Developing advanced datadriven framework to predict the bearing capacity of piles on rock by Kennedy C. Onyelowe, Shadi Hanandeh, Viroon Kamchoom, Ahmed M. Ebid, Fabián Danilo Reyes Silva, José Luis Allauca Palta, José Luis Llamuca Llamuca, Siva Avudaiappan

“…Abstract Developing accurate predictive models for pile bearing capacity on rock is crucial for optimizing foundation design and ensuring structural stability. …”
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From Extractive to Generative: An Analysis of Automatic Text Summarization Techniques by Liu Zixu

“…The review highlights significant milestones in the development of summarization algorithms, including the emergence of Transformer-based models like Bidirectional Encoder Representations from Transformers (BERT) and Generative Pre-trained Transformer (GPT), which have significantly improved the quality and coherence of generated summaries. …”
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Research on the Virtual Coupling Technology for Urban Rail Transit Trains by YANG Weifeng, LEI Chengjian, HAN Chen, LI Qihe, XIE Xing

“…The paper additionally highlights the technical challenges faced in engineering applications, including the need for improved cooperative control algorithms, enhanced vehicle-to-vehicle communication mechanisms, and optimized safety braking models.…”
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Analytical and Geometric Foundations and Modern Applications of Kinetic Equations and Optimal Transport by Cécile Barbachoux, Joseph Kouneiher

“…By bridging entropy dissipation, optimal transport, and geometric analysis, our work offers a new perspective on stability, convergence, and structure in high-dimensional kinetic models and applications.…”
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Filter-enhanced adiabatic quantum computing on a digital quantum processor by Erenay Karacan, Conor Mc Keever, Michael Foss-Feig, David Hayes, Michael Lubasch

“…We demonstrate a significant improvement in ground-state accuracies for paradigmatic quantum spin models.…”
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Enhancing stone matrix asphalt performance with sugarcane bagasse ash: Mechanical properties and machine learning-based predictions using XGBoost and random forest by Hamed Khani Sanij, Rezvan Babagoli, Reza Mohammadi Elyasi

“…Among the models, XGBoost achieved the best performance (R² > 0.93), followed closely by RF, while SVR showed relatively lower accuracy. …”
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Artificial Intelligence in Revolutionizing Kidney Care and Beyond: Kid-AI Revolution by Kounaina Khan, Farhan Zameer, Pratheek Jain, Ravi KR, Vidya Niranjan, Manoj S, Ravish H, Subrahmanya Padyana

“…Despite the transformative potential, the review acknowledges challenges such as data privacy, interpretability of AI models, the imperative need for trust in AI-driven recommendations for broad applicability, external validation, and improved clinical decision-making. …”
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Artificial Intelligence in Gastroenterology: A Comprehensive Review by Raju Vaishya, Mahesh Kumar Goenka, Anupam Sibal, Sujoy Kar, Sangita Reddy

“…In capsule endoscopy, AI can significantly reduce reading time with improved algorithms to increase sensitivity and specificity in identifying gastrointestinal abnormalities. …”
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Analytical review of visualization methods for launch and landing of spacecraft with consideration of 64-bit system boundary value issues by Oleksii Vynokur, Iryna Perova, Polina Zhernova

“…The development of 128-bit systems for space simulations is critically important for enhancing the accuracy and realism of the modeling. The Unity and Unreal Engine 5 game engines although having the potential for adaptation to aerospace simulations, require significant improvements in handling large scales and detailed aspects. …”
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Advancement in public health through machine learning: a narrative review of opportunities and ethical considerations by Sumit Singh Dhanda, Deepak Panwar, Chia-Chen Lin, Tarun Kumar Sharma, Deependra Rastogi, Shantanu Bindewari, Anand Singh, Yung-Hui Li, Neha Agarwal, Saurabh Agarwal

“…In genomics, ML methods enabled nuanced disease subtype discovery and improved the accuracy of cancer risk assessment and pharmacogenomic modeling. …”
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Research progress in predicting the conversion from mild cognitive impairment to Alzheimer’s disease via multimodal MRI and artificial intelligence by Min Ai, Yu Liu, Dan Liu, Chengxi Yan, Xia Wang, Xun Chen

“…Artificial intelligence (AI) methods based on deep learning and machine learning, with their powerful data processing and pattern recognition capabilities, have shown great potential in mining the features of multimodal MRI data and constructing prediction models for MCI conversion. Therefore, this paper systematically reviews the research progress of multimodal MRI techniques in capturing brain changes related to MCI conversion, as well as the practical experience of AI algorithms in constructing efficient prediction models, analyses the current technical challenges faced by the research, and discusses future directions, with the goal of providing a scientific reference for the early and accurate prediction of MCI conversion and the formulation of intervention strategies.…”
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Enhancing predictive maintenance in automotive industry: addressing class imbalance using advanced machine learning techniques by Yashashree Mahale, Shrikrishna Kolhar, Anjali S. More

“…Six machine learning models, including logistic regression, support vector machine, decision tree, and random forest, along with gradient boosting algorithms using extreme gradient boost (XGBoost) and light gradient boosting machine frameworks, were implemented. …”
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Smart Farming: AI and IoT-Based Solutions for Real-Time Agriculture Monitoring by Kadao Anjali Krushna, Shivaji Ghorpade Bipin

“…The machine learning models are used to predict possible points of problems like disease outbreaks or nutrient deficiencies so that appropriate steps can be taken preemptively. …”
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Low-Light Image and Video Enhancement for More Robust Computer Vision Tasks: A Review by Mpilo M. Tatana, Mohohlo S. Tsoeu, Rito C. Maswanganyi

“…The review analyzes both traditional and deep learning-based enhancers and provides a comparative analysis on recent models in the field. The review also analyzes how popular computer vision tasks are improved and made more robust when coupled with light enhancement algorithms. …”
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Dynamic Skin: A Systematic Review of Energy-Saving Design for Building Facades by Jian Wang, Shengcai Li, Peng Ye

“…The research results indicate that various types of dynamic skins, intelligent materials, multi-layer facades, dynamic shading, and biomimetic facades are commonly used core technologies for dynamic facades. Parametric modeling, computer simulation, and multi-objective algorithms are commonly used to optimize the performance of dynamic skin. …”
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Hyperspectral data of understory elements in boreal forests: In situ and laboratory measurementsMendeley DataMendeley Data by Audrey Mercier, Susanna Karlqvist, Aarne Hovi, Miina Rautiainen

“…These data support the analysis of vegetation characteristics, training of classification algorithms and improvement of forest radiative transfer models, and could be used to evaluate the potential of hyperspectral data to discriminate the understory elements of boreal forest.…”
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Advanced Deep Learning Based Predictive Maintenance of DC Microgrids: Correlative Analysis by M. Y. Arafat, M. J. Hossain, Li Li

“…This study can contribute to the emerging technology for predictive maintenance of MGs and provide significant insights into the modeling and performance of RNN architectures for improving fault detection in MG systems. …”
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Optimization Scheduling of Multiple Heterogeneous Energy Sources by Ying Zhao, Zhiwen Yu, Xiaobin Wang, Jianlin Tang, Xiaoming Lin, Fan Zhang, Bin Qian

“…The study summarizes the mainstream mathematical modeling and optimization algorithms, intelligent optimization techniques, and real-time data processing technologies. …”
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Hole Cleaning during Drilling Oil and Gas Wells: A Review for Hole-Cleaning Chemistry and Engineering Parameters by Mohammed Al-Rubaii, Mohammed Al-Shargabi, Dhafer Al-Shehri

“…The development of hole-cleaning models is important for use as hole-cleaning indicators to ensure optimized drilling efficiency. …”
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Airport-FOD3S: A Three-Stage Detection-Driven Framework for Realistic Foreign Object Debris Synthesis by Hanglin Cheng, Yihao Li, Ruiheng Zhang, Weiguang Zhang

“…Traditional Foreign Object Debris (FOD) detection methods face challenges such as difficulties in large-size data acquisition and the ineffective application of detection algorithms with high accuracy. In this paper, image data augmentation was performed using generative adversarial networks and diffusion models, generating images of monitoring areas under different environmental conditions and FOD images of varied types. …”
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