In situ fully vectorial tomography and pupil function retrieval of tightly focused fields by Xin Liu, Shijie Tu, Yiwen Hu, Yifan Peng, Yubing Han, Cuifang Kuang, Xu Liu, Xiang Hao

“…In this article, we develop an in situ method for reconstructing the fully vectorial information of tightly focused fields in 3D space, while simultaneously retrieving the pupil functions. …”
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Balancing resource utilization and slice dissatisfaction through dynamic soft slicing for 6G wireless networks by Zeinab Sasan, Siavash Khorsandi

“…The problem is formulated as a Mixed Integer Linear Programming (MILP) model, aiming to maximize network utilization while minimizing slice dissatisfaction. Given the NP-hard nature of the problem, we propose the Heuristic Resource Allocation for Soft Slicing (HRASS) algorithm, which achieves near-optimal performance with significantly reduced computational complexity. …”
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ReliefSeq: a gene-wise adaptive-K nearest-neighbor feature selection tool for finding gene-gene interactions and main effects in mRNA-Seq gene expression data. by Brett A McKinney, Bill C White, Diane E Grill, Peter W Li, Richard B Kennedy, Gregory A Poland, Ann L Oberg

“…Relief-F is a nonparametric, nearest-neighbor machine learning method that has been successfully used to identify relevant variables that may interact in complex multivariate models to explain phenotypic variation. While several tools have been developed for assessing differential expression in sequence-based transcriptomics, the detection of statistical interactions between transcripts has received less attention in the area of RNA-seq analysis. …”
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Preoperative prediction of pulmonary ground-glass nodule infiltration status by CT-based radiomics combined with neural networks by Kun Mei, Zikang Feng, Hui Liu, Min Wang, Chao Ce, Shi Yin, Xiaoying Zhang, Bin Wang

“…Feature selection was performed using the Lasso algorithm to identify the most predictive variables, which were subsequently incorporated into the radiomics-based neural network model. …”
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Predicting the subclinical carotid atherosclerosis in overweight and obese patients using a machine learning model by D. V. Gavrilov, T. Yu. Kuznetsova, M. A. Druzhilov, I. N. Korsakov, A. V. Gusev

“…The introduction of such risk stratification algorithms into practice will increase the accuracy and quality of CVR prediction and optimize the system of preventive measures.…”
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Cascade-Based Input-Doubling Classifier for Predicting Survival in Allogeneic Bone Marrow Transplants: Small Data Case by Ivan Izonin, Roman Tkachenko, Nazarii Hovdysh, Oleh Berezsky, Kyrylo Yemets, Ivan Tsmots

“…The approach utilizes two artificial intelligence tools: the first Probabilistic Neural Network generates output signals that expand the independent attributes of an augmented dataset, while the second machine learning algorithm performs the final classification. …”
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Impedance-Driven Decoupling Water–Nitrogen Stress in Wheat: A Parallel Machine Learning Framework Leveraging Leaf Electrophysiology by Shuang Zhang, Xintong Du, Bo Zhang, Yanyou Wu, Xinyi Yang, Xinkang Hu, Chundu Wu

“…A parallel modelling strategy was implemented employing Gradient Boosting, Random Forest, and Ridge Regression, selecting the optimal algorithm per feature based on predictive performance. …”
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Remote Target High-Precision Global Geolocalization of UAV Based on Multimodal Visual Servo by Xuyang Zhou, Ruofei He, Wei Jia, Hongjuan Liu, Yuanchao Ma, Wei Sun

“…We design a step-convergent target geolocation optimization algorithm. By adjusting the step size and the scale factor of the cost function, we achieve fast accuracy convergence for different UAV reconnaissance modes, while maintaining the geolocation accuracy without divergence even when the laser ranging sensor is turned off for a short period. …”
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XSQ-Learning: Adaptive Similarity Thresholds for Accelerated and Stable Q-Learning by Ansel Y. Rodríguez González, Roberto E. López Díaz, Shender M. Ávila Sansores, María G. Sánchez Cervantes

“…Reinforcement Learning (RL) enables agents to learn optimal policies through environment interaction, with Q-learning being a fundamental algorithm for Markov Decision Processes (MDPs). …”
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Lightweight UAV Detection Method Based on IASL-YOLO by Huaiyu Yang, Bo Liang, Song Feng, Ji Jiang, Ao Fang, Chunyun Li

“…To address this, we propose the IASL-YOLO algorithm, which optimizes the YOLOv8s model to enhance detection accuracy and lightweight efficiency. …”
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Heterogeneity-aware device selection for efficient federated edge learning by Yiran Shi, Jieyan Nie, Xingwei Li, Hui Li

“…To address these issues, this paper proposes a novel Quality-aware online Device Selection (QDS) algorithm. The QDS algorithm leverages a greedy selection method that guarantees the deadline restrictions and reflects upon their historical performance metrics, as indicated by their loss function values from preceding training rounds. …”
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Pseudolite Self-Interference Cancellation Without Additional GNSS Signal Distortion by Lichao Gao, Jin He, Yuanxin Wu, Wenxian Yu

“…However, the low-orbit pseudolite also needs satellite navigation to get continuous and effective high-precision positioning. So, while effectively filtering the self-interference, the GNSS navigation signals need low distortion. …”
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Mapping the landscape of machine learning in chronic disease management: A comprehensive bibliometric study by Shiying Shen, Wenhao Qi, Sixie Li, Jianwen Zeng, Xin Liu, Xiaohong Zhu, Chaoqun Dong, Bin Wang, Qian Xu, Shihua Cao

“…Logistic regression (n = 459) remains the most widely used algorithm, while neural networks (n = 183) show promising potential. …”
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A YOLOv8n-T and ByteTrack-Based Dual-Area Tracking and Counting Method for Cucumber Flowers by Liyang Su, Shujuan Zhang, Hongtu Zhang, Xiangsen Meng, Xiongkui He

“…The YOLOv8n-T incorporates a Coordinate Attention (CA) mechanism and lightweight modules while optimizing the loss function, thereby improving floral feature extraction capabilities and reducing computational complexity. …”
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Energy-Efficient Route and Velocity Planning for Electric Vehicles: A Hierarchical Eco-Driving Framework Integrating Traffic and Road Information by Dong Xie, Jianhua Guo, Yu Jiang, Zhuoran Hou, Jintao Deng

“…Then, an energy-efficient route is efficiently planned using the proposed multi-heuristic A* algorithm. Finally, based on the route information from the upper level, along with traffic, kinematic, and road information, a convex optimization algorithm is employed to achieve accurate and efficient velocity planning. …”
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A Maximal Concurrency and Low Latency Distributed Scheduling Protocol for Wireless Sensor Networks by Xiaohui Liu, Yu Chen, Hongwei Zhang

“…Existing work that schedules concurrent transmissions without collisions suffers from low channel utilization. We propose the Optimal Node Activation Multiple Access (ONAMA) protocol to achieve maximal channel spatial reuse through a distributed maximal independent set (DMIS) algorithm. …”
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Voronoi-GRU-Based Multi-Robot Collaborative Exploration in Unknown Environments by Yang Lei, Jian Hou, Peixin Ma, Mingze Ma

“…In the lower layer, navigation planning is achieved using A* and Timed Elastic Band (TEB) algorithms, while an improved Cartographer algorithm is used to construct a joint map for the multi-robot system. …”
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Deep Reinforcement Learning-Based Impact Angle-Constrained Adaptive Guidance Law by Zhe Hu, Wenjun Yi, Liang Xiao

“…This study presents an advanced second-order sliding-mode guidance law with a terminal impact angle constraint, which ingeniously combines reinforcement learning algorithms with the nonsingular terminal sliding-mode control (NTSM) theory. …”
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Enhancing high pressure pulsation test bench performance: a machine learning approach to failure condition tracking by Aslı Aksoy, Ömer Haki

“…The objective of this study is to enhance the efficiency of HPPT benches by addressing specimen, bench, and test environment- based problems and to develop a failure condition tracking tool (FCTT) by using machine learning (ML) algorithms. The findings of this study provide a basis for the development of the company’s data-driven smart predictive maintenance applications while providing an increase in the operational efficiency of HPPT benches. …”
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Expansion Planning of Electrical Distribution Systems Considering Voltage Quality and Reliability Criteria by Marco Israel Zuñiga Villarreal, Alexander Aguila Téllez, Narayanan Krishnan, Marcelo García

“…A 5% annual load growth was simulated for each system, which involved analyzing key parameters, such as the power loss, voltage deviation, and average failure rate. An algorithm was developed to perform a multi-criteria analysis, which provided optimal solutions for the system behavior in response to increasing demand. …”
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