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 e...
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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. Resc...
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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 regulation...
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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 liva...
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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...
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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 solutio...
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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 ...
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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 ...
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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
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 electr...
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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,...
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