ABSTRACT
Mental stress has been recognized as an essential risk factor for hypertension. Therefore, experts specializing in cardiology, psychiatry, and Traditional Chinese Medicine organized by the Psycho-cardiology Group, College of Cardiovascular Physicians of Chinese Medical Doctor Association, and Hypertension Group of the Chinese Society of Cardiology proposed the expert consensus on the diagnosis and treatment of adult mental stress-induced hypertension in March 2021, which includes the epidemiology, etiology, diagnosis, and treatment of the mental stress-induced hypertension. This consensus will hopefully facilitate the clinical practice of this disorder. In addition, the COVID-19 pandemic has become one of the primary global sources of psychosocial stressors since the beginning of 2020, and the revision of this expert consensus in 2022 has increased the relevant content. This consensus consists of two parts. The sections of Part A include (I) Background and epidemiological characteristics, (II) Pathogenesis, and (III) Diagnosis. The sections of Part B contain (IV) Treatment recommendations, and (V) Prospects. This article presents Part B of the consensus. © 2022 Heart and Mind ;Published by Wolters Kluwer - Medknow.
ABSTRACT
Mental stress has been recognized as an essential risk factor for hypertension. Therefore, experts specializing in cardiology, psychiatry, and Traditional Chinese Medicine organized by the Psycho-Cardiology Group of College of Cardiovascular Physicians of Chinese Medical Doctor Association and Hypertension Group of Chinese Society of Cardiology proposed the expert consensus on the diagnosis and treatment of adult mental stress-induced hypertension in March 2021, which includes the epidemiology, etiology, diagnosis, and treatment of the mental stress-induced hypertension. This consensus will hopefully facilitate the clinical practice of this disorder. In addition, the COVID-19 pandemic has become one of the primary global sources of psychosocial stressors since the beginning of 2020, and the revision of this expert consensus in 2022 has increased the relevant content. This consensus consists of Part A and Part B. Part A includes (I) Background and epidemiological characteristics, (II) Pathogenesis, and (III) Diagnosis and Part B includes (IV) Treatment recommendations and (V) Prospects. This part presents the content of Part A. © 2022 Heart and Mind ;Published by Wolters Kluwer - Medknow.
ABSTRACT
Background: This study was designed to investigate patterns and risk factors for substance use among obstetrical patients who gave birth during the early period of the pandemic, and their partners. Method(s): Cross-sectional survey of obstetrical patients between March 17th and June 16th, 2020, at The Ottawa Hospital, Ottawa, Canada. Substance use was a composite measure of any alcohol, tobacco, or cannabis use since COVID-19 began. Four outcomes included: any participant substance use or increase in substance use, any partner substance use or increase in substance use. Adjusted risk ratios (ARR) and 95% confidence intervals (CI) are presented. Finding(s): Of 216 participants, 113 (52.3%) and 15 (6.9%) obstetrical patients reported substance use and increased use, respectively. Those born in Canada (ARR: 2.03;95% CI: 1.27-3.23) and those with lower household income (ARR: 1.38;95% CI: 1.04-1.85) had higher risk of substance use. Those with postpartum depression (ARR: 5.78;95%CI: 2.22-15.05) had the highest risk of increased substance use. Families affected by school/daycare closure reported a higher risk of increased partner substance use (ARR: 2.46;95% CI:1.38-4.39). Conclusion(s): This study found that risk factors for substance use included demographics (i.e., being born in Canada, income), mental health (postpartum depression), and school/childcare closures.Copyright © 2023 Taylor & Francis Group, LLC.
ABSTRACT
Human-beings are suffering from the rapid spread of COVID-19 throughout the world. In order to quickly identify, quarantine and cure the infected people, and to stop further infections, it is crucial to expose those origins who have been infected but are asymptomatic. However, this task is not easy, especially when the rigid security and privacy constraints on health records are taken into consideration. In this paper, we develop a new method to solve this problem. In the outbreak of a disease like COVID-19, the proposed method can find hidden infected people (or communities) through volunteered share of health data by some mobile users. Such volunteers only reveal whether they are healthy or infected e.g. through they mobile apps. This approach minimises health data disclosure and preserves privacy for the others. There are three steps in the proposed method. First, we borrow the idea from traditional epidemiology and design a novel algorithm to estimate the number of infection origins based on a Susceptible-Infected model. Second, we introduce the concept of 'heavy centre' to locate those origins. The probability of each node being infected will then be derived by building a spreading model based on the origins. To evaluate our method, we conduct a series of experiments on various networks with different structures. We examine the performance in estimating the number of origins as well as their origins. The results show that the proposed method yields higher accuracies than the existing methods, even when the fraction of volunteers is small. © 2001-2012 IEEE.
Subject(s)
COVID-19 , Drugs, Chinese Herbal , Humans , Drugs, Chinese Herbal/therapeutic use , SARS-CoV-2 , Drug CombinationsABSTRACT
The spread of COVID-19 has caused irreparable and enormous damage to many families around the world, so using mathematical models to further study the changing pattern of the infection's population caused by the spread of the coronavirus can help people to predict the trend of its changes. In this paper, on top of the logistic growth and classical SIR epidemiological models, the author develops a new SIRV model, including the effect of reinfection and breakthrough infection, to illustrate some properties of the spread of COVID-19. This study identified several fundamental properties and basic reproduction numbers of this SIRV COVID-19 model and further searched for the steady-state or equilibrium point of the model using dimensionless methods. This study demonstrated the following: first, the author proved that the solution of the model is positive under non-negative conditions. Second, the author applied the next generation matrix method to determine the basic reproduction number of the COVID-19 virus in the model and found that the calculation of the basic reproduction number in the model is the same as in the classical SIR model. Finally, the author used the dimensionless method to obtain expressions for the equilibrium points of the model in both disease-free and diseased cases. © 2022 SPIE.
ABSTRACT
Human-beings are suffering from the rapid spread of COVID-19 throughout the world. In order to quickly identify, quarantine and cure the infected people, and to stop further infections, it is crucial to expose those origins who have been infected but are asymptomatic. However, this task is not easy, especially when the rigid security and privacy constraints on health records are taken into consideration. In this paper, we develop a new method to solve this problem. In the outbreak of a disease like COVID-19, the proposed method can find hidden infected people (or communities) through volunteered share of health data by some mobile users. Such volunteers only reveal whether they are healthy or infected e.g. through they mobile apps. This approach minimises health data disclosure and preserves privacy for the others. There are three steps in the proposed method. First, we borrow the idea from traditional epidemiology and design a novel algorithm to estimate the number of infection origins based on a Susceptible-Infected model. Second, we introduce the concept of ’heavy centre’to locate those origins. The probability of each node being infected will then be derived by building a spreading model based on the origins. To evaluate our method, we conduct a series of experiments on various networks with different structures. We examine the performance in estimating the number of origins as well as their origins. The results show that the proposed method yields higher accuracies than the existing methods, even when the fraction of volunteers is small. IEEE
ABSTRACT
Social network users receive a large amount of social data every day. These data may contain malicious unwanted social spams, even though each social network has its social spam filtering mechanism. Moreover, spammers may send spam to multiple social networks concurrently, and the spam on the same topic from different social networks has similarities. Therefore, it is crucial to building a universal spam detection system across different social networks that can effectively fend off spam continuously. In this paper, we designed and implemented a tool Spam-Fender to facilitate spam detection across social networks. In order to utilize the raw social data obtained from multiple social networks, we utilized a semi-supervised learning method to convert unlabelled data into usable data for training the model. Moreover, we developed an incremental learning method to enable the model to learn new data continuously. Performance evaluations demonstrate that our proposed system can effectively detect social spam with satisfactory accuracy levels. In addition, we conducted a case study on the COVID-19 dataset to evaluate our system.
ABSTRACT
In the last two years, the outbreak of COVID-19 has significantly affected human life, society, and the economy worldwide. To prevent people from contracting COVID-19 and mitigate its spread, it is crucial to timely distribute complete, accurate, and up-to-date information about the pandemic to the public. In this article, we propose a spatial-temporally bursty-aware method called STBA for real-time detection of COVID-19 events from Twitter. STBA has three consecutive stages. In the first stage, STBA identifies a set of keywords that represent COVID-19 events according to the spatiotemporally bursty characteristics of words using Ripley's K function. STBA will also filter out tweets that do not contain the keywords to reduce the interference of noise tweets on event detection. In the second stage, STBA uses online density-based spatial clustering of applications with noise clustering to aggregate tweets that describe the same event as much as possible, which provides more information for event identification. In the third stage, STBA further utilizes the temporal bursty characteristic of event location information in the clusters to identify real-world COVID-19 events. Each stage of STBA can be regarded as a noise filter. It gradually filters out COVID-19-related events from noisy tweet streams. To evaluate the performance of STBA, we collected over 116 million Twitter posts from 36 consecutive days (from March 22, 2020 to April 26, 2020) and labeled 501 real events in this dataset. We compared STBA with three state-of-the-art methods, EvenTweet, event detection via microblog cliques (EDMC), and GeoBurst+ in the evaluation. The experimental results suggest that STBA outperforms GeoBurst+ by 13.8%, 12.7%, and 13.3% in terms of precision, recall, and F ₁score. STBA achieved even more improvements compared with EvenTweet and EDMC.
ABSTRACT
Light food refers to healthy and nutritious food that has the characteristics of low calorie, low fat, and high fiber. Light food has been favored by the public, especially by the young generation in recent years. Moreover, affected by the COVID-19 epidemic, consumers’ awareness of a healthy diet has been improved to a certain extent. As both take-out and in-place orders for light food are growing rapidly, there are massive customer reviews left on the Meituan platform. However, massive, multi-dimensional unstructured data has not yet been fully explored. This research aims to explore the customers’ focal points and sentiment polarity of the overall comments and to investigate whether there exist differences of these two aspects before and after the COVID-19. A total of 6968 light food customer reviews on the Meituan platform were crawled and finally used for data analysis. This research first conducted the fine-grained sentiment analysis and classification of the light food customer reviews via the SnowNLP technique. In addition, LDA topic modeling was used to analyze positive and negative topics of customer reviews. The experimental results were visualized and the research showed that the SnowNLP technique and LDA topic modeling achieve high performance in extracting the customers’ sentiments and focal points, which provides theoretical and data support for light food businesses to improve customer service. This research contributes to the existing research on LDA modeling and light food customer review analysis. Several practical and feasible suggestions are further provided for managers in the light food industry. © 2021, Springer Nature Singapore Pte Ltd.
ABSTRACT
This study examined the role of individual differences in horizontal and vertical individualism and collectivism, trust and worries, and concerns about COVID-19 in predicting the attitudes toward compliance of health advice and psychological responses during the COVID-19 pandemic. Chinese university students (N=384, 324 female) completed measures of individualism and collectivism, trust, attitudes toward compliance, and psychological responses to the pandemic. Results showed that not only vertical collectivist orientation but also horizontal individualist orientation significantly predicted higher willingness to comply, whereas vertical individualist orientation significantly predicted lower willingness to comply. Vertical individualist and vertical collectivist orientations predicted higher psychological response in terms of distress, anxiety, and depression, while horizontal collectivistic orientation significantly predicted less psychological problems. Implications of the effect of individual-level cultural orientations on attitudes toward public health compliance and psychological well-being during global health crises are discussed.
ABSTRACT
Since the outbreak of the COVID-19, small and medium-sized enterprises have been greatly affected. In order to cope with the difficulty of capital turnover for small and medium-sized enterprises, the government has successively introduced a series of financial policies to increase credit support and reduce financing costs. The rapid development of technology has also prompted further innovations in the operating models of banks and other credit platforms. However, banks and credit platforms must consider practical issues such as their own capital costs and risk assessment while they help small and medium-sized enterprises reduce financing costs. This paper aims to study and design a credit risk assessment system based on big data technology and machine learning algorithms. It is hoped that the system will enhance the bank's ability to identify the credit risks of small and medium-sized enterprises, so as to solve the problem of difficult and expensive financing for small and medium-sized enterprises. At the same time, it will reduce the bank's own bad loan ratio and increase profit margins. Achieving a win-win situation for small and medium-sized enterprises and banks, it's crucial to promote jointly the development of economy.
ABSTRACT
In the covid-19 epidemic period, in order to investigate the research and application of various ultraviolet disinfection technologies, low-pressure mercury lamp and ultraviolet light-emitting diode ( UV-LED ) as the mainstream ultraviolet disinfection light sources in the market are investigated. The principle, application and main characteristics of the two light sources are briefly introduced and the research progress, application sites and potential of UV-LED single wavelength irradiation, pulsed irradiation and multi-wavelength synergistic irradiation are introduced and evaluated. The 222nm safety ultraviolet disinfection technology was introduced and its application was prospected. Finally, the problems of UV-LED at the present stage are discussed and the prospect of the development of UV-LED disinfection market is presented. It is expected that the future UV disinfection market will form mercury lamp, LED as a supplement, the two complementary pattern. © 2020, Editorial Board of Optical Technique. All right reserved.