Transforming urinary stone disease management by artificial intelligence-based methods: A comprehensive review by Anastasios Anastasiadis, Antonios Koudonas, Georgios Langas, Stavros Tsiakaras, Dimitrios Memmos, Ioannis Mykoniatis, Evangelos N. Symeonidis, Dimitrios Tsiptsios, Eliophotos Savvides, Ioannis Vakalopoulos, Georgios Dimitriadis, Jean de la Rosette

“…The terms ‘‘endourology’’, ‘‘artificial intelligence’’, ‘‘machine learning’’, and ‘‘urolithiasis'’ were used for searching eligible reports, while review articles, articles referring to automated procedures without AI application, and editorial comments were excluded from the final set of publications. …”
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Deep reinforcement learning agents for dynamic spectrum access in television whitespace cognitive radio networks by Udeme C. Ukpong, Olabode Idowu-Bismark, Emmanuel Adetiba, Jules R. Kala, Emmanuel Owolabi, Oluwadamilola Oshin, Abdultaofeek Abayomi, Oluwatobi E. Dare

“…Cognitive Radio (CR) technology has emerged as a pivotal solution, enabling dynamic spectrum sharing among secondary users while respecting the rights of primary users. However, the basic setup of CR technology is insufficient to manage spectrum congestion, as it lacks the ability to predict future spectrum holes, leading to interferences. …”
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Seminal plasma proteomics of asymptomatic COVID-19 patients reveals disruption of male reproductive function by Jialyu Huang, Yufang Su, Jiawei Wang, Zheng Fang, Yiwei Zhang, Hong Chen, Xinxia Wan, Yuanhuan Xiong, Ning Song, Houyang Chen, Xingwu Wu

“…Abstract Background A considerable proportion of males suffer from asymptomatic SARS-CoV-2 infection, while the effect on reproductive function and underlying pathomechanisms remain unclear. …”
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Rethinking the Paradigm of Using Ps for Diagnosing Compartment Syndrome by Yasser Bouklouch, BSc, MPH, July Agel, MA, ATC, William T. Obremskey, MD, MPH, MMHC, Andrew H. Schmidt, MD, Kathy Liu, MB, ChB, Jerald R. Westberg, MPH, Matthew Zakariah, BSc, Eli Bunzel, MD, Greer Henry, MSc, Andres Fidel Diaz, MD, Thierry Bégué, MD, Mitchell Bernstein, MD, Edward J. Harvey, MDCM, MSc

“…The combinations were tested for predictive power using 2 machine learning algorithms. Results:. Pressure on palpation was the strongest clinical predictor of ACS while pain was the weakest. …”
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Exploration of ductility for refractory high entropy alloys via interpretive machine learning by Shaolong Zheng, Lingwei Yang, Liyang Fang, Chenran Xu, Guanglong Xu, Yifang Ouyang, Xiaoma Tao

“…RHEAs with compressive ductility ≥30 % hold particular commercial value. While ductility can be engineered through alloying composition and processing condition optimization, this remains challenging due to the virtually infinite and vast unexplored compositional space. …”
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Oral microbiome-derived biomarkers for non-invasive diagnosis of head and neck squamous cell carcinoma by Jingtai Zhi, Yibo Liang, Wang Zhao, Jie Qiao, Yongzhe Zheng, Xin Peng, Li Li, Xianfeng Wei, Wei Wang

“…Unsupervised clustering algorithms of relative microbial abundance profiles revealed three distinctive microbiome clusters. …”
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Predicting Olympic Medal Performance for 2028: Machine Learning Models and the Impact of Host and Coaching Effects by Zhenkai Zhang, Tengfei Ma, Yunpeng Yao, Ningjia Xu, Yujie Gao, Wanwan Xia

“…This study develops two machine learning models to predict the medal performance of countries at the 2028 Olympic Games while systematically analyzing and quantifying the impacts of the host effect and exceptional coaching on medal gains. …”
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Effect of tool angle in nanocutting of single crystal GaN using diamond cutter by Yongqiang WANG, Hao XIA, Zhihang HU, Shuaiyang ZHANG, Shaohui YIN

“…This promotes more efficient and continuous chip formation while effectively suppressing undesirable lateral atomic flow and material pile-up at the groove sides, leading to improved groove definition. …”
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Anellovirus abundance as an indicator for viral metagenomic classifier utility in plasma samples by Gabriel Montenegro de Campos, Luan Gaspar Clemente, Alex Ranieri Jerônimo Lima, Eleonora Cella, Vagner Fonseca, João Paulo Bianchi Ximenez, Milton Yutaka Nishiyama, Enéas de Carvalho, Sandra Coccuzzo Sampaio, Marta Giovanetti, Maria Carolina Elias, Svetoslav Nanev Slavov

“…Interestingly, despite employing different algorithms and data types (protein-based vs. nucleotide-based), Kaiju and Kraken2 performed similarly. …”
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Abnormal eye movement, brain regional homogeneity in schizophrenia and clinical high-risk individuals and their associated gene expression profiles by Zhaobin Chen, Yangpan Ou, Yudan Ding, Ying Wang, Huabing Li, Feng Liu, Ping Li, Dongsheng Lv, Yong Liu, Bing Lang, Jingping Zhao, Wenbin Guo

“…FSZ exhibited a wide range of eye movement abnormalities across multiple tasks, while certain abnormalities were already present in CHR. …”
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Nonlinear Model Predictive Yaw Moment Control Through Electric Axle and Friction Brake Torque Distribution by Carmine Caponio, Gaetano Tavolo, Davide Tavernini, Ahu Ece Hartavi Karci, Javad Ahmadi, Basilio Lenzo, Giulio Reina, Giacomo Mantriota, Pietro Perlo, Aldo Sorniotti

“…A broad literature deals with torque-vectoring (TV) algorithms based on direct yaw moment control (DYC), which continuously generate longitudinal tire forces that are different on the left and right vehicle sides. …”
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SUMOylation-related genes define prognostic subtypes in stomach adenocarcinoma: integrating single-cell analysis and machine learning analyses by Kaiping Luo, Kaiping Luo, Donghui Xing, Donghui Xing, Xiang He, Yixin Zhai, Yanan Jiang, Hongjie Zhan, Zhigang Zhao

“…A SUMOylation Risk Score (SRS) model was developed using 69 machine learning models across 10 algorithms, with performance evaluated by C-index and AUC. …”
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Mapping Antarctic Blue Ice Areas With Sentinel-2A/B Images and LightGBM Model by Xiaolong Teng, Jiahui Xu, Xiangbin Cui, Guitao Shi, Zhengyi Hu, Qingyu Gu, Bailang Yu, Jianping Wu, Yan Huang

“…Random forest, XGBoost, and LightGBM integrated learning algorithms were used to model the extraction of Antarctic blue ice. …”
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An International Longitudinal Natural History Study of Patients With Danon Disease: Unique Cardiac Trajectories Identified Based on Sex and Heart Failure Outcomes by Kimberly N. Hong, Emily Eshraghian, Tarek Khedro, Alessia Argirò, Jennifer Attias, Garrett Storm, Melina Tsotras, Tanner Bloks, Isaiah Jackson, Elijah Ahmad, Sharon Graw, Luisa Mestroni, Quan M. Bui, Jonathan Schwartz, Stuart Turner, Eric D. Adler, Matthew Taylor

“…Left ventricular wall hypertrophy continues in men who have not yet experienced a HF outcome and stabilizes before HF outcome, while females have progressive left ventricular thinning regardless of HF outcome. …”
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Classification of SERS spectra for agrochemical detection using a neural network with engineered features by Mateo Frausto-Avila, Monserrat Ochoa-Elias, Jose Pablo Manriquez-Amavizca, María del Carmen González-López, Gonzalo Ramírez-García, Mario Alan Quiroz-Juárez

“…Compared to other machine-learning algorithms, our approach offers reduced computational complexity while maintaining or exceeding the accuracy of more complex models. …”
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Optimized AI and IoT-Driven Framework for Intelligent Water Resource Management by Mahmoud Badee Rokaya Mahmoud, Dalia Ismaeil Ibrahim Hemdan, Samah Hazzaa Alajmani, Raneem Yousif Alyami, Ghada Elmarhomy, Hassan Hashim, El-Sayed Atlam

“…The architecture combines the ensemble-learning algorithms (XGBoost, LightGBM), hybrid AIs (XGBoost + Autoencoder), and metaheuristic feature selection (GA, PSO, SA) for making intelligent decisions. …”
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Deep unsupervised clustering for prostate auto-segmentation with and without hydrogel spacer by Hengrui Zhao, Biling Wang, Michael Dohopolski, Ti Bai, Steve Jiang, Dan Nguyen

“…In recent years, hydrogel spacers have been used in some prostate cancer patients receiving radiotherapy to separate the prostate and the rectum to better spare the rectum while achieving adequate dose coverage on the prostate. …”
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Data fusion-based improvements in empirical regression and machine learning for global daily ∼ 8 km resolution sea surface nitrate estimation and interpretation by Aifen Zhong, Difeng Wang, Fang Gong, Jingjing Huang, Zhuoqi Zheng, Xianqiang He, Qing Zhang, Qiankun Zhu

“…Assessing sea surface nitrate (SSN) concentrations and dynamics is crucial for understanding marine ecosystem health, yet optical remote sensing of SSN remains challenging because of the lack of distinct spectral features. While various global-scale SSN regression and machine learning algorithms based on SSN-environment variable relationships have been developed, the prediction accuracy and spatiotemporal resolution of their applications continue to face limitations. …”
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A Novel Autonomous Robotic Vehicle-Based System for Real-Time Production and Safety Control in Industrial Environments by Athanasios Sidiropoulos, Dimitrios Konstantinidis, Xenofon Karamanos, Theofilos Mastos, Konstantinos Apostolou, Theocharis Chatzis, Maria Papaspyropoulou, Kalliroi Marini, Georgios Karamitsos, Christina Theodoridou, Andreas Kargakos, Matina Vogiatzi, Angelos Papadopoulos, Dimitrios Giakoumis, Dimitrios Bechtsis, Kosmas Dimitropoulos, Dimitrios Vlachos

“…More specifically, the Q-CONPASS system is based on an AMR equipped with a plethora of machine learning algorithms that enable the vehicle to safely navigate in a dynamic industrial environment, avoiding humans, moving machines, and stationary objects while performing important tasks. …”
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In-depth insight into structure-reactivity/regioselectivity relationship of Lewis acid catalyzed cascade 4πe-cyclization/dicycloexpansion reaction by Ka Lu, Pan-Pan Zhou, Yong-Qiang Tu, Fu-Min Zhang, Xiao-Ming Zhang, Kai Li, Kun Fang, Yun-Peng Wang, Zi-Hao Li, Jia-Qi Li

“…Then, DFT calculations combined with machine learning algorithms establish a prediction model for first cycloexpansion (i e. regioselectivity). …”
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