Identifying Influential Nodes in Complex Networks via Transformer with Multi-Scale Feature Fusion by Tingshuai Jiang, Yirun Ruan, Tianyuan Yu, Liang Bai, Yifei Yuan

“…Through the transformer module, node information is effectively aggregated, thereby improving the model’s ability to recognize key nodes. …”
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SDGTrack: A Multi-Target Tracking Method for Pigs in Multiple Farming Scenarios by Tao Liu, Dengfei Jie, Junwei Zhuang, Dehui Zhang, Jincheng He

“…This method improves tracking performance across various farming environments by enhancing the model’s adaptability to different domains and integrating an optimized tracking strategy, significantly increasing the generalization of group pig tracking technology across different scenarios. …”
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Precise application of water and fertilizer to crops: challenges and opportunities by Yingying Xing, Xiukang Wang

“…It examines the integration of advanced sensors, remote sensing, and machine learning algorithms in precision agriculture, assessing their roles in optimizing irrigation and nutrient management. …”
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Decentralized Coordination of Temperature Control in Multiarea Premises by Maria Yukhymchuk, Volodymyr Dubovoi, Viacheslav Kovtun

“…The proposed coordination algorithms make it possible to optimize the operating modes of the system automatically when its structure and/or settings are changed. …”
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Advanced imaging techniques and artificial intelligence in pleural diseases: a narrative review by Guido Marchi, Mattia Mercier, Jacopo Cefalo, Carmine Salerni, Martina Ferioli, Piero Candoli, Leonardo Gori, Federico Cucchiara, Giovanni Cenerini, Giacomo Guglielmi, Michele Mondoni

“…Finally, the role of deep-learning models in early complication detection and automated analysis of follow-up imaging studies is examined. …”
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The Impact of a Deep Learning Self-Adaptive Colour Restoration Pipeline for Deep Underwater Images in 3D Reconstruction by M. Vlachos, D. Skarlatos, S. Demesticha

“…The findings are intended to inform future development of hybrid approaches that combine physical modelling with deep learning, aiming to optimize both visual clarity and geometric fidelity in underwater mapping and documentation. …”
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Machine learning-based prediction of physical parameters in heterogeneous carbonate reservoirs using well log data by Fuyong Wang, Xianmu Hou

“…Machine learning models are trained and evaluated to predict carbonate rock properties. …”
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Transformative applications in biology education: A case study on the efficacy of adaptive learning with numerical insights by Shin I. Aleksandrovich, Tyan Ramazan, Raissa Utegaliyeva, Balzat Sarimbayeva, Gaukhar Keubassova, Rakhima Bissalyyeva, Kuanysh Syman, Gaukhar Abdikarimova

“…Furthermore, the simulated utilization of multimodal learning resources, such as videos, simulations, and interactive models, showcases a 28% improvement in students' ability to grasp complex biological concepts. …”
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Advanced Estimation of Winter Wheat Leaf’s Relative Chlorophyll Content Across Growth Stages Using Satellite-Derived Texture Indices in a Region with Various Sowing Dates by Jingyun Chen, Quan Yin, Jianjun Wang, Weilong Li, Zhi Ding, Pei Sun Loh, Guisheng Zhou, Zhongyang Huo

“…Following a two-step variable selection method, Random Forest (RF)-LassoCV, five machine learning algorithms were applied to develop estimation models. …”
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ABL-SMOTE: A Novel Resampling Method by Handling Noisy and Borderline Challenge for Imbalanced Dataset for Software Defect Prediction by Kamal Bashir, Sara Abdelwahab Ghorashi, Ali Ahmed, Abdolraheem Khader

“…Machine learning algorithms face important implementation difficulties due to imbalanced learning since the Synthetic Minority Oversampling Technique (SMOTE) helps improve performance through the creation of new minority class examples in feature space before preprocessing. …”
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Monitoring of vegetation chlorophyll content in photovoltaic areas using UAV-mounted multispectral imaging by Ming Li, Weiyi Wang, Haoran Li, Zekun Yang, Jianjun Li

“…Moreover, the fusion of vegetation indices and texture features effectively improved the accuracy of chlorophyll inversion models; among the six regression algorithms tested, the multilayer perceptron model achieved the highest performance (R² = 0.874, RMSE = 3.725, MAPE = 3.982%). …”
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Carbon Sequestration Strategies in Regenerative Agricultural Systems by Leveraging Wireless Sensor Networks for Precision Carbon Management by Al-Jawahry Hassan M., AI_Sadi Hafidh l., Rajasekhar Boddu

“…In this study, a total approach towards optimizing carbon sequestration strategies using advanced technologies like Wireless Sensor Network (WSN), Digital Twin model, and predictive algorithms like Random Forest Regression and gradient boosting are presented. …”
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Enhancing Crop Type Mapping in Data-Scarce Regions Through Transfer Learning: A Case Study of the Hexi Corridor by Jingjing Mai, Qisheng Feng, Shuai Fu, Ruijing Wang, Shuhui Zhang, Ruoqi Zhang, Tiangang Liang

“…As target domain data were gradually incorporated, the total accuracy for all models ranged from 0.77 to 0.92, with F1-scores ranging from 0.76 to 0.92, showing a consistent improvement in model performance. …”
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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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Robust EEG Characteristics for Predicting Neurological Recovery from Coma After Cardiac Arrest by Meitong Zhu, Meng Xu, Meng Gao, Rui Yu, Guangyu Bin

“…By integrating machine learning (ML) algorithms, such as Gradient Boosting Models and Support Vector Machines, with SHAP-based feature visualization, robust screening methods were applied to ensure the reliability of predictions. …”
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Continual Learning With Neuromorphic Computing: Foundations, Methods, and Emerging Applications by Mishal Fatima Minhas, Rachmad Vidya Wicaksana Putra, Falah Awwad, Osman Hasan, Muhammad Shafique

“…., sparse spike-driven operations and bio-plausible learning rules) for improving energy efficiency and performance, thereby enabling efficient CL algorithms (e.g., unsupervised learning approach) executed in dynamically-changed environments with resource-constrained computing systems. …”
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Modifiable Factors and 10‐Year and Lifetime Risk of Cardiovascular Disease in Adults With New‐Onset Diabetes: The Kailuan Cohort Study by Shouling Wu, Yuntao Wu, Yi Ning, Xiang Peng, Haiyan Zhao, Jun Feng, Liming Lin, Chunyu Ruan, Shuohua Chen, Jinwei Tian, Cheng Jin

“…However, the extent to which optimizing modifiable lifestyle and clinical factors can mitigate this risk remains insufficiently assessed across both short‐ and long‐term risk periods. …”
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Automated segmentation of brain metastases in T1-weighted contrast-enhanced MR images pre and post stereotactic radiosurgery by Hemalatha Kanakarajan, Wouter De Baene, Patrick Hanssens, Margriet Sitskoorn

“…Abstract Background and purpose Accurate segmentation of brain metastases on Magnetic Resonance Imaging (MRI) is tedious and time-consuming for radiologists that could be optimized with deep learning (DL). Previous studies assessed several DL algorithms focusing only on training and testing the models on the planning MRI only. …”
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Electrophysiological changes in the acute phase after deep brain stimulation surgery by Lucia K. Feldmann, Diogo Coutinho Soriano, Jeroen Habets, Valentina D'Onofrio, Jonathan Kaplan, Varvara Mathiopoulou, Katharina Faust, Gerd-Helge Schneider, Doreen Gruber, Georg Ebersbach, Hayriye Cagnan, Andrea A. Kühn

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Quantitative Analysis of Sulfur Elements in Mars-like Rocks Based on Multimodal Data by Yuhang Dong, Zhengfeng Shi, Junsheng Yao, Li Zhang, Yongkang Chen, Junyan Jia

“…To validate the advantages of the multimodal approach, comparative analyses were conducted against unimodal methods. Furthermore, to optimize model performance, different feature selection algorithms were evaluated. …”
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