State-of-the-Art Fault Detection and Diagnosis in Power Transformers: A Review of Machine Learning and Hybrid Methods by Lebo Dina Mashifane, Bongumsa Mendu, Bessie Baakanyang Monchusi

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A Nonlinear Integer Programming Model for Integrated Location, Inventory, and Routing Decisions in a Closed-Loop Supply Chain by Hao Guo, Congdong Li, Ying Zhang, Chunnan Zhang, Yu Wang

“…Second, we develop a novel heuristic approach that incorporates simulated annealing into adaptive genetic algorithm to solve the model efficiently. Last, numerical analysis is presented to validate our solution approach, and it also provides meaningful managerial insight into how to improve the closed-loop supply chain under study.…”
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Deep Reinforcement Learning Based Active Disturbance Rejection Control for ROV Position and Attitude Control by Gaosheng Luo, Dong Zhang, Wei Feng, Zhe Jiang, Xingchen Liu

“…The deep deterministic policy gradient (DDPG) algorithm was used to optimize the linear extended state observer (LESO). …”
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Addressing the Return Visit Challenge in Autonomous Flying Ad Hoc Networks Linked to a Central Station by Ercan Erkalkan, Vedat Topuz, Ali Buldu

“…This paper presents different approaches to efficiently directing UAVs and explains how heuristic algorithms can enhance our understanding and improve current methods for task assignments.…”
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Enhancing Performance and Stability of Wing-Alone UAV: A Comprehensive Mathematical Model and Simulation Approach Using MATLAB and Simulink by G. Ramanan, N. Rahul, V. E. Sathishkumar, Indraraj Upadhyaya

“…The novel objective of this study is enhancing the performance and stability aspects of the wing-alone UAV through code-based reports and the implementation of custom MATLAB algorithms. The wing-alone UAV demonstrated a 15% improvement in aerodynamic efficiency and a 10% reduction in overall weight compared to baseline designs. …”
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Prediction Model of Household Carbon Emission in Old Residential Areas in Drought and Cold Regions Based on Gene Expression Programming by Shiao Chen, Yaohui Gao, Zhaonian Dai, Wen Ren

“…Key influencing factors (e.g., electricity usage and heating energy consumption) were selected using Pearson correlation analysis and the Random Forest (RF) algorithm. Subsequently, a hybrid prediction model was constructed, with its parameters optimized by minimizing the root mean square error (RMSE) as the fitness function. …”
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Dengue Early Warning System and Outbreak Prediction Tool in Bangladesh Using Interpretable Tree‐Based Machine Learning Model by Md. Siddikur Rahman, Miftahuzzannat Amrin, Md. Abu Bokkor Shiddik

“…The optimal tree‐based ML model with strong interpretability was created by comparing various ML models using the hyperparameter optimization technique. …”
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A novel bivariate regression model derived from the clayton copula and the Odd Dagum-G family and its application by Julius Kwaku Adu-Ntim, Akoto Yaw Omari-Sasu, Maxwell Akwasi Boateng, Isaac Adjei Mensah

“…The models cumulative distribution function (CDF) and probability distribution function (PDF) are derived and the parameters were estimated using the maximum likelihood estimation (MLE) where the likelihood function was optimized using the Broyden-Fletcher-Goldfarb-Shannon (BFGS) algorithm.Simulation is conducted under various scenarios to validate the model’s robustness, exhibiting consistent estimators, reduced bias, and decreasing mean square errors (MSEs) with increasing sample size. …”
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FPA-based weighted average ensemble of deep learning models for classification of lung cancer using CT scan images by Liang Zhou, Achin Jain, Arun Kumar Dubey, Sunil K. Singh, Neha Gupta, Arvind Panwar, Sudhakar Kumar, Turki A. Althaqafi, Varsha Arya, Wadee Alhalabi, Brij B. Gupta

“…These results confirm the model’s effectiveness in accurate and reliable cancer diagnosis. …”
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Leveraging Thermal Infrared Imaging for Pig Ear Detection Research: The TIRPigEar Dataset and Performances of Deep Learning Models by Weihong Ma, Xingmeng Wang, Simon X. Yang, Lepeng Song, Qifeng Li

“…Overall, the TIRPigEar dataset demonstrates optimal performance when applied to the YOLOv9m algorithm. …”
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Predictive model for determining the indications for automated 3D ultrasound for screening patients at low risk of developing breast tumors by A. E. Garanina, A. V. Kholin

“…The developed algorithms will help optimize screening and referral for additional examinations, which is of practical importance for improving diagnostics and optimizing healthcare resources.…”
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Prediction of three-year all-cause mortality in patients with heart failure and atrial fibrillation using the CatBoost model by Jiacan Wu, Guanghong Tao, Siyuan Xie, Han Yang, Fenglin Qi, Naiyue Bao, Zhuo Li, Guanglei Chang, Hua Xiao

“…Conclusions CatBoost was identified as the optimal model for predicting three-year all-cause mortality in HF-AF patients, potentially aiding clinicians in risk stratification and individualized treatment planning to improve patient outcomes.…”
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Computational Modelling of Tunicamycin C Interaction with Potential Protein Targets: Perspectives from Inverse Docking with Molecular Dynamic Simulation by Vivash Naidoo, Ikechukwu Achilonu, Sheefa Mirza, Rodney Hull, Jeyalakshmi Kandhavelu, Marushka Soobben, Clement Penny

“…Following this, molecular dynamics modelling revealed that Tunicamycin C binding induced a conformational perturbation in the 3D structures of TK1 and PKAc, inhibiting their activities. …”
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A Predictive Method for Greenhouse Soil Pore Water Electrical Conductivity Based on Multi-Model Fusion and Variable Weight Combination by Jiawei Zhao, Peng Tian, Jihong Sun, Xinrui Wang, Changjun Deng, Yunlei Yang, Haokai Zhang, Ye Qian

“…We propose a hybrid prediction model—PSO–CNN–LSTM–BOA–XGBoost (PCLBX)—that integrates a particle swarm optimization (PSO)-enhanced convolutional LSTM (CNN–LSTM) with a Bayesian optimization algorithm-tuned XGBoost (BOA–XGBoost). …”
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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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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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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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