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Current plans to decarbonize the electric supply system imply that the generation from wind and solar sources will grow substantially. This growth will increase the uncertainty of system operations due to the inherent variability of these renewable sources, and as a result, more reserve capacity will be required to provide the ramping (flexibility) needed for reliable operations. This paper assumes that all of the increased uncertainty comes from wind farms on the grid, and it shows how distributed storage managed locally by aggregators can provide the ramping needed without introducing a separate market for flexibility. This can be accomplished when the aggregators minimize the expected daily cost of the energy purchased from the grid for their customers by submitting optimal bids into the wholesale market with high and low price thresholds for discharging and charging the storage. This model is illustrated using a stochastic multi-period security constrained optimal power flow together with realistic data for a reduction of the network in the Northeast Power Coordinating Council region of the United States. The results show that the bidding strategy for distributed storage provides ramping to the grid just as effectively as storage managed by a system operator. [ABSTRACT FROM AUTHOR]

Purpose: Intimate partner cyber abuse (IPCA) is a prevalent form of intimate partner violence (IPV) that has detrimental effects on victims' well-being. Although research has documented associations with other forms of IPV perpetration, additional research is needed to identify IPCA perpetration risk factors. One of the most common risk factors for offline IPV is perpetrators' alcohol use; however, less is known about how this translates to online contexts. There is also a need to identify protective factors that mitigate the effects of alcohol. Methods: This study evaluated associations between drinking, relationship satisfaction, and IPCA perpetration via self-report questionnaires within a longitudinal framework. Participants included 544 adults in an intimate relationship (n = 296 at T2). Results: Results indicated that relationship satisfaction buffered the effects of problem drinking on IPCA perpetration at T1, but not at T2. Further, 20.2% of individuals who perpetrated IPCA at T1 drank alcohol during at least one incident, and these individuals reported more problem drinking and more frequent IPCA perpetration compared to those who reported IPCA without alcohol. Conclusion: Results from this study provide insight into both risk and protective factors for IPCA perpetration among adults and have the potential to guide concurrent prevention strategies that target intersections between problem drinking, IPCA, and offline IPV, and promote healthy and satisfying intimate relationships. [ABSTRACT FROM AUTHOR]

In the United States, recent changes to the National Statistical System have amplified the geographic-demographic resolution trade-off. That is, when working with demographic and economic data from the American Community Survey, as one zooms in geographically one loses resolution demographically due to very large margins of error. In this paper, we present a solution to this problem in the form of an AI based open and reproducible geodemographic classification system for the United States using small area estimates from the American Community Survey (ACS). We employ a partitioning clustering algorithm to a range of socio-economic, demographic, and built environment variables. Our approach utilizes an open source software pipeline that ensures adaptability to future data updates. A key innovation is the integration of GPT4, a state-of-the-art large language model, to generate intuitive cluster descriptions and names. This represents a novel application of natural language processing in geodemographic research and showcases the potential for human-AI collaboration within the geospatial domain. [ABSTRACT FROM AUTHOR]

The increasing vulnerability of the population from frequent disasters requires quick and effective responses to provide the required relief through effective humanitarian supply chain distribution networks. We develop scenario-robust optimization models for stocking multiple disaster relief items at strategic facility locations for disaster response. Our models improve the robustness of solutions by easing the difficult, and usually impossible, task of providing exact probability distributions for uncertain parameters in a stochastic programming model. Our models allow decision makers to specify uncertainty parameters (i.e., point and probability estimates) based on their degrees of knowledge, using distribution-free uncertainty sets in the form of ranges. The applicability of our generalized approach is illustrated via a case study of hurricane preparedness in the Southeastern United States. In addition, we conduct simulation studies to show the effectiveness of our approach when conditions deviate from the model assumptions. [ABSTRACT FROM AUTHOR]

Respiratory Syncytial Virus (RSV) is a leading cause of acute respiratory tract infection, with the greatest impact on infants, immunocompromised individuals, and older adults. RSV prevalence decreased substantially in the United States (US) following the implementation of COVID-19-related non-pharmaceutical interventions but later rebounded with abnormal seasonality. The biological and epidemiological factors underlying this altered behavior remain poorly defined. In this retrospective cohort study from 2009 to 2023 in Chicago, Illinois, US, we examined RSV epidemiology, clinical severity, and genetic diversity. We found that changes in RSV diagnostic platforms drove increased detections in outpatient settings post-2020 and that hospitalized adults infected with RSV-A were at higher risk of intensive care admission than those with RSV-B. While population structures of RSV-A remained unchanged, RSV-B exhibited a genetic shift into geographically distinct clusters. Mutations in the antigenic regions of the fusion protein suggest convergent evolution with potential implications for vaccine and therapeutic development. Non-pharmaceutical interventions for COVID-19 also impacted the transmission of other viruses including respiratory syncytial virus (RSV). Here the authors describe the changing epidemiology, clinical severity, and genetic diversity of RSV in Chicago, Illinois, from July 2010 to April 2023. [ABSTRACT FROM AUTHOR]

Background: Congenital cytomegalovirus (cCMV) is the most common congenital viral infection in the United States. Symptomatic infections can cause severe hearing loss and neurological disability, although ~ 90% of cCMV infections are asymptomatic at birth. Despite its prevalence, the long-term neurobehavioral risks of asymptomatic cCMV infections are not fully understood. The objective of this work was to evaluate for potential long-term neurobehavioral sequelae in infants with asymptomatic cCMV. Methods: Infants with cCMV were identified from a universal newborn cCMV screening study in a metropolitan area in the midwestern United States. Asymptomatic infants with cCMV were enrolled in a longitudinal neurodevelopmental study (N = 29). Age- and sex-matched healthy control infants (N = 193) were identified from the Baby Connectome Project (BCP), a longitudinal study of brain and behavioral development. The BCP sample supplemented an additional group of healthy control infants (N = 30), recruited from the same participant registry as the BCP specifically for comparison with infants with asymptomatic cCMV. Neurobehavioral assessments and parent questionnaires, including the Mullen Scales of Early Learning, the Repetitive Behavior Scales for Early Childhood (RBS-EC), and the Infant Toddler Social Emotional Assessment (ITSEA) were administered at 12 months of age. Neurobehavioral scores were compared between infants with asymptomatic cCMV and all identified healthy control infants. Results: Infants with asymptomatic cCMV performed equivalently compared to healthy control infants on the neurobehavioral measures tested at 12 months of age. Conclusions: These results indicate that at 12 months of age, infants with asymptomatic cCMV are not statistically different from controls in a number of neurobehavioral domains. Although follow-up is ongoing, these observations provide reassurance about neurobehavioral outcomes for infants with asymptomatic cCMV and inform the ongoing discussion around universal screening. Additional follow-up will be necessary to understand the longer-term outcomes of these children. [ABSTRACT FROM AUTHOR]

The invasive Asian longhorned tick Haemaphysalis longicornis that vectors and transmits several animal pathogens is significantly expanding in the United States. Recent studies report that these ticks also harbor human pathogens including Borrelia burgdorferi sensu lato, Babesia microti, and Anaplasma phagocytophilum. Therefore, studies that address the interactions of these ticks with human pathogens are important. In this study, we report the characterization of H. longicornis organic anion-transporting polypeptides (OATPs) in interactions of these ticks with A. phagocytophilum. Using OATP-signature sequence, we identified six OATPs in the H. longicornis genome. Bioinformatic analysis revealed that H. longicornis OATPs are closer to other tick orthologs rather than to mammalian counterparts. Quantitative real-time PCR analysis revealed that OATPs are highly expressed in immature stages when compared to mature stages of these ticks. In addition, we noted that the presence of A. phagocytophilum upregulates a specific OATP in these ticks. We also noted that exogenous treatment of H. longicornis with xanthurenic acid, a tryptophan metabolite, influenced OATP expression in these ticks. Immunoblotting analysis revealed that antibody generated against Ixodes scapularis OATP cross-reacted with H. longicornis OATP. Furthermore, treatment of H. longicornis with OATP antibody impaired colonization of A. phagocytophilum in these ticks. These results not only provide evidence that the OATP-tryptophan pathway is important for A. phagocytophilum survival in H. longicornis ticks but also indicate OATP as a promising candidate for the development of a universal anti-tick vaccine to target this bacterium and perhaps other rickettsial pathogens of medical importance. [ABSTRACT FROM AUTHOR]

Homozygous familial hypercholesterolemia (HoFH) is an underdiagnosed and undertreated ultra-rare disease. We utilized claims data from the Komodo Healthcare Map database to develop a machine-learning model to identify potential HoFH patients. We tokenized patients enrolled in MyRARE (patient support program for those prescribed evinacumab-dgnb in the United States) and linked them with their Komodo claims. A true positive HoFH cohort (n = 331) was formed by including patients from MyRARE and patients with prescriptions for evinacumab-dgnb or lomitapide. The negative cohort (n = 1423) comprised patients with or at risk for cardiovascular disease. We divided the cohort into an 80% training and 20% testing set. Overall, 10,616 candidate features were investigated; 87 were selected due to clinical relevance and importance on prediction performance. Different machine-learning algorithms were explored, with fast interpretable greedy-tree sums selected as the final machine-learning tool. This selection was based on its satisfactory performance and its easily interpretable nature. The model identified four useful features and yielded precision (positive predicted value) of 0.98, recall (sensitivity) of 0.88, area under the receiver operating characteristic curve of 0.98, and accuracy of 0.97. The model performed well in identifying HoFH patients in the testing set, providing a useful tool to facilitate HoFH screening and diagnosis via healthcare claims data. [ABSTRACT FROM AUTHOR]

Background: Patients with migraine often have poor sleep quality between and during migraine attacks. Furthermore, extensive research has identified photophobia as the most common and most bothersome symptom in individuals with migraine, second only to headache. Seeking the comfort of darkness is a common strategy for managing pain during an attack and preventing its recurrence between episodes. Given the well-established effects of daily light exposure on circadian activity rhythms and sleep quality, this study aimed to investigate the relationship between photophobia symptoms and sleep quality in a cohort of patients with migraine. Methods: A cross-sectional observational study was conducted using existing data extracted from the American Registry for Migraine Research (ARMR). Participants with a migraine diagnosis who had completed the baseline questionnaires (Photosensitivity Assessment Questionnaire (PAQ), Generalized Anxiety Disorder-7 (GAD-7), Patient Health Questionnaire-2 (PHQ-2)), and selected questions of the ARMR Sleep questionnaire were included. Models were created to describe the relationship of photophobia and photophilia with various sleep facets, including sleep quality (SQ), sleep disturbance (SDis), sleep onset latency (SOL), sleep-related impairments (SRI), and insomnia. Each model was controlled for age, sex, headache frequency, anxiety, and depression. Results: A total of 852 patients meeting the inclusion criteria were included in the analysis (mean age (SD) = 49.8 (13.9), 86.6% (n = 738) female). Those with photophobia exhibited significantly poorer sleep quality compared to patients without photophobia (p < 0.001). Photophobia scores were associated with SQ (p < 0.001), SDis (p < 0.001), SOL (p = 0.011), SRI (p = 0.020), and insomnia (p = 0.005) after controlling for age, sex, headache frequency, depression, and anxiety, signifying that higher levels of photophobia were associated with worse sleep-related outcomes. Conversely, photophilia scores were associated with better sleep-related outcomes for SQ (p < 0.007), SOL (p = 0.010), and insomnia (p = 0.014). Conclusion: Results suggest that photophobia is a significant predictor of poor sleep quality and sleep disturbances in migraine. These results underscore the necessity for comprehensive and systematic investigations into the intricate interplay between photophobia and sleep to enhance our understanding and develop tailored solutions for individuals with migraine. [ABSTRACT FROM AUTHOR]

Digital health technologies (DHTs) can transform neurological assessments, improving quality and continuity of care. In the United States, the Food & Drug Administration (FDA) oversees the safety and efficacy of these technologies, employing a detailed regulatory process that classifies devices based on risk and requires rigorous review and post-market surveillance. Following FDA approval, DHTs enter the Current Procedural Terminology, Relative Value Scale Update Committee, and Centers for Medicare & Medicaid Services coding and valuation processes leading to coverage and payment decisions. DHT adoption is challenged by rapid technologic advancements, an inconsistent evidence base, marketing discrepancies, ambiguous coding guidance, and variable health insurance coverage. Regulators, policymakers, and payers will need to develop better methods to evaluate these promising technologies and guide their deployment. This includes striking a balance between patient safety and clinical effectiveness versus promotion of innovation, especially as DHTs increasingly incorporate artificial intelligence. Data validity, cybersecurity, risk management, societal, and ethical responsibilities should be addressed. Regulatory advances can support adoption of these promising tools by ensuring DHTs are safe, effective, accessible, and equitable. [ABSTRACT FROM AUTHOR]

The presence of Artificial Intelligence (AI) and Machine Learning (ML) applications has led to its widespread adoption across diverse domains. AI is making its way into industry, beyond research and academia. Concurrently, the water sector is undergoing a digital transformation. Water utilities in the United States are at different stages in their journey of digital transformation, and the decision makers in water sector, who are non-expert stakeholders in AI applications, need to better understand this technology to make informed decisions. While AI has numerous benefits to offer, there are also many challenges related to data, model development, knowledge integration and ethical concerns that should be considered before implementing it for real world applications. Civil engineering is a licensed profession where critical decision making is involved. Therefore, trust in any decision support technology is critical for its acceptance in real-world applications. Therefore, this research proposes a framework called aiWATERS (Artificial Intelligence for the Water Sector) which can serve as a guide for the water utilities to successfully implement AI in their system. Based on this framework, we conduct pilot interviews and surveys with various small, medium, and large water utilities in the United States (US) to capture their current state of AI implementation and identify the challenges faced by them. The research findings reveal that most of the water utilities in the United States are at an early stage of implementing AI as they face concerns regarding the black box nature, trustworthiness, and sustainability of AI technology in their system. The aiWATERS framework is intended to help the utilities navigate through these issues in their journey of digital transformation. [ABSTRACT FROM AUTHOR]

Background: This study aims to conduct a scientometric analysis and thematically-based narrative review of a century of Taraxacum officinale research (TOR), uncovering patterns, trends, themes, and advancements in the field to provide insights for future investigations. The study followed PRISMA guidelines and utilized the Scopus database with MeSH terms for bibliographic data retrieval. Scientometric mapping employed VOSviewer and R-package-based Bibliometrix, while extracted themes were reviewed narratively. A detailed analysis of TOR was achieved by including only original studies. Results: The findings include the extensive duration of TOR since 1908 and its significant growth, particularly in the last two decades. China emerges as the most productive country, but the United States leads in recognizable and collaborative TOR. The thematic map displays dynamic and diverse themes, with a rich knowledge structure revealed through the analysis of term co-occurrence. The year 2016 represents a turning point in the thematic map, marked by numerical growth and thematic bifurcation. The study extracted several main research topics within the field of TOR, including germination, antioxidant activity, bioherbicide, oxidative stress, Taraxacum kok-saghyz, and heavy metals. These topics represent key areas of investigation and provide insights into the diverse aspects of research surrounding T. officinale. Additionally, emerging topics in TOR encompass toxicity, metabolomics, dandelion extract, and diabetes mellitus. Conclusions: The study consolidated knowledge, highlighted research gaps, and provided directions for future investigations on TOR. [ABSTRACT FROM AUTHOR]

Background: Police violence is increasingly recognized as an urgent public health problem. Basic questions about police violence, however, remain unanswered, including which types of law enforcement agency are responsible for fatal police violence deaths. Methods: We estimated the proportion of police violence deaths in the U.S. (2013–2022) that were attributable to local, county, state, federal, or tribal police agencies, using mapping police violence data. We examined proportions overall, by decedent race/ethnicity, and by state. Results: Nationally, 60% of decedents were killed by municipal, 29% by county, 8% by state, and 3% by federal, police, with < 1% killed by tribal or other officers. These proportions varied by race/ethnicity, with 56% of Native American decedents killed by municipal police compared to 70–75% among other racially minoritized people. While municipal police were responsible for most deaths in most states, in the Southeast, county police predominated. In some Northeastern states (and Alaska), state police were responsible for > 40% of deaths. Conclusions: We identify wide geographic & racial/ethnic variation in the agencies responsible for fatal police violence. Findings suggest that the budgetary and infrastructural shifts required to prevent fatal police violence need to occur at multiple levels of government. [ABSTRACT FROM AUTHOR]

Introduction: The emergence of artificial intelligence (AI) chat programs has opened two distinct paths, one enhancing interaction and another potentially replacing personal understanding. Ethical and legal concerns arise due to the rapid development of these programs. This paper investigates academic discussions on AI in medicine, analyzing the context, frequency, and reasons behind these conversations. Methods: The study collected data from the Web of Science database on articles containing the keyword "ChatGPT" published from January to September 2023, resulting in 786 medically related journal articles. The inclusion criteria were peer-reviewed articles in English related to medicine. Results: The United States led in publications (38.1%), followed by India (15.5%) and China (7.0%). Keywords such as "patient" (16.7%), "research" (12%), and "performance" (10.6%) were prevalent. The Cureus Journal of Medical Science (11.8%) had the most publications, followed by the Annals of Biomedical Engineering (8.3%). August 2023 had the highest number of publications (29.3%), with significant growth between February to March and April to May. Medical General Internal (21.0%) was the most common category, followed by Surgery (15.4%) and Radiology (7.9%). Discussion: The prominence of India in ChatGPT research, despite lower research funding, indicates the platform's popularity and highlights the importance of monitoring its use for potential medical misinformation. China's interest in ChatGPT research suggests a focus on Natural Language Processing (NLP) AI applications, despite public bans on the platform. Cureus' success in publishing ChatGPT articles can be attributed to its open-access, rapid publication model. The study identifies research trends in plastic surgery, radiology, and obstetric gynecology, emphasizing the need for ethical considerations and reliability assessments in the application of ChatGPT in medical practice. Conclusion: ChatGPT's presence in medical literature is growing rapidly across various specialties, but concerns related to safety, privacy, and accuracy persist. More research is needed to assess its suitability for patient care and implications for non-medical use. Skepticism and thorough review of research are essential, as current studies may face retraction as more information emerges. [ABSTRACT FROM AUTHOR]

Groundwater wells are critical infrastructure that enable the monitoring, extraction, and use of groundwater, which has important implications for the environment, water security, and economic development. Despite the importance of wells, a unified database collecting and standardizing information on the characteristics and locations of these wells across the United States has been lacking. To bridge this gap, we have created a comprehensive database of groundwater well records collected from state and federal agencies, which we call the United States Groundwater Well Database (USGWD). Presented in both tabular form and as vector points, USGWD comprises over 14.2 million well records with attributes, such as well purpose, location, depth, and capacity, for wells constructed as far back as 1763 to 2023. Rigorous cross-verification steps have been applied to ensure the accuracy of the data. The USGWD stands as a valuable tool for improving our understanding of how groundwater is accessed and managed across various regions and sectors within the United States. [ABSTRACT FROM AUTHOR]

This paper investigates the homogeneity of United States aircraft reconnaissance data and the impact of these data on the homogeneity of the tropical cyclone (TC) best track data for the seasons 1949–1987 generated by the China Meteorological Administration (CMA). The evaluation of the reconnaissance data shows that the minimum central sea level pressure (MCP) data are relatively homogeneous, whereas the maximum sustained wind (MSW) data show both overestimations and spurious abrupt changes. Statistical comparisons suggest that both the reconnaissance MCP and MSW were well incorporated into the CMA TC best track dataset. Although no spurious abrupt changes were evident in the reconnaissance-related best track MCP data, two spurious changepoints were identified in the remainder of the best-track MCP data. Furthermore, the influence of the reconnaissance MSWs seems to extend to the best track MSWs unrelated to reconnaissance, which might reflect the optimistic confidence in making higher estimates due to the overestimated extreme wind "observations". In addition, the overestimation of either the reconnaissance MSWs or the best track MSWs was greater during the early decades compared to later decades, which reflects the important influence of reconnaissance data on the CMA TC best track dataset. The wind–pressure relationship (WPR) used in the CMA TC best track dataset is also evaluated and is found to overestimate the MSW, which may lead to inhomogeneity within the dataset between the aircraft reconnaissance era and the satellite era. [ABSTRACT FROM AUTHOR]

Indian Americans now constitute the nation's second largest foreign-born population group in the United States (U.S.). Concerning Indian American youth, there is a paucity of information about their experiences in the U.S., particularly with respect to cultural stress and model minority stress, and whether these stressors have an impact on their mental health. This article describes the dual experiences of Indian American youth in the Indian and American cultures and discusses two theoretical frameworks (cultural stress and model minority stress) that illuminate dynamics influencing the lives of these youth. We illustrate three core pathways (independent, synergistic and indirect) by which cultural stress and model minority stress can influence the mental health of Indian American youth. Social workers and other behavioral health providers can assist Indian American youth by examining their own biases related to the model minority stereotype, helping school personnel assess the impact of this stereotype, and remembering that Indian American youth are not a monolith. Researchers need to disaggregate data on Asian Americans to gather accurate statistics on the Indian American population and refine model-minority-stereotype assessment instruments. [ABSTRACT FROM AUTHOR]

There were 9.7 million Native Americans (American Indian, Alaska Native-AI/AN- these acronyms will be used interchangeably with Native Americans throughout the paper) in 2019 comprising 2.9% of the U.S. population. Native American populations have disproportionately higher rates of mental illnesses compared to other racial groups in the U.S. Mental health is a significant public health concern for this population, impacting different areas of their lives including employment. Additionally, Native Americans continue to experience significant disparities in access to Vocational Rehabilitation (VR) services and have poor employment outcomes. However, little is known about the relationships among demographic factors, vocational rehabilitation services, and employment outcomes of Native Americans with mental illness. Consequently, the current study examined how demographic factors and VR services are related to successful employment outcomes for Native American VR clients with mental illnesses using data from the Rehabilitation Services Administration (RSA) program year (2019) Case Service Report (9–11). Both descriptive analysis and data mining approaches were used to answer the research questions. Chi-square Automatic Interaction Detector (CHAID) analysis was used to determine which of the VR services could best predict the successful employment outcome of Native Americans with mental illness. The findings of the data mining approach revealed that among all the vocational rehabilitation services, job placement assistance was the strongest predictor of successful employment among Native American clients with mental illnesses. The second most important service predicting successful employment for those who received job placement assistance was shown to be maintenance. Implications for rehabilitation counselors and future research are discussed. [ABSTRACT FROM AUTHOR]

Fake news (FN) on social media (SM) rose to prominence in 2016 during the United States of America presidential election, leading people to question science, true news (TN), and societal norms. FN is increasingly affecting societal values, changing opinions on critical issues and topics as well as redefining facts, truths, and beliefs. To understand the degree to which FN has changed society and the meaning of FN, this study proposes a novel conceptual framework derived from the literature on FN, SM, and societal acceptance theory. The conceptual framework is developed into a meta-framework that analyzes survey data from 356 respondents. This study explored fuzzy set-theoretic comparative analysis; the outcomes of this research suggest that societies are split on differentiating TN from FN. The results also show splits in societal values. Overall, this study provides a new perspective on how FN on SM is disintegrating societies and replacing TN with FN. [ABSTRACT FROM AUTHOR]

Purpose: Sexual violence (SV) and adolescent relationship abuse (ARA) are common in the U. S. and have strong associations with negative health and wellbeing outcomes. Manhood 2.0 is the first U.S. program designed for community settings to build bystander skills while also challenging harmful gender norms. A cluster-randomized trial comparing Manhood 2.0 to Job Skills, a job readiness training control condition, demonstrated that it is a promising strategy to prevent sexual violence and adolescent relationship abuse. Such community-based interventions may be particularly relevant in lower resource urban settings, and the costs of such prevention programs have not been considered previously. Methods: The aim of the present study is to perform systematic and standardized cost calculations associated with implementing Manhood 2.0 among adolescent males. In addition, this study provides detailed cost information of the community-based intervention program, as well as costs associated with implementing the Job Skills control program. Program implementation data were recorded throughout the study period (2015–2019) by the Manhood 2.0 study team. Results: The cost of implementing Manhood 2.0 is $4,771 per complete round of program delivery and $451 per participant, which is approximately the same cost as the control Job Skills program ($4,432 and $453 per participant). The marginal cost per additional round of Manhood 2.0 program is $3,682. Conclusion: Implementation of a community-based program requires substantial resources and collaborations with community partners especially in economically disadvantaged neighborhoods. This study provides a snapshot of the cost information of a community-based intervention program from the implementing agency's perspective, which is essential in helping decision-makers understand the costs they will incur by implementing prevention programs and ensuring program feasibility and sustainability. [ABSTRACT FROM AUTHOR]

The growing prevalence of cardiovascular diseases in the United States (US) has disproportionately affected minority populations more than their white counterparts. A population that is often overlooked is the Asian American population, particularly Southeastern Asian immigrants. Despite having relatively favorable socioeconomic indicators compared to the general US population, Asian Americans, specifically Southeast Asian individuals, face a significant burden of traditional cardiovascular risk factors and are considered a high cardiovascular disease risk group. In addition, most studies have aggregated Asian populations into one major racial group rather than analyzing the different ethnicities among the Asian categorization. While some studies suggest that the acculturation process has some degree of impact on cardiovascular health, there has not been a widely-used tool to measure or ascertain the totality of acculturation. Instead, multiple proxies have been used to measure acculturation, and prior studies have argued for more culturally-tailored acculturation proxies. This paper aims to assess the implications of different acculturation measures on cardiovascular health among Asian Americans, particularly Southeastern Asian immigrants. The following proxies were expanded on in this paper: English spoken at home, length of stay in the US, religiosity and spirituality, and admixed family structures. Previous studies showed that as the length of stay in the US increases, the burden of cardiovascular risk factors increases. However, the impact of English spoken at home, religiosity, and admixed family structure are still inconclusive given the extent of current studies. While most studies suggest that an increase in acculturation increases the risk of cardiovascular disease, it is critical to note that acculturation is a multifaceted process. Therefore, more studies are necessary to appropriately examine the implications of various acculturation processes on cardiovascular risk factors in Asians, specifically Southeastern Asian individuals in the US. [ABSTRACT FROM AUTHOR]

In prior work, we fit the mixture Rasch model to item responses from a fractions survey administered to a nationwide sample of middle grades mathematics teachers in the United States. The mixture Rasch model located teachers on a continuous, unidimensional scale and fit best with 3 latent classes. We used item response data to generate initial interpretations of the reasoning characteristic of each latent class. Our results suggested increasing facility reasoning about fraction arithmetic from one class to the next. The present study contributes two further arguments for the validity of our initial interpretations. First, we administered the same survey to a new sample of future middle grades mathematics teachers before and after 20 weeks of instruction on multiplication, division, and fractions, and we found that from pretest to posttest future teachers transitioned from one latent class to another in ways consistent with increased proficiency in fraction arithmetic. Second, we interviewed 8 of the future teachers before and after the instruction and found that future teachers' reasoning during interviews was largely consistent with our original interpretation of the 3 latent classes. These results provide further support for our original interpretation of the mixture Rasch analysis, demonstrate the utility of our approach for capturing growth and change in future teachers' reasoning during teacher education coursework, and contribute innovative applications of psychometric models for surveying teachers' reasoning at scale. [ABSTRACT FROM AUTHOR]

Suicide is a critical public health issue in the United States, recognized as the tenth leading cause of death across all age groups (Centers for Disease Control and Prevention, 2020). Despite the Islamic prohibition on suicide, suicidal ideation and suicide mortality persist among Muslim populations. Recent data suggest that U.S. Muslim adults are particularly vulnerable, with a higher attempt history compared to respondents from other faith traditions. While the underlying reasons for this vulnerability are unclear, it is evident that culturally and religiously congruent mental health services can be utilized to steer suicide prevention, intervention, and postvention in Muslim communities across the United States. However, the development of Suicide Response toolkits specific to Muslim populations is currently limited. As a result, Muslim communities lack a detailed framework to appropriately respond in the event of a suicide tragedy. This paper aims to fill this gap in the literature by providing structured guidelines for the formation of a Crisis Response Team (CRT) through an Islamic lens. The CRT comprises of a group of individuals who are strategically positioned to respond to a suicide tragedy. Ideally, the team will include religious leaders, mental health professionals, healthcare providers, social workers, and community leaders. The proposed guidelines are designed to be culturally and religiously congruent and take into account the unique cultural and religious factors that influence Muslim communities' responses to suicide. By equipping key personnel in Muslim communities with the resources to intervene in an emergent situation, provide support to those affected, and mobilize community members to assist in prevention efforts, this model can help save lives and prevent future suicide tragedies in Muslim communities across the United States. [ABSTRACT FROM AUTHOR]

Suicide among American Muslims is understudied, despite recent research highlighting increased suicide attempts among this population. While suicide is forbidden in Islam, formal guidelines for addressing and responding to suicide within Muslim communities did not exist until recently. The Stanford Muslim Mental Health and Islamic Psychology Lab has responded to a number of suicides in Muslim communities across North America and implemented an original model for suicide response and community healing. This approach incorporates Islamic principles and values to create a culturally and religiously congruent response to suicide that can support loss survivors and steer impacted communities toward healing. The Muslim Postvention Community Healing session described in this paper aims to provide a safe space for individuals impacted by suicide to come together and process their emotions, while also using Islamic teachings to guide the healing process. This unique model has the potential to serve as a valuable resource for Muslim communities across North America, and beyond, in addressing and responding to suicide. [ABSTRACT FROM AUTHOR]

There is a pressing debate in the United States concerning the implied physicians' obligation to do no harm and the status of legalizing physician-assisted suicide (PAS). Key issues that underpin the debate are important to consider. These include: (1) foundational medical beginnings; (2) euthanasia's historical and legal background context; and (3) the key arguments held by those for and against legalization of PAS. This paper reviews the major claims made by proponents for the legalization of PAS and the associated complexities and concerns that help underscore the importance of conscience freedoms. Relief of suffering, respect for patient autonomy, and public policy arguments are discussed in these contexts. We argue here that the emphasis by healthcare providers should be on high quality and compassionate care for those at the end of life's journey who are questioning whether to prematurely end their lives. If medicine loses its chief focus on the quality of caring—even when a cure is not possible—it betrays its objective and purpose. In this backdrop, legalization of PAS harms not only healthcare professionals, but also the medical profession's mission itself. Medicine's foundation is grounded in the concept of never intentionally to inflict harm. Inflicting death by any means is not professional or proper, and is not trustworthy medicine. [ABSTRACT FROM AUTHOR]

We examined differences in reactions to climate change as a function of belief in God. We studied four samples, convenience samples of university students in the USA (n = 627) and in Poland (n = 628), a nationally representative sample of adults in Poland (n = 1154), and a nationally representative sample of adults in the USA (n = 1098). In each study we measured the distress people felt about climate change, belief in God, and left–right political orientation. These constructs were measured slightly differently across the studies. Regardless of how these constructs were measured, believers were less distressed by climate change than non-believers, and with only a few exceptions, these differences disappeared after covarying political orientation (left–right or liberal-conservative). Contrary to those who argue that there is something inherent in religious belief that predisposes people to deny or ignore climate change, the present results suggest that it is the (growing) confluence of faith and conservative political orientation that is responsible for the fact that some people of faith tend to deny climate change or actively oppose efforts to combat it. [ABSTRACT FROM AUTHOR]

Research on conspiracy theories has proliferated since 2016, in part due to the US election of President Trump, the COVID-19 pandemic, and increasingly threatening environmental conditions. In the rush to publication given these concerning social consequences, researchers have increasingly treated as definitive a 2016 paper by Michael Wood (Political Psychology, 37(5), 695–705, 2016) that concludes that the phrase "conspiracy theory" has no negative effect upon people's willingness to endorse a claim. We revisit Wood's findings and its (re)uptake in the recent literature. Is the label "conspiracy theory" a pejorative? If so, does it sway or affect people's belief in specific claims of conspiracy (i.e. particular conspiracy theories), or is the effect one that concerns claims of conspiracy more generally (i.e. all conspiracy theories)? Through an examination of the conceptual and methodological scope of Wood's work and the results of our similar quasi-experimental design, we argue that it is premature to suggest the label "conspiracy theory" has no impact on the believability of a claim, or that it has no rhetorical power. [ABSTRACT FROM AUTHOR]

Transportation practitioners and scholars increasingly view car-free mobility as an essential component of a sustainable, healthy, and safe transportation future. In the United States, however, strikingly few households—only around 9%—do not own cars. Moreover, most zero-vehicle individuals—around 80%—are without vehicles due of constraint (e.g., because they cannot afford one), while a much smaller fraction—around 20%—are car-free because they can meet their mobility needs without one. This latter group is of interest to transportation researchers and practitioners, given the imperative to reduce driving and its associated negative externalities. In this paper, I examine factors that contribute to individuals' decisions to live car-free. This research is based on 430 comments posted on the social content-sharing website Reddit, in which commenters shared their first-hand experiences related to the prospect of going car-free. These comments were obtained from 46 threads situated in seven different US cities, focusing specifically on car ownership decisions. Commenters described accessible neighborhoods as foundational to decisions to forego a vehicle. While such conditions were typically necessary, however, they sometimes were not sufficient to foster car-free choices. Costs of car ownership, especially parking costs, were often the deciding factor for commenters who opted not to have a car. Finally, inconveniences related to car ownership and travel, like traffic-related stress, were discussed as benefits of living car-free. In sum, the observed individual decisions about car ownership tend to consist of people balancing supportive conditions for non-car travel against costs of and constraints on car-travel. [ABSTRACT FROM AUTHOR]

City-scale traffic data, such as traffic flow, speed, and density on every road segment, are the foundation of modern urban research. However, accessing such data on a city scale is challenging due to the limited number of sensors and privacy concerns. Consequently, most of the existing traffic datasets are typically limited to small, specific urban areas with incomplete data types, hindering the research in urban studies, such as transportation, environment, and energy fields. It still lacks a city-scale traffic dataset with comprehensive data types and satisfactory quality that can be publicly available across cities. To address this issue, we propose a unified approach for producing city-scale traffic data using the classic traffic assignment model in transportation studies. Specifically, the inputs of our approach are sourced from open public databases, including road networks, traffic demand, and travel time. Then the approach outputs comprehensive and validated citywide traffic data on the entire road network. In this study, we apply the proposed approach to 20 cities in the United States, achieving an average correlation coefficient of 0.79 in average travel time and an average relative error of 5.16% and 10.47% in average travel speed when compared with the real-world data. [ABSTRACT FROM AUTHOR]

Rural youth suicide represents a major source of mental health inequity in the United States (US). School-based suicide prevention programs may provide an effective avenue to address this mental health crisis among rural youth. This study's primary goal is to demonstrate the feasibility and acceptability of a novel implementation approach (utilizing teachers from a neighboring school) for delivering the Youth Aware of Mental Health (YAM) program, an evidence-based, universal youth suicide prevention curriculum, to high school students in rural Montana. We will recruit approximately 1300 9th grade student in four Montana schools. Using a non-inferiority design, we will randomize classrooms in each school to be instructed by a teacher from another school (YAM-TE; experimental) or a traditional external instructor (YAM-EXT; control). We will assess program fidelity of both YAM training and implementation via independent observer and instructor-reporter ratings. Youth will complete measures of acceptability at post-YAM and outcome measures at pre-YAM, post-YAM, and 12 month follow-up. Standard and mixed linear and logistic regression models will be used to test the main hypothesis that the YAM-TE does not differ from YAM-EXT regarding fidelity (teachers) and acceptability (teachers and youth). Exploratory analyses will test moderation of the intervention effect (e.g., sex, poverty) and the mediating effect of mental health literacy, belongingness, and perceived burdensomeness on the intervention effect. Results of this pilot study will inform the development of subsequent, fully powered noninferiority trials. Our long-term goal is to scale YAM-TE for implementation across rural US communities or, if culturally appropriate, more globally. [ABSTRACT FROM AUTHOR]

Type 1 diabetes (T1D) is a chronic condition characterized by glucose fluctuations. Laboratory studies suggest that cognition is reduced when glucose is very low (hypoglycemia) and very high (hyperglycemia). Until recently, technological limitations prevented researchers from understanding how naturally-occurring glucose fluctuations impact cognitive fluctuations. This study leveraged advances in continuous glucose monitoring (CGM) and cognitive ecological momentary assessment (EMA) to characterize dynamic, within-person associations between glucose and cognition in naturalistic environments. Using CGM and EMA, we obtained intensive longitudinal measurements of glucose and cognition (processing speed, sustained attention) in 200 adults with T1D. First, we used hierarchical Bayesian modeling to estimate dynamic, within-person associations between glucose and cognition. Consistent with laboratory studies, we hypothesized that cognitive performance would be reduced at low and high glucose, reflecting cognitive vulnerability to glucose fluctuations. Second, we used data-driven lasso regression to identify clinical characteristics that predicted individual differences in cognitive vulnerability to glucose fluctuations. Large glucose fluctuations were associated with slower and less accurate processing speed, although slight glucose elevations (relative to person-level means) were associated with faster processing speed. Glucose fluctuations were not related to sustained attention. Seven clinical characteristics predicted individual differences in cognitive vulnerability to glucose fluctuations: age, time in hypoglycemia, lifetime severe hypoglycemic events, microvascular complications, glucose variability, fatigue, and neck circumference. Results establish the impact of glucose on processing speed in naturalistic environments, suggest that minimizing glucose fluctuations is important for optimizing processing speed, and identify several clinical characteristics that may exacerbate cognitive vulnerability to glucose fluctuations. [ABSTRACT FROM AUTHOR]

Language change is influenced by many factors, but often starts from synchronic variation, where multiple linguistic patterns or forms coexist, or where different speech communities use language in increasingly different ways. Besides regional or economic reasons, communities may form and segregate based on political alignment. The latter, referred to as political polarization, is of growing societal concern across the world. Here we map and quantify linguistic divergence across the partisan left-right divide in the United States, using social media data. We develop a general methodology to delineate (social) media users by their political preference, based on which (potentially biased) news media accounts they do and do not follow on a given platform. Our data consists of 1.5M short posts by 10k users (about 20M words) from the social media platform Twitter (now "X"). Delineating this sample involved mining the platform for the lists of followers (n = 422M) of 72 large news media accounts. We quantify divergence in topics of conversation and word frequencies, messaging sentiment, and lexical semantics of words and emoji. We find signs of linguistic divergence across all these aspects, especially in topics and themes of conversation, in line with previous research. While US American English remains largely intelligible within its large speech community, our findings point at areas where miscommunication may eventually arise given ongoing polarization and therefore potential linguistic divergence. Our flexible methodology — combining data mining, lexicostatistics, machine learning, large language models and a systematic human annotation approach — is largely language and platform agnostic. In other words, while we focus here on US political divides and US English, the same approach is applicable to other countries, languages, and social media platforms. [ABSTRACT FROM AUTHOR]

Over decades, many cities have been expanded and functionally diversified by population activities, socio-demographics and attributes of the built environment. Urban expansion and development have led to the emergence of spatial structures of cities. Uncovering cities' spatial structures is critical to understanding various urban phenomena such as segregation, equity of access, and sustainability. In this study, we propose using a neural embedding model—graph neural network (GNN)—that leverages the heterogeneous features of urban areas and their interactions captured by human mobility networks to obtain vector representations of these areas. Using large-scale high-resolution mobility data sets from millions of aggregated and anonymized mobile phone users in 16 metropolitan counties in the United States, we demonstrate that our embeddings encode complex relationships among features related to urban components (such as distribution of facilities) and population attributes and activities. The clustered representations of urban areas show the shared characteristics among urban areas in the same cluster. We show that embeddings generated by a model trained on a different county can capture 50% to 60% of the spatial structure in another county, allowing us to make cross-county comparisons and inferences. The findings reveal complex relationships among urban components in cities. Since the identified multifaceted spatial structures capture the combined effects of various mechanisms, such as segregation, disparate facility distribution, and human mobility, the findings could help identify the limitations of the current city structure to inform planning decisions and policies. Also, the model and findings set the stage for a variety of research in urban planning, engineering and social science through an integrated understanding of how the complex interactions between urban components and population activities and attributes shape the spatial structures in cities. [ABSTRACT FROM AUTHOR]

Questions abound over how universities should teach and prepare the next generation of researchers to confront current and future wicked problems. With so much focus on curriculum and training, it is crucial that we step back and reflect on higher education's capabilities to foster solution-oriented, collaborative research. What do the institutional incentive structures in higher education support, in terms of practices and outputs related to scholarship? And are those structures felt evenly across the academy? Those doing research in these spaces—in terms of title, autonomy, power, privilege, and status—vary widely by their institutional locations as well as in terms of their ties to broader disciplinary norms. To assess whether these dynamic, contested institutional landscapes afford so-called wicked problem scholarship, this paper draws from survey and interview data collected from 44 researchers working at the nexus of food, energy, and water systems at Carnegie Research 1 universities in the United States. Findings point to an uneven institutional landscape, which is shown to shape in different ways the type of solutions-oriented, collaborative scholarship fostered across the five positions examined. The paper concludes by reflecting on the paper's findings, particularly in terms of what the data tell us about higher education as a place that fosters wicked problems scholarship, while also highlighting the study's limitations. [ABSTRACT FROM AUTHOR]

The purpose of this paper is twofold: analyzing stationarity of energy consumption by source in the United States and studying their cycles and pairwise synchronization. We study a panel of nine time series of monthly energy consumption for the period 1973–2022. Four of the series (namely coal, natural gas, petroleum, and nuclear electric power consumption) are non-renewables, whereas the remaining ones (hydroelectric power, geothermal, biomass, solar, and wind energy consumption) are renewable energy sources. We employ a nonparametric, panel stationarity testing approach. The results indicate that most of the series may be trend-stationarity, with nuclear and geothermal energy consumption being the only exceptions. Additionally, a study on potential cycles in the series of energy consumption by source is carried out, and subsequently we analyze pairwise concordance between states of different energy sources and between states of energy sources and the business cycle. Significant correlations are detected in the latter analysis, which are positive in the case of fossil fuel sources and negative for two renewable sources, namely geothermal and biomass energy consumption. [ABSTRACT FROM AUTHOR]

During the start of the COVID-19 pandemic in 2020, lockdowns and movement restrictions were thought to negatively impact population mental health, since depression and anxiety symptoms were frequently reported. This study investigates the effect of COVID-19 mitigation measures on mental health across the United States, at county and state levels using difference-in-differences analysis. It examines the effect on mental health facility usage and the prevalence of mental illnesses, drawing on large-scale medical claims data for mental health patients joined with publicly available state- and county-specific COVID-19 cases and lockdown information. For consistency, the main focus is on two types of social distancing policies, stay-at-home and school closure orders. Results show that lockdown has significantly and causally increased the usage of mental health facilities in regions with lockdowns in comparison to regions without such lockdowns. Particularly, resource usage increased by 18% in regions with a lockdown compared to 1% decline in regions without a lockdown. Also, female populations have been exposed to a larger lockdown effect on their mental health. Diagnosis of panic disorders and reaction to severe stress significantly increased by the lockdown. Mental health was more sensitive to lockdowns than to the presence of the pandemic itself. The effects of the lockdown increased over an extended time to the end of December 2020. [ABSTRACT FROM AUTHOR]

Accurate and timely inland waterbody extent and location data are foundational information to support a variety of hydrological applications and water resources management. Recently, the Cyclone Global Navigation Satellite System (CYGNSS) has emerged as a promising tool for delineating inland water due to distinct surface reflectivity characteristics over dry versus wet land which are observable by CYGNSS's eight microsatellites with passive bistatic radars that acquire reflected L-band signals from the Global Positioning System (GPS) (i.e., signals of opportunity). This study conducts a baseline 1-km comparison of water masks for the contiguous United States between latitudes of 24°N-37°N for 2019 using three Earth observation systems: CYGNSS (i.e., our baseline water mask data), the Moderate Resolution Imaging Spectroradiometer (MODIS) (i.e., land water mask data), and the Landsat Global Surface Water product (i.e., Pekel data). Spatial performance of the 1-km comparison water mask was assessed using confusion matrix statistics and optical high-resolution commercial satellite imagery. When a mosaic of binary thresholds for 8 sub-basins for CYGNSS data were employed, confusion matrix statistics were improved such as up to a 34% increase in F1-score. Further, a performance metric of ratio of inland water to catchment area showed that inland water area estimates from CYGNSS, MODIS, and Landsat were within 2.3% of each other regardless of the sub-basin observed. Overall, this study provides valuable insight into the spatial similarities and discrepancies of inland water masks derived from optical (visible) versus radar (Global Navigation Satellite System Reflectometry, GNSS-R) based satellite Earth observations. [ABSTRACT FROM AUTHOR]

Assessing and improving the effectiveness of evacuation orders is critical to improving hurricane emergency response, particularly as the frequency of hurricanes increases in the United States. However, our understanding of causal relationships between evacuation orders and evacuation decision-making is still limited, in large part due to the lack of standardized, high-temporal-resolution data on historical evacuation orders. To overcome this gap, we developed the Hurricane Evacuation Order Database (HEvOD) – a comprehensive database of hurricane evacuation orders issued in the United States between 2014 and 2022. The database features evacuation orders that were systematically retrieved and compiled from a wide range of resources and includes information on order type, announcement time, effective time, and evacuation area. The rich collection of attributes and the resolution of the data in the database will allow researchers to systematically investigate the impact of evacuation orders, as a vital public policy instrument, and can serve as an important resource to identify gaps in current policies, leading to more effective policy design in response to hurricanes. [ABSTRACT FROM AUTHOR]

We investigate how in the context of Corporate Venture Capital (CVC), the investment decisions affect the likelihood of their subsequent exit strategies. We use OLS and probit regression as well as Weibull distribution of residual values, given its reliability and validity for studying lifetime analysis. Based on a sample of 8722 VC-backed ventures with the first investment dates between 1999 and 2018 in United States (US) and United Kingdom (UK), the results show that the presence of CVCs positively affects the funding amounts and the duration of the investment. CVC funds are more generous and more patient than Independent Venture Capital (IVC) funds regarding their investments in ventures. Moreover, the findings provide evidence that the exit strategies are directly influenced by the funding amounts and the duration of the investment which are influenced, in turn, by the fund type. Greater funding increases the likelihood of IPO exit which is reduced by longer investment duration. Our results are robust to alternative estimation methods, namely two-stage treatment-effects regressions. These results help the various stakeholders (VC funds, investors, ventures) make crucial decisions regarding investment amounts and duration, and exit. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has had profound adverse effects on public health and society, with increased mobility contributing to the spread of the virus and vulnerable populations, such as those with pre-existing health conditions, at a higher risk of COVID-19 mortality. However, the specific spatial and temporal impacts of health conditions and mobility on COVID-19 mortality have yet to be fully understood. In this study, we utilized the Geographical and Temporal Weighted Regression (GTWR) model to assess the influence of mobility and health-related factors on COVID-19 mortality in the United States. The model examined several significant factors, including demographic and health-related factors, and was compared with the Multiscale Geographically Weighted Regression (MGWR) model to evaluate its performance. Our findings from the GTWR model reveal that human mobility and health conditions have a significant spatial impact on COVID-19 mortality. Additionally, our study identified different patterns in the association between COVID-19 and the explanatory variables, providing insights to policymakers for effective decision-making. [ABSTRACT FROM AUTHOR]

I study institutional and political determinants of migrants' immigration strategies using the United States' immigration system as a case study. Drawing from work that theoretically connects decisions to immigrate legally vs. illegally as well as theoretical insights from literature on the economics of crime, I show how relative probabilities of successful migration using different strategies and relative utility gained using different strategies weigh heavily in immigrants' calculus. To do so, I use qualitative evidence of migrant strategies in the face of migration policy constraints. These various policy constraints lead to a variety of different tactics revolving around decisions regarding whether to immigrate legally, to commit visa fraud, or to choose one type of visa over another. In the process, I also build on a rich body of Public Choice literature to demonstrate how complicated regulatory systems, in this case regulatory systems that quantitatively restrict migration, encourage rent-seeking by both migrants and nonmigrants. [ABSTRACT FROM AUTHOR]

Minimizing teacher stress is an essential aspect of effective teaching and supporting a healthy school climate. Previous research has indicated that teachers who experience elevated levels of occupational stress may have a negative perception of their school's climate, poor mental/physical health, poor relationships with their students, and are less likely to stay within their occupations. In addition, recent research has shown that Americans have experienced an increase in stress levels since the beginning of 2020 due to COVID-19-related stressors. This study sought to expand on previous research that investigated how teacher occupational stress is associated with teachers' perceptions of school climate. This study also examined how COVID-19-related stressors are associated with teachers' perceptions of school climate. Data from 111 elementary, middle, and high school teachers in the Southeastern United States were collected using self-report surveys on school climate, teacher occupational stress, and COVID-19-related stress. Linear regression analysis revealed that teacher occupational stress was not significantly associated with teachers' perceptions of school climate; however, a negative association between teachers' COVID-19-related stress and their perceptions of school climate was observed. This finding suggests that as teachers' COVID-19-related stress increased, their perceptions of school climate tended to decrease. The findings of this study provide insight into how to better support teachers' well-being so they can effectively perform their jobs and contribute to positive outcomes for students. [ABSTRACT FROM AUTHOR]

This paper addresses the notion of electronic economics by examining financial technology (fintech) firms' performance and corporate governance quality, using data from the United States. The findings support our maintained assumption that due to the economics of electronic platforms perused by financial technology firms, these firms outperform firms from other industries (non-Fintech). The final sample comprises 1,712 company-year observations between 2010 and 2019 (pre-COVID-19). The evidence suggests that our corporate governance quality index, developed from Organisation of Economic Cooperation and Development (www.oecd.org/corporate/corporate-governance-factbook.htm, 2019) guidelines, accurately captures corporate governance quality in the United States, principally due to the inclusion of an anti-bribery policy indicator, in the index. The results suggest that this evidence is not merely an artefact due to the corporate governance quality index potentially capturing priced risk factors. Our findings reveal that fintech firms have superior corporate governance quality than non-fintech and that fintech firms place more reliance on internal versus external corporate governance mechanisms, vis-a-vis companies in other industries. [ABSTRACT FROM AUTHOR]

Cannabis legalization policies are rapidly changing in the United States. While there are concerns that recreational legalization will negatively affect young people, previous reviews have not provided clear indication of such effects. The purpose of this rapid systematic review was to examine whether recreational legalization was associated with increases in prevalence of cannabis use and use disorder among adolescents and young adults. PubMed/MEDLINE, Embase, Scopus, PsycInfo, Web of Science, Dissertations & Theses Global, the Trip Database, and OpenGrey were searched from date of inception through Marcy 17, 2022 to retrieve all relevant records. English language and human subject filters were applied. Two reviewers screened abstracts and titles, assessed full text articles, and coded the final included articles. Studies including primarily 10- to 19–year-olds were classified as adolescent, and those between 18 and 26 years as young adult. Our search identified 33 research reports (22 with adolescent samples; 14 young adult). For adolescents, ten studies reported no change in use prevalence associated with legalization, six reported a decrease, and seven reported an increase. Among young adults, most studies (8) showed an increase in at least one prevalence measure, four showed no change, and one showed a decrease. Only two adolescent and one young adult study examined cannabis use disorder, both adolescent studies showed an increase, and the young adult showed no change. The majority of studies had risk of bias. Recreational legalization may be associated with increases in prevalence of cannabis use in young adults while results for adolescents are mixed. Policymakers and practitioners should consider appropriate prevention and treatment options for young people. Trial Registration: PROSPERO #CRD42021276984. [ABSTRACT FROM AUTHOR]

The United States (U.S.) Department of Health and Human Services is interested in increasing geographical equity in access to liver transplant. The geographical disparity in the U.S. is fundamentally an outcome of variation in the organ supply to patient demand (s/d) ratios across the country (which cannot be treated as a single unit due to its size). To design a fairer system, we develop a nonlinear integer programming model that allocates the organ supply in order to maximize the minimum s/d ratios across all transplant centers. We design circular donation regions that are able to address the issues raised in legal challenges to earlier organ distribution frameworks. This allows us to reformulate our model as a set-partitioning problem. Our policy can be viewed as a heterogeneous donor circle policy, where the integer program optimizes the radius of the circle around each donation location. Compared to the current policy, which has fixed radius circles around donation locations, the heterogeneous donor circle policy greatly improves both the worst s/d ratio and the range between the maximum and minimum s/d ratios. We found that with the fixed radius policy of 500 nautical miles (NM), the s/d ratio ranges from 0.37 to 0.84 at transplant centers, while with the heterogeneous circle policy capped at a maximum radius of 500 NM, the s/d ratio ranges from 0.55 to 0.60, closely matching the national s/d ratio average of 0.5983. Our model matches the supply and demand in a more equitable fashion than existing policies and has a significant potential to improve the liver transplantation landscape. [ABSTRACT FROM AUTHOR]

Introduction: Long-acting lipoglycopeptides such as dalbavancin may have utility in patients with Gram-positive bloodstream infections (BSI), particularly in those with barriers to discharge or who require prolonged parenteral antibiotic courses. A retrospective cohort study was performed to provide further multicenter real-world evidence on dalbavancin use as a sequential therapy for Gram-positive BSI. Methods: One hundred fifteen patients received dalbavancin with Gram-positive BSI, defined as any positive blood culture or diagnosed with infective endocarditis, from 13 centers geographically spread across the United States between July 2015 and July 2021. Results: Patients had a mean (SD) age of 48.5 (17.5) years, the majority were male (54%), with many who injected drugs (40%). The most common infection sources (non-exclusive) were primary BSI (89%), skin and soft tissue infection (SSTI) (25%), infective endocarditis (19%), and bone and joint infection (17%). Staphylococcus aureus accounted for 72% of index cultures, coagulase-negative Staphylococcus accounted for 18%, and Streptococcus species in 16%. Dalbavancin started a median (Q1–Q3) of 10 (6–19) days after index culture collection. The most common regimen administered was dalbavancin 1500 mg as one dose for 50% of cases. The primary outcome of composite clinical failure occurred at 12.2%, with 90-day mortality at 7.0% and 90-day BSI recurrence at 3.5%. Conclusions: Dalbavancin may serve as a useful tool in facilitating hospital discharge in patients with Gram-positive BSI. Randomized controlled trials are anticipated to validate dalbavancin as a surrogate to current treatment standards. [ABSTRACT FROM AUTHOR]

Disasters share a common potential for significant ecological and psychosocial disruption at the individual, community, and societal levels, and are especially harmful to members of social groups in vulnerable situations, including youth in foster care and those recently emancipated from care. These young people are susceptible to mental health challenges and understanding their resiliency is key to mitigating pandemic-related harms. This qualitative study aims to (1) understand how the COVID-19 pandemic affected the mental health of older youth between the ages of 18 and 23 (M = 20.5 years, S.D. = 1.7 years), currently in or aged out of foster care (M = 5.9 years in care, S.D. = 4.4 years), and experiencing mental health challenges, and (2) gain insight into the resiliency that supports young people in dealing with these challenges. Twenty-six young people (77% female-identifying, 38% White, 27% Hispanic) in the USA participated in in-depth interviews. Salient themes include: 1) supportive relationships, 2) adaptive coping skills and mindsets, and 3) environmental, institutional, and social supports. Results amplify the voices of these young people concerning the impact of COVID-19 on their mental health and foreground the dynamic strategies they are using to alleviate their distress. [ABSTRACT FROM AUTHOR]

The need to better understand the nature of work related to unpaid caregiving to older persons and its effect on the caregivers has become a pressing issue given the worldwide trend toward population aging. This study focuses on those who frequently provide this type of unpaid care in the US. Using pooled 2011–19 American Time Use Survey data for individuals aged 25–61-years old and a correlated system of regressions, we examine the gendered time allocation pattern of those who frequently provide care to older persons. We find that female caregivers spend less time on market work compared to male caregivers. Our results also suggest that frequent caregiving is associated with more time spent on domestic and care work and less time on market work and self-care. On days when they don't perform or just do little care work, frequent caregivers tend to spend long hours performing market work and reduce their time on domestic chores, care work, and leisure. Sensitivity checks confirm these findings. [ABSTRACT FROM AUTHOR]

Student engagement is determined by the degree to which students perceive that their psychological needs for autonomy, competence, and relatedness are met as prescribed. The COVID-19 pandemic, with its sudden and dramatic shift to mandatory HyFlex (alternating online and face-to-face student attendance) or online learning only, meant less or no face-to-face interactions with peers and faculty, which had an impact on student motivation and engagement in the classroom, as reflected in the National Survey of Student Engagement. This new educational landscape will likely remain in effect to a certain extent, and, thus, there is a need for a deeper understanding of its impact on students' basic psychological needs and ultimately their engagement in the classroom. Building on self-determination theory and self-system processes, we studied 329 student responses to a survey conducted at a private Northeastern university in the United States and analyzed. We found that the impact of need for autonomy on student engagement is mediated by need for competence, and need for relatedness is directly impacting student engagement. Theoretical and practical implications are discussed in detail. [ABSTRACT FROM AUTHOR]