Analysis of the Possibility of Using CO₂ Capture in a Coal-Fired Power Plant by Łukasz Mika, Karol Sztekler

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Development platform for artificial pancreas algorithms. by Mohamed Raef Smaoui, Remi Rabasa-Lhoret, Ahmad Haidar

“…<h4>Results</h4>The platform facilitates development by solving the ODE model in the cloud on large CPU-optimized machines, providing a 62% improvement in memory, speed and CPU utilization. …”
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Online Tool Wear Monitoring via Long Short-Term Memory (LSTM) Improved Particle Filtering and Gaussian Process Regression by Hui Xu, Hui Xie, Guangxian Li

“…Accurate prediction of tool wear plays a vital role in improving machining quality in intelligent manufacturing. However, traditional Gaussian Process Regression (GPR) models are constrained by linear assumptions, while conventional filtering algorithms struggle in noisy environments with low signal-to-noise ratios. …”
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Enhancing Attention Network Spatiotemporal Dynamics for Motor Rehabilitation in Parkinson’s Disease by Guangying Pei, Mengxuan Hu, Jian Ouyang, Zhaohui Jin, Kexin Wang, Detao Meng, Yixuan Wang, Keke Chen, Li Wang, Li-Zhi Cao, Shintaro Funahashi, Tianyi Yan, Boyan Fang

“…The identified brain spatiotemporal neural markers were validated using machine learning models to assess the efficacy of MIRT in motor rehabilitation for PD patients, achieving an average accuracy rate of 86%. …”
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Leveraging AI to Drive Timely Improvements in Patient Experience Feedback: Algorithm Validation by Mustafa Khanbhai, Catalina Carenzo, Sarindi Aryasinghe, David Manton, Erik Mayer

“…While the algorithm yielded strong and reusable models in relatively stable environments, such as adult inpatient care settings, the initial accuracy was notably lower in organizations providing services such as pediatrics and mental health. …”
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新型铝锭堆垛机传动系统耦合振动计算 by 罗德春, 张玲, 芮执元, 赵俊天, 李鄂民, 王鹏

“…The buncher of aluminium ingot is the key equipment of continuously casting machines. The lifting finder of buncher of aluminium ingot highly active is introduced,and the natural frequency calculation and the model shape analysis for it’s drive system are carried out based on the coupled vibration of the spur gear by the means of the whole transfer matrix. …”
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Applying Acoustic Signals to Monitor Hybrid Electrical Discharge-Turning with Artificial Neural Networks by Mehdi Soleymani, Mohammadjafar Hadad

“…Artificial intelligence (AI) models have demonstrated their capabilities across various fields by performing tasks that are currently handled by humans. …”
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Estimation and Forecasting of Nigeria&#x2019;s Residential, Commercial, and Industrial Electricity Demands by Prosper O. Ugbehe, Ogheneruona E. Diemuodeke, Daniel O. Aikhuele, Kenneth E. Okedu, Gauri Kalnoor

“…These needs were analyzed using the developed models and sourced data to obtain advanced estimates of electricity consumption. …”
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Mapping the rapid growth of multi-omics in tumor immunotherapy: Bibliometric evidence of technology convergence and paradigm shifts by Huijing Dong, Xinmeng Wang, Yumin Zheng, Jia Li, Zhening Liu, Aolin Wang, Yulei Shen, Daixi Wu, Huijuan Cui

“…Keyword co-occurrence and citation burst analyses reveal evolving frontiers: early emphasis on “PD-1/CTLA-4 blockade” has transitioned toward “machine learning,” “multi-omics,” and “lncRNA,” reflecting a shift to predictive modeling and biomarker discovery. …”
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Enhanced classification of tinnitus patients using EEG microstates and deep learning techniques by Zahra Raeisi, Abolfazl Sodagartojgi, Fahimeh Sharafkhani, Amirsadegh Roshanzamir, Hossein Najafzadeh, Omid Bashiri, Alireza Golkarieh

“…Validation with pre-trained models showed ResNet50 with SVM classifier using 6-state configurations provided optimal discrimination (100% accuracy). …”
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Leveraging AI for early cholera detection and response: transforming public health surveillance in Nigeria by Adamu Muhammad Ibrahim, Mohamed Mustaf Ahmed, Shuaibu Saidu Musa, Usman Abubakar Haruna, Mohammed Raihanatu Hamid, Olalekan John Okesanya, Aishat Muhammad Saleh, Don Eliso Lucero-Prisno III

“…This paper explores the potential of artificial intelligence (AI) and machine learning (ML) to address these challenges. …”
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Development and validation of a CT-based multi-omics nomogram for predicting hospital discharge outcomes following mechanical thrombectomy by Feifan Liu, Jiayi Hong, Yuhan Chen, Huan Liu, Yue Wang, Lijian Su, Sheng Hu, Jingjing Fu

“…Our nomogram significantly outperformed clinical, radiomics, and DTL models, as well as physician assessments (senior physicians: 0.693, p = 0.001; junior physicians: 0.600, p &lt; 0.001).ConclusionThis multi-omics nomogram, integrating HIM-derived, clinical, radiomic, and DTL features, accurately predicts post-MT discharge outcomes, enabling early identification of high-risk patients and optimizing management to improve prognosis.…”
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Enhancing Radiologist Productivity with Artificial Intelligence in Magnetic Resonance Imaging (MRI): A Narrative Review by Arun Nair, Wilson Ong, Aric Lee, Naomi Wenxin Leow, Andrew Makmur, Yong Han Ting, You Jun Lee, Shao Jin Ong, Jonathan Jiong Hao Tan, Naresh Kumar, James Thomas Patrick Decourcy Hallinan

“…A few studies also explored machine learning-based automation software and, more recently, large language models. …”
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Evaluation of Weed Infestations in Row Crops Using Aerial RGB Imaging and Deep Learning by Plamena D. Nikolova, Boris I. Evstatiev, Atanas Z. Atanasov, Asparuh I. Atanasov

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Better Pseudo-Labeling for Semi-Supervised Domain Generalization in Medical Magnetic Resonance Image Segmentation by Liangqing Hu, Zuqiang Meng, Chaohong Tan, Yumin Zhou

“…Moreover, due to variations in MRI machines, ensuring the independence and identical distribution between model training data and real-world data is difficult, which may lead to noisy model predictions and weak generalization ability. …”
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Bayesian neural networks for predicting tokamak energy confinement time with uncertainty quantification by Enliang Gao, Chenguang Wan, Youjun Hu, Minglong Wang, Jingjing Lu, Zhisong Qu, Xinghao Wen, Jia Huang, Ying Chen, Heru Guo, Zhengping Luo, Zhi Yu, Xiaojuan Liu, Qiping Yuan, Jiangang Li

“…Evaluations on the multi-machine ITPA global H-mode confinement database and individual datasets from JET, DIII-D, and ASDEX-Upgrade, both models outperform conventional methods in terms of accuracy and provide reliable uncertainty estimates. …”
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The Impact of Exercise Training on the Brain and Cognition in Type 2 Diabetes, and its Physiological Mediators: A Systematic Review by Jitske Vandersmissen, Ilse Dewachter, Koen Cuypers, Dominique Hansen

“…Studies directly investigating the effect of any kind of exercise training on the brain or cognition in patients with T2DM, or animal models thereof, were included, with the exception of human studies assessing cognition only at one time point, and studies combining exercise training with other interventions (e.g. dietary changes, cognitive training, etc.). …”
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What is the Role of Medical Informatics in Digital Healthcare? by Mihai TĂRÂŢĂ

“… • Big Data & Predictive Analytics – Analyzing large datasets to identify disease patterns, optimize hospital efficiency, and advance personalized medicine through risk stratification and predictive modeling…”
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LensNet: Enhancing Real-time Microlensing Event Discovery with Recurrent Neural Networks in the Korea Microlensing Telescope Network by Javier Viaña, Kyu-Ha Hwang, Zoë de Beurs, Jennifer C. Yee, Andrew Vanderburg, Michael D. Albrow, Sun-Ju Chung, Andrew Gould, Cheongho Han, Youn Kil Jung, Yoon-Hyun Ryu, In-Gu Shin, Yossi Shvartzvald, Hongjing Yang, Weicheng Zang, Sang-Mok Cha, Dong-Jin Kim, Seung-Lee Kim, Chung-Uk Lee, Dong-Joo Lee, Yongseok Lee, Byeong-Gon Park, Richard W. Pogge

“…The internal model of the pipeline employs a multibranch Recurrent Neural Network architecture that evaluates time-series flux data with contextual information, including sky background, the full width at half-maximum of the target star, flux errors, point-spread function quality flags, and air mass for each observation. …”
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Advancements in Herpes Zoster Diagnosis, Treatment, and Management: Systematic Review of Artificial Intelligence Applications by Dasheng Wu, Na Liu, Rui Ma, Peilong Wu

“…AI applications were analyzed across three domains: (1) diagnosis, where mobile deep neural networks, convolutional neural network ensemble models, and mixed-scale attention-based models have improved diagnostic accuracy and efficiency; (2) treatment, where machine learning models, such as deep autoencoders combined with functional magnetic resonance imaging, electroencephalography, and clinical data, have enhanced treatment outcome predictions; and (3) management, where AI has facilitated case identification, epidemiological research, health care burden assessment, and risk factor exploration for postherpetic neuralgia and other complications. …”
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