Texas A&M University – Commerce

The COVID-19 pandemic has impacted people’s lives in various cities across the United States. This study explored the factors influencing anticipated migration patterns as indicated by the house search on the Redfin platform in 132 United States cities before, during, and after the COVID-19 Pandemic. The research addresses a crucial gap in the existing literature on recent trends in migration patterns, factors influencing this, and the potential impact of the pandemic. We employed a multivariate regression model to study overall migration and additional models to examine yearly data, identifying key factors that drive people to relocate. We identified that regions with better healthcare services, lower income inequality, and lower unemployment rates increasingly attract residents. Cities with greater racial and ethnic diversity, as well as high school completion rates, were also appealing until 2020 when the importance of education in migration decisions was overshadowed by housing affordability issues. Meanwhile, proximity to larger cities played a significant role, with people favoring smaller cities nearby for better growth opportunities. Our findings suggest that developing cities with smaller populations are becoming preferred destinations due to their mix of affordability, job opportunities, and better living conditions. These insights are invaluable for policymakers, city planners, and community organizations, helping guide urban development strategies and address challenges such as income inequality, neighborhood segregation, and healthcare accessibility.

This study focuses on investigating the conformational structure and zinc(II) affinity of a zinc finger‐like motif (ZFM) peptide with the sequence acetyl‐ His 1 ‐Cys 2 ‐Gly 3 ‐Pro 4 ‐Gly 5 ‐ His 6 ‐Cys 7 , where bold highlights the potential zinc(II) binding sites. Zinc fingers are crucial protein motifs known for their high specificity and affinity for zinc ions. The ZFM peptide's sequence contains the 2His‐2Cys zinc‐binding sites similar to those in natural zinc finger proteins but without the hydrophobic core, making it a valuable model for studying zinc(II)–peptide interactions. Previous research on related peptides showed that collision cross sections and B3LYP modeling predicted that the His‐2Cys‐carboxyl terminus coordination of zinc(II) was more stable than the 2His‐2Cys. Employing a comprehensive approach integrating ion mobility–mass spectrometry and theoretical modeling techniques, various zinc(II) binding modes of the ZFM have been thoroughly compared to ascertain their influence on the competitive threshold collision‐induced dissociation method for measuring the relative gas‐phase Zn(II) affinity of the ZFM peptide. The measured Zn(II) affinity of ZFM is greater than those measured recently for two peptides with similar primary structures, acetyl‐ His 1 ‐Cys 2 ‐Gly 3 ‐Pro 4 ‐Gly 5 ‐Gly 6 ‐ Cys 7 and acetyl‐ Asp 1 ‐ His 2 ‐Gly 3 ‐Pro 4 ‐Gly 5 ‐Gly 6 ‐ Cys 7 , indicating the preference for the His 1 ‐Cys 2 ‐His 6 ‐Cys 7 side groups for coordinating zinc(II) over the His‐2Cys‐carboxyl terminus or Asp‐His‐Cys‐carboxyl terminus in these related heptapeptides.

Twin stars-two stable neutron stars (NSs) with the same mass but different radii have long been proposed to appear as a consequence of a possible first-order phase transition in NS matter. Within a meta-model for the EOS of hybrid stars, we revisit the viability of twin stars and its dependence on numerous parameters characterizing the EOS of nuclear matter, quark matter, and the phase transition between them. While essentially no experimental constraint exists for the last two, parameters characterizing the EOS of neutron-rich nucleonic matter have been constrained within various ranges by terrestrial experiments and astrophysical observations. Within these ranges, the impact of nuclear EOS and crust-core transition density on the formation of twin stars is studied. It is found that the symmetry energy of neutron-rich nucleonic matter notably influences the formation of twin stars, particularly through its slope L and curvature $K_\textrm{sym}$. Conversely, varying the EOS of symmetric nuclear matter within their currently known uncertainty ranges shows minimal influence on the formation of twin stars.

The TOV equations govern the radial evolution of pressure and energy density in static neutron stars (NSs) in hydrodynamical equilibrium. Using the reduced pressure and energy density with respect to the NS central energy density, the original TOV equations can be recast into dimensionless forms. While the traditionally used integral approach for solving the original TOV equations require an input nuclear Equation of State (EOS), the dimensionless TOV equations can be anatomized by using the reduced pressure and energy density as polynomials of the reduced radial coordinate without using any input nuclear EOS. Interesting and novel perspectives about NS core EOS can be extracted directly from NS observables using this new approach based on Intrinsic and Perturbative Analyses of the Dimensionless (IPAD) TOV equations (IPAD-TOV). In this review, we first discuss the length and energy density scales of NSs as well as the dimensionless TOV equations for scaled variables and their perturbative solutions near NS cores. We then review several new insights into NS physics gained from using the IPAD-TOV. We also demonstrate that the strong-field gravity plays a fundamental role in extruding a peak in the density/radius profile of the speed of sound squared (SSS) in massive NS cores independent of the nuclear EOS. Finally, some future perspectives of NS research using the IPAD-TOV are outlined.

Drawing from the National Hispanic Male Teacher Survey (NHMTS) pilot in Texas, this paper aims to expand efforts to recruit, prepare, support, and sustain Hispanic male teachers in K-12 institutions. The findings drawn from 839 educators present becoming a future role model to Hispanic students, dismantling stereotypical assumptions around being Hispanic and male, confronting racism within the teacher role, and the burden of being the translator, disciplinarian, and educator all in one. Our study contributes to the differentiation of teacher recruitment and retention strategies when addressing the underrepresentation of racially and ethnically minoritized educators in the teacher workforce.

Women living in an impoverished environment after birth have an increased risk of developing postpartum depression (PP-Dep) and hypertension (PP-HTN). The mechanisms underlying these heightened risks are unknown and understudied. To examine the relation between reduced environmental resources, PP-Dep, and PP-HTN; postpartum rodent dams were exposed to the low-resource limited bedding and nesting (LBN) chronic stress model during weaning. Postpartum dams were divided into control (CTL) and experimental (LBN) groups, in which the experimental group experienced LBN. At six weeks postpartum, blood pressure, sucrose preference test (a proxy for anhedonia and depression), corticosterone, and markers of neuroinflammation were measured. We hypothesized that postpartum dams exposed to LBN will have increased corticosterone, neuroinflammation, depression-like behaviors, and HTN. Results show that postpartum dams exposed to an impoverished environment exhibit decreased sucrose preference, increased circulating corticosterone, and elevated neuroinflammation (~ 150% increased TNF-α and astrocyte activation in the cerebrum). No changes in blood pressure were observed. However, there was a strong correlation between postpartum blood pressure and corticosterone and blood pressure and TNF-α levels. Importantly, this study provides insights into the pathology and development of PP-HTN and PP-Dep in the postpartum period, which will enable the discovery of novel therapeutic approaches.

Background Preeclampsia (PE) is characterized as de novo hypertension (HTN) with end-organ damage, especially in the brain. PE is hypothesized to be caused by placental ischemia. PE affects ~5–8% of USA pregnancies and increases the risk for HTN and cerebrovascular diseases (CVD) later in life. We hypothesize that blood pressure (BP), cerebral oxidative stress, and cerebral inflammation will increase in postpartum (PP) placental ischemic dams. Methods Placental ischemia was induced in pregnant Sprague Dawley dams, utilizing reduced uterine perfusion pressure (RUPP) surgery. At 6 weeks PP (~3 human years), BP was measured via carotid catheterization, and cerebral oxidative stress and inflammation were assessed via ELISAs, biochemical assays, and Western blots. Results BP, cerebral pro-inflammatory cytokines (TNF-α and IL-6), and GFAP (a marker of astrocyte activity) were increased in PP RUPP dams. Cerebral hydrogen peroxide (H2O2) was also increased in PP RUPP dams, and had a strong correlation with PP RUPP BP, proinflammatory cytokines (TNF- α and IL-6), and GFAP astrocyte activation. Conclusion PP RUPP dams have increased BP, cerebral oxidative stress, and cerebral inflammation at 6 weeks postpartum. These changes in cerebral inflammation and oxidative stress may contribute to the pathology and development of HTN and CVDs in postpartum dams.

Integrating generative artificial intelligence (AI) tools like ChatGPT into education has reshaped academic practices, offering students innovative ways to engage with learning tasks. This study examines the cognitive, emotional, and behavioral factors influencing undergraduate students’ engagement with ChatGPT, guided by the technology acceptance model, the stimulus-organism-response framework, and attachment theory. Using a sample of 398 US undergraduates, the study employed confirmatory factor analysis and structural equation modeling to validate relationships among perceived usability, enjoyment, responsiveness, learning motivation, emotional attachment, satisfaction, and continuance intention. Results revealed that perceived responsiveness is a significant driver of learning motivation, emotional attachment, and satisfaction, while perceived enjoyment enhances learning motivation and satisfaction. Emotional attachment strongly predicts continuance intention, emphasizing the role of affective connections in sustained use. The findings indicate that successful AI tools must balance usability, responsiveness, and emotional engagement to foster long-term adoption. This research advances the theoretical understanding of human-AI interactions in education and provides practical recommendations for designing effective, student-centered AI tools.

In this article, we propose (re)designing privacy literacy as an essential component of our digital lives in an age of Generative Artificial Intelligence (genAI). Our study emphasizes the layered digital, technical, rhetorical, and algorithmic literacies associated with design thinking and genAI to support theorizing privacy literacy. We introduce Design as an analytical element complementary to Woods and Wason's (2021) multi-pronged framework for analyzing Terms of Service (ToS) documents. Using a cluster of Adobe Generative AI ToS, we illustrate the necessity of including Design , which allows those invested in Communication Design (CD) and Technical and Professional Communication (TPC) to interrogate how or if design supports or undermines values related to user privacy, data ownership, and informed consent. We conclude by detailing how collective surveillance apathy regarding emergent data infrastructures signal a Post-Surveillance era in our global society and digital lives.

Variance-based sensitivity analysis serves as a crucial tool for assessing the variability of inputs on the output of complex mathematical models. In this study, we examine Sobol indices, a class of variance-based sensitivity analysis, to quantify the importance of each input variable on the overall variability of the model output. We apply Sobol indices within the framework of a regression model to identify the most importance features (also known as predictors) in predicting total medical expenses charged per year for the health insurance plan. Our findings reveal that “smoker” emerged as the most important feature impacting health insurance charges, followed by “age” and “bmi” as the second and third most important features, respectively. This application not only demonstrates the effectiveness of Sobol’s indices in regression models but also suggests possible model simplifications.

In this article, we propose (re)designing privacy literacy as an essential component of our digital lives in an age of Generative Artificial Intelligence (genAI). Our study emphasizes the layered digital, technical, rhetorical, and algorithmic literacies associated with design thinking and genAI to support theorizing privacy literacy. We introduce Design as an analytical element complementary to Woods and Wason's (2021) multi-pronged framework for analyzing Terms of Service (ToS) documents. Using a cluster of Adobe Generative AI ToS, we illustrate the necessity of including Design , which allows those invested in Communication Design (CD) and Technical and Professional Communication (TPC) to interrogate how or if design supports or undermines values related to user privacy, data ownership, and informed consent. We conclude by detailing how collective surveillance apathy regarding emergent data infrastructures signal a Post-Surveillance era in our global society and digital lives.

A significant challenge faced by many students and parents is the lack of clear information about the diverse range of available courses and career paths in the ever-evolving landscape of higher education. This informational gap often results in students making uninformed choices, leading to academic struggles and disappointment for both students and their parents. Particularly in the field of computer science, where numerous bright majors and rewarding careers exist, many students lack awareness of the available opportunities and how to navigate the learning process. Recognizing the pivotal role of emerging technologies, this paper introduces an AI-driven chatbot called CSACbot (Computer Science as Career bot) developed using neural networks, sentence transformers, and PyTorch. This chatbot serves as an innovative solution to provide comprehensive information about the field of computer science, guiding students and parents in making informed decisions about courses, majors, and potential careers. By efficiently and accurately addressing user queries, this AI-driven chatbot aims to bridge the information gap, empowering students to embark on successful academic journeys in computer science.

Although American news satire trends towards the left side of the political spectrum, satire is not unique to liberal viewpoints. Conservative news satire, published by outlets such as The Babylon Bee, lend evidence to the ability for right-wing views to be advanced using satirical methods. Despite political differences, existing comparisons of left- and right-wing news satire suggest a high degree of structural similarity. In contrast, qualitative content-based analyses suggest clear political biases which differentiate the satire from these sources. In this study, we compare left- and right-wing American news satire across different structural and content-based features using a combination of quantitative, computational, and qualitative approaches. Our results mirror those of prior studies, indicating similarities in terms of structure but not content. These results further speak to the importance of taking conservative satire seriously, as well as to the necessity of a multi-pronged approach when studying the complex nature of satirical discourse.

In this editorial for the first issue of the International Journal of Disney Studies ( IJDS ), the founding editors map the field of Disney Studies as it currently stands and then place this new journal within it. In particular, we explain the need for such a Disney Studies journal at this specific moment in time. The editorial ends with an introduction to this issue and thanks and welcomes to our editorial team, both at Intellect and on the journal team specifically.

monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Paralfetch speeds up application launches on personal computing/communication devices, by means of: 1) accurate collection of launch-related disk read requests, 2) pre-scheduling of these requests to improve I/O throughput during prefetching, and 3) overlapping application execution with disk prefetching for hiding disk access time from the execution of the application. We implemented Paralfetch under Linux kernels on a desktop/laptop PC, a Raspberry Pi 3 board, and an Android smartphone. Tests with popular applications show that Paralfetch significantly reduces application launch times on flash-based drives and hard disk drives, and it outperforms GSoC Prefetch [18] and FAST [21], which are representative application prefetchers available for Linux-based systems.

With the interrelatedness between global health and mental health equity being evident, this article provides an overview of several related areas and contemporary issues that can be addressed through the globalization of counseling and international counseling. Additionally, with global service-learning in counselor education having several benefits that can lead to the advancement and globalization of counseling, essential elements that relate to implementation in specialty areas are discussed. Contemporary research also indicates the potential of service-learning in counselor education to contribute towards advancing health equity, mental health counseling equity, and international counseling. Therefore, recommendations, implications, and a case example are included for counselor educators, counselors-in-training, clinicians, and stakeholders interested in global service-learning course development and implementation.