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In this study, we propose a tetrahedral mesh generation and adaptive refinement method for multi-chamber, multi-facet, multiscale and surface-solid mesh coupling with extremely thin layers, solving the two challenges of mesh generation and refinement in current electromagnetic simulation models.

Utilizing innovative topology transformation techniques, high-precision intersection judgment algorithms and highly reliable boundary recovery algorithms to reduce the number of Steiner locking points. The feasible space for the reposition of Steiner points is determined by using the linear programming. During mesh refinement, an edge-split method based on geometric center and boundary facets node size is devised. Solving the problem of difficult insertion of nodes in narrow geometric spaces, capable of filtering the longest and boundary edges of tetrahedrons, refining the mesh layer by layer through multiple iterations, and achieving collaborative optimization of surface and tetrahedral mesh. Simultaneously, utilizing a surface-facet preserving mesh topology optimization algorithm to improve the fit degree between the mesh and geometry.

Initial mesh generation for electromagnetic models, compared to commercial software, the method proposed in this paper has a higher pass rate and better mesh quality. For the adaptive refinement performance of high-frequency computing, this method can generate an average of 50% fewer meshes compared to commercial software while meeting simulation accuracy.

This paper proposes a complete set of mesh generation and adaptive refinement theories and methods designed for the structural characteristics of electromagnetic simulation models, which meet the needs of real-world industrial applications.

Consumers nowadays are no longer bystanders in the process of production but are proactive collaborators with the power to co-create value with brands. This study aims to explore the impact of social commerce on the co-creation process of brand value in a social commerce setting.

A questionnaire survey was conducted online to gather 300 eligible responses. The data were empirically validated using the partial least squares structural equation modelling (PLS-SEM) method.

The results indicated that brand engagement (BEN) is vital to brand co-creation (BCC) in social commerce, which could be driven by social-hedonic value (SHV) and social information sharing (SIS).

This study stresses the influence of consumer autonomy in the process of BCC by probing the role of SIS. Moreover, by considering the prevailing trend in social media, this study offers a nuanced perspective on the values of social commerce from the viewpoint of SHV.

This study may serve as a useful guide for practitioners to improve their digital outreach strategy on social commerce to forge stronger relationships, encourage further engagements and promote value co-creation within their brand community.

This examines the effect of relationship quality (RQU) and BEN on BCC through a relational viewpoint.

The main aim of this study is to examine the behavior of reinforced concrete short columns strengthened using longitudinal near surface mounted (NSM)-carbon fiber reinforced polymer (CFRP) strips.

A full 3D-finite element (FE) model was developed using ABAQUS in order to conduct the analysis. The model is first validated based on experimental data available in the literature, and then the effect of concrete compressive strength, number of CFRP strips that are used and the spacing between them were taken in consideration for both concentric and eccentric loading cases. The parametric study specimens were divided into three groups. The first group consisted of unstrengthened columns and served as control specimens. The second group consisted of columns strengthened by longitudinal CFRP strips at two opposite column faces.

The results of this study are used to develop interaction diagrams for CFRP-strengthened short columns and to develop best-fit equations to estimate the nominal axial load and moment capacities for these strengthened columns. The results showed that the specimens that were strengthened using more longitudinal CFRP strips showed a significant increase in axial load capacity and a significant improvement in the interaction diagram, especially at large load eccentricity values. This result can be justified by the fact that longitudinal strips effectively resist the bending moment that is generated due to eccentric loading. Generally, the process of strengthening using longitudinal strips only has a reasonable effect and it can be typically considered an excellent choice considering the economic aspect when the budget of strengthening is limited.

This research aims at studying the performance of strengthened rectangular reinforced concrete short columns with CFRP strips using FE method, developing interaction diagrams of strengthened columns in order to investigate the effect of different parameters such as compressive strength (20, 30 and 40 MPa), number of CFRP strips (1, 2, 3 and 4) and the spacing between CFRP strips in terms of the ratio of CFRP center point distance to column outside dimension ratio (0.60, 0.70 and 0.80) on the behavior of strengthened RC columns and improving empirical formulas to predict the nominal axial load and moment capacities of strengthened RC columns. These parameters that directly affect short column load carrying capacity are presented in ACI-318 (2014).

Unethical pro-organizational behavior (UPB) harms organizations’ long-term development; hence, all sectors of society view it as highly concerning. Optimizing leadership and curbing this behavior is a key managerial challenge. This study takes the relationship between temporal leadership and UPB as its object and examines the direct and indirect paths of temporal leadership’s influence on UPB based on the conservation of resources theory. It further dissects the mediating mechanism of emotional exhaustion and the regulating mechanism of job complexity and constructs the mechanism through which temporal leadership affects UPB.

Data gathered from a sample of 380 employees in 24 provinces and cities were employed for empirical testing using validated factor analysis, hierarchical regression analysis, and a bootstrap method.

The results show that temporal leadership inhibits UPB, while emotional exhaustion partially mediates the relationship between temporal leadership and UPB. That is, temporal leadership inhibits pro-organizational unethical behavior by alleviating emotional exhaustion. In addition, job complexity negatively moderates the relationship between emotional exhaustion and UPB and positively moderates the mediating role of emotional exhaustion between temporal leadership and UPB.

First, although the data used in the study were collected at two different times, they were obtained through self-assessment; therefore, the subjective component and the potential problem of common method bias is evident. Second, the study’s sample size and types of respondents are limited.

1. This study found that temporal leadership can inhibit UPB by reducing employee emotional exhaustion. Therefore, organizations should place greater emphasis on the time factor. 2. In terms of emotional factors, organizations should actively focus on the impact of emotional exhaustion on employees' UPBs. 3. In management practice, managers should adjust their leadership modeling behaviors according to the different degrees of job complexity to replace UPBs with conscious and rational behaviors.

The study reveals how temporal leadership affects UPB and provides a theoretical basis for organizations to mitigate employees' UPB by optimizing their leadership style.

Current research on temporal leadership primarily focuses on the positive predictive effects on individual behaviors and attitudes (Zhang and Ling, 2016), but neglects its effects on negative behaviors. This study’s results complement research on the relationship between temporal leadership and employees' negative behaviors and responds to the call by Zhang and Ling (2015) to conduct research related to temporal leadership in China. On the other hand, current research on employees’ UPB largely focuses on its causative factors, while less research has been conducted on the disincentives for UPB, which to some extent limits systematic and sound research on UPB.

To comprehend the influence of green HR practices (GHRM) on employee outcomes, strategic HRM researchers are gradually adopting an employee-centric approach, a subject that has sparked recent discussions among scholars in the field of green HR. These scholars have emphasized the need for studies that shed light on the reasons behind the differences in employees' perceptions of GHRM. To address this concern, we investigated (1) supervisors perceived GHRM (SUP-GHRM) and subordinates perceived GHRM (SUB-GHRM) as the fundamental source of variation in employee eco-friendly behavior and green performance, (2) the association between SUP-GHRM and SUB-GHRM, (3) the mediation role of SUB-GHRM toward green performance and eco-friendly behavior, and (4) the moderation of perceived HRM system strength (HRMSS) on supervisor-subordinate perceived GHRM.

Applying a survey approach, we collected data from 217 supervisors and 624 subordinates from Large-Scale Manufacturing Organizations in the Textile sector of Pakistan. Since the data is hierarchical, we applied the Hierarchical Linear Model (HLM) and bootstrapping techniques to examine the hypothesized relationship.

The results of HLM revealed that (1) the SUP-GHRM and SUB-GHRM were key in determining green performance and eco-friendly behavior, (2) the SUP-GHRM significantly influenced SUB-GHRM, (3) the SUP-GHRM indirectly affected the eco-friendly behavior and green performance through SUB-GHRM, (4) the HRM system’s strength positively moderated the association between the SUP-GHRM and SUB-GHRM.

The corporations need to ensure that both supervisors and subordinates have a consistent understanding of GHRM practices and foster positive relationships between them. It is also important for companies to actively enhance supervisors' knowledge of GHRM and encourage them to effectively communicate the company’s GHRM practices to their subordinates. This is vital for improving employee job-related outcomes. Furthermore, corporations should emphasize developing a strong HRM system designed to create a climate where employees understand the behaviors and responses that are valued and recognized, leading them to perceive situations in line with their managers.

This study suggests SUP-GHRM and SUB-GHRM as critical factors that influence eco-friendly behavior and green performance, and HRMSS is key to aligning the perception gaps between subordinates and supervisors about what GHRM is in place in their organization, which is empirically analyzed in a developing country context.

This empirical research aims to provide organisations with useful information on how teleworking has affected employee commuting, allowing organisations to adapt working conditions to the new social context. This research sets out to provide evidence on the behaviour of teleworkers in terms of the mode of transport chosen when commuting to work physically.

This research is based on a survey conducted in Madrid based on a theoretical approach grounded in the theory of planned behaviour (TPB) and discrete choice models. Exploratory factor analysis (EFA) and discriminant analysis have been used to determine the variables that influence the mode of transport chosen by teleworkers when commuting in person.

The results indicate the prevalence of private car use by teleworkers when working at the office, finding that attitudes and satisfaction with modes of transport significantly influence their choice. The characteristics and frequency of teleworking, however, are not significant, which indicates that teleworkers attach greater importance to their preferred mode of transport, regardless of the disadvantages.

This research provides empirical evidence using an inductive approach to understand the challenges that organisations are facing with regard to teleworking since commuting behaviour has changed for teleworkers. This requires organisations to give significant reconsideration to the issue of commuting as an important component of working time that could condition employee satisfaction and, therefore, the retention or recruitment of talent. Furthermore, residence and mobility changes afford organisations better access for recruitment, notwithstanding urban planning implications.

The purpose of this paper is to present an automatic approach for mesh sizing field generation of complicated  computer-aided design (CAD) models.

In this paper, the authors present an automatic approach for mesh sizing field generation. First, a source point extraction algorithm is applied to capture curvature and proximity features of CAD models. Second, according to the distribution of feature source points, an octree background mesh is constructed for storing element size value. Third, mesh size value on each node of background mesh is calculated by interpolating the local feature size of the nearby source points, and then, an initial mesh sizing field is obtained. Finally, a theoretically guaranteed smoothing algorithm is developed to restrict the gradient of the mesh sizing field.

To achieve high performance, the proposed approach has been implemented in multithreaded parallel using OpenMP. Numerical results demonstrate that the proposed approach is remarkably efficient to construct reasonable mesh sizing field for complicated CAD models and applicable for generating geometrically adaptive triangle/tetrahedral meshes. Moreover, since the mesh sizing field is defined on an octree background mesh, high-efficiency query of local size value could be achieved in the following mesh generation procedure.

How to determine a reasonable mesh size for complicated CAD models is often a bottleneck of mesh generation. For the complicated models with thousands or even ten thousands of geometric entities, it is time-consuming to construct an appropriate mesh sizing field for generating high-quality mesh. A parallel algorithm of mesh sizing field generation with low computational complexity is presented in this paper, and its usability and efficiency have been verified.

This study seeks to establish a new framework for categorizing incivility, differentiating between explicit and implicit forms, and to investigate their respective abilities to proliferate and mobilize conversations, along with behavioral outcomes in various social contexts.

Employing computational techniques, this research analyzed 10,145 protest-related threads from the HK Golden Forum, a prominent online discussion board in Hong Kong.

Our analysis revealed divergent effects of explicit and implicit incivility on their diffusion, influences on deliberative discussions, and user participation. Explicit incivility was found to impede deliberative conversations, while implicit incivility tended to provoke more responses. Explicit uncivil expressions encouraged the propagation of incivility but reduced the likelihood of individual involvement. In contrast, implicit incivility had a stronger dampening effect on further uncivil comments and achieved greater thread popularity. The results showed strong associations between uncivil expressions and the contextual norms surrounding social movements.

Theoretically, this research introduced a classification of incivility and underscored the importance of differentiating between implicit and explicit incivility by examining their effects on deliberation and engagement. Although previous studies have extensively covered explicit incivility, this study goes further by analyzing implicit incivility and comparing both forms of uncivil discourse in a less-studied context. Methodologically, the study developed a Cantonese dictionary to differentiate between two types of incivility, providing a practical reference for more nuanced analyses. By revealing how varying movement norms moderate the interplay between deliberative and uncivil expressions, the study drew attention to the highly situational nature of incivility.

Augmented reality (AR) adoption has boomed globally in recent years. The prospective of AR to seamlessly integrate digital information into the actual environment has proven to be a challenge for academics and industry, as they endeavor to understand and predict the influence on users' perceptions, adoption intentions and usage. This study investigates the factors affecting consumers’ behavioral intention to adopt AR technology in shopping malls by offering the mobile technology acceptance model (MTAM).

This conceptual framework is based on mobile self-efficacy, rewards, social influence and enjoyment of existing MTAM constructs. A self-administered questionnaire, constructed by measuring questions modified from previous research, elicited 311 usable responses from mobile respondents who had recently used AR technology in shopping malls. This analysis was performed using SmartPLS3.0.

Grounded on the findings of the study, it was found that, aside from factors such as mobile usefulness, ease of use and social influence, the remaining independent variables had the most significant impact on adopting AR technologies. Considering the limitations of this study, the paper concludes by discussing the significant implications and insinuating avenues for future research.

To better investigate mobile AR app adoption in Pakistan’s shopping malls, the researchers modified the newly proposed MTAM model by incorporating mobile self-efficacy theory, social influence, rewards and perceived enjoyment. However, the extended model has not been extensively studied in previous research. This study is the first to examine the variables that affect an individual’s intention to accept mobile AR apps by using a novel extended MTAM.

This study investigates the influence of the wisdom of the crowd (WSC), trust and perceived value on consumer purchase intentions within social commerce (s-commerce) platforms. By constructing a novel theoretical model, the research aims to delineate the interrelationships among these variables, addressing an emerging area in social interactions and information technology.

To empirically test and validate the proposed model, the study collected data from 442 Facebook users familiar with online shopping. The analysis employed Structural Equation Modeling – Partial Least Squares (SEM-PLS) to test the hypotheses and examine the relationships between WSC, trust, perceived value and purchase intentions.

The findings confirm that WSC significantly influences trust, perceived value and the intention to purchase on s-commerce platforms. Both perceived value and trust are substantial determinants of purchase intentions. Notably, the PLS analysis revealed that WSC accounts for 36.8% of the variance in trust and 33.1% of the variance in perceived value related to purchasing decisions on s-commerce platforms.

This research contributes uniquely to the ongoing discourse on s-commerce adoption by integrating WSC as a pivotal factor in understanding perceived value and consumer adoption intentions. It provides a fresh perspective on how collective intelligence affects consumer behavior in digital marketplaces, enriching the theoretical and practical understanding of s-commerce dynamics.