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

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

Large thermoelectric power factor from crystal symmetry-protected non-bonding orbital in half-Heuslers.

Modern society relies on high charge mobility for efficient energy production and fast information technologies. The power factor of a material-the combination of electrical conductivity and Seebeck coefficient-measures its ability to extract electrical power from temperature differences. Recent advancements in thermoelectric materials have achieved enhanced Seebeck coefficient by manipulating the electronic band structure. However, this approach generally applies at relatively low conductivities, preventing the realization of exceptionally high-power factors. In contrast, half-Heusler semiconductors have been shown to break through that barrier in a way that could not be explained. Here, we show that symmetry-protected orbital interactions can steer electron-acoustic phonon interactions towards high mobility. This high-mobility regime enables large power factors in half-Heuslers, well above the maximum measured values. We anticipate that our understanding will spark new routes to search for better thermoelectric materials, and to discover high electron mobility semiconductors for electronic and photonic applications

Large thermoelectric power factor from crystal symmetry-protected non-bonding orbital in half-Heuslers

Modern society relies on high charge mobility for efficient energy production and fast information technologies. The power factor of a material-the combination of electrical conductivity and Seebeck coefficient-measures its ability to extract electrical power from temperature differences. Recent advancements in thermoelectric materials have achieved enhanced Seebeck coefficient by manipulating the electronic band structure. However, this approach generally applies at relatively low conductivities, preventing the realization of exceptionally high-power factors. In contrast, half-Heusler semiconductors have been shown to break through that barrier in a way that could not be explained. Here, we show that symmetry-protected orbital interactions can steer electron-acoustic phonon interactions towards high mobility. This high-mobility regime enables large power factors in half-Heuslers, well above the maximum measured values. We anticipate that our understanding will spark new routes to search for better thermoelectric materials, and to discover high electron mobility semiconductors for electronic and photonic applications.United States. Department of Energy. Office of Science. Basic Energy Sciences (Award # SC0001299/DE-FG02-09ER46577 (for fundamental research on electron–phonon interaction in thermoelectric materials))United States. Defense Advanced Research Projects Agency. Materials for Transduction program (Grant HR0011-16-2-0041 (for code development to support practical thermoelectric devices)