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Drawing on conservation of resources (COR) theory, this study examined the effect of developmental human resource (HR) practices on employee workplace procrastination and investigated the mediation effect of boredom at work and the moderation effects of exploitative leadership and self-leadership.

Data were collected from 443 employees across companies in China. Hypotheses were tested using hierarchical regression analysis and indirect effect testing via bootstrapping in SPSS and Mplus.

This study found that developmental HR practices were negatively related to employee workplace procrastination and that boredom at work mediated the relationship between developmental HR practices and employee workplace procrastination. Moreover, exploitative leadership strengthened the negative relationship between developmental HR practices and boredom at work, whereas self-leadership weakened the positive relationship between boredom at work and employee workplace procrastination. The indirect relationship between developmental HR practices and employee workplace procrastination through boredom at work was moderated by exploitative leadership and self-leadership.

This study extended the literature on the antecedents of employee workplace procrastination. Moreover, by investigating the mediation effect of boredom at work, this study extended the underlying mechanism by which developmental HR practices affect subsequent employee outcomes. Finally, by testing the moderation effect of exploitative leadership and self-leadership, respectively, this study offered insights into the boundary conditions resultant from developmental HR practices.

In the brain imaging based on electrical impedance tomography, it is sometimes not able to attach 16 electrodes due to space restriction caused by craniotomy. As a result of this, the number of boundary measurements decreases, and spatial resolution of reconstructed conductivity distribution is reduced. The purpose of this study is to enhance reconstruction quality in cases of limited measurement.

A new data expansion method based on the shallow convolutional neural network is proposed. An eight-electrode model is built from which fewer boundary measurements can be obtained. To improve the imaging quality, shallow convolutional neural network is constructed which maps limited voltage data of the 8-electrode model to expanded voltage data of a quasi-16-electrode model. The predicted data is compared with the quasi-16-electrode data. Besides, image reconstruction based on L1 regularization method is conducted.

The results show that the predicted data generally coincides with the quasi-16-electrode data. It is found that images reconstructed with the data of eight-electrode model are the poorest. Nevertheless, imaging results when the limited data is expanded by the proposed method show large improvement, and there is a minor difference with the images recovered with the quasi-16-electrode data. Also, the impact of noise is studied, which shows that the proposed method is robust to noise.

To enhance reconstruction quality in the case of limited measurement, a new data expansion method based on the shallow convolutional neural network is proposed. Both simulation work and phantom experiments have demonstrated that high-quality images of cerebral hemorrhage and cerebral ischemia can be obtained when the limited measurement is expanded by the proposed method.

In the process of making agricultural production decisions in rural households, severe weather conditions, either extreme cold or heat, may squeeze the labor input in the agricultural sector, leading to a reallocation of labor between the agricultural and non-agricultural sectors. By applying a dataset with a wide latitude range, this study empirically confirms the influence of extreme temperatures on the agricultural labor reallocation, reveal the mechanism of farmers’ adaptive behavioral decision and therefore enriches the research on the impact of climate change on rural labor markets and livelihood strategies.

This study utilizes data from Chinese meteorological stations and two waves of China Household Income Project to examine the impact and behavioral mechanism of extreme temperatures on rural labor reallocation.

(1) Extremely high and low temperatures had led to a reallocation of labor force from agricultural activities to non-farm employment, with a more pronounced effect from extreme high temperature events. (2) Extreme temperatures influence famers’ decision in abandoning farmland and reducing investment in agricultural machinery, thus creating an interconnected impact on labor mobility. (3) The reallocation effect of rural labor induced by extreme temperatures is particularly evident for males, persons that perceives economic hardship or labor in economically active areas.

By applying a dataset with a wide latitude range, this study empirically confirms the influence of extreme temperatures on the agricultural labor reallocation, and reveals the mechanism of farmers’ adaptive behavioral decision and therefore enriches the research on the impact of climate change on rural labor markets and livelihood strategies.

This paper analyzes the application of digital twin technology in the field of intelligent operation and maintenance of high-speed railway infrastructure from the perspective of top-level design.

This paper provides a comprehensive overview of the definition, connotations, characteristics and key technologies of digital twin technology. It also conducts a thorough analysis of the current state of digital twin applications, with a particular focus on the overall requirements for intelligent operation and maintenance of high-speed railway infrastructure. Using the Jinan Yellow River Bridge on the Beijing–Shanghai high-speed railway as a case study, the paper details the construction process of the twin system from the perspectives of system architecture, theoretical definition, model construction and platform design.

Digital twin technology can play an important role in the whole life cycle management, fault prediction and condition monitoring in the field of high-speed rail operation and maintenance. Digital twin technology is of great significance to improve the intelligent level of high-speed railway operation and management.

This paper systematically summarizes the main components of digital twin railway. The general framework of the digital twin bridge is given, and its application in the field of intelligent operation and maintenance is prospected.

The research explores how artificial intelligence-driven natural language processing can be integrated into digital game-based learning to enhance science education worldwide. It conducted a thorough literature review focusing on electronic bibliographic databases, identifying nine key themes. The chapter discusses the potential of digital games to improve science education, including theoretical frameworks, integration techniques and problem-solving methods. It also examines the impact of gaming on critical thinking and natural language processing. The findings suggest that integrating natural language processing into digital game-based learning shows promise for improving inquiry-based teaching and student performance in science education. Digital platforms can offer personalized feedback, support natural language interactions and customize content for each student. However, addressing pedagogical concerns and technological limitations is crucial for fully realizing these benefits. Practically, the research offers guidance for educators, curriculum developers and policymakers. It emphasizes the importance of educating educators on natural language processing, developing inquiry-driven digital learning platforms and establishing supportive policies. This signifies a move toward student-centered learning and the integration of innovative technologies into education. In terms of originality and value, the study contributes to the existing knowledge by discussing challenges and potential strategies for integrating digital game-based learning with natural language processing globally. It suggests that future research and practical efforts in this area can benefit from a comprehensive understanding of the opportunities and implications of this educational approach, informed by extensive research across various themes.

Regardless of the diverse research on big data analytics (BDA) across different supply chains, little attention has been paid to exploit this information across service supply chains. The healthcare supply chains, where supply chain operations consume the second highest expenditures, have not completely attained the potential gains from data analytics. So, this paper explores the challenges of BDA at various levels of healthcare supply chains.

Drawing on the resource-based view (RBV), this research explores the various challenges of big data at organizational and operational level of different nodes in healthcare supply chains. To demonstrate the links among supply chain nodes, the authors have used a supplier-input-process-output-customer (SIPOC) chart to list healthcare suppliers, inputs (such as employees) supplied and used by the main healthcare processes, outputs (products and services) of these processes, and customers (patients and community).

Using thematic analysis, the authors were able to identify numerous challenges and commonalities among these challenges for the case of healthcare supply chains across United Arab Emirates (UAE). An applicable exploration on organizational (Socio-technical) and operational challenges to BDA can enable healthcare managers to acclimate efficient and effective strategies.

The identified common socio-technical and operational challenges could be verified, and their impacts on the sustainable performance of various supply chains should be explored using formal research methods.

This research advances the body of literature on BDA in healthcare supply chains in that (1) it presents a structured approach for exploring the challenges from various stakeholders of healthcare chain; (2) it presents the most common challenges of big data across the chain and finally (3) it uses the context of UAE where government is focusing on medical tourism in the coming years.

Originality of this work stems from the fact that most of the previous academic research in this area has focused on technology perspectives, a clear understanding of the managerial and strategic implications and challenges of big data is still missing in the literature.

In laser powder bed fusion (L-PBF) additive manufacturing, spatter particles are ejected from the melt pool and can be detrimental to material performance and powder recycling. Quantifying spatter generation with respect to processing conditions is a step toward mitigating spatter and better understanding the phenomenon. This paper reveals process insights of spatter phenomena by automatically annotating spatter particles in high-speed video observations using machine learning.

A high-speed camera was used to observe the L-PBF process while varying laser power, laser scan speed and scan strategy on a constant geometry on an EOSM290 using Ti-6Al-4V powder. Two separate convolutional neural networks were trained to segment and track spatter particles in captured high-speed videos for spatter characterization.

Spatter generation and ejection angle significantly differ between keyhole and conduction mode melting. High laser powers lead to large ejections at the beginning of scan lines. Slow and fast build rates produce more spatter than moderate build rates at constant energy density. Scan strategies with more scan vectors lead to more spatter. The presence of powder significantly increases the amount of spatter generated during the process.

With the ability to automatically annotate a large volume of high-speed video data sets with high accuracy, an experimental design of observed parameter changes reveals quantitively stark changes in spatter morphology that can aid process development to mitigate spatter occurrence and impacts on material performance.

The aim of this study is to review the literature on how intellectual capital (IC) contributes to the decarbonization efforts of firms. It explores how carbon accounting can measure the components of IC in decarbonization efforts to balance profitability with environmental and social goals, particularly in promoting decent work and economic growth (Sustainable Development Goal [SDG] 8 and its targets [2, 5, 6, 8]). Moreover, it emphasises the importance of multi-stakeholder partnerships for sharing knowledge, expertise, technology, and financial resources (SDG17-Target 17.G) to meet SDG8.

As a consolidated methodological approach, a systematic literature review (SLR) was used in this study to fill the existing research gaps in sustainability accounting. To consolidate and clarify scholarly research on IC towards decarbonization, 149 English articles published in the Scopus database and Google Scholar between 1990 and 2024 were reviewed.

The results highlight that the current research does not sufficiently cover the intersection of carbon accounting and IC in the analysis of decarbonization practices. Stakeholders and regulatory bodies are increasingly pressuring firms to implement development-focused policies in line with SDG8 and its targets, requiring the integration of IC and its measures in decarbonization processes, supported by SDG17-Target 17.G. This integration is useful for creating business models that balance profitability and social and environmental responsibilities.

The integration of social dimension to design sustainable business models for emission reduction and provide a decent work environment by focusing on SDG17-Target 17.G has rarely been investigated in terms of theory and practice. Through carbon accounting, IC can be a key source of SDG8-Targets 8.[2, 5, 6, 8] and SDG17-Target 17.G. Historically, these major issues are not easily aligned with accounting research or decarbonization processes.

This study investigates the impact of universities' social marketing initiatives on students’ development of personal (altruistic, biospheric and egoistic) and social values, leading to their pro-environmental behaviors.

This study applies quantitative deductive research. This study examined the value-belief-norms (VBN) theory, adding social values to the framework. This study took place in Egypt from January 2023 to March 2023. The population of focus was college students (whether at public or private universities). Students were requested to fill out the questionnaire by scanning a quick-response (QR) code, which linked to a Google Form. After data collection, 410 questionnaires were analyzed using statistical package for social science.

This study developed empirical evidence that clarifies that social marketing initiatives done by universities have the power to develop students’ personal and social values. Values trigger behavior change. Social values lead to students’ pro-environmental behaviors; personal egoistic values lead to students’ pro-environmental behaviors; personal biospheric values lead to students’ pro-environmental behaviors and personal altruistic values does not lead to students’ pro-environmental behaviors.

This study offers firsthand insight in understanding how social marketing is an effective tool to develop students’ values that are needed to inspire the right behaviors to preserve and protect the environment. This study builds upon the VBN theory, explaining the significant underlying environmental values that should be developed through universities’ non-academic initiatives (such as marketing activities) to inform behaviors needed to better the community, such as pro-environmental behaviors.

The purpose of this research is to enhance the Laser Powder Bed Fusion (LPBF) additive manufacturing technique by addressing its susceptibility to defects, specifically lack of fusion. The primary goal is to optimize the LPBF process using a digital twin (DT) approach, integrating physics-based modeling and machine learning to predict the lack of fusion.

This research uses finite element modeling to simulate the physics of LPBF for an AISI 316L stainless steel alloy. Various process parameters are systematically varied to generate a comprehensive data set that captures the relationship between factors such as power and scan speed and the quality of fusion. A novel DT architecture is proposed, combining a classification model (recurrent neural network) with reinforcement learning. This DT model leverages real-time sensor data to predict the lack of fusion and adjusts process parameters through the reinforcement learning system, ensuring the system remains within a controllable zone.

This study's findings reveal that the proposed DT approach successfully predicts and mitigates the lack of fusion in the LPBF process. By using a combination of physics-based modeling and machine learning, the research establishes an efficient framework for optimizing fusion in metal LPBF processes. The DT's ability to adapt and control parameters in real time, guided by machine learning predictions, provides a promising solution to the challenges associated with lack of fusion, potentially overcoming the traditional and costly trial-and-error experimental approach.

Originality lies in the development of a novel DT architecture that integrates physics-based modeling with machine learning techniques, specifically a recurrent neural network and reinforcement learning.