This paper evaluates the short-term impact of the coronavirus outbreak on 21 leading stock market indices in major affected countries including Japan, Korea, Singapore, the USA, Germany, Italy, and the UK etc. The consequences of infectious disease are considerable and have been directly affecting stock markets worldwide. Using an event study method, our results indicate that the stock markets in major affected countries and areas fell quickly after the virus outbreak. Countries in Asia experienced more negative abnormal returns as compared to other countries. Further panel fixed effect regressions also support the adverse effect of COVID-19 confirmed cases on stock indices abnormal returns through an effective channel by adding up investors’ pessimistic sentiment on future returns and fears of uncertainties.

Mobile-edge computing (MEC) has been garnering considerable level of interests by processing computation tasks nearby mobile devices (MDs). With limited computation and communication resources and strict task deadline, balancing the energy consumption and time delay of computational tasks will be highly focused. MDs deployed energy harvesting (EH) modules can always provide service to continuous task requests, and finer-grained offloading schemes of the MEC system will significantly affect the time delay of computation tasks. However, when combined them together, the energy causal constraint and the coupling between offloading ratios and resources allocation will cause new challenges for the computation offloading problem. To address these issues, we investigate the partial computation offloading schemes for multiple MDs enabled by harvested energy in MEC. Specifically, we build models for two computing modes and EH process. Subsequently, we formulate a nonconvex optimization problem by minimizing the energy consumption of all the MDs while satisfying the constraint of time delay. Furthermore, we propose and design a novel algorithm based on the Lyapunov optimization to achieve optimal solution, that is, Lyapunov-optimization-based partial computation offloading for multiuser (LOMUCO). Then, we take the long-term average energy consumption and the discarding ratio of computation tasks as the quantitative metrics and conduct extended simulation experiments to confirm the performance of LOMUCO. Finally, compared to several baseline or state-of-the-art algorithms, including local computing all (LCA), offloading computing all (OCA), randomly partial computation offloading (RPCO), and Lyapunov-optimization-based dynamic computation offloading (LODCO), we can demonstrate the superiority of LOMUCO.

With the development of big data and blockchain, an increasing number of scholars have begun to study blockchain for data sharing. By studying data sharing models that are based on blockchain, we find that almost all of them have the following problems: 1) it is difficult to protect the privacy and integrity of users’ data, along with users’ data ownership; 2) the storage burden of blockchain is heavy, and blockchain lacks a mechanism for dealing with data with diverse types and inconsistent formats; and 3) the consensus mechanism has low fairness or low efficiency. Therefore, we propose a data-sharing privacy protection model (DS2PM) that is based on blockchain and a federated learning mechanism for solving these problems. The safety analysis and experimental results show that the DS2PM outperforms the previously established schemes.

This paper established a combined dataset from 2004 to 2015 for 129 host countries and matched them with 1193 Outward Foreign Direct Investment (OFDI) events by Chinese listed firms. Four variables were designed to proxy the agglomeration effects of Chinese OFDI at both an industry and country level. Probit and FGLS estimation methods were used to analyze whether the Chinese listed firm location choices were affected by the agglomeration level. It was found that the agglomeration effect of Chinese OFDI on host country selection was obvious, as Chinese firms were often found to follow other Chinese firms and invest in host countries where Chinese investment was concentrated; however, it was also found that Chinese firms did not choose countries where there was a high concentration of non-Chinese FDI. The agglomeration effect on Chinese OFDI industry selection was also significant. Firms were found to invest in industries that already had large Chines OFDI agglomeration or high host-country industrial agglomeration. Further investigations found that the “Belt and Road” Initiative (BRI) was effective in guiding location decisions. Firms were found to invest more along the “Belt and Road” route after the BRI was launched; however, large State-owned listed firms with higher leverage but lower productivity and profitability were found to more often invest along the BRI routes.

Mobile edge computing (MEC) can sufficiently meet the computing demands of complex application consists of multiple interdependent tasks which can be represented by a directed acyclic graph (DAG). For tasks in a DAG, different scheduling orders and offloading decisions will generate different completion time, which further affects the Quality of Experiences (QoEs). So it is important to study the scheduling and offloading schemes for tasks in MEC scenarios. To this end, we first designed a scheme that schedules tasks with the highest response ratio and offloads tasks to the optimal processor with the optimization method for a DAG, which is termed as HRRO algorithm. Then, considering the complexity of the reality, we extended the HRRO to the ultradense MEC system and achieved the optimal joint scheduling and offloading scheme for multi-DAG based on the genetic algorithm, which can be concluded as HRRO based on the genetic algorithm (HRRO-GA). Subsequently, to evaluate the performance of the algorithms, we conducted amounts of the simulation experiments and compared the results with several state-of-the-art algorithms, including distributed earliest finish-time offloading (DEFO), potential game-based offloading algorithm (PGOA), and GA-based multiuser earliest finish time (GA-MEFT). Meanwhile, we selected some random strategies to verify the schemes of HRRO-GA are the best. Finally, we concluded that HRRO-GA is more suitable for the ultradense MEC system.