Machine Learning Models to Predict Individual Cognitive Load in Collaborative Learning: Combining fNIRS and Eye-Tracking Data by Wenli Chen, Zirou Lin, Lishan Zheng, Mei-Yee Mavis Ho, Farhan Ali, Wei Peng Teo

“…These findings have implications for understanding cognitive load dynamics and designing effective collaborative learning environments and human–computer interfaces.…”
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DAGSLAM: causal Bayesian network structure learning of mixed type data and its application in identifying disease risk factors by Yuanyuan Zhao, Jinzhu Jia

“…However, existing DAG structure learning algorithms still have limitations in handling mixed-type data (including continuous and discrete variables), which restricts their practical utility. …”
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Should GPS data be normalized for performance and fatigue monitoring in soccer? A theoretical-practical discussion on high-speed running by Ricardo Pimenta, Ricardo Pimenta, Ricardo Pimenta, Ricardo Pimenta, Hugo Antunes, João Ribeiro, João Ribeiro, Fábio Yuzo Nakamura, Fábio Yuzo Nakamura

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From weather data to water fluxes simulation in Mediterranean greenhouses through a combined climate and hydrological modelling approach by D. la Cecilia, A. Venezia, D. Massa, M. Camporese

“…Importantly, the crop potential evapotranspiration estimated from climate data either measured indoor or simulated with the greenhouse model were identical. …”
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Multi-environment trials data analysis: linear mixed model-based approaches using spatial and factor analytic models by Tarekegn Argaw, Berhanu Amsalu Fenta, Habtemariam Zegeye, Girum Azmach, Assefa Funga

“…These insights have important implications for improving the efficiency and accuracy of MET data analysis, which is crucial for improving genetic gain estimation in plant breeding and agricultural research, ultimately accelerating the delivery of high-performing crop varieties to farmers and consumers.…”
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Instance maps as an organising concept for complex experimental workflows as demonstrated for (nano)material safety research by Benjamin Punz, Maja Brajnik, Joh Dokler, Jaleesia D. Amos, Litty Johnson, Katie Reilly, Anastasios G. Papadiamantis, Amaia Green Etxabe, Lee Walker, Diego S. T. Martinez, Steffi Friedrichs, Klaus M. Weltring, Nazende Günday-Türeli, Claus Svendsen, Christine Ogilvie Hendren, Mark R. Wiesner, Martin Himly, Iseult Lynch, Thomas E. Exner

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Discontinuation of Cerebro-Spinal Fluid (CSF) Drainage in Acute Hydrocephalus: A Prospective Cohort Study and Exploratory Data Analysis by Anand S. Pandit, Joanna Palasz, Lauren Harris, Parashkev Nachev, Ahmed K. Toma

“…The consequences of temporary CSF diversion have significant implications at financial and patient levels, but the quality of evidence regarding weaning remains poor. …”
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Associations between anthropometric indices and biological age acceleration in American adults: insights from NHANES 2009–2018 data by Xinyun Chen, Xia Chen, Fangyu Shi, Wenhui Yu, Chang Gao, Shenju Gou, Ping Fu

“…Abstract Population aging has become a global phenomenon with significant implications for public health. Aging accelerates the development of age-related diseases, leading to increased social and economic burdens. …”
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Bi-branch Neural Network for Urban Functional Zone Mapping: Combining Remote Sensing Imagery and Point-of-Interest Data by L. Ying, X. Chen, X. Chen, X. Chen, X. Chen, J. Zhao, Y. Zhang, H. Sun, W. Tu, W. Tu, W. Tu, W. Tu

“…This study proposes a novel multi-modal bi-branch deep learning model, named BibDL, which integrates remote sensing imagery with Point-of-Interest (POI) data for UFZ classification. The BibDL model leverages the complementary strengths of these data sources: remote sensing provides spatial and structural information, while POI data offers insights into human activities and land use patterns. …”
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Gestational weight gain in low-income and middle-income countries: a modelling analysis using nationally representative data by Goodarz Danaei, Wafaie W Fawzi, Enju Liu, Anne Marie Darling, Dongqing Wang, Nandita Perumal

“…However, the average levels of GWG across all low-income and middle-income countries of the world have not been characterised using nationally representative data.Methods GWG estimates across time were computed using data from the Demographic and Health Surveys Program. …”
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Analysing LULC transformations using remote sensing data: insights from a multilayer perceptron neural network approach by Khadim Hussain, Kaleem Mehmood, Sun Yujun, Tariq Badshah, Shoaib Ahmad Anees, Fahad Shahzad, Nooruddin, Jamshid Ali, Muhammad Bilal

“…The accuracy of the LULC estimates for 2022 was verified by comparing them with observed data, ensuring the model’s reliability. Moreover, the presence of evidence likely was found to be a significant factor that had a substantial impact on the accuracy of the model. …”
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Exploring the association between socio-economic and environmental factors and food consumption in Iran: insights from time series data by Pegah Rahbarinejad, Seyyed Reza Sobhani, Negar Sangsefidi, Kiyavash Irankhah, Maryam Mohamadinarab

“…Method We analyzed secondary data from Iran’s annual Household Income and Expenditure Survey (1991–2019; n = 756,232 households), using a three-stage cluster sampling method to ensure national representativeness. …”
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Multi-omic integration of single-cell data uncovers methylation profiles of super-enhancers in skeletal muscle stem cells by Anyu Zeng, Hailong Liu, Shuling He, Xuming Luo, Zhiqi Zhang, Ming Fu, Baoxi Yu

“…Methods The ROSE software was employed to identify super enhancers from the ChIP-seq data obtained from the ENCODE database. Additionally, the ALLCools and Methylpy packages were applied to analyze the methylation profile of SEs and to identify differentially methylated regions (DMRs) between aged and control samples using single-cell bisulfite sequencing (scBS-seq) data from the Gene Expression Omnibus (GEO) database. …”
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ADVANCED SYSTEMS FOR RELIABLE STORAGE OF BIOMEDICAL INFORMATION by H. Hristov, P. Batalov

“…It outlines key challenges including the harmonization of different data formats, growing cybersecurity threats, patients' privacy rights, and the ethical implications of processing sensitive health data. …”
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Statistical and probabilistic modeling of groundwater contamination: A data-driven uncertainty and risk assessment in Barind Tract, Rajshahi, Bangladesh by Mir Md Tasnim Alam, Md Opu Sarker, Mahfuzur Rahman, Nazmus Sakib, Mahir Tajwar, Md Yousuf Gazi, Shamiha Shafinaz Shreya, Saika Islam Meim, Anwar Zahid

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Unlocking the transformative potential of data science in improving maternal, newborn and child health in Africa: a scoping review protocol by Eric Ohuma, Agbessi Amouzou, Abiy Seifu Estifanos, Phillip Wanduru, Samson Yohannes Amare, Joseph Akuze, Bancy Ngatia, Grieven P Otieno, Rornald M Kananura, Kirakoya-Samadoulougou Fati

“…They will also propose next steps for integrating data science in MNCH programmes in Africa. The implications of our findings will be examined in relation to possible methods for enhancing data science in MNCH, such as community and clinical settings, monitoring and evaluation. …”
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Prediction of Soil Organic Carbon Content in <italic>Spartina alterniflora</italic> by Using UAV Multispectral and LiDAR Data by Jiannan He, Yongbin Zhang, Mingyue Liu, Lin Chen, Weidong Man, Hua Fang, Xiang Li, Xuan Yin, Jianping Liang, Wenke Bai, Fuping Li

“…These results hold significant implications for the study of SOC content in <italic>S. alterniflora</italic>.…”
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Predictive models of recurrent implantation failure in patients receiving ART treatment based on clinical features and routine laboratory data by Qunying Fang, Zonghui Qiao, Lei Luo, Shun Bai, Min Chen, Xiangjun Zhang, Lu Zong, Xian-hong Tong, Li-min Wu

“…The patients’ basic characteristics, clinical treatment data, and laboratory test indices were compared. …”
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Direct measurement of the $$^{39}$$ 39 Ar half-life from 3.4 years of data with the DEAP-3600 detector by P. Adhikari, R. Ajaj, M. Alpízar-Venegas, P.-A. Amaudruz, J. Anstey, D. J. Auty, M. Batygov, B. Beltran, M. A. Bigentini, C. E. Bina, W. M. Bonivento, M. G. Boulay, J. F. Bueno, M. Cadeddu, B. Cai, M. Cárdenas-Montes, S. Cavuoti, Y. Chen, S. Choudhary, B. T. Cleveland, R. Crampton, S. Daugherty, P. DelGobbo, P. Di Stefano, G. Dolganov, L. Doria, F. A. Duncan, M. Dunford, E. Ellingwood, A. Erlandson, S. S. Farahani, N. Fatemighomi, G. Fiorillo, R. J. Ford, D. Gahan, D. Gallacher, A. Garai, P. García Abia, S. Garg, P. Giampa, A. Giménez-Alcázar, D. Goeldi, V. V. Golovko, P. Gorel, K. Graham, A. Grobov, A. L. Hallin, M. Hamstra, S. Haskins, J. Hu, J. Hucker, T. Hugues, A. Ilyasov, B. Jigmeddorj, C. J. Jillings, A. Joy, G. Kaur, A. Kemp, M. Khoshraftar Yazdi, M. Kuźniak, F. La Zia, M. Lai, S. Langrock, B. Lehnert, J. LePage-Bourbonnais, N. Levashko, M. Lissia, L. Luzzi, I. Machulin, A. Maru, J. Mason, A. B. McDonald, T. McElroy, J. B. McLaughlin, C. Mielnichuk, L. Mirasola, A. Moharana, J. Monroe, A. Murray, C. Ng, G. Oliviéro, M. Olszewski, S. Pal, D. Papi, B. Park, M. Perry, V. Pesudo, T. R. Pollmann, F. Rad, C. Rethmeier, F. Retière, I. Rodríguez García, L. Roszkowski, R. Santorelli, F. G. Schuckman II, S. Seth, V. Shalamova, P. Skensved, T. Smirnova, K. Sobotkiewich, T. Sonley, J. Sosiak, J. Soukup, R. Stainforth, M. Stringer, J. Tang, R. Turcotte-Tardif, E. Vázquez-Jáuregui, S. Viel, B. Vyas, M. Walczak, J. Walding, M. Ward, S. Westerdale, R. Wormington, A. Zuñiga-Reyes, DEAP Collaboration

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Modeling the 2023 Türkiye Earthquakes and Strain Accumulation Along the East Anatolian Fault Zone: Insights from InSAR, GNSS, and Small-Magnitude Seismicity, with Implications for... by Daniele Cheloni, Nicola Angelo Famiglietti, Aybige Akinci, Riccardo Caputo, Annamaria Vicari

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