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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 paper aims to investigate the variations in the flow fields induced by transition regions in the windbreak structures between the flat ground and the cutting along a railway and to propose mitigation measures to improve the windproof ability of the windbreak.

The improved delayed detached eddy simulation method was used to simulate the impact of the windbreak transition on flow structures of the high-speed railway under different wind angles, and also the accuracy of the numerical results was validated with those of the wind tunnel test.

The results showed that the original windbreak transition region resulted in a dimensionless peak wind velocity of 0.62 and 0.82 for railway line-1 at wind angles of 90° and 75°, respectively, and the corresponding values were 0.81 and 0.97 for railway line-2. The flow structure analysis revealed the reason for the mismatched height in the transition region, and the right-angle structures of the windbreaks resulted in ineffective protection and sudden changes in the wind speed and direction. Two mitigation measures – oblique structure (OS) and circular curve structure (CCS) transition walls – were developed to reduce the peak wind speed. The OS provided superior protection. The peak value of dimensionless wind velocity was all less than 0.2 for OS and CCS.

The flow field deterioration mechanism induced by the inappropriate form of a windbreak transition at different wind angles was examined, and effective mitigation and improvement measures were proposed and compared with the original transition.

Based on the aerodynamic loads and dynamic performances of trains, this study aims to investigate the effect of crosswinds and raindrops on intercity trains operating on viaducts to ensure the safe operation of intercity railways in metropolitan areas.

An approach coupled with the Euler multiphase model as well as the standard k-ɛ turbulence model is used to investigate the coupled flow feature surrounding trains and viaducts, including airflow and raindrops, and the numerical results are validated with those of the wind tunnel test. Additionally, the train’s dynamic response and the operating safety region in different crosswind speeds and rainfall is investigated based on train’s aerodynamic loads and the train wheel–rail dynamics simulation.

The aerodynamic loads of trains at varying running speeds exhibit an increasing trend as the increase of wind speed and rainfall intensity. The motion of raindrop particles demonstrates a significant similarity with the airflow in wind and rain environments, as a result of the dominance of airflow and the supplementary impacts of droplets. As the train’s operating speed ranged between 120 and 200 km/h and within a rainfall range of 20–100 mm/h, the safe operating region of trains decreased by 0.56%–7.03%, compared with the no-rain condition (0 mm/h).

The impact of crosswind speeds and rainfall on the train’s aerodynamic safety is studied, including the flow feature of crosswind and different particle-sized raindrops around the train and viaduct, aerodynamic loads coefficients suffered by the intercity train as well as the operating safety region of intercity trains on the viaduct.

Physics-informed neural networks (PINNs) have become a new tendency in flow simulation, because of their self-advantage of integrating both physical and monitored information of fields in solving the Navier–Stokes equation and its variants. In view of the strengths of PINN, this study aims to investigate the impact of spatially embedded data distribution on the flow field results around the train in the crosswind environment reconstructed by PINN.

PINN can integrate data residuals with physical residuals into the loss function to train its parameters, allowing it to approximate the solution of the governing equations. In addition, with the aid of labelled training data, PINN can also incorporate the real site information of the flow field in model training. In light of this, the PINN model is adopted to reconstruct a two-dimensional time-averaged flow field around a train under crosswinds in the spatial domain with the aid of sparse flow field data, and the prediction results are compared with the reference results obtained from numerical modelling.

The prediction results from PINN results demonstrated a low discrepancy with those obtained from numerical simulations. The results of this study indicate that a threshold of the spatial embedded data density exists, in both the near wall and far wall areas on the train’s leeward side, as well as the near train surface area. In other words, a negative effect on the PINN reconstruction accuracy will emerge if the spatial embedded data density exceeds or slips below the threshold. Also, the optimum arrangement of the spatial embedded data in reconstructing the flow field of the train in crosswinds is obtained in this work.

In this work, a strategy of reconstructing the time-averaged flow field of the train under crosswind conditions is proposed based on the physics-informed data-driven method, which enhances the scope of neural network applications. In addition, for the flow field reconstruction, the effect of spatial embedded data arrangement in PINN is compared to improve its accuracy.

The purpose of this paper is to investigate the forces driving organizational innovation, particularly CEO transformational leadership as it affects external and internal social capital in top management teams.

Survey questionnaires were administered to 90 Chinese top management teams. Structural equation modeling was used to test the hypothesized relationships.

Both internal and external social capital mediated the relationship between transformational leadership and organizational innovation.

Organizations should strengthen internal and external capital of top management teams to reap maximal innovation outcomes from transformational leadership.

The findings contribute to the transformational leadership, social capital, and innovation literature first by showing how leadership influences innovation through largely neglected mechanisms – internal and external social capital. Second, a social capital focus challenges the tacit assumption that transformational leadership has only internal influences by showing that it potentially spills over to the external domain.

This paper explores the effectiveness of using blockchain technology to solve financial constraints faced by small- and medium-sized suppliers in a capital-constrained supply chain.

To characterize the impact of blockchain on credit period and enterprise credit level, the study formulates a newsvendor model to analyze a supply chain in which a financially constrained supplier sells products to a financially sound manufacturer, subject to uncertain demand. The study investigates three repayment methods: the benchmark case without blockchain and two blockchain-enabled cases with the hybrid repayment mode and single repayment mode (SRM), respectively. The study derives and compares the equilibria under each repayment method to characterize their impact.

When the bank interest rate is low and the carbon cap is also low, choosing to implement blockchain technology leads to higher profitability for the manufacturer than not utilizing it. Within the framework of blockchain technology, when comparing the two repayment models, the manufacturer exhibits a preference for SRM. Furthermore, under specific conditions of the bank interest rate, blockchain technology can effectively facilitate consensus among supply chain members in terms of channel selection.

The results derived in this paper provide novel managerial implications to the capital-constrained members in terms of pricing decisions and order quantity under demand uncertainty considering blockchain technology, which transfers the creditor's rights to the bank and shortens the collection time. In addition, blockchain technology enables efficient and intelligent collaborative development of supply chains, which can reduce carbon emissions during the transportation of goods.

Few studies incorporate blockchain technology into supply chain finance, and this paper considers the credit period and capital's time value for a capital-constrained supplier facing the adoption of blockchain technology within a stochastic demand environment.

The purpose of this paper is to investigate employee turnover in a leading Chinese retail organisation, where high turnover rates are recognised as being a major issue for human resource strategy. The study seeks to focus on the job satisfaction‐turnover relationship, examining how this is moderated by occupation.

A literature review on employee job satisfaction and employee turnover provides the basis for the research model and hypotheses. A self‐completion questionnaire survey, involving 164 respondents (95.4 per cent response rate) from a leading Chinese retail organisation was used to gather data and test existing theory in a Chinese context.

The results provided strong support for the hypothesised negative relationship between employee turnover intention and job satisfaction. Occupation is also significantly associated with job satisfaction, turnover intention and the job satisfaction‐turnover relationship: non‐management/frontline employees expressed higher levels of intention to leave their job than management/office employees.

The sample was limited to one retail organisation in China. It may not be appropriate to generalise the findings across other populations or settings. However, the sample can be viewed as a representative case typical of many other organisations in the same industry.

The results provide insight into the impact of employee job satisfaction on turnover intention in the particular Chinese retail setting which could benefit managers and policy makers in the focus organisation as well as other organisations operating in the same sector in general.

The paper studies problems that characterise the Chinese retail sector.

Loitering aerial vehicle (LAV) swarm safety flight control is an unmanned system control problem under multiple constraints, which are derived to prevent the LAVs from suffering risks inside and outside the swarms. The computational complexity of the safety flight control problem grows as the number of LAVs and of the constraints increases. Besides some important constraints, the swarms will encounter with sudden appearing risks in a hostile environment. The purpose of this study is to design a safety flight control algorithm for LAV swarm, which can timely respond to sudden appearing risks and reduce the computational burden.

To address the problem, this paper proposes a distributed safety flight control algorithm that includes a trajectory planning stage using kinodynamic rapidly exploring random trees (KRRT*) and a tracking stage based on distributed model predictive control (DMPC).

The proposed algorithm reduces the computational burden of the safety flight control problem and can fast find optimal flight trajectories for the LAVs in a swarm even there are multi-constraints and sudden appearing risks.

The proposed algorithm did not handle the constraints synchronously, but first uses the KRRT* to handle some constraints, and then uses the DMPC to deal with the rest constraints. In addition, the proposed algorithm can effectively respond to sudden appearing risks by online re-plan the trajectories of LAVs within the swarm.

This study investigates the way in which acquisition-related human factors affect knowledge transfer in the context of Chinese cross-border M&A for strategic assets. The authors find that the process of knowledge transfer is reciprocal for revenue and cost synergies, including explicit and tacit knowledge. The establishment of joint ventures (JV) in China after the takeover boosts product-oriented knowledge transfer from overseas-acquired firms in mature markets to Chinese acquirers. The promotion of overseas synergies stimulates complementary knowledge transfer flow, which is reversely transferred from Chinese acquirers to overseas-acquired subsidiaries such as low-saving sourcing and new market applications. This study identifies three acquisition-related human factors that impact overseas knowledge senders for knowledge transfer. These human factors are implemented by Chinese strategic investors as new shareholders during the loosen integration phase. The first facilitator is all-round communication programs with top management involvement, aiming to build up constructive communication channels to boost knowledge transfer. The second facilitator is competence-based trust, which stimulates cooperation and application based on similar professional competence between Chinese acquirers and their overseas-acquired subsidiaries. The impeder is a high turnover of key skilled workers at Chinese acquirers to undermine the effectiveness of knowledge transfer.

The research question is how home country corruption and nationalism may affect operations of BRIC multinational enterprises. BRIC composition permits a comparison of two authoritarian regimes and two constitutional democracies. Each BRIC features a different combination of corruption and nationalism. The chapter adds South Africa information for two limited reasons. First, from 2010 South Africa is a member of the BRIC summit process. South Africa is an important entry point to Africa, for BRIC multinationals and particularly for China. Second, concerning corruption and nationalism South Africa is analytically useful as a control context that helps illustrate but does not appear to change highly exploratory BRIC findings.

The chapter draws on limited literature and information concerning corruption and nationalism in BRICs to suggest tentative possibilities. Transparency International provides bribe payers index estimates for 28 large economies, with important multinational enterprises, and corruption perceptions index estimates including those 28 countries. These estimates include the four BRICs and South Africa. The available sources suggest some suggested findings about varying impacts of home country corruption and nationalism on operations of BRIC multinationals.

China and Russia are authoritarian regimes in transition from central planning-oriented communist regimes. They are global military powers, expanding influence in their respective regions. Brazil, India, and South Africa are constitutional democracies. India, a nuclear-armed military power, seeks a regional leadership role in South Asia. Brazil and South Africa are key countries economically in their regions. BRIC multinationals are positioned between home country and host country conditions. Chinese and Russian multinationals may reflect a stronger nationalistic tendency due to home country regimes and ownership structure.

The chapter is an original but highly exploratory inquiry into impacts of corruption and nationalism on BRIC multinationals. Extant BRIC literature tends to understudy effects of home country corruption and nationalism on managerial mindset and incentives in either commercial or state-owned enterprises.