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3D steady heat conduction analysis considering heat source is conducted on the fundamental of the fast multipole method (FMM)-accelerated line integration boundary element method (LIBEM).

Due to considering the heat source, domain integral is generated in the traditional heat conduction boundary integral equation (BIE), which will counteract the well-known merit of the BEM, namely, boundary-only discretization. To avoid volume discretization, the enhanced BEM, the LIBEM with dimension reduction property is introduced to transfer the domain integral into line integrals. Besides, owing to the unsatisfactory performance of the LIBEM when it comes to large-scale structures requiring massive computation, the FMM-accelerated LIBEM (FM-LIBEM) is proposed to improve the computation efficiency further.

Assuming N and M are the numbers of nodes and integral lines, respectively, the FM-LIBEM can reduce the time complexity from O(NM) to about O(N+ M), and a full discussion and verification of the advantage are done based on numerical examples under heat conduction.

(1) The LIBEM is applied to 3D heat conduction analysis with heat source. (2) The domain integrals can be transformed into boundary integrals with straight line integrals by the LIM. (3) A FM-LIBEM is proposed and can reduce the time complexity from O(NM) to O(N+ M). (4) The FM-LIBEM with high computational efficiency is exerted to solve 3D heat conduction analysis with heat source in massive computation successfully.

Surrogate models are extensively used to substitute real models which are expensive to evaluate in the time-dependent reliability analysis. Normally, different surrogate models have different scopes of application. However, information is often insufficient for analysts to select the most appropriate surrogate model for a specific application. Thus, the result precited by individual surrogate model tends to be suboptimal or even inaccurate. Ensemble model can effectively deal with the above concern. This work aims to study the application of ensemble model for reliability analysis of time-independent problems.

In this work, a method of reliability analysis for time-dependent problems based on ensemble learning of surrogate models is developed. The ensemble of surrogate models includes Kriging, radial basis function, and support vector machine. The prediction is approximated by the weighted average model. The ensemble learning of surrogate models is updated by finding and adding the sample points with large prediction errors throughout the entire procedure.

The effectiveness of the proposed method is verified by several examples. The results show that the ensemble of surrogate models can effectively propagate the uncertainty of time-varying problems, and evaluate the reliability with high prediction accuracy and computational efficiency.

This work proposes an adaptive learning framework for the uncertainty propagation of time-dependent problems based on the ensemble of surrogate models. Compared with individual surrogate models, the ensemble model not only saves the effort of selecting an appropriate surrogate model especially when the knowledge of unknown problem is lacking, but also improves the prediction accuracy and computational efficiency.

This study aims to investigate the motivations for the adoption of Industry 4.0 technologies among manufacturing firms in developing economies. Specifically, the effects of relative advantage of the technologies, competitive pressure, and government support on the adoption are explored. Moreover, the mediating role of top management support between environmental factors (government support and competitive pressure) and the adoption of Industry 4.0 technologies is examined.

A research model is developed based on the technology-organization-environment (TOE) framework strengthened by institutional theory. Structural equation modeling (SEM) approach is employed to evaluate the model using data obtained from 215 manufacturing firms through a cross-industry survey. Additionally, a post-hoc analysis is conducted using cluster analysis and ANOVA.

The results show that competitive pressure and government support significantly promote top management support, which in turn contributes to the adoption of Industry 4.0 technologies. Relative advantage of the technologies is not significantly related to the adoption.

This study does not explore the relationship between technology type and the specific needs of manufacturing firms. Future researchers can conduct a more comprehensive analysis by examining how different technology types align with the unique needs of individual companies.

The findings of this study have implications for both policymakers and managers. Policymakers can leverage these insights to understand the underlying motivations behind manufacturing firms' adoption of Industry 4.0 technologies and develop promoting policies. In turn, managers should keep an eye on government policies and utilize government support to facilitate technology adoption.

This study uncovers the underlying motivations—government support and competitive pressure—for the adoption of Industry 4.0 technologies among manufacturing firms in developing economies. Meanwhile, it complements previous research by showing the mediating role of top management support between environmental factors (government support and competitive pressure) and the adoption of Industry 4.0 technologies.

This paper aims to build an improved grey panel clustering evaluation model and evaluate the comprehensive development potential of industrial economy, society and ecological environment in the Yangtze River Economic Belt of China. The purpose of this study is to provide some theoretical basis and tool support for management departments and relevant researchers engaged in industrial sustainable development.

This study uses the driving force pressure state impact response analysis framework to build a comprehensive evaluation index system. Based on the center point triangle whitening weight function, it classifies the panel grey clustering of improvement time and index weight.

The results show that there are great differences in the level of industrial ecological development in different regions of the Yangtze River Economic Belt, which further illustrates the scientificity and rationality of the evaluation method proposed in this paper.

Due to the industrial ecological development is in a constantly changing state, and the information is uncertain. Whitening weight function is introduced to represent the complete information of relevant data. The industrial ecological evaluation involves a comprehensive complex system, which belongs to the panel data analysis problem. The improved grey panel clustering evaluation model is applied to grade the industrial ecological development level of the Yangtze River Economic Belt. The results have important guiding significance for the balanced development of industrial ecology in the region.

Due to the industrial ecological development is in a constantly changing state, and the information is uncertain. Whitening weight function is introduced to represent the complete information of relevant data. The industrial ecological evaluation involves a comprehensive complex system, which belongs to the panel data analysis problem. In order to improve the effectiveness of industrial ecological evaluation, the improved grey panel clustering evaluation model is applied to grade the industrial ecological development level of the Yangtze River Economic Belt. The results have important guiding significance for the balanced development of industrial ecology in the region.

the new model proposed in this paper complements and improves the grey clustering analysis theory of panel data, that is, aiming at the subjective limitation of using time degree to determine time weight in panel grey clustering, a comprehensive theoretical method for determining time weight is creatively proposed. Combining the DPSIR (Driving force-Pressure-State-Influence-Response) model model with ecological development, a comprehensive evaluation model is constructed to make the evaluation results more authentic and comprehensive.

The current research aimed to investigate the external enablement role of Digital Infrastructures (DI) in the interplay of entrepreneurial cognitions and innovation.

Data from the Global Entrepreneurship Monitor (GEM) and Digital Economy and Society Index (DESI) were used for analyses. This yielded a sample of 8,601 Generation Z entrepreneurs operating in 25 European countries.

Applying hierarchical moderated regressions showed that socio-cognitive components of an entrepreneurial mindset (self-efficacy, risk propensity, opportunity identification) affect innovation among Generation Z entrepreneurs. More importantly, DI plays an external enablement role in the interplay of cognitions and innovation among Generation Z entrepreneurs.

This study contributes to the socio-cognitive theory of entrepreneurship by integrating an external enablement perspective into the study of cognitions and entrepreneurial outcomes (here, innovation). It contributes to the digital technology perspective of entrepreneurship by connecting the conversation about the socio-cognitive perspective of entrepreneurship regarding the role of cognitions in innovation to the conversation in information systems (IS) regarding technology affordances and constraints. This study extends the application of the external enabler framework to the post-entry stage of entrepreneurial activity and integrates a generational perspective into it.

The purpose of this paper is to investigate the effect of job insecurity on employee silence by developing a moderated mediation model. The model focuses on the mediating role of ego depletion underpinning the relationship between job insecurity on employee silence and the moderating role of perceived coworker support and career growth opportunity in influencing the mediation.

This study conducts a questionnaire from 309 employees of six Chinese financial enterprises in two waves, with a one-month interval between the two waves. Moreover, this study uses bootstrapping and confirmatory factor analysis to verify the hypothesis.

Job insecurity has a significant positive impact on employee silence, and ego depletion partly mediated the relationship between job insecurity and employee silence. Perceived coworker support and career growth opportunity negatively moderated the relationship between job insecurity and ego depletion and also moderated the indirect effect of job insecurity on employee silence through ego depletion.

The study provides evidence for the positive effects of job insecurity on ego depletion, which, in turn, is significantly associated with employee silence. It highlights the important role of perceived coworker support and career growth opportunities in reducing employee negative perceptions and behaviors.

This empirical study provides preliminary evidence of the mediating role of ego depletion in the positive relationship between job insecurity and employee silence. The moderated mediation model also extends the existing finding by adding substantive moderators (perceived coworker support and career growth opportunity) to explain how the effect of job insecurity on employees’ behaviors unfolds.

Following the positive call for a special issue on Architectural Pedagogies and Sustainable Development Goals (SDGs), the purpose of this overview article is to contextualise reflections on nine selected articles, within recent efforts made by professional organisations, which aspire to blend sustainable development into the collective psyche of both academics and future professionals.

This article adopts four lines of inquiry by capturing key insights on the place of sustainable design knowledge in architectural education validation and accreditation at both national and global scales; presenting analytical narratives on the recent global efforts that embrace excellence in architectural pedagogy through addressing SDGs; elucidating the two knowledge spaces, centred on pedagogy and sustainability, which are strengthened and supported by Archnet-IJAR, and offering reflections on the nine articles published in this special issue that aims at integration of the two knowledge spaces.

Contextualisation and reflective narratives offer insights into current efforts and demonstrate a clear commitment of professional organisations to embed values relevant to SDGs. Efforts of the Education Commission of the International Union of Architects and the UNESCO-UIA Validation Council of Architectural Education appear to have advanced significantly over recent years with a clear prospect for a sustainable future. The nine articles published in this special issue respond clearly to the goal of Quality Education (SDG4), but not all of them have addressed the goals related to Good Health and Well-being (SDG3) and Sustainable Cities and Communities (SDG11), and their place in architectural pedagogy. However, they take a step further to address aspects of climate change, globalisation, sustainable architecture and urbanism, social sustainability, global north/global south dialectics and decolonisation.

The findings offer opportunities to recognise efforts by professional organisations, map key pedagogical experiments into these efforts, while providing lessons learned from best practices aiming to effectively integrate SDGs into architectural pedagogy.

No serious effort has been made to articulate the integration of SDGs into architectural education at the level of research or design studio pedagogical practice. Addressing architectural pedagogies and sustainable development is predicated on the fact that there is very little written or known on integrating SDGs into architectural education and design pedagogy. Understanding, appreciating, and sharing various efforts and approaches to incorporate SDGs into architectural pedagogy is a key step towards a sustainable future.

This paper explores the relationships between firm absorptive capacity, novel business model design (NBMD), product differentiation strategy and performance in a transition economy.

The study uses structural equation modeling (SEM) to analyze firm-level data from a unique sample of Albanian manufacturing and service firms.

The study shows that absorptive capacity enables and shapes the NBMD that, in turn, leads to performance gains. The authors also find that the NBMD capacity mediates the impact of realized absorptive capacity on performance, whereas product differentiation strategy moderates the relationship between new business model and performance.

All variables were measured based on a self-assessed scale leading to potential method bias. Also, based on relevant literature, the study focuses on only one type of business model (BM) design.

Since dynamic capabilities are the foundation of NBMD, firms should invest carefully in developing such capabilities. Thus, the study results provide an integrative framework for understanding the role of absorptive capacity in NBMD adoption and for explaining the relationship between NBMD adoption and performance, an aspect that helps organizations in a dynamic environment.

This study strives to investigate the relationships between absorptive capacity, business model design, product strategies and performance by answering the call of Teece (2018) to “flesh out the details” of such relationships.

This study aims to propose a facilitating methodology for the application of Fuzzy FMEA (Failure Mode and Effect Analysis), comparing the traditional approach with fuzzy variations, supported by a case application in the aeronautical sector.

Based on experts' opinions in risk analysis within the aeronautical sector, rules governing the relationship between severity, occurrence, detection and risk factor were defined. This served as input for developing a fuzzyfied FMEA tool using the Matlab Fuzzy Logic Toolbox. The tool was applied to the sealing process in a company within the aeronautical sector, using triangular and trapezoidal membership functions, and the results were compared with the traditional FMEA approach.

The results of the comparative application of traditional FMEA and fuzzyfied FMEA using triangular and trapezoidal functions have yielded valuable insights into risk analysis. The findings indicated that fuzzyfied FMEA maintained coherence with the traditional analysis in identifying higher-risk effects, aligning with the prioritization of critical failure modes. Additionally, fuzzyfied FMEA allowed for a more refined prioritization by accounting for variations in each variable through fuzzy rules, thereby improving the accuracy of risk analysis and providing a more realistic representation of potential hazards. The application of the developed fuzzyfied FMEA approach showed promise in enhancing risk assessment in the aeronautical sector by considering uncertainties and offering a more detailed and context-specific analysis compared to conventional FMEA.

This study emphasizes the potential of fuzzyfied FMEA in enhancing risk assessment by accurately identifying critical failure modes and providing a more realistic representation of potential hazards. The application case reveals that the proposed tool can be integrated with expert knowledge to improve decision-making processes and risk mitigation strategies within the aeronautical industry. Due to its straightforward approach, this facilitating methodology could also prove beneficial in other industrial sectors.

This paper presents the development and application of a facilitating methodology for implementing Fuzzy FMEA, comparing it with the traditional approach and incorporating variations using triangular and trapezoidal functions. This proposed methodology uses the Toolbox Fuzzy Logic of Matlab to create a fuzzyfied FMEA tool, enabling a more nuanced and context-specific risk analysis by considering uncertainties.

The purpose of this study is to propose a new consumer value segmentation method for low-dimensional dense market datasets to quickly detect and cluster the most profitable customers for the enterprises.

In this study, the comprehensive segmentation bases (CSB) with richer meanings were obtained by introducing the weighted recency-frequency-monetary (RFM) model into the common segmentation bases (SB). Further, a new market segmentation method, the CSB-MBK algorithm was proposed by integrating the CSB model and the mini-batch k-means (MBK) clustering algorithm.

The results show that our proposed CSB model can reflect consumers' contributions to a market, as well as improve the clustering performance. Moreover, the proposed CSB-MBK algorithm is demonstrably superior to the SB-MBK, CSB-KMA and CSB-Chameleon algorithms with respect to the Silhouette Coefficient (SC), the Calinski-Harabasz (CH) Index , the average running time and superior to the SB-MBK, RFM-MBK and WRFM-MBK algorithms in terms of the inter-market value and characteristic differentiation.

This paper provides a tool for decision-makers and marketers to segment a market quickly, which can help them grasp consumers' activity, loyalty, purchasing power and other characteristics in a target market timely and achieve the precision marketing.

This study is the first to introduce the CSB-MBK algorithm for identifying valuable customers through the comprehensive consideration of the clustering quality, consumer value and segmentation speed. Moreover, the CSB-MBK algorithm can be considered for applications in other markets.