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Resilience has a critical role to promote human well-being in turbulent environments. However, extensive interventions and stimulus measures not only achieve resilience goals with numerous unacceptable consequences, but also highlight the narrow definition of resilience. The study proposes the concept of resilience effectiveness to constrain the unsustainability of resilience in concept and practice. The resilience effectiveness of Chinese cities in the early stages of the pandemic is first implemented for evaluation based on input-output relationship, and then patterns and determinants are investigated using the GIS methods and the geographically optimal zones-based heterogeneity (GOZH) model. The study finds that the average resilience effectiveness is not high, indicating that China's strong resilience is the loss of safety, greenness, inclusiveness, and efficiency in the early stage of the pandemic. Resilience effectiveness has significant spatial heterogeneity and is dominated by innovation ability. The optimal geographic zones of Chinese cities for resilience effectiveness are identified, revealing that it is a complex and multifactorial process. This paper broadens resilience research and provides implications for city planning in the post-pandemic era. [ABSTRACT FROM AUTHOR]

The Coronavirus Disease 2019 (COVID-19) has proved a globally prevalent outbreak since December 2019. As a focused country to alleviate the epidemic impact, China implemented a range of public health interventions to prevent the disease from further transmission, including the pandemic lockdown in Wuhan and other cities. This paper establishes China's mobility network by a flight dataset and proposes a model without epidemiological parameters to indicate the spread risks through the network, which is termed as epidemic strength. By simply adjusting an intervention parameter, traffic volumes under different travel-restriction levels can be simulated to analyze how the containment strategy can mitigate the virus dissemination through traffic. This approach is successfully applied to a network of Chinese provinces and the epidemic strength is smoothly interpreted by flow maps. Through this node-to-node interpretation of transmission risks, both overall and detailed epidemic hazards are properly analyzed, which can provide valuable intervention advice during public health emergencies. [ABSTRACT FROM AUTHOR]

The novel coronavirus disease 2019 (COVID-19) has affected almost all the countries globally, and it is considered a global pandemic. Hence, this study conducted a bibliometric analysis of scientific output on the COVID-19 pandemic outbreak using the Web of Science (WoS) database. A specific search technique was developed based on Corona virus-related keywords from January 1, 2019, until June 24, 2020. A total of 10850 documents related to COVID-19 published from January 1, 2019, to June 24, 2020, were retrieved from the WoS database. The researchers downloaded the data from WoS as a WoS plain text file, and the data were analyzed using R studio (biblioshiny) software. Out of the total of 10850 documents published in 1736 scientific journals, 10845 papers belonging to the year 2020, which constituents 99.954% of the total documents (N=10850). Wang Y is the most productive author who contributed 0.60% (n=65) of the total documents published. USA is the predominant country that produced 2701 documents related to COVID-19, and China follows it with 1937 documents. The most active collaboration held between the USA and China, and it accounted for 2.75% (n=298) of the entire documents (N=10850). [ABSTRACT FROM AUTHOR]

Purpose: The present study reflects the trend and growth of publications on COVID-19 viral disease spreading around the world. With the emergence of COVID-19 in Wuhan, China in December 2019, scholars started publishing their researches in form of different kind of publications. Methodology: The data has been extracted from the Scopus database using key TERMS Covid-19 and corona virus 2019. Findings: During six month period around ten thousand publications are contributed by scientists around the globe. The literature produced in different fields but highest output is made in the field of Medicine. BMJ and Journal of Medical Virology journals produced highest amount of research on COVID. Highest numbers of publications have been contributed by China based two institutions viz. Huazhong University of Science and Technology and Tongji Medical College, China. Mahase, E. of UK contributed maximum papers followed by Wiwanitkit, V. of China. [ABSTRACT FROM AUTHOR]

In this paper, the interaction strategies and the evolutionary game analysis of the actions taken by the government and the public in the early days of the epidemic are incorporated into the natural transmission mechanism model of the epidemic, and then the transmission frequency equations of COVID-19 epidemic is established. According to the cumulative confirmed cases of COVID-19 in the UK and China, the upper limit of the spread of COVID-19 in different evolutionary scenarios is set. Using SPSS to perform logistic curve fitting, the frequency fitting equations of cumulative confirmed cases under different evolution scenarios are obtained respectively. The analysis result shows that the emergency response strategy adopted by the government in the early days of the epidemic can effectively control the spread of the epidemic. Combined with the transmission frequency equation of COVID-19 epidemic, measures taken by the government are analyzed. The influence of each measure on the frequency variable is judged and then the influence on the spread of the epidemic is obtained. Finally, based on the above analysis, the government is advised to adhere to the principles of scientific, initiative and flexibility when facing major epidemics. [ABSTRACT FROM AUTHOR]

This paper uses resilience as a lens through which to analyse disasters and other major threats to patterns of criminal behaviour. A set of indicators and mathematical models are introduced that aim to quantitatively describe changes in crime levels in comparison to what could otherwise be expected, and what might be expected by way of adaptation and subsequent resumption of those patterns. The validity of the proposed resilience assessment tool is demonstrated using commercial theft data from the COVID-19 pandemic period. A 64 per cent reduction in crime was found in the studied city (China) during an 83-day period, before daily crime levels bounced back to higher than expected values. The proposed resilience indicators are recommended as benchmarking instruments for evaluating and comparing the global impact of COVID-19 policies on crime and public safety. [ABSTRACT FROM AUTHOR]

• This paper summarised the major types of SARS-COV-2 cluster infections worldwide. • Sixty-five studies were included, involving 108 cluster infections. • The major types of clusters were families, community transmission, etc. To summarise the major types of SARS-CoV-2 cluster infections worldwide through a comprehensive systematic review. All studies published between 01 January–15 June 2020 on COVID-19 cluster infections in English electronic databases were searched, including PubMed, Embase, Web of Knowledge, and Scopus. All included studies were independently screened and evaluated by two authors, and information from each study was extracted using a standard form. Sixty-five studies were included, which involved 108 cluster infections from 13 countries, areas or territories. Seventy-two (66.7%) of the cluster infections were reported in China. The major types of cluster infections were families, community transmission, nosocomial infection, gatherings, transportation, shopping malls, conferences, tourists, religious organisations, workers, prisons, offices, and nursing homes. The SARS-CoV-2 can be transmitted in various circumstances, and cluster infections play an important role in the rapid evolution of COVID-19 transmission. Prevention and control measures such as social distancing must be strictly implemented to contain these cluster infections. [ABSTRACT FROM AUTHOR]

This paper seeks to understand whether a catastrophic and urgent event, such as the first months of the COVID-19 pandemic, accelerates or reverses trends in international collaboration, especially in and between China and the United States. A review of research articles produced in the first months of the COVID-19 pandemic shows that COVID-19 research had smaller teams and involved fewer nations than pre-COVID-19 coronavirus research. The United States and China were, and continue to be in the pandemic era, at the center of the global network in coronavirus related research, while developing countries are relatively absent from early research activities in the COVID-19 period. Not only are China and the United States at the center of the global network of coronavirus research, but they strengthen their bilateral research relationship during COVID-19, producing more than 4.9% of all global articles together, in contrast to 3.6% before the pandemic. In addition, in the COVID-19 period, joined by the United Kingdom, China and the United States continued their roles as the largest contributors to, and home to the main funders of, coronavirus related research. These findings suggest that the global COVID-19 pandemic shifted the geographic loci of coronavirus research, as well as the structure of scientific teams, narrowing team membership and favoring elite structures. These findings raise further questions over the decisions that scientists face in the formation of teams to maximize a speed, skill trade-off. Policy implications are discussed. [ABSTRACT FROM AUTHOR]

Pneumonia caused by SARS-CoV-2 infection emerged in Wuhan City, Hubei Province, China in December 2019. Widespread outbreaks of infectious disease are associated with psychological distress and symptoms of mental illness. The COVID-19 outbreak has caused a public health crisis. In response to the rapidly increasing number of publications on this subject, this article attempts to provide a comprehensive review of early reports that appeared in the international literature until the beginning of May 2020. This paper details the effects on the general population as medical staff are exposed to different, specific stressors. The available literature has emerged from only a few of the most affected countries; therefore, there is a need for more representative research from other affected regions. The majority of studies focus on depression, anxiety, insomnia and distress. Subsyndromal mental health problems are a common response to the COVID-19 pandemic. There is a huge need for further research. In particular, attention must be paid to the potentially devastating effects on population-wide mental health. Pandemic crisis shifts from acute to protracted, and the long-term mental-health consequences of the pandemic will manifest themselves in the future. [ABSTRACT FROM AUTHOR]

We propose a varying coefficient Susceptible-Infected-Removal (vSIR) model that allows changing infection and removal rates for the latest corona virus (COVID-19) outbreak in China. The vSIR model together with proposed estimation procedures allow one to track the reproductivity of the COVID-19 through time and to assess the effectiveness of the control measures implemented since Jan 23 2020 when the city of Wuhan was lockdown followed by an extremely high level of self-isolation in the population. Our study finds that the reproductivity of COVID-19 had been significantly slowed down in the three weeks from January 27th to February 17th with 96.3% and 95.1% reductions in the effective reproduction numbers R among the 30 provinces and 15 Hubei cities, respectively. Predictions to the ending times and the total numbers of infected are made under three scenarios of the removal rates. The paper provides a timely model and associated estimation and prediction methods which may be applied in other countries to track, assess and predict the epidemic of the COVID-19 or other infectious diseases. [ABSTRACT FROM AUTHOR]

This paper studied the relationship between personality traits and mental health conditions of medical personnel to provide a basis and reference for the implementation of targeted education on mental health. A self-report inventory, the Symptom Checklist-90 (SCL-90), was used to investigate the mental health status of 548 medical personnel dealing with the new coronavirus pneumonia in eight provinces and cities of China. The overall mean SCL-90 score and mean values of factors (somatization, obsessive-compulsive, anxiety, phobic anxiety, and psychoticism) of the medical personnel were significantly higher than in the norm group (p < 0.05), while their average interpersonal sensitivity score was significantly lower (p < 0.01). In addition, personal factors affecting the mental health status of medical personnel were identified (all p < 0.05). The overall mental health status of medical personnel responding to new coronavirus pneumonia is generally higher than that of the norm group in China. The results of this study should contribute to measures to alleviate the psychological pressures on medical personnel dealing with the new coronavirus epidemic in China. [ABSTRACT FROM AUTHOR]

Background: A novel coronavirus pneumonia outbreak began in Wuhan, Hubei Province, in December 2019; the outbreak was caused by a novel coronavirus previously never observed in humans. China has imposed the strictest quarantine and closed management measures in history to control the spread of the disease. However, a high level of evidence to support the surgical management of potential trauma patients during the novel coronavirus outbreak is still lacking. To regulate the emergency treatment of trauma patients during the outbreak, we drafted this paper from a trauma surgeon perspective according to practical experience in Wuhan. Main body: The article illustrates the general principles for the triage and evaluation of trauma patients during the outbreak of COVID-19, indications for emergency surgery, and infection prevention and control for medical personnel, providing a practical algorithm for trauma care providers during the outbreak period. Conclusions: The measures of emergency trauma care that we have provided can protect the medical personnel involved in emergency care and ensure the timeliness of effective interventions during the outbreak of COVID-19. [ABSTRACT FROM AUTHOR]

The emergence of an unusual Corona virus (COVID-19) flu pandemic starting in China in December 2019, spreading all around the globe is a major threat to public health. The investigations have shown this virus originated from a seafood market in Wuhan. However, the unavailability of medicines for the new disease is a big challenge all around. An attempt has been made in the present article to familiarise about the morphology of the virus. The study of effect of pH, temperature and relative humidity is also depicted. Various preventive measures have also been discussed. The natural dietary measures suggested in the paper would be very beneficial to improve and boost the immunity of the mankind. [ABSTRACT FROM AUTHOR]

The global spread of COVID-19 has caused a substantial societal burden and become a major global public health issue. The COVID-19 elderly population with hypertension, diabetes, cardiovascular, and cerebrovascular diseases are at risk. Mortality rates are highest in these individuals if infected with COVID-19. Although the lungs are the main organs involved in acute respiratory distress syndrome caused by COVID-19 infection, COVID-19 triggers inflammatory and immune mechanisms, inducing a "cytokine storm" that aggravates disease progression and may lead to death. Presently, effective drugs are lacking, although current studies have confirmed that drugs with therapeutic potential include redaciclovir, lopinavir/ritonavir combined with interferon-β, convalescent plasma, and monoclonal antibodies. Currently, the most reasonable and effective way to prevent COVID-19 is to control the source of infection, terminate routes of transmission, and protect susceptible populations. With the rise of COVID-19 in China and worldwide, further prevention, diagnosis, and treatment measures are a critical unmet need. Cerebrovascular disease has high incidence, disability rate, and fatality rate. COVID-19 patient outcomes may also be complicated with acute stroke. This paper summarizes the influence of COVID-19 on cerebrovascular disease and discusses possible pathophysiological mechanisms to provide new angles for the prevention and diagnosis of this disease. [ABSTRACT FROM AUTHOR]

• None of SARS-CoV-2 RNA was detected among the 36 objects surface samples and 9 staffs PPE samples in isolation wards. • Though SARS-CoV-2 RNA of the sewage samples were positive from inlets of the sewage disinfection pool and negative from the outlet of the last sewage disinfection pool, no viable virus was detected by culture. The aim of this paper was to monitor the presence of SARS-Cov-2 among hospital environment surfaces, sewage, and personal protective equipment (PPE) of staffs in isolation wards in the First Affiliated Hospital of Zhejiang University, China. Surfaces of objects were routinely wiped with 1000 mg/L chlorine containing disinfectant. Air and sewage disinfection was proceeded routinely and strictly. Hospital environmental surfaces and PPE of staffs in isolation wards were sampled using swabs. The sewage from various inlet and outlets were sampled. The respiratory and stool specimens of patients were collected. The respiratory specimens of staffs in the isolation wards were also sampled once a week. Quantitative real-time reverse transcription PCR (qRT-PCR) methods were used to confirm the existence of SARS-Cov-2 RNA. Viral culture was done for the samples positive for SARS-Cov-2 RNA. During the study period, 33 laboratory-confirmed patients were hospitalized in isolation wards in the hospital. None of SARS-Cov-2 RNA was detected among the 36 objects surface samples and 9 staffs PPE samples in isolation wards. Though the 3 sewage samples from the inlet of preprocessing disinfection pool were positive for SARS-CoV-2 RNA and the sample from the outlet of preprocessing disinfection pool was weakly positive, the sewage sample from the outlet of the last disinfection pool was negative. All of the 5 sewage samples from various points were negative by viral culture of SARS-Cov-2. None of the respiratory specimens of staffs in the isolation wards were positive. Though SARS-Cov-2 RNA of the sewage samples were positive from inlets of the sewage disinfection pool and negative from the outlet of the last sewage disinfection pool, no viable virus was detected by culture. The monitoring data in this study suggested that the strict disinfection and hand hygiene could decrease the hospital-associated COVID-19 infection risk of the staffs in isolation wards. [ABSTRACT FROM AUTHOR]

• The latest summary of the COVID-19 outbreak in China; • There might be an oral-fecal transmission of the virus; • Aggregates and consolidates the epidemiology, clinical manifestations, diagnosis, treatments and preventions of this new type of coronavirus. There is a current worldwide outbreak of the novel coronavirus Covid-19 (coronavirus disease 2019; the pathogen called SARS-CoV-2; previously 2019-nCoV), which originated from Wuhan in China and has now spread to 6 continents including 66 countries, as of 24:00 on March 2, 2020. Governments are under increased pressure to stop the outbreak from spiraling into a global health emergency. At this stage, preparedness, transparency, and sharing of information are crucial to risk assessments and beginning outbreak control activities. This information should include reports from outbreak site and from laboratories supporting the investigation. This paper aggregates and consolidates the epidemiology, clinical manifestations, diagnosis, treatments and preventions of this new type of coronavirus. [ABSTRACT FROM AUTHOR]

The emergence of an unusual Corona virus (COVID-19) flu pandemic starting in China in December 2019, spreading all around the globe is a major threat to public health. The investigations have shown this virus originated from a seafood market in Wuhan. However, the unavailability of medicines for the new disease is a big challenge all around. An attempt has been made in the present article to familiarise about the morphology of the virus. The study of effect of pH, temperature and relative humidity is also depicted. Various preventive measures have also been discussed. The natural dietary measures suggested in the paper would be very beneficial to improve and boost the immunity of the mankind. [ABSTRACT FROM AUTHOR]

"Conoronavirus Disease" (COVID-19) abbreviation is used for the identification of the new disease alerted in Wuhan city of China at the end of year 2019. COVID-19 is caused by "Severe Acute Respiratory Syndrome Coronavirus-2" (SARS-CoV-2). Special pandemic precautions are designed for the control of virus spread by inhaling droplets or contact touching surfaces. Authorities were concentrated on the education of healthcare workers, personal protective equipment (PPE) were pursued, and the hospital wards were re-designed during COVID-19 pandemic. In this paper, infection control precautions conducted in health facilities were reviewed on the basis of practices of Infection Control Committee (ICC) of Gazi University Hospital following the first COVID-19 case reported at 11 March 2020 in our country. Technical properties of PPEs, donning and doffing procedures, recommendations of PPE for each specific clinical situation for each specific healthcare worker were evaluated in this review. [ABSTRACT FROM AUTHOR]

The ongoing pandemic due to SARS CoV2 is really a big one, which would never welcomed from any countries anytime, but knocked the door all of a sudden! The SARS CoV-2 first appeared in China in late December 2019. The virus isolated on January 7, 2020; the disease named as COVID-19 afterwars. Since this was a SARS virus not an influenza, nobody seem to expect it as a pandemic agent, spread so fast and so globally. But actually it did spread to many countries and declared as a pandemic by the World Health Organization on March 11, 2020. The pandemic hit the universities hard. The most important topic for universities during the pandemic was of course distance and remote learning, which become widespread already. The pandemic will cause profound impacts and changes on the higher education system around the world in terms of education-teaching methods, research, internationalization and mobility. The response of Council of Higher Education of Turkey to pandemic could be evaluated in four phases: 1) Close monitoring, 2) Preparing, 3) Action, 4) New normalization. In this paper we will review the response of Turkish higher education institutions and Council of Higher Education to the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

• Ventilation rate and arrangement are crucial to prevent airborne disease in healthcare facilities. • Local ventilation is practical for supply fresh air and diluting contaminants in high-risk areas. • Microplastics emerged as potential virus carriers in indoor environments, warrant further investigations. The prevalence and transmission of airborne infections pose significant challenges to public health, leading to consequential negative impacts. Ventilation strategies are crucial in mitigating airborne infections in healthcare settings, preventing the spread of infectious diseases, thus protecting patients and healthcare workers. While individual studies have explored various aspects of ventilation in healthcare settings, a synthesized overview of the current state of knowledge is lacking. Therefore, this study attempts to bridge the gap by conducting a review and bibliometric analysis based on 198 papers published within the recent decades (1993–2022). The findings will provide a holistic understanding on the innovative interventions and identify areas for future research, thereby contributing to the development of effective guidelines for controlling airborne infections. The co-authorship analysis revealed that China emerged as the main contributor in the current research field due to the keen interest of researchers and the substantial financial support provided by funding agencies. The keyword co-occurrence analysis identified three primary research hotspots within the field, (i) the optimized ventilation strategies for combating airborne infection, (ii) the transition towards the application of computational fluid dynamics (CFD) in analyzing airflow patterns, and, (iii) the characteristics of airborne particles and their impact on infection transmission. The present review highlights the significance of local ventilation as an additional measure to the main ventilation system for enhanced dilution of contaminants in high-risk areas. The transition towards dynamic airflow analysis considered human movement's effect, allowing for more precise assessments and tailored ventilation strategies optimization. The review also underscores the emerging concern of microplastic as an indoor contaminant with potential implications as a virus carrier, emphasizing further research on its behavior and impact on airborne infection transmission. [ABSTRACT FROM AUTHOR]

This paper examines the impact of female analysts' coverage on firm's philanthropic activities amidst the outbreak of the COVID-19 pandemic in China. Using a hand-collected dataset of corporate philanthropy, the paper provides robust evidence that firms covered by female analysts are more likely to contribute actively to the well-being of societies by increasing corporate donation. This positive relationship is more pronounced if the company is privately controlled or covered by female analysts with more working experience, or located in more infectious provinces. Overall, our findings call for more female analyst recruitment, yielding benefits of pressuring firms to engage in philanthropy. • Firms covered by more female analysts provide more donation during the pandemic. • This positive impact is more pronounced if analysts have more working experience. • The positive relationship is more pronounced if a firm is privately controlled. • The positive impact is more pronounced if a firm is in more infectious province. • Firm site visit is the channel that female analysts affect corporate donation. [ABSTRACT FROM AUTHOR]

Coronavirus disease 2019 (COVID-19), first reported in December 2019 in Wuhan, China, has progressed to a pandemic associated with substantial morbidity and mortality. Little is known about the healthcare workers who died fighting the disease in China. This paper analyzed the data of 78 Chinese healthcare workers who died in the fight against COVID-19 between 23 January and 2 June, 2020, and revealed the following characteristics. First, compared to the number of deaths directly attributable to COVID-19, more healthcare workers died from pre-existing disease attack induced by excessive fatigue or died from accidents. Second, the median age of the healthcare workers who died directly from COVID-19 was younger than that of the Wuhan non- healthcare workers who died of COVID 19. Third, although more women than men were involved in fighting the pandemic, more men died. Fourth, more healthcare workers died in Hubei than in other provinces. Fifth, most of the healthcare workers who died directly from COVID-19 were non-professionals. [ABSTRACT FROM AUTHOR]

Apart from the direct health and behavioral influence of the COVID-19 pandemic itself, COVID-19 rumors as an infodemic enormously amplified public anxiety and cause serious outcomes. Although factors influencing such rumors propagation have been widely studied by previous studies, the role of spatial factors (e.g., proximity to the pandemic) on individuals' response regarding COVID-19 rumors remain largely unexplored. Accordingly, this study, drawing on the stimulus-organism-response (SOR) framework, examined how proximity to the pandemic (stimulus) influences anxiety (organism), which in turn determines rumor beliefs and rumor outcomes (response). Further, the contingent role of social media usage and health self-efficacy were tested. The research model was tested using 1246 samples via an online survey during the COVID-19 pandemic in China. The results indicate that: (1)The proximity closer the public is to the pandemic, the higher their perceived anxiety; (2) Anxiety increases rumor beliefs, which is further positively associated rumor outcomes; (3) When the level of social media usage is high, the relationship between proximity to the pandemic and anxiety is strengthened; (4) When the level of health self-efficacy is high, the effect of anxiety on rumor beliefs is strengthened and the effect of rumor beliefs on rumor outcomes is also strengthened. This study provides a better understanding of the underlying mechanism of the propagation of COVID-19 rumors from a SOR perspective. Additionally, this paper is one of the first that proposes and empirically verifies the contingent role of social media usage and health self-efficacy on the SOR framework. The findings of study can assist the pandemic prevention department in to efficiently manage rumors with the aim of alleviating public anxiety and avoiding negative outcomes cause by rumors. [ABSTRACT FROM AUTHOR]

An outbreak of coronavirus disease (COVID-19) in Wuhan, China caused by SARS-CoV-2 has led to a serious epidemic in China and other countries, resulting in worldwide concern. With active efforts of prevention and control, more and more patients are being discharged. However, how to manage these patients normatively is still challenging. This paper reports an asymptomatic discharged patient with COVID-19 who retested positive for SARS-CoV-2, which arouses concern regarding the present discharge standards of COVID-19. [ABSTRACT FROM AUTHOR]

The papers in this special section focus on modeling and simulation in disaster and emergency management. The COVID-19 pandemic of 2020 has wrought damage globally to a degree that is totally unprecedented in human history.Within three months that the first known death from the illness caused by the so-called novel Coronavirus was reported by state media in China on the 11th of January 2020, close to 1.8 million people worldwide had tested positive, with more than 100,000 deaths reported. By the time the pandemic will have been declared to be under control, millions more are expected to have been infected and tens (possibly hundreds) of thousands more will have perished as a result of the disease. Not only are the numbers of COVID-19 infected people and deaths staggering. Economies throughout the world have suffered tremendously, with many companies forced to shut down, or at the very least significantly scale down, their production of goods and services as a result of the need for social distancing and self-isolation required to prevent the unmitigated transmission of the disease. All this has led to hundreds of millions worldwide losing their employment or other sources of livelihood. Governments of all nations, large and small, developed and underdeveloped, have needed to pump into their economy and onto their struggling citizens whatever financial resources they could manage to generate. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has impacted electricity consumption patterns and such an impact cannot be analyzed by simple data analytics. In China, specifically, city lock-down policies lasted for only a few weeks and the spread of COVID-19 was quickly under control. This has made it challenging to analyze the hidden impact of COVID-19 on electricity consumption. This paper targets the electricity consumption of a group of regions in China and proposes a new clustering-based method to quantitatively investigate the impact of COVID-19 on the industrial-driven electricity consumption pattern. This method performs K-means clustering on time-series electricity consumption data of multiple regions and uses quantitative metrics, including clustering evaluation metrics and dynamic time warping, to quantify the impact and pattern changes. The proposed method is applied to the two-year daily electricity consumption data of 87 regions of Zhejiang province, China, and quantitively confirms COVID-19 has changed the electricity consumption pattern of Zhejiang in both the short-term and long-term. The time evolution of the pattern change is also revealed by the method, so the impact start and end time can be inferred. Results also show the short-term impact of COVID-19 is similar across different regions, while the long-term impact is not. In some regions, the pandemic only caused a time-shift in electricity consumption; but in others, the electricity consumption pattern has been permanently changed. The data-driven analysis of this paper can be the first step to fully interpret the COVID-19 impact by considering economic and social parameters in future studies. [ABSTRACT FROM AUTHOR]

Nitrogen dioxide (NO 2) is an important target for monitoring atmospheric quality. Deriving ground-level NO 2 concentrations with much finer resolution, it requires high-resolution satellite tropospheric NO 2 column as input and a reliable estimation algorithm. This paper aims to estimate the daily ground-level NO 2 concentrations over China based on machine learning models and the TROPOMI NO 2 data with high spatial resolution. In this study, four tree-based algorithm machine learning models, decision trees (DT), gradient boost decision tree (GBDT), random forest (RF) and extra-trees (ET), were used to estimate ground-level NO 2 concentrations. In addition to considering many influencing factors of the ground-level NO 2 concentrations, we especially introduced simplified temporal and spatial information into the estimation models. The results show that the extra-trees with spatial and temporal information (ST-ET) model has great performance in estimating ground-level NO 2 concentrations with a cross-validation R2 of 0.81 and RMSE of 3.45 μg/m3 in test datasets. The estimated results for 2019 based on the ST-ET model achieves a satisfactory accuracy with a cross-validation R2 of 0.86 compared with the other models. Through time-space analysis and comparison, it was found that the estimated high-resolution results were consistent with the ground observed NO 2 concentrations. Using data from January 2020 to test the prediction power of the models, the results indicate that the ST-ET model has a good performance in predicting ground-level NO 2 concentrations. Taking four ground-level NO 2 concentrations hotspots as examples, the estimated ground-level NO 2 concentrations and ground-based observation data during the coronavirus disease (COVID-19) pandemic were lower compared with the same period in 2019. The findings offer a solid solution for accurately and efficiently estimating ground-level NO 2 concentrations by using satellite observations, and provide useful information for improving our understanding of the regional atmospheric environment. • The spatiotemporal information can enhance the estimated ability of the models. • ST-ET model has high accuracy in estimating ground NO 2 concentration with high R2 and low RMSE. • The estimated results can capture the spatial and temporal pattern especially during the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic lockdowns led to a sharp drop in socio-economic activities in China in 2020, including reductions in fossil fuel use, industry productions, and traffic volumes. China's economy suffered a serious negative effect from COVID-19. However, there is a "positive effect" on CO 2 emissions reduction. Here, for the first time, this paper constructs a new model named "Weighted Multi-regional Hypothetical Extraction Method (WMHEM)" based on a multiregional input-output model. It not only solves the problems of traditional HEM methods such as improper use of assumptions, excessive reliance on industry intermediate input, but also accurately reflects the impact of external shocks on the inter-industry linkages. By using the monthly economic data of each provinces in China during COVID-19 (except Hong Kong，Macao and Taiwan) an the latest Multi-regional input-output tables, the "economic negative effect" and "CO 2 emission positive effect" under COVID-19 in China are measured. Results show that COVID-19 lockdown was estimated to have reduced China's CO 2 emissions substantially between January and March in 2020, with the largest reductions in February. With the spread of coronavirus controlled, China's CO 2 emissions rebounded in April. In addition, key emission reduction sectors and key development encouraged sectors are selected by combining "economic negative effect" and "CO 2 emission positive effect" during COVID-19. Therefore, policies recommendations are put forward based on forward and backward linkages respectively which are from two ends of the supply chain to turn pandemic-related CO 2 emissions declines into firm climate action. • COVID-19 has negative effects on economy, but positive effects on CO 2 emissions. • A new model is used to measure economic and CO 2 emission effects during COVID-19. • Hubei had the biggest economic and CO 2 emission effects on China due to COVID-19. • Economic and CO 2 emission effects of COVID-19 are combined to select key sectors. [ABSTRACT FROM AUTHOR]

The research of traffic revitalization index can provide support for the formulation and adjustment of policies related to urban management, epidemic prevention and resumption of work and production. This paper proposes a deep model for the prediction of urban Traffic Revitalization Index (DeepTRI). The DeepTRI builds model for the data of COVID-19 epidemic and traffic revitalization index for major cities in China. The location information of 29 cities forms the topological structure of graph. The Spatial Convolution Layer proposed in this paper captures the spatial correlation features of the graph structure. The special Graph Data Fusion module distributes and fuses the two kinds of data according to different proportions to increase the trend of spatial correlation of the data. In order to reduce the complexity of the computational process, the Temporal Convolution Layer replaces the gated recursive mechanism of the traditional recurrent neural network with a multi-level residual structure. It uses the dilated convolution whose dilation factor changes according to convex function to control the dynamic change of the receptive field and uses causal convolution to fully mine the historical information of the data to optimize the ability of long-term prediction. The comparative experiments among DeepTRI and three baselines (traditional recurrent neural network, ordinary spatial–temporal model and graph spatial–temporal model) show the advantages of DeepTRI in the evaluation index and resolving two under-fitting problems (under-fitting of edge values and under-fitting of local peaks). [ABSTRACT FROM AUTHOR]

• A deep learning COVID-19 diagnostic system is developed using state-of-the-art deep learning architectures. • A detailed analysis of different methods used for detecting COVID-19 is presented in this paper. • Chest X-rays from different sources are collected to build a robust classification model. • The developed diagnostic model provided efficient results, even with a limited number of input images. • The results produced in this work are validated by an expert radiologist. In recent months, a novel virus named Coronavirus has emerged to become a pandemic. The virus is spreading not only humans, but it is also affecting animals. First ever case of Coronavirus was registered in city of Wuhan, Hubei province of China on 31st of December in 2019. Coronavirus infected patients display very similar symptoms like pneumonia, and it attacks the respiratory organs of the body, causing difficulty in breathing. The disease is diagnosed using a Real-Time Reverse Transcriptase Polymerase Chain reaction (RT-PCR) kit and requires time in the laboratory to confirm the presence of the virus. Due to insufficient availability of the kits, the suspected patients cannot be treated in time, which in turn increases the chance of spreading the disease. To overcome this solution, radiologists observed the changes appearing in the radiological images such as X-ray and CT scans. Using deep learning algorithms, the suspected patients' X-ray or Computed Tomography (CT) scan can differentiate between the healthy person and the patient affected by Coronavirus. In this paper, popular deep learning architectures are used to develop a Coronavirus diagnostic systems. The architectures used in this paper are VGG16, DenseNet121, Xception, NASNet, and EfficientNet. Multiclass classification is performed in this paper. The classes considered are COVID-19 positive patients, normal patients, and other class. In other class, chest X-ray images of pneumonia, influenza, and other illnesses related to the chest region are included. The accuracies obtained for VGG16, DenseNet121, Xception, NASNet, and EfficientNet are 79.01%, 89.96%, 88.03%, 85.03% and 93.48% respectively. The need for deep learning with radiologic images is necessary for this critical condition as this will provide a second opinion to the radiologists fast and accurately. These deep learning Coronavirus detection systems can also be useful in the regions where expert physicians and well-equipped clinics are not easily accessible. [ABSTRACT FROM AUTHOR]

This paper examines and projects the water use and wastewater generation during and after the SARS-CoV-2 (COVID-19) in China, and discussed the water use/wastewater generation pattern changes among different sectors. Existing studies on the impact of pandemic spread-prevention measures on water consumption and wastewater treatment during the pandemic are reviewed. The water use and wastewater discharge in China through the COVID-19 period are then projected and analyzed using Multivariate Linear Regression. The projection is carried out for years 2019–2023 and covers an (estimated) full process of pre-pandemic, pandemic outbreak, and recovery phase and provides essential information for determining the complete phase impact of the COVID-19. Two scenarios, i.e. the recovery scenario and the business as usual scenario, are set to investigate the water use and wastewater generation characteristics after the pandemic. The results imply that in both scenarios, the water use in China shows a V-shaped trend from 2019 to 2023 and reached a low point in 2020 of 5,813✕108 m3. The wastewater discharge shows an increasing trend throughout the COVID period in both scenarios. The results are also compared with the water consumption and wastewater generation during the SARS-CoV-1 period. The implication for policymakers is the possible increase of water use and wastewater discharge in the post COVID period and the necessity to ensure the water supply and control of water pollution and wastewater discharge. • The impact of the SARS-CoV-2 pandemic on the water sector are reviewed. • Water use and wastewater discharge throughout the COVID-19 period are projected. • Water use in China shows a V-shaped trend from 2019 to 2023. • Wastewater discharge shows an increasing trend throughout the COVID period. • Water use and wastewater discharge during the SARS and the COVID-19 are compared. [ABSTRACT FROM AUTHOR]

The COVID-19 outbreak has led to a substantial loss of human life throughout the world and has a tremendous impact on healthcare services. Industry 4.0 technologies have established effective supply chain management towards the fulfillment of customized demands in the healthcare field. In addition, the internet of things, artificial intelligence, big data analytics, and 3D printing have been extensively used to combat the COVID-19 pandemic and assist in providing value-added services in the healthcare sector. Henceforth, this paper presents a scientometric analysis on the literature of aforementioned Industry 4.0 technologies in the context of COVID-19. It provides extensive insights into co-citation and co-occurrence analysis of high cited publications, participating countries, influential authors, prolific journals, and keywords using the CiteSpace tool. The analyses reveal that China has produced the highest research outputs, although India is the most collaborative country in this field. The current research hotspots include supply chain, 4D printing, and social distancing technologies. Furthermore, it explores emerging trends, intellectual structure of publications, research frontiers, and potential research directions for further work in the Industry 4.0 assisted healthcare domain. [Display omitted] • Industry 4.0 technologies have emerged in the healthcare domain as an area of growing interest in academia and industry discipline. • Artificial intelligence, big data analytics, internet of things and 3D printing have been extensively used to control the COVID-19 spread and assist in providing value-added services in the healthcare sector. • It explores emerging trends, intellectual structure of publications, and potential research directions for further work in the Industry 4.0 assisted healthcare domain. [ABSTRACT FROM AUTHOR]

Achieving the peak of carbon dioxide (CO 2) emissions requires a large amount of green and low-carbon investment. Whether the green finance system can efficiently support the capital need for achieving the CO 2 emissions target in the context of the COVID-19 epidemic is a matter of concern. This paper constructs a system dynamics model (SD model) to illustrate the quantitative relationship between the green finance system and CO 2 emissions and introduce the COVID-19 epidemic as a variable to analyze ten simulation scenarios regarding the carbon emissions commitment realization under different green finance and economic growth status. It is shown that: (1) Regardless of the impact of COVID-19, China can meet its commitment by reaching its CO 2 emissions peak in 2030 and realizing a 20% non-fossil energy proportion in 2025; (2) Under the impact of the epidemic, the goal can not be obtained in all energy consumption scenarios when the government expenditure on the environment is low. The target year of reaching CO 2 emissions peak becomes 2033, 2037, and 2040. The results indicate that reducing government expenditure on environment protection makes the CO 2 emissions peak target less likely to be achieved within a given time frame. We also concluded with important policy implications according to the result of the simulations. Overall, this study makes a reference for other economies and researchers to quantitatively predict the interaction relationship between the green finance system and CO 2 emissions in the context of COVID-19, which provides policymakers with insights into a joint power of energy consumption upgrading and green capital guidance. • Providing empirical studies regarding the mechanism of the inhibitory effect of green finance on CO 2 emissions. • Using systems dynamics model to address the missing data problem. • Making solid suggestions under COVID-19 Epidemic. • Building the relationship between the green finance system and CO 2 emissions and making forecasts for "3060" mission result. [ABSTRACT FROM AUTHOR]

Socio-economic development is undergoing changes in China, such as the recently proposed carbon peak and carbon neutral targets, new infrastructure development strategy and the Coronavirus disease 2019 (COVID-19) pandemic. Meanwhile, the new-round marketization reform of the electricity industry has been ongoing in China since 2015. Therefore, it is urgent to evaluate the risk of electric power grid investment in China under new socio-economic development situation, which can help the investors manage risk and reduce risk loss. In this paper, a hybrid novel multi-criteria decision making (MCDM) method combining the latest group MCDM method, namely, Bayesian best–worst method (BBWM) and improved matter-element extension model (IMEEM) is proposed for risk evaluation of electric power grid investment in China under new socio-economic development situation. The BBWM is used for the weights' determination of electric power grid investment risk criteria, and the IMEEM is employed to rank risk grade of electric power grid investment. The risk evaluation index system of electric power grid investment is built, including economic, social, environmental, technical and marketable risks. The risk of electric power grid investment under new socio-economic development situation in Inner Mongolia Autonomous Region of China is empirically evaluated by using the proposed MCDM method, and the results indicate that it belongs to "Medium" grade, but closer to "High" grade. The main contributions of this paper include: (1) it proposes a hybrid novel MCDM method combining the BBWM and IMEEM for risk evaluation of electric power grid investment; and (2) it provides a new view for risk evaluation of electric power grid investment including economic, social, environmental, technical and marketable risks. The proposed hybrid novel MCDM method for the risk evaluation of electric power grid investment is effective and practical. [ABSTRACT FROM AUTHOR]

• Propose an innovative Bayesian model to control the risk of COVID-19 patients. • Propose two indicators to verify the feasibility and effectiveness of the model. • Provide the tool for risk assessment in the field of epidemics. In the context of the outbreak of coronavirus disease (COVID-19), this paper proposes an innovative and systematic decision support model based on Bayesian networks (BNs) to identify and control the risk of COVID-19 patients spreading the virus, which requires the following three steps. First, by consulting the related literature and combining this with expert knowledge, we identify and classify the characteristics (risk factors) of COVID-19 and obtain a conceptual framework for COVID-19 Risk Assessment Bayesian Networks (CRABNs). Second, data on COVID-19 patients with expert scoring results on patient risk levels were collected from hospitals in Hubei Province of China and are used as the training set, and the structure and parameters of the CRABNs model are obtained through machine learning. Finally, we propose two indicators, namely, Model Bias and Model Accuracy, and use the remaining data to verify the feasibility and effectiveness of the CRABNs model to ensure that there are no significant differences between the predicted results of the model and the actual results provided by experts who have relevant experience in treating COVID-19. At the same time, we compared the CRABNs model with the support vector machine (SVM), random forest (RF), and k-nearest neighbour (KNN) models through four indicators: accuracy, sensitivity, specificity, and F-score. The results suggest the reliability of the model and show that it has promising application potential. The proposed model can be used globally by doctors in hospitals as a decision support tool to improve the accuracy of assessing the severity of COVID-19 symptoms in patients. Furthermore, with the further improvement of the model in the future, it can be used for risk assessments in the field of epidemics. [ABSTRACT FROM AUTHOR]

Corporate financial distress prediction research has been ongoing for more than half a century, during which many models have emerged, among which ensemble learning algorithms are the most accurate. Most of the state-of-the-art methods of recent years are based on gradient boosted decision trees. However, most of them do not consider using feature importance for feature selection, and a few of them use the feature importance method with bias, which may not reflect the true importance of features. To solve this problem, a heuristic algorithm based on permutation importance (PIMP) is proposed to modify the biased feature importance measure in this paper. This method ranks and filters the features used by machine learning models, which not only improves accuracy but also makes the results more interpretable. Based on financial data from 4,167 listed companies in China between 2001 and 2019, the experiment shows that compared with using the random forest (RF) wrapper method alone, the bias in feature importance is indeed corrected by combining the PIMP method. After the redundant features are removed, the performance of most machine learning models is improved. The PIMP method is a promising addition to the existing financial distress prediction methods. Moreover, compared with traditional statistical learning models and other machine learning models, the proposed PIMP-XGBoost offers higher prediction accuracy and clearer interpretation, making it suitable for commercial use. • The model Combines a corrected feature selection measure and XGBoost. • Permutation importance can correct the bias of feature importance. • The model is validated on Chinese listed companies datasets over five metrics. • The model is proved to outperform several benchmark techniques. • The feature importance and partial dependence plot enhance model interpretation. [ABSTRACT FROM AUTHOR]

• A transfer learning framework and a CNN model are proposed for the black swan small-sample events. • (BEST-L). • Experiments show that the BEST-L outperforms traditional methods. • The transfer learning method can effectively use small samples to improve the model's generalization. • performance. • The BEST-L is tested for the load forecasting case with the COVID-19 scenario in Central China. The black swan event will usually cause a great impact on the normal operation of society. The scarcity of such events leads to a lack of relevant data and challenges in dealing with related problems. Different situations also make the traditional methods invalid. In this paper, a transfer learning framework and a convolutional neuron network are proposed to deal with the black swan small-sample events (BEST-L). Taking the COVID-19 as a typical black swan event, the BEST-L is utilized to achieve accurate mid-term load forecasting using the relationship between economy and electricity consumption. The experiment results show that the transfer learning model can effectively learn the basic knowledge about the relationship between the adopted input and output data and use a relatively small amount of data during the black swan event to improve the target areas' generalization. The approach and results can provide an effective approach to respond and react to sudden changes quickly and effectively in similar open problems. [ABSTRACT FROM AUTHOR]

COVID-19 has had a huge impact on many industries around the world. Internationally-funded enterprises have been greatly affected by COVID-19 prevention and control measures, such as border controls. However, few studies have examined the impact of COVID-19 on internationally-funded enterprises. To this end, this paper considered 12 of China's industrial parks situated in Southeast Asia, while comparing the operation status before and after the outbreak of COVID-19 based on remote sensing of nighttime lights (NTL). The NTL is generally used as a proxy for economic activity. First, six parameters were proposed to quantify and monitor the operation status based on NTL data. Subsequently, these parameters were calculated for the parks and for 10 km buffer zones surrounding them to analyze the differences in operating conditions. The results showed that (1) despite the negative impact of COVID-19, 9 out of the 12 parks had a mean NTL greater than 1, indicating that these parks are in better operating condition in 2020 than 2019; (2) 7 out of the 10 km buffer zones around the parks showed a decline in mean NTL. Only three parks showed a decline in mean NTL. The impact of COVID-19 on surrounding areas was greater than the impact on parks. [ABSTRACT FROM AUTHOR]

Emotional intelligence is a main area in educational psychology and a key factor in the academic life of students. It deals with deviant behavior through self-awareness and self-motivation, regulates emotional and social skills, and converts emotional energy into positive energy. This study examined direct and indirect relationships between emotional intelligence and study habits in blended learning environments. Blended learning is conceptualized as a hybrid learning approach that combines online learning opportunities and the traditional classroom approach. Furthermore, the study explored the mediating role of cognitive engagement in the relationship between emotional intelligence and study habits. We used 26 items in a paper-based questionnaire in a quantitative study to collect data on emotional intelligence, cognitive engagement and study habits from health sciences students (N = 338) enrolled in blended learning courses in universities in the Hunan province of China. Emotional intelligence included self-awareness, self-motivation, and the regulation of emotion; social skills were also examined. A partial least squares structural-equation modeling approach was applied through SmartPLS software to explore the relationships. The results indicate that self-awareness and self-motivation have direct, significant, and positive connections with study habits. Similarly, the results indicate that all four dimensions of emotional intelligence (self-awareness, self-motivation, emotion regulation and social skills) had indirect, significant, and positive relationships with study habits using cognitive engagement as a mediator variable. It was concluded that students face higher-than-usual challenges in building study habits in blended learning environments during the COVID-19 pandemic, and that emotional intelligence helps them to develop their study habits to greater effect. Similarly, it was concluded that cognitive engagement strengthens the connection between emotional intelligence and study habits. Therefore, it is recommended that universities take specific measures to enhance students' emotional intelligence and cognitive engagement, which will ultimately improve their study habits. Moreover, valuable and practical implications for teachers, practitioners, and university management were also discussed in the study. [ABSTRACT FROM AUTHOR]

LINKED CONTENT This article is linked to Rhodes et al and Liu & Li papers. To view these articles, visit https://doi.org/10.1111/apt.15777 and https://doi.org/10.1111/apt.16048 [ABSTRACT FROM AUTHOR]

LINKED CONTENT This article is linked to Rhodes et al papers. To view these articles, visit https://doi.org/10.1111/apt.15777 and https://doi.org/10.1111/apt.16058 [ABSTRACT FROM AUTHOR]

In November 2019, the novel coronavirus disease COVID-19 was reported in Wuhan city, China, and was reported in other countries around the globe. COVID-19 is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Strategies such as contact tracing and a vaccination program have been imposed to keep COVID-19 under control. Furthermore, a fast, noninvasive and reliable testing device is needed urgently to detect COVID-19, so that contact can be isolated and ringfenced before the virus spreads. Although the reverse transcription polymerase chain reaction (RT-PCR) test is considered the gold standard method for the diagnosis of SARS-CoV-2 infection, this test presents some limitations which cause delays in detecting the disease. The antigen rapid test (ART) test, on the other hand, is faster and cheaper than PCR, but is less sensitive, and may limit SARS-CoV-2 detection. While other tests are being developed, accurate, noninvasive and easy-to-use testing tools are in high demand for the rapid and extensive diagnosis of the disease. Therefore, this paper reviews current diagnostic methods for COVID-19. Following this, we propose the use of expired carbon dioxide (CO2) as an early screening tool for SARS-CoV-2 infection. This system has already been developed and has been tested on asthmatic patients. It has been proven that expired CO2, also known as capnogram, can help differentiate between respiratory conditions and, therefore, could be used to detect SARS-CoV-2 infection, as it causes respiratory tract-related diseases. [ABSTRACT FROM AUTHOR]

Healthcare researchers have been working on mortality prediction for COVID-19 patients with differing levels of severity. A rapid and reliable clinical evaluation of disease intensity will assist in the allocation and prioritization of mortality mitigation resources. The novelty of the work proposed in this paper is an early prediction model of high mortality risk for both COVID-19 and non-COVID-19 patients, which provides state-of-the-art performance, in an external validation cohort from a different population. Retrospective research was performed on two separate hospital datasets from two different countries for model development and validation. In the first dataset, COVID-19 and non-COVID-19 patients were admitted to the emergency department in Boston (24 March 2020 to 30 April 2020), and in the second dataset, 375 COVID-19 patients were admitted to Tongji Hospital in China (10 January 2020 to 18 February 2020). The key parameters to predict the risk of mortality for COVID-19 and non-COVID-19 patients were identified and a nomogram-based scoring technique was developed using the top-ranked five parameters. Age, Lymphocyte count, D-dimer, CRP, and Creatinine (ALDCC), information acquired at hospital admission, were identified by the logistic regression model as the primary predictors of hospital death. For the development cohort, and internal and external validation cohorts, the area under the curves (AUCs) were 0.987, 0.999, and 0.992, respectively. All the patients are categorized into three groups using ALDCC score and death probability: Low (probability < 5%), Moderate (5% < probability < 50%), and High (probability > 50%) risk groups. The prognostic model, nomogram, and ALDCC score will be able to assist in the early identification of both COVID-19 and non-COVID-19 patients with high mortality risk, helping physicians to improve patient management. [ABSTRACT FROM AUTHOR]

The accurate and timely estimation of regional crop biomass at different growth stages is of great importance in guiding crop management decision making. The recent availability of long time series of remote sensing data offers opportunities for crop monitoring. In this paper, four machine learning models, namely random forest (RF), support vector machine (SVM), artificial neural network (ANN), and extreme gradient boosting (XGBoost) were adopted to estimate the seasonal corn biomass based on field observation data and moderate resolution imaging spectroradiometer (MODIS) reflectance data from 2012 to 2019 in the middle reaches of the Heihe River basin, China. Nine variables were selected with the forward feature selection approach from among twenty-seven variables potentially influencing corn biomass: soil-adjusted total vegetation index (SATVI), green ratio vegetation index (GRVI), Nadir_B7 (2105–2155 nm), Nadir_B6 (1628–1652 nm), land surface water index (LSWI), normalized difference vegetation index (NDVI), Nadir_B4 (545–565 nm), and Nadir_B3 (459–479 nm). The results indicated that the corn biomass was suitably estimated (the coefficient of determination (R2) was between 0.72 and 0.78) with the four machine learning models. The XGBoost model performed better than the other three models (R2 = 0.78, root mean squared error (RMSE) = 2.86 t/ha and mean absolute error (MAE) = 1.86 t/ha). Moreover, the RF model was an effective method (R2 = 0.77, RMSE = 2.91 t/ha and MAE = 1.91 t/ha), with a performance comparable to that of the XGBoost model. This study provides a reference for estimating crop biomass from MOD43A4 datasets. In addition, the research demonstrates the potential of machine learning techniques to achieve a relatively accurate estimation of daily corn biomass at a large scale. [ABSTRACT FROM AUTHOR]

[Display omitted] • Clean energy transitions based on the circular economy (CE) are analysed for a town in China. • This could reduce energy use, CO 2 emissions and PM 2.5 emissions compared to the BAU scenario. • COVID pandemic is a new opportunity to introduce circular economy measures. • Circular economy measures could accelerate clean energy transitions. This paper models the energy and emissions scenarios for a circular economy based clean energy transitions in a 140,000-population town in China, taking into account the new situation encountered by the COVID-19 pandemic. The modelled scenarios propose new clean energy transition roadmaps towards a sustainable urban system through the implementation of circular economy strategies. This is represented by the cascading use of industrial excess heat to form symbiosis between factories and to cover the growing building heat demand, as well as by the electrification of the transport sector and reusing the batteries for a second life as energy storage devices. The results show that for a circular economy scenario, during 2020–2040, an accumulated saving of 7.1 Mtoe final energy use (34%), a decline in 14.5 Mt CO 2 emissions (40%) and 592 t PM 2.5 emissions (43%) could be achieved compared with the business-as-usual scenario. The outcomes of the circular economy strategies are at least 7% better than the new policy scenario which simply has energy efficiency improvements. The outbreak of the COVID-19 tremendously impacts the socio-economic activities in the town. If taking the pandemic as an opportunity to enhance the circular economy, by 2040, compared with the scenario without introducing circular economy measures, the extra avoided final energy use, CO 2 emissions and PM 2.5 emissions could be 1.6 Mtoe (8%), 3.8 Mt (11%) and 229 t (17%) respectively. [ABSTRACT FROM AUTHOR]

The aim of this paper is the generation of a time-series based statistical data-driven procedure in order to track an outbreak. At first are used univariate time series models in order to predict the evolution of the reported cases. Moreover, are considered combinations of the models in order to provide more accurate and robust results. Additionally, statistical probability distributions are considered in order to generate future scenarios. Final step is the build and use of an epidemiological model (tSIR) and the calculation of an epidemiological ratio (R 0) for estimating the termination of the outbreak. The time series models include Exponential Smoothing and ARIMA approaches from the classical models, also Feed-Forward Artificial Neural Networks and Multivariate Adaptive Regression Splines from the machine learning toolbox. Combinations include simple mean, Newbolt-Granger and Bates-Granger approaches. Finally, the tSIR model and the R 0 ratio are used for estimating the spread and the reversion of the pandemic. The suggested procedure is used to track the COVID-19 epidemic in Greece. This epidemic has appeared in China in December 2019 and has been widespread since then to all over the world. Greece is the center of this empirical study as is considered an early successful paradigm of resistance against the virus. [ABSTRACT FROM AUTHOR]

In this paper, we report the results obtained from one year of real-time measurement (i.e., from December 2019 to November 2020) of atmospheric black carbon (BC) under a rural environment in Qingdao of Northeastern China. The annual average concentration of BC was 1.92 ± 1.89 μg m−3. The highest average concentration of BC was observed in winter (3.65 ± 2.66 μg m−3), followed by fall (1.73 ± 1.33 μg m−3), spring (1.53 ± 1.33 μg m−3), and summer (0.83 ± 0.56 μg m−3). A clear weekend effect was observed in winter, which was characterized by higher BC concentration (4.60 ± 2.86 μg m−3) during the weekend rather than that (3.22 ± 2.45 μg m−3) during weekdays. The influence of meteorological parameters, including surface horizontal wind speed, boundary layer height (BLH), and precipitation, on BC, was investigated. In particular, such BLH influence presented evidently seasonal dependence, while there was no significant seasonality for horizontal wind speed. These may reflect different roles of atmospheric vertical dilution on affecting BC in different seasons. The △BC/△CO ratio decreased with the increase of precipitation, indicative of the influence of below-cloud wet removal of BC, especially during summertime where rainfall events more frequently occurred than any of other seasons. The bivariate-polar-plot analysis showed that the high BC concentrations were mainly associated with low wind speed in all seasons, highlighting an important BC source originated from local emissions. By using concentration-weighted trajectory analysis, it was found that regional transports, especially from northeastern in winter, could not be negligible for contributing to BC pollution in rural Qingdao. In the coronavirus disease 2019 (COVID−19) case analysis, we observed an obvious increase in the BC/NO2 ratio during the COVID-19 lockdown, supporting the significant non-traffic source sector (such as residential coal combustion) for BC in rural Qingdao. [ABSTRACT FROM AUTHOR]

The Novel Coronavirus Disease 2019 (COVID-19) has swept the world and caused a global pandemic. SARS-CoV-2 seems to have originated from bats as their reservoir hosts over time. Similar to SARS-CoV, this new virus also exerts its action on the human angiotensin-converting enzyme 2. This action causes infections in cells and establishes an infectious disease, COVID-19. Against this viral invasion, the human body starts to activate the innate immune system in producing and releasing proinflammatory cytokines such as IL-6, IL-1β, IL-8, TNF-α, and other chemokines, such as G-CSF, IP10 and MCPl, which all develop and increase the inflammatory response. In cases of COVID-19, excessive inflammatory responses occur, and exaggerated proinflammatory cytokines and chemokines are detected in the serum, resulting in cytokine release syndrome or cytokine storm. This causes coagulation abnormalities, excessive oxidation developments, mitochondrial permeability transition, vital organ damage, immune system failure and eventually progresses to disseminated intravascular coagulation and multiple organ failure. Additionally, the excessive inflammatory responses also cause mitochondrial dysfunction due to progressive and persistent stress. This damages cells and mitochondria, leaving products containing mitochondrial DNA and cell debris involved in the excessive chronic inflammation as damage-associated molecular patterns. Thus, the respiratory infection progressively leads to disseminated intravascular coagulation from acute respiratory distress syndrome, including vascular endothelial cell damage and coagulation-fibrinolysis system disorders. This condition causes central nervous system disorders, renal failure, liver failure and, finally, multiple organ failure. Regarding treatment for COVID-19, the following are progressive and multiple steps for mitigating the excessive inflammatory response and subsequent cytokine storm in patients. First, administering of favipiravir to suppress SARS-CoV-2 and nafamostat to inhibit ACE2 function should be considered. Second, anti-rheumatic drugs (monoclonal antibodies), which act on the leading cytokines (IL-1β, IL-6) and/or cytokine receptors such as tocilizumab, should be administered as well. Finally, melatonin may also have supportive effects for cytokine release syndrome, resulting in mitochondrial function improvement. This paper will further explore these subjects with reports mostly from China and Europe. [ABSTRACT FROM AUTHOR]

Since December 2019, a new form of coronavirus, SARS-CoV-2, has spread from China to the whole word, raising concerns regarding Coronavirus Disease 2019 (COVID-19) endangering public health and life. Over 1.5 million deaths related with COVID-19 have been recorded worldwide, with wide variations among countries affected by the pandemic and continuously growing numbers. The aim of this paper was to provide an overview of the literature cases of deaths involving COVID-19 and to evaluate the application of the COVID-19 Significance Score (CSS) in the classification of SARS CoV-2-related fatalities, comparing it with the Hamburg rating scale. The results obtained allowed us to highlight that CSS used after a complete accurate post-mortem examination, coupled to the retrieval of in vivo data, post-mortem radiology, histology and toxicology, as well as to additional required analyses (e.g., electronic microscopy) is a useful and concise tool in the assessment of the cause of death and the role played by this virus. A shared use of this scale might hopefully lower the inhomogeneities in forensic evaluation of SARS CoV-2-related fatalities. [ABSTRACT FROM AUTHOR]

With the coronavirus pandemic wreathing havoc around the world, power industry has been hit hard due to the proposal of lockdown policies. However, the impact of lockdowns and shutdowns on the power system in different regions as well as periods of the pandemic can hardly be reflected on the foundation of current studies. In this paper, a prediction-based analysis method is developed to point out the electricity consumption gap resulted from the pandemic situation. The core of this method is a novel optimized grey prediction model, namely Rolling IMSGM(1,1) (Rolling Mechanism combined with grey model with initial condition as Maclaurin series), which achieves better prediction results in the face of long-term emergencies. A novel initial condition is adopted to track data with various characteristics in the form of higher-order polynomials, which are then determined by intelligent algorithms to realize accurate fitting. Historical power consumption data in China are utilized to carry out the monthly forecasts during COVID-19. Compared with other competitive models' prediction results, the superiority of IMSGM(1,1) are demonstrated. Through analyzing the gap between predicted consumption values and the actual data, it can be found that the impact of the pandemic on electricity varies in different periods, which is related to its severity and the local lockdown policies. This study helps to understand the impact on power industry in the face of such an emergency intuitively so as to respond to possible future events. [ABSTRACT FROM AUTHOR]