Recently, the Healthcare Internet of Things (H-IoT) has been widely applied to alleviate the global challenge of the coronavirus disease 2019 (COVID-19) pandemic. However, security and limited energy capacity issues remain the two main factors that prevent the large-scale application of the H-IoT. Therefore, a permissioned blockchain and deep reinforcement learning (DRL)-empowered H-IoT system is presented in this research to address these two issues. The proposed H-IoT system can provide real-time security and energy-efficient healthcare services to control the propagation of the COVID-19 pandemic. To address the security issue, a permissioned blockchain method is adopted to guarantee the security of the proposed H-IoT system. As for handling the limited energy constraint, we employ the mobile edge computing (MEC) method to offload the computing tasks to alleviate the computational burden and energy consumption of the proposed H-IoT system. We also adopt an energy harvesting method to improve performance. In addition, a DRL method is employed to jointly optimize both the security and energy efficiency performance of the proposed system. The simulation results demonstrate that the proposed solution can balance the requirements of security and energy efficiency issues and hence can better respond to the COVID-19 pandemic.

How is the discretion of police officers influenced by clients’ gender and situational visibility under emergency regulations? To answer this question, this study uses the scenario-based-experimental-survey data collected from 878 police officers from three cities in China under the COVID-19 pandemic. The results show that the effect of gender congruence on the leniency of officers is only significantly positive when police and clients are both female. However, situational visibility will weaken the gender effect, reducing the officers’ leniency towards females. Moreover, visibility makes police even harsher towards male violators. The findings provide some practical implications.

Frontline information disclosure refers to the endeavor to make the information of interactions between frontline officials and their clients available to policy stakeholders. This study proposes two sets of competing hypotheses to examine the relationship between frontline information disclosure and the willingness of street-level bureaucrats to follow the rules: facilitation effect and inhibition effect hypotheses. The facilitation effect assumes that frontline information disclosure may increase street-level bureaucrats’ willingness to follow the rules, while the inhibition effect expects bureaucrats will be reluctant to follow the rules under circumstances where more frontline information can be disclosed. These hypotheses are tested using data from a survey of 133 managers and 1,610 Chengguan officials in local regulatory agencies from 24 cities in China. Our statistical analyses consistently show that street-level bureaucrats who are exposed to a higher level of objective information disclosure or perceive a higher level of subjective information disclosure are more willing to follow the rules during regulatory enforcement. This finding supports facilitation effect hypotheses. The key implications of our research are discussed in the conclusion.

With the fact of harmfulness, X-ray radiation dose reduction received more and more attention in CT research field nowadays. As Compressed Sensing theory applied into Computed Tomography reconstruction algorithms make it possible to reconstruct high-quality CT images with the requirement of low radiation dose. One of research hot point in CS-based CT reconstruction algorithms is Total Variation, using the information of x-coordinate and y-coordinate gradients to reconstruct CT images, which implies that it will reconstruct better CT images by using gradients of more directions. In this paper, a CT reconstruction algorithm based on Multi-directional Total Variation (MdTV) was proposed, by using the information of x-coordinate, y-coordinate and diagonal gradient together to reconstruct CT images, which theoretically will reconstruct images with higher quality. Experimental results shows that, MdTV based reconstruction algorithm reconstruct better images than TV based algorithm, which is more helpful to reduce X-ray radiation dose as much as possible.