Knoxville College is a historically black liberal arts college in Knoxville, Tennessee, United States, which was founded in 1875 by the United Presbyterian Church of North America. It is a United Negro College Fund member school. A slow period of decline began in the 1970s, and by 2015, the school had an enrollment of just 11 students. In May 2015, the college suspended classes until Fall 2016 term in hopes of reorganizing. On May 17, 2018, the Tennessee Higher Education Commission gave its approval for Knoxville College to once again reopen its doors and offer classes. On July 1, 2018, Knoxville College website announced resumption of enrolling students for fall 2018 semester.
This paper provides evidence on how having violence-exposed peers who migrated to nonviolent areas affects students’ educational trajectories in receiving schools. To recover our estimates, we exploit the variation in local violence across different municipalities in the context of Mexico’s war on drugs and linked administrative records on students’ educational trajectories. We find that peer exposure to violence in elementary school imposes persistent negative effects on students in nonviolent areas. Having elementary school violence-exposed peers has detrimental effects on students’ academic performance in a high school admission exam and grade progression. For every 100 students previously exposed to local violence who migrated to Mexico City’s metro area, approximately 17 incumbent students in safe municipalities are placed in lower-ranked and less-preferred schools.
As blockchain technology reshapes operations and supply chain management (OSCM), understanding its capability-driven adoption pathways is crucial for enhancing supply chain efficiency, resilience, and decision-making. This study applies Fuzzy-Set Qualitative Comparative Analysis (fsQCA) within the Technology-Organisation-Environment (TOE) framework to analyse data from supply chain managers with blockchain implementation experience. Our findings reveal three adoption pathways: (1) Dynamic Collaborators, who integrate blockchain through external collaboration and agility; (2) Resilient Specialists, who prioritise internal resilience over partnerships; and (3) Proficient Collaborators, who emphasise information sharing and agility without deep IT integration. These results highlight the causal complexity of blockchain adoption, demonstrating that firms follow multiple strategic configurations based on their capability portfolios. By uncovering scalable, context-specific adoption pathways, this study provides actionable insights for managers and policymakers, advancing both blockchain integration strategies and the broader OSCM literature.
This paper presents a Gaussian process regression (GPR)-based surrogate modeling approach for predicting the pitch-damping sum, a critical parameter for understanding the dynamic stability of reentry vehicles in 1-DoF motion scenarios. Markov chain Monte Carlo (MCMC) sampling is first used to evaluate discrete points of the pitch-damping sum at various values of the angle of attack, providing a robust dataset for model construction. Using these discrete data, GPR predicts the continuous functional relationship of the pitch-damping sum across the entire range of angle of attack, while incorporating the uncertainty derived from the MCMC samples. To refine the model further, the upper confidence bound (UCB) criterion is applied, identifying and prioritizing regions of high epistemic uncertainty for improvement. The UCB criterion identifies high-uncertainty regions at.. = 15., 25., and 2. for additional MCMC sampling, iteratively reducing epistemic uncertainty until the surrogate achieves convergence across 0. =.. = 30.. Validation against held-out trajectories at..0 = 2. and 20. yields RMSEs of 0.84. and 1.13., respectively, with propagated +/- 2.. envelopes reliably capturing dynamic response variability. This integrated approach delivers an accurate and reliable uncertaintyaware surrogate model, enabling the accurate predictions of aerodynamic coefficients and dynamic stability, thereby advancing the modeling capabilities for reentry vehicle dynamics.
Artificial intelligence (AI) –assisted technologies are widely available and can be used in the research and publication processes, creating unique considerations for kinesiology authors, editors, and readers. An overview of AI policies in sport-science journals listed in Scimago is provided in this mixed-methods study. A chi-square analysis was used to determine if a journal’s 2023 quartile ranking was related to the presence of an AI policy. Using semantic qualitative analysis, common themes among the policies were identified. The research team analyzed the journal/publisher website(s) of 124 journals and found AI policies for 77.8% of the journals. The presence of a policy differed based on quartile ranking. Common themes included transparency and ethical disclosure, human accountability, ethical boundaries, and the need for adaptable policies. Recommendations are provided to guide editors and researchers in navigating the responsible integration of AI technologies in kinesiology research and writing.
