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The primary objective of this study is to tackle the enduring challenge of preserving feature integrity during the manipulation of geometric data in computer graphics. Our work aims to introduce and validate a variational sparse diffusion model that enhances the capability to maintain the definition of sharp features within meshes throughout complex processing tasks such as segmentation and repair.

We developed a variational sparse diffusion model that integrates a high-order L₁ regularization framework with Dirichlet boundary constraints, specifically designed to preserve edge definition. This model employs an innovative vertex updating strategy that optimizes the quality of mesh repairs. We leverage the augmented Lagrangian method to address the computational challenges inherent in this approach, enabling effective management of the trade-off between diffusion strength and feature preservation. Our methodology involves a detailed analysis of segmentation and repair processes, focusing on maintaining the acuity of features on triangulated surfaces.

Our findings indicate that the proposed variational sparse diffusion model significantly outperforms traditional smooth diffusion methods in preserving sharp features during mesh processing. The model ensures the delineation of clear boundaries in mesh segmentation and achieves high-fidelity restoration of deteriorated meshes in repair tasks. The innovative vertex updating strategy within the model contributes to enhanced mesh quality post-repair. Empirical evaluations demonstrate that our approach maintains the integrity of original, sharp features more effectively, especially in complex geometries with intricate detail.

The originality of this research lies in the novel application of a high-order L₁ regularization framework to the field of mesh processing, a method not conventionally applied in this context. The value of our work is in providing a robust solution to the problem of feature degradation during the mesh manipulation process. Our model’s unique vertex updating strategy and the use of the augmented Lagrangian method for optimization are distinctive contributions that enhance the state-of-the-art in geometry processing. The empirical success of our model in preserving features during mesh segmentation and repair presents an advancement in computer graphics, offering practical benefits to both academic research and industry applications.

This paper aims to investigate the moisture diffusion behavior in a system-in-package module systematically by moisture-thermalmechanical-coupled finite element modeling with different structure parameters under increasingly harsh environment.

A finite element model for a system-in-package module was built with moisture-thermal-mechanical-coupled effects to study the subsequences of hygrothermal conditions.

It was found in this paper that the moisture diffusion path was mainly dominated by hygrothermal conditions, though structure parameters can affect the moisture distribution. At lower temperatures (30°C~85°C), the direction of moisture diffusion was from the periphery to the center of the module, which was commonly found in simulations and literatures. However, at relatively higher temperatures (125°C~220°C), the diffusion was from printed circuit board (PCB) to EMC due to the concentration gradient from PCB to EMC across the EMC/PCB interface. It was also found that there exists a critical thickness for EMC and PCB during the moisture diffusion. When the thickness of EMC or PCB increased to a certain value, the diffusion of moisture reached a stable state, and the concentration on the die surface in the packaging module hardly changed. A quantified correlation between the moisture diffusion coefficient and the critical thickness was then proposed for structure parameter optimization in the design of system-in-package module.

The different moisture diffusion behaviors at low and high temperatures have seldom been reported before. This work can facilitate the understanding of moisture diffusion within a package and offer some methods about minimizing its effect by design optimization.

Due to the cross-network effect, two-sided users communicate with each other, producing a coupling network. To study the spread of platform self-operation in two-sided users' marketing and purchasing tactics, this paper considers the differences in reputation acquired by platform-owned and third-party operating channels.

This study proposes a two-layer network with cross-network links: one layer represents the social network of consumers, while the other layer represents the competitive network of buyers. A closed system of differential equations, based on the binary dynamics of the stochastic network, is developed to study the trend and stability points of the platform self-operation dissemination. Then the overall benefits of platform are analyzed to unify the platform diffusion and pricing strategies.

The degree of difference in social influence and cross-network effects affect diffusion synergistically. Cross-network effects hinder diffusion when there is a significant difference of social influence between consumers and sellers but promote diffusion when there is little difference of social influence between consumers and sellers. Additionally, the network weights and reputation gap exhibit a nonlinear correlation with diffusion. For pricing strategy of the platform, it can achieve maximum profit when the pricing of self-operated goods and third-party-operated goods is equal.

This study considers the complex network architecture created by bilateral markets and the dynamic influence of group interactions on product. Additionally, this study takes reputation into account when considering the price and dissemination tactics of various operating channels, offering guidelines for platforms to control merchants and mediate disputes between various operating channels.

Building on social media and destination brand-related literature, this study aims to explore World Heritage Sites’ (WHSs) brand diffusion and formation process from long-term and short-term perspectives, which includes brand diffusion, user-generated content (UGC), opinion leaders and brand events’ impact.

This study uses a mixed-method including text mining, keyword analysis and social network analysis to explore the brand formation process of four popular WHSs in Beijing, namely, the Palace Museum, Great Wall, Summer Palace and Temple of Heaven and more than 10,000,000 users’ data on Sina Weibo has been implemented to uncover the underlying social media branding mechanism.

The results show that the number of postings keeps in a stable range in most months, but, in general, there are no common rules for changing trends among the four WHSs; long-term high-frequency keywords related to history and culture account for a higher percentage; different kinds of accounts have varying impacts on information diffusion, in which media accounts lead to a bigger influence. However, more followers do not necessarily mean more interactions and most of the interaction ratio is much lower than 0.01000; brand events facilitate brand dissemination and have an impact on the creation of UGC.

This study is valuable for destination marketers to deeper understand brand diffusion and formation and provides valuable insights for developing effective destination marketing strategies.

Unlike previous studies that only concern a few parts of destination brand formation via social media (e.g. brand diffusion, brand events or opinion leaders’ impact), this study takes a more comprehensive perspective by systematically analyzing the brand formation process of WHSs on social media. By considering both long-term diffusion and short-term representative events, this study provides a more holistic understanding of the branding mechanism.

Despite the critical role of the policy environment in facilitating the advancement of building information modeling (BIM) as a systemic innovation to reshape traditional facility design, construction and operation processes, scant scholarly attention has been paid to systematically investigating how and why complex BIM policies are concretely and gradually implemented in different regional contexts from a dynamic policy diffusion perspective. This study aims to empirically investigate how different types of BIM policy instruments are dynamically implemented in heterogeneous regions over time and how the diffusion of BIM policies across different regions is comprehensively impacted by both internal efficiency needs and external legitimacy pressures.

This study employed a positivist research paradigm in which BIM policy data from 182 prefecture-level and above cities in China during 2011–2022 were analyzed with quantitative approaches for theory verification. Based on the content analysis of the evolutionary characteristics of the adopted BIM policy instruments in heterogeneous regions over time, the event history analysis (EHA) method was then used to further examine the mechanisms underlying the diffusion of BIM policies across different regions.

The content analysis results show that while environmental instruments (such as technological integration and goal planning) are the primary policy instruments currently adopted in China, recent years have also witnessed increasing adoptions of supply-side instruments (such as fiscal support and information support) and demand-side instruments (such as demonstration projects and tax incentives). After controlling for the impacts of regional fiscal and technical resources, the EHA results illustrate that BIM policy adoption positively relates to regional construction industry scale but negatively relates to regional industry productivity and that compared with public pressures from industry participants, vertical pressures from the central government and horizontal pressures from neighboring regions are more substantial drivers for policy adoption.

As an exploratory effort of using a dynamic policy diffusion perspective to systematically investigate how BIM policies are adopted in heterogeneous regional contexts to facilitate BIM advancement, this study not only characterizes the complexity and dynamics of BIM policies but also provides deepened understandings of the mechanisms underlying policy adoption in the conservative construction industry. The findings hold implications for how multifarious policy instruments can be more effectively and dynamically adopted to facilitate the advancement of BIM and related technologies as innovative solutions in the construction domain.

This study sought to examine the interaction between rural bank performance, information and communication technology (ICT) investment, ICT diffusion and financial development.

Data were sourced from the Association of Rural Banks (ARB) Apex and World Development Indicators (WDI) for the period 2014–2020. A total of 122 rural banks were used for this study. The study adopted the two-step system generalized method of moments (SGMM) estimation technique in assessing the interactions among variables.

This study found compelling evidence to support the positive effect of ICT investment on banks’ performance (return on asset and net interest margin). Further, ICT diffusion and financial development positively influence banks’ performance. The results show a positive moderating effect exerted by ICT diffusion and financial development on the impact of bank risk (bank stability) and ICT investment on all three performance measures.

The study focuses on the rural banking sector in the Ghanaian economy, compared to related studies that examine the subject matter for commercial banks. The moderating effects of ICT diffusion and financial development are assessed to guide policy on rural banking development in Ghana.

Drawing on social network and information diffusion theories, the authors study the impact of the structural characteristics of a seller’s local social network on her promotion effectiveness in social commerce.

The authors define a local social network as one formed by a focal seller, her directly connected users and all links among these users. Using data from a large social commerce website in China, the authors build econometric models to investigate how the density, grouping and centralization of local social networks affect the number of likes received by products posted by sellers.

Local social networks with low density, grouping and centralization are associated with more likes on sellers’ posted products. The negative effects of grouping and centralization are reduced when density is high.

The paper deepens the understanding of the determinants of social commerce success from a network structure perspective. In particular, it draws attention to the role of sellers’ local social networks, forming a foundation for future research on social commerce.

The main purpose of this paper is to explore whether the nature of innovation is accumulative or radical and to what extent past year accumulation of technology stock can predict future innovation. More importantly, the authors are concerned with whether a change of policy regime or a variance in the quality of technology will moderate the nature of innovation.

The authors examined a dataset of 3.6 million Chinese patents during 1985–2015 and constructed more than 5 million citation pairs across 8 sections and 128 classes to track knowledge spillover across technology fields. The authors used this citation dataset to calculate the technology innovation network. The authors constructed a measure of upstream invention, interacting the pre-existing technology innovation network with historical patent growth in each technology field, and estimated measure's impact on future innovation since 2005. The authors also constructed three sets of metrics – technology dependence, centrality and scientific value – to identify innovation quality and a policy dummy to consider the impact of policy on innovation.

Innovation growth is built upon past year accumulation and technology spillover. Innovation grows faster for technologies that are more central and grows more slowly for more valuable technologies. A pro-innovation and pro-intellectual property right (IPR) policy plays a positive and significant role in driving technical progress. The authors also found that for technologies that have faster access to new information or larger power to control knowledge flow, the upstream and downstream innovation linkage is stronger. However, this linkage is weaker for technologies that are more novel or general. On most occasions, the nature of innovation was less responsive to policy shock.

This paper contributes to the debate on the nature of innovation by determining whether upstream innovation has strong predictive power on future innovation. The authors develop the assumption used in the technology spillover literature by considering a time-variant, directional and asymmetric matrix to model technology diffusion. For the first time, the authors answer how the nature of innovation will vary depending on the technology network configurations and policy environment. In addition to contributing to the academic debate, the authors' study has important implications for economic growth and industrial or innovation management policies.

The purpose of this study is to reveal the dynamics of house prices and sales in spatial and temporal dimensions across British regions.

This paper incorporates two empirical approaches to describe the behaviour of property prices across British regions. The models are applied to two different data sets. The first empirical approach is to apply the price diffusion model proposed by Holly et al. (2011) to the UK house price index data set. The second empirical approach is to apply a bivariate global vector autoregression model without a time trend to house prices and transaction volumes retrieved from the nationwide building society.

Identifying shocks to London house prices in the GVAR model, based on the generalized impulse response functions framework, I find some heterogeneity in responses to house price changes; for example, South East England responds stronger than the remaining provincial regions. The main pattern detected in responses and characteristic for each region is the fairly rapid fading of the shock. The spatial-temporal diffusion model demonstrates the presence of a ripple effect: a shock emanating from London is dispersed contemporaneously and spatially to other regions, affecting prices in nondominant regions with a delay.

The main contribution of this work is the betterment in understanding how house price changes move across regions and time within a UK context.

Cross-laminated timber (CLT) is an innovative construction material that provides a balanced mix of structural stiffness, fabrication flexibility and sustainability. CLT development and innovation diffusion require close collaborations between its supply chain architectural, engineering, construction and manufacturing (AECM) stakeholders. As such, the purpose of this study is to provide a preliminary understanding of the knowledge diffusion and innovation process of CLT construction.

The study implemented a longitudinal social network analysis of the AECM companies involved in 100 CLT projects in the UK. The project data were acquired from an industry publication and decoded in the form of a multimode project-company network, which was projected into a single-mode company collaborative network. This complete network was filtered into a four-phase network to allow the longitudinal analysis of the CLT collaborations over time. A set of network and node social network analysis metrics was used to characterize the topology patters of the network and the centrality of the companies.

The study highlighted the scale-free structure of the CLT collaborative network that depends on the influential hubs of timber manufacturers, engineers and contractors to accelerate the innovation diffusion. However, such CLT supply collaborative network structure is more vulnerable to disruptions due to its dependence on these few prominent hubs. Also, the industry collaborative network’s decreased modularity confirms the maturity of the CLT technology and the formation of cohesive clusters of innovation partners. The macro analysis approach of the study highlighted the critical role of supply chain upstream stakeholders due to their higher centralities in the collaborative network. Stronger collaborations were found between the supply chain upstream stakeholders (timber manufacturers) and downstream stakeholders (architects and main contractors).

The study contributes to the field of industrialized and CLT construction by characterizing the collaborative networks between CLT supply chain stakeholders that are critical to propose governmental policies and industry initiatives to advance this sustainable construction material.