Texas A&M University–Commerce

Neutron-skin thicknesses provide a sensitive probe of the isovector sector of the nuclear equation of state and its density dependence, commonly characterized by the symmetry-energy slope parameter L. A wide variety of experimental and observational methods have been used to extract neutron skins, ranging from hadronic and electromagnetic probes of finite nuclei to inferences from neutron-star observations. Each approach carries distinct theoretical and systematic uncertainties, complicating global interpretations and obscuring genuine physical trends. In this work we present a hierarchical Bayesian framework for the statistically consistent synthesis of heterogeneous neutron-skin constraints. The neutron-skin thickness is modeled as a smooth latent function of isospin asymmetry and nuclear size, while method-dependent bias parameters and intrinsic nuisance widths are introduced to account for unmodeled experimental and theoretical systematics. Focusing on the tin isotopes, we infer probabilistic neutron-skin trends from 100Sn to 140Sn, finding minimal uncertainties near stability and increasing uncertainties toward the proton-rich and neutron-rich extremes. We assess the consistency of nuclear energy-density functionals and obtain conditional constraints on the symmetry-energy parameters. The resulting posterior exhibits a pronounced compression of the symmetry-energy slope parameter L, reflecting the dominant sensitivity of neutron skins to sub-saturation symmetry pressure. We demonstrate that our hierarchical Bayesian framework provides robust and transparent constraints on the sub-saturation isovector sector of the nuclear equation of state.

Abstract I was born and raised in Agua Dulce, Veracruz, Mexico. When I was 11, I moved to Commerce, Texas and began 5th grade without knowing any English. Learning the language was a difficult and lengthy process, but I never missed a day of school unless I was sick. In 2005, I finally graduated from Commerce High. I earned my undergraduate degree in 2010, and I began my Master’s program in the spring of 2011 and graduated with an MBA and an MS in Marketing in the spring of 2014. In the summer of 2015, I had the opportunity to teach a Marketing class to the Trio Upward Bound Program, and I’ve been working with them ever since. The Trio Upward Bound Program is a federally funded program for high school students preparing to go to college. My job entails working with high school students. In the spring and fall semesters, I visit schools and interact with students during their lunch hours and work with them on campus during the summer. Working with students and helping them realize their goals has been the best part of my job.

Purpose The purpose of this study is to examine the longitudinal structural relationships among top management's support for employee development, the perceived strength of the HR department's strategic role and organizational commitment in the context of South Korean business. In particular, this study focuses on the mediating role of the perceived strength of the HR department's strategic role in bridging the relationship between top management's support for employee development and organizational commitment during the COVID-19 pandemic, when external uncertainty was prevalent. Design/methodology/approach Multivariate Latent Growth Modeling was applied to analyze three-wave longitudinal data collected from 2020 to 2022. Findings Significant and positive relationships were found among top management's support for employee development, the perceived strength of the HR department's strategic role and organizational commitment over time. This study confirms the mediating effect of the perceived strength of the HR department's strategic role in the relationship between top management's support for employee development and organizational commitment over time. Originality/value The results of this study extend the concept of HR system strength by highlighting the importance of consistency in implementing HR practices, particularly during environmental disruptions.

Depression treatment outcomes remain difficult to predict for selective serotonin reuptake inhibitor (SSRI) and repetitive transcranial magnetic stimulation (rTMS) therapies, leading to trial-and-error treatment selection and delayed clinical benefit. This study develops an EEG-based prediction model using local Fibonacci pattern (LFP) features to classify treatment responders before therapy initiation. Pre-treatment EEG signals from the Mumtaz SSRI dataset and the Atieh Hospital rTMS datasets were processed using finite impulse response filtering and multi-scale principal component analysis. The LFP method extracts nonlinear temporal features directly from 19-channel EEG recordings through Fibonacci-indexed local differences and histogram encoding. Features were ranked using neighborhood component analysis and classified with a feedforward neural network under segment-level 10-fold cross-validation. The model achieved accuracies of 99.12% for SSRI, 100.00% for the small rTMS dataset, and 94.51% for the big rTMS dataset under this protocol. To address the risk of overfitting and to provide a stricter estimate of subject-level generalization, subject-wise leave-one-subject-out (LOSO) validation was also performed. Under LOSO validation, the corresponding accuracies were 61.83%, 77.93%, and 71.57%, with balanced accuracy/macro-F1 values of 59.07%/59.16%, 65.49%/66.68%, and 75.43%/71.51% for SSRI, small rTMS, and big rTMS datasets, respectively. Key discriminative channels corresponded to frontal, temporal, and parietal regions involved in emotion regulation and cognitive control. The revised findings support LFP as a computationally efficient EEG representation, but the lower LOSO results indicate that larger independent cohorts are required before any clinical-deployment claim can be made.