Relationship between Lightning, Lightning Fire and Human Activities

Lightning is the main source of natural fire, and lightning fire and other types of forest fires together constitute the global forest fire system. It is generally believed that lightning fire, as a natural fire source, has nothing to do with human beings and is different from man-made fire sources, but in fact, human activities have inextricable links with the occurrence of lightning fire. Since 2019, due to the severe impact of COVID-19 lockdowns, non-essential activities and mobility have decreased, which has led to a significant decrease in pollutant concentrations and lightning. In this paper, we linked the lightning fire with modernization process of human beings, the expansion of habitation, the change of underlying surface, the development of prediction technology and firefighting technology, and the laws and regulations of the country, to explore the impact of human activities on the occurrences of lightning and the forest lightning fire. Lightning is the fire source of the three elements in lightning fire occurrence, the lightning that can cause lightning fire is mainly cloud-to-ground lightning. The human activities in recent decades have profoundly affected the content of aerosols in environment. Aerosols are the main factors affecting lightning, and the large amount of pollution aerosols emitted from urban areas, soot aerosols emitted from biomass combustion and urban heat island effect have all increased the probability of lightning occurrence. The average annual ground lightning density of different land cover types is obviously different, and the construction land has the highest average annual ground lightning density. Intense lightning in forest areas has a higher density and slope. Most of the forests are located in high altitude areas, which is consistent with previous studies showing high lightning frequency in high altitude areas. The lightning in forests is intenser, steeper and more destructive, so forest areas are prone to lightning strikes. Lightning has the characteristic of selective discharge, that is, it will discharge into some special areas, which are also known as lightning selection areas, such as the place groundwater is exposed to the ground, where different conductive soils are connected, and where there are underground metal mines, such as copper and iron mines, and underground lake and water reservoir areas. Lightning strikes are caused by changes in soil conductivity caused by human activities such as mining waste rock sites, reservoir construction on mountain tops, and power transmission lines in mountainous areas. At the same time, due to the abundant trees in the mountainous area, it is also important to avoid the resulting lightning fire. With the development of lightning monitoring technology, a lightning location monitoring system has been established in some areas of China. Especially in 2021, the National Forestry and Grassland Administration launched the "Enlisting and Leading" emergency science and technology project of forest lightning fire prevention and control, and the project team has constructed a lightning fire sensing system in the Daxing'anling region with three-dimensional lightning full-wave detection network as the main body, covering the forest area of the Daxing'anling forest region, which can accurately locate the location of cloud-to-ground lightning in real time, improve the monitoring and warning ability of lightning fires, and improve the efficiency of lightning fire discovery. National laws and regulations indirectly affect lightning fires by affecting forest cover and climate change. This paper is expected to provide reference for the occurrence, prevention and control of forest lightning fire in the future, and provide a basis for the formulation of corresponding policies.

Application of Machine Learning in the Fight against the COVID-19 Pandemic: A Review

Introduction: Machine learning (ML) plays a significant role in the fight against the COVID-19 (officially known as SARS-CoV-2) pandemic. ML techniques enable the rapid detection of patterns and trends in large datasets. Therefore, ML provides efficient methods to generate knowledge from structured and unstructured data. This potential is particularly significant when the pandemic affects all aspects of human life. It is necessary to collect a large amount of data to identify methods to prevent the spread of infection, early detection, reduction of consequences, and finding appropriate medicine. Modern information and communication technologies (ICT) such as the Internet of Things (IoT) allow the collection of large amounts of data from various sources. Thus, we can create predictive ML-based models for assessments, predictions, and decisions. Method(s): This is a review article based on previous studies and scientifically proven knowledge. In this paper, bibliometric data from authoritative databases of research publications (Web of Science, Scopus, PubMed) are combined for bibliometric analyses in the context of ML applications for COVID-19. Aim(s): This paper reviews some ML-based applications used for mitigating COVID-19. We aimed to identify and review ML potentials and solutions for mitigating the COVID-19 pandemic as well as to present some of the most commonly used ML techniques, algorithms, and datasets applied in the context of COVID-19. Also, we provided some insights into specific emerging ideas and open issues to facilitate future research. Conclusion(s): ML is an effective tool for diagnosing and early detection of symptoms, predicting the spread of a pandemic, developing medicines and vaccines, etc.Copyright © 2022 Sciendo. All rights reserved.

Linguistic repercussions of COVID-19: A corpus study on four languages

The global reach of the COVID-19 pandemic and the ensuing localized policy reactions provides a case to uncover how a global crisis translates into linguistic discourse. Based on the JSI Timestamped Web Corpora that are automatically POS-tagged and accessible via SketchEngine, this study compares French, German, Dutch, and English. After identifying the main names used to denote the virus and its disease, we extracted a total of 1,697 associated terms (according to logDice values) retrieved from news media data from January through October 2020. These associated words were then organized into categories describing the properties of the virus and the disease, their spatio-temporal features and their cause-effect dependencies. Analyzing the output cross-linguistically and across the first 10 months of the pandemic, a fairly stable semantic discourse space is found within and across each of the four languages, with an overall clear preference for visual and biomedical features as associated terms, though significant diatopic and diachronic shifts in the discourse space are also attested.

The Association of Social Emotions, Perceived Efficiency, Transparency of the Government, Concerns about COVID-19, and Confidence in Fighting the Pandemic under the Week-Long Lockdown in Shenzhen, China.

The outbreak and spread of the COVID-19 pandemic have had a far-reaching impact. The present study investigated the factors primarily affecting the execution of the control measures, including social emotions, concerns about the pandemic, perceived efficiency, transparency of the government in publishing the pandemic-related information, and confidence in fighting the pandemic. Specifically, we examined the differences in these factors across four areas (i.e., lockdown area, control area, prevention area, and safe area) according to different COVID control measures under the week-long lockdown in Shenzhen. We found that social emotions, concerns about the pandemic, perceived efficiency of the government, and confidence in fighting the pandemic were more negative in the lockdown area than that in other areas. More importantly, after controlling for areas and education level of participants, the emotion of optimism, concerns about the COVID-19 pandemic, perceived efficiency, and perceived transparency of the government in releasing COVID-19 relevant information positively predicted confidence in fighting the pandemic, while anger negatively predicted confidence in fighting the pandemic. Therefore, the government and communities could make efforts at effective communication and find innovative approaches to make individuals (especially in the lockdown area) maintain social connections, reduce negative emotions, and enhance confidence in combating the pandemic.

Super-resolution wavelets for recovery of arbitrarily close point-masses with arbitrarily small coefficients

Three families of super-resolution (SR) wavelets Ψv,ng(x), Ψu,n,ms(x) and Ψw,n,mds(x), to be called Gaussian SR (GSR), spline SR (SSR) and dual-spline SR (DSSR) wavelets, respectively, are introduced in this paper for resolving the super-resolution problem of recovering any point-mass h(y)=∑ℓ=1Lcℓδ(y−σℓ), with ;σℓ−σk;≥η for ℓ≠k, σℓ≠0, and ;cℓ;>η⁎ for all ℓ,k=1,…,L, where η>0 and η⁎>0 are allowed to be arbitrarily small. Let Ψα,n=Ψv,ng, Ψu,n,ms or Ψw,n,mds, with α=v,u or w, respectively, where m=12,1,2,⋯ is suppressed. The SR wavelets are designed to have the n-th order of vanishing moments, with Fourier transform of their complex conjugates Ψ¯ˆα,n(x) to possess the following properties: (i) Ψ¯ˆα,n(x)≥0 for all x∈R, (ii) maxx⁡Ψ¯ˆα,n(x)=Ψ¯ˆα,n(κ)=ξn, where κ≐2.331122371 and ξ≐1.449222080 are positive constants independent of n,α and m, and (iii) the widths (or standard deviations) of Ψ¯ˆα,n(x), with center at κ, tends to 0 very fast for large values of α. While the most popular approach to resolve this super-resolution problem is to consider the Fourier transform d(x)=∑ℓ=1Lcℓe−iσℓx of h(y) as the “data function” for solving the inverse problem of recovering L, σ1,⋯,σL and c1,⋯,cL of the point-mass d(x), our proposed approach is to consider the “enhanced data function” D(a;α,n):=FΨα,n(a)=∑ℓ=1LcℓΨ¯ˆα,n(aσℓ), where FΨα,n(a), to be called the search function in this paper, is obtained by taking the continuous wavelet transform (CWT): (WΨα,nd)(t,a)=∫−∞∞Ψα,n(y−ta)‾d(y)dya of the data function d(x), with Ψα,n as the analysis wavelet, followed by applying wavelet thresholding to “de-noise” the data function d(x), by choosing an appropriate thresholding parameter γ>0, with γ<η⁎×ξn, in order not to remove any of the coefficients cℓ, where ℓ=1,⋯,L;and finally by setting t=0. Hence, the enhanced data function D(a;α,n) is at least cleaner than the data function d(x). In our proposed approach, instead of directly recovering σ1,⋯,σL as in the published literature, we propose a “divide and conquer” strategy: first by applying “bottom-up thresholding” of the search function FΨα,n(a), with thresholding parameter γ⁎>0 close to but not exceeding η⁎×ξn, to separate the set of the local extrema locations aℓ:=κσℓ of the function FΨα,n(a) in {a∈R:a≠0} into disjoint intervals of clusters, with more and smaller intervals and less number of local extrema aℓ in each interval for larger values of α;and secondly, by applying “top-down thresholding” to extract, one-by-one, of all local maxima, followed by all local minima (after a sign change), for each and every cluster. A desired leeway Δ>0 and lower bounds of the choice of the width parameter α are derived for the iterative application of top-down thresholding. Extension to Rs for s≥2 is also studied in this paper. For s=2, we observe that the imagery of the enhanced data function for a single point-mass at (σ1,σ2) where σ1,σ2≠0, resembles that of an “Airy disk” with center at (κ/σ1,κ/σ2) in light microscopy and celestial telescopy of point-masses.

Artificial Intelligence and Internet of Things (AI-IoT) Technologies in Response to COVID-19 Pandemic: A Systematic Review

The origin of the COVID-19 pandemic has given overture to redirection, as well as innovation to many digital technologies. Even after the progression of vaccination efforts across the globe, total eradication of this pandemic is still a distant future due to the evolution of new variants. To proactively deal with the pandemic, the health care service providers and the caretaker organizations require new technologies, alongside improvements in existing related technologies, Internet of Things (IoT), Artificial Intelligence (AI), and Machine Learning in terms of infrastructure, efficiency, privacy, and security. This paper provides an overview of current theoretical and application prospects of IoT, AI, cloud computing, edge computing, deep learning techniques, blockchain technologies, social networks, robots, machines, privacy, and security techniques. In consideration of these prospects in intersection with the COVID-19 pandemic, we reviewed the technologies within the broad umbrella of AI-IoT technologies in the most concise classification scheme. In this review, we illustrated that AI-IoT technological applications and innovations have most impacted the field of healthcare. The essential AI-IoT technologies found for healthcare were fog computing in IoT, deep learning, and blockchain. Furthermore, we highlighted several aspects of these technologies and their future impact with a novel methodology of using techniques from image processing, machine learning, and differential system modeling.

The need for a financial sector legal standard to support the NIST Cybersecurity Framework

The National Institute of Standards and Technology (NIST) published the NIST Framework for Improving Critical Infrastructure Cybersecurity of 2014, followed by an updated version in 2017. The Framework, which was developed as a joint effort between the U.S. Federal Government and the private sector, serves only as a guideline and is not mandated by any legal authority. Currently, adoption of the Framework is voluntary. The Financial Sector, one of sixteen Department of Homeland Security critical infrastructure sectors, should be incentivized to adopt the framework, based on inconsistency and accountability of best practices implementation across the sector. Global cyber attack opportunists used the 2020 COVID-19 pandemic to exploit cybersecurity vulnerabilities and gaps in the U.S. Financial Sector. The NIST Cybersecurity Framework provides guidelines for strengthening cybersecurity and identifies areas of potential cyber attack impacts. This paper is a summary of the author's published 2021 doctoral dissertation, which includes research and analysis of reported Financial Sector risks, failures and impacts due to weak or lack of cybersecurity controls. The study also provides analysis of success stories of Financial Sector and other entities which have adopted the NIST Cybersecurity Framework. Dr. Goodwin is a Senior Member of IEEE. © 2022 IEEE.

NICOV : a model to analyse impact of nutritional status and immunity on COVID-19.

A few months back there was no medication and vaccine for COVID-19. Yet, most of the infected people got recovered. A very small portion of the infected people could not recover. A lion's share of the fatal cases were the patients suffering from some kind of chronic critical diseases. Due to that, their nutritional status and immunity were not normal. In this study, we have proposed a model called NICOV (Nutritional status, Immunity and COVID) that establishes the relationship among nutritional status, immunity, and COVID-19. This model formulates the relations considering all possible states of nutritional status and immunity of the body. We have numerically simulated the model for four different sets of values and found that susceptible, infected, and recovered cases of COVID-19 are significantly related to different states of nutritional status and immunity. It is also evident from numerical simulation that the effect of nutritional status and immunity varies with variation of other parameters associated with the formulation of the model. This model can help the concerned in decision making for mitigation of the losses that arise due to COVID-19-like situations.

A Historical Perspective to Inform Strategic Planning for 2020 End-of-Year Wildland Fire Response Efforts

A severe outbreak of wildfire across the US Pacific Coast during August 2020 led to persistent fire activity through the end of summer. In late September, Fire Weather Outlooks predicted higher than usual fire activity into the winter in parts of California, with concomitant elevated fire danger in the Southeastern US. To help inform the regional and national allocation of firefighting personnel and equipment, we developed visualizations of resource use during recent late season, high-demand analogs. Our visualizations provided an overview of the crew, engine, dozer, aerial resource, and incident management team usage by geographic area. While these visualizations afforded information that managers needed to support their decisions regarding resource allocation, they also revealed a potentially significant gap between resource demand and late-season availability that is only likely to increase over time due to lengthening fire seasons. This gap highlights the need for the increased assessment of suppression resource acquisition and allocation systems that, to date, have been poorly studied.

Energy Management in the Multi-Source Systems

[...]it is clear by the policies and scenarios which are already laid out, that we need to investigate in detail and improve the existing technologies in an effective manner. [13] present a frequency splitting based energy management strategy in a microgrid consisting of a diesel generator, supercapacitors, wind turbine, solar photovoltaic generator, and lithium batteries. A dedicated building has been recommended for the electrode station for the sake of limiting energy consumption, preventing electrochemical corrosion, protecting from environmental constraints, and reducing the risk of fire hazards by increasing the fire resistance by installing a firefighting system. [...]a design process for the electrode station building has been proposed in detail.

Specialized Training on Sulphur Fire Fighting - Competency Enhancement

Multiple incidents in the Sulphur Handling facility in the past and significant findings from the investigations revealed the need for competency enhancement of employees and Central Fire Brigade team in terms of firefighting with specific reference to Sulphur fires. With immense support and leadership commitment, we focused to enhance the competency level of employees and Contractors those who are identified as first responders. We have undergone multilevel search to provide Sulphur firefighting techniques and concluded with a specialized third party training with an intensive core idea to conduct the training utilizing the firefighting equipment's and facilities available within the plant to make it lively and specific to the work environment. - 1st Time Specialized Sulphur Fire Fighting Training in ADNOC Group of Companies. - Utilizing Plant Assets and Real Time Scenarios - Switch from Conventional methods to Modern techniques. - Theory & Practical - COVID 19 compliance - Knowledge sharing with other Operating Companies. © Copyright 2021, Society of Petroleum Engineers

Dutch wildcats celebration

[...]we are like an oiled Swiss Army knife, which has proven its added value over the years, and is quickly available when called upon". The 'Cougars' of 300 squadron also deployed to Afghanistan several times during 2006 to 2010 Operations took place out of Kandahar Airfield in support of the Task Force Uruzgan. Besides a transport role of supplies also missions to support Special Operations Forces (SOF) where executed. 300 Squadron also supports humanitarian missions like fighting forest fires (Fire Bucket Operations - FBO), in 2004 the squadron deployed two Cougars to Portugal, and in August 2007 towards Greece to support the firefighting there. For these amphibious overseas missions six Cougars (serials S-419, 441, 442, 445 and 447) are available with maritime equipment like floats to prevent the helicopter from sinking when it comes in the water. [...]2015, the squadron was tasked as Patients transport (Air Ambulance) from the Wadden Islands, which are located in the upper north of the Netherlands in the Wadden Sea, to hospitals on the mainland. According to 1st Lieutenant Niels Henderson, Chief Helicopter Maintenance of 300 squadron, this is a unique opportunity.

Risk Mitigation, Vulnerability Management, and Resilience under Disasters

Development has been associated with positive economic expectations, income increases, and improved habitation opportunities [4]. [...]spontaneous spatial development and statutory planning follow social aspirations that may even be hazardous to develop privileged but sensitive and/or hazardous environments (e.g., coastal zones, riverbanks, peri-urban forest land, etc.). [...]activation of both the public and private sector for vulnerability management and resilience-building necessitates sufficient knowledge on the part of the respective actors: what are the major local hazards? The authors criticized the obsession of the Greek state with the fire-suppression component/stage of the forest fire risk management cycle by referring to the metaphor “the fire-fighting trap” as a quick fix (rather than a long-term management strategy), inducing negative, unintended consequences. [...]the authors emphasized that the risk-communication strategies should be tailored to the at-risk communities’ skills, habits, norms, beliefs, and risk culture.

Hybrid Model of Machine Learning Refractory Data Prediction Based on IoT Smart Cities

With the advent of the digital age in recent years, the application of artificial intelligence in urban Internet of Things (IoT) systems has become increasingly important. The concept of smart cities has gradually formed, and smart firefighting under the smart city system has also become important. The method of machine learning is now applied in various fields, but seldom to the data prediction of smart firefighting. Various types of applications including data applications of machine learning algorithms in smart firefighting have yet to be explored. In this article, we propose using machine learning algorithms to predict building fire-resistance data, aiming to provide more theoretical and technical support for IoT smart cities. This article adopts the fire-resistance data of building beam components in a real fire environment, using three integrated machine learning algorithms, Extreme random Tree (ET), AdaBoost, and Gradient Boosting Machine (GBM), and the grey wolf optimization algorithm to optimize. We improve the grey wolf algorithm and combine the grey wolf algorithm with the machine learning model. The algorithm constitutes three machine learning hybrid models: GWO-ET, GWO-AdaBoost, and GWO-GBM. Compared with traditional grid tuning, particle swarm optimization (PSO), and genetic algorithm (GA) optimization, the robustness and accuracy of the three optimization algorithms and the machine learning hybrid algorithm on the data set are compared and analyzed. Performance is measured through various performance comparisons and experimental result comparisons. For various building beam component data sets under real fires, the optimization and comparison show that the mean square error (MSE) of the proposed algorithm is extremely small. The results indicate that the GWO machine learning hybrid model is superior to other models and has a smaller prediction error.

Collective Action of the 'Fandom Girls' in Fighting the Epidemic - - Based on the perspective of social identity theory

The collective action of the 'Fandom Girls' to collect supplies in the fight against the COVID-19 coronavirus pandemic in 2020 demonstrated strong organizational executive skills, changing the public's prejudice and stereotype. Based on the perspective of social identity theory, this paper uses case study and text analysis to explore the identity construction of this collective action. The author finds that 'Fandom Girls' identify with their country, idols, norms, and communities, which function as the motivation of their collective action. The identity mechanism is developed from internal and external identities, which are expressed as self-categorization and evolution through questioning. The social identity theory is validated in this collective action, but it also shows new characteristics. Understanding the group's identity motivations and mechanisms can help to utilize the group's power better. © 2021 IEEE.

Jedi ops over Sicily

The military-civilian SAR duties of 82nd squadron includes regular cooperation with the mountain rescue unit CNSAS, the police and the firefighting forces of Vigili del Fuoco. Since 2018, the unit is also equipped with water-bambi buckets to assist with forest fires. The region around Trapani air base is an ideal for these purposes, not only for sea ops training, but also the rough Sicilian mountainous landscape is perfect to practice in a wide variety of circumstances", Capt Conforti informed us. When needed the HH-139 can be made available to transfer patients between hospitals, with a supplementary bio-containment cradle, the patient was isolated to secure a safe working area for the crew.

The Association between Participation in Fights and Bullying and the Perception of School, Teachers, and Peers among School-Age Children in Serbia.

Participating in physical fighting and bullying can be a cause of severe injury and death among school-age children. Research evidence can support school and health actors' efforts to improve school-age children's development and health capacity for life. The study aims to assess the prevalence of school-age children's participation in fights and bullying in Serbia, and to examine the relevance of students' socio-demographic characteristics and perceptions of school and relations with other students and professors for participation in fights and bullying. A secondary analysis is also performed on the original data of the 2017 HBSC study, which was conducted on 3267 students in a nationally representative sample of primary and high schools in Serbia. We sought to investigate the relationship between eight socio-demographic characteristics and nine school-related perceptions, with two outcome variables: taking part in fights and taking part in bullying, examined by using univariate and multivariate logistic regression. The main results show that 50.8% of boys and 17.1% of girls have taken part in fights, while 17.7% boys and 10.4% of girls have taken part in bullying. Students who felt a large and very large burden of school obligations were 1.43 times more likely to participate in bullying at least once, while they were 1.38 and 2.12 times more likely to participate in multiple fights and 4.04, 1.24, and 2.78 times more likely to participate multiple times in bullying. Multiple participation in fights and in bullying is significantly negatively associated with female gender, younger age years, good and very good perception of family financial status and quality of life, and positive perceptions of school and relations they have with other students and professors. Fights among school-age children are significantly positively associated with living with relatives/legal guardians and poor quality of life. In conclusion, the prevalence of participating in at least one fight/bullying is higher than in multiple fights/bullying. These associations suggest a necessity to enhance the monitoring and control of peer behavior among school-age children. The findings of the study imply key enablers of protection, such as building relationships based on team spirit and work, friendly behavior, empathy, and help, which should be included in the value system of school and family activities in programs to combat fights and bullying in school-age children.

Status quo and influencing factors of post-traumatic stress disorder among front-line anti-epidemic medical staff

[Background] Front-line medical staff are an important group in fighting against Coronavirus Disease 2019 (COVID-19), and their mental health should not be ignored. [Objective] This study investigates the current situation and influencing factors of post-traumatic stress disorder (PTSD) among front-line anti-epidemic medical staff during COVID-19 epidemic. [Methods] Medical staff who had participated in fighting against the COVID-19 epidemic wereselected from three grade III Class A hospitals and four grade II Class A hospitals in a city of Hubei Province by convenient sampling method in May 2020. The survey was conducted online using the Post-traumatic Stress Checklist-Civilian Version (PCL-C) as the main survey tool to investigate current situation and characteristics of PTSD among these participants. A total of 1120 questionnaires were collected, of which 1071 were valid, and the effective rate was 95.6%. [Results] Of the 1071 participants, the average age was (32.59±5.21) years;the ratio of male to female was 1: 5.02;the ratio of doctor to nurse was 1:5.8;nearly 70% participants came from grade III Class A hospitals;married participants accounted for 75.4%;most of them held a bachelor degree or above (86.5%);members of the Communist Party of China (CPC) accounted for 22.9%;50.9% had junior titles;the working years were mainly 5−10 years (42.8%);more than 80.0% participants volunteered to join the front-line fight;95.1% participants received family support;43.0% participated in rescue missions;78.1% participants fought the epidemic in their own hospitals;more than 60% participants considered the workload was greater than before;34.4% participants fought in the front-line for 2−4 weeks, and 23.5% participants did for more than 6 weeks. There were 111 cases of positive PTSD syndromes (PCL-C total score ≥38) with an overall positive rate of 10.4%, and the scores of reexperience [1.40 (1.00, 1.80)] and hypervigilance [1.40 (1.00, 2.00)] were higher than the score of avoidance [1.14 (1.00, 2.57)]. The results of univariate analysis revealed that PTSD occurred differently among participants grouped by age, political affiliation, working years, anti-epidemic activities location, accumulated working hours in fighting against COVID-19, having child parenting duty, voluntariness, family support, whether family members participated in front-line activities, and rescue mission assignment (P<0.05). The results of logistic regression analysis showed that the incidence rates of reporting PTSD syndromes in medical personnel aged 31−40 years (OR=0.346, 95%CI: 0.164−0.730) and aged 41 years and above (OR=0.513, 95%CI: 0.319-0.823) were lower than that in those aged 20−30 years;the incidence rates of reporting PTSD syndromes in medical staff who were CPC members (OR=0.499, 95%CI: 0.274−0.909), volunteered to participate (OR=0.584, 95%CI: 0.360−0.945), and received family support (OR=0.453, 95%CI: 0.222-0.921) were lower than those did not (P<0.05);the incidence rates of reporting PTSD syndromes among medical workers who had child parenting duty (OR=2.372, 95%CI: 1.392−4.042), whose family members participated in front-line activities (OR=1.709, 95%CI: 1.135−2.575), and who participated in rescue missions (OR=1.705, 95%CI: 1.133-2.565) were higher than those who did not (P<0.05). [Conclusion] The positive PTSD syndrome rate is 10.4% in the front-line anti-epidemic medical staff. Age, political affiliation, voluntariness, family support, having child parenting duty, with a family members participating in the fight, and rescue mission assignment are the influencing factors of PTSD.

COVID-19 and Conflict: Major Risks and Policy Responses.

The COVID-19 pandemic entails a medium- and long-run risk of heightened political conflict. In this short essay we distinguish four major consequences of COVID-19 that may fuel social tensions and political violence, namely i) spiking poverty, ii) education under stress, iii) potential for repression, and iv) reduced inter-dependence. After discussing them in turn, we will formulate policy recommendations on how to attenuate these risks.

Psychological Behavior of Frontline Medical Staff in the Use of Preventive Medication for COVID-19: A Cross-Sectional Study.

PURPOSE: To understand the current pandemic, levels of anxiety in frontline staff, and whether they have been using medication to prevent COVID-19. METHODS: Between January 10 and March 10, 2020, 290 frontline staff completed a questionnaire incorporating the Generalized Anxiety Disorder Scale 7 (GAD-7) to indicate their psychological behavior in the use of preventive medication. RESULTS: Of those who participated in the study, 77.6% used preventive medication, with 47.5, 40.9, and 11.6% using these as part of routine preventive treatment, to fight infection after it was contracted, and after occupational exposure, respectively. There was a statistically significant relationship between the anxiety scale scores and the frequency of medication use (P < 0.05). Comparative analyses revealed that the scores of those in the group taking medication after occupational exposure (to respiratory and blood-borne pathogens) were significantly different from other groups. The proportion of participants choosing Western medicine, traditional Chinese medicine, and integrated Chinese and Western medicine was 24.4, 28.0, and 47.6%, respectively. Additionally, the relationship between the anxiety scale scores and the three types of medication was statistically significant (P < 0.05), as was the difference between Western medicine and other groups. According to Multinomial logistic regression based on the adjustment to gender, age, educational level, marital status, current workplace, and profession, participants with moderate to severe anxiety, had higher odds (OR = 10.331, 95%CI:1.453-73.429) of using Western medicine than participants with no anxiety. Participants with moderate anxiety were 6.399 times more likely to use an integrated combination of traditional Chinese and Western medicine compared to those with no anxiety (OR = 6.399, 95%CI:1.007-40.658). Furthermore, those with mild anxiety were 2.656 times more likely to use integrated traditional Chinese and Western medicine than those with no anxiety (OR = 2.657, 95%CI:1.075-6.570). The probability that frontline medical staff with moderate anxiety took preventive medication after occupational exposure to COVID-19 was 8.066 times (OR = 8.066, 95%CI:1.043-62.353) higher than that of staff without anxiety. DISCUSSION: This study revealed that there was more anxiety among frontline medical staff who took medication after unexpected occupational exposure. There was less anxiety among those using an integrated course of Chinese and Western medicine than Western medicine alone. It was also observed that anxiety affects the types and frequency of the preventive medication taken. Frontline medical staff who suffer from anxiety are also more likely to use medication to prevent COVID-19.