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Current methods for flow field reconstruction mainly rely on data-driven algorithms which require an immense amount of experimental or field-measured data. Physics-informed neural network (PINN), which was proposed to encode physical laws into neural networks, is a less data-demanding approach for flow field reconstruction. However, when the fluid physics is complex, it is tricky to obtain accurate solutions under the PINN framework. This study aims to propose a physics-based data-driven approach for time-averaged flow field reconstruction which can overcome the hurdles of the above methods.

A multifidelity strategy leveraging PINN and a nonlinear information fusion (NIF) algorithm is proposed. Plentiful low-fidelity data are generated from the predictions of a PINN which is constructed purely using Reynold-averaged Navier–Stokes equations, while sparse high-fidelity data are obtained by field or experimental measurements. The NIF algorithm is performed to elicit a multifidelity model, which blends the nonlinear cross-correlation information between low- and high-fidelity data.

Two experimental cases are used to verify the capability and efficacy of the proposed strategy through comparison with other widely used strategies. It is revealed that the missing flow information within the whole computational domain can be favorably recovered by the proposed multifidelity strategy with use of sparse measurement/experimental data. The elicited multifidelity model inherits the underlying physics inherent in low-fidelity PINN predictions and rectifies the low-fidelity predictions over the whole computational domain. The proposed strategy is much superior to other contrastive strategies in terms of the accuracy of reconstruction.

In this study, a physics-informed data-driven strategy for time-averaged flow field reconstruction is proposed which extends the applicability of the PINN framework. In addition, embedding physical laws when training the multifidelity model leads to less data demand for model development compared to purely data-driven methods for flow field reconstruction.

This study aims to test the validity of the Rajan theory in South Africa and other selected emerging markets (Chile, Peru and Brazil) during the period 1975–2019.

In this study, the researchers used time-series data to estimate a Bayesian Vector Autoregression (BVAR) model with hierarchical priors. The BVAR technique has the advantage of being able to accommodate a wide cross-section of variables without running out of degrees of freedom. It is also able to deal with dense parameterization by imposing structure on model coefficients via prior information and optimal choice of the degree of formativeness.

The results for all countries except Peru confirmed the Rajan hypotheses, indicating that inequality contributes to high indebtedness, resulting in financial fragility. However, for Peru, this study finds it contradicts the theory. This study controlled for monetary policy shock and found the results differing country-specific.

The findings suggest that an escalating level of inequality leads to financial fragility, which implies that policymakers ought to be cautious of excessive inequality when endeavouring to contain the risk of financial fragility, by implementing sound structural reform policies that aim to attract investments consistent with job creation, development and growth in these countries. Policymakers should also be cautious when implementing policy tools (redistributive policies, a sound monetary policy), as they seem to increase the risk of excessive credit growth and financial fragility, and they need to treat income inequality as an important factor relevant to macroeconomic aggregates and financial fragility.

The paper aims to examine the impact of social capital on the size of the shadow economy in the BIRCS countries over the period 1995–2014.

The authors employ the Bayesian linear regression method to uncover the relationship between social capital and the shadow economy. The method applies a normal distribution for the prior probability distribution while the posterior distribution is determined using the Markov chain Monte Carlo technique.

The results indicate that the unemployment rate and tax burden positively affect the size of the shadow economy. By contrast, corruption control and trade openness are negatively associated with the development of this informal sector. Moreover, the paper's primary finding is that social capital represented by social trust and tax morale can hinder the size of the shadow economy.

This study is limited to the case of the BRICS countries for the period 1995–2014. The determinants of the shadow economy in different groups of countries can be heterogeneous. Moreover, social capital is a multidimensional concept that may consist of various components. This difficulty of measuring the social capital calls for further research on the relationship between other dimensions of social capital and the shadow economy.

Many studies investigate the effect of economic factors on the size of the shadow economy. This paper applies a new approach to discover the issue. Notably, the authors use the Bayesian linear regression method to analyze the relationship between social capital and the shadow economy in the BRICS countries.

Environmentally-friendly certifications have increased over the past decade within food supply chains. Although a large body of literature has explored the drivers leading firms to adopt such certifications, it has not closely examined the strategic motivations associated with their adoption. This paper aims to investigate an environmentally-friendly certification, VIVA, examining its role as an alternative form of supply chain governance. The aim is to investigate the drivers affecting the adoption of VIVA and to assess managerial perceptions related to transaction-related characteristics and the firm’s internal resources and capabilities.

This study draws upon both an extended transaction cost economics perspective, which is based on transaction risks and the resource-based view, which examines a firm’s internal resources. A survey was conducted via a structured questionnaire sent to all of the wine producers in charge of the decision regarding whether to adopt VIVA certification. A Hierarchal Bayesian Model was applied to analyse questionnaire responses. Such a model allows us to specify the probabilistic relationship between questions and latent constructs and to carry over uncertainty across modelling levels.

The adoption of this environmentally-friendly certification is envisioned as a tool to curb internal risks, and thus to manage behavioural uncertainty within the supply chain. A high level of exposure to exogenous transaction risks discourages firms from adopting VIVA certification. The certification system is not perceived as a promoter of operational capabilities. Managers are more likely to implement the certification when they expect that its adoption will leverage their potential knowledge of the supply chain or prompt new and better collaborations with the suppliers. Therefore, the certification can become a resource that interacts with the capabilities of the firm, expressing complementarities that stimulate the formation of dynamic capabilities.

The identification of drivers from the two theoretical perspectives offers insights into the attributes that are perceived as important by managers and which, therefore, could be leveraged to foster the adoption of the environmental certification. The external validity of the study could be improved by extending the sample to other certifications and supply chains.

The study offers a different perspective on environmental certification. It demonstrates that considering the certification as an alternative form of supply chain governance opens up a set of efficiency and strategic considerations that could be addressed to promote the effectiveness of an environmental strategy within a supply chain

Aiding employment is an important poverty reduction strategy in many countries’ social welfare systems, as this strategy can help empower the recipients with a better living standard, development and social inclusion. The purpose of this paper is to identify the most significant individual and systematic variables for the employment status of low-income groups in urban China.

The data of this study are drawn from “Social Policy Support System for Poverty-stricken Families in Urban and Rural China 2015” report. The Ministry of Civil Affairs of the People’s Republic of China appointed and funded the Institute of Social Science Survey (ISSS) at Peking University to deliver the related project and organize a research team to write the report. Multiple binary logistic regression analysis is adopted to identify both individual and systematic factors that affect the employment status among low-income groups in urban China.

According to the results of the binary logistic regression model, individual factors, including: gender; householder status; education; and self-rated health status, play a significant role in determining the employment status of low-income groups in urban China. Clearly, the impacts of individual factors are more influential to marginal families than to families entitled to receive Basic Living Allowance. In contrast, compared with marginal families, systematic factors are more influential to families entitled to receive Basic Living Allowance.

This study highlights the importance of precise poverty reduction strategy and the issue of “welfare dependence” among low-income groups in urban China. Policy recommendations derived from the findings are hence given, including: the promotion of family-friendly policies; the introduction of a smart healthcare system; the establishment of a Basic Living Allowance adjustment mechanism; and the provision of related social services.

Purpose: The study elaborates the contextual conditions of the academic workplace in which gender, age, and nationality considerably influence the likelihood of self-categorization as being affected by workplace bullying. Furthermore, the intersectionality of these sociodemographic characteristics is examined.

Basic Design: The hypotheses underlying the study were mainly derived from the social role, social identity, and cultural distance theory, as well as from role congruity and relative deprivation theory. A survey data set of a large German research organization, the Max Planck Society, was used. A total of 3,272 cases of researchers and 2,995 cases of non-scientific employees were included in the analyses performed. For both groups of employees, binary logistic regression equations were constructed. the outcome of each equation is the estimated percentage of individuals who reported themselves as having experienced bullying at work occasionally or more frequently in the 12 months prior to the survey. The predictors are the demographic and organization-specific characteristics (hierarchical position, scientific field, administrative unit) of the respondents and selected interaction terms. Using regression equations, hypothetically relevant conditional marginal means and differences in regression parameters were calculated and compared by means of t-tests.

Results: In particular, the gender-related hypotheses of the study could be completely or conditionally verified. Accordingly, female scientific and non-scientific employees showed a higher bullying vulnerability in (almost) all contexts of the academic workplace. An increased bullying vulnerability was also found for foreign researchers. However, the patterns found here contradicted those that were hypothesized. Concerning the effect of age analyzed for non-scientific personnel, especially the age group 45–59 years showed a higher bullying probability, with the gender gap in bullying vulnerability being greatest for the youngest and oldest age groups in the sample.

Interpre4tation and Relevance: The results of the study especially support the social identity theory regarding gender. In the sample studied, women in minority positions have a higher vulnerability to bullying in their work fields, which is not the case for men. However, the influence of nationality on bullying vulnerability is more complex. The study points to the further development of cultural distance theory, whose hypotheses are only partly able to explain the results. The evidence for social role theory is primarily seen in the interaction of gender with age and hierarchical level. Accordingly, female early career researchers and young women (and women in the oldest age group) on the non-scientific staff presumably experience a masculine workplace. Thus, the results of the study contradict the role congruity theory.

The impact of financial reforms and financial development on an economy has received considerable attention over the recent past. This paper aims to investigate whether financial liberalisation and financial development increase the likelihood financial crises in Southern African development community (SADC) countries.

Due to the binary nature of the dependent variable, the logit model is used for the analysis using data for the period 1990 to 2015.

The results showed that financial liberalisation captured by real interest rates reduces the likelihood of financial crises. Furthermore, regulatory quality strengthens this reductive effect of financial liberalisation on the probability of financial crises. On the other hand, financial development represented by bank credit increases the incidence of financial crises. The results also suggest that financial liberalisation may increase the likelihood of financial crises indirectly through financial development.

The study recommends that a sound regulatory and supervisory framework be established as well as institutional quality raised to curb the effect of financial development on the incidence of financial crises.

There is scant evidence on the role that financial liberalisation and financial development play in the incidence of financial crises in the SADC. This study incorporates the effect of institutional quality in the analysis which has been neglected by most studies on financial reforms in SADC countries. A number of recent studies in SADC countries conclude that financial development resulting from financial reforms, may hinder economic growth. Therefore, this study sheds light on this negative relationship.

While there exist many surveys on the use stochastic frontier analysis (SFA), many important issues and techniques in SFA were not well elaborated in the previous surveys, namely, regular models, copula modeling, nonparametric estimation by Grenander’s method of sieves, empirical likelihood and causality issues in SFA using regression discontinuity design (RDD) (sharp and fuzzy RDD). The purpose of this paper is to encourage more research in these directions.

A literature survey.

While there are many useful applications of SFA to econometrics, there are also many important open problems.

This is the first survey of SFA in econometrics that emphasizes important issues and techniques such as copulas.