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11th International Conference on Industrial engineering and Applications, ICIEA 2024

作者： Mulyasari, Wisda Ciptomulyono, Udisubakti Sudiarno, Adithya Industrial and Systems Engineering Department Institut Teknologi Sepuluh Nopember Surabaya Indonesia Department of Industrial Engineering Universitas Muhammadiyah Gresik Gresik Indonesia Department of Technology Management Institut Teknologi Sepuluh Nopember Surabaya Indonesia Jawa Timur Jakarta Indonesia

ISBN: (纸本)9789819764914

Macroergonomics aims to create entirely "harmonious" work systems that increase many areas of organisational efficiency and effectiveness. In macroergonomics, all work system components must collaborate to ensure safety. As a result, while the previous SLR only examined validity and reliability, a study of measurement variables is required to create an SCMM measuring instrument utilising a macroergonomic method. This project will conduct a systematic literature review (SLR) of SCMM publishing papers during the last 22 years (2001–2023). This SLR tries to classify the variables used in SCMM measurement into sociotechnical (macroergonomic) work subsystems, study fields, models, and measurement methods. This paper applied the SLR methodology to 53 SCMM articles in the last two decades, from 2001 to 2023. Data was collected by searching for published articles in Scopus, Google Scholar and Semantic Scholar, UK. Health and Safety Executive (HSE), ProQuest and Occupational Safety and Health Resource based on article title, abstract and keywords. Filtering was done by using keywords and evaluating the content's suitability by examining the article’s abstract and overall content (Output 53 articles). The results found valuable gaps in the use of the five sociotechnical work subsystems in measuring performance. The SLR found 18 organisation variables, 3 job and task variables, 7 human variables, 5 environmental variables and 7 tools and technological variables. The results indicate that the primary research areas in SCMM are health care and oil and gas, with ten publications. Besides, the SLR results indicated that the Hudson Model was the most utilised method, and the most used method was a questionnaire. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Economic and social effects

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An integrated machine learning approach for identifying emergency rescue messages on social media during natural disasters

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Social Network Analysis and Mining 2025年第1期15卷 1-19页

作者： Khallouli, Wael Li, Jiang Huang, Jingwei Rabadi, Ghaith Kovacic, Samuel Department Engineering Management and Systems Engineering Old Dominion University NorfolkVA United States Department Electrical and Computer Engineering Old Dominion University NorfolkVA United States University of Texas Southwestern Medical Center DallasTX United States School of Modeling Simulation and Training University of Central Florida OrlandoFL United States

During large-scale disasters, emergency call centers are often overwhelmed by the large volume of rescue requests and calls for help. Consequently, people are turning to social media platforms to seek assistance. Rescue information posted on these platforms is extremely valuable for first responders to make informed rescue decisions. Therefore, the automatic identification of these requests from the vast amount of data posted on social media during crises is critical yet challenging. This work presents our ongoing research on applying deep learning techniques to extract actionable rescue information from social media during crises. We proposed a novel deep learning model that integrates a fine-tuned BERT to extract low-level statistical features and rule-based Regex filters to extract problem-specific features for emergency tweet identification. The proposed model was evaluated on labeled tweets collected from three hurricane events (Harvey, Ian, and Ida). Experimental results showed that our model performed better than several machine learning and deep learning methods in terms of the Area Under the Precision-Recall Curve (AUC-PR) metric for all events. This study contributed to the crisis informatics literature by introducing a novel deep learning approach for automatically identifying actionable information in social media, which can be adapted for similar natural language processing (NLP) tasks. © The Author(s) 2025.

关键词： Tweets

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A comparison study between Modbus and IEC 61850 MMS Protocols 33

A comparison study between Modbus and IEC 61850 MMS Protocol...

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33rd Southern African Universities Power engineering Conference, SAUPEC 2025

作者： Johnson, Rayner Krishnamurthy, Senthil Mataifa, Haltor Esmail, Mohammed Cape Peninsula University of Technology Center for Substation Automation and Energy Management Systems Department of Electrical Electronic and Computer Engineering Cape Town7530 South Africa

ISBN: (纸本)9798331535162

The growing adoption of renewable energy primary plants has been seen globally. South Africa has seen a significant development in utility-scale photovoltaic (PV) farms in recent years due to the alleviated regulations stipulated in the latest Electricity Regulation Amendment Act. Connecting the PV plants to the power grid has also undergone a technical evolution by incorporating modern embedded computers known as Intelligent Electronic Devices (IEDs). The International Electrotechnical Committee (IEC) 61850 standard defines a suite of protocols used in modern power systems. This study compares the IEC 61850 Manufacturing Messaging Specification (MMS) with the well-known MODBUS protocol. The comparison is achieved by simulating a solar farm's Power Plant Controller (PPC). © 2025 IEEE.

关键词： Solar power plants

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A dynamic simulation model to improve the livability of transportation systems

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Public Transport 2025年第1期17卷 177-220页

作者： Suryani, Erma Hendrawan, Rully Agus Adipraja, Philip Faster Eka Widodo, Basuki Chou, Shuo-Yan Az-Zahra, Alifia Department of Information Systems Institut Teknologi Sepuluh Nopember Surabaya60111 Indonesia Department of Informatics Engineering Institut Teknologi Dan Bisnis Asia Malang Surabaya65113 Indonesia Department of Mathematics Institut Teknologi Sepuluh Nopember Surabaya60111 Indonesia Department of Industrial Management National Taiwan University of Science and Technology Taipei Taiwan Department of Civil Engineering Institut Teknologi Sepuluh Nopember Surabaya60111 Indonesia

Transportation users often face shortcomings, such as unreliable service causing uncertainty in transit, low quality of service, and loss of time due to road and highway congestion. This study aims to improve the livability of the transportation system by considering several factors. System dynamics is used as a model development method to analyze the relationship between complex and nonlinear variables affecting the livability of the transportation system. The system structure of a valid model can be modified by adding multiple feedback loops, new parameters, and changing the feedback loop structure to see how it affects other variables. The scenario model is built from a valid model by changing the model structure to improve the livability of the transportation system through improvements in the perception, safety, security, and health level of public transport, land use accessibility, and mitigation of air pollution. These strategies are expected to increase the livability of the transportation system by 71%, driven by the improvement in perception, safety, security level, and health level of public transport, land use accessibility, and decreased air pollution. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Land use

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Waste Vegetable Oil as a Sustainable Additive for Enhancing Asphalt Binder Properties: A Review 8th

Waste Vegetable Oil as a Sustainable Additive for Enhancing ...

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8th International Conference on Material engineering and Nanotechnology, ICMEN 2024

作者： Khasawneh, Mohammad Ali Sawalha, Ansam Khasawneh, Ahmad Ali Mohammed, Nayeemuddin Ahmed, Danish Department of Civil Engineering Prince Mohammad Bin Fahd University Al Khobar Saudi Arabia Department of Civil Engineering American University of Madaba Madaba Jordan Management Engineering and Process Improvement Department/Integrated Systems Engineering Department Ohio State University Wexner Medical Center and Ohio State University 660 Ackerman Road ColumbusOH43202 United States

ISBN: (纸本)9789819636235

This review paper presents a comprehensive and systematic analysis of the performance improvements achieved by incorporating waste vegetable oil (WVO) into asphalt binders. The study encompasses a thorough examination of various factors, including rheological characteristics, durability, environmental impacts, and economic viability. The results demonstrate that the utilization of WVO-modified asphalt binders offers a promising solution for environmentally friendly pavement construction practices. These binders exhibit enhanced performance qualities, reduced environmental effects, and the potential for cost savings. This review highlights the feasibility and benefits of WVO-modified asphalt binders as a sustainable choice for the construction industry, paving the way for more environmentally conscious and economically viable pavement construction approaches. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Binders

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Multi-Mode Inflow Prediction for Urban Transportation systems 14th

Multi-Mode Inflow Prediction for Urban Transportation System...

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14th International Conference on Frontier Computing, FC 2024

作者： Hung, Chih-Chieh Hung, Min-Hsien Chang, Jia-Wei Department of Management Information Systems National Chung Hsing University Taichung Taiwan Department of Computer Science and Information Engineering National Taichung University of Science and Technology Taichung Taiwan

ISBN: (纸本)9789819623945

Traffic congestion poses significant challenges to modern cities, leading to increased energy use, pollution, and long commute times. Optimizing public transit systems and encouraging their use is an effective solution. However, public transit integrates various modes like subways, buses, and shared bikes, requiring transfers between modes. Therefore, understanding the relationships between modes and accurately predicting passenger flows for each mode is crucial. Accurate predictions enable better capacity allocation, scheduling, and seamless transfers between modes, enhancing accessibility and convenience to attract more riders. Existing multi-modal prediction methods typically focus on station-to-region or region-to-region flows, overlooking station-to-station predictions that can better capture inter-modal relationships and provide fine-grained insights for traffic management. This study proposes a multi-modal framework for MRT and YouBike data that employs a spatial-temporal module to effectively model the spatiotemporal dependencies in the data. Evaluations on real-world datasets demonstrate the framework outperforms benchmark methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Traffic congestion

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Using Local Gradient Compensation in Federated Learning for Improving Privacy and Performance 14th

Using Local Gradient Compensation in Federated Learning for ...

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14th International Conference on Frontier Computing, FC 2024

作者： Chang, Jia-Wei Chen, Wei-Cheng Hung, Chih-Chieh Department of Computer Science and Information Engineering National Taichung University of Science and Technology Taichung Taiwan Department of Management Information Systems National Chung Hsing University Taichung Taiwan

ISBN: (纸本)9789819623945

Federated learning (FL) has emerged as a leading approach for decentralized model training, preserving data privacy by exchanging only model parameters. However, recent studies have exposed vulnerabilities, revealing that attackers can reconstruct private data from shared model parameters, compromising user privacy. To address this, various defense methods have been proposed, with gradient perturbation being a lightweight yet effective strategy. However, it often incurs a trade-off with model performance. In this paper, we introduce a lightweight defense mechanism tailored for Federated Stochastic Gradient Descent (FedSGD). Our method leverages maximum gradient values to gauge noise scale, while also preserving and compensating for previous noise iterations to mitigate performance impacts. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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Explainable artificial intelligence model for accident severity modeling

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Asian Journal of Civil engineering 2025年第6期26卷 2433-2445页

作者： Khasawneh, Mohammad Ali Umar, Ibrahim Khalil Khasawneh, Ahmad Ali Civil Engineering Department Prince Mohammad Bin Fahd University P.O. Box 1664 Al Khobar 31952 Saudi Arabia Department of Civil Engineering Technology Kano State Polytechnic Kano 700282 Nigeria Management Engineering and Process Improvement Department/Integrated Systems Engineering Department Ohio State University Wexner Medical Center Ohio State University 660 Ackerman Rd Columbus 43202 OH United States

Accident severity prediction is a critical challenge in traffic safety management, emergency response, and urban mobility planning. Road accidents remain a leading cause of fatalities worldwide, yet existing accident analysis frameworks often lack predictive accuracy, interpretability, and real-time decision-making capabilities. Traditional statistical models fail to capture complex interactions between vehicle attributes, driver behavior, and environmental factors, limiting their effectiveness in accident severity assessment. This study addresses these gaps by developing an explainable artificial intelligence (XAI) framework for accident severity prediction, leveraging machine learning models (Random Forest, Extreme Gradient Boosting, Support Vector Machine, and Naïve Bayes) and SHapley Additive exPlanations (SHAP) analysis to enhance model transparency. The dataset, sourced from the Fatality Analysis Reporting System (FARS), consists of 7394 recorded crashes and incorporates key predictors such as airbag deployment, control devices, seatbelt usage, and driver demographics. Experimental results demonstrate that XGBoost outperforms other models, achieving the highest accuracy (80.8%), recall (80.8%), and F1-score (81.0%), making it the most reliable classifier for distinguishing between severe and non-severe accidents. SHAP analysis reveals that airbag deployment, seatbelt usage, and control devices significantly impact accident severity outcomes, providing valuable insights into policy-driven interventions and traffic management strategies. Despite its effectiveness, the study highlights limitations such as data imbalance, lack of real-time behavioral factors, and exclusion of non-fatal crashes, suggesting deep learning integration, real-time telematics, and hybrid AI models in future research. The proposed framework offers a data-driven approach to accident severity prediction, improving road safety policies, vehicle design enhancements, and emergency response eff

关键词： Accident severity prediction Explainable AI Feature importance Machine learning models Risk assessment Traffic safety

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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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Optimal Energy Scheduling of Load Demand with Two-Level Multi-objective Functions in Smart Electrical Grid

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Operations Research Forum 2025年第2期6卷 1-23页

作者： Du, T.X. Jorshary, Kamilia Mehrabi Seyedrezaei, Mirali Uglu, Valisher Sapayev Odilbek School of Management Harbin Institute of Technology Harbin Heilongjiang 150001 China Energy Systems Department Ajman University Ajman United Arab Emirates Department of Industrial and Systems Engineering Russ College of Engineering and Technology Ohio University Athens OH United States Department of Economics and Business Colorado School of Mines Golden 80401 CO United States General Professional Science Department Mamun University Khiva Uzbekistan

In this paper, electrical grid scheduling with hybrid demand management via a multi-level optimization approach is implemented. At the upper level, demand management approaches are implemented, and at the lower level, multi-objective functions are optimized. The implementation of demand management approaches, including load shifting and interruption approaches, is carried out. These approaches are optimized subject to power prices in the upstream grid. The multi-objectives like reliability index and operation costs are optimized subject to demand management approaches at the upper level. By using the linear programming technique for the multidimensional analysis of preference (LINMAP) and fuzzy methods, the optimal solution in Pareto solutions for multi-objectives is selected. The performance of the approach is analyzed on the 69-bus network by employing power scheduling and integrating results from mathematical modeling through several case studies. With the implementation of the demand management approaches in optimal power consumption, the reliability index is improved by 0.6% and operating costs are minimized by 2.43% compared to the lack of demand management. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Demand management approaches Multi-level optimization approach Multi-objective functions Power prices

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