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Obstructive Sleep Apnea (OSA) is a prevalent health issue affecting 10-25% of adults in the United States (US) and is associated with significant economic consequences. Machine learning methods have shown promise in improving the efficiency and accessibility of OSA diagnoses, thus reducing the need for expensive and challenging tests. A comparative analysis of Logistic Regression (LR), Support Vector Machine (SVM), Gradient Boosting (GB), Gaussian Naive Bayes (GNB), Random Forest (RF), and K-Nearest Neighbors (KNN) algorithms was conducted to predict Obstructive Sleep Apnea (OSA). To improve the predictive accuracy of these models, Random Oversampling was applied to address the imbalance in the dataset, ensuring a more equitable representation of the minority class. Patient demographics, including age, sex, height, weight, BMI, neck circumference, and gender, were employed as predictive features in the models. The RFC provided outstanding training and testing accuracies of 87% and 65%, respectively, and a Receiver Operating Characteristic (ROC) score of 87%. The GBC and SVM classifiers also demonstrated good performance on the test dataset. The results of this study show that machine learning techniques may be effectively used to diagnose OSA, with the Random Forest Classifier demonstrating the best results.

Objective Preterm birth (PTB) is one of the leading causes of infant and neonatal mortality. Prepregnancy body mass index (BMI; kg/m2) has been linked to PTB but the evidence of this association by weight gain during pregnancy, race, and ethnicity is limited. This study aimed to assess the association between maternal prepregnancy BMI and PTB stratified by weight gain during pregnancy, race, and ethnicity. Study Design The U.S. natality data from 2017 to 2021 were used. In this analysis, we included mothers who had a live singleton birth and available data for prepregnancy BMI, gestational age at birth, weight gain during pregnancy, race, and ethnicity. Logistic regression models were used to assess the association between prepregnancy BMI categories and PTB stratified by weight gain during pregnancy, race, and ethnicity. Results A total of 17,311,509 singleton live births were included of which 1,393,889 (8.05 %) were PTBs. After adjusting for confounders, compared with normal prepregnancy BMI mothers (18.5–24.9), those with underweight BMI (<18.5) were at increased odds of PTB regardless of weight gain during pregnancy, race, and ethnicity. However, for mothers with a prepregnancy BMI above the normal weight (≥25), the association between prepregnancy BMI and PTB differs by weight gain during pregnancy, race, and ethnicity. Asian mothers with obesity II (35.0–39.9) had 93% (odds ratio [OR] = 1.93, 95% confidence interval [CI]: 1.62–2.30) increased odds of PTB for weight gain during pregnancy of 31 to 40 pounds. Their White, Hispanic, and Black counterparts experienced lower odds of PTB for similar weight gain during pregnancy (White: OR = 1.56, 95% CI: 1.51–1.60; Hispanic: OR = 1.48, 95% CI: 1.41, 1.54; and Black: OR = 1.22, 95% CI: 1.17–1.27). Conclusion Mothers with underweight BMI were at increased risk of PTB regardless of weight gain during pregnancy, race, and ethnicity. However, the association between high prepregnancy BMI and PTB varied by weight gain during pregnancy, race, and ethnicity. Key Points

Background: In the United States, chronic obstructive pulmonary disease (COPD) is a significant cause of mortality. As far as we know, it is a chronic, inflammatory lung condition that cuts off airflow to the lungs. Many symptoms have been reported for such a disease: breathing problems, coughing, wheezing, and mucus production. Patients with COPD might be at risk, since they are more susceptible to heart disease and lung cancer. Methods: This study reviews COPD diagnosis utilizing various machine learning (ML) classifiers, such as Logistic Regression (LR), Gradient Boosting Classifier (GBC), Support Vector Machine (SVM), Gaussian Naïve Bayes (GNB), Random Forest Classifier (RFC), K-Nearest Neighbors Classifier (KNC), Decision Tree (DT), and Artificial Neural Network (ANN). These models were applied to a dataset comprising 1603 patients after being referred for a pulmonary function test. Results: The RFC has achieved superior accuracy, reaching up to 82.06% in training and 70.47% in testing. Furthermore, it achieved a maximum F score in training and testing with an ROC value of 0.0.82. Conclusions: The results obtained with the utilized ML models align with previous work in the field, with accuracies ranging from 67.81% to 82.06% in training and from 66.73% to 71.46% in testing.

Meta-heuristic optimization algorithms have become widely used due to their outstanding features, such as gradient-free mechanisms, high flexibility, and great potential for avoiding local optimal solutions. This research explored the grey wolf optimizer (GWO) to find the ideal configuration for a six-element Yagi–Uda antenna. The GWO algorithm adjusted the lengths of the antenna wires and the spacings between them. The goal was to maximize the antenna’s ability to transmit signals (throughput gain). Optimal antenna selection relies on various parameters, including gain, bandwidth, impedance matching, frequency, side-lobe levels, etc. The optimization of a six-element Yagi–Uda antenna presents a challenging engineering design problem due to its multimodal and nonlinear nature. Achieving optimal performance hinges on the intricate interplay between the lengths of the constituent elements and the spacing configurations. To this end, a multiobjective function was adopted to design this antenna. The performance of several meta-heuristic algorithms, including genetic algorithms, biogeography-based optimization, simulated annealing, and grey wolf optimizer, was compared. The GWO-based approach has performed better than its competitors. This optimized antenna design based on GWO reported a gain of 14.21 decibel. Therefore, the GWO-based method optimizes antennas that can be further investigated for other antenna design problems.

Structured faculty development programs focused on integrating health equity into medical education curricula remain limited.

Background: HIV risk behavior in women who use drugs is related to myriad psychosocial issues, including incarceration. The experience of incarceration elevates women’s HIV risk by disrupting social networks, housing, employment, and access to health care. During the COVID-19 pandemic, changes in criminal-legal practices resulted in decreased incarceration, especially among women. These changes may have largely altered HIV risk among women who use drugs, depending on their access to care in the community. Objective: This study seeks to build knowledge about the impact of shifts in criminal-legal practices during the COVID-19 pandemic on HIV risk behaviors of justice-involved women who use drugs. Methods: Qualitative methods are used to gather and analyze women’s narratives about their life experiences before and during the COVID-19 pandemic, with a focus on individual and structural determinants of HIV risk behaviors. Thirty formerly incarcerated women with a history of substance use are being recruited through collaboration with community partners. Each participant completes a sociodemographic survey and two interviews. The first interview uses a life history instrument that invites participants to reflect on key turning points in their lives. The second interview uses a calendar approach to gather information about participants’ lives during the first year of the COVID-19 pandemic (March 2020-March 2021). The interviews (1 hour each) are audio-recorded and transcribed for analysis. Rapid Qualitative Inquiry and thematic analysis are being used to manage, organize, and interpret the data. The study team will collaborate with a subset of participants to develop digital stories about their COVID-19 experiences, a process that allows for member-checking and triangulation. Findings will be disseminated to program and policy makers in academic venues, community settings, and social service agencies. Results: To date, 10 women’s data have been collected. In total, two themes have been identified in this preliminary data: (1)the chaos and instability of participants’ lives increased during the COVID-19 pandemic: participants reported a wide range of psychosocial and health problems and limited engagement with social service systems. Interaction with criminal-legal systems was rife with uncertainty; participants described living in a state of limbo, which was extremely stressful. (2) When asked to describe a “turning point” in their lives, many participants attributed their substance use to the traumatic loss of a child due to death, incarceration, or termination of parental rights. During the COVID-19 pandemic, participants’ struggles to cope with these unresolved experiences of grief and loss were intensified by the widespread death and dying of the pandemic. Conclusions: Preliminary findings suggest that HIV risk factors increased for participants during the COVID-19 pandemic and invite further investment in community-based harm reduction programs, especially housing, that support women who use drugs. Interventions that address experiences of maternal grief and loss may reduce women’s substance use. Trial Registration:

Despite the persistence of breastfeeding racial and ethnic disparities in the United States, little is known about Black fathers' perceptions of breastfeeding and breastfeeding support services (e.g., maternity hospital-based care and lactation management care). This qualitative, community-based participatory research study reports Black fathers' perceptions of barriers and facilitators to breastfeeding, including the provision of breastfeeding support services in Connecticut. A focus group guide was co-developed with community partners and adapted from the Barrier Analysis Tool to identify breastfeeding facilitators, barriers, and service improvement areas. Four focus groups were conducted with 30 Black fathers who were Connecticut residents with a child under 3 years old. Qualitative data were analyzed using rapid template analysis involving deductive and inductive coding. We identified factors influencing breastfeeding and fathers' ability to support breastfeeding across all levels of the Socio-Ecological Model. Facilitators included high paternal breastfeeding knowledge, paternal breastfeeding involvement, parents' shared decision-making, extensive maternity hospital discharge support, ongoing breastfeeding support into the postnatal period, availability of community breastfeeding resources, and designated spaces for public breastfeeding. Barriers included low paternal breastfeeding knowledge, familial discouragement, insufficient prenatal breastfeeding education, exclusion of the father from breastfeeding support services, and stigma against breastfeeding in public. Understanding breastfeeding perceptions among members of a mother's support network, including their partners, is key for developing effective person- and family-centered breastfeeding education and counseling services that are well coordinated from the prenatal to postnatal periods with strong direct engagement from fathers.

This research examines the impact of generative artificial intelligence (AI) on the perception of educational content quality, specifically by comparing AI-generated and human-generated course syllabi in marketing education. Results from four studies indicate a general preference for AI-generated syllabi, attributed to their greater perceived objectivity. This preference is more pronounced in conventional courses but diminishes in unconventional ones, suggesting that the unique aspects of these courses may reduce the advantages of generative AI. In addition, disclosing the AI authorship of syllabi significantly affects their perceived quality negatively, underscoring the impact of transparency on the acceptance of AI-generated educational materials. These findings highlight the potential of generative AI in educational content creation and its limitations in certain contexts. They offer valuable insights for enhancing educational practices and shaping policy decisions to enrich student experiences in the era of AI integration.

The purpose of this study is to learn more about virtual reality (VR) and augmented reality (AR) practices at the United States’ top one hundred university libraries, as well as how they are engaging with the metaverse. We conducted qualitative and descriptive analysis on the websites of the top one hundred university libraries in the United States to determine the application fields and application proportions of VR and AR technologies and found good practice examples of using VR and AR technologies in this field. The findings show that 86 percent of the top one hundred US university libraries have implemented VR and AR technologies, with practice areas focused on: VR/AR studio and VR/AR makerspace; immersive learning services and virtual exhibitions/conference services; visual geographic information system and VR navigation services; virtual reading services and visual retrieval services; and VR reference services. The study provides university library administrators and professionals with the most up-to-date information and best practices of VR and AR engagement areas and the proportion of use, which can aid in the development of strategies to leverage VR and AR technologies to improve patron service and embrace the metaverse for the communities they serve.

This assignment is designed to enhance resilience among students in leadership courses. It leverages the US Army’s Master Resilience Training (MRT) framework and positive psychology to develop resiliency skills.,A three-part experiential workshop integrates academic readings (providing a foundation of resilience concepts), explores the influence of personal identities on leadership and connects leadership skills with resilience concepts.,Participants reflect on self-awareness tools and positive psychology and create personalized action plans. Participants' resilience skills are enhanced with their personalized resiliency plan.,The program provides a structured approach to resilience training, which can be integrated into university curriculums. Students gain self-awareness and psychological tools to manage challenges, which are valuable for personal growth and professional development. There is a persistent gender gap in leadership, and for women to attain greater parity in leadership positions, resilience skills are imperative. By focusing on identity-related factors, the program prepares future leaders for challenges in attaining leadership positions.,This program is uniquely tailored for students aspiring to leadership positions, with an emphasis on the role of identity, such as gender, in leader emergence and overcoming related challenges.

Universities and colleges are organizations that significantly impact students, their communities, and society. This forum explores how organizational communication scholars who are university leaders have applied their scholarly backgrounds to inform their roles. The forum participants engage in the work of being reflective practitioners to shed light on how organizational communication theory can help in negotiating the everyday lived experience of academic leadership. Three key issues are explored: (1) in what ways are organizational communication scholars uniquely positioned to assume a university leadership role? (2) how do communication concepts inform the communication practices of administrators? and (3) how do communication practices contribute to universities as multi-faceted institutions? The participants conclude by reflecting on current challenges in higher education and the potential of organizational communication scholars to play a vital role in navigating those challenges.

Across three online studies, we examined the relationship between the Fear of Missing Out (FoMO) and moral cognition and behavior. Study 1 (N = 283) examined whether FoMO influenced moral awareness, judgments, and recalled and predicted behavior of first-person moral violations in either higher or lower social settings. Study 2 (N = 821) examined these relationships in third-person judgments with varying agent identities in relation to the participant (agent = stranger, friend, or someone disliked). Study 3 (N = 604) examined the influence of recalling activities either engaged in or missed out on these relationships. Using the Rubin Causal Model, we created hypothetical randomized experiments from our real-world randomized experimental data with treatment conditions for lower or higher FoMO (median split), matched for relevant covariates, and compared differences in FoMO groups on moral awareness, judgments, and several other behavioral outcomes. Using a randomization-based approach, we examined these relationships with Fisher Tests and computed 95% Fisherian intervals for constant treatment effects consistent with the matched data and the hypothetical FoMO intervention. All three studies provide evidence that FoMO is robustly related to giving less severe judgments of moral violations. Moreover, those with higher FoMO were found to report a greater likelihood of committing moral violations in the past, knowing people who have committed moral violations in the past, being more likely to commit them in the future, and knowing people who are likely to commit moral violations in the future.

Despite being a fundamental concept, the field of supply chain management (SCM) exhibits a significant lack of consensus regarding the definition of supply chain flows (SCFLOWS). Additionally, there has been an over-reliance on three flows – material, information and finance – while various other flows crucial to SCM performance have been overlooked. Hence, the purpose of this study is twofold: (1) to explore the multi-dimensional nature of SCFLOWS and (2) to identify additional flows beyond the commonly acknowledged ones that are vital for SCM performance.,This study employs various qualitative methods as part of the abduction process. The methods include in-depth interviews with logistics professionals, a Delphi study involving SCM scholars and a focus group comprising airline industry practitioners.,Seven SCFLOWS dimensions are identified and presented as SCFLOWS framework. Also, two additional flows, i.e. human and capital equipment, are proposed as vital to SCM performance.,This is the first study to introduce SCFLOWS framework to achieve consensus in the field. By introducing two additional flows, it proposes extending the SCFLOWS boundary to include various flows overlooked previously but pertinent to SCM performance. The SCFLOWS framework serves as a systematic guide to validate additional flows and represents an important step towards building SCM theory.

Marker variables provide an efficacious means of post hoc detection of common method variance (CMV) in data. These variables are measured in the same way as substantive variables, but because they are conceptually unrelated to the variables of interest, they are believed to be a proxy for CMV. Although marker variables have demonstrated effectiveness, questions remain as to what they actually measure, and thus, why they work. This lack of knowledge prevents researchers from choosing appropriate marker variables to include in same source surveys. The purpose of this research is to determine how four different marker variables account for common rater effects which can cause CMV. A metacognitive approach is used to develop an empirical study using two samples, with a focus on the specific rater effects of mood state, transient mood, consistency motif, and illusory correlations. Findings indicate that these marker variables elicit similar respondent reactions and do not create a notable psychological separation between substantive variables. Additionally, there is evidence that respondents’ use of consistency motifs and illusory correlations influence substantive variable relations. Finally, using the confirmatory factor analysis marker technique, data from two samples indicate the presence of CMV, but not bias from CMV, indicating that the problem of artificially inflated results due to CMV may be overstated.

We measure the absolute proper motion of Andromeda III (And III) using Advanced Camera for Surveys/Wide Field Channel and WFPC2 exposures spanning an unprecedented 22 yr time baseline. The WFPC2 exposures have been processed using a deep-learning centering procedure recently developed as well as an improved astrometric calibration of the camera. The absolute proper motion zero point is given by 98 galaxies and 16 Gaia EDR3 stars. The resulting proper motion is (μ α , μ δ ) = (−10.5 ± 12.5, 47.5 ± 12.5) μas yr−1. We perform an orbit analysis of And III using two estimates of M31's mass and proper motion. We find that And III’s orbit is consistent with dynamical membership to the Great Plane of Andromeda system of satellites although with some looser alignment compared to the previous two satellites NGC 147 and NGC 185. And III is bound to M31 if M31's mass is M vir ≥ 1.5 × 1012 M ⊙.

States and districts share an obligation to provide Multilingual Learners (MLLs) with access to high quality language programs that are proven to be effective in minimizing opportunity gaps between MLLs and non-MLLs. This article reviews how local education agencies (LEAs) allocated their state-issued funding to improve MLL language programs and increase student outcomes. Findings reveal that of the total state-issued MLL funding, LEAs used 88.7% on teacher salaries and benefits, 5.1% on teacher professional development, 4.9% on language program implementation, 0% on language program evaluation, and a small percentage of funding remained unspecified. Collectively, these findings indicate that LEAs did not adhere to the state's funding policies, nor did the state follow their own policies to regulate the LEAs' expenditures. We close with a discussion on how the state can improve their function as an organizational leader and serve as a model for other stakeholders in the shared obligation of the education of MLLs.

Abstract Despite the prominent argument for equal educational opportunity for women in Republic V, commentators frequently question Plato’s sincerity, the quality of the case made, or its significance. Undermining confidence in Plato’s advocacy of female equality are derogatory remarks about women in this and other dialogues. Since we take Plato to be sincere in the argument in Republic V, we reconcile his conclusions there about the equal educational opportunity for women with these seemingly problematic remarks by suggesting that the remarks reflect the interlocutors involved in the dialogue and conventional Athenian prejudices of that time rather than ideas that Plato held to be true. We take seriously the observation of Levin 1996, 14, “in none of those passages in which Plato makes derogatory remarks about women does he use phusis to explain why they behave in the ways of which he is critical.” Hence Plato never suggested that women were by nature limited to the position they occupied socially and politically in 5th or 4th century Athens; he understood the difference between the way they were by convention and the way that they could be, in accordance with their nature, were they to develop their natural capacities through education. We examine carefully Plato’s argument for the equal nature of women in Republic V to defend its viability. The provocation is our not finding in the extensive secondary literature a really detailed treatment of the actual argument and appreciation that it is intended as a sound philosophical argument. We then turn to the devolution schemes in Timaeus 41e–44d and 86b–92c, which again touch on the nature of women and appear to counter the position we attribute to Plato, to show that they are really supportive of our account. Both the Republic and Timaeus limit the natural differences between males and females to body-type. Therefore, even relative physical weakness of women’s bodies does not much problematize for Plato that their natural abilities are equal to those of men, where nature in these contexts means suitability to perform certain functions.

Today's practicing marketers and scholars are confronted with a wide array of conflicting and imprecise information about best practices by which to search, gather, consolidate and interpret market information. Consequently, the need has never been greater to optimize market sensing to generate managerial actions that efficiently and effectively utilize knowledge of emerging consumer needs and competitive threats. This book addresses these urgent concerns. In essence, Market Sensing Today will cover, in ground-breaking ways, the following marketing managerial areas: * marketing opportunities associated with conventional and progressive bases of segmentation. * trends in market segment size and growth affecting long-range planning. * strategic direction for reaching future goals. * managerial understanding of assumptions competitors make about themselves. * the direction of current market strategies. * adding to the knowledge of a firm's core competencies. * how new market knowledge is best integrated into a firm's market intelligence system. * best ways to ensure the quality of information underlying decisions. * how benchmarking improves with market sensing. * best approaches for translating business issues into projects. * ways that key information may be disseminated within firms. * how proposed strategic changes are promoted by market sensing. * roles customer satisfaction insights play in policy. This book will address these key issues and more, to advance theory, research and practice based on latest developments in this vital field. It will show how to re-formulate traditional models that no longer work.

Thinking "Green" has become a significant economic trend lately. This research paper offers a better understanding of the background facts of the current hype surrounding "Green Energy." First, the problem of global warming is investigated. Europe has taken a lead role in the fight against global warming in order to meet their objective of 20% renewable energy production by 2020. Many possibilities exist to transform raw biomass into bioenergy fuels, which can then be used for specific energy production purposes: electricity production, warming or transport. Biodiesel, bioethanol, biogas and many other fuels can be produced from biomass.

Consumers and businesses have an ethical obligation to do their part to reuse or recycle unwanted items. However, while some consumers and businesses do reuse or recycle e-waste, glass, metal, plastic, paper or textiles, more could be done. Even with environmentalists warning of potential chemical hazards these items produce in landfills, an estimated 85 percent of landfills are filled with unwanted waste. The recycling industry along with local government run media campaigns to raise awareness; however, as a result consumers and businesses may be experiencing information overload which may have a negative impact on consumers ' recycling efforts.