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Surface texturing has emerged in the past two decades as a viable option of surface engineering, resulting in significant improvement in wear resistance and friction coefficient. The purpose of this study is to find the appropriate surface texture to reduce vibration and improve the stability of journal bearings.

Micro-dimples, evenly distributed in a square array, were selected as the texture pattern and formed on the lower surface of bush by the laser surface texturing technique. Experiments were carried out to evaluate the effects of micro-dimples under different depths, densities and distributions.

The results are summarized in the form of shaft center orbits, waterfall illustrations and Hilbert-Huang transforms. In the entire test, it was found that an optimum geometric and distributive range of micro-dimples exists, where vibration acceleration can be decreased at least 3dB and stability can be greatly improved.

A majority of researchers devoted to studying on static characteristics, such as friction coefficient, load carrying capacity, pressure distribution and cavitation model. Besides, the influence of surface texture on stability of rotor-journal bearing system was rarely investigated and the recent examples can be found in Refs. (Ausas et al. 2007). However, a complete study of textured journal bearings has not been undertaken in the dynamic properties. Therefore, the purpose of this paper is to experimentally investigate the comprehensive effects of density, depth and distribution of micro-dimples on bearing vibration and stability.

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 purpose of this paper is to summarize the existing point cloud target detection algorithms based on deep learning, and provide reference for researchers in related fields. In recent years, with its outstanding performance in target detection of 2D images, deep learning technology has been applied in light detection and ranging (LiDAR) point cloud data to improve the automation and intelligence level of target detection. However, there are still some difficulties and room for improvement in target detection from the 3D point cloud. In this paper, the vehicle LiDAR target detection method is chosen as the research subject.

Firstly, the challenges of applying deep learning to point cloud target detection are described; secondly, solutions in relevant research are combed in response to the above challenges. The currently popular target detection methods are classified, among which some are compared with illustrate advantages and disadvantages. Moreover, approaches to improve the accuracy of network target detection are introduced.

Finally, this paper also summarizes the shortcomings of existing methods and signals the prospective development trend.

This paper introduces some existing point cloud target detection methods based on deep learning, which can be applied to a driverless, digital map, traffic monitoring and other fields, and provides a reference for researchers in related fields.

Most of the existing ground filtering algorithms are based on the Cartesian coordinate system, which is not compatible with the working principle of mobile light detection and ranging and difficult to obtain good filtering accuracy. The purpose of this paper is to improve the accuracy of ground filtering by making full use of the order information between the point and the point in the spherical coordinate.

First, the cloth simulation (CS) algorithm is modified into a sorting algorithm for scattered point clouds to obtain the adjacent relationship of the point clouds and to generate a matrix containing the adjacent information of the point cloud. Then, according to the adjacent information of the points, a projection distance comparison and local slope analysis are simultaneously performed. These results are integrated to process the point cloud details further and the algorithm is finally used to filter a point cloud in a scene from the KITTI data set.

The results show that the accuracy of KITTI point cloud sorting is 96.3% and the kappa coefficient of the ground filtering result is 0.7978. Compared with other algorithms applied to the same scene, the proposed algorithm has higher processing accuracy.

Steps of the algorithm are parallel computing, which saves time owing to the small amount of computation. In addition, the generality of the algorithm is improved and it could be used for different data sets from urban streets. However, due to the lack of point clouds from the field environment with labeled ground points, the filtering result of this algorithm in the field environment needs further study.

In this study, the point cloud neighboring information was obtained by a modified CS algorithm. The ground filtering algorithm distinguish ground points and off-ground points according to the flatness, continuity and minimality of ground points in point cloud data. In addition, it has little effect on the algorithm results if thresholds were changed.

The purpose of this paper is to help understand consumer acceptance of online health information services by integrating the health belief model and extended valence framework.

A laboratory-based, experimental-scenarios research design is used to collect data, and the structural equation modeling technique is used to test the research model.

The model explains 47.6 percent of the variance in intentions to use online health information services. Trust appeared to have the strongest effect on acceptance. Perceived risk also had a significant impact on acceptance. Furthermore, health belief variables are confirmed as important factors for consumer acceptance. Self-efficacy was found to moderate the effect of perceived severity on acceptance.

This study helped identify the relative salience of the health belief model and extended valence framework in consumer acceptance of online health information services.

This study can help practitioners better understand the development of trust and the profiles of consumers who may browse their sites. When online health service providers promote their information to encourage potential online health information seekers, they should use countermeasures against risk perceptions.

This study attempted to extend the valence framework to the non-commercial service context. Moreover, health beliefs and the valence framework are two fundamental aspects that health information seekers consider when making decisions about online health services.

Building information modeling (BIM) is recognized as one of the technologies to upgrade the informatization level of the architecture engineering and construction (AEC) industry. However, the level of BIM implementation in the construction phase lags behind other phases of the project. Assessing the level of BIM implementation in the construction phase from a system dynamics (SD) perspective can comprehensively understand the interrelationship of factors in the BIM implementation system, thereby developing effective strategies to enhance BIM implementation during the construction phase. This study aims to develop a model to investigate the level of BIM implementation in the construction phase.

An SD model which covered technical subsystem, organizational subsystem, economic subsystem and environmental subsystem was developed based on questionnaire survey data and literature review. Data from China were used for model validation and simulation.

The simulation results highlight that, in China, from 2021 to 2035, the ratio of BIM implementation in the construction phase will rise from 48.8% to 83.8%, BIM model quality will be improved from 27.6% to 77.2%. The values for variables “BIM platform”, “organizational structure of BIM” and “workflow of BIM” at 2035 will reach 65.6%, 72.9% and 72.8%, respectively. And the total benefits will reach 336.5 billion yuan in 2035. Furthermore, the findings reveal five factors to effectively promote the level of BIM implementation in the construction phase, including: policy support, number of BIM standards, owners demand for BIM, investment in BIM and strategic support for BIM.

This study provides beneficial insights to effectively enhance the implementation level of BIM in the construction phase. Meanwhile, the model developed in this study can be used to dynamically and quantitatively assess the changes in the level of BIM implementation caused by a measure.

The purpose of this study is, to compare laser-based additive manufacturing and subtractive methods. Laser-based manufacturing is a widely used, noncontact, advanced manufacturing technique, which can be applied to a very wide range of materials, with particular emphasis on metals. In this paper, the governing principles of both laser-based subtractive of metals (LB-SM) and laser-based powder bed fusion (LB-PBF) of metallic materials are discussed and evaluated in terms of performance and capabilities. Using the principles of both laser-based methods, some new potential hybrid additive manufacturing options are discussed.

Production characteristics, such as surface quality, dimensional accuracy, material range, mechanical properties and applications, are reviewed and discussed. The process parameters for both LB-PBF and LB-SM were identified, and different factors that caused defects in both processes are explored. Advantages, disadvantages and limitations are explained and analyzed to shed light on the process selection for both additive and subtractive processes.

The performance of subtractive and additive processes is highly related to the material properties, such as diffusivity, reflectivity, thermal conductivity as well as laser parameters. LB-PBF has more influential factors affecting the quality of produced parts and is a more complex process. Both LB-SM and LB-PBF are flexible manufacturing methods that can be applied to a wide range of materials; however, they both suffer from low energy efficiency and production rate. These may be useful when producing highly innovative parts detailed, hollow products, such as medical implants.

This paper reviews the literature for both LB-PBF and LB-SM; nevertheless, the main contributions of this paper are twofold. To the best of the authors’ knowledge, this paper is one of the first to discuss the effect of the production process (both additive and subtractive) on the quality of the produced components. Also, some options for the hybrid capability of both LB-PBF and LB-SM are suggested to produce complex components with the desired macro- and microscale features.

The research aims to explore the influence mechanism of peer feedback and users' knowledge contribution behavior. This study draws on the social identity theory and considers social identity as a mediating factor into the research framework.

This paper collected users' activity data of 142,191 ideas submitted by 76,647 users from the MIUI community between October 2010 and May 2018 via Python software, and data were processed using Stata 16.0.

The results indicate that knowledge feedback and social feedback positively influence users' knowledge contribution (quantity and quality), respectively. User's cognitive identity positively mediates the relationship between peer feedback and knowledge contribution behavior, affective identity positively mediates the relationship between peer feedback and knowledge contribution behavior, while evaluative identity positively mediates the relationship between peer feedback and knowledge contribution quality, but there is no mediating effect between peer feedback and knowledge contribution quantity.

This study advances knowledge management by highlighting peer feedback on online innovation communities. By demonstrating the significant mediating effect of social identity, this study empirically clarifies the relationships of peer feedback (knowledge feedback and social feedback) to specific dimensions of knowledge contribution, thereby providing managerial guidance to the online innovation community on incentivizing and managing user interaction to foster the innovation development of firms.

The Western-centric nature of research into corporate social responsibility (CSR) has left gaps in one’s understanding of local culture's role in augmenting or undermining the impact of firms' CSR policies. This paper constructs and tests variables measuring “Confucian values” mediation between Chinese employees' perceived CSR and their job satisfaction, organizational commitment and organizational citizenship behavior.

The hypotheses were tested through structural equation modeling, using data from 311 responses completed by employees at Chinese private companies, located in China's Cheng-Yu economic area (Chongqing and Chengdu).

Chinese employees' perceived CSR had a positive significant effect on job attitudes (job satisfaction and organizational commitment). However, perceived CSR had no significant positive impact on organizational citizenship behavior. The authors also found that Confucian values are a partial mediator between perceived CSR and job attitudes and a full mediator between perceived CSR and organizational citizenship behavior.

The results enrich one’s understanding of cultural values in these relationships and suggest further research into how firms and governments in Confucian-based societies can better operationalize Confucian values to argument the firm's and country's CSR identity, thus improving job attitudes and public relations among customers who share this cultural heritage. For non-Confucian societies and foreign firms operating in China, the results encourage searches for Confucian value substitutes, such as trust and education, to incorporate into CSR mechanisms that promote these values among employees. The authors suggest approaches for furthering these agendas.