Growth of Smaller Grain Attached on Larger One: Algorithm to Overcome Unphysical Overlap between Grain

As a smaller grain, which is attached on larger one, is growing, it pushes also the larger one and other grains in its surrounding. In a simulation of similar system, repulsive force such as contact force based on linear spring-dashpot model can not accommodate this situation when cell growing rate is faster than simulation time step, since it produces sudden large overlap between grains that makes unphysical result. An algorithm that preserves system linear momentum by introducing additional velocity induced by cell growth is presented in this work. It should be performed in an implicit step. The algorithm has successfully eliminated unphysical overlap.Comment: 6 pages, 4 figures, conference paper (ICMNS 2014, 2-3 November 2014, Bandung, Indonesia

生体ナノクラスタ自己集積集合体における構造揺らぎモードと同期パターン

取得学位：博士(理学)，学位授与番号：博甲第1047号，学位授与年月日：平成20年9月26

PEMODELAN ALIRAN DEBRIS FLOW UNTUK ANALISIS POTENSI LONGSORAN STUDI KASUS: PEGUNUNGAN FISHHWAK, CALIFORNIA

Aliran debris (debris flow) merupakan aliran yang terdiri dari campuran material halus berukuran kecil (clay, lumpur, pasir) sampai material berukuran kasar (kerikil, bongkahan bebatuan) dengan sejumlah volume air. Material debris flow mengalir dengan volume besar dan kecepatan tinggi menghasilkan momentum yang bisa menyebabkan kerusakan infrastruktur, lingkungan, bahkan korban jiwa. Interaksi fisis antara campuran air dan material debris melibatkan interaksi yang sangat kompleks membuat mekanisme transpor aliran debris sulit untuk dikaji menggunakan pendekatan numerik. Prediksi distribusi aliran debris dapat dilakukan menggunakan pendekatan statistik empirik. Pendekatan ini dengan menghubungkan parameter volume debris flow dengan luas genangan yang akan dihasilkan. Analisis menggunakan pendekatan statistik empirik didukung oleh data spasial dapat dijalankan menggunakan perangkat lunak DFLOWZ.  Parameter input dalam DFLOWZ yaitu data digital ketinggian (DEM), data polyline, dan volume debris flow.  DFLOWZ berhasil diterapkan pada debris flow di pegunungan Alpen, Italia. Pada penelitian ini, DFLOWZ diaplikasikan di salah satu daerah yang pernah terjadi debris flow di wilayah California, Amerika Serikat. Didapatkan luas genangan debris flow sebesar 285.447 m2. Kata kunci: debris flow, pemodelan, DFLOW

Effect of Growth Space on The Productivity of Maize Using Three Sisters Cultivation with Bee Pollination

he increasing number of food needs is one of the driving factors for increasing agricultural production, but there are constraints on the availability of land. A polyculture system with corn, beans, and pumpkins, commonly known as the three sisters, can create positive interactions that can enhance the growth and development of each plant. This system has a vast potency to be applied to urban farming inside a grow bag because it does not require ample space, the placement of plants is flexible, and it produces a variety of yields. However, it is necessary to assess the effect of growing space on the growth of maize (Zea mays) cultivated by the three-sister system. This study used a completely randomized design with three treatments and six replications. The treatment consists of three planting spaces with various growing bags (treatment A:75 L, B:100 L, and C:200 L). The results of this study showed that the highest corn productivity was in the largest growing space (treatment C), which weighed 318.40 g/cob, and without husks 246.42 g/cob, but not significantly different from treatment B (grow bag 100 L), which weights 316.20 g/cob and without pods of 240.63 g/cob. This study found that the 100 L grow bag was the optimal growing space for planting corn in containers using the three sisters technique

Prediction of Carbon Monoxide Concentration with Variation of Support Vector Regression Kernel Parameter Value

Human and industrial activities produce air pollutants that can cause a decline in air quality. In urban areas, transportation activities are the main source of air pollution. One of the emitted air pollutants produced by transportation is carbon monoxide (CO). The understanding of CO concentration is crucial since its overabundance beyond a certain limit will have a negative impact on human health and the environment. In this study, the support vector regression (SVR) method was used to predict CO concentration. The purpose of this study was to predict the hourly CO concentration in the Ujung Berung district, Bandung City, West Java, Indonesia with optimal prediction accuracy. An experiment was carried out by modeling the CO concentration with varying kernel parameter values to obtain accurate prediction results. The suitability of the values between error (ɛ), a trade-off constant (C), and variation mismatch (γ) is vital to obtain optimal prediction results. The results showed that the best prediction accuracy value was 97.68% with kernel parameter values ɛ = 0.02, γ = 30, and C = 0.006. These results may lead to proper decision making on environmental issues and can improve air pollution control strategies

Prediction of Carbon Monoxide Concentration with Variation of Support Vector Regression Kernel Parameter Value

Human and industrial activities produce air pollutants that can cause a decline in air quality. In urban areas, transportation activities are the main source of air pollution. One of the emitted air pollutants produced by transportation is carbon monoxide (CO). The understanding of CO concentration is crucial since its overabundance beyond a certain limit will have a negative impact on human health and the environment. In this study, the support vector regression (SVR) method was used to predict CO concentration. The purpose of this study was to predict the hourly CO concentration in the Ujung Berung district, Bandung City, West Java, Indonesia with optimal prediction accuracy. An experiment was carried out by modeling the CO concentration with varying kernel parameter values to obtain accurate prediction results. The suitability of the values between error (ɛ), a trade-off constant (C), and variation mismatch (γ) is vital to obtain optimal prediction results. The results showed that the best prediction accuracy value was 97.68% with kernel parameter values ɛ = 0.02, γ = 30, and C = 0.006. These results may lead to proper decision making on environmental issues and can improve air pollution control strategies

The Geometrical Effects of Substitutional Impurities on Electric and Magnetic Properties of (10,0) Carbon Nanotube by using Density Functional Theory

We have studied the properties of CNT (10, 0) with the impurities of gallium arsenide and nitrogen. We constructed five geometrically different structures and investigated their electric and magnetic properties by using density functional theory (DFT) and generalized gradient approximation (GGA) exchange correlation. We found that the structural variations of gallium arsenide and nitrogen substitution impurity on CNT (10, 0) show the effects on their electronic and magnetic properties such as the different magnetic moments on several types of CNT (10, 0) impurity