Polishing inner wall of crossed deep micropores using magnetic microabrasive jet technology by Zezhi WANG, Jie WANG, Xiaogang MA, Fan LI, Xinya FAN, Yan CHEN

“…By constructing a physical model of the machining process and using the simulation form of coupling the finite element method and the discrete element method, it is possible to more clearly simulate the motion of the abrasive and flow field in the abrasive water jet during the machining process, as well as the force situation of the workpiece being machined. …”
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Automated Recognition of Abnormalities in Gastrointestinal Endoscopic Images – Evaluation of an AI Tool for Identifying Polyps and Other Irregularities by Weronika Jarych, Elżbieta Tokarczyk, Patryk Iglewski, Daria Ziemińska, Karina Motolko, Rafał Burczyk, Konrad Duszyński, Michał Kociński, Jan Reinald Wendt

“…Key advantages of AI integration in endoscopy include improved sensitivity, minimized detection errors, and the potential to optimize clinical workflow efficiency. However, the study also addresses significant challenges, including the necessity for large, heterogeneous datasets for model validation, the need for standardized AI applications, and the ethical implications of AI-assisted clinical decision-making. …”
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Comprehensive study on phase stability of lead-free Sn-based perovskite FAxMA1-xSnI3 by Zih-Lie Huang, Cheng-Hsien Yeh, Ming-Yao Wang, Vincent Wing-hei Lau, Hong-Kang Tian, Chuan-Feng Shih

“…Using Density Functional Theory (DFT) and a pre-trained, fine-tuned machine-learning model, we explore the favorable molecular structures and configurations within FAxMA1-xSnI3. …”
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Diagnostic Applications of AI in Sports: A Comprehensive Review of Injury Risk Prediction Methods by Carmina Liana Musat, Claudiu Mereuta, Aurel Nechita, Dana Tutunaru, Andreea Elena Voipan, Daniel Voipan, Elena Mereuta, Tudor Vladimir Gurau, Gabriela Gurău, Luiza Camelia Nechita

“…AI models improve the accuracy and reliability of injury risk assessments by tailoring prevention strategies to individual athlete profiles and processing real-time data. …”
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The use of artificial intelligence in the diagnosis of oral carcinoma by Francesco Gianfreda, Andrea Danieli, Maria Scarpati Cioffari di Castiglione, Marco Gargari, Patrizio Bollero, Mirko Martelli

“…Results: Integrating AI in the diagnosis of oral lesions has resulted in accuracy rates exceeding 99% and significantly reduced diagnostic waiting times. Machine learning models effectively detect subtle anomalies and offer diagnostic performance that matches or surpasses expert clinical pathologists. …”
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Revolutionizing Clear-Sky Humidity Profile Retrieval with Multi-Angle-Aware Networks for Ground-Based Microwave Radiometers by Yinshan Yang, Zhanqing Li, Jianping Guo, Yuying Wang, Hao Wu, Yi Shang, Ye Wang, Langfeng Zhu, Xing Yan

“…Based on the 7-year (2018–2024) in situ measurements from Beijing, Nanjing, and Shanghai, validation results reveal that AngleNet achieves substantial improvements, with an average R2 of 0.71 and a root mean square error (RMSE) of 10.39%, surpassing conventional models such as LGBM (light gradient boosting machine) and RF (random forest) by over 10% in both metrics, and demonstrating a remarkable 41% increase in R2 and a 10% reduction in RMSE compared to the previous BRNN method (batch normalization and robust neural network). …”
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Altered static and dynamic spontaneous brain activity in patients with dysthyroid optic neuropathy: a resting-state fMRI study by Jinling Lu, Hao Hu, Jiang Zhou, Wenhao Jiang, Xiongying Pu, Huanhuan Chen, Xiaoquan Xu, Feiyun Wu

“…When detecting DON, the dALFF model showed optimal diagnostic performance (AUC 0.9987).ConclusionDysthyroid optic neuropathy patients exhibited both static and dynamic brain functional alterations in visual, cognitive, and emotion-related brain regions, deepening our current understanding of the underlying neural mechanisms of this disease. …”
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How do palatal extensions and brands affect the retention of custom-made mouthguards? An in vitro study by Zhe Sun, Ruitong Sun, Meng Zhang, Qingqiu Zhong, Minghao Huang, Xu Yan, Liye Shi, Jian Li

“…The current study aimed to evaluate the influence of palatal extensions and brands on the retention of MGs and to provide a theoretical basis for designing comfortable MGs. Methods Using an optimized fully dentate maxillary anatomical teaching model, two brands of sports MGs (Erkoflex (E) and Supsmile (S)) were created, with each brand preparing three identical splints, resulting in a total of six groups (E18, E28, E38, S18, S28, S38) with palatal margin extending 8 mm past the gingival margin. …”
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Spatiotemporal Variation and Driving Factors of Carbon Sequestration Rate in Terrestrial Ecosystems of Ningxia, China by Yi Zhang, Chunxiao Cheng, Zhihui Wang, Hongxin Hai, Lulu Miao

“…Based on ground observation data and multimodal datasets, the optimal machine learning model (EXT) was used to invert a 30 m high-resolution vegetation and soil carbon density dataset for Ningxia from 2000 to 2023. …”
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Sustainable extraction of phytochemicals from Mentha arvensis using supramolecular eutectic solvent via microwave Irradiation: Unveiling insights with CatBoost-Driven feature analy... by Zubera Naseem, Muhammad Bilal Qadir, Abdulaziz Bentalib, Zubair Khaliq, Muhammad Zahid, Fayyaz Ahmad, Nimra Nadeem, Anum Javaid

“…The categorical boosting (CatBoost) machine learning model was applied to optimize the extraction process against time (4–8 min), microwave power (160–320 W), and biomass quantity (1–2.0 g/10 mL) with DES. …”
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Genomic Prediction of Kernel Water Content in a Hybrid Population for Mechanized Harvesting in Maize in Northern China by Ping Luo, Ruisi Yang, Lin Zhang, Jie Yang, Houwen Wang, Hongjun Yong, Runze Zhang, Wenzhe Li, Fei Wang, Mingshun Li, Jianfeng Weng, Degui Zhang, Zhiqiang Zhou, Jienan Han, Wenwei Gao, Xinlong Xu, Ke Yang, Xuecai Zhang, Junjie Fu, Xinhai Li, Zhuanfang Hao, Zhiyong Ni

“…The average prediction accuracy of machine learning methods was lower than that of linear statistical methods for KWC-related traits, but the random forest model had a high prediction accuracy of 0.510 for GY. …”
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ON TECHNIQUE OF FUZZY EXPERT KNOWLEDGE REPRESENTATION by Lyudmila Victorovna Borisova, Valery Petrovich Dimitrov, Inna Nikolayevna Nurutdinova

“…The improved background for choosing terms of the linguistic variables allows optimizing the KB parameters based on the fuzzy production rules. …”
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Applied intelligence in clinical drug development: Potential benefits and emerging concerns by Arun Bhatt

“…The use of artificial intelligence (AI) technology and machine learning (ML) is growing exponentially and is moving from AI to applied intelligence. …”
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Design of federated routing mechanism in cross-domain scenario by Peizhuang CONG, Yuchao ZHANG, Ye TIAN, Wendong WANG, Dan LI

“…With the development of multi-network integration,how to ensure efficient interconnections among multiple independent network domains is becoming a key problem.Traditional interdomain routing protocol fails due to the limitation of domain information privacy,where each autonomous domain doesn’t share any specific intra-domain information.A machine learning-based federated routing mechanism was proposed to overcome the existing shortcomings.Each autonomous domain shares intra-domain information implicitly via neural network models and parameters.It not only breaks data islands problems but also greatly reduces the amount of transmitted data shared between domains,then decreases convergence delay of entire network information.Based on the federated routing mechanism,border routers can formulate global optimal routing strategies according to the status of entire network.…”
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Applications of Artificial Intelligence in Biotech Drug Discovery and Product Development by Yuan‐Tao Liu, Le‐Le Zhang, Zi‐Ying Jiang, Xian‐Shu Tian, Peng‐Lin Li, Pei‐Huang Wu, Wen‐Ting Du, Bo‐Yu Yuan, Chu Xie, Guo‐Long Bu, Lan‐Yi Zhong, Yan‐Lin Yang, Ting Li, Mu‐Sheng Zeng, Cong Sun

“…Recent advances in AI—particularly generative models such as generative adversarial networks, variational autoencoders, and diffusion models—have introduced data‐driven, iterative workflows that dramatically accelerate and enhance pharmaceutical R&D. …”
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Estimation of mangrove heights and aboveground biomass using UAV-LiDAR, Sentinel-1 and ZY-3 stereo images by Bolin Fu, Yingying Wei, Linhang Jiang, Hang Yao, Xiaomin Li, Yanli Yang, Mingming Jia, Weiwei Sun

“…In this study, we constructed mangrove height inversion models using multiple types of remote sensing data and machine learning algorithms (partial least squares regression (PLSR), random forest (RF), and mixture density network (MDN)). …”
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Logistics solutions and supply management in the company: the challenges of time by H.M.

“…In particular, attention is paid to the peculiarities of use of Big Data Analytics, Machine Learning, and Internet of Things technologies. …”
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Structural Biomimetics of Non-smooth Pits for Ceramic Extruders and Its Reducing Adhesion and Resistance by Shengrui YU, Ping JI, Zhemin DAI, Lei XU, Zhihuan LIU, Wei LI, Huiting WU

“…Furthermore, a geometric model of the dung beetlex’s pit-shaped bionic structure is established for the extruder’s head. …”
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Integrative single-cell and exosomal multi-omics uncovers SCNN1A and EFNA1 as non-invasive biomarkers and drivers of ovarian cancer metastasis by Liping Tang, Dong Pang, Chengbang Wang, Jiali Lin, Shaohua Chen, Jiangchun Wu, Junqi Cui

“…Candidate genes were validated in clinical specimens using qPCR and immunohistochemistry. We then applied ten machine learning algorithm to exosomal transcriptomic data to evaluate diagnostic performance and identify the optimal classifier. …”
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Artificial intelligence based assessment of clinical reasoning documentation: an observational study of the impact of the clinical learning environment on resident documentation qu... by Verity Schaye, David J. DiTullio, Daniel J. Sartori, Kevin Hauck, Matthew Haller, Ilan Reinstein, Benedict Guzman, Jesse Burk-Rafel

“…Clinical reasoning documentation quality of admission notes was determined to be low or high-quality using a supervised machine learning model. From note-level data, the shift (day or night) and note index within shift (if a note was first, second, etc. within shift) were calculated. …”
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