Computational modelling and optimization of porous plates for mandibular fracture fixation accounting for bone healing by Ali Mehboob, Imad Barsoum, Hassan Mehboob, Abdelhak Ouldyerou, Rashid K. Abu Al-Rub

“…The healing process was predicted using a mechano-regulation algorithm. Conventional plate showed excessive interfragmentary strain (IFS) of approximately 1.4 and 0.6 in the 0.5 mm and 2.0 mm fracture gaps, respectively, which resulted in delayed healing. 2 MPP bone plates exhibited improved biomechanical performance with less stress in the bone plates and improved micromovement at the fracture site, promoting early bone union. …”
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Crop choice advisory for the West African Sudan Savanna based on soil type and presowing rainfall forecasts: A machine learning residual model approach by Toshichika Iizumi, Kohtaro Iseki, Kenta Ikazaki, Toru Sakai, Shintaro Kobayashi, Benoit Joseph Batieno

“…Using the random forest (RF) algorithm, residual models were developed for cowpea, groundnut, soybean, maize, millet, and sorghum cultivated at three locations in Burkina Faso. …”
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The Immune‐Related Gene CD48 Is a Prognostic Biomarker Associated With the Breast Cancer Tumor Microenvironment by Yi Zhao, Hengheng Zhang, Wenwen Wang, Guoshuang Shen, Miaozhou Wang, Zhen Liu, Jiuda Zhao, Jinming Li

“…Conclusions CD48 may serve as an effective biomarker for predicting BCa patient prognosis and a potential immune‐related therapeutic target.…”
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Early breast cancer detection via infrared thermography using a CNN enhanced with particle swarm optimization by Riyadh M. Alzahrani, Mohamed Yacin Sikkandar, S. Sabarunisha Begum, Ahmed Farag Salem Babetat, Maryam Alhashim, Abdulrahman Alduraywish, N. B. Prakash, Eddie Y. K. Ng

“…An Enhanced Particle Swarm Optimization (EPSO) algorithm is integrated to automatically fine-tune CNN hyperparameters, thereby minimizing manual effort and enhancing computational efficiency. …”
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The Water Table Model (WTM) (v2.0.1): coupled groundwater and dynamic lake modelling by K. L. Callaghan, K. L. Callaghan, K. L. Callaghan, A. D. Wickert, A. D. Wickert, A. D. Wickert, R. Barnes, J. Austermann

“…The dynamic lake component makes use of the Fill–Spill–Merge (FSM) algorithm to move surface water into lakes, where it may evaporate or affect groundwater flow. …”
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Berg Balance Scale Scoring System for Balance Evaluation by Leveraging Attention-Based Deep Learning with Wearable IMU Sensors by Zhangli Lu, Huiying Zhou, Honghao Lyu, Haiteng Wu, Shaohua Tian, Geng Yang

“…The key limitations included: a limited generalizability to severely impaired patients who were unable to walk independently, and the inability to predict the score of individual tasks.…”
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Chemical Composition, Chemometric Analysis, and Sensory Profile of <i>Santolina chamaecyparissus L.</i> (<i>Asteraceae</i>) Essential Oil: Insights from a Case Study in Serbia and... by Biljana Lončar, Mirjana Cvetković, Milica Rat, Jovana Stanković Jeremić, Jelena Filipović, Lato Pezo, Milica Aćimović

“…Chemometric analysis proved effective in predicting the oil’s composition, and sensory evaluation revealed a herbal aroma with earthy, woody, and camphoraceous notes. …”
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Modeling saturation exponent of underground hydrocarbon reservoirs using robust machine learning methods by Abhinav Kumar, Paul Rodrigues, A. K. Kareem, Tingneyuc Sekac, Sherzod Abdullaev, Jasgurpreet Singh Chohan, R. Manjunatha, Kumar Rethik, Shivakrishna Dasi, Mahmood Kiani

“…A well-known outlier detection algorithm is applied on the gathered data to assess the data reliability before model development. …”
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Deep Learning-Based Secrecy Performance of UAV-IRS NOMA Systems With Friendly Jamming by Kajal Yadav, Prabhat K. Upadhyay, Jules M. Moualeu, Amani A. F. Osman, Pedro H. J. Nardelli

“…Subsequently, the accuracy of the proposed theoretical framework is validated through comprehensive Monte Carlo simulations. We also propose an algorithm that determines an optimal power allocation. …”
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Machine Learning-Driven Optimization of Transport Layers in MAPbI&#x2083; Perovskite Solar Cells for Enhanced Performance by Velpuri Leela Devi, Piyush Kuchhal, Debasis de, Abhinav Sharma, Neeraj Kumar Shukla, Mona Aggarwal

“…In this research work, among those eight ML models, the XGBoost algorithm shows high accuracy for predicting the power conversion efficiency (PCE) of the cell, achieving root mean square error (RMSE) of 0.052 and a coefficient of determination (R2) of 0.999. …”
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Optimizing Automatic Voltage Control Collaborative Responses in Chain-Structured Cascade Hydroelectric Power Plants Using Sensitivity Analysis by Li Zhang, Jie Yang, Jun Wang, Lening Wang, Haiming Niu, Xiaobing Liu, Simon X. Yang, Kun Yang

“…Subsequently, a regional-voltage-coordinated regulation model is developed using sensitivity analysis, followed by the establishment of a mathematical model, solution algorithm, and operational procedure for multi-station AVC-coordinated response optimization. …”
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Data-driven machine learning with lattice distortion and thermodynamic parameters guided strength optimization of refractory high-entropy alloys by Shujian Ding, Yifan Zhang, Siyang Lei, Xiang Weng, Wenhui Li, Wei Ren, Jian Chen, Weili Wang

“…The LightGBM algorithm was utilized to train the model based on a RHEA yield strength dataset. …”
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Rapid screening and optimization of CO2 enhanced oil recovery operations in unconventional reservoirs: A case study by Shuqin Wen, Bing Wei, Junyu You, Yujiao He, Qihang Ye, Jun Lu

“…Based on the results of model interpretability, the genetic algorithm (GA) was coupled with RF (RF-GA model) to optimize the CO2-EOR process. …”
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Development of an MRI based artificial intelligence model for the identification of underlying atrial fibrillation after ischemic stroke: a multicenter proof-of-concept analysisRes... by Zijie Zhang, Yang Ding, Kaibin Lin, Wenli Ban, Luyue Ding, Yudong Sun, Chuanliang Fu, Yihang Ren, Can Han, Xue Zhang, Xiaoer Wei, Shundong Hu, Yuwu Zhao, Li Cao, Jun Wang, Saman Nazarian, Ying Cao, Lan Zheng, Min Zhang, Jianliang Fu, Jingbo Li, Xiang Han, Dahong Qian, Dong Huang

“…Area under the curve (AUC), sensitivity, specificity, accuracy, positive predictive value, and negative predictive value were calculated for model evaluation. …”
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Prevention and management of degenerative lumbar spine disorders through artificial intelligence-based decision support systems: a systematic review by Paolo Giaccone, Federico D’Antoni, Fabrizio Russo, Luca Ambrosio, Giuseppe Francesco Papalia, Onorato d’Angelis, Gianluca Vadalà, Albert Comelli, Luca Vollero, Mario Merone, Rocco Papalia, Vincenzo Denaro

“…Several different machine learning and deep learning algorithms were employed, and their predictive ability on clinical, demographic, psychosocial, and imaging data was assessed. …”
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Exploring high entropy alloys: A review on thermodynamic design and computational modeling strategies for advanced materials applications by Peter Ifeolu Odetola, Bukola Joseph Babalola, Ayodeji Ebenezer Afolabi, Ufoma Silas Anamu, Emmanuel Olorundaisi, Mutombo Christian Umba, Thabang Phahlane, Olusoji Oluremi Ayodele, Peter Apata Olubambi

“…By dissecting the foundational ''four core effects'' intrinsic to HEAs—high entropy, sluggish diffusion, severe lattice distortion, and cocktail effect—we illuminate the path towards predictable and tailored material properties. Central to the present discourse is the application of valence electron concentration (VEC) and cutting-edge strategies, including the CALculation of PHAse Diagrams (CALPHAD) method, first-principles approach, and machine-learning algorithms, which collectively empower the prediction and understanding of HEA behavior. …”
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Scientific planning of dynamic crops in complex agricultural landscapes based on adaptive optimization hybrid SA-GA method by Changlong Li, Zengye Su, Yudan Nie, Zhiyi Ye, Jinyi Li, Jing Wang, Zicong Yang, Xuxin Li, Weijian Zeng, Yanjian Chen

“…This framework is underpinned by a sophisticated model integrating advanced monitoring systems with a Hybrid Simulated Annealing Genetic Algorithm (H-SAGA), further enhanced by neural network-driven real-time predictions. …”
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Iron Ore Information Extraction Based on CNN-LSTM Composite Deep Learning Model by Haili Chen, Mengxiang Xia, Yaping Zhang, Ruonan Zhao, Bingran Song, Yang Bai

“…In contrast, with the use of data-driven and sophisticated algorithms, modern hyperspectral technology can quickly deliver high-precision iron ore information, increasing efficiency. …”
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Multi-modality radiomics diagnosis of breast cancer based on MRI, ultrasound and mammography by Jiao Wu, YongXin Li, Wanqing Gong, Qian Li, Xue Han, Tingting Zhang

“…Conclusions The multi-modality radiomics model based on MRI, ultrasound, and mammography can predict benign and malignant breast lesions.…”
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A Meta-Learning-Based Ensemble Model for Explainable Alzheimer’s Disease Diagnosis by Fatima Hasan Al-bakri, Wan Mohd Yaakob Wan Bejuri, Mohamed Nasser Al-Andoli, Raja Rina Raja Ikram, Hui Min Khor, Zulkifli Tahir, The Alzheimer’s Disease Neuroimaging Initiative

“…The findings demonstrated significant progress in diagnostic transparency, as the algorithm consistently linked predictions to observed structural changes in the dilated lateral ventricles of the brain, which serve as a clinically reliable biomarker for AD and can be easily verified by medical professionals. …”
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