ABSTRACT
The Moderate Resolution Imaging Spectroradiometer (MODIS) 1 km aerosol product based on the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm has great potential in understanding the interaction between human activities and the atmospheric environment. In this paper, the MODIS 1 km aerosol product over China during the Coronavirus Disease 2019 (COVID-19) pandemic was validated against with the ground measured data collected from the Aerosol Robotic Network (AERONET). The result shows a good agreement between the two datasets. The spatiotemporal analyses of three selected regions, which are Beijing-Tianjin-Hebei, Hubei and Guangdong-Hong Kong-Macao, indicate that the COVID-19 pandemic has a significant impact on human activities and aerosol loadings.
ABSTRACT
In order to save manpower, improve the management of COVID-19 prevention and prevent the spread of the epidemic, this paper proposes and designs a medical robot based on a one-chip computer. The single-chip STC89C52 is used as the main control core. Obstacles are detected by infrared sensors. And the robot uses the tracking module to determine the path. The working states of the two DC motors are then changed by the IO-port control L298N drive template, thereby changing the motion state of the robot through the speed difference between the motors on both sides. In the intelligent tracking module, the robot first uses a genetic algorithm to find the best path forward inspection and then enters the ward. After disinfection, the robot uses STM32F4 to drive the OV2640 camera to collect data and detect the mask using the yolov5s algorithm. Finally, it sends the collected information to the computer to realize the real-time monitoring of the patient's condition. The simulation results show that the medical robot can effectively and accurately realize the requirements of path planning, facial mask recognition, and wireless communication. This will significantly improve the efficiency and safety of medical staff. © 2022 IEEE.
ABSTRACT
Due to the Coronavirus Disease (COVID-19) pandemic, the human activities in China and even in the world were reduced in 2020, which also caused the variation of the atmospheric environment, especially atmospheric aerosol emissions. In this paper, the MODIS level-3 gridded atmosphere monthly global joint product in 2019 and 2020 were collected and processed. After preliminary analysis, we found that MODIS annual aerosol optical depth (AOD) over China in 2020 is generally lower than in 2019. In some regions such as Beijing-Tianjin-Hebei and Yangtze River Delta, AOD values dropped the most in February. However, in some months and regions, AOD in 2020 is even higher than in 2019. More studies are still ongoing. © 2021 IEEE.
ABSTRACT
The coronavirus (COVID-19) pandemic has significantly impacted and changed our daily routines. Worldwide, people have had to adapt by undergoing remote work and self-quarantine. This situation has required transforming strategies for various logistics services for a variety of service providers, such as retail stores and restaurants. The concept of contactless delivery has emerged to help prevent the spread of the coronavirus. However, contactless delivery only reduces the direct interaction between the delivery personnel and the customer. In addition to peer-to-peer contact, items still go through insecure interactions between and among the delivery personnel and other unknown third parties. Even if the items are delivered without physical contact, concerns remain about their routes in the supply chain. In this paper, we present a novel blockchain-based framework to enable the traceability of products in the supply chain. This framework records and tracks delivery traces and the medical status of delivery personnel in a privacy-preserved manner, ultimately contributing to COVID-19 prevention and control. We build a Hyperledger Fabric-based blockchain prototype system as our testbed. Several smart contract functions are implemented and evaluated to show the efficiency of the proposed framework. In conjunction with the implementation and evaluation, we also perform comprehensive security and privacy analyses of this framework. © 2021 IEEE.
ABSTRACT
Graph is a natural representation encoding both the features of the data samples and relationships among them. Analysis with graphs is a classic topic in data mining and many techniques have been proposed in the past. In recent years, because of the rapid development of data mining and knowledge discovery, many novel graph analytics algorithms have been proposed and successfully applied in a variety of areas. The goal of this tutorial is to summarize the graph analytics algorithms developed recently and how they have been applied in healthcare. In particular, our tutorial will cover both the technical advances and the application in healthcare. On the technical aspect, we will introduce deep network embedding techniques, graph neural networks, knowledge graph construction and inference, graph generative models and graph neural ordinary differential equation models. On the healthcare side, we will introduce how these methods can be applied in predictive modeling of clinical risks (e.g., chronic disease onset, in-hospital mortality, condition exacerbation, etc.) and disease subtyping with multi-modal patient data (e.g., electronic health records, medical image and multi-omics), knowledge discovery from biomedical literature and integration with data-driven models, as well as pharmaceutical research and development (e.g., de-novo chemical compound design and optimization, patient similarity for clinical trial recruitment and pharmacovigilance). We will conclude the whole tutorial with a set of potential issues and challenges such as interpretability, fairness and security. In particular, considering the global pandemic of COVID-19, we will also summarize the existing research that have already leveraged graph analytics to help with the understanding the mechanism, transmission, treatment and prevention of COVID-19, as well as point out the available resources and potential opportunities for future research. © 2020 Owner/Author.
ABSTRACT
Objective: To analyze the epidemiological characteristics of imported COVID-19 cases in early phase in Shanghai, introduce measures and provide reference for prevention and control of imported COVID-19 cases. Methods: Data of imported COVID-19 cases in Shanghai reported as of 30 March, 2020 were obtained from National Notifiable Disease Report System of China CDC and field epidemiological investigation reports by CDCs in Shanghai. The information about measures of prevention and control was collected from official websites and platforms of the governments. Data cleaning and statistical analysis were performed with softwares of EpiData 3.1, Excel 2019 and SAS 9.4. Results: A total of 171 imported COVID-19 cases had been reported as of 30 March, 2020 in Shanghai, including 170 confirmed cases and 1 asymptomatic infection case. Among them, cases of Chinese nationality accounted for 71.3% (122/171) and cases of foreign nationality accounted for 28.7% (49/171). The median age of the cases was 23 years (P(25), P(75): 18, 35 years), and the male to female ratio of the cases was 1.3â¶1. Students accounted for 56.6% (97/171). About 45.6% (78/171) of the cases fell ill before arriving in Shanghai. The cases with mild or common clinical manifestation accounted for 96.5% (165/171) and no significant difference in clinical type was observed between overseas Chinese cases and foreign cases. The epidemic curve by diagnosis date reached peak on March 24, and the number of the cases gradually declined due to the closed-loop management process of joint port prevention and control mechanism. The 171 imported COVID-19 cases were mainly from 24 countries and regions, including the United Kingdom (64 cases, 37.3%), the United States (32 cases, 18.6%), France (19 cases, 11.0%) and Italy (16 cases, 9.4%). About 40.4% of the cases (69/171) planned to continue travelling to 21 other provinces and municipalities in China. Customs quarantine and community observation/detection points identified 43.9% (75/171) cases and 31.0% (53/171) cases, respectively. Conclusions: The imported COVID-19 cases in early phase in Shanghai were mainly young population and students accounted for high proportion. The imported risk of COVID-19 was consistent with the severity of the epidemic in foreign countries. The closed-loop management model of the joint port prevention and control mechanism plays an important role in the identification and management of the imported COVID-19 cases.