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This study aims to provide a systematic and complete knowledge map for use by researchers working in the field of research data. Additionally, the aim is to help them quickly understand the authors' collaboration characteristics, institutional collaboration characteristics, trending research topics, evolutionary trends and research frontiers of scholars from the perspective of library informatics.

The authors adopt the bibliometric method, and with the help of bibliometric analysis software CiteSpace and VOSviewer, quantitatively analyze the retrieved literature data. The analysis results are presented in the form of tables and visualization maps in this paper.

The research results from this study show that collaboration between scholars and institutions is weak. It also identified the current hotspots in the field of research data, these being: data literacy education, research data sharing, data integration management and joint library cataloguing and data research support services, among others. The important dimensions to consider for future research are the library's participation in a trans-organizational and trans-stage integration of research data, functional improvement of a research data sharing platform, practice of data literacy education methods and models, and improvement of research data service quality.

Previous literature reviews on research data are qualitative studies, while few are quantitative studies. Therefore, this paper uses quantitative research methods, such as bibliometrics, data mining and knowledge map, to reveal the research progress and trend systematically and intuitively on the research data topic based on published literature, and to provide a reference for the further study of this topic in the future.

This study aims to reduce the redundant weight of the anti-roll torsion bar brought by the traditional empirical design and improving its strength and stiffness.

Based on the finite element approach coupled with the improved beluga whale optimization (IBWO) algorithm, a collaborative optimization method is suggested to optimize the design of the anti-roll torsion bar structure and weight. The dimensions and material properties of the torsion bar were defined as random variables, and the torsion bar's mass and strength were investigated using finite elements. Then, chaotic mapping and differential evolution (DE) operators are introduced to improve the beluga whale optimization (BWO) algorithm and run case studies.

The findings demonstrate that the IBWO has superior solution set distribution uniformity, convergence speed, solution correctness and stability than the BWO. The IBWO algorithm is used to optimize the anti-roll torsion bar design. The error between the optimization and finite element simulation results was less than 1%. The weight of the optimized anti-roll torsion bar was lessened by 4%, the maximum stress was reduced by 35% and the stiffness was increased by 1.9%.

The study provides a methodological reference for the simulation optimization process of the lateral anti-roll torsion bar.

The purpose of this paper is to investigate if country development indicators, i.e. gross domestic product per capita (GDPPC), literacy rate, internet penetration and urban population, influence the generation of e-waste on a global level. The moderation effect due to differences between countries in terms of absence or presence of e-waste policy and level of development is also checked.

This is an archival study that builds upon data from United Nations (UN), World Bank and Global E-waste Statistics Partnership. The authors did a path analysis comprising mediation and multigroup analyses to decipher the proposed rese arch model containing data from 172 countries.

The results indicate that GDPPC, literacy rate, internet penetration and urban population do not directly influence the generation of e-waste. However, higher internet penetration in developing countries leads to higher e-waste, while higher literacy rates in developed countries suppress e-waste generation. When it comes to e-waste policy, a higher urban population without a regulatory legal framework boosts higher e-waste. The authors observed that higher internet penetration leads to higher e-waste in the presence of e-waste policy as well.

This is the first study to include economic well-being indicators in elaborating e-waste generation, on a global scale. No previous study has observed differences between countries nested in e-waste policy and level of development.

This paper aims to focus on the relationship between female leadership and the environmental, social and governance (ESG) performance of firms. Specifically, the study examines if firms with women as chief executive officers (CEOs) and/or board chairpersons have higher environmental and social scores.

The study uses data on publicly listed Nordic firms in a panel regression approach to establish the relationship between female leadership and the environmental and social performance of firms.

The result of this study shows that women have a leadership characteristic that increases the weighted average of environmental (E) and social (S) performance of a firm. In particular, pillar score results indicate a positive relationship between female CEOs and the social scores of a firm but no relationship between a female board chairperson and the environmental or social scores of a firm. This implies that gender-based differences affect the CEO’s success, especially in a firm’s social performance. Further analyses show a more significant impact on the E and S performance when a woman replaces a man as CEO of a firm.

While prior research has explored various aspects of gender diversity in corporate leadership and its potential impact, the focus on the Nordic context in this study provides a unique perspective, given the region’s distinct business environment and societal factors. In addition, by examining the collective influence of female leaders and both female CEOs and board chairpersons separately, this study provides a nuanced understanding of how different leadership roles may impact a firm’s ESG performance.

In this study, AlCoCrFeNi–Cu (Cu-based) and AlCoCrFeNi–Ti (Ti-based) high entropy alloys (HEAs) were fabricated using a direct blown powder technique via laser additive manufacturing on an A301 steel baseplate for aerospace applications. The purpose of this research is to investigate the electrical resistivity and oxidation behavior of the as-built copper (Cu)- and titanium (Ti)-based alloys and to understand the alloying effect, the HEAs core effects and the influence of laser parameters on the physical properties of the alloys.

The as-received AlCoCrFeNiCu and AlCoCrFeNiTi powders were used to fabricate HEA clads on an A301 steel baseplate preheated at 400°C using a 3 kW Rofin Sinar dY044 continuous-wave laser-deposition system fitted with a KUKA robotic arm. The deposits were sectioned using an electric cutting machine and prepared by standard metallographic methods to investigate the electrical and oxidation properties of the alloys.

The results showed that the laser power had the most influence on the physical properties of the alloys. The Ti-based alloy had better resistivity than the Cu-based alloy, whereas the Cu-based alloy had better oxidation residence than the Ti-based alloy which attributed to the compositional alloying effect (Cu, aluminum and nickel) and the orderliness of the lattice, which is significantly associated with the electron transportation; consequently, the more distorted the lattice, the easier the transportation of electrons and the better the properties of the HEAs.

It is evident from the studies that the composition of HEAs and the laser processing parameters are two significant factors that influence the physical properties of laser deposited HEAs for aerospace applications.

This study aims to explore the correlation between Management Control Systems, Green Innovation, Social Media Networks, and Company Performance in medium-sized construction and real estate firm in Indonesia.

This research method uses quantitative approach. The sample selection technique uses simple random sampling. The analytical method in this study uses structural equation models based on variance. Statistical test tool used, is Smart PLS 3.0.

The management control systems have a significant and positive impact on social media networks, green innovation, and company performance in the upper-middle-class construction and real estate businesses in Java. Furthermore, social media networks and green innovation were found to mediate the strong relationship between management control systems and firm performance in medium-sized construction and real estate businesses in Java.

This research should provide a detailed, technical, and structured explanation of how companies assess suitability standards for implementing green innovation in Indonesia’s construction and real estate sectors.

The finding emphasize the importance of the management control system in enhancing firm performance. If, the elements of the management control system are met or adequate, it can improve the performance of those in charge, leading to satisfactory performance.

This finding is the first of its kind in Indonesia. It will contribute to shaping future development policies for government and private projects, ensuring they are more advance and environmentally conscious.

In order to solve the problems faced by First Order Reliability Method (FORM) and First Order Saddlepoint Approximation (FOSA) in structural reliability optimization, this paper aims to propose a new Reliability-based Design Optimization (RBDO) strategy for offshore engineering structures based on Original Probabilistic Model (OPM) decoupling strategy. The application of this innovative technique to other maritime structures has the potential to substantially improve their design process by optimizing cost and enhancing structural reliability.

In the strategy proposed by this paper, sequential optimization and reliability assessment method and surrogate model are used to improve the efficiency for solving RBDO. The strategy is applied to the analysis of two marine engineering structure cases of ship cargo hold structure and frame ring of underwater skirt pile gripper. The effectiveness of the method is proved by comparing the original design and the optimized results.

In this paper, the proposed new RBDO strategy is used to optimize the design of the ship cargo hold structure and the frame ring of the underwater skirt pile gripper. According to the results obtained, compared with the original design, the structure of optimization design has better reliability and stability, and reduces the risk of failure. This optimization can also better balance the relationship between performance and cost. Therefore, it is recommended for related RBDO problems in the field of marine engineering.

In view of the limitations of FORM and FOSA that may produce multiple MPPs for a single performance function, the new RBDO strategy proposed in this study provides valuable insights and robust methods for the optimization design of offshore engineering structures. It emphasizes the importance of combining advanced MPP search technology and integrating SORA and surrogate models to achieve more economical and reliable design.

Image segmentation is one of the most essential tasks in image processing applications. It is a valuable tool in many oriented applications such as health-care systems, pattern recognition, traffic control, surveillance systems, etc. However, an accurate segmentation is a critical task since finding a correct model that fits a different type of image processing application is a persistent problem. This paper develops a novel segmentation model that aims to be a unified model using any kind of image processing application. The proposed precise and parallel segmentation model (PPSM) combines the three benchmark distribution thresholding techniques to estimate an optimum threshold value that leads to optimum extraction of the segmented region: Gaussian, lognormal and gamma distributions. Moreover, a parallel boosting algorithm is proposed to improve the performance of the developed segmentation algorithm and minimize its computational cost. To evaluate the effectiveness of the proposed PPSM, different benchmark data sets for image segmentation are used such as Planet Hunters 2 (PH2), the International Skin Imaging Collaboration (ISIC), Microsoft Research in Cambridge (MSRC), the Berkley Segmentation Benchmark Data set (BSDS) and Common Objects in COntext (COCO). The obtained results indicate the efficacy of the proposed model in achieving high accuracy with significant processing time reduction compared to other segmentation models and using different types and fields of benchmarking data sets.

The proposed PPSM combines the three benchmark distribution thresholding techniques to estimate an optimum threshold value that leads to optimum extraction of the segmented region: Gaussian, lognormal and gamma distributions.

On the basis of the achieved results, it can be observed that the proposed PPSM–minimum cross-entropy thresholding (PPSM–MCET)-based segmentation model is a robust, accurate and highly consistent method with high-performance ability.

A novel hybrid segmentation model is constructed exploiting a combination of Gaussian, gamma and lognormal distributions using MCET. Moreover, and to provide an accurate and high-performance thresholding with minimum computational cost, the proposed PPSM uses a parallel processing method to minimize the computational effort in MCET computing. The proposed model might be used as a valuable tool in many oriented applications such as health-care systems, pattern recognition, traffic control, surveillance systems, etc.

This study investigates whether Chinese local governments’ environmental attention can mitigate corporate “greenwashing”, focusing on the extent of environmental content in annual government work reports as indicative of government environmental attention. This study aims to determine whether enterprises respond to changes in local governmental attention by improving the quality of their environmental information disclosures.

Data from China’s A-share listed companies spanning 2013–2021 were sourced from the CSMAR database and company annual reports. Environmental attention data were manually gathered from local government work reports published on official local government websites by using text analysis methods. These datasets were analyzed empirically to assess the impact of local governments’ environmental attention on corporate greenwashing behavior.

Results show that increased governmental environmental attention significantly reduces corporate greenwashing behavior by alleviating corporate financing constraints, enhancing independent engagement in environmental initiatives and bolstering stakeholder oversight. Moreover, heterogeneity analysis indicates that the influence of government environmental concerns is pronounced in non-state-owned enterprises, firms with subpar audit quality and those exhibiting myopic management tendencies.

This study enriches the existing literature on the government–business nexus. It also introduces methodological innovations by employing a lexical analysis of environmental themes in local government work reports instead of using typical event study approaches. Furthermore, it uses a mediating effect model to identify the mechanisms through which government environmental attention influences corporate greenwashing, namely, government subsidies, corporate environmental initiatives and external stakeholder oversight.