Search results

The selection of sensitive temperature measurement points is the premise of thermal error modeling and compensation. However, most of the sensitive temperature measurement point selection methods do not consider the influence of the variability of thermal sensitive points on thermal error modeling and compensation. This paper considers the variability of thermal sensitive points, and aims to propose a sensitive temperature measurement point selection method and thermal error modeling method that can reduce the influence of thermal sensitive point variability.

Taking the truss robot as the experimental object, the finite element method is used to construct the simulation model of the truss robot, and the temperature measurement point layout scheme is designed based on the simulation model to collect the temperature and thermal error data. After the clustering of the temperature measurement point data is completed, the improved attention mechanism is used to extract the temperature data of the key time steps of the temperature measurement points in each category for thermal error modeling.

By comparing with the thermal error modeling method of the conventional fixed sensitive temperature measurement points, it is proved that the method proposed in this paper is more flexible in the processing of sensitive temperature measurement points and more stable in prediction accuracy.

The Grey Attention-Long Short Term Memory (GA-LSTM) thermal error prediction model proposed in this paper can reduce the influence of the variability of thermal sensitive points on the accuracy of thermal error modeling in long-term processing, and improve the accuracy of thermal error prediction model, which has certain application value. It has guiding significance for thermal error compensation prediction.

The key to preventing employees from engaging in deceptive knowledge hiding is fostering a responsible environment. Drawing on social cognitive theory, this study aims to explore the factors that inhibit deceptive knowledge hiding and to construct potential pathways for enhancing individual moral cognition. This study further analyzes the moderating effect of leader–follower value congruence on these relationships.

Using data from 341 full-time employees in various service industries in China, this study conducted path analysis, the product-of-coefficients method and bootstrapping to test the hypotheses through a three-stage, time-lagged survey.

The empirical results show that responsible leadership is negatively associated with employees’ deceptive knowledge hiding. Employee moral reflectiveness mediates this relationship, whereas leader–follower value congruence moderates the indirect effect of responsible leadership on deceptive knowledge hiding through moral reflectiveness.

First, this study extends field research by introducing positive leadership factors to reduce deceptive knowledge hiding, whereas prior studies focused mainly on negative leadership antecedents. Second, this study sheds light on the underlying moral cognitive mechanisms and explains how responsible leadership can prevent implicit unethical behavior. Third, it reveals how leader–follower value congruence can enhance the impact of responsible leadership on moral reflectiveness, offering novel insights into the role of value-based fit in reducing deceptive knowledge hiding.

Metro stations have become a crucial aspect of urban rail transportation, integrating facilities, equipment and pedestrians. Impractical physical layout designs and pedestrian psychology impact the effectiveness of an evacuation during a metro fire. Prior research on emergency evacuation has overlooked the complexity of metro stations and failed to adequately consider the physical heterogeneity of stations and pedestrian psychology. Therefore, this study aims to develop a comprehensive evacuation optimization strategy for metro stations by applying the concept of design for safety (DFS) to an emergency evacuation. This approach offers novel insights into the management of complex systems in metro stations during emergencies.

Physical and social factors affecting evacuations are identified. Moreover, the social force model (SFM) is modified by combining the fire dynamics model (FDM) and considering pedestrians' impatience and panic psychology. Based on the Nanjing South Metro Station, a multiagent-based simulation (MABS) model is developed. Finally, based on DFS, optimization strategies for metro stations are suggested.

The most effective evacuation occurs when the width of the stairs is 3 meters and the transfer corridor is 14 meters. Additionally, a luggage disposal area should be set up. The exit strategy of the fewest evacuees is better than the nearest-exit strategy, and the staff in the metro station should guide pedestrians correctly.

Previous studies rarely consider metro stations as sociotechnical systems or apply DFS to proactively reduce evacuation risks. This study provides a new perspective on the evacuation framework of metro stations, which can guide the designers and managers of metro stations.

This study examines how local governments and enterprises can implement ecological restoration of abandoned mines based on ecology-oriented development (EOD), which will be more beneficial to local environmental protection and economic development under the central government’s policy of outcome incentives or process subsidies.

We construct a dynamic differential game model to simulate the interactions between local governments and enterprises during the ecological restoration of abandoned mines from an EOD perspective.

The findings suggest that under the central government’s outcome incentive policy, cooperation between local governments and enterprises is an optimal strategy. Under the process subsidy policy, while neither cooperative nor non-cooperative models significantly affect the investment levels of local governments and enterprises, a cooperative approach ensures optimal investments from both without solely relying on the process subsidy. Additionally, incorporating altruistic preferences can lead to Pareto improvements in economic and environmental results under central government outcome incentives.

This research offers a policy foundation for governments to encourage the EOD model in the ecological restoration of abandoned mines. It provides theoretical support for achieving environmental sustainability and high-quality economic development, and is particularly significant for resource-depleted cities seeking to transform their development strategies.

Through a dynamic differential game model involving government agencies and enterprises to simulate decision-making in the ecological restoration of abandoned mines, incorporating altruistic preferences into this restoration process, and identifying optimal strategies and policies for ecological restoration.

The inclusion of esports as an official event in the Hangzhou Asian Games is an important step towards the institutionalisation of esports. The significance of this event marks that Asia once again takes a lead in the global esportisation. This chapter investigates a series of history events in the inclusion process of esports into the comprehensive Games in Asia using process sociology and actor network theory (ANT). This study will analyse the type characteristics of esports events in Hangzhou Asian Games, whilst examining how key stakeholders' interact and balance in the network composed of international sports organisations, host of the event, emerging esports organisations and esports game companies. The chapter also examines the functions of global game industrial economic geography, local cultural politics, esports geopolitics and Olympic values in esports sportization, aiming to reveal the implications of esports inclusion in the Asian Games on the debate of whether esports meets the criteria to be classified as a ‘sport’ and its enlightenment of digital strategy to the inclusion esports in the Olympics.

This study investigates the impact of corporate governance on FinTech disclosure levels in Jordanian conventional and Islamic banks. It aims to determine whether governance mechanisms affect disclosure practices in the FinTech sector, exploring the interplay between governance and transparency in financial innovations.

The research methodology entails a thorough analysis of data from all 15 Jordanian conventional and Islamic banks listed on the Amman Stock Exchange, covering the period from 2015 to 2022. This study uses manual content analysis using a custom FinTech Disclosure Index (FDI) and quantitative analysis with a two-way clustered error regression model.

The findings show that corporate governance mechanisms, particularly board size, board meetings and “Big4” audit firms, are crucial in enhancing FinTech disclosure across conventional and Islamic banks. However, Islamic banks consistently show higher disclosure levels than their conventional counterparts, attributed to their distinct governance structures that emphasize ethical governance and transparency. These results indicate an awareness among decision-makers about the importance of business model transformation toward FinTech.

This study pioneers the introduction of FDI, using it for a novel comparative analysis of FinTech disclosure levels between Islamic and conventional banks. By exploring how various governance structures influence FinTech disclosure, this research provides fresh insights into the interplay between corporate governance and financial technologies in the banking sector.

This paper aims to examine the association between perceived firm risk and two reporting mechanisms: risk disclosure and earnings smoothing in the UK context.

This study juxtaposes three competing views, the “null”, the “divergence” and the “convergence” hypotheses, and empirically investigates whether risk disclosure and earnings smoothing affect firm perceived risk for a sample of large UK firms with rich and poor information environments. This study also uses the global financial crisis as an external shock on overall risk in the economy to investigate when and how managers use these two reporting mechanisms to shape the firm perceived risk.

This paper documents that risk disclosures have no significant effect on investors’ risk perceptions, consistent with risk disclosures containing boilerplate and generic statements about firm risk. This paper also finds that earnings smoothing reduces investors’ risk perceptions, reflecting investors’ interpretations about future firm performance. Additional tests reveal that earnings smoothing is not associated with perceived firm risk for firms with rich information environments and expanded risk disclosures. Furthermore, reporting smooth earnings decreases perceived firm risk following the global financial crisis. These findings are robust to alternative specifications and measures of earnings smoothing as well as post-filing perceived firm risk.

This study does not distinguish between the garbling role and the informational role of earnings smoothing. The risk disclosure measurement used in this study, developed based on UK annual reports, may limit the generalizability of findings to other countries.

The findings suggest that managers should revise their risk disclosure strategies to provide in-depth details on firm risk. Investors might require information and thorough assessment to evaluate investment risks when firms provide generic risk disclosures and smoothed earnings by consulting sources like financial intermediaries. Regulators should keep an eye on firms reporting boilerplate risk disclosures and on how smoothing earnings impacts the firm perceived risk following economic turmoil, to guide interventions that promote market stability.

The findings provide new insights into when and how managers use their financial reporting discretion to make firms appear less risky and, therefore, influence investors’ risk perceptions.

Productivity is often cited as a key barrier to the adoption of metal laser-based powder bed fusion (ML-PBF) technology for mass production. Newer generations of this technology work to overcome this by introducing more lasers or dramatically different processing techniques. Current generation ML-PBF machines are typically not capable of taking on additional hardware to maximise productivity due to inherent design limitations. Thus, any increases to be found in this generation of machines need to be implemented through design or adjusting how the machine currently processes the material. The purpose of this paper is to identify the most beneficial existing methodologies for the optimisation of productivity in existing ML-PBF equipment so that current users have a framework upon which they can improve their processes.

The review method used here is the preferred reporting items for systematic review and meta-analysis (PRISMA). This is complemented by using an artificial intelligence-assisted literature review tool known as Elicit. Scopus, WEEE, Web of Science and Semantic Scholar databases were searched for articles using specific keywords and Boolean operators.

The PRIMSA and Elicit processes resulted in 51 papers that met the criteria. Of these, 24 indicated that by using a design of experiment approach, processing parameters could be created that would increase productivity. The other themes identified include scan strategy (11), surface alteration (11), changing of layer heights (17), artificial neural networks (3) and altering of the material (5). Due to the nature of the studies, quantifying the effect of these themes on productivity was not always possible. However, studies citing altering layer heights and processing parameters indicated the greatest quantifiable increase in productivity with values between 10% and 252% cited. The literature, though not always explicit, depicts several avenues for the improvement of productivity for current-generation ML-PBF machines.

This systematic literature review provides trends and themes that aim to influence and support future research directions for maximising the productivity of the ML-PBF machines.

Drawing an inference from institutional theory and dynamic capabilities view, this study empirically examined the impact of three institutional dimensions (regulative, normative and cognitive) and green entrepreneurial orientation (GEO) on a business firm’s performance. In addition, the moderating effect of dynamic capabilities on the relationship between GEO and firm performance was also explored.

The data were collected from 527 information technology (IT) firms in Pakistan using paper–pencil questionnaires, and the hypotheses were tested using structural equation modeling.

The findings showed that the regulative and normative institutional dimensions enhance GEO and firm performance in the selected developing country. However, the cognitive institutional dimension fails to report any substantial influence on GEO and firm performance. The findings raised concerns about lower individual accountability as well as the promotion of green practices and firm performance. In addition, dynamic capabilities positively moderate the GEO influence on firm performance.

With the interplay of institutional dimensions, GEO (as mediator) and dynamic capabilities (as moderator), this study developed and tested a unique framework to understand their influence on firm performance. Specifically, we extended the literature by giving evidence that among the three institutional dimensions, only regulative and normative are considered more important because of their direct and indirect (through GEO) positive effect on firm performance. In contrast, the cognitive institutional dimension failed to report any significant direct or indirect impact on firm performance in our study.

The present research aims to analyze the influence of Green Supply Chain Management (GSCM) on the Sustainable Performance (SP) of Spanish wineries, examining the intermediary roles of Circular Economy Capability (CEC) and Green Ambidexterity Innovation (GAI) within this primary relationship. In particular, the study seeks to answer the following Research Questions (RQs): (RQ1), does GSCM exert a positive impact on wineries’ SP? (RQ2), does CEC play a mediating role in the interconnection between GSCM and wineries’ SP? And (RQ3), does GAI function as a mediator in the interplay between GSCM and wineries’ SP?

The research utilizes Partial Least Squares Structural Equation Modeling (PLS-SEM) to evaluate the hypothesized theoretical model using primary data collected from 196 Spanish wineries from September 2022 to January 2023.

The results reveal a positive relationship between GSCM and SP in Spanish wineries, as well as the existence of positive partial mediations of CEC and GAI in this main linkage, which allows an affirmative answer to the three RQs formulated.

Distinguished by its innovative exploration, this research marks a pioneering effort in the uncharted field of scrutinizing CEC and GAI as mediating factors in the GSCM-SP nexus, thereby enriching the existing body of knowledge and opening new avenues for future academic research on sustainability in the wine industry.