The Emergence of AI-Driven Virtual Hospitals: Redefining Patient Care Beyond Physical Boundaries by Ifrah Hameed, Hafiz Muhammad Haseeb Khaliq

“…The involvement of AI in the development of virtual hospitals has transformed diagnostics and clinical decision-making. Sophisticated AI algorithms have started to analyze complex types of medical data such as medical images, patient reports, and histories. …”
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A Deep Learning-Based Echo Extrapolation Method by Fusing Radar Mosaic and RMAPS-NOW Data by Shanhao Wang, Zhiqun Hu, Fuzeng Wang, Ruiting Liu, Lirong Wang, Jiexin Chen

“…Furthermore, as the extrapolation time increases, the smoothing effect inherent to convolution operations leads to increasingly blurred predictions. To address the algorithmic limitations of deep learning-based echo extrapolation models, this study introduces three major improvements: (1) A Deep Convolutional Generative Adversarial Network (DCGAN) is integrated into the ConvLSTM-based extrapolation model to construct a DCGAN-enhanced architecture, significantly improving the quality of radar echo extrapolation; (2) Considering that the evolution of radar echoes is closely related to the surrounding meteorological environment, the study incorporates specific physical variable products from the initial zero-hour field of RMAPS-NOW (the Rapid-update Multiscale Analysis and Prediction System—NOWcasting subsystem), developed by the Institute of Urban Meteorology, China. …”
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A Framework for High-Spatiotemporal-Resolution Soil Moisture Retrieval in China Using Multi-Source Remote Sensing Data by Zhuangzhuang Feng, Xingming Zheng, Xiaofeng Li, Chunmei Wang, Jinfeng Song, Lei Li, Tianhao Guo, Jia Zheng

“…Four machine learning and deep learning algorithms are applied, including Random Forest Regression (RFR), Extreme Gradient Boosting (XGBoost), Long Short-Term Memory (LSTM) networks, and Ensemble Learning (EL). …”
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Demethylase FTO mediates m6A modification of ENST00000619282 to promote apoptosis escape in rheumatoid arthritis and the intervention effect of Xinfeng Capsule by Fanfan Wang, Fanfan Wang, Jianting Wen, Jianting Wen, Jian Liu, Jian Liu, Ling Xin, Yanyan Fang, Yanyan Fang, Yue Sun, Mingyu He, Mingyu He

“…This study aimed to elucidate the mechanism by which the demethylase FTO promoted FLS apoptosis escape through the m6A modification of lncRNA ENST00000619282 and to reveal the therapeutic targets of XFC in treating RA by intervening in this m6A-dependent pathway.MethodsA retrospective analysis was conducted on 1603 RA patients using association rule mining and random walk algorithms to evaluate the efficacy of XFC. The proliferation and apoptosis of co-cultured RA-FLS were assessed using CCK-8, flow cytometry (FCM), and molecular biology techniques. …”
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Research Progress on Selective Depolymerization of Waste Plastics to High-Quality Liquid Fuels by Xinze LI, Zhicheng LUO, Rui XIAO

“…For instance, microwave-enhanced light absorption in TiO2-MoS2 hybrids doubled charge carrier density, potentially reducing energy consumption by 30% – 40%. (3) Intelligent reactors: IoT-enabled sensors and machine learning algorithms stabilized multiphase reactions in simulated trials, minimizing yield fluctuations to ±5% versus ±15% in batch modes. …”
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MAPPING GENERATIVE AI'S ETHICAL ISSUES IN HIGHER EDUCATION: A FELT-GUIDED SYSTEMATIC REVIEW [PEMETAAN ISU ETIKA GENERATIVE AI DI PENDIDIKAN TINGGI: TINJAUAN SISTEMATIS BERPANDUAN... by okky barus, Achmad Nizar Hidayanto, Imairi Eitiveni

“…The SLR revealed seven prominent ethical concerns: (1) academic integrity and plagiarism, highlighting issues of unauthorized assistance and false authorship; (2) bias and fairness, manifested through algorithmic and linguistic biases; (3) data privacy and security, concerning unauthorized access and re-identification risks; (4) impact on critical thinking and learning outcomes, fostering over-reliance; (5) authorship, intellectual property, and copyright ambiguities; (6) misinformation, hallucinations, and deepfakes, eroding trust; and (7) broader environmental and labor impacts. …”
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INTERNATIONAL FORUM “OLD AND NEW MEDIA: ALONG THE PATH TOWARDS A NEW AESTHETICS” / МЕЖДУНАРОДНЫЙ ФОРУМ «СТАРЫЕ И НОВЫЕ МЕДИА: ПУТИ К НОВОЙ ЭСТЕТИКЕ»... by BOGATYRYOVA ELENA A.

“…Notice was made of alarming tendencies of transforming consciousness into that of “gamers,” reacting in correlations with certain algorithms, and a transference from discourse and substantiations towards reactions and evaluations. …”
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Evaluating the Accuracy and Reliability of Real-World Digital Mobility Outcomes in Older Adults After Hip Fracture: Cross-Sectional Observational Study by Martin A Berge, Anisoara Paraschiv-Ionescu, Cameron Kirk, Arne Küderle, Encarna Micó-Amigo, Clemens Becker, Andrea Cereatti, Silvia Del Din, Monika Engdal, Judith Garcia-Aymerich, Karoline B Grønvik, Clint Hansen, Jeffrey M Hausdorff, Jorunn L Helbostad, Carl-Philipp Jansen, Lars Gunnar Johnsen, Jochen Klenk, Sarah Koch, Walter Maetzler, Dimitrios Megaritis, Arne Müller, Lynn Rochester, Lars Schwickert, Kristin Taraldsen, Beatrix Vereijken

“… BackgroundAlgorithms estimating real-world digital mobility outcomes (DMOs) are increasingly validated in healthy adults and various disease cohorts. …”
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Comparison of cardiovascular risk prediction models developed using machine learning based on data from a Sri Lankan cohort with World Health Organization risk charts for predictin... by Anuradhani Kasturiratne, Hithanadura Janaka de Silva, Chamila Mettananda, Anuradha Supun Dassanayake, Norihiro Kato, Rajitha Wickremasinghe, Maheeka Solangaarachchige, Prasanna Haddela

“…The ML models were derived using classification algorithms of the supervised learning technique. We compared the predictive performance of our ML models with WHO risk charts (2019, Southeast Asia) using area under the receiver operating characteristic curves (AUC-ROC) and calibration plots. …”
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Translational medicine research on the role of key gene network modulation mediated by procyanidin B2 in the precise diagnosis and treatment of multiple sclerosis by Jian Liu, Meng Pu, Di Guo, Ying Xiao, Jin-zhu Yin, Dong Ma, Cun-gen Ma, Qing Wang

“…Eight machine learning algorithms were employed to screen key genes, and nomograms and ROC curves were constructed to assess the value of the screened biomarker genes in MS diagnosis. …”
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Proteomic signatures and predictive modeling of cadmium-associated anxiety in middle-aged and elderly populations: an environmental exposure association study by Sheng Wan, Yong Yang, Qihan Zhao, Zelong Xing, Jie Li, Hao Gao, Yinghui Yin, Zhenzhong Liu, Qiwen Chen, Maoqin Tian, Xinxin Shi, Ziyue Ji, Shaoxin Huang

“…By utilizing the XGBoost and LASSO machine learning algorithms, combined with validation through animal experiments, CCDC126 was identified as a diagnostic biomarker derived from the plasma proteome. …”
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ARTS AND MACHINE CIVILIZATION INTERNATIONAL SCIENTIFIC CONFERENCE / МЕЖДУНАРОДНАЯ НАУЧНАЯ КОНФЕРЕНЦИЯ «ИСКУССТВО И МАШИННАЯ ЦИВИЛИЗАЦИЯ»... by DUKOV YEVGENY V. / ДУКОВ Е.В., EVALLYO VIOLETTA D. / ЭВАЛЛЬЕ В.Д.

“…The purpose of the conference was to comprehend the artistic practices in the era of machine civilization, get acquainted with current hypotheses, publish new facts and discuss modern terminologies (law of spontaneity, law of semantic uncertainty, algorithmic apophenia, post-opera, artificial life and new vitality). …”
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Interpretable Machine Learning for Legume Yield Prediction Using Satellite Remote Sensing Data by Theodoros Petropoulos, Lefteris Benos, Remigio Berruto, Gabriele Miserendino, Vasso Marinoudi, Patrizia Busato, Chrysostomos Zisis, Dionysis Bochtis

“…Subsequently, six ML models were evaluated representing different algorithmic strategies. Among them, XGBoost showed the best performance (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mi>R</mi><mn>2</mn></msup></mrow></semantics></math></inline-formula> = 0.8756) and low error values across <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>M</mi><mi>A</mi><mi>E</mi></mrow></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>M</mi><mi>S</mi><mi>E</mi></mrow></semantics></math></inline-formula>, and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>R</mi><mi>M</mi><mi>S</mi><mi>E</mi></mrow></semantics></math></inline-formula> metrics. …”
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Multi-Focus Image Fusion Based on Dual-Channel Rybak Neural Network and Consistency Verification in NSCT Domain by Ming Lv, Sensen Song, Zhenhong Jia, Liangliang Li, Hongbing Ma

“…Experimental results show that our method consistently outperforms several state-of-the-art image fusion techniques, including both traditional algorithms and deep learning-based approaches, in terms of visual quality and objective performance metrics (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>Q</mi><mrow><mi>A</mi><mi>B</mi><mo>/</mo><mi>F</mi></mrow></msub></mrow></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>Q</mi><mrow><mi>C</mi><mi>B</mi></mrow></msub></mrow></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>Q</mi><mi>E</mi></msub></mrow></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>Q</mi><mrow><mi>F</mi><mi>M</mi><mi>I</mi></mrow></msub></mrow></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>Q</mi><mrow><mi>M</mi><mi>I</mi></mrow></msub></mrow></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>Q</mi><mrow><mi>M</mi><mi>S</mi><mi>E</mi></mrow></msub></mrow></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>Q</mi><mrow><mi>N</mi><mi>C</mi><mi>I</mi><mi>E</mi></mrow></msub></mrow></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>Q</mi><mrow><mi>N</mi><mi>M</mi><mi>I</mi></mrow></msub></mrow></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>Q</mi><mi>P</mi></msub></mrow></semantics></math></inline-formula>, and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>Q</mi><mrow><mi>P</mi><mi>S</mi><mi>N</mi><mi>R</mi></mrow></msub></mrow></semantics></math></inline-formula>). …”
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Digital Land Suitability Assessment for Irrigated Cultivation of Some Agricultural Crops Using Machine Learning Approaches (Case Study: Qazvin-Abyek) by F. Jannati, F. Sarmadian

“…The utilization of modern mapping techniques such as digital soil mapping and machine learning algorithms can significantly improve the accuracy of land suitability assessment and crop performance prediction. …”
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Modeling and validation of wearable sensor-based gait parameters in Parkinson’s disease patients with cognitive impairment by Guo Hong, Guo Hong, Fengju Mao, Fengju Mao, Mingming Zhang, Fei Zhang, Fei Zhang, Xiangcheng Wang, Kang Ren, Kang Ren, Zhonglue Chen, Zhonglue Chen, Xiaoguang Luo, Xiaoguang Luo

“…The logistic regression model demonstrated superior predictive performance (test set AUC: 0.957), outperforming other machine learning algorithms. SHAP analysis revealed that Step Length, UPDRS-III score, Duration of PD, and Peak angular velocity during steering were the most influential predictors in the logistic regression model. …”
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Preliminary Results in the Investigation of In Vivo Iliac and Coronary Flow Collision, Vortex Formation, and Disorganized Flow Degeneration: Insights from Invasive Cardiology Based... by Thach Nguyen, Hieu D. Nguyen, Hoang V. K. Dinh, Tien H. T. Dinh, Khiem Ngo, Hieu H. Truong, Hien Q. Nguyen, Vu Tri Loc, Thien Le, Nhi Vo, Trung Q. T. Le, Tam Tran, Chau Dang, Vy Le, Dat Q. Ha, Hadrian Tran, Mihas Kodenchery, Marco Zuin, Gianluca Rigatelli, Miguel Antunes, Quynh T. N. Nguyen, Aravinda Nanjundappa, C. Michael Gibson

“…Additionally, in two sub-studies, we analyzed the relationship between retrograde flow and blood pressure, and artificial intelligence algorithms were used to detect the retrograde flow in the right coronary artery. …”
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Improving accessibility to radiotherapy services in Cali, Colombia: cross-sectional equity analyses using open data and big data travel times from 2020 by Luis Gabriel Cuervo, Carmen Juliana Villamizar, Daniel Cuervo, Pablo Zapata, Maria B. Ospina, Sara Marcela Valencia, Alfredo Polo, Ángela Suárez, Maria O. Bula, J. Jaime Miranda, Gynna Millan, Diana Elizabeth Cuervo, Nancy J. Owens, Felipe Piquero, Janet Hatcher-Roberts, Gabriel Dario Paredes, María Fernanda Navarro, Ingrid Liliana Minotta, Carmen Palta, Eliana Martínez-Herrera, Ciro Jaramillo, on behalf of the AMORE Project Collaboration

“…The platform integrates open data, including the location of radiotherapy services, the disaggregated sociodemographic microdata for the population and places of residence, and big data for travel times from Google Distance Matrix API. We used genetic algorithms to identify optimal locations for new services. …”
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Sepsis-Associated Acute Kidney Disease Incidence, Trajectory, and Outcomes by Hsiu-Yin Chiang, Chih-Chia Liang, Ya-Luan Hsiao, Uyen-Minh Le, Yi-Ching Chang, Pei-Shan Chen, David Ray Chang, I-Wen Ting, Hung-Chieh Yeh, Chin-Chi Kuo

“…This study aimed to use data algorithms on the electronic health records to trace the SA-AKD trajectory from acute kidney injury (AKI) to chronic kidney disease (CKD). …”
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Development and validation of an explainable machine learning model for predicting postoperative pulmonary complications after lung cancer surgery: a machine learning studyResearch... by Shaolin Chen, Ting Deng, Qing Yang, Jin Li, Juanyan Shen, Xu Luo, Juan Tang, Xulian Zhang, Jordan Tovera Salvador, Junliang Ma

“…Feature selection involved univariate analysis, collinearity analysis, nine ML algorithms, and expert consensus. Twelve independent ML models and 26 stacking ensemble models were developed. …”
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