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The purpose of this paper is to gain a deeper insight on how education boosts economic progress in key emerging economies. This project is aimed at exploring the interactive dynamics between the tertiary education sector and economic development in BRICS countries. The author also aims to examine how the structure of higher education contributes to economic expansion.

The author uses the time series data of BRICS countries across approximately two decades to determine the statistical causality between the size of tertiary enrollment and economic development. The linear regression model is then used to figure out the different impact levels of academic and vocational training programs at the tertiary level to economic development.

Data from all BRICS countries exhibited a unidirectional statistical causality relationship, except the Brazilian data. The national economic expansion Granger Caused increased tertiary enrollment in Russia and India, while in China and South Africa, higher education enrollment Granger Caused economic progress. The impact from tertiary academic training is found to be positive for all BRICS nations, while tertiary vocation training is shown to have impaired the Russian and South African economy.

This project is based on a rather small sample size, and the stationary feature of the time series could be different should a larger pool of data spanning a longer period of time is used. In addition, the author also neglects other control variables in the regression model. Therefore, the impact level could be distorted due to possible omitted variable bias.

Tertiary academic study is found to have a larger impact level to all countries’ economic advancement, except for China, during the time frame studied. There is a statistical correlation between the education and economic progress. This is particularly true for BRICS countries, especially China. But the exception is Brazil.

The government should provide education up to the certain level, as there is a direct correlation to the job creation and economic progress. Furthermore, the government should also work closely with industry to ensure growth of industry and creation of new jobs.

The comparative analysis and evaluation of the dynamic interaction of tertiary enrollment and economic output across all five BRICS nations is unique, and it deepens the understanding of the socioeconomic development in these countries from a holistic management perspective.

The purpose of this study is to obtain both the numerical and asymptotic solutions of the unsteady mixed convection flow and heat transfer over an expanding or contracting cylinder which is placed vertically.

Solutions of the governing ordinary (similarity) differential equations for the fluid flow and temperature field are obtained using the function bvp4c from MATLAB. The problem involves the Prandtl number σ, the mixed convection parameter λ and unsteadiness parameter S that characterize an expanding or contracting cylinder. This solution approach is capable of producing multiple solutions once the necessary assumptions are provided.

It is found that solutions exist for all negative values of S, expanding cylinder, and only for small positive values of S, contracting cylinder. Further, more than one solution is observed; numerical computation shows that the critical point of S becomes singular as λ approaching zero. For the case of expanding cylinder, the mixed convection parameter has a significant effect on both the flow and heat transfer characteristics. Asymptotic analysis also shows that when σ is large, dual solutions exist for some values of S and λ.

The present results are new and original for the study of the unsteady mixed convection flow and heat transfer over an expanding/contracting cylinder where numerical solutions are obtained for representative values of the involved parameters. Asymptotic solutions for large λ and large σ are derived.

This study investigates whether listed firms using equity incentive plans (EIPs) adopt more conservative accounting in China's unique corporate setting.

Based on a sample of 2,243 listed firms and 9,950 firm-year observations for the period of 2008–2017, this study employs piecewise cross-sectional regression models with year and industry fixed effects to examine the associations proposed in the research hypotheses.

This study finds a positive relationship between the adoption of EIPs and accounting conservatism in listed Chinese firms. Further analyses reveal that this positive relationship is more pronounced when listed Chinese firms use restricted stock units (RSUs), instead of stock options, in their EIPs.

Unlike many early studies, this paper empirically investigates the impacts of two different types of equity incentives – stock options and RSUs – and thus contributes to accounting and corporate governance literature by providing a better understanding of the impacts of different types of equity incentives on financial reporting quality. However, this study does not consider other alternative equity incentive measurements because of the limited data regarding Chinese firm's executive compensation.

This study offers investors and policymakers in China some insight into how accounting conservatism in listed firms might be shaped by equity incentives used in their managerial compensation schemes.

This study is one of the few that examines the effects of using equity incentives in a large emerging market. It offers support for the view that the recent introduction of policies on EIPs by the Chinese government has an overall positive impact on listed firm's financial reporting quality, as reflected by greater degrees of accounting conservatism.

Digital tools have been used to document cultural heritage with high-quality imaging and metadata. However, some of the historical assets are totally or partially unlabeled and some are physically damaged, which decreases their attractiveness and induces loss of value. This paper introduces a new framework that aims at tackling the cultural data enrichment challenge using machine learning.

This framework focuses on the automatic annotation and metadata completion through new deep learning classification and annotation methods. It also addresses issues related to physically damaged heritage objects through a new image reconstruction approach based on supervised and unsupervised learning.

The authors evaluate approaches on a data set of cultural objects collected from various cultural institutions around the world. For annotation and classification part of this study, the authors proposed and implemented a hierarchical multimodal classifier that improves the quality of annotation and increases the accuracy of the model, thanks to the introduction of multitask multimodal learning. Regarding cultural data visual reconstruction, the proposed clustering-based method, which combines supervised and unsupervised learning is found to yield better quality completion than existing inpainting frameworks.

This research work is original in sense that it proposes new approaches for the cultural data enrichment, and to the authors’ knowledge, none of the existing enrichment approaches focus on providing an integrated framework based on machine learning to solve current challenges in cultural heritage. These challenges, which are identified by the authors are related to metadata annotation and visual reconstruction.

Activity sampling is a technique to monitor onsite labourers' time utilisation, which can provide helpful information for the management level to implement suitable labour productivity improvement strategies continuously. However, there needs to be a review paper that compiles research on activity sampling studies to give readers a thorough grasp of the research trend. Hence, this paper aims to investigate the activity sampling techniques applied in earlier research from the angles of activity categories formation, data collection methods and data analysis.

The method used in this paper is a systematic review guided by the PRISMA framework. The search was conducted in Scopus and Web of Science. The inclusion and exclusion criteria were applied, selecting 70 articles published between 2011 and 2022 for data extraction and analysis. The analysis method involved a qualitative synthesis of the findings from the selected articles.

Activity sampling is broadly divided into four stages: targeting trade, determining activity categories, data collection and data analysis. This paper divides the activity categories into three levels and classifies the data collection methods into manual observation, sensor-based activity sampling and computer vision-based activity sampling. The previous studies applied activity sampling for two construction management purposes: labour productivity monitoring and ergonomic safety monitoring. This paper also further discusses the scientific research gaps and future research directions.

This review paper contributes to the body of knowledge in construction management by thoroughly understanding current state-of-the-art activity sampling techniques and research gaps.

The purpose of this study is to scrutinise individual learning strategies in their workplace when they were experiencing digital innovation. Moreover, the respective enablers and challenges of each category of strategies were explored to conceptualise supporting features needed in the digital learning environment.

This study adopts a qualitative inquiry to understand employees’ workplace learning strategies and the respective enablers, and challenges in each category of strategies. Twenty employees across different industries were interviewed, and a two-cycle inductive coding analysis was adopted to identify the categories of learning strategies.

Four categories of workplace learning strategies were identified: self-driven, social-leveraged, enterprise-oriented and information and technology-enhanced (IT-enhanced) strategies. Enablers and challenges of each category were also presented in this study.

The findings provide insights for individuals to establish a repertoire of learning strategies. The inadequate use of IT-enhanced strategies is additionally addressed.

This study examined workplace learning strategies in the context of digital innovation. Based on the empirical findings and existing literature, this study proposes a framework with the supporting features for a digital learning environment.

The purpose of this study is to review the literature on the Delphi method, its characteristics and current applications through an analysis of recent most-cited scientific papers, with an emphasis on three axes, namely, the number of rounds used, stakeholder participation relevance or only academic experts’ participation and the possibility of using indicators or techniques different from those related to descriptive statistics.

In total, 57 papers were initially reviewed, 10 of them with a high citation rate. Then, an analysis was made of papers in Scopus for the period 2015-2018 published in the Technological Forecasting and Social Change Journal and in the Futures and Foresight Journal, which had the characteristic of displaying quartile Q1 or Q2 in Scimago in addition to being in Scopus.

Among the main results, the authors observe the tendency to use fewer rounds, a higher prevalence of stakeholder participation and not only academic experts but also the use of new types of modified Delphi such as real-time spatial Delphi, Delphi group, market Delphi, real-world Delphi and policy Delphi.

Among the conclusions, the possibility of using other indicators or complementary techniques to the descriptive statistics is highlighted such as number of justifications or comments between rounds, coefficients to quantify the competence or degree of expertise of the participants, measures of the perception of the expert on the usefulness of the presented feedback, graphs of the number of arguments according to the number of questions, the Wilcoxon Ranked Pairs Test, the k means, Kolmogorov–Simonov test and the Mann–Whitney U-test.

The purpose of this study is to present the magnetohydrodynamic (MHD) flow and heat transfer in an accelerating film of a non-Newtonian pseudo-plastic nanofluid along an inclined surface with viscous dissipation and Joule heating.

An incompressible and inelastic fluid is assumed to obey the Ostwald-de-Waele power law model and the action of viscous stresses is confined to the developing momentum boundary layer adjacent to the solid surface. Viscous dissipation and Joule heating on the flow of electrically conducting film in the presence of uniform transverse magnetic field is considered for the Carboxyl Methyl Cellulose (CMC) water-based nanofluid. The fluid is the CMC-water-based with concentration (0.1-0.4 per cent) containing three types of nano-solid particles Cu, Al₂O₃ and TiO₂. The modeled boundary layer conservation equations are transformed to dimensionless, coupled and highly non-linear system of differential equations, and then solved numerically by means of a local non-similarity approach with shooting technique. To validate the numerical results, a comparison of the present results is made with the earlier published results and is found to be in good agreement.

The effects of magnetic parameter, Prandtl number, Eckert number and Biot numbers on the velocity and temperature fields are presented graphically and discussed for various values of thermo-physical parameters. It has been found that magnetic field decelerates the fluid velocity for both cases of Newtonian nanofluid and pseudo-plastic nanofluid because of the generated drag-like Lorentz force. This is of great benefit in magnetic materials processing operations, utilizing static transverse uniform magnetic field, as it allows a strong regulation of the flow field.

The numerical study is valid for two-dimensional, steady, laminar film flow of Ostwald-de-Waele power law non-Newtonian nanofluid along an inclined plate. A uniform transverse magnetic field of strength B₀ is applied perpendicular to the wall. Assume that the base fluid and the nano-solid particles are in thermal equilibrium with no slip effects. The interaction of magnetic field with nanofluid has several potential implications and may be used to deal with the problems such as cooling nuclear reactors by liquid sodium and inducting the flow meter which depends on the potential difference in the fluid along the direction perpendicular to the motion and to the magnetic field.

The study has significant applications in magnetic field control of materials processing systems.

The results of the present study may be attentiveness to the engineers and applied mathematicians who are interested in hydrodynamics and heat transfer enhancement associated with film flows.

The operating wagon records were produced from distinct railway information systems, which resulted in the wagon routing record with the same oriental destination (OD) was different. This phenomenon has brought considerable difficulties to the railway wagon flow forecast. Some were because of poor data quality, which misled the actual prediction, while others were because of the existence of another actual wagon routings. This paper aims at finding all the wagon routing locus patterns from the history records, and thus puts forward an intelligent recognition method for the actual routing locus pattern of railway wagon flow based on SST algorithm.

Based on the big data of railway wagon flow records, the routing metadata model is constructed, and the historical data and real-time data are fused to improve the reliability of the path forecast results in the work of railway wagon flow forecast. Based on the division of spatial characteristics and the reduction of dimension in the distributary station, the improved Simhash algorithm is used to calculate the routing fingerprint. Combined with Squared Error Adjacency Matrix Clustering algorithm and Tarjan algorithm, the fingerprint similarity is calculated, the spatial characteristics are clustering and identified, the routing locus mode is formed and then the intelligent recognition of the actual wagon flow routing locus is realized.

This paper puts forward a more realistic method of railway wagon routing pattern recognition algorithm. The problem of traditional railway wagon routing planning is converted into the routing locus pattern recognition problem, and the wagon routing pattern of all OD streams is excavated from the historical data results. The analysis is carried out from three aspects: routing metadata, routing locus fingerprint and routing locus pattern. Then, the intelligent recognition SST-based algorithm of railway wagon routing locus pattern is proposed, which combines the history data and instant data to improve the reliability of the wagon routing selection result. Finally, railway wagon routing locus could be found out accurately, and the case study tests the validity of the algorithm.

Before the forecasting work of railway wagon flow, it needs to know how many kinds of wagon routing locus exist in a certain OD. Mining all the OD routing locus patterns from the railway wagon operating records is helpful to forecast the future routing combined with the wagon characteristics. The work of this paper is the basis of the railway wagon routing forecast.

As the basis of the railway wagon routing forecast, this research not only improves the accuracy and efficiency for the railway wagon routing forecast but also provides the further support of decision-making for the railway freight transportation organization.

Prior studies have confirmed market segmentation as an important shackle to China's macroeconomy upgrade, but the systematic analysis of microenterprise upgrade remains inadequate. This paper aims to investigate the nonlinear impact of market segmentation on microenterprise upgrade.

The price method was applied to calculate China's provincial market segmentation, including commodity, capital, labor and energy. The generalized method of moments was employed to examine the nonlinear impact of market segmentation on the upgrading of manufacturing enterprises based on microenterprise data from 2003 to 2019.

First, China’s heterogeneous market segmentations have been significantly reduced. Macroeconomics and policies are critical factors in market integration. Second, there is an inverted U-shaped relationship between China’s total market segmentation and the upgrading of manufacturing enterprises. Third, the relationship between the commodity market segmentation, labor market segmentation, energy market segmentation and manufacturing enterprises' upgrade is shown as an inverted U-shaped. Nevertheless, the relationship between the capital market segmentation and upgrading of manufacturing enterprises exhibits a U-shape.

The impact of market segmentation on the upgrading of manufacturing enterprises in China performs nonlinearly. An inverted U-shaped relationship exists between market segmentation in commodity, labor and energy and the upgrading of manufacturing enterprises, while a U-shaped relationship prevails between capital market segmentation and the upgrading of manufacturing enterprises.