Simulating the root-to-shoot ratio of natural grassland biomass in China by the AutoGluon framework by Rui Guo, Xiaodong Huang, Yangjing Xiu, Minglu Che, Jinlong Gao, Shuai Fu, Qisheng Feng, Tiangang Liang

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Machine Learning-Powered Smart Healthcare Systems in the Era of Big Data: Applications, Diagnostic Insights, Challenges, and Ethical Implications by Sita Rani, Raman Kumar, B. S. Panda, Rajender Kumar, Nafaa Farhan Muften, Mayada Ahmed Abass, Jasmina Lozanović

“…The review focuses on the role of big data analysis and ML towards better diagnosis, improved efficiency of operations, and individualized care for patients. …”
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Anomaly detection with grid sentinel framework for electric vehicle charging stations in a smart grid environment by V. Thiruppathy Kesavan, Md. Jakir Hossen, R. Gopi, Emerson Raja Joseph

“…This technology can also detect and respond to suspicious movements dynamically using powerful machine learning algorithms (long short-term memory (LSTM), random forest, and autoencoder models), ensuring safety. …”
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Integrative review in PhD admissions: A case study of efficiently minimizing bias while maximizing the student narrative. by Minerva A Orellana, Danielle J Beetler, Carmen J Silvano, Ryan Wuertz, Jennifer L Weisbrod, Lewis R Roberts, Anthony J Windebank, Felicity T Enders, Marina R Walther-Antonio

“…This case study details the conceptualization of an integrative application review process to challenge and improve classic application review frameworks which gatekeep admissions opportunities from under-represented (UR) applicants. …”
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HOW IS ARTIFICIAL INTELLIGENCE CHANGING HR? ADAPTIVE MANAGEMENT FOR THE NEW ENVIRONMENT by Dmytro Antoniuk, Bjoern Sven Ivens, Olexandr Kolyada

“…The practical significance of the study lies in its recommendations for organisations seeking to implement adaptive AI-based HR models. It provides insights on how to optimise the use of AI for talent management, improve HR efficiency and address ethical considerations. …”
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Opportunities of Digitalization of Economic Subjects in Educational Process by L. N. Babkina, O. V. Skotarenko

“…Thus, institutions of higher professional education should become so-called points of growth or locomotives of growth of the level of application of information and computer technologies, algorithms for solving economic and management problems based on the use of economic and mathematical methods and models that students master in the process of studying the relevant disciplines. …”
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Domain adaptation of deep neural networks for tree part segmentation using synthetic forest trees by Mitch Bryson, Ahalya Ravendran, Celine Mercier, Tancred Frickey, Sadeepa Jayathunga, Grant Pearse, Robin J.L. Hartley

“…Results of our work indicated that models trained on our synthetic data pipeline were competitive with models trained on real data, in particular when real data came from non-target sites, and our unsupervised domain adaptation method further improved performance. …”
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MSIMRS: Multi-Scale Superpixel Segmentation Integrating Multi-Source Remote Sensing Data for Lithology Identification in Semi-Arid Area by Jiaxin Lu, Liangzhi Li, Junfeng Wang, Ling Han, Zhaode Xia, Hongjie He, Zongfan Bai

“…In addition, pixel-level K-Nearest Neighbor (KNN), Random Forest (RF) and SVM classification algorithms, as well as deep-learning models including Resnet50 (Res50), Efficientnet_B8 (Effi_B8), and Vision Transformer (ViT) were chosen for a comparative analysis. …”
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Artificial Intelligence and Smart Technologies in Safety Management: A Comprehensive Analysis Across Multiple Industries by Jiyoung Park, Dongheon Kang

“…AI-driven solutions, such as predictive analytics, machine learning algorithms, IoT sensor integration, and digital twin models, are shown to proactively identify and mitigate potential hazards, optimize energy consumption, and enhance operational efficiency. …”
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Precise application of water and fertilizer to crops: challenges and opportunities by Yingying Xing, Xiukang Wang

“…Advanced irrigation methods, such as subsurface drip irrigation and micro-sprinkling, improve water-use efficiency and reduce salinity levels, while precision fertilization techniques optimize nutrient uptake and minimize leaching. …”
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Recent technical advancements and clinical applications of MR-guided radiotherapy in lung cancer treatment by Chi Ma, Xiao Wang, Ke Nie, Zhenyu Xiong, Keying Xu, Ning Yue, Yin Zhang

“…Additionally, MRgRT could potentially allow multileaf collimator (MLC) tracking to further improve the treatment efficiency. Recent technological innovations, including AI-powered auto-contouring algorithms, deep-learning (DL) based prediction models, and adaptive treatment strategies, further optimize MRgRT by improving workflow efficiency and reducing treatment time. …”
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Effect of the time factor on the hydraulic activity of mechanically activated saponin-containing powder by Marina V. Morozova, Maria A. Frolova

“…Studies have shown that mechanically activated saponite-containing powder (MSP) improves concrete performance and reduces binder consumption. …”
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The risks of telehealth in radiation oncology: challenges and mitigation strategies by Sotiri Stathakis, Niko Papanikolaou, Jessica Ashford, Jason Stephens, Aaron LaRose

“…The integration of telehealth into radiation oncology represents a significant evolution in healthcare delivery, driven by the potential for enhanced patient accessibility, convenience, and improved multidisciplinary collaboration and operational efficiency. …”
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UAV Collision Avoidance in Unknown Scenarios with Causal Representation Disentanglement by Zhun Fan, Zihao Xia, Che Lin, Gaofei Han, Wenji Li, Dongliang Wang, Yindong Chen, Zhifeng Hao, Ruichu Cai, Jiafan Zhuang

“…By concentrating on causal aspects during the policy learning phase, the impact of non-causal factors is minimized, thereby improving the generalizability of DRL models. Experimental results demonstrate that our technique achieves reliable navigation and effective collision avoidance in unseen scenarios, surpassing state-of-the-art deep reinforcement learning algorithms.…”
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Deep learning-based crop health enhancement through early disease prediction by Venkata Santhosh Yakkala, Krishna Vamsi Nusimala, Badisa Gayathri, Sriya Kanamarlapudi, S. S. Aravinth, Ayodeji Olalekan Salau, S. Srithar

“…Additionally, the model aims to identify specific diseases affecting the crops if they are found to be diseased. …”
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Artificial intelligence in atrial fibrillation: emerging applications, research directions and ethical considerations by Ibrahim Antoun, Ibrahim Antoun, Ahmed Abdelrazik, Mahmoud Eldesouky, Xin Li, Georgia R. Layton, Georgia R. Layton, Georgia R. Layton, Mustafa Zakkar, Mustafa Zakkar, Mustafa Zakkar, Riyaz Somani, Riyaz Somani, G. André Ng, G. André Ng, G. André Ng

“…Furthermore, AI-powered remote monitoring integrates wearable-derived insights with real-time decision support, improving patient engagement and adherence. Despite these advances, significant challenges persist, including algorithm transparency, bias, data integration, and regulatory hurdles. …”
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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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FTIR-Based Microplastic Classification: A Comprehensive Study on Normalization and ML Techniques by Octavio Villegas-Camacho, Iván Francisco-Valencia, Roberto Alejo-Eleuterio, Everardo Efrén Granda-Gutiérrez, Sonia Martínez-Gallegos, Daniel Villanueva-Vásquez

“…The results showed that Z-score normalization significantly improved stability and generalization across most models, with CNN, MLP, and RF achieving near-perfect values in accuracy, precision, recall, and F1-score. …”
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Dynamic Dwell Time Adjustment in Wire Arc-Directed Energy Deposition: A Thermal Feedback Control Approach by Md Munim Rayhan, Abderrachid Hamrani, Fuad Hasan, Tyler Dolmetsch, Arvind Agarwal, Dwayne McDaniel

“…Future work should focus on integrating multi-axis thermal sensors, extending the framework to multi-material deposition scenarios and implementing machine learning algorithms for predictive thermal modeling.…”
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Research on Quantitative Evaluation of Defects in Ferromagnetic Materials Based on Electromagnetic Non-Destructive Testing by Xiangyi Hu, Ruijie Xie, Ruotian Wang, Jiapeng Wang, Haichao Cai, Xiaoqiang Wang, Xiang Li, Qingzhu Guan, Jianhua Zhang

“…To accurately describe the quantitative relationship between ENDT signals and defect dimensional parameters, the electromagnetic model and electromagnetic induction model are introduced in this paper to elucidate the physical mechanism of ENDT, as both models provide a basis for the selection of the constitutive relationship for simulation analysis. …”
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