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Mental health issues are prominent among gender minorities; however, research examining these issues among college campuses is limited, and has often overlooked individuals who identify outside of transgender or cisgender identities. Our findings indicated that transgender participants reported the highest rates and frequencies of non-suicidal self injury, suicidal thoughts, and suicide attempt compared to cisgender peers; followed closely by gender-expansive participants. Gender minority participants reported higher rates of family distress and social anxiety.

Introduction This study aims to examine (1) the impact of the pandemic phases on overall and preventable hospitalizations and emergency department (ED) visits, and (2) the effect of the pandemic on these outcomes within subgroup populations including gender, race, patients’ residence in health professional shortage areas (HPSA), and residence in a federal poverty level. Study Design We used electronic medical record (EMR) data for the year 2019 and 2020 from a large health system predominantly serving medically underserved patients in the South. We used a difference-in-differences approach to examine changes in weekly rates of overall and preventable hospitalizations and ED visits in the pandemic phase 1 (Mid-March to June of 2020) and phase 2 (July-September of 2020) compared to the same period in 2019 after adjusting for weekly outcome rates in the baseline period (January to Mid-March of 2020) compared to the same period in 2019. Results The study sample included 1.4 million hospitalizations and ED encounters. In phase 1 of the pandemic, there were significant reductions in overall (−108) and preventable (−75.3) hospitalizations, and overall (−408) and preventable (−306) ED visits when compared to the same period in 2019. In phase 2 of the pandemic, there were significant reductions in overall (−60) and preventable (−43) hospitalizations and in overall (−360) and preventable (−258) ED visits as compared to 2019. We found greater reductions in ED visits, both overall and preventable, during the early pandemic phases among Black patients than among White patients. Similar patterns in the reduction of ED visits were found in Black versus White patients within subgroups of women, men, and those residing in a HPSA and low-income areas. Discussion Substantial reductions in utilization were observed in Black patients in comparison to white patients and these differences persisted among men, women, and those living in underserved and low-income areas.

Regulators’ increasingly stringent carbon rules to protect the environment are encouraging practitioners to modify their operational activities that are accountable for releasing emissions into the atmosphere. Thereby, practitioners dealing with product inventory planning are seeking proper management strategies not only to increase profits but also to reduce released carbons from operations. In addition, increasing uncertainty in supply operations has motivated suppliers to impose prepayment mechanisms in recent decades. This study examines the best prepayment installment policy for a practitioner for the first time, where the consumption behavior of consumers changes as a result of the combined effects of unit selling price and storage time. Moreover, to make the present inventory planning more realistic, the unit holding cost function is adopted as a power function of the inventory unit's storage period. The goal of this study is to provide the best combined installment for advance payment, price, and replenishment strategies for a practitioner under cap-and-price, cap-and-trade, and carbon tax environmental guidelines by ensuring maximum profit. For this purpose, an algorithm is created by combining all derived theoretical results from the analytical study, whereas the efficacy of the algorithm is assessed through the examination of five illustrative numerical instances. A plethora of noteworthy management insights for the practitioner are obtained by investigating the dynamic shifts in optimal strategies resulting from fluctuations in system parameters. The results reveal that if the demand is low in the nascent phases of the business cycle, then the prudent approach for the practitioner entails procuring a comparatively smaller lot-size using a modest number of payment frequencies and then setting a relatively small unit selling price to increase profits.

Fast-paced markets require complex interactions from all supply-chain agents to satisfy customer demands and needs. The manufacturing industries face some difficulties in terms of production amounts and smooth delivery rates. Technical experts found that a warm-up period before a production run helps address those challenges and improves the workability of machine tools in the manufacturing process. The use of a warm-up process causes a reduction of faulty products (an adverse production outcome) and improves operational efficiency. Also, a shortage in the supply of commodities creates difficult conditions for inventory management decisions, posing the same production problems as mentioned above. Consideration of the warm-up process has recently been included in the scope of operations research, but it is necessary to study its interaction with the presence of shortage. This study presents a system where a manufacturing environment utilizes the warm-up process in its initial phase and shortages are allowed during the production period, in addition, the study takes into account carbon emissions during manufacturing to integrate environmental concerns. We assume that the company has the capability to trade the surplus carbon capacity it hasn't produced. This study offers a comprehensive framework that incorporates former research that addresses warm-up process, carbon emissions, shortages, and defective items. To solve the proposed non-linear programming problem with inequality constraints, we employ the Karush-Kuhn-Tucker (KKT) conditions method to determine the optimal solutions. Managerial insights are derived, and sensitivity analysis highlights the effects of the system parameters on the decision variables. The sensitivity analysis results indicate that the carbon trading cost has a significant impact on the overall cost, and subsequently, the company's profit.

Some sensitive industries with precise specifications in their manufacturing process, such as aircraft engine parts, impose 100% inspection processes in their inventory-manufacturing system. In some cases, a lot of small dimensions are measured and compared with strict acceptable tolerances during these inspection processes, causing manufacturing process times (machine time speed) to be less than inspection times. This study is relevant to manufacturing enterprises that implement comprehensive 100% inspection protocols within their production procedures, encompassing sectors such as pharmaceuticals, aerospace, electronics, medical devices, and textiles. In this paper, such a phenomenon is investigated, as an inventory system with a strict inspection model is deployed under three conditions: a) an inventory-manufacturing system with scrapped items without shortage, b) an inventory-manufacturing system with reworked items without shortage, and c) an inventory-manufacturing system with scrapped and reworkable items with backorder. The aim is to minimize total cost by considering strict inspection during production process. These proposed models are suitable for managing systems where quality control is a bottleneck due to its speed such as high-precision manufacturing systems. The optimal solutions for each model are obtained as a closed-form expression. In models 1 and 2, where the shortage is not allowed, the closed-form expression is focused on determining the economic production quantity. However, in model 3, there are two expressions of significance. First, it addresses the economic production quantity, like models 1 and 2. Additionally, it provides an optimal value for the shortage in each cycle, as it considers a scenario where shortages are permitted within the inventory-manufacturing system. This study has included a numerical example to illustrate the practical applicability of each model. Based on the optimal solutions obtained, it is evident that Model 3 stands out as the superior choice. This outcome aligns with expectations, as Model 3 encompasses and comprehensively addresses the scenarios presented in the two other models. Finally, a sensitivity analysis is performed to investigate the effects of key parameters on the optimal solutions of each model, and the results are summarized to present a comparison between them.

Our study sought to provide a more nuanced understanding of the significant issues experienced by young adult survivors of child-maltreatment (CM) prior to seeking counseling services. More specifically, we examined differences between survivors of CM and participants with no CM histories related to non-suicidal self-injury (NSSI), suicidal ideation, and suicide attempt, and scores on the Counseling Center Assessment of Psychological Symptoms (CCAPS). In our sample of N = 2604 post-secondary students seeking counseling services, CM histories were significantly associated with higher frequencies of NSSI, suicidal ideation, and suicide attempt. Further, results indicated that survivors of CM reported significantly higher mean scores on all CCAPS subscales and were more likely to report scores that were considered "elevated" when compared to peers with no reported CM history. Implications for college counseling centers and college mental health providers are discussed.

Humanitarian logistics face significant challenges during natural disasters due to operational uncertainties. Humanitarian logistics networks such as food banks must manage both regular operations and disaster-induced supply and demand. The study aims to develop and assess two-stage stochastic models that support decision-making under these dual operations. We evaluate various decisional strategies through extensive numerical experiments inspired in the operation of the food bank network Bancos de Alimentos de México (BAMX), highlighting the importance of suitable timeframes for reactive and anticipative decisions. The findings offer valuable insights for managers in balancing routine and emergency responses efficiently.

The Energy Exascale Earth System Model Atmosphere Model version 1, the atmospheric component of the Department of Energy's Energy Exascale Earth System Model is described. The model began as a fork of the well-known Community Atmosphere Model, but it has evolved in new ways, and coding, performance, resolution, physical processes (primarily cloud and aerosols formulations), testing and development procedures now differ significantly. Vertical resolution was increased (from 30 to 72 layers), and the model top extended to 60 km (~0.1 hPa). A simple ozone photochemistry predicts stratospheric ozone, and the model now supports increased and more realistic variability in the upper troposphere and stratosphere. An optional improved treatment of light-absorbing particle deposition to snowpack and ice is available, and stronger connections with Earth system biogeochemistry can be used for some science problems. Satellite and ground-based cloud and aerosol simulators were implemented to facilitate evaluation of clouds, aerosols, and aerosol-cloud interactions. Higher horizontal and vertical resolution, increased complexity, and more predicted and transported variables have increased the model computational cost and changed the simulations considerably. These changes required development of alternate strategies for tuning and evaluation as it was not feasible to “brute force” tune the high-resolution configurations, so short-term hindcasts, perturbed parameter ensemble simulations, and regionally refined simulations provided guidance on tuning and parameterization sensitivity to higher resolution. A brief overview of the model and model climate is provided. Model fidelity has generally improved compared to its predecessors and the CMIP5 generation of climate models.

For patients hospitalized with COVID-19, delirium is a serious and under-recognized complication, and people experiencing homelessness (PEH) may be at greater risk. This retrospective cohort study compared delirium-associated risk factors and clinical outcomes between PEH and non-PEH. This study used patient records from 154 hospitals discharged from 2020–2021 from the Texas Inpatient Public Use Data file. Study subjects (n = 878) were patients, aged 18–69 years, who were hospitalized with COVID-19 and were identified as homeless. The baseline group included (n = 176,518) patients with COVID-19 aged 18–69 years who were not homeless. Logistic regression models were used to identify risk factors for delirium. Relevant risk factors included chronic comorbidities, substance use disorders, and traumatic brain injury (TBI). Seven of the delirium-associated risk factors were more prevalent among PEH compared to baseline. PEH had higher rates of TBI, alcohol, cannabis, and opioid use disorders. PEH had significantly higher rates of delirium (10.6% vs. 8.1%; P<0.01). However, PEH had fewer respiratory complications, including pneumonia (48.5% vs. 81.9%; P<0.001) and respiratory failure (28.7% vs. 61.9%; P<0.001), and lower in-hospital mortality (3.3% vs. 9.5%; P<0.001). The anti-viral Remdesivir had a protective effect against delirium (AOR = 0.63; CI: 0.60, 0.66). Mean hospital length of stay (LOS) was more than twice as long for delirious patients compared with non-delirious patients (18.4 days vs. 7.7 days; P<0.001). Delirium greatly increased the risk of in-hospital mortality (AOR = 3.8; CI: 3.6, 4.0). For PEH (n = 29) who died from COVID-19, delirium was present in more than half (52%) of cases. Hospitals should screen PEH for delirium and adopt nursing protocols to prevent delirium and reduce its severity. [ABSTRACT FROM AUTHOR]

Introduction The COVID-19 pandemic led to a major transition for patients from routine ambulatory-care-based in-person primary care visits to telehealth visits to manage chronic diseases. However, it remains unclear the extent to which individuals access telehealth services and whether such utilization varies along neighborhood characteristics, especially among racial minorities. This study aims to examine the association of outpatient telehealth utilization with sociodemographic, clinical, and neighborhood characteristics among adults with ambulatory care sensitive conditions (ACSCs) during the COVID-19 pandemic. Methods We included adults treated for an ACSC between March 5, 2020, and December 31, 2020, at a single ambulatory-care-based healthcare system, which serves a large population of low-income patients in the South region of the United States (i.e., Memphis, TN, Metropolitan Statistical Area). Telehealth utilization was defined by outpatient procedural codes and providers’ notes on the type of visits. Generalized linear mixed models were used to examine the association of sociodemographic, clinical, and neighborhood factors with telehealth utilization in the overall cohort and the racial subpopulations. Results Among the 13,962 adults with ACSCs, 8583 (62.5%) used outpatient telehealth services. Patients who were older, female, with mental disorders, and who had more comorbidities had higher rates of telehealth services ( p < .05). Controlling for covariates, we observed 75.2% and 23.1% increased use of telehealth services among Hispanics and other race groups, respectively, compared to Whites. Patients who commuted more than 30 minutes to health facilities were slightly less likely to use telehealth services [OR: 0.994 (0.991,0.998)]. Racial minorities (Blacks and Hispanics) with mental disorders were more likely to use telehealth service when compared to Whites. Discussion We found that among patients being treated for ACSCs, the use of telehealth services was highly prevalent in Hispanic patients in general and were more pronounced among both Hispanics and Black patients who have mental disorders.

Inventory models that consider environmental and quality concerns have received some attention in the literature, yet no model developed to date has investigated these features in combination with growing items. Therefore, there is a need to incorporate these three relevant aspects together in a single inventory model to support decisions, compare results, and obtain new knowledge for the complexities of the real world. Moreover, current sustainable inventory management practices aim at mitigating the ecological consequences of an industry while preserving its profitability. The present study aligns with this perspective and introduces an economic order quantity (EOQ) model that considers imperfect quality while also accounting for sustainability principles. More specifically, the model addresses growing items, which have a demand dependent on selling price and the unique ability to grow while being stored in inventory. Additionally, the analysis acknowledges the possibility of classification errors during the inspection process, encompassing both Type-I and Type-II inspection errors. Furthermore, the model permits shortages and ensures that any shortage is completely fulfilled through backorders. The optimization model produces an optimal solution for the proposed model that is derived by optimizing three decision variables: order quantity of newborn items, backordering quantity, and the selling price of perfect items. A numerical example is presented, and the results are discussed. Finally, a sensitivity analysis on variations of parameters such as Type-I and Type-II errors shows that it is advantageous to reduce the percentage of good items that are misclassified as defective (i.e., Type-I error). As there is a direct impact of such errors on sales, it is imperative to address and mitigate this issue. When defective items are mistakenly classified as good Type-II errors, adverse consequences ensue, including a heightened rate of product returns. This, in turn, results in additional costs for the company, such as penalties and diminished customer confidence. Hence, the findings clearly suggest that the presence of Type-I and Type-II errors has a negative effect on the ordering policy and on the total expected profit. Moreover, this work provides a model that can be used with any growing item (including plants), so the decision-maker has the opportunity to analyze a wide variety of scenarios.

The classical economic growing quantity (EGQ) model is a key concept in the inventory control problems research literature. The EGQ model is commonly employed for the purpose of inventory control in the management of growing items, such as fish and farm animals, within industries such as livestock, seafood, and aviculture. The economic order quantity (EOQ) model assumes that customer demand is satisfied without interruption in each cycle; however, this assumption is not always true for some companies as they do not have continuous operations, except for item storage, during non-working times such as weekends, natural idle periods, or spare time. In this study, we extend the traditional EGQ model by incorporating the concept of working and non-working periods, resulting in the development of a new model called discontinuous economic growing quantity (DEGQ). Unlike the conventional EGQ model, the DEGQ model considers the presence of intermittent operational periods, in which the firm is actively engaged in its activities, and non-working periods, during which only storage-related operations occur. By incorporating this discontinuity, the DEGQ model provides a more accurate representation of real-world scenarios where businesses operate in a non-continuous manner, thus enhancing the effectiveness of inventory control and management strategies. The study aims to obtain the optimal number of periods in each cycle and the optimal slaughter age for the breeding items, and, subsequently, to find the optimal order size to minimize the total cost. Finally, we propose an optimal analytical procedure to determine the optimal solutions. This procedure entails finding the optimal number of periods using a closed-form equation and determining the optimal slaughter age by exhaustively searching the entire range of possible growth times.

Humanitarian logistics face significant challenges during natural disasters due to operational uncertainties. Humanitarian logistics networks such as food banks must manage both regular operations and disaster-induced supply and demand. The study aims to develop and assess two-stage stochastic models that support decision-making under these dual operations. We evaluate various decisional strategies through extensive numerical experiments inspired in the operation of the food bank network Bancos de Alimentos de México (BAMX), highlighting the importance of suitable timeframes for reactive and anticipative decisions. The findings offer valuable insights for managers in balancing routine and emergency responses efficiently. [ABSTRACT FROM AUTHOR]

Introduction With the recent emergence of the Omicron variant, there has been a rapid and alarming increase in the number of COVID-19 cases among pediatric populations. Yet few US pediatric cohort studies have characterized the clinical features of children with severe COVID-19. The objective of this study was to identify those chronic comorbidities that increase the risk of hospitalization for pediatric populations with severe COVID-19. Methods A retrospective cohort study that utilized the Texas Inpatient Public Use Data file was conducted. The study included 1187 patients (ages 5 to 19) from 164 acute-care Texas hospitals with the primary or secondary ICD-10CM diagnosis code U07.1 (COVID-19, virus identified). The baseline comparison group included 38 838 pediatric patients who were hospitalized in 2020. Multivariable binary logistic regression, controlling for patient characteristics, sociodemographic factors, and health insurance, was used to estimate the adjusted risk of hospitalization for COVID-19. Results Obesity and type 1 diabetes increased the risk of hospitalization with COVID-19 among both children (5-12 years) and adolescents (13-19 years). Adolescents with morbid obesity were 10 times more likely to have severe COVID-19 (p

Success in the industrial sector is compromised by diverse conditions such as imperfect product production, manufacturing line interruptions, and unscheduled maintenance. The precise use of common practices in production environments is an available solution to eliminate some of these issues. Applying a warm-up period in a manufacturing process is adequate and cost-effective for almost all companies. It improves the equipment’s productivity and helps the manufacturing line generate fewer defective products. Even though several inventory management studies have included a warm-up phase in their models, its use in economic production quantity (EPQ) models remains largely unexplored. Adding a warm-up phase to the production cycle minimizes maintenance expenses and defective products and increases the machine’s performance. In this study, the dependency between the machine downtime and the warm-up length is examined for the first time. The warm-up time depends on the machine’s off-state period: if the machine has a longer operation timeout, then a longer warm-up period is needed. The model includes a function to model the warm-up time relative to the machine downtime and two types of defective products: scrapping and reworking items. The study is concluded with some numerical examples, a sensitivity analysis, and some management insights related to the EPQ.

Determining the optimal slaughter age of fast-growing animals regarding the mortality rates and breeding costs plays an important and major role for companies that benefit from their meat. Additionally, the effects of carbon dioxide (CO2) emissions during the growth cycle of animals are a significant concern for governments. This study proposes an economic order quantity (EOQ) for growing items with a mortality function under a sustainable green breeding policy. It assumes that CO2 production is a practical polynomial function that depends on the age of the animals as well as the mortality function. The aim of the model is to determine the optimal slaughter age and the optimal number of newborn chicks, purchased from the supplier, to minimize the total costs. We propose an analytical approach, with five simple steps, to find the optimal solutions. Finally, we provide a numerical example and some model management insights to help practitioners in this area.