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The purpose of this paper is to construct an integrated theoretical framework of firm resilience, and examine the relationship between resource reconfiguration, firm resilience, disruption impact, profit growth, innovation and environmental uncertainty in the context of COVID-19.

A survey was distributed to 220 companies and a total of 207 respondents returned the survey. chief executive officer (CEO) and chief financial officer (CFO) of each company participants in the survey. The hypotheses are tested using structural equation modeling (SEM) technique.

The results showed that firm resilience can be stimulated through the reconstruction of existing resources, and environmental uncertainty played a moderating role in this process; in turn, the improvement of firm resilience enabled companies to reduce the impact of disruptions, achieve profit growth and promote innovation.

This study provides practical implications for how business management shapes firm resilience and promotes organization recovery and development.

This study expands the literature of firm resilience by providing an integrated theoretical framework of firm resilience. Firstly, based on the perspective of dynamic capabilities, this study reveals that resource reconfiguration plays a key role in shaping firm resilience. Secondly, this study enriches the boundary research on firm resilience by incorporating environmental uncertainty into the research framework. Thirdly, this study validates the impact of firm resilience on disruption impact, profit growth and innovation of companies, providing sufficient empirical evidence for the outcomes of firm resilience.

The purpose of this paper is to propose a binocular visual odometry algorithm based on the Random Sample Consensus (RANSAC) in visual navigation systems.

The authors propose a novel binocular visual odometry algorithm based on features from accelerated segment test (FAST) extractor and an improved matching method based on the RANSAC. Firstly, features are detected by utilizing the FAST extractor. Secondly, the detected features are roughly matched by utilizing the distance ration of the nearest neighbor and the second nearest neighbor. Finally, wrong matched feature pairs are removed by using the RANSAC method to reduce the interference of error matchings.

The performance of this new algorithm has been examined by an actual experiment data. The results shown that not only the robustness of feature detection and matching can be enhanced but also the positioning error can be significantly reduced by utilizing this novel binocular visual odometry algorithm. The feasibility and effectiveness of the proposed matching method and the improved binocular visual odometry algorithm were also verified in this paper.

This paper presents an improved binocular visual odometry algorithm which has been tested by real data. This algorithm can be used for outdoor vehicle navigation.

A binocular visual odometer algorithm based on FAST extractor and RANSAC methods is proposed to improve the positioning accuracy and robustness. Experiment results have verified the effectiveness of the present visual odometer algorithm.

The purpose of this paper is to examine the impact of urbanicity on rural–urban migrants' dietary diversity and nutrition intake and whether its effect differs across various urban environments of migrants.

Using the individual- and time-invariant fixed effects (two-way FE) model and five-year panel data from the China Health and Nutrition Survey (CHNS), this paper estimates a linear and nonlinear relationship between urbanicity and nutrition. The paper also explores the spatial heterogeneity between rural–urban migrants and rural–suburban migrants. Dietary diversity, total energy intake and the shares of energy obtained from protein and fat, respectively, are used to measure rural–urban migrants' nutrition on both quality and quantity aspects.

The study shows that rural–urban migrants have experienced access to more diverse, convenient and prepared foods, and the food variety consumed is positively associated with community urbanicity. Energy intake is positively and significantly affected by community urbanicity, and it also varies with per capita household income. The obvious inverse U-shaped relationship reveals that improving community urbanicity promotes an increase in the shares of energy obtained from protein and fat at a decreasing rate, until reaching the urbanicity index threshold of 66.69 and 54.26, respectively.

This paper focuses on the nutritional status of rural–urban migrants, an important pillar for China's development, which is often neglected in the research. It examines the urbanicity and the nutrition of migrants in China, which provides a new perspective to understand the dietary and nutritional intake among migrants in the economic and social development. Moreover, the urbanicity index performs better at measuring urban feathers rather than the traditional rural/urban dichotomous classification.

A new backward punch shape was designed and used in the hydroforming process of double-layer Y-shaped tubes to achieve uniform wall thickness. This study focuses on the implementation and effectiveness of this novel punch shape.

A numerical simulation and experimental validation of the hydroforming process of double-layer Y-shaped tubes under various backward punch, replenishment ratios (left and right feed ratios) and internal pressure loading paths was performed using finite elements. During the hydroforming process, an analysis was made on the distribution of stress, strain and wall thickness in both the inner and outer layers of the Y-shaped conduit.

The novel backward punch parallel to the main tube has been found to improve the distribution of wall thickness in Y-shaped tubes. By controlling the feeding ratio and modifying the loading path of the internal pressure, it is possible to obtain the optimal forming part of the double-layer Y-shaped tube. The comparison between the simulation and experimental results of the double-layer Y-shaped tube formed under the optimal path indicates that the error is within 5% and the distribution of wall thickness is consistent.

A novel backward punch technique is employed to control the hydroforming process in a Y-shaped tube. A study on hydroforming of double-layer Y-shaped tubes with asymmetric features and challenging forming conditions is being suggested.

Studies on organizational failure learning have focused on whether and how organizations learn from failures but have paid limited attention on the persistence of failure learning. This study centers on failure recidivism and answers why organizations would fall into repeated failures after learning from them.

Based on a sample of Chinese publicly listed firms that once recovered from special treatment status, the authors use event history technique and Cox proportional hazards regression model.

The authors find that reviviscent firms with higher interlock centrality are less likely to decline again, and underperforming partners can strengthen the role of interlock tie in failure recidivism. By contrast, politically connected reviviscent firms are more likely to decline again, and this effect attenuates for firms located in more market-oriented regions.

The authors’ contribution comes from the close integration of literature on failure learning and network embeddedness perspective to examine how social networks affect the learning process of failure recidivism.

The study provides important practical implications for organizations, especially those that once experienced failures or are experiencing failures.

Combining organizational learning theory and network embeddedness perspective, the study provides novel insights into answering how firms embedded in different types of social networks affect failure learning persistence differently.

This paper aims to address the following issues: (1) most existing methods are based on recurrent network, which is time-consuming to train long sequences due to not allowing for full parallelism; (2) personalized preference generally are not considered reasonably; (3) existing methods rarely systematically studied how to efficiently utilize various auxiliary information (e.g. user ID and time stamp) in trajectory data and the spatiotemporal relations among nonconsecutive locations.

The authors propose a novel self-attention network–based model named SanMove to predict the next location via capturing the long- and short-term mobility patterns of users. Specifically, SanMove uses a self-attention module to capture each user's long-term preference, which can represent her personalized location preference. Meanwhile, the authors use a spatial-temporal guided noninvasive self-attention (STNOVA) module to exploit auxiliary information in the trajectory data to learn the user's short-term preference.

The authors evaluate SanMove on two real-world datasets. The experimental results demonstrate that SanMove is not only faster than the state-of-the-art recurrent neural network (RNN) based predict model but also outperforms the baselines for next location prediction.

The authors propose a self-attention-based sequential model named SanMove to predict the user's trajectory, which comprised long-term and short-term preference learning modules. SanMove allows full parallel processing of trajectories to improve processing efficiency. They propose an STNOVA module to capture the sequential transitions of current trajectories. Moreover, the self-attention module is used to process historical trajectory sequences in order to capture the personalized location preference of each user. The authors conduct extensive experiments on two check-in datasets. The experimental results demonstrate that the model has a fast training speed and excellent performance compared with the existing RNN-based methods for next location prediction.

The purpose of this paper is to draw on the experience of students, employers, and tutors in the two product design degree programmes, respectively, delivered at the University of Salford in the UK and at Shanghai Jiao Tong University, China, to examine the differences in employer engagement embedded within the curriculum.

Two in‐depth case studies supported by interviews have been conducted, which are represented as two distinctive employer engagement models. These two models are compared in terms of context, employer perception, curriculum development, and challenges for sustainability.

An understanding has been generated of the differences and suggests a collaborative framework between these two programmes, which draws the advantages of both sides together. The findings also shed light on the development of curriculum to engage employers, recognise a move away from “teaching” towards “managing learning opportunities” and the complexities of employer engagement, and explore how this may be embedded.

To address employability agenda, one of the main problems faced by higher education institutions is the content of curriculum and its relevance to the employment market. Engaging employers in the curriculum becomes key in addressing this issue, and this is especially typical for the design industry, given its unique characteristics. However, little has been researched as to how universities across different cultures achieve employer engagement. Understanding of the differences helps universities from different regions to seek potential sustainability solutions that may be bred on the basis of collaboration.

The purpose of this paper is to try to identify the motivation of firms that announce share repurchase but do not follow it up with the actual purchase. The authors conjecture that the long-term earnings quality of such firms is low, which makes them poor candidates for actual stock repurchase. Their intention is to mimic actual repurchasers and their motivation appears to be just to get a bounce in their stock price normally associated with such announcements.

The authors use probit analysis to ascertain whether earnings quality can predict the subsequent repurchase behavior of firms that announce share repurchase. As Gong et al. point out, the relationship between earnings management and the percentage of shares repurchased may be endogenous. In order to mitigate the potential endogeneity bias, the authors use a two-stage instrumental variable probit model adapted for this study from Lee and Masulis (2009).

The results show that non-carry-through firms have lower earnings quality than carry-through firms in the pre-announcement period in all of the metrics the authors use to measure earnings quality. In the post-announcement period, the earnings quality of the non-carry-through firms declines still further and the difference in the quality becomes more pronounced. The results of probit regression show that lower earnings quality increases the likelihood of becoming a non-carry-through company.

The finding has interesting implications for investment management as investors can differentiate non-carry-through firms from carry-through repurchasers by examining the firm's earnings quality.

The analysis shows that poor long-term earnings quality increases the chance of not carrying through on the repurchase announcement. The authors also find that the poor earnings quality of non-carry-through firms limits their ability to manage earnings downward prior to the repurchase announcement.

As more and more people are beginning to take virtual communities (VC) as effective communication channels and the main places to get information and knowledge, understanding the factors that can support or hinder one’s knowledge sharing seems important for the management of VCs. The purpose of this paper is to explore the antecedents that influence people’s knowledge sharing in VCs, and to explore influence mechanism and the boundary condition of how the antecedent affect people’s knowledge sharing in VCs.

The authors conducted empirical research to test our hypotheses. The authors designed a questionnaire based on previous research and collected the questionnaires from seven VCs in China, including two specific topic-based forums in Baidu Tieba which is the largest Chinese online communication platform, three in traditional university bulletin board system (BBS) forums and another two based on instant messaging service. The final sample the authors got included 96 individuals. Then the authors used the hierarchical linear modeling (HLM) technique to analysis the data.

The results suggest that community member’s attachment can be a strong indicator of his/her knowledge-sharing intention which will possibly lead to knowledge-sharing behavior. However, this effect can be contingent on individual centrality and community member fluctuations. In a stable community, those who are in the peripheral position are more likely to transfer their attachment into knowledge sharing because they have the intention to move into central positions and knowledge sharing can help them gain status. While in an unstable environment, it does not make any difference between people in different position in the social network.

First, this paper reveals member’s attachment as the antecedent of people’s knowledge sharing in VCs. Second, this paper adopts a network perspective to construct the research model. And the basic point made is that knowledge sharing can be seen as a channel to attain status and centrality in a community. Thus, people who are in periphery positions are more likely to transfer their community attachment into knowledge-sharing intention. Third, this paper emphasizes the dynamic characteristic of members in VCs and proves the moderation effect of community member fluctuations.

Considering the problem that the high recognition rate of deep learning requires the support of mass data, this study aims to propose an insulating fault identification method based on small data set convolutional neural network (CNN).

Because of the chaotic characteristics of partial discharge (PD) signals, the equivalent transformation of the PD signal of unit power frequency period is carried out by phase space reconstruction to derive the chaotic features. At the same time, geometric, fractal, entropy and time domain features are extracted to increase the volume of feature data. Finally, the combined features are constructed and imported into CNN to complete PD recognition.

The results of the case study show that the proposed method can realize the PD recognition of small data set and make up for the shortcomings of the methods based on CNN. Also, the 1-CNN built in this paper has better recognition performance for four typical insulation faults of cable accessories. The recognition performance is improved by 4.37% and 1.25%, respectively, compared with similar methods based on support vector machine and BPNN.

In this paper, a method of insulation fault recognition based on CNN with small data set is proposed, which can solve the difficulty to realize insulation fault recognition of cable accessories and deep data mining because of insufficient measure data.