Résumé
With the gradual normalization of the COVID-19, unmanned delivery has gradually become an important contactless distribution method around China. In this paper, we study the routing problem of unmanned vehicles considering path flexibility and the number of traffic lights in the road network to reduce the complexity of road conditions faced by unmanned vehicles as much as possible. We use Monte Carlo Tree Search algorithm to improve the Genetic Algorithm to solve this problem, first use Monte Carlo Tree Search Algorithm to compute the time-saving path between two nodes among multiple feasible paths and then transfer the paths results to Genetic Algorithm to obtain the final sequence of the unmanned vehicles fleet. And the hybrid algorithm was tested on the actual road network data around four hospitals in Beijing. The results showed that compared with normal vehicle routing problem, considering path flexibility can save the delivery time, the more complex the road network composition, the better results could be obtained by the algorithm.
Résumé
Objective: To analyze the international scientific research collaboration network of coronavirus disease 2019 (COVID-19), and provide reference for researchers to grasp the research status and explore collaborative paths in future. Methods: The ESI highly cited papers on COVID-19, which were collected from the core collection of Web of Science, were divided into four stages according to the publication time, and the multivariate statistics and social network analysis were used to reveal the international research collaboration rate, team size, collaboration network characteristics and its evolution rule. Results: Up to Apr 30, 2021, a total of 3 794 ESI highly cited papers were initially collected.According to the literature screening criteria, 3 715 papers were finally included in the current research. The overall international research collaboration rate reached 34.27%.Among the 10 countries with the highest number of papers published, China had the lowest research collaboration rate (34.53%) with other countries, and the rests were more than 50%.The collaboration rate between China and the United States showed a gradual downward trend in the four stages.The overall scientific research team size showed an upward trend in general.In the entire scientific research collaboration network, the second stage was the largest and the most compact one, Since then the network scale became shrinking, and the collaboration network had been gathering to the center. The core degree of China in the first stage was 0.741, indicating that China was at the core position. Since then the United States replaced China and became the core of the scientific research collaboration network, and England came in second. Conclusion: The overall level of international scientific research collaboration of COVID-19 is high, and the collaboration network presents a trend of centripetal aggregation.The United States is still the core of the collaboration network. © 2021, Editorial Department of Fudan University Journal of Medical Sciences. All right reserved.
Résumé
The controversy over fairness and objectivity in the job market, due to hiring irregularities, has led to calls for transparent and fair recruitment procedures. Advances in IT technology have led to the emergence of a non-face-to-face “AI recruitment system” in which artificial intelligence (AI) conducts interviews, instead of human interviews. As the introduction of the non-face-to-face method is encouraged in the hiring process due to the COVID-19 virus pandemics, the number of companies introducing AI recruitment systems is steadily increasing. In this study, the factors affecting the intention of use of AI-based recruitment system were analyzed by utilizing TOE and TAM. As a result, it was shown that the reliability, security, suitability, new technology, partiality, readiness, and legal and policy environment of the TOE affected the intention of using the system. It was also identified to have the moderating effect of the number of employees in the firm. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.