An ODE control system of a rigid body on an ocean wave for a surfer simulation in the SPH method

In this work we use a smoothed particle hydrodynamics (SPH) method coupledwith a rigid body simulation to simulate a surfing board on top of an ocean wave. Externalforces are applied to the board to represent a surfer trying to control a surfing board. Anordinary differential equation (ODE) control is used to manipulate the external forces basedon a position, velocity, and an inclination angle of the surfing board. The control systemsuccessfully helps the surfing board to move to and maintain its desired position

Coupling Efficiency of a Spot Size Converter for Optical Fiber-Chip Connections

The light propagation in optical waveguide must be able to maintain low propagation loss, low coupling loss and scattering loss condition, especially in the junction. In this research, a spot size converter is proposed to preserve the lowest coupling loss. This optical converter is composed of a single mode optical fiber (SiO2) including inversed taper. The optical input signal from the optical fiber is launched into photonic integrated circuits and then coupled into the Si-Slab waveguide. Furthermore, linear form with the length dependence has been studied to obtain the optimal position of optical fiber and the chip and analyzed the coupling efficiency of it. The purpose of this research is to procure the optimal form of spot size converter. The simulation result shows the coupling loss of linear form is 0.62 dB and 0.24 dB on TE and TM mode condition respectively. Along with the increase in the taper length, the coupling loss obtained tends to decrease as well. So that, it can be assumed the design of a linear form with 100 μm taper length provides the highest coupling efficie ncy

On a Coupled SPH-Rigid Body Method for the Surfing Problem

13301甲第4816号博士（理学）金沢大学博士論文要旨Abstract 以下に掲載予定：The Science Reports of Kanazawa University 62 2018. The Institute of Science and Engineering, Kanazawa University. 共著者：Reza Rendian Septiawa

On a Coupled SPH-Rigid Body Method for the Surfing Problem

13301甲第4816号博士（理学）金沢大学博士論文本文Full 以下に掲載予定：The Science Reports of Kanazawa University 62 2018. The Institute of Science and Engineering, Kanazawa University. 共著者：Reza Rendian Septiawa

The Effect of Force Ratio Multiplier on A Control System for Surfing Problem Simulation

A surfing problem is a control problem of the surfing board to maintain its position on top of an ocean wave as long as possible. There are some physical and mathematical problems regarding a surfing problem that have not yet been solved. One of them is on translating a target inclination problem from an ordinary differential equation (ODE) control system to the inclination control system via the distribution of a surfers weight. To move the surfing board swiftly, a correct value of the multiplier, which is notated by σ, is needed on the weight distribution system. In this work, an investigation has been done on the effect of the multiplier in an attempt to help moves the surfing board fulfils the target inclination angle needed by using a smoothed particle hydrodynamics (SPH) simulation. The result from this work shows that the best value for the multiplier is σ=10 that gives the smallest average positional error for some variations of a given target position. This work gives a contribution on an attempt to model a surfers phenomenon mathematically

Development of mobile billing application system for PAMDES water meter data logging

Along with the growth of Sindangsari's population, the water requirement in this village has increased. So, the drinking water company (PAMDES) in this village must be able to manage the available water to meet the necessities of public life. Now, the water company is still collecting and recording water meters manually. It is very risky to consider the water condition in the village because an officer can make a human error while recording the water meter's value. When the water meter recorder is damaged, the officer estimates the water meter's value. An application is needed to manage bills and record water meters to avoid this in previous studies using the Internet of Things (IoT) or mobile applications that must be sent online to the server in real-time. This solution is not suitable for the internet condition in Indonesia, which is not evenly distributed to remote villages. This study proposes to use a mobile application that can store data on mobile devices. When the internet connection is unavailable, it can be sent later when it is available again. In this study, data obtained that the condition of the meter recorder from 672 customers, 37 water meters recorder is damaged. In addition, water meter data is also obtained for the following month's bill and data on average water usage of 10,661m3 per month. With these data, it is found that the minimum water requirement is 10,661 m3 per month. It is hoped that the application for billing management and recording of PAMDES water meters in Sindangsari village, Cikoneng sub-district, Ciamis district can help increase PAMDES management capacity

QSAR Study of Larvicidal Phytocompounds as Anti-Aedes Aegypti by using GA-SVM Method

Aedes aegypti is one of the most dangerous mosquitoes that can cause several deadly diseases, such as dengue fever, Chikungunya, Zika, and jaundice with high mortality rate. For now, no specific drug has been found that can cure the disease caused by Aedes Aegypti. One possible solution for handling this problem is to inhibit the growth and development of Aedes aegypti larvae. This study aims to implement Genetic Algorithm-Support Vector Machine to develop Quantitative Structure-Activity Relationship model for identification larvicidal phytocompounds as anti-aedes-aegypti. Hyperparameter tuning was performed to improve the performance of the models. Based on the result, we found that the best model was developed by the RBF kernel with the value of    and  score are 0.64 and 0.64, respectively.Aedes aegypti is one of the most dangerous mosquitoes that can cause several deadly diseases, such as dengue fever, Chikungunya, Zika, and jaundice with high mortality rate. For now, no specific drug has been found that can cure the disease caused by Aedes Aegypti. One possible solution for handling this problem is to inhibit the growth and development of Aedes aegypti larvae. This study aims to implement Genetic Algorithm-Support Vector Machine to develop Quantitative Structure-Activity Relationship model for identification larvicidal phytocompounds as anti-aedes-aegypti. Hyperparameter tuning was performed to improve the performance of the models. Based on the result, we found that the best model was developed by the RBF kernel with the value of    and  score are 0.64 and 0.64, respectively. Aedes aegypti is one of the most dangerous mosquitoes that can cause several deadly diseases, such as dengue fever, Chikungunya, Zika, and jaundice with high mortality rate. For now, no specific drug has been found that can cure the disease caused by Aedes Aegypti. One possible solution for handling this problem is to inhibit the growth and development of Aedes aegypti larvae. This study aims to implement Genetic Algorithm-Support Vector Machine to develop Quantitative Structure-Activity Relationship model for identification larvicidal phytocompounds as anti-aedes-aegypti. Hyperparameter tuning was performed to improve the performance of the models. Based on the result, we found that the best model was developed by the RBF kernel with the value of    and  score are 0.64 and 0.64, respectively

DPP IV Inhibitors Activities Prediction as An Anti-Diabetic Agent using Particle Swarm Optimization-Support Vector Machine Method

Diabetes mellitus is a chronic illness that can affect anyone, while the medicine that can entirely cure diabetes has not been discovered yet. Dipeptidyl Peptidase IV (DPP IV) inhibitor is one of the agents with potency as an anti-diabetic treatment. In this work, we utilized the machine learning method to predict the activity of DPP IV as an anti-diabetic agent. We combined Particle Swarm Optimization (PSO) method for features selection and the Support Vector Machine (SVM) for the prediction model. Three SVM kernels, i.e., radial basis function (RBF), polynomial, and linear, were utilized, and their performance was compared. A Hyperparameter tuning procedure was conducted to improve the performance of models. According to the results, we found that the best model obtained from SVM with RBF kernel with the value R2 of train and test set are 0.79 and 0.85, respectively.  Diabetes mellitus is a chronic illness that can affect anyone, while the medicine that can entirely cure diabetes has not been discovered yet. Dipeptidyl Peptidase IV (DPP IV) inhibitor is one of the agents with potency as an anti-diabetic treatment. In this work, we utilized the machine learning method to predict the activity of DPP IV as an anti-diabetic agent. We combined Particle Swarm Optimization (PSO) method for features selection and the Support Vector Machine (SVM) for the prediction model. Three SVM kernels, i.e., radial basis function (RBF), polynomial, and linear, were utilized, and their performance was compared. A Hyperparameter tuning procedure was conducted to improve the performance of models. According to the results, we found that the best model obtained from SVM with RBF kernel with the value R2 of train and test set are 0.79 and 0.85, respectively

QSAR Study on Diacylgycerol Acyltransferase-1 (DGAT-1) Inhibitor as Anti-diabetic using PSO-SVM Methods

Diabetes mellitus is a chronic disease that can occurred to anyone. Up until now, there are no specific drugs that have been found which can completely cure diabetes. One of the possible steps to treat diabetes mellitus is by inhibiting the growth of Diacylglycerol Acyltransferase-1 (DGAT-1) enzyme. This study aims to build a QSAR model on DGAT-1 inhibitors as anti-diabetic using Particle Swarm Optimization (PSO) and Support Vector Machine (SVM). Acyl-CoA: DGAT1 is a mikrosomal enzyme in lipogenesis which is increased in metabolically active cells to meet nutrient requirements. Microsomal-enzymes-that have an-important in the triglyceride-synthesis process-of 1,2-diacylglycerol by-catalyzing-acyl-coa-dependent-acylations as anti-diabetics. The dataset used in this study consists of 228 samples containing molecular structures and their inhibitor activities. We reduce the number of features by removing features with a standard deviation less than the threshold value, followed by the PSO algorithm. The best-predicted result is obtained through the implementation of SVM with RBF kernel, with the score of and  are 0.75 and 0.67, respectively.  Diabetes mellitus is a chronic disease that can occurred to anyone. Up until now, there are no specific drugs that have been found which can completely cure diabetes. One of the possible steps to treat diabetes mellitus is by inhibiting the growth of Diacylglycerol Acyltransferase-1 (DGAT-1) enzyme. This study aims to build a QSAR model on DGAT-1 inhibitors as anti-diabetic using Particle Swarm Optimization (PSO) and Support Vector Machine (SVM). Acyl-CoA: DGAT1 is a mikrosomal enzyme in lipogenesis which is increased in metabolically active cells to meet nutrient requirements. Microsomal-enzymes-that have an-important in the triglyceride-synthesis process-of 1,2-diacylglycerol by-catalyzing-acyl-coa-dependent-acylations as anti-diabetics. The dataset used in this study consists of 228 samples containing molecular structures and their inhibitor activities. We reduce the number of features by removing features with a standard deviation less than the threshold value, followed by the PSO algorithm. The best-predicted result is obtained through the implementation of SVM with RBF kernel, with the score of and  are 0.75 and 0.67, respectively. &nbsp