Farah Yuki, Dewanto Prasetyawati, Ahmad Harjunowibowo, Bayu Fauzi, Utomo Dani, Harmanto, D. Harjunowibowo, Ahmad Fauzi, Dani Harmanto
Electricity demand which increases up to 2.7%, needs to be evaluated to prevent power wastage. This paper proposes an INA219 sensor and a power monitoring solution based on the ESP8266. Power Monitoring stores and displays real-time data in Google Sheets via Blynk version 1.0.1. The system has been calibrated with a fixed LED and resistor as a voltage calibration load. Meanwhile, the lamp and shunt resistors calibrate the shunt voltage. The measuring tools for comparison in calibration are digital multimeters, oscilloscopes, and power data loggers. Calibration using the linear regression technique with accuracy, precision, and uncertainty analysis are determined by Mean Absolute Percent Error (MAPE), Relative Standard Deviation (RSD), and Gaussian distribution. Successively, the sensor coefficient of determination (R2), accuracy, precision, and uncertainty of the load voltage and shunt voltage are 0.999 and 0.997, 99.27% and 93.71%, 99.82% and 99.55%, 0.37 V and 0.89 mV.
电力需求增长高达 2.7%，需要对电力需求进行评估，以防止电力浪费。本文提出了一种基于 ESP8266 的 INA219 传感器和电力监控解决方案。电力监控系统通过 Blynk 1.0.1 版在 Google Sheets 中存储和显示实时数据。系统使用固定的 LED 和电阻作为电压校准负载进行校准。同时，灯管和分流电阻校准分流电压。校准中用于比较的测量工具是数字万用表、示波器和功率数据记录器。校准采用线性回归技术，准确度、精确度和不确定性分析由平均绝对百分比误差 (MAPE)、相对标准偏差 (RSD) 和高斯分布确定。负载电压和分流电压的传感器判定系数（R2）、准确度、精确度和不确定性分别为 0.999 和 0.997、99.27% 和 93.71%、99.82% 和 99.55%、0.37 V 和 0.89 mV。
{"title":"Calibration and Validation of INA219 as Sensor Power Monitoring System using Linear Regression","authors":"Farah Yuki, Dewanto Prasetyawati, Ahmad Harjunowibowo, Bayu Fauzi, Utomo Dani, Harmanto, D. Harjunowibowo, Ahmad Fauzi, Dani Harmanto","doi":"10.53799/ajse.v22i3.595","DOIUrl":"https://doi.org/10.53799/ajse.v22i3.595","url":null,"abstract":"Electricity demand which increases up to 2.7%, needs to be evaluated to prevent power wastage. This paper proposes an INA219 sensor and a power monitoring solution based on the ESP8266. Power Monitoring stores and displays real-time data in Google Sheets via Blynk version 1.0.1. The system has been calibrated with a fixed LED and resistor as a voltage calibration load. Meanwhile, the lamp and shunt resistors calibrate the shunt voltage. The measuring tools for comparison in calibration are digital multimeters, oscilloscopes, and power data loggers. Calibration using the linear regression technique with accuracy, precision, and uncertainty analysis are determined by Mean Absolute Percent Error (MAPE), Relative Standard Deviation (RSD), and Gaussian distribution. Successively, the sensor coefficient of determination (R2), accuracy, precision, and uncertainty of the load voltage and shunt voltage are 0.999 and 0.997, 99.27% and 93.71%, 99.82% and 99.55%, 0.37 V and 0.89 mV.","PeriodicalId":224436,"journal":{"name":"AIUB Journal of Science and Engineering (AJSE)","volume":"105 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139154008","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}
In the influence of fluid buoyancy forces, the ferrofluid combined convective flow in porous curvilinear surfaces is studied with thermal generation/absorption effect. In the ambient flow conditions, the pressure gradient terms and ferrofluid buoyancy forces are replaced by the free steam velocity . The governing equations of the present problem are converted to ODEs by introducing non-dimensional functions and similarity variable. Boundary conditions of first derivative of velocities and temperature of our problem were constructed by the initial value problem, also the unknown initial conditions are found by shooting methods, and then a set of ODEs is solved numerically by the integration scheme of the six-order Range-Kutta method. The results of the solutions are presented graphically of velocity and thermal profiles with the help of MATLAB for different values of suction parameter and heat absorption parameter . Finally, the comparisons of the results highlight the justification of the numerical calculation accepted in the presence study. The problems in curvilinear surface study of boundary layer flow are complicated in fluid mechanics with applications of natural science and engineering.
{"title":"The Thermal Absorption/Generation on Ferro-fluid Combined Convective Flow Over Curvilinear Porous Surfaces","authors":"Maleque Kh. Abdul","doi":"10.53799/ajse.v22i3.546","DOIUrl":"https://doi.org/10.53799/ajse.v22i3.546","url":null,"abstract":"In the influence of fluid buoyancy forces, the ferrofluid combined convective flow in porous curvilinear surfaces is studied with thermal generation/absorption effect. In the ambient flow conditions, the pressure gradient terms and ferrofluid buoyancy forces are replaced by the free steam velocity . The governing equations of the present problem are converted to ODEs by introducing non-dimensional functions and similarity variable. Boundary conditions of first derivative of velocities and temperature of our problem were constructed by the initial value problem, also the unknown initial conditions are found by shooting methods, and then a set of ODEs is solved numerically by the integration scheme of the six-order Range-Kutta method. The results of the solutions are presented graphically of velocity and thermal profiles with the help of MATLAB for different values of suction parameter and heat absorption parameter . Finally, the comparisons of the results highlight the justification of the numerical calculation accepted in the presence study. The problems in curvilinear surface study of boundary layer flow are complicated in fluid mechanics with applications of natural science and engineering.","PeriodicalId":224436,"journal":{"name":"AIUB Journal of Science and Engineering (AJSE)","volume":"77 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139154403","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}
A new mathematical framework is proposed in this study to comprehend the impact of program architecture on input random variables, the IF statement was the main topic. The primary idea that is theoretically and experimentally supported in this study is that the part of the joint pmf of a collection of random variables that represents the condition will be shifted to the part that represents the action. After sorting two random variables, the framework is used with four random variables, and the theoretically produced results were realistically validated. The study's equations can be applied to assessing probabilistic models of various sorting algorithms or other intricate program structures. This may also result in future investigations formalizing more precise execution time expectations.
本研究提出了一个新的数学框架，以理解程序结构对输入随机变量的影响，其中 IF 语句是主要课题。本研究中得到理论和实验支持的主要观点是，随机变量集合的联合 pmf 中代表条件的部分将转移到代表动作的部分。在对两个随机变量进行分类后，该框架被用于四个随机变量，理论上得出的结果得到了现实验证。本研究的方程可用于评估各种排序算法或其他复杂程序结构的概率模型。这也可能导致未来的研究正式确定更精确的执行时间预期。
{"title":"Probabilistic Modeling for Conditional Statements","authors":"Alaa Ghazi, Yasir Hashim","doi":"10.53799/ajse.v22i3.841","DOIUrl":"https://doi.org/10.53799/ajse.v22i3.841","url":null,"abstract":"A new mathematical framework is proposed in this study to comprehend the impact of program architecture on input random variables, the IF statement was the main topic. The primary idea that is theoretically and experimentally supported in this study is that the part of the joint pmf of a collection of random variables that represents the condition will be shifted to the part that represents the action. After sorting two random variables, the framework is used with four random variables, and the theoretically produced results were realistically validated. The study's equations can be applied to assessing probabilistic models of various sorting algorithms or other intricate program structures. This may also result in future investigations formalizing more precise execution time expectations.","PeriodicalId":224436,"journal":{"name":"AIUB Journal of Science and Engineering (AJSE)","volume":"52 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139164660","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}
Nurakmal Ahmad Mustaffa, M. Zulkifli, Manzur Hossain, Khan, Malaysia Utara Malaysia, Manzur Hossain Khan
The 4th Industrial Revolution, more commonly referred to as Industry 4.0, has brought about a wave of multifaceted changes across the industrial spectrum around the world, and it has triggered the digitalisation of supply chains and their management regardless of the type of organisation. With increasing interconnectivity through various sectors, digital supply chain (DSC) practices and intentions have also become integral to higher education institutions. As streamlined, automated administrative processes and virtual classes conducted through online platforms become the norm, digitalisation has been catalysed in the education sector. However, several sociocultural, economic, and psychographic factors influence the adaptation of new technologies, especially in developing countries such as Bangladesh. This study uses the composite index approach to determine the Index derived from the correlation between the factors and their impact on the DSC practices and intentions. The study indicates that Trust (T) is the primary influencer, along with Performance Expectancy (PE), closely followed by Facilitating Value (FV), Facilitating Conditions (FC), and Digital Literacy (DL).
{"title":"DSC Index: Measuring the Digital Supply Chain Practice among the Higher Education Institutions Community in Least Developed Countries","authors":"Nurakmal Ahmad Mustaffa, M. Zulkifli, Manzur Hossain, Khan, Malaysia Utara Malaysia, Manzur Hossain Khan","doi":"10.53799/ajse.v22i3.886","DOIUrl":"https://doi.org/10.53799/ajse.v22i3.886","url":null,"abstract":"The 4th Industrial Revolution, more commonly referred to as Industry 4.0, has brought about a wave of multifaceted changes across the industrial spectrum around the world, and it has triggered the digitalisation of supply chains and their management regardless of the type of organisation. With increasing interconnectivity through various sectors, digital supply chain (DSC) practices and intentions have also become integral to higher education institutions. As streamlined, automated administrative processes and virtual classes conducted through online platforms become the norm, digitalisation has been catalysed in the education sector. However, several sociocultural, economic, and psychographic factors influence the adaptation of new technologies, especially in developing countries such as Bangladesh. This study uses the composite index approach to determine the Index derived from the correlation between the factors and their impact on the DSC practices and intentions. The study indicates that Trust (T) is the primary influencer, along with Performance Expectancy (PE), closely followed by Facilitating Value (FV), Facilitating Conditions (FC), and Digital Literacy (DL).","PeriodicalId":224436,"journal":{"name":"AIUB Journal of Science and Engineering (AJSE)","volume":"23 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139164862","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}
Abhijit Bhowmik, Noorhozaimi Mohd. Nur, M. Saef, U. Miah, Debajyoti Karmekar
Evaluating teachers' performance is a fundamental pillar of educational enhancement, guiding the evolution of pedagogical practices and fostering enriched learning environments. This study pioneers an innovative approach by harnessing sentiment analysis within an aspect-based framework to decipher the intricate emotional nuances embedded within students' feedback. By categorizing sentiments as positive, negative, and neutral, we delve into the diverse perceptions of teaching aspects, offering a multifaceted portrait of educators' contributions. Through meticulous data collection, preprocessing, and a deep learning sentiment analysis model, we dissected student comments into distinct teaching aspects. The subsequent sentiment analysis unearthed positive, negative, and neutral sentiments. Positive sentiments highlighted strengths and effective communication, while negative sentiments illuminated areas for growth. Neutral sentiments provided contextual equilibrium, forming a holistic tapestry of teachers' performance. The proposed model achieved 86% F1 score for classifying sentiments into three classes.
对教师的绩效进行评估是提高教育质量的一个基本支柱，它可以指导教学实践的发展，营造丰富的学习环境。本研究开创了一种创新方法，在基于方面的框架内利用情感分析来解读学生反馈中蕴含的复杂的情感细微差别。通过将情感分为积极、消极和中性三种类型，我们深入探讨了对教学方面的不同看法，为教育工作者的贡献提供了一幅多面的画卷。通过细致的数据收集、预处理和深度学习情感分析模型，我们将学生的评论剖析为不同的教学方面。随后的情感分析揭示了正面、负面和中性情感。积极情感突出了学生的优势和有效沟通，而消极情感则揭示了需要改进的地方。中性情感提供了背景平衡，形成了教师表现的整体挂毯。所提出的模型在将情绪分为三类方面取得了 86% 的 F1 分数。
{"title":"Aspect-based Sentiment Analysis Model for Evaluating Teachers' Performance from Students' Feedback","authors":"Abhijit Bhowmik, Noorhozaimi Mohd. Nur, M. Saef, U. Miah, Debajyoti Karmekar","doi":"10.53799/ajse.v22i3.921","DOIUrl":"https://doi.org/10.53799/ajse.v22i3.921","url":null,"abstract":"Evaluating teachers' performance is a fundamental pillar of educational enhancement, guiding the evolution of pedagogical practices and fostering enriched learning environments. This study pioneers an innovative approach by harnessing sentiment analysis within an aspect-based framework to decipher the intricate emotional nuances embedded within students' feedback. By categorizing sentiments as positive, negative, and neutral, we delve into the diverse perceptions of teaching aspects, offering a multifaceted portrait of educators' contributions. Through meticulous data collection, preprocessing, and a deep learning sentiment analysis model, we dissected student comments into distinct teaching aspects. The subsequent sentiment analysis unearthed positive, negative, and neutral sentiments. Positive sentiments highlighted strengths and effective communication, while negative sentiments illuminated areas for growth. Neutral sentiments provided contextual equilibrium, forming a holistic tapestry of teachers' performance. The proposed model achieved 86% F1 score for classifying sentiments into three classes.","PeriodicalId":224436,"journal":{"name":"AIUB Journal of Science and Engineering (AJSE)","volume":"49 18","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139165903","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}
This study aims to investigate the prevalence and determining factors of Type 2 Diabetes (T2D) among youths in Bangladesh using a statistical approach. The research objectives were to determine the prevalence of T2D in this population and identify the factors associated with its occurrence. A survey questionnaire was formed encompassing certain relevant variables. A sample of youths was selected through cluster sampling strategy. By collecting relevant data and employing appropriate statistical analyses, the study provided insights into the prevalence and associated factors of T2D among the youths, which can contribute to the development of effective prevention and management strategies. Statistical analyses were performed using chi-square tests and logistic regression, to explore the relationships between T2D prevalence and the determining factors identified in the study. Lifestyle factors played a significant role in the development of T2D among youths. Besides, certain socio-demographic factors like occupation, education, income, age, marital status, and residential origin were found to be associated with an increased risk of T2D among youths in Bangladesh. These findings highlight the multifactorial nature of T2D among youths in Bangladesh. Addressing these factors through targeted interventions and public health policies can play a crucial role in preventing and managing T2D in this population. The study emphasized the importance of health awareness and education programs targeting youths in Bangladesh. The findings from this study can contribute to the development of evidence-based strategies to prevent and manage T2D in this population, ultimately reducing the burden of T2D in Bangladesh
{"title":"Unraveling the Burden of T2D among the Adolescents in Bangladesh: A Statistical Exploration of Prevalence and Influencing Factors","authors":"M. Ahmmed, M. M. Rahman, Mahfuz Khatun","doi":"10.53799/ajse.v22i3.786","DOIUrl":"https://doi.org/10.53799/ajse.v22i3.786","url":null,"abstract":"This study aims to investigate the prevalence and determining factors of Type 2 Diabetes (T2D) among youths in Bangladesh using a statistical approach. The research objectives were to determine the prevalence of T2D in this population and identify the factors associated with its occurrence. A survey questionnaire was formed encompassing certain relevant variables. A sample of youths was selected through cluster sampling strategy. By collecting relevant data and employing appropriate statistical analyses, the study provided insights into the prevalence and associated factors of T2D among the youths, which can contribute to the development of effective prevention and management strategies. Statistical analyses were performed using chi-square tests and logistic regression, to explore the relationships between T2D prevalence and the determining factors identified in the study. Lifestyle factors played a significant role in the development of T2D among youths. Besides, certain socio-demographic factors like occupation, education, income, age, marital status, and residential origin were found to be associated with an increased risk of T2D among youths in Bangladesh. These findings highlight the multifactorial nature of T2D among youths in Bangladesh. Addressing these factors through targeted interventions and public health policies can play a crucial role in preventing and managing T2D in this population. The study emphasized the importance of health awareness and education programs targeting youths in Bangladesh. The findings from this study can contribute to the development of evidence-based strategies to prevent and manage T2D in this population, ultimately reducing the burden of T2D in Bangladesh","PeriodicalId":224436,"journal":{"name":"AIUB Journal of Science and Engineering (AJSE)","volume":"35 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139164592","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}
Maria Sultana Rupa, M.Tanseer Ali, Mohammad Abdul Mannan, Mehedi Hasan
The work that has been presented here aims to simulate a multijunction transparent solar cell and analyze its performance in terms of simulated short-circuit current density, open circuit voltage, efficiency, and fill factor. The model structure is created by COMSOL Multiphysics and consists of five layers of InAs/InSb/AlGaAs/GaN/Si, taking into account the source materials' properties. Its electromagnetic wave is used to report on the optical and electrical properties. It is assumed that the cell is working at room temperature (300K). A maximum conversion of 15.2655% would be achieved for this model's simulation exposures at fill factor (FF)=0.6531 from the I-V curve and for such a combination and transparency.
{"title":"Modeling and performance analysis of a transparent multilayer solar cell","authors":"Maria Sultana Rupa, M.Tanseer Ali, Mohammad Abdul Mannan, Mehedi Hasan","doi":"10.53799/ajse.v22i3.580","DOIUrl":"https://doi.org/10.53799/ajse.v22i3.580","url":null,"abstract":"The work that has been presented here aims to simulate a multijunction transparent solar cell and analyze its performance in terms of simulated short-circuit current density, open circuit voltage, efficiency, and fill factor. The model structure is created by COMSOL Multiphysics and consists of five layers of InAs/InSb/AlGaAs/GaN/Si, taking into account the source materials' properties. Its electromagnetic wave is used to report on the optical and electrical properties. It is assumed that the cell is working at room temperature (300K). A maximum conversion of 15.2655% would be achieved for this model's simulation exposures at fill factor (FF)=0.6531 from the I-V curve and for such a combination and transparency.","PeriodicalId":224436,"journal":{"name":"AIUB Journal of Science and Engineering (AJSE)","volume":"236 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139169130","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}
Md. Abu Jafor, Md. Anwar Hussen Wadud, Kamruddin Nur, Mohammad Motiur Rahman
Employee promotion is an important aspect of the human resource management process. Due to different factors, it refers to the automatic improvement among the employees in an organization. Promoting employees from the lower level to the higher level brings a feeling of satisfaction among the employees. It improves their job satisfaction and motivation by providing more significant income, status, and responsibilities. By building up loyalty, promotion reduces employee attrition. Thus, it is difficult to accurately decide, whether an employee should or should not be promoted based on their current and past performance. So, human resource management does research about promotion, because there are a limited number of research about the finding of employee promotion prediction in the existing studies. First, to find the reasons for employee promotion, we need to analyze the research study for finding the factors which are related to the promotion. The aim of this research study is to implement an employee promotion prediction framework using machine learning. A modified AdaBoost classifier is used for automatic promotion prediction, and six machine learning techniques for instance, Support Vector Machine (SVM), Logistic Regression (LR), Artificial Neural Network (ANN), Random Forest (RF), XGBoost (XGB), and AdaBoost are applied in performance comparison. Through a complex assessment process, the performance of these supervised machine learning algorithms for predicting employee advancement is analyzed using assessment metrics on the employees' evaluation dataset for promotion prediction. The Artificial Neural Network (ANN) and AdaBoost model provide better results on this dataset than all traditional machine learning techniques. Finally, Our proposed modified AdaBoost approach outperformed all other methods evaluated with an accuracy of 95.30%.
{"title":"Employee Promotion Prediction Using Improved AdaBoost Machine Learning Approach","authors":"Md. Abu Jafor, Md. Anwar Hussen Wadud, Kamruddin Nur, Mohammad Motiur Rahman","doi":"10.53799/ajse.v22i3.781","DOIUrl":"https://doi.org/10.53799/ajse.v22i3.781","url":null,"abstract":"Employee promotion is an important aspect of the human resource management process. Due to different factors, it refers to the automatic improvement among the employees in an organization. Promoting employees from the lower level to the higher level brings a feeling of satisfaction among the employees. It improves their job satisfaction and motivation by providing more significant income, status, and responsibilities. By building up loyalty, promotion reduces employee attrition. Thus, it is difficult to accurately decide, whether an employee should or should not be promoted based on their current and past performance. So, human resource management does research about promotion, because there are a limited number of research about the finding of employee promotion prediction in the existing studies. First, to find the reasons for employee promotion, we need to analyze the research study for finding the factors which are related to the promotion. The aim of this research study is to implement an employee promotion prediction framework using machine learning. A modified AdaBoost classifier is used for automatic promotion prediction, and six machine learning techniques for instance, Support Vector Machine (SVM), Logistic Regression (LR), Artificial Neural Network (ANN), Random Forest (RF), XGBoost (XGB), and AdaBoost are applied in performance comparison. Through a complex assessment process, the performance of these supervised machine learning algorithms for predicting employee advancement is analyzed using assessment metrics on the employees' evaluation dataset for promotion prediction. The Artificial Neural Network (ANN) and AdaBoost model provide better results on this dataset than all traditional machine learning techniques. Finally, Our proposed modified AdaBoost approach outperformed all other methods evaluated with an accuracy of 95.30%.","PeriodicalId":224436,"journal":{"name":"AIUB Journal of Science and Engineering (AJSE)","volume":"174 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139169809","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}
The nozzle design is one of the most important issues because it determines the pressure range and the other dimensions to guarantee an adequate performance of a jet_pump. An incorrect design of this part can cause shock waves and unnecessary overexpansion of the power fluid. The nozzle’s main purpose is to allow the high-pressure, low-velocity primary fluid to be accelerated in such a way as to substantially decrease the fluid pressure while increasing its velocity. This is achieved because the subsonic flow accelerates when entering the convergent part of the nozzle, obtaining a sonic or supersonic flow at the nozzle throat that accelerates even more when entering the divergent part of the nozzle. Therefore, to achieve the highest possible nozzle discharge velocity, the nozzle must be able to change the flow conditions from subsonic to supersonic. Considering the high importance of the nozzle design in the jet_pump performance, five cases are simulated in the present work, where the ratio of nozzle inlet to nozzle throat areas is modified (10,15,20,25 y 30), to study the behavior of three performance parameters, namely, drag coefficient (Cd), pressure ratio (PR) and Energy Efficiency (η), as well as the Mach number (Ma) and velocity fields.
喷嘴的设计是最重要的问题之一，因为它决定了压力范围和其他尺寸，以保证喷射泵具有足够的性能。该部件的错误设计会导致冲击波和动力流体不必要的过度膨胀。喷嘴的主要作用是使高压、低速的主流体加速，从而在提高流速的同时大幅降低流体压力。这是因为亚音速流体在进入喷嘴的汇聚部分时会加速，在喷嘴喉部会产生超声速或超音速流体，在进入喷嘴的发散部分时会进一步加速。因此，要获得尽可能高的喷嘴排出速度，喷嘴必须能够将流动条件从亚音速变为超音速。考虑到喷嘴设计对喷射泵性能的高度重要性，本研究模拟了五种情况，分别改变喷嘴入口与喷嘴喉部的面积比（10、15、20、25 y 30），以研究三个性能参数（即阻力系数 (Cd)、压力比 (PR) 和能效 (η)）以及马赫数 (Ma) 和速度场的行为。
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Sayeed Hasan, Md. Rifat Hazari, Effat Jahan, Mohammad Abdul Mannan
The article focuses on sizing and designing microgrids with pvlib-python and the Python programming language. Pvlib-python is a free and open-source program for simulating solar photovoltaic (PV) systems. For the design, an existing case study of an agricultural microgrid comprised of PV arrays, batteries, and a biogas-based generator in an off-grid configuration was explored. The solar resources and PV system were modeled using pvlib-python, while the rest of the microgrid was built and simulated using a custom dispatch algorithm written in Python. The study also discussed an in-depth strategy for modeling PV utilizing various data sources using the included modules and functions. A similarly specified microgrid was also modeled in Homer Pro software and the results from the designed microgrid in Python were compared. The hourly distribution of data for both tools exhibits a noticeable deviation. The daily and annual distribution of most of the parameters, on the other hand, produce comparable results.
这篇文章的重点是利用 pvlib-python 和 Python 编程语言确定微电网的规模并进行设计。Pvlib-python 是一款免费的开源程序，用于模拟太阳能光伏（PV）系统。在设计过程中，我们对现有的一个农业微电网案例进行了研究，该微电网由光伏阵列、蓄电池和沼气发电机组成，采用离网配置。太阳能资源和光伏系统使用 pvlib-python 进行建模，微电网的其他部分则使用 Python 编写的自定义调度算法进行构建和模拟。该研究还深入讨论了利用所含模块和函数的各种数据源为光伏建模的策略。还在 Homer Pro 软件中对一个类似的微电网进行了建模，并对 Python 中设计的微电网的结果进行了比较。两种工具的每小时数据分布存在明显偏差。另一方面，大多数参数的日分布和年分布结果相当。
{"title":"Modeling an Agricultural Microgrid using pvlib-python: A Case Study in Bangladesh","authors":"Sayeed Hasan, Md. Rifat Hazari, Effat Jahan, Mohammad Abdul Mannan","doi":"10.53799/ajse.v22i3.733","DOIUrl":"https://doi.org/10.53799/ajse.v22i3.733","url":null,"abstract":"The article focuses on sizing and designing microgrids with pvlib-python and the Python programming language. Pvlib-python is a free and open-source program for simulating solar photovoltaic (PV) systems. For the design, an existing case study of an agricultural microgrid comprised of PV arrays, batteries, and a biogas-based generator in an off-grid configuration was explored. The solar resources and PV system were modeled using pvlib-python, while the rest of the microgrid was built and simulated using a custom dispatch algorithm written in Python. The study also discussed an in-depth strategy for modeling PV utilizing various data sources using the included modules and functions. A similarly specified microgrid was also modeled in Homer Pro software and the results from the designed microgrid in Python were compared. The hourly distribution of data for both tools exhibits a noticeable deviation. The daily and annual distribution of most of the parameters, on the other hand, produce comparable results.","PeriodicalId":224436,"journal":{"name":"AIUB Journal of Science and Engineering (AJSE)","volume":"593 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139170217","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}
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