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The authors aim to develop a conceptual framework for longitudinal estimation of stress-related states in the wild (IW), based on the machine learning (ML) algorithms that use physiological and non-physiological bio-sensor data.

The authors propose a conceptual framework for longitudinal estimation of stress-related states consisting of four blocks: (1) identification; (2) validation; (3) measurement and (4) visualization. The authors implement each step of the proposed conceptual framework, using the example of Gaussian mixture model (GMM) and K-means algorithm. These ML algorithms are trained on the data of 18 workers from the public administration sector who wore biometric devices for about two months.

The authors confirm the convergent validity of a proposed conceptual framework IW. Empirical data analysis suggests that two-cluster models achieve five-fold cross-validation accuracy exceeding 70% in identifying stress. Coefficient of accuracy decreases for three-cluster models achieving around 45%. The authors conclude that identification models may serve to derive longitudinal stress-related measures.

Proposed conceptual framework may guide researchers in creating validated stress-related indicators. At the same time, physiological sensing of stress through identification models is limited because of subject-specific reactions to stressors.

Longitudinal indicators on stress allow estimation of long-term impact coming from external environment on stress-related states. Such stress-related indicators can become an integral part of mobile/web/computer applications supporting stress management programs.

Timely identification of excessive stress may improve individual well-being and prevent development stress-related diseases.

The study develops a novel conceptual framework for longitudinal estimation of stress-related states using physiological and non-physiological bio-sensor data, given that scientific knowledge on validated longitudinal indicators of stress is in emergent state.

Background: Rising food bank usage in the UK suggests a growing prevalence of food insecurity. However, a formalised, representative measure of food insecurity was not collected in the UK until 2019, over a decade after the initial proliferation of food bank demand. In the absence of a direct measure of food insecurity, this article identifies and summarises longitudinal proxy indicators of UK food insecurity to gain insight into the growth of insecure access to food in the 21st century.

Methods: A rapid evidence synthesis of academic and grey literature (2005–present) identified candidate proxy longitudinal markers of food insecurity. These were assessed to gain insight into the prevalence of, or conditions associated with, food insecurity.

Results: Food bank data clearly demonstrates increased food insecurity. However, this data reflects an unrepresentative, fractional proportion of the food insecure population without accounting for mild/moderate insecurity, or those in need not accessing provision. Economic indicators demonstrate that a period of poor overall UK growth since 2005 has disproportionately impacted the poorest households, likely increasing vulnerability and incidence of food insecurity. This vulnerability has been exacerbated by welfare reform for some households. The COVID-19 pandemic has dramatically intensified vulnerabilities and food insecurity. Diet-related health outcomes suggest a reduction in diet quantity/quality. The causes of diet-related disease are complex and diverse; however, evidence of socio-economic inequalities in their incidence suggests poverty, and by extension, food insecurity, as key determinants.

Conclusion: Proxy measures of food insecurity suggest a significant increase since 2005, particularly for severe food insecurity. Proxy measures are inadequate to robustly assess the prevalence of food insecurity in the UK. Failure to collect standardised, representative data at the point at which food bank usage increased significantly impairs attempts to determine the full prevalence of food insecurity, understand the causes, and identify those most at risk.

The objective of this study is to investigate the impact of longitudinal forces on extreme-long heavy-haul trains, providing new insights and methods for their design and operation, thereby enhancing safety, operational efficiency and track system design.

A longitudinal dynamics simulation model of the super long heavy haul train was established and verified by the braking test data of 30,000 t heavy-haul combination train on the long and steep down grade of Daqing Line. The simulation model was used to analyze the influence of factors on the longitudinal force of super long heavy haul train.

Under normal conditions, the formation length of extreme-long heavy-haul combined train has a small effect on the maximum longitudinal coupler force under full service braking and emergency braking on the straight line. The slope difference of the long and steep down grade has a great impact on the maximum longitudinal coupler force of the extreme-long heavy-haul trains. Under the condition that the longitudinal force does not exceed the safety limit of 2,250 kN under full service braking at the speed of 60 km/h the maximum allowable slope difference of long and steep down grade for 40,000 t super long heavy-haul combined trains is 13‰, and that of 100,000 t is only 5‰.

The results will provide important theoretical basis and practical guidance for further improving the transportation efficiency and safety of extreme-long heavy-haul trains.

This paper aims to discuss multilevel modeling for longitudinal data, clarifying the circumstances in which they can be used.

The authors estimate three-level models with repeated measures, offering conditions for their correct interpretation.

From the concepts and techniques presented, the authors can propose models, in which it is possible to identify the fixed and random effects on the dependent variable, understand the variance decomposition of multilevel random effects, test alternative covariance structures to account for heteroskedasticity and calculate and interpret the intraclass correlations of each analysis level.

Understanding how nested data structures and data with repeated measures work enables researchers and managers to define several types of constructs from which multilevel models can be used.

In this paper, the C80 special coal gondola car was taken as the subject, and the load test data of the car body at the center plate, side bearing and coupler measured on the dedicated line were broken down to generate the random load component spectrums of the car body under five working conditions, namely expansion, bouncing, rolling, torsion and pitching according to the typical motion attitude of the car body.

On the basis of processing the measured load data, the random load component spectrums were equivalently converted into sinusoidal load component spectrums for bench test based on the principle of pseudo-damage equivalence of load. Relying on the fatigue and vibration test bench of the whole railway wagon, by taking each sinusoidal load component spectrum as the simulation target, the time waveform replication (TWR) iteration technology was adopted to create the drive signal of each loading actuator required for the fatigue test of car body on the bench, and the drive signal was corrected based on the equivalence principle of measured stress fatigue damage to obtain the fatigue test loads of car body under various typical working conditions.

The fatigue test results on the test bench were substantially close to the measured test results on the line. According to the results, the relative error between the fatigue damage of the car body on the test bench and the measured damage on the line was within the range of −16.03%–27.14%.

The bench test results basically reproduced the fatigue damage of the key parts of the car body on the line.

This paper aims to focus on non-poor households that during the Great Recession experienced economic stress (ES). That is, whose economic comfort was reduced taking into account their previous living standards. The paper seeks to determine how the crisis has affected this extensive (and key) social group.

The analysis has been performed in a dynamic way. The non-poor households ES situation and its changes are studied throughout the period 2008-2016 by taking the four-year intervals provided by the longitudinal Spanish Living Conditions Survey. The authors discuss and select the circumstances to determine whether ES has occurred. To identify which variables determine the probability of suffering ES the authors use a standard logit model.

The main variable is the tenure status of the dwelling: property with a mortgage or rented multiply the risk of ES by up to 3.5 times. ES falls as the household’s work intensity increases. However, an improvement in the employment situation cannot be associated with a reduction in ES probability. The main socio-demographic variables behave as predicted: woman householder, grow in the number of household members and bad health increase the risk of ES, and the higher the level of education of the householder, the lower the risk.

There are very few studies regarding the people above the poverty line. Exploring and analyzing the factors determining the sensitivity of the largest part of the population to the crisis is very relevant, as the pace of the economic recovery depends largely on them.

Poverty transitions can be explained by two opposing theories: the traditional sociological approach that focusses on social stratification and individualisation theory, which emphasises on life course risks for all strata. Both perspectives have been investigated extensively for income poverty while neglecting other important poverty indicators, such as deprivation or the receipt of social assistance. The purpose of this paper is to focus on the latter to investigate the impact of social stratification (e.g. social class), life course risks (e.g. health problems), and their interactions on the probability of social assistance entry for Germany.

The analysis utilises survey data containing a sample of first-time social assistance entrants and a sample of the residential population. Applying case-control methodology, logistic regression is conducted to model the impact of social stratification determinants, life course risks, and their interactions on the probability of social assistance entry.

Social stratification determinants, particularly social class, have a significant effect. However, their effect is weaker than the effect of life course risks. Contrary to the prediction of individualisation theory, the poverty-triggering impact of life course risks varies substantially by social stratum. The combination of both theories yields high predictive power.

This paper is the first to comprehensively test social stratification and individualisation theory with respect to social assistance receipt as a poverty indicator. It is the first paper that investigates the entire population at risk of social assistance entry in Germany.

Over 60% of armed conflicts re-occur; the seed of future conflict is sown even as a peace agreement is signed. The cyclical nature of war calls for a focus on youth who can disrupt this pattern over time. Addressing this concern, the developmental peace-building model calls for a dynamic, multi-level and longitudinal approach. Using an innovative statistical approach, this study aims to investigate the associations among four youth peace-building dimensions and quality peace.

Multi-level time-series network analysis of a data set containing 193 countries and spanning the years between 2011 and 2020 was performed. This statistical approach allows for complex modelling that can reveal new patterns of how different youth peace-building dimensions (i.e. education, engagement, information, inclusion), identified through rapid evidence assessment, promote quality peace over time. Such a methodology not only assesses between-country differences but also within-country change.

While the within-country contemporaneous network shows positive links for education, the temporal network shows significant lagged effects for all four dimensions on quality peace. The between-country network indicates significant direct effects of education and information, on average, and indirect effects of inclusion and engagement, on quality peace.

This approach demonstrates a novel application of multi-level time-series network analysis to explore the dynamic development of quality peace, capturing both stability and change. The analysis illustrates how youth peace-building dimensions impact quality peace in the macro-system globally. This investigation of quality peace thus illustrates that the science of peace does not necessitate violent conflict.

Lockdowns and the forced closure of certain industries during the COVID-19 pandemic severely impacted workers, particularly entrepreneurs, who were financially and emotionally involved in their businesses. Two studies have shown that entrepreneurs have a lower willingness to get vaccinated against COVID-19 than employees. In this study, the authors try to replicate the vaccination gap between the two groups. Second, the authors study whether the difference persists when controlling for demographics, vaccination attitudes and the COVID-19 context, including the financial impact of the pandemic, its effect on the wellbeing of workers, and government attitudes. Third, the authors study whether there are differences in how the context of the pandemic relates to vaccination willingness for entrepreneurs and employees.

The authors conduct regression analyses using three large datasets. The authors study vaccination status (February 2022) in a 27-country Eurobarometer sample, vaccination intention (December 2020) in a Dutch sample from the LISS panel and vaccination status (July 2021) in a sample from the Understanding America Study (UAS).

All datasets confirm that entrepreneurs have lower vaccination intention and coverage than employees. Even when controlling for the variables described in the LISS and UAS datasets, this negative difference remains. The study results also indicate that demographics, especially vaccination attitudes, are much more important than contextual influences in the decision to get vaccinated against COVID-19.

The authors are the first to dive further into the vaccination differences between entrepreneurs and employees. They advise further research into the drivers of this gap, specifically relating to the role of personality and social normative influences.

This study presents and validates a theoretical model linking individual characteristics of the founding or lead innovative entrepreneur of a start-up venture – the entrepreneur's values, entrepreneurial attitudes and entrepreneurial self-efficacy – to the firm's entrepreneurial orientation (EO) and market orientation (MO) and, ultimately, to firm performance.

We conducted a survey on a stratified random sample of founders of early-stage South Australian micro- and small enterprises with a response rate of 24% (N = 204). Structural equation modelling was used to evaluate the model.

The study found that there is a significant relationship between the individual lead entrepreneur and firm strategies developed in early-stage firms in explaining firm performance. It also found that internal values are positively related to entrepreneurial attitude. Entrepreneurial attitude is positively related to entrepreneurial self-efficacy and EO innovativeness. In turn, entrepreneurial self-efficacy is related to innovativeness, proactiveness and risk-taking. The proactiveness dimension of EO and entrepreneurial attitude is related to MO. Entrepreneurial self-efficacy, innovativeness and MO are related to firm performance.

This research was limited to entrepreneurial ventures in South Australia and may lack generalisability in other states and countries.

The research contributes to the understanding of the heterogeneity within self-employed individuals, in particular among innovative entrepreneurs, by expanding insights regarding antecedents and consequences of the entrepreneurial process. It develops insights into the links of individual-level constructs with firm-level constructs to develop a more meaningful understanding of new venture creation and performance. It enhances our knowledge of the heterogeneity within the group of self-employed by exploring the individual entrepreneurial antecedents of performance in early-stage firms.