Air quality improvement assessment and exposure risk of Shandong Province in China during 2014 to 2020.

As one of the most polluted provinces in China, air pollution events occur frequently in Shandong. Based on the hourly (or daily) concentrations of six air pollutants (PM2.5, PM10, O3, NO2, SO2 and CO), the situations of air quality improvement in three kinds of cities (key cities, coastal cities and general cities) are assessed comprehensively during 2014-2020. Contrary to the daily maximum 8-h average ozone (MDA8 O3), the annual average concentrations of other pollutants show the downward trends during 2014-2020. Therein, the improvement rates of annual average concentrations of air pollutants in key cities are highest. By 2020, the day proportions of O3 as the primary pollutant are up to 38% in three kinds of cities. Besides, due to the impact of COVID-19, the monthly average concentrations of PM2.5, PM10, NO2, SO2 and CO in February 2020 decrease by 32.1-49.5% year-on-year. There are still about 50% of population exposed to high-risk regions (R i > 2), which are mainly concentrated in main urban areas and industrial areas. Thus, the adjustment of industrial structure and energy composition in the context of carbon peak and carbon neutrality should be implemented in the future. Supplementary Information: The online version contains supplementary material available at 10.1007/s13762-022-04651-5.

Blockchain-Enabled Federated Learning for UAV Edge Computing Network: Issues and Solutions

Unmanned aerial vehicles (UAVs) extend the traditional ground-based Internet of Things (IoT) into the air. UAV mobile edge computing (MEC) architectures have been proposed by integrating UAVs into MEC networks during the current novel coronavirus disease (COVID-19) era. UAV mobile edge computing (MEC) shares personal data with external parties (such as edge servers) during intelligent medical analytics. However, this technique raises privacy concerns about patients' health data. More recently, the concept of federal learning (FL) has been set up to protect mobile user data privacy. Compared to traditional machine learning, federated learning requires a decentralized distribution system to enhance trust for UAVs. Blockchain technology provides a secure and reliable solution for FL settings between multiple untrusted parties with anonymous, immutable, and distributed features. Therefore, blockchain-enabled FL provides both theories and techniques to improve the performance of intelligent UAV edge computing networks from various perspectives. This survey begins by discussing the current state of research on blockchain and FL. Then, compare the leading technologies and limitations. Second, we will discuss how to integrate blockchain and FL into UAV edge computing networks and the associated challenges and solutions. Finally, we discuss the fundamental research challenges and future directions.

Temperature-Responsive Multilayer Films Based on Block Copolymer-Coated Silica Nanoparticles for Long-Term Release of Favipiravir

Using surface-initiated atom-transfer radical polymerization, temperature-responsive block polymers were functionalized on the surface of silica nanocapsules (SNCs) by a "grafting from" technique. Favipiravir, a potential medicine candidate for the treatment of coronavirus disease (COVID-19), was encapsulated in polymer-coated SNCs and further incorporated into welldefined films by layer-by-layer self-assembly. The multilayer films composed of polymer-coated SNCs and poly(methacrylic acid) (PMAA) homopolymers exhibited swelling/deswelling behaviors under the trigger of a temperature stimulus. For the first time, the impact of steric hindrance on the assembling behavior, swelling/ deswelling transition, and delivering capacity of nanocapsule-based multilayer films was investigated. SNCs with coronae of higher steric hindrance resulted in a larger layering distance during film growth. Moreover, the difference in the sustained release rates of the drug indicated their diverse diffusion coefficients and intermolecular interactions within the multilayer films, due to the presence of a methyl spacer at the amino group of nanocapsule coronae and weaker ionic pairing between SNC coronae and PMAA homopolymers. The profile of drug release from the films was dependent on the temperature value of the surrounding environment. At 37 and 40 degrees C, the films were able to efficiently entrap favipiravir, with as low as 50% released in 80 days, whereas a faster favipiravir release was triggered by exposure to a lower temperature value at 25 degrees C. This work demonstrates the first proof-of-concept platform of temperature-responsive SNC-incorporated multilayered films with a well-defined internal structure and a sustained release profile for on-demand in vitro drug delivery.