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As the crucial support component of the propeller power system, the reliability of the operation of submersible pumps is influenced by the lubrication performance of water-lubricated thrust bearings. When the water-lubricated thrust bearings are under start-stop or heavy load conditions, the effect of surface morphology is crucial as the mixed lubrication regime is encountered. This paper aims to develop one mixed lubrication model for the water-lubricated thrust bearings to predict the effects of surface skewness, kurtosis and roughness orientation on the loading carrying capacity and tribological behavior.

This paper developed one improved mixed lubrication model specifically for the water-lubricated thrust bearing system. In this model, the hydrodynamic model was improved by using the height of the rough surface and its probability density function, combined with the average flow model. The asperity contact model was improved by using the equation for the Pearson system of frequency curves to characterize the non-Gaussian aspect of surface roughness distribution.

According to the results, negative skewness, large kurtosis and lateral surface pattern can improve the tribological performance of water-lubricated thrust bearings. Optimizing the surface morphology is a reasonable design method that can improve the performance of water-lubricated thrust bearings.

In this paper, one mixed lubrication model specifically for the water-lubricated thrust bearing with the effect of surface roughness into consideration was developed. Based on the developed model, the effect of surface morphology on tribological behavior can be evaluated.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-08-2023-0247/

This article develops an innovative multidisciplinary conceptual framework in the field of refugee entrepreneurship by combining the theory of mixed embeddedness with the concepts of intersectionality and agency. Focusing on the phenomenon of refugee entrepreneurship, this conceptual framework addresses the following questions: how is entrepreneurship informed by the various intersectional positions of refugees? And how do refugees exert their agency based on these intersecting identities?

By revising the mixed embeddedness approach and combining it with an intersectional approach, this study aims to develop a multidimensional conceptual framework.

This research illustrates how the intersectional positions of refugees impact their entrepreneurial motivations, resources and strategies. The authors' findings show that refugee entrepreneurship not only contributes to the economic independence of refugees in new societies but also creates opportunities for refugees to exert their agency.

This conceptual framework can be applied in empirical research and accordingly contributes to refugee entrepreneurship studies and intersectionality theory.

The purpose of this study is to explore the transient characteristics of mixed-flow pumps during startup process.

This study uses a full-flow field transient calculation method of mixed-flow pump based on a closed-loop model.

The findings show the hydraulic losses and internal flow characteristics of the piping system during the start-up process.

Large computational cost.

Improve the accuracy of current numerical simulation results in transient process of mixed-flow pump.

Simplify the setting of boundary conditions in the transient calculation.

This interdisciplinary study aims to analyze how service organizations communicate sustainable beliefs in their social media narratives and use them to generate brand awareness, customer recognition and ongoing demand for sustainable service.

A two-phase exploratory analysis of 10,342 tweets from 2019–2020 was conducted by sustainable global corporations to identify best practices for their social media teams operating within a service-based business model. First, the significant themes were identified using an unguided machine learning approach of three types of firms: services, goods and mixed. Next, the full set of tweets with linguistic sentiment analysis was analyzed followed by a deeper view of the services-based organizations based on their strategic focus (business-to-business [B2B] versus mixed).

The findings indicate that tweets that appear to create the highest customer engagement are characterized as having high levels of analytical language, high clout (i.e. are socially relevant), a positive tone, a high number of words and a high number of words per sentence. On the other hand, having complex language in terms of six-letter words does not seem to associate with customer engagement. The last level of analysis shows that B2B services-based corporations with positive tone and higher word count exhibit higher levels of retweets. Implications include providing rational and informational tweets to increase engagement and highlight societal relevance.

Climate change has negative consequences on human and physical capital, and ecosystems across the globe. This study provides specific recommendations for how services corporations can increase their sustainable communications and actions.

The key implication of our research is that corporations must strategically design social media narratives about climate change as part of their online branding and communications process.

This paper investigates a problem in a reverse logistics (RLs) network to decide whether to dispose of unsold goods in primary stores or re-commercialize them in outlet centers. By deducting the costs associated with each policy from its revenue, this study aims to maximize the profit from managing unsold goods.

A new mixed-integer linear programming model has been developed to address the problem, which considers the selling prices of products in primary and secondary stores and the costs of transportation, cross-docking and returning unwanted items. As a result of uncertain nature of the cost and time parameters, gray numbers are used to deal with it. In addition, an innovative uncertain solution approach for gray programming problems is presented that considers objective function satisfaction level as an indicator of optimism.

According to the results, higher costs, including transportation, cross-docking and return costs, make sending goods to outlet centers unprofitable and more goods are disposed of in primary stores. Prices in primary and secondary stores heavily influence the number of discarded goods. Higher prices in primary stores result in more disposed of goods, while higher prices in secondary stores result in fewer. As a result of the proposed method, the objective function satisfaction level can be viewed as a measure of optimism.

An integral contribution of this study is developing a new mixed-integer linear programming model for selecting the appropriate goods for re-commercialization and choosing the best outlet center based on the products' price and total profit. Another novelty of the proposed model is considering the matching percentage of boxes with secondary stores’ desired product lists and the probability of returning goods due to non-compliance with delivery dates. Moreover, a new uncertain solution approach is developed to solve mathematical programming problems with gray parameters.

This paper aims to introduce a heteroskedastic hidden truncation normal (HTN) model that allows for conditional volatilities, skewness and kurtosis, which evolve over time and are linked to economic dynamics and have economic interpretations.

The model consists of the HTN distribution introduced by Arnold et al. (1993) coupled with the NGARCH type (Engle and Ng, 1993). The HTN distribution nests two well-known distributions: the skew-normal family (Azzalini, 1985) and the normal distributions. The HTN family of distributions depends on a hidden truncation and has four parameters having economic interpretations in terms of conditional volatilities, kurtosis and correlations between the observed variable and the hidden truncated variable.

The model parameters are estimated using the maximum likelihood estimator. An empirical application to market data indicates the HTN-NGARCH model captures stylized facts manifested in financial market data, specifically volatility clustering, leverage effect, conditional skewness and kurtosis. The authors also compare the performance of the HTN-NGARCH model to the mixed normal (MN) heteroskedastic MN-NGARCH model.

The paper presents a structure dynamic, allowing us to explore the volatility spillover between the observed and the hidden truncated variable. The conditional volatilities and skewness have the ability at modeling persistence in volatilities and the leverage effects as well as conditional kurtosis of the S&P 500 index.

This paper aims to focus on a medical goods distribution problem and pharmacological waste collection by plug-in hybrid vehicles with some real-world restrictions. In this research, considering alternative energy sources and simultaneous pickup and delivery led to a decrease in greenhouse gas emissions and distribution costs, respectively.

Here, this problem has been modeled as mixed-integer linear programming with the traveling and energy consumption costs objective function. The GAMS was used for model-solving in small-size instances. Because the problem in this research is an NP-hard problem and solving real-size problems in a reasonable time is impossible, in this study, the artificial bee colony algorithm is used.

Then, the algorithm results are compared with a simulated annealing algorithm that recently was proposed in the literature. Finally, the results obtained from the exact solution and metaheuristic algorithms are compared, analyzed and reported. The results showed that the artificial bee colony algorithm has a good performance.

In this paper, medical goods distribution with pharmacological waste collection is studied. The paper was focused on plug-in hybrid vehicles with simultaneous pickup and delivery. The problem was modeled with environmental criteria. The traveling and energy consumption costs are considered as an objective function.

Empirical studies regarding the impact of the real exchange rate (RER) on economic growth are extensively available. However, the literature as a whole appears to report varying results, while the causes of such differences have not been analyzed systematically. The present study aims to fill the gap in the literature.

In this paper, the authors compile 543 empirical estimates from 51 studies of the exchange rate-growth nexus in order to meta-analyze its relationship. Meta-analysis allows the authors to quantitatively synthesize previous empirical studies and explain the variation in the results. This method also enables us to investigate the possibility of publication bias, as there is a tendency in research only to report results that are both statistically significant and show the expected signs.

After addressing publication bias and heterogeneity in the estimates, the meta-regression results show that RER depreciation (or undervaluation) genuinely favors economic growth. On average, RER depreciation has a greater impact on economic growth in developing countries than the developed ones. The study’s results imply that maintaining an undervalued RER could be favorable to spur economic growth, especially in developing countries.

Initially predominant in the medical literature, meta-analysis has been on a rising edge in economics. This progress has produced many systematic quantitative review analyses with continuously improved statistical-econometric practices related to economic variables. However, to the authors’ knowledge, no comprehensive meta-regression analysis of the relationship between exchange rate and economic growth has been conducted and published in any publicly accessible academic outlet. Therefore, this study aims to fill this gap in the literature.

High levels of youth unemployment in Africa, the difficulty of accessing salaried jobs, and the weakness of public institutions in charge of employment issues push youths towards informal channels that can help them find jobs. Among these informal channels, religion has been playing an increasingly important role. Thus, this study aimed to analyse the effects of religion on youths' access to self-employment.

This study used data from the survey on the improvement of youth employment policies in Francophone Africa—conducted in 2018 by the Laboratory for Economic and Social Research and Studies (LARES) of Marien Ngouabi University—to estimate the effects of religion on access to self-employment. The econometric model employed is a two-stage model. Conditional mixed process developed by Roodman (2011) was used to verify the model's robustness.

The results indicate that religion exhibits a positive and significant effect on access to self-employment. This effect is stronger for youths from Muslim communities than for those from other religious communities, compared to youths who do not engage in religious communities.

Based on the current dynamics observed in numerous African countries with respect to employment access, these results imply that religious denominations should be considered when developing policies and programs related to employment, particularly for youths.

The approach followed in this study contributes to the literature predominantly by demonstrating how the network theory approach helps explain, to some extent, the link between religion and access to employment in general and access to self-employment, particularly in developing economies—mainly in sub-Saharan Africa, where the recourse to informal channels of access to self-employment constitutes a significant solution approach for youths.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-02-2023-0097

Assembly line is a common production form and has been effectively used in many industries, but the imprecise processing time of each process makes production line balancing and capacity forecasting the most troublesome problems for production managers. In this paper, uncertain man-hours are represented as interval grey numbers, and the optimization problem of production line balance in the case of interval grey man-hours is studied to better evaluate the production line capacity.

First, this paper constructs the basic model of assembly line balance optimization for the single-product scenario, and on this basis constructs an assembly line balance optimization model under the multi-product scenario with the objective function of maximizing the weighted greyscale production line balance rate, second, this paper designs a simulated annealing algorithm to solve problem. A neighborhood search strategy is proposed, based on assembly line balance optimization, an assembly line capacity evaluation method with interval grey man-hour characteristics is designed.

This paper provides a production line balance optimization scheme with uncertain processing time for multi-product scenarios and designs a capacity evaluation method to provide managers with scientific management strategies so that decision-makers can scientifically solve the problems that the company's design production line is quite different from the actual production situation.

There are few literary studies on combining interval grey number with assembly line balance optimization. Therefore, this paper makes an important contribution in this regard.