Research hotspots and trends of fresh e-commerce in China: A knowledge mapping analysis based on bibliometrics

The fresh e-commerce industry has seen a sudden and substantial rise since the outbreak of COVID-19. The rapid development of this industry calls for a comprehensive and systematic review of its research status, hotspots and future trends, which will have significant implications for researchers in related fields. This paper first conducts a current situation analysis of the core literature on fresh e-commerce retrieved from four databases – CNKI, CSSCI, Wanfang and VIP – to categorize the research status of fresh e-commerce in three dimensions: the year of publication, article sources, and distribution of subjects. CiteSpace is then used to perform a bibliometric analysis of the data and to create visualized knowledge maps. The results show that the research on fresh e-commerce can be divided into three stages: rapid development (2012-2015), exploration and transformation (2016-2019), maturity and upgrade (2020-present). At each stage, the research evolves toward diversity and maturity with policy developments and changes in the external environment. Cold chain logistics, business models, freshness-keeping of products and e-commerce are ongoing research hotspots in fresh produce e-commerce, while later studies focus more on the transformation and upgrade of products, logistics, distribution and platforms to better serve consumers’ consumption habits and environmental requirements. This study provides valuable insights for researchers and enterprises who are engaged in the industry and for those who are interested in the development of fresh e-commerce in China

Sales forecasting of stores in shopping malls: A study based on external data and transaction data

To improve the forecast accuracy of the sales of stores in shopping malls, this paper proposes a prediction method based on deep learning that comprehensively considers the external data, such as online review data of shopping mall stores, weather data, weekday/weekend data, and historical transaction data of the stores. To begin with, the online review data of the stores are pre-trained with BERT (Bidirectional Encoder Representations from Transformers) to complete the multi-label sentiment classification and obtain the intensity index of perceived sentiment of reviews. The index, together with other external data, such as online ratings, weather, weekday/weekend differences, and historical transactions of the stores, is pre-processed. At last, the Long Short-Term Memory (LSTM) and the Attention models are used to predict the sales volume of stores in a certain shopping mall. The results show that the addition of external data – weather, weekday/weekend, online ratings and intensity index of sentiment of reviews – to the historical sales data-based model can effectively improve the forecast accuracy of store sales

PUBLIC OPINION ANALYSIS BASED ON PROBABILISTIC TOPIC MODELING AND DEEP LEARNING

With the rapid development of Internet, especially the social media technologies, the public have gradually published their perception of social events online through social media. In Web2.0 era, with the concept of extensive participation of public in social-event-related information sharing, the effective content analysis and better results presentation for these media published online thus possesses significant importance for public opinion analysis and monitoring. In view of this, this paper proposes a novel method for public opinion analysis on social media website. First, the probabilistic topic model of Latent Dirichlet Allocation (LDA) is adopted to extract the public ideas about the distinct topics of certain event, and then the deep learning model named word2vec is used to calculate the emotional intensity for each text. Next, the underlying themes in the whole as well as the events of emotional intensity are investigated, and the variation trend of public’s emotion intensities is tracked based on time series analysis. Finally, the rationality and effectiveness of the method are verified with the analysis of a real case

Understanding Consumers’ Loyalty to an Online Outshopping Platform: The Role of Social Capital and Perceived Value

We draw upon the social capital theory in order to discuss how three dimensions of social capital affect consumer value and loyalty to online outshopping platforms. After considering the characteristics of consumers, we propose that the structural, relational, and cognitive dimensions of social capital promote consumers’ perceptions of utilitarian and idea shopping value, and that those perceived values increase loyalty to online outshopping platforms. The survey data of 291 Chinese consumers with online outshopping platform experience are used to test the model. The results show that different dimensions of consumers’ social capital influence their loyalty through different values. Utilitarian value mediates the effects of structural capital and cognitive capital on loyalty, whereas hedonic value (ideal shopping value) mediates the effects of structural and relational capital on loyalty

Examining Protection Motivation and Network Externality Perspective Regarding the Continued Intention to Use M-Health Apps

M-health apps have developed rapidly and are widely accepted, but users’ continued intention to use m-health apps has not been fully explored. This study was designed to obtain a better understanding of users’ continued intention to use m-health apps. We developed a theoretical model by incorporating the protection motivation theory and network externalities and conducted an empirical study of a 368-respondent sample. The results showed that: (1) perceived vulnerability has a direct impact on users’ self-efficacy and response efficacy; (2) self-efficacy and response efficacy have a direct impact on users’ attitudes and continued intention; (3) network externalities affect users’ attitudes and continued intention, among which direct network externalities have an indirect impact on users’ continued intention through attitude; and (4) the impacts of self-efficacy, response efficacy, and indirect network externalities on continued intention are partially meditated by attitudes

Ensemble Learning-Based Person Re-identification with Multiple Feature Representations

As an important application in video surveillance, person reidentification enables automatic tracking of a pedestrian through different disjointed camera views. It essentially focuses on extracting or learning feature representations followed by a matching model using a distance metric. In fact, person reidentification is a difficult task because, first, no universal feature representation can perfectly identify the amount of pedestrians in the gallery obtained by a multicamera system. Although different features can be fused into a composite representation, the fusion still does not fully explore the difference, complementarity, and importance between different features. Second, a matching model always has a limited amount of training samples to learn a distance metric for matching probe images against a gallery, which certainly results in an unstable learning process and poor matching result. In this paper, we address the issues of person reidentification by the ensemble theory, which explores the importance of different feature representations, and reconcile several matching models on different feature representations to an optimal one via our proposed weighting scheme. We have carried out the simulation on two well-recognized person reidentification benchmark datasets: VIPeR and ETHZ. The experimental results demonstrate that our approach achieves state-of-the-art performance

Agent-Based Modeling and Simulation of Tourism Market Recovery Strategy after COVID-19 in Yunnan, China

The tourism industry hit severely by COVID-19 faces the challenge of developing effective market recovery strategies. Nonetheless, the existing literature is still limited regarding the dynamic evolution process and management practice. Hence, this study chose several famous spots in the Yunnan Province of China as the focus for a case study and utilized an agent-based simulation method for the decision-making process of tourists’ destination selection and the dynamic recovery process of the destinations under different price and information strategies. The study found that the recovery effects of information strategies are positive, negative, or have no effect in different destinations. In contrast, price strategies can significantly stimulate an increase in the market share of destinations. When price strategy and information strategy are applied simultaneously, the interaction effects are inconsistent in different destinations. The findings contribute to the prediction of the recovery effect of strategies, can reduce trial and error costs, and can improve the scientific understanding of tourism market recovery

Exploring the gamification of cybersecurity education in higher education institutions: An analytical study

Our world has become increasingly dependent on electronic technology. As most economic, cultural, and social activities are conducted in cyberspace, how to protect data from cyberattacks has arisen as a prominent challenge. Cybersecurity education and training that improves awareness among personnel is recognized as an effective approach. Higher education institutions (HEIs) have become prime cyberattack targets as they hold vast amounts of valuable research and personal data. This paper analyses the state of cybersecurity in HEIs and the problems of cybersecurity education, and proposes the solution of gamification of cybersecurity education. A detailed feasibility analysis and recommendations for developing cybersecurity education games are provided. This paper expands the theories of gamified cybersecurity education in China, and sheds light on enhancing the effectiveness of cybersecurity education in HEIs through games

Exploring the gamification of cybersecurity education in higher education institutions: An analytical study

Our world has become increasingly dependent on electronic technology. As most economic, cultural, and social activities are conducted in cyberspace, how to protect data from cyberattacks has arisen as a prominent challenge. Cybersecurity education and training that improves awareness among personnel is recognized as an effective approach. Higher education institutions (HEIs) have become prime cyberattack targets as they hold vast amounts of valuable research and personal data. This paper analyses the state of cybersecurity in HEIs and the problems of cybersecurity education, and proposes the solution of gamification of cybersecurity education. A detailed feasibility analysis and recommendations for developing cybersecurity education games are provided. This paper expands the theories of gamified cybersecurity education in China, and sheds light on enhancing the effectiveness of cybersecurity education in HEIs through games

A yigP mutant strain is a small colony variant of E. coli, and shows pleiotropic antibiotic resistance

Small colony variants (SCVs) are a commonly observed subpopulation of bacteria that have a small colony size and distinctive biochemical characteristics. SCVs are more resistant to some antibiotics than the wild-type, and usually cause persistent infections in the clinic. SCV studies have been very active during the past two decades, especially of Staphylococcus aureus. However, fewer studies on Escherichia coli SCVs exist, so we studied an E. coli SCV during an experiment involving the deletion of the yigP locus. PCR and DNA sequencing revealed that the SCV was attributable to a defect in the yigP function. Furthermore, we investigated the antibiotic resistance profile of the SCV and it showed increased erythromycin, kanamycin and D-cycloserine resistance, but collateral sensitivity to ampicillin, polymyxin, chloramphenicol, tetracycline, rifampin, and nalidixic acid. We tried to determine the association between yigP and the pleiotropic antibiotic resistance of the SCV by analyzing biofilm formation, cellular morphology and coenzyme Q (Q8) production. Our results indicated that impaired Q8 biosynthesis was the primary factor that contributed to the increased resistance and collateral sensitivity of the SCV. This study offers a novel genetic basis for E. coli SCVs and an insight into the development of alternative antimicrobial strategies for clinical therapy.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author