SC-Route: A Scalable Cross-Layer Secure Routing Method for Multi-Hop Inter-Domain Wireless Networks by Yanbing Li, Yang Zhu, Shangpeng Wang

“…Experimental results demonstrated that the proposed method achieves at least 28% improvement in effective throughput, reduces average authentication delay by approximately 30%, and increases the secure link ratio by at least 10%, outperforming mainstream routing algorithms under multi-constraint conditions.…”
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Collaborative altitude-adaptive reinforcement learning for active search with unmanned aerial vehicle swarms by XIAO Zijian, Chen-Chun Hsia, XU Yanggang, REN Jiyuan, CHEN Xinlei

“…To address these challenges, collaborative altitude-adaptive reinforcement learning (CARL) was proposed which incorporated an altitude-aware sensor model, a confidence-informed assessment module, and an altitude-adaptive planner based on proximal policy optimization (PPO) algorithms. …”
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A mechanism for value-sensitive decision-making. by Darren Pais, Patrick M Hogan, Thomas Schlegel, Nigel R Franks, Naomi E Leonard, James A R Marshall

“…We present a dynamical systems analysis of a decision-making mechanism inspired by collective choice in house-hunting honeybee swarms, revealing the crucial role of cross-inhibitory 'stop-signalling' in improving the decision-making capabilities. We show that strength of cross-inhibition is a decision-parameter influencing how decisions depend both on the difference in value and on the mean value of the alternatives; this is in contrast to many previous mechanistic models of decision-making, which are typically sensitive to decision accuracy rather than the value of the option chosen. …”
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Hardware-software complex for experimental research of electric drives of asynchronous motors with squirrel-cage rotor with traditional winding and motors with combined winding by A. N. Tsvetkov, Doan Ngok Shi

“…The structure of the HSC included the developed frequency converter with the possibility of adjusting the algorithms for controlling the electric motor and the mathematical model of the electric motor itself. …”
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Travel Time Variability and Spatio-Temporal Analysis of Urban Streets Using Global Positioning System: A Review by Zainab Ahmed Alkaissi, Ruba Yousif Hussain

“… Travel Time estimation is largely caused by the stochastic process of arrivals and departures of vehicles and its reliability measurements considering important issues for improving operational efficiency and safety for traffic road networks. …”
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An investigation into the zoning of ecosystem sensitivity control areas in Mentougou District (Beijing, China). by Xiaodan Li, Haoyu Tao, Jing Li, Zhen Liu, Zhiping Liu

“…This method significantly improves the accuracy and scientific credibility of ecosystem sensitivity zoning, providing a versatile approach to meet the varied zoning needs of different regions. …”
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A Review of Artificial Intelligence Applications in Predicting Faults in Electrical Machines by Mathew Habyarimana, Abayomi A. Adebiyi

“…This review highlights the revolutionary potential of artificial intelligence (AI) in improving the sustainability, efficiency, and dependability of electrical machine systems, especially in the context of rotating machines, by addressing existing constraints and suggesting future research routes.…”
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Enhanced YOLOv8 with lightweight and efficient detection head for for detecting rice leaf diseases by Bo Gan, Guolin Pu, Weiyin Xing, Lianfang Wang, Shu Liang

“…The LEDH enhances detection speed by simplifying the network structure while maintaining accuracy, reducing computational demands. The MSPPF improves the model’s ability to capture intricate details of rice leaf diseases at various scales by fusing multi-level feature maps. …”
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Simultaneous Instance and Attribute Selection for Noise Filtering by Yenny Villuendas-Rey, Claudia C. Tusell-Rey, Oscar Camacho-Nieto

“…This can result in more accurate and reliable models, improving their ability to generalize and make accurate predictions on new data. …”
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Optimizing Gammatone Cepstral Coefficients for Gear Fault Detection by Zrar Kh Abdul, Abdulbasit K. Al-Talabani, Wisam Hazim Gwad, Entisar Alkayal, Halgurd S. Maghdid, Safar Maghdid Asaad

“…Furthermore, optimizing the number of GTCC coefficients helps reduce model complexity. Experimental results demonstrate that optimizing GTCC parameters with GWO improves fault detection performance using an SVM classifier, achieving over 1% and 3% accuracy improvements on the PHM09 and DDS datasets, respectively.…”
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Energy-Accuracy Trade-Offs for Resistive In-Memory Computing Architectures by Saion K. Roy, Naresh R. Shanbhag

“…This result clearly implies the need for other approaches, e.g., algorithmic- and learning-based methods, to improve the inference accuracy of resistive IMC architectures.…”
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Prediction of Soil Organic Carbon Content in <italic>Spartina alterniflora</italic> by Using UAV Multispectral and LiDAR Data by Jiannan He, Yongbin Zhang, Mingyue Liu, Lin Chen, Weidong Man, Hua Fang, Xiang Li, Xuan Yin, Jianping Liang, Wenke Bai, Fuping Li

“…We compared the predictive performance of these different machine learning algorithms to identify the most effective one. The results show that the following. 1) The prediction accuracy is improved by classifying the data into three types: unlodging <italic>S. alterniflora</italic> (ULSA), lodging <italic>S. alterniflora</italic> (LSA), and mudflats. 2) XGBoost outperformed RF and SVM in accurately predicting SOC content, with <italic>R</italic>²; values of 0.743 for ULSA, 0.731 for LSA, and 0.705 for mudflats; 3) In the XGBoost models constructed for ULSA, LSA, and mudflats, spectral features contributed 75.7&#x0025;, 73.1&#x0025;, and 63.1&#x0025;, respectively, with the normalized difference vegetation index emerging as the most critical spectral feature. …”
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Combined assessment of stress hyperglycemia ratio and glycemic variability to predict all-cause mortality in critically ill patients with atherosclerotic cardiovascular diseases ac... by Fuxu Wang, Yu Guo, Yuru Tang, Shuangmei Zhao, Kaige Xuan, Zhi Mao, Ruogu Lu, Rongyao Hou, Xiaoyan Zhu

“…This integrated approach could inform personalized glycemic management strategies, potentially improving clinical outcomes. Graphic abstract…”
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A Review of Intrusion Detection for Railway Perimeter Using Deep Learning-Based Methods by Jin Wang, Hongyang Zhai, Yang Yang, Niuqi Xu, Hao Li, Di Fu

“…This article reviews the background and importance of detecting railway perimeter intrusion, summarizes the limitations of traditional detection methods, and emphasizes the potential of improving detection accuracy and efficiency in image recognition with deep learning models. …”
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Enhancing healthcare AI stability with edge computing and machine learning for extubation prediction by Kuo-Yang Huang, Ying-Lin Hsu, Che-Liang Chung, Huang-Chi Chen, Ming-Hwarng Horng, Ching-Hsiung Lin, Ching-Sen Liu, Jia-Lang Xu

“…Notably, the edge-based architecture reduced server data transmissions by 83.33%, while improving system stability, resilience, and sustainability. …”
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On the Extrapolation of Generative Adversarial Networks for Downscaling Precipitation Extremes in Warmer Climates by Neelesh Rampal, Peter B. Gibson, Steven Sherwood, Gab Abramowitz

“…We assess the extrapolation capabilities of a deterministic Convolutional Neural Network baseline and a Generative Adversarial Network (GAN) built with this baseline, trained to predict daily precipitation simulated by a Regional Climate Model (RCM) over New Zealand. Both approaches emulate future changes in annual mean precipitation well, when trained on historical data, though training on a future climate improves performance. …”
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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

“…Our findings ultimately inform practitioners about system efficiencies and limitations, but also the recent advances in hardware accelerators and algorithmic improvements aimed at shaping the new graph processing frontier in diverse technology domains.…”
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Smart diabetes management: remote monitoring and predictive health insights by K.S. Smelyakov, I.A. Lurin, K.V. Misiura, A.S. Chupryna, T.V. Tyzhnenko, O.D. Dolhanenko, V.M. Repikhov

“…A key innovation discussed is GluComp, a modular digital health platform designed to improve diabetes management. GluComp integrates CGM systems with personalized machine learning models to deliver real-time alerts, predictive insights, and adaptive care. …”
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Tumor tissue-of-origin classification using miRNA-mRNA-lncRNA interaction networks and machine learning methods by Ankita Lawarde, Ankita Lawarde, Masuma Khatun, Prakash Lingasamy, Prakash Lingasamy, Andres Salumets, Andres Salumets, Andres Salumets, Vijayachitra Modhukur, Vijayachitra Modhukur

“…Using transcriptomic profiles from 14 cancer types in The Cancer Genome Atlas (TCGA), we constructed co-expression networks and applied multiple feature selection techniques including recursive feature elimination (RFE), random forest (RF), Boruta, and linear discriminant analysis (LDA) to identify a minimal yet informative subset of miRNA features. Ensemble ML algorithms were trained and validated with stratified five-fold cross-validation for robust performance assessment across class distributions.ResultsOur models achieved an overall 99% classification accuracy, distinguishing 14 cancer types with high robustness and generalizability. …”
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Contrastive Disentangled Variational Autoencoder for Collaborative Filtering by Woo-Seong Yun, Seong-Min Kang, Yoon-Sik Cho

“…The representations learnt from our proposed scheme better reflect salient latent factor instead of being washed out by latent factor of popular items. Consequently, our model improves the predictive performance by effectively learning the representations toward salient latent features while excluding the effects from popularity. …”
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