Constitutive modeling and workability characterization of pre-deformed AZ31 magnesium alloy during hot shear-compression deformation by Junsong Jin, Fangtao Chai, Jinchuan Long, Chang Gao, Shaolei Wang, Pan Zeng, Xuefeng Tang, Pan Gong, Mao Zhang, Lei Deng, Xinyun Wang

“…The deformation characteristics, flow behavior and microstructure/texture evolution mechanisms of pre-deformed AZ31 alloy were systematically investigated under varying process parameters. A genetic algorithm-optimized artificial neural network (GA-ANN) constitutive model was developed using machine learning methods, and hot processing maps were established based on this model. …”
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GYS-RT-DETR: A Lightweight Citrus Disease Detection Model Based on Integrated Adaptive Pruning and Dynamic Knowledge Distillation by Linlin Yang, Zhonghao Huang, Yi Huangfu, Rui Liu, Xuerui Wang, Zhiwei Pan, Jie Shi

“…Secondly, the model adopts two model optimization strategies: (1) The Group_taylor local pruning algorithm is used to reduce memory occupation and the number of computing parameters of the model. (2) The feature-logic knowledge distillation framework is proposed and adopted to solve the problem of information loss caused by the structural difference between teachers and students, and to ensure a good detection performance, while realizing the lightweight character of the model. …”
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Plasma methylated HIST1H3G as a non-invasive biomarker for diagnostic modeling of hepatocellular carcinoma by Weiwei Zhu, Weiwei Zhu, Huifen Wang, Huifen Wang, Yudie Cai, Yudie Cai, Jun Lei, Jun Lei, Jia Yu, Jia Yu, Ang Li, Zujiang Yu

“…HIST1H3G, PIVKA-II, total bilirubin (TBIL) and age were selected as the optimal markers and were included in the development of a diagnostic model. …”
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Rapid and Accurate Measurement of Major Soybean Components Using Near-Infrared Spectroscopy by Chenxiao Li, Jiatong Yu, Sheng Wang, Qinglong Zhao, Qian Song, Yanlei Xu

“…Comparative analysis revealed that the RF model consistently outperformed the others across most combinations. …”
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Protection scheme of flexible MTDC transmission line based on ISSA-BiLSTM by LI Zheng, CHEN Tangxian, ZHANG Yunning, LIU Shuangyang, SUN Peisheng

“…Based on wavelet transform technology, the characteristics of transmission line faults are extracted as model input to train the model; the original sparrow search algorithm is improved by using Sine chaotic mapping, learning particle swarm algorithm strategy, and introducing Gaussian disturbance term. …”
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Characterization of Irrigated Rice Cultivation Cycles and Classification in Brazil Using Time Series Similarity and Machine Learning Models with Sentinel Imagery by Andre Dalla Bernardina Garcia, Ieda Del’Arco Sanches, Victor Hugo Rohden Prudente, Kleber Trabaquini

“…However, challenges such as managing large volumes of data, addressing data gaps, and optimizing available data are key focuses in remote sensing research using automated machine learning models. …”
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Matlab-Based Modeling and Simulations to Study the Performance of Different MPPT Techniques Used for Photovoltaic Systems under Partially Shaded Conditions by Jehun Hahm, Jaeho Baek, Hyoseok Kang, Heejin Lee, Mignon Park

“…The proposed method applied a model to simulate the performance of the PV system for solar energy usage, which is compared to the conventional methods under nonuniform insolation improving the PV system utilization efficiency and allowing optimization of the system performance. …”
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A Two-Sided Stable Matching Model of Cloud Manufacturing Tasks and Services considering the Nonlinear Relationship between Satisfaction and Expectations by Yujie Zheng, Meiyan Li, Jiakun Liu

“…Finally, an adaptive genetic algorithm (AGA) is designed to solve the proposed two-sided matching model. …”
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Maximum likelihood self-calibration for direction-dependent gain-phase errors with carry-on instrumental sensors:case of deterministic signal model by WANG Ding1, PAN Miao2, WU Ying1

“…Aim at the self-calibration of direction-dependent gain-phase errors in case of deterministic signal model,the maximum likelihood method（MLM） for calibrating the direction-dependent gain-phase errors with carry-on instrumental sensors was presented.In order to maximize the high-dimensional nonlinear cost function appearing in the MLM,an improved alternative projection iteration algorithm,which could optimize the azimuths and direction-dependent gain-phase errors was proposed.The closed-form expressions of the Cramér-Rao bound（CRB） for azimuths and gain-phase errors were derived.Simulation experiments show the effectiveness and advantage of the novel method.…”
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Maximum likelihood self-calibration for direction-dependent gain-phase errors with carry-on instrumental sensors:case of deterministic signal model by WANG Ding1, PAN Miao2, WU Ying1

“…Aim at the self-calibration of direction-dependent gain-phase errors in case of deterministic signal model,the maximum likelihood method（MLM） for calibrating the direction-dependent gain-phase errors with carry-on instrumental sensors was presented.In order to maximize the high-dimensional nonlinear cost function appearing in the MLM,an improved alternative projection iteration algorithm,which could optimize the azimuths and direction-dependent gain-phase errors was proposed.The closed-form expressions of the Cramér-Rao bound（CRB） for azimuths and gain-phase errors were derived.Simulation experiments show the effectiveness and advantage of the novel method.…”
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Predicting Earthquake Casualties and Emergency Supplies Needs Based on PCA-BO-SVM by Fuyu Wang, Huiying Xu, Huifen Ye, Yan Li, Yibo Wang

“…Subsequently, the optimal hyperparameters for the SVM model are obtained using the Bayesian Optimization algorithm. …”
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Development and validation of a quick screening tool for predicting neck pain patients benefiting from spinal manipulation: a machine learning study by Changxiao Han, Guangyi Yang, Haibao Wen, Minrui Fu, Bochen Peng, Bo Xu, Xunlu Yin, Ping Wang, Liguo Zhu, Minshan Feng

“…Among the algorithms tested, the Multilayer Perceptron (MLP) model demonstrated optimal performance with an AUC of 0.823 (95% CI 0.750, 0.874) in the test set, showing consistency between training (AUC = 0.829) and test performance. …”
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Comparing Grid Model Fitting Methodologies for Low-temperature Atmospheres: Markov Chain Monte Carlo versus Random Forest Retrieval by Anna Lueber, Adam J. Burgasser

“…Here, we compare two grid model fitting approaches: a Markov Chain Monte Carlo (MCMC) algorithm interpolating across spectral fluxes, and a random forest retrieval (RFR) algorithm trained on a grid model set. …”
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Predicting bearing capacity of gently inclined bauxite pillar based on numerical simulation and machine learning by Deyu WANG, Defu ZHU, Biaobiao YU, Chen WANG

“…Additionally, two optimization algorithms, Genetic Programming (GP) and Improved Quantum Particle Swarm Algorithm (IQPSO), were used to enhance model performance and establish a non-linear mapping relationship between the influencing factors and the strength of the gently inclined pillars. …”
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Three-dimensional visualization of maize roots based on magnetic resonance imaging by Fang Xiaorong, Wang Nanfei, Zhang Jianfeng, Gong Xiangyang, Liu Fei, He Yong

“…A procedure to obtain root architecture system of maize was developed by computer image graphics technology. The root model was reconstructed with improved volume rendering algorithm in the environment of Visualization Toolkit 5.4. …”
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Enhancing Model Accuracy of UAV-Based Biomass Estimation by Evaluating Effects of Image Resolution and Texture Feature Extraction Strategy by Yaxiao Niu, Xiaoying Song, Liyuan Zhang, Lizhang Xu, Aichen Wang, Qingzhen Zhu

“…Maize AGB estimation models were established based on SIs only and combination of SIs and TFs using machine learning algorithms. …”
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Prediction of recurrence after surgery for pituitary adenoma using machine learning- based models: systematic review and meta-analysis by Ibrahim Mohammadzadeh, Bardia Hajikarimloo, Behnaz Niroomand, Nasira Faizi, Pooya Eini, Mohammad Amin Habibi, Alireza Mohseni, Mohammadmahdi Sabahi, Abdulrahman Albakr, Michael Karsy, Hamid Borghei-Razavi

“…Abstract Background Predicting pituitary adenoma (PA) recurrence after surgical resection is critical for guiding clinical decision-making, and machine learning (ML) based models show great promise in improving the accuracy of these predictions. …”
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Machine Learning Model Based on Prognostic Nutritional Index for Predicting Long‐Term Outcomes in Patients With HCC Undergoing Ablation by Nan Zhang, Ke Lin, Bin Qiao, Liwei Yan, Dongdong Jin, Daopeng Yang, Yue Yang, Xiaohua Xie, Xiaoyan Xie, Bowen Zhuang

“…ABSTRACT Aims To develop multiple machine learning (ML) models based on the prognostic nutritional index (PNI) and determine the optimal model for predicting long‐term survival outcomes in hepatocellular carcinoma (HCC) patients after local ablation. …”
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Inflow Prediction for Agricultural Reservoirs Using Disaster Prevention Measurement Data: A Comparison of TANK Model and Machine Learning by Bong-Kuk Lee, Joonyoung Choi, Kyoung Jae Lim, Jeongho Han

“…The results of this study demonstrate the potential of machine learning techniques for inflow prediction in agricultural reservoirs and suggest the need for further research on model improvement using various algorithms and input variables.…”
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Ensemble Machine Learning Model Prediction and Metaheuristic Optimisation of Oil Spills Using Organic Absorbents: Supporting Sustainable Maritime by Le Quang Dung, Pham Duc, Bui Thi Anh Em, Nguyen Lan Huong, Nguyen Phuoc Quy Phong, Dang Thanh Nam

“…To close this gap, our work combines metaheuristic algorithms with ensemble machine learning and suggests a hybrid technique for the precise prediction and improvement of oil removal efficiency. …”
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