International journal of biomaterials research and engineering最新文献_第3页

Modeling of Phosphorous Acid Fuel Cell in PSCAD PSCAD中磷酸燃料电池的建模

International journal of biomaterials research and engineering Pub Date : 2018-11-01 DOI: 10.21276/IJRE.2018.5.9.6

Sundari Ramabhotla, S. Bayne

引用次数: 1

Big Data and Big Data Analytics: concepts, types and technologies 大数据和大数据分析:概念、类型和技术

International journal of biomaterials research and engineering Pub Date : 2018-11-01 DOI: 10.21276/IJRE.2018.5.9.5

Youssra Riahi, Sara Riahi

引用次数: 57

Optimal Energy Storage Sizing in Photovoltaic and Wind Hybrid Power System Meeting Demand-Side Management Program in Viet Nam 满足越南需求侧管理计划的光伏和风力混合发电系统的最佳储能规模

International journal of biomaterials research and engineering Pub Date : 2018-11-01 DOI: 10.21276/IJRE.2018.5.9.3

N. M. Cuong, T. Vinh, L. T. Phong, Vu Phuong Lan

引用次数: 1

Solving Vehicle Routing Problem using Ant Colony Optimisation (ACO) Algorithm 用蚁群优化算法求解车辆路径问题

International journal of biomaterials research and engineering Pub Date : 2018-11-01 DOI: 10.21276/IJRE.2018.5.9.2

W. Othman, A. Wahab, S. Alhady, H. N. Wong

{"title":"Solving Vehicle Routing Problem using Ant Colony Optimisation (ACO) Algorithm","authors":"W. Othman, A. Wahab, S. Alhady, H. N. Wong","doi":"10.21276/IJRE.2018.5.9.2","DOIUrl":"https://doi.org/10.21276/IJRE.2018.5.9.2","url":null,"abstract":"Engineering field usually requires having the best design for an optimum performance, thus optimization plays an important part in this field. The vehicle routing problem (VRP) has been an important problem in the field of distribution and logistics since at least the early 1960s. Hence, this study was about the application of ant colony optimization (ACO) algorithm to solve vehicle routing problem (VRP). Firstly, this study constructed the model of the problem to be solved through this research. The study was then focused on the Ant Colony Optimization (ACO). The objective function of the algorithm was studied and applied to VRP. The effectiveness of the algorithm was increased with the minimization of stopping criteria. The control parameters were studied to find the best value for each control parameter. After the control parameters were identified, the evaluation of the performance of ACO on VRP was made. The good performance of the algorithm reflected on the importance of its parameters, which were number of ants (nAnt), alpha (α), beta (β) and rho (ρ). Alpha represents the relative importance of trail, beta represents the importance of visibility and rho represents the parameter governing pheromone decay. The route results of different iterations were compared and analyzed the performance of the algorithm. The best set of control parameters obtained is with 20 ants, α = 1, β = 1 and ρ = 0.05. The average cost and standard deviation from the 20 runtimes with best set of control parameters were also evaluated, with 1057.839 km and 25.913 km respectively. Last but not least, a conclusion is made to summarize the achievement of the study.","PeriodicalId":89778,"journal":{"name":"International journal of biomaterials research and engineering","volume":"14 5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82879728","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7

Failure Analysis in Conceptual Phase toward a Robust Design: Case Study in Monopropellant Propulsion System 面向稳健设计的概念阶段失效分析:以单推进剂推进系统为例

International journal of biomaterials research and engineering Pub Date : 2018-11-01 DOI: 10.21276/IJRE.2018.5.9.7

E. Keshavarzi, K. Goebel, I. Tumer, C. Hoyle

引用次数: 0

Method to Harness Maximum Power from Photovoltaic Power Generation Basing on Completely Mathematical Model 基于完全数学模型的光伏发电最大功率控制方法

International journal of biomaterials research and engineering Pub Date : 2018-09-01 DOI: 10.21276/IJRE.2018.5.8.4

L. T. Phong, N. M. Cuong, T. Vinh, N. Minh

{"title":"Method to Harness Maximum Power from Photovoltaic Power Generation Basing on Completely Mathematical Model","authors":"L. T. Phong, N. M. Cuong, T. Vinh, N. Minh","doi":"10.21276/IJRE.2018.5.8.4","DOIUrl":"https://doi.org/10.21276/IJRE.2018.5.8.4","url":null,"abstract":"This paper introduces a new method that no previous study has been done in this photovoltaic power generation similar to this paper to harness maximum potential power from photovoltaic power generation. The completely mathematical model added the relation between diode factor of the generation and p-n junction temperature is proposed to use in this method. The maximum power point tracker combines the iterative and bisectional technique, the completely mathematical model of PVgPVG and the system of equations that converts value of parameters from standard test condition to any working condition, measuring sensors to measure power of solar irradiance and p-n junction temperature to determine parameters at maximum power point at any working condition. The voltage controller is designed to drive this generation to expect working state to harness maximum power. An experimental model corresponding to this method was designed and operated in real conditions in Viet Nam. Experimental results show the high accuracy of analyzing in theory and high capability to bring this method out real applications to harness all available energy of this generation.","PeriodicalId":89778,"journal":{"name":"International journal of biomaterials research and engineering","volume":"44 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82219062","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Exploring the Therapeutic Effects of Bisphenol-A on Prostate Cancer 探讨双酚a对前列腺癌的治疗作用

International journal of biomaterials research and engineering Pub Date : 2018-09-01 DOI: 10.21276/ijre.2018.5.8.2

Jerry G. Zhang

{"title":"Exploring the Therapeutic Effects of Bisphenol-A on Prostate Cancer","authors":"Jerry G. Zhang","doi":"10.21276/ijre.2018.5.8.2","DOIUrl":"https://doi.org/10.21276/ijre.2018.5.8.2","url":null,"abstract":"Prostate cancer (PCa) is the most common cancer in American men and its metastatic spread is responsible for its high mortality rates. Since PCa cells evolve against various treatment approaches, there is an urgent need for new treatment solutions. Bisphenol A (BPA) is an industrial chemical with structural similarities to androgens and estrogens and is postulated to interfere with androgen receptor (AR) and estrogen receptor function by competitive inhibition. Previous studies reported that low concentrations of BPA stimulate the proliferation of cancer cells expressing AR, whereas high concentrations of BPA unexpectedly inhibited the growth of those cells. This prompted the hypothesis that high concentrations of BPA might kill AR-positive, treatment-resistant PCa cells. In this study, the effect of high concentrations of BPA on AR-positive, Enzalutamide (a recent oral AR inhibitor)-resistant PCa cells were evaluated. At high concentrations, BPA was found to reduce cell growth by 90% and to trigger cell death. To explore the underlying molecular mechanisms, the expression and activity of AR were examined; both were decreased upon treatment of high BPA concentrations. In summary, high concentrations of BPA can inhibit cell growth and stimulate cell death of Enzalutamide-resistant PCa by targeting AR signaling. These findings may pave the road for novel approaches for treatment-resistant PCa.","PeriodicalId":89778,"journal":{"name":"International journal of biomaterials research and engineering","volume":"69 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78964792","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Method and Apparatus for a Differential Localized Microscopy System Based on Position Sensitive Detector 一种基于位置敏感检测器的微分定位显微系统的方法和装置

International journal of biomaterials research and engineering Pub Date : 2018-09-01 DOI: 10.21276/IJRE.2018.5.8.1

Venky Dijkkomp, Kanai Ogale

引用次数: 0

Myanmar WarningBoard Recognition System 缅甸警示牌识别系统

International journal of biomaterials research and engineering Pub Date : 2018-09-01 DOI: 10.21276/IJRE.2018.5.8.3

Kyi Pyar Zaw, Zin Mar Kyu

引用次数: 0

Comparative Study of MFCC Feature with Different Machine Learning Techniques in Acoustic Scene Classification MFCC特征与不同机器学习技术在声学场景分类中的比较研究

International journal of biomaterials research and engineering Pub Date : 2018-08-07 DOI: 10.21276/IJRE.2018.5.7.1

Mie Mie Oo

{"title":"Comparative Study of MFCC Feature with Different Machine Learning Techniques in Acoustic Scene Classification","authors":"Mie Mie Oo","doi":"10.21276/IJRE.2018.5.7.1","DOIUrl":"https://doi.org/10.21276/IJRE.2018.5.7.1","url":null,"abstract":"The task of labeling the audio sample in outdoor condition or indoor condition is called Acoustic Scene Classification (ASC). The ASC use acoustic information to imply about the context of the recorded environment. Since ASC can only applied in indoor environment in real world, a new set of strategies and classification techniques are required to consider for outdoor environment. In this paper, we present the comparative study of different machine learning classifiers with Mel-Frequency Cepstral Coefficients (MFCC) feature. We used DCASE Challenge 2016 dataset to show the properties of machine learning classifiers. There are several classifiers to address the ASC task. In this paper, we compare the properties of different classifiers: K-nearest neighbors (KNN), Support Vector Machine (SVM), Decision Tree (ID3) and Linear Discriminant Analysis by using MFCC feature. The best of classification methodology and feature extraction are essential for ASC task. In this comparative study, we extract MFCC feature from acoustic scene audio and then extracted feature is applied in different classifiers to know the advantages of classifiers for MFCC feature. This paper also proposed the MFCC-moment feature for ASC task by considering the statistical moment information of MFCC feature.","PeriodicalId":89778,"journal":{"name":"International journal of biomaterials research and engineering","volume":"53 1","pages":"439-444"},"PeriodicalIF":0.0,"publicationDate":"2018-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83825457","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 8