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IEEE engineering Management Review 2025年

作者： Ratna, Silvia Saide, Saide Rater, Salya Svyazhanovna, Baizakova Saule Herzavina, Herzavina Faculty of Information Technology Department of Informatics Engineering and Department of Information Systems Banjarmasin Indonesia State Islamic University of Sultan Syarif Kasim Riau Universitas Islam Negeri Sultan Syarif Kasim Riau-UIN SUSKA Riau Department of Information Systems Faculty of Science and Technology Indonesia PRO Knowledge Indonesia Pekanbaru Indonesia Universitas Pahlawan Tuanku Tambusai Indonesia Lembaga Enreach UIN Suska Riau Indonesia Politeknik Aceh Selatan Department of Informatics Engineering Aceh Indonesia Information Communication Technologies Robotics in Arkalyk Pedagogical Institute Master of Natural Sciences Educational Program for the Informatics Computer Science Kazakhstan Universitas Pahlawan Tuanku Tambusai Department of Informatics Engineering Indonesia National Taiwan University of Science and Technology Taiwan

Knowledge resource and information system/technology (IS/IT) capability have been considered to improve firm performance, however there is still a gap regarding the sustainability of supply chain to face and recover from disruption (e.g., COVID-19). Questionnaire-based surveys was adopted and received feedback from sixty (160) Indonesian companies or about 28.07% of response rate. © 1973-2011 IEEE.

关键词： customer knowledge transfer disruption information system/technology (IS/IT) responding knowledge management resource-based view (RBV) Supply chain (SC) sustainability supply chain knowledge transfer

Diabetes Prediction Model for Better Clarification by using Machine Learning

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Diabetes Prediction Model for Better Clarification by using ...

International Conference on Inventive Computation Technologies (ICICT)

作者： J. Lysa Eben R. Jayasudha S. Ramya S. Kaliappan Shobha Aswal Khalid Ali Salem Al-Salehi Department of Computer Applications School of Computing Sciences Vels Institute of Science Technology and Advanced Studies Chennai Tamilnadu India Department of Mathematics Dr.N.G.P.Institute of Technology Coimbatore India Department of Computer Science and Engineering Kongu Engineering College Erode Tamilnadu India Division of Research and Development Lovely Professional University Phagwara Punjab India School of Computing Graphic Era Hill University Dehradun India Biomedical engineering study program faculty of science and technology Airlangga University Surabaya Indonesia

Diabetes mellitus is one of the most pressing health concerns because so many people are afflicted by its disabling symptoms. Factors such as age, excess body fat, insufficient physical activity, a history of diabetes in one's family, a sedentary lifestyle, an unhealthy diet, hypertension, etc., all increase the likelihood of developing diabetes mellitus. Health complications are more common in people with diabetes, including cardiovascular disease, renal failure, stroke, blindness, and nerve injury. To validate a diagnosis of diabetes, hospitals typically perform a battery of procedures on the patient. Big data analytics has many vital applications in the healthcare sector. Numerous large computer systems are used in the healthcare sector. With the help of big data analytics, researchers can sift through mountains of data in search of previously unseen patterns and insights. Current techniques have a poor degree of precision in classification and forecast. While previous research has focused on factors such as glucose, body mass index, age, insulin, etc., the proposed model takes these into account and also the other factors that may be more relevant to the development of diabetes. The newer sample is superior to the older one based on categorization accuracy. A workflow algorithm for diabetes prognosis is also required to improve the accuracy.

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Learn How to Design High-Quality Qualitative Educational Research! - A Workshop for Disciplinary STEM Faculty by Disciplinary STEM Faculty 53

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Learn How to Design High-Quality Qualitative Educational Res...

53rd IEEE ASEE Frontiers in Education International Conference, FIE 2023

作者： Morelock, John R. Jarvie-Eggart, Michelle Chenette, Heather Hooshangi, Sara Chestnutt, Betsy Wilson, Sarah Bolhari, Azadeh Dodson, Kirsten Pavlova, Iglika Reck, Rebecca Transformations Institute University of Georgia Engineering Education AthensGA United States Michigan Technological University Engineering Fundamentals HoughtonMI United States Rose-Hulman Institute of Technology Chemical Engineering Terre HauteIN United States Virginia Tech Computer Science - Northern Virginia Center WashingtonDC United States University of Tennessee Knoxville Engineering Fundamentals KnoxvilleTN United States University of Kentucky Chemical Engineering LexingtonKY United States University of Colorado Boulder Environmental Engineering Program BoulderCO United States Lipscomb University Mechanical Engineering NashvilleTN United States University of North Carolina Greensboro Department of Biology GreensboroNC United States University of Illinois Urbana-Champaign Bioengineering ChampaignIL United States

ISBN: (纸本)9798350336429

The purpose of this workshop-designed for instructional and disciplinary STEM faculty interested in learning about qualitative research-is to (1) introduce participants to high-quality qualitative research design and (2) practice this design process alongside disciplinary STEM faculty to expand their STEM education research abilities and network. We will do so using the ProQual approach, a methodologically unencumbered and widely accessible way of thinking about qualitative research design that was deployed and refined over the last three years as part of the NSF-funded ProQual Institute for Research Methods [1]. This workshop will be conducted by ProQual Institute alumni, who are culturally sensitive to the challenges faced by disciplinary STEM faculty. Leveraging a propagation model of effecting academic change [2], the workshop leaders will serve as a community of practice to help participants move their educational research ideas forward during and after the workshop. In doing so, we strive to further FIE's mission to create a collaborative, supportive, and inclusive community of educational researchers. © 2023 IEEE.

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Sensing property of Ga2O3-based extended-gate field-effect transistors for a living cell viability sensor

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Sensors and Actuators A: Physical 2023年 349卷

作者： Chou, Hsin-Yu Chiang, Jung-Lung Yu, Chang-Tze Ricky Chen, Jo-Mei Maureen Wuu, Dong-Sing Department of Materials Science and Engineering National Chung Hsing University Taichung40227 Taiwan Ph.D. Program Prospective Technology of Electrical Engineering and Computer Science National Chin-Yi University of Technology Taichung411030 Taiwan Department of Applied Chemistry National Chi-Nan University Nantou54561 Taiwan Department of Applied Materials and Optoelectronic Engineering National Chi Nan University Nantou54561 Taiwan

In this study, gallium oxide (Ga2O3) nanorods were deposited onto an indium tin oxide (ITO) glass substrate to develop a real-time living cell viability sensor. Ga2O3 nanorods had characteristics of cell population sensing based on cellular metabolism to detect cell viability. The pH sensing characteristics of Ga2O3-based extended-gate field-effect transistors (EGFETs) with and without the annealing process were measured from pH 1–11. Three different sensor heads, ITO/glass, GaOOH/ITO/glass, and Ga2O3/ITO/glass, were produced as EGFETs and immersed into a pH standard buffer and high-glucose Dulbecco's modified Eagle's medium (DMEM) to investigate hydrogen ion sensing. Results showed that the Ga2O3/ITO/glass probe had the best hydrogen ion sensing sensitivity and stability in different sample buffers. In contrast, GaOOH/ITO/glass had instability and lower sensitivity due to interference with other components in the medium. The drift characteristic of sensors was analyzed to detect long-term stability. The Ga2O3/ITO/glass probe showed lower drift properties, which can potentially monitor the DMEM situation in real-time for a long-term period. During in vitro detection, the Ga2O3/ITO/glass probe revealed a 70 μA/cells change in IDS-VDS analysis and a 10 μV/cells change in IDS-VGS analysis, which was superior linearly dependent within 10,000 cells. The biocompatibility of the Ga2O3/ITO/glass probe was also detected by 3-(4,5-dimethylthiazol-2-yl)− 2,5-diphenyltetrazolium bromide assay and showed no cytotoxicity. Overall, the Ga2O3/ITO/glass EGFET sensor has potential applications as a living cell viability device in microenvironmental studies in the future. © 2022 Elsevier B.V.

关键词： Nanorods

Parallel Approximations for High-Dimensional Multivariate Normal Probability Computation in Confidence Region Detection Applications

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arXiv 2024年

作者： Zhang, Xiran Abdulah, Sameh Cao, Jian Ltaief, Hatem Sun, Ying Genton, Marc G. Keyes, David E. Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Saudi Arabia Statistics Program King Abdullah University of Science and Technology Saudi Arabia Extreme Computing Research Center King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia Department of Mathematics The University of Houston HoustonTX United States

Addressing the statistical challenge of computing the multivariate normal (MVN) probability in high dimensions holds significant potential for enhancing various applications. For example, the critical task of detecting confidence regions where a process probability surpasses a specific threshold is essential in diverse applications, such as pinpointing tumor locations in magnetic resonance imaging (MRI) scan images, determining hydraulic parameters in groundwater flow issues, and forecasting regional wind power to optimize wind turbine placement, among numerous others. One common way to compute high-dimensional MVN probabilities is the Separation-of- Variables (SOV) algorithm. This algorithm is known for its high computational complexity of O(n3) and space complexity of O(n2), mainly due to a Cholesky factorization operation for an n × n covariance matrix, where n represents the dimensionality of the MVN problem. This work proposes a high-performance computing framework that allows scaling the SOV algorithm and, subsequently, the confidence region detection algorithm. The framework leverages parallel linear algebra algorithms with a task-based programming model to achieve performance scalability in computing process probabilities, especially on largescale systems. In addition, we enhance our implementation by incorporating Tile Low-Rank (TLR) approximation techniques to reduce algorithmic complexity without compromising the necessary accuracy. To evaluate the performance and accuracy of our framework, we conduct assessments using simulated data and a wind speed dataset. Our proposed implementation effectively handles high-dimensional multivariate normal (MVN) probability computations on shared and distributed-memory systems using finite precision arithmetics and TLR approximation computation. Performance results show a significant speedup of up to 20X in solving the MVN problem using TLR approximation compared to the reference dense solution without sacrificing the

关键词： Statistics

Model-Assisted Probabilistic Safe Adaptive Control With Meta-Bayesian Learning

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arXiv 2023年

作者： Wang, Shengbo Li, Ke Yang, Yin Cao, Yuting Huang, Tingwen Wen, Shiping College of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China Department of Computer Science University of Exeter ExeterEX4 4QF United Kingdom College of Science and Engineering Hamad Bin Khalifa University Doha5855 Qatar Science Program Texas A & M University at Qatar Doha23874 Qatar Australian AI Institute Faculty of Engineering and Information Technology University of Technology SydneyNSW2007 Australia

Breaking safety constraints in control systems can lead to potential risks, resulting in unexpected costs or catastrophic damage. Nevertheless, uncertainty is ubiquitous, even among similar tasks. In this paper, we develop a novel adaptive safe control framework that integrates meta learning, Bayesian models, and control barrier function (CBF) method. Specifically, with the help of CBF method, we learn the inherent and external uncertainties by a unified adaptive Bayesian linear regression (ABLR) models, which consists of a forward neural network (NN) and a Bayesian output layer. Meta learning techniques are leveraged to pre-train the NN weights and priors of the ABLR model using data collected from historical similar tasks. For a new control task, we refine the meta-learned models using a few samples, and introduce pessimistic confidence bounds into CBF constraints to ensure safe control. Moreover, we provide theoretical criteria to guarantee probabilistic safety during the control processes. To validate our approach, we conduct comparative experiments in various obstacle avoidance scenarios. The results demonstrate that our algorithm significantly improves the Bayesian model-based CBF method, and is capable for efficient safe exploration even with multiple uncertain constraints. Copyright © 2023, The Authors. All rights reserved.

关键词： Multilayer neural networks

Ideation of engineering Solutions for Sustainable Development Goals: A Collaborative Course Between Thai and Japanese Students

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Ideation of Engineering Solutions for Sustainable Developmen...

IEEE Region 10 International Conference TENCON

作者： Kodchakorn Krutphong Natt Leelawat Jing Tang Eri Ota Rie Murakami Suzuki Yuki Taoka Proadpran Punyabukkana Environmental Engineering and Disaster Management Program Mahidol University Thailand Department of Industrial Engineering Faculty of Engineering Chulalongkorn University and Disaster and Risk Management Information Systems Research Unit Chulalongkorn University Thailand International School of Engineering Chulalongkorn University Center for International Education Tokyo Institute of Technology Japan School of Environmet and Society Tokyo Institute of Technology Japan Department of Computer Engineering Faculty of Engineering Chulalongkorn University and the Learning Innovation Center Chulalongkorn University Thailand

A collaborative course called Global Awareness for technology Implementation was conducted by the Faculty of engineering, Chulalongkorn University, Thailand, and the Tokyo Institute of technology, Japan. The objective of this course was to facilitate the exchange of knowledge, perspectives, and experiences between Thai and Japanese students through project work, using an engineering design approach and the design thinking process to create innovation relevant to the Sustainable Development Goals (SDGs). During the class, experts shared their knowledge and experiences with the students. The students then worked on their respective projects to develop solution concepts using the design thinking process. The proposed ideas include the application for English class, smart water quality checker, hydrogen-based energy system for rural area, reduced wasted fashion solution, and auto waste sorting machine. Finally, this study concluded with a summary of the results and findings on the empathy process in the chosen SDGs, and of the commonalities and differences between Thailand and Japan in terms of ideation.

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Generative AI-enabled Vehicular Networks: Fundamentals, Framework, and Case Study

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arXiv 2023年

作者： Zhang, Ruichen Xiong, Ke Du, Hongyang Niyato, Dusit Kang, Jiawen Shen, Xuemin Poor, H. Vincent The Engineering Research Center of Network Management Technology High Speed Railway of Ministry of Education School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China The Collaborative Innovation Center of Railway Traffic Safety Beijing Jiaotong University Beijing100044 China The National Engineering Research Center of Advanced Network Technologies Beijing Jiaotong University Beijing100044 China The School of Computer Science and Engineering The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore The School of Automation Guangdong University of Technology China The Department of Electrical and Computer Engineering University of Waterloo Canada The Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States

Recognizing the tremendous improvements that the integration of generative AI can bring to intelligent transportation systems, this article explores the integration of generative AI technologies in vehicular networks, focusing on their potential applications and challenges. Generative AI, with its capabilities of generating realistic data and facilitating advanced decision-making processes, enhances various applications when combined with vehicular networks, such as navigation optimization, traffic prediction, data generation, and evaluation. Despite these promising applications, the integration of generative AI with vehicular networks faces several challenges, such as real-time data processing and decision-making, adapting to dynamic and unpredictable environments, as well as privacy and security concerns. To address these challenges, we propose a multi-modality semantic-aware framework to enhance the service quality of generative AI. By leveraging multi-modal and semantic communication technologies, the framework enables the use of text and image data for creating multi-modal content, providing more reliable guidance to receiving vehicles and ultimately improving system usability and efficiency. To further improve the reliability and efficiency of information transmission and reconstruction within the framework, taking generative AI-enabled vehicle-to-vehicle (V2V) as a case study, a deep reinforcement learning (DRL)-based approach is proposed for resource allocation. Finally, we discuss potential research directions and anticipated advancements in the field of generative AI-enabled vehicular networks. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

Empowering Machines to Think Like Chemists: Unveiling Molecular Structure-Polarity Relationships with Hierarchical Symbolic Regression

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arXiv 2024年

作者： Lou, Siyu Liu, Chengchun Chen, Yuntian Mo, Fanyang Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China Ningbo Institute of Digital Twin Eastern Institute of Technology Ningbo315200 China School of Materials Science and Engineering Peking University Beijing100871 China -Preferred Program Peking University Shenzhen Graduate School Shenzhen518500 China

Thin-layer chromatography (TLC) is a crucial technique in molecular polarity analysis. Despite its importance, the interpretability of predictive models for TLC, especially those driven by artificial intelligence, remains a challenge. Current approaches, utilizing either high-dimensional molecular fingerprints or domain-knowledge-driven feature engineering, often face a dilemma between expressiveness and interpretability. To bridge this gap, we introduce Unsupervised Hierarchical Symbolic Regression (UHiSR), combining hierarchical neural networks and symbolic regression. UHiSR automatically distills chemical-intuitive polarity indices, and discovers interpretable equations that link molecular structure to chromatographic behavior. Copyright © 2024, The Authors. All rights reserved.

关键词： Thin layer chromatography

Sodium-ion battery cycle life prediction using machine learning

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Sodium-ion battery cycle life prediction using machine learn...

International Conference on Green Energy, Computing and Intelligent technology 2024 (GEn-CITy 2024)

作者： Linfeng Liu Filbert H. Juwono W. K. Wong Erick Purwanto Tingyan Jin Yuyang Zhen Sustainable Energy Technology Program Xi'an Jiaotong-Liverpool University Suzhou People's Republic of China Department of Electrical and Electronic Engineering Xi'an Jiaotong-Liverpool University Suzhou People's Republic of China Department of Electrical and Computer Engineering Curtin University Malaysia Miri Malaysia Department of Computing Xi'an Jiaotong-Liverpool University Suzhou People's Republic of China School of Mechanical and Electrical Engineering Beijing Information Science and Technology University Beijing People's Republic of China School of Instrument Science and Technology Beijing University of Technology Beijing People's Republic of China

ISBN: (数字)9781837242863

In this study, we explore the classification and prediction capabilities of three models—Genetic programming (GP), Logistic Regression (LR), and the Kolmogorov-Arnold Network (KAN)—on the task of sodium-ion battery life prediction. By leveraging a dataset composed of multiple battery characteristics, we aim to determine the remaining power of sodium-ion batteries using these machine learning models. The KAN model, being a novel approach, demonstrates superior performance across various metrics, including accuracy, precision, recall, and F1 score, when compared to the other two models. This highlights the potential of KAN as a robust model for complex classification tasks in the field of battery life prediction.

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