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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.

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.

Quality failures in grid infrastructure construction would cause large-scale collapses in power supply and additional expenditures by reworks and repairs. Learning from quality failures (LFQF) extracts experience from previous quality events and converts them into preventive measures to reduce or eliminate future construction quality issues. This study aims to investigate the influence factors of LFQF in the construction of grid infrastructure.

The related factors of LFQF, including quality management (QM) practices, quality rectification, and individual learning, were identified by reviewing literature about organizational learning and extracting experience from previous failures. A questionnaire survey was distributed to the grid companies in North, Northeast, Northwest, East, Central, and Southwest China. 381 valid responses collected and analyzed using structural equation modeling (SEM) to test the influence of these factors on LFQF.

The SEM results support that QM practices positively affect individual learning and LFQF. Quality rectification indirectly impacts LFQF via individual learning, while the results did not support the direct link between quality rectification and LFQF.

The findings strengthen practical insights into extracting experience from poor-quality issues and continuous improvement. The contributory factors of LFQF found in this study benefit the practitioners by taking effective measures to enhance organizational learning capability and improve the long-term construction quality performance in the grid infrastructure industry.

Existing research about the application of LFQF still stays at the explorative and conceptual stage. This study investigates the related factors of LFQF, including QM practices, quality rectification, and individual learning, extending the model development of learning from failures (LFF) in construction QM.

This paper investigates the ways digital media applications in rural areas have transformed the influence of social networks (SN) on farmers' adoption of various climate change mitigation measures (CCMM), and explores the key mechanisms behind this transformation.

The study analyzes data from 1,002 farmers’ surveys. First, a logit model is used to measure the impact of SN on the adoption of different types of CCMM. Then, the interaction term between digital media usage (DMU) and SN is introduced to analyze the moderating effect of digital media on the impact of SN. Finally, a conditional process model is used to explore the mediating mechanism of agricultural socialization services (ASS) and the validity of information acquisition (VIA).

The results reveal that: (1) SN significantly promotes the adoption of CCMM and the marginal effect of this impact varies with different kinds of technologies. (2) DMU reinforces the effectiveness of SN in promoting farmers' adoption of CCMM. (3) The key mechanisms of the process in (2) are the ASS and the VIA.

This study shows that in the context of DMU, SN’s promotion effect on farmers' adoption of CCMM is strengthened.

This study is aimed at making an inspection of the effects of collaborative innovation network characteristics on firm innovation performance, and the intermediary roles of knowledge transfer efficiency is taken into account.

This study used a convenient sampling method to obtain population and samples. Using data obtained by publishing online and paper questionnaires, and using on-site interviews in Anhui Province in the Yangtze River Delta region of China, descriptive analysis, regression analysis and correlation analysis are utilized to study the direct influence of collaborative innovation network characteristics on knowledge transfer efficiency as well as firm innovation performance, and the intermediary roles of knowledge transfer efficiency on firm innovation performance, respectively. In this study, 3,000 questionnaires were distributed to the employees of enterprises engaged in research and development (R&D) activities, of which 2,560 were valid. With the help of SPSS24.0 software, the reliability and validity of the questionnaire was analyzed.

The results are indicative of that network centrality and relationship strength positively affect knowledge transfer efficiency and firm innovation performance. Nevertheless, network scale has no significant correlation with knowledge transfer efficiency and enterprise innovation performance. In addition, knowledge transfer efficiency is an intermediary between collaborative innovation network characteristics and enterprise innovation performance, and positively affects enterprise innovation performance, which demonstrated that managers should take advantage of collaborative innovation network characteristics to elevate knowledge transfer efficiency because well-realized transferals of knowledge can help accelerate the coordination of resources in knowledge, and finally bring about the advancement of firm's innovation abilities and performance.

There are few previous studies that fully examined the relationships among collaborative innovation network characteristics, knowledge transfer efficiency and firm innovation performance. This paper developed previous researches on the relationships between collaborative innovation network characteristics, knowledge transfer efficiency and firm innovation performance. The mediation of knowledge transfer efficiency on the relationship between collaborative innovation network characteristics and firm innovation performance is analyzed. Further, studies on collaborative innovation network characteristics using data obtained from employees engaged in R&D activities are very limited in the literature. On account of that, the findings in this study may make sense to the innovation ability of innovative enterprise and expand the literature in the field of enterprise strategic management and knowledge management.

This analysis shows that collaborative innovation network characteristics have both positive and negative effects on firm innovation performance. Therefore, business managers should pay attention to their position in the collaborative innovation network and maintain the relationship strength with other innovation subjects. Special consideration should be given to the knowledge transfer of innovative enterprises, so as to improve firm innovation performance practically.

The study may provide additional understandings for researchers, government managers, universities and enterprises with regard to strategic management from the visual angle of innovation ecosystems. It is instrumental in the exploration of the mechanisms enabling firm innovation performance.

This study aims to study the distribution characteristics of antibiotic resistance in direct-eating food and analysis of Citrobacter freundii genome and pathogenicity. Residual antibiotics and antibiotic resistance genes (ARGs) in the environment severely threaten human health and the ecological environment. The diseases caused by foodborne pathogenic bacteria are increasing daily, and the enhancement of antibiotic resistance of pathogenic bacteria poses many difficulties in the treatment of disease.

In this study, six fresh fruits and vegetable samples were selected for isolation and identification of culturable bacteria and analysis of antibiotic resistance. The whole genome of Citrobacter freundii isolated from cucumber was sequenced and analyzed by Oxford Nanopore sequencing.

The results show that 270 strains of bacteria were identified in 6 samples. From 12 samples of direct food, 2 kinds of probiotics and 10 kinds of opportunistic pathogens were screened. The proportion of Citrobacter freundii screened from cucumber was significantly higher than that from other samples, and it showed resistance to a variety of antibiotics. Whole genome sequencing showed that Citrobacter freundii was composed of a circular chromosome containing signal peptides, transmembrane proteins and transporters that could induce antibiotic efflux, indicating that Citrobacter freundii had strong adaptability to the environment. The detection of genes encoding carbohydrate active enzymes is more beneficial to the growth and reproduction of Citrobacter freundii in crops. A total of 29 kinds of ARGs were detected in Citrobacter freundii, mainly conferring resistance to fluoroquinolones, aminoglycosides, carbapenem, cephalosporins and macrolides. The main mechanisms are the change in antibiotic targets and efflux pumps, the change in cell permeability and the inactivation of antibiotics and the detection of virulence factors and ARGs, further indicating the serious risk to human health.

The detection of genomic islands and prophages increases the risk of horizontal transfer of virulence factors and ARGs, which spreads the drug resistance of bacteria and pathogenic bacteria more widely.

The service environment of urban polyethylene (PE) pipes has a crucial influence on their long-term safety and performance. Based on the application and structural performance analysis of PE pipe failure cases, this study aims to investigate the impact of organic substances in the soil on the aging behavior of PE pipes by designing organic solutions with different concentrations, which are based on the composition of organic substances in the soil environment, and periodic immersion tests.

Soil samples in the vicinity of the failed pipes were analyzed by gas chromatography-mass spectrometry, sensitive organic substances were screened and soaking solutions of different concentrations were designed. After the soaking test, the PE pipe samples were analyzed using differential scanning calorimetry, Fourier-transform infrared spectroscopy and other testing methods.

The performance difference between the outer surface and the middle of the cross section of PE pipes highlights the influence of the soil service environment on their aging. Different organic solutions can have varying impacts on the aging behavior of PE pipes when immersed. For instance, when exposed to amine organic solutions, PE pipes may have an increased weight and decreased material yield strength, although there is no reduction in their thermal or oxygen stability. On the contrary, when subjected to ether organic solutions, the surface of PE pipe specimens may be affected, leading to a reduction in material fracture elongation and a decrease in their thermal and oxygen stability. Furthermore, immersion in either amine or ether organic solutions may result in the production of hydroxyl and other aging groups on the surface of the material.

Understanding the potential impact of organic substances in the soil environment on the aging of PE pipe ensures the long-term performance and safety of urban PE pipe. This research approach will provide valuable insights into improving the durability and reliability of urban PE pipes in soil environments.

The aim of this study is to empirically investigate the impact of marketing analytics capability on business performance from the perspective of RBV theory.

This study used a survey method to gather information from 225 food processing SMEs registered with the Ghana Enterprise Agency (GEA) in Ghana’s eastern region. A structural equation modeling (SEM) path analysis was used to assess the impact of marketing analytics capability (MAC) on the performance of SMEs.

The results of the study show that MAC significantly and positively affect the financial performance (FP), customer performance (CF), internal business process performance (IBPP) and learning and growth performance (LGP) of Ghanaian SMEs. The findings of this study also illustrated the significance of MAC determinants, including marketing analytics skills (MAS), data resource management (DRM) and data processing capabilities (DPC), in achieving SME success in Ghana.

The research’s conclusions give RBV theory strong credence. The results of this study also provide credence to previous research finding that SMEs should view MAC and its determinants (i.e. DRM, DPC, MAS) as a crucial strategic capability to improve their performance (i.e. FP, CF, IBPP, LGP). With regard to its contribution, this study broadens the body of knowledge on MAC and SME performance, particularly in the context of an emerging economy.

In modern urban governance, rescue materials storage points (RMSP) are a vital role to be considered in responding to public emergencies and improving a city's emergency management. This study analyzes the siting of community-centered relief supply facilities.

Combining grey relational analysis, complex network and relative entropy, a new multi criteria method is proposed. It pays more attention to the needs of the community, taking into account the use of community hospitals, fire centers and neighborhood offices to establish small RMSP.

The research results firstly found suitable areas for RMSP site selection, including Hanyang, Qiaokou, Jiangan and Wuchang. The top 10 nodes in each region are found as the location of emergency facilities, and the network parameters are higher than ordinary nodes in traffic networks. The proposed method was applied in Wuhan, China and the method was verified by us-ing a complex network model combined with multi-criteria decision-making for emergency facility location.

This method solves the problem of how to choose the optimal solution and reduces the difficulty for decision makers. This method will help emergency managers to locate and plan RMSP more simply, especially in improving emergency siting modeling techniques and additionally in providing a reference for future research.

The method proposed in this study is beneficial to improve the decision-making ability of urban emergency departments. Using complex networks and comprehensive evaluation techniques, RMSP is incorporated into the urban community emergency network as a critical rescue force. More importantly, the findings highlight a new direction for further research on urban emergency facilities site selection based on a combination of sound theoretical basis as well as empirical evidence gained from real life case-based analysis.

1. Material reserve points are incorporated into the emergency supply network to maintain the advantage of quantity.

2. Build emergency site selection facilities centered on urban communities.

3. Use a complex network model to select the location of emergency supplies storage sites.

Material reserve points are incorporated into the emergency supply network to maintain the advantage of quantity.

Build emergency site selection facilities centered on urban communities.

Use a complex network model to select the location of emergency supplies storage sites.

In response to the dynamic demands of the contemporary business landscape, this study critically examines the pivotal role of ethical leadership in shaping employee’s innovative behavior within organizations. Our research delves into the nuanced interplay between ethical leadership, psychological well-being and innovative work behavior. Drawing from the principles of social exchange theory, our study addresses a critical gap in the literature by exploring the mediating role of psychological well-being in the relationship between ethical leadership and employees' innovative work behavior.

In this quantitative research, data were collected from 384 employees and their direct supervisors in Pakistan’s IT sector using “Google Forms” through a convenience sampling method facilitated by the “LinkedIn” platform. Additionally, the study applied a two-stage structural equation modeling approach, first to assess the uni-dimensionality, and subsequently, to evaluate the proposed hypotheses.

The research results unveiled a robust and positive impact of ethical leadership on innovative work behavior, operating through both direct and indirect pathways mediated by psychological well-being. Intriguingly, the moderating role of perceived organizational support adds depth to our understanding, revealing nuanced conditions under which ethical leadership influences employees' well-being and, subsequently, their innovative contributions.

Beyond theoretical contributions, our study provides practical insights for managers seeking to leverage employees' innovative work behavior for organizational success. By emphasizing ethical leadership as a catalyst, we advocate for its integration into HRM practices. However, recognizing the contextual nature of organizational support, our findings underscore the importance of adaptable leadership strategies to maximize positive outcomes.

Grounded in the principles of social exchange theory, this research marks a pioneering effort to shed light on the link between ethical leadership and innovative work behavior through the mediation of psychological well-being. Additionally, this study makes a valuable contribution to the current body of knowledge by investigating the contingent influence of perceived organizational support on the relationship between ethical leadership and employees' psychological well-being.