Search Results (3)

This paper proposes an advanced deep convolutional neural network model for motor bearing fault detection that was designed to overcome the limitations of traditional models in feature extraction, accuracy, and generalization under complex operating conditions. The model combines multi-scale residuals, hybrid attention mechanisms, and dual global pooling to enhance the performance. Convolutional layers efficiently extract features, while hybrid attention mechanisms strengthen the feature representation. The multi-scale residual network structure captures features at various scales, and fault classification is performed using global average and max pooling. The model was trained with the Adam optimizer and sparse categorical cross-entropy loss by incorporating a learning rate decay mechanism to refine the training process. Experiments on the University of Paderborn bearing dataset across four conditions showed that the model had superior performance, where it achieved a diagnostic accuracy of 99.7%, which surpassed traditional models, like AMCNN, LeNet5, and AlexNet. Comparative experiments on rolling bearing vibration and motor current datasets across four bearing conditions highlighted the model’s effectiveness and broad applicability in motor fault detection. Its robust feature extraction and classification capabilities make it a reliable solution for motor bearing fault diagnosis, with significant potential for real-world applications. This makes it a reliable solution for motor bearing fault diagnosis with significant potential for practical applications. Full article

This study reviewed the global prevalence, health and socioeconomic impact, and management approaches of obstructive sleep apnea. The narrative review examined three key dimensions: (1) worldwide OSA prevalence across different regions, accounting for variations in diagnostic standards; (2) OSA’s effects on health outcomes and socioeconomic conditions across diverse populations and healthcare systems; and (3) current global approaches to OSA diagnosis, treatment, and public health management. Despite advances in diagnosis and treatment, a large proportion of OSA cases remain undiagnosed or inadequately managed. The findings show that untreated OSA significantly increases public safety risks, particularly regarding motor vehicle and occupational accidents, while also creating a substantial pool of patients at high risk for systemic complications with severe impacts on overall health. There is a critical need for increased public awareness, universal screening approaches, and integrated care strategies to address this global health challenge and reduce its considerable socioeconomic burden. Our review uniquely addresses global disparities in OSA prevalence, clarifies the health and socioeconomic impacts that remain underexplored in the current literature, and suggests concrete strategies for public health and clinical management improvement worldwide. Full article

Managing a fleet efficiently to address demand within cost constraints is a challenge. Mismatched fleet size and demand can create suboptimal budget allocations and inconvenience users. To address this problem, many studies have been conducted around heterogeneous fleet optimization. That research has not included an examination of different vehicle types with travel distance constraints. This study focuses on optimizing the University of Tennessee (UT) motor pool which has a heterogeneous fleet that includes electric vehicles (EVs) with a travel distance and recharge time constraint. After assessing UT motor pool trip patterns as a case study, a queuing model was used to estimate the maximum number of each vehicle type needed to minimize the expected customer wait time to near zero. The break-even point is used for the optimization model to constrain the minimum number of years that electric vehicles should be operated under the no-subsidy assumption. The results show that the fleet has surplus vehicles. In addition to reducing the number of vehicles, total fleet costs could be minimized by using electric vehicles for all trips less than 100 miles. The models are flexible and can be applied and help fleet managers make decisions about fleet size and EV adoption. Full article