Machine learning‐based model for worsening heart failure risk in Chinese chronic heart failure patients by Ziyi Sun, Zihan Wang, Zhangjun Yun, Xiaoning Sun, Jianguo Lin, Xiaoxiao Zhang, Qingqing Wang, Jinlong Duan, Li Huang, Lin Li, Kuiwu Yao

“…The best models were identified by integrating nine ML algorithms and interpreted using SHAP, and to develop a final risk calculation tool. …”
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Automatic location and recognition of horse freezing brand using rotational YOLOv5 deep learning network by Zhixin Hua, Yitao Jiao, Tianyu Zhang, Zheng Wang, Yuying Shang, Huaibo Song

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Dynamic Virtual Simulation with Real-Time Haptic Feedback for Robotic Internal Mammary Artery Harvesting by Shuo Wang, Tong Ren, Nan Cheng, Rong Wang, Li Zhang

“…Our key innovations include a topology-preserving cutting algorithm, a bidirectional tissue coupling mechanism, and dual-channel haptic feedback for electrocautery simulation. …”
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Lifetime Prediction Analysis of Proton Exchange Membrane Fuel Cells Based on Empirical Mode Decomposition—Temporal Convolutional Network by Chao Zheng, Changqing Du, Jiaming Zhang, Yiming Zhang, Jun Shen, Jiaxin Huang

“…This study proposes a novel EMD-TCN-GN algorithm, which, for the first time, integrates empirical mode decomposition (EMD), temporal convolutional network (TCN), and group normalization (GN) by using EMD to adaptively decompose non-stationary signals (such as voltage fluctuations), the dilated convolution of TCN to capture long-term dependencies, and combining GN to group-calibrate intrinsic mode function (IMF) features to solve the problems of modal aliasing and training instability. …”
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A hybrid data-driven method for voltage state prediction and fault warning of Li-ion batteries by Yufeng Huang, Xuejian Gong, Zhiyu Lin, Lei Xu

“…Secondly, a Bi-LSTM module is used to learn long-term dependence relationships among fused features while integrating a self-attention mechanism. To further verify the algorithm's effectiveness, a new 18650 battery dataset has been set up under various conditions between day and night. …”
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The PLSR-ML fusion strategy for high-accuracy leaf potassium inversion in karst region of Southwest China by Zhihao Song, Zhihao Song, Wen He, Yuefeng Yao, Ling Yu, Jinjun Huang, Yong Xu, Haoyu Wang

“…Validation coefficient of determination (R²) values reached 0.89, 0.94, and 0.96, respectively—representing improvements of 206%, 147%, and 108% over standalone algorithms. …”
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Capacity Estimation and Knee Point Prediction Using Electrochemical Impedance Spectroscopy for Lithium Metal Battery Degradation via Machine Learning by Qianli Si, Shoichi Matsuda, Yasunobu Ando, Toshiyuki Momma, Yoshitaka Tateyama

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Spatiotemporal Analysis and Anomalous Trends of Asia AOD (2001–2024): Insights from a Deep Learning Fusion Model and EOF Decomposition by Yu Ding, Wenjia Ni, Jiaxin Dong, Jie Yang, Shiyao Meng, Siwei Li

“…However, satellite-derived AOD datasets frequently suffer from missing values due to factors such as cloud cover, algorithmic limitations, and various atmospheric conditions. …”
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Grid tied hybrid PV fuel cell system with energy storage and ANFIS based MPPT for smart EV charging by Suresh vendoti, Narasimha Prasad Tulasi, Ravi Kumar Jalli, Sudhakiran Ponnuru, Zhang Jin, P. Yakaiah, Mamdooh Alwetaishi, S. Prabhakar

“…An adaptive neuro-fuzzy inference system (ANFIS)-based maximum power point tracking (MPPT) algorithm is employed to enhance PV power extraction under dynamically varying environmental conditions. …”
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Modelling Ergonomic Hazard Risks in Manual Handling: Insights from Ponorogo’s Traditional Industry by Dian Afif Arifah, Ratih Andhika Akbar Rahma, Triana Harmini, Dhiya Irsyad Hafidz

“…This study aims to develop a model to evaluate and predict ergonomic hazards using a neural network algorithm, focusing on the relationship between manual handling postures and musculoskeletal pain in 12 body regions. …”
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Attention-Based CNN Fusion Model for Emotion Recognition During Walking Using Discrete Wavelet Transform on EEG and Inertial Signals by Yan Zhao, Ming Guo, Xiangyong Chen, Jianqiang Sun, Jianlong Qiu

“…To effectively improve the performance of the recognition system, the proposed decision fusion algorithm combines Critic method and majority voting strategy to determine the weight values that affect the final decision results. …”
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Method and application of stability prediction model for rock slope by Yun Qi, Chenhao Bai, Xuping Li, Hongfei Duan, Wei Wang, Qingjie Qi

“…To accurately and efficiently predict the stability state of slopes, we propose a combined model that integrates the Newton–Raphson optimization algorithm (NRBO) with an optimized extreme gradient boosting tree (XGBoost). …”
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Continuously Variable Geometry Quadrotor: Robust Control via PSO-Optimized Sliding Mode Control by Foad Hamzeh, Siavash Fathollahi Dehkordi, Alireza Naeimifard, Afshin Abyaz

“…A sliding mode control algorithm, optimized using particle swarm optimization, is implemented to ensure stability and high performance in the presence of uncertainties and noise. …”
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Enhancing LiDAR Mapping with YOLO-Based Potential Dynamic Object Removal in Autonomous Driving by Seonghark Jeong, Heeseok Shin, Myeong-Jun Kim, Dongwan Kang, Seangwock Lee, Sangki Oh

“…In this study, we propose an enhanced LiDAR-based mapping and localization system that utilizes a camera-based YOLO (You Only Look Once) algorithm to detect and remove dynamic objects, such as vehicles, from the mapping process. …”
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A Portable Smartphone-Based 3D-Printed Biosensing Platform for Kidney Function Biomarker Quantification by Sangeeta Palekar, Sharayu Kalambe, Jayu Kalambe, Madhusudan B. Kulkarni, Manish Bhaiyya

“…The platform features a 3D-printed enclosure with integrated diffused LED lighting to ensure a controlled environment for image acquisition. …”
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Development of a postoperative recurrence prediction model for stage Ⅰ non-small cell lung cancer patients using multimodal data based on machine learning by ZHANG Di, WU Yi, XU Yu

“…By integrating the 7 radiomic features and 4 clinical features using a feature-level fusion strategy, the combined model exhibited further improved predictive performance, with an AUC value of 0.953 (95% CI: 0.924~0.983; accuracy: 0.884, specificity: 0.860) and 0.852 (95% CI: 0.729~0.976; accuracy: 0.682, specificity: 0.629), respectively in the training set and the validation set. …”
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Advancing Marine Surveillance: A Hybrid Approach of Physics Infused Neural Network for Enhanced Vessel Tracking Using Automatic Identification System Data by Tasmiah Haque, Md Asif Bin Syed, Srinjoy Das, Imtiaz Ahmed

“…Recognizing the strengths and limitations of the LSTM model, we propose a hybrid machine-learning algorithm that integrates LSTM with a physics-based model. …”
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Inversion and validation of soil water-holding capacity in a wild fruit forest, using hyperspectral technology combined with machine learning by Tingwei Song, Liang Guo, Qian Sun, Guizhen Gao, Jing Chen, Qikun Zhang

“…The spectral characteristics of the forest canopy were employed as a bridge to enhance the sensitivity between the SWHC and various vegetation indices using mathematical statistical methods. This study integrated hyperspectral technology with machine learning algorithms to model complex nonlinear relationships and to select the optimal SWHC model. …”
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Mathematical approach for rapid determination of pull-in displacement in MEMS devices by Yongcun Shao, Yutong Cui

“…These equations are transformed into an iterative algorithm for calculating pull-in displacement, with nonlinear terms addressed through approximation and perturbation techniques tailored to the MEMS system’s characteristics.ResultsValidation using specific examples demonstrates the method's accuracy in determining pull-in displacement and voltage. …”
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Predicting hospital outpatient volume using XGBoost: a machine learning approach by Lingling Zhou, Qin Zhu, Qian Chen, Ping Wang, Hao Huang

“…This study aims to develop a predictive model for daily hospital outpatient volume using the XGBoost algorithm. Meanwhile, the forecasting performance was compared with that of the Seasonal AutoRegressive Integrated Moving Average with exogenous regressors (SARIMAX) and Random Forest (RF) models. …”
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