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Articles 1 - 4 of 4
Full-Text Articles in Other Statistics and Probability
Emotionality Stigma Scale: Measurement Development, Reliability, And Validity., Hayley D. Seely
Emotionality Stigma Scale: Measurement Development, Reliability, And Validity., Hayley D. Seely
Electronic Theses and Dissertations
Emotions are biological responses to stimuli that allow individuals to derive meaning, appraise experiences, and prepare to respond. However, individuals perceive emotions differently based on emotion socialization which not only dictates the way emotions are viewed and managed but also has been directly linked with mental health outcomes. Furthermore, research shows emotion socialization is informed by demographic variables such as gender such that the expectations of emotionality differ; where women are taught to express emotions, men are taught to conceal. Given the societal rules regarding emotionality, it is possible that emotionality stigma – the stigma around the experience and expression …
A Data-Driven Multi-Regime Approach For Predicting Real-Time Energy Consumption Of Industrial Machines., Abdulgani Kahraman
A Data-Driven Multi-Regime Approach For Predicting Real-Time Energy Consumption Of Industrial Machines., Abdulgani Kahraman
Electronic Theses and Dissertations
This thesis focuses on methods for improving energy consumption prediction performance in complex industrial machines. Working with real-world industrial machines brings several challenges, including data access, algorithmic bias, data privacy, and the interpretation of machine learning algorithms. To effectively manage energy consumption in the industrial sector, it is essential to develop a framework that enhances prediction performance, reduces energy costs, and mitigates air pollution in heavy industrial machine operations. This study aims to assist managers in making informed decisions and driving the transition towards green manufacturing. The energy consumption of industrial machinery is substantial, and the recent increase in CO2 …
Penalized Bayesian Exponential Random Graph Models., Vicki Modisette
Penalized Bayesian Exponential Random Graph Models., Vicki Modisette
Electronic Theses and Dissertations
Networks have the critical ability to represent the complex interconnectedness of social relationships, biological processes, and the spread of diseases and information. Exponential random graph models (ERGM) are one of the popular statistical methods for analyzing network data. ERGM, however, struggle with computational challenges and degeneracy issues, further exacerbated by their inability to handle high-dimensional network data. Bayesian techniques provide a promising avenue to overcome these two problems. This paper considers penalized Bayesian exponential random graph models with adaptive lasso and adaptive ridge penalties to perform variable selection and reduce multicollinearity on a variety of networks. The experimental results demonstrate …
Factor Structure And Measurement Invariance Of The Maslach Burnout Inventory In Emergency Medicine Residents, Tim P. Moran, Nicole Battaglioli, Simiao Li-Sauerwine
Factor Structure And Measurement Invariance Of The Maslach Burnout Inventory In Emergency Medicine Residents, Tim P. Moran, Nicole Battaglioli, Simiao Li-Sauerwine
Journal of Wellness
Introduction: Emergency medicine residents suffer from high rates of occupational burnout. Recent research has focused on identifying risk and protective factors for burnout as well as targets for intervention. This research has primarily employed the Maslach Burnout Inventory to evaluate burnout in this population. Factor analytic work has identified three underlying factors measured by the Maslach Burnout Inventory: Emotional Exhaustion, Depersonalization, and Personal Accomplishment. However, this three-factor structure has not been evaluated in emergency medicine residents. Furthermore, its structural equivalence has not been demonstrated across commonly-studied risk factors, such as gender and year of post-graduate training. In the present study, …