Temporal Muscle Thickness as a New Biomarker: Its Association With Alzheimer Pathology and Cognitive Impairment in Chinese Adults by Yunxia Zhu, Yingying Ke, Chenxi Ren, Jiehua Zhu, Zhen Zhang, Liang Cui, Yuehua Li, Fang Xie, Qihao Guo

“…Functional performance was evaluated using the Functional Activities Questionnaire. …”
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Models for the Design and Operation of Digital Educational Environments by A. V. Solovov, A. A. Menshikova

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Machine learning-driven prediction model for cuproptosis-related genes in spinal cord injury: construction and experimental validation by Yimin Zhou, Xin Li, Zixiu Wang, Liqi Ng, Rong He, Chaozong Liu, Gang Liu, Xiao Fan, Xiaohong Mu, Yu Zhou, Yu Zhou

“…Abnormal expression of the four candidate genes affects mitochondrial function, energy metabolism, oxidative stress, and the immune response, which is detrimental to the recovery of neurological function in SCI. …”
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Student behavior recognition based on multi-view learning via mask RCNN and its application in education management by Fei Xiao

“…Therefore, this paper proposes a novel student behavior recognition combined with multi-view learning and mask RCNN (Region-based convolutional neural network. …”
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Identification of podocyte molecular markers in diabetic kidney disease via single-cell RNA sequencing and machine learning. by Hailin Li, Quhuan Li, Zuyan Fan, Yue Shen, Jiao Li, Fengxia Zhang

“…Podocyte heterogeneity and functional differences in DKD were observed. Multiple machine-learning algorithms were used to screen and construct diagnostic models to identify hub differentially expressed podocyte marker genes (DE-podos), revealing ARHGEF26 as a significantly downregulated marker in DKD. …”
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Machine learning based assessment of hoarseness severity: a multi-sensor approach centered on high-speed videoendoscopy by Tobias Schraut, Anne Schützenberger, Tomás Arias-Vergara, Melda Kunduk, Matthias Echternach, Stephan Dürr, Julia Werz, Michael Döllinger

“…Integrating time-synchronized acoustic and HSV recordings could allow for an objective visual and acoustic assessment of vocal function based on a single HSV examination. This study investigates a machine learning-based approach for hoarseness severity assessment using synchronous HSV and acoustic recordings, alongside conventional voice examinations.MethodsThree databases comprising 457 HSV recordings of the sustained vowel /i/, 634 HSV-synchronized acoustic recordings, and clinical parameters from 923 visits were analyzed. …”
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Using machine learning to predict depression among middle-aged and elderly population in China and conducting empirical analysis. by Zhe Wang, Ni Jia

“…For depression prediction, factors such as life satisfaction, self-reported health, pain, sleep duration, and cognitive function are identified as highly significant predictive factors.…”
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Energy Trading in Local Energy Markets: A Comprehensive Review of Models, Solution Strategies, and Machine Learning Approaches by Sania Khaskheli, Amjad Anvari-Moghaddam

“…This classification examines various algorithms for energy transactions considering the agent type, learning methods, policy, state space, action space, and action selection for state, action, and reward function outputs. …”
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Federated deep reinforcement learning-based edge collaborative caching strategy in space-air-ground integrated network by LIU Liang, JING Tengxiang, DUAN Jie, MAO Wuping, YAN Hongcheng, MA Wenjie

“…With the reconfigured reward function as the optimization objective, the optimal cache replacement policy was learned by maximizing the expectation of negative long-term rewards. …”
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Optimal Iterative Learning Fault-Tolerant Guaranteed Cost Control for Batch Processes in the 2D-FM Model by Limin Wang, Weiwei Dong

“…The robust iterative learning reliable guaranteed cost controller (ILRGCC), which includes a robust extended feedback control for ensuring the performances over time and an iterative learning control (ILC) for improving the tracking performance from cycle to cycle, is formulated such that it cannot only guarantee the closed-loop convergency along both the time and the cycle directions but also satisfy both the H∞ performance level and a cost function having upper bounds for all admissible uncertainties and any actuator failures. …”
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Reinforcement-Learning-Based Fixed-Time Prescribed Performance Consensus Control for Stochastic Nonlinear MASs with Sensor Faults by Zhenyou Wang, Xiaoquan Cai, Ao Luo, Hui Ma, Shengbing Xu

“…The reinforcement-learning-based backstepping method is proposed to realize the optimal control of the system. …”
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Prediction of Seronegative Hashimoto's thyroiditis using machine learning models based on ultrasound radiomics: a multicenter study by Wenjun Wu, Shengsheng Yao, Daming Liu, Yuan Luo, Yihan Sun, Ting Ruan, Mengyou Liu, Li Shi, Chang Liu, Mingming Xiao, Qi Zhang, Zhengshuai Liu, Xingai Ju, Jiahao Wang, Xiang Fei, Li Lu, Yang Gao, Ying Zhang, Liying Gong, Xuanyu Chen, Wanli Zheng, Xiali Niu, Xiao Yang, Huimei Cao, Shijie Chang, Jianchun Cui, Zuoxin Ma

“…Combining ultrasound radiomics with machine learning offers potential for early detection in patients with normal thyroid function. …”
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Deep reinforcement learning model for Multi-Ship collision avoidance decision making design implementation and performance analysis by Rongjun Pan, Wei Zhang, Shijie Wang, Shuhua Kang

“…Our innovation lies in the integration of a comprehensive state representation capturing key inter-ship relationships, a reward function that dynamically balances safety, efficiency, and COLREGs compliance, and an enhanced DQN architecture with dueling networks and double Q-learning specifically optimized for maritime scenarios. …”
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Individual Travel Knowledge Graph-Based Public Transport Commuter Identification: A Mixed Data Learning Approach by Song Hu, Jiancheng Weng, Quan Liang, Wei Zhou, Peizhao Wang

“…The optimal model structure of neuron node number, transfer function, and learning rate are discussed quantitatively according to the minimization of model errors. …”
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Biomechanical optimization and reinforcement learning provide insight into transition from ankle to hip strategy in human postural control by Seongwoong Hong, Sukyung Park

“…The control objective function featured a novel CoP constraint penalty, complemented by terms for upright posture recovery and control effort minimization. …”
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Fatigue and stamina prediction of athletic person on track using thermal facial biomarkers and optimized machine learning algorithm by P. K. Santhosh, B. Kaarthick

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A Deep Learning Inversion Method for 3D Temperature Structures in the South China Sea with Physical Constraints by Dongcan Xu, Yahao Liu, Yuan Kong

“…The proposed approach advances ocean remote sensing by synergizing data-driven learning with thermodynamic principles, offering a robust framework for understanding the South China Sea’s thermal variability.…”
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Multi-Agent Deep Reinforcement Learning for Large-Scale Traffic Signal Control with Spatio-Temporal Attention Mechanism by Wenzhe Jia, Mingyu Ji

“…Recently, deep reinforcement learning (DRL) has emerged as a promising solution for optimizing signal control. …”
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Fuzzy Neural Network Q-Learning Method for Model Disturbance Change: A Deployable Antenna Panel Application by Zhiyong Liu, Hong Bao, Song Xue, Jingli Du

“…In the proposed method, the error of the model disturbance is reduced by introducing the fuzzy radial basis function (RBF) neural network into Q-learning, and the parameters of the fuzzy RBF neural network were optimized and adjusted by a Q-learning method. …”
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Determining Point of Economic Cattle Milk Production through Machine Learning and Evolutionary Algorithm for Enhancing Food Security by Sonam Bhardwaj, Ayon Tarafdar, Manoj Baghel, Triveni Dutt, Gyanendra Kumar Gaur

“…The results showed that BR with 2 hidden layer neurons showed excellent prediction ability (R2 = 0.999, MSE < 10−6) and was therefore used as an objective function by GA for optimization. The optimized results revealed that higher milk production is achievable at lower investment if the age at first calving is 768 days with a body weight of ∼281 kg. …”
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