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The study investigates persistence of individuals' labour market activity with a focus on examining whether and to what extent there is genuine state dependence in six labour market states: not-in-labour-force, unemployment, self-employment, casual employment, fixed term contracts, and ongoing employment, and how the persistence and genuine state dependence of the labour market states change with education levels.

A dynamic multinomial logit model that accounts for observed and unobserved individual heterogeneity is estimated, using the first 19 waves of the Household, Income, and Labour Dynamics in Australia Survey.

While observed and unobserved individual heterogeneity plays an important role in the persistence of each of the labour market states examined, genuine state dependence is found to be present for all the states. It is also found that the persistence and genuine state dependence of unemployment is larger among those with a low education attainment than among those with higher education.

The existence of genuine state dependence of labour market states calls for early interventions to prevent people from losing jobs.

Earlier studies often focus on persistence of a particular labour market state such as unemployment, while this study examines the persistence simultaneously of six labour market states.

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.

The purpose of this study is to propose a systematic model selection procedure for long-term load forecasting (LTLF) for ex-ante and ex-post cases considering uncertainty in exogenous predictors.

The different variants of regression models, namely, Polynomial Regression (PR), Generalised Additive Model (GAM), Quantile Polynomial Regression (QPR) and Quantile Spline Regression (QSR), incorporating uncertainty in exogenous predictors like population, Real Gross State Product (RGSP) and Real Per Capita Income (RPCI), temperature and indicators of breakpoints and calendar effects, are considered for LTLF. Initially, the Backward Feature Elimination procedure is used to identify the optimal set of predictors for LTLF. Then, the consistency in model accuracies is evaluated using point and probabilistic forecast error metrics for ex-ante and ex-post cases.

From this study, it is found PR model outperformed in ex-ante condition, while QPR model outperformed in ex-post condition. Further, QPR model performed consistently across validation and testing periods. Overall, QPR model excelled in capturing uncertainty in exogenous predictors, thereby reducing over-forecast error and risk of overinvestment.

These findings can help utilities to align model selection strategies with their risk tolerance.

To propose the systematic model selection procedure in this study, the consistent performance of PR, GAM, QPR and QSR models are evaluated using point forecast accuracy metrics Mean Absolute Percentage Error, Root Mean Squared Error and probabilistic forecast accuracy metric Pinball Score for ex-ante and ex-post cases considering uncertainty in the considered exogenous predictors such as RGSP, RPCI, population and temperature.

Home is a place/system/product that becomes increasingly occupied with various tasks used to be performed in workplaces. However, the knowledge of the relationship between residential physical environments and occupant experience is limited, especially when considering the effect of indoor plants (IPs) and climate zones. To address the gap, this study conducted a questionnaire survey in three cities across different regions in China.

Based on User Experience and Customer Satisfaction Index theory, following the research paradigm, a total of 627 valid samples were collected and analyzed in a stepwise statistical analysis, including descriptive statistics, reliability and validity test, correlation test and region comparison, then the model of PROCESS was adopted to examine the hypotheses that are given based on the former studies.

The results showed that residential physical environments have a significant effect on occupant satisfaction (OS) in all regions, as well as OS on occupant performance. However, regional differences were found that OS is a complete mediator in the Middle region, while a partial mediator in the North and South. A slight moderating effect of IPs was also found in the region of South. Nevertheless, both the number of plants and plant types have a significant moderating effect on the mechanism.

Besides combining two theories and confirming the mechanism in the residential physical environment, it is also the first study to consider the moderating effects of IPs and climate zones, providing potential empirical support for not only design and management stages but also facing global challenges of working at home and climate changes.

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.

This study aims to assess support vector machine (SVM) models' predictive ability to estimate half-cell potential (HCP) values from input parameters by using Bayesian optimization, grid search and random search.

A data set with 1,134 rows and 6 columns is used for principal component analysis (PCA) to minimize dimensionality and preserve 95% of explained variance. HCP is output from temperature, age, relative humidity, X and Y lengths. Root mean square error (RMSE), R-squared, mean squared error (MSE), mean absolute error, prediction speed and training time are used to measure model effectiveness. SHAPLEY analysis is also executed.

The study reveals variations in predictive performance across different optimization methods, with RMSE values ranging from 18.365 to 30.205 and R-squared values spanning from 0.88 to 0.96. Additionally, differences in training times, prediction speeds and model complexities are observed, highlighting the trade-offs between model accuracy and computational efficiency.

This study contributes to the understanding of SVM model efficacy in HCP prediction, emphasizing the importance of optimization techniques, model complexity and dimensionality reduction methods such as PCA.

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.

Construction waste reduction (CWR) plays a vital role in achieving sustainability in construction. A good CWR practice can result in optimizing material usage, conserving natural resources, limiting environmental pollution, protecting the environment and enhancing human health. In this regard, the purpose of the current study is to identify the most relevant organizational policies that aid in waste reduction and concurrently explores the congruent measures to be adopted during the construction process in the Indian high-rise building sector.

The research findings were obtained through a mixed- method approach. Content analysis was used to identify waste reduction measures (variables) targeting on the two domains of construction – “waste-efficient execution” and “waste – mitigating organizational policies.” Furthermore, the authors explored and documented the key measures from the identified waste reduction measures using the constraint value of the relative importance index. As the next step, the study listed the theoretical hypothesis based on expert interviews and tested the theory through confirmatory factor analysis.

The results revealed that “waste sensitive construction techniques and strategies” as the most significant category under the domain “Execution” with a path coefficient of 0.85. Concurrently, the study has also determined that “control procedures for budget, quality and resources” as the most effective organizational approach in reducing construction waste in the Indian building industry, with a path coefficient of 0.83.

The current research is context-sensitive to the Indian construction sector. It presents the stakeholder’s perspective on construction waste reduction and the relevant measures to be implemented to reduce construction waste in high-rise building projects in India. It can also act as a concordance for decision-makers to further focus on CWR management and aid in formulating policies suitable for the Indian context.

Success in the transition between the university and the labor market is an important indicator of the adaptation of newly graduates to the worker’s role in life. This study aims to describe the validity and reliability of the University-to-Work Success Scale based on its internal structure and relationship with measures of career success, protean career orientation and life satisfaction in newly Portuguese graduates.

Using an online protocol, responses were collected from 576 graduates for less than twelve months (74.1% women), aged between 20 and 64 years (M = 25.8, SD = 6.693). Instruments included a socio-demographic questionnaire and measures of transition success, career success and life satisfaction.

The internal structure of the scale was evaluated through exploratory and confirmatory factor analyses that supported a four-factor hierarchical structure with a good fit. The reliability of the factors evaluated by Cronbach’s Alpha was satisfactory. The scale consists of 29 items divided into four subscales (professional insertion and satisfaction, confidence in the future of career, income and financial independence and adaptation to work).

These results support the use of the scale as a valid and reliable measure to assess success in the transition between university and the labor market in newly Portuguese graduates.

This study is very important because this measure can serve as a basis for both preventive and corrective career interventions and policies. The preventive approach can help graduates in their transition to the labor market by promoting career resources. The corrective approach can include re-evaluating organizational integration practices after employment, with an emphasis on promoting gender equality.

The proposed model has been aimed to predict stock market signals by designing an accurate model. In this sense, the stock market is analysed by the technical analysis of Japanese Candlestick, which is combined by the following meta heuristic algorithms: support vector machine (SVM), meta-heuristic algorithms, particle swarm optimization (PSO), imperialist competition algorithm (ICA) and genetic algorithm (GA).

In addition, among the developed algorithms, the most effective one is chosen to determine probable sell and buy signals. Moreover, the authors have proposed comparative results to validate the designed model in this study with the same basic models of three articles in the past. Hence, PSO is used as a classification method to search the solution space absolutelyand with the high speed of running. In terms of the second model, SVM and ICA are examined by the time. Where the ICA is an improver for the SVM parameters. Finally, in the third model, SVM and GA are studied, where GA acts as optimizer and feature selection agent.

Results have been indicated that, the prediction accuracy of all new models are high for only six days, however, with respect to the confusion matrixes results, it is understood 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 authors to analyze the data the long length of time between the years 2013–2021, makes the input data analysis challenging. They must be changed with respect to the conditions.

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