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This paper aims to develop an innovative 3D printer based on material extrusion to expand applied material field and shorten the production cycle. The developed 3D printer can fabricate products directly using various powders, including polymers and fillers. In addition, the influence of extrusion on the orientation of thermal conductive filler is also investigated.

To ensure the plasticizing effect and the mixing ability, the printing head is a conical twin-screw extruder, which have a smaller volume. PA12 and h-BN powders were selected for printing as matrix and filler, respectively. The properties of printing products were characterized.

The results show that the new printer can fabricate products directly using polymer powders because of the mixing ability of the twin-screw. The h-BN filler orient in the PA12 matrix and form thermal conduction paths due to the extrusion process, which make the printed samples have an anisotropic thermal conductivity.

The innovative 3D printer provides a method of printing products directly using powders, which can expand material field and shorten the production cycle. For composites, the extrusion process can make fillers orient in the matrix to fabricate products with anisotropic characteristics.

With the advancements in photo editing software, it is possible to generate fake images, degrading the trust in digital images. Forged images, which appear like authentic images, can be created without leaving any visual clues about the alteration in the image. Image forensic field has introduced several forgery detection techniques, which effectively distinguish fake images from the original ones, to restore the trust in digital images. Among several forgery images, spliced images involving human faces are more unsafe. Hence, there is a need for a forgery detection approach to detect the spliced images.

This paper proposes a Taylor-rider optimization algorithm-based deep convolutional neural network (Taylor-ROA-based DeepCNN) for detecting spliced images. Initially, the human faces in the spliced images are detected using the Viola–Jones algorithm, from which the 3-dimensional (3D) shape of the face is established using landmark-based 3D morphable model (L3DMM), which estimates the light coefficients. Then, the distance measures, such as Bhattacharya, Seuclidean, Euclidean, Hamming, Chebyshev and correlation coefficients are determined from the light coefficients of the faces. These form the feature vector to the proposed Taylor-ROA-based DeepCNN, which determines the spliced images.

Experimental analysis using DSO-1, DSI-1, real dataset and hybrid dataset reveal that the proposed approach acquired the maximal accuracy, true positive rate (TPR) and true negative rate (TNR) of 99%, 98.88% and 96.03%, respectively, for DSO-1 dataset. The proposed method reached the performance improvement of 24.49%, 8.92%, 6.72%, 4.17%, 0.25%, 0.13%, 0.06%, and 0.06% in comparison to the existing methods, such as Kee and Farid's, shape from shading (SFS), random guess, Bo Peng et al., neural network, FOA-SVNN, CNN-based MBK, and Manoj Kumar et al., respectively, in terms of accuracy.

The Taylor-ROA is developed by integrating the Taylor series in rider optimization algorithm (ROA) for optimally tuning the DeepCNN.

The purpose of this paper is to analyze the effects of private investor's fair preference on the governmental compensation mechanism based on the uncertainty of income for the public-private-partnership (PPP) project.

Based on the governmental dilemma for the compensation of PPP project, a generalized compensation contract is designed by the combination of compensation before the event and compensation after the event. Then the private investor's claimed concession profit is taken as its fair reference point according to the idea of the BO model, and its fair utility function is established by improving the FS model. Thus the master-slave counter measure game is applied to conduct the behavior modeling for the governmental compensation contract design.

By analyzing the model given in this paper, some conclusions are obtained. First, the governmental optimal compensation contract is fair incentive for the private investor. Second, the private fair preference is not intuitively positive or negative related to the social efficiency of compensation. Only under some given conditions, the correlation will show the consistent effect. Third, the private fair behavior’s impact on the efficiency of compensation will become lower and lower as the social cost of compensation reduces. Fourth, the governmental effective compensation scheme should be carried out based on the different comparison scene of the private claimed portfolio profit and the expected revenue for the project.

This study analyzes the effects of private investor's fair preference on the validity of governmental generalized compensation contract of the PPP project for the first time; and the governmental generalized compensation contract designed in this study is a pioneering and exploratory attempt.

As China has pressed ahead with rural revitalization in recent years, its rural financial sector has also developed rapidly and the financial environment has been greatly improved. But compared with urban areas, the rural financial sector makes rather limited contributions to rural economic development for a variety of reasons, including single types of service providers, narrow coverage, and lack of services and products. The underdevelopment of the rural financial system is closely related to the characteristics of its target customers and the economic system. The deficient rural financial credit system, the low level of IT application, the difficulty in data collection and integration, and the insufficient collateral of farmers pose high costs and huge risks for financial institutions when providing credit and other financial services.

In the present case, fintech and financial innovation complement each other: The application of fintech makes innovation possible, and the need for financial development fuels the development of fintech. Leveraging fintech and new business models, MYbank has overcome the main obstacles in the development of rural finance to provide convenient financial services for farmers and rural MSEs. Fintech is the abbreviation of “financial technology.” It can be understood as the combination of finance and technology for easier understanding, but it is more than that. Fintech refers to the innovation of traditional financial products and services with various technologies to improve efficiency and reduce operating costs. The emergence and development of fintech have led to the creation of new business models, applications, and processes, which have triggered major changes in financial markets, financial institutions, and the ways financial services are delivered, and are reshaping the financial landscapes of countries and even the world.

There are three major problems in the development of rural finance: difficult access to data, difficult risk management, and difficult market penetration. In order to gradually remove the obstacles and guarantee sustainable business development, MYbank has created three new business models with the power of fintech: digital inclusive finance at the county level, industrial finance, and platform finance. With these models, MYbank is searching for a “Chinese solution” to the worldwide problem of rural inclusive finance.

This study aims to present that the chemo-responsive shape recovery of thermoplastic polyurethane (TPU) is tunable by solvents with different solubility parameters, and it is generic for chemo-responsive shape-memory polymer and its composites.

Two kinds of commercial TPU samples with different thicknesses were prepared by panel vulcanizer and injection molding (an industrial manner) to investigate their chemo-responsive shape memory properties in acetic ether and acetone.

Results showed that all of TPU films with different thicknesses can fully recover their original shapes weather they recover in acetic ether or acetone. But the recovery time of TPU films in acetone is greatly reduced, especially for the twisting samples. The residual strains of recovery TPU samples after extension reduce obviously.

The great decrement of recovery time is related to two factors. One is due to the bigger solubility parameter of acetone with higher dipole moment compared with those of acetic ether, and the other is the remained internal stress of TPU films after preparation. The internal stress is identified to have an effect on the shape-memory properties by comparing the recovery process of samples with/without annealing. The reduced residual strains of recovery TPU samples after extension is due to the increasing mobility of polymer segments after molecules of acetic ether penetrates into the polymeric chains.

This is a universal strategy to control the recovery process of shape-memory materials or composites. The underlying mechanism is generic and should be applicable to chemo-responsive shape-memory polymers or their composites.

Extant literature has indicated that government support is one of the main drivers of international expansion of Chinese multinational enterprises. However, research on the influence of governors on firm internationalization is still limited. Drawing upon the institution-based view, we theorize a novel concept of institutional enablement to illustrate the influence of a governor's pro-market ideology on Chinese firms' internationalization.

We analyze the relationship between a governor's pro-market ideology (consisting of a pro-market political ideology, an overseas educational background and a business background) and firm internationalization with a sample of Chinese public companies during 2014–2017.

We find a direct and positive effect of a governor's pro-market ideology on firm internationalization. We also find an indirect and positive effect of a governor's pro-market ideology through regional, inward foreign direct investment.

To the best of our knowledge, this paper is the first to investigate an underexplored question of the impact of governors on firm internationalization and to develop a novel concept of institutional enablement, based on discursive institutionalism.

Employing multi-type laborers (MLs) is common in multinational and cross-culture projects (MPCs). Different attributes of MLs can lead to uncertain and dynamic laborer behaviors (i.e. behavioral diversities), which may cause project deviations. Previous studies do not consider the uncertainties or dynamics of behaviors adequately or they only provide general suggestions. The purpose of this paper is to combine system dynamics (SD) and agent-based modeling (ABM) to build an integrated model. The proposed ABM-SD can gain better understanding of MLs’ behavioral diversities, reveal the associated impacts and improve project management.

Based on extensively review in construction labor management and computer simulation, architecture is built to depict the relationships between the affecting factors of MLs’ behaviors, MLs’ behavioral diversities and project performance. Second, conceptual structures of the ABM-SD model are developed. Third, methods to implement the model in practice are introduced, focusing on data collection and model structure adjustment. Finally, the model is tested in a case study.

Different ML groups have distinctive behaviors which constantly change through interactions between MLs, engineers and external environment. Inadequate consideration of the diversities can result in inaccurate estimation of productivity, work quality and absenteeism, causing severe project deviations such as schedule delay, cost overrun and high absenteeism. On the other hand, using the ABM-SD model, the root causes of project deviations are analyzed from the perspective of MLs’ behavioral diversities and the optimization of labor management can significantly improve project performance.

This paper supplements previous studies because the ABM-SD model takes fully use of the strength of simulation of solving uncertain and dynamic problems and combines both qualitative and quantitative findings in existing studies of labor management. Besides, the ABM-SD model is also a practical management tool to better monitor laborer behaviors and forecast the impacts. The limitation is mainly about the small scale of the case study. However, the ABM-SD model already demonstrates the mechanism about how MLs’ different behaviors affect a project, which fulfill the aim of the study.

The ABM-SD model can simulate MLs’ behavioral diversities and produce reliable estimations of project performance. It also allows to optimize management plans. Furthermore, The ABM-SD model is adjustable based on specific project conditions, which make it applicable for different tasks, different laborer compositions and even different projects. Thus, the ABM-SD model can be a practical tool for engineers in MCPs.

SD and ABM are applied to study behaviors with well-known benefits in both separated and integrated manner. However, few studies use the approach to investigate MLs’ behaviors in MCPs. Hence, the proposed ABM-SD model is an original attempt to improve the laborer management level in MCPs.

The emergence of live streaming commerce has injected promising impetus into rural development and attracted many rural streamers. This study aims to explore the influencing factors of rural streamers’ engagement intentions to help promote the sustainable development of rural live streaming commerce.

Grounded in the extended valence framework, this research employs a mixed-methods approach encompassing both qualitative and quantitative methodologies. In the qualitative phase, the authors conduct in-depth interviews with 15 rural streamers, employing data coding techniques to uncover underlying factors. Subsequently, in the quantitative phase, the authors analyze survey data from 282 rural streamers, subjecting hypotheses to validation through structural equation modeling.

The findings derived from the analysis of both interviews and questionnaires reveal that several platform qualities, including platform rural-aiding support, perceived effectiveness of dispute resolution, perceived interactivity and platform reputation, have a positive effect on trust in the platform and validate the extended valence framework in understanding rural streamers’ live streaming intention. In addition, ties with customers have a moderating effect. Specifically, the stronger the ties with customers, the stronger the positive effect of perceived benefits and the weaker the positive effect of trust in the platform on live streaming intention will be.

This study contributes to the rural live streaming commerce literature and trust research from the sellers’ perspective and provides practical implications for policymakers and live streaming platform managers on enhancing rural streamers’ participation.

The COVID-19 pandemic has significantly raised economic risk and uncertainty worldwide. How does COVID-19 affect urban housing markets? Is there any difference when different areas encounter COVID-19? This study aims to investigate the impacts of the pandemic on housing prices by using Beijing’s housing markets data in 2020.

The authors use transaction-level data from April to September in 2020 to conduct a hedonic price analysis of the housing markets in Beijing. The data included 70,843 transactions scraped from a real estate agent’s website. The authors use the difference-in-differences approach to evaluate the impacts of the COVID-19 outbreak from the Beijing Xinfadi market (the largest and most important food wholesale market in Beijing) in 2020.

This outbreak of COVID-19 caused a 6.3% drop in housing prices in Beijing from April to September in 2020. However, the impacts of COVID-19 on housing prices in different urban neighbourhoods were spatially heterogeneous. Housing prices in neighbourhoods with industries that rely on face-to-face communication were more affected by the pandemic, while those that can work remotely were less affected.

By investigating the impacts of COVID-19 on housing prices in Beijing, this study illustrates that urban housing prices would be impacted by the pandemic, at least in the short term. While the rise and fall of housing prices were found spatially heterogeneous in Beijing, it suggests that urban neighbourhoods with specific socioeconomic characteristics and geographic locations would unfold different resilience when encountering pandemic. By using data scraping and rigorous statistical tools, the study is probably one of the first ones examining the consequences of COVID-19 in intra-urban housing markets.

The study aims to analyze the relationship between tourists' satisfaction and their sense of belonging in a specific cultural destination by also investigating the moderating role of their on-site involvement, both offline (on-site activities at the destination) and online (social media activities). A further objective lies in exploring the relationship between tourists' sense of belonging and their adoption of environmentally responsible behavior while at the destination. Finally, the paper attempts to determine whether said behavior can predict tourists' pro-environmental behavior and recommendation intention.

To reach these objectives, a survey and a structural equation model, based on a sample of 647 visitors of an important world Heritage site UNESCO (i.e. Urbino), have been adopted.

Findings reveal a positive influence of sense of belonging on satisfaction and environmentally responsible behavior, which, in turn, influences pro-environmental behavior and Recommendation intention, thus triggering a virtuous process in the tourists' formation as responsible and loyal travelers. Moreover, results underline how the relationship between satisfaction and sense of belonging is significantly strengthened when tourists' on-site activity involvement increases. Conversely, the moderating role of social media involvement is not supported.

The present study offers important implications for different actors in the tourism sector, such as policy makers, destination marketing organizations (DMOs) and tourism operators.

The present study explores the role of social media involvement, specifically during the tourist's holiday.

This study enriches the empirical evidence in the cultural tourism through an analysis focused on the tourists' perspective, especially by investigating the relationship between satisfaction and emotional constructs (i.e. sense of belonging) that are able to bring environmental and loyalty benefits to the destination. Moreover, although existing research has highlighted the positive influence of the tourists' involvement on their experience, there is a paucity of studies jointly analyzing the on-site and online activities in the sustainability field.