Secondary System Fault Detection Method Based on Association Rules and Reconstruction Error by Yang WANG, Weidong MA, Jiming LIU, Boshi WANG, Kai YAO, Wei HAN, Juan YU

“…Finally, the ensemble learning model is optimized to improve the accuracy of secondary equipment anomaly warning. …”
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Performance of M-QAM MIMO PNC Based on MRC and ZR Techniques by Alaa A. Yassin, Ebtihal H. G. Yousif, Rashid A. Saeed, Hashim Elshafie, Abdullah Alenizi, Hesham Alhumyani

“…Maximum Ratio Combining (MRC) and Zero-Forcing (ZF) for the receive/transmit diversity techniques are used at the relay node to reduce multi-user interference. The system model is based on denoise-and-forward (DNF) relaying along with the Latin Square (LS) mapping algorithm at the relay node and square M-ary Quadrature Amplitude Modulation (M-QAM) at all nodes. …”
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Emergency evacuation planning in natural disasters under diverse population and fleet characteristics by Evangelia Baou, Vasilis P. Koutras, Vasileios Zeimpekis, Ioannis Minis

“…Design/methodology/approach – The problem is formulated using a mixed integer linear programming model and solved using a heuristic algorithm. The authors analyze the selected heuristic with respect to key parameters and use it to address theoretical and practical case studies. …”
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A novel hybrid nonlinear control of four switch buck-boost converter feeding constant power load in fuel cell-based DC microgrid by Hafiz Mian Muhammad Adil, Hassan Abbas Khan

“…Controller parameters are optimized using the slime mould algorithm, minimizing the integral time absolute error (ITAE) to address both transient and steady-state behavior. …”
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Benchmarking Big Data Systems: Performance and Decision-Making Implications in Emerging Technologies by Leonidas Theodorakopoulos, Aristeidis Karras, Alexandra Theodoropoulou, Georgios Kampiotis

“…GraphX’s strength was in processing batch in-memory workloads typical of blockchain and machine learning model optimization, while Flink excelled in processing stream data, which is timely and important to the IoT world. …”
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Dynamic Pricing Strategy for Data Product Through Deep Reinforcement Learning by Junxin Shen, Yashi Wang, Fanghao Xiao

“…First, the dynamic pricing problem for data products is modeled as a Markov Decision Process (MDP). Then, a dynamic pricing algorithm based on the Deep Q-learning algorithm is designed, incorporating an annealing mechanism to optimize the exploration strategy. …”
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Parallel boosting neural network with mutual information for day-ahead solar irradiance forecasting by Ubaid Ahmed, Anzar Mahmood, Ahsan Raza Khan, Levin Kuhlmann, Khurram Saleem Alimgeer, Sohail Razzaq, Imran Aziz, Amin Hammad

“…Traditional deep learning (DL) models and decision tree (DT)-based algorithms have been widely employed for this purpose. …”
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A Review of Research on Flight Delay Propagation: Current Situation and Prospect by N. Li, H. G. Yao

“…At the same time, researchers also need to constantly explore and optimize algorithms to overcome their current limitations and further improve the reliability of predictions.…”
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Artificial Neural Network Framework for Hybrid Control and Monitoring in Turning Operations by Bogdan Felician Abaza, Vlad Gheorghita

“…This paper proposes a hybrid control and monitoring framework designed to enhance turning operations by integrating artificial neural networks (ANNs) for predictive modeling and adaptive recalibration. The system leverages machine learning (ML) to improve machining efficiency, tool longevity, and energy consumption optimization. …”
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Parallel Machine Production and Transportation Operations’ Scheduling with Tight Time Windows by Yang Jiang, Tong He, Jie Xiong, Xin Wu, Yao Chen

“…The results show that the use of the system coordination model in this method obviously improves the number of successful transfers and deliveries.…”
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Risk Limiting Based Resilient Operation Method for Power Grid by Xiyue WANG, Hao ZHANG, Chaoyi PENG

“…By deriving three intermediate state models of RLB and their analytical solutions, we present an iterative RLB solution algorithm based on intermediate state models and provide a rigorous proof of its global optimality. …”
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Machine learning-based brain magnetic resonance imaging radiomics for identifying rapid eye movement sleep behavior disorder in Parkinson’s disease patients by Yandong lian, Yibin Xu, Linlin Hu, Yuguo Wei, Zhaoge Wang

“…Furthermore, based on the optimal cut-off value of the model, subjects were categorized into low- and high-risk groups, and differences in the actual number of RBD patients between the two sets were compared to assess the clinical effectiveness of the model. …”
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Full lifecycle security management scheme for terminal keys under power IoT by Wang Hui, Yuan Jiahui, Shi Zhentong, Fang Mu

“…Firstly, the two-stage key distribution architecture based on the state secret algorithm is adopted in the scheme to achieve secure access authentication of power terminals at different stages. …”
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Three-Dimensional in Vivo Imaging of Green Fluorescent Protein-Expressing T Cells in Mice with Noncontact Fluorescence Molecular Tomography by Anikitos Garofalakis, Giannis Zacharakis, Heiko Meyer, Eleftherios N. Economou, Clio Mamalaki, Joseph Papamatheakis, Dimitris Kioussis, Vasilis Ntziachristos, Jorge Ripoll

“…By measuring multiple projections, large data sets can be obtained, thus improving the accuracy of the inversion models used for quantitative three-dimensional reconstruction of fluorochrome distribution, as well as facilitating a higher spatial resolution. …”
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The ensemble transform Schmidt–Kalman filter: A novel method to compensate for observation uncertainty due to unresolved scales by Zackary Bell, Sarah L. Dance, Joanne A. Waller

“…Observation error due to unresolved scales occurs when there is a difference in scales observed and modeled. To obtain an optimal estimate through data assimilation, the error due to unresolved scales must be accounted for in the algorithm. …”
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Sparse Convolution FPGA Accelerator Based on Multi-Bank Hash Selection by Jia Xu, Han Pu, Dong Wang

“…However, many computing devices that claim high computational power still struggle to execute neural network algorithms with optimal efficiency, low latency, and minimal power consumption. …”
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YOLO-MES: An Effective Lightweight Underwater Garbage Detection Scheme for Marine Ecosystems by Chengxu Huang, Wenyuan Zhang, Beitian Zheng, Jiahao Li, Bochen Xie, Ruisi Nan, Zongming Tan, Baohua Tan, Neal N. Xiong

“…However, existing high-precision detection algorithms are challenging to deploy on performance-constrained IoT underwater devices due to their large computational complexity and model size. …”
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Ischemic Stroke Lesion Segmentation on Multiparametric CT Perfusion Maps Using Deep Neural Network by Ankit Kandpal, Rakesh Kumar Gupta, Anup Singh

“…The study aims to investigate the potential of deep learning (DL) algorithms for the improved segmentation of ischemic lesions. …”
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AI-driven energy management system based on hesitant bipolar complex fuzzy Hamacher power aggregation operators and their applications in MADM by Walid Emam, Hafiz Muhammad Waqas, Tahir Mahmood, Ubaid ur Rehman, Dragan Pamucar

“…Abstract Artificial Intelligence (AI) based energy management systems utilize sophisticated AI algorithms to improve and control the consumption of energy in various sectors, such as power utilities, industrial systems, and smart buildings. …”
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RivAIr: A custom-designed UAV-based sensor for real-time water area segmentation and surface velocity estimation by Marco La Salandra, Rosa Colacicco, Simone Panza, Giovanni Fumai, Pierfrancesco Dellino, Domenico Capolongo

“…Unlike existing methods that rely on post-processing, RivAIr is purpose-built for real-time field deployment in data-scarce or emergency scenarios.RivAIr integrates YOLOv8 with an optimized Farneback dense optical flow algorithm focused on water bodies, improving motion estimation accuracy while reducing runtime for real-time UAV processing. …”
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