Optimizing physical education schedules for long-term health benefits by Liang Tan, Qin Chen, Jianwei Wu, Mingbang Li, Tianyu Liu

“…The developed DL model integrates convolutional neural network (CNN) layers to capture spatial features and long short-term memory (LSTM) layers to extract temporal patterns from demographic and activity-related variables. These features are combined through a fusion layer, and a customized loss function is employed to accurately predict fitness scores.ResultsExtensive experimental evaluation demonstrates that the proposed model consistently outperforms competitive baseline models. …”
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Optimizing the Production of LNG and NGL from Arab Crudes and Wet Gases by Adel M. Hemeida, Mohammed S. Al-Blehed, Saad El-Din Desouky

“…The explanatory variables of the model are LNG and NGL volumes and prices, oil volume and prices, the initial costs of separators, chillers, demethanizer, compressor, partial condenser and boiler, the running cost which includes cost of refrigerants, cost of steam and other continuous operating and transportion cost. …”
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Flow-based cytometric analysis of cell cycle via simulated cell populations. by M Rowan Brown, Huw D Summers, Paul Rees, Paul J Smith, Sally C Chappell, Rachel J Errington

“…We present a new approach to the handling and interrogating of large flow cytometry data where cell status and function can be described, at the population level, by global descriptors such as distribution mean or co-efficient of variation experimental data. …”
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A Knowledge-Driven Smart System Based on Reinforcement Learning for Pork Supply-Demand Regulation by Haohao Song, Jiquan Wang

“…Around the core of the system, a nonlinear constrained optimization model is established, which uses adjustments to newly retained gilts as decision variables and minimizes supply-demand squared errors as its objective function, incorporating multi-dimensional factors such as pig growth dynamics, epidemic impacts, consumption trends, and international trade into its analytical framework. …”
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A LINEAR SIMULATION MODEL FOR OPTIMIZING CROP STRUCTURE IN ORDER TO MAXIMIZE INCOME IN A VEGETAL AGRICULTURAL FARM by Sorin IONITESCU

“…The model included: the 8 unknown variables for the cultivated area with 8 crops: wheat, rye, barley, peas, rape, soybean, maize and sunflower, 14 restrictions regarding Diesel fuel, fertilizers, herbicides, total surface, expenditures, income, and area per each crop, and objective - function f(Max) Income. …”
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Bayesian inference for peak feature extraction and prediction of material property in X-ray diffraction data by Ryo Murakami, Taisuke T. Sasaki, Hideki Yoshikawa, Yoshitaka Matsushita, Keitaro Sodeyama, Tadakatsu Ohkubo, Hiroshi Shinotsuka, Kenji Nagata

“…We then applied Bayesian linear regression to the maximum energy product [Formula: see text], using the extracted peak features as the explanatory variable. The outcomes yielded reasonable and robust regression results. …”
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Fast 3D localization of nano-objects in wide-field interferometric scattering microscopy via vectorial diffraction model-derived analytical fitting by Liaoliao Wei, Caroline Livan Anyi, Huakun Li, Haitao Nie, Bihan Wen, Tong Ling

“…The simplified analytic function includes a quadratically scaling amplitude term and a linearly scaling phase term, both of which change with the nano-object’s axial position. …”
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Reconfiguration of dynamic brain networks in heart failure with preserved ejection fraction: Linking neurovascular coupling and cardiac dysfunction by Xiulin Liang, Qinghua Zhang, Chuanlong Zhang, Jingjing Liu, Pengcheng Sang, Qing Mao, Lei Wang

“…Results: (1) Static network topology properties exhibited significantly decreased local network efficiency among patients with HFpEF. The connectivity strength and information processing efficiency were diminished in the left parahippocampal gyrus, left cingulate gyrus, and right insular gyrus brain regions, and improved in the left thalamus, right fusiform gyrus, and right precuneus regions. (2) Dynamic network topology properties of patients with HFpEF showed decreased variability in critical nodes and brain subregions (e.g., the superior frontal gyrus, left amygdala, and left fusiform gyrus) and compensatory increases in the variability of specific regions (e.g., right insular gyrus, right postcentral gyrus, and right temporal gyrus). (3) In HFpEF, the static and dynamic functional brain network topology properties of the fusiform gyrus, cingulate gyrus, superior temporal gyrus, precuneus, parahippocampal gyrus, insular gyrus, and amygdala regions were significantly correlated with cardiac structural and functional indices, such as LVDd, LVMI, and E/e′ ratio. …”
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Deep Learning Approaches for Morphological Classification of Intestinal Organoids by Giovanni Cicceri, Sebastiano Di Bella, Simone Di Franco, Giorgio Stassi, Matilde Todaro, Salvatore Vitabile

“…The use of deep learning (DL) in organoid image analysis becomes crucial to handle complexity, variability, and large amounts of data efficiently and accurately, overcoming the limitations of traditional image processing approaches. …”
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Load frequency control in isolated island city microgrids using deep graph reinforcement learning considering extensive scenarios by Ping He, Xiongwei Huang, Ruobing He, Linkun Yuan

“…Demonstrated effectively in China Southern Grid’s island microgrid setup, LE-LFC emerges as an advanced solution for modern grid variability, offering superior robustness, adaptability, and learning speed, thus enabling flexible and efficient energy system management.…”
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Organizational and Economic Factors of Health System Optimization by Oleksandr Telizhenko, Yuliia Halynska, Nataliia Baistriuchenko, Volodymyr Potseluiev, Kateryna Demchuk

“…To this aim, the factors that influence the efficiency of the organization and functioning of the medical system are identified. …”
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Field Testing of a Smart Geomat Based on Fiber Optic Sensors and Sensor Fusion for Levee Monitoring by Manuel Bertulessi, Francesca Ceccato, Francesco Ioli, Federico Barbieri, Livio Pinto, Paolo Simonini, Giovanni Menduni

“…ABSTRACT Real‐time monitoring of the functionality of river embankments, especially during flood events, is an increasingly important topic in the new scenario outlined by climate change. …”
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PM21-particle stimulation augmented with cytokines enhances NK cell expansion and confers memory-like characteristics with enhanced survival by Jeremiah L. Oyer, Tayler J. Croom-Perez, Md Faqrul Hasan, Javier A. Rivera-Huertas, Sarah B. Gitto, Joanna M. Mucha, Xiang Zhu, Deborah A. Altomare, Robert Y. Igarashi, Alicja J. Copik

“…This is typically done by ex vivo stimulation with cytokines to enhance functionality or expansion for 10-14 days to increase both their activity and quantity. …”
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Deletion of genes linked to the C1-fixing gene cluster affects growth, by-products, and proteome of Clostridium autoethanogenum by Ugochi Jennifer Nwaokorie, Kristina Reinmets, Lorena Azevedo de Lima, Pratik Rajendra Pawar, Kurshedaktar Majibullah Shaikh, Audrey Harris, Michael Köpke, Kaspar Valgepea

“…However, our understanding of the functionalities of the genes involved in the C1-fixing gene cluster and its closely-linked genes is incomplete. …”
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Classification of traffic accidents’ factors using TrafficRiskClassifier by Wei Sun, Lili Nurliyana Abdullah, Fatimah binti Khalid, Puteri Suhaiza binti Sulaiman

“…Furthermore, the TrafficRiskClassifier model delineates a functional nexus between accident risk and variables such as weather, road conditions, and personal factors, employing a polynomial regression approach. …”
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Enhancer chip: detecting human copy number variations in regulatory elements. by Marco Savarese, Giulio Piluso, Daniela Orteschi, Giuseppina Di Fruscio, Manuela Dionisi, Francesca del Vecchio Blanco, Annalaura Torella, Teresa Giugliano, Michele Iacomino, Marcella Zollino, Giovanni Neri, Vincenzo Nigro

“…Validation experiments have demonstrated that Enhancer Chip efficiently detects duplications and deletions covering enhancer loci, demonstrating that it is a powerful instrument to detect and characterize copy number variable enhancers.…”
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Characterization and treatment monitoring of ureagenesis disorders using stable isotopes by Gabriella Allegri, Martin Poms, Nadia Zürcher, Véronique Rüfenacht, Nicole Rimann, Déborah Mathis, Beat Thöny, Matthias Gautschi, Ralf A. Husain, Daniela Karall, Karolina Orchel-Szastak, Francesco Porta, Dominique Roland, Barbara Siri, Carlo Dionisi-Vici, René Santer, Johannes Häberle

“…Thus, the method proved to be safe and efficient to monitor UCD patients of variable severity pre- and post-therapy, being suitable as physiological endpoint for development of therapies.…”
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Structural network topologies are associated with deep brain stimulation outcomes in Meige syndrome by Bin Liu, Zhiqi Mao, Xinyuan Yan, Hang Yang, Junpeng Xu, Zhebin Feng, Yanyang Zhang, Xinguang Yu

“…However, the DBS efficacy varies across MS patients and the factors contributing to the variable responses remain enigmatic. We aim to explain the difference in DBS efficacy from a network perspective. …”
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Office-in-the-Loop: an investigation into Agentic AI for advanced building HVAC control systems by Tomoya Sawada, Masahiro Mizuno, Takaomi Hasegawa, Keiichi Yokoyama, Mayuka Kono

“…Regression analysis confirmed the robustness of our approach against confounding variables like outdoor conditions and occupancy levels. …”
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Achievement motivation and attitude of medical laboratory assistants to continuing education by K. S. Kichukova, T. G. Taneva

“…The applicability of the results is very clearly visible in the formulated thesis of the necessary change in the educational policy in the country through new forms of continuous education of medical laboratory assistants which would update their achievement motivation and would result in increase in the efficiency of their professional functioning.…”
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