PROTOCOLS FOR RESTARTING TOURISM IN COVID-19 CONDITIONS AND DEVELOPING PANDEMIC IMMUNITY.

The purpose of the study is to provide a better understanding of the protocols for restarting tourism considered as an international prior action plan, and to present an academic output supporting the development of pandemic immunity. In this direction, the global guidelines of United Nations World Tourism Organization dated 28 May 2020 and new-normal protocols of World Travel and Tourism Council dated 29 May 2020 were translated into Turkish and subjected to content analysis. The entire analysis process, from coding to Sankey diagram creation, conducted via ATLAS.ti - v.22.1.4.0, a computer-aided qualitative data analysis software. Among the results;the role in restarting tourism and the aspects of developing pandemic immunity of implementing hygiene-oriented innovative practices even if they will require changes in the organizational chart, prioritizing coordination, communication and cooperation not only in the steps taken by the government and businesses but also by the other interested parties, making the dynamism within the internal processes brought about by the turbulent conditions efficient and standardized via the adoption of protocols establishing a specific business manner and procedure are stand out. (English) [ FROM AUTHOR] Çalışmanın amacı;uluslararası nitelikteki turizmi yeniden başlatma protokollerinin detaylı bir şekilde incelenerek somutlaştırılmasıdır. Böylece benzer krizlerin şok etkisini kısaltan, eyleme geçme sürecini daha etkili ve hızlı kılan bir pandemi bağışıklığının geliştirilmesine katkı sağlanması hedeflenmektedir. Bu doğrultuda, Türkçeye çevrilmiş olan Birleşmiş Milletler Dünya Turizm Örgütü'nün 28 Mayıs 2020 tarihli küresel yönergesi ile Dünya Seyahat ve Turizm Konseyi'nin 29 Mayıs 2020 tarihli yeni-normal protokolleri içerik analizine tabi tutulmuştur. Kodlama işleminden Sankey diyagramı oluşturmaya kadar tüm analiz süreci, bilgisayar destekli bir nitel veri analiz yazılımı olan ATLAS.ti - v.22.1.4.0 vasıtasıyla gerçekleştirilmiştir. Sonuçlar arasından;örgüt şemasında değişimleri gerektirecek olsa dahi hijyen odaklı inovatif uygulamaların hayata geçirilmesinin, yalnızca hükümetin ve işletmelerin değil;diğer ilgili tarafların atacağı adımlarda da koordinasyonun, iletişimin ve iş birliğinin öncelikli olarak değerlendirilmesinin, çalkantılı koşulların işletme içi süreçlerde meydana getirdiği olumsuzlukların protokollerin benimsenmesi aracılığıyla standardize edilmesinin ve verimli kılınmasının, turizmin yeniden başlatılmasındaki rolü ve pandemi bağışıklığını geliştirici yönleri ön plana çıkmaktadır. (Turkish) [ FROM AUTHOR] Copyright of Pamukkale University Journal of Social Sciences Institute / Pamukkale Üniversitesi Sosyal Bilimler Enstitüsü Dergisi is the property of Pamukkale University, Social Sciences Institute and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

The Relationship Between the Fear of COVID-19 in the Elderly Aged 65 Years and Over and Their Levels of Adaptation to the "New Normal": A Cross-Sectional Study.

This study was carried out to determine the relationship between the fear of COVID-19 in the elderly aged 65 years and over and their levels of adaptation to the "new normal." This descriptive cross-sectional study was completed with 623 elderly individuals. It was determined that the individuals who adapted well to the "new normal" had high levels of adaptation to old age, while their levels of fear of COVID-19 were slightly above average (p < 0.01). Elderly individuals have tried to adapt to the "new normal" while also experiencing fear of COVID-19. In order to minimize the fear experienced by the elderly during COVID-19, adequate support and psychological support should be provided.

MASCULINIDAD HEGEMÓNICA, ROLES DE GÉNERO Y VIOLENCIA INTRAFAMILIAR EN PUEBLA-TLAXCALA, MÉXICO

El objetivo del artículo es analizar, desde la perspectiva de género, la incidencia de la masculinidad hegemónica y los roles de género estereotipados en la armonía familiar en la Zona Metropolitana de Puebla-Tlaxcala (ZMTP), donde se reporta un incremento de violencia durante el confinamiento por Covid-19 como resultado del reparto desigual en los quehaceres domésticos y el machismo en México. Es una investigación cualitativa donde se empleó el método de encuesta telefónica y descriptivo-exploratorio. Al final del trabajo, se evidencia que las tradiciones culturales y la normalización de la dominación masculina impiden la erradicación de los abusos en el hogar.Alternate :The objective of the article is to analyze, from the gender perspective, the incidence of hegemonic masculinity and stereotyped gender roles in family harmony where it refers to an increase in violence during confinement by Covid-19 in the Metropolitan Area Puebla-Tlaxcala (ZMTP) as a result of the unequal distribution of domestic chores and sexism in Mexico. It is a qualitative investigation where the method of telephone and descriptive-exploratory survey was used. At the end of the work, it shows that cultural traditions and the normalization of male domination prevent the eradication of abuse in the home.

Exploration on the construction of the regional center of continuing medical education under the background of normalization of epidemic prevention and control

The COVID-19 epidemic is running at a high level globally, affecting all aspects of society, and medical education is no exception. With the rapid development of medical science, continuing medical education is an important way for medical workers to receive lifelong education. Meanwhile, attending continuing medical education is an inevitable requirement to ensure clinical ability. Under the background of normalization of epidemic prevention and control and the new situation of medical development, the management of continuing medical education in hospitals must follow the current situation and keep pace with the times. Therefore, the Internet support system to continuing education has emerged. This study used PDSA method to explore the construction of the regional center of continuing medical education through Internet under the background of normalization of epidemic prevention and control, aiming to promote the integration of medical education resources under the new situation, expand the learning channels of medical staff, and improve the level of medical education and teaching. © 2022 IEEE.

Green bond market boom: did environmental, social and governance criteria play a role in reducing health-related uncertainty?

Recent years have been characterized by considerable growth of the green bond market in Europe, particularly in the domain of social bond issuance. Considering the recent pandemic, it is also a stylized fact that this growth is positively correlated with the concept of health-related uncertainty, as the green bond market aims to acquire financing in order to allow the development of projects that comply with the so-called environmental (E), social (S) and governance (G) criteria. This study then applies a dynamic spatial econometric analysis and several robustness checks to assess the extent to which each E, S and G criterion contributes to the societal dynamics of health-related uncertainty. The analysis takes advantage of available data on the number of confirmed cases of COVID-19 to measure health-related uncertainty at the municipal level, so that a higher (lower) number of confirmed cases constitutes a proxy for a greater (smaller) degree of uncertainty, respectively. To reinforce the need to evaluate impacts in a context characterized by health-related uncertainty, the time span covers the first wave of COVID-19, which is the period when uncertainty reached its highest peak. Additionally, the geographical scope is mainland Portugal since this country has become a breeding ground for startups and new ideas, being currently one of the world leaders in hosting businesses that reached Unicorn status. The main result of this research is that only the social dimension has a significant, positive and permanent impact on health-related uncertainty. Therefore, this study empirically confirms that the European green bond market has been and can be further leveraged by the need to finance projects with a social scope.

Covid-19 Forecasting Using CNN Approach With A Halbinomial Distribution And A Linear Decreasing Inertia Weight-Based Cat Swarm Optimization

In recent times, the COVID-19 epidemic has spread to over 170 nations. Authorities all around the world are feeling the strain of COVID-19 since the total of infected people is rising as well as they does not familiar to handle the problem. The majority of current research effort is thus being directed on the analysis of COVID-19 data within the framework of the machines learning method. Researchers looked the COVID 19 data to make predictions about who would be treated, who would die, and who would get infected in the future. This might prompt governments worldwide to develop strategies for protecting the health of the public. Previous systems rely on Long Short -Term Memory (LSTM) networks for predicting new instances of COVID-19. The LSTM network findings suggest that the pandemic might be over by June of 2020. However, LSTM may have an over-fitting issue, and it may fall short of expectations in terms of true positive. For this issue in COVID-19 forecasting, we suggest using two methods such as Cat Swarm Optimization (CSO) for reducing the inertia weight linearly and then artificial intelligence based binomial distribution is used. In this proposed study, we take the COVID-19 predicting database as an contribution and normalise it using the min-max approach. The accuracy of classification is improved with the use of the first method to choose the optimal features. In this method, inertia weight is added to the CSO optimization algorithm convergence. Death and confirmed cases are predicted for a certain time period throughout India using Convolutional Neural Network with Partial Binomial Distribution based on carefully chosen characteristics. The experimental findings validate that the suggested scheme performs better than the baseline system in terms of f-measure, recall, precision, and accuracy.

Convolutional Neural Networks for Detection of COVID-19 from Chest X-Rays

The coronavirus (COVID-19) pandemic was rapid in its outbreak, and the contagion of the virus led to an extensive loss of life globally. This study aims to propose an efficient and reliable means to differentiate between chest x-rays indicating COVID-19 and other lung conditions. The proposed methodology involved combining deep learning techniques such as data augmentation, CLAHE image normalization, and transfer learning with eight pre-trained networks. The highest performing networks for binary, 3-class (normal vs. COVID-19 vs. viral pneumonia) and 4-class classifications (normal vs. COVID-19 vs. lung opacity vs. viral pneumonia) were MobileNetV2, InceptionResNetV2, and MobileNetV2, achieving accuracies of 97.5%, 96.69%, and 92.39%, respectively. These results outperformed many state-of-the-art methods conducted to address the challenges relating to the detection of COVID-19 from chest x-rays. The method proposed can serve as a basis for a computer-aided diagnosis (CAD) system to ensure that patients receive timely and necessary care for their respective illnesses. Copyright © 2022, IGI Global.

Effects of COVID‐19 lockdown measures on nitrogen dioxide and black carbon concentrations close to a major Italian motorway

During the first half of 2020, the Italian government imposed several restrictions to limit the spread of the COVID‐19 pandemic: at the beginning of March, a heavy lockdown regime was introduced leading to a drastic reduction of traffic and, consequently, traffic‐related emissions. The aim of this study is to evaluate the effects of these restrictions on pollutant concentrations close to a stretch of the Italian A22 motorway lying in the Alpine Adige valley. In particular, the analysis focuses on measured concentrations of nitrogen dioxide (NO2) and black carbon (BC). Results show that, close to the motorway, NO2 concentrations dropped by around 45% during the lockdown period with respect to the same time period of the previous 3 years. The equivalent analysis for BC shows that the component related to biomass burning, mostly due to domestic heating, was not particularly affected by the restrictions, while the BC component related to fossil fuels, directly connected to traffic, plummeted by almost 60% with respect to the previous years. Since atmospheric concentrations of pollutants depend both on emissions and meteorological conditions, which can mask the variations in the emission regime, a random forest algorithm is also applied to the measured concentrations, in order to better evaluate the effects of the restrictions on emissions. This procedure allows for obtaining business‐as‐usual and meteorologically normalized time series of both NO2 and BC concentrations. The results derived from the random forest algorithm clearly confirm the drop in NO2 emissions at the beginning of the lockdown period, followed by a slow and partial recovery in the following months. They also confirm that, during the lockdown, emissions of the BC component due to biomass burning were not significantly affected, while those of the BC component related to fossil fuels underwent an abrupt drop.

Novel neural network architecture using sharpened cosine similarity for robust classification of Covid-19, pneumonia and tuberculosis diseases from X-rays

COVID-19 is a rapidly proliferating transmissible virus that substantially impacts the world population. Consequently, there is an increasing demand for fast testing, diagnosis, and treatment. However, there is a growing need for quick testing, diagnosis, and treatment. In order to treat infected individuals, stop the spread of the disease, and cure severe pneumonia, early covid-19 detection is crucial. Along with covid-19, various pneumonia etiologies, including tuberculosis, provide additional difficulties for the medical system. In this study, covid-19, pneumonia, tuberculosis, and other specific diseases are categorized using Sharpened Cosine Similarity Network (SCS-Net) rather than dot products in neural networks. In order to benchmark the SCS-Net, the model's performance is evaluated on binary class (covid-19 and normal), and four-class (tuberculosis, covid-19, pneumonia, and normal) based X-ray images. The proposed SCS-Net for distinguishing various lung disorders has been successfully validated. In multiclass classification, the proposed SCS-Net succeeded with an accuracy of 94.05% and a Cohen's kappa score of 90.70%;in binary class, it achieved an accuracy of 96.67% and its Cohen's kappa score of 93.70%. According to our investigation, SCS in deep neural networks significantly lowers the test error with lower divergence. SCS significantly increases classification accuracy in neural networks and speeds up training. [ FROM AUTHOR] Copyright of Journal of Intelligent & Fuzzy Systems is the property of IOS Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Exploring the role of policy actors in the implementation of social distancing: a case of COVID-19 in Pakistan

Purpose: At the outbreak of the COVID-19 pandemic, the absence of pharmaceutical agents meant that policy institutions had to intervene by providing nonpharmaceutical interventions (NPIs). To satisfy this need, the World Health Organization (WHO) issued policy guidelines, such as NPIs, and the government of Pakistan released its own policy document that included social distancing (SD) as a containment measure. This study explores the policy actors and their role in implementing SD as an NPI in the context of the COVID-19 pandemic. Design/methodology/approach: The study adopted the constructs of Normalization Process Theory (NPT) to explore the implementation of SD as a complex and novel healthcare intervention under a qualitative study design. Data were collected through document analysis and interviews, and analysed under framework analysis protocols. Findings: The intervention actors (IAs), including healthcare providers, district management agents, and staff from other departments, were active in implementation in the local context. It was observed that healthcare providers integrated SD into their professional lives through a higher level of collective action and reflexive monitoring. However, the results suggest that more coherence and cognitive participation are required for integration. Originality/value: This novel research offers original and exclusive scenario narratives that satisfy the recent calls of the neo-implementation paradigm, and provides suggestions for managing the implementation impediments during the pandemic. The paper fills the implementation literature gap by exploring the normalisation process and designing a contextual framework for developing countries to implement guidelines for pandemics and healthcare crises. © 2023, Muhammad Fayyaz Nazir, Ellen Wayenberg and Shahzadah Fahed Qureshi.

Integrating machine learning with linguistic features: A universal method for extraction and normalization of temporal expressions in Chinese texts.

BACKGROUND AND OBJECTIVE: With the rapid development of information dissemination technology, the amount of events information contained in massive texts now far exceeds the intuitive cognition of humans, and it is hard to understand the progress of events in order of time. Temporal information runs through the whole process of beginning, proceeding, and ending of events, and plays an important role in many natural language processing applications, such as information extraction, question answering, and text summary. Accurately extracting temporal information from Chinese texts and automatically mapping the temporal expressions in natural language to the time axis are crucial to understanding the development of events and dynamic changes in them. METHODS: This study proposes a method integrating machine learning with linguistic features (IMLLF) for extraction and normalization of temporal expressions in Chinese texts to achieve the above objectives. Linguistic features are constructed by analyzing the expression rules of temporal information, and are combined with machine learning to map the natural language form of time onto a one-dimensional timeline. The web text dataset we build is divided into five parts for five-fold cross-validation, to compare the influence of different combinations of linguistic features and different methods. In the open medical dialog dataset, based on the training model obtained from the web text dataset, 200 disease descriptions are randomly selected each time for three rounds of experiments. RESULTS: The F1 of multi-feature fusion is 95.2%, which is better than the single-feature and double-feature combination. The results of experiments showed that the proposed IMLLF method can improve the accuracy of recognition of temporal information in Chinese to a greater extent than classical methods, with an F1-score of over 95% on the web text dataset and medical conversation dataset. In terms of the normalization of time expressions, the accuracy of the IMLLF method is higher than 93%. CONCLUSIONS: IMLLF has better results in extracting and normalizing time expressions on the web text dataset and the medical conversation dataset, which verifies the universality of IMLLF to identify and quantify temporal information. IMLLF method can accurately map the time information to the time axis, which is convenient for doctors to intuitively see when and what happened to the patient, and helps to make better medical decisions.

The governance shock doctrine: Civic space in the pandemic

MotivationEmergencies heighten societies' need to be governed. Accordingly, the COVID‐19 pandemic put systems of public governance under severe pressure across the globe. Civic freedoms were widely curtailed for public health reasons. Scarce resources needed to be allocated swiftly, with little opportunity for debate.PurposeIn settings characterized by authoritarianism, violent conflict, and restricted civic space, relations between governments, civil society, and citizens at best tend to be fragile and fraught even in "normal” times. What happens when these settings are rocked by a profound shock such as the onset of a global pandemic?Methods and approachThis article is based on research on civic space and civic action shortly after the onset of the pandemic in three such settings—Mozambique, Nigeria, and Pakistan. Civil society advocates in each country tracked and interpreted events in real time, debated their responses, supplemented their own knowledge through key informant interviews, and compared experiences across countries.FindingsI argue that the three governments' responses to the COVID‐19 pandemic constitute a "governance shock doctrine,” based on the premise that shocks bring responses from the powerful that advance certain agendas. This patterned phenomenon, visible across the three countries, consists of "securitization” of the public health emergency, suppression of dissent, extension and centralization of executive powers, curtailment of press freedoms, and tightened regulation of civic space, including online space. Civic activism navigated or combated these attacks in various ways.Policy implicationsMeasures adopted in emergency situations tend to persist, threatening to lock civil society into living with pandemic‐era restrictions. Preventing this should be a global priority, and especially important where authoritarianism already looms. An energetic mobilization among national and international actors to reassert and protect civic space is needed if the erosion of civil liberties and normalization of autocratic governance wrought by the political‐military apparatus in so many countries during the COVID‐19 pandemic is not to become permanent, and if the inspired and progressive innovations in organic civic activism over the 2020–2021 crisis period are to survive and flourish.

Social media in the election during the COVID-19 pandemic in South Korea: Candidates' use of social media when offline gatherings are prohibited

Objective: : This study investigated the utilization of social media during the 2020 South Korean general election, which took place during the COVID-19 pandemic, using the equalization versus normalization framework. Methods: : This study estimated the associations between the characteristics of candidates and their respective constituencies and the use of various social media platforms by the candidates. Results: : Dominant political actors were more active social media users, supporting the normalization hypothesis. However, when considering the candidates' chances of winning the election, social media's normalizing effect was weakened. Conclusion: : This study provides new insights into the equalization versus normalization debate by analyzing social media use in a context where offline campaigning was restricted. © 2023 by the Southwestern Social Science Association.

Vloga socialnega dela v izrednih razmerah

Zaradi katastrof, kot so naravne nesreće, onesnaženje okolja, oboroženi spopadi in pandēmija koronovirusne bolezni, se posamezne skupnosti ali celotne družbe znajdejo v krizi in vzpostavijo izredne razmere, v katerih z bolj ali manj usklajenimi ukrepi poskušajo zajeziti nevarnost, omiliti ali odpraviti tveganja in ponovno vzpostaviti stabilne razmere v družbi. V izrednih razmerah ima poleg drugih služb za zašćito in reševanje tudi socialno delo pomembno vlogo pri pomoći in podpori ljudem - predvsem ranljivim posameznikom, družinam, skupinam ali celotnim skupnostim. V príspevku je prikazan pregled znanstvene in strokovne literature o socialnem delu priodpravljanju psihosocialnih in družbenih posledic razlićnih vrst katastrof. Predstavljeni so učinkoviti pristopi in metode socialnega dela pri pomoći in podpori ljudem in skupnostim v izrednih razmerah, posebej vsedanji pandemiji covida-19. Posebna pozornost je namenjena obravnavi pomoći in podpore socialnim delavkam, ki zagotavljajo psihosocialno pomoć in podporo ljudem v stiski zaradi katastrofe.Alternate :Disasters such as natural disasters, environmental pollution, armed conflicts and the coronavirus pandemic, affect the population pushing individual communities or entire societies in a crisis and state of emergency. More or less coordinated measures are used to restrain danger, mitigate or eliminate risks and restore stable conditions in society, in order to enable the normalization of everyday life as soon as possible. In addition to other protection and rescue services, social work plays an important role in helping and supporting people in disasters - in particular the vulnerable individuals, families, groups or whole communities. The article presents a review of scientific and professional literature on social work in eliminating the psychosocial and societal consequences of various types of disasters. Effective approaches and methods of social work in helping and supporting people and communities in emergencies are presented, especially in the current Covid-19 pandemic. Special attention is also paid to provision of support for the social workers, who secure psychosocial help and support to the people affected in disasters.

Text Monitoring with NLP for Covid Related Data

Since the adoption of the internet as a medium of communication of information, fake or false information or news has always been a major issue. Incidents of false information have always increased at times of crisis on national or international scales. The world witnessed a global pandemic from the Coronavirus, causing a complete disruption in the functioning of society. News of bogus cures, home remedies, and medicines started to make their way around the world. The number of incidents of such false news only increased as the pandemic worsened and more people were falling sick and dying. In times of desperation, people can easily be persuaded to try unverified and possibly dangerous medicines or cures, that can cost them their money as well as health. In this paper, natural language processing is used to first identify and differentiate text that has information regarding Covid 19 from the text that does not contain information regarding Covid 19. Word frequency scores like TF and IDF scores are then calculated. The intent of the text is then analyzed by observing the mannerisms detected in false news. With this analysis, the potential of the text to be false or fake is then determined. This research intends to explore the linguistics of false news and to get one step ahead in identifying fake news. The same methodology can be used to analyze data related to other specific topics. © 2022 IEEE.

Dynamic connectedness between COVID-19 news sentiment, capital and commodity markets

This study investigates the dynamic transmission mechanism between COVID-19 news sentiment (Google Trends Index), and S&P100, crude oil and gold volatility indices using the recently developed time-varying parameter vector autoregressive (TVP-VAR)-based extended joint connectedness approach. This framework corrects for the Generalized Forecast Error Variance Decomposition (GFEVD) normalization problem. The obtained empirical results suggest that dynamic total connectedness is heterogeneous over time and severely affected by COVID-19. More importantly, we identify COVID-19 news sentiment to be the main driver of spillover shocks indicating that it is indeed an important predictor of the volatility indices employed in our study. Thus, our findings have important implications for policymakers, private investors, as well as for portfolios and risk managers.

Reflections on the Pain Points of Elderly Care during the Epidemic Prevention and Control Stage from Two Cases of Disease Narration

The COVID-19 has had a profound impact on human society, the elderly, as a vulnerable group, are the most affected. Based on two cases of disease narrative collected by the department of neurology of a hospital in Guiyang, this paper analyzed the shortcomings of elderly care in the context of epidemic prevention and control. The overall health information literacy of the elderly was low, which made it difficult to obtain correct epidemic related information. The lifestyle of the elderly has changed during the epidemic prevention and control stag. The long time isolation at home has reduced their constitution and made them prone to illness, thus affecting the quality of healthy elderly care. In this stage, the psychological burden of the middle-aged and elderly people has been increasing, which reduced the life satisfaction and subjective well-being of the elderly, and increased the risk of depression. In view of these outstanding problems, this paper proposed corresponding measures, aiming to improve the quality of life and physical and mental health of the elderly during the epidemic prevention and control stage, and provide reference for realizing healthy aging in China. © 2023, Editorial department of Chinese Medical Ethics. All rights reserved.

Quantification of COVID-19 impacts on NO2 and O3: Systematic model selection and hyperparameter optimization on AI-based meteorological-normalization methods

Since the unprecedented outbreak of the COVID-19, numerous meteorological-normalization techniques have been developed in lockdown-imposed regions to decouple the impacts of meteorology and emissions on the atmospheric environment. However, the application of normalization techniques in regions without lockdown is limited. In this study, we propose a novel research framework to investigate the observed and meteorological-normalized concentrations of nitrogen dioxide (NO2) and ozone (O3) across 62 cities in Taiwan. Four meteorological-normalization techniques, namely, the generalized additive model (GAM), generalized linear model (GLM), gradient boosting machine (GBM), and random forest (RF), were developed, optimized, and compared using meteorological and temporal variables. The models were optimized using a systematic trial-and-error approach for data distribution type in GAM and GLM and a grid-search approach for tree numbers in GBM and RF. Based on the findings, for GLM, the optimal data distribution for both NO2 and O3 modeling was Gaussian, whereas for GAM, the optimal data distribution for NO2 and O3 simulation was quasi- Gaussian and Poisson, respectively. In contrast, for RF and GBM, the optimized number of trees varied significantly by site, ranging from 10 to 6310. The simulation performance of RF and GBM was better than that of GAM and GLM across Taiwan and the best-performing optimized model was selected to identify changes in NO2 and O3 concentrations during COVID-19. Throughout 2020, even in the absence of a lockdown, the daily mean meteorological-normalized NO2 and O3 levels across Taiwan decreased by 14.9% and 5.8%, respectively, providing novel insights for sustainable air quality management.Copyright © 2023

Relationship between Anxiety, Depression and Learning Burnout of Nursing Undergraduates after the COVID-19 Epidemic: The Mediating Role of Academic Self-Efficacy.

BACKGROUND: Although cross-sectional studies on the learning status of nursing undergraduates during the COVID-19 epidemic have surged, few studies have explored the normalization of COVID-19 on students' learning burnout and mental health. The study was designed to investigate the learning burnout of nursing undergraduates in school under the normalization of the COVID-19 epidemic and explore the hypothesized mediation effect of academic self-efficacy in the relationship between anxiety, depression and learning burnout in Chinese nursing undergraduates. METHODS: A cross-sectional study was conducted among nursing undergraduates in the school of nursing of a university in Jiangsu Province, China (n = 227). A general information questionnaire, College Students' Learning Burnout Questionnaire, Generalized Anxiety Disorder Scale (GAD-7), and Patient Health Questionnaire depression scale (PHQ-9) were administered. Descriptive statistical analysis, Pearson correlation analysis, and multiple linear regression analysis were performed via SPSS 26.0. Process plug-in (Model 4) was used to test the mediating effect of academic self-efficacy (bootstrap 5000 iterations, α = 0.05). RESULTS: Learning burnout (54.1 ± 0.656) was positively correlated with anxiety (4.6 ± 0.283) and depression (5.3 ± 0.366) (p < 0.01) and was negatively correlated with academic self-efficacy (74.41 ± 0.674) (p < 0.01). Academic self-efficacy plays a mediating role between anxiety and learning burnout (0.395/0.493, 80.12%) and a mediating role between depression and learning burnout (0.332/0.503, 66.00%). CONCLUSION: Academic self-efficacy has a significant predictive effect on learning burnout. Schools and teachers should strengthen the screening and counselling of students' psychological problems, detect learning burnout caused by emotional problems in advance and improve students' initiative and enthusiasm for learning.

Internal reference genes with the potential for normalizing quantitative PCR results for oral fluid specimens.

In basic research, testing of oral fluid specimens by real-time quantitative polymerase chain reaction (qPCR) has been used to evaluate changes in gene expression levels following experimental treatments. In diagnostic medicine, qPCR has been used to detect DNA/RNA transcripts indicative of bacterial or viral infections. Normalization of qPCR using endogenous and exogenous reference genes is a well-established strategy for ensuring result comparability by controlling sample-to-sample variation introduced during sampling, storage, and qPCR testing. In this review, the majority of recent publications in human (n = 136) and veterinary (n = 179) medicine did not describe the use of internal reference genes in qPCRs for oral fluid specimens (52.9% animal studies; 57.0% human studies). However, the use of endogenous reference genes has not been fully explored or validated for oral fluid specimens. The lack of valid internal reference genes inherent to the oral fluid matrix will continue to hamper the reliability, reproducibility, and generalizability of oral fluid qPCR assays until this issue is addressed.