Predicting Breast Cancer Screening Behaviors Using Protective Motivation Theory: A Structural Equation Modeling Approach by Yizheng Gao, Jianwei Yu, Hairuo Wang, Binhao Liu, Shunhua Zhang

“…The findings of this study suggest that PMT can effectively predict breast screening behavior, and interventions based on the substructure of PMT to develop screening behavior in women may be more effective.…”
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Polygenic risk score models based on anxiety-related traits as endophenotypes to predict unipolar depression and suicidal behavior by A. V. Kazantseva, R. F. Enikeeva, Yu. D. Davydova, R. G. Valinurov, I. Yu. Ahmerova I.Yu., S. B. Malykh, M. M. Lobaskova, E. K. Khusnutdinova

“…The present study included the construction of polygenic risk score (PRS) models of individual differences in anxiety level, which increased values represent the endophenotype of depression and suicidal behavior, to predict these psychopathologies based on genotyping of 63 SNPs of serotoninergic, hypothalamicpituitary-adrenal and neurotrophic factors systems together with the relative leukocyte telomere length. …”
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Effect of fracture energy estimation on the predictions of mode II behavior of bonded joints using cohesive zone models by Michele Perrella, Enrico Armentani, Giuseppe Lamanna, Valentino Paolo Berardi

“…The mode II cohesive traction-separation law was identified by numerical differentiation of SERR and best fit equation systems were adopted for an analytical description of cohesive interface behavior. Moreover, the obtained CZM laws were used for predicting the decohesion process by finite element analyses. …”
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Big Data in Leadership Studies: Automated Machine Learning Model to Predict Preferred Leader Behavior Across Cultures by Erik Lankut, Gillian Warner-Søderholm, Ilan Alon, Inga Minelgaité

“…We offer a robust empirical measurement of culturally contingent leader behavior and entrepreneurship behaviors and provide a tool for assessing the cultural predictors of preferred leader behavior to minimize predictive errors, explore patterns in the data and make predictions in an empirically robust way. …”
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Research on predicting the thermocompression deformation behavior of Mg–Li matrix composite using machine learning and traditional techniques by Dandan Li, Xiaoyu Hou, Yangfan Liu, Linhao Gu, Jinhui Wang, Jiaxuan Ma, Xiaoqiang Li, Zhi Jia, Qichi Le, Dexue Liu, Xincheng Yin

“…Artificial intelligence and machine learning (ML) technologies have emerged as powerful tools for analyzing the thermal compression deformation behavior of metal matrix composites, offering significant potential to optimize their plastic deformation processing techniques. …”
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Integrating Learning-Driven Model Behavior and Data Representation for Enhanced Remaining Useful Life Prediction in Rotating Machinery by Tarek Berghout, Eric Bechhoefer, Faycal Djeffal, Wei Hong Lim

“…The increasing complexity of modern mechanical systems, especially rotating machinery, demands effective condition monitoring techniques, particularly deep learning, to predict potential failures in a timely manner and enable preventative maintenance strategies. …”
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Applying social cognitive theory to predict physical activity and dietary behavior among patients with type-2 diabetes by Ann Tresa Sebastian, Eslavath Rajkumar, P Tejaswini, R Lakshmi, J Romate

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Analyzing and predicting short-term substance use behaviors of persons who use drugs in the great plains of the U.S. by Nguyen Thach, Patrick Habecker, Bergen Johnston, Lillianna Cervantes, Anika Eisenbraun, Alex Mason, Kimberly Tyler, Bilal Khan, Hau Chan

“…<h4>Objective</h4>This study examines the short-term (within a year) behavioral changes in substance use of PWUDs at the population and individual levels.…”
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An iterative approach to identify key predictive features of fear reactivity and fearfulness in horses (Equus caballus) by Elena Gobbo, Oleksandra Topal, Inna Novalija, Dunja Mladenić, Manja Zupan Šemrov

“…However, key predictive features differed between the two traits, with correlation coefficients not exceeding 0.57. …”
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Prediction of the Charging Probability of Electric Vehicles with Different Power Levels by Wei Zeng, Jun-Jie Xiong, Xin Li, Xiang-Yu Liu, Zhao-Xia Xiao

Subjects: “…charging probability prediction…”
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Independent associations of social determinants of health with mortality and added predictive value beyond life’s essential 8 by Yuansong Zhuang, Xinlong Zhao, Siqi Tang, Yakun Zhao, Yanbo Liu, Yuxiong Chen, Yitao Han, Jinyan Lei, Zhongjie Fan

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COVID-19 risk stratification among older adults: a machine learning approach to identify personal and health-related risk factors by Arezoo Abasi, Seyed Abbas Motevalian, Haleh Ayatollahi

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A Machine Learning Approach for User Behavior Analysis in Developing Websites by Aeesha Shaheen

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Predicting humor effectiveness of robots for human line cutting by Yuto Ushijima, Satoru Satake, Takayuki Kanda

“…In the process of preparing this machine learning, we discovered that considering the situation and the discomfort caused by the phrase is crucial for predicting the effectiveness of humor. Next, we constructed a system to select the best humorous phrase for the line-cutting behavior using this predictor. …”
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Exploring the Predictive Role of Demographic Characteristics and Health Literacy in the Self-Care Behaviors of Pregnant Women at Risk of Preterm Labor in Bushehr, Iran by Mahsa Rezaei, Marziye Mahmoodi, Mahnoush Reisi

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Exploring rounD Dataset for Domain Generalization in Autonomous Vehicle Trajectory Prediction by Zikai Zhang

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The multifaceted role of the inferior colliculus in sensory prediction, reward processing, and decision-making by Xinyu Du, Haoxuan Xu, Peirun Song, Yuying Zhai, Hangting Ye, Xuehui Bao, Qianyue Huang, Hisashi Tanigawa, Zhiyi Tu, Pei Chen, Xuan Zhao, Josef P Rauschecker, Xiongjie Yu

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Improved Crime Prediction Using Hybrid Neural Architecture Search Together with Hyperparameter Tuning by Rami Ayied Alshahrani, Tariq Jamil Saifullah Khanzada

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Predicting Pregnancy in Preconception Weight Loss Trials: Is it Possible? by Jacqueline F. Hayes, Suzanne Phelan, Elissa Jelalian, Aaron B. Caughey, Kristin Castorino, Casey Heaney, Angelica Mchugh, Rena R. Wing

“…The current study examined whether self‐reported pregnancy likelihood and timing were predictive of conception. Methods Adults (n = 184; 58% Hispanic; age = 33.4 ± 5.1; BMI = 33.6 ± 6.6) with overweight or obesity, prior gestational diabetes mellitus, and plans to become pregnant participated in a pre‐conception behavioral weight loss intervention or control. …”
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Incorporating Behavioral Adaptation of Human Drivers in Predicting Traffic Efficiency of Mixed Traffic: A Case Study of Priority T-Intersections by Nagarjun Reddy, Narayana Raju, Haneen Farah, Serge Hoogendoorn

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