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Some papers have investigated the complex factors impacting building information modeling (BIM) application in prefabricated buildings (PBs), but few paid attention to their interaction relationships. Ignoring the fact that different factors are not isolated may lead to some key factors being overlooked without appropriate improvement strategies being proposed. This paper aims to analyze those factors and their inter-relationships, with the view to identify the critical factors and their interaction relationships so as to derive constructive strategies that would effectively facilitate BIM adoption in Chinese prefabrication.

First, factors influencing BIM application in prefabrication are extracted and collated by literature review, expert interview and analysis of PBs characteristics. Thereafter, an evaluation laboratory (decision-making trial and evaluation laboratory) and interpretive structural modeling are used to explore the relationships and hierarchy among the factors. Based on the degree of cause and centrality, critical factors are extracted and the interaction relationship are investigated.

The results show that BIM policies and standards for PBs are the main causal factors. The maturity of BIM software and BIM data interface for PBs, willingness to share data, the strategic goals of the enterprise, BIM law and BIM input and benefit are the main transitional factors while BIM staff and workflow, enterprise attitude, distribution of BIM liability and cooperation of participants are the main direct factors.

Based on the above findings, corresponding improvement strategies are proposed so as to promote BIM application in prefabrication and the rapid development of China’s PBs efficiently.

The paper aims to investigate the possible changes in mean temperature in the Mongolian Plateau associated with the 1.5 and 2°C global warming targets and how snow changes in the Mongolian Plateau when the mean global warming is well below 2°C or limited to 1.5°C.

In total, 30 model simulations of consecutive temperature and precipitation days from Coupled Model Inter-comparison Project Phase 5 (CMIP5) are assessed in comparison with the 111 meteorological monitoring stations from 1961–2005. Multi-model ensemble and model relative error were used to evaluate the performance of CMIP5 models. Slope and the Mann–Kendall test were used to analyze the magnitude of the trends and evaluate the significance of trends of snow depth (SD) from 1981 to 2014 in the Mongolian Plateau.

Some models perform well, even better than the majority (80%) of the models over the Mongolian Plateau, particularly HadGEM2-CC, CMCC-CM, BNU-ESM and GFDL-ESM2M, which simulate best in consecutive dry days (CDD), consecutive wet days (CWD), cold spell duration indicator (CSDI) and warm spell duration indicator (WSDI), respectively. Emphasis zones of WSDI on SD were deeply analysed in the 1.5 and 2 °C global warming period above pre-industrial conditions, because it alone has a significant negative relation with SD among the four indices. It is warmer than before in the Mongolian Plateau, particularly in the southern part of the Mongolian Plateau, indicating less SD.

Providing climate extremes and SD data sets with different spatial-temporal scales over the Mongolian Plateau. Zoning SD potential risk areas and proposing adaptations to promote regional sustainable development.

The purpose of this paper is to study the wire and arc additive manufacturing (WAAM) method and path planning algorithm of truss structure parts, to realize the collision-free rapid prototyping of truss structures with complex characteristics.

First, a point-by-point stacking strategy is proposed based on the spot-welding mode of cold metal transfer welding technology. A force analysis model of the droplet is established, which can be used to adjust the posture of the welding torch and solve the collapse problem in the WAAM process of the truss structure. The collision detection model is developed to calculate the interference size between the truss structure and the welding torch, which is used to control the offset of the welding torch. Finally, the ant colony algorithm has been used to optimize the moving path of welding torch between truss with considering the algorithm efficiency and collision avoiding and the efficiency of the algorithm is improved by discretizing the three-dimensional workspace.

A series of experiments were conducted to prove the validity of the proposed methods. The results show that the wire feeding speed, welding speed are the important parameters for controlling the WAAM process of truss parts. The inclination angle of the welding torch has an important influence on the forming quality of the truss.

The force analysis model of truss structure in the WAAM process is established to ensure the forming quality and a collision-free path planning algorithm is proposed to improve forming efficiency.

The purpose of this paper is to improve the surface electrical conductivity and corrosion resistance of AISI 430 stainless steel (430 SS) as bipolar plates for proton exchange membrane fuel cells (PEMFCs), a protective Nb-modified layer is formed onto stainless steel via the plasma surface diffusion alloying method. The effect of diffusion alloying time on electrochemical behavior and surface conductivity is evaluated.

In this work, the surface electrical conductivity and corrosion resistance of modified specimen are evaluated by the potentiodynamic and potentionstatic polarization tests. Moreover, the hydrophobicity is also investigated by contact angle measurement.

The Nb-modified 430 SS treated by 1.5 h (1.5Nb) presented a lower passivation current density, lower interfacial contact resistance and a higher hydrophobicity than other modified specimens. Moreover, the 1.5 Nb specimen presents a smoother surface than other modified specimens after potentionstatic polarization tests.

The effect of diffusion alloying time on electrochemical behavior, surface conductivity and hydrophobicity of modified specimen is evaluated. The probable anti-corrosion mechanism of Nb-modified specimen in simulated acid PEMFC cathode environment is presented.

Construction schedule delays and quality problems caused by construction errors are common in the field of prefabricated buildings. The effective monitoring of the construction project process is one of the key factors for the success of a project. How to effectively monitor the construction process of prefabricated building construction projects is an urgent problem to be solved. Aiming at the problems existing in the monitoring of the construction process of prefabricated buildings, this paper proposes a monitoring method based on the feature extraction of point cloud model.

This paper uses Trimble X7 3D laser scanner to complete field data collection experiments. The point cloud data are preprocessed, and the prefabricated component segmentation and geometric feature measurement are completed based on the PCL platform. Aiming at the problem of noisy points and large amount of data in the original point cloud data, the preprocessing is completed through the steps of constructing topological relations, thinning, and denoising. According to the spatial position relationship and geometric characteristics of prefabricated frame structure, the segmentation algorithm flow is designed in this paper. By processing the point cloud data of single column and beam members, the quality of precast column and beam members is measured. The as-built model and as-designed model are compared to realize the visual monitoring of construction progress.

The experimental results show that the dimensional measurement accuracy of beam and column proposed in this paper is more than 95%. This method can effectively detect the quality of prefabricated components. In the aspect of progress monitoring, the visualization of real-time progress monitoring is realized.

This paper proposed a new monitoring method based on feature extraction of the point cloud model, combined with three-dimensional laser scanning technology. This method allows for accurate monitoring of the construction process, rapid detection of construction information, and timely detection of construction quality errors and progress delays. The treatment process based on point cloud data has strong applicability, and the real-time point cloud data transfer treatment can guarantee the timeliness of monitoring.

China has a new rural revitalization strategy to stimulate rural transformation through modernizing rural areas and resolving their social contradictions. While social capital is recognized as an important element to rural revitalization and entrepreneurship, research into the role of psychological capital is less developed. Therefore, this paper assesses the impact of both social and psychological capital on entrepreneurial performance of Chinese new-generation rural migrant entrepreneurs (NGRMEs) who have returned to their homes to develop businesses as part of the rural revitalization revolution.

Based on a survey, data were collected from 525 NGRMEs in Shaanxi province. This paper uses factor analysis to determine variables for a multiple linear regression model to investigate the impacts of dimensions of both social capital and psychological capital on NGRMEs’ entrepreneurial performance.

Through the factor analysis, social capital of these entrepreneurs consists of five dimensions (reputation, participation, networks, trust and support), psychological capital has three dimensions (innovation and risk-taking, self-efficacy and entrepreneurial happiness) and entrepreneurial performance contains four dimensions (financial, customer, learning and growth, and internal business process). Furthermore, the multiple linear regression model empirically verifies that both social capital and psychological capital significantly influence and positively correlate with NGRMEs' entrepreneurial performance.

This study shows the importance of how a mixture of interrelated social and psychological dimensions influence entrepreneurial performance that may contribute to the success of the Chinese rural revitalization strategy. This has serious implications when attempting to improve the lives of over 100 million rural Chinese citizens.

The purpose of this paper is to examine the relationship between bank diversity and small- and medium-sized enterprise (SME) firm innovation in China to evaluate the impact of recent bank deregulation.

Using a large data set that includes 8,143 firm-year observations of 1,122 listed SME firms in China and baseline and robustness regression analyses, the authors identify how bank diversity affects firm innovation and via what economic mechanisms. Potential endogeneity problems are considered and addressed in the design and analysis to minimize research bias.

The authors find robust evidence that bank diversity improves firm innovation. Specifically, the findings suggest that the positive effects of bank diversity on firm innovation are only significant for the firms which are more external finance dependent, have fewer growth opportunities and/or located in the provinces having low financial market development.

This study provides novel evidence and insights into the relationship between banking market structure and the determinants of firm innovation in the Chinese context, as a result of China’s banking deregulation.

Accurately judging the quality of marine economic development is the premise of grasping the level and status of marine economic development. In order to scientifically evaluate the development quality of regional marine economy, the purpose of this paper is to select the marine area of Qingdao as the research object, and construct a marine economic development quality evaluation index system with 16 indicators.

The raw data is normalized by the range conversion method, and the weight of the index is determined by the information entropy model. Further, the grey relational analysis (GRA) method is used to evaluate the quality of marine economic development of Qingdao from 2012 to 2017.

The results show that the marine economic development capacity of Qingdao is with the generally increasing trend, the total marine economy is with on the rising trend, the marine storage and transportation capacity, and marine ecological environment are first decreased, and then increased. The utilization of marine resources is generally decreasing, and the comprehensive management of oceans varies with the changes of environment and economy. Therefore, in view of the development capacity of marine economy, the coordinated development of economy and environment should be carried out.

This paper uses the GRA to evaluate the quality of marine economic development and provides a reference for the development of marine economy in Qingdao.

Urban water bodies play an important role in reducing summertime urban heat island (UHI) effects. Previous studies focused mainly on the impact of water bodies of large areas, and there is no analysis of the efficacy and scale effect of how small and medium-sized water bodies reduce the UHI effects. Hence, these studies could not provide theoretical support for the scientific planning and design of urban water bodies. This study aims to confirm, within different scale ranges, the efficacy of a water body in reducing the summertime UHI effects. We propose a scale sensitivity method to investigate the temporal and spatial relationship between urban water bodies and UHI. Based on the scale theory and geostatistical analysis method in landscape ecology, this study used the platforms of 3S, MATLAB, and SPSS to analyze the distance-decay law of water bodies in reducing summertime UHI effects, as well as the scale response at different water surface ratios. The results show that the influence of water surfaces on UHIs gradually decreases with increasing distance, and the temperature rises by 0.78 °C for every 100 m away from the water body. During daytime, there is a scaled sensitivity of how much water surfaces reduce the summertime UHI effects. The most sensitive radius from the water was found at the core water surface ratio of 200 m. A reduction of UHI intensity by 2.3 °C was observed for every 10% increase of the average core water surface ratio. This study provides a theoretical reference to the control of heat islands for the planning and design of urban water bodies.

The paper aims to build a new objective evaluation method of fabric pilling by combining an integrated image analysis technology with a deep learning algorithm.

Series of image analysis techniques were adopted. First, a Fourier transform transformed images into the frequency domain. The optimal resolution matrix of an exponential high-pass filter was determined by combining the energy algorithm. Second, the multidimensional discrete wavelet transform determined the optimal division level. Third, the iterative threshold method was used to enhance images to obtain a complete and clear pilling ball images. Finally, the deep learning algorithm was adopted to train data from pilling ball images, and the pilling levels were classified according to the learning features.

The paper provides a new insight about how to objectively evaluate fabric pilling grades. Results of the experiment indicate that the proposed objective evaluation method can obtain clear and complete pilling information and the classification accuracy rate of the deep learning algorithm is 94.2%, whose structures are rectified linear unit (ReLU) activation function, four hidden layers, cross-entropy learning rules and the regularization method.

Because the methodology of the paper is based on woven fabric, the research study’s results may lack generalizability. Therefore, researchers are encouraged to test other kinds of fabric further, such as knitted and unwoven fabrics.

Combined with a series of image analysis technology, the integrated method can effectively extract clear and complete pilling information from pilled fabrics. Pilling grades can be classified by the deep learning algorithm with learning pilling information.