Prevalence and patterns of antiretroviral resistance in HIV-infected Latin American asylum seekers by Samuel Manzano, Juan Torres-Macho, Neda Deihim-Rahampour, Guillermo Cuevas, Felipe Perez-Garcia, Luz Balsalobre, Salvador Resino, Matilde Sanchez-Conde, Jorge Valencia, Pablo Ryan

“…These findings underscore the need for optimized treatment strategies and improved healthcare access for migrant populations with HIV.…”
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Application of collaborative innovation between the logical brain and the associative brain in oil and gas gathering and transportation systems by Jing GONG, Siheng SHEN, Daqian LIU, Qi KANG, Shangfei SONG, Haihao WU, Bohui SHI

“…There is an urgent need to overcome bottlenecks in areas such as algorithmic fusion, dynamic data sharing, and deep AI integration to enable a leap from localized optimization to system-wide intelligent decision-making. …”
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Development and validation of novel machine learning-based prognostic models and propensity score matching for comparison of surgical approaches in mucinous breast cancer by Chunmei Chen, Jundong Wu, Yutong Fang, Yong Li, Qunchen Zhang

“…We have successfully developed 6 optimal prognostic models utilizing the XGBoost algorithm to accurately predict the survival of MBC patients. …”
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Reducing Safety Risks in Construction Tower Crane Operations: A Dynamic Path Planning Model by Binqing Cai, Zhukai Ye, Shiwei Chen, Xun Liang

“…To reduce safety risks in construction tower crane operations, this research proposes a dynamic path planning model for tower crane operations based on computer vision technology and dynamic path planning algorithms. The proposed model consists of three modules: first, a path information collection module preprocessing the video data to capture relevant operational path information; second, a path safety risk evaluation module employing You Only Look Once version 8 (YOLOv8) instance segmentation to identify potential risk factors along the operational path, e.g., potential drop zones and the positions of nearby workers; and finally, a path planning module utilizing an improved Dynamic Window Approach for tower cranes (TC-DWA) to avoid risky areas and optimize the operational path for enhanced safety. …”
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Distinguishing novel coronavirus influenza A virus pneumonia with CT radiomics and clinical features by Lianyu Sui, Huan Meng, Jianing Wang, Wei Yang, Lulu Yang, Xudan Chen, Liyong Zhuo, Lihong Xing, Yu Zhang, Jingjing Cui, Xiaoping Yin

“…Firstly, voxel-based gray-level discretization (binWidth = 25) and Z-Score normalization were applied to preprocess the patient's ROI and normalize the extracted features. Then, the most predictive radiomic features were selected and their corresponding coefficients were evaluated using the correlation coefficient algorithm and the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm. …”
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Predicting Student Performance and Enhancing Learning Outcomes: A Data-Driven Approach Using Educational Data Mining Techniques by Athanasios Angeioplastis, John Aliprantis, Markos Konstantakis, Alkiviadis Tsimpiris

“…Five machine learning algorithms—k-nearest neighbors, random forest, logistic regression, decision trees, and neural networks—were applied to identify correlations between courses and predict grades. …”
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Determination of lithium concentration in black mass using laser-induced breakdown spectroscopy hand-held instrumentation by Elisa Galli, Mattia Massa, Alessandra Zanoletti, Silvana De Iuliis, Elza Bontempi, Laura Eleonora Depero, Vincenzo Palleschi, Laura Borgese

“…Abstract Lithium has become one of the most strategic materials in the industry, given its wide use for the realization of efficient energy storage devices and for improving the chemical and physical characteristics of advanced ceramic and glass materials. …”
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An adaptive intelligent thermal-aware routing protocol for wireless body area networks by Abdollah Rahimi, Mehdi Jafari Shahbazzadeh, Amid Khatibi

“…In the first phase, sensor nodes exchange vital network status information, including residual energy, node temperature, link reliability, and delay, to build an optimized network topology. Instead of relying solely on shortest-path routing, a multi-criteria decision-making algorithm is employed to select the most efficient paths, prioritizing those that balance energy consumption, temperature regulation, and communication stability. …”
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Lung Cancer Prediction Using an Enhanced Neutrosophic Set Combined with a Machine Learning Approach by Vakeel A. Khan, Asheesh Kumar Yadav, Mohammad Arshad, Nadeem Akhtar

“…To address this issue, we propose an Enhanced Neutrosophic Set (ENS) framework integrated with machine learning algorithms to improve the prediction accuracy of lung cancer. …”
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Prediction of formation pressure in underground gas storage based on data-driven method by SUI Gulei, FU Yujiang, ZHU Hongxiang, LI Zunzhao, WANG Xiaolin

“…The experimental results show that predictive performances of three predictive models are ranked from high to low: SVR, XGBoost, LSTM, among which the predictive performance of SVR is the most stable. Introducing the proportion of gas injection-production to screen pressure monitoring wells can improve the predictive performance of the data-driven model. …”
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Artificial Intelligence and Machine Learning Approaches for Target-Based Drug Discovery: A Focus on GPCR-Ligand Interactions by M. O. Otun

“…This review explores the integration of AI and ML techniques in GPCR-targeted drug discovery, highlighting their potential to accelerate lead identification, optimize ligand binding predictions, and improve structure-activity relationship modeling. …”
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Machine Learning-Based Analysis of Travel Mode Preferences: Neural and Boosting Model Comparison Using Stated Preference Data from Thailand’s Emerging High-Speed Rail Network by Chinnakrit Banyong, Natthaporn Hantanong, Supanida Nanthawong, Chamroeun Se, Panuwat Wisutwattanasak, Thanapong Champahom, Vatanavongs Ratanavaraha, Sajjakaj Jomnonkwao

“…Practical implications include optimizing fare structures, enhancing service quality, and improving station accessibility to support sustainable adoption.…”
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Comparative Analysis of Hybrid Model Performance Using Stacking and Blending Techniques for Student Drop Out Prediction In MOOC by Muhammad Ricky Perdana Putra, Ema Utami

“…The use of ensemble techniques to build models can improve performance, but previous research has not reviewed the most optimal ensemble technique for this case study. …”
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Machine Learning Applications in Gray, Blue, and Green Hydrogen Production: A Comprehensive Review by Xuejia Du, Shihui Gao, Gang Yang

“…ML algorithms such as artificial neural networks (ANNs), random forest (RF), and gradient boosting regression (GBR) have been widely applied to predict hydrogen yield, optimize operational conditions, reduce emissions, and improve process efficiency. …”
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Prediction of porosity, hardness and surface roughness in additive manufactured AlSi10Mg samples. by Fatma Alamri, Imad Barsoum, Shrinivas Bojanampati, Maher Maalouf

“…Advanced machine learning techniques to predict part quality can improve repeatability and open additive manufacturing to various industries. …”
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Advanced GPU Techniques for Dynamic Remeshing and Self-Collision Handling in Real-Time Cloth Tearing by Jong-Hyun Kim, Jung Lee

“…We also present a method to optimize kernels based on a complete binary tree in arbitrary triangular meshes, improving performance. …”
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Robust vector-weighted and matrix-weighted multi-view hard c-means clustering by Zhe Liu, Sarah Aljohani, Sijia Zhu, Tapan Senapati, Gözde Ulutagay, Salma Haque, Nabil Mlaiki

“…With the rapid advancement of information technology, multi-view data has become ubiquitous, prompting extensive attention towards multi-view clustering algorithms. Despite significant strides, several challenges persist: (1) the prevalence of noise and outliers in real-world multi-view data often compromises the efficacy of clustering; (2) most existing multi-view clustering algorithms predominantly assess the overall contribution of each view, while neglecting the intra-view contributions. …”
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FedACT: An adaptive chained training approach for federated learning in computing power networks by Min Wei, Qianying Zhao, Bo Lei, Yizhuo Cai, Yushun Zhang, Xing Zhang, Wenbo Wang

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The geriatric 5Ms, artificial intelligence, and Hannah Arendt’s critique: ethical reflections within contemporary gerontology by Virgílio Garcia Moreira, Andréia Pain, Ivan Aprahamian

“…The integration of AI into geriatrics has the potential to improve diagnostic accuracy, optimize therapies, and individualize interventions. …”
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Comparison of Machine Learning Methods for Predicting Electrical Energy Consumption by Retno Wahyusari, Sunardi Sunardi, Abdul Fadlil

“…Data pre-processing, specifically min-max normalization, is crucial for improving the accuracy of distance-based algorithms like KNN. …”
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