Rolling Prediction of Emergency Supplies Based on Postdisaster Multisource Time-Varying Information by Wei Li, Ming Zhang, Boquan Li, Songrui Li, Zhifeng Qiu

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The theory of preloading: A cognitive-behavioural motivational model by Jacob G. Sorbello, Grant J. Devilly

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Optimizing mRNA Vaccine Degradation Prediction via Penalized Dropout Approaches by Hwai Ing Soon, Azian Azamimi Abdullah, Hiromitsu Nishizaki, Latifah Munirah Kamarudin

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Petrological controls on the engineering properties of carbonate aggregates through a machine learning approach by Javid Hussain, Tehseen Zafar, Xiaodong Fu, Nafees Ali, Jian Chen, Fabrizio Frontalini, Jabir Hussain, Xiao Lina, George Kontakiotis, Olga Koumoutsakou

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Predicting the Activity Level of the Great Gerbil (Rhombomys opimus) via Machine Learning by Fan Jiang, Peng Peng, Zhenting Xu, Yu Xu, Ding Yang, Shouquan Chai, Shuai Yuan, Limin Hua, Dawei Wang, Xuanye Wen

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Predictive factors of early left ventricular remodeling and reverse remodeling in patients with STEMI after successful reperfusion therapy by Yanbo Wang, Qing Zhou, Lifang Su, Yajing Miao, Xiaoxu Wang, Yunfa Jiang, Xinshun Gu, Hongning Yin

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Securing fruit trees future: AI-driven early warning and predictive systems for abiotic stress in changing climate by Muhammad Ahtasham Mushtaq, Muhammad Ateeq, Muhammad Ikram, Shariq Mahmood Alam, Muhammad Mohsin Kaleem, Muhammad Atiq Ashraf, Muhammad Asim, Khalid F. Almutairi, Mahmoud F. Seleiman, Fareeha Shireen

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Development of a Fault Prediction Algorithm for Marine Propulsion Energy Storage System by Jaehoon Lee, Sang-Kyun Park, Salim Abdullah Bazher, Daewon Seo

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Reference Levels of Plasma D-dimer for Normal Pregnant and Their Association with Pregnancy Complications: A Single-center Retrospective Analysis by Wenhuan Wang, Qiqi Dai, Liqin Pan, Hongping Zhang, Yeping Wang

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Multi-Dimensional Feature Fusion and Enhanced Attention Streaming Movie Prediction Algorithm by Hanqing Hu, Tianmu Tian, Chengjing Liu, Xueyuan Bai

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Postoperative Apnea‐Hypopnea Index Prediction of Velopharyngeal Surgery Based on Machine Learning by Jingyuan You, Juan Li, Yingqian Zhou, Xin Cao, Chunmei Zhao, Yuhuan Zhang, Jingying Ye

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Current Situation and Developing Trend of Fatigue Life Prediction of Components based on Data-driven by Xiuhua Zhang, Huaiju Liu, Caichao Zhu, Peitang Wei, Shaojie Wu

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Experimental and theoretical analysis of ultrasonic separation in bioethanol purification by Junli Liu, Hao Feng

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Comparative performance analysis of ensemble learning methods for fetal health classification by Tasnim Bill Zannah, Sadia Islam Tonni, Md. Alif Sheakh, Mst. Sazia Tahosin, Afjal Hossan Sarower, Mahbuba Begum

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A Predictive Nomogram for Development of Lymph Node Metastasis in Muscle-Invasive Bladder Cancer Following Neoadjuvant Therapy by Garrett K. Harada, MD, Steven N. Seyedin, MD, Olivia Heutlinger, BS, Armon Azizi, BS, Audree Hsu, BS, Arash Rezazadeh, MD, Michael Daneshvar, MD, MS, Greg E. Gin, MD, Edward M. Uchio, MD, Giovanna A. Giannico, MD, Jeremy P. Harris, MD, Aaron B. Simon, MD, PhD, Jeffrey V. Kuo, MD, Nataliya Mar, MD

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Predicting suicidality in people living with HIV in Uganda: a machine learning approach by Anthony B. Mutema, Anthony B. Mutema, Anthony B. Mutema, Lillian Linda, Lillian Linda, Daudi Jjingo, Segun Fatumo, Segun Fatumo, Eugene Kinyanda, Allan Kalungi, Allan Kalungi, Allan Kalungi

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Prediction and Optimization of Civil Aviation Flight Delays Based on Machine Learning Algorithms by Qingwei Zhong, Yingxue Yu, Yiru Huang, Tianhang Zhang

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Machine learning and multicriteria analysis for prediction of compressive strength and sustainability of cementitious materials by Khuram Rashid, Fatima Rafique, Zunaira Naseem, Fahad K. Alqahtani, Idrees Zafar, Minkwan Ju

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Growth Differentiation Factor 15 Predicts Cardiovascular Events in Peripheral Artery Disease by Ben Li, Farah Shaikh, Houssam Younes, Batool Abuhalimeh, Abdelrahman Zamzam, Rawand Abdin, Mohammad Qadura

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Machine Learning Prediction of Airfoil Aerodynamic Performance Using Neural Network Ensembles by Diana-Andreea Sterpu, Daniel Măriuța, Grigore Cican, Ciprian-Marius Larco, Lucian-Teodor Grigorie

“…The best-performing single-seed model tested (seed 0) achieves a mean absolute percentage error (MAPE) of 1.1% with an <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mrow><mi mathvariant="normal">R</mi></mrow><mrow><mn>2</mn></mrow></msup></mrow></semantics></math></inline-formula> of 0.9998 for the lift coefficient prediction and 0.57% with an <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mrow><mi mathvariant="normal">R</mi></mrow><mrow><mn>2</mn></mrow></msup></mrow></semantics></math></inline-formula> of 0.9954 for the drag coefficient prediction. …”
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