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 developed model should be utilized as a useful tool to help the design of an efficient processing of natural gases. A great deal of the unlimited what if questions can be answered using this model. …”
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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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HEE-SegGAN: A holistically-nested edge enhanced GAN for pulmonary nodule segmentation. by Yong Wang, Seri Mastura Mustaza, Mohammad Syuhaimi Ab-Rahman, Siti Salasiah Mokri

“…We also improved the loss function to better capture edge-level details and enhance segmentation precision in edge regions. …”
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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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Fuzzy volume fraction model representation of blood-based sisko tri-hybrid nanofluid flow via a stretching cylinder by N. Deepa, P. Kavya, N. Thamaraikannan, S. Madhanraj, P. Asaigeethan, K. Loganathan

“…The triangular membership function (TMF) is utilized to analyze the variability of uncertainty, while the α− cut is responsible for controlling the TFNs. …”
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Analysis of factors influencing the increase of extracellular water ratio in tumor patients without edema signs by Heling Zhu, Panpan Gan, Hao Jiang, Liangliang Bao, Chengjiang Liu, Jiawen Yu, Jiawen Yu

“…PA was the most influential factor among all independent variables affecting ECW/TBW (B = −1.006, p < 0.001). …”
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Optimized Temporal Interference Stimulation Based on Convex Optimization: A Computational Study by Chao Geng, Yang Li, Long Li, Xiaoqi Zhu, Xiaohan Hou, Tian Liu

“…CVXTI accommodates various optimization objectives by incorporating different objective functions, thereby enhancing the focality of the stimulation field. …”
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Elucidating Genotypic Variation in Quinoa via Multidimensional Agronomic, Physiological, and Biochemical Assessments by Samreen Nazeer, Muhammad Zubair Akram

“…The results revealed that significant variation was observed across all measured parameters, highlighting the diverse adaptive strategies and functional capacities among the tested genotypes. More specifically, genotypes Q4, Q11, Q15, and Q126 demonstrated superior agronomic potential and canopy-level physiological efficiencies, including high biomass accumulation, low infrared canopy temperatures and sustained NDVI values. …”
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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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Interpretable machine learning for predicting isolated basal septal hypertrophy. by Lei Gao, Boyan Tian, Qiqi Jia, Xingyu He, Guannan Zhao, Yueheng Wang

“…This is a common echocardiographic finding with a prevalence of approximately 7-20%, which may indicate early structural and functional remodeling of the left ventricle in certain pathologies. …”
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Hierarchical GraphCut Phase Unwrapping Based on Invariance of Diffeomorphisms Framework by Xiang Gao, Xinmu Wang, Zhou Zhao, Junqi Huang, Xianfeng David Gu

“…However, the presence of noise, occlusions, and piecewise continuous phase functions induced by complex 3D surface geometry makes the inverse reconstruction of the true phase extremely challenging. …”
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Smart Fermentation Technologies: Microbial Process Control in Traditional Fermented Foods by Chong Shin Yee, Nur Asyiqin Zahia-Azizan, Muhamad Hafiz Abd Rahim, Nurul Aqilah Mohd Zaini, Raja Balqis Raja-Razali, Muhammad Ameer Ushidee-Radzi, Zul Ilham, Wan Abd Al Qadr Imad Wan-Mohtar

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AI-guided laser purification of human iPSC-derived cardiomyocytes for next-generation cardiac cell manufacturing by Prakaimuk Saraithong, Peyton Krajcarski, Yukako Kusaka, Moe Yamada, Junichi Matsumoto, Hailey Cunningham, Sama Salih, Darby Jones, Devika Baddhan, Christian Hausner, Justus Anumonwo, Anthony Rosenzweig, Mary M. Navarro, Luis Villa Diaz, Joseph Criscione, Deok-Ho Kim, Todd J. Herron

“…This streamlined process preserves cardiomyocyte viability and function, offering a scalable and efficient solution for cardiac regenerative medicine, disease modeling, and drug discovery.…”
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Correlation of magnetic resonance imaging parameters with montreal cognitive assessment-Indonesian (MOCA-INA) score in dementia patients at Prof. Dr. IGNG Ngoerah General Hospital,... by Made Widhi Asih, Elysanti Dwi Martadiani, I Wayan Gede Artawan Eka Putra, Felicia Nike, Putri Ayu Ratnasari, Nugra Arenz Raturandang, Jessieca Liusen, Anak Agung Parama Swari Khrisna, Luh Dindi Ayu Surya Kanti, Adsel Kartadinata

“…Correlation between variables was evaluated based on the correlation coefficient, which indicates the direction and strength of the correlation. …”
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Ultrasonographic Nomograms of Foetal Fourth Ventricle Biometry and its Correlation with Transcerebellar Diameter and Gestational Age among Normal Foetuses: A Cross-sectional Study by N Padmalatha, Paarthipan, Parthasarathy, Francis Nanda Prakash Monteiro

“…Regression analysis was performed to determine the equation of the linear fitted function with each of the independent variables (GA, TCD). …”
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Compositional and nutritional value of lupin cultivars: Identifying high-protein seeds for enhanced protein isolate production and phytochemical valorisation by Pau Taberner-Pibernat, Albert Ribas-Agustí, Gisela Quinteros, Gerard Sabeña, Miguel López-Gómez, Rubén Domínguez-Valencia, Ricard Bou

“…The findings revealed significant inter- and intra-species variability in protein, total dietary fibre, and fat content among the studied cultivars. …”
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Advancing geohazard risk assessment – A digital tool for steep slope management by Cherry Rose Godes, One-Bin Lim, Shanelle Aira Rodrigazo, Yongseong Kim, Yongjin Kim, Seungjoo Lee, Jaeheum Yeon

“…Growing climatic variability and rapid urbanization have increased landslide occurrences, posing significant challenges for risk assessment. …”
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On the added value of sequential deep learning for the upscaling of evapotranspiration by B. Kraft, B. Kraft, B. Kraft, J. A. Nelson, S. Walther, F. Gans, U. Weber, G. Duveiller, M. Reichstein, W. Zhang, M. Rußwurm, D. Tuia, M. Körner, Z. Hamdi, M. Jung

“…We compared different types of covariates (meteorological without precipitation, precipitation, remote sensing, and plant functional types) and their impact on model performance at the site level in a cross-validation setup.…”
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