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Green finance is an essential instrument for achieving sustainable development. Objectively addressing correlations among different green finance markets is conducive to the risk management of investors and regulators. This paper presents evidence on the time-varying correlation effects and causality among the green bond market, green stock market, carbon market, and clean energy market in China at multi-frequency scales by combining the methods of Ensemble Empirical Mode Decomposition Method (EEMD), Dynamic Conditional Correlation (DCC) GARCH model, Time-Varying Parameter Vector Autoregression with Stochastic Volatility Model (TVP-VAR-SV), and Time-varying Causality Test. In general, the significant negative time-varying correlations among most green finance markets indicate a prominent benefit of risk hedging and portfolio diversification among green financial assets. In specific, for different time points and lag periods, the green finance market shock has obvious time-varying, positive and negative alternating effects in the short-term scales, while its time delay and persistence are more pronounced in the medium-term and long-term scales. Interestingly, a positive event shock will generate positive connectivity among most green finance markets, whereas a negative event including the China/U.S. trade friction and the COVID-19 pandemic may exacerbate the reverse linkage among green finance markets. Furthermore, the unidirectional causality of "green bond market - carbon market - green stock and clean energy markets” was established during 2018–2019. © 2023
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Acid deposition and particulate matter (PM) pollution have declined considerably in China. Although metal(loid) and acid deposition and PM have many common sources, the changes of metal(loid) deposition in China in the recent decade have not been well explored by using long-term monitoring. Therefore, we analyzed the dry and wet deposition of eleven metal(loid)s (including Al, As, Ba, Cd, Cu, Cr, Fe, Mn, Pb, Sr, and Zn) from 2017 to 2021 at Mount Emei, which is adjacent to the most economic-developed region in western China (Sichuan Basin (SCB)). Anthropogenic emissions contributed to over 80% of the annual wet deposition fluxes of metal(loid)s and acids (SO4 2-, NO3 -, and NH4 +) at Mount Emei, and the major source regions were the SCB, the Yunnan-Guizhou Plateau, and Gansu Province. Metal(loid) and acid deposition had similar seasonal variations with higher wet deposition fluxes in summer but higher wet deposition concentrations and dry fluxes in winter. The seasonal variations were partially associated with higher precipitation but lower pH in summer (968 mm and 5.52, respectively) than in winter (47 mm and 4.73, respectively). From 2017 to 2021, metal(loid) deposition did not decline as substantially as acid deposition (5.6%-30.4%). Both the annual total deposition fluxes and concentrations of Cr, Cu, Sr, Ba, and Pb were even higher in 2020-2021 than in 2017-2018. The inter-annual and seasonal changes implied the responses of metal(loid) deposition to anthropogenic emission changes were buffered (e.g., transformation, dilution, and degradation) by precipitation rates, acidity, natural emissions, and chemical reactions in the atmosphere, among others.Copyright © 2023 Elsevier Ltd
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The great anti-epidemic spirit is the spirit of the times formed by all Chinese people in the great struggle against COVID-19 under the leadership of the Communist Party of China. The reason for the spatial transformation of the integration of anti-epidemic spirit education is that the great anti-epidemic spirit is the product of specific space environment. Modern space theory provides a new perspective for the integration of anti-epidemic spirit education.and With the end of the epidemic, the era of the epidemic has changed from the anti-epidemic space to the anti-epidemic spirit education space. The principle of spatial dimension of anti-epidemic spirit education lies in the environment creats people, the anti-epidemic spirit and the spatial environment are mutually embedded and mutually constructed, and the formation of the anti-epidemic spirit is the unity of perceptual cognition and rational cognition.education, and the formation of the anti-epidemic spirit is the unity of perceptual cognition and rational cognition. At the practical level, considering the integration of anti epidemic spirit education from the spatial dimension, we should not only integrate the anti epidemic spirit elements into the multi dynamic spatial environment, but also give full play to the role of multi spatial collaborative education and the supervision role of multi subjects in the spatial environment. © 2022, Editorial department of Chinese Medical Ethics. All rights reserved.
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The sudden outbreak of COVID-19 has greatly affected the development of all industries, and the development of many enterprises has been severely impacted. In the context of epidemic prevention and control, the Internet has brought new development space for enterprise marketing, so more and more enterprises begin to enter the field of online marketing. With the continuous progress of Internet technology and the deepening of informatization, China's big data industry has made qualitative progress. The traditional marketing model cannot meet the needs of the increasingly fierce market competition. By applying big data technology to network marketing, enterprises can dig deeply into user information and formulate corresponding marketing strategies based on users' preferences, behavior patterns and shopping habits, so as to realize precise marketing and improve their economic benefits by mining potential customers. However, there are also some problems in the process of using big data technology to move towards precision, such as serious homogenization, low application level, and privacy security issues. Only by solving these problems can enterprises use big data to achieve higher quality development. © 2022 IEEE.
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Coronavirus disease 2019 (COVID-19) has become a global pandemic that significantly challenged healthcare systems worldwide, with over 4 million deaths among 18.6 million identified cases as of June 2021. Understanding the current COVID-19 cases and determining clinical solutions is of paramount importance. In this chapter, we describe an exploratory study of identifying risk factors associated with COVID-19 inpatient care. Based on a set of COVID-19 inpatient medical health records in a US hospital system, we used both unsupervised and supervised machine learning methods to explore risk factors associated with hospitalized COVID-19 patients. We found that the most important features related to the COVID-19 disease include (1) influenza vaccines, (2) pneumococcal vaccines, and (3) weight-related variables (i.e., weight, height, and BMI). As such, we provide a use case that machine learning methods are valuable for predicting COVID-19 inpatient risk factors, and the results are promising to guide further research in this area. © 2022 Elsevier Inc. All rights reserved.
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Objective: To analyze the mental health status and influencing factors of China medical team (CMT) members in Africa during COVID-19 pandemic. Methods: From July 2021 to August 2021, 72 members of the 8th CMT in Malawi, the 36th CMT in Sudan and the 22nd CMT in Zambia were tested by 12-item General Health Questionnaire (GHQ-12), Generalized Anxiety Disorder-7 (GAD-7), and Patient Health Questionnaire-9(PHQ-9), general information form and influencing factors form. Results: The results of GHQ-12 were positive for 33.3% (24/72) of the CMT members. 51.4% (37/72) of the CMT members showed different levels of anxiety: 40.3% (29/72) of them had mild anxiety, 8.3% (6/72) of them had moderate anxiety, and 2.8% (2/72) of them had severe anxiety. 52.8% (38/72) of the CMT members had different degrees of depression: 34.7% (25/72) of them had mild depression, 11.1% (8/72) of them had moderate depression, 4.2% (3/72) of them had moderate-severe depression, and 2.8% (2/72) of them had severe depression. The CMT members who contacted with COVID-19 patients got significantly high scores of GHQ-12, GAD-7 and PHQ-9 (P<0.05) compared to those who did not have contact with COVID-19 patients. And CMT members who did not adapt to the local social life got significantly higher scores than those who adapted to the local social life (P<0.05). These factors were the main factors affecting the mental health of the CMT members. Conclusion: During COVID-19, the psychological pressure of CMT members was increased significantly, and both the incidence and severity of anxiety and depression were increased. Paying attention to and improving CMT members' mental health status can ensure the smooth development of medical aid to Africa.
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Vaccines are one of the biggest successes in modern history and are particularly important in light of the multiple ongoing epidemics. Recently, vaccines have protected peoples' health and lives around the world during the coronavirus disease 2019 pandemic. Different types of vaccines have their own characteristics and advantages and are used in the context of different epidemics. Responses to vaccination are also different, and can include adverse reactions and absent responses. These individual differences are thought to be influenced by host genes. In this review, we first discuss vaccine types and characteristics. Second, we discuss different responses to vaccination, primarily focusing on the association between genetic variation and inter-individual differences.
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The COVID-19 pandemic is highly infectious and has caused many deaths. The COVID-19 infection diagnosis based on blood test is facing the problems of long waiting time for results and shortage of medical staff. Although several machine learning methods have been proposed to address this issue, the research of COVID-19 prediction based on deep learning is still in its preliminary stage. In this paper, we propose four hybrid deep learning models, namely CNN+GRU, CNN+Bi-RNN, CNN+Bi-LSTM and CNN+Bi-GRU, and apply them to the blood test data from Israelta Albert Einstein Hospital. We implement the four proposed models as well as other existing models CNN, CNN+LSTM, and compare them in terms of accuracy, precision, recall, F1-score and AUC. The experiment results show that CNN+Bi-GRU achieves the best performance in terms of all the five metrics (accuracy of 0.9415, F1-score of 0.9417, precision of 0.9417, recall of 0.9417, and AUC of 0.91).
ABSTRACT
Up to now, the coronavirus disease 2019 (COVID-19) has been sweeping across all over the world, which has affected individual's lives in an overwhelming way. To fight efficiently against the COVID-19, radiography and radiology images are used by clinicians in hospitals. This article presents an integrated framework, named COVIDNet, for classifying COVID-19 patients and healthy controls. Specifically, ResNet (i.e., ResNet-18 and ResNet-50) is adopted as a backbone network to extract the discriminative features first. Second, the spatial pyramid pooling (SPP) layer is adopted to capture the middle-level features from the features of ResNet. To learn the high-level features, the NetVLAD layer is used to aggregate the features representation from middle-level features. The context gating (CG) mechanism is adopted to further learn the high-level features for predicting the COVID-19 patients or not. Finally, extensive experiments are conducted on the collected database, showing the excellent performance of the proposed integrated architecture, with the sensitivity up to 97% and specificity of 99.5% of the ResNet-18, and with the sensitivity up to 99% and specificity of 99.4% of the ResNet-50. © 2014 IEEE.
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The long-lasting global pandemic of Coronavirus disease 2019 (COVID-19) has changed our daily life in many ways and put heavy burden on our mental health. Having a predictive model of negative emotions during COVID-19 is of great importance for identifying potential risky population. To establish a neural predictive model achieving both good interpretability and predictivity, we have utilized a large-scale (n =542) longitudinal dataset, alongside two independent samples for external validation. We built a predictive model based on psychologically meaningful resting state neural activities. The whole-brain resting-state neural activity and social-psychological profile of the subjects were obtained from Sept. to Dec. 2019 (Time 1). Their negative emotions were tracked and re-assessed twice, on Feb 22 (Time 2) and Apr 24 (Time 3), 2020, respectively. We first applied canonical correlation analysis on both the neural profiles and psychological profiles collected on Time 1, this step selects only the psychological meaningful neural patterns for later model construction. We then trained the neural predictive model using those identified features on data obtained on Time 2. It achieved a good prediction performance (r =0.44, p =8.13 ×10-27). The two most important neural predictors are associated with self-control and social interaction. This study established an effective neural prediction model of negative emotions, achieving good interpretability and predictivity. It will be useful for identifying potential risky population of emotional disorders related to COVID-19. IEEE
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In this study, three greenhouse gases (CO2, CH4, and N2O) and one conventional gas (CO) were observed at a roadside station in Shenzhen from September, 2019 to July, 2020. The average concentration of CO2, CH4, N2O, and CO was (430.8±6.1)×10-6, (2318.5±137.9)×10-9, (332.6±1.6)×10-9, and (333.4±121.2)×10-9, respectively. Seasonal variation of CO2 and CO were high in winter and low in summer, Seasonal variation of CH4 and N2O were high in autumn and low in summer. The high concentration in autumn and winter is due to the long-distance transmission of fossil fuel emissions during the heating period, and the low concentration in summer is mainly due to the reduction of long-distance transmission sources and the enhancement of sinks such as plant photosynthesis and photochemical reactions. The diurnal variation of CO2 concentration showed a two-peak and one-valley pattern, which was mainly affected by plant photosynthesis and morning and evening traffic peak;The diurnal variation of CO concentration showed a two-peak pattern, which was mainly affected by the morning and evening traffic peaks. The diurnal variation of CH4 and N2O concentration was high at night and low at day, which was mainly affected by daytime photochemical reaction. Among them, the concentration of CO2 and CO is more sensitive to the emission of traffic sources. In addition, this study compared the COVID-19 lockdown period in 2020 with the same period in 2021, and the results showed that the concentration of CO2, CH4, N2O, and CO decreased by 3.1%, 10.6%, 0.5% and 13.9%, respectively, indicating that traffic control can play an important role in reducing urban greenhouse gas emissions. © 2022, Editorial Board of China Environmental Science. All right reserved.
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In their response to the initial outbreak of COVID-19 in China, Chinese social workers were able to take their place on the international stage and share their experiences and knowledge with the rest of the world. Thus, we aim to examine the experiences of social workers during the initial outbreak of COVID-19 in China to promote public health. Based on a quasi-scoping review of articles on social work practice during the initial outbreak of COVID-19 in China, this reflective article elucidates how social workers contributed in different roles and focuses over three stages. Three suggestions are also made in terms of confronting the challenges arising in each stage: increase the independence and visibility of social work in the system, prioritise practice and flexibility over rigid procedures and increase professional collaborations and do away with disputes around the provision of community and public health social work services. Social workers contributed significantly to the promotion of public and community health during the initial outbreak of COVID-19 in China. Based on a quasi-scoping review of articles on social work practice during that outbreak, this reflective article elucidates how social workers contributed in three chronological stages. In the early stage (late January and February 2020), social workers provided community services offered as part of the governmental structure (moderate information and resource provision);in the middle stage (March 2020), social workers provided services to vulnerable groups alongside supporting the quarantine strategy;and in the late stage (April 2020 onwards), their services were focused on recuperation and recovery after the national lockdown was lifted. In the meanwhile, several issues for public and community health social work as a profession in terms of how it was able to support anti-COVID-19 practices became clear, including a lack of independence and stability, the need for better flexibility and greater ability to act pragmatically and lack of professional agreement. This article aims to enlighten the development of a (re)emerging field-public health and community health social work in China in the wake of the COVID-19 pandemic.
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The impact on public emergencies such as COVID-19 has created a greater disconnect from producers and consumers, which has also put pressure on economic growth. Distributed systems can create sustainable development by connecting individual production units and sharing resources between them. Based on the investigation of agricultural products of Qihe County, the distributed system theory and product service system design method are applied to analyze the needs of consumers, producers and their stakeholders of local agricultural products. With the help of small-scale interconnected regional production units, a distributed subsystem based on the characteristics of its own agricultural products is established. Through the design of agricultural products brands, traceable agricultural products safety system and agricultural products online sales APP, the production and sales forms can be flexibly adjusted to increase online purchasing and offline experience forms. Finally, it solves the pain points of users, adds local cultural elements, improves the added value of agricultural products, enhances the income of farmers, improves the quality of life of urban residents, and promotes the sustainable development of economy, society and environment. © 2021 IEEE.
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INTRODUCTION: Poor sleep quality among college students is a global problem. Chinese college students were required to home quarantine, social distance and participate in online learning during the COVID-19 epidemic. This study aimed to investigate the sleep quality of college students during the epidemic and identify the factors related to poor sleep quality. METHODS: Study participants completed an online survey that included questionnaires about sleep symptoms and lifestyle during the COVID-19 outbreak. The study participants included 3416 college students (mean age 20.4 ± 1.8 years). The Pittsburgh Sleep Quality Index (PSQI) was used to measure sleep quality, and a PSQI score >7 was defined as poor sleep quality. A logistic regression model was used to analyze the factors related to sleep quality. RESULTS: The percentage of college students with poor sleep quality was 15.97 % in southern Anhui province during the COVID-19 pandemic. The majority of the students were female (67.4%) and most were from urban areas (53.9%). Single-parent (adjusted odds ratio [aOR], 1.39;95% CI, 1.02-1.89) domestic violence incidents ≥5×/yr (aOR, 3.68;95% CI, 1.70 to 7.96), nap time >4 hr/d (aOR, 1.90;95% CI, 25-2.90) were significantly associated with poor sleep quality. While knowledge of COVID-19 was prevalent (aOR, 0.71;95% CI, 0.53 to 0.96) light exercise >1 hour/day (aOR, 0.47;95% CI, 0.28 to 0.78), parent-accompanied exercise >3×/wk (aOR, 0.59;95% CI, 0.38 to 0.90) were protective factors against poor sleep quality. CONCLUSIONS: The present study found that college students in single-parent families and students who had experienced domestic violence had a high risk of poor sleep quality during the COVID-19 pandemic in China. College students who were familiar with COVID-19 and had light exercise habits or parent-accompanied exercise habits had better sleep quality. At the time of writing, COVID-19 was still pandemic worldwide, so targeted sleep health interventions must be established to actively guide college students' healthy living habits. In addition, the sleep disorders and other health problems that may occur in college students should be dealt with in advance, and should be part of the routine work of global disease prevention.