Designing an Empathy Training for Depression Prevention Using Virtual Reality and a Preliminary Study

As virtual reality (VR) is labeled by many as 'an ultimate empathy machine,' immersive VR applications have the potential to assist in empathy training for mental healthcare such as depression [21]. In responding to the increasing numbers of diagnosed depression throughout COVID-19, a first-person VR adventure game called 'Schwer' was designed and prototyped by the authors' research team to provide a social support environment for depression treatment. To continue the study and assess the training effectiveness for an appropriate level of empathy, this current article includes a brief survey on data analytics models and features to accumulate evidence for the next phase of the study, an interactive game-level design for the 'Reconstruction' stage, and a preliminary study with data collection. The preliminary study was conducted with a post-game interview to evaluate the design of the levels and their effectiveness in empathy training. Results showed that the game was rated as immersive by all participants. Feedback on the avatar design indicated that two out of three of the non-player characters (NPCs) have made the intended effect. Participants showed mostly positive opinion towards their experienced empathy and provided feedback on innovative teleport mechanism and game interaction. The findings from the literature review and the results of the preliminary study will be used to further improve the existing system and add the data analytics model training. The long-term research goal is to contribute to the healthcare field by developing a dynamic AI-based biofeedback immersive VR system in assisting depression prevention. © 2023 IEEE.

Validation and Spatiotemporal Analysis of the Modis 1 Km Aerosol Product over China during the Covid-19 Pandemic

The Moderate Resolution Imaging Spectroradiometer (MODIS) 1 km aerosol product based on the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm has great potential in understanding the interaction between human activities and the atmospheric environment. In this paper, the MODIS 1 km aerosol product over China during the Coronavirus Disease 2019 (COVID-19) pandemic was validated against with the ground measured data collected from the Aerosol Robotic Network (AERONET). The result shows a good agreement between the two datasets. The spatiotemporal analyses of three selected regions, which are Beijing-Tianjin-Hebei, Hubei and Guangdong-Hong Kong-Macao, indicate that the COVID-19 pandemic has a significant impact on human activities and aerosol loadings.

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

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

Comparison of Atmospheric Environment in China during Sars and Covid-19 Pandemics

Satellite remote sensing has advantages in monitoring environmental changes during the global pandemics such as the Severe Acute Respiratory Syndrome Coronavirus (SARS) and the Corona Virus Disease 2019 (COVID-19). In this paper, the variations of atmospheric environment during SARS and COVID-19 pandemics were calculated and analyzed based on the Moderate Resolution Imaging Spectroradiometer (MODIS) Atmosphere Monthly Global Product. Preliminary results show that: (1) aerosol optical depth is most affected by the pandemics, especially the duration and prevention and control measures;(2) the correlations between the variables of aerosol optical depth, cloud fraction, total column ozone and precipitable water vapor were not very strong during the two pandemics.

Impact of Covid-19 Pandemic on Atmospheric Environment over China: A Satellite Perspective

The Coronavirus Disease 2019 (COVID-19) pandemic, which has lasted for more than two years, has had a huge impact on human health and the global economy, as well as the ecological environment. In this study, the variations of atmospheric environment over China from 2019 to 2020 were calculated and analyzed based on the measured total columns of ozone (O-3), sulfur dioxide (SO2), nitrogen dioxide (NO2) and aerosol optical depth (AOD) from the Ozone Monitoring Instrument (OMI) aboard NASA's Aura satellite. The study shows the impact of the epidemic prevention and control measures and the resumption of work and production on atmospheric environment, and demonstrates that satellite remote sensing can play an important role in the monitoring of the COVID-19 pandemic, especially its impact on atmospheric environment.

Analysis of the nucleic acid detection quality assessment results from novel coronavirus laboratories in 43 disease control institutions in Qinghai province in 2021

Objective To assess the quality of nucleic acid testing in 43 Novel Coronavirus laboratories of disease control institutions in Qinghai Province that have passed the acceptance inspection, so as to ensure the accuracy and reliability of nucleic acid testing results. Methods Five samples for quality control assessment were distributed to each testing institution. The quality of nucleic acid testing was carried out following the requirements of the testing technical guidelines in the Novel Coronavirus Pneumonia Prevention and Control Program (Seventh Edition) issued by the Chinese Center for Disease Control and Prevention. Each detection reagentwas prepared by the member units participating in the assessment. The nucleic acid detection ability of each institution was evaluated by comparing the testing results with the expected results of the assessment samples. Results Fortythree disease control institutions participated in the assessment, all the testing results were collected within the specified time. The overall compliance rate of this assessment was 100. 00%. A total of 10 manufacturers' nucleic acid extraction kits and 9 manufacturers' nucleic acid amplification kits were involved. One hundred percent of the kits detected the ORF1ab gene and N gene of the Novel Coronavirus. Conclusion The nucleic acid detection capacity of the disease control institutions in Qinghai has been further improved by making this Novel Coronavirus quality control assessment. The assessment createsa solid foundation for the prevention and control of the Novel Coronavirus epidemic in Qinghai. © 2023, Editorial Department of Medical Pest Control. All rights reserved.

Analysis of the nucleic acid detection quality assessment results from novel coronavirus laboratories in 43 disease control institutions in Qinghai province in 2021

Objective To assess the quality of nucleic acid testing in 43 Novel Coronavirus laboratories of disease control institutions in Qinghai Province that have passed the acceptance inspection, so as to ensure the accuracy and reliability of nucleic acid testing results. Methods Five samples for quality control assessment were distributed to each testing institution. The quality of nucleic acid testing was carried out following the requirements of the testing technical guidelines in the Novel Coronavirus Pneumonia Prevention and Control Program (Seventh Edition) issued by the Chinese Center for Disease Control and Prevention. Each detection reagentwas prepared by the member units participating in the assessment. The nucleic acid detection ability of each institution was evaluated by comparing the testing results with the expected results of the assessment samples. Results Fortythree disease control institutions participated in the assessment, all the testing results were collected within the specified time. The overall compliance rate of this assessment was 100. 00%. A total of 10 manufacturers' nucleic acid extraction kits and 9 manufacturers' nucleic acid amplification kits were involved. One hundred percent of the kits detected the ORF1ab gene and N gene of the Novel Coronavirus. Conclusion The nucleic acid detection capacity of the disease control institutions in Qinghai has been further improved by making this Novel Coronavirus quality control assessment. The assessment createsa solid foundation for the prevention and control of the Novel Coronavirus epidemic in Qinghai. © 2023, Editorial Department of Medical Pest Control. All rights reserved.

Evaluation and Prediction of Coupling Coordinated Development of Compound System of Key State-owned Forest Region in Daxing'anling, Heilongjiang Province

Coordination is an important part of the new development philosophy. Promoting the coordinated development is the main goal of deepening the reform and development in state-owned forest region. Aiming to provide scientific basis and theoretical supports for promoting the continuous deepening of reform of state-owned forest region and realizing comprehensive and high-quality coordinated development, the key state-owned forest region in Daxing'anling, Heilongjiang Province was chosen as the research object. A compound system covering ecological conservation, industrial development, enterprise management, well-being of the people and support capability was constructed. The coupling coordination model was used to quantitatively evaluate the coupling coordination status of the compound system from 2000 to 2020. The Grey Markov model was used to predict the trend of coupling coordination development in this compound system from 2021 to 2022. Results show that, after 21-year of transformation and development, the development index of each subsystem of state-owned forest region in Daxing'anling, Heilongjiang Province has been changed, however, the process were different among subsystems. The growth rates of the subsystems of well-being of the people and resource conservation have been high, while the subsystems of enterprise management and the support capability have been lagged dramatically behind. The development stage of coupling coordination of the compound system has changed from misalignment to coordination, nevertheless, the coordinated development level was regressive in recent years due to certain factors such as policy, COVID-19, etc. It is predicted that by 2022, the development stage of coupling coordination of compound system will be recovered to the benign coordinated development type, however, there is still a big gap before it reaches the high-quality coordinated development type. It is suggested that the existing support policies and inputs should be kept stable, moreover, the enterprise management and support capability should be strengthened, in order to promote the stable and high-quality coupling coordinated development in the key state-owned forest regions in Daxing'anling, Heilongjiang Province. © 2022, China Environmental Science Press. All rights reserved.

Deep Active Context Estimation for Automated COVID-19 Diagnosis

Many studies on automated COVID-19 diagnosis have advanced rapidly with the increasing availability of large-scale CT annotated datasets. Inevitably, there are still a large number of unlabeled CT slices in the existing data sources since it requires considerable consuming labor efforts. Notably, cinical experience indicates that the neighboring CT slices may present similar symptoms and signs. Inspired by such wisdom, we propose DACE, a novel CNN-based deep active context estimation framework, which leverages the unlabeled neighbors to progressively learn more robust feature representations and generate a well-performed classifier for COVID-19 diagnosis. Specifically, the backbone of the proposed DACE framework is constructed by a well-designed Long-Short Hierarchical Attention Network (LSHAN), which effectively incorporates two complementary attention mechanisms, i.e., short-range channel interactions (SCI) module and long-range spatial dependencies (LSD) module, to learn the most discriminative features from CT slices. To make full use of such available data, we design an efficient context estimation criterion to carefully assign the additional labels to these neighbors. Benefiting from two complementary types of informative annotations from K-nearest neighbors, i.e., the majority of high-confidence samples with pseudo labels and the minority of low-confidence samples with hand-annotated labels, the proposed LSHAN can be fine-tuned and optimized in an incremental learning manner. Extensive experiments on the Clean-CC-CCII dataset demonstrate the superior performance of our method compared with the state-of-the-art baselines.

A CASE STUDY ON CHINA'S POLICIES OF PROMOTING WORK RESUMPTION DURING THE COVID-19 PANDEMIC - PERSPECTIVE OF POLICY RESILIENCE

During the COVID-19 pandemic, China has achieved high recovery efficiency. One of the most important reasons behind this is the effective policies of promoting work resumption. Why can such policies maintain steady performance despite the high level of environmental uncertainties? This question can be answered from the perspective of policy resilience. This study employed a policy evaluation model for analyzing quantitative data of 342 policies of promoting work resumption. We evaluate the policies through the Policy Modeling Consistency (PMC-index) model and text mining methods. The results show that: first, the contents and elements of all policies have consistent characteristics, including the combination of multiple policy tools, the combination of support for work resumption and pandemic control, the incentives to support effective policy implementation, and the reasonable match between macro and micro policies as well as short-term and long-term policies. Second, among the nine policies that are randomly selected from the sample, one is rated excellent and the other eight are good, indicating that China's policies of promoting work resumption have good resilience.

Covid-19, urban economic resilience and the pandemic pivot: Toronto's restaurant scene

Restaurants, fundamental to Toronto's urban and cultural economy, experienced significant disruption because of extended closures during the Covid-19 pandemic. We examine data harvested from Yelp Business Search Endpoint on restaurant openings and closures in Toronto between May 2020 and May 2021. Our analysis shows that, despite expectations to the contrary, more restaurants opened than closed during this time. Geographically, similar numbers of restaurants both opened and closed in the city's downtown core, demonstrating that early pandemic predictions suggesting the end of concentration are exaggerated. Overall, restaurants and restaurateurs exhibited resilience during the pandemic. We attribute this resilience, in part, to an ability to pivot to takeout-friendly foods, digital ordering and delivery and because of government funding supports.

Selective exposure to COVID-19 vaccination information: the influence of prior attitude, perceived threat level and information limit

Purpose The massive amount of available information and functionality of the Internet makes selective information seeking effortless. This paper aims to understand the selective exposure to information during a health decision-making task. Design/methodology/approach This study conducted an experiment with a sample of 36 students to examine the influence of prior attitude, perceived threat level and information limit on users' selective exposure to and recall of coronavirus disease 2019 (COVID-19) vaccination information. Participants were assigned to two conditions with or without an upper limit of the number of articles to be examined, and this study collected the number of articles read, the number of articles included in the report and recall score of the articles after one day of the experiment. Findings This study found that (1) participants with a negative attitude were more inclined to view attitude-consistent information and recalled attitude-consistent information more accurately, while participants with a positive attitude viewed more balanced information;(2) participants perceiving higher health threat level recalled attitude-consistent information more accurately;and (3) an upper limit on the number of articles to be viewed does not have any impact on selective exposure. Research limitations/implications The findings of this paper pinpoint the disparity of influence of positive and negative attitudes on selective exposure to and selective recall of health information, which was not previously recognized. Practical implications Vaccination campaigns should focus on reaching people with negative attitudes who are more prone to selective exposure to encourage them to seek more balanced information. Originality/value This is the first paper to explore selective exposure to COVID-19 vaccination information. This study found that people with a negative attitude and a higher level of perceived health threat are more prone to selective exposure, which was not found in previous research.

Digital technologies’ impacts on student social adaptation during Coronavirus pandemic

The coronavirus pandemic has caused a rather difficult period of adaptation of students to the university system and the new educational process. Digital technologies came to the rescue, which contributed to some solution of emerging adaptation issues for nonresident students. This article is aimed at identifying the features of social adaptation of nonresident students to the educational process at the university. As a research method, the questionnaire method was used, which allowed to identify and analyze the peculiarities of adaptation of first-year students from other cities to the university environment and university requirements. The article reveals the influence of digital technologies on the adaptation of students to the university environment and new living conditions. It was determined that, in general, the participants have a high level of adaptation to the university environment, even in the conditions of the coronavirus pandemic. © 2021. Birlesik Dunya Yenilik Arastirma ve Yayincilik Merkezi.

Stance Detection in COVID-19 Tweets

The prevalence of the COVID-19 pandemic in day-to-day life has yielded large amounts of stance detection data on social media sites, as users turn to social media to share their views regarding various issues related to the pandemic, e.g. stay at home mandates and wearing face masks when out in public. We set out to make use of this data by collecting the stance expressed by Twitter users, with respect to topics revolving around the pandemic. We annotate a new stance detection dataset, called COVID-19-Stance. Using this newly annotated dataset, we train several established stance detection models to ascertain a baseline performance for this specific task. To further improve the performance, we employ self-training and domain adaptation approaches to take advantage of large amounts of unlabeled data and existing stance detection datasets. The dataset, code, and other resources are available on GitHub.(1)

Passive Adaptation or Active Learning: A Survey on the Subjective Feelings of University Teachers in Mainland China in the Online Education Environment during the COVID-19 Pandemic

This paper proposes that we should focus on the subjective feelings of university teachers in the online education environment during the COVID-19 pandemic. Using the questionnaire survey method and choosing university teachers in mainland China as the objects of investigation, it obtained a total of 256 survey samples and puts forward two assumptions. One is that changes brought by the online education environment during the pandemic have a significant influence on university teachers' subjective feelings. The other is that university teachers with different backgrounds have different subjective feelings in the online education environment. The survey results showed university teachers' subjective feelings overall were at the upper middle level, but their attitudes towards and adaption to online classes were diverse. Among the five dimensions of teachers' subjectivity, the score of self-consciousness was the highest, and the score of autonomy was the lowest. During the pandemic, the online teaching environment has a significant impact on the subjective feelings of university teachers in mainland China. Differences in the subjective feelings of university teachers with different backgrounds mainly exist in gender variables, while there is no significant difference in age, years of teaching, and teaching subjects.

Opinion Mining about Online Education Basing on GDELT and Twitter Data

This public health incident transformed teaching activities from offline to online. The media content and comments on social media provide a dataset for digging the public opinion on online learning. This study uses the GDELT and TWITTER platforms' data, searching "COVID" and "online education" as keywords;the relevant information is collected and analyzed in python. The results of this public opinion mining will play an essential role in discovering the problem of online teaching in this pandemic.

A Hybrid Data Mining Method for EEG Analysis in Online Education Enhanced by Background Music

Oitline teaching arc facing drikmatic challenges due to the COVIDI9 pandemic requiring massive online education. Students are experiencing mental and physical isolation during this period. This research :Aims to find an efficient may to discover students emotion status through 1,;El: patient recognition (1'10. Traditional PR !method,' haie been applied eNtensikeh in 1":Ft;recognition including Artificial Neuron Networks iANNi, Support Vector Nlak'hinekSVNI Nearest Xeighhorc (KNN), and so on. In this paper, a association rule -based PR method has been introduced through incorporating clustering and Apriori association rube methods. The experimental results demonst rale that Ute tgdimized rule -based 1":F:t;PR model Can improve real-time recognition eflicienc. Tlic proposed model can Ike used for identifying students cognitive statuses and improve educational perfikrrnunce in (.0\11/19 period