Adolescent dropouts’ functioning in relationships with parents and peers by Ewa Rzechowska

“…Generalizations are made based on the decision tree generated by Quinlan’s algorithm. Based on it, a model of the variant modes of adolescent dropouts’ functioning is constructed. …”
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Development of an Integrated Cooling System Controller for Hybrid Electric Vehicles by Chong Wang, Qun Sun, Limin Xu

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APPLICATION OF POWER INDICES BY NATIONAL ECONOMIC ENTITIES FOR ULTIMATE BENEFICIAL OWNER IDENTIFICATION by Володимир Вареник

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Application of Motion Video Analysis System Based on SISA Neural Network in Sports Training by Shishun Ge, Chunhong Zhu

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Intelligent multi-modeling reveals biological relationships and adaptive phenotypes for dairy cow adaptation to climate change by Robson Mateus Freitas Silveira, Angela Maria de Vasconcelos, Concepta McManus, Luiz Paulo Fávero, Iran José Oliveira da Silva

“…Applying the random forest method to classify predictions of adaptive responses based on climatic variables showed that all thermoregulatory, hormonal, biochemical, and hematological responses are important, except for urea and T₃ concentrations, which had negative importance values. …”
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Multiperson Target Dynamic Tracking Method for Athlete Training Based on Wireless Body Area Network by Diandian Du

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Hexapeptide dalargin in clinical gastroenterology: 30 years' of clinical application of the drug by S. A. Bulgakov

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Realistic challenges and path options for the high-quality development of smart stadiums in China by Shaoquan Chu

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The Application of the Internet of Things Framework in the Ideological and Political Teaching System by Bei Qiu, Pakiza Ikram

“…This study uses Internet of Things technology to build a smart system for ideological and political education in order to remove several flaws in present ideological and political teaching. …”
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Descriptions of self-treatment for the middle-aged and elderly in Shanxi, China. by Rui Wang, Chenjin Ma, Kun Jiang, Ming Li, Shuangge Ma

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Adverse events in the neonatal intensive care unit identified by triggers by Fabiana Bragança Albanese, Deise de Souza Ventura, Deise de Souza Ventura, Maurício Wesley Perroud, Maurício Wesley Perroud, Rafael Nogueira de Souza, Mariana Vieira Morau, Marília Berlofa Visacri, Marília Berlofa Visacri, Patricia Moriel, Patricia Moriel

“…AE severity was assessed using the Neonatal Adverse Event Severity Scale (NAESS) and the World Health Organization (WHO) classification, while ADR causality was evaluated using the WHO criteria and the algorithm proposed by Du et at. Risk factors such as gestational age, birth weight, and length of hospital stay were also analyzed.ResultsA total of 249 triggers identified 168 confirmed AEs, resulting in a Positive Predictive Value (PPV) of 67.5%. …”
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Mapping Soil Available Nitrogen Using Crop-Specific Growth Information and Remote Sensing by Xinle Zhang, Yihan Ma, Shinai Ma, Chuan Qin, Yiang Wang, Huanjun Liu, Lu Chen, Xiaomeng Zhu

“…These remote sensing variables were combined with soil sample data, crop type information, and crop growth period data as predictive factors and input into a Random Forest (RF) model optimized using the Optuna hyperparameter tuning algorithm. …”
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Assessment of prostate cancer aggressiveness through the combined analysis of prostate MRI and 2.5D deep learning models by Yalei Wang, Yuqing Xin, Baoqi Zhang, Fuqiang Pan, Xu Li, Manman Zhang, Yushan Yuan, Lei Zhang, Peiqi Ma, Bo Guan, Yang Zhang

“…Model classification performance in the test set was evaluated using the area under the receiver operating characteristic curve (AUC).ResultsIn the test set, the nomogram demonstrated the highest predictive ability for prostate cancer aggressiveness (AUC = 0.919, 95% CI: 0.8107–1.0000), with a sensitivity of 0.966 and specificity of 0.833. …”
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Enhancing deep learning methods for brain metastasis detection through cross-technique annotations on SPACE MRI by Tassilo Wald, Benjamin Hamm, Julius C. Holzschuh, Rami El Shafie, Andreas Kudak, Balint Kovacs, Irada Pflüger, Bastian von Nettelbladt, Constantin Ulrich, Michael Anton Baumgartner, Philipp Vollmuth, Jürgen Debus, Klaus H. Maier-Hein, Thomas Welzel

“…Multiple DL methods were developed with NAQ or HAQ using either SPACE or MRPAGE images and evaluated on their detection performance using positive predictive value (PPV), sensitivity, and F1 score and on their delineation performance using volumetric Dice similarity coefficient, PPV, and sensitivity on one internal and four additional test datasets (660 patients). …”
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Novel bacterial cluster “Prevotella, Bacteroides and Suterella” associated with mortality in Mexican patients with acute-on-chronic liver failure (ACLF) and clinical utility of sys... by Paula A. Castaño Jiménez, Tonatiuh A. Baltazar-Díaz, Rodrigo Hernández-Basulto, Mayra P. Padilla-Sánchez, Ksenia K. Kravtchenko, Roxana García-Salcido, María T. Tapia-De la paz, Kevin J. Arellano-Arteaga, Luz A. González-Hernández, Miriam R. Bueno-Topete

“…Furthermore, α and β diversity analyses, relative abundances, and ANCOM-BC compositional analysis were performed in the QIIME2 package. Predictive values and associations were performed using ROC curves and Spearman correlations, respectively. …”
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Enhancing depression recognition through a mixed expert model by integrating speaker-related and emotion-related features by Weitong Guo, Qian He, Ziyu Lin, Xiaolong Bu, Ziyang Wang, Dong Li, Hongwu Yang

“…Abstract The World Health Organization predicts that by 2030, depression will be the most common mental disorder, significantly affecting individuals, families, and society. …”
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Evaluating the acceptance of CBDCs: experimental research with artificial intelligence (AI) generated synthetic response by Sergio Luis Náñez Alonso, Peterson K. Ozili, Beatriz María Sastre Hernández, Luís Miguel Pacheco

“…Generative AI, with its algorithmic fidelity, has great potential for predicting human behavior in economic contexts. …”
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Artificial intelligence technology in the clinical analysis of a patient with a mental disorder (case report) by Dmitry I. Nozdrachev, Maksim P. Marachev, Pavel S. Evgenov, Natalia N. Petrova

“…Modern neural networks, such as neural networks and large language models (LLMs), claim to be able to diagnose, prescribe treatments, and predict the course of mental disorders based on historical and clinical data. …”
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REinforcement learning to improve non-adherence for diabetes treatments by Optimising Response and Customising Engagement (REINFORCE): study protocol of a pragmatic randomised tria... by Lily G Bessette, Elad Yom-Tov, Niteesh K Choudhry, Julie C Lauffenburger, Nancy Haff, Marie E McDonnell, Constance P Fontanet, Ellen S Sears, Erin Kim, Kaitlin Hanken, Renee A Barlev, Punam A Keller, J Joseph Buckley

“…The messages will be individually adapted using a reinforcement learning prediction algorithm based on daily adherence measurements from the pill bottles. …”
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Emerging Diagnostic Approaches for Musculoskeletal Disorders: Advances in Imaging, Biomarkers, and Clinical Assessment by Rahul Kumar, Kiran Marla, Kyle Sporn, Phani Paladugu, Akshay Khanna, Chirag Gowda, Alex Ngo, Ethan Waisberg, Ram Jagadeesan, Alireza Tavakkoli

“…Artificial intelligence and machine learning algorithms now automate image interpretation, predict clinical outcomes, and enhance clinical decision support, complementing conventional clinical evaluations. …”
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