Georgia State University

The emergence of Large Language Models (LLM), such as ChatGPT, is considered a productivity revolution in many areas of business and society. For a classroom setting, especially, it would be useful to understand whether, and how, to incorporate ChatGPT, similar to any other productivity revolution technology, such as calculators or a Google search engine. Although there are concerns regarding the use of LLMs in business education, the positive or negative impact of LLM use is not well-understood. In this research, we examine the substitution and complementarity effects of using ChatGPT in business curricula on learning outcomes and well-being in a socially supportive learning environment. Specifically, we examine whether technology anchors impact students’ goal orientation, learning outcomes, and well-being by conducting an empirical study with students majoring in Information Systems. Our analysis reveals that a technology anchor (computer playfulness) can complement the effects of social support on learning outcomes, while enhancing well-being for simple tasks. Students’ well-being and learning outcomes are hindered by LLM use (specifically, the computer anxiety anchor), substituting social support for simple and difficult tasks. These findings have implications for educational institutions that are assessing how to incorporate LLMs into business curricula.

Graph neural networks (GNNs) rely heavily on graph structures and artificial hyperparameters, which may increase computation and affect performance. Most GNNs use original graphs, but the original graph data has problems with noise and incomplete information, which easily leads to poor GNN performance. For this kind of problem, recent graph structure learning methods consider how to generate graph structures containing label information. The settings of some hyperparameters will also affect the expression of the GNN model. This paper proposes a genetic graph structure learning method (Genetic-GSL). Different from the existing graph structure learning methods, this paper not only optimizes the graph structure but also the hyperparameters. Specifically, different graph structures and different hyperparameters are used as parents; the offspring are cross-mutated through the parents; and then excellent offspring are selected through evaluation to achieve dynamic fitting of the graph structure and hyperparameters. Experiments show that, compared with other methods, Genetic-GSL basically improves the performance of node classification tasks by 1.2%. With the increase in evolution algebra, Genetic-GSL has good performance on node classification tasks and resistance to adversarial attacks.

Violence against women with disabilities has received more attention in recent years recognizing the intersectionality of experiences of abuse, yet little is known about the less visible forms of disability such as speech and language disorders. This review aimed to identify and synthesize existing literature exploring the relationship between speech and language disorders and victimization, including child sexual abuse (CSA), exposure to domestic violence in childhood, and intimate partner violence (IPV) and sexual assault in adulthood. Five electronic databases were systematically searched using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Scoping Reviews guidelines. Studies were included if they were English-language peer-reviewed articles or grey literature publications focusing on domestic violence and/or sexual assault and speech and language disorders. Twenty studies met the inclusion criteria. The findings showed a clear link between childhood exposure to domestic violence and/or CSA and speech and language disorders. Women with speech and language disorders appear to be at heightened risk of IPV and sexual assault relative to the general population. Nonfatal strangulation emerged as a growing area of concern in the literature with acute and chronic symptoms requiring treatment from speech and language therapists. Practice implications include enhanced training at the undergraduate and professional level for therapists to better identify and respond to suspected abuse in their patients. Emergency and specialist support services need protocols and training to better support women with speech and language disorders. Future research should examine the help-seeking behavior of women with speech and language disorders post-victimization and collect longitudinal data to understand the long-term consequences of abuse.

While the effects of external factors like fluid mechanical forces and scaffold geometry on tissue growth have been extensively studied, the influence of cell behavior—particularly nutrient consumption and depletion within the scaffold—has received less attention. Incorporating such factors into mathematical models allows for a more comprehensive understanding of tissue-engineering processes. This work presents a comprehensive continuum model for cell proliferation within two-dimensional tissue-engineering scaffolds. Through mathematical modeling and asymptotic analysis based on the small aspect ratio of the scaffolds, the study aims to reduce computational burdens and solve mathematical models for tissue growth within porous scaffolds. The model incorporates fluid dynamics of nutrient feed flow, nutrient transport, cell concentration, and tissue growth, considering the evolving scaffold porosity due to cell proliferation, with the crux of the work establishing the ideal pore shape for channels within the tissue-engineering scaffold to obtain the maximum tissue growth. We investigate scaffolds with specific two-dimensional initial porosity profiles, and our results show that scaffolds which are uniformly graded in porosity throughout their depth promote more tissue growth.

This qualitative case study delved into students’ understanding and positioning while they participated in solving an authentic, conceptually‐based problem in a high‐school chemistry class. Verbal and nonverbal cues, particularly gestures, offered broader awareness of students’ engagement in sensemaking during the learning experience. The chemistry classroom emerged as a dynamic space where intricate scientific thinking unfolded during this experience, and our embodied, multimodal analysis focused on unraveling this complexity. Our analysis determined the ways that various features of the contextual configuration—the intersection of different semiotic fields in the social setting—affected student thinking and participation. For example, the lack of specific reference to semiotic resources and the lack of attention to a key gesture influenced the way ideas evolved in the solution generation phase. The analysis also revealed the teacher's impact on the contextual configuration at critical junctures, including her influence on the use of semiotic resources and on student positioning. Finally, the embodied and multimodal analysis provided insights into the affordances and constraints of the activity structure and modes of communication on student's involvement in scientific practices. These insights highlighted the importance of educators recognizing diverse forms of student expression, including gestures, as essential for nurturing scientific sensemaking and supporting students in utilizing different modalities productively. Our approach can assist researchers in holistically investigating pedagogical strategies that can facilitate reform‐based science teaching. It can also assist teachers in fostering effective communication—both verbal and non‐verbal, while simultaneously guiding positioning within and between student groups, establishing an environment conducive to equitable sensemaking.

Family‐to‐work conflict (FWC) bias captures an erroneous assumption that women have more FWC than men. Existing research has relied on a “lack of fit” perspective (i.e., women have less person–job and person–organization fit compared with men) to explain why this bias detracts from women's work outcomes. Building on this, we propose a novel social exchange cost explanation for these effects. We argue that FWC bias promotes a belief in supervisors that female subordinates are less reliable in fulfilling work duties and, therefore, less able to reciprocate resources invested in them. This concern, we maintain, is manifested in their diminished cognitive trust in their female (vs. male) subordinates. In turn, we argue that supervisors, because of their lower cognitive trust, will reciprocate by engaging in greater ostracism of their female (vs. male) employees. To test these predictions, we conducted three studies, including an experimentally randomized instrumental variable design, a multisource field survey using supervisor–subordinate dyads, and an experiment in which we utilized a bias‐disrupting strategy. Overall, our findings suggest that women are perceived as having greater FWC than men, leading supervisors to have less cognitive trust in them relative to men, which in turn, manifests in greater ostracism of female subordinates.

Prior research indicates that young people who engage in thoughtful and reflective decision-making (TRDM) are less prone to criminal and delinquent behavior, although certain factors (e.g., stress, lack of sleep) can undermine the crime preventive effect of TRDM. We build on this research by examining the possibility that adolescent alcohol use may also undermine the relationship between TRDM and youth crime. Drawing on data from a nationally representative sample of adolescents, we conduct negative binomial regression analyses and test for interaction effects between TRDM and alcohol use. The results indicate that frequent heavy drinking tends to weaken the crime preventive effect of TRDM, although no such effect emerged when the sheer frequency of alcohol was the focus of our analyses. Implications for adolescent decision-making, alcohol use, and crime prevention are discussed.

This study's objective was to investigate the extent to which two different levels of low‐intensity vibration training (0.6 g or 1.0 g ) affected musculoskeletal structure and function after a volumetric muscle loss (VML) injury in male C57BL/6J mice. All mice received a unilateral VML injury to the posterior plantar flexors. Mice were randomized into a control group (no vibration; VML‐noTX), or one of two experimental groups. The two experimental groups received vibration training for 15‐min/day, 5‐days/week for 8 weeks at either 0.6 g (VML‐0.6 g ) or 1.0 g (VML‐1.0 g ) beginning 3‐days after induction of VML. Muscles were analyzed for contractile and metabolic adaptations. Tibial bone mechanical properties and geometric structure were assessed by a three‐point bending test and microcomputed tomography (µCT). Body mass‐normalized peak isometric‐torque was 18% less in VML‐0.6 g mice compared with VML‐noTx mice ( p = 0.030). There were no statistically significant differences of vibration intervention on contractile power or muscle oxygen consumption ( p ≥ 0.191). Bone ultimate load, but not stiffness, was ~16% greater in tibias of VML‐1.0 g mice compared with those from VML‐noTx mice ( p = 0.048). Cortical bone volume was ~12% greater in tibias of both vibration groups compared with VML‐noTx mice ( p = 0.003). Importantly, cross‐section moment of inertia, the primary determinant of bone ultimate load, was 44% larger in tibias of VML‐0.6 g mice compared with VML‐noTx mice ( p = 0.006). These changes indicate that following VML, bones are more responsive to the selected vibration training parameters than muscle. Vibration training represents a possible adjuvant intervention to address bone deficits following VML.

Objective Using an innovative data sharing model, we assessed the impacts of the COVID-19 pandemic on the health of people who inject drugs (PWID). Design The PWID Data Collaborative was established in 2021 to promote data sharing across PWID studies in North America. Contributing studies submitted aggregate data on 23 standardized indicators during four time periods: pre-pandemic (Mar 2019 – Feb 2020), early-pandemic (Mar 2020 – Feb 2021), mid-pandemic (Mar 2021 - Feb 2022), and late pandemic (Mar 2022 - Feb 2023). Methods We present study-specific and meta-analyzed estimates for the percentage of PWID who took medications for opioid use disorder, received substance use treatment, shared syringes or injection equipment, had a mental health condition, had been incarcerated, or had experienced houselessness. To examine change over time across indicators, we fit a random effects meta-regression model to prevalence estimates using time as a moderator. Results Thirteen studies contributed estimates to the Data Collaborative on these indicators, representing 6,213 PWID interviews. We observed minimal change across prevalence of the six indicators between the pre-pandemic (March 2019 – February 2020) and three subsequent time periods, overall or within individual studies. Considerable heterogeneity was observed across study- and time-specific estimates. Conclusions Limited pandemic-related change observed in indicators of PWID health is likely a result of policy and supportive service-related changes and may also reflect resilience among service providers and PWID themselves. The Data Collaborative is an unprecedented data sharing model with potential to greatly improve the quality and timeliness of data on the health of PWID.

The hypothesis that certain psychiatric or neurological diseases can be best understood—and therefore treated—at a systems level is an attractive one. For testing such a hypothesis, animal models are useful, and the ability to meaningfully compare multicellular activity between brains is needed. Identifying when population-level functional dynamics are typical or healthy, and when they are aberrant or pathological, is not straightforward, especially when there exists no within-animal baseline state to which to compare. This chapter focuses on practical considerations for applying popular ensemble-identification analyses (such as tSNE, SVM, k-means) to comparisons between groups, such as one might carry out between a wild-type animal and an animal with a genetic mutation affecting a disease-relevant pathway. While the methods are many, the principles are few(er). We focus first and foremost on the choice of metric: precisely what is thought to differ between groups? What aspect of multineuronal activity—stability, number, size, diversity, etc.—might be depleted or augmented in the disease state of interest? And could other behavioral or neural properties, such as arousal, motor output, or baseline neural firing rates, better account for change in your chosen metric, rather than a specific loss or disorganization of neural ensembles? We provide an example analysis pipeline wherein these points are considered.

Researchers working with thin samples, such as monolayer graphene, are consistently struggling against contamination. Indeed, the problem of hydrocarbon contamination is known from the earliest days of electron microscopy and efforts to reduce this problem are ubiquitous to almost all high‐vacuum experiments. Accurate knowledge of the behavior of such contamination is essential for electron beam (e‐beam) based atomic fabrication, where it is aspired to select and control matter on an atom‐by‐atom basis. Here, the vexing question of hydrocarbon contamination on graphene is taken up. Image intensity is used to directly reveal the presence of diffusing hydrocarbons on ostensibly clean graphene. These diffusing hydrocarbons are previously inferred but not directly observed. Surprising dynamic variations of the concentration of these hydrocarbons impels questions about their origin. Here, some possible explanations are presented and some tentative conclusions are drawn. This work updates the conceptual model of “clean graphene” and offers refinements to the description of e‐beam induced hydrocarbon deposition.

Background People living with Parkinson's disease (PD) commonly experience heat sensitivity—worsening symptoms and restricted daily activities in heat. Objective This study aimed to develop a scale of heat sensitivity for people with PD. Methods Through a search of the scientific literature and online forums, we developed 41 items relating to experiences of heat for people with PD to assess heat sensitivity. A panel of experts was then consulted to review the scale items critically. After two rounds of review, the scale was refined to 36 items with an overall scale content validity index of 0.89. Via an online survey, 247 people with PD responded to the items. Results The items were examined with exploratory factor analysis to determine the underlying factors therein. After several iterations, a simple structure was achieved with 29 items loading uniquely onto one of four factors: daily activities, sweating and exercise, heat‐related illness, and symptoms and medications. The model had acceptable to excellent fit statistics (root mean square error of approximation = 0.073 [90% confidence interval 0.067–0.081], root mean square of the residuals = 0.03, comparative fit index = 0.93, and Tucker‐Lewis index = 0.91), and each factor showed high reliability (Cronbach's α ≥0.89). Factor and total scale scores were significantly higher among those reporting sensitivity to heat and poor health status. Conclusion This new heat sensitivity scale for people living with PD can enable health professionals and clients to assess the severity and impact of heat sensitivity.

Alcohol misuse increases infections and cancer fatalities, but mechanisms underlying its toxicity are ill‐defined. We show that alcohol treatment of human tracheobronchial epithelial cells leads to inactivation of giantin‐mediated Golgi targeting of glycosylation enzymes. Loss of core 2 N‐acetylglucosaminyltransferase 1, which uses only giantin for Golgi targeting, coupled with shifted targeting of other glycosylation enzymes to Golgi matrix protein 130‐Golgi reassembly stacking protein 65, the site normally used by core 1 enzyme, results in loss of sialyl Lewis x and increase of sialyl Lewis a and α2‐6sialo mucin O‐glycans. The α2‐6sialo mucin O‐glycans induced by alcohol cause death of U937 macrophages mediated by sialic acid‐binding immunoglobulin‐like lectin 7. These results provide a mechanistic insight into the cause of the toxic effects of alcohol and might contribute to the development of therapies to alleviate its toxicity.