Search results

Vertical electrical sounding (VES) and Rayleigh wave exploration are widely used in the exploration of near-surface structure, but both have limitations. This study aims to make full use of the advantages of the two methods, reduce the multiple solutions of single inversion and improve the accuracy of the inversion. Thus, a nonlinear joint inversion method of VES and Rayleigh wave exploration based on improved differential evolution (DE) algorithm was proposed.

Based on the DE algorithm, a new initialization strategy was proposed. Then, taking AK-type with high-velocity interlayer model and HA-type with low-velocity interlayer model near the surface as examples, the inversion results of different methods were compared and analyzed. Then, the proposed method was applied to the field data in Chengde, Hebei Province, China. The stratum structure was accurately depicted and verified by drilling.

The synthetic data and field data results showed that the joint inversion of VES and Rayleigh wave data based on the improved DE algorithm can effectively improve the interpretation accuracy of the single-method inversion and had strong stability and large generalizable ability in near-surface engineering problems.

A joint inversion method of VES and Rayleigh wave data based on improved DE algorithm is proposed, which can improve the accuracy of single-method inversion.

This study aims to identify the intellectual structure of expanded audit reporting (EAR), offers a quantitative summation of prominent themes, contributors and knowledge gaps and provides suggestions for further research.

This research uses various bibliometric techniques, including co-word and co-citation analysis for EAR science mapping, based on 123 papers from Scopus Database between 1991 and 2022.

The results show EAR research is focused on Audit Quality; Auditor Liability and Litigation; Communicative Value and Readability; Audit Fees; and Disclosure. Regarding EAR research, Brasel et al. (2016), article is the most cited paper, Bédard J. is the most cited author, Laval University is the most influential university, The Accounting Review is the most cited journal and USA is the leading country. Furthermore, the results show that in common law countries, in which shareholder rights and litigation risk is high, topics such as disclosure quality and audit litigation have been addressed more; and in civil legal system countries, which usually favor stakeholders’ rights, topics of gender diversity or corporate governance have been more studied.

This research has practical implications for standard setters and regulators, who can identify important, overlooked and emerging issues and consider them in future policies and standards.

This paper contributes to the literature by providing a more objective and comprehensive status of the accounting research on EAR, identifying the gaps in the literature and proposing a direction for future research to continue the discussion on the value-relevance of EAR to achieve more transparency and less audit expectation gap.

The purpose of this paper is to propose an new online path planning method for porcine belly cutting. With the proliferation in demand for the automatic systems of pork production, the development of efficient and robust meat cutting algorithms are hot issues. The uncertain and dynamic nature of the online porcine belly cutting imposes a challenge for the robot to identify and cut efficiently and accurately. Based on the above challenges, an online porcine belly cutting method using 3D laser point cloud is proposed.

The robotic cutting system is composed of an industrial robotic manipulator, customized tools, a laser sensor and a PC.

Analysis of experimental results shows that by comparing with machine vision, laser sensor-based robot cutting has more advantages, and it can handle different carcass sizes.

An image pyramid method is used for dimensionality reduction of the 3D laser point cloud. From a detailed analysis of the outward and inward cutting errors, the outward cutting error is the limiting condition for reducing the segments by segmentation algorithm.

Big data challenges and opportunities on the Internet of Vehicles (IoV) have emerged as a transformative paradigm to change intelligent transportation systems. With the growth of data-driven applications and the advances in data analysis techniques, the potential for data-adaptive innovation in IoV applications becomes an outstanding development in future IoV. Therefore, this paper aims to focus on big data in IoV and to provide an analysis of the current state of research.

This review paper uses a systematic literature review methodology. It conducts a thorough search of academic databases to identify relevant scientific articles. By reviewing and analyzing the primary articles found in the big data in the IoV domain, 45 research articles from 2019 to 2023 were selected for detailed analysis.

This paper discovers the main applications, use cases and primary contexts considered for big data in IoV. Next, it documents challenges, opportunities, future research directions and open issues.

This paper is based on academic articles published from 2019 to 2023. Therefore, scientific outputs published before 2019 are omitted.

This paper provides a thorough analysis of big data in IoV and considers distinct research questions corresponding to big data challenges and opportunities in IoV. It also provides valuable insights for researchers and practitioners in evolving this field by examining the existing fields and future directions for big data in the IoV ecosystem.

Consumers and businesses are becoming increasingly conscious of sustainable business practices and are often willing to pay a premium for responsibly sourced and manufactured products. Many countries and organizations have implemented regulations and standards for sustainability and companies face penalties or are barred from exporting for not meeting the requirements. Rooted in the resource-based view theory, this study aims to test a moderated mediation model to improve the sustainability performance of exporting firms.

Textile firms generating more than 25% of export revenues were targeted for this research. The data collected from 245 middle management-level employees were tested for reliability and validity. The structural equation modelling in AMOS 26 was used to test hypotheses.

Organizational readiness for green innovation (ORGI) has a direct positive effect on sustainability performance. The mediation analysis implies that ORGI translates into sustainability performance through improvement in green innovation performance. The moderating effect of knowledge integration highlights the importance of being prepared internally and actively seeking and incorporating external knowledge to improve green innovation performance.

The findings offer a solid foundation for informed decision-making, policy development and strategies to improve sustainability performance while aligning with the global nature of the textile industry and its inherent challenges. The proposed model and practical implications guide policymakers and managers of exporting firms to foster a culture of green innovation to leverage the effect of their readiness for green innovation on sustainability performance.

Disruptions at ports may destroy the planned ship schedules profoundly, which is an imperative operation problem that shipping companies need to overcome. This paper attempts to help shipping companies cope with port disruptions through recovery scheduling.

This paper studies the ship coping strategies for the port disruptions caused by severe weather. A novel mixed-integer nonlinear programming model is proposed to solve the ship schedule recovery problem (SSRP). A distributionally robust mean conditional value-at-risk (CVaR) optimization model was constructed to handle the SSRP with port disruption uncertainties, for which we derive tractable counterparts under the polyhedral ambiguity sets.

The results show that the size of ambiguity set, confidence level and risk-aversion parameter can significantly affect the optimal values, decision-makers should choose a reasonable parameter combination. Besides, sailing speed adjustment and handling rate adjustment are effective strategies in SSRP but may not be sufficient to recover the schedule; therefore, port skipping and swapping are necessary when multiple or longer disruptions occur at ports.

Since the port disruption is difficult to forecast, we attempt to take the uncertainties into account to achieve more meaningful results. To the best of our knowledge, there is barely a research study focusing on the uncertain port disruptions in the SSRP. Moreover, this is the first paper that applies distributionally robust optimization (DRO) to deal with uncertain port disruptions through the equivalent counterpart of DRO with polyhedral ambiguity set, in which a robust mean-CVaR optimization formulation is adopted as the objective function for a trade-off between the expected total costs and the risk.

Based on the density dependence theory, this paper attempts to explore how two types of interdependence among firms located in the same national high-tech zones (NHTZs) – mutualism and competition – affect entrepreneurship in the NHTZs. The authors suggest that increasing firm density can help enhance legitimacy and form mutual networks. However, as the competition becomes fierce, the above positive relationship will weaken when the firm density exceeds a certain level. In addition, the authors are interested in whether the age of NHTZs would affect their sensitivity to legitimacy and competition and whether firm density affects entrepreneurship.

This article formulates two hypotheses from the theoretical deduction. The hypotheses are examined using the ordinary least squares (OLS) regression with a unique, unbalanced panel dataset of Chinese NHTZs spanning from 2014 to 2021. Considering potential endogeneity risk among the variables, the authors attempt to lag variables and ultimately find the results are still robust.

Drawing upon the density dependence theory, the empirical results show firm density is conducive to promoting entrepreneurship, while the positive relationship between community density and NHTZs' entrepreneurship gradually weakens as the firm density surpasses a certain level. The dynamics between mutualism and competition have different impacts on NHTZs' entrepreneurship. In addition, the results demonstrate that the linkage between firm interdependence and entrepreneurship is stronger for younger NHTZs. Firm density has an impact on entrepreneurship through legitimacy and excessive competition effects.

On the one hand, the research period of this paper is 2014–2021, as the China Torch Statistical Yearbook only started to publish operating revenues in 2014, so the data period of this paper is relatively short. More research can be done in the future when more data is disclosed. On the other hand, the qualitative analysis cannot be conducted because of the limited data and materials. In future research, the qualitative analysis of entrepreneurial activities in NHTZs, such as questionnaires or case studies, needs to be supplemented, which will be an interesting direction.

Most existing research has not distinguished the differences between NHTZs (Wang et al., 2019), especially the differences in legitimacy and access to resources caused by the age of NHTZs. This article considers the heterogeneity between NHTZs, which helps to provide theoretical and practical evidence for a transition economy like China to make trade-off decisions on balancing absorbing new entrants with promoting the efficient allocation of resources based on the density and age of NHTZs.

Drawing upon density dependency theory, this paper enriches the literature on agglomeration and entrepreneurship with a new perspective and extends the study to NHTZs.

First, this paper provides new evidence on how agglomeration affects entrepreneurship from an ecological perspective with the help of mutualism and competition interdependence. Most studies have explored the role of agglomeration in entrepreneurship, focussing on social networks, knowledge spillovers or resource endowments (Acs et al., 2013; Capozza et al., 2018; Yu, 2020). Drawing upon density dependency theory, this paper enriches the literature on agglomeration and entrepreneurship with a new perspective and extends the study to NHTZs. Second, the emphasis of science parks has been primarily on qualitative or case studies (Salvador et al., 2013; Guo and Verdini, 2015; Xie et al., 2018). We have diversified the quantitative research between agglomeration and entrepreneurship by using panel data from Chinese NHTZs from 2014 to 2021. Third, most existing research has not distinguished the differences between NHTZs (Wang et al., 2019), especially the differences in legitimacy and access to resources caused by the age of NHTZs. This article considers the heterogeneity between NHTZs, which helps to provide theoretical and practical evidence for a transition economy like China to make trade-off decisions on balancing absorbing new entrants with promoting the efficient allocation of resources based on the density and age of NHTZs. Finally, this paper meticulously investigates the profound influence and underlying mechanisms of firm density within NHTZs on entrepreneurship. It discerns two distinct mechanisms at play: the legitimacy effect and the impact of excessive competition resulting from firm density. This comprehensive analysis significantly contributes to our comprehension of the intricate interplay between firm density and entrepreneurship, shedding light on the dynamics of competition and mutual benefits.

This study aims to examine how board gender diversity and foreign directors influence the sector-wise corporate philanthropic giving (donation) of Islamic banks in Bangladesh.

Unbalanced panel data were extracted from the annual reports of Islamic banks in Bangladesh over 11 years, from 2010 to 2020.

The findings indicate that gender diversity significantly improves corporate philanthropic giving for the education sector but insignificantly influences corporate philanthropic giving for health and humanitarian and disaster relief sectors. In contrast, the results show that foreign directors significantly and positively affect the banks' corporate philanthropic giving for the three sectors.

This paper used only secondary data extracted from the annual reports of Islamic banks in Bangladesh between 2010 and 2020. Besides, only three sectors of corporate social responsibility activities were considered. Hence, the findings could not be generalized, as the study used only data from one country.

The findings can be useful to policymakers and regulators to provide policies and regulations that ensure the appointment of women and foreign directors to boards that can competently promote Islamic banks' charitable donations.

Inducing Islamic banks to provide corporate donations for activities related to education, health and humanitarian and disaster relief can contribute directly to achieving sustainable development goals (SDGs) like SDG-3 (good health and well-being) and SDG-4 (quality education) and impliedly support attaining some indicators of SDG-1 (no poverty), SDG-2 (zero hunger) and SDG-10 (reduced inequality).

This study contributes to the literature by investigating how board gender diversity and foreign directors influence sector-wise corporate donations for the education, health and human and disaster relief sectors instead of aggregate donations studies concentrated by previous studies.

For policymakers and participants of financial markets, predictions of trading volumes of financial indices are important issues. This study aims to address such a prediction problem based on the CSI300 nearby futures by using high-frequency data recorded each minute from the launch date of the futures to roughly two years after constituent stocks of the futures all becoming shortable, a time period witnessing significantly increased trading activities.

In order to answer questions as follows, this study adopts the neural network for modeling the irregular trading volume series of the CSI300 nearby futures: are the research able to utilize the lags of the trading volume series to make predictions; if this is the case, how far can the predictions go and how accurate can the predictions be; can this research use predictive information from trading volumes of the CSI300 spot and first distant futures for improving prediction accuracy and what is the corresponding magnitude; how sophisticated is the model; and how robust are its predictions?

The results of this study show that a simple neural network model could be constructed with 10 hidden neurons to robustly predict the trading volume of the CSI300 nearby futures using 1–20 min ahead trading volume data. The model leads to the root mean square error of about 955 contracts. Utilizing additional predictive information from trading volumes of the CSI300 spot and first distant futures could further benefit prediction accuracy and the magnitude of improvements is about 1–2%. This benefit is particularly significant when the trading volume of the CSI300 nearby futures is close to be zero. Another benefit, at the cost of the model becoming slightly more sophisticated with more hidden neurons, is that predictions could be generated through 1–30 min ahead trading volume data.

The results of this study could be used for multiple purposes, including designing financial index trading systems and platforms, monitoring systematic financial risks and building financial index price forecasting.

This paper aims to explore the moderating role of state ownership variables on the relationship between market concentration (MC) and financial statement comparability (FSC) in Vietnam.

This study uses data from the financial statements of 475 nonfinancial listed companies for the period from 2010 to 2019. This study uses both the system generalized method of moments and fuzzy-set qualitative comparative analysis (fsQCA) to consider the correlation and causal–effect relationships of the variables in the model.

The results show that MC has a positive relationship with FSC, and MC tends to exert a stronger impact on FSC for firms with higher state ownership. In addition, this study suggests that some combinations help improve FSC. This study has important implications for investors, managers and especially state-owned organizations when market power becomes fierce.

This study contributes to the literature on the comparability of financial statements in the context of developing countries that have not fully adopted International Financial Reporting Standards. Furthermore, this study applies the fsQCA method to complement the linear regression method.