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The purpose of this study is to investigate e-commerce as a new means to ensure that the urban demand for food can be met during the coronavirus disease 2019 (COVID-19) outbreak. Because a number of COVID-19 e-commerce models have emerged, this paper discusses whether and (if so) why and how e-commerce can ensure the food supply for urban residents if social distancing becomes a norm and the transport and logistics systems are hindered.

This study used qualitative research methods following the lack of empirical data. The authors referred to relevant literature, statistical data and official reports and comprehensively described the importance of e-commerce in ensuring the safety of food supply to Chinese urban residents under the impact of the epidemic. Corresponding to the traditional case study, this study presented a Chinese case on ensuring food supply through e-commerce during an epidemic.

The authors found that three e-commerce models played a substantial role in preventing the spread of the epidemic and ensuring the food supply for urban residents. The nationwide e-commerce platforms under market leadership played their roles by relying on the sound infrastructure of large cities and its logistics system was vulnerable to the epidemic. In the worst-affected areas, particularly in closed and isolated communities, the local e-commerce model was the primary model, supplemented by the unofficial e-commerce model based on social relations. Through online booking, centralized procurement and community distribution, the risk of cross infection could be effectively reduced and the food demand could be effectively satisfied. The theoretical explanation further verifies that, apart from e-commerce, a governance system that integrates the government, e-commerce platform, community streets and the unofficial guanxi also impels the success of these models.

Lessons from China are drawn for other countries struggling to deliver food to those in need under COVID-19. The study not only provides a solution that will ensure constant food supply to urban residents under the COVID-19 epidemic but also provides some reference for the maintenance of the food system of urban residents under the impact of a globalization-related crisis in future.

In autonomous driving, the inherent sparsity of point clouds often limits the performance of object detection, while existing multimodal architectures struggle to meet the real-time requirements for 3D object detection. Therefore, the main purpose of this paper is to significantly enhance the detection performance of objects, especially the recognition capability for small-sized objects and to address the issue of slow inference speed. This will improve the safety of autonomous driving systems and provide feasibility for devices with limited computing power to achieve autonomous driving.

BRTPillar first adopts an element-based method to fuse image and point cloud features. Secondly, a local-global feature interaction method based on an efficient additive attention mechanism was designed to extract multi-scale contextual information. Finally, an enhanced multi-scale feature fusion method was proposed by introducing adaptive spatial and channel interaction attention mechanisms, thereby improving the learning of fine-grained features.

Extensive experiments were conducted on the KITTI dataset. The results showed that compared with the benchmark model, the accuracy of cars, pedestrians and cyclists on the 3D object box improved by 3.05, 9.01 and 22.65%, respectively; the accuracy in the bird’s-eye view has increased by 2.98, 10.77 and 21.14%, respectively. Meanwhile, the running speed of BRTPillar can reach 40.27 Hz, meeting the real-time detection needs of autonomous driving.

This paper proposes a boosting multimodal real-time 3D object detection method called BRTPillar, which achieves accurate location in many scenarios, especially for complex scenes with many small objects, while also achieving real-time inference speed.

The purpose of this paper is to reveal the distributional characteristics and evolutional patterns in source periodicals, topics, authors, funding, and institutes of research papers in Chinese Agricultural Economics so as to understand the current situations and developmental tendency of Chinese agricultural economics research over the past decade.

Using the citation analysis method, this paper analyzed the distributional characteristics and evolution of source periodicals, fields, authors and topics of 2,203 highly cited journal papers from the database of China National Knowledge Infrastructure (CNKI) and 189 cited journal papers from database of Social Sciences Citation Index (SSCI) in agricultural economics first-authored by Chinese scholars from 2006 to 2015.

First, over the past decade, agricultural economics research in China has seen a rapid development. Specially, 103 scholars and 42 institutes have played key roles in the development, and 12 Chinese periodicals and 3 international journals have been the most influential outlets. Second, the coverage of the topics in Chinese agricultural economics research is broad and has expanded over the past decade. The rural land issue has been the most popular topic, while the issues regarding rural institutional arrangements and industrialization in rural areas have been explored extensively. However, issues in other fields, such as agricultural markets and trade, rural labor, food safety, etc. have to be further studied. Third, the improvements of economic theory and quantitative analytic techniques, the supports from research funding, and an increase in the collaboration between Chinese scholars and those from other countries have made great contribution to the rapid development of Chinese agricultural economics research over the past decade.

This paper is an original work that identifies the most influential journal papers including highly cited journal papers from CNKI and cited journal papers from SSCI, using citation frequency and standard Essential Science Indicators method. This is a contribution relative to the methods used by previous studies, which did not account for frequency of citation of a paper. Moreover, this study is based on data from two databases, CNKI and SSCI, suggesting that the coverage of sample papers is broader compared to those of previous studies.