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This study aims to examine the development of a social enterprise in China’s tourism industry by analyzing the coevolution of the social entrepreneur and the social system.

Purposeful sampling was used to select 1kg.org as a single-case study. Semistructured in-depth interviews of three informants were conducted to obtain organization-specific insights. Interview data were analyzed following structuration theory. Secondary data and interviews of other social entrepreneurs and experts were used to support the findings. Research findings were validated using triangulation and member-checking methods.

Because of the institutional environment, most nonprofit organizations in China do not have resource independence and clear legal identity. Meanwhile, social enterprise has emerged as a new organizational form with the objective of creating social value through profitable business operations.

Social enterprise is still in its infancy in China, particularly in the tourism field. This study reveals an innovative and sustainable model for nonprofit organizations in China facing institutional challenges and competitive funding environments. It provides recommendations to policymakers for improving mechanisms to increase social services through social enterprises.

This study proposes a new conceptual framework for studying social entrepreneurship by adapting structuration theory to address contemporary social and business issues.

This study aims to establish a bio-inspired controller for realizing the bounding gait of a quadruped robot system presented in this paper.

The bio-inspired controller is divided into three levels to mimic the biological patterns of animals. First, the high-level sub-controller is equivalent to the cerebellum, which could plan and control the motion of animals. Second, the effect of the middle-level sub-controller corresponds to the central nervous system. The central pattern generators in the spine generate the stable and cyclic signals as the fundamental rhythm for periodic motion of the leg and spine joints. Third, the low-level sub-controller is equal to the end effector, which adopts the simple proportional-derivative (PD) control to realize the specific motion trajectory of the legs and spine.

Combined with the stability criterion presented previously and the delayed feedback control method, the bounding gait of the cheetah virtual prototype could be actuated and stabilized by the bio-inspired controller. Moreover, the bio-inspired controller is applied to realize the bounding gait of an SQBot, which is a quadruped robot with a spine joint. Meanwhile, the validity and practicability of the bio-inspired controller for the control of quadruped robot have been verified against different forward velocities.

The bio-inspired controller and bionic quadruped robot system are instructive for the designing and actuating of the real quadruped robot.

No study to date has addressed the construct of event tourist experience value and there is no well-accepted value measurement method. This study aims to examine the underlying dimensions of event tourist experience value from a holistic perspective.

The research process consists of two steps. First, the dimensions of event tourist experience value are investigated by using grounded theory and content analysis of travel blogs. The second step is to explore and verify the scale of event tourist experience value.

The items show that event tourist experience value can be comprehensively understood from three dimensions: functional value (functional factors); hedonic value (relaxation, escape, restoration and interaction); and symbolic value (social prestige, belonging and self-transcendence).

The findings are useful indicators explaining events attendance, and can help event organizers and destination managers to better understand the needs of tourists and design event experiences in a more sophisticated way.

This work integrates a series of varied events to suggest a holistic view of event tourist experience value, which provides a theoretical basis for other scholars to compare and monitor findings in this field.

Although collaborative research is believed to be an important means of accessing external knowledge, research on whether taking a strategic network position benefits new product development (NPD) is inconclusive. This study aims to unravel the conditions under which taking a strategic position within a collaborative research network is conducive for a firm’s NPD.

Drawing on social network theory, absorptive capacity theory and knowledge recombinant studies, this study examines how strategic network positions (i.e. degree centrality and structural holes) and knowledge base cohesion (i.e. local and global cohesion) in tandem affect a firm’s NPD. A panel data set of 366 firms in the Chinese automobile sector (2002–2010) is empirically analyzed, using the panel negative binomial approach with random effects and several alternate estimation approaches.

This study reveals that, rather than the volume of a firm’s knowledge base, its cohesion determines how it absorbs and uses knowledge accrued from collaborative research for NPD. Specifically, this paper finds that centrally positioned firms have greater NPD when their knowledge bases are locally cohesive, while firms spanning structural holes have more NPD when their knowledge bases are globally cohesive.

Successfully transferring collaborative research outcomes into product innovation is difficult. This study contributes to the literature on strategic network positions and NPD. The findings advance the understanding of knowledge base cohesion’s moderating role in explaining how firms absorb and exploit external knowledge for internal innovation. The findings also have important implications for managers who wish to promote product innovation by engaging in collaborative research with external partners.

The purpose of this study is to analyze Chinese tourists' perceptions toward the 2010 World Expo and the impact of a mega event on the host city and visitor satisfaction.

Guided by the conceptualization of hosting special events for destination branding, this study examines empirically tourists' perceptions and experiences of the 2010 Shanghai World Expo and the host city. Ten hypotheses were tested using SEM to identify and analyze the factors and relationships that influence mega events and host cities.

Data collected from 600 tourists who visited the 2010 Shanghai World Expo provide support for the proposed model. The empirical test provides support for eight of the ten hypotheses and it reveals that the hosting of the World Expo had significant positive impact on Shanghai but not vice versa, and the Chinese tourists exhibited loyalty to the host city.

Using a moving event such as Expo, the Shanghai brand is simplified. The sample includes only domestic tourists and future research should include an international sample for comparison.

The results help in identifying and managing the factors that contribute positively to the host destination brand when hosting mega events.

This study is the first attempt to empirically test the conceptual framework of leveraging a mega event for enhancing the host destination brand by using the 2010 Shanghai Expo as a case study. It validates that the strategic fit between the event and host city, community support and event quality are essential in enhancing host destination brand and tourist loyalty.

The purpose of this paper is to propose a prediction method of gas-liquid two-phase flow patterns and reveal the flow characteristics in the suction chamber of a centrifugal pump.

A transparent model pump was experimentally studied, and the gas-liquid two-phase flow in the pump was numerically simulated based on the Eulerian–Eulerian heterogeneous flow model. The numerical simulation method was verified from three aspects: the flow pattern in the suction chamber, the gas spiral length and the external characteristics of the pump. The two-phase flow in the suction chamber was studied in detail by using the numerical simulation method.

There are up to eight flow patterns in the suction chamber. However, at a certain rotational speed, only six flow patterns are observed at the most. At some rotational speeds, only four flow patterns appear. The gas spiral length has little relationship with the gas flow rate. It decreases with the increase of the liquid flow rate and increases with the increase of the rotational speed. The spiral flow greatly increases the turbulence intensity in the suction chamber.

A method for predicting the flow pattern was proposed. Eight flow patterns in the suction chamber were identified. The mechanism of gas-liquid two-phase flow in the suction chamber was revealed. The research results have reference values for the stable operation of two-phase flow pumps and the optimization of suction chambers.

The purpose of this paper is to find an efficient fabric defect detection algorithm by means of exploring the sparsity characteristics of main local binary pattern (MLBP) extracted from the original fabric texture.

In the proposed algorithm, original LBP features are extracted from the fabric texture to be detected, and MLBP are selected by occurrence probability. Second, a dictionary is established with MLBP atoms which can sparsely represent all the LBP. Then, the value of the gray-scale difference between gray level of neighborhood pixels and the central pixel, and the mean of the difference which has the same MLBP feature are calculated. And then, the defect-contained image is reconstructed as normal texture image. Finally, the residual is calculated between reconstructed and original images, and a simple threshold segmentation method can divide the residual image, and the defective region is detected.

The experiment result shows that the fabric texture can be more efficiently reconstructed, and the proposed method achieves better defect detection performance. Moreover, it offers empirical insights about how to exploit the sparsity of one certain feature, e.g. LBP.

Because of the selected research approach, the results may lack generalizability in chambray. Therefore, researchers are encouraged to test the proposed propositions further.

In this paper, a novel fabric defect detection method which extracts the sparsity of MLBP features is proposed.

The purpose of this paper is to investigate the literature on the treatment of primary pupils and inspecting the role of environmental psychology, e-learning, learning style and school design on the behavior of students in elementary schools.

A questionnaire was designed to evaluate the components of the model. Experts with significant experiences in the field of students’ behavior revised the surveys. Data were collected from 400 teachers of the elementary schools in Iran. The SMART-PLS 3.2 and SPSS 22 software package were used in the field of questionnaires’ statistical analysis.

Findings confirmed the suggested model’s validity for elementary students’ behavior assessment. The consequences of this research illustrated the effect of environmental psychology on the behavior of elementary students. In addition, the authors were concluded that intention to e-learning has a significant role in developing the action and behavior of the elementary students. Moreover, the learning style has an affirmative and considerable impact on the behavior of elementary students. Finally, school design has an affirmative and significant effect on the manner of the elementary students.

The consequences of this research have provided some traces about the basic perspectives, which have to be in the center of attention of administrators. For instance, school design and learning style sound to be a decisive mechanism for improving action and learning behavior. In addition, educational leaders may use the findings to evaluate their school facilities and define where developments will have the most significant impact or planners may use the results to assist architects in the design and construction of new educational services.

This study builds a valuable contribution by focusing on pupil environmental psychology, e-learning, learning style and school design in elementary schools by enlightening the connection between them and students’ manner.

The purpose of this paper is to focus on the design of a dual-branch balance saliency model based on fully convolutional network (FCN) for automatic fabric defect detection, and improve quality control in textile manufacturing.

This paper proposed a dual-branch balance saliency model based on discriminative feature for fabric defect detection. A saliency branch is firstly designed to address the problems of scale variation and contextual information integration, which is realized through the cooperation of a multi-scale discriminative feature extraction module (MDFEM) and a bidirectional stage-wise integration module (BSIM). These modules are respectively adopted to extract multi-scale discriminative context information and enrich the contextual information of features at each stage. In addition, another branch is proposed to balance the network, in which a bootstrap refinement module (BRM) is trained to guide the restoration of feature details.

To evaluate the performance of the proposed network, we conduct extensive experiments, and the experimental results demonstrate that the proposed method outperforms state-of-the-art (SOTA) approaches on seven evaluation metrics. We also conduct adequate ablation analyses that provide a full understanding of the design principles of the proposed method.

The dual-branch balance saliency model was proposed and applied into the fabric defect detection. The qualitative and quantitative experimental results show the effectiveness of the detection method. Therefore, the proposed method can be used for accurate fabric defect detection and even surface defect detection of other industrial products.