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Predicting Factors Influencing the Actual Use of E-Learning Platform among Medical Students in the Philippines: Unified Theory of Acceptance and Use of Technology Approach 24

Predicting Factors Influencing the Actual Use of E-Learning ...

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4th International Conference on Internet and E-Business, ICIEB 2024

作者： Benito, Omar Paolo Prasetyo, Yogi Tri Roque, Ralph Andre C. Flores, Fernan Patrick Cahigas, Maela Madel L. Nadlifatin, Reny Ayuwati, Irene Dyah International Bachelor Program in Engineering Yuan Ze University Taiwan Department of Industrial Engineering and Management Yuan Ze University Taiwan School of Industrial Engineering and Engineering Management Mapua University Philippines School of Graduate Studies Mapua University Philippines Department of Information Systems Sepuluh Nopember Institute of Technology Indonesia University of Surabaya Indonesia

ISBN: (纸本)9798400709739

eLearning has been utilized as a mode of instruction among medical students amidst the COVID-19 pandemic. The present study aimed to investigate the factors influencing the acceptance of eLearning platforms in medical education during the COVID-19 pandemic in the Philippines by employing the latent variables of UTAUT2, along with two additional latent variables gathered from various related literature. A sum of 360 medical students participated and answered an online survey that consists of 40 questions. A stepwise multiple linear regression was utilized and found that performance expectancy (PE), habit (HB), and instructor characteristics (IC), can successfully predict the actual use (AU) of medical students with a precision of 54.22%. A higher performance expectancy (PE), habit (HB), and instructor characteristics (IC) can enhance and promote the acceptance of medical eLearning platforms. This is the first study that has examined the acceptance of eLearning platforms among medical students in the Philippines during the COVID-19 pandemic. The outcomes of this research can serve as guidelines for the Commission on Higher Education of the Philippines for the enhancement of eLearning platforms. Although this study only utilized the latent variables of UTAUT2 along with two additional variables: Learning Value and Instructor Characteristics;the findings of this study would still hold significant value for promoting open innovation in eLearning platforms in medical fields worldwide. © 2024 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： COVID-19

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Multi-Objective Grey Wolf Optimizer for the Identification Process of a real MIMO System

Multi-Objective Grey Wolf Optimizer for the Identification P...

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IEEE/IAS International Conference on Industry Applications (INDUSCON)

作者： Allan Christian Krainski Ferrari Gideon Villar Leandro Leandro dos Santos Coelho Eletrical Engineering Department Federal University of Paraná UFPR Curitiba Brazil Electrical Engineering Graduate Program (PPGEE) Federal University of Paraná (UFPR) and Industrial and Systems Engineering Graduate Program (PPGEPS) Pontifical Catholic University of Paraná (PUCPR)

This article implements the multi-objective grey wolf optimizer (MOGWO) in the identification process of a multivariable system. For a sequence of 100 runs, MOGWO approach had compared and analyzed with the grey wolf optimizer (GWO), the single-objective version. In the computational simulation, the determination coefficient values and mean square error had used to analyze the quality of the models obtained. In the results, MOGWO showed superior to GWO to the differences between the order of magnitude of the system outputs. The implementation shows that MOGWO and other multi-objective metaheuristics can be an alternative method to solve problems of identification processes of multivariable systems.

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Urban Digital Twins for Synthetic Data of Individuals with Mobility Aids in Curitiba, Brazil, to Drive Highly Accurate AI Models for Inclusivity

Urban Digital Twins for Synthetic Data of Individuals with M...

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8th International Conference on Science, Technology and Innovation for Society, CITIS 2023

作者： Luna-Romero, Santiago Felipe Stempniak, Charles Roberto de Souza, Mauren Abreu Reynoso-Meza, Gilberto Health Technology Graduate Program Pontifícia Universidade Católica do Paraná Curitiba80215-901 Brazil Automa Geo "Digital Twin Platform" Curitiba Brazil Industrial and Systems Engineering Graduate Program Pontifícia Universidade Católica do Paraná Curitiba80215-901 Brazil

ISBN: (纸本)9783031520891

This article delves into the significance of urban digital twins in generating synthetic data specifically tailored for individuals with disabilities. It explores how this data can be effectively utilized to construct artificial intelligence (AI) models that promote inclusivity and enhance accessibility in urban environments. The article thoroughly examines the challenges and opportunities associated with acquiring data on individuals with disabilities, taking into account the ethical and practical implications of generating and utilizing synthetic data for the development of accessible and inclusive technologies. Using the city of Curitiba, Brazil as a case study, which has undergone scanning, digitization, and transformation into an urban digital twin, avatars have been strategically placed to enable the detection of individuals with disabilities within diverse scenarios and perspectives in urban environments. Consequently, this approach provides a diverse and realistic representation of the situations in which individuals with disabilities may find themselves in urban settings, contributing to advancements in urban planning and technology by emphasizing the importance of digital twins in generating synthetic data to promote inclusivity and accessibility for individuals with disabilities. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Computer vision

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3D Cloud of Points AI-Based Analysis to Wheels Detection in the Free-Flow Tolling Context

3D Cloud of Points AI-Based Analysis to Wheels Detection in ...

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IEEE Conference on industrial Electronics and Applications (ICIEA)

作者： Erick Lemmy Dos Santos Oliveira Luiz Gustavo Schitz Da Rocha Marcelo Rudek Dept. of Computer Engineering Pontifical Catholic University of Paraná - PUCPR Curitiba Brazil Pumatronix Curitiba Brazil Industrial and Systems Engineering Graduate Programme - PPGEPS Pontifical Catholic University of Paraná - PUCPR Curitiba Brazil

ISBN: (数字)9798350360868

ISBN: (纸本)9798350360875

This work addresses the challenge of vehicle parameters' identification for the automation of efficient traffic management from vehicle classification, speed measurement, and axles counting by proposing a method based on computer vision and artificial intelligence. Once a vehicle is classified the research problem investigation is to differentiate the number of wheels that can be identified for each vehicle class as trucks, buses, cars pulling trailers due to the different price policies according to the roads' characteristics. We explore wheels and tires' identification by the 3D cloud of points analysis by artificial intelligence and computational vision technologies. The proposal aims to overcome the limitations of current 2D image systems. Also, we discuss the integration approach based on the integration of a variety of technologies for classifying and tracking and by exploring the main libraries for image processing and 3D data analysis. Additionally, the text describes a virtual testing environment as a digital twin for different scenarios and climate conditions involved in the process. As a result, the research brings the comparative study between the real and simulated virtual data.

关键词： Cloud computing Three-dimensional displays Laser radar Wheels Cameras Tires Digital twins

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Multi-objective Machine Learning for control performance assessment in PID control loops ⁎

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IFAC-PapersOnLine 2024年第7期58卷 168-173页

作者： Gilberto Reynoso-Meza Jesús Carrillo-Ahumada Tainara Marques Industrial and Systems Engineering Graduate Program (PPGEPS) Pontifícia Universidade Católica do Paraná (PUCPR) Rua Imaculada Conceição 1155 Curitiba Brazil Control Systems Optimization Laboratory (LOSC) Pontifícia Universidade Católica do Paraná (PUCPR) Ingeniería en Alimentos. Universidad del Papaloapan. Circuito Central 200 Col. Parque Industrial 68301 Tuxtepec Oaxaca Mex

Control Performance Assessment (CPA) is a critical endeavor in industrial processes, ensuring optimal functioning of control systems. Traditionally, CPA has been addressed through solutions using some control performance indicators. Nowadays, the integration of data science and machine learning has emerged as a viable alternative, particularly in classification tasks related to CPA. That is, in a binary classification scheme, the goal is to predict whether incoming data from the control loop belongs to class 0 or 1, representing the absence or presence of an anomaly (performance degradation). In such a case, a trade-of between false positives and false negatives should be obtained, via the training phase of a given supervised machine learning structure for example. Usually, this is a conflicting trade-of, where multi-objective optimization techniques in the training phase of such learners could bring interesting results. In this paper, we explore the usability of multi-objective optimization training in machine learning, for control performance assessment classification. A database describing 30 control performance indicators (features) in a PID control loop is used. The obtained results indicate that the proposed approach could bring interesting applications to improve the performance of CPA classification systems.

关键词： Control performance assessment machine learning multi-objective optimization PID control multi-criteria analysis

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Agent-Based Modeling for Forest Fire Simulation in Chiang Mai, Thailand 23

Agent-Based Modeling for Forest Fire Simulation in Chiang Ma...

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23rd International Symposium on Communications and Information Technologies, ISCIT 2024

作者： Tappanom, Teerapat Leelawat, Natt Saengtabtim, Kumpol Tang, Jing Risk and Disaster Management Program Graduate School Chulalongkorn University Bangkok Thailand Chulalongkorn University Faculty of Engineering Department of Industrial Engineering Bangkok Thailand Graduate School of System Design and Management Keio University Yokohama Japan Disaster and Risk Management Information Systems Research Unit Chulalongkorn University Bangkok Thailand

ISBN: (纸本)9798350353952

Forest fires have a significant impact on ecosystems and human life, especially in northern Thailand, where Chiang Mai is severely affected. Traditional methods for detecting forest fires have limitations, so there is a need for advanced simulation models. This research uses Agent-Based Modeling (ABM) to develop a forest fire simulation for Chiang Mai. The methodology involves collecting historical data, performing Multiple Regression Analysis, designing the simulation, and testing it. Data from 1998 to 2021, including temperature, relative humidity, wind speed/direction, burn area, and slope, were collected from various sources. Multiple regression analysis identified wind speed as the most significant factor affecting burn area. The forest fire simulation, designed using ZF Wang's spread model and tested with AnyLogic, showed that wind direction and speed are crucial in fire spread. The simulation accurately predicted high-risk areas, helping in proactive planning and response. This study confirms that wind is a critical factor in forest fire spread, providing a valuable tool for fire districts to improve preparedness and management. Future research should focus on refining the model with localized data and integrating real-time detection to improve its accuracy and applicability. © 2024 IEEE.

关键词： Forest ecology

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Manta Ray Foraging Optimization Approaches on Multivariable PID Controller Tuning

Manta Ray Foraging Optimization Approaches on Multivariable ...

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Congress on Evolutionary Computation

作者： Yan Lieven Souza Luúio Luiza Scapinello Aquino Da Silva Viviana Cocco Mariani Leandro Dos Santos Coelho Electrical Engineering Graduate Program (PPGEE) Federal University of Parana (UFPR) Curitiba Brazil Department of Electrical Engineering Mechanical Engineering Graduate Program (PPGEM) Pontifical Catholic University of Parana (PUCPR) and Federal University of Parana (UFPR) Curitiba Parana Brazil Department of Electrical Engineering Industrial and Systems Engineering Graduate Program (PPGEPS) Pontifical Catholic University of Parana (PUCPR) and Federal University of Parana (UFPR) Curitiba Brazil

This work presents a performance comparison between the newly developed Manta Ray Foraging Optimization (MRFO) and two different variants created for tuning a decentralized fractional order proportional-integral-derivative (FOPID) controller for a multiple-input multiple-output (MIMO) application. The application consists of a ball mill pulverizing system to pulverize coal and maximize fuel efficiency. MRFO and its applications have the task of finding the optimal controller variable values to control the system's temperature and pressure. The MRFO is a metaheuristic based on the behavior of manta rays, and although it shows efficient performance it may be improved through the use of techniques which generate new variants of the algorithm. Two techniques were used in the de-velopment of new variants: Quantum mechanics and opposition-based learning. The three different versions of MRFO were used to minimize a custom fitness function which is a combination of the integral time squared error (ITSE) and the overshoot of the system response. Simulations were carried using Simulink and Matlab softwares. For analyzing the performances, statistical measures such as minimum, maximum, best, mean, median, and standard deviation of the fitness function over 50 runs were used. Additionally, the different variants were also compared for minimizing 10 benchmark functions. The results show that the use of the previously mentioned techniques improve the performance of the original MRFO in optimizing the FOPID to control the ball mill pulverizing system.

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A Conjoint Analysis on the Preference of Pipe Welding Materials and Procedures

A Conjoint Analysis on the Preference of Pipe Welding Materi...

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2024 IEEE International Conference on industrial engineering and engineering Management, IEEM 2024

作者： Dela Cruz, Erick D. Prasetyo, Yogi Tri Ayuwati, Irene Dyah Cahigas, Maela Madel L. Nadlifatin, Reny School of Industrial Engineering and Engineering Management Mapúa University Manila Philippines School of Graduate Studies Mapúa University Manila Philippines International Bachelor Program in Engineering Yuan Ze University Taoyuan Taiwan Department of Industrial Engineering and Management Yuan Ze University Taoyuan Taiwan University of Surabaya Surabaya Indonesia Department of Information Systems Institut Teknologi Sepuluh Nopember Surabaya Indonesia

ISBN: (纸本)9798350386097

Welding is both as art and science and its common use is the jointing of points. Some of the welding procedures used for jointing metal pipes are Tungsten Inert Gas and Gas Metal Arc welding and in common practice plastic pipes are bonded with the use of butt-fusion welding procedure. Since 1970’s conjoint analysis was used as a way of finding out what the preference of consumers. The objective of this study is to determine the combination welding attributes that were most preferred using a conjoint analysis approach. With conjoint analysis, together with, the orthogonal design the preference for welding materials and procedures were analyzed. It showed that pipe material with 29.24% was the most preferred attribute and Nondestructive test as the least preferred with 2.66%. The result of this study may be utilized in future related reviews and could be applicable in other related Mechanical systems requiring welding works. © 2024 IEEE.

关键词： Nondestructive examination

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Effect of Sn addition on electrochemical activity of ribbons of Mg-Al-Ca anode material for rechargeable magnesium batteries fabricated by rapid solidification

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Keikinzoku/Journal of Japan Institute of Light Metals 2025年第1期75卷 9-13页

作者： Fukuta, Yoshitaka Kikura, Kensei Tsukeda, Tadayoshi Aida, Tetsuo Tabata, Hironobu Kurihara, Hideki Suzuki, Mayumi Program of Materials Science and Engineering Studies in Science and Engineering Graduate School of Science and Engineering University of Toyama 3190 Gofuku Toyama-shi Toyama930-8555 Japan Graduate School of Science and Engineering University of Toyama Toyama-shi Toyama Japan Faculty of Sustainable Design Academic Assembly University of Toyama Toyama-shi Toyama Japan CHUETSU METAL WORKS CO. LTD. Nakaniikawagun Toyama Japan Saitama Industrial technology Center Kawaguchi-shi Saitama Japan Department of Mechanical Systems Engineering Faculty of Engineering Toyama Prefectural University Imizu-shi Toyama Japan

Magnesium-based batteries are potential candidates for next-generation rechargeable batteries due to the divalent nature of magnesium cations and the natural abundance of magnesium resources. In this study, the electrochemical activity of Mg-6mass%Al-3mass%Ca anodes combined with Sn was investigated. Thin ribbons of the anode specimens were prepared by a single-roll rapid solidification method. The results showed that the electrochemical activity increased upon adding 1 mass% Sn, while it decreased for 3 mass% Sn. Based on the results of an X-ray diffraction analysis, the addition of Sn led to the formation of Mg2Sn and Ca2Sn phases, which increased the lattice distortion. Scanning electron microscopy observations indicated that these secondary phases were homogeneously distributed for 1 mass% Sn addition, while they were coarsely distributed for 3 mass% Sn. © 2025 The Japan Institute of Light Metals.

关键词： X ray diffraction analysis

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A Machine Learning-Based Digital Twin Model for Pressure Prediction in the Fuel Injection System 48

A Machine Learning-Based Digital Twin Model for Pressure Pre...

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48th Annual Conference of the IEEE industrial Electronics Society, IECON 2022

作者： Duarte, Edwin P. Viegas, Eduardo K. Santin, Altair O. Graduate Program in Computer Science PPGIa Pontificia Universidade Catolica Do Parana-Pucpr Brazil Robert Bosch GmbH Engineering Systems Curitiba Brazil Secure Systems Research Center Abu Dhabi United Arab Emirates

ISBN: (数字)9781665480253

ISBN: (纸本)9781665480253

Over the last years, the engine calibration task has mostly been conducted based on the engineers' knowledge. As a result, considering the complexity of modern engines, finding the most suitable configuration for each situation has become an impractical and expensive task. Apart from causing engines to be produced with inadequate calibration configuration, it can also decrease the lifespan of their components, degrading their efficiency. This paper proposes a machine learning-based digital twin model for pressure prediction in a fuel injection system, split into two steps. First, we extract statistical engine features based on a predefined time window to represent the engine behavior over time. Second, a digital twin implemented through a machine learning model is used to predict pressure levels in the fuel injection system. As a result, the predicted values can be used to assist the engine common rail system module in avoiding undesired engine states. Experiments performed on a new dataset, built over a real diesel-based engine, consisting of 208 features and over 1.3 million instances, have shown the feasibility of our proposal. The proposed scheme can predict in an advance time of 0.1 seconds the pressure levels for a fuel injection system with only 0.057 RMSE. Moreover, it increases its error rate by only 10.6% if a 0.5-second time advance is required. © 2022 IEEE.

关键词： Machine learning

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