With the continuous improvement of people’s living standards and their increasing demand for fresh food, the cold chain logistics industry has developed rapidly. One of the biggest challenges to today’s cold chain logistics is to offer fresh food while minimizing CO₂ emissions. The fresh degree and CO₂ emissions are involved in the vehicle routing optimization problem in the cold chain logistics. In order to meet the quality requirement for fresh agricultural products and low carbon logistics, a novel routing optimization model considering the costs of quality deterioration and carbon emissions (Low Carbon and Freshness Degrees Vehicle Routing Problem (LCFD-VRP)) for cold chain distribution was established in this study. This model takes into account the fixed cost, fuel cost and time window penalty cost. An improved ant colony algorithm (IACA) is used to optimize the whole vehicle distribution routing with its strong global search ability. Tabu Search (TS) algorithm is used to search the single vehicle distribution routing with its good local search ability. An IACA combined with TS (IACATS) was proposed to solve the above LCFD-VRP model. The practicability of the model and the effectiveness of the above improved algorithm are verified using a real case study. The results of Zhoushan Dayang Refrigerated Logistics Co., Ltd. showed that, compared with the traditional algorithm, IACATS could reduce the dispatching of two refrigerated vehicles, thus lowering the total cost by 4.94%, shortening the actual transportation distance by 5.50% and cutting the total CO₂ emissions by 8.9%. Therefore, the LCFD-VRP model can effectively help to achieve the low carbon emissions, multi-variety and low-cost distribution of fresh agricultural products. The proposed model and IACATS algorithm would be used to optimize VRP in cold chain enterprises. The results of this study also provide management suggestions for cold chain enterprises to effectively balance economic cost and environmental cost.

VR technology can help create optimal virtual learning spaces. Such spaces offer new visual experiences that break through the limitations of time and space and greatly stimulate people’s imagination and creativity in learning. Currently, the bandwidth required for such spaces limits the large-scale application of virtual reality (VR) technology for this purpose. With the large-scale deployment and application of high-speed networks, however, online education platforms based on VR technology will be better able to meet the diversified and personalized learning needs of learners. To promote the development and popularization of new online education platforms based on VR, the factors influencing the migration of learners from traditional online education platforms to new platforms need to be understood more clearly. A model based on the theory of negative, positive, and anchoring effects can explain learners’ migration behavior in this connection. To this end, a structural equation model based on the PLS variance algorithm was used to analyze data obtained through offline and online questionnaires. It was found that in terms of “negative effects”, the afunction and loyalty associated with traditional online education platforms reduced learners’ willingness to migrate to new platforms based on VR technology. In terms of “positive effects”, the novel interactivity and personalization brought by the new platform increased the willingness of users of traditional platforms to migrate to new platforms. In terms of “anchoring effects”, the system quality and relationship quality of learners’ use of traditional online education platforms, as well as the transfer costs associated with the new platform, generated learners’ risk perception about platform migration. In addition, risk perception not only negatively affects learners’ migration to the new platforms, but also strengthens their cognition of the system quality and relationship quality of the traditional platforms, while reducing their interactive awareness of those platforms. Therefore, by adjusting the psychological component of virtual learning, the online education platforms based on VR technology can create high-quality platforms migrating from traditional platforms.

Rural revitalization is an important strategy to promote sustainable development of rural areas in China. Rural revitalization listed companies play an important role in implementing the rural revitalization strategy and developing the agricultural industry. However, the financing problem has always been a bottleneck problem with Chinese listed companies. This study used a two-stage DEA (data envelopment analysis) method to evaluate the funds raising efficiency, funds using efficiency, and overall financing efficiency of 34 rural revitalization listed companies in 2018. The results show that the financing efficiencies of 34 sample companies were low, only six companies have overall efficient financing, and there was much room for improvement. Financing efficiency varied greatly depending on the nature of the company, the industry, and the listing board. The efficiency of funds using of state-owned enterprises was much lower than that of private companies. The average efficiency value of agricultural company funds raising was lower than that of manufacturing. The efficiency of small and middle-size enterprises (SMEs) was lower than that of main board companies, but the growth enterprise market (GEM) companies achieved higher efficiency in the funds using. Further, by using the grey relational analysis (GRA) method, we found that the key factors affecting the financing efficiency of sample companies included capital structure, debt-paying ability, governance structure, company age, and operating ability. To improve financing efficiency, the companies should not only optimize their capital structure and governance structure but also improve their management and innovation capabilities. At the same time, the state also needs to give different policies support according to the characteristics of the companies.