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Postneonatal mortality (PNM), which differs from infant and perinatal mortality, has been observed in the past 25 years with respect to the health outcomes of children. While infant and perinatal mortality have been well-evaluated regarding racial differentials, there are no substantial data on PNM in this perspective. The purpose of this study was to assess whether or not social determinants of health adversely affect racial/ethnic PNM differentials in the USA.

A cross-sectional, nonexperimental epidemiologic study design was used to assess race as an exposure function of PNM using Cohort Linked Birth/Infant Death Data (2013). The outcome variable assessed PNM, while the main independent variables were race, social demographic variables (i.e. sex and age) and social determinants of health (i.e. marital status and maternal education). The chi-square statistic was used to assess the independence of variables by race, while the logistic regression model was used to assess the odds of PNM by race and other confounding variables.

During 2013, there were 4,451 children with PNM experience. The cumulative incidence of PNM was 23.6% (n = 2,795) among white infants, 24.3% (n = 1,298) among Black/African-Americans (AA) and 39.5% (n = 88) were American-Indian infants (AI), while 21.3% (n = 270) were multiracial, χ² (3) = 35.7, p < 0.001. Racial differentials in PNM were observed. Relative to White infants, PNM was two times as likely among AI, odds ratio (OR) 2.11 (95% confidence interval [CI] 1.61, 2.78). After controlling for the confounding variables, the burden of PNM persisted among AI, although slightly marginalized, adjusted odds ratio (aOR) 1.70, (99% CI 1.10, 2.65).

In a representative sample of US children, there were racial disparities in PNM infants who are AI compared to their white counterparts, illustrating excess mortality. These findings suggest the need to allocate social and health resources in transforming health equity in this direction.

We propose a risk factor for idiosyncratic entropy and explore the relationship between this factor and expected stock returns.

We estimate a cross-sectional model of expected entropy that uses several common risk factors to predict idiosyncratic entropy.

We find a negative relationship between expected idiosyncratic entropy and returns. Specifically, the Carhart alpha of a low expected entropy portfolio exceeds the alpha of a high expected entropy portfolio by −2.37% per month. We also find a negative and significant price of expected idiosyncratic entropy risk using the Fama-MacBeth cross-sectional regressions. Interestingly, expected entropy helps us explain the idiosyncratic volatility puzzle that stocks with high idiosyncratic volatility earn low expected returns.

We propose a risk factor of idiosyncratic entropy and explore the relationship between this factor and expected stock returns. Interestingly, expected entropy helps us explain the idiosyncratic volatility puzzle that stocks with high idiosyncratic volatility earn low expected returns.

In this study, the central objective is to foresee stock market signals with the use of a proper structure to achieve the highest accuracy possible. For this purpose, three hybrid models have been developed for the stock markets which are a combination of support vector machine (SVM) with meta-heuristic algorithms of particle swarm optimization (PSO), imperialist competition algorithm (ICA) and genetic algorithm (GA).All the analyses are technical and are based on the Japanese candlestick model.

Further as per the results achieved, the most suitable algorithm is chosen to anticipate sell and buy signals. Moreover, the authors have compared the results of the designed model validations in this study with basic models in three articles conducted in the past years. Therefore, SVM is examined by PSO. It is used as a classification agent to search the problem-solving space precisely and at a faster pace. With regards to the second model, SVM and ICA are tested to stock market timing, in a way that ICA is used as an optimization agent for the SVM parameters. At last, in the third model, SVM and GA are studied, where GA acts as an optimizer and feature selection agent.

As per the results, it is observed that all new models can predict accurately for only 6 days; however, in comparison with the confusion matrix results, it is observed that the SVM-GA and SVM-ICA models have correctly predicted more sell signals, and the SCM-PSO model has correctly predicted more buy signals. However, SVM-ICA has shown better performance than other models considering executing the implemented models.

In this study, the data for stock market of the years 2013–2021 were analyzed; the long length of timeframe makes the input data analysis challenging as they must be moderated with respect to the conditions where they have been changed.

In this study, two methods have been developed in a candlestick model; they are raw-based and signal-based approaches in which the hit rate is determined by the percentage of correct evaluations of the stock market for a 16-day period.

Guided by the affective events theory (AET), the purpose of this paper was to explore the impact of interpersonal trust in peers, as an affective work event, on two affect-driven behaviors (i.e. job performance and organizational citizenship behavior toward individuals [OCBI]) via positive affect during the Covid-19 pandemic, particularly in the Asia–Pacific region.

This study is quantitative in approach, and longitudinal survey study in design. The authors collected data from lecturers in 2020 at the beginning, at the end and two months after the first Covid-19 lockdown in Malaysia. Then, the authors utilized the efficient partial least squares (PLSe2) estimator to investigate the relationships between the variables, while also considering gender as a control variable.

The findings show that positive affect fully mediates the relationship between interpersonal trust in peers and job performance and partially mediates the relationship between interpersonal trust in peers and OCBI. Given that gender did not demonstrate any significant relationships with interpersonal trust in peers, positive affect, job performance and OCBI, the recommended policies can be universally developed and applied, irrespective of the gender of academics.

This research contributes originality by integrating the widely recognized theoretical framework of AET and investigating a less explored context, specifically the Malaysian higher education sector during the challenging initial phase of the Covid-19 pandemic. Furthermore, the authors adopt a novel and robust methodological approach, utilizing the efficient partial least squares (PLSe2) estimator, to thoroughly examine and validate the longitudinal theoretical model from both explanatory and predictive perspectives.

Despite large bodies of research related to the impacts of e-commerce on last-mile logistics and sustainability, there has been limited effort to evaluate urban freight using an equity lens. Therefore, this study proposes a modeling framework that enables researchers and planners to estimate the baseline equity performance of a major e-commerce platform and evaluate equity impacts of possible urban freight management strategies. The study also analyzes the sensitivity of various operational decisions to mitigate bias in the analysis.

The model adapts empirical methodologies from activity-based modeling, transport equity evaluation, and residential freight trip generation (RFTG) to estimate person- and household-level delivery demand and cargo van traffic exposure in 41 U.S. Metropolitan Statistical Areas (MSAs).

Evaluating 12 measurements across varying population segments and spatial units, the study finds robust evidence for racial and socio-economic inequities in last-mile delivery for low-income and, especially, populations of color (POC). By the most conservative measurement, POC are exposed to roughly 35% more cargo van traffic than white populations on average, despite ordering less than half as many packages. The study explores the model’s utility by evaluating a simple scenario that finds marginal equity gains for urban freight management strategies that prioritize line-haul efficiency improvements over those improving intra-neighborhood circulations.

Presents a first effort in building a modeling framework for more equitable decision-making in last-mile delivery operations and broader city planning.

The internal (farmer-controlled) and external (non-farmer-controlled) factors affect crop yield. However, not a single study has identified and analyzed yield predictors in India using effective predictive models. Thus, this study aims to investigate how internal and external predictors impact pearl millet yield and Stover yield.

Descriptive analytics and artificial neural network are used to investigate the impact of predictors on pearl millet yield and Stover yield. From descriptive analytics, 473 valid responses were collected from semi-arid zone, and the predictors were categorized into internal and external factors. Multi-layer perceptron-neural network (MLP-NN) model was used in Statistical Package for the Social Sciences version 25 to model them.

The MLP-NN model reveals that rainfall has the highest normalized importance, followed by irrigation frequency, crop rotation frequency, fertilizers type and temperature. The model has an acceptable goodness of fit because the training and testing methods have average root mean square errors of 0.25 and 0.28, respectively. Also, the model has R² values of 0.863 and 0.704, respectively, for both pearl millet and Stover yield.

To the best of the authors’ knowledge, the current study is first of its kind related to impact of predictors of both internal and external factors on pearl millet yield and Stover yield.

The literature reveals that most studies have estimated crop yield using limited parameters and forecasting approaches. However, this research will examine the impact of various predictors such as internal and external of both yields. The outcomes of the study will help policymakers in developing strategies for stakeholders. The current work will improve pearl millet yield literature.

In recent years, the global consumption of craft beer witnesses remarkable growth. This growth is attributed to the evolving demographics of beer consumers, particularly the emergence of a new generation known as Digitarians or Generation Z. This study aims to analyze the key determinants influencing craft beer consumption among Digitarians.

An online questionnaire is administered, and a total of 296 completed responses are included in the statistical analysis. The methodology uses logistic regressions combined with a backward selection process and variance inflation factor analysis to address multicollinearity. The logistic regressions are conducted in three steps to delve into the research objective and gain insights into the behavior of young consumers. The stepwise backward selection aids in obtaining robust coefficients as a variable selection tool.

The results shed light on how Digitarians’ preferences for craft beer are influenced by various factors, including self-perceived knowledge, alcohol content, gender, food pairings, environment and companionship.

To the best of the authors’ knowledge, this paper contributes novel insights by being the first study to explore the significance of craft beer choices among Digitarians, identifying the role of several predictors in their consumption patterns.

The purpose of the paper is to predict mapping of areas vulnerable to flooding in the Ourika watershed in the High Atlas of Morocco with the aim of providing a useful tool capable of helping in the mitigation and management of floods in the associated region, as well as Morocco as a whole.

Four machine learning (ML) algorithms including k-nearest neighbors (KNN), artificial neural network, random forest (RF) and x-gradient boost (XGB) are adopted for modeling. Additionally, 16 predictors divided into categorical and numerical variables are used as inputs for modeling.

The results showed that RF and XGB were the best performing algorithms, with AUC scores of 99.1 and 99.2%, respectively. Conversely, KNN had the lowest predictive power, scoring 94.4%. Overall, the algorithms predicted that over 60% of the watershed was in the very low flood risk class, while the high flood risk class accounted for less than 15% of the area.

There are limited, if not non-existent studies on modeling using AI tools including ML in the region in predictive modeling of flooding, making this study intriguing.

The paper identifies the influence of socio-economic factors and livelihood training in stimulating micro-entrepreneurship among women self-help group (SHG) members.

The study is based on a sample of 416 women SHG members drawn from all the four districts of Sikkim using cluster sampling procedure. A multivariate binary logistic model is used to find the impact of socio-economic factors, and a Poisson regression has been used to find the impact of training on fostering micro-entrepreneurship. The result is validated using a propensity score matching approach which corrects for the potential self-selection bias in the sample. Subsequently, a covariate adjustment estimator verifies the robustness of the approach.

The study finds that “size of landownership”, “amount of loan borrowed”, “member's age”, “number of earning and dependent members”, “number of years of SHG enrolment” as well as the “district to which the member belongs to” have a statistically significant influence on the graduation of SHG members to micro-entrepreneurs. Furthermore, it is found that members attending the livelihood training programmes had a significantly higher number of microenterprises.

The study differentiates itself by providing empirical evidence on how socio-economic factors and livelihood training stimulate micro-entrepreneurship among SHG women of Sikkim, which has so far remained unexplored. Moreover, advanced econometric method has been used to eliminate the possible self-selection bias involved with training participation and thereby provides reliable and robust results.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-01-2023-0070

The effect of vicarious learning during clinical or medical internships on graduates' adaptive career behaviours has attracted scant attention from healthcare researchers, particularly, in the developing world context. Drawing upon the social cognitive career theory model of career self-management (SCCT-CSM), the current study examines how vicarious learning influences the clinical graduates' adaptive career behaviours (i.e. career exploration and decision-making) via career exploration and decision-making self-efficacy (CEDSE) and career intention.

Data were collected from 293 nursing graduates undertaking clinical internships in 25 hospitals across Nigeria who willingly participated in this study as they were also assured of confidentiality at two-waves. The proposed hypotheses were tested using a path analysis.

The findings showed that vicarious learning during clinical internship had a direct effect on career exploration, decision-making and career decision self-efficacy among graduate trainees. Also, the findings revealed that the effects of vicarious learning on the graduates' career exploration and career decision-making were significantly mediated by career decision self-efficacy and career intentions.

The findings of this study have important practical implications for higher education institutions and industries that send and receive clinical graduates for clinical internships to gain more skills. More emphasis should be on encouraging learners to learn vicariously in addition to other forms of learning experiences available during clinical internships.

The study explains that the graduates' higher engagement in clinical career exploration and decision-making was based on a higher level of vicarious learning during internships. The results suggest that higher education institutions and healthcare service providers can derive greater benefits from more emphasis on promoting vicarious learning during clinical internships.