Deciphering organic substrate impacts in Anammox systems: A machine learning driven framework for predictive classification and process mechanism analysis by Zemin Li, Yulun Wu, Tao Chen, Bo Yan, Chaohai Wei

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Establishment of prognostic risk model related to disulfidptosis and immune infiltration in hepatocellular carcinoma by Zhe Xu, Chong Pang, Xundi Xu

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Application and feasibility analysis of knowledge-based machine learning in predicting fatigue performance of stainless steel by Jia Wang, Dongkui Fan, C.S. Cai

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Predicting the impact of non-pharmaceutical interventions against COVID-19 on Mycoplasma pneumoniae in the United States by Sang Woo Park, Brooklyn Noble, Emily Howerton, Bjarke F. Nielsen, Sarah Lentz, Lilliam Ambroggio, Samuel Dominguez, Kevin Messacar, Bryan T. Grenfell

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Theoretical prediction of hysteretic rubber friction in ball on plate configuration by finite element method

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Machine-learning synergy in high-entropy alloys: A review by Sally Elkatatny, Walaa Abd-Elaziem, Tamer A. Sebaey, Moustafa A. Darwish, Atef Hamada

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Experimental evaluation of an advanced predictive control technique for variable-speed wind turbine systems by Farah Echiheb, Btissam Majout, Ismail EL Kafazi, Badre Bossoufi, Abdelhamid Rabhi, Nicu Bizon, Anton Zhilenkov, Saleh Mobayen

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Experimental Application of Predictive Controllers by C. H. F. Silva, H. M. Henrique, L. C. Oliveira-Lopes

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A GNN-Based QSPR Model for Surfactant Properties by Seokgyun Ham, Xin Wang, Hongwei Zhang, Brian Lattimer, Rui Qiao

“…However, the relationship between surfactant structure and these properties is complex and difficult to predict theoretically. Here, we develop a graph neural network (GNN)-based quantitative structure–property relationship (QSPR) model to predict the CMC, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>γ</mi></mrow><mrow><mi>c</mi><mi>m</mi><mi>c</mi></mrow></msub></mrow></semantics></math></inline-formula>, and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>Γ</mi></mrow><mrow><mi>m</mi><mi>a</mi><mi>x</mi></mrow></msub></mrow></semantics></math></inline-formula>. …”
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Predictability problems of global change as seen through natural systems complexity description. 1. General Statements by Vladimir V. Kozoderov, Victor A. Sadovnichii, Sergey A. Ushakov, Oleg A. Timoshin

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Predicting the direction and magnitude of small mammal disturbance effects on plant diversity across scales by Meredith Root-Bernstein

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Dynamic Evaluation and Prediction of Ecological Carrying Capacity of Water Resources in Tarim River Basin by ZHU Xueying, HUANG Shengzhi, HUANG Qiang, ZHENG Xudong, ZHANG Shaoqing, GAO Yuejiao

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Bayesian network modeling applied to food risks: Data from General Administration of Customs of China as an example by Xuanyu Ying, Anni Lu, Qiang Cai, Jun Lu

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Combinatorial machine learning approaches for high-rise building cost prediction and their interpretability analysis by Zenghui Liu, Jing Lin

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Prediction of long-term HBsAg seroclearance in patients with HBeAg-negative chronic hepatitis B by Hae Lim Lee, Soon Kyu Lee, Ji Won Han, Hyun Yang, Heechul Nam, Pil Soo Sung, Hee Yeon Kim, Sung Won Lee, Do Seon Song, Jung Hyun Kwon, Chang Wook Kim, Si Hyun Bae, Jong Young Choi, Seung Kew Yoon, Jeong Won Jang

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Task offloading delay minimization in vehicular edge computing based on vehicle trajectory prediction by Feng Zeng, Zheng Zhang, Jinsong Wu

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Factors Predicting the standard precautions for infection control pre-hospital emergency staff of Hamadan based on the Health Belief Model by Masoud Khodaveisi, Mahnaz Mohamadkhani, Roya Amini, Manoochehr Karami

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Prediction of fatigue cracking in asphaltic pavement under local traffic conditions: Application to Semel-Patel highway-Duhok by Noor Akram Alaani

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IRnet: Immunotherapy response prediction using pathway knowledge-informed graph neural network by Yuexu Jiang, Manish Sridhar Immadi, Duolin Wang, Shuai Zeng, Yen On Chan, Jing Zhou, Dong Xu, Trupti Joshi

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A comprehensive machine learning-based models for predicting mixture toxicity of azole fungicides toward algae (Auxenochlorella pyrenoidosa) by Li-Tang Qin, Xue-Fang Tian, Jun-Yao Zhang, Yan-Peng Liang, Hong-Hu Zeng, Ling-Yun Mo

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