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Introduction: Beyond Hollow Materialism
No full textThis introduction to the special issue, “Transformative Approaches to Materiality in Appalachian Studies,” provides theoretical background and examples that demonstrate applications of new material culture and materiality theory to key Appalachian studies themes. It offers new directions for the study of material entanglement in social relationships by reviewing the history of material culture studies in the Appalachian context, fundamental theoretical propositions emerging from the “material turn” in social theory, and examples drawn from ethnographic and archaeological research into the patterns of use of material culture in the timber country of West Virginia and coalfields of eastern Kentucky. Finally, this introduction summarizes key questions offered by the contributors and invites further discourse amongst scholars of Appalachia about the role of material culture and materiality in shaping our research and engaged practices
Testing an Ironic Process Model of Emotion Expression in Daily Life
No full textExpressive suppression, or suppressing the expression of an emotion, is a common emotion regulation approach that has been found to have substantial negative implications for one’s mental health, physical health, and relationship functioning. However, expression of emotion, and of negative emotion in particular, has not consistently been found to be beneficial for mood or relationships. Moreover, the social expression of emotion that is culturally valued among Black-identifying Americans is different in White-identifying Americans, particularly in regard to expression of negative affect. However, it is unknown whether this cultural context mitigates the potential consequences of expressive suppression and emotion expression.
Expressive suppression and expression of emotion have been established as distinct constructs rather than two ends of one continuum. However, whether expressive suppression serves as a context that influences the expression of negative emotion and the associated outcomes has not been thoroughly explored. The present study tested an ironic process effect of emotion expression, by which suppression of emotion may result in paradoxical expression of that emotion after a delay. Using 14 days of daily-diary surveys, the present study tested an ironic process model of emotion expression in emerging adulthood in daily life (Aim 1), examined whether expressive suppression moderated associations between emotion expression and outcomes for mood and relationships (Aim 2) and whether these effects varied across White and Black Americans (Aim 3). Results indicated that expressive suppression was not directly associated with intensity of negative emotion expression, failing to support the hypothesis in Aim 1. However, results did reveal a significant interaction between expressive suppression and intensity of negative emotion expression in models predicting emotional intimacy, both within and between participants. Between participants, the effect was in the direction hypothesized in Aim 2, but within-participants, the direction of the effect was reversed. Similarly, in the between-person model predicting anxiety symptoms, the interaction was significant, but not in the direction that would be consistent with an ironic process effect. In the model predicting depression symptoms, the interaction was not significant within or between participants after controlling for race. Cumulatively, these results provide partial support for the hypothesized ironic process effect in Aim 2. Lastly, there was no support for a three-way interaction between racial identity and the ironic process effect, suggesting that effects did not vary across race. However, in models predicting depression and anxiety, the positive association between expression of negative emotion and mood was moderated by race such that there was no significant association in participants who identified as Black. Clinical implications and future directions are discussed
Overcoming Resistance to AI in Higher Education: A Case Study
No full textWhile the deployment of AI in business and industry is widely acknowledged for its potential to improve efficiencies, decision-making, and automation, its implementation often encounters several challenges. These include integrating AI with existing systems, ensuring data quality and availability, bridging the employee skills gap, overcoming resistance to change, addressing ethical concerns, managing costs, complying with regulations, and handling ongoing maintenance and updates. In the context of higher education, however, the challenges take a different shape. Faculty members may express concerns about how AI could disrupt traditional teaching methods, necessitate changes in course delivery, and raise issues related to job security and evolving roles. To address this potential resistance, universities are increasingly developing professional development programs to help faculty integrate AI into their teaching. By adopting a human-centered approach, fostering a culture of continuous learning, providing AI literacy resources, and promoting faculty involvement in AI-related research, universities can support faculty in embracing AI more effectively. This paper presents a case study of a large, research-intensive university in the mid-Atlantic region of the U.S. and the steps it took to tackle these challenges. The paper concludes with the suggestion that by engaging faculty in a value/non-value-added task analysis of their own roles, they are more likely to better understand, appreciate, and adopt AI where it adds value
Impacts of Ramadan Fasting During Pregnancy on Pregnancy and Birth Outcomes: An Umbrella Review
Get PDFBackground
Despite a large number of primary research studies, and systematic and narrative reviews, there is no consensus on the impact of fasting during Ramadan while pregnant on pregnancy and birth outcomes. Currently, there is no evidence-based guideline for Muslim women regarding Ramadan fasting during pregnancy and clinicians cannot provide firm recommendations.
Objectives
To review the current evidence regarding the impact of Ramadan fasting during pregnancy on pregnancy and birth outcomes.
Search Strategy
We conducted an umbrella review of all systematic and narrative reviews examining the impacts of fasting during Ramadan while pregnant on pregnancy and birth outcomes by searching PubMed, CINAHL, and Cochrane Registry of Systematic Reviews databases between November 2023 and February 2024.
Selection Criteria
We included all systematic and narrative reviews examining the impacts of Ramadan fasting on pregnancy and birth outcomes. The primary outcome was the change in birth weight, gestational age at birth, fetal growth indices, and Apgar score as well as the risk of delivery by cesarean section and the risks of gestational diabetes and pre-eclampsia.
Data Collection and Analysis
We summarized the data using narrative synthesis and descriptive statistics as appropriate. This study was registered with PROSPERO, ID: CRD42023478819.
Main results
Out of 943 published reports identified across all database searches, 13 systematic and narrative reviews were included, of which three were systematic reviews with meta-analysis, six were systematic reviews without meta-analysis, and the remaining four were narrative reviews. There is no sufficient evidence that Ramadan fasting during pregnancy may reduce gestational age at birth or increase the risk of preterm birth (PTB). There is little evidence to support the hypothesis that maternal Ramadan fasting may reduce birth weight or increase the risk of low birth weight (LBW). Systematic reviews showed pooled estimates of odds ratios ranging between 0.93 (95% confidence interval [CI] 0.60-1.44) and 0.99 (95% CI 0.72-1.37) for PTB, and between 1.05 (95% CI 0.87-1.26) and 1.37 (95% CI 0.74-2.53) for LBW. There is no sufficient evidence that Ramadan fasting during pregnancy may increase the risk of delivery by cesarean section, gestational diabetes, or the risk of pre-eclampsia. None of the reviews reported evidence regarding the impacts of fasting during pregnancy on rare but clinically significant pregnancy and birth outcomes such as stillbirth, miscarriage, congenital anomalies, or neonatal deaths.
Conclusion
There is little evidence that Ramadan fasting during pregnancy can negatively impact pregnancy and birth outcomes. Primary research studies on this issue suffered from significant methodologic limitations and systematic reviews showed significant heterogeneity for several pregnancy and birth outcomes. High-quality primary research studies that collect data on multiple confounders and effect modifiers are needed to investigate this issue and help reaching evidence-based recommendations
Why AI Monitoring Faces Resistance and What Healthcare Organizations Can Do About It: An Emotion-Based Perspective
Get PDFContinuous monitoring of patients\u27 health facilitated by artificial intelligence (AI) has enhanced the quality of health care, that is, the ability to access effective care. However, AI monitoring often encounters resistance to adoption by decision makers. Healthcare organizations frequently assume that the resistance stems from patients\u27 rational evaluation of the technology\u27s costs and benefits. Recent research challenges this assumption and suggests that the resistance to AI monitoring is influenced by the emotional experiences of patients and their surrogate decision makers. We develop a framework from an emotional perspective, provide important implications for healthcare organizations, and offer recommendations to help reduce resistance to AI monitoring
Sounds of Silence: Using the Stereotype Content Model to Understand Perceptions of Introverts and Extraverts at Work
Get PDFThis paper drew on the stereotype content model (SCM) to clarify cultural stereotypes about introverted and extraverted people at work to increase our understanding of the stereotype-driven process that may lead to negative responses to introversion and subsequent detriment to employee health and well-being. The hypothesis that introverted workers would be rated as lower in warmth and competence than extraverted workers was examined across three studies. Study 1 used qualitative content analyses to assess open responses that freely described introverted and extraverted colleagues. Study 2 tested the hypothesis quantitatively using established measures of warmth and competence. Finally, Study 3 tested if one’s self-identified personality impacted perceptions of warmth and competence. Across all studies, introverted employees were endorsed as being lower in warmth and competence than extraverted employees. In Study 3, warmth and competence stereotypes held regardless of one’s identification as introverted or extraverted. Finally, social interaction requirements of the job moderated perceptions of competence in Study 1, but not in Study 2 or 3. The present findings extend the SCM to new groups and provide empirical evidence to support a key driver in negative responses to introversion in the workplace. The results also suggest that job demands and personality identity salience are important considerations, and a need for organizations to engage in best practices to reduce the negative impact of these stereotypes on employees’ health and well-being
Determination of Structural Factors Contributing to Protection of Zinc Fingers in Estrogen Receptor α through Molecular Dynamic Simulations
Get PDFThe ERα transcription factor that induces tumor growth is a potential target for breast cancer treatment. Each monomer of the ERα DNA-binding domain (ERαDBD) homodimer has two conserved (Cys)4-type zinc fingers, ZF1 (N-terminal) and ZF2 (C-terminal). Electrophilic agents release Zn2+ by oxidizing the coordinating Cys of the more labile ZF2 to inhibit dimerization and DNA binding. Microsecond-length molecular dynamics (MD) simulations show that greater flexibility of ZF2 in the ERαDBD monomer leaves its Cys more solvent accessible and less shielded from electrophilic attack by sulfur-centered hydrogen bonds than ZF1 which is buried in the protein. In the unreactive DNA-bound dimer, the formation of the dimer interface between the highly flexible D-box motif of ZF2 decreases the solvent accessibility of its Cys toward electrophiles and increases the populations of sulfur-containing hydrogen bonds that reduce their nucleophilicity. Examination of these factors in ERαDBD and other proteins with labile ZF motifs may reveal new targets to treat viral infections and cancer
LRTM Left-Right Transition Matrices for Molecular Interaction Prediction
Get PDFMolecular interactions are central to most biological processes. The discovery and identification of potential associations between molecules can provide insights into biological exploration, diagnostic and therapeutic interventions, and drug development. So far many relevant computational methods have been proposed, but most of them are usually limited to specific domains and rely on complex preprocessing procedures, which restricts the models’ ability to be applied to other tasks. Therefore, it remains a challenge to explore a generalized approach to accurately predicting potential associations. In this study, We propose Left-Right Transition Matrices (LRTM) for molecular interaction prediction. From the perspective on the diffusion model, we construct two transition matrices to model undirected graph information propagation. This allows modeling the transition probabilities of links, which facilitates link prediction in molecular bipartite networks. The extensive experimental results show that the proposed LRTM algorithm performs better than the compared methods. Also, the proposed algorithm has the potential for cross-task prediction. Furthermore, case studies show that LRTM is a powerful tool that can be effectively applied to practical applications
5G-Practical Byzantine Fault Tolerance: An Improved PBFT Consensus Algorithm for the 5G Network
Get PDFThe consensus algorithm is the core technology of blockchain systems to maintain data consistency, and its performance directly affects the efficiency and security of the whole system. Practical Byzantine Fault Tolerance (PBFT) plays a crucial role in blockchain consensus algorithms by providing a robust mechanism to achieve fault-tolerant and deterministic consensus in distributed networks. With the development of 5G network technology, its features of high bandwidth, low latency, and high reliability provide a new approach for consensus algorithm optimization. To take advantage of the features of the 5G network, this paper proposes 5G-PBFT, which is an improved practical Byzantine fault-tolerant consensus algorithm with three ways to improve PBFT. Firstly, 5G-PBFT constructed the reputation model based on node performance and behavior. The model dynamically selected consensus nodes based on the reputation value to ensure the reliability of the consensus node selection. Next, the algorithm selected the primary node using the reputation model and verifiable random function, giving consideration to the reliability of the primary node and the randomness of the selection process. Finally, we take advantage of the low latency feature of the 5G network to omit the submission stage to reduce the communication complexity from ON² to ON, where N denotes the number of nodes. The simulation results show that 5G-PBFT achieves a 26% increase in throughput and a 63.6% reduction in transaction latency compared to the PBFT, demonstrating significant performance improvements