February 12-13rd 2014

th

The 4 Annual Basic Science International Conference

BIOLOGY

Batu, East Java, Indonesia | 1

February 12-13rd 2014

2 | Batu, East Java, Indonesia

th

The 4 Annual Basic Science International Conference

February 12-13rd 2014

th

The 4 Annual Basic Science International Conference

Resistance Evaluation of Large Seeded Soybean Lines to Rust Disease Sumartini 1 and Heru Kuswantoro 1*) Indonesian Legume and Tuber Crops Research Institute Indonesian Agency for Agricultural Research and Development 1)

*)

Corresponding author: [email protected]

Abstract— Rust disease is a major disease on soybean, wide spreading, existing in almost all soybean producer countries, and causing yield losses up to 80%. The objective was to evaluate the resistance of soybean lines to rust disease. The research was conducted in the greenhouse of Indonesian Legume and Tuber Crops Research Institute, from March to June 2013. Design was randomized completely block design with three replications. Materials were 10 soybean lines and five check varieties. Inoculating rust spores was carried out at 3 weeks after planting by using spore suspension (104/ml density) derived from infected soybean leaf. Inoculation was performed by spraying the spore suspension on the first leaves at 16.00-18.00. Observations rust disease resistance based on the system of International Working Group on Soybean Rust. Results showed that there was no soybean line identified as resistant line. Of a total ten soybean lines, seven soybean lines (Tgm/Anj-908, Tgm/Anj-909, Tgm/Anj-910, Tgm/Anj-919, Tgm/Anj-932, Tgm/Anj-957, and Tgm/Anj-995 were identified as moderately resistant, two soybean lines (Tgm/Anj933, and Tgm/Anj-991) were susceptible, and one soybean line (Tgm/Anj-931) was susceptible. The three check varieties (Wilis, Tanggamus and Anjasmoro) were moderately resistant, while two check varieties (Grobogan and Argomulyo) were moderately susceptible. Keywords— soybean lines, resistance, rust disease.

I. INTRODUCTION ASED on the seed size, soybean seeds are classified as small seed, medium seed and large seed. Usually, medium and small seeds are used for soy sprouts, soy sauce, tofu and soy milk ingredients, while large seeds is used for tempeh ingredient. However, the main usage is for tempeh ingredient as well as tofu. Therefore, one of the Iletri’s soybean breeding programs is directed to develop large seeded soybean variety. In developing soybean variety, the resistance of the new variety to rust disease should be tested to provide supporting data for release variety; because rust disease is one of the major soybean disease in Indonesia. Rust disease also known as the "Asian soybean rust", caused by the fungus Phakopsora pachyrhizi. Rust disease is widespread in the area of soybean production centers in the world and causing significant yield loss. Distribution of rust disease started from Japan and East Asia in 1902, entered into Southeast Asia (Indonesia) and Australia in 1914, while in 1950 has reached India, and in 1994 en-

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tered Hawaii. Then it entered South Africa in 1920 and has reached Uganda in 1996. In the years 2001 - 2002 rust disease infestation appeared in South America, and in 2004 had spread out to the north reaching the United States [1]. Yield losses can reach more than 85% if suitable environment for disease development [2]. Furthermore, it is stated that there are four types of genes that responsible for controlling rust disease resistance in soybean, i.e. Rpp1 , Rpp2 , Rpp3, and Rpp4 [2]. One of the techniques for controlling soybean rust disease is to grow resistant varieties. There are several soybean varieties having rust disease resistance that have been released. The resistance to rust disease is not durable and someday the resistance will be broken because the fungus P. pachyrhizi can mutate to be new races. Therefore, developing new of superior that resistant to rust disease is still needed. The purpose of the study is to evaluate the resistance of large seeded soybean lines to rust diseases. II. MATERIALS AND METHOD The study was conducted in Indonesian Legume and Tuber Crops Institute, in the dry season (March-June 2013). The design was randomized completely block, with three replications. The materials research were 10 soybean lines (Tgm/Anj 908, Tgm/Anj-909, Tgm/Anj910, Tgm/Anj-919, Tgm/Anj-931, Tgm/Anj-932 Tgm/Anj-933, TGM / Anj-957, Tgm/Anj-991, Tgm/Anj-995) and four check varieties (Wilis, Tanggamus, Anjasmoro, Grobogan, Argomulyo). Soybean seeds were grown in plastic polybag (Φ = 15cm), two plants per polybag. At one month old, plants were inoculated with rust disease by spraying spore suspension (104/ml) to soybean leaves. Spore suspension was originated of rust-infected leaves from inoculum plantation sources. The day before inoculation, infected leaves were taken to be incubated at 100% humidity conditions in the laboratory. After 24 hours, the spores were taken by using a brush, and then the spores were diluted. The spore suspension was homogenized by using Tween 20, two drops per liter. To avoid pest attacking, spraying insecticides (carbofuran, sipemetrin, cyhalothrin) was carried out several times alternately. Intensity of rust disease was observed in all tested plants at 7 and 9 weeks after planting by IWGSR (International Working Group on Soybean Rust) method [3] (Table 1).

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February 12-13rd 2014

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The 4 Annual Basic Science International Conference

TABLE I DETERMINATION OF RESISTANCE CRITERIA TO RUST DISEASE Position of the disease on the plant 1 : 1/3 of lower part of the plant 2

:

2/3 of middle part of the plant

3

:

1/3 of upper part of the plant

Disease intensity 1

:

no pustule

2

:

mild ( 1 – 8 pustul/cm2 )

3

:

medium ( 9 – 16 pustul/cm2 )

4

:

high ( > 16 pustul/cm2 )

Resistance criteria Imun

:

Score 111

Resistant

:

Score 122, 123, 132, 133, 222. 223

Moderately resistant

:

Moderately susceptible

:

Score 142, 143, 232, 233, 242, 243, 322, 323 Score 332, 333

Susceptible

:

Score 343

In addition to the intensity of rust disease, observation was also carried out on yield component, such as plant height, number of branches per plant, shoot fresh weight, shoot dry weight, number of filled and unfilled pods per plant, and seed weight. III. RESULTS AND DISCUSSION The results showed that until week 9th, position of

contained, there was no resistant genotype, seven genotypes were moderately resistant (MR), two genotypes were moderately susceptible, and one genotype was susceptible (Table 2). In this experiment, the three checks varieties (Wilis, Tanggamus and Anjasmoro) were classified as moderately resistance. This results were similar to the varieties description [4]. However, Argomulyo was moderately susceptible, different to the description that Argomulyo is tolerant to rust disease. Similar results also reported by investigators from Nigeria using simpler methods based solely on the intensity of rust disease on a scale 1-5. With this method they can classify resistance of soybean genotypes to rust disease. Of the 28 tested genotypes, most of the genotypes were susceptible to moderatly resistance. However, they found seven resistant genotypes [5]. Beside, they found dominant genes controlling rust disease found resistance in three different loci. Investigators from the United States classified resistance to soybean rust disease into four types of Phakopsora pachyrhizi fungi isolates and the symptoms by two types of symptoms i.e. reddish-brown (RB) and TAN. Of 34 tested genotypes, 28 genotypes were included as TAN type with many sporulating and six genotypes were included as RB (reddish-brown) [6]. Plant height is an important character because it has a positive correlation to grain yield [7]. Beside, plant height had indirect effect on grain yield through number of branches per plant, number of pods per plant, number of grain per plant and 100 grains weight [8]. Plant height of the tested genotypes were classified as normal. However, three genotypes had abnormal plant height, i.e. Tgm/Anj-995, Tgm/Anj-919 and Tgm/Anj-931

TABLE II SCORES AND RESISTANCE CRITERIA OF LARGE SEEDED SOYBEAN GENOTYPES TO RUST DISEASE

Genotypes

Pustul position

Number of pustul/cm2

Disease intensitas

Spore existence

Score

Resistance criteria

Tgm/Anj-908 Tgm/Anj-909 Tgm/Anj-910 Tgm/Anj-919 Tgm/Anj-931 Tgm/Anj-932

2 2 2 2 3 2

14 11 9 13 18 11

3 3 3 3 4 3

3 3 3 3 3 3

233 233 233 233 343 233

Moderately resistance Moderately resistance Moderately resistance Moderately resistance

Tgm/Anj-933 Tgm/Anj-957

3 2

11 13

3 3

Tgm/Anj-991 Tgm/Anj-995 Wilis Tanggamus Anjasmoro

3 2 2 2 2

10 11 12 10 10

3 3 3 3 3

Grobogan 3 16 3 plant disease was in the middle to upper canopy, or 2 to 3 score. The intensity of rust disease scores ranged 3-4, or the number of pustules from 8 to more than 16 pustules per cm2. It means that the intensity was mild to high, and many pustules had spores. Of the 10 genotypes

4 | Batu, East Java, Indonesia

Susceptible Moderately resistance Moderately suscepti3 333 ble 2 232 Moderately resistance Moderately suscepti3 333 ble Moderately resistance 3 233 Moderately resistance 3 233 Moderately resistance 2 232 Moderately resistance 3 233 Moderately suscepti3 333 ble Moderately suscepti(Table 3). The three genotypes had different response to rust disease; where Tgm/Anj-995 and Tgm/Anj-919 were moderately resistant and Tgm/Anj-931 was susceptible. In this case, the rust disease did not cause plant height character. Probably, other unknown

February 12-13rd 2014

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The 4 Annual Basic Science International Conference

TABLE III PLANT HEIGHT, SHOOT FRESH WEIGHT AND SHOOT DRY WEIGHT OF LARGE SEEDS SOYBEAN. ILETRI GREENHOUSE, DRY SEASON 2013 Genotypes

Plant height (cm)

Tgm/Anj-908 Tgm/Anj-909 Tgm/Anj-910 Tgm/Anj-919

68.17 63.83 52.11 43.94

a ab bc c

Tgm/Anj-931 Tgm/Anj-932 Tgm/Anj-933 Tgm/Anj-957 Tgm/Anj-991 Tgm/Anj-995

44.62 56.61 67.17 67.89 62.06 18.44

c abc a a ab d

Wilis Tanggamus Anjasmoro Grobogan Argomulyo

61.56 56.44 62.39 52.11 65.39

ab abc ab bc a

Shoot fresh weight (g)

Shoot dry weight (g)

15.34 abcd

8.567 abc

17.76ab 18.75ab

10.22 ab 9.753 ab

9.40 cde 8.66 de 13.98 bcd 18.78 ab 19.54 ab 21.14 a

6.653 5.400 8.453 9.180 9.610 11.01

c c abc abc ab a

3.03 f 16.79 ab 16.59 ab 19.14 ab 15.95 abc 14.25 bcd

1.117 8.787 8.770 9.733 9.150 7.957

d abc abc ab abc bc

environmental factors affected the plant height performance in this study. The performance of fresh weight and dry weight of plants were similar to plant height performance. Plant with having higher fresh weight and dry weight also had higher plant height, and vice versa. The three shortest lines (Tgm/Anj-995, Tgm/Anj-919 and Tgm/Anj-931) also had the lowest fresh weight and dry weight of plants (Table 3). The low value of fresh weight and dry weight may be caused by inability of the short plant to develop larger or more organs than high/normal plants. Plant dry weight described photosynthate which produced by the plant. The photosynthate is partitioned into plant organs, and it leads the magnitude of grain yield.

determined by the number of seeds [9], and the number of seeds is determined by the number of pods. The number of pods per plant varied among tested genotypes, where Tgm/Anj-995 showed the lowest number of filled pods and Tgm/Anj-909 showed the highest number of filled pods. Similar to fresh weight and dry weight of plants, soybean lines with lower plant height also showed lower number of pods (Table 4). The number of pods per plant and the number of reproductive nodes have a positive correlation [10]. It is because the formed organs (pods) are larger and more on normally growing plants. In contrast to the number of pods, number of unfilled pods did not seem related to plant height (Table 4). Unfilled pod is a pod that cannot be formed completely, because the lack of photosynthate for pod developing in the end of seed filling period. In addition, the number of unfilled pods is determined by genetic factors, where the broad sense heritability of this character was high (93.1%) [11]. The most number of branches per plant was indicated by the check variety of Anjasmoro, while the lowest by Tgm/Anj-995. Soybean lines with low plant height were still able to produce number of branches as much as normally growing genotypes (Table 4). Branch is a large plant organ where the development of this organ requires extremely high energy. Therefore the number of branches was relatively similar between the tested genotypes. Grain yield is not an independent character [7], but a complex character where the expression is determined by genetic and environmental factors [12]. The highest grain yield was showed by Tgm/Anj-991 while the lowest by Tgm/Anj-995 (Table 4). Both of these lines had different responses to rust disease, where Tgm/Anj991 was moderately susceptible and Tgm/Anj-995 was

TABLE IV NUMBER OF FILLED AND UNFILLED PODS, NUMBER OF BRANCHES, AND GRAIN YIELD PER PLANT OF LARGE SOYBEAN SEEDS. ILETRI GREENHOUSE, DRY SEASON 2013

Genotypes

Number of filled pods per plant

Number of unfilled pods per plant

Number of branches per plant

Grain yield per plant (g)

Tgm/Anj-908

30.11 bcd

1.05 cd

2.17 cd

3.96 ab

Tgm/Anj-909

40.89 a

1.00 cd

2.55 bc

3.84 ab

Tgm/Anj-910

31.06 bcd

1.33 bcd

2.17 cd

3.98 ab

Tgm/Anj-919

14.05 e

2.28 ab

2.22 cd

2.67 c

Tgm/Anj-931

14.95 e

0.83 cd

2.06 cd

2.56 c

Tgm/Anj-932

27.44 cd

1.11 cd

2.61 bc

3.59 abc

Tgm/Anj-933

29.39 bcd

0.83 cd

1.94 cd

3.35 abc

Tgm/Anj-957

32.22 bc

1.06 cd

2.06 cd

3.30 abc

Tgm/Anj-991

29.22 bcd

0.95 cd

2.16 cd

4.28 a

Tgm/Anj-995

0.167 f

0.33 d

0.22 e

0.01 d

Wilis

33.56 abc

1.17 cd

3.28 ab

3.08 bc

Tanggamus Anjasmoro Grobogan Argomulyo

28.33 cd

1.44 abc 1.22 cd 2.39 a 1.28 bcd

2.28 cd 3.56 a 2.00 cd 1.61 d

3.34 abc

36.61 ab 27.95 cd 23.94 d

The number of pods determine grain yield because grain yield is the total photosynthate partitioned into seeds, and the magnitude of grain yield was also

3.85 ab 2.93 bc 3.39 abc

moderately resistant. In this study, grain yield was not affected by the response to rust disease. Grain yield is a plant characters that affected by other yield components,

Batu, East Java, Indonesia | 5

February 12-13rd 2014

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The 4 Annual Basic Science International Conference

especially the number of pods and seed size. There is a positive correlation between grain yield and 100 seeds weight [13], [14]. Contribution of yield components to grain yield may a direct effect or an indirect effect, and varies depending on the environmental conditions [15], [16]. IV. CONCLUSION Of the 10 soybean genotypes, there was no resistant genotype, seven genotype were moderately resistant (Tgm/Anj-908, Tgm/Anj-909, Tgm/Anj-910, Tgm/Anj919, Tgm/Anj-932, Tgm/Anj -957, and Tgm/Anj-995), two genotypes were moderately susceptible (Tgm/Anj933, and Tgm/Anj-991) and one genotype was susceptible (Tgm/Anj-931). Rust disease infestation did not affect yield and yield components.

[6]

[7]

[8]

[9]

[10]

[11]

REFERENCES [1]

[2]

[3]

[4]

[5]

R. M. Miles, R. D. Frederick, and G. L. Hartman. 2003. Soybean Rust : Is The US Soybean Crop at Risk? [http://www.apsnet. org/online/feature/rust/] G. L. Hartman, M. R. Miles, and R. D. Frederick, 2005. Breeding for resistance to soybean rust. Plant Disease 89(6): 664-666. The American Phytopathological Society. USA. Shanmugasundaram. 1977. The International working group on soybean rust and Its proposed soybean rust rating system. Work shop on rust of soybean. The problem and research needs. Manila, Philippines. 28 Feb – 4 March 1977. Balitkabi. 2009. Deskripsi Varietas Unggul Kacang-kacangan dan Umbi-umbian. Balai Penelitian Tanaman Kacang-kacangan dan Umbi-umbian. 175 Hlm. G. A. Iwo, M. A. Ittah, and E. O. Osai. 2012. Source of genetics of resistance to soybean rust Phakopsora pachyrhizi (H. Sydow & Sydow) in Nigeria. Journal of Agricultural Science 4(10). Canadian Centre of Science and Education.

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[12]

[13]

[14]

[15]

[16]

C. Paul, C. B. Hill, and G. L. Hartman. 2011. Comparisons of visual rust assessments and DNA levels of Phakopsora pachyrhizi in soybean genotype varying in rust resistant. Plant Disease 95:1007-1012. The American Phytopathological Society. USA. M. F. A. Malik, M. Ashraf, A. S. Qureshi and M. R. Khan. 2011. Investigation and comparison of some morphological traits of the soybean populations using cluster analysis. Pak. J. Bot. 43: 1249-1255. M. El. M . El-Badawy,. and S. A. S. Mehasen, 2012. Correlation and Path Coefficient Analysis for Yield and Yield Components of Soybean Genotypes Under Different Planting Density. Asian Journal of Crop Science, 4: 150-158. Harmida. 2010. Respons pertumbuhan galur harapan kedelai (Glycine max (L.) Merril) pada lahan masam. Jurnal Penelitian Sains 13: 13209-13248 T. Machikowa, and P. Laosuwan. 2011. Path coefficient analysis for yield of early maturing soybean. Songklanakarin J. Sci. Technol. 33:365-368. G. Sahay, B. K. Sarma and A. A. Durai. 2005. Genetic variabiuty and interrelationship in f2 segregating generation of soybean {Glycine max (L) Merril} in mid-altitude ofmeghalaya. Agric. Sci. Digest, 25 (2) : 107 – 110. A. Sudaric, and M. Vrataric. 2002. Variability and interrelationships of grain quantity and quality characteristics in soybean. Die Bodenkultur 53: 137-142. M. Arshad, N. Ali and A. Ghafoor. 2006. Character correlation and path coefficient in soybean Glycine max (L.) Merrill. Pak. J. Bot. 38: 121-130. M. Showkat and S. D. Tyagi. 2010. Correlation and path coefficient analysis of some quantitative traits in soybean (Glycine max L. Merrill.). Research Journal of Agricultural Sciences 1:102-106. R. A. Ball, R. W. McNew, E. D. Vories, T. C. Keisling, and L. C. Purcell. 2001. Path analyses of population density effects on short-season soybean yield. Agron. J. 93:187–195. S. Kobraee and K. Shamsi. 2011. Evaluation of soybean yield under drought stress by path analysis. Australian Journal of Basic and Applied Sci. 5:890-895.

February 12-13rd 2014

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The 4 Annual Basic Science International Conference

Leaf Characteristics of Some Trees Species in Accumulating SO2 and NO2 Pollutant in Park Area of Padjadjaran University Jatinangor Fauziah Hafidha *), Mohamad Nurzaman, Nurullia Fitriani, and Teguh Husodo Department of Biology, Padjadjaran University, Sumedang, Indonesia *)

Corresponding author: [email protected]

Abstract— Research about the relation of leaf characteristics in some trees species to the accumulation of sulfate and nitrate has been done. The purpose of this research is to find out the leaf characteristics that related to the sulfate and nitrate accumulation. This research is using survey method and correlation analysis. The plants that used in this research were dadap merah (Erythrina crista-galli), suren (Toona sureni), bungur (Lagerstroemia speciosa), ki acret (Spathodea campanulata), kayu afrika (Maesopsis eminii) dan mahagoni (Swietenia mahagoni). The results show that bungur has the largest leaf surface and dadap merah has the thickest leaf. Mahagoni has the highest value of stomatal density and kayu afrika has the largest size of stomata. Dadap merah has the highest value of total chlorophyll content. The highest value of sulfate accumulation was in ki acret and the highest value of nitrate accumulation was in bungur. The leaf width has the positive correlation with sulfate accumulation. The leaf width, leaf thickness, stomatal size and chlorophyll content have the positive correlation with nitrate accumulation. Keywords— tree, leaf characteristics, sulfate, nitrate, pollutant accumulation

I. INTRODUCTION IR pollution is the introduction of pollutants into the atmosphere that can cause disruption and discomfort to living organisms and also cause environmental damages[1]. Air pollution emissions are released naturally from smokes of forest or volcanic eruption but mostly cause due to anthropogenic sources[2,3] such as human activities, economic development, increasing population, use of transport, industrial development and higher level of energy consumption[1,4]. Sulfur oxides (SOx) are one of the gaseous pollutants. SOx pollution is mainly caused by sulfur dioxide (SO2) and sulfur trioxide (SO3). Most of SO2 gases in the atmosphere are the result of human activities that comes from burning fuel, such as coal, charcoal, wood and the results of industrial processes[5]. SOx pollution can cause human respiratory disease[6] and can cause tissue damage (leaf necrosis), even on the higher expose of SO2 can cause cholorosis to the plant[29]. Nitrogen oxides (NOx) can be found as nitrogen monoxides (NO) and nitrogen dioxides (NO2), but NO2 are the most common gases that found as air pollutant. The

A

primary sources of NOx pollutants are motor vehicle emissions, electric tools and human activities such as industrial, trading and the result of the household burning[7]. Chronic exposure of NOx can cause immune system decrease and toxicity of NO2 can cause health problems, especially lungs problem to human[8]. NO absorption by plant can cause leaf flecks and higher concentration of NO2 exposures can cause leaf necrosis[9]. Plants are one of the living organisms that receive a lot exposure from air pollution[4]. Most of pollutants enter the plant trough leaf and can cause some damages, although the plant still has a defense mechanism to minimize the damages. The mechanism is conducted through the movement of opening and closing the stomata and also detoxification process[10]. Plants like trees have the potency to reduce pollutants by providing large leaf area to accumulate pollutants[7,11]. Trees provide a leaf surface onto which particles are deposited and gases like SO2 and NO2 are removed[12]. Plant ability in absorbing gaseous pollutants was determined by some factors such as morphology, anatomy and physiology of the leaf. The absorption of gaseous pollutants process is mainly occurs through stomata[7]. The stomatal density affects the potential of leaf to absorb pollutants[3]. Moreover, morphological characters such as leaf surface and leaf thickness affect the absorption of pollutant too[13]. Chlorophyll content of the leaf may change due to air pollution[11]. Park is a green area which has a function to reduce air pollutant concentration. The vegetation at the park is effective to adsorb and absorb pollutants that come from motor vehicles emissions[14]. Front park area of Padjadjaran University is areas that close to Jatinangor – Sumedang highway road. The relation between plant leaf characteristic with the absorption of SO2 and NO2 can be discovered by calculate the accumulation of sulfate and nitrate concentration then analyzed with correlation analysis. The plants selected for this research are the trees that exist in the front park area and are tolerant species, especially SO2 and NO2 pollutant, based on the study literature. II. MATERIAL AND METHODS Study area : The area of this study was in front park

Batu, East Java, Indonesia | 7

February 12-13rd 2014

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area of Padjadjaran University near the Jatinangor – Sumedang highway road and located on 06º55’55.7”S and 107º46’21.7”E. This park location is near Jatinangor – Sumedang highway road and Arboretum Unpad.. The trees that are used for this study is the trees that exist on the park, near the road and have the height around 1 – 3 m and also based on literature have the ability to absorb SO2 and NO2 pollutant. Collected sample : Leaf were collected from the study area in the morning around 08.30 WIB. The taken leaves were mature leaves and relative have the same size. Temperature and light intensity were also measured when the leaves were taken. Leaf characteristics : Leaf areas were measured using leaf area meter and the leaf thicknesses were measured by micrometer screw. Stomata were observed under light microscope with 400x magnificent. Total chlorophyll content were measured using chlorophyll meter. Sulfate and nitrate accumulation : Sulfate accumulation were measured by forming BaSO4 and then calculating SO4 concentration. Nitrate accumulation were measured by calculating total N of leaf and then converted to NO3. SO2 and NO2 concentration measurement : SO2 concentration were measured using the absorption method with absorption solution of TCM. NO2 concentration were measured using the absorption method with absorption solution of Saltzman-Griess. Analysis of data : The data were analyzed using correlation analysis on Microsoft Excel. III. RESULT AND DISCUSSION Study area : The average of light intensity in study area were 805,7 x 100 lux and the average temperature on site were 27,2 – 28,5ºC. Average pH soils in the location were 5.4 to 6.8. Leaf characteristic : The data of leaf characteristic can be seen on Table 1. All the leaves had a dark green color on the upper side and lighter green on the bottom side. Bungur has the largest leaf surface with 130,20 cm2 and suren has the smallest leaf surface with 34,31 cm2. Dadap merah has the thickest leaf with 0,23 mm and suren has the thinnest leaf with 0,07 mm. Leaf thickness is one of internal factors that affect the transpiration process to lowering plant temperature[16]. Stomatal type on each plant was anomocytic type, except dadap merah which has parasitic type. Stomata of all studied species were only found on the bottom side of leaves. Stomata were found more numerous on the bottom side although can be found on both sides of leaves[17]. This is an adaptation to reduce rate of transpiration by plant, because 90% of the transpiration occurs trough stomata. Beside play a role in transpiration process, stomata were also having a function for gases exchange, like CO2, in the physiological process that related to photosynthesis[18].

8 | Batu, East Java, Indonesia

TABLE 1 LEAF CHARACTERISTIC Characteristic

Leaf surface (cm2) Leaf thickness (mm) Stomatal type Stomatal density 2 (/mm ) Stomatal size (µm) Total chlorophyll content (cci)

Suren

Bungur

Species Ki acret

Dadap merah 99,59

34,31

130,20

94,11

Kayu afrika 36,32

73,09

Mahagoni

0,23

0,07

0,21

0,12

0,13

0,19

Parasi-tic 105,3

Anomocy-tic 288,95

Anomocy-tic 210,2

Anomocy-tic 122,2

Anomocy-tic 142,3

Anomocy-tic 289,9

21,5

19,2

26

21,8

29

12,1

41,5

13,5

32,8

27,7

39,4

28,6

Mahagoni has the highest value of stomatal density with 289,9/mm2 and dadap merah has the lowest value of stomatal density with 105,3/mm2. Kayu afrika has the highest value of stomatal size with 29µm and mahagoni has the lowest value of stomatal size with 12,1µm. Stomatal density of each plant can be different according to the type of the plant[17]. The result shows that all the plants have low stomatal density. Stomatal density categorized as low if the density is 200/mm2 or less[3]. This low value may be related to plant adaptation toward dryness. Some plants reduce the size and number of stomata to adapt a dry environment[18]. Total chlorophyll content was measured using chlorophyll meter. Leaf plant which has the highest chlorophyll content is dadap merah with 41,5 cci and suren leaf has the lowest with 13,5 cci. Dadap merah leaf has darker green color, have a fairly broad leaf surface and thicker than suren leaf which has thinner leaf, lighter color and smaller leaf surface. Chlorophyll is one of substantial pigment that used by plant to absorbs light for photosynthesis process. Leaf color is closely related to the chlorophyll content on leaf plant. Leaf plant with normal green color relatively has higher chlorophyll than the leaf with yellow or light green color[19]. Sulfate and nitrate accumulation : Sulfate and nitrate accumulation can be seen in Table 2. The highest value of sulfate accumulation was found in ki acret leaf with 15,0834 mg and the lowest value was found in kayu afrika leaf with sulfate content around 0,0467 mg. Sulfur (S) is secondary nutrients and generally required for optimum growth around 0,1% - 0,5% of the dry weight of plants. The structures of proteins in plants are largely determined by the S group, such as methionine and cysteine amino acids. S is also known as an essential nutrient for the production of chlorophyll which is closely related to photosynthesis process[20]. Sulfur is absorbed by plants in the form of SO4 and stored in the cytosol[21]. Varying value of sulfate accumulation in leaf can be caused by genetic factors and environmental factors around plants[22]. The highest value of nitrate accumulation was found in bungur leaf with 87,844 mg and the lowest value was found in mahagoni with 2,982 mg. Around 5 – 6% nitrogen needs for the plants are come from NO2 absorption from the atmosphere. Nitrogen especially needed for supporting the vegetative growth, such as leaf growth rate[22]. Most of absorbed NO2 from the atmosphere remain in the leaves and only small portion were

February 12-13rd 2014

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The 4 Annual Basic Science International Conference

distributed to stems and roots[7].NO2 gas which entering the leaf through stomata will react with H2O and form nitric acid (H2NO3)[22]. Nitric acid will ionized to H+ and nitrate, so nitrate will be assimilated and turn into amino acid which is needed by plant[8].

for sulfate uptake and transport in plants.

TABLE 2 SULFATE AND NITRATE ACCUMULATION Species

Dadap merah Suren Bungur Ki acret Kayu afrika Mahagoni

Leaf weight (g) 2,58

Sulfate (%)

Sulfate (mg)

Nitrate (%)

0,0914

2,3581

2,91

75,08

0,58 3,16 1,67

0,5868 0,0993 0,9032

5,75 2,78 2,45

33,35 87,844 40,915

0,57 1,42

0,0082 0,2242

3,4034 3,1379 15,083 4 0,0467 3,1863

4,29 0,21

24,453 2,982

Nitrate (mg)

Correlation between accumulation of sulfate and nitrate with leaf characteristic : The result of this study showed that sulfate accumulation on leaf is related to some leaf characters such as leaf area, leaf thickness and stomatal density. There was a positive correlation between sulfate accumulations with leaf surface with a correlation coefficient 0,2935 (Fig 1). Sulfate accumulation has negative correlation with leaf thickness with a correlation coefficient -0,2734. Leaf thickness related to tissue thickness and gas become relatively difficult to enter the tissue leaf, so it causes the absorption of the gas is relatively small[8]. The correlation between sulfate accumulation with stomatal density and chlorophyll content showed a negative correlation with a correlation coefficient -0,2707 and -0,2789. Accumulation of air pollutant in leaf can affect the chlorophyll content[11]. This showed that greater accumulation sulfate can cause decreasing of leaf chlorophyll content.

Fig. 1. Relation between sulfate accumulation with leaf area (r = 0,2935).

Ki acret leaf show greater sulfate uptake and accumulation can be caused by a relatively large leaf surface with low leaf thickness and the value of stomatal density which is not too high, compared to the others studied species. Suren leaf which has the thinnest leaf among all studied species can’t accumulate sulfate as much as ki acret because suren has relatively small leaf area that is 34,31 cm2 and also the higher value of stomatal density which is around 288,95/mm2. Sulfate absorption by leaf, beside affected by plant characteristic such as leaf area, leaf thickness and stomatal density, can also influenced by genetic trait of each plant. Each plant has a different transporter gene

Fig. 2. Relation between nitrate accumulation with leaf area (r = 0,7303) and between nitrate accumulation with leaf thickness (r = 0,4909).

The result showed that nitrate accumulation was related to some leaf characteristics such as leaf area, leaf thickness, stomatal density and size. Figure 2 shows that nitrate accumulation has a positive correlation with leaf area and leaf thickness. The correlation coefficient between nitrate accumulation with leaf thickness is 0,4909. This positive correlation is contrary to Patra[8] and Nugrahani et al.[7] which were reported that nitrate accumulation has negative correlation with leaf thickness. This difference may be caused by the absorption of NO2 which not only depend on leaf thickness but also depend on other factors such as leaf area and stomatal size. The research of Patra[8] showed that there was a plant which has relatively thick leaf were also accumulate high nitrate. The total chlorophyll content has positive correlation with nitrate accumulation with correlation coefficient around 0,334 (Fig 3). Chlorophyll content will be influenced by the pollutant accumulation on leaf[11]. Chlorophyll synthesis is also influenced by several factors, such as light, carbohydrate, water, temperature, genetic factors and some elements such as nitrogen, magnesium, iron, manganese, sulfur, iron and oxygen[24]. Nitrogen (N) is closely related to chlorophyll synthesis[17], also for synthesis protein and enzymes such as rubisco[25]. Hendriyani and Setiari[25] reported that N is main factors to forming chlorophyll. The result showed that nitrate accumulation has a negative correlation with stomatal density. Patra[8] reported that the high value of stomatal density will cause a high concentration of nitrate accumulation, but Nugrahani et al.[7] reported that there was no correlation between stomatal density and nitrate accumulation. This difference suggests that stomatal density is not the only one factor that affected nitrate accumulation on leaf. showed that there is a positive correlation between nitrate accumulations with the size of stomata (r = 0,5331). This suggests that the greater the value of stomatal size tend to cause greater accumulation of nitrate on leaf. SO2 and NO2 concentration : SO2 gas concentration in the air are one of factors that directly affect sulfate accumulation in leaf[21]. The results of ambient air quality measurements showed that the concentration of SO2 gas at the study site was not detected, so it can be categorized as an uncontaminated area. The very small concentration of sulfate accumulation on leaf may be due to the good quality of ambient air for SO2 in the study area.

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The concentration of SO2 gas was not detected in the study area can be caused due to the distance of SO2 main pollutant sources, such as coal industry, not close to the area. Concentration of SO2 is mainly influenced by coal combustion emissions[21], coal industry, oil and biomass burning[2]. Yanismai[26] reported that great reactions of SO2 can also cause the concentration of SO2 in the atmosphere become not detected.

The humidity at the location when conducted air sampling was around 35 – 41,35%. Istantinova et al.[27] reported that the higher humidity will cause the decreasing concentration of SO2 in the atmosphere. Temperature measurement was 28.8 to 30ºC and categorized as relatively normal. Air temperature has the positive correlation with the concentration of gaseous pollutants in the atmosphere. The increasing of air temperature will cause to the increasing of gaseous pollutants too [28,27]. IV. CONCLUSION

Fig. 3. Relation between nitrate accumulation with the chlorophyll content (r = 0,3340).

The results of ambient air quality measurements showed that the concentrations of NO2 gas at the study site was 49.64 µg/Nm3/hour and it categorized as good because the concentration of NO2 were lower than the standard quality which is 400 µg/Nm3/hour. High concentration of NO2 is generally found in areas with the high transport activity[22]. Yanismai[26] reported that there is tendency of increasing concentrations of NO2 along with the increasing number of vehicles. The number of vehicles that pass through the campus road and in front of Jatinangor – Sumedang highway can be seen in Table 3. The differences between NO2 gas concentration in campus road and highway road were in line with the research done by Yanismai[26] which reported that there is an increase in NO2 concentrations with increasing number of vehicles. TABLE 3 THE NUMBER OF VEHICLES Location Campus road Highway road

Numbers of vehicles (/hour) 671 2828

NO2 gas concentration (µg/Nm3/hour) 24,41 74,67

The wind speed, air temperature and humidity are some of the meteorological factors that could affect the concentration of gaseous pollutant in the atmosphere[27]. According to Subaid[28], wind speed can affect spreading and mixing process of air pollutants. Higher wind speed can cause the increasing of spreading process of air pollutants from the sources. Istantinova et al.[27] reported that the wind speed has a negative correlation to the concentration of gaseous pollutant in the atmosphere. According to Yanismai[26], high wind speed can cause levels of gaseous pollutant like NO2 are distributed to other locations, so the concentration of NO2 in the location that close to the source is reduced. The measurement of wind speed at the study site ranged from 0.53 to 3.02 m/sec and it categorized as normal. The wind speed was relatively normal, but the low concentration of SO2 and NO2 can cause gases with a low concentration were dispersed to other locations by wind.

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1) The results are bungur (Lagerstoemia speciosa) has the largest leaf surface with 130,20 cm2 and dadap merah (Erythrina crista-galli) has the thickest leaf with 0,23 mm. Mahagoni (Swietenia mahagoni) has the highest value of stomatal density with 289,9/mm2 and kayu afrika (Maesopsis eminii) has the largest size of stomata with 29µm. Dadap merah (Erythrina crista-galli) has the highest value of total chlorophyll content with 41,5 cci. 2) The highest value of sulfate accumulation was in ki acret (Spathodea campanulata) with 15,0834 mg and the highest value of nitrate accumulation was in bungur (Lagerstoemia speciosa) with 87,848 mg. 3) The leaf width has the positive correlation with sulfate accumulation. The leaf width, leaf thickness, stomatal size and chlorophyll content has the positive correlation with nitrate accumulation. REFERENCES [1]

Wagh, N.D., P.V. Shukla, S.B. Tambe, and S.T. Ingle, Biological Monitoring of Roadside Plants Exposed to Vehicular Pollution in Jalgaon City.Journal of Environmental Biology, India, 2006. [2] De Kok, L.J., M. Durenkamp, L. Yang, and I. Stulen, Sulfur in Plants, an Ecological Prespective. New York: Springer, 2007. [3] S.R. Hidayati, Analisis Karakteristik Stomata, Kadar Klorofil dan Kandungan Logam Berat pada Daun Pohon Pelindung Jalan Kawasan Lumpur Porong Sidoarjo. Skripsi Jurusan Biologi Fakultas Sinstek dan Teknologi Universitas Islam, Malang, 2009. [4] Pant, P.P., and A.K. Tripathi, Analysis of Some Biochemical Parameters of Plants as Indicator of Air Pollution. Journal of Environmental Science, Computer Science and Engineering and Technology.Vol.1, No.1, 14-21, 2012. [5] Depkes. (2012, May 7) Parameter Pencemar Udara dan Dampaknya terhadap Kesehatan. Available: www.depkes.go.id [6] M. Soedomo, Kumpulan Karya Ilmiah mengenai Pencemaran Udara. Bandung : Penerbit ITB, 2001. [7] Nugrahani, P., N. Nasrullah, dan E.L. Sisworo, Faktor Fisiologi Tanaman Tepi Jalan yang Menentukan Kemampuan Serapan Polusi Udara Gas NO2. Risalah Seminar Ilmiah Aplikasi Isotop dan Radiasi, 2006. [8] A.D. Patra, The Techincal Writers Handbook. Mill Valley, CA: University Science, 1989. Faktor Tanaman dan Faktor Lingkungan yang Mempengaruhi Kemampuan Tanaman dalam Menyerap Polutas Gas NO2. Bogor : Program Pasca Sarjana IPB, 2002. [9] R.Y. Maulana, Identifikasi Respon Anatomi Daun dan Pertumbuhan Kenari, Akasia dan Kayu Manis terhadap Emisi Gas Kendaraan Bermotor. Bogor : Skripsi.Fakultas Kehutanan.Institut Pertanian, 2004. [10] A. Wijaya, Penggunaan Tumbuhan sebagai Bioindikator dalam Pemantauan Pencemaran Udara. Surabaya : Skripsi Jurusan Teknik Lingkungan ITS, 2010. [11] Seyyednjad, S.M., K. Majdian, H. Koochak, and M. Niknejad. (2012, December 7) Air Pollution Tolerance Indices of Some Plants Around Industrial Zone in South of Iran. Available:

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http://scialert.net/fulltext/?doi=ajbs.2011.300.305 [12] Begum, A., and S. Harikhrisna, Evaluation of Some Tree Species to Absorb Air Pollutants in Three Industrial Locations of South Bengaluru. India : E-Journal of Chemistry, 2010. [13] Samsoedin, I. dan E. Subiandono, Pembangunan dan Pengelolaan Hutan Kota. Prosiding Ekspose Hasil – Hasil Penelitian, 2007. [14] I. Samsoedin, Kajian Status IPTEK dan Pengembangan Ekosistem Hutan di Perkotaan. [15] E.B. Hidayat, Anatomi Tumbuhan Berbiji. Bandung : Penerbit ITB, 1995. [16] D. Dwidjoseputro, Pengantar Fisiologi Tumbuhan. Jakarta : Penerbit Gramedia, 1985. [17] Salisbury, F.B. and Cleon W. Ross, Fisiologi Tumbuhan Jilid I. Bandung : Penerbit ITB, 1995. [18] E. Lestari, Hubungan antara Kerapatan Stomata dengan Ketahanan Kekeringan pada Somaklon Padi Gajahmungkur, Towuti, dan IR 64. Biodiversitas ISSN: 1412-033X Volume 7, Nomor 1 Januari 2006 Halaman : 44-48, 2005. [19] P. Bakti, Analisis Kandungan Klorofil dan Laju Fotosintesis Tebu Transgenik PS-IPB 1 yang ditanam di Kebun Percobaan PG Djatiroto, Jawa Timur. Bogor : Skripsi Program Studi Manajemen Sumberdaya Lahan.Departemen Ilmu Tanah dan Sumberdaya Lahan.Fakultas Pertanian.IPB, 2009. [20] N. Danapriatna. (2013, March 30) Peranan Sulfur bagi Pertumbuhan Tanaman. Available: www.ejournal-unisma.net/ojs/index.php/.../238 [21] Dwivedi, A.K. and Shashi, Ambient Air Sulphur Dioxide and Sulphate Accumulation in Deciduous and Evergreen Plants. J.

[22]

[23] [24]

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Environ. Biol. 33, 1-3 (2012) ISSN: 0254-8704 CODEN: JEBIDP, 2010. Sulistijorini, Keefektifan dan Toleransi Jenis Tanaman Jalur Hijau dalam Mereduksi Pencemar NO2 Akibat Aktivitas Transportasi.Bogor : Sekolah Pasca Sarjana IPB, 2009. M.J. Hawkesford, Sulfur and Plant Ecology.Sulfur in Plant : an Ecological Perspective. New York : Springer, 2007. Hendriyani, I. dan N. Setiari, Kandungan Klorofil dan Pertumbuhan Kacang Panjang (Vigna sinensis) pada Tingkat Penyediaan Air yang Berbeda. Semarang : Artikel Penelitian.Jurusan Biologi FMIPA Universitas Diponegoro, 2009. Suharja dan Sutarno, Biomassa, Kandungan Klorofil dan Nitrogen Daun Dua Varietas Cabai (Capsicum annum) pada Berbagai Perlakuan. Bioteknologi 6 (1): 11-20, Mei 2009, ISSN: 0216-6887. Yanismai. (2013, November 25) Hubungan Antara Kepadatan Lalu Lintas dengan Kualitas Udara di Kota Padang.Available : _repository.unand.ac.id/412/1/yanismai_01209040.rtf Istantinova, D.B., M. Hadiwidodo dan D.S. Handayani, Pengaruh Kecepatan Angin, Kelembaban dan Suhu Udara, terhadap Konsentrasi Gas Pencemar Sulfur Dioksida (SO2) dalam Udara Ambien di sekitar PT Inti General Yaja Steel Semarang. 2012. M.S. Subaid, Pengaruh Suhu Udara, Curah Hujan, Kelembaban Udara, dan Kecepatan Angin, terhadap Fluktuasi Konsentrasi Gas – Gas NO2, O3, dan SO2 di Area PLTP Gunung Salak. Sukabumi. 2002. R. Achmad, Kimia Lingkungan. Jakarta : Penerbit Andi, 2004.

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Biological Control of Cabbage Head Caterpillar Crocidolomia binotalis, Zeller by Using Fusants Bacillus thuringiensis var. kurstaki and Bacillus thuringiensis var. Israelensis Cultured in Whole Coconut Fruits Siti Sumarmi1*), Retno Peni Sancayaningsih2), Sebastian Margino3) and RC. Hidayat Soesilohadi4) 1),2),4) Faculty of Biology UGM 3) Faculty of Agriculture UGM *)Correspondent author : [email protected] Abstract—Pathogenicity of fusants Bacillus thuringiensis isolates against cabbage head caterpillar larvae, Crocidolomia binotalis Z.was examined. Of the three Bt. fusants (F28, F31 and F33) were tested against C. binotalis, the second and third instar larvae. Pathogenicity of each isolate Bt. fusants varied for the second and third instar larvae. Among the 3 tested Bt. fusants. F28, F31 and F33 isolates showed that the Bt. F28 was the most pathogenic to the second instar larvae. The mortality of the second instar larvae were from 3,33 to 29.66 per cent recorded at 24 h after the treatment. While the Bt. fusant F31 recorded 4,17 per cent and the Bt. fusant F33 registered 2,50 per cent mortality of the second instar larvae. The Bt. fusant F 28 was the most pathogenic to the second instar larvae with the value LC50 and LC90 were 2,74 x1011 and 6,10 x 1013 respectively. However the LC50 and LC90 valued of Bt. fusant F31 and F33 were 1,03 x 10 17 and 2,14 x 10 23, and 2.33 x 1018 and 1,47x10 2 respectively. Therefore, the Bt. fusant F33 was not pathogenic to the second instar larvae. At 72 h of the treated time, the Bt. fusant F 28 was the most pathogenic to the third instar larvae. The percentage of mortality of the larvae caused by Bt. fusant F28 was 29,17 per cent, while the Bt. fusant F31 registered 13,33, per cent mortality whereas Bt. fusant F33 showed 10 per cent mortality. The value LC50 and LC90 of Bt. fusant F28 to the third instar larvae were 7,06 x108 and 4,98 x 109, whereas Bt. fusant F31 , and F33 respectively were 3,12 x 10 29 and 6,4 x 10 46, whereas Bt. fusant F33 was 8,74 x 1024 and 1,20 x10 42. In conclusion, all Bt. fusant strains of F28, F31, and F33 were pathogenic to C. Binotalis larvae. The third instar larvae was more susceptible than the second instar larva Keywords—Crocidolomia binotalis, Bacillus thuringiensis, fusants, Biological control

I. INTRODUCTION HE Cabagge head caterpillar Crocidolomia binotalis, Zeller (Lepidoptera: Pyralidae), is a serious insect pest especially of cruciferous vegetables is widely distributed in tropical and sub tropical region as South and Southeast Asia, Australia, South Africa, Tanzania and the Pacific Islands [1]. In Indonesia, C. binotalis and Plutella xylostella together may caused the

T

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yield loss up to 100 per cent, if suitable control for the insects is not undertaken, especially in the dry season, [2] The life history parameters and its biology has reported by [3]. Chemicals are the frontline defense for control of insect pests. The sole reliance on pesticides is however cause many problems. Resurgence of insect pests, development of resistance of residue of toxic chemicals in food stuffs are the major problems [4]. Biological control approach appears to be the best alternative to chemical control which are practical, effective, economical and safe. Bacillus thuringiensis (Berliner), a rod shaped gram positive entomopathogenic bacterium is abundant in soil [5]. B. thuringiensis is the most successful commercial biocontrol agent against insect pests more than 50th years [6]-[7]. B. huringiensis is an aerobic spore former is well known for its ability to produce crystal proteins during sporulation [8]-[9]-[10]. The crystal protein designated as delta endotoxin is toxic to many insect larvae, such as lepidopterans, coleopterans and dipteran larvae [10]-[11]. Since 2005, more than 200 crystal protein gen (cry gens) have been identified and classified to 44 different families [12]. II. MATERIALS AND METHODS The research was carried out in 2013. Cabbage head caterpillar collected from Cabagge crops in Kopeng Magelang, Central Java, Indonesia. The insect was mass reared in the insectary of Laboratory of Entomology, Faculty of Biology, University of Gadjah Mada, Indonesia.The larvae collected from the infested fields of cabbage were reared separately on cabbage leaves raised in green house under insecticide free condition. Pupae thus obtained were kept in a sterilized Petri dish and placed in the wooden cage of 60x30 cm. for adult emergence. When the moth started emerging, 25 – 30 days old small cabbage heads were provided for oviposition. Fifth teen per cent honey solution was provided as food for adults in sterilized vial with cotton plug. The moth laid eggs both on ventral and dorsal surface of cabbages leaves. Leaves with eggs were transferred to the cage for mass rearing of larvae. The second and the third instar F1generation larvae were used for bioassay (Figure 3). Of three Fusants B. thuringiensis (F28, F31,and F33

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isolates collected from the previous research maintained at Laboratory of Entomology, Faculty of Biology, University of Gadjah Mada, were used for bioassay to assess their pathogenicity against tested insect. [13]-[14]. To multiply the isolates they were streaked on plain Brain Hearth Infusion Agar (BHIA) plates and incubated for 24 h which was later inoculated in whole coconut [15]-[16]-17]; and was kept for growth under shaking condition at room temperature (28 0C) and incubated for 72 h. Then, the culture was examined under phase-contras microscope (magnification of 1200x). haversted in 10 ml of sterile water for taking colony count. A concentration of B. thuringiensis (1.2X107cfu/ml) to assess its toxicity against the test insects (Dip Leaf bioassay, described by [18] Tabashnik and Crushing (1987) was adopted with modification. Cabbage leaves were cut square of 6 cm These leaves were dipped in aqueous solution of the test isolates for 10 minutes. Excess fluid was drained off and the cutting cabbage leaves were dried under shade for 10 min. before transferring to glass jars (5 cm height and 3 cm diameter) covered with absorbent gauze. Cutting Leaves were placed slantingly so that larvae can move and feed on either side. Bioassays were done with three replications per treatment and ten larvae of test insect were released on each the container was covered with absorbent gauze. The cabbage leaves dipped in distilled water alone served as control. Mortality was observed at 24 h, 48 h, and 72 h after treatment and data were subjected to analysis of variance after suitable transformation (arcsine) and the means were separated by Duncan’s Multiple Range Test (DMRT)[19] and the LC50 and LC 96 were determine by Probit analysis [20] III. RESULT AND DISCUSSION Among three strains of fusants Bt. F28, F31 and F33 have variations result, the Bt. Fusant F28 was the most pathogenic to the C. binotalis larvae than the others. According to Knowles [21] explored that the variations in efficacy against different lepidopterans may be due to varying number of cry genes and the absence of specific binding sites. The mortality of the second instar larvae of C. binotaliscaused by the Bt. fusants F28 was from 3,33 to 29.66 per cent recorded at 24 h of the treatment. The Bt. fusants F31 recorded 4,17 per cent while the Bt. fusants F33 registered 2,50 per cent mortality of the second instar larvae (Table 1). Fusant F 28 was the most pathogen to the larvae with the value LC50 and LC90 was 2,74 x1011 and 6,10 x 1013 respectively than the fusant F31 and F33 with LC50 and LC90 valued were 1,03 x 10 17 and 2,14 x 10 23, and 2.33 x 1018 and 1,47x10 2 respectively. But the F33 was not pathogenic to the second instar larvae. TABEL 1. PATHOGENICITY TEST OF Bt. FUSANTS TO THE SECOND INSTAR LARVAE OF C. binotalis. Mortality of the Second Instar Fusant Larvae of Insect at : Total Bt.strains 24 h 48 h 72 h F28 A 16 12 1 29

B C D Per cent LC50 LC90 F31 A B C D Per cent LC50 LC90 F33 A B C D Per cent LC50 LC90 Control

1 2 1 16,67 3,05x109 1,75x1011

1 1 0 11,67 3,8x108 4,9x109

0 0 0 0,83

3 0 1 1 4,17 2,57x1017 2,61x1024

7 3 1 0 9,17 2,12x1010 8,37x1012

5 15 8 11 0 2 0 1 10,83 24,17 1,25x109 1,06x1011

1 1 1 0 2,50 2,33x1018 1,47x1025 0

0 1 0 0 0,83 1,03x1017 2,14x1023 0

0 1 0 2 0 1 0 0 0,00 3,33 1,03x1017 2,14x1023 0 0

2 3 1 29,17 3,47x108 3,89x109

Note : Concentration of the treatments A : 1,75 x 109,; B: 1,75 x 108, C: 1,75 x 107: D: 1,75 x 106

The mortality of the second instar larvae at 48 h after treatments can be seen at Table 1. The Fusant F28 recorded 11,67 per cent mortality to the second instar larvae while Bt. fusant F 31 was 9.17 per cent and Bt. fusant F33 was 0,83 per cent mortality of the second larvae. After 72 h of the treated time F28, F31, and F33 recorded 29.66 per cent, 24,83 per cent, and 3.33 per cent mortality to the insect larvae respectively . Fusant F 28 was the most pathogenic to the second instar larvae with the value LC50 and LC90 was 2,74 x1011 and 6,10 x 1013 respectively, whereas F31, and F33 were 12,57 x 10 17 and 2,61 x 10 24, however F33 was 2.33 x 1018 and 1,47x10 25. The mortality of the third insect larvae can be seen at Table 2. The Bt. fusants F28, F31, and F33 recorded 6,67 per cent while isolate F31 registered 3,33, per cent mortality whereas F33 showed 2,50 per cent mortality. The values LC50 and LC90 of F28 . were 2,74 x1011 and 6,10 x 1013 the value LC50 and LC90 F 31 were 1,03 x 10 17 and 2,14 x 10 23, However, F33 was not pathogenic to the third instar larvae. At 48 h of the treatments, fusant F28 showed 20, per cent mortality of the third instar larvae whereas F31 was 3,33 per cent mortality , while F33 showed 5,00 per cent mortality. The values LC50 and LC90 of F28 . were 8,89 x108 and 4,51 x 109 whereas F 33 were 3,12 x 10 29 and 6,4 x 10 48, respectively. However, F31 was not pathogenic to the third instar larvae. At 72 of the treatments, of Bt. fusant F 28 was the most pathogenic to the third instar showed 29,17 per cent mortality, Isolate F31 registered 13,33, per cent mortality whereas F33 showed 10 per cent mortality. The value LC50 and LC90 of F28 was 7,06 x108 and 4,98 x 109, whereas F31 , and F33 were 3,12 x 10 29 and 6,4 x 10 46, whereas F33 was 8,74 x 1024 and 1,20 x10 42. The F33 was not pathogenic to the third instar larvae

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TABEL 2. PATHOGENICITY TEST OF Bt. FUSANTS TO THE THIRD INSTAR LARVAE OF C. binotalis. Fusant Bt.strains

Mortality of the third instar larvae of insect at : 24 h. 48 h. 72 h.

Total

F28 A B C D Per cent LC50 LC90

4 3 1 0 6,67 2,74x1011 6,1x1013

19 2 1 2 20,00 8,98x108 4,51x109

2 25 0 5 1 3 0 2 2,50 29,17 7,06x108 4,98x109

A B C D Per cent LC50 LC90

1 2 1 0 3,33 1,03x1017 2,14x1023

0 1 3 1 3,33 0 0

3 4 3 6 1 5 0 1 5,83 13,33 3,42x1014 1,71x1022

A B C D Per cent LC50 LC90 Control

0 1 1 1 2,50 0 0 1 Per cent

4 0 0 2 5,00 3,12x1029 6,4x1048 1

0 4 2 3 1 2 0 3 2,50 10,00 8,74x1024 1,20x1042 0 2 6,67

F31

F33

Note : : Concentration of treatments A : 1,75 x 109,; B: 1,75 x 108, C: 1,75 x 107: D: 1,75 x 106

IV. CONCLUSION All fusants Bt strains ; F28, F31, and F33 were pathogenic to C. Binotalis. The third instar larvae was more susceptible than the second. Fusant Bt F28 and F31 will be assessed their pathogenicity to the instar larvae at small scale field. ACKNOWLEDGEMENT The investigators wish to thank to DP2M DIKTI RI for the financial support of the research. 2. LPPM UGM Indonesia for the research coordination and the Dean of Faculty of Biology UGM, Indonesia for give me opportunity to do this research and also Special thanks to I Nyoman Sumerta, SPd. for his sincere assistance in carrying out the experiments, and Suparmin for administration tasks. REFERENCES [1] Kalshoven, L.G.E. 1981. The Pests of Crops in Indonesia. Pt Ichtiar Baru-Van Hoeve, Jakarta. P. 341-344.

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Sudarwohadi S. 1975. Correlation between planting time of cabbage and population dynamics of Plutella maculipennis Curt. and Crocidolomia binotalis Zell. Bull. Penel. Hort. ,3, 314 (in Indonesian with English Summary) [4] Rao, V. P., M. A. Ghani, T. Sankaran & K. C. Mathur. 1971. A review of the biological control of insects and other pests in the south-east Asia and the Pacific region. Commonwealth Inst. Biol. Contr. Tech. Comm. No. 6: 149 p. [5] Bora, R.S., Murthy, M.G., Shenbagarathai, R. and Sekar, V., 1993, Introduction of a lepidopteran specific crystal protein gene of Bacillus thuringiensis sub sp. kurstaki by conjugal transfer into a Bacillus megaterium strain that persist in the cotton phyllosphere. App. Envoy. Mic., 60: 214-22. [6] Federici, B.A. (1999). Bacillus thuringiensis in Biological Control. . In: Handbook of Biological Control. T. Fisher (Ed.)Academic Press (Ed.) 575-593, ISBN 10: 0-12-257305-6 [7] Navon, A. (2000). Bacillus thuringiensis insecticides in crop protection-reality and prospects. Crop Protection 19(8-10): 669 – 676. [8] Krieg, A., 1961, Bacillus thuringiensis, Berliner. Mitt. Boil. Bundesantatt land- Forstwirtsch, Berlin- Dahlem, 103: 3-79. [9] Heimpel, A.M., 1963, The status of Bacillus thuringiensis. Bull. Am.Chem. Soc., 41: 64-74. [10] Heimpel, A.M. and Angus, T.A., 1959, Diseases caused by certain spore forming bacteria, In: Insect Pathology: An advanced Treatise 2, Academic Press New York, pp. 68. [11] Feitelson, 1. S., Payne, 1. & Kim, L. (1992). Bacillus tburingiensis:insects and beyond. Biol Technology 10, 271-275. [12] Crickmore N, Zeigler DR, Feitelson J, Schnepf E, van Rie J. Lereclus D, BaumJ, Dean DH (1998) Revision of the nomenclature for the Bacillus thuringiensis pesticidal crystal proteins. Microbiol Mol Biol Rev 62:807–813. [13] Sumarmi, S., S. Margino , S. Yuwono 2006. Efikasi Fusan Bacillus thuringiensis kurstaki dan Bt. Israelensis terhadap larva Aedes aegypti dan Plutella xyllostela. ( laporan penelitian Hibah bersaing 2006) [14] Sumarmi, S., S. Margino, D.T. Buwono, dan RC. Hidayat. 2010. Pengendalian Nyamuk Vektor Malaria Anopheles aconitus dan Ulat Jagung Helicoverpha armigera (Hubner) Hardwick Secara Hayati dengan Fusan Bacillus thuringiensis var kurstaki dan Bt. var israelensis) ( laporan penelitian Stranas 2010). [15] Prabakaran, G.; Hoti, S. L.; Manonmani, A. M.; Balaraman, K. (2008), Coconut water as a cheap source for the production of δ endotoxin of Bacillus thuringiensis var israelensis - a mosquito control agent. Acta Tropica, 105, 35–38 [16] Poopathi and Kumar, 2003 S. Poopathi and K.A. Kumar, Novel fermentation media for production of Bacillus thuringiensis subsp. israelensis, J. Econ. Entomol. 96 (2003), pp. 1039–1044. [17] Poopathi et al., 2002. S. Poopathi, K. Anup Kumar, L. Kabilan and S. Vaithilingam, Development of low cost media for the culture of mosquito larvicides, Bacillus sphaericus and Bacillus thuringiensis serovar. israelensis, World J. Microbiol. Biotechnol. 18 (2002), pp. 209–216. [18] Tabashnik, B.E. and Cushing, N.L., 1987, Leaf residue Vs topical bioassay for assessing insecticide resistance in the Diamond back Moth, Plutella xylostella L. FAO Pl. Prot. Bull., 35: 11-14. [19] Duncan, D.B., 1955, Multiple range and multiple ‘F’ tests. Biometrics,11: 1-42. [20] Finney, 1971 D.J. Finney, Probit Analysis (3rd ed.), S. Chand and Co. Ltd., New Delhi (1971) pp. 50–80. [21] Knowles, B.H., 1994, Mode of action of Bacillus thuringiensis upon feeding on insects. Adv. Insect Physiol., 24: 275-308.

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The Effect of Trehalose Levels on Post-Thaw Sperm Membrane Integrity of Boer Goat Semen Nurul Isnaini Faculty of Animal Husbandry, Brawijaya University, Malang, Indonesia *)

Corresponding author: [email protected]

Abstract— This study was conducted to evaluate the effect of trehalose levels on post-thaw sperm membrane integrity at different level of trehalose in tris based diluter of Boer Goat semen. Fresh semen was collected from six aged male Boer goats. Immediately after collection using artificial vagina, the semen was evaluated for quality and diluted with tris aminomethane-base extender in 10 folds (1 semen: 9 extender). The effect of different levels of trehalose (1.5%; 2.5% and 3.5%) in the diluter on the sperm membrane integrity was evaluated in this study. The diluted semen was freezed with standard method. The result showed that fresh semen collected from Boer bucks in this study indicated a normal quality and therefore, could be used for further treatment. According to the varian analysis it was shown that 2.5% trehalose resulted higher sperm quality than those the other levels on post-thaw of Boer Goat semen. It was concluded that the addition 2.5% trehalose in tris-based medium resulting optimal sperm membrane integrity of Boer goat semen post thawing. It was suggested, that for resulting optimal sperm membrane integrity post thawing of Boer goat semen in tris-based medium should be supplemented with 2.5% trehalose. Keywords— Boer goat, trehalose, cryopreservation, membrane integrity

I. INTRODUCTION uring dilution, cooling and freezing, the sperm quality reduce corresponding to appropriate processing technique and cryoprotectant used. The addition of cryoprotectant in the medium or semen extender can retard the reduce of semen quality in those process. Disaccharides have a stabilizing effect on biological membrane. Trehalose is found in animals capable of enduring cold temperatures, whereas sucrose is found in plants [1]. Cryopreservation are known to damage sperm membranes [2]. This damage includes swelling and disruption of plasma and outer acrosome membranes [3], changes in membrane fluidity [4], disregulation of intracellular Ca2+ influx [5] and changes in enzyme activity [6]. Membrane integrity is not only important for sperm metabolism, but also a correct change in the properties of the membrane is required for successful union of the male and female gametes, i.e. for sperm capacitation, the acrosome reaction and the binding of the spermatozoa to the egg surface. The integrity and functional activity of the sperm membrane is of fundamental importance in

D

the fertilization process and assessment of membrane function may be useful indicator of the fertilizing ability of spermatozoa. The objective of the present study was conducted to evaluate the effect of trehalose levels on postthaw sperm membrane integrity at different level of trehalose in tris based diluter of Boer Goat semen. II. MATERIAL AND METHOD a. Animal and Sperm Preparation Ejaculated were obtained from 6 male Boer goats (originated from Australia Breeding Herd) aged of 2.0 – 2.5 years with about 100 kgs in weight, using artificial vagina. The bucks were maintained at Field Laboratory of the Faculty of Animal Husbandry, University of Brawijaya Malang. After collection, the semen was evaluated macroscopic and microscopically. Sperm motility was evaluated by placing a drop of well mixed semen on a prewarmed glass slide under a coverslip and examining it at x100 and x400 magnification by phase contrast microscopy. Motility was assessed subjectively on the basis of spermatozoa that were moving either progressively or non progressively or those that were nonmotile. Viability and abnormality were evaluated by eosinenegrosine staining. One hundred sperm cells were scored per slide. Sperm concentration was measured with a Thoma hemocytometer. Only semen with individual motility of sperm of more than 70% and mass motility of 2+ and 3+ was used for research material. Semen collection was regularly conducted twice a week per individu of animal. The selected semen was diluted with tris-base diluent containing 1.5%; 2.5% or 3.5% trehalose and equilibrated at 5˚C for 2 h. Before loading into 0.25 ml straw, semen was evaluated for the quality and then straw containing diluted semen was horizontally placed on liquid nitrogen vapour (-140˚C) for 9 min for pre-freezing. Immediately thereafter, the straw was plunged into liquid nitrogen for at least 24 h. Thawing was conducted by transfer the frozen straw into the warm water (37˚C) for 30 sec. For each treatment (1.5%; 2.5% and 3.5% trehalose) was taken for evaluation of membrane integrity by hypoosmotic swelling test (HOS test). 2. Hypoosmotic Swelling (HOS) test The HOS test solution contained 0.49 g Na-sitrate x 2H2 O and 0.9 g fructose in 100 ml aquadest. The os-

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motic swelling technique consisted of mixing 0.1 ml semen with 1.0 ml hypo-osmotic solution and allowing the mixture to incubate at 37 C for at least 30 min. The mixture was allowed to stand for at least 1 min before observations were made by phase-contrast microscopy at x400 ; 100 spermatozoa were observed. The percentage spermatozoa that showed typical tail abnormalities indicative of swelling was calculated. III. RESULT If you are using Word, use either the Microsoft Equation Editor or the MathType add-on (http://www.mathtype.com) for equations in your paper (Insert | Object | Create New | Microsoft Equation or MathType Equation). “Float over text” should not be selected. a. Characteristics of fresh semen Table 1 shows that the characteristics of Boer goat fresh semen used in this study was normal. TABLE 1. CHARACTERISTICS OF FRESH SEMEN Parameter Mean + SD Color

Creamy

Consistency

Less opaque

pH

7.00 + 0.0

Volume (ml)

0.64 + 0.27

Concentration (106 / ml)

3238.00 + 187.78

Mass motility

2+ - 3+

Individual motility (%)

74.50 – 4.38

Life sperm (%)

86.88 + 2.31

Abnormal sperm (%)

8.63 + 1.97

Membrane integrity (%)

72.59 + 6.21

b. Sperm membran integrity post thawing Sperm membrane integrity post thawing diluted with tris-base diluent containing different levels of trehalose is shown in Table 2. TABLE 2. MEAN (+ SD) OF MEMBRANE INTEGRITY SPERM FOLLOWING TREATMENT WITH DIFFERENT TREHALOSE LEVEL POST THAWING Trehalose levels Membrane integrity 1.5%

38.34 + 5.69b

2.5%

42.12 + 2.66 b

3.5%

29.17 + 6.93a

a.b.c within column significant difference (P 200 mg/dL and/or HbA1c > 6.5 % were considered to have diabetes; subjects with a fasting plasma glucose 100 to 125 mg/dL (IFG) or 2 hour plasma glucose level 140 to 199 mg/dL (IGT) or HbA1c 5.7 to 6.4% were considered to have increased risk for diabetes (prediabetes); subjects with a fasting plasma glucose < 110 mg/dL or 2 hour plasma glucose level < 140 mg/dL or HbA1c < 5.6 % were regarded as a having normal glucose tolerance (NGT) [1]. The exocrine insufficiency was assessed according to increasing of amylase and or lipase level. The considered reference range of normal amylase was 30 - 110 U/L and lipase was 30 210 U/L. Fasting venous blood was collected from all of subjects, it was centrifuged (at 1500 g for 15 minutes). The separated plasma was used to assay the HbA1c. HbA1c was measured with ion-exchange high-performance liquid chromatography using an automated analyzer (BioRad D10). The separated serum was divided into four aliquot. One was designed for immediate assay of glucose and lipid profile which included Triglyceride (TG), total cholesterol (CHOL), high density lipoprotein (HDL), low density lipoprotein (LDL). The other aliquots were stored at -20oC for subsequent assay for amylase, lipase, insulin and proinsulin. The assay of sample analysis was carried out by using different reagent kits as per procedure which was defined by manufacturer. The immediate assay of sample analysis was measured on a fully automated analyzer. The fasting plasma glucose was measured by the hexokinase method. The serum triglyceride was measured by the enzymatic method (GPO-POD method, End Point). For determination of total cholesterol, an enzymatic (CHOD-POD) colorimetric method was used. The direct measurement for HDL and LDL were done by using enzymatic methods. Insulin concentration was measured by Sandwich enzyme immunoassay method. Insulin concentration was measured using kit from Ucsn, China. Homeostasis model assessment of insulin resistance (HOMA-IR) was used for the direct measurement of insulin resistance and was calculated as follows: HOMA-IR = [fasting insulin (µU/mL) (mg/dL)]/405 [4]

x

fasting glucose

The cut-off point to define insulin resistance corresponds to HOMA-IR ≥ 3.8 [4][5] The quantitative insulin sensitivity check index (QUICKI) was calculated from fasting plasma glucose (mg/dL) and insulin (µIU/mL) concentrations, as follows: QUICKI = 1/(log Io + log Go ) [6] Amylase and lipase activity were measured by photometric enzymatic method. The amylase activity was assayed using BioAssay Systems’ QuantiChromTM αAmylase Assay Kit (DAMY-100). Lipase activity was

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assayed using BioAssay Systems’ QuantiChromTM Lipase Assay Kit (DLPS-100). The results were analyzed statistically using SPSS version 16.0 statistical software. The results were expressed as mean ± SD if the variables were continuous, and as percentage, if categorical. Multivariate analysis of variance was used for differences in continuous variables. Multiple regression was applied for correlation studies. All statistical tests were two-side and a P99.99%) from local vendor was used without further purification. Nickel nitrate hexahydrate (Ni(NO3)2.6H2O)and aluminum oxide (γ-Al2O3) were supplied by Merck Germany.

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B. Preparation of catalysts The catalysts were prepared according to the method given by Miloneet al. [8]. Aluminum oxide (γ-Al2O3) having surface area 150-400 m2/g, pore size 0.5-1 cm3/g with radius 3-12 nm was slowly added to methanol solution contain5%, 10%, and 15% ofNi salt and stirred for 24 h at ambient temperature. The solvent were slowly removed by rotary evaporator at 35 oC for 1 hour. All the catalysts were dried at 120 oC for 2 hours and calcined at 450 oC for 3 hours. Before hydrogenation reaction, all the catalysts were reduced at 450 oC for 5 hours. C. Catalysts characterization The characteristic of Ni/γ-Al2O3 catalyst were investigated by X-ray diffraction (XRD, Philips X’pert with Ni-filtered Cu Kα radiation) operated at 40 kV and 30 mA. The surface morphology and the particle size was determinedby Scanning Electronic Microscopy (SEM) TM3000. The BET surface area was evaluated from nitrogen adsorption isotherms at 77 K performed in Quantochrome NovaWin2.

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D. Liquid phase hydrogenation of furfural Catalytic hydrogenation of furfural was performed in modified two-neckedglass reactor fitted with sampling and hydrogen valve. Before used, the Ni/γ-Al2O3catalyst (0.5 g) wasreduced in glass reactor with hydrogen flowed for 30 minute at 100 oC(95 mmHg). The activation of catalysts was repeated for three times to completely replace air in the reactor. Then 5 ml of furfural were added to the reactor and stirred. After the necessary connection between reactor and the hydrogen gas cylinder was duly made, H2 passed into the reactor and reaction temperature gradually increased up to 150 oC. The reaction time was counted after the setting temperature obtained. The progress of the hydrogenation reaction maintain by sampling a sufficient number of microsamplein 30, 60, 90, 120, 150, and 180 minutes. After the reaction complete, all the products analyzed by means of a Shimadzu QCMS-QP2010S gas chromatograph spectrometer massa with RastekstabilwakR-DA column and FID detector.

The XRD patterns of the 5Ni/Al and 10Ni/Al catalysts compared to γ-Al2O3 after reduced at 450oC under hydrogen were depicted in Figure 2. The sharp diffraction at 2θ = 44 and 66o correspond to the γ-Al2O3(440).

III. RESULT AND DISCUSSION A. Catalysts characterization Figure 1 compile the profile of surface morphology (SEM) of 5% Ni/γ-Al2O3(5Ni/Al), 10% Ni/γ-Al2O3 (10Ni/Al), and 15% Ni/γ-Al2O3 (15Ni/Al). Due to the different concentration of nickel deposited on Al2O3, the distinct differences of the morphology and particle size were observed. The particle size of catalyst (A) 5Ni/Al is in the range 50-100 µm. The sample (B) 10Ni/Al andsample (C) 15Ni/Al is in the similar particle size range 30-60 µm. The smaller particle size implies a higher surface energy of the particle [9]. Figure 2. XRD spectra of the γ-Al2O3, 5Ni/Al, and 10Ni/Al reduced at 450oC

The new peaks appear at the 2θ = 44.5, 52, and 76o in agreement with Ni(111), Ni(200), and Ni(222) species, respectively [5]. The specific diffraction of NiO at 2θ = 43 and 63 didn’t observed which indicate that Ni2+ transformed to Ni0 after H2 treatment at 450oC. Another peak detected were at 2θ = 37 and 66o correspond with NiAl2O4 from the reaction of Ni2+ with γ-Al2O3. TABLE 1. BET SURFACE AREA AND POROSITY OF THE CATALYSTS Catalyst

BET surface area (m2/g)

Pore volume (cm2/g)

Average pore diameter (Å)

5Ni/Al 10Ni/Al 15Ni/Al

123.919 108.270 101.905

0.252

40.8268

0.221

41.0688 40.7094

0.206

Figure 1. SEM of (A) 5Ni/Al; (B) 10Ni/Al; (C) 15Ni/Al calcined at 450oC

The BET surface area and the pore volume of the synthesized Ni/γ-Al2O3 catalysts are shown in Table 1. The BET surface and the pore volume of 5Ni/Al is the largest compared with 10Ni/Al, and 15Ni/Al. No significant change in the pore size distribution from 5Ni/Al, 10Ni/Al, and 15Ni/Al was observed. The increasing of the salt loading decreases the surface area as well as the pore volume of γ-Al2O3. The decreasing of surface area

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was attributed to nickel which fills up the pores of the support [8]. B. Influence of reaction time on activity and selectivity The hydrogenation of furfural (FFald) was studied at 150 oC (95 mmHg) in the modified glass reactor. Scheme 1 showed the hydrogenation of furfural on 5~15% Ni/γ-Al2O3. The result showed a continuous decrease in the furfural (FFald) concentration and the formation of furfuryl alcohol (FFalc) as a majorreaction product. Unreacted 5-methylfurfural (MF) was also observed in the final reaction product. It is suggested that the catalyst is highly selective to reduce C=O bond from furfural but unreactive for any starting material.According to the detection of MF, lies in fact that all raw materials used for the manufacture of furfural contain some methyl pentosanwhich might hydrolyzed to MF [1]. The direct catalytic reaction of alkylmethylfurfuralwithin the starting materialalso reported to give MF [10].

Scheme 1. Hydrogenation product of furfural on 5~15% Ni/γ-Al2O3

The conversion of FFald and the selectivity of FFalcon 5Ni/Al, 10Ni/Al, and 15Ni/Al are shown in Figure 3. Product conversion by the activity of15Ni/Al gave the highest result compared to 5Ni/Aland 10Ni/Al. This catalyst converted FFald to FFalc up to 15% within 150 minutes of reaction time. The 5Ni/Al catalyst is considerably as good catalyst by 2.1% conversion of starting material after 120 minutes. Theactivity of 10Ni/Al showed 2.0% conversion ofFFald after 180 minutes. It is noteworthy that the higher the salt loading gives the increasing activity of the catalysts. However, conversion of FFald to FFalc should be optimizedto increase the product formation. Mäki-Arvela reported that temperature of the reductionand time were influence the activity of the catalysts. Catalytic activity and selectivity of Au/TiO2 catalyst in the hydrogenation of crotonaldehyde exhibit a maximum after increasing catalysts-reduction temperature [11]. The selectivity formation of FFalcbyhydrogenation reaction of FFaldon Ni/γ-Al2O3 is depicted in Figure 3. It observed that within 60 minute of reaction time, the selectivity of 5Ni/Al increased sharply from 27.9% to 85.7% then prolonged the reaction to 120 minutes completely reduce FFald to FFalc up to 100% conversion. Similarly, 10Ni/Al and 15Ni/Al also exhibit a similar selectivity, raised up from 63.4% and 77.5% to 100%, after 60 minute of reaction time. However, prolonged the reaction time until 180 minutes reduce the selectivity of 5Ni/Al catalyst to 46.9%, but 10Ni/Al and 15Ni/Al remain unchanged until 180 minutes of reaction time.

Figure 3. Conversion of furfural and selectivity for furfuryl alcohol as a function of reaction time. Reaction conditions: T = 150 oC (90 mmHg); furfural = 3 ml; catalyst = 0.5 g; and for 5Ni/Al; and for 10Ni/Al; and for 15Ni/Al.

It is likely that within the range of the reaction time, all the catalysts showed the higher selectivity to the formation of FFald. However, the decreasing selectivity of 5Ni/Al after 180 minutes is considered bythe formation of side product.Structure determination of side product is under consideration.Baijunet al.reported the formation of furfuryl alcohol (FFald) and tetrahydrofurfuryl alcohol (THFald) are parallel reaction, whereas THFald is the final product from further hydrogenation of FFald [12]. IV. CONCLUSION A series of 5~15% Ni/γ-Al2O3 catalysts were prepared by wet impregnation method. The characterization of catalysts conducted by SEM, XRD and BET surface area. The activity of these catalysts utilized for hydrogenation reaction of furfural to furfuryl alcohol at 150 oC for 30 to 180 minutes of reaction time. On the basis of the result presented herein, 15% Ni/γ-Al2O3showed the higher activities among others by 15% conversion of furfural to furfuryl alcohol with the selectivity up to 100% after 150 minutes reaction. ACKNOWLEDGMENT This work was supported by DPP/SPP research grant through DIPA Faculty of Science, University of Brawijaya No. 16/UN10.9/PG/2013and Student Creativity Program (PKM-P) research grant from The Directorate General of Higher Education, Indonesian Ministry of Education. REFERENCES [1]

[2]

[3]

[4]

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K. J. Zeitsch, The Chemistry and Technology of Fufural and Its Many by-products, Amsterdam, Elsevier, vol. 3, 2000, pp. 77– 78. R. H. Kottice, ”Furfural derivatives,” in Kirk-Othmer Encyclopedia of Chemical Technology, 4th ed., J. Kroschwitz, M. Home-Grant, Eds. New York: John Wiley and Sons, 1997, pp. 155. A. Corma, S. Iborra, and A. Velty, “Chemical routes for the transformation of biomass into chemicals,” Chem. Rev., vol. 107, pp. 2411–2502, 2007. L. J. Friner and H. Fineberg, “Copper chromite catalyst, process for its production, and process for production of unsaturated aldehydes from alcohols,” DE Patent 3007139, 1980.

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[6]

[7]

[8]

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Rodiansono, T. Hara, N. Ichikuni, and S. Shimazu, “ A novel preparation method of Ni–Sn alloy catalysts supported on aluminium hydroxide: Application to chemoselective hydrogenation of unsaturated carbonyl compounds,” Chem. Lett., vol. 41, pp. 769–771, 2012. A. Kaufman and J. C. Adams, “The use of platinum oxide as catalyst in the reduction of organic compounds. IV. Reduction of furfural and its derivatives,” J. Am. Chem. Soc., vol. 41, pp. 769–771, 2012. J. Kijenski, P. Winiarek, T. Paryjczak, A. Lewicki, and A. Mikolajska, “Platinum deposited on monolayer supports in selective hydrogenation of furfural to furfuryl alcohol,”Appl. Catal. A: Gen, vol. 233, pp. 171–182,2002. C. Milone, C. Gangemi, G. Neri, A. Pistine, and S. Galvagno, “Selective one step synthesis of (–)menthol from (+)citronellal on Ru supported on modified SiO2,” Appl. Catal. A: Gen., vol. 199, pp. 239–244, 1999.

[9]

S-P Lee and Y-W Chen, “ Selective hydrogenation of furfural on Ni–P, Ni–B, and Ni–P–B ultrafine materials”, Ind. Eng. Chem. Res., vol. 38, pp. 2548–2556, 1999. [10] W. Yang and A. Sen, “Direct catalytic synthesis of 5methylfurfural from biomass-derived carbohydrates,” Chem. Sus. Chem., vol. 4, pp. 349–352, 2011. [11] P. Mäki-Arvela, J. Hájek, T. Salmi, and D. Yu Murzin, “Chemoselective hydrogenation of carbonyl compounds over heterogenous catalysts,” Appl. Catal. A: Gen., vol. 292, pp. 1–49, 2005. [12] L. Baijun, L. Lianhai, W. Bingchun, C. Tianxi, and K. Iwatani, “Liquid phase selective hydrogenation of furfural on Raney nickel modified by impregnation of salts of heteropolyacids”, Appl. Catal. A: Gen., vol. 171, pp. 117–122, 1998.

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A New Oxalate Ion Sensors Based on Chitosan Membrane Atikah1*), R. Retnowati2), H. Sulistyarti3), B.Siswojo4), Z. Rismiati5) Department of Chemistry, Faculty of Mathematics and Natural Sciences, University of Brawijaya 4) Department of Electrical Engineering, Faculty of Engineering, University of Brawijaya 1,2,3,5)

*)

Corresponding author: [email protected]

Abstract—The aim of this research is to prepare potentiometric sensor prototype as a coated wire oxalate ion selective electrodes (CWE) for urinary oxalate determination by potentiometric method. The sensor is composed of a platinum (Pt) wire that is coated directly on the membrane surface.The membrane’s sensor consist of a mixture an active material of Chitosan was protonation using acetic acid 3%v/v in order to have anion exchange properties and Aliquat-336-oxalate as additive material, polyvinylchloride (PVC) as supporting material, dibuthylphtalate (DBP) as plasticizer = 4:1:33.5:61.5 (% w/w) dissolved in tetrahydrofuran (THF) solvent (1:3 w/v).The characterization of the basic properties of sensor included : sensitivity and linearity of response (detection limit), response time, influence of pH and temperature, soaking time, selectivity against foreign ions and also life time. The sensor shows a good Nernstian slope of 29.9 ± 0.1mV/ decade in wide linear range concentration from 1.0 × 10 -5 to 1.0× 10 -1 M .The detection limit of 2,56x10-6M (0.22 ppm),respond time fast (20 seconds) and was found usable in pH range of 3.0 – 7.0 and temperature of 2050oC. Selectivity was obtained over HPO42-,SO42-, PO43-, Cl,H2PO4-,I-,SCN- ,creatinine and also urea thats contained in the urine. The electrode is reproducible and stable for nearly 2 months. This kind of CWE was successfully applied in determination of oxalate anion in urine samples at concentrations corresponding levels of oxalate kidney stones light and medium provide average accuracy of 98.72% and an average precision of 99.81%. Keywords—Coated Wire Ion Selective Electrode (CWE), potentiometric sensor ,Chitosan, oxalate,membrane.

I. INTRODUCTION ROLITHIASIS is a symptom that is mostly caused by a multifactorial metabolic disorder. Originators in part because a diet rich in fat and protein, lacking fiber intake combined with inactivity, resembling the so-called modern industrialised life style and genetic predisposition enhance developing urolithiasis [4] Typical symptoms of an acute stone colic are, inter alia, agony, sickness and hematuria. Urinary calculus formation is caused by disturbed urinary compositions with altered urinary pH, increased concentrations of lithogenic components as, e.g. calcium, oxalate, phosphate and a lack in inhibitoric substances as, e.g. citrate and magnesium. Calcium oxalate represents the most frequent mineral phase found in uroliths with a frequency of approximately 70–75% [1]

U

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Oxalate is one of the important nutrients in the human diet found principally in spinach, beet leaves, etc. Oxalate is primary chelator of calcium ion, so it forms chelates with calcium. Oxalate ion also inhibits the calcium adsorption in the body and if it is not sufficiently degraded, it may accumulate in the body. It plays a crucial role in the formation of most renal stones. In the body oxalates can be found as two forms in vivo: oxalate ions and calcium oxalate monohydrate (COM) crystals that readily form in the presence of calcium[ 2]. Owing to the high recurrence rate of calcium oxalate stone formation in case of inadequate treatment, evaluation of the individual causes for calcium oxalate urolithiasis is of utmost clinical importance [1]. Urinary stone formation has been evolved to a widespread disease during the last years. The reasons for the formation of urinary stones are little crystals, mostly composed of calcium oxalate, which are formed in human kidneys. The early diagnosis of the risk for urinary stone formation of patients can be determined by the “Bonn-Risk-Index” method based on the potentiometric detection of the Ca2+-ion or oxalate ion concentration and an optical determination of the triggered crystallisation of calcium oxalate in unprocessed urine [1]. Most of the analytical methods like ion chromatography, neutron activation analysis, atomic absorption spectrophotometry and mass spectrometry etc. have been reported for the determination of oxalate ions at a very low concentration levels, but these methods require expensive instrumentation, delicate and expert handling of the sample and instrument. Hence potentiometric determination based on ion selective electrodes offer several advantages such as ease of preparation, low cost, simple procedure and easy instrumentation. It gives relatively fast response, wide linear range, good selectivity, high detection limit and online applications as compared to other analytical methods[1,3]. During the last decade, a number of studies have focused on properties of functionalized chitosan, a relatively new class of an adsorbent, chelating agent and ion exchanger. Chitosan, a poly-[1-4]β-D-glucosamine, is a derivative of chitin, a naturally occurring polysaccharide found in insects, arthropods and crustaceans. Chitosan is hydrophobic, bio-degradable, bio-compatible and low toxicity so widely used for various applications including pharmaceutical, biotechnology and wastewater treatment. Chitosan is well known for complexing transition metal ions, through chelation at its amino group[1]. It was also shown that chitosan can be used as

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potent ionophores for the preparation of ion selectivesensors[4,5]. The potentiometric selective coefficients of ISEs sensitive to organic anion included oxalate ion have been reviewed in detail. The application of function so far, several experimental studies have demonstrated that the generation of a membrane potential of those type of ISEs could be attributed to permselective ion transport across the liquid membrane/solution interface, i.e., charge separation through a preferential uptake of a primary ion by a sensing element in the liquid membrane, leaving its hydrophilic counter ion in an aqueous sample solution and usually exhibit the Hofmeister pattern with the largest selectivity to lipophilic cations [6,7]. These can also be used in complex and coloured media. Therefore, there has been progressive growth in the development and application of potentiometric sensors based on polymeric membrane ion selective electrodes incorporating ionophores for the detection of different cations and anions and other biologically important compounds[3]. Recent studies in different laboratories showed incorporation of different novel materials as ionophores for the ion selective electrodes[4]. A strong interaction of the anions and the ionophore as well as the steric effect associated with the structure of the ligand gives rise to selectivity sequence. Thus the research on sensing materials for anion as well as developments including new synthetic ionophores, miniaturization of the detecting device like coated wire selective ion electrode (CWE) etc. makes it an ever-expanding culture for research in chemical sensors [2,8]. In this paper, we wish to introduce a highly oxalate ion selective potentiometric sensor based on a heterogeneous membrane of chitosan as ion carrier membranes and Aliquat-336-oxalae as additive material supported by polymeric polyvinyl chloride (PVC) of high molecular weight and plasticizer dibutyl phthalate (DBP) then its application for the determination of urinary oxalae ion as early diagnosis of the risk for urinary stone formation of patients can be determined by the “Bonn-RiskIndex” method based on the potentiometric detection of the oxalae ion concentration combine with Ca2+ determination by atomic absorption spectrophotometric (AAS) and their result compared by an optical determination of the triggered crystallisation of calcium oxalate in unprocessed urine.

II. METHODOLOGY A. Apparatus and emf measurements All potential measurements were performed using the following assembly: Hg, Hg2Cl2 (Sat’d)//sample solution/PVC membrane/Pt-wire electrode. A pH-meter (Fisher E 520) was used for potential measurements at 26°C ± 0.5oC. The activities of ioxalae ion (C2O42-) ions in the urine were calculated according to the Debye– Hückel approximation. B.Reagent and solution Chitosan powder isolation results from the shell of jerbung shrimp (Penaeus merguinensis) with a degree of deacetylation 68% (w/w) is use as ionophore was protonated using Acetic Acid (3%), Aliquat-336 oxalae

as additive material,polyvinyl chloride (PVC) of high molecular weight , dibutylphtalate (DBP) as a plsticizer were purchased from sigma, tetrahydrofuran is products from E.Merck. Platinum wire (99,9% ; ∅ 0.5 mm) is products from Aldrich and RG-58 Coaxial cable as connector ISE to mV potentiometer. All other reagent used were of analytical reagent grade, and doubly distilled water was used throughout. Ca oxalate, Acetic Acid (3%), Aliquat 336-S,NaOH, Na3PO4, CaCl2, creatinine, uric acid. C. Construction and calibration of the electrodes The membranes electrode was prepared by mixing thoroughly by dissolving protonated chitosan, Aliquat336-Oxalat, PVC, DBP plasticizer in THF solvent (1:2 v/w). This solution was deposited directly onto a platinum wire approximately 0.5 mm in diameter and 10 cm in length whose tip had been melted in flame to form a spherical button was soldered to a length of RG-58 coaxial cable, and the solvent was evaporated for approximately 30 minutes and then allowed to stand overnight in the oven at 50oC. A membrane was formed on the platinum surface and the electrode was allowed to stabilize overnight. Prior to use the electrode was initially conditioned by soaking it overnight in a 0.1M solution of Naoxalate (Na2C2O4) to be measured. When not use, the electrode was store in air between use and reconditioning immediately before using by soaking for at least 1 hour in a 0.1M solution of Na2C2O4. The utility, composition of polymer membrane, respond characteristic, and selectivity coated wire oxalae ion selective electrode (CWE) were investigated. The electrode potential measurement was made under constant conditions by taking 25 mL of solution for each measurement in a cell thermostated at 26 ± 0.5 oC , immersing the electrode to a constant depth in the solution, and stirring at a constant rate by means of a magnetic stirring bar. In all experiments the electrode potential measurement was carried out from low concentration to high concentration. The electrode tip was rinsed with deionized water and then immersed in one of the standard solution. D. determination of oxalate ion in urine Urine samples were taken from the Central Laboratory of Clinical Pathology Hospital laboratory which have completed used for clinical pathology examination (samples are no longer used or discarded) of patients with kidney stones (calcium oxalate crystals containing microscopic examination based on positive optical crystal with no indication of the patients showed symptomatic urolithiasis) taken from 50 patients with the risk of kidney stones and high light each 25 samples and 10 urine samples taken from normal patients as a whole amounted to 92 samples. Each urine sampel put in a polyethylene tubes. The samples were immediately centrifuged and stored at 4oC. A 1.0 mL aliquot of the sample was transferred into a 10-mL measuring flash and diluted with distilled water. For each analysis, the oxalae sensor and double –junction Ag/AgCl reference electrode were immersed in the same solution, and the potential reading were recorded. A typical potentiometric calibration plot was made by plotting the potential

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change against the logarithm [C2O42-] concentration. The obtain calibration curve was used for subsequent determination of C2O42- in unknown samples. The results of determination of C2O42- on both the optical microscopy method and the potentiometric method using oxalate ion sensors tested for their accuracy, precision and also for early diagnosis of the risk for urinary stone formation of patients can be determined by the “Bonn-Risk-Index” (BRI) method based on the ratio of potentiometric detection of the urinary oxalae ion and Ca2+-ion concentration by AAS and their result compare to an optical microscopy determination of the triggered crystallisation of calcium oxalate in unprocessed urine. III.

RESULT AND DISCUSSION

A. Influence of membrane composition The different aspect of membrane preparation based on protonated chitosan as ionophore and Aliquat 336oxalate as additive material containing different PVC/plasticizer ratios were mix in THF solvent (1:2 ratio v/w) were studied and the results revealed that the amount of ionophore, the nature of solvent mediator, the plasticizer/PVC ratio significantly influence the sensitivity of ion selective electrodes. Membrane with a composition ratio of wt% PVC: DBP: the active ingredient Chitosan-oxalate; additives Aliquat 336oxalate = 4: 1: 33: 62 in THF 1: 3 volume gives Nernstian properties with prices Nernst factor of 29.9 mV / decade concentration, means that the optimum composition meets the theoretical Nernst factor for monovalent anion, because the active ingredient membrane forming a homogeneous phase with membranes visible supporter of the smallest ∆ dm price for the active ingredient chitosan [4,5]. Non Nernstian response of the oxalate ion CWE, most probably due to saturation or non-uniformity of the membrane. Use of the DBP plasticizer as a solvent mediator for preparing a coated wire oxalate ion-selective electrode(oxalae ion CWE) need to fulfill four principal criteria: high lipophilicity, solubility in the polymeric membrane (no crystallization) as well as no exudation (one phase system) and good selectivity behavior of the resulting membrane. It should be noted that the nature of plasticizer influences both the dielectric constant of the membrane and the mobility of ionophore and its complexed associatiated with oxalae ion [9,10]. Thus, based on the result obtained on the optimazation of the membrane composition, the membrane 4 with the optimized composition of percent ratio (w/w) of the active ingredient protonated chitosan -oxalate; Aliquat 336-oxalate additives:: PVC:DBP = 4: 1: 4: 1 in THF 1: 3 volume was selected for preparation the polymeric membrane electrode for Iion. Nernstian responses obtained on the composition ratio of PVC / plasticizer 1.2 as obtained by other researchers [3]. The specifications of oxalate ion CWE base on chitosan carrier are as follows: Sensitivity (Nernst factor) of 29,9± 0,252 mV / concentration decade of oxalate concentration over the range 1.10-5-1.10-1 M, with the detection limit of 42.56x10-6 (0.22 ppm), They have relatively fast response (20 seconds), satisfactory reproducibility, and life times more than two months

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and was found to be very selective toward oxalate ions with the selectivity sequence against foreign ions in order: oksalat2-> HPO42->SO42-> PO43->Cl-≈H2PO4-≈I≈SCN-> kreatinin> urea. The observed selectivity pattern for proposed sensor significantly same from the Hofmeister selectivity sequence (i.e. selectivity based on lipophilicity and charge density of anions), usable in wide pH range of 3-7and temperature of 20-50oC, need soaking time of 75 minutes in 0.1M in oxalate solution. This result states that oxalate ion CWE has a optimal character for the potentiometric measurement of oxalate analysis. However, the lipophilicity of the anion still plays an important role, and only the simultaneous consideration of both the lipophilicity and interaction of the anion with zeolite allows one to explain the selectivity patterns. Therefore, ion exchange selectivity is mainly determined by two factors:i.e the charge of an ion and its solvation, since the interaction between anions and ion exchange groups on chitosan is electrostatic [8]. B. Application The new coated wire oxalae ion selective electrode was satisfactorily applied to the determination of oxalae ion cover from 9 urine samples of kidney stone patients were examined in the Clinical Pathology Laboratory and measurements performed 20 times at room temperature 27 ± 1oC.The analysis were performed by direct potentiometry using the standard curve technique. Good recoveries in all matrices were obtained. From this results we can conclude that the proposed sensor was successfully applied to determining the oxalae content in biological samples .The results obtained that coated wire oxalate ion selective electrode can be used as sensors for the determination of oxalate in urine to detect renal stone disease before clinical symptoms arise with giving rat average accuracy of 98.72% and an average precision of 99, 81% which shows measurement accuracy and good precision. Distribution potentiometric measurements oxalate concentration in urine and measurement of calcium ions in the urine by AAS method and also early detection of urolithiasis risk categories based on price of BRI and optical microscopy base on the statistical test Chi Square. Percentage error determination urolithiasis risk category according the “Bonn-Risk-Index” (BRI) method (The BRI method is based on the potentiometric detection of the free Ca2+-ion concentration(activity) by means of an ion-selective electrode (ISE) together with an optical determination of the induced crystallization of calcium oxalate in native urine. The BRI is determined as ratio: BRI = [Ca2+]/(Ox2-) [1] and optical microscopy said that Count X2 = 72.35 while the table on the confidence limits P X2 = 95% and degrees of freedom (DB) = (3-1) (3-1) = 4, then X2 (0.95) (4) = 9.49. Arithmetic mean X2> X2 table, which means there is a very real relationship between the % difference BRI results using oxalate sensor with each category on the risk of urolitiasis. It means the measurement results urinary oxalate causing urolithiasis in all population categories are independent. The validation results of potentiometric method according to the calculation

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results indicated by the percentage of correspondence between the two methods, i.e., 69.6% or 30.5% gives an error, that is 12% sensor method smaller than the microscope method and 18.5% error sensor method greater than microscope metod.To test whether % differences in BRI outcomes toward the optical microscope with a population of BRI category is independent or not chi squared test needs to be done and the result test state that BRI method are sensitive and specific for high-risk category of patients suffering from urolithiasis because of the formation of calcium oxalate crystals. For patients with a high risk of urolithiasis giving a sensitivity of 75% and specificity of 75%. Moderate risk for urolithiasis patients, giving a sensitivity of 95% and specificity was also 60%, whereas the risk for urolithiasis patients with mild risk gave a sensitivity of 86.6% and a specificity of 72.1%. Thus potentiometric method can be used as an alternative method besides optical microscopy method IV. CONCLUSION The membrane composition influence the Nernstian character of oxalae sensor. The membrane with the composition of ratio of wt% the active ingredient Chitosan-oxalate; additives Aliquat 336-oxalate: PVC: DBP= 4: 1: 33: 62 in THF 1: 3 dissolved in THF solvent (1:2 w/v) was selected for preparation the polymeric membrane electrode for oxalae ion and can be use as chemical sensor for oxalae ion in the construction of coated wire oxalae ion selective electrode which has optimum characteristics for oxalate ion analysis. Method validation results showed that oxalate ion CWE produced have optimum characteristics for sensor oxalate ions suitable for urinary oxalate analysis provides of an accuracy of 98.72% and precision of 99.81%beside to the soptical microscopy. This kind of CWE was successfully applied to detect high-risk patients with urolithiasis (sensitivity 75% and specificity 75%), moderate risk of urolithiasis (sensitivity 95% and specificity 60%) as well as mild risk of urolithiasis (sensitivity 86.8% and specificity of 72.1% ), have compatibility with the optical microscope method of error of 69.6% or 30.5% error, that is 12%

smaller than of optical microscope and 18.5% larger than of optical microscopy method ACKNOWLEDGMENT The study was funded by Competitive Research Grant, the Directorate General of Higher Education, Indonesia Ministry of National Education with the contract number: 366/SK/2012 To the Ministry of National Education and University of Brawijaya are gratefully Acknowledged REFERENCES [1] Beging, S., D. Mlyneka, S. Hataihimakula, A. Poghossian., G.Baldsiefenc,H.Buschc.,N. Laubed, L. Kleinene, M. J. Schöninga (2010) Field-effect calcium sensor for the determination of the risk of urinary stone formation, Sensors and Actuators B 144, 374–379. [2] R, A., Chandra, Sulekh., Sarkar, Anjan (2010) Highly Selective Potentiometric Oxalate Ion Sensors Based on Ni(II) Bis-(mamino acetophenone)ethylenediamine, Chin.J.Chem., 28, 1140—1146. [3] Ardakani,M. M., F. Iranpoor., M. A. Karimi, and M. SalavatiNiasari (2008) A New Selective Membrane Electrode for Oxalate Based on N,N'-Bis(salicylidene)-2,2-dimethylpropane1,3-diamine Ni(II), Bull. Korean Chem. Soc., Vol. 29, No. 2 pp 398- 403 [4] Cruz, J.,M. Kawasaki.,and W.Gorski.(2000,February).Electrode Coatings Based on Chitosan Scaffolds, Anal. Chem,72: (4):680-686 [5] Isa,I.M.,S.Ab Ghani.(2007).Development of Prototype heterogeneous Chitosan Membrane using Different Plasticizer for Glutamate Sensing, Talanta,71:452-455 from http://www.elsevier.com/locate/talanta [6] Pretsch,E. (2007). The New Wave of Potentiometric Ion Sensors,Trends in Analytical Chemistry., 26(1): 46-51 from http://www.elsevier.com/locate/trac [7] Okada,M.H, and T.Ohki, (2009), Hydration of Ions in Confines Spaces and IonRecognition Selectivity, Analytical Science., vol. 25,pp 167-175,

[8]

Gustavo , A.D., A.Z-Guillén, G. A. Crespo, S. Macho and J. R. F. X. Rius.(2011). Nanostructured Materials in Potentiometry, Anal Bioanal Chem, 399:171–181 from http://www.elsevier.com/ [9] Sulekh C, S. Raizada and S. Sharma (2012) Highly selective oxalate – membrane electrode based on [CuL, IOSR Journal of Applied Chemistry (IOSRJAC) ISSN : 2278-5736 Volume 1, Issue 5 (July-Aug 2012), PP 39-48 www.iosrjournals.org [10] Stefan R. I. Draghici, G. E. Baiulescu (2000), Sensors and Actuators B, 65, 250–252

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PHYSICS

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Relocation of Hypocentrum Earthquake in Mentawai Island Region as Data Support Determination of Earthquake Early Warning 1)

Ahmad Marzuki S 1*), Munawarah1), and Iven Ganesja 1) Department of Physics, University of Indonesia, Depok, Indonesia *)

Corresponding author : [email protected]

Abstract—Indonesia is one of the most country in the world which has the high tectonic activity. It caused by ring of fire passed Indonesia Region. Ring of fire zone is the subduction zone or convergent plate motion. It cause earthquake often occured on Indonesia region. Mentawai Island region is one of the common area of earthquake in Indonesia region. This area located at west of Sumatera which subduction zone between Eurasia plate tectonic (continental crust) and Indo-Australia plate tectonic (oceanic crust). Relocation of hypocentrum earthquake on this area is important because it as early warning determination. The relocation hypocentrum earthquake on this area using Modified Joint Hypocentrum Determination (MJHD) method.The earthquake data on this area is taken from 2009-2012. Moreover our research create seismicity mapping in the Mentawai Island region based on result of relocation hypocentrum earthquake using MJHD method. The results used to early warning determination on Mentawai island as to minimize seismic hazard

II. RESEARCH METHOD The method on this research is using Modified Joint Hypocentrum Determination (MJHD). This method is created by [1], Japan seismologist (1990-1992), This method is used to determination relocation of earthquake. The following inversion steps of this method is : Start

Reformat data to mjhd format

Input data for mjhd07.f : mjhd07.inp

No mjhd07.f

Keywords—Earthquake, MJHD, Subduction zone, Plate tectonic

I. INTRODUCTION NDONESIA is one of the most country in the world which has the high tectonic activity. It caused by indonesia has three plate tectonic motion, they are Eurasia, Indo-Australia, and Pasifik. This subduction zone is called ring of fire zone. It starts from west of Indonesia region continuous to east of Indonesia region. That region to common area of earthquake on Indonesia. One of our focus is Mentawai island, it located on the west of Sumatera. This zone belonging subduction zone (Eurasia plate tectonic and Indo-Australia plate tectonic), and the common area of earthquake on the west of Sumatera Island. Moreover, the geological setting at this area is dominated sedimentary and mixing from continental and oceanic. Relocation of hypocentrum earthquake on this area is important because this relocation of hypocentrum to identification tectonic condition on this area including the knowing the seismic gap which may be a big source of earthquake in the future. The results is used to early warning determination as to minimisize seismic hazard especially for occupant on this area and the surrounding.

Station selection process : station.f

The desired residual

I

mjhdoutselect.f

mjhd.outp

mjhd.out mjhd.print

Yes

STOP STOP

The earthquake parameters is lattitude and longitude of earthquake, time of common earthquake, the change of that parameters influence the change significant the hyprocentrum of earthquake depth. The excellent of this method calculate many hypocentrum of earthquake simultaneously and correction about lateral heterogenity in subsurface. Lateral heterogenity in subsurface attenuate the p-wave propagation, the effect is mislocation the hypocentrum of earthquake. The input data is taken from 2009-2012 years and recorded by

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BMKG (Badan Meteorologi dan Geofisika). The data has magnitude > 5 SR, including time of common earthquake, the recorded station, arrival time p-wave, longitude and lattitude coordinate, and the depth of hypocentrum. The start of inversion using MJHD step of determine the value of Minimum Number of Earthquake at Each Station (MNEQ), and Minimum Number of station at Each Earthquake (MNST) This value as input data of station program, the function to determine the number of station that match the requirments of the value MNEQ and MNST. The others parameters is lattitude and longitude the earthquake as initial data, the depth of margin earthquake in km, maximum residu of travel time (RESS), the number of maximum iteration (ITRT) is 5, standar deviation, the number of station and the number of earthquake which not participate in calculation, reading accurate (RANKAB), minimum magnitude is > 5 SR, the value of slope, the hypocentrum corrected using MJHD is plotting in General Mapping Tool (GMT), the function of GMT showing the hypocentrum corrrected distribution from the surface and their cross section.

Picture 2. Hypocentrum of Earthquake in Mentawai Island before relocation correction 2009-2012 data with all magnitude > 5 SR

III. RESULTS, ANALYSIS, AND DISCUSSION At this part we show the inversion results using MJHD method, compare the results before relocation correction (picture 1), and after relocation correction (picture2),then interpretation the results. At the picture 1 (before relocation correction), the distribution of hypocentrum on Mentawai island dominated at shallow depth (0-20 km), and but at picture 2 (after relocation correction), the distribution of hypocentrum depth on Mentawai island dominated at 25-50 km, and the distribution on seismicity mapping show dominated at shallow-intermediet depth earthquake near the Mentawai island, and deeper far from Mentawai island. In geological setting the enough great angle of subduction zone between Eurasia plate motion and Indo-Australia plate motion at west of Sumatera island. It caused by as oceanic crust ages and cools, so it thickens. Thick crust (far the spreading zone) tends to subduct at a greater angle than thin crust (near the spreading zone). The more depth of hypocentrum, indicating that the great angle of convergent plate motion. The plate has point of rupture, where if the force between the plate (ocean crust and continental crust) at subduction zone more great than rupture point of the plate then the plate will be rupture. This rupture create a propogate wave on the subsurface medium. Its traveling on the subsurface medium get attenuate the energy when this wave propagate on the weathering zone. So, the observer at the surface calculated the time traveling of wave from source to surface. In reality, time of calculation and time of observe get shiftting because weathering zone at subsurface. So, time of calculated and time of of observed must be correction. It’s match with seismicity mapping. Based on seismicity mapping after relocation correction, the Mentawai island dominated by shallow to intermediet depth earthquake, this earthquake dangerous for Mentawai occupant and its surrounding.

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Picture 3. Hypocentrum of Earthquake in Mentawai Island after relocation correction 2009-2012 data with all magnitude > 5 SR

Picture 3. The Seismicity Mapping After Relocation Correction using MJHD

On this seismicity mapping we can look that the red circle indicate that the earthquake at the shallow to intermediet depth, and the yellow circle indicate that the earthquake at the intermediet to deeper. The red circle dominated at the subduction zone with dominated

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intensity is 3-7 SR (Richter Scale), and the yellow circle dominated at the far of subduction zone with dominated intensity is 3-5 SR ( Richter Scale ). It indicated that the collison between Eurasia plate and Indo-Australia plate at subduction on the flattening angle (0-80 degree). Based on seismicity mapping the Mentawai island dominated with variate earthquake intensity with the focal depth near the Mentawai island. Based on geological setting, lithology of Mentawai island dominated from continental and oceanic rock ( Melange formation ) with active fault such as reversed fault or thrust fault. This zone called fore arc zone. Focal depth shallow to intermediet because Mentawai island near the subduction zone, it very dangerous for local citizens because source of earthquake near the surface. The effect of this earthquake very destruct. We can not to predict when the earthquake create, but we can predict that the earthquake at the Mentawai island variate intensity, because the lithology at the Mentawai island is dominated clastic sediment. In general the earthquake at the Mentawai island created by collision between Eurasia plate and Indo-Australia plate at the subduction zone with focal depth shallow to intermediet.

IV. CONCLUSION The depth of hypocentrum on the Mentawai island is shallow to intermediet (25-50 km), and their distribution on the west of Mentawai island indicating that subduction zone between Eurasia plate motion and IndoAustralia plate motion. The convergent plate motion between Eurasia and Indo-australia has the enough great angle. ACKNOWLEDGEMENT The Author Thank to BMKG for their cooperation in providing the earthquake data on the Mentawai island from 2009-2012 year used in this study and Mr. DR. ENG. Supriyanto as geophysics lecture on Department of Physics University of Indonesia for his constructive suggestion and transfer his knowledge to interpretation the result. REFERENCES [1]

N. Hurukawa and Imoto. 1992. Modified Joint Hypocentrum Determination. Japan.

[2]

Thompson &Turk. 2000. Introduction to Physical Geologi. USA.

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Reducing Deforestation and GHG Emission with UB Biomass Stove and Fuel as Alternative Energy for Community Muhammad Nurhuda 1*), Setyowati Rahayu 2), and Dhoni Saputra 2) 1) Faculty of Science, Brawijaya University, Malang, Indonesia 2) Yayasan Inovasi Indonesia (INOTEK), Jl. Jenggala 2 No 9, Kebayoran Baru, Jakarta *)

Corresponding author: [email protected]

nesia’s consumption on wood-fuel was among the highest Abstract— A pilot project for implementing the UB bio- in the world i.e. 70.72 million m3 in 2006 and 65.03 milmass stoves and measuring their impacts over 100 households lion m3 in 2008 [4]. in Palangka Raya rural area has been performed. The reciCentral Kalimantan is one of province in Indonesia pients were located in two villages, one village (Petuk Bukit) which has been experiencing highest rate of deforestation. whose inhabitants was predominated by Dayak Native and Deforestation is mostly attributed to logging for the conthe other (Habaring Hurung) by transmigrants from Java. version of forests to plantations for palm oil and to supply The pilot project was supported by Energy and Environment the pulp and paper industry. In addition, it also has major Partnership (EEP) Indonesia for one year. It was found that after intensive coaching and monitoring, energy crisis whereas supply of fossil fuel is limited. Due beneficiaries increase the usage of biomass stoves and adapt to poor availability of kerosene and gas fuel, households in new habits in cooking preparation. By the end of project, 100 Central Kalimantan commonly use firewood for cooking % of households in Habaring Hurung used the UB biomass harvesting it from forests surrounding their villages. stoves for their daily cooking, while in Petuk Bukit the numAs an agricultural-based nation, Indonesia possesses an ber was 78%. It was also found that 60% households in Ha- abundant source of biomass from residues, e.g. rice, subaring Hurung relied on UB biomass stove only, while the garcane, palm oil, logging, sawn timber, coconut, and othrest 40% were in combination with traditional woodstove and er agricultural wastes. These residues and wastes are ackerosene stoves. In practical use, the firewood reduction accounted for 59% less than that of traditional wood stove and tually sources of energy that can provide fuel for rural the stoves produce much less smoke than the traditional households. It was with this justification that INOTEK Foundation had been partnering with Muhammad Nurhuda woodstoves. in developing and disseminating an innovative UB BioKeywords—UB stove, biomass, deforestation. mass Stove and a range of biomass fuel. Nurhuda has received mentoring facilitation in RAMP Indonesia proI. INTRODUCTION gram that is implemented by INOTEK. Technologically, VER 40% of the world's population still burns vari- the UB biomass Stove is designed with innovative preous forms of biomass, such as wood, dung, charcoal, heating, gasification, counter flow and turbulence mechanor crop residues or coal as a cooking fuel, see e.g [1,2]. ism [5,6]. The external laboratory tests has shown that the They cook over open fires or on rudimentary cookstoves. stoves can reach thermal efficiency as high as 49%, which This way of cooking is not only inefficient, but also emits is comparable to the fossil-based cooking stove [7]. With a harmful smoke that causes range of deadly chronic and such high efficiency, it is expected that the use of UB bioacute health effects such as child pneumonia, lung cancer, mass stove can reduce the amount of firewood needed for chronic obstructive pulmonary disease, heart disease, and daily cooking as well as reduce the green house gas (GHG) produced from combustion process low birth-weight [3]. Though currently the Indonesia’s government has II. METHOD launched kerosene to LPG national conversion program, Through the project, 100 energy efficient UB biomass the use of biomass as daily cooking fuels is still predominate in most rural area. Moreover, inadequate supply of stoves were taken into use in the villages of Habaring Huhydrocarbon fuel at an affordable price and unsafe lique- rung and Petuk Bukit, which are rural areas in Palangka fied petroleum gas stoves for low income communities Raya, Central Kalimantan. Petuk Bukit village is predomihave led to the growing use of firewood as fuel for daily nated by Dayak Native whereas Habaring Hurung is by activities, including by cutting down trees in nearby fo- transmigrants from Java. The choice of two villages sociorests. The combination of deforestation, inefficient fuel logically were based on different characters of inhabitants. combustion and high consumption has significantly created Each village received 50 stoves, and the stoves were then vast environmental degradation and global warming. Indo- distributed among the households readily for usage in their

O

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daily cooking activities. The project was funded by the Energy and Environment Partnership (EEP) Indonesia (www.eepindonesia.org) for one year. The data collection were made by interviewing the beneficiaries of the stoves in every three months. Socialization, coaching and mentoring were carried out by involving Yayasan Mitra Insani as a local partner of the project. III. RESULTS AND DISCUSSION The project started after the signing of the contract between INOTEK and EEP-Indonesia in May 24, 2012 and were accomplished in the period of June 2012 up to June 2013. The project was designed from the beginning to involve active participation of local people with the goal to raise awareness among the people on the significance of health and nature conservation. In the beginning of the project, there were some constraints, both technically and non-technical. The non technical problem was due to refusal from the local leaders, such as installment scheme, the selection criteria of beneficiaries, and also collecting data base and conducting interview. All barriers could be solved with intensive approach and explanation on the purpose of the project, and by identifying formal and informal leaders and opinion leaders to get support from them. The technical problem was due to fuel requirement, which, if not carefully prepared, could lead into improper usage of the stove such that the combustion emits a lot of smokes in their kitchen. However, after extensive coaching and monitoring, the beneficiaries started to accept the new habits in cooking using the biomass stove. To introduce the UB biomass stoves, knowledge transfer was achieved by giving training, and one to one coaching. The training did not relate the technical aspects only, such as operating biomass stove, preparing woods or biomass fuel, maintenance of the stoves, but also income generating activities and simple accounting. To involve active participation among beneficiaries and raising awareness and ownership, the selected beneficiaries are requested to pay IDR 200,000,- . The payment was made using an installment scheme. The installment payment was collected by representative of beneficiaries and the money will be used as revolving fund. Dissemination of project were achieved through media and by distributing promotion materials. In the beginning of the project, very rare beneficiaries used the stoves for daily cooking. It was found that most beneficiaries put their stoves in rack after non-successful first usage. The reason was the beneficiaries considering the combustion in the stoves as it in their traditional clay stove, though each stove was accompanied with manual use procedure. However, after 6 month the project was running, the monitoring track showed some good trends and encouraging results. The beneficiaries were getting used to biomass stoves. It was found that 99% of beneficiaries continue to use biomass stoves every day or in combination with other stoves. It was only one beneficiary in Petuk Bukit which returned the stove to the project implementers, and claimed to be reluctant to cutting woods into pieces.

Fig. 1. Usage of biomass stove in kitchen. TABEL 1 PERCENTAGE OF USAGE OF UB BIOMASS STOVE IN COMBINATION WITH OTHER COOKSTOVES: Type of stoves UB stove only UB + kerosene UB + clay Clay+UB+ Kerosene Clay+UB+LPG+ Kerosene NA Total

Habaring Hurung 60% 10% 16% 14%

Pethok Bukit 16% 76% 0% 4%

0%

2%

0% 100%

2% 100%

Referring to table 1, it is shown that among 100 households, 38% have used the UB stove exclusively. Further data analysis has shown that the stoves were used to boil water (78%), side dish (81%), and cooking rice (20%). The lower percentage in using the stove for cooking rice were due the fact that the size of the stoves were too small for cooking rice for large family members, since it is normal in the rural area of Palangka Raya that one house is occupied with family member that is larger than 10. The controllable flame intensity of UB biomass stove may be the reason why the stoves are mostly used for preparing the side dish. Finally, in Tab. 2 we show the saving potential of both firewood and kerosene. The data was collected at the time the project was approaching to end. On average, the use of UB biomass stove could reduce the real consumption of woods up to 59% compared to using traditional clay stoves, and reduce the use of kerosene up to 50%. The comparisons were made based on the monthly needs, before and after implementing the UB biomass stove. The saving in using kerosene was found to be depend on the kerosene supply and whether the biomass stoves were used exclusively or in combination with other stoves. From Tab. 1 and Tab 2 we can immediately see that the households in Habaring Hurung, which are mostly transmigrant from Java, could be better accepting the cooking new habits compared to households in Petuk Bukit, which are predominated by Dayak native. The reason are proba-

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bly the Java transmigrant could better adapt the new habits, that by the condition, they are far away separated from their families in Java and thus must hardly struggle for surviving in the new land. TABLE. 2: THE COMPARISON OF MONTHLY NEEDS OF FUELS, BEFORE AND AFTER IMPLEMENTING THE UB STOVE. Project tion

Loca-

Habaring Hurung Petuk Bukit

Type of fuel

Monthly amount of fuel Before

After

Woods Kerosene Wood Kerosene

102 kg 11.8 L 86.9 kg 15.1 L

41.3kg 5.8 L 40.8 kg 7.7 L

Saving (%) 59% 51% 53% 49%

which represent two different cultures. It is found that the potential reduction for fire woods accounts for 60% compared to the traditional clay stove. Furthermore, adaptation of new cooking habits can only be realized after intensive coaching and monitoring. ACKNOWLEDGMENT The authors and involved project members greatly appreciate the Energy and Environment Partnership with Indonesia (EEP Indonesia) for their financial supports. The project is of coded under project ID 2055060401741509211. REFERENCES

Despite the difference level of results in habaring Hurung and Petuk Bukit, it is clear for Tab. 2 that the beneficiaries required less wood for their daily cooking. Less fuel means also less smoke in the kitchen, since the emission is always proportional the amount of combusted fuel. Thus, in regards to the deforestation, the use of UB biomass stove could help preventing the people from cutting the woods in forest and thus reducing the potential of deforestation. IV. CONCLUSION As conclusion, a UB biomass stove pilot project to measure the real reduction of biomass usage for cooking has been performed in rural area of Palangka Raya district, Central Kalimantan for one year. The sample areas were chosen to be the Habaring Hurung and Petuk Bukit village,

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[13] Global Alliances for Clean Cookstoves: http://www.cleancookstoves.org/ [14] Hedon Household Energy Network: http://www.hedon.info/tikiindex.php [15] Carlos Torres-Duque, Darío Maldonado, Rogelio Pérez-Padilla, Majid Ezzati, and Giovanni Viegi "Biomass Fuels and Respiratory Diseases", Proceedings of the American Thoracic Society, Vol. 5, No. 5 (2008), pp. 577-590. [16] http://www.fao.org/docrep/013/i2000e/i2000e00.htm. [17] M. Nurhuda, Kompor Biomass Dengan Gasifikasi Terpanaskan Dan Pembakaran Turbulen, paten register number P00201000217, 2010. [18] M, Nurhuda, Kompor Briket Dengan Pre-Heating dan Pembakaran Secara Counter Flow, patent register number P00201100059, 2011. [19] David Beritault and Veronique Lim, Stove Performance Report, UB03, Envirofit G3300 and M5000, Ezystove, Geres, Phnom Penh, Cambodia, 2013. [20] Anonymous, INOTEK END-PROJECT COMPLETION REPORT FOR EEP INDONESIA, 2013.

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Measurement of Bioefficacy and Its Effects on One Push Aerosol Insecticide by Using Glass Chamber Firdy Yuana1), Chomsin S. Widodo2), and Sukainah Quraisyiyah3) 1) Faculty of Science, Brawijaya University, Malang, Indonesia 2) Faculty of Science, Brawijaya University, Malang, Indonesia *)

Corresponding author: [email protected]

Abstract — Research has been conducted to examine the bioefficacy of one push aerosol that is currently on the market. This study aims to determine bioefficacy (KT) of one push aerosols on insects and determine whether there is a residue generated by these insecticides within 1 hour of observation by using a glass chamber size of 70x70x70 cm and particle counters Ptrak 8525 models. The average percentage mortality of Aedes aegypti mosquito is entirely equal 100 %. The fastest KT 50 and KT 90 on a product A (transfluthrin 21.3 %) is 573 s and 1462 s , followed by product B ( metofluthrin 3.5 % ) which has a value of 792 s and 1879 s . Product C (transfluthrin 25 %) has KT 50 and KT in 1277 s and 2867 s . Within 60 minutes of observation was found residue on each product , product A have particle concentration 897 pt / cc , product B 1047 pt/cc , and the product C 493 pt/cc .Product A is the the most effective to kill mosquitoes because it has a greater concentration of particles is 17703 pt / cc rather than product C and also for product) as the active ingredient contained in a product that is transfluthrin 21.3 % even though the product B has an average concentration greater than the average concentration of product A is 18350 pt /cc . Keywords— bioefficacy, one push aerosol, insecticide

I. INTRODUCTION HE use of insecticides is one of the effective way to control mosquitoes, cockroaches, ants or other insects that are common in the home. Insecticides are sold widely in various forms both fuels and aerosols. For aerosol itself there are various brands available in the market such as Baygon, vape, Mortein and others. Bioefficacy emerging insecticides that used today is one push aerosols. Aerosol is a term used for the preparation of thin mist spray with high-pressure system. Aerosol types can also be distinguished by size. Aerosol particle size is usually expressed in particle radius assuming a sphereshaped particles. According to the version of the Aitken particle size divided into three categories, namely: • Aitken particles (nucleation mode) with a size range between 0.001-0.1 µ m; • large particles (accumulation mode) measuring between 0.1-1 µ m, and • giant particles (particle coarsa mode) which size > 1 µ m radius. [2]. The use of one push aerosol somewhat more efisient because just a single tap is enough to free us from mosqui-

T

toes for a 10 hours . At the time inhaled, aerosol particles can get rid of the respiratory system's natural defenses and lodge deep in the lungs. Aerosol very dangerous for people with diseases such as asthma, bronchitis, and emphysema (swelling in the lungs because blood vessels intruding air), as dangerous for people with liver disease. High levels of these objects in the air can trigger asthma attacks, lung damage, and supports carcinogenesis, and premature death. In contrast to the usual aerosol spray which should in some places into the room to obtain the same results. Given these differences, there are needs to determine the bioefficacy (knock down time) of one push aerosols on insects which is Aedes aegypti and determine whether there is a residue generated by these insecticides. II. RESEARCH METHODS Stages of the study are as follows : 1 . Testing Using Glass Chamber Glass Chamber is a box made of glass measuring 70x70x70cm with one wall open the door. Glass Chamber must be ensured in a contaminated state. Insecticides are used in this case is one push Vape ( Transflutrin 21.3 % ), Force Magic Microns ( 3.5 % Metoflurin ) and Hit (Transflutrin 25 %). Gauging levels of mosquito repellent spray is done in the following way : one push- aerosol mosquito to be tested , weighed , and then sprayed for one second outdoors . Then after a severe insect repellent sprayed weighed again and the difference in weight is recorded (in grams). Gauging levels of spray made with three replications. A total of 20 Aedes aegypti (age 7-8 days) is released into the Glass Chamber and wait one minute. One pushaerosol insect repellent sprayed 1 time press into the Glass Chamber. Observations were made for 60 minutes. The number of mosquitoes fainting calculated at any specified time interval , which are : 0:50 ; 1:25 ; 2:00 ; 2:50 ; 3:00 ; 3:50 ; 5:00 ; 15:00 ; 30.00 and 60.00 minutes . Then all the mosquitoes moved into the plastic tube , given the wet cotton sugar solution and stored ( holding ) for 24 hours at room temperature 27 °C. To determine the time fall / lame ( Knock-down Time ) 50 % ( KT-50 ) used probit analysis. KT-50 is the time required for the drop / knock out 50 % of the population of mosquitoes in certain doses. As for knowing the difference bioefficacy between the treatment

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and control groups performed the t-test as a condition of ANOVA test. Bioefficacy insecticides have a sense of the effectiveness of the insecticide itself. Percentage collapsed and died in the experiment was 100 % after bioefficacy calculated using the formula : { ( P + Q ) : R } X 100 % (1) Description : P : Number of mosquitoes fainting Q : The number of dead mosquitoes R : Number of mosquitoes tested 2 . Preparation of test animals Test animals used is dengue fever mosquito (Aedes aegypti) were sterile dengue virus androgynous females , aged 7-8 days as many as 400 individuals . 3 . Particle measurements using P-trak 8525 models The next stage is to measure the concentration of particles contained in a spray product using the particle counter is the P-Trak 8525 Model. The first step is to measure the temperature in the chamber by using a thermometer and the pressure using a barometer. Then, the P-trak hose connected to a glass chamber as well as the hose connects to the glass chamber with air pump. After that, one push aerosol insect repellent manually sprayed into the glass chamber for a second . Then spray the glass allowed to stand in the chamber during a predetermined time is: 0:50 ; 1:25 ; 2:00 ; 2:50 ; 3:00 ; 3:50 ; 5:00 ; 15:00 ; 30.00 and 60.00 minutes . After that, the concentration of particles is measured using a P-Trak 8525 Model and be repeated three times for each time. Automatically particle measurement data will be stored in the P-Trak then the data will be processed by using the 0rigin 8.1 software. The concentration of particles obtained from the difference between the minimum and maximum values contained in the P-Trak program. Shape measurement circuit is shown in Figure 1.

minutes and 2:50 minutes. This can be happen because most of the particles of the active materials undergo deposition (particles to the surface of the glass forming chamber, causing droplets with larger size). In the next minute is minute 3:00, the particle concentration increased on average significantly due to the 3:00 minute droplets stick to the glass chamber will change the shape of solids into gases that can be caught by the P-Trak. And at minute of 3:50, the particle concentration decreased again from the previous minutes. The decrease is largely due to the concentration of the particles that have accumulated particles into the air to experience deposition on the surface of the glass chamber. In the next minute, the concentration of particles continued to decline slowly, until the last minute which are 60.00 , a product of particle concentration 897 pt/cc , product B 1047 pt/cc , and the product C 493 pt/cc. This indicates that after 60 minutes the particle is not evaporated entirely or the particles accumulate in the air is not all evaporated.

Fig. 1. Total concentration of particles.

Figure 1. Shape measurement circuit is shown

To calculate the total concentration of particles of insecticide used the following equation (2) Information:

III. RESULT

From the measurement, the total concentration of aerosol particles in one push can be seen in Figure 1. The concentration of particles in the early minutes to 0.50 minutes product A the average particle concentration reached 17703 pt/cc , product B 18350 pt / cc, and for products C 10037 pt /cc. And then decreased at minute 1:25, 2:00

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By using ANOVA analysis shows that the three products had average concentrations were significantly different. From the ANOVA analysis was obtained that Product B has an average particle concentrations are higher than most other products, whis are 7427.7 pt/cc . The average particle concentration of the second highest is a product that is 4478.9 pt / cc and the lowest concentration is the product C is 3340.9 pt / cc. Can be seen in Figure 1 that at minute 3:00, the particle concentration decreased , suggesting that at minute 1:25 ; 2:00 and 2:50 of the particles undergo deposition around the surface of the glass and experience the deposition chamber where the material will undergo a process of change in the form of a gas or liquid that is small be solid also called desublimasi so that the particles can not be exploited by the P - trak . At minute 3:00, the active ingredient volatile at temperatures above the room temperature, causing the particles are shaped colorless crystals undergo evaporation. When it is evaporated, the solid particles are transformed into a gas that can be sucked back by the P-Trak. This is why at the minute 3:00, particle concentration values increase. The workings of the active ingredients contained in insect repellent one push aerosol is is pyrethroid which attack the nervous system of the mosquito Aedes aegypti that can

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cause paralysis. The active ingredients are micro-sized so it also attacks the circulatory system, the hormonal system, and respiratory system and can settle in the lungs and led to the death of the Aedes aegypti mosquitoes. Henceforth , the testing of bioefficacy KT 50 and KT 90 by using three types of drugs push one of the aerosol mosquito Aedes aegypti mosquitoes were placed in a glass chamber. Test results bioefficacy KT 50 and KT 90 can be seen in table 1. TABLE 1. The relationship between the concentration of particles produced per insect repellent spray for each product with the duration of paralysis

Test bioefficacy one push repellent aerosol showed that mosquito mortality on all products are 100 % . KT 50 and KT 90 of the most effective is a product containing 21.3 % with variation transfluthrin paralysis time mosquitoes. After that followed by B product with the active ingredient metofluthrin 3.5 %. KT 50 and KT 90 a C product with the active ingredient transfluthrin 25 % has the lowest amount. Differences KT 50 and KT 90 at any one push repellent products due to differences in aerosol particle number concentrations in each product has a different amount. It is seen that the product A , which contains 21.3 % transfluthrin , faster crippling mosquito Aedes aegypti is 11 seconds ( KT 50 minutes to 0.5 ) than the product C is 33 seconds ( KT 50 minutes to 0.5 ) which contains transfluthrin 25 % , even though the active ingredient product C higher than product A. This is because the value of the average concentration of product A is higher than product C. Types of active ingredients listed on the packaging of the product also affect the timing of mosquitoes death. It is shown in the tables above indicate that the product kills mosquito A faster than product B even though the products have an average concentration value higher than a product , for example in 0.5 minutes , the product A has a value of 17703 particle concentration pt / cc whereas product B has a particle concentration of 1830 pt / cc IV. CONCLUSION

From the results of the research can be concluded that Product B has the largest concentration followed by product A and product C. Bioefficacy test bioefikasi of three different insect repellent products have a value of KT 50 and KT 90 distinct and not depending on the magnitude of the concentration of the active substance and the number of particles generated by one push-aerosol insect repellent. Product A has the most rapid efficacy followed by product B and C. There is still a residue of insecticide after 60 mi-

nutes of observation, this is indicated by the presence of a concentration of particles in the observation area REFERENCES [1] Ardley, Neil. 1998. Percobaan Ilmu Pengetahuan. Semarang. PT Mandiri Jaya. [2] Biswas, P. 2009. Measurement and Capture of Fine and Ultrafine Particles from a Pilot-Scale Pulverized Coal Combuster with an Electrostatic Precipitator. J. Air&Waste Manage. Assoc. [3] Departemen Pendidikan Nasional. 2004. Ensoklopedi Sains dan Kehidupan. Jakarta. CV Tarity Samudra Berlian. [4] Djojosumarto, Panut. 2012. Available : http://www.google.co.id/Panduan%20Lengkap%20Pestisida %20&Aplikasinya.html. [5] Ganiswara, S.G., Setiabudi, R., Suyatna, F.D., Purwantyastuti, Nafrialdi. 1995. Farmakologi dan Terapi (Edisi 4). Bagian Farmakologi FK UI: Jakarta [6] Gillett, J. D. 1972. The Mosquito: It’s life, Activities and Impact on Human Affairs. Doubleday, Garden City. New York [7] Hartanto, L.N. 2004. Biologi Dasar. Yogyakarta: Penebar Surabaya. [8] Heller, J.L. 2010. Insectisida Poisoning. Medline plus. English. [9] Kurnianti, Novik. 2013. Available: www.google.co.id/Petunjuk Aplikasi Pestisida.html [10] Lucas, JR; Shono, Y; Iwasaki, T; Ishiwatari, T; Spero, N; Benzon, G. 2007. Journal of the American Mosquito Control Association. Laboratory and field trials of metofluthrin (SumiOne) emanators for reducing mosqito biting outdoors. US. 23(1): 47-54 [11] M, Sugono. 2005. The biological activity of a novel pyrethroid :Metofluthrin. Sumitomo Chemical Co., Ltd., Hyogo. Japan [12] Sigit SH, Koesharto FX, Hadi UK, Gunandini DJ, Soviana S, Wirawan IA, Chalidaputra M, Rivai M, Priyambodo, Yusuf S dan Utomo S. 2006. Hama Pemukiman Indonesia. Pengenalan, Biologi, dan Pengendalian. Bogor. Penerbit Unit Kajian Pengendalian Hama Pemukiman. Fakultas Kedokteran Hewan IPB. [13] Staf pengajar Farmokologi. 1995. Absorbsi dan Ekskresi. Bagian Farmakologi FK UNLAM: Banjarbaru. [14] T, Nazimek. et al. Content of Transfluthin. Departement of Toxicology, Institute of Rural health. Lublin. Poland [15] Widiarti, dkk. 1997. Uji Bioefikasi Beberapa Insektisida Rumah Tangga terhadap Nyamuk Vektor Demam Berdarah. Stasiun Penelitian Vektor Penyakit, Pusat Penelitian Ekologi Kesehatan Badan Penelitian dan Pengembangan Kesehatan. Salatiga. [16] Womack, M. 1993. The yellow fever mosqito, Aedes aegypti. Wing Beats. Florida [17] Zoolner, G; Orshan, L. 2011. Journal of the Society for vector Ecology. Evaluation of metofluthrin fan vaporizer device against phlebotomine sand flies (Diptera: Psychodidae) in a cutaneous leishmaniasis focus in the judean Desert. Israel. 36 Suppl: S157-65

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The Effect of Probe Pulse in a Double Pulses Experiment Abdurrouf Faculty of Mathematics and Natural Sciences, Brawijaya University, Malang, Indonesia Corresponding author: [email protected] Abstract—This paper investigates numerically the probe the double pulses experiment can be arranged in several pulse effect on the dynamic alignment, generated by pump different ways. pulse, in a double pulses experiment. To this end, we compare the dynamic alignment by pump pulse only, with that obtained by using both pump and probe pulses. The numerical calculation demonstrates that the probe pulse slightly raises the degree of alignment without changing its general characteristics, both in time and frequency domain Keywords—double pulses experiment, probe, alignment, time domain, frequency domain

I. INTRODUCTION

T

he double pulses experiment is one of the interesting research topics in atomic and molecular physics, no less because of its potential applications as a source of coherent ultraviolet light and/or generation of ultrashort attosecond laser pulses [1], for molecular imaging [2], for controlling molecule-laser interaction [3], etc. In double pulses experiments, an ensemble of gas is first subject to a femtosecond pump pulse to controls their wave-function to set them in rotational motions and wait for the molecules to be aligned at a later time (a few picoseconds) when they could undergo a ‘rotational revival’ [3]. The second more intense femtosecond probe pulse, was delayed with respect to the first by successively increasing the time intervals td, is given to generate the observed signals [4], either in ionization [5], dissociation, optical Kerr effect (OKE) [6], photoelectron angular distribution (PAD) [7], above threshold ionization (ATI) [8], or high harmonic generation (HHG) schema [4]. By plotting the observed signal as a function of delay time between two pulses, one obtains the dynamic signal describing the molecular behavior due to the interaction. The typical pump-probe experiment set up by using HHG as a probe, is depicted in Fig. 1. A laser beam is split into two parts by a beam splitter (BS) to separate the pump and probe pulses, with a specific ratio of intensity between them. The probe pulse is then delayed with a controllable delay line system D and, if needed, can be also rotated with a polarizer P. The two pulses is then mixed again with a beam mixer (BM) where the probe pulse is delayed by from the pump pulse and its polarization is rotated with respect to that of the pump pulse by an angle , if desired. The pump-probe pulse sequence is focused by a system of lenses (F) and is then subjected to a molecular gas jet: the pump pulse dynamically ‘aligns’ the molecules whereas the probe pulse generates HHG signal from the aligned molecules. The HHG signal is then recorded by a detector system. Depending on the experimental purpose,

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Fig. 1. Schema of pump-probe experiment. The explanation is given in text

So far it is assumed that the alignment is due to the pump pulse only, while the second one only gives the observed signal, without rotating it during the short duration of the probe pulse. However, it is worthwhile to observe, whether the probe pulse affects on the dynamic signal, or not. The question has been raised in Ref. [9]. Here, the discussion is extended, including the eighth revival and its Fourier spectrum. II. COMPUTATIONAL DETAILS To overcome the question, I directly compare here the alignment by the pump pulse only, with that obtained by using both the pump and the probe pulses. For the second schema, we allow the possibility that the probe pulse also may rotate the molecule before generating the observed signal. The total field is assumed to consist of the sum of the two fields:

In the above indices 1 and 2 stand for pump and probe pulse, respectively. td is the delay time between the two pulses. In above, ε10 and ε10 are peak field whereas and are time profile of the field. The effective pulse then reads

For two pulses, dynamic alignment is characterized by pump and probe peak intensity, pump and probe time profiles, delay time between them, and interaction time (i.e. delay time between entering probe pulse and observing

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signal). The schema for alignment by the pump and the probe pulses is the following. The probe pulse is given after the pump pulse. We let the probe pulse to interact with the molecule at a given time and then take the some expectation values and plot as function of the delay is the quantum measurement of dytime . namical alignment of a rotating molecule, given by , which is the expectation value of the with θ is the angle between the molecular axis and the pump/probe polarization direction; the double angular brackets stand for the expectation value with respect to the wave packet states (inner brackets) and the statistical average with respect to the Boltzmann distribution (outer brackets) of the initially occupied rotational states. In this paper, I calculate two expectation values of a diatomic linear molecule , those are the first alignment degree , which is the first term in ionization signal [5], and the second alignment degree , which is the first term in high harmonic generation signal [4]. The properties of are listed in Tab. I. TABLE I PROPERTIES OF O2 Symbol

Quantity

Value

Rotational constant parallel polarizability perpendicular polarizability number of even-j wave function number of odd-j wave function

1.4297 cm-1 2.35 (A3) 1.21 (A3) 0 1

Fig. 2. Dynamics alignment of (upper panel) and (lower panel) of at 300 K subject to laser pulses of III. RESULTS AND DISCUSSION and with FWHM 40 fs. We In experiment, I use here the pump and probe pulses keep the second pulse to interact for 40 fs. Solid lines for with peak intensity of and double pulse and dashed lines for one pulse. . Both pulses have Gaussian profile with FWHM (full width at half maximum) 40 fs. In general, the probe pulse does not change the dynamThe gas of is kept at room temperature 300 K. I choose ics of , except in three aspects. First, the the pulse duration, 40 fs, as the delay time between enter- probe pulse intensity adds up the intensity of the pump ing probe pulse and observing signal pulse and enhances the alignment process prepared and The results are shown in Fig. 2, for (up- generated by the pump pulse, so that the expectation value per panel) and (lower panel). From of with two pulses is higher than the one the upper figure, it is shown that there is no difference be- with pump pulse only. Second, the alignment enhancement tween the signal due to the double pulses (black, solid by the probe pulse depends on the delay time between the lines) compare to that of single pulse (blue, dashed lines). two pulses and reaches a maximum when the slope of Both signal revive with a period of , which is given alignment degree is positive. This means that the degree of by alignment is enhanced before its maximum and as a consequence reaches the maximum earlier than the alignment with one pulse only. Third, there are a small revival structure at in signal due to two pulses (as shown in Fig. 3), where they are absent in the case of single pulse signal. For O2, . The signals also show a half and The similar situation occurs in (Fig quarter revival, which is a character of . 2. lower panel). Both signal has a period of 11.6 ps and show a half, quarter, and eighth revivals, as a characteristics of . In general, there is no significant difference between the signal due to single and double pulses.

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because of its small depth modulation, which is about one by hundred compare to that of half and fourth revival.

Fig. 4. The Fourier spectrum of of for single pulse (upper panel) and double pulses (lower panel). The pulses parameters and temperature are similar to those in Fig. 2.

Fig.

shows the Fourier spectrum of . The dynamic of is associated with phase differences . While the transition with is related to series of Fig. 3. The revival structure at (panel a), (panel (10,18,26,34,….)Bc, the transition with is assob), (panel c), and (panel d) of of ciated with phase differences with for double pulses. The pulses parameters and temperature are similar to those in Fig. 2. , (5) To see more clearly, we Fourier transform the dynamic signal and get the spectrum in frequency domain, as shown in Fig. 4 for and Fig. 5 for . From Fig. 4, one can see that there is no difference between spectrum of single and double pulses. Both spectrum consist of peak series located at (10,18,26,34,….)Bc or . As we know, the generates a transition with The transition with ∆J = 0 creates a peak at frequency zero, whereas the transition with is associated with phase differences with

(4) with c in cm/s. The absence of series (4Jeven+6)Bc indicating that O2 has Jodd only. The series is peaked at 50 Bc corresponding to . I note here, that the revival structure at does not contribute in Fourier spectrum

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5

and related to series of (28, 44, 60, 76, 92, 108, 124, 140,….) Bc. According to Eqs. (4) and (5), the Fourier spectrum of of O2 consists of series of and . It was shown in Fig. 5, that both Fourier spectrums show series of (10, 18, 26, 34,….) Bc as representation of and series of (28, 44, 60, 76, 92….)Bc due to transition. The series is peaked at 50 Bc corresponding to , similar to those of . On the other hand, the series is peaked at 140 Bc corresponding to . The detail of Fourier spectrum and its role in dynamic alignment can be found in Ref. [10]. I also note that the Fourier spectrum of of double pulses has similar intensity compare to that of single pulse. It is related to the depth modulation of two dynamic signals. On the other hand, the intensity of the Fourier spectrum of is smaller than that of single pulse. It can be understood, because the depth

February 12-13rd 2014 modulation of similar to that of single pulse.

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double pulse is not in the observed signal in a double pulses experiment. REFERENCES

Fig. 5. The Fourier spectrum of of for single pulse (upper panel) and double pulses (lower panel). The pulses parameters and temperature are similar to those in Fig. 2.

IV. CONCLUSION In summary, it has been demonstrated that the probe pulse does not change the signal, in both time and frequency domain. Then, we can neglect the effect of probe pulse

1) A.H. Zewail, “Femtochemistry: Atomic-scale dynamics of the chemical bond”, J. Phys. Chem. A, vol. 104, no. 24, pp. 5660 – 5694, June 2000. 2) J. Itatani, J. Levesque, D. Zeidler, H. Niikura, H. Pepin, J.C. Kieffer, P.B. Corkum, and D.M. Villeneuve, “Tomographic imaging of molecular orbitals”, Nature, Vol 432, no 7019, pp. 867 – 871, Dec. 2004. 3) J. Itatani, D. Zeidler, J. Levesque, M. Spanner, D.M. Villaneuve, and P.B. Corkum, “Controlling high harmonics generation with molecular wave packets”, Phys. Rev. Lett., vol. 94, no. 12, pp. 123902, March 2005. 4) K. Miyazaki, M. Kaku, G. Miyaji, A. Abdurrouf, and F.H.M. Faisal, “Field-Free Alignment of Molecules Observed with High-Order Harmonic Generation”, Phys. Rev. Lett., vol. 95, no. 24, pp. 243903 (1-4), Dec. 2005. 5) P. W. Dooley, I. V. Litvinyuk, K. F. Lee, D. M. Rayner, M. Spanner, D. M. Villeneuve, and P. B. Corkum, “ Direct imaging of rotational wave-packet dynamics of diatomic molecules”,. Phys. Rev. A, vol. 68, no. 2, pp 023406, August 2003. 6) B.J. Sussman, J.G. Underwood, R. Lausten, M.Y. Ivanov, and A. Stolllow, “\Quantum control via the dynamic Strak effect: Application to switch rotational wave packets and molecular axis alignment”, Phys. Rev. A, vol. 73, no. 5, pp. 053403, May 2006. 7) M. Tsubouchi and T. Suzuki, “Photoionization of homonuclear diatomic molecules aligned by an intense femtosecond laser pulse”, Phys. Rev. A, vol. 72, no. 2, pp. 022512, 2005. 8) T. K. Kjeldsen and L. B. Madse, “Alignment-dependent abovethreshold ionization of molecules”, J. Phys. B: At. Mol. Opt. Phys., vol. 40, no. 1, pp. 237 – 245, Jan. 2007 9) A. Abdurrouf and F. H. M. Faisal, “Theory of intense-field dynamic alignment and high-order harmonic generation from coherently rotating molecules and interpretation of intense-field ultrafast pump-probe experiments”, Phys Rev. A, vol. 79, no 2, pp. 023405, Feb. 2009 10) F. H. M. Faisal, A. Abdurrouf, K. Miyazaki, and G. Miyaji, “Origin of Anomalous Spectra of Dynamic Alignments Observed in N2 and O2”, Phys. Rev. Lett., vol 98, no, 14 pp. 143001, April 2007.

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Gravity Data Analysis of Teluk Mandar, Makassar Strait Supriyanto 1*), T. Noor 1), Y. Mark 1), and Y. Sofyan 2) 1) Department of Physics, Faculty of Mathematics and Natural Science, University of Indonesia, Kampus UI Depok, Indonesia, 16424 2) Aso Volcanological Laboratory, Graduate School of Science, Kyoto University, Minami Aso, Aso, Kumamoto 869-1404, Japan *)

Corresponding author: [email protected] II. GRAVITY METHOD

Abstract— The Teluk Mandar is located around Makassar strait, and is situated within a complex tectonic region at the edge of Eurasian plate. Gravity data analysis have been performed to identify the subsurface structure Complete Bouguer gravity anomalies derived from satellites altimeter measurements were used in this analysis. We separate the Bouguer gravity into two parts, the regional and the residual gravity, by using Power Spectrum based filtering. The residual gravity data were then analyzed using integrated gradient interpretation techniques, such as the Horizontal Gradient (HG), and Second Vertical Derivative (SVD). These techniques detected many faults and several sedimentary basin that characterized by negative Bouguer gravity anomalies. The results of present study will lead to an improved understanding of the geological structure in Parepare region, especialy inside Sengkang sedimentary basin area.

In principal, the goal of gravity surveying is to locate and describe subsurface structures from the gravity effects caused by their anomalous densities. Gravity studies in regioanal basin area, in particular, can provide unique insights into shallow sub-surface density variations associated with the structural and magmatic activity of basinal system [2].

Keywords—Gravity, Teluk Mandar, Horizaontal Gradient, Second Vertical Derivative

I. INTRODUCTION HE Teluk Mandar is located around Makassar strait between 3.556 oS and 4.9853 oS latitudes; and 119.4083 oE -- 120.3083 oE longitudes, is situated within a complex tectonic region at the edge of Eurasian plate, covers an area of about 12.869 km2(Figure 1). It is well-known that Sengkang sedimentary basin is located in the onshore of South Sulawesi province. Gravity data from Geosat and ERS-1 satelites are analyzed using advanced gravity data processing to determine structures that affected the basin and to estimate the sedimentary thickness. It is known that gravity interpretation suffers from non-uniqueness. Different subsurface features produce same gravity field. Care was taken to constrain the analysis results using integrated gradient method [4].

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1) Department of Physics, Faculty of Mathematics and Natural Science, University of Indonesia, Kampus UI Depok, Indonesia, 16424 2) Aso Volcanological Laboratory, Graduate School of Science, Kyoto University, Minami Aso, Aso, Kumamoto 869-1404, Japan *) Corresponding author: [email protected]

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Fig 1. Teluk Mandar region, South Sulawesi Province

Nowadays, gravity data could be acquared from satellites. Satellite altimetry has provided the most comprehensive images of gravity field at ocean basins with accuracies and resolution approaching typical shipboard gravity data. The analysis of gravity data uses three approaches to reduce the error in satellite-derived gravity anomalies to 2– 3 mGal from 5 to 7 mGal. First, the raw waveforms were retracked from 11 months of ERS-1 satellite data [4] and 18 months of Geosat/GM satellite data resulting in improvements in range precision of 40% and 27%, respectively. Second, the recently published EGM2008 global gravity model at 5 min resolution [1] were used in the remove/restore method to provide 5-min resolution gravity over the land and 1-min resolution (8 km 1/2 wavelength) over the ocean with a seamless land to ocean transition. Third, a biharmonic spline interpolation method including tension [6] were used to construct residual

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vertical deflection grids from seven types of inconsistent along-track slope measurements. Comparisons between shipboard gravity and the global gravity grid show errors ranging from 2.0 mGal in the Gulf of Mexico to 4.0 mGal in areas with rugged seafloor topography. The largest errors of up to 20 mGal occur on the crests of large seamounts. III. GRAVITY MEASUREMENT AND DATA PROCESSING Gravity data retracked from Geosat and ERS-1 altimetry satelites have been provided by The Satellite Geodesy research group at Scripps Institution of Oceanography, University of California San Diego. A total of 3904 point of gravity measurement were distributed at Teluk Mandar region at the total area of about 12.869 km2 ; with point interval of about 1.8 km (1 minute). The gravity data set consists of longitude and latitude coordinates; and complete bouguer anomaly (CBA). All data processing steps are shown in Figure 2. In this study, we assume that the final output of several steps of processing of satellite-derived gravity anomaly explained above is a Complete Bouguer Anomaly (CBA) as shown in 3. Complete Bouguer Anomaly (CBA) map of Teluk Mandar Figure 3. The CBA data ranges between -21 and 114 Fig. region, South of Sulawesi. Coast line and Sengkang Basin boundary are mGal. Negative anomalies were observed mainly in the shown in blue line and black line respectiveley. offshore of Teluk Mandar region. Meanwhile, highest positive gravity values were detected on the onshore of Teluk Mandar region and, it can be interpreted by high density of volcanic rock instrusion.

Fig 2. Flow processing of gravity data

There is a general assumption that the negative value of the CBA could be interpreted as a sedimentary basin. However, the CBA value come from all bodies anomaly at various depth. Moreover, in contrast, the Sengkang sedimentary basin is covered by relatively high of CBA, not negative value. Therefore, to be more confident for determining the location of sedimentary basin, we need to extract residual gravity anomaly from the CBA. We have separated the CBA into two parts, the regional and the residual gravity anomalies, by using power spectrum based filtering. The regional gravity reflects source of gravity anomaly from deeper part which is more than 10 km. We have decided to analize residual gravity, rather than regional gravity. In term of gravity data, we have expected that sedimentary basin indicated by low residual gravity anomaly inside Sengkang sedimentary basin area (Figure 4).

Fig 4. Residual gravity anomaly of Teluk mandar region

The residual gravity resulted from filtering were then analyzed using two integrated gradient methods, such as the Horizontal Gradient (HG) and Second Vertical Derivative (SVD) methods. These techniques detected many faults as the boundary of the sedimentary basin. Moreover, the depth structure of formation inside the Sengkang sedimentary basin have been delineated. IV. RESULT AND DISCUSSION A. Horizontal Gradient The horizontal gradient method was used extensively to locate the boundaries of regions of contrasting density from gravity data [3]. The greatest advantage of the

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horizontal gradient method is that it is least susceptible to noise in the data because it requires the calculation of only two first-order, horizontal derivatives of the field [4].

 ∂H   ∂H  H ( x, y ) =    +  ∂x   ∂y  2

2

(1) The map of horizontal gradient from Teluk Mandar is shown in Figure 5. It shows that the boundaries/faults are located at the maxima of the horizontal gradient. Some new faults were detected as well. These faults are located at, or near the volcanic area. Moreover, some geological faults are not corroborated by the horizontal gradient technique. This discrepancy may be the fact that the horizontal gradient detects only faults that displaced formations vertically causing density contrasts.

Fig 6. Second Vertical Derivative map of Teluk Mandar region

Fig 5. Result of Horizontal Gradient analysis

B. Second Vertical Derivative In order to identify certain type of fault, the Second Vertical Derivative (SVD) analysis has been aplied to the CBA free noise. The SVD shows and isolates structural features that are identical and complimentary to those already identified with the horizontal gradient maxima. The Euler solution superimposed on the SVD clearly outlines the various contacts and structures of the faults and basin boundaries. To determine fault type, we first make a line that perpendicular to the certain fault line interpreted by Euler calculation. Second, evaluate the maxima and minima of the SVD value along the line. If the absolute of maxima number is greater than absolute minima number, the fault type is identified as a normal fault. Otherwise, the fault type is reverse fault.

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Fig 7. SVD value variation along AB line. The fault type is normal fault because absolute maxima number is greater than absolute minima number.

V. CONCLUSION Integrated gradient interpretation techniques, Horizontal Gradient (HG) and Second Vertical Derivative (SVD). Have ssuccessfully detected many faults and several sedimentary basin that are characterized by negative Bouguer gravity anomalies. The structural high interpreted from SVD analysis high has covered the area around of 250 km2. ACKNOWLEDGMENT We would like to acknowledge the support of the Directorate of Research and Public Service, University of Indonesia, who gave us financial support under the Hibah Penelitian Unggulan Perguruan Tinggi (PUPT) – BOPTN 2013 scheme with the contract number: 1350/H2.PPK/HKP.05.00/2013.

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REFERENCES [1]

[2]

Mikhailov, V., Galdeano, A., Diament, M., Gvishiani, A., Agayan, S., Bogoutdinov, S., Graeva, E., Sailhac, P., (2003). Application of artifcial intelligence for Euler solutions clustering. Geophysics 68, 168e180. Mikhailov et al., 2003 Pavlis, , N. K., S. A. Holmes, S. C. Kenyon, and J. K. Factor (2008), An Earth Gravitational model to degree 2160, paper presented at General Assembly, Eur. Geosci. Union, Vienna.

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Reid, A.B., Allsop, J.M., Granser, H., Millett, A.J., Somerton, I.W., (1990). Magnetic interpretation in three dimensions using Euler deconvolution. Geophysics 55, 80e91 Sandwell, D. T., and W. H. F. Smith (2005), Retracking ERS-1 altimeter waveforms for optimal gravity field recovery, Geophys. J. Int., 163, 79–89, doi:10.1111/j.1365-246X.2005.02724.x. Thompson, D.T., (1982). EULDPHda new technique for making computer-assisted depth estimates from magnetic data. Geophysics 47, 31e37. Wessel, P., and D. Bercovici (1998), Interpolation with splines in tension: A Green’s function approach, Math. Geol., 30(1), 77 – 93, doi:10.1023/A:1021713421882.

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Modeling of Relative Dating Using Spectroscopy Analysis of Tamban’s Subfossil T.B. Susilo*#, U. B. L. Utami*, R. Nurmasari*, R. Mujianti*, M. Habibi***, S. Mawadah***, A. Pradana***, S. Hayati***, Y. Seftiawan***, O. Soesanto**, B. I. Prayogo**** and Satrio ***** *

Prodi Kimia FMIPA UNLAM, Jl. A. Yani Km 35,5 Banjarbaru, Kal Sel; ***Mahasiswa Kimia FMIPA UNLAM, Jl. A. Yani Km 35,5 Banjarbaru, Kal Sel; **Prodi Matematika FMIPA UNLAM, Jl. A. Yani Km 35,5 Banjarbaru, Kal Sel; ****Museum Lambung Mangkurat, Jl. A Yani Km 35, Banjarbaru, ***** Pusat Aplikasi Teknologi Isotop dan Radioaktif-Badan Teknologi Atom Nasional (PATIRBATAN), Jl. Lebak Bulus Raya No. 49, Pasar Jumat, Jakarta, *#

Corresponding author: [email protected]

Abstract—Archaeological fragments of bone that are exposed to a wetland environment take up fluorine from the surrounding soil. The fluorine ion exchanged the hydroxyl group in the hydroxyapatite (Ca10(PO4)6(OH)2)of the bone, forming chemically more stable fluorapatite(Ca10(PO4)6(F)2). Based on our data 14C radiocarbon, the age of two Tamban’s subfossil are 485-± 5 and 5684 ± 16year ago, respectively. The IR spectrum is sharp band 3500-4000 cm-1 in the hydroxyapatite. Tamban’s subfossil and Tahura’s bone that spectrum is assigned to the OH stretching mode and considering the fossilization have been a conservations in wetland environment. In the region 800-900 cm1, the subfossil and bone implies that carbonate and silicon substitution don’t induce vacancies at the OH- site. In here, we report that modeling Ca2+replaces Cu2+, Cd2+ and Zn2+ions, which can be described by a diffusion model, contain information on the exposure duration of the Tamban’s subfossil object, several attempts to use metal profiling as a relative dating method. Keywords—14C, FTIR and relative dating method.

I. INTRODUCTION HE chemical composition of the mineral and the organic part of bones has been used palaeodiet and palaeoclimate reconstruction. However, the burial period, bones have been in contact with sediments, soil and water. Partial of complete dissolution, erosion, and precipitation, recrystallization, ion uptake by sorption and diffusion, hydrolysis, and polymerization may lead to changes in the chemical composition and structures. The state of preservation is very variable and depends mainly on direct environmental conditionsuch as groundwater and sediment temperature, soil hydrology, and pH, reductionoxidation (redox) potential and temperature, mechanical pressure, biological factors and particle transport. They are of great importance to understand the alteration process in soils and the impact of environments conditions on bone/fossil conservation[1]. Very few studies have addressed multi-element ionic exchanges between soil solution and bones. Ionic interactions with soil solution would

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involve competition between a wide range of ions for the various lattice position in bone mineral (apatites) [2]. In this context, the preservation of the geochemical signal in biological apatites that are relevant for studies related to between 14C dating, rate diffusion of ion metal (Cu2+, Cd2+ and Zn2+), and shift of FTIR spectra. In the Borneo, elephants have a very limited distribution, being restricted to approximately 5% in northeast. Fossil evidence for the prehistoric presence of elephants on Borneo is limited to a single specimen of tooth from a cave in Brunei. Two history popular, considering the geographic proximity to Borneo, the elephant trade that flourished in Sumatra, Java and Paninsular Malaya during 16th18th centuries may have been the source. Alternatively, Borneo’s elephant presented to the Sultan of Sulu in 1750 to Borneo northeast by East India Trading Company. These animals presumably originated in India [3]. Conversely, if elephant occurred naturally on Borneo, they would have colonized the island during Pleistocene glaciations, when much of the Sunda shelf was exposed and the western Indo-Malayan archipelago formed a single landmass designated Sundaland. Thus, the isolation of Borneo’s elephant from other conspecific population would minimally date from the last glacial maximum 18,000 year ago, when land bridges last linked the Sunda islands and the mainland [4]. If Borneo’s elephants are indigenous origin, this would push the natural range of Asian elephant 1300 Km to east, and as a unique population at an extreme of the species’ range, Borneo elephants’ in situ conservation would be apriority and ex situ crossbreeding with other population would be contraindicated [3]. Initially, Borneo elephant were classified as a unique subspecies (Elephas maximus borneoensis) based on morphological differences other population [5]. Subsequently, they were subsumed under the Indian Elephas maximus indicus[6] or the Sumatran Elephas maximus sumatrensis[7] subspecies, based on assumption of their introduction to the region or on the reasoning that morphological divergence was insufficient to warrant separated status. According to Fernando (2003), HVS mtDNA analysis showed that Borneo’s elephant are genetically distinct with

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molecular divergence indicative of a Pleistocene coloniza- conclusions. The sharp band 3500-4000 cm-1 in the HAp, tion of Borneo and subsequent isolation. Tamban’s subfossil and Tahura’s bone spectrum is assigned to the OH stretching mode; presence this mode from the spectra of Tahura’s bone and Tamban’s subfossil II. MATERIALS AND METHODS The Fourier Transform Infrared (FTIR) spectra were implies the carbonate content and silicon substitutions did recorded on Bruker Optic IFS66s/S interferometer not induce vacancies at the OH sites. The increase in inequipped with an attenuated total reflectance (ATR) unit. tensity with decrease in carbonate content and the presence The range frequencies was 650-4000 cm-1 and the typical of structurally bound OH in the little carbonated Tahura’s experimental condition utilized a resolution of 4cm-1, a bone and Tamban’s subfossil has been reported in a very velocity of 6-10 KHz, a gain of 16x, an apodization Black recent work. Table 1, shows the 650-900cm-1 region of the IR specHarris 3-term, a Mertz phase correction and zero filling 2, on a double sided, forward-backward acquisition mode. A tra of all the samples. In the region, all substituted samples KBr beam splitter was used for the M-IR source. Subse- display very weak bands that can be assigned to the vibraquently, aliquots of approximately 2 mg subfossil Tam- tion4CO32- (ν4 CO32-)and ν2 CO32- modes at energies ban’s elephant were ground and pressed into a KBr pellet similar to the previously reported exceptfor the Tahura’s and the infrared spectra were measured on a Perkin Elmer bone and Tamban’s subfossil [8]. The absence ν2CO32-, the subfossil and bone implies that carbonate and silicon Spectrum One instrument [8]. For isotope 14C dating, carbonate in calcined subfossil substitution don’t induce vacancies at the OH- site, probaTamban’s elephant obtained is the most reliable source of bly considering the fossilization have been a conservation inorganic carbon. The subfossil was demineralized in a 1% in wetland environment. The weak intensity of the absorphydrochloric acid (HCl) solution several days. The ex- tion band near 1640cm-1 corresponding to νCNH of the tracted gelatin-like collagen was thoroughly washed with amide group [1]. According to Abeyratne et al (1997) [14], distilled water. In order to remove the humic acid, the col- FTIR trace for bone and fossil, phosphate is indicated by lagen was treated with an 0.1 N sodium hydroxide (NaOH) double troughs around 600cm-1 and width trough at 1036 for several days. The remaining collagen was again washed cm-1. Carbonate is shown bay the narrow dip at about 875 with distilled water, dried and carbonized by heating at cm-1 and the wider one at 1425cm-1. The Tamban’s sub800oC in an oxygen-free environment. The phosphorous fossil and Tahura’s bone was measured with FTIR by excompounds were removed by treating the collagen with amining the splitting factor of PO4 anti-asymmetric bend“aqua regia”, a mixture of nitric acid (HNO3) and hy- ing mode peak at wave number 563cm-1. The FTIR spectra of both bone and subfossil are characdrochloric acid (HCl). The cleaned collagen was then terized by intense band between 1300-2000cm-1 and washed with distilled water, dried, and used for carbon 2300-3000 cm-1 indicating an abundant contribution of dioxide gas (CO2) preparation [9].For metal Cu, Cd and Zn carried out by atomic absorption spectroscopy (AAS) alkyl chains. Strong aliphatic absorptions centered at [10]. Modeling dating relative referenced to Lagrange me- around 2860-2930cm-1 are assigned to asymmetric stretching vibrations from CH2 and symmetric stretching vibrathod of interpolation [11], [12] and [13]. tions from CH2 methylene group, respectively. The absorption of symmetric bending of CH3 with possible conIII. RESULTS AND DISCUSSION tribution from (CH)n bending. The presence a long polymethelynic chains (n ≥ 4) is indicated by the absorption at A. Examination of sample preservation by FTIR around 720cm-1. The absorption at 1710cm-1 shows the The major peculiarities for a diagenetically altered bone presence of carboxyl group. The weak absorption signal are an increase in crystal size and a decrease in protein attributed to aromatics C=C ring stretching vibration peaks content. Thus information on the state of degradation can at round 1620cm-1. be obtained from FTIR spectroscopy (Fourier Transform Infrared Spectroscopy) by observing the characteristic splitting of the double peaks at 563-604 cm-1 correspondB. Radiocarbon Dating and Originality of Boring to phosphate vibration ν(PO4)3- indicating mineral neo’sElephant phase modification, e. g changes in crystallinity and ion In the Indian subcontinent, the dispersal elephants carexchange. A low value for the splitting factor (SF) indi- ried out by human intervention through trade and warfare. cates a high amount of amorphous material in the mineral Human activity has changed the natural distribution pattern phase and obtained as described in Reiche et al. (2003). of elephants. Special originality Borneo’s elephants still The intensity of the organic CO-signal at ≈ 1650 cm- discussion. There are two popular hypotheses, first, refer 1(compared to the signal of inorganic CO32- at ≈ 1450 to the documents that the sultan of Sulu has received a gift cm-1 provides an indication of the degree of collagen de- elephant from the Indian (Elephas maximus indicus) in gradation and expressed as C=O and C-C bonding, where a 1750 and domesticated in North Borneo[6]. According to high value represents a high degradation of collagen in the Medway (1977)[7], elephant Borneo derived from Sumasample [8]. tran elephant or Elephas maximus sumatrensis. The next The IR spectra of all specimens (Tahura’s bone , Tam- hypothesis is indigenous or not introducer. Fernando ban’s subfossil and Antonakos’s data) show broad bands in (2003) [3] stated that based on the analysis of D-loop the high energy region that are propably water related band mtDNA fragment, population showed evidence of indi(table 1). They show peak positions without leading to any genous and not introducer. Mac Kinnon (1996) [4] also

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stated that the Borneo elephants have colonized the Pleistocene about 18,000 years ago when the bridge was formed Sundaland shift. Our 14C dating data also show that the findings of the elephant subfosil from Tamban village, district Batola shows the age range 485±5 and 5684±16 years ago. This information supported the second hypothesis. TABLE 1. ROOM TEMPERATURE OBSERVED IR MODES AND THEIR ASSIGNMENT HAp 574s 856 wbr

CHAp 15M 577s

CHAp 25M 584s

SiHAp

CFAp 530

CFAp 840

592s

590s

602s

674 vm noisy [759vw 805vw 844m] 872s 880m 893w 954sh 961s

668 m [713vm 760vw 813vw 847sh] 873s 880m 933sh 961s

680vw spectrum

668 vw 690w 720w

668w 754vm

865s 877s

864m 876m

ν2 CO3

956sh 992sh

ν1 PO4

1029vs 1060sh 1094s

1029vs1061sh1093vs

961s

noisy

873w 882w

936sh 962s

964s

Tamban’s Subfossil 563s 727w

871vw vm

Tahura’s Bone 563s 760

871vw

963s

1015s 1029vs 1060sh 1029vs

3400br 3567m

1029vs 1045vs 1060sh 1091s 1174m 1223m 1409m 1427m 1444m 1468sh 3700br 4073br

1410s 1450s 1470s 1498sh 1568sh

3553br

3460br

1030vs1060sh 1093vs 1160m 1427s 1456s 1468s [1482sh 1506vm 1518vm 1538vm 1558vm]

3400br

1025vs1045sh 1093s 1146m 1162w 1424m 1452m [1470sh 1506vm 1518vm 1538vm 1558vm]

3750br

1033vs 1334vm 1411m 1550vm 1658m 1982vm 2075vm 2252vm 2337m 2939m 2970m 3425br 3927m

1041vs

Assignment ν4 CO3and ν1 PO4 ν4 CO3

ν3 PO4 and ν1 CO3 ν3 CO3 and SiO4

1411m 1627m 2291vm 2368m

3410br 3749m

-

OH ion or moisture

Code: [HAp: (Ca10(PO4)6(OH)2)]; [CHAp 15M: (Ca10(PO4)6(OH)2) 15M]; [CHAp25M: (Ca10(PO4)6(OH)2) 25M]; [SiHAp : (Ca10(PO4)6-x(SiO4)x(OH)2)]; [CFAp 530: (Ca10(PO4)6(F)2) heat treat at 530 oC] [CFAp 840: (Ca10(PO4)6(F)2) heat treat at 840 oC, 4,8 % loss CO2]

C. Modeling Relative Datingand Cation Exchange Cu2+, Cd2+ and Zn2+ The use of entropy of hydration (∆Hh) in addition to crystallographic ionic radii improve predictions concerning the abilities of various cationic to substitute in to Ca2+ lattice position. The heat of hydration provides a measure of the strength of ion water molecule bond. Strong bonds are indicated by highly negative ∆Hh values. In the context fossilization, the Cu2+(-2100 kalJ/mol), Zn2+(2+ 2044kalJ/mol), Cd (-1806 kalJ/mol) cationic substitutein to Ca2+(-1592 kalJ/mol) lattice hydroxyapatite. Larger cationic tend to associate less strongly with water due to their increased radii (Å) and reduced surfaced area[2].For radii Cu2+, Zn2+, Cd2+and Ca2+cations are 0.73Å; 0.74Å, 0.95Å, and 1.00Å, respectively.The degree of hydration increases with increasing ionic charge. In the case of this experimental, the third cationicchargeare the same so that is 2+. According Scimiklas (2003) [15] and Smiliklas (2007) [16] and Chen (1997) [17], the exchange Cu2+, Zn2+, and Cd2+cationicin to Ca2+ lattice hydroxyapatite can be expressed as follows.

substitution of solution ions for normal hydroxyapatite lattice position [2]. In our data, Cu2+, Cd2+ and Zn2+cationic replace lattice Ca2+, after elephant subfossil from Tamban (Batola-Kalsel) and elephant bone (Tahura park) have been buried for 1, 2, 4, and 6 month, respectively. Carbon dating showed 485-± 5 and 5684 ± 16 BP (Before Present) (table 2). Modeling dating relative have been construct based on radiocarbon dating versus spectroscopy data (table 2) using multi-interpolation method (table 3 and figure 1) [12].e. g. relative dating Y(Zn) = 1.4333e-007t2 +0.00063794t + 0.14854 and rate diffusion Y(Zn)/dt = -2.8666e-007t + 0.00063794 (table 3 and figure 1). TABLE 2. PERCENT OF MATERIAL SUBFOSSIL VERSUS FOR BURIED (YEAR) Year 485.08 3 485.16 7 485.25 485.5 7.083 7.167 7.250 7.500 485

Sample

FG

Ash%

Organic%

Cu%

Cd%

76.667

21.260

0.011

0.008

61.290

38.710

0.003

0.005

60.000 61.290 70.968 73.333 67.857 63.333 23

40.000 34.630 18.832 14.907 18.143 24.907 70

Zn% 0.488 0.004

0.009 0.007 0.253 0.005 0.007 0.002 0.008 0.008 0.4 0.009 0.009 0.499 RG 0.013 0.007 0.406 0.006 0.008 0.32 0.118 0.017 0.692 0.0008 0.0003 0.0180 4148 59.70 32.29 3 1 FG: Elephant subfossil buried 485 and 4148 year (BP) from Tamban village; RG: Elephant bone buried 7 year from Tahura park TABLE 3. MODELING DATING RELATIVE BASED ON MATERIAL VS TIME (YEAR). Materit2 (year) t (year) C (conal stant) Ash 1.2178e-005 -0.049814 64.138 Organic -1.163e-005 0.045528 33.06 Cu -3.2659e-008 0.00013609 0.004815 Cd -3.1.3912e1.3912e0.0053648 005 005 Zn -1.4333e-007 0.00063794 0.14854

[Ca10(PO4)6(OH)2] + n X2+→ [CA10-nXn(PO4)6(OH)2] + n Ca2+ X2+ : Cu2+, Zn2+, or Cd2+cationic n : number of moles

A B Figure 1. Equation shown that type related between ash and organic material (%) vs t (time-year) (A), equation Cu, Cd and Zn content(%) versus t (time-year) (B).

Ionic exchange between soil solution and bone should IV. CONCLUSION be a dominant process in apatite mineral diagenesis. ExpeFigure FTIR analysisand14Cdatingsuggest thatthere is rimental evidence indicates that Ba2+, Mg2+, Pb2+, Sr2+, in the Borneo. Na+, CO32- an F- enter the crystal surface from the bound Elephas maximusborneensisandindigenous A Combinationanalysis ofCu, CdandZncontent of elephant hydration layer of mineral hydroxyapatite 14 subfossiland Cradiocarbondatingshownnon-linear equa(Ca10(PO4)6(OH)2). Ion exchange involves the isomorphic tions.

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Medway L., (1977), Mammals of Borneo, Monogr Malay Br, R

Asian Soc, 7: 1-72. ACKNOWLEDGEMENT [8] Antonakos, A., Liarokapis, E’, and Leventaouri, T., (2007), MicroHigest thank to head of Lambung MangkuratMuseuRaman and FTIR studies of synthetic and natural apamandheadof community forestpark(Tahura-Banjarbaru) for tites,Biomaterilas, 28: 3043-3054. [9] Keates, S. G., (2010), The Chronology of Pleistocene Modern gave elephant bone and subfossil sample.

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Reiche, I., Favre-Quattropani, L., Vignaud, C., Bocherens, H., Charlet, L., Menu, M., 2003. Amulti-analytical study off bone diagenesis: the Neolithic site of Bercy (Paris,France), Measurement Science and Technology 14, 1608–1619. Pate, F. D., Hutton, J. T., Norrish, K, (1989), Ionic exchange between soil solution and bone: toward a predive model, Applied Geochemistry, vol.4, pp. 303-316. Fernando, P., Vidya, T. N. C., Payne, J., Stuewe, M., Davison, G., Alfred, R. J., Andau, Patrick, Edwin, B., Kilbaourn, K., Melnick, D. J., (2003), DNA Analysis Indicates That Asians Elephant Are Native to Borneo and are Therefore a High Priority for Conservation, Plos Biology, 1: 110-115. Mac Kinnon, K., Hatta, G., Halim, H., Manggalik, A., (1996), The Ecology of Kalimantan, Hong Kong, Periplus Edition, Ltd. 802 p. Deraniyagala,P.E.P., (1955), Some extinct elephants, theirs relatives, and the two living species, Colombo, Ceylon, government Press, 161.p. Shoshani, J., dan Eisenberg, J. F., (1982), Elephas Maximus, Mamm sp., 182: 1-8.

Humans In China, Korea, and Japan, Radiocarbon, 52: 428-465 [10] Walsh, A., (1955), The application of atomic absorption spectra to chemical analysis, Spectrochim. Acta 7: 108–117. [11] Hussein, K. A., (2011), The Lagrange Interpolation Polynomial for Neural Network Learning, International Journal of Computer Science and Network Security, vol. 11, no.3. [12] Bozogmanesh, A. R., Otadi, M., Kordi, A. A. S., Zahibi, F., and Ahmadi, M. B., (2009), Lagrange two-dimentional interpolation method for modeling nanoparticle formation during RESS process, Int. J. Industrial Mathematics vol. 1, No. 2, 175-181. [13] Gasca,M., and Sauer, T., (2001), Polynomial interpolation in variables, Advances in Computational Mathematics, [14] Abeyratne , M., Spooner, N.A., Grun, R., and Head, J., (1997), Multidating studies of Batadomba Cave, Sri Langka, Quaternary Science Reviews, vol.16, pp. 243-255. [15] Scimiklas I., D., (2003), Cadmium immobilization by Hydroxyapatite, Chem, Ind, 57(3), 101-106. [16] Scimiklas, I., Onjia, A., Raicevic, S., Janakovic, D., and Mitric, M., (2007), Factor influencing the removal of divalent cations by hydroxyapatite, Journal Hazardous Material, 152, 876-884. [17] Chen, X., Wright, J. V., Conca, James, J. L., and Peurrung, L. M., (1997), Effect of pH on Heavy Metal Sorption on Mineral Apatite, Environmental Science & Technology, vol 31, no. 3

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Particle Motion of Seismic Waves Recorded from Hydrothermal Area at Cangar, East Java, Indonesia 1

Wasis1*), Sukir Maryanto1, Dahlia Kurniawati1 Geophysics Lab., Dept. of Physics, Brawijaya Univ., Jl. Veteran, Malang 65145, Indonesia *)email : [email protected]

Abstract—The particle motion analysis of seismic waves around Cangar hydrothermal area has been investigated to estimate the epicenter and hypocenter. The determination of epicenter and hypocenter are based on the direction of the particle motion, by using single-h methods. On each station CGR01 and CGR02 three events are chosen. From the particle motion analysis, five epicenters obtained, based on the intersection of particle motion direction on both stations. They located at (112°32’2,04” E ; 7°44’32,208” S), (112°32’2,04” E; 7°44’32,1” S), (112°32’0,96” E ; 7°44’31,49” S), (112°32’3,57” E ; 7°44’32,58” S), and (112°32’3,44” E; 7°,67” S). Whereas the hypocenter of earthquake ranging from 30–60 meters depth. The epicenter and hypocenter related to hydrothermal activities in subsurface.

Keywords—sparticle motion analysis, seismic, hydrothermal, Cangar.

I. INTRODUCTION EOTHERMAL is one of the natural energy sources originated from the rocks interaction and heat flow in the earth. Indonesia has 40% of the world geothermal sources, from Sumatera, Java, Nusa Tenggara to Sulawesi. One of them is Cangar hotspring in East Java. Some researchs have been conducted to find geothermal potential using some geophysics methods such as: geoelectric, geomagnetic and gravity. Research results used geoelectric method showed the existence of the geothermal potential south to the hotspring in the depth of 24,7 meter [1]. Research used geomagnetic method showed the existence of the geothermal sources in the north and west direction from the hotsprings[2]. Meanwhile, researchs used gravity method predicting that there is geothermal potential as much as ±2.064.640 m3 in the coordinates of 7.7406° S and 112.5339°E [3]. Nevertheless, research using seismic method based on the microseimic analysis to find area having geothermal potential has never been conducted yet. Geothermal energy can be defined as energy that naturally produced by the earth. Earthquake in the geothermal area connected to sesar movement along the geothermal fluids flow [4]. Earthquake with magnitude less than 3, known as microearthquake. According to Holland (2002) [5], by studying microeartquake on the geothermal location, inrerrelationship of the crack sytems

G

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that control fluids migration on the geothermal area can be determined.

According to Utama et al (2013) [6], microseismic or microtremor is one of the passive seismic methods for recording the vibration of the earth caused by vulcanic activity, waves, meteorology regional condition, human activities, etc. Microseismic method usually used for exploration, mining, as well as geothermal. One of the methods to investigate the crack existence in the geothermal field is the microseimic particle motion. Horizontal and vertical components of the particle motion used for determining epicenter and hypocenter of the microseismic. II. RESEARCH PROCEDURE This research used microseismic data in Cangar area, East Java, with two recording stations CGR01 and CGR02. Research flow as seen in figure 1. Data recorded by TDS have 3 components which are North-South (NS), EastWest (EW) and Up-Down (UD). These three components will be used for analysing microseismic particle motion. Data in the time domain will be transformed to the frequency domain using FFT (Fast Fourier Transform) so that frequency spectrum for each component obtained. Spectogram analysis is needed to know variation of the harmonic signal frequency to time. This process aims to determine frequency limit that will be used in filtering process. Filtering used Butterworth band-pass filter, because filter of this type has an advantage in band-pass filtering. Particle motion plotting in horizontal and vertical components was used for determining epicenter and hypocenter of a microseismic. Microseismic epicenter predicted by examining the particle motion direction, and then calculated roughly. The same process was applied for determining hypocenter distance of a microseismic in a grothermal field. Interpretation on the existence of the geothermal potential, need to be correlated to the results of the prior researchs.

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Particle Motion

Interpretation

Frequency limit determined by the frequency spectrum analysis and spectogram will be applied in filtering process. Filtered signal will be sampled every 1 second in order to know particle motion direction. Based on the particle motion plotting for horizontal and vertical components, position of the epicenter point can be determined. N 70

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Frequency spectrum analyzed using FFT, where data recorded in time domain will be transformed to frequency domain. According to Ihsan (2011) [7], frequency limit determination based on dominant frequency, showed that there was a signal in that frequency. Microseismic data in Cangar area has a dominant frequency more than 15 Hz (Figure 2). This high dominant frequency could becaused by hydrothermal activities influence. The same result yielded by the spectogram that applied the STFT (Short Time Fourier Transform) principles that is frequency variation to time.

Figure 4. Particle Motion CGR01: a) horizontal component (b) vertical component

Based on the particle motion analysis, seismic activities in Cangar area, East Java, there are 5 epicenter points with subsurface hydrothermal activities. This is supported by geothermal manifestation distribution around that area. The dominant rocks in Cangar, according to geoelectric, geomagnetic, and gravity surveys are basalt and tuff. Tuff rocks contain many cracks from where fluids (water) flow (Rakhmanto, 2011). Cracks happened because of volcanic activities or tectonic in Mount Arjuno-Welirang area. Fluids under the surface will be heated by hot rocks, so that the hot fluids activities increased and earthquake took place. Figure 5 shows three epicenter points of the microseismic that could be represent unrevealed hydrothermal sources

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The 4 Annual Basic Science International Conference

30 Meters T B " 8 0 ' 2 3 ° 2 1 1

T B " 7 0 ' 2 3 ° 2 1 1

T B " 6 0 ' 2 3 ° 2 1 1

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T B " 4 0 ' 2 3 ° 2 1 1

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. Figure 5. Geothermal Distribution Map in Cangar Area, East Java

III. CONCLUSION Particle motion analysis on the horizontal and vertical components can be used for predicting the location of the epicenter and hypocenter of the microeartquake. Based on the research, the center of the earthquake is in depth of 16 to 60 meter. Epicenter distribution are on the 5 points which are: (112°32’2,04” E; 7°44’32,208” S), (112°32’2,04” E; 7°44’32,1” S), (112°32’0,96” E; 7°44’31,49” S), (112°32’3,57” E;7°44’32,58” S), and (112°32’3,44” E; 7°44’32,67” S). 1. Epicenter and hypocenter determination related to the hydrothermal activities in the subsurface. A high dominant frequency spectrum showed that there are fluid activities heated by the hot rocks around them.

112 | Batu, East Java, Indonesia

REFERENCES [1] Rakhmanto, F., 2011. Tomografi Geolistrik Daerah Panasbumi Welirang-Arjuno (Studi Sumber Air Panas Cangar Batu). Tesis S2. Universitas Brawijaya Malang. [2] Afandi, Akhmad. 2011. Studi Potensi Panas Bumi Di Daerah Cangar Kota Batu Jawa Timur [3] Zaman, Muhammad Badaruz. 2011. Studi Potensi Panas Bumi Di Pemandian Air Panas Cangar, Kota Batu, Jawa Timur Dengan Menggunakan Metode Gayaberat. Skripsi S1. Universitas Brawijaya Malang. [4] Holland, Austin Adams. 2002. Microearthquake Study Of The Salton Sea Geothermal Field, California: Evidence Of Stress Triggering. The University Of Texas. El Paso. [5] Utama, W., Tri Martha Kp, Dwa Desa W., And Makky S. Jaya. 2013. Application Of Ensemble Empirical Mode Decomposition (Eemd) For Identification Of Hydrothermal Dynamics In The Subsurface, Case Study Mt. Lamongan, East Java. Proceeding Itb Geothermal Workshop. Bandung. [6] Ihsan, Agung Budi. 2011. Karakterisasi Mikrotremor Di Daerah Sekitar Sungai Porong Desa Kebonagung Sidoarjo. Skripsi S1. Universitas Brawijaya Malang.

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MATHEMATIC AND STATISTIC

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Model Components Selection in Bayesian Model Averaging Using Occam's Window for Microarray Data 1)

Ani Budi Astuti1*), Nur Iriawan2), Irhamah3) and Heri Kuswanto4) PhD. Student at Statistics Department of Mathematics and Natural Sciences, Institut Teknologi Sepuluh Nopember (ITS), Surabaya, Indonesia 1)

Mathematics Department of Mathematics and Natural Sciences, Brawijaya University, Malang, Indonesia

2), 3), 4)

Statistics Department of Mathematics and Natural Sciences, Institut Teknologi Sepuluh Nopember (ITS), Surabaya, Indonesia *)

e-mail: [email protected]

Abstract—Microarray is an analysis for monitoring gene expression activity simultaneously. Microarray data is data generated from microarray experiments having characteristics of very few numbers of samples where the shape of distribution is very complex and the number of measured variables is very large. Due to this specific characteristic, it requires special method to overcome this. Bayesian Model Averaging (BMA) is a Bayesian solution method that is capable to handle microarray data with a best single model constructed by combining all possible models in which the posterior distribution of all the best models will be averaged. There are several method that can be used to select the model components in Bayesian Model Averaging (BMA). One of the methods that can be used is the Occam's Window method. The purpose of this study is to measure the performance of Occam's Window method in the selection of the best model components in the Bayesian Model Averaging (BMA). The data used in this study are some of the gene expression data as a result microarray experiments used in the study of Sebastiani, Xie and Ramoni in 2006. The results showed that the Occam's Window method can reduce a number of models that may be formed as much as 65.7% so that the formation of a single model with BMA only involves the model of 34.3%. Keywords—Bayesian Model Averaging, Microarray Data, Model Components Selection, Occam’s Window Method.

I. INTRODUCTION Microarray data is the data obtained from a microarray experiment that is an experiment with a particular analysis technique to monitor the activity of thousands genes expression simultaneously [1]. Microarray data have several characteristics i.e. -limited availability of the number of samples because of limited budget, resources and time. Though the availability of the number of samples is limited, the measurable characteristic variables can be hundreds or even thousands of gene expression. By these special characteristics, it

114 | Batu, East Java, Indonesia

is possible that the nature of the distribution of gene expression data will be very complex in which the distribution of the data is probably not a normal distribution [2]. Due to these specific characteristics, it requires special method to overcome this. Bayesian is a statistical analysis method that does not consider the number of samples (especially for very small sample size) and to any form of distribution. Moreover, Bayesian method is based on information from the original data (driven data) to obtain the posterior probability distribution which is a product of the prior distribution and the likelihood function [3]. Model Parameter in Bayesian method is viewed as a random variable in the space of model parameter and allows for the formal combination of different from the prior distribution and facilitates the iterative updating of new information which thus overcome the problem of uncertainty and complexity of the model in the data [4] . Bayesian Model Averaging (BMA) is a Bayesian solution to model uncertainty in which the completion of the model by averaging the posterior distribution of all the best models. The basic principles of the BMA is form the best single model by considering all possible models that could be formed so that the purpose of the BMA is models incorporate uncertainty and obtain the best model [5]. There are several method that can be used for the model components selection in the BMA of which Occam 's Window method of [5]. This method is quite simple and widely used in research related the BMA in which obtained quite good results in the model components selection in the modeling of the BMA [5] and [6]. Various studies have been done related to the Bayesian Model Averaging (BMA), among others [6], [7], [8], [9], [10] and [11]. In this study will be used Occam's Window method of [5] to select the component model in the modeling of the BMA for microarray data.

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II. MICROARRAY, BAYESIAN MODEL AVERAGING AND OCCAM’S WINDOW METHOD A. Microarray Techniques and Microarray Data. According to [1], microarray technique is a technique of data collection by using the platform (reference) which is a duplicate of the original object identifier. The measurement data of a microarray technique called Microarray Data [12]. There are a variety of different technologies have been developed for microarray techniques, among which is a Synthetic Oligonucleotide Microarray Technology [13]. Gene expression data is the measurement data from Microarray techniques so that the gene expression data has the characteristics of microarray data. According to [2], the data obtained from experiments with microarray technique has the following characteristics: 1. The number of samples that can be observed very limited (slightly) because of limited budget, resources and time. Though the availability of the number of samples is limited, the measurable characteristic variables can be hundreds or even thousands of gene expression. 2. The nature of the distribution of data will be very complex in which the distribution of the data is probably not a normal distribution. By looking at the characteristics possessed by the microarray data then to analyze of the microarray data requires special handling because it is generally the basis of parametric statistical method, especially for the comparative analysis requires a large number of samples. If the basis of this statistical method is not fulfilled then the conclusion of the analysis can not be accounted for [9]. Bayesian Method. Bayesian is a statistical method based on the combination of two information that is the past of data information as the prior information and the observations data as a constituent likelihood function to update the prior information in the form of posterior probability distribution model. Bayesian method is based on information from the original data (driven data) to obtain the posterior probability distribution and it is does not consider the number of samples (especially for very small sample size) and to any form of distribution. Bayesian method allow for the formal combination of different from the prior distribution and facilitates the iterative updating of new information which thus overcome the problem of uncertainty and complexity of the model in the data. The Rational of Bayesian method derived from Bayes Theorem thinking concept invented by Thomas Bayes in 17021761[3], [4], [14] and [15]. In Bayesian method, the parameters of the model θ is seen as a random variable in the parameter space θ . Suppose there are x observational data with likelihood function f ( x | θ ) then the known information about the parameters

θ before

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the observations were made is referred to as

θ namely p(θ ) . Posterior probability distribution of θ , namely p(θ | x) determined by the rules of probability

prior

in Bayes theorem [3] as follows:

f ( x | θ ) p (θ ) f (x)

p (θ | x ) =

( 2 . 1)

where

f ( x) = E[ f ( x | θ )] =

f ( x | θ ) f (θ ) d θ

∫

θ

if

x∈ R

continous and

f ( x) = E[ f ( x | θ )] = ∑ f ( x | θ ) p(θ ) if θ discrete. x∈B

f (x) is a constant called the normalized constant [4]. So that the equation (2.1) can be written as:

p(θ | x) ∝ f ( x | θ ) p(θ ) Posterior ∝ Likelihood Function x Prior

(2.2)

Equation (2.2) shows that the posterior probability is proportional to the product of the likelihood function and the prior probability of the model parameters. This means that the update's information prior to use information of samples in the data likelihood to obtain the posterior information that will be used for decision making [16]. B.1.1. Markov Chain Monte Carlo (MCMC) Algorithms with Gibbs Sampler Approach. According to [17], [18] and [19], MCMC algorithms with Gibbs sampler approach can be described as: Step 1. Set initial values for θ

θ

(0)

(

=θ

(0) 1

,...,θ

(0) r

(k )

)

at

k = 0 so that

Step 2. Sampling process to obtain the value of

θ j from the

conditional distribution by the sampling for r steps as follows: 2.1. Sampling

θ1( k +1)

from

(

p θ1 | x, θ 2( k ) ,..., θ r( k )

θ 2( k +1)

2.2. Sampling

(

θ ( k +1) in

)

)

from

)

from

p θ 2 | x, θ 1( k ) , θ 3( k ) ,..., θ r( k ) . .

θ r( k +1)

2.r. Sampling

(

p θ r | x, θ

(k ) 1

,θ

(k ) 2

,..., θ

(k ) r −1

Step 3. Doing iteration in step 2 as M times with

M →∞

C. Bayesian Model Averaging (BMA). C.1. Basic Concepts of Bayesian Model Averaging (BMA) The basic concept of Bayesian Model Averaging (BMA) is the best single model formed by considering all

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possible models that could be formed. BMA is a Bayesian solution for model uncertainty in which the completion of the model uncertainty by averaging the posterior distribution of all the best models. The purpose of the BMA is to combine models of uncertainty in order to obtain the best model [5] and [6]. According to [20], the prediction parameters using the BMA approach uses data derived from a combination of hierarchical models. If known {M 1 , M 2 ,..., M q } is the set of models which may be formed from M and ∆ is the value to be predicted, then the BMA prediction begins with determining the prior probability distribution of all the parameters of the model and the model M k . Posterior distribution of

∆ if x is known to the data as follows: q

P(∆ | x) = ∑ P(∆ | M k , x) P( M k | x)

( 2.3)

k =1

where q is the sum of all the models that may have formed. Posterior distribution of ∆ if known the data x is the average of the posterior distribution if known models weighted by posterior probability models. While the posterior probability of the model M k is:

P( M k | x) =

P(Y | M k ) P( M k ) q

∑ P(Y | M l =1

l

Mk . is

p (θ k | M k ) and and p ( M k ) is the prior probability of M k if model M k is true. Implicitly, all probabilities depend on the model M so that the expected value of the coefficient of ∆ obtained by averaging the model of M , that is: q

( 2 .6 )

k =1

E (∆ | x) in the equation (2.6) shows the weighted expected value of ∆ in every model possible comThe value of

bination (weights determined by the prior and the model). While the variance of (∆ | x) is: Var(∆ | x) = ∑ (var(∆ | x, M k ) + [E(∆ | M k , x]2 )P(M k | x) − E(∆ | x)2 ) (2.7) k =1

where A’ is the posterior odds to the model- k and c values

highest posterior probability score and P ( M k

θ k if known model M k p(θ k | M k ) is the likelihood

q

( 2.8)

( 2.5)

Equation (2.5) is the marginal likelihood of the model

E (∆ | x) = ∑ P( M k | x) E (∆ | M k , x)

max l ( P ( M l | x )) ≤ c} P( M k | x)

(2.4)

where

Prior probability of

A’= {M k :

of 20 is equivalent to α = 5% if using the test criteria with p-value [21]. If a model has a value of A’ is greater than 20, then the model is not included in the modeling of the BMA and must be removed from the equation (2.3) and otherwise if a model has a value of A’ is less than or equal to 20, then the model will be included in the modeling of the BMA and should be included in the calculation of equation (2.3). In the equation (2.8), max l ( P ( M l | x )) is the model with the

) P(M l )

P ( x | M k ) = ∫ P ( x | θ k , M k ) P (θ k | M k ) dθ k

C.2. Model Components Selection in Bayesian Model Averaging (BMA). Based on the basic concept of Bayesian Model Averaging (BMA), the components of the model will be selected to be included in the equation (2.3) of q number of models that may be formed. There are several method for selecting the components model that will be included in the equation (2.3) based on its posterior probabilities, which are Occam's Window method [5]. Occam's Window method is quite simple and widely used in research related to the BMA and give good results in the selection of components model in the BMA [5] and [6]. According to [5], the rationale of Occam's Window method in selecting the component model in the BMA modeling based on the posterior probability of the model. The model that will be accepted by this method (the model can fit in modeling BMA) must satisfy the following equation:

| x) is the

value of the posterior probability of the model-k. In the various applications of Occam's Window method is generally able to reduce the large number of components model so that it becomes less than 100 models of even less than 10 models. Reduction of component model that only one or two models are very rare but may occur [5]. III. MATERIAL AND METHODS The data used in this study are some of the data used in the study [22]. Selection of component models in the BMA modeling begins with determining the most appropriate form of distribution to the data and parameter estimator and then based on the distribution model raised several distribution models by MCMC method with the Gibbs sampler approach to obtain some models that might be formed. Selection of component in the BMA modeling using Occam's window method [5] with the following formula:

A’= {M k :

maxl ( P(M l | x)) ≤ c}. The BMA Modeling in P(M k | x)

the equation (2.3) is based on the result of model components selection from Occam's Window method.

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IV. RESULTS AND DISCUSSION

DISTRIBUTION SHAPE DAN ESTIMATOR PARAMETER FOR GENE EXPRESSION DATA WITH POLY DETECTOR METHOD

A. Description of Gene Expression Data on Diseased and Health Conditions with Poly Detector and mRNA Method. Results of Descriptive statistics for gene expression data on the deseased and healthy condition can be seen in the following figure:

TABLE 4.2 DISTRIBUTION SHAPE DAN ESTIMATOR PARAMETER FOR GENE EXPRESSION DATA WITH MRNA METHOD Figure 4.1. Mean Value of Gene Expression with Poly Detector Method

Figure 4.2. Mean Value of Gene Expression with mRNA Method

Based on Figure 4.1 and Figure 4.2 for the 10 ID genes were observed known that there are differences in gene expression for diseased and healthy conditions in which there are several ID genes showed that in healthy condition is more expressive than the deseased condition that is H55933, R39465-2, U14973, R02593, T51496, H80240 and T55131 for Poly Detector method and U14973 for mRNA method and otherwise there are several ID genes showed that in deseased condition is more expressive than the healthy condition that is R39465-1, R85482 and T65938 for Poly Detector method and H55933, R39465-1, R39465-2, R85482, R02593, T51496, H80240, T65938 and T55131 for the mRNA method. B. Identification of Distribution and Parameter Estimator for the Data The results of the identification to distribution and parameter estimator for gene expression data can be seen in Table 4.1 and Table 4.2 below: TABLE 4.1

Based on Table 4.1 and Table 4.2, it can be seen that there are some differences in the distribution of ID genes in diseased and healthy conditions that is 6 ID genes with Poly Detector method and 5 on the mRNA method and some other ID genes that have the same distribution that is 4 ID genes in Poly Detector method and 5 on the mRNA method. In addition, most of the data have non normal distributions that is lognormal distribution and there are some others have 2parameter exponential distibution. C. Model Components Selection in BMA with Occam's Window method. The results of the identification to model components selection in BMA with Occam’s Window method can be seen in Table 4.3 and Table 4.4 below:

TABLE 4.3

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PERCENTAGE OF COMPONENT MODELS INCLUDED IN THE BMA MODELING WITH OCCAM'S WINDOW FOR POLY DETECTOR METHOD.

eral genes ID that have the value of the expression in healthy condition is stronger than diseased condition. The average percentage of the component model that can be included in the BMA modeling with Occam's Window method as much as 34.3%. This means that the Occam 's Window method can reduce the component model may be formed as much as 65.7% so that in the form of the BMA modeling involve only 34.3% where it would further simplify the model without reducing the validity of the model is formed. ACKNOWLEDGMENT

TABLE 4.4 PERCENTAGE OF COMPONENT MODELS INCLUDED IN THE BMA MODELING WITH OCCAM'S WINDOW FOR MRNA METHOD.

This research is part of the doctoral research at the Statistics Department of Institut Teknologi Sepuluh Nopember (ITS), Surabaya, Indonesia. We would like to thank the group of researchers Sebastiani, P., Xie H. and Ramoni, M.F. to the use of data, Head of the Mathematics Department and Dean of FMIPA UB Malang. REFERENCES [1] Knudsen, S. (2004). A Guide to Analysis of DNA Microarray Data.

Based on Table 4.3 and Table 4.4, it can be seen that the total of overall mean to percentage of the component models included in the BMA modeling at 34.3% that is derived from this calculations (32.54+63.64+4.17+36.88)/4). This means that in a study with Occam's Window method can reduce the component models in the BMA modeling was 65.7% so that in the formation of the BMA modeling involves only 34.3% of the overall model may be formed. V. CONCLUSION Based on the results of research conducted, it can be concluded that most of the gene expression data as a result of microarray experiments have nonnormal distributions both in healthy and diseased conditions. In addition, there are different type of data distribution in healthy and diseased conditions and there is also the same type of data distribution in healthy and diseased conditions . There are several genes ID that have the value of the expression in diseased condition is stronger than healthy condition and otherwise there are sev-

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Second Edition. John Wiley & Sons, Inc., New Jersey, Canada. [2] Muller, P., Parmigiani, G., Robert, C., and Rouseau, J. (2002), “Optimal Sample Size for Multiple Testing: the Case of Gene Expression Microarrays,” Tech. rep., University of Texas, M.D. Anderson Cancer Center. [3] Gosh, J. K., Delampady, M. and Samanta, T. (2006). An Introduction to Bayesian Analysis Theory and Method. Springer, New York. [4] Gelman, A., Carlin, J. B., Stern, H. S. and Rubin, D. B. (1995). Bayesian Data Analysis. Chapman & Hall, London. [5] Madigan, D. and Raftery, A. E. (1994). Model Selection and Accounting for Model Uncertainty in Graphical Models Using Occam’s Window. Journal of the American Statistical Association.Vol.89. 428: 1535-1546. [6] Hustianda, V. F. (2012). Comparison of Bayesian Model Averaging and Multiple Linear Regression in Predicting Factors Affecting Number of Infant Death in East Java. Thesis. Statistics Department. FMIPA-ITS, Surabaya. (in Indonesia). [7] Liang, F. M, Troung, Y, and Wong, W. H. (2001). Automatic Bayesian Model Averaging for Linear Regression and Applications in Bayesian Curve Fitting. Statistical Science, 11(4): 1005-1029. [8] Brown, P.J., Vannucci, M. and Fearn, T. (2002). Bayesian Model Averaging with Selection of Regressors. J. R. Statist. Soc. B Part 3. 519– 536. [9] Sebastiani, P., Xie H. and Ramoni, M.F. (2006). Bayesian Analysis of Comparative Microarray Experiments By Model Averaging. International Society For Bayesian Analysis.1, number 4, pp. 707-732. [10] Purnamasari, R. (2011). The use of Bayesian Model Averaging (BMA) method with Markov Chain Monte Carlo (MCMC) approach for Wind Speed Daily Averages Forecasting in Juanda Meteorological Station. Thesis. Statistics Department. FMIPA-ITS, Surabaya. (in Indonesia). [11] Kuswanto, H. and Sari, M. R. (2013). Bayesian Model Averaging with Markov Chain Monte Carlo for Calibrating Temperature Forecast from Combination of Time Series Models. (on Review). [12] Shoemaker, J. S. and Lin, S. M. (2005). Method of Microarray Analysis IV. Springer, New York. [13] Duggan, J. D., Bittner, M., Chen, Y., Meltzer, P. and Trent, J. M. (1999). Expression Profiling Using CDNA Microarrays. Nature Genetics. 21: 10-14. [14] Box, G. E. P. and Tiao. (1973). Bayesian Inference in Statistical Analysis. MA: Addison-Wesley, Massachusetts.

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[15] Zellner, A. (1971). An Introduction to Bayesian Inference in Econometrics. John Wiley, New York. [16] Iriawan, N. (2003). Simulation Technique. Teaching Modules. ITS, Surabaya. (in Indonesia).

[17] Gamerman, D. (1997). Markov Chain Monte Carlo. Chapman & Hall, London.

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Monitoring of High-Yielding Varieties of Rice Plants Using Multi Temporal Landsat-8 Data Candra Dewi 1) 1)

Program of Information Technology and Computer Science, Brawijaya University

Abstract—Monitoring spectral characteristic of rice plant is important to obtain information about the age of rice during it growth. This study examines multi temporal spectral characteristics of three varieties of high yielding rice plant in Malang using Landsat 8 image. The varieties consist of IR64, Ciherang and Memberamo. Normalized Difference Vegetation Index (NDVI) is used to detect the condition and the age of rice plants. The comparison of their vegetation indices shows that all these three varieties have different growth patterns, where the most distinct pattern found in IR64. Keywords—High Yielding Rice Plant, Landsat-8, Monitoring, Spectral Characteristic

I. INTRODUCTION ICE is a staple food source in almost all regions of Indonesia. Therefore, the increasing of population resulted the increasing of the demand for rice. However, the extent of agricultural area more and more reduced and turned into residential area and other uses. Thus, continuos monitoring and identification of the rice plant are needed to determine the availability of rice. Satellite image is one of the method that can be used to monitor rice plant during it’s growth. This process can be done by using the data of spectral characteristics of the plant during its growth phase. Some research on monitoring crop growth have been carried out. Most of these research utilizes a medium resolution imagery such as NOAA-AVHRR and MODIS [1]-[5] and RADARSAT [6]–[7]. In Indonesia, the prediction of the greenery rate of agricultural crops, especially rice has been conducted continuously by Space Agency (LAPAN) using NOAA and MODIS satellite imagery. But, for a small scale of agricultural land parcell, this image could not be used because in one pixel contains a variety information of land uses. This will reduce the accuracy of the identification process [8]. Occording to this limitation of small resolution imagery, some research were conducted by using medium resolution imagery such as Landsat ETM + [9]–[10]. This image with a resolution of 30 m could be used for small scale agricultural land parcel. ETM+ imagery also has a revisit time every 16 days, so that appropriate if it is used for monitoring the growth of rice that has a growth cicle between 110 to 125 days . In the study was conducted by Nuarsa et al, the spectral

R

120 | Batu, East Java, Indonesia

identification process was carried out on Ciherang varieties. As in Indonesia, the type of rice planted is vary widely. In East Java, especially in Malang district, a type of rice that are usually planted is high yielding varieties of rice. Each type of high yielding varieties has difference characteristics and difference growth cycle until harvest time. In addition, each type also has different yields product. According to the Center for Rice Research Bereau [11] , rice IR64 ages ranged from 110 to 120 days with average yield is 5 tons/ ha and can reach a maximum of 6. For Ciherang variety has a life cycle of 116 to 125 days with an average yield about 6 tons/ha and can reach a maximum of around 8 tons. While Membramo variety has a lifespan of 115 to 120 days with the average yield is 6.5 tons/ha. As already known, that the Landsat 7 satellite was damaged since May 2003 and resulted the captured images that contain stripping data. This image causes the identification process does not produce optimum accuracy. To replace this satellite, in February 2013, NASA launched Landsat 8 with characteristics similar to the Landsat ETM+ in contextual of resolution, correction methods, and the characteristics of the sensor (http://landsat.usgs.gov). However, Landsat 8 has added characteristic as perfecting of Landsat ETM+ such as the number of bands, the lower range of the electromagnetic waves spectrum that can be captured by the sensor and 16 bit value range of each pixel. The increasing of quantification for each pixel will improve the ability to distinguish each interpreted object. To determine the sufficiency of rice in more detail, needs a research to monitor the growth of each rice plant varieties. Therefore, this study is aimed to monitor several varieties of rice plant during their growth using Landsat 8 image data. II. MATERIAL AND METHODS A. Study Area Location of study is Malang regency. Administratively, the area is part of East Java province, Indonesia. Geographically, the study area is located between 7o50 ' – 8o09 ' South Latitude and 112o33 ' – 112o44 ' East Longitude. Rice plant phase consists of three phases namely vegetative (early growth until panicle formation/primordial), reproductive (primordial until flowering) and maturation (flowering to mature grain). In general, most tropical rice varieties

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have reproductive phase of approximately 35 days and a maturation phase of approximately 30 days. According to the Center for Agricultural Research and Development of Agriculture Ministry (BPPT), the rice planting follows certain patterns season called dasarian. The dasarian is calendar system for rice cultivating established by the Ministry of Agriculture. However, based on the data obtained from the field survey, the majority of agricultural areas have cultivating time that does not comply with this calendar system (TABLE 1). The field survey found that the average rice cultivating season is twice a year with each planting cycle was about 4 months (120 days). TABLE I RICE CULTIVATING SEASON AT SOME SUB DISTRIC IN MALANG (SOURCE: [12], [13] AND FIELD SURVEY) No

Sub District

Dasarian (BPPT) *MT I/ *MT III / MH MK II

Field Survey

1

Blimbing

Oct II - III

Jun II – III

Jul, Sep

2

Kedungkandang

Nov I - II

Jul I – II

Jul, Aug, Sep, Oct

3

Lowokwaru

Oct II - III

Jun II - III

Aug, Sep, Oct

4

Sukun

Nov I - II

Jul I – II

Aug, Sep., Oct

5

Karangploso

Oct II - III

Jun II - III

6

Kepanjen

Nov I - II

Jul I – II

7

Lawang

Oct II - III

Jun II - III

8

Pakis

Nov I - II

Jul I – II

Sep, Oct, Nov

9

Pakisaji

Oct II - III

Jun II - III

Aug, Sep, Oct

10

Singosari

Oct II - III

Jun II - III

Aug, Sep, Oct

Sep, Oct Aug, Sep, Oct, Nov Sep, Oct

*MT (Cultivating Season), MT I (Wet Season/MH), MT III (Dry Season/MK).

B. Landsat-8 Data Landsat 8 satellite provide data in the form of digital values with a spatial resolution (pixel) 30m to the visible region, near infrared and middle infrared. The characteristics of Landsat 8 are recognized using Operational Land Manager sensors (OLI). Landsat 8 has shorter bands interval than Landsat ETM+ intervals and with the addition of two bands. Landsat-8 allegedly had better geodetic and geometric accuracy. Data collected to LDCM Mission by the Operational Land Imager (OLI) instrument will improve the measurement capability in the future. With the "Ultra-Blue" band (Band 1) which is used for coastal and aerosols study, as well as Band 9 is useful for detecting cirrus clouds and two thermal bands provide more accurate surface temperature (TIRS 1 and TIRS 2). The spectral characteristics of Landsat-8 are shown in TABLE 2. TABLE 2 SPECTRAL CHARACTERISTICS OF LANDSAT-8

Band

Spectral Range (µ µm)

Band Division

Spatial Resolution (m)

1 2 3 4 5 6 7 8 9 10 11

0,43 – 0,45 0,45 – 0,51 0,53 – 0,59 0,64 – 0,67 0,85 – 0,88 1,57 – 1,65 2,11 – 2,29 0,50 – 0,68 1,36 – 1,38 10,6 – 11,19 11,5 – 12,51

Ultra Blue Blue Green Red NIR SWIR1 SWIR2 PAN Cirrus TIRS 1 TIRS2

30 30 30 30 30 30 30 15 30 100 100

Overall Malang is located in Path 118 Row 066 on Landsat 8 image. Based on the field survey, the monitoring is examined for three varieties namely Ciherang, IR64 and Membramo. For this study, the sample data of rice field is planted on early August. To monitor the characteristics for one rice growth cycle is used as much as 6 time series images with different acquisition date (August 13, 2013; August 29, 2013; September 14, 2013; September 30, 2013; October 16, 2013 and November 1, 2013). Some of the images that are acquired on November and December can not be used due to the existing of the clouds with a fairly high percentage. C. Methods The step of monitoring spectral characteristic for this study consists of two parts. The first is calculating the vegetation index value to determine differences in the pattern of the three varieties in one rice growth cycle. The second is determines the relationship between the vegetation index and rice age. This study uses 135 pixels that representing the pixels for IR64, Ciherang and Membramo for the analysis. These pixels are taken from 5 different agricultural areas in each acquisition date of Landsat image. Then, the average value was used to represent the value of each variety. The vegetation indices analysis in this study used normalized difference vegetation index (NDVI). The calculation of NDVI uses formula as in (1).

NDVI =

NIR − R NIR + R

(1)

Where NIR and R are Near Infra Red and Red bands

III. RESULT AND DISCUSSION Spectral reflectance patterns of some plants will be different depending on the color of the leaves (chlorophyll), leaf structure and water content in the leaves. This spectral will of course influence the vegetation indices pattern of three rice varieties (Figure 1). The picture shows that the differences of vegetation indices patterns for the three varieties are clearly seen by using NDVI. Of the three varieties, IR64 has the most different pattern than the other two varieties. The peak reflectance of

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IR64 occurs at the age of 33 days and 68 days. For Ciherang, the peaks reflectance can be found on days 17 and 68. While peak reflectance of Membramo found at days 68. Of the three varieties, it can be identified that in average the peak reflectance is at the age of 68 days. At this age, the three varieties are in the reproductive phase in which the leaves of paddy is lush and entered a flowering period.

Ciherang 100

Rice Age

80

NDVI

y = -2106,1x 2 + 1483,1x - 203,37 R2 = 0,1034

60 40 20

0,5

0

NDVI

0,4

0

0,05

0,1

0,15

0,2

0,25

0,3

0,35

NDVI

0,3 0,2 0,1

Fig. 3. Relationship between NDVI and rice age of Ciherang

0 17

33

49

65

81

Memberam o

Age (Day) IR64

Ciherang

100

Memberamo Rice Age

80

Fig. 1. NDVI graph of IR64, Ciherang and Memberamo.

Due to the study was conducted by Nuarsa et all, using multiple bands give better relationship than using single band. Therefore, this study uses NDVI to utilize relationship between rice age and spectral value of Landsat-8 data. Furthermore, the relationship between rice age and NDVI was evaluated using determination coefficient (R2). Base on the experiment, the best equation that can be used to show the relationship between rice age and NDVI was polynomial for Ciherang and Memberamo, while for IR64 was power. Figure 2 to Figure 4 show this relationship for three varieties of rice that are evaluated in this study. The higher of R2 value shows the stronger relationship between rice age and NDVI. The figure shows that the strong relationship of these varieties can be seen on Memberamo variety with the value of R2 is 0.9175. While the other shows the weak relationship with the values of R2 are about 0.2928 and 0.1034 respectively for IR64 and Ciherang.

y = -4290,7x 2 + 2739,4x - 361,5 R2 = 0,9175

60 40 20 0 0

0,1

0,2

0,3

0,4

NDVI

Fig.3.Relationship between NDVI and rice age of Memberamo

IV. CONCLUSION This study shows that all three varieties have different pattern of growth since it is evaluated using NDVI. Based on the calculation of vegetation indices can be seen that the three varieties have a different growth pattern, where IR64 variety has a growth pattern that is most easily recognized than Membramo and Ciherang varieties. Otherwise, the stronger relationship between rice age and NDVI can be found in Memberamo variety.

IR64 100

REFERENCES

Rice Age

80

[1] y = 228,64x 1,5556 R2 = 0,2928

60 40

[2]

20 0 0

0,1

0,2

0,3

0,4

0,5

[3]

NDVI

Fig. 2. Relationship between NDVI and rice age of IR64

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[4]

S. Panigrahy, J. S. Parihar, and N. K. Patel, “Rice Estimation in Orissa Using NOAA-AVHRR Data”, Journal of the Indian Society, of Remote Sensing, 20, 35-42, 1992. X. Xiao, S. Boles, J. Liu, D. Zhuang, S. Frolking, C. Li, W. Salas, and B. Moore, “Mapping Paddy Rice Agriculture in South and Southeast Asia Using Multi-Temporal MODIS Image”, Remote Sensing of Environment, 100, 95-113, 2005. T. Wataru, O. Taikan, and Y. Yoshifumi, “Investigating an Integrated Approach on Rice Paddy Monitoring Over Asia with MODIS and AMSR-E”, Proceedings of The Conference of The Remote Sensing Society in Japan, 40, 173-174, 2006. H. Sun, J. Huang, A. R. Huete, D. Peng, F. Zhang, “Mapping Paddy Rice with Multi-Date Moderate-Resolution Imaging Spectroradiome-

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[5]

[6]

[7]

[8]

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ter (MODIS) Data in China”, Journal of Zhejiang University SCIENCE A, Vol. 10, Issue 10, 1509-1522, 2009. H. O. Kim, and J. M. Yeom, “Multi-Temporal Spectral Analysis of Rice Fields in South Korea Using MODIS and RapidEye Satellite Imagery”, Journal of Astronomy and Space Sciences, 29(4), 407-411, 2012, http://dx.doi.org/10.5140/JASS.2012.29.4.407. F. Ribbes, T. le Toan, « Rice Field Mapping and Monitoring with RADARSAT Data”, International Journal Remote Sensing, 20(4): 745-765, 1999. Y. Shao, X. Fan, H. Liu, J. Xiao, S. Ross, B. Brisco, R. Brown, and G. Staples, “Rice Monitoring and Production Estimation Using Multitemporal RADARSAT”, Journal of Remote Sensing for Environment, 76, 310-325, 2001. A. H. Strahler, L. Boschetti, G. M. Foody, M. A Friedl, M. C. Hansen, M. Herold, P. Mayaux, J. T. Morisette, S. V. Stehman, and C. E. Woodcock, “Global Land Cover Validation : Recommendation for Evaluation and Accuracy Assessment of Global Land Cover Maps”, Office for Official Publication of European Communities, Available:http://wgcv.ceos.org/docs/wgcv26/GloballandCoverValidati on_JefMorisette.pdf, 2006.

[9]

[10]

[11] [12]

[13]

S. Uchida, “Monitoring of Planting Paddy Rice With Complex Cropping Pattern int The Tropical Humid Climate Region Using Landsat and MODIS Data – A Case of West Java, Indonesia, International Archieves of The Photogrammetry, Remote Sensing and Spatial Information Science, Volume XXXVIII, Paet 8, Kyoto, Japan, 477- 481, 2010. I. W. Nuarsa, F. Nishio, and C. Hongo, “Spectral Characteristics and Mapping of Rice Plants Using Multi-Temporal Landsat Data”, Journal of Agricultural Science Vol. 3, No. 1: 54-67, 2011. BPPT, “Description the Variety of Paddy”, Centre of Research of Paddy Plant (BPPT), Agriculture Department, 2009. BPPT, “Integrated Planting Calendar on Cultivating Season (MT) I 2013/2014 Malang City, East Java Province”, The Center for Agricultural Research and Development (BPPT), Ministry of Agriculture, 2013. BPPT, “Integrated Planting Calendar on Cultivating Season (MT) I 2013/2014 Malang Regency, East Java Province”, The Center for Agricultural Research and Development (BPPT), Ministry of Agriculture, 2013.

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The Courant–Friedrichs–Lewy Number Influences the Accuracy of Finite Volume Methods Sudi Mungkasi1,*) and Noor Hidayat2,3) Department of Mathematics, Faculty of Science and Technology, Sanata Dharma University, Mrican, Tromol Pos 29, Yogyakarta 55002, Indonesia 2) Doctoral Program, Faculty of Science and Technology, Airlangga University, Surabaya, Indonesia 3) Department of Mathematics, Brawijaya University, Malang, Indonesia 1)

*)

Corresponding author: [email protected]

Abstract— The shallow water (wave) equations govern shallow water flows. We solve the shallow water equations using a finite volume method. A necessary condition for a consistent finite volume method to be stable (hence, convergent) is that the method satisfies the Courant–Friedrichs–Lewy (CFL) condition. Numbers representing this condition are called CFL numbers. In this paper, the effects of CFL numbers to the convergence rate of the finite volume method are investigated. Setting a CFL number to the method gives varying time steps in the numerical evolution. We compare results between those produced by imposing a CFL number and imposing a fixed time step to the numerical method. We shall show which strategy is more efficient and produces more accurate solutions in solving the shallow water equations. Keywords—convergence rate, Courant–Friedrichs–Lewy, finite volume method, shallow water equations.

I. INTRODUCTION HE system of shallow water equations is a well-known mathematical model that describes shallow water waves and flows. We are interested in solving these equations as the solutions are useful in the simulations of real world problems such as dam break floods and tsunamis. In this paper we implement a finite volume method to solve the shallow water equations. The method is chosen due to its robustness in dealing with smooth and non-smooth solutions [9, 10]. In finite volume methods, a necessary condition for convergence is that the Courant–Friedrichs–Lewy (CFL) condition be satisfied [3, 9, 10]. This condition is related to the time stepping in the integration of the shallow water equations with respect to time after the equations are discretized with respect to space. This means that we can use either a fixed time step as long as the CFL condition is satisfied at every time step or a varying time step based on a fixed CFL number. Here a CFL number represents a positive number such that the CFL condition is satisfied. This paper investigates the influence of CFL number to the accuracy of numerical solutions produced by the finite volume method. The accuracy of the finite volume method, of course,

T

124 | Batu, East Java, Indonesia

depends on the accuracy of the integration technique implemented to the space and time. To focus on our investigation, we use a single integration technique for the space variable, that is, we use a second order method for the space integration. Then we compare the performance of a second order method for the time integration by presenting the errors between implementing a fixed time step and a fixed CFL number. This paper is organized as follows. In Section II we recall the shallow water equations in one dimension. The finite volume method is presented in Section III. Numerical results are given in Section IV. Finally some concluding remarks are drawn in Section V. II. GOVERNING EQUATIONS The shallow water equations are ht + (hu ) x = 0 ,

(hu ) t +

(

1 2

gh + hu 2

2

)

x

(1)

= − ghBx .

(2)

where t denotes the time variable, x denotes the space variable, h( x, t ) is water height or depth, u ( x, t ) is velocity, B ( x) represents the bottom elevation or topography, and g is the acceleration due to gravity. The absolute water level (stage) is defined as (3) w( x, t ) := h( x, t ) + B ( x) . A number of authors have proposed numerical techniques to solve these shallow water equations (1) and (2). Some of them are [1, 2, 5-8, 11, 12, 15, 16]. III. NUMERICAL METHOD As we mentioned, we use a finite volume numerical method to solve the shallow water equations. In a semi-discrete form, the finite volume method is d 1 Q j (t ) = − F 1 (t ) − F j − 1 (t ) + S nj (4) 2 dt ∆x j + 2 where Q is an approximation of the conserved quantity, F is an approximation of the analytical flux and S is a discretization of the analytical source term. See the References [1, 6, 15]

(

)

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for more details of this type of scheme. This scheme is called semi-discrete because we have discretize the shallow water equations with respect to space, but the time variable is still continuous [3, 9, 10]. To get a second order method in space, we use a linear reconstruction for quantities stage, height, bed, velocity and momentum. Then in order to suppress artificial oscillation due to the space reconstruction, we implement the minmod limiter. This limiter gives a limitation to the values of the gradients in the linear reconstruction of the aforementioned quantities. After that, numerical fluxes are computed based on these reconstructions. We use the Lax-Friedrichs numerical flux function. We refer to [9, 10] for the formulation of this flux function. The next step is to integrate the semi-discrete form (4) with respect to time. We actually can use any standard method of Ordinary Differential Equations (ODEs) solver. However, because we have used a second order method in space, it is better to use either a first or second order method in time. This is because we will never get a finite volume method of order higher than two, even if we use higher order method in time. In this paper we implement the second order Runge-Kutta method to integrate the semi-discrete form (4) with respect to time. IV. NUMERICAL RESULTS This section provides numerical results regarding two different strategies for the numerical evolution. The first strategy is imposing a fixed time step in the second order Runge-Kutta integration. The second strategy is imposing a fixed CFL number where in our simulations we use CFL number to be 1.0 in one case and 0.01 in another case. Details about CFL conditions and CFL numbers can be found in the References [9, 10, 17]. Numerical settings are as follow. We use SI units for measured quantities, so we omit the writing of units. Errors are quantified using absolute L1 formula as used in [13, 14]. In this paper we consider one test case. Standard test cases are available in the References [4, 18]. As a test case we consider the dam break problem. We assume that the topography is given by a flat horizontal bottom B( x) = 0 , where −1 ≤ x ≤ 1 . Therefore we have that stage equals to water height. The water height is initially given by 10 , x < 0 , h( x,0) =  (5) 4 , x > 0. The analytical solution of this problem has been found by Stoker [18] and an extension to the debris avalanche problem has been solved by Mungkasi and Roberts [13, 14]. TABLE I COMPARISON OF STAGE ERRORS BETWEEN IMPOSING A FIXED TIME STEP AND IMPOSING FIXED CFL NUMBERS. THE FIXED TIME STEP IS 0.05 TIMES THE CELLWIDTH, WHEREAS FIXED CFL NUMBER ARE 1.0 AND 0.01. Cell number 100

Fixed time step Error 0.058 9

RC

CFL=1.0 Error 0.058 2

RC

CFL=0.01 Error 0.056 9

RC

200

0.030 8 400 0.014 4 800 0.007 2 1600 0.003 6 Average rate of convergence

0.934 3 1.094 0 1.001 4 0.992 5 1.005 5

0.030 4 0.014 3 0.007 1 0.003 6

0.935 9 1.091 7 1.001 4 0.990 0 1.004 7

0.029 6 0.014 0 0.007 0 0.003 5

0.940 5 1.082 3 0.999 4 0.986 0 1.002 0

TABLE II COMPARISON OF DISCHARGE ERRORS BETWEEN IMPOSING A FIXED TIME STEP AND IMPOSING FIXED CFL NUMBERS. THE FIXED TIME STEP IS 0.05 TIMES THE CELL-WIDTH, WHEREAS FIXED CFL NUMBER ARE 1.0 AND 0.01. Cell number

Fixed time step Error

0.471 4 200 0.244 8 400 0.117 7 800 0.058 9 1600 0.029 9 Average rate of convergence

RC

100

0.945 5 1.056 2 0.998 4 0.979 1 0.994 8

CFL=1.0 Error 0.465 2 0.241 6 0.116 4 0.058 3 0.029 6

RC

0.945 3 1.053 3 0.996 4 0.977 8 0.993 2

CFL=0.01 Error 0.452 0 0.234 1 0.113 8 0.057 1 0.029 1

RC

0.948 8 1.041 4 0.993 8 0.970 7 0.988 7

TABLE III COMPARISON OF VELOCITY ERRORS BETWEEN IMPOSING A FIXED TIME STEP AND IMPOSING FIXED CFL NUMBERS. THE FIXED TIME STEP IS 0.05 TIMES THE CELL-WIDTH, WHEREAS FIXED CFL NUMBER ARE 1.0 AND 0.01. Cell number

Fixed time step Error

0.073 2 200 0.038 8 400 0.017 8 800 0.008 8 1600 0.004 4 Average rate of convergence

RC

100

0.915 7 1.127 6 1.009 0 1.003 2 1.013 9

CFL=1.0 Error 0.072 4 0.038 4 0.017 6 0.008 7 0.004 4

RC

0.915 6 1.126 8 1.008 8 1.003 5 1.013 7

CFL=0.01 Error 0.070 5 0.037 3 0.017 2 0.008 5 0.004 3

RC

0.916 6 1.120 9 1.006 7 0.996 6 1.010 2

Our simulation results are summarized in Tables 1-3. Table 1 shows error comparison for stage (water surface) between three scenarios of simulations. Errors for discharge (momentum) and velocity are summarized in Table 1 and Table 2 respectively. From these three tables, the highest convergence rate is achieved by setting a fixed time step, rather than imposing a fixed CFL number. We should note that the average rate of convergence for imposing CFL number 1.0 produces a very close average rate of convergence to the fixed time step setting. Furthermore imposing CFL number to be 1.0 gives the most efficient computation as it takes the shortest running time. Setting CFL to be too small such as 0.01 gives a low rate of convergence. Of course setting CFL number too

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small makes the computation be expensive, so the running computation is long. Here the fixed time step scenario used is ∆t = 0.05 ∆x , with the time step ∆t and the cell width ∆x .

true only when the solution of the shallow water equations is smooth [9, 10]. As our solution in this paper is nonsmooth due to discontinuities, it is not surprising that we obtain that the rate of convergence is less than two, that is, about one. Even though we have a fixed formal order, the numerical order or rate of convergence is obviously dependent on the numerical strategy that we use. This has been shown in this paper. Taking a fixed time step in the finite volume method gives different convergence rate from taking a varying time step with imposing a CFL number. In addition, imposing a specific CFL number gives different convergence rate from imposing another CFL number. V. CONCLUSION

Fig. 1. The initial condition of the dam break problem (at time t = 0 using 100 cells. Solid line represents the exact solution. Dotted line represents the numerical solution.

We have investigated the CFL effects on the convergence rate of finite volume methods used to solve the shallow water equations. Our simulations indicate that the use of CFL number 1.0 for solving the dam break problem gives the best combination between efficiency and accuracy. Note that setting the CFL number greater than 1.0 may make the numerical method unstable when we solve the shallow water equations in general.

REFERENCES [1]

[2]

[3]

[4] Fig. 2. Solution to the dam break problem at time t = 0.05 using 100 cells with the fixed time step. Solid line represents the exact solution. Dotted line represents the numerical solution.

Figure 1 shows the initial condition for the test case. The first subfigure is the stage or water level (free surface). The second and third subfigures are the momentum and velocity respectively. It is clear that initially we have only discontinuity in the stage, while the momentum and velocity are continuous. Figure 2 shows the stage, discharge and velocity of water after 0.05 seconds of dam break using the fixed time step. The numerical solutions approximate the analytical solution well based on this Figure 2. Here we see discontinuities in the stage, momentum and velocity. The convergence rate in our simulation is about 1.0 even though we have implemented a second order finite volume method, that is, second order in space and second order in time. This is because the discontinuities of the solution occur. The discontinuity appears in the measured quantities as well as the derivative of the quantities. Again, see Figure 2 for these discontinuities. It is worth to note that the formal convergence rate of our numerical method is two, because we use a second order method in space as well as in time. However this formal order is

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[5] [6]

[7]

[8]

[9] [10] [11]

[12]

[13]

J. Balbas and S. Karni, A central scheme for shallow water flows along channels with irregular geometry, ESAIM: Mathematical Modelling and Numerical Analysis, 43 (2009), 333–351. F. Bianco, G. Puppo and G. Russo, High order central schemes for hyperbolic systems of conservation laws, SIAM Journal on Scientific Computing, 21 (1999), 294–322. F. Bouchut, Nonlinear stability of finite volume methods for hyperbolic conservation laws and well-balanced schemes for sources, Birkhauser, Bassel, 2004. N. Goutal and F. Maurel, Proceedings of the 2nd Workshop on DamBreak Wave Simulation, No. HE-43/97/016/B, Departement Laboratoire National d’Hydraulique, Groupe Hydraulique Fluviale, EDF, Chatou, 1997. A. Harten, High resolution schemes for hyperbolic conservation laws, Journal of Computational Physics, 135 (1997), 260–278. A. Kurganov and D. Levy, Central-upwind schemes for the SaintVenant system, ESAIM: Mathematical Modelling and Numerical Analysis, 36 (2002), 397–425. A. Kurganov, S. Noelle and G. Petrova, Semidiscrete central-upwind schemes for hyperbolic conservation laws and Hamilton–Jacobi equations, SIAM Journal on Scientific Computing, 23 (2001), 707–740. A. Kurganov and E. Tadmor, New high-resolution central schemes for nonlinear conservation laws and convection-diffusion equations, Journal of Computational Physics, 160 (2000), 241–282. R. J. LeVeque, Numerical methods for conservation laws, 2nd Edition, Birkhauser, Basel, 1992. R. J. LeVeque, Finite-volume methods for hyperbolic problems, Cambridge University Press, Cambridge, 2004. D. Levy, G. Puppo and G. Russo, Central WENO schemes for hyperbolic systems of conservation laws, ESAIM: Mathematical Modelling and Numerical Analysis, 33 (1999), 547–571. X. D. Liu and E. Tadmor, Third order nonoscillatory central scheme for hyperbolic conservation laws, Numerische Mathematik, 79 (1998), 397– 425. S. Mungkasi, A study of well-balanced finite volume methods and refinement indicators for the shallow water equations, Thesis of Doctor of Philosophy, The Australian National University, Canberra, 2012; Bulletin of the Australian Mathematical Society, 88 (2013), 351–352, Cambridge University Press.

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[14] S. Mungkasi and S. G. Roberts, Analytical solutions involving shock waves for testing debris avalanche numerical models, Pure and Applied Geophysics, 169 (2012), 187–1858. [15] R. Naidoo and S. Baboolal, Application of the Kurganov–Levy semidiscrete numerical scheme to hyperbolic problems with nonlinear source terms, Future Generation Computer Systems, 20 (2004), 465–473.

[16] H. Nessyahu and E. Tadmor, Non-oscillatory central differencing for hyperbolic conservation laws, Journal of Computational Physics, 87 (1990), 408–463. [17] S. Osher and E. Tadmor, On the convergence of difference approximation to scalar conservation laws, Mathematics of Computation, 50 (1988), 19–51. [18] J. J. Stoker, Water Waves: The Mathematical Theory with Application, Interscience Publishers, New York, 1957.

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Bayesian Migration Schedule Model: An Aplication to Migration in East Java 1)

Preatin1), Iriawan, N. 1), Zain, I.1), and Hartanto, W. 2) Statistics Departement , Sepuluh Nopember Institute of Technology, Indonesia 2) National Family Planning Coordination Board (BKKBN), Indonesia

Abstract— From several migration models for individual data, the schedule model has advantages over logistic models and event histories analysis. In terms of data, the schedules model is more simple because does not involve non-migrants such as the logistic model. While the event history analysis requires special survey because it need detailed information. Schedule model show regular features as a peak in young, declining migration in old age, and may be elevated migration in retirement age. These features can characterize migration flows that associated with labour migration, return migration or familial affiliations. This paper using Bayesian approach to apply schedule model to see the pattern of in-migration to East Java by age so that it can be used for development planning.

Migration

Keywords— Migration, Schedule Model, Bayesian, East Java

I. INTRODUCTION ANY discipline of sciences interested in developing migration model. It is because migrations are a complex phenomenon that involves many dimensions. It requires a comprehensive understanding which is not limited to particular disciplines. Multidisciplinary modeling approach couple with the right chosen variables would be more beneficial than just using any particular theory approach [2]. There are two aspects that follow the process, those are individuals and regions. The individual data or the micro data requires specific modeling to the individual characteristic related to the decision to migrate. While the region data or macro data requires different modeling to characterize the region, as the origin and the destination of migration. Figure 1 shows separation some models that are used to elaborate migration viewed from the availability of data. This paper using individual data from populations census in 2010 by BPS. For individual data there are 3 options, namely logistic model, event history analysis, and schedule models. Each model has its advantages and weakness when applied to migration data in Indonesia. Based on the data used in logistic model, it would be very hard in preparation for analysis. It is due to the difficulty to have the entire population data. Using logistic models on individual data migration, on the other hand, will involve migrants and non-migrants. Indonesia which still relies on census data for the analysis of migrations, therefore, need the use of computational intensive approaches due to the involving large data.

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Figure 1. Separation of Selected Migration Models Event history analysis requires a special survey to see the migration history of each individual during every individual lifetime, and Indonesia not already have migration surveys. So, the using of schedule model is the most probable to applied. II. BAYESIAN MIGRATION SCHEDULE MODEL Schedule model show regular features as a peak in young, declining migration in old age, and may be elevated migration in retirement age. These features can characterize migration flows that associated with labour migration, return migration or familial affiliations [6].This paper using Bayesian approach to apply schedule model with purely parametric model. Let Yx is migration flows at individual years of ages (x=1,2,3,…), Nx is mid year populations, and assume Yx ~ Poi(Nxmx) where mx are migrations rates.

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mx = a1 exp(−α1 x) + a2 exp{−α 2 ( x − µ2 ) − exp[−λ2 ( x − µ 2 )]} + a3 exp{−α 3 ( x − µ3 ) − exp[−λ3 ( x − µ3 )]} + a4 exp(λ4 x) + c The component with parameters ɑ1 and α1 represent child migration, the component with parameters (ɑ2, α2, µ2, λ2) represent young migration which in mainly labour migration, the retirement age represented by the shifted exponential term with parameters (ɑ3, α3, µ3, λ3), and post retirement age represented by parameters ɑ4 and λ4. Figure 3. Type of Migration Schedules Model Where κ is an additional positive parameter. The mean mx and the variance mx2/κ of mx represent that mx declines as κ increases. III. MIGRATION FLOWS IN EAST JAVA

α1 λ2 α2 λ3 α3 c

= = = = = =

rate of descent of pre-labor force component rate of ascent of labor force component rate of descent of labor force component rate of ascent of post-labor force component rate of descent of post-labor force component Constant

xj xh xr X A B

= = = = = =

low peak high peak retirement peak labor force shift parental shift Jump

East Java province including the migrants sender to other provinces in Indonesia and abroad. In the province itself there is mobility between district / city or even in-migration from outside. In-migration data in 2010 was 1.5% of the total population.

Figure 2 Migration Schedule Model Figure 2 shows migration patterns according to age. Its graduation was changed by a scheduled model, which is defined as a sum of four components: 1. Pre-labor force, a single negative exponential curve with its rate of decent 1. 2. Labor-force, a left skewed unimodal curve with mean age µ2, rate of ascent λ2, and rate of decent 2. 3. Post-labor force, an almost bell shaped curve, with mean age µ3, rate of ascent λ3, and rate of decent 3. 4. Post-retirement peak, exponential curve with rate of ascent λ4. 5. Constant c. Combination from above components form the 4 types of schedule models adjusted of data conditions, as shown in figure 3. Substantively there is some conditions likely to be over dispersion that excess heterogeneity, this lead to allow hierarchical model. The conjugate option for mx as gamma mixing. Yx ~ Poi(Nxmx) mx ~ Ga(κ, κ/mx)

mx = a0 + a1 exp(−α1 x)

Figure 4. In-migration Rates by Age and Gender Figure 4 shows that the in-migration balanced between male and female, where higher female in-migration in young ages and vice versa in old age. From the image identification shows that schedule models is appropriate to type 2 and type 4, where there is a post-retirement peak. Table 1 and Table 2 contains the estimated parameters and summary measure of fit. Schedule models for male and female have the same conclusion, that fit with type 2 if seen from DIC values, but fit with type 4 if seen from MSE values.

+ a2 exp{−α 2 ( x − µ2 ) − exp[−λ2 ( x − µ 2 )]} + a3 exp{−α 3 ( x − µ3 ) − exp[−λ3 ( x − µ3 )]} + a4 exp(λ4 x)

TABLE I MALE SCHEDULE MODEL PROPERTIES

Parameters

1 a0

Type 1

Type 2

Type 3

Type 4

0,003712

0,000128

0,003916

0,000004

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a1 a2 a3 a4 alpha alpha1 alpha2 alpha3 lambda2 lambda3 lambda4 mu2 mu3 deviance DIC mse

0,027810 0,104500

0,008710 0,069230

6,15 0,230700 0,137100

0,000000 23,97 0,026480 0,093340

0,326000

0,646100

15,81

0,207100 13,33

903,79 994,12 0,000021

875,10 957,22 0,000009

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The 4 Annual Basic Science International Conference 0,077260 0,087320 0,022260 4,86 0,459800 0,181800 0,351200 0,534000 0,448100 13,84 29,29 894,40 985,20 0,000015

TABLE 2 FEMALE SCHEDULE MODEL PROPERTIES Parameters Type 1 Type 2 Type 3 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

a0 a1 a2 a3 a4 alpha alpha1 alpha2 alpha3 lambda2 lambda3 lambda4 mu2 mu3 deviance DIC mse

0,003026 0,030100 0,097130

0,000267 0,008568 0,068920

4,50 0,223700 0,151800

0,000000 20,34 0,031550 0,116800

0,511800

0,900600

14,20

0,196700 12,73

882,30 874,02 0,000022

858,30 942,76 0,000010

0,003106 0,069240 0,103500 0,014190 4,01 0,366500 0,192300 0,366800 0,635400 0,517600 13,42 29,95 878,10 969,89 0,000023

0,004615 0,082050 0,007638 0,000000 154,10 0,056460 0,121400 0,058040 0,509000 0,024270 0,186900 13,97 59,45 897,60 964,48 0,000006

[4] [5]

[6]

[7]

[8]

[9]

[10] Type 4 0,000022 0,008067 0,133600 0,002120 0,000001 141,60 0,024090 0,329600 0,280700 0,419700 0,089270 0,185300 15,43 55,24 900,70 969,74 0,000004

IV. CONCLUSION Modeling migration must be adapted to the purpose of research and the availability of data. For in-migration in East Java having limited data requires the selection of an appropriate model. For individual data, schedule models is more probable because does not involve non-migrants such as the logistic model and special surveis as event history analysis. Bayesian approach was recommended, because it would be more flexible as data driven approaches, but it requires computational intensive capabilities. Pattern of in-migrations to East Java by ages still characterize as young migration at labor force migrants. Post retirement peak shows return migrations is significant but further research is needed. REFERENCES [1] n Research (CEFMR) Assuncao, R.M., Schmertmann, C.P., Potter, J.E., and Cavenaghi, S.M., “Emprical Bayes Estimation of Demographic Schedules for Small Areas”, Demography, Vol.2, No.3, pp. 537-558, 2005. [2] Bijak, J., “Forecasting International Migration: Selected Theories, Models, and Methods”, Central European Forum For MigratioWorking Paper No. 04, Warsaw, Poland, 2006.

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[20]

[21]

[22]

[23] [24]

Bijak, J., Bayesian methods in international migration forecasting in “International Migration in Europe: Data, Models and Estimates”, J. Raymer and F. Willekens, Chichester, GB, John Wiley, pp. 255-281, 2008. Bijak, J., “Forecasting International Migration in Europe: A Bayesian View”, Springer, London, 2011. Butzer, R., Mundlak, Y., and Larson, D.F., “Intersectoral Migration in Southeast Asia: Evidence from Indonesia, Thailand, and the Philippines”, Journal of Agricultural and Applied Economics, Vol.35, pp.105-117, 2003. Congdon, P., “A Bayesian Approach to Prediction Using the Gravity Model, with an Aplication to Patient Flow Modeling”, Geographical Analysis, Vol. 32, No.3, pp.205-224, 2000. Courgeau, D., “Interaction between Spatial Mobility, Family and Career Life Cycle: A French Survey”, European Sociological Review, Vol.1, No.2, pp.139–162, 1985. Courgeau, D., “Migration theories and behavioural models”, International Journal of Population Geography, vol.1, No.1, pp.19–27, 1995. Courgeau, D., “From the Macro-Micro Opposition to Multilevel Analysis in Demography” in Methodology and Epistemology of Multilevel Analysis, D. Courgeau, Dordrecht, Kluwer, 2003. Courgeau, D. and Lelièvre, E., Event history analysis in demography. Oxford University Press, Oxford, 1992. Garip, F. and Western, B., Model Comparison and Simulation for Hierarchical Models: Analyzing Rural-Urban Migration in Thailand, Weatherhead Center for International Affairs (WCFIA) Working Paper No. 0056, Harvard University, Cambridge, 2008. Ginsberg, R.B., “Probability Models of Residence Histories: Analysis of Times between Moves”, in Population Mobility and Residential Change, Clark, W.A.V. and Moore, E.G., Northwestern University, Evanston, IL, 1978. Gullickson, A., Multiregional Probabilistic Forecasting, presented in “The Young Scientists Summer Program Midsummer Workshop, International Institute for Applied Systems Analysis”, Vienna-Austria, July 2001, printed at www.demog.berkeley.edu/~aarong/PAPERS/ gullick_iiasa_stochmig.pdf McCullagh, P. and Nelder, J. , Generalized Linear Models, Second Edition, Chapman and Ppl, Boca Raton, 1989. Muhidin, S, The Population of Indonesia, Rozenberg Publishers, Amserdam, 2002. Pellegrini, P.A. and Fotheringham, A.S., “Intermetropolitan Migration and Hierarchical Destination Choice: A Disaggregate Analysis from the US Public Use Microdata Samples”, Environment and Planning A, Vol.31, pp.1093-1118, 1999. Perrakis, K, Karlis, D., Cools, M., Janssens, D., Vanhoof, K. And Wets, G., “A Bayesian Approach for Modeling Origin-Destination Matrices”, Trasportation Research part A: Policy and Practice, Vol. 46, Issue 1, pp.200-212, 2012. Phouxay, K., Malmberg, G., and Tollefsen, A., “Internal Migration and Socio-Economic Change in Laos”, Migration Letters, Vol.7, No.1, pp. 91-104,2010. Poncet, S., “Provincial Migration Dynamics in China: Borders Costs and Economic Motivations”, Regional Science and Urban Economics, Vol.36, pp.385-398, 2006. Raymer, J.,” The estimation of international migration flows: A general technique focused on the origin-destination association structure”, Environment and Planning A, Vol.39, No.4, pp.985-995,2007. doi:10.1068/a38264. Rogers, A., “Model Migration Schedules: A Aplication Using Data for The Soviet Union”, Canadian Studies in Population, Vol.5, pp.85-98, Canada,1978. Rogers, A.,” Parameterized multistate population dynamics and projections”, Journal of the American Statistical Association, Vol.81, No.393, pp. 48-61, 1986. Rogers, A., “Age patterns of elderly migration: An international comparison”, Demography, Vol.25, No.3, pp355-370,1988. Rogers, A., Demographic Modeling of the Geography of Migration and Population : A Multiregional Perspective, Population Program Working Paper No.02, Institute of Behavioral Science, University of Colorado, Boulder,2007.

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[25] Rogers, A. and Castro, L.J., “ What the Age Composition of Migrants Can Tell Us”, Population Bulletin of the United Nations, No. 15, pp. 66-79,1983. [26] Rogers, A. and Little, J.S., “Parameterizing age patterns of demographic rates with the multiexponential model schedule”, Mathematical Population Studies, ratesol.4., No.3, pp. 175-195,1994. [27] Rogers, A. and Watkins, J.F., “General versus elderly interstate migration and population redistribution in the United States”, Research on Aging, Vol.9, No.4, pp.483-529,1987. [28] Rogers, A., and Raymer, J., “The Spatial Focus of U.S. Interstate Migration Flows”, International Journal of Population Geography, Vol.4, pp.63-80,1998. [29] Rogers, A., and Raymer, J., “Estimating the regional migration patterns of the foreign-born population in the United States: 1950-1990”, Mathematical Population Studies, Vol. 7, No.3, pp. 181-216, 1999. [30] Rogers, A., and Raymer, J., “Fitting observed demographic rates with the multiexponential model schedule: An assessment of two estimation programs”, Review of Urban and Regional Development Studies, Vol.11, No.1, pp.1-10, 1999a. doi:10.1111/1467-940X.00001. [31] Rogers, A., and Raymer, J., “Using Age and Spatial Flow Structures in the Indirect Estimation of Migration Streams”, Demography, Vol.44, No.2, pp.199-223, 2007. [32] Rogers, A., Little, J., and Raymer, J., The Indirect Estimation of Migration, Springer, London, 2010.

[33] Rogers, A., Willekens, F., and Raymer, J., “Imposing age and spatial structures on inadequate migration flow data sets”, The Professional Geographer, Vol. 55, No.1, pp. 56-69, 2003. doi:10.1111/0033-0124. 01052 [34] Safrida, S.B.M., Siregar, H., and Harianto, “Dampak Kebijakan Migrasi Internal terhadap Perilaku Pasar Kerja di Indonesia”, IPB E-Jurnal, 2008, printed at http://repository.ipb.ac.id/handle/123456789/45432. [35] Smith, P.W.F., Raymer, J., and Giulietti, C., “Combining available migration data in England to study economic activity flows over time”, Journal of the Royal Statistical Society: Series A (Statistics in Society), Vo. 173, No.4, pp. 733-753, 2010. doi:10.1111/j.1467-985X.2009. 00630.x. [36] Tsegai, D. And Le, B.Q., District-level Spatial Analysis of Migration Flows in Ghana: Determinants and Implications for Policy, Zentrum fur Entwicklungforschung Discussion Papers on Development Policy No. 144, Universiy of Bonn, Germany, 2010. [37] Tsutsumi, M. and Tamesue, K.,” Intraregional Flow Problem in Spatial Econometric Models for Origin-Destination Flows”, Procedia Social and Behavioral Sciences, Vol.21, pp.184-192, 2011. [38] Van Imhoff, E., and Post, W.,” Microsimulation methods for population projection”, Population–E, Vol.10, No.1, pp. 97–138, 1998. [39] Wilson, T., “Model Migration Schedules Incorporating Student Migration Peaks”, Demographic Research, Vol 23, No. 8, pp.191-222, 2010.

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A Totally Irregular Total Labeling Disjoint Union of Wheel Graph Diana Kurnia Sari Sudirman1*), Rismawati Ramdani 1), and Siti Julaeha2) Department of Mathematics, SunanGunungDjati State Islamic University Bandung, Indonesia 2) Department of Mathematics, SunanGunungDjati State Islamic University Bandung, Indonesia 1)

*)

Diana Kurnia Sari Sudirman:[email protected]

Abstract—A total labeling is called totally irregular total -labeling of if every twodistinct vertices and in satisfies , and every two distinct edges and in satisfies , where and . The minimum for which a graph has totally irregular total k-labeling is called the total irregularity strength of , denoted by . In this paper determined for disjoint union from copies of wheel denoted by . Keywords—the total edge irregularity strength, the total vertex irregularity strength, total irregularity strength, totally irregular total k-labeling, wheel.

in a paper entitled On the total Irregularity strength on cycles and paths [3]. Suppose is a graph. Total ling is calleda total k - labeling irregular total of G if any two points and are different in and any two sides and satisfies different in satisfies , where and . The minimum value of k so that G has a total k - labeling irregular total called total value of the total (total Irregularity strength) of G and is denoted by [3]. In this paper, we determined the total irregular total labeling disjoint union of wheel graph. II. THEORY

I. INTRODUCTION athematics is a branch of science known as the Queen of Science. Evident from many other disciplines that employ methods contained in mathematics. One area in mathematics that great attention is graf.Teori graph theory is part of mathematics that is widely used as a tool to describe or represent a problem so that it is easier to understand, be understood and resolved. Many issues will be clearer to explain if it can be formed into a graph [5]. Until now the use of graph theory is perceived role in various sectors of other sciences. One of the uses of science graph in other disciplines, namely in the fields of chemistry, including hydrocarbon compounds that can be formed into a tree graph. Over time, the growing study of graph theory. One of the topics in graph theory is graph labeling. Labeling graphs was first introduced by Rosa in 1967 [2]. Labeling on the graph is the mapping that carries graph elements to the values [1]. Based on the domain, labeling is divided into three, namely the point of labeling, the labeling, and the labeling of the total. Labeling is the point of labeling with domain the set point, the labeling is labeling with domain the set of sides, and the total labeling is labeling combined with domain the set of points with the set side. One topic of total labeling of a graph is irregular total labeling introduced by Marzuki, Salman, and Miller

M

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Irregular total labeling was introduced by Baca, et al in 2007 in a paper entitled On irregular total labelings. In this paper, Baca, et al introduce two types of irregular total labeling, ie irregular total labeling and labeling the total irregular point. Definitions and results-labeling studies of labeling is given below. A. An Edge IrregularTotal Labeling is called an edge irA total labeling regular total -labelingin if every two different edges and in satisfies , where . The smallestvalue ofksuch that agraph has anedge irregular total -labeling is called the total edgeIrregularitystrengthof agraph isdenoted by [1]. In the paper [1] Ba a, et al provides a lower limit of the andthe total edgeIrregularitystrength for some graph, including path and circle graph. The results of these studies are given in the following theorems. Theorem 2.1 [1] Let is a graph with a nonempty and , then

Theorem 2.2 [1] Let is a path, with

, then

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Let n be a positive integer number and n vertex, then

Theorem 2.3 [1] Let is a circle graph, with

, then

B. A Vertex Irregular Total Labeling is called a vertex irA total labeling regular total -labelingin if every two different vertex and in satisfies , where . The smallestvalue ofksuch that agraph has a vertex irregular total -labeling is called the total vertex Irregularity strength of a graph is denoted by [1]. In the paper [1] Ba a, et al provides a lower limit of the and the total vertex Irregularity strength for some graph, ie star graph. The results of these studies are given in the following theorems. Theorem 2.4 [1] Let is a graph with and , minimum degree , and maksimum degree , then

Theorem 2.5 [1] Let is a star graph with

then

C. Totally Irregular Total Labeling Marzuki, et al. [3]combine the idea of an edge Irregular total labeling and a vertex irregular total labeling into a new labeling called a totally irregular total labeling. A total labeling is called a totally irregular total -labelingin if every two different vertex and in satisfies and every two different edges and in satisfies where and . The smallest value of k such that a graph has a totally irregular total -labeling is called the total Irregularity strength of a graph is denoted by [3]. In the paper [3], Marzuki, et al provides a lower limit of . In addition, the same paper has determined the total irregularity strength of path and circle graphs are summarized in the following theorems. Theorem 2.6 [3] Let is a graph. Then Theorem 2.7 [3] Let be a positive integer number and is a circle graph with n edge, then

Theorem 2.8 [5]

is a path graph with

Research on the total irregular total labeling was also performed by Ramdani and Salmanin a paperen titled on the total Irregularity strength of some Cartesian product graphs[6]. Inthe paper, given the total irregulariy strength of some Cartesian product graphs, ie , where is a path graph with n order, is a circle graph with n order and is a star graph with order. The results are summarized in the following theorems. Theorem 2.9 [6] For Theorem 2.10 [6] For Theorem 2.11 [6] For Theorem 2.12 [6] For D. Disjoint Union Definition 2.13 [2] Two graphs and said disjoint if . Definition 2.14 [2] Let and are two disjoint graph. Disjoint union from and , denoted are graphwith the set of vertex and the set of edges . Example 2.15

Definition 2.16 [2]

Figure 2.1 Disjoint Union form

and

III. RESULT A. Wheel Graph Definition 3.1 [2]

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Wheel graph with vertices denotedby formed from the cycle by adding a vertex ing each point in to the vertex . Example 3.2

is a graph and connect-

Moreover, based on theorem 2.4

Thus Moreover, based on theorem 2.6

Thus,

(3.1)

2. Will prof that Total labeling given the graph

is as follows: untuk

b. Labeling on

is as follows:

Figure3.1Wheel Graph B. A Totally Irregular Total Labeling Disjoint Union of Wheel Graph Definition 3.2 [2] graphis a disjoint union from copies of wheel graph

Figure 3.2 graph has a set of vertex , where:

as follows:

a. Labeling on

.

Graph and a set of edges

and

Theorem 3.3 Let graph is a disjoint union from graph , then

copies of wheel

Proof. There are two steps to proof theorem 3.3, ieby determining the lower limit and the upper limit from , as can be seen in the following description

1. Will proof that graph has vertices and edges. The smallest degree from graph is andthe greatest degree from is . Based on the theorem 2.1,

To show that is totally irregular total labeling, then it will be shown that by labeling , the weights of all vertices on and weights on all edges are different.The weight of a vertices and an edges obtained by labeling is as follows:

(i)

The weight of the edge

for

(ii)

The weight of the edge

for

is Thus

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is

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(iii)

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The weight of edge

for

is (ix)

The weight of the edge

for

is

. (iv)

The weight of edge is

for (x)

The weight of the edge

for

is

(v)

(vi)

The weight of edge

for

is

The weight of the edge

(xi)

The weight of the edge

for

(xii)

The weight of the edge

for

is

for

is

is

(vii)

The weight of theedge

, for

(xiii)

The weight of the vertices

for

(xiv)

The weight of the vertices , for

is

is

.

(viii)

The weight of the edge is

for

is

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(xv)

The weight of the vertices

for

(xvi)

The weight of the vertices

, for

is

(xvii)

The weight of the vertices is

, for

(xviii)

The weight of the vertices

, for

is

Based on the formula weigh to the vertices and edges above, it can be seen that the weight of all edges and the weight of all vertices are different. Thus, the total labeling satisfy a totally irregular total labeling with the biggest labels . Therefore, (3.2) Based on theequation(3.1) and(3.2), it can be concludedthat ∎ Example 3.4 As an illustration, the following will be given a totally irregular total labeling disjoint union of based formula totally irregular total labeling on theorem 3.3

is Figure 3.3 Graph Labeling the edges and vertices of graph as follows

(xix)

The weight of the vertices

, for

(xx)

The weight of the vertices

, for

is Figure 3.4 A totally irregular labeling of graph Sothere is noequal weight toeach vertices and no equal weight on each edge.

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[7] [8]

Figure 3.5Weight on all edges

Figure 3.6 Weight on all vertices

R. Ramdani dan A. N. M. Salman. On the total irregularity strength of some cartesian product graphs,AKCE Int. J. Graphs Comb., 10, No 2 (2013), pp. 199-209. S. Slamet.PengantarTeori Graf, Universitas Indonesia, Jakarta (1998). R. J. Wilson. dan J. J. Watkins. Graph:An Introductory Approach, Simultaneously, Canada (1990).

graph

graph

IV. CONCLUSION In this paper determined for disjoint union from copies of wheel denoted by obtained from theorem 3.3 that .The discussion of thetotalirregulartotallabelingis stillopenfor other researcherstoconductsimilarstudieswithdifferenttypes ofgraphs, includinggraph with . REFERENCES [1] [2] [3] [4]

[5]

M. Ba a, S. Jendrol. M. Miller. Dan J. Ryan. On irregular total labellings.Discrete Mathematics 307(2007) 1378-1388. J.A.Bondydan U.S.R. Murty, Graf Theory with Application, The Macmillan Press Ltd, New York (1976). J. A. Galian. A Dynamic Survey of Graph Labeling. The Electronic journal of combinatorics18 (2011). V. E. Levit danE.Mandrescu.The Independence Polynomial of a Graph--A Survey.Dalam proses untuk the 1st International Conference on Algebraic Informatics(2005). C. C. Marzuki, A. N. M. Salman, dan M. Miller. On the total irregularity strength on cycles and paths. Diterima untuk dipublikasikan di Far East Journal of Mathematical Sciences.

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The Implementation of The Meshless Local PetrovGalerkin on Calculating Volume of River Sedimentation in The Confluence of Two Rivers Inu Laksito Wibowo1, Suhariningsih2 and Basuki Widodo3 PhD Student in FST UNAIR/Lecturer of Math. Dept. of ITS, 2 Professor in Physics FST UNAIR 3 Professor in Applied Mathematics Department Math of ITS 1

Abstract---The occurrence of sedimentation in a confluence two rivers can be formulated into mathematical model and simulated numerically, so the morphological changes due to the sedimentation of the river can be unpredictable. Mathematical modeling and numerical simulation results of solutions can be used as a material consideration in the adoption of a policy, so the impact will be caused by the sedimentation of the river can be prevented as early as possible or at least be reduced. In this paper, we consider about a model of sedimentation in the river which is formulated by using control volume and be solved using the method of Meshless Local Petrov- Galerkin (MLPG). Themain purpose of meshless method is to get rid of the grid or to reduce the difficulty in making a grid with points. We obtain that the sedimentation distribution in the confluences of two rivers is influenced by the shape of river morphology. The higher of the river velocity the higher the erotion in the river. Keywords: Sedimentation, Confluence two rivers, MLPG

method is predicted to replace the FEM method in the future (Atlury and Lin, 2001). In this paper, we consider, the method Meshless Local PetrovGalerkin (MLPG) which is used to solve the model which has been obtained from the Finite Volume Method approach on the sedimentation at the confluence of two rivers. Furthermore, by using the approach of Moving Least Square (MLS) as a function of the shape and Heavyside function as a function of test completion the solution sought. Settlement obtained is then made using the computer program MATLAB programming language to be solved numerically using a computer, and by varying the input variables and parameters subsequently simulated to obtain the characteristics of the variables and parameters of the system being modeled. The results of the simulation is then visualized in the form of pictures of the calculation results of the numerical simulations and compared with the results of visualization using the software.

I. INTRODUCTION

o

ne of the benefits of river is very important is to store water during the rainy season. Siltation of the river due to sediment deposition causes water or undrained cannot be accommodated to the maximum, it cause flooding. The process of sedimentation in the river can be constructed into a mathematical model and numerically simulated, so that the process of morphological changes due to sedimentation of the river can be predicted. Mathematical modeling and numerical simulation results that the solution can be used as one consideration in making a policy, so the impact will be caused by the presence of river sedimentation can be prevented as early as possible or at least be reduced. River sedimentation model is built using the approach volume method and it is solved by using the meshless local Petrov-Galerkin (MLPG). This method is relatively new, and still being developed in the fluid dynamics problems. Meshless method which is developed in this study is used to resolve those problems. The main purpose meshless method is to eliminate or to reduce the difficulty in making the grid by using the points (nodes) as his successor. This method is very flexible, accurate and not at all in the use of grid application , either for interpolation purposes or for purposes of calculating the integral . Complexity thing, the MLPG is good when compared with the method that uses mesh (Widodo, 2009).This

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II. SEDIMENTATION FORMATION PROCESS The main function of the river is flowing rain water so that it is possible silting. This is due to deposition of sediment in certain places at the bottom of the river. Sedimentation occurs because of the presence of solid particles (sediment) that is carried on by the flow of water. Sediment transport mechanism is categorized into two, namely bed load and suspended load. Bed load sediment movement is sediment moves on the river bottom by rolling, sliding and jumping around. While the suspended load, consisting of fine granules suspended in water (Widodo 2012). 2.1 Sedimentation Calculation Basic Equation Bed load is grains / particles / sediment material that generally occurs in the watershed. There are several kinds of mathematical formulas that can be used to calculate the amount of sediment in this type of sediment transport. One of the formulas / mathematical formula which is popular is formula Meyer - Peter & Müller (Yang, 1996). In the study Yang (1996), changes in river morphology is assumed to occur only at the bottom of the river and caused by the presence of scour and deposition processes. Changes in the river bed can be calculated using the equation of conservation

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of mass equation for sediment transport. Yang (1996), Widodo (2012), namely :

∂y 1 ∂qb + =0 ∂t (1 − p ) ∂x where: y = height of the river bed, p = porosity, qb= bed load 2.2 Confluence of Watershed Confluence of two rivers is an interesting phenomenon and very complex. Soburo Komura (Widodo, 2009) conducted a study of the phenomena occurring at the confluence of two streams using the balance equation, which is obtained by using the equations of motion, continuity equation for sediment transport, and the continuity equation for the shear velocity of water flow. The equation obtained is less reflect the real situation on the ground. So that this equation can be used or can be applied to things that are ideal. It is therefore necessary to find or develop a new mathematical model to another or explain the phenomenon approaching real state (Widodo,2009). 2.3 Two forms of Morphology Meeting Watershed Morphology shape of confluence of two rivers is a natural phenomenon that is very interesting, because we will see the confluence of two rivers form the model that various kinds. Some of these models have been widely studied as a model and a model developed Shazy Shabayek, ie numeca. In models shaped river Main stream and lateral stream has been shown to result in sedimentation in the riverbed on the research results (Widodo, 2009). In the study described also that sedimentation is not only dependent on the flow of the river upstream but also the morphology of the river, the river mouth will be eroded due to the presence of scours caused as a result of back water gate block of sedimentation (Widodo 2009). Model confluence of two rivers that form a quarter-circle is arc numeca as depicted at the Figure (2.1).

Figure 2.1 Watershed Model Numeca Bow Quarter Circle.

In the quarter-circle model of domain Numeca river is divided into 2 parts by volume control. The main river (main stream) and two tributaries (lateral stream) on the flow curve is expressed as a volume control 1 while at the confluence straight expressed as volume control 2. For the forces acting on the second volume of this control include hydrostatic force,

the frictional force on the bottom of the river, the frictional forces that occur at the boundary between the two volume control, gravitydue to the influence of Earth's gravity and friction forces on the surface of the river water. Some of the characteristics of the main river will change with the influx of tributary streams. Such changes include the change in mass of the depth, direction , and flow rate , as well as other changes . Markup (2001) have mathematically derive the equation of conservation of mass and momentum for flow in tributaries entering from the side of the main stream. Mass.and momentum equations are:

B

∂z ∂Q + =q ∂t ∂x

∂Q ∂  β Q 2  ∂z gQ | Q | +  = vq cos φ  + gA + ∂t ∂x  A  ∂x AC 2 R With: A = cross sectional area of the flow B = width of the surface flow Q = flow rate z = height of surface flow v = velocity of lateral flow φ = angle between the main flow and lateral flowφ q = lateral flow width unity C = Chezy coefficient g = gravity 2.4 Method of Meshless Local Petrov - Galerkin (MLPG) The main purpose of the meshless method is to avoid the use mesh / grid. This method is very useful in problems with the domain boundary that is not continuous or moving, or other difficulties may be found in the use of the finite element method (Atlury and Lin, 2001). Meshless method is known to be very effective implemented in the field of computational science and engineering, but in terms of speed and reliability still needs to be developed. Integration numerically to determine the convergence of this method numerical solution generated. Nodal shape functions of the Moving Least Square (MLS) is used in this method is very complex, so as to obtain accurate numerical Integration results in weak form is very difficult to do , especially for a method that is included in this type meshlessGalerkin (Ottevanger, 2005). MLPG predicted could replace the finite element method (FEM) in the future (Widodo, 2009). 2.5 Numerical Methods Basic Search Volume Search volume can be approximated by the approach area of the base multiplied by the height, so that the search is necessary to find the approach area of the base and height function numerically. III.

FRAMEWORK CONCEPT

Numerical methods, in addition to methods meshless local Petrov - Galerkin (MLPG), there have been widely applied in

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solving fluid dynamics problems. One fundamental thing in common and that became the basis of the above methods is the use grid or cells in its application. The use of the grid determines the level of accuracy of these methods. The smaller the grid is created, or in other words the more the number of the grid , the more accurate the output (output) produced , but the more expensive the cost of computation to be done (Atlury and Lin, 2001). Even the grid itself to the manufacture of complex domains is very hard to do. Then Meshless Local PetrovGalerkin (MLPG) was introduced and applied to the problem known as the ideal fluid Navier Stokes equations (Atlury and Lin 2001) which followed (Atlury and Shen 2002). Meshless method (without mesh / grid) that developed in subsequent studies used to address the problem of sedimentation. The main purpose meshless method (without mesh / grid) grid is to eliminate or to reduce the difficulty in making the grid by using the points (nodes) as successor (Widodo 2011). This method is very flexible, accurate and not at all in the use of grid application, either for interpolation purposes or for purposes of calculating the integral. This method is known as the MLPG (Meshless Local PetrovGalerkin) method is applied to obtain the distribution pattern of sedimentation with a case study times SurabayaWidodo 2011), and then proceed to the case of the application of MLPG shaped river Numeca. This study followed a combination of curved and straight rivers with MLPG and refined with the application of the Moving Least Square (MLS) (Wibowo 2012) which is then used as an initial study of this dissertation. Sedimentation volume produced will be very instrumental in determining the gate that creates large blocks scour the river mouth. This phenomenon makes it clear that the large volume of sedimentation factor is an important factor to be addressed in managing a river in relation to keeping the river mouth from fell out. Sedimentation volume will be constructed from the results of the determination of the location of sedimentation to be made sedimentation area L (A) multiply by a function of position in the sediment height F (P) and summedto determine the volume of sedimentation can be done with a numerical approach. Shape 3 dimensions are sought and can use some help in getting the visualization software. IV. BUILDING A MODEL SEDIMENTATION As has been described in the literature review , that the sedimentation process can be divided into two parts , namely : the hydrodynamic flow of the river and river morphology .

lume control to be modeled is described as follows:

Figure 4.1 (a) Model Numeca Bow River Quarter Circle, and (b) Volume Control

4.2 Morphology River Changes in river morphology is assumed to occur only in the river bed due to scour and deposition processes. Changes in the river bed can be calculated using the equation of conservation of mass transport of sediment. As for the formula used to calculate the amount of sediment Meyer - Peter & Muller. In the Application, the second equation is expanded into a two-dimensional equation for. So the formula used to calculate the change of the river bed due to sediment transport and sediment transport to calculate the amount is as follows : Sediment mass conservation :

Lateral Stream (Meandering Flow):

Main Stream :

Sediment transport :

With, Number of bed load sediment

4.1 Hydrodynamic Watershed Profile river models numeca quarter-circle arc and the vo-

: 8.0 ρs=density of the sediment and ρ = density of water, g = acceleration due to gravity , d50 = median diameter of sediment, µ = 1.0, θc = 0.047,

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In Figure 4.10 that seemingly stream with initial condition velocity = 0.9 at all positions (x) increased approximately 0.068586 . , U : velocity of the river flow, h :depth 4.3 Simulation Calculation of Volume Sedimentation Sedimentation volume will be constructed from the results of the determination of the location of sedimentation to be made sedimentation area L(A) multiply by a function of position in the sediment height F(P). This volume is calculated from the two creeks to flow Cornering and meet at the creek with a straight flow. 4.3.1 Simulation Flow Creeks Cornering This volume is calculated from the two creeks to flow Cornering obtained by simulating it as follows : Simulation I Initial depth h, = 0.3 Initial velocity v, = 0.9 Initial height of the sediment, =0.3 Time t, = 20 Delta t, = 5

Figure 4.9 Plot Depth River in Simulation I

simulation I, it appears that the flow with initial conditions at depth = 0.3 speed = 0.9 and after the time of the decline in river depth of approximately 0.015832.

Figure 4.10 Plot of Speed on the Flow Simulation I

Figure 4.11 Plot Sediment Elevation in Simulation I

In Figure 4.11 shows that the flow with initial conditions sediment height = 0.3 in all positions (x) and after the time of the change of height of the sediment that is down about 0.166570. From the results shown above plot (Figure 4.6 - 4:11) shows that the depth h, velocity v, and the sediment height zb undergo different changes at the position (x) after a certain time. When the river flow rate increased from 0.1 into 0.9 visible increase in the depth of the river, a decrease in flow velocity, and rise sediments occur also increases. 4.3.2 Simulation Flow Straight Creeks Search volumes were then computed sediment yield of peertemuan two creeks with Cornering the flow then continues on sediments from tributary streams straight. simulation II Initial depth h, = 0.3 Velocity v, = 0.2 Initial height of the sediment, =0.3 Time t, = 5 =1 Delta t, The angle of the river 1, = The angle of the river 2, = The river1 discharge, =0.5 The river 2 discharge, =0.5

Figure 4.12 Plot of the depth of the river simulation II

In the third simulation, shows that the flow with the initial condition and depth = 0.3 after which time an increase in depth up approximately 2.792678.

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Two river discharge, = 0.9 The resulting volume is = 1.5414e +007 = 1.5414 x 107 m3 Sediment volume charts

Figure 4.13 Plot of flow velocity in simulation II

In Figure 4.13 that seemingly stream with initial conditions a = streamflowtwo = 0.5 and after the time of the change is the speed drops 5.172373.

Figure 4.18 The graph plots the volume of sediment

V. CONCLUSIONS In this chapter provides the conclusions of the analysis and discussion that has been done. Moreover, given also the advice to do as a continuation or development of this research.

Figure 4.14 Plot Sediment height on Simulation II

In Figure 4.14shows that the flow with initial conditions sediment height = 0.3 in all positions (x) and after the time of the change in height of the sediment that is down approximately 2.792678. 4.4 Calculation Of Total Volume Sedimentation Sedimentation Volume Calculation constructed from the results of the determination of the location of sedimentation to be made sedimentation area L(A) multiply by a function of position in the sediment height F (P) and summed according to the grid (interval) is the sum Reimann as follows :

Limit

∑ ∑ L(A) F(P) ∆a∆p = ∫∫ l(a) f(p) dp da ∆p →0 ∆a→0

and used GAUSS Quadrature order to determine the sedimentation volume , this technique can produce a numerical 3D shapes using software assistance in getting visualization. For the data: Initial depth h, = 0.3 Velocity v, = 0.2 Initial height of the sediment, =0.3 Time t, = 5 Delta t, = 1 The angle of the river, = The angle of the river, = The river discharge, =0.3

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5.1 Conclusion • From the analysis and discussion that has been done, it is concludedthat: Sediment distribution patterns along the flow is influenced by the shape of its morphology. Streams quarter-circle arc -shaped curve or a straight stream to experience the difference at each change of position of the point, either change the depth, speed, and changes in sediment height after a certain time interval. • River velocity will increase the speed of the bow section of the river that can allow scouring the bow section of the river. At confluence, the vector velocity will increase and form a vortex as a result of the convergence of two different direction vectors river. • Sedimentation volume can be constructed from the results of the determination of the location of sedimentation to be made sedimentation area L(A) multiply by a function of position in the sediment height F (P) and summed according to the grid (interval) is the sum numerical approaches can be searched and visualized in the form of 3 dimensions .

REFERENCES 1. Apsley, D. 2005. “Computational Fluid Dynamic”, Springer. New York. 2. Asahi. 2003. “Estimation of Sediment Discharge into Account Tributaries to the Ishikari River”, Journal of Natural Disaster Science. Vol 25 No 1 pp. 17-22. 3. Atlury and Lin. 2000.“The meshless local Petrov-Galerkin (MLPG) method for convection-diffusion problems”, CMES. Vol. 1, No. 2, pp. 42-60. 4. Atlury and Lin. 2001.“The meshless local Petrov-Galerkin (MLPG) method for solving incompressible Navier-Stokes Equations”, CMES. Vol. 2, No. 2, pp. 117-142. 5. Atlury and Shen. 2002. “The Meshless Local Petrov-Galerkin (MLPG) Method: A Simple & Less-costly Alternative to the Finite Element and Boundary Element Methods”, CMES, vol.3, no.1, pp.11-51.

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6. Atlury and Zhu. 1998. A New Meshless Local Patrov-Galerkin, In Computational mechanics. New York. 7. Koolahdoozan 2003. “Three-Dimensional Geo-Morphological Modeling of Astuarine Waters”,International Journal of Sediment Research. Vol 18, No.1, pp. 1-16. 8. Ottevanger, W. 2005.Discontinuous Finite Element Modeling of River Hydraulics and Morphology with Application to the Parana River, Master Tesis.Department of Applied Mathematics.University of Twente. 9. Shabayek, S., dkk. 2002. “Dynamic model for sub critical combining flows in channel junction”, Journal of Hydraulic Engineering, ASCE, pp. 821-828

10. Wang. 2004. “River Sedimentation and Morphology Modeling-The State of The Art and Future Development”, Proceedings of the Ninth Symposium on River Sedimentation, Yichang-China. 11. Wibowo, I L and Widodo B, 2013, “Numerical Simulation on Calculating Volume Sedimentation On Two Rivers Confluences” Far East Journal of Mathemathical Sciences (FJMS) Vol 76, ISSN 0972-0871, PUSPHA Publishing House India. 12. Yang, C. T. 1996.Sediment Transport, Theory and Practice, McGraw Hill. New York.

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On The Total Irregularity Strength Offriendship Rismawati Ramdani1), A.N.M. Salman2), and Hilda Assiyatun2) 1)

Faculty of Sciences and Technologies Universitas Islam Negeri Sunan Gunung Djati Bandung 2) 3) Combinatorial Mathematics Research Group Faculty of Mathematics and Natural Sciences, Institut Teknologi Bandung *)

Corresponding author : [email protected]

Abstract—Let be a graph and k be a positive integer. Total k-labeling of G is a mapping . A total k-labeling of Gis called totally irregular total k-labeling of G if every two distinct vertices x and y inV satisfies and every two distinct edges and in Esatisfies , and . The minimum k for which a graph G has a totally irregulartotal k-labeling is called the total irregularity strength of G, denoted by ts(G). Thefriendship is a graph obtained from wheel by missing every alternate rimedge. In this paper, we consider the total irregularity strength of friendship. where

=

Keywords— friendship, the edge irregularity strength, the total irregularity strength, the vertex irregularity strength, totally irregular total k-labeling.

I. INTRODUCTION

L

et be a graph. A labeling of a graph is a mapping that carries graphelements to the numbers (usually to the positive or non-negative integers). A labeling f is called edge labeling if the domain of f is E, a labeling f is called vertex labeling if the domain of f is V , , then the labeling and if the domain of a labeling f is fis called total labeling. Graph labeling was introduced in 1963 by Sadlacek. There aremany kinds of graph labeling, such as graceful labeling, harmonious labeling, magic labeling, and anti magic labeling. In 2007, Ba a, Jendro , Miller, and Ryan [1] introduced irregular total k-labeling.They studied two kinds of irregular total labeling, namely edge irregular total labelingand vertex irregular total labeling. Let be a graph. For an integer k, atotal labeling is called an edge irregular total klabeling ofG if every two distinct edges and in E satisfy , where = and . The minimum k for which a graph G has anedge irregular total k-labeling, denoted by , is called the total edge irregularitystrength of G. Some results about the edge irregular total k–labelingweregiven by Nurdin, Salman, and Baskoro in

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[7] and Jendro , Mi kuf, and Sot k in [2].For an integer k, a total labeling is called avertex irregular total k-labeling of G if every two distinct vertices , where x and y in Vsatisfy . The minimum k for which a graphG has a vertex irregular total k-labeling, denoted by , is called the total vertexirregularity strength of G.Some results about the vertex irregular total k-labeling were given by Nurdin, Baskoro, Salman, and Gaos in [4]-[5]-[8]. Combining both of these notions, Marzuki, Salman, and Miller [3] introduced anew irregular total k-labeling of a graph G called 'totally irregular total k-labeling'.A totally irregular total k-labeling of G is a mapping such that is distinct for every and is distinct for every . The minimum k for which a graph Ghas a totally irregular total k-labelng, denoted by , is called the total irregularity strength of G. Marzuki, Salman, and Miller [3] provided an upper bound and a lower. Besides that, they determined the total bound on irregularity strength of cyclesand paths. In [1], Ba a, Jendro , Miller, and Ryanderive a lower and an upper bounds on the total edge irregularitystrength of any graph as follows.

In the same paper, Ba a, Jendro , Miller, and Ryan[1] also derive a lower and an upper bounds of the total vertex irregularity strength of any graph with minimum degree and maximum degree , thefollowing bounds hold.

Marzuki, Salman, and Miller [3] given a lower bound of as follows. Some results about the totally irregular total k-labeling were given by Ramdaniand Salman in [9]. In the paper, they have given the total irregularity strength ofsome Cartesian product graphs.

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II. MAIN RESULTS Friendship

The weights of all vertices and the weights of all edges under the totally irregulartotal 6-labeling are given in Figure 3.

is a graph with the vertex set

and the edge set For ilustration, friendship are given in Figure 1.

Figure 3.The weights of vertices and edges under the labeling of Figure 2 Figure 1. Friendship III. CONCLUSION , then . Theorem 2.1.Let Proof. has vertices and edges. The minimum degree of is and the maximum degree of is . From (1) and (2), weget and . There-

Friendship is a graph which has the total irregularity strength which is equal to its lower bound, so that it completes other graph other graphs classes on paper [9].

fore, from (3), we get

[1] M. Ba a, S. Jendro , M. Miller, and J. Ryan, On irregular total labelings,Discrete Mathematics, vol. 307, 1378-1388, 2007. [2] S. Jendro , J. Mi kuf, and R. Sot k, Total edge irregularity strength of completegraphs and complete bipartite graphs, Discrete Mathematicsvol. 310, 400-407, 2010. [3] C. C. Marzuki, A. N. M. Salman, and M. Miller, On the total irregularitystrength on cycles and paths, Far East Journal of Mathematical Sciences, to be published. [4] Nurdin, E. T. Baskoro, A. N. M. Salman, and N. N. Gaos, On the total vertexirregularity strength of trees, Discrete Mathematics, vol. 310, 3043-3048, 2010. [5] Nurdin, E. T. Baskoro, A. N. M. Salman, and N. N. Gaos, On the total vertexirregularlabelings for several types of trees, UtilitasMathematica, vol. 83, 277-290, 2010. [6] Nurdin, E. T. Baskoro, and A. N. M. Salman, The total edge irregular strengthof the union of ,JurnalMatematikadanSains FMIPA-ITB, vol. 11,105-109, 2006. [7] Nurdin, A. N. M. Salman, and E. T. Baskoro, The total edgeirregular strengthof the corona product of paths with some graphs, Journal of CombinatorialMathematics and Combinatorial Computing,vol. 65, 163-175, 2008. [8] Nurdin, A. N. M. Salman, N. N. Gaos, and E. T. Baskoro, On the total vertex-irregular strength of a disjoint union of t copies of a path, Journal of Combinatorial Mathematics and Combinatorial Computing, vol. 71, 227-233, 2009. [9] R. Ramdani, A.N.M. Salman, On the total irregularity strength of some Cartesian product graphs, AKCE International. Journal of Graphs and Combinatorics, vol. 10, No.2, pp. 199-209, 2013.

Next, we will show that Define a total labeling of

. . as follows:

We can see that f is a labeling from . Next,we can check that:

into

Hence, there are no two vertices of the same weight and there are no two edgesof the same weight. So, f is a totally irregular total -labeling. We conclude that

REFERENCES

For ilustration, we give a totally irregular total 6-labeling for in Figure 2.

Figure 2. A totally irregular total 6-labeling for

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EXISTENCE AND UNIQUENESS SOLUTION OF EULER-LAGRANGE EQUATION IN Ω ⊂ Rn Ratna Dwi Christyanti 1*), Ratno Bagus Edy Wibowo 2), Abdul Rouf Alghofari 2) Student of Magister Mathematics, Faculty of Mathematics and Natural Sciences, Brawijaya University, Malang 2) Department of Mathematics, Faculty of Mathematics and Natural Sciences, Brawijaya University, Malang 1)

Keywords— Euler-Lagrange equation, Existence, Uniqueness

Fu ( u%, ∇u%, x ) − ∑ where Fu =

ALCULUS of variations is a branch of mathematics that deals with optimization problem to find the extremum for a functional. Functionals is function of the functions. It can be expressed as integrals and derivatives of the function. Some methods that used to solve the problem of the calculus of variations are classical and direct methods. In the classical method, the most important tool is the EulerLagrange equation, see [2]. The existence and uniqueness of the solution of Euler-Lagrange equation in the finite dimensional has been discussed in [5]-[7]. Unlike in the finite dimensional, the classical method in the infinite dimensional can not be used directly since u% in

(C

or C

) is difficult to be proved, see [2].

call such equation as Euler-Lagrange equation in

Ω ⊂ Rn.

In order to prove existence and uniqueness of minimizers for a functional in Sobolev spaces, we need some theorem as in the following Theorem 1 (Holder inequality) Let

Ω ⊂ R n be open and 1 ≤ p ≤ ∞. If u ∈ Lp ( Ω )

and

v ∈ Lp ( Ω) where

and moreover

uv

Ω

where

u : Ω→R, F ∈C1 ( R×Rn ×Ω) , F = F( u,∇u, x) and

∇u =

∂u , i = 1, 2, L , n . ∂ xi

Moreover, we find the minimizer u% for a functional (P) which satisfy the equation

L1

≤ u

Lp

v

Lp

'

Theorem 2 (Poincare inequality)

Let

Ω ⊂ R n be a bounded open set and 1 ≤ p ≤ ∞. u

Lp

γ = γ ( Ω, p ) > 0

≤ γ ∇u

Lp

W 1, p

≤ γ ∇u

so that

, ∀u ∈ W01, p ( Ω ) ,

or equivalently

u (P)

1 1 + = 1, then uv ∈ L1 ( Ω) p p'

'

Then there exists

In this paper, we prove the existence and uniqueness solution u% of Euler-Lagrange equation in Ω ⊂ R n for a functional

I ( u ) = ∫ F ( u, ∇u, x ) dx,

∀x ∈ Ω

II. PRELIMINARIES

C

2

)

∂F ∂F and Fu x = , i = 1, 2,L , n. We i ∂u ∂u xi

I. INTRODUCTION

1

(

∂ Fux ( u%, ∇u%, x ) = 0, i i =1 ∂xi n

Abstract— This paper discusses the existence and uniqueness of minimizers of a functional in Sobolev spaces with Direct method, and by introducing of Euler-Lagrange equation with the Classical method. Finally, we prove the existence solution of Euler-Lagrange equation in Ω ⊂ R n .

Lp

, ∀u ∈ W01, p ( Ω ) .

Theorem 3 Let Ω ∈ R be convex. The function f : Ω → R said to be convex if for every x, y ∈ Ω n

and every

λ ∈ [ 0,1] ,

the following inequality holds

f ( λ x + (1 − λ ) y ) ≤ λ f ( x ) + (1 − λ ) f ( y ) . Theorem 4 Let

f : R n → R and f ∈ C1 ( R ) , the

function f is convex if only if

f ( x ) ≥ f ( y ) + ∇f ( y ) ; x − y , ∀x, y ∈ R n

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The 4th Annual Basic Science International Conference

 ∂f ( y ) ∂f ( y ) ∂f ( y )  , ,L , ∇f ( y ) =   and ∂x2 ∂xn   ∂x1 .;. denotes the scalar product in  n .

where

Direct method. Step 1: (Compactness)

I ( u0 ) < ∞ and from (A4), we get

Since

−∞ < m ≤ I ( u0 ) < ∞.

Theorem 5 (Fundamental Lemma of the Calculus of variations)

Let

Let Ω ⊂ R be an open set and

(1), i.e.

n

u ∈ L1loc ( Ω ) so that

∫u ( x ) ϕ ( x ) dx = 0,

uv ∈ u0 + W01, p ( Ω ) be a minimizing sequence of

I ( uv ) → inf { I ( u )} = m, as v → ∞. From (A4) that for v large enough

∀ϕ ∈ C0∞ ( Ω )

Ω

m + 1 ≥ I ( u v ) ≥ α 1 ∇u

then u = 0, almost everywhere in Ω.

and hence there exists III. RESULTS

Let Ω ⊂ R boundary. Let F ∈ C

0

n

be a bounded open set with Lipschitz

( R × R n × Ω ) , F = F ( u, ∇u, x ) , satisfy

∇u → F ( u , ∇u , x )

(A1)

is

convex

for

Applying Theorem 2 (Poincare inequality), we can find constants α 5 , α 6 > 0 so that

α 4 ≥ ∇u L ≥ α 5 uv and we can find

uv

α1 > 0,α 2 , α 3 ∈ R

such that

use

the

fact

W 1, p

n

Step 2: (lower semicontinuity)

  inf I ( u) = ∫F ( u, ∇u, x) dx : u ∈u0 +W01, p ( Ω)  = m (1) Ω   1, p where u0 ∈ W ( Ω ) with I ( u0 ) < ∞. Then there

We now show that

u% ∈ u0 + W

(Ω)

Proof: We will assume that F ∈ C

1

( u, ∇u ) → F ( u, ∇u, x )

(A3)

x ∈ Ω. (A4) there exist p > 1 and

(R × R

n

× Ω ) and

is convex for every

α1 > 0,α 3 ∈ R

I ( u% ) weakly lower semicontinuity,

uv  u% in W 1, p ( Ω ) ⇒ lim inf I ( uv ) ≥ I ( u% ) . v →∞

( uv , ∇uv ) → F ( uv , ∇uv , x )

x ∈ Ω, then from Theorem 4 we get F ( uv , ∇uv , x) ≥ F ( u%, ∇u%, x) + Fu ( u%, ∇u%, x)( uv − u% ) + F∇u ( u%, ∇u%, x) ;∇uv −∇u% . '

n

( u , ∇u , x ) ∈ R × R n × Ω p −1 p −1 Fu ( u , ∇u , x ) ≤ β (1 + u + ∇u )

(

F∇u ( u, ∇u , x ) ≤ β 1 + u where

and

Fu =

(

)

p −1

+ ∇u

F∇u = Fux , Fux ,L, Fux , Fux = 1

2

n

i

p −1

(2)

Fu ( u%, ∇u%, x ) ∈ Lp ( Ω ) and

F ( u, ∇u, x ) ≥ α1 ∇u + α3 , ∀ ( u, ∇u, x ) ∈ R × R × Ω. ' F∇u ( u%, ∇u%, x ) ∈ Lp ( Ω; R n ) . (A5) there exist a constant β ≥ 0 so that for every

and

is convex for every

Furthermore, we need to show that

such that

p

exists

this mean that

Since

a minimizer of (1).

p > 1, there

that

Let

exists

≤ α7.

uv  u% di W 1, p ( Ω ) as v → ∞.

F ( u, ∇u, x) ≥ α1 ∇u +α2 u +α3, ∀( u, ∇u, x) ∈R × R ×Ω.

1, p 0

− α6 ,

u% ∈ u0 + W01, p ( Ω ) and subsequence (still denoted uv ) so that

q

W 1, p

α 7 > 0 so that

every We

p

≤ α4.

Lp

p

( u , x ) ∈ R × Ω, (A2) there exist p > q ≥ 1 and

α 4 > 0 so that ∇u

Using Direct method we have Theorem 6 (Existence)

+ α3 ,

p Lp

)

∂F , i =1,2,L, n ∂uxi

∂F . ∂u

From

(A5)

and

u% ∈W1, p ( Ω)

where

 1 1 p  + ' = 1  p' = , p p p −1  we have

Fu ( u% , ∇u%, x )

p'

∫ F ( u%, ∇u%, x )

p'

∫ Ω

≤ β1 1 + u%

(

W 1, p

(

W 1, p

p

) 0,α 2 , α 3 ∈ R such

there exist p > q ≥ 1 and that

F ( u, ∇u, x) ≥ α1 ∇u +α1 u +α3, ∀( u, ∇u, x) ∈ R× Rn ×Ω. p

q

It is shown that if function

( u , ∇u ) → F ( u , ∇u , x )

is

Ω ⊂ R n have solution if function ( u , ∇u ) → F ( u , ∇u , x ) is convex. Moreover, Euler-Lagrange equation in

Furthermore, we shown that Euler-Lagrange equation in (7)

Ω

u% be a solution of (4). Since ( u , ∇u ) → F ( u , ∇u , x ) is convex for every

Proof: Let

x ∈ Ω, then from Theorem 4 we deduce that for every u ∈ u0 + W01, p ( Ω ) the following holds

(9)

strictly convex, then the minimizer of (P) is unique.

convex for every

I ( u ) = ∫F ( u , ∇u, x ) dx.

x ∈ Ω, then u% is a unique minimizer of

IV. CONCLUSIONS

Theorem 9 (Existence solution of Euler-Lagrange equation) If u% satisfies n ∂ Fu ( u%,∇u%, x) − ∑ Fux ( u%,∇u%, x) = 0,∀x∈Ω i i=1 ∂xi

stricly convex for

Proof: The proof of Corollary 10 is analog with Theorem 9, and by Theorem 7 we deduce u% is a unique minimizer of (9).

or in other words n

(8)

Ω

Fu ( u%, ∇u%, x ) − ( ∇.F∇u ( u%, ∇u%, x ) ) = 0, Fu ( u%, ∇u%, x) − ∑

( u, ∇u ) → F ( u, ∇u, x ) is

I ( u ) = ∫F ( u , ∇u, x ) dx.

∫ F (u%,∇u%, x) −(∇.F ( u%, ∇u%, x))ηdx = 0,∀η ∈W ( Ω) . From Theorem 5, u% ∈ C

)

n ∂ Fu ( u%,∇u%, x) − ∑ Fux ( u%,∇u%, x) = 0,∀x∈Ω i i=1 ∂xi

∇u

u

)

I ( u ) ≥ I ( u% ) . We deduce u% that is

we get indeed that

(

∫ F ( u%, ∇u%, x)η + F ( u%, ∇u%, x) ; ∇η  dx = u

(

∂ Fux ( u% , ∇u%, x ) = 0, i ∂xi

a minimizer of (7). Corollary 10 (uniqueness) If u% satisfies

then u

n

Fu ( u%, ∇u%, x ) − ∑

Ω

Ω ⊂ R n have a unique solution if function ( u, ∇u ) → F ( u, ∇u, x ) is stricly convex. REFERENCES [1] [2]

Adams R.A., Sobolev spaces, Academic Press, New York, 1975. Dacorogna B., Introduction to the Calculus of Variations, Imperial College Press, French, 1992.

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The 4th Annual Basic Science International Conference

Sun W. and Yuan Y., Optimization theory and methods, Springer, New York, 2006. Clarke F.H., The Euler-Lagrange Differential Inclusion, J. Of Diferential Equations. 19 (1975), 80-90. Bohner M. and Guseinov G.Sh., Double integral calculus of variations on time scales, J. of Computers and Mathematics with Applications. 54 (2007), 45-57.

150 | Batu, East Java, Indonesia

[6] [7]

Christyanti R.D, Euler-Lagrange Equation, Research report, 2011. Orpel A., The existence of minimizers of the action functional without convexity assumption, J. of the Juliusz Schauder Center. 20 (2002), 179-193

February 12-13rd 2014

The 4th Annual Basic Science International Conference

APPLICATION BARRO MODEL ON ECONOMIC GROWTH VIA HEALTH IN CENTRAL JAVA Caroline Universitas Sultan Fatah Demak, Demak, Indonesia *)

Corresponding author: [email protected]

Abstract—Central Java Through Health Spending, Education, health and income are the three pillars that are important in the economic development of a region (World Bank, 1993). By considering the importance of health for the improvement of the health of a region need to get the government's attention. Barro model offers economic growth through health channels. With the healthy person's productivity will increase, so that the output will be generated will increase the economic growth of a region. Central Java is one of the provinces in Indonesia, which has a human development index (HDI) which is lower than the HDI and the Indonesian island of Java. With the improvement of health in Central Java, is expected to boost economic growth in Central Java, so that with the economic growth of Central Java which will increase Indonesia's economic growth. Keywords— Economic growth, health

I. INTRODUCTION evelopment is a tool used to achieve the goals of ecoDnomic growth of the nation and is one of the indicators to assess the success of a country's development. Development paradigm that is being developed at this time is economic growth measured by the human development that seen with the level of quality of human life in each country. One of the benchmarks used in looking at the quality of human life is the Human Development Index (HDI) which is measured by the quality of education, health and economic (purchasing power). Through the third increase this indicator is expected to increase the quality of human life. To see the extent of development and human wellbeing, UNDP has issued an indicator of the Human Development Index (HDI) to measure the success of a country's development and prosperity. Human Development Index (HDI) is a benchmark figure of a region or state welfare is seen by three dimensions: life expectancy at birth, literacy rates and average length of the school, and purchasing power. Life expectancy is an indicator to measure the health, indicator of the adult population literacy rate and the average length of the school to measure education and the last indicator measures the purchasing power of the standard of living. (United Nations Development Programme, UNDP, 1990). The rate of economic growth in Central Java Province from 2005 to 2012 has increased. This suggests that the economic development in Central Java Province has increased. It will boost economic development and human

development. Regional economic growth positively and significantly influenced by human development. TABLE I GROSS REGIONAL DOMESTIC PRODUCT AT CONSTAN 2000 MARKET PRICE 2005-2012 Year

Gross Regional Domestic Product at Constan 2000 Market Price

2005

5.35

2006

5.33

2007

5.59

2008

5.61

2009

5.14

2010

5.84

2011

6.03

2012 6.34 Source : BPS-Statistics of Central Java Province

HDI achievement targets in Central Java in 2013 is expected to experience a significant increase in the amount of 74.3% with a life expectancy indicators (life expectancy) of 73.8 years, the average length of 7.0 years of school, literacy rates for 97.3%, and the per capita expenditure of IDR. 626,200. It became a Central Java in order to make the target unable to compete with other regions, especially in Java and outside Java in general, which is expected to improve the competitiveness in terms of quality of human resources. The elements of human development underlines explicitly targets to be achieved, namely a healthy life and longevity, educated and can enjoy a decent living. This means that human development aims to improve the welfare of the community with regard to the quality of human and society. Therefore, human is central to the development process. Health, education and income has been regarded as the three pillars of human development in the Human Development Index (HDI) (UNDP, 1990). Health is an important form of human capital. This can increase worker productivity by improving their physical capacity, such as strength and durability, as well as their mental capacity, such as cognitive functioning and reasoning abilities. Health factors closely related to the quality of human resources (quality of human resources) itself. High and low quality of human resources (HR) will be determined by health status, education and income levels per

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capita (Ananta and Hatmadji, 1985). In economic activities, the three indicators of the quality of human resources will also indirectly impact on the level of productivity of human resources, in this case, especially labor productivity.

Year s

TABLE II HUMAN DEVELOPMENT INDEX IN CENTRAL JAVA PROVINCE 2005-2013 Life The averReal exLiteracy Expecage Old penditure / rates HDI tation School per capita (%) (Year) (Years) (Rp.000)

2005

70.60

6.60

87.40

621.40

69.8

2006

70.80

6.80

88.20

621.70

70.25

2007

70.90

6.80

88.62

628.53

70.92

2008

71.10

6.86

89.24

633.58

71.60

2009

71.25

7.07

95.60

624.20

72.10

2010

71.40

7.24

96.10

624.60

72.49

2011

73.20

6.90

96.60

625.30

72.94

2012

73.50

7.00

97.00

625.80

71.72

97.30

626.20

71.68

2013 73.80 7.00 Source : BPS-Statistics of Central Java Tengah Province

The quality of labor, in the form of human capital, contributing significantly to economic growth. Workers are physically fit and mentally more energetic and robust. They are more productive so that it will get higher wages. They also tend to be absent from work due to illness (or disease in their family). Disease and disability reduce hourly wages substantially. Todaro and Smith (2006) that health is at the core of the welfare and education is essential to achieve a satisfying and worthwhile life. Education plays a major role in shaping the ability of developing countries to absorb modern technology and to develop the capacity to create sustainable growth and development. Health is a prerequisite for improved productivity, while educational success also relies on good health. Its dual role as both input and output cause health and education is very important in economic development and economic growth. So research on the application dieprlukan barro models in promoting economic growth through health channels in Central Java. II. THEORY A. Economic Growh Teory The theory of economic growth has a long history dating back to the late 18 century when the analysis of economic growth was at the center of attention of classical economists such as Smith (1776), Malthus (1798) and Ricardo (1817). These studies identified important causes and mechanisms that affect economic growth. The most important result from them is that the accumulation and investment of the production output is the main driving force behind economic growth. The much later works of Ramsey (1928), Young (1928), Schumpeter (1934) and Knight (1944), which emphasize the elements of competition, equilibrium dynamics, diminishing returns, the accumulation of physical and human capital and the monopoly power gained from

152 | Batu, East Java, Indonesia

technology advances, formed a good basis for the neoclassical growth theories and the endogenous growth theories developed after the middle 20 century. The models of Solow (1956) and Swan (1956) use a production function approach where there are constant returns to scale but diminishing return to each input. The equilibrium will exist if certain conditions are satisfied. The growth rate of the economy is determined exclusively by the exogenous technology. In other words, there will be long-term economic growth only if there are continuous new technologies available. One important finding of the neoclassical model is neoclassical models explain everything except long-term growth. To overcome this modeling deficiency, researches on endogenous growth such as Romer (1986), Lucas (1988) and Romer (1990), which emphasize the roles technology changes and human capital accumulation in the form of education play, help to generate some important results confirming the important roles of technology changes and education in promoting long-term growth. The theory of "conditional convergence" which shows that the growth rate of the economy will be faster the further this economy is below its own equilibrium level. The historical facts show that the positive rate of economic growth persists over a century and there is no trend of decline. The property of diminishing return of the inputs determines that the neoclassical models explain everything but long-term growth. To overcome this modeling deficiency, researches on endogenous growth such as Romer (1986), Lucas (1988) and Romer (1990), which emphasize the roles technology changes and human capital accumulation in the form of education play, help to generate some important results confirming the important roles of technology changes and education in promoting long-term growth. A. Economi Growth Via Health with Barro Model Fogel (1991, 1997, and 2000) have used historical facts to demonstrate that health is a powerful engine of economic growth. Barro (1991), used a-cross sectional framework; that is, the growth and the explanatory were observed only once per country. The main reason extend to a panel set up is to expand the sample information. Although the main evidence turn out to ceme from the crosssectional (between-country) variation, the time-series (within-country) dimension provides some additional information. Barro Model with initial level of GDP,initial level of schooling and initial health status. • Initial Level of GDP For given of the explanatory variables, the neoclassical model predicts a negatif coefficient on initial GDP, which enters in the system in logarithmic form. The coefficient on log of initial GDP has the interpretation of a conditional rate of convergence. • Initial Level of Schooling Education appears in two in system : average years of attaiment for male age 25 and over in secondary and higher schools at the start of each period and an interaction between the log of initial GDP and theyears of male secondary and higher schooling. Female schooling is importanat for other indicators of economic development, such as fertility, infant morality and political freedom. Specifically, female primary education has a strong negative relation with the fertility rate (see Schultz [1989], Behrman [1990], and Barro [1994]).

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The 4th Annual Basic Science International Conference

• Initial Health Status The population’s overall health status is measured here by the log of life expectancy at birth at the start of each period. The results are, however, similar with some alternative aggregate indicators of health, such at the infant mortality rate, the mortality rate up to age five, or expectancy at age five. • Fertility Rate If the population is growing, then a portion of the economy’s investment is used to provide capital for new workers, rather than to raisecapital per worker. For this reason, a higher rate of population groeth has a negative effect on y*, the steady-state level of output per effective workers in the neoclassical growth model. Another, reinforcing, effect is that a higher fertility rate means that increased must be devoted to childerearing, rather than to production of goods (see Beckers and Barro [1988]). Fertility decision are surely endogenous; previous research has shown that fertility rypically declines with measures of prosperity, especiall female primary education and health status (see Schultz [1989], Berhman [1990], and Barro and Lee [1994]).

Based on the Barro (1996) framework, inspired by the argument made by Grossman (1972) that health depreciation rate should not be constant, we endogenize the health depreciation rate by considering the following two cases: (1) health depreciation is determined exclusively by health; (2) health depreciation rate is jointly determined by health and education. In these two cases, the negative effect of health on economic growth is reflected explicitly by the endogenous health depreciation rate which is a positive function of health. The optimization results show that when the endogenous health depreciation rate is determined only by health, the negative effect of health on economic growth would be too strong to generate endogenous growth in the long-term. In contrast, if we consider the effect of education on lowering the health depreciation rate simultaneously with the positive effect of health on health. In the Barro (1996b) model, health affects economic growth by entering the production function directly, which corresponds to part I of Figure 1. Health affect economic growth through labor productivity. Improvement in health would allow the worker to work more efficiently, increase the amount of effective working hours and lower the probability of being absent from work either by the worker or his/her family members. Better health status would also increase the life expectancy and thus prolong the working ages which would encourage investment in education because the return on education investment is higher with longer effective working time. All these channels would lead to improvement in labor productivity which results in economic growth.

Our idea of endogenous health depreciation rate is supported by Grossman (1972). In the Grossman (1972) paper, health has been identified as another important form of human capital, which provides a good starting point for researchers to analyze the relationship between health and economic growth. However, as accepted by Grossman, health depreciation rate should vary over time. To understand why the health depreciation rate should not be constant, we should first understand the definition of health depreciation rate, which is the cost of maintaining the current level of health. There are many examples to show why the health depreciation rate should not be a constant. For example, before a major competition like the Olympic Games, an athlete needs to spend time on training, to eat following the instruction of dietitian and to check his/her body fitness regularly. In order to keep the match fitness, the investment is huge. However, after the competition, he/she no longer needs to keep that high level of match fitness and the expenditure to keep his/her non-match fitness level of health would be lower. Another example is that one of the significant indicators of better health is life expectancy. C. The Barro model of health and economic growth Barro (1996b) proposed a one-sector model which extended the neoclassical model to incorporate the impact of health on economic growth. In his model, health affects economic growth both directly and indirectly. First, health directly enters production function indicating a direct impact of health on productivity. In other words, if other determinants of the production function, such as physical capital, labor and schooling, are all constant, an improvement in an individual's health would increase the productivity. Second, health also determines the depreciation rate of both health and education. Therefore, health contributes to economic growth indirectly through its effect on education. D. The Barro growth model revisited In the Barro model, health is a private good that is financed totally by the individuals themselves. Investments in health include activities such as the purchase of nutrition products, the leisure time spent on sports, the money paid on doctors and medicines, a regular body check, etc. The economy is a one sector economy. First, total output Y is determined in a Cobb-Douglas function by physical capital inputs, K,individual's schooling and other education related factors, S, the health capital of individual, H, and the amount of labor provided, L: Y = AKα S β H χ (L)1-α-β-χ where A is the knowledge stock parameter, which represents the exogenously determined technology level. The model assumes that α > 0, β > 0 , χ > 0 and α + β + χ < 1.

B. The health depreciation rate As health depreciation is one component in the health accumulation function, we are interested in endogenizing the health depreciation rate in order to reflect the negative effect of health in promoting economic growth.

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Higher labor productivity

Less illness

Increased effective labor

Longe working hours

More Work Energy

Higher Cognitive Ability

Increasing Return on Education

Longer years of working

Health

Higher life expectancy

Population structure change

Lower fertility

Lower morbidity Goods production function: Y=AF (K, H)

Fig. 1 The interaction network between health and growth at Barro Model

That is, in this production function, Barro assumes constant returns to scale with respect to the four inputs (physical capital, education, health and labor) but diminishing returns with respect to each of the inputs respectively. This is a key assumption to derive the results of the Barro model. One thing to pay attention to these assumptions is that constant returns to scale with respect to the four inputs imply diminishing returns to scale with respect to the inputs of physical capital, education and health together.

IV. ESTIMATION AND RESULTS From the SPSS 16 output display, Model summary magnitude of R2 is 0.746, meaning 74.6 GDP variation is explained by the variation of the four independent variables POP, LABORFORCE, LABOR and LIFE EXPECTANCY. While the remaining (100% - 74.6% = 25.4%) is explained by other causes outside the model. TABEL III DESCRIPTIVE STATISTICS Mean

III.DATA

PDRB

We construct a panel of Central Java over 1988-2012. Output data Gross Regional Domestic Product Constant 2000 from BPS-Statisctics of Jawa Tengah Province. We measure a province’s labor supply by the size of its economically active population using data from BPSStatisctics of Jawa Tengah Province. Life expectancy date from BPS-Statisctics of Jawa Tengah Province.

Std. Deviation

N

8.49E7

2.935E11

19

Pop

3.1136E7

1.00281E10

19

Laborforce

1.7185E7

3.73047E10

19

Labor

1.4683E7

5.92126E9

19

69.7355

4335.65823

19

Lifeexpectation

a. Weighted Least Squares Regression - Weighted by Education

Standard error of estimate (SEE) is 1.478 Milyar . The smaller the value of SEE will make more precise regression models in predicting the dependent variable.

TABEL IV Model Summaryb,c Change Statistics

Model 1

R .896a

R Square .803

Adjusted R Square .746

Std. Error of the Estimate 1.478E11

a. Predictors: (Constant), Lifeexpectation, Laborforce, Labor, Pop b. Dependent Variable: PDRB c. Weighted Least Squares Regression - Weighted by Education

154 | Batu, East Java, Indonesia

R Square Change .803

F Change 14.242

df1

Sig. F Change

df2 4

14

.000

DurbinWatson 2.065

February 12-13rd 2014

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The 4 Annual Basic Science International Conference

From the ANOVA F test obtained or calculated value F calculated at 14.242 with probability 0.000. Because the probability is much smaller than 0.05 then the regression model can be

used to predict the GDP or POP, LABORFORCE, LABOR and Life Expectancy jointly affect the GDP.

TABLE V ANOVAb,c Model

Sum of Squares

1

df

Mean Square

F

Regression

1.244E24

4

3.111E23

Residual

3.058E23

14

2.184E22

Total

1.550E24

18

Sig. 14.242

.000a

a. Predictors: (Constant), Lifeexpectation, Laborforce, Labor, Pop b. Dependent Variable: PDRB c. Weighted Least Squares Regression - Weighted by Education TABLE VI Coefficientsa,b Unstandardized Coefficients Model 1

(Constant)

B

Std. Error

Standardized Coefficients Beta

-4.290E9 7.221E8

95% Confidence Interval for B Lower Bound

Upper Bound

t

Sig.

-5.941

.000

-5.839E9 -2.741E9

Collinearity Statistics

Correlations Zeroorder

Partial

Part

Tolerance

VIF

Pop

-3.948

5.198

-.135

-.759

.460

-15.096

7.201

.576

-.199

-.090

.447

2.239

Laborforce

-.837

1.027

-.106

-.816

.428

-3.040

1.365

-.339

-.213

-.097

.827

1.209

Labor

-1.250

6.512

-.025

-.192

.851

-15.217

12.718

.327

-.051

-.023

.816

1.225

3.867E7

9.126E7

Lifeexpectation 6.496E7 1.226E7

.960 5.299

.000

.888

.817

.629

.429 2.329

a. Dependent Variable: PDRB b. Weighted Least Squares Regression - Weighted by Education

The coefficient of the independent variable (independent) can use unstandarized coefficients and standarized coefficients. Unstandarized beta coefficients : The four independent variables included in the model OLS, Life expectancy variables significant at 0.05, while the other three variables were not significant (because of the above 0:05). Mathematical equation: PDRB = -4.290E9 -3.948 Pop – 0,837 Laborforce – 1.250 Labor + 6.496E7 Life expectancy. REFERENCES [1] Baldacci, E. Hillman, A. and Kojo, N. 2004. Growth governance, and fiscal policy transmission channels in low-income countries. European Journal of Political Economy, 20 (3), 517-549. [2] BPS-Statistics of Jawa Tengah Province [3] Barro, Robert J. 1990. Government spending in a simple model of endogenous growth. Journal of Political Economy, 98, October, part II,103125. [4] Barro, Robert J. 1991. Economic growth in a cross section of countries. Quarterly Journal of Economics, 106, May, 407-443. [5] Barro, Robert J. and Xavier Sala-I-Martin. 1991. Convergence across states and regions. Brookings Papers on Economic Activity, 1, 107-158. [6] Barro, Robert J. and Xavier Sala-i-Martin. 1992. Convergence. Journalof Political Economy, 100, 223-251. [7] Barro, Robert J. and Lee, J. 1993. International comparisons of educational attainment. Journal of Monetary Economics, 32 (3),363-394.

[8] Barro, Robert J. 1996a. Determinants of economic growth: A crosscountry empirical study. NEBR Working Paper No.5968. Cambridge, MA: National Bureau of Economic Research [9] Barro, Robert J. 1996b. Health, human capital and economic growth, Paper for the program on Public Policy and Health, Pan American Health Organization and World Health Organization. Washington, DC: Pan American Health Organization [10] Barro, Robert. J. 1997. Determinants of economic growth: a cross country empirical study, MIT Press [11] Barro, Robert. J. and Lee, J. 2000. International data on education attainment: Updates and implications. Center for International Development Working Paper No 42. Cambridge, MA: Harvard University. [12] Barro, Robert J. and Xavier Sala-i-Martin. 2005. Economic Growth.New York: McGraw-Hill, Inc. [13] Bassanini, A., and Scarpetta, S. 2001. Does human capital matter for growth in OECD countries? Evidence from pooled mean-group estimates, Economics Department Working Paper No. 282, Paris, France: OECD. [14] Becker, G.S. 1962. Investment in human capital: a theoretical analysis. Journal of Political Economy, 70, 9-49. [15] Becker, G.S. and Barro, Robert J. 1989. Fertility choice in a model of economic growth. Econometrica. 16, 481-501. [16] Bloom David E. and David Canning. 2000. Demographic change and economic growth: The role of cumulative causality, in Nancy Birdsall, Allen C. Kelley, and Stephen Sinding, eds., Population Does Matter: Demography, Growth, and Poverty in the Developing World. New York: Oxford University Press [17] Bloom, David E., Canning, D., and Sevilla, J. 2001. The effect of health on economic growth: theory and evidence. NBER Working Papers 8587,National Bureau of Economic Research, Inc. [18] Bloom, David E. and Canning, D. 2003. The health and poverty of nations: From theory to practice. Journal of Human Development, 4(1), 47-71.

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[19] Bloom, David E. and Canning, D., and Sevilla, D. 2004. The effect of health on economic growth: A production function approach. World Development, 32 (1), 1-13 [20] Chakraborty, S., and Das, M. 2005. Mortality, human capital and persistent inequality. Journal of Economic Growth, 10, 159-192. [21] Durlauf, S. 1996. A theory of persistent income inequality. Journal of Economic Growth, 1, 75-94. [22] Fogel, Robert W. 1991. New sources and new techniques for the study of secular trends in nutritional status, health, mortality, and the process of aging, National Bureau of Economic Research Working Paper Series on Historical Factors and Long Run Growth: 26, May. [23] Fogel, Robert W. and Wimmer, L. T. 1992. Early indicators of later work levels, disease, and death, Bureau of Economic Research Working Paper Series on Historical Factors and Long Run Growth: 38, June. [24] Grossman, M. 1972. The Demand for Health: A Theoretical and Empirical Investigation. NBER, Occasional Paper 119, Columbia University Press.

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[25] Gupta, S., Verhoeven, M., and Tiongson, E. 2003. Public spending on health care and the poor. Health Economics, 12(8), 685-696. [26] Islam, N. 1995. Growth empirics: A panel data approach. Quarterly Journal of Economics, 110, 1127-1170 [27] Musgrove, P. 1996. Public and private roles in health: Theory and financing patterns, World Bank Discussion Paper No. 339, Washington DC: World Bank. [28] Mushkin, S.J. 1962. Health as an investment. Journal of Political Economy, 70, 129-157. [29] Romer, P. M. 1986. Increasing returns and long-run growth. Journal of Political Economy, 94 (5), 1002-37 [30] Schultz, T.P. 1961. Investment in human capital. American Economic Review, 51, 1-17. [31] Solow, R.M. 1956. A contribution to the theory of economic growth Quarterly Journal of Economics, 70, 65-94.

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The Development of Gui Simulation-Based Media for Geometry Transformation Lilik Hidayati1*) 1) SMKN 2 Lingsar West Lombok, West Nusa Tenggara, Indonesia *)

Corresponding author: [email protected]

Abstract— This study aimed to develop Graphical User Interface (GUI) simulation-based media that involved material concepts and problem-solving of mathematical processes, particularly pertaining to the transformation of geometry material. The rationale for this is that the language of mathematics that employs symbols and abstract meanings are hardly understood by students, and therefore it is regarded as a difficult subject to study. To deal with this condition, it is necessary to develop teaching media that can transform the abstract mathematical concepts into more concrete ones. Generally, the mathematics software for learning simulation does not show the process of solving mathematical problems. The software only generates the final result without helping students to do the thinking process. With this in mind, it is necessary to develop a mathematical learning simulation that contains mathematical processes pertaining to the mathematical problemsolving. To conduct the current research, the researcher used a flow chart design. Keywords— Abstract, Math Problems, GUI-Based Media, Symbols, Software

I. INTRODUCTION HE specific objective of mathematics teaching at school is to train students with logical, critical, precise, and applicale thinking as well as providing students with ability to study sciences and technology for further education. The teaching of mathematics has experienced paradigm changes of learning technique. This can be seen from the development of the cooperative models in mathematics teachings which focus on the students as the learning indivduals. [Herman]1 On the other hand, according to Rastaman & Rastaman (1997), laboratorium is a supporting media in the process of teaching and learning. Optimum result will be achieved when students are involved both physically and mentally in the teaching process. Through the simulation media being developed, it is expected that teachers can easily explain the abstract mathematical concepts becoming more concrete ones. The problem to solve is how the Graphic User interface (GUI-based) simulation media in the teaching of mathematics can help convey materials and concepts of mathematics problems solving. This study aims at developing a GUI-based simulation media for mathematics teaching that contain maerials and concepts of mathematics problems solving.

T

II. LITERATURE RIVIEW

A. The Mathematics Learning Theory of Dienes According to Dienes, mathematical games are really important because the math operation in the game illustrates concrete rules that guide and sharpen the mathematic understanding of the learners. Thus, the concrete objects in the form of game play an important role in mathematics teaching when manipulated well. The more varied the the concepts introduced, the clearer the understanding of the students is. B. Graphical User Interface (GUI) So far, the teaching of mathematics is dominantly performed in the traditional way in which everything is written on the board. Nowadays, the teaching process has has progressed where Matlab is used as computation device that help teachers in teaching mathematics. Matlab is a programming language with high performance in computation that is highly qualified for technical computation. It is also an interactive system for numeric computation and data visuaization. C. Simulation Method This simulation method develops learners’ skills, both mental and pysical/technical skills. The method transfers a real-life situation into a learning activity or learning room as there is difficulty to do a practice in the real situation. For instance, an aviation school student does a flying operation simulaton. The situation faced in the simulation must be created in such that it appears like the real true one (reality replication). III. RESEARCH METHOD

Research design is the procedure concept to guide the research implementation so that it runs correctly to achieve the goal. This is a developing type of research with the following design:

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Start

Designing GUI Model

IV. UNITS Scripting

Implementation

No Successful

Yes Project Application

% TRANSLASI, by itself, creates a new TRANSLASI or raises the existing % singleton*. % % H = TRANSLASI returns the handle to a new TRANSLASI or the handle to % the existing singleton*. % % TRANSLASI('CALLBACK',hObject,eventData,handles, ...) calls the local % function named CALLBACK in TRANSLASI.M with the given input arguments. % % TRANSLASI('Property','Value',...) creates a new TRANSLASI or raises the % existing singleton*. Starting from the left, property value pairs are % applied to the GUI before translasi_OpeningFcn gets called. An % unrecognized property name or invalid value makes property application % stop. All inputs are passed to translasi_OpeningFcn via varargin. % % *See GUI Options on GUIDE's Tools menu. Choose "GUI allows only one % instance to run (singleton)". % % See also: GUIDE, GUIDATA, GUIHANDLES % Edit the above text to modify the response to help translasi % Last Modified by GUIDE v2.5 23-Nov-2013 11:58:56 % Begin initialization code - DO NOT EDIT

End Chart 1. Diagram of the Developing of GUI-Application for Maths Teaching Simulation IV. RESULT AND DISCUSSION

........ function edit7_CreateFcn(hObject, eventdata, handles) % hObject handle to edit7 (see GCBO) % eventdata reserved - to be defined in a future version of MATLAB % handles empty - handles not created until after all CreateFcns called

A. Design of the GUI Model % Hint: edit controls usually have a white background on Windows. % See ISPC and COMPUTER. if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor')) set(hObject,'BackgroundColor','white'); end

Figure 1.This is GUI Geometry Transformation concept Note the Figure is designed for GUI model. The program is used for calculating geometric transfomation. By input the data into the boxes (A,B,and C). The data is operated by pressing the button (translasi, refleksi, rotasi, dilatasi) and the result will appear in the screen.

B. Scripting After designing the GUI model, the computation code is added to the m-file. This scripting process can be seen in details in the appendix. Some parts of the stages are as the following: function varargout = translasi(varargin) % TRANSLASI MATLAB code for translasi.fig

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C. Implementation and Discussion The result of the developing of the simulation media is to be applied for problem solving in transformational geometry. The samples of problems are chosen on the basis of their representativeness covering items such as translation, reflection, rotation, and dilation. The project application from the development of of the GUI-based simulation media is in the form of software compilation in CD application. The GUI - Model is equipped with scripting (computation language). After verification and application test in which the media is considered successful, it is then applied into teaching practice. The data of the implementation/application stage is analyzed to see the relation among variables and also to identify the pattern of the data. The advantage for teacher: The first, process of teaching leaarning is effective and effesient, the second the target of learning achieved or sucessfull, for students: The first, the whole process appear in this

February 12-13rd 2014 program, the second the program appearing the animation process make the process of teaching reality without leaving the mathematic terms, third constructed the students’ knowledge systematicly. V.

CONCLUSION

Based on the implementation result of the GUIModel simulation media and the study on the learning theory of Dienes, the teaching media is found to be potential to contribute much in increasing the students’ understanding towards the concept of transformational geometry. REFERENCES [1]

[2] [3]

Bloom, Benyamin S., et. all (1971), Handbook on Formative and Summative Evaluation of Student Learning, McGraw-Hill Book Company, New York. Djamarah, Syaiful Bahri, Drs. Strategi Belajar Mengajar. Jakarta: Rineka Cipta. 2002. Gronlund, Norman E. (1985) Measurement and Evaluation in Teaching, Fifth Edition, Macmillan Publishing Company New York.

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Guilford (1973). Fundamental Statistic in Psychology and Education, Tokyo: Mc Graw-Hill Kogakusha. [5] Herman Hudoyo. 1998. Belajar Mengajar Matematika. Jakarta: Depdikbud P2LPTK. [6] Hidayati, L., (2012), Aplikasi Berbasis Gui (Grafik User Interfaces) Untuk Simulasi Limit Fungsi, Jurnal, AVISENA, Vol.4/No.2/ISSN2086-8960/Desember/2012 UNIZAR Mataram. [7] Hidayati, L., (2013), Pengembangan Media Pembelajaran Matematika Berbasis Gui (Grafik User Interfaces), Makalah Seminar Nasional MIPA dan Pendidikan Matematika, UNESA, 2013. [8] Ibrahim, (2012), Teori Dienis, Makalah tugas mata kuliah Psikologi Belajar Matematika, Jurusan Matematika UIN Sunan Kalijaga Yogyakarta. [9] Muslihati, (2012), Teori Belajar Permainan Dienes Dalam Pembelajaran matematika, Jurnal, STKIP PGRI Metro. [10] Purwanto, Drs. Strategi Pembelajaran Matematika. Surakarta: Sebelas Maret University Press. 2003. [11] Sardiman, A.M. Interaksi dan Motivasi Belajar Mengajar. Cet. IV; Jakarta: Rajawali Pers. 1992. [12] Sri Wulandari Danoebroto. 2008. “Meningkatkan Kemampuan Pemecahan Masalah Melalui Pendekatan PMRI dan Pelatihan Metakognitif”. Jurnal Penelitian dan Evaluasi Pendidikan. Nomor 1 Tahun XI. 69 – 81.

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