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The purpose of this paper is to present an automatic approach for mesh sizing field generation of complicated  computer-aided design (CAD) models.

In this paper, the authors present an automatic approach for mesh sizing field generation. First, a source point extraction algorithm is applied to capture curvature and proximity features of CAD models. Second, according to the distribution of feature source points, an octree background mesh is constructed for storing element size value. Third, mesh size value on each node of background mesh is calculated by interpolating the local feature size of the nearby source points, and then, an initial mesh sizing field is obtained. Finally, a theoretically guaranteed smoothing algorithm is developed to restrict the gradient of the mesh sizing field.

To achieve high performance, the proposed approach has been implemented in multithreaded parallel using OpenMP. Numerical results demonstrate that the proposed approach is remarkably efficient to construct reasonable mesh sizing field for complicated CAD models and applicable for generating geometrically adaptive triangle/tetrahedral meshes. Moreover, since the mesh sizing field is defined on an octree background mesh, high-efficiency query of local size value could be achieved in the following mesh generation procedure.

How to determine a reasonable mesh size for complicated CAD models is often a bottleneck of mesh generation. For the complicated models with thousands or even ten thousands of geometric entities, it is time-consuming to construct an appropriate mesh sizing field for generating high-quality mesh. A parallel algorithm of mesh sizing field generation with low computational complexity is presented in this paper, and its usability and efficiency have been verified.

The purpose of this study is to generate insight into the effects of entrepreneurship education in China by conducting a preliminary scoping study of the enterprising tendency of university students studying business.

This study used a self-administered questionnaire based on the General Measure of Enterprising Tendency v2 (GET2) test to measure the enterprising tendency of a group of Chinese university students. Decision trees, using the Chi-square automatic interaction detector (CHAID) approach, and multiple regression analyses were used to investigate the enterprising tendency of respondents.

The findings from this study indicate that the students have an overall medium level of enterprising tendency and strengths in some enterprising characteristics. The findings reveal that gender, family business, hometown and entrepreneurship education are significantly related to enterprising tendency but that age, household income, parents’ education and occupation are not.

Although the study is based on a relatively small sample taken from just one university in Beijing, the findings suggest that the enterprising tendency of students can be encouraged by entrepreneurship education. Combined with evidence that entrepreneurship education is at a relatively early stage of development in China, this finding suggests considerable scope to increase student’s enterprising tendency by extending, creating a more favourable environment for and improving the methods used to deliver entrepreneurship education. Enterprising tendency can be argued to naturally result in entrepreneurial intention; however, this extension is beyond the scope of this study, which is restricted to the analysis of enterprising tendency.

This study makes an original contribution to knowledge as it is one of the first studies to explore enterprising tendency among university students in China. It has value for government, policymakers and university program designers in that it provides direction for entrepreneurship education in China.

The purpose of this paper is to study the tribological properties of Cu nanoparticles (NPs) as lubricant additives in three kinds of commercially available lubricants.

A four-ball machine is used to estimate the tribological properties of Cu NPs as lubricant additives in three kinds of commercially available lubricants. Three-dimensional optical profiler and electrical contact resistance are evaluated to investigate the morphology of the worn surfaces and the influence of Cu NPs on tribofilms.

Wear tests show that the addition of Cu NPs as lubricant additives could reduce wear and increase load-carrying capacity of commercially available lubricants remarkably, indicating that Cu NPs have a good compatibility with the existing lubricant additives in commercially available lubricants.

The tribological properties of Cu NPs as lubricant additives in three kinds of commercially available lubricants were investigated in this paper. These results are reliable and can be very helpful for application of Cu NPs as lubricant additives in industry.

The inconsistent results shown in previous group faultline research have created a need for investigating the underlying mechanisms of the faultline's effects. This study focuses on clarifying the competing mediating roles of information diversity and team conflict in the nonlinear relationship between board faultlines (BF) and decision quality.

This study is empirically tested with the questionnaire data from 105 Chinese listed companies.

This study finds: (1) an inverted U-shaped curve relationship between BF and board decision quality and (2) that the joint mediating effect of team conflict and information diversity leads to the inverted U-shaped curve relationship between BF and decision quality. Specifically, BF shows a U-shaped curve relationship with team conflict and an inverted U-shaped curve relationship with information diversity. Either too weak or too strong faultlines will inhibit the positive effects of information diversity and amplify the negative effects of team conflicts, leading to low-quality decisions.

This study contributes to the research on: (1) board governance as it clarifies the effect of BF on the board decision-making process and its quality, which helps to open the black box of board decision-making and (2) group faultlines as it reveals how information diversity and team conflict can play a joint mediating role in the functioning of team faultlines.

The purpose of this paper is to model and predict suitable gait trajectories of lower-limb exoskeleton for wearer during rehabilitation walking. Lower-limb exoskeleton is widely used for assisting walk in rehabilitation field. One key problem for exoskeleton control is to model and predict suitable gait trajectories for wearer.

In this paper, the authors propose a Deep Spatial-Temporal Model (DSTM) for generating knee joint trajectory of lower-limb exoskeleton, which first leverages Long-Short Term Memory framework to learn the inherent spatial-temporal correlations of gait features.

With DSTM, the pathological knee joint trajectories can be predicted based on subject’s other joints. The energy expenditure is adopted for verifying the effectiveness of new recovery gait pattern by monitoring dynamic heart rate. The experimental results demonstrate that the subjects have less energy expenditure in new recovery gait pattern than in others’ normal gait patterns, which also means the new recovery gait is more suitable for subject.

Long-Short Term Memory framework is first used for modeling rehabilitation gait, and the deep spatial–temporal relationships between joints of gait data can obtained successfully.

Despite the fact that user participation (UP) has been highlighted as an important aspect in innovation, previous findings on its relationship with service innovation performance (SIP) are inconsistent. This study aims to investigate the relationships among UP, knowledge management capability (KMC) and SIP, especially in the digital age, inspired by the theories of knowledge-based and absorptive capacity.

Based on a sample of 252 Chinese e-commerce enterprises, this study adopts a hierarchical regression analysis and bootstrap method to test the theoretical framework and research hypotheses.

UP and KMC have positive effects on SIP, respectively. KMC plays a mediating role in the effect of UP on SIP. Furthermore, the intermediary role of KMC varies in different sub-paths between UP and SIP.

First, this study provides some explanations for inconsistent arguments on the relationship between UP and service innovation. Second, with the consideration of specific dimensions of UP and SIP, the mediation role of KMC varies in different sub-paths has been recognized, which provides a deeper understanding of the relationship between UP and SIP. Third, this study opens the discussion about how to realize SIP more effectively in the digital age, advancing theoretical and practical developments on service innovation.

In the era of big data, various industries are generating large amounts of text data every day. Simplifying and summarizing these data can effectively serve users and improve efficiency. Recently, zero-shot prompting in large language models (LLMs) has demonstrated remarkable performance on various language tasks. However, generating a very “concise” multi-document summary is a difficult task for it. When conciseness is specified in the zero-shot prompting, the generated multi-document summary still contains some unimportant information, even with the few-shot prompting. This paper aims to propose a LLMs prompting for multi-document summarization task.

To overcome this challenge, the authors propose chain-of-event (CoE) prompting for multi-document summarization (MDS) task. In this prompting, the authors take events as the center and propose a four-step summary reasoning process: specific event extraction; event abstraction and generalization; common event statistics; and summary generation. To further improve the performance of LLMs, the authors extend CoE prompting with the example of summary reasoning.

Summaries generated by CoE prompting are more abstractive, concise and accurate. The authors evaluate the authors’ proposed prompting on two data sets. The experimental results over ChatGLM2-6b show that the authors’ proposed CoE prompting consistently outperforms other typical promptings across all data sets.

This paper proposes CoE prompting to solve MDS tasks by the LLMs. CoE prompting can not only identify the key events but also ensure the conciseness of the summary. By this method, users can access the most relevant and important information quickly, improving their decision-making processes.

Internet marketing underground industry users refer to people who use technology means to simulate a large number of real consumer behaviors to obtain marketing activities rewards illegally, which leads to increased cost of enterprises and reduced effect of marketing. Therefore, this paper aims to construct a user risk assessment model to identify potential underground industry users to protect the interests of real consumers and reduce the marketing costs of enterprises.

Method feature extraction is based on two aspects. The first aspect is based on traditional statistical characteristics, using density-based spatial clustering of applications with noise clustering method to obtain user-dense regions. According to the total number of users in the region, the corresponding risk level of the receiving address is assigned. So that high-quality address information can be extracted. The second aspect is based on the time period during which users participate in activities, using frequent item set mining to find multiple users with similar operations within the same time period. Extract the behavior flow chart according to the user participation, so that the model can mine the deep relationship between the participating behavior and the underground industry users.

Based on the real underground industry user data set, the features of the data set are extracted by the proposed method. The features are experimentally verified by different models such as random forest, fully-connected layer network, SVM and XGBOST, and the proposed method is comprehensively evaluated. Experimental results show that in the best case, our method can improve the F1-score of traditional models by 55.37%.

This paper investigates the relative importance of static information and dynamic behavior characteristics of users in predicting underground industry users, and whether the absence of features of these categories affects the prediction results. This investigation can go a long way in aiding further research on this subject and found the features which improved the accuracy of predicting underground industry users.

This study investigates how Danmu (danmaku) technological features (DTFs) of recommendation vlogs (rec-vlogs) impact consumer experiences and decisions.

The authors adopt the partial least squares structural equation modeling (PLS-SEM) technique with a sample of 422 viewers of Danmu-enabled rec-vlogs to examine the proposed model.

The DTFs positively affect consumer loyalty intentions through the mediation of social presence and immersion. Also, vlogger-product congruency significantly moderates the relationship between social presence and immersion.

With the increasing popularity of vlogs, retailers have adopted the vlog as a new marketing channel to connect with consumers. Although a growing body of studies focuses on this phenomenon, little is known about how DTFs help to increase consumer loyalty toward using rec-vlogs as product information sources. Anchored on the stimulus–organism–response framework, this study investigates how pseudo-synchronicity, Danmu-content congruency and parallelism, three DTFs used in rec-vlogs, impact consumer experiences of social presence and immersion, eventually influence the consumers' loyalty intentions toward rec-vlogs. The authors also examine whether the effect of social presence on immersion is contingent on vlogger-product congruency. These findings have implications for both researchers and practitioners to understand the effect of DTFs in the context of rec-vlogs.

As wheeled mobile robots find increasing use in outdoor applications, it becomes more important to reduce energy consumption to perform more missions efficiently with limit energy supply. The purpose of this paper is to survey the current state-of-the-art on energy-efficient motion planning (EEMP) for wheeled mobile robots.

The use of wheeled mobile robots has been increased to replace humans in performing risky missions in outdoor applications, and the requirement of motion planning with efficient energy consumption is necessary. This study analyses a lot of motion planning technologies in terms of energy efficiency for wheeled mobile robots from 2000 to present. The dynamic constraints play a key role in EEMP problem, which derive the power model related to energy consumption. The surveyed approaches differ in the used steering mechanisms for wheeled mobile robots, in assumptions on the structure of the environment and in computational requirements. The comparison among different EEMP methods is proposed in optimal, computation time and completeness.

According to lots of literature in EEMP problem, the research results can be roughly divided into online real-time optimization and offline optimization. The energy consumption is considered during online real-time optimization, which is computationally expensive and time-consuming. The energy consumption model is used to evaluate the candidate motions offline and to obtain the optimal energy consumption motion. Sometimes, this optimization method may cause local minimal problem and even fail to track. Therefore, integrating the energy consumption model into the online motion planning will be the research trend of EEMP problem, and more comprehensive approach to EEMP problem is presented.

EEMP is closely related to robot’s dynamic constraints. This paper mainly surveyed in EEMP problem for differential steered, Ackermann-steered, skid-steered and omni-directional steered robots. Other steering mechanisms of wheeled mobile robots are not discussed in this study.

The survey of performance of various EEMP serves as a reference for robots with different steering mechanisms using in special scenarios.

This paper analyses a lot of motion planning technologies in terms of energy efficiency for wheeled mobile robots from 2000 to present.