The Impact of COVID-19 Lockdown on Ambient Air Quality in Shanghai, 2022

The COVID-19 lockdown contributes to the improvement of air quality. Most previous studies have attributed this to the reduction of human activity while ignoring the meteorological changes, this may lead to an overestimation or underestimation of the impact of COVID-19 lockdown measures on air pollution levels. To investigate this issue, we propose an XGBoost-based model to predict the concentrations of PM2.5 and PM10 during the COVID-19 lockdown period in 2022, Shanghai, and thus explore the limits of anthropogenic emission on air pollution levels by comprehensively employing the meteorological factors and the concentrations of other air pollutants. Results demonstrate that actual observations of PM2.5 and PM10 during the COVID-19 lockdown period were reduced by 60.81% and 43.12% compared with the predicted values (regarded as the period without the lockdown measures). In addition, by comparing with the time series prediction results without considering meteorological factors, the actual observations of PM2.5 and PM10 during the lockdown period were reduced by 50.20% and 19.06%, respectively, against the predicted values during the non-lockdown period. The analysis results indicate that ignoring meteorological factors will underestimate the positive impact of COVID-19 lockdown measures on air quality. © 2023 by the authors.

Modeling and Analysis of Infectious Disease Transmission Considering Protection and Isolation

Based on the SEIR model, two compartments for self-protection and isolation are introduced, and a more general infectious disease transmission model is proposed.Through qualitative analysis of the model, the basic reproduction number of the model is calculated, and the local asymptotic stability of the disease-free equilibrium point and the endemic equilibrium point of the model is analyzed through eigenvalue theory and Routh-Hurwitz criterion.The numerical simulation and fitting results of COVID-19 virus show that the proposed SEIQRP model can effectively describe the dynamic transmission process of the infectious disease.In the model, the three parameters, i.e.protection rate, incubation period isolation rate, and infected person isolation rate play a very critical role in the spread of the disease.Raising people's awareness of self-protection, focusing on screening for patients in the incubation period, and isolating and treating infected people can effectively reduce the spread of infectious diseases. © 2023 Northeastern University.All rights reserved.

Research progress on factors, risk assessment, and prevention strategies associated with SARS-CoV-2 survival in food and media.

This paper presented a brief introduction to the outbreak process and symptoms of coronavirus disease 2019 (COVID-19), elucidated the detection methods and transmission modes of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that caused the disease, and summarized the survival time of SARS-COV-2 in different media and different physical and chemical conditions and factors that could affect the long-term survival of the virus. This paper also summed up current risk assessments of SARS-COV-2 in food safety conducted in various countries, and concluded that the risk of SARS-COV-2 to food safety is very low, but preventive measures are still in need after referring to latest research. Finally, some methods to prevent SARS-COV-2 contamination in food were introduced, aiming to provide a basis for the formulation of policy measures in the future.Copyright © 2021, Shanghai Municipal Center for Disease Control and Prevention. All rights reserved.

Dynamic Properties for a Second-Order Stochastic SEIR Model with Infectivity in Incubation Period and Homestead-Isolation of the Susceptible Population

In this article, we analyze a second-order stochastic SEIR epidemic model with latent infectious and susceptible populations isolated at home. Firstly, by putting forward a novel inequality, we provide a criterion for the presence of an ergodic stationary distribution of the model. Secondly, we establish sufficient conditions for extinction. Thirdly, by solving the corresponding Fokker–Plank equation, we derive the probability density function around the quasi-endemic equilibrium of the stochastic model. Finally, by using the epidemic data of the corresponding deterministic model, two numerical tests are presented to illustrate the validity of the theoretical results. Our conclusions demonstrate that nations should persevere in their quarantine policies to curb viral transmission when the COVID-19 pandemic proceeds to spread internationally. © 2023 by the authors.

Police Social Capital and the Effectiveness of Emergency Policing in China During COVID-19

Emergency policing has played a significant role in controlling the spread of COVID-19 in various countries. China is one of the few countries that has successfully controlled the pandemic. What are the factors that determine the effectiveness of emergency policing in China? This study argues that the social capital played a supportive role in emergency policing during COVID-19. Based on the data analysis, we construct a theoretical framework to explain why pandemic control in China has been effective. The effectiveness of the police response to the COVID-19 pandemic in Zhejiang, China, displays the importance of all three types of social capital for emergency policing, including interaction-assisted social capital represented by 'grid-governance', technology-driven social capital represented by 'data sharing', and service-assisted social capital represented by 'safety construction'.

Topology and evolution of international trade network for fish and fish products

In this study, a complex network method was employed to quantify the changing role of countries in fish trade and the dynamic characteristics of fish globalization. Based on the United Nations Comtrade Database, the International Trade Network for Fish and Fish Products (ITN-Fish) was constructed as a series of weighted-directed networks for each year from 1990 to 2018. Almost all countries and territories worldwide have participated in the fish trade. In 2018, the network identified 229 fish traders. The share of developing countries in imports and exports has increased. Traders actively establish new trade relations, which improve network connectivity. However, these relations only account for a small part of the fish trade. The high connectivity allows risks to spread rapidly in the world through hubs such as the United States and China, which raises concerns about the robustness of these weak links in the Sino-US trade conflict and the outbreak of COVID-19. However, we have optimistic expectations on this issue. The dynamic of network topology property shows that the globalization of fish trade flourished between 1990 and 2018. Although, due to the financial crisis and its subsequent impact, the total amount of fish trade declined in 2009 and 2015, the network structure was not seriously affected, and the trend of topology property remained unchanged. Based on the construction of the international trade network, its node attribute, and its structural attribute, fish trade maintains the trend of globalization. Countries should actively adhere to trade globalization to promote the development of the fish trade.

Developing Contemporary Survivorship Clinic for Recipients of Hematopoietic Cell Transplantation: A Single Center Experience

Introduction: Survival after hematopoietic cell transplantation (HCT) has improved tremendously over the last few decades. HCT survivors are at increased risk of long-term complications and secondary cancers. This poses unique challenges to the HCT-related healthcare system given the growing need for survivorship care. Developing a HCT survivorship program with a dedicated clinic to survivors ensures equitable access to care and ongoing patient education. Herein, we describe our program survivorship model and our initial experience. Method(s): The Moffitt Cancer Center (MCC) survivorship clinic (SC) planning committee was initiated in September 2019. The SC was launched in January 2021 with the mission to provide high-quality, comprehensive, and personalized survivorship care and to empower patients and community health care providers with education and a roadmap for screening for late effects. The SC initially focused on allogeneic (allo) HCT patients and later opened to autologous (auto) HCT recipients in February 2022. HCT patients are referred by primary HCT team after HCT with an emphasis on preferred timeframe of initial SC visit no earlier than 3 months but less than 12 months from HCT. SC is located at 2 physical locations: main campus and satellite, with virtual visit options to account for the distance from MCC and COVID considerations. SC applies a consultative model. SC is staffed by dedicated advanced practice professional (APP), supervised by SC faculty. The scope of SC care includes but is not limited to prevention of infections (education, vaccinations), surveillance of late effects (endocrine, pulmonary function, cardiac, bone health), and general cancer screenings (breast, colon, skin cancer). Patients' clinical data from SC inception to August 2022 were reviewed. Result(s): From January 2021 to August 2022, a total of 138 patients were seen in SC. The majority were seen in person (62% in clinic, 38% by virtual visit). Median age was 58 years (range, 19-82). Median time to first SC visit was 21 months (range, 3-1464) after HCT. Allo HCT was the most common type of HCT seen in clinic (87%, n=120). Most common diagnoses were acute myeloid leukemia (43%, n=59), myelodysplastic syndrome (17%, n=23), and acute lymphoblastic leukemia (10%, n=14). Only 17 patients (12%) were seen in 2021 but the volume increased significantly in 2022. Currently there are more than 10 patients seen in SC per month. Conclusion(s): We report successful experience in launching a contemporary HCT SC despite the challenges of an ongoing COVID pandemic. As a stand-alone cancer center, we serve a wide geographical location with subspecialty and primary care providers dispersed throughout the community. Our consultative model and experience could provide a useful guide for other programs. In 2023, we plan to expand our SC to a broader population of patients receiving other cellular immunotherapies.Copyright © 2023 American Society for Transplantation and Cellular Therapy

Contributing Factors to the Changes in Public and Private Transportation Mode Choice after the COVID-19 Outbreak in Urban Areas of China

The COVID-19 pandemic has tremendously affected the whole of human society worldwide. Travel patterns have greatly changed due to the increased risk perception and the governmental interventions regarding COVID-19. This study aimed to identify contributing factors to the changes in public and private transportation mode choice behavior in China after COVID-19 based on an online questionnaire survey. In the survey, travel behaviors in three periods were studied: before the outbreak (before 27 December 2019), the peak (from 20 January to 17 March 2020), and after the peak (from 18 March to the date of the survey). A series of random-parameter bivariate Probit models was developed to quantify the relationship between individual characteristics and the changes in travel mode choice. The key findings indicated that individual sociodemographic characteristics (e.g., gender, age, ownership, occupation, residence) have significant effects on the changes in mode choice behavior. Other key findings included (1) a higher propensity to use a taxi after the peak compared to urban public transportation (i.e., bus and subway);(2) a significant impact of age on the switch from public transit to private car and two-wheelers;(3) more obvious changes in private car and public transportation modes in more developed cities. The findings from this study are expected to be useful for establishing partial and resilient policies and ensuring sustainable mobility and travel equality in the post-pandemic era.

Optimal Hospital Care Scheduling During the SARS-CoV-2 Pandemic

The COVID-19 pandemic has seen dramatic demand surges for hospital care that have placed a severe strain on health systems worldwide. As a result, policy makers are faced with the challenge of managing scarce hospital capacity to reduce the backlog of non-COVID patients while maintaining the ability to respond to any potential future increases in demand for COVID care. In this paper, we propose a nationwide prioritization scheme that models each individual patient as a dynamic program whose states encode the patient's health and treatment condition, whose actions describe the available treatment options, whose transition probabilities characterize the stochastic evolution of the patient's health, and whose rewards encode the contribution to the overall objectives of the health system. The individual patients' dynamic programs are coupled through constraints on the available resources, such as hospital beds, doctors, and nurses. We show that the overall problem can be modeled as a grouped weakly coupled dynamic program for which we determine near-optimal solutions through a fluid approximation. Our case study for the National Health Service in England shows how years of life can be gained by prioritizing specific disease types over COVID patients, such as injury and poisoning, diseases of the respiratory system, diseases of the circulatory system, diseases of the digestive system, and cancer.

Guest Editorial Introduction to the Special Issue on Intelligent Transportation Systems in Epidemic Areas

The COVID-19 pandemic has posed significant challenges to transportation systems in various aspects, such as transferring patients and medical resources, enforcing physical distancing in public transportation, and controlling virus transmission through transportation networks. To address these challenges, a variety of artificial intelligence technologies, such as autonomous driving, big data analytics, intelligent vehicle routing and scheduling, and intelligent traffic control, have been employed in the design of intelligent transportation systems. This Special Issue provides a forum for researchers and practitioners to present the most recent advances in presenting and applying intelligent technologies to promote transportation systems in large-scale epidemics.

The transformation of microclimate adaption in public spaces by smart ventilation approach: a case study of Eastern Banlieue memory industrial Park, China

With the normalization of the COVID-19 pandemic prevention and control, there is an urgent need to develop a healthy urban public space. However, because of the fast urbanization process with a series of problems, such as PM2.5 air pollution, the Urban Heat Island, and the relatively high frequency of static winds under the influence of its topography, the ventilation problem in the public spaces of Chengdu is of great importance. Along these lines, in this work, the history of theoretical research on urban ventilation is summarized and reviewed first to evaluate the urban wind environment. Second, so far, qualitative methods are mainly adopted for the evaluation methods of microclimate adaptation. However, the practical application has achieved few results. Meanwhile, there is still a lack of comprehensive and unified research on the multi-element of human microclimate comfort in public space. For this reason, the urban ventilation assessment system was established in this work according to the physical, physiological, and psychological aspects, with 9 indices selected and ranked. Then, an optimization strategy for rebuilding the urban public space was proposed for improving the wind environment microclimate adaption on three levels: macro city-regional level, meso block linear space, and micro space node. By taking Eastern Banlieue Memory Industrial Park as an example, the statistical data were systematically investigated on the spot from the results of 249 wind environment questionnaires, and 30 Delphi expert consultation questionnaires. Combined with the Computational Fluid Dynamics (CFD) simulation, the results reveal that most public spaces in the study area were below 0.6 m/s in more than 80% of the public space, and wind-based environmental problems obviously exist without any ventilation improvement measures. Combined with the background of the carbon peak era, the ventilation environment of the urban public space is not conducive to using active ventilation equipment. The solution of a complete set of regional intelligent ventilation systems was thoroughly discussed here, while some innovative sustainable systematic solutions and urban ventilation furniture combined with a geothermal heat pump and cloud data platform were formulated.

The time-varying impact of uncertainty on oil market fear: Does climate policy uncertainty matter?

Implied volatility index is a popular proxy for market fear. This paper uses the oil implied volatility index (OVX) to investigate the impact of different uncertainty measures on oil market fear. Our uncertainty measures consider multiple perspectives, specifically including climate policy uncertainty (CPU), geopolitical risk (GPR), economic policy uncertainty (EPU), and equity market volatility (EMV). Based on the time-varying parameter vector autoregression (TVP-VAR) model, our empirical results show that the impact of CPU, GPR, EPU, and EMV on OVX is time-varying and heterogeneous due to these uncertainty measures containing different information content. In particular, the CPU has become increasingly important for triggering oil market fear since the recent Paris Agreement. During the COVID-19 pandemic, CPU, EPU, and EMV, rather than GPR, play a prominent role in increasing oil market fear. © 2023 Elsevier Ltd

Measuring the frequency and quantile connectedness between policy categories and global oil price

This study explores the connectedness between various categories of economic policy uncertainty (EPU) and global crude oil prices in different frequencies and quantiles using the generalized forecast error variance decomposition and data in the US, China, and Japan from January 2000 to May 2022. The empirical results may be summarized as follows. First, total short and long term connectedness exhibits different patterns and is more sensitive to extreme positive and negative shocks than regular shocks. Second, fiscal policy uncertainty (FPU) and monetary policy uncertainty (MPU) tend to act as net transmitters of shocks, while the roles of trade policy uncertainty (TPU) are mixed in the short term, irrespective of the country. However, under extreme market conditions, no specific-category EPU features a clear net transmitter/recipient. Finally, the results are qualitatively and quantitatively unaffected by the chosen proxy of crude oil prices and are not altered by global real economic activity. © 2023 Elsevier Ltd

Research on two-dimensional code scanning and automatic recording device for human body temperature measurement based on stm32

With the outbreak and normal development of COVID-19, the effective detection and recording of body temperature has become a new focus of our attention. At present, there is no complete system to measure temperature, automatic record and specific information at home and abroad. To this end, combined with professional knowledge, our team designed a two-dimensional code scanning and human body temperature automatic recording device with STM32F1 as the core. The device STM32F1 development board is the main control chip. By connecting the WIFI module through the serial port, STM32F1 uses the function of wireless communication. Through the communication protocol, the link between the router and the ESC cloud server of Ali Cloud is utilized. The router or mobile data is transmitted to the user side (APP, applets) according to the specified communication protocol. Inside the development board, the code of each part is written to complete the device integrating code scanning and temperature measurement, which can be displayed and alarm through the node (OLED display screen). This will play a good role in preventing the spread of COVID-19. The system can be used in hospitals, communities, railway stations, shopping malls and many other public places. © 2022 IEEE.

A more accurate mask detection algorithm based on Nao robot platform and YOLOv7

With the global outbreak of Corona Virus Disease 2019(COVID-19), many countries had made it mandatory for people to wear masks in public places. This paper proposed a novel mask detection algorithm RMPC (Restructing the Maxpool layer and the Convolution layer)-YOLOv7 based on YOLOv7 for detecting whether people wear masks in public places. The RMPC-YOLOv7 algorithm reconstructed the downsampling structure in the original YOLOv7 algorithm. We changed the stacking of the maxpooling layer and the convolutional layer. This enabled the feature information to be fully integrated to achieve the accuracy improvement of the new model. Through comparison experiments, our proposed RMPC-YOLOv7 had was improved 0.9% and 1.2% for mAP0.5 and mAP0.5:0.95, respectively. The experimental results demonstrated the feasibility of RMPC-YOLOv7. © 2023 IEEE.

How does indoor physical environment differentially affect learning performance in various classroom types?

Classroom indoor physical environment (CIPE) crucially impacts learning performance (LP). Along with the extended school hours caused by COVID-19, an investigation was conducted at Zhejiang Sci-Tech University to explore the effect of CIPE on LP in different classroom types under natural working condition of transitional seasons. Based on a six-day physical environment measurement and learning performance test, then five CIPE parameters and three LP indicators of four learning abilities were obtained. Through the statistical analysis, the results demonstrated that all CIPE parameters had some correlation or influence on LP, briefly, (1) Low carbon dioxide concentration (CCD, below 700 ppm) was a positive significant factor for all learning abilities, and relative humidity (RH) was a negative factor for comprehension memory ability (CMA) (significant) and logic deduction ability (LDA) (general) to varying degrees, with center illumination (Ic) being a positive significant factor for CMA only. (2) Deeper abilities, like CMA and LDA, were more susceptible to air temperature (Ta) and RH, with the former being positive and the latter being negative. (3) Compared to other types, LP in compact classrooms was more vulnerable to CIPE parameters, such as the positive influence of Ta and CCD, due to the greater variation in CIPE. The findings revealed the differential relationships between the CIPE and LP in various classroom types, guiding classroom design that couples the dual optimization of CIPE and LP. Limitations remain, however, and need to be supplemented by more future research, e.g. year-round experiments and medical instrumentation assistance. © 2023 Elsevier Ltd

Application of low dose radiation therapy in the treatment of COVID-19 pneumonia.

With the global pandemic of COVID-19, cytokine storms in critical patients with pneumonia is really a problem and need to be solved immediately.Low dose radiation therapy (LDRT) has been temporarily used to treat pneumonia.In the past decades, researchers were dedicated to clarify the biological mechanism of LDRT.LDRT plays a unique role in the suppression of inflammation, preliminary outcomes have been acquired in critical patients with COVID-19 pneumonia, and radiotherapy community is paying attention to this treatment strategy.This review summarizes the application of LDRT in pneumonia, its biological mechanism, the result of LDRT in COVID-19 pneumonia, the existing problems and prospective in clinic.Copyright © 2021 Chinese Medical Association

An Effective COVID-19 CT Image Denoising Method Based on a Deep Convolutional Neural Network

Background: Faced with the global threat posed by SARS-CoV-2 (COVID-19), low-dose computed tomography (LDCT), as the primary diagnostic tool, is often accompanied by high levels of noise. This can easily interfere with the radiologist's assessment. Convolutional neural networks (CNN), as a method of deep learning, have been shown to have excellent effects in image denoising. Objective: The objective of the study was to use modified convolutional neural network algorithm to train the denoising model. The purpose was to make the model extract the highlighted features of the lesion region better and ensure its effectiveness in removing noise from COVID-19 lung CT images, preserving more important detail information of the images and reducing the adverse effects of denoising. Methods: We propose a CNN-based deformable convolutional denoising neural network (DCDNet). By combining deformable convolution methods with residual learning on the basis of CNN structure, more image detail features are retained in CT image denoising. Results: According to the noise reduction evaluation index of PSNR, SSIM and RMSE, DCDNet shows excellent denoising performance for COVID-19 CT images. From the visual effect of denoising, DCDNet can effectively remove image noise and preserve more detailed features of lung lesions. Conclusion: The experimental results indicate that the DCDNet-trained model is more suitable for image denoising of COVID-19 than traditional image denoising algorithms under the same training set. © 2023 Bentham Science Publishers.

An Analysis of Factors Influencing the Intention to Use "Untact” Services by Service Type

Since COVID-19, social distancing has become common, and the demand for untact services has increased rapidly, resulting in an economic phenomenon centered on untact worldwide. Due to social distancing, the untact service area is expanding not only to shopping but also to online learning, home training, and telemedicine, and untact services are expected to expand to more diverse areas in the future. This study investigates four types of untact services: online lectures, online meetings related to work and study, online seminars, and online performances, and the effects of concerns about untact services on the intention of use have been examined using a path analysis model. As a result of the analysis, the perceived usefulness had a positive effect on the user's continuous intention to use untact services. However, depending on the type of untact service, it can be confirmed that the factors that affect the intention to continue using the service differ from each other. Practitioners can use the results of this study when designing untact services in the future. © 2023 by the authors.