Optimization of energy acquisition system in smart grid based on artificial intelligence and digital twin technology by Zhen Jing, Qing Wang, Zhiru Chen, Tong Cao, Kun Zhang

“…Through data synchronization and interaction between the physical power grid and the digital twin model, the operational efficiency and reliability of the power grid are improved. …”
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Hierarchical Sensing Framework for Polymer Degradation Monitoring: A Physics-Constrained Reinforcement Learning Framework for Programmable Material Discovery by Xiaoyu Hu, Xiuyuan Zhao, Wenhe Liu

“…This paper introduces a novel physics-informed deep learning framework that integrates multi-scale molecular sensing data with reinforcement learning algorithms to enable intelligent characterization and prediction of polymer degradation dynamics. …”
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Integrating cyber-physical systems with embedding technology for controlling autonomous vehicle driving by Manal Abdullah Alohali, Hamed Alqahtani, Abdulbasit Darem, Monir Abdullah, Yunyoung Nam, Mohamed Abouhawwash

“…To address these constraints, this study incorporates double deep Q networks (DDQN) to improve the agent’s adaptability under uncertain driving conditions. …”
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The role of artificial intelligence in occupational health in radiation exposure: a scoping review of the literature by Zohreh Fazli, Mehran Sadeghi, Mohebat Vali, Parvin Ahmadinejad

“…Abstract Introduction Artificial intelligence (AI) has the potential to significantly enhance workplace safety and mitigate occupational radiation exposure risks by improving the accuracy of assessment and management of these hazards. …”
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Radiology AI and sustainability paradox: environmental, economic, and social dimensions by Burak Kocak, Andrea Ponsiglione, Valeria Romeo, Lorenzo Ugga, Merel Huisman, Renato Cuocolo

“…Socially, AI risks perpetuating healthcare disparities through biases in algorithms and unequal access to technology. On the other hand, AI has the potential to improve sustainability in healthcare by reducing low-value imaging, optimizing resource allocation, and improving energy efficiency in radiology departments. …”
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Range Space and Similarity Transformation Using Symbolic Mathematics in Python by Chainarong Wisassakwichai, Prasert Phaochoo

“…The effectiveness of these classes has been validated through numerical results, which, when compared to MATLAB functions (null(), pinv(), sym(), and jordan()), demonstrate that the exact symbolic solutions from both classes significantly improve numerical precision for selected application examples from numerical algorithms, power systems and linear algebra. …”
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Enhancing dosimetric precision in the treatment of cancerous tumors: Gamma Index validation and Monte Carlo simulations of 6 and 12 megavoltage photon beams from Varian Medical lin... by Ali H. D. Alshehri

“…These findings underscore the potential of MC simulations to improve the accuracy and reliability of radiotherapy treatment plans. …”
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Diagnostic value of leukocytosis in patients presenting to the emergency department with abdominal pain: A retrospective observational study by Erkan Boğa

“…Further multicenter prospective studies are needed to validate these findings and improve diagnostic algorithms.…”
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Body-part-based individual feral cat identification from camera trap images using deep learning by Rio Rifqi Syah Akbar, Matthew W. Rees, Patricia A. Fleming, Ferdous Sohel

“…We trained deep-learning models with a ResNet-50 backbone on these body parts indivdually as well as combinations of multiple body parts through feature concatenation. …”
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Debris-covered glaciers mapping based on machine learning and multi-source satellite images over Eastern Pamir by Aamir Feroz, Shuang Liang, Gunayu Li, Xinwu Li, Zahid Ur Rahman, Bojin Yang

“…Conventional glacier mapping techniques often misclassify debris-covered areas due to their spectral similarity to the surrounding terrain, making this work crucial in addressing these limitations. To improve the accuracy of recognition between debris-covered glaciers and non-glaciated areas by capitalizing on the strengths of multiple machine-learning algorithms and diverse data sources. …”
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Enhancing Ophthalmic Diagnosis and Treatment with Artificial Intelligence by David B. Olawade, Kusal Weerasinghe, Mathugamage Don Dasun Eranga Mathugamage, Aderonke Odetayo, Nicholas Aderinto, Jennifer Teke, Stergios Boussios

“…AI algorithms, particularly those utilizing machine learning (ML) and deep learning (DL), have demonstrated remarkable success in diagnosing conditions such as diabetic retinopathy (DR), age-related macular degeneration, and glaucoma with precision comparable to, or exceeding, human experts. …”
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Artificial Intelligence in cancer epigenomics: a review on advances in pan-cancer detection and precision medicine by Karishma Sahoo, Prakash Lingasamy, Masuma Khatun, Sajitha Lulu Sudhakaran, Andres Salumets, Vino Sundararajan, Vijayachitra Modhukur

“…Future directions include integrating multi-omics data, developing explainable AI frameworks, and addressing ethical concerns, such as data privacy and algorithmic bias. By overcoming these gaps, AI-powered epigenetic diagnostics can enable earlier detection, more effective treatments, and improved patient outcomes, globally. …”
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Beyond Linearity: Uncovering the Complex Spatiotemporal Drivers of New-Type Urbanization and Eco-Environmental Resilience Coupling in China’s Chengdu–Chongqing Economic Circle with... by Caoxin Chen, Shiyi Wang, Meixi Liu, Ke Huang, Qiuyi Guo, Wei Xie, Jiangjun Wan

“…A random forest (RF) model, interpreted with Shapley Additive exPlanations (SHAP) and Partial Dependence Plot (PDP) algorithms, explores nonlinear driving mechanisms, while Geographically and Temporally Weighted Regression (GTWR) assesses drivers’ spatiotemporal heterogeneity. …”
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An ensemble machine learning framework with explainable artificial intelligence for predicting haemoglobin anaemia considering haematological markers by Dhruva Darshan B S, Punit Sharma, Krishnaraj Chadaga, Niranjana Sampathila, G. Muralidhar Bairy, Sushma Belurkar, Srikanth Prabhu, Swathi K S

“…The ML models' judgments are interpreted using explainable artificial intelligence (XAI) methods. …”
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An intelligible AI-driven decision support system for poststroke mobility assessment by Jin Cheng Liaw, Dominik Raab, Malte Weber, Mario Siebler, Harald Hefter, Dörte Zietz, Marcus Jäger, Andrés Kecskeméthy, Francisco Geu Flores

“…Results: The models yielded good to very good (Cohen) coefficients of determination. …”
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Analyzing Post-fire Vegetation Dynamics with Ultra-high Resolution Remote Sensing Data by O. Petrov, O. Petrov, A. Medvedev

“…Co-alignment was applied to multi-temporal UAV datasets, and tree segmentation was performed using the algorithms developed for Airborne Laser Scanning (ASL) forest point clouds. …”
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Machine learning for asphaltene polarizability: Evaluating molecular descriptors by Arun K. Sharma, Owen McMillan, Selsela Arsala, Supreet Gandhok, Rylend Young

“…Accurate prediction of polarizability is essential for modeling intermolecular forces and improving force field design in petroleum and materials chemistry, issues that are central to industrial and chemical applications.…”
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Integrative machine learning and bioinformatics analysis to identify cellular senescence-related genes and potential therapeutic targets in ulcerative colitis and colorectal cancer by Tianle Xue, Yunpeng Chen, Xiaomeng Li, Zhixiang Zhou, Qiyang Chen

“…An integrated machine learning (IML) model—utilizing 12 algorithms with 10-fold cross-validation—was constructed to pinpoint key diagnostic biomarkers. …”
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Early detection of woody plant encroachment in Canadian prairies using UAV imagery and transformer-based deep learning by Yihan Pu, Xiaojing Lu, Irini Soubry, Xulin Guo

“…Transfer learning improved the model performance in SegFormer by more than 5 % in the mF1-score and 7 % in mIoU. …”
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Experimental investigation of shaft misalignment effects on bearing reliability through vibration signal analysis using machine learning and deep learning by Fransiskus Tatas Dwi Atmaji, Jamasri, Hari Agung Yuniarto, I Made Miasa

“…Statistical time-domain feature extraction notably improved the performance of classical models, with KNN achieving a maximum accuracy of 92.9%. …”
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