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Coronavirus disease 2019 (COVID-19) has endured constituting formidable economic, social, educational, and phycological challenges for the societies. Moreover, during pandemic outbreaks, the hospitals are overwhelmed with patients requiring more intensive care units and intubation equipment. Therein, to cope with these urgent healthcare demands, the state authorities seek ways to develop policies based on the estimated future casualties. These policies are mainly non-pharmacological policies including the restrictions, curfews, closures, and lockdowns. In this paper, we construct three model structures of the S p I n I t I b D-N (suspicious S p , infected I n , intensive care I t , intubated I b , and dead D together with the non-pharmacological policies N) holding two key targets. The first one is to predict the future COVID-19 casualties including the intensive care and intubated ones, which directly determine the need for urgent healthcare facilities, and the second one is to analyse the linear and non-linear dynamics of the COVID-19 pandemic under the non-pharmacological policies. In this respect, we have modified the non-pharmacological policies and incorporated them within the models whose parameters are learned from the available data. The trained models with the data released by the Turkish Health Ministry confirmed that the linear S p I n I t I b D-N model yields more accurate results under the imposed non-pharmacological policies. It is important to note that the non-pharmacological policies have a damping effect on the pandemic casualties and this can dominate the non-linear dynamics. Herein, a model without pharmacological or non-pharmacological policies might have more dominant non-linear dynamics. In addition, the paper considers two machine learning approaches to optimize the unknown parameters of the constructed models. The results show that the recursive neural network has superior performance for learning nonlinear dynamics. However, the batch least squares outperforms in the presence of linear dynamics and stochastic data. The estimated future pandemic casualties with the linear S p I n I t I b D-N model confirm that the suspicious, infected, and dead casualties converge to zero from 200000, 1400, 200 casualties, respectively. The convergences occur in 120 days under the current conditions. • We construct three S p I n I t I b D-N model structures; namely, linear S p I n I t I b D-N model, non-linear S p I n I t I b D-N model, and strongly non-linear S p I n I t I b D-N model to reveal the linear and non-linear characters of the COVID-19 casualties. To the best of our knowledge, this is the first paper examining the linear and non-linear properties of a pandemic disease. • We modify the non-pharmacological policies based on their changing characters with the occurrence of the second peak in the COVID-19 casualties. Re-opening the schools partial-by-partial and imposed self-curfews, for instance, are modelled and incorporated into the S p I n I t I b D-N models. • We enrich the SpID-N model with the intensive care I t and intubated I b , which will greatly help to estimate the hospital requirements. [ABSTRACT FROM AUTHOR]

[Display omitted] • A ready-to-use colorimetric device for oxidative disinfectants evaluation is proposed. • The colorimetric sensing element relies on a nanodecorated PET-sheet. • The device is fully realized with office-grade materials and instruments. • H 2 O 2 , NaClO, and C 2 H 4 O 3 were successfully determined obtaining LODs ∼ 1 ppb. • Disinfectants, disinfection treatments, and relative residues were analyzed. In this work, a ready-to-use colorimetric device for oxidative disinfectants (i.e., H 2 O 2 , C 2 H 4 O 3 , and NaClO) detection has been developed and proved for the evaluation of disinfection treatments in the COVID-19 containment framework. The sensing element of the proposed colorimetric indicator consists of a transparent flexible PET substrate integrating gold and silver nanostructures. The nano-equipped indicator was integrated into different devices realized using a craft-cutting plotter taking advantage of adhesive/thermo-adhesive flexible pouches and filter paper. In particular, two devices are presented (i) a flexible multi-analysis sampling plate and (ii) a paper-based sampling pipette , for the determination of oxidant disinfectants concentration, evaluation of disinfection treatments by spraying/fogging, and for the analysis of disinfectant residues. The sensing strategy relies on the ability of the disinfectants to oxidize the silver nanostructures of the indicator substrate, producing a colorimetric variation monitorable by a smartphone, maximizing the sensitivity using a colored background. The proposed colorimetric assay occurs in 5 min and does not use external reagents; contact of the disinfectant with the sensing surface is the only requirement. The devices had very low limits of detection (∼1 ppb) and good reproducibility (RSD ≤ 2 %, n = 3) for direct disinfectants analysis. They were challenged for the evaluation of indoor disinfection treatments, allowing to evaluate spraying and fogging treatments, as well as residues monitoring. The here proposed system is disposable, low-cost both in therm of material and reagents (0.05 €) and instruments required, portable, and able to return information on the good practices recommended for environmental disinfection. [ABSTRACT FROM AUTHOR]

Introduction: The immunogenicity and efficacy of COVID-19 vaccination varied by demographic, including solid organ transplant recipients on immunosuppressive therapy. Aim: This purpose of this study is to assess seropositivity and seroconversion in solid-organ transplant recipients before and after third-dose COVID-19 vaccination. Methods: This study is a systematic review and meta-analysis performed using PRISMA guidelines. To analyze clinical and cohort studies reporting immunologic response and seroconversion third-dose vaccination, a systematic search was performed using electronic databases (PubMed, Scopus, Cochrane, Directory of Open Access Journal (DOAJ), and Clinicaltrials.gov). Result: There were 18 full-text papers that could be analyzed qualitatively and quantitatively. After the third vaccination, the pooled rate seropositivity was 67.00% (95% CI 59.511; 74.047, I2 = 93.82%), and the pooled rate seroconversion was 52.51% (95% CI 44.03; 60.91, I2 = 92.15%). The pooled rate of seroconversion after the mRNA-based booster was 52.380% (95% CI 40.988; 63.649, I2 = 94.35%), and after the viral-vector-based booster was 42.478% (95% CI 35.222; 49.900, I2 = 0.00%). Conclusion: Based on the analysis of immunologic responses and seroconversion findings, the third-dose vaccination of solid organ transplant recipients is an effective method in establishing better immunity against COVID-19. [ABSTRACT FROM AUTHOR]

Hypersphere Guided Embedding for Masked Face Recognition has been proposed to address the problem encountered in the Masked Face Recognition task, which arises due to non-biological information from occlusions. While some existing algorithms prefer to digesting the existence of masks by probing and covering, others aim to integrate face recognition and masked face recognition tasks into a unified solution domain. In this paper, we propose a framework to enable existing methods to accommodate multiple data distributions by orthogonal subspaces. Specifically, We introduce constraints on multiple hypersphere manifolds via Multi-Center Loss and employ a Spatial Split Strategy to ensure the orthogonality of base vectors associated with different hypersphere manifolds, corresponding to distinct distribution. Our method is extensively evaluated on publicly available datasets on face recognition, mask face recognition and occlusion, demonstrating promising performance. Our code is available on an anonymous website: https://github.com/CaptainKai/HE_MFR. [Display omitted] • Elucidated the adverse impact of mask information. • Explored the similarities and disparities between different data distribution. • Experiments proved the effectiveness of our method. [ABSTRACT FROM AUTHOR]

To describe CT findings in patients with confirmed Covid-19 infection and correlate them with the disease evolution stages. This is a historical cohort observational analytical study carried out with outpatients, inpatients, and emergency patients from a private hospital in Maceió/AL, Brazil. The final sample consisted of 390 patients with positive RT-PCR for Covid-19 with available laboratory tests and chest CT results. The most frequent initial symptoms were cough, fever, dyspnea and headache. The most commonly found comorbidities were hypertension, diabetes mellitus and obesity. A total of 22% of the CT scans showed no alterations; ground-glass opacity was the most frequently found one. There was a significant association between age, comorbidities, pulmonary involvement, ground-glass opacity, mosaic attenuation and percentage of pulmonary involvement with death. The analysis of the disease stages showed a significant association with laboratory data (CRP and platelet levels), ground-glass opacity and mosaic attenuation with the disease evolution stages in relation to the days since symptom onset. The disease evolution of Covid-19 occurs in stages, and this study describes tomographic findings in patients with confirmed Covid-19 infection and shows they vary depending on the disease evolution stages. This paper provides important addition to the various records that have been accumulated through the Covid-19 pandemic. [ABSTRACT FROM AUTHOR]

The Kingdom of Saudi Arabia has developed rigorous strategies to control and prevent the spread of COVID-19. However, the effectiveness of these measures in containing and mitigating the epidemic has yet to be studied. This paper aims to assess the efficiency of preventive policy initiatives that Saudi Arabia has taken to reduce the spread of COVID-19, which was rapid and progressive in nature. Information on the effectiveness of measures applies to help the Saudi government adjust policy responses when considering which measures to relax once the epidemic is controlled. Data for this study were retrieved via publicly available data sources such as the Saudi Arabia Ministry of Health and the government's official news agency—Saudi Press Agency (SPA) websites. Other datasets, such as prevention measures, were gathered from the Country Policy Tracker website. Our dataset's time component extends over 590 consecutive days from 20 January 2020–31 August 2021. Moreover, a mixed-method approach combining COVID-19 data and prevention measures was adopted to assess preventative measures practice. We compiled the dataset used in this study in a Microsoft Excel database. The significance of observed differences among implementing effective strategies was determined using ANOVA and Mixed methods approach. Noticeably, the statistical analysis was performed using the open-source statistical system R version 4.2 (available at http://cran.r-project.org). Our analysis showed that only three out of the 32 (9.4%) measures significantly reduced the spread of COVID-19. Our results also show substantial variations in the spread of COVID-19 associated with preventive measures in Saudi Arabia. There was a significant positive correlation between activating and massive testing in communities and cases of COVID-19 (measure effect = 923.086 and p < 0.05). A similar result was found for complete curfew across the Kingdom and cases of COVID-19 (measure effect = 621.389 and p < 0.10). Removing slum areas interrupted the spread of Covid-19 (measure effect = 305.689 and p < 0.01). The other preventive measures did not significantly affect the COVID-19 pandemic distribution. These findings consistently concluded that activating and massive testing in communities, complete curfew across the Kingdom, and removal of slum areas were the most effective measures for reducing the impact of the COVID-19 pandemic. Only by understanding these correlations will it be possible to control and reduce the rate of COVID-19 spread and, therefore, suggest a possible exit strategy once the epidemic is controlled. [ABSTRACT FROM AUTHOR]

[Display omitted] • An alternative to vaccination to combat the attack of coronavirus. • First simple model to determine the frequency range for coronavirus inactivation. • First to use the tensegrity concept to model the coronavirus. • Coronavirus inactivation by envelope fracturing is evident in model failure mode. • First natural frequency map for coronavirus vs healthy human cells as reference. COVID-19 is a worldwide health hazard. In discovering a fully efficient vaccination for continually evolving viruses, alternatives such as the sonication treatment methods, which have seen encouraging outcomes in disinfection and medical therapies, are worth exploring. Such treatments incapacitate microbes or diseased cells by selectively invoking large deformation at the resonant frequency that initiates structural failure. Nevertheless, due to biodiversity, the precise range of resonant frequencies for different biological bodies can be widely varying, its determination of which is best achieved with the cost-effective computational simulation approach. The paper proposes, therefore, the numerical determination of the resonant frequency of the coronavirus employing the tensegrity method, due to its efficacy in modeling various biomechanical behaviors of different biological bodies and systems. Initiating from successfully verifying the resonant frequency with existing findings, the model then determines the operative sonication frequencies range for the resonant state of the virus. At these sonication frequencies, the modal analysis exhibits a large structurally destructive deformation of the coronavirus. For practical convenience, the operative range of sonication resonant frequencies of the coronavirus has been mapped alongside healthy human cells. These findings offer an alternative technological avenue in combating the COVID-19 progressive threat. [ABSTRACT FROM AUTHOR]

• In this article, an optimal evaluation of re-opening policies for COVID-19 is developed. • The method provides effective re-opening policies using agent-based models and the whale optimization algorithm. • Agent-based models simulate the transmission risk in scenarios that are impossible to evaluate in real conditions. • The whale optimization algorithm is used to find the best scenario where the lowest risk of infection is reached. • Experiments demonstrate that the proposed approach delivers realistic predictions to formulate re-opening policies. A new contagious disease or unidentified COVID-19 variants could provoke a new collapse in the global economy. Under such conditions, companies, factories, and organizations must adopt reopening policies that allow their operations to reduce economic effects. Effective reopening policies should be designed using mathematical models that emulate infection chains through individual interactions. In contrast to other modeling approaches, agent-based schemes represent a computational paradigm used to characterize the person-to-person interactions of individuals inside a system, providing accurate simulation results. To evaluate the optimal conditions for a reopening policy, authorities and decision-makers need to conduct an extensive number of simulations manually, with a high possibility of losing information and important details. For this reason, the integration of optimization and simulation of reopening policies could automatically find the realistic scenario under which the lowest risk of infection was attained. In this paper, the metaheuristic technique of the Whale Optimization Algorithm is used to find the solution with the minimal transmission risk produced by an agent-based model that emulates a hypothetical re-opening context. Our scheme finds the optimal results of different generical activation scenarios. The experimental results indicate that our approach delivers practical knowledge and essential estimations for identifying optimal re-opening strategies with the lowest transmission risk. [ABSTRACT FROM AUTHOR]

This paper investigates the effects of social learning on the transmission of Covid-19 in a network model. We calibrate our model to detailed data for Cape Town, South Africa and show that the inclusion of social learning improves the prediction of excess fatalities, reducing the best-fit squared difference from 19.34 to 11.40. The inclusion of social learning both flattens and shortens the curves for infections, hospitalizations, and excess fatalities, which is qualitatively different from flattening the curve by reducing the contact rate or transmission probability through non-pharmaceutical interventions. While social learning reduces infections, this alone is not sufficient to curb the spread of the virus because learning is slower than the rate at which the disease spreads. We use our model to study the efficacy of different vaccination strategies and find that vaccinating vulnerable groups first leads to a 72% reduction in fatalities and a 5% increase in total infections compared to a random-order benchmark. By contrast, using a contact-based vaccination strategy reduces infections by only 0.9% but results in 42% more fatalities than the benchmark. [ABSTRACT FROM AUTHOR]

• Transmissibility of three variants was Omicron BA.2>Omicron BA.1 > Delta. • Omicron BA.2 variant has less pathogenicity than Omicron BA.1 and Delta variant. • Transmissibility in cycle threshold value should be discussed in further research. The Omicron BA.2 variant is probably the main epidemic strain worldwide at present. Comparing the epidemiological characteristics, transmissibility, and influencing factors of SARS-CoV-2, the results obtained in this paper will help to provide theoretical support for disease control. This study was a historical information analysis, using the R programming language and SPSS 24.0 for statistical analysis. The Geoda and Arc GIS were used for spatial autocorrelation analysis. Local spatial autocorrelations of the incidence rate were observed in Delta and Omicron BA.1 outbreaks, whereas Omicron BA.2 outbreaks showed a random distribution in incidence rate. The time-dependent reproduction number of Delta, Omicron BA.1, and Omicron BA.2 were 3.21, 4.29, and 2.96, respectively, and correspondingly, the mean serial interval were 4.29 days (95% confidence interval [CI]: 0.37-8.21), 3.84 days (95% CI: 0-8.37), and 2.77 days (95% CI: 0-5.83). The asymptomatic infection rate of cases in Delta, Omicron BA.1, and Omicron BA.2 outbreaks were 21.71%, 6.25%, and 4.35%, respectively. The Omicron BA.2 variant had the greatest serial interval, transmissibility, and transmission speed, followed by BA.1, and then Delta. Compared with Delta and Omicron BA.1 variants, the Omicron BA.2 variant may be less pathogenic and more difficult to control than Omicron BA.1 and Delta. [ABSTRACT FROM AUTHOR]

In July 2020, the Japanese government launched the "Go To Travel" campaign, providing subsidies for domestic travel, during the coronavirus disease (COVID-19) pandemic. While the campaign aimed to revitalize the declining tourism industry, concerns were raised about its potential to spread infection. Although the literature indicates that the campaign increased hotel reservations, its causal effects on infection are not well-known. Using a difference-in-differences framework, this paper examines whether the campaign increased the number of domestic hotel guests and caused the virus to spread. The results show that the campaign increased the number of domestic hotel guests, as travel was not a Giffen good even at the risk of contracting COVID-19. However, the campaign did not increase infection spread in Japan, as it did not increase the transient population in the downtown areas. Moreover, this study provides a comprehensive cost–benefit analysis of the campaign. [ABSTRACT FROM AUTHOR]

This paper examines the effectiveness of four major COVID-19 social distancing policies, (i) shelter-in-place orders (SIPO), (ii) non-essential business closures, (iii) mandatory quarantine for travelers, and (iv) bans on large gatherings, on both COVID cases and COVID deaths. Results indicate that states are highly ineffective in producing the fraction of the population that does not have COVID-19 or the fraction of the population that does not die from COVID-19. We find that having any form of social distancing policies increases the fraction of the population not considered a positive COVID-19 case by 23.5 percentage points. Results also show that having any of the four major social distancing policies reduces the fraction of the population who has died of COVID-19 by 1.3 percentage points between March 1, 2020 and September 1, 2020; during the first 100 days, effectiveness would improve by 2.1 percentage points. Evidence suggests that there is no effective uniform national COVID-19 social distancing policy. Furthermore, conditional efficiency regressions after 100 days suggest that behavioral noncompliance and premature expiration of social distancing policies both negatively impact effectiveness. Partial regression plots suggest that bans on large gatherings and the closure of non-essential businesses were the two most impactful COVID-19 social distancing policies. [ABSTRACT FROM AUTHOR]

This paper aims to analyse the relationships among tourism, sustainability, and innovation in the Bangladesh economy. Based on annual data covering 1980 to 2019, we apply robust and standard time series econometric techniques and reveal that innovation and sustainability are significantly linked with tourism in Bangladesh. Furthermore, we use Computable General Equilibrium (CGE) modelling method to find the impact of the Covid-19 pandemic on the tourism industry. We find that the tourism industry is one of the highly affected industries in Bangladesh. As a recovery strategy, we reveal that innovation efficiency and government stimulus packages are much more effective in tackling the pandemic's adverse impacts. Based on the holistic analysis, we also provide key policy suggestions for the sustainable development of the tourism industry. [ABSTRACT FROM AUTHOR]

The study aims to assess the effect of personal protective equipment (PPE) on the physical and psychological well-being of health care workers (HCWs) and its impact on patient safety. After ethical approval, a 14-point questionnaire was circulated offline and online among the HCWs of ours institute, who were involved in performing invasive procedures while wearing a PPE. The responses were analysed using the SPSS software version 26. Of 198 responses, the mean duration of PPE use was 4.6 ± 1.52 h. Seventy percent of respondents suggested <4 h of continuous use of PPE. Seventy-seven percent found difficulties during the procedures while wearing PPE and agreed to errors while performing a procedure. Poor visibility (95.5%), fogging (84.9%), communication difficulty (75.3%), sweating (74.2%), posture-related discomfort (56.1%) and poor concentration (51%) were major causes. Anxiety (39.9%) and fear of spreading an infection to the family (42.9%) were major psychological effects. Eighty percent of HCWs raised concern over the quality of PPE, N95 mask and eye protector. The HCWs felt the need to improve the quality of PPEs, use extra padding around the ears, sealing the N95 mask with adhesive tape, besides using sign language for communication for more safety. Fifty percent graded the procedure-related difficulty level >6 on a Likert scale of 1–10. PPE-related discomfort is common among the HCWs and could contribute to errors during an invasive procedure. Efforts to alleviate the physical and psychological well-being of the HCWs will be essential for reducing procedural error while wearing a PPE. [ABSTRACT FROM AUTHOR]

• COVID-19 vaccine hesitancy was heterogeneous both in terms of time and magnitude. • Following COVID-19 news, trust levels, risk preferences, and vaccine confidence predicted intention reversals. • Lowering the cost of following the news for the population may raise vaccine uptake. This paper investigates the prevalence and determinants of three main states of people's willingness to be vaccinated (WTBV) against COVID-19 – willing, unwilling and hesitant – and the occurrence and predictors of shifts between these states over time. Understanding the dynamics of vaccine intentions is crucial for developing targeted campaigns to increase uptake and emergency response preparedness. A panel survey consisting of 9 quarterly waves of data collected between April 2020 and January 2022. Baseline data included 24 952 adults from Germany, UK, Denmark, the Netherlands, France, Portugal, and Italy recruited from online panels to construct census-matched nationally representative samples. Self-reported COVID-19 vaccine intention was the main outcome. Multinomial logit random effects models were used to analyze the relationships of interest. All results reported as relative risk ratios (RRR). Hesitancy to get vaccinated was the most unstable vaccine intention, with on average 42% of ever hesitant respondents remaining in this state through future waves, followed by the 'unwilling' (53%) and 'willing (82%). Following COVID-19 news, trust in information from the government, GPs and the WHO, risk preferences, risk perceptions, and confidence in vaccines (or lack thereof) predicted vaccination intention reversals. Risk preferences acted both as an impediment and as a facilitator for the vaccine uptake depending on the initial vaccine intention. This study revealed the dynamic nature of COVID-19 vaccine intentions and its predictors in 7 European countries. The findings provide insights to policymakers for designing more effective communication strategies, particularly targeted at hesitant and unwilling to vaccinate population groups, to increase vaccine uptake for future public health emergencies. [ABSTRACT FROM AUTHOR]

Coronavirus disease 2019 (COVID-19) is associated with excess risk of cardiovascular and thrombotic events in the early post-infection period and during convalescence. Despite the progress in our understanding of cardiovascular complications, uncertainty persists with respect to more recent event rates, temporal trends, association between vaccination status and outcomes, and findings within vulnerable subgroups such as older adults (aged 65 years or older), or those undergoing hemodialysis. Sex-informed findings, including results among pregnant and breastfeeding women, as well as adjusted comparisons between male and female adults are similarly understudied. Adult patients, aged ≥18 years, with polymerase chain reaction-confirmed COVID-19 who received inpatient or outpatient care at the participating centers of the registry are eligible for inclusion. A total of 10,000 patients have been included in this multicenter study, with Brigham and Women's Hospital (Boston, MA) serving as the coordinating center. Other sites include Beth Israel Deaconess Medical Center, Anne Arundel Medical Center, University of Virginia Medical Center, University of Colorado Health System, and Thomas Jefferson University Health System. Data elements will be ascertained manually for accuracy. The two main outcomes are 1) a composite of venous or arterial thrombotic events, and 2) a composite of major cardiovascular events, defined as venous or arterial thrombosis, myocarditis or heart failure with inpatient treatment, new atrial fibrillation/flutter, or cardiovascular death. Clinical outcomes are adjudicated by independent physicians. Vaccination status and time of inclusion in the study will be ascertained for subgroup-specific analyses. Outcomes are pre-specified to be reported separately for hospitalized patients versus those who were initially receiving outpatient care. Outcomes will be reported at 30-day and 90-day follow-up. Data cleaning at the sites and the data coordinating center and outcomes adjudication process are in-progress. The CORONA-VTE-Network study will share contemporary information related to rates of cardiovascular and thrombotic events in patients with COVID-19 overall, as well as within key subgroups, including by time of inclusion, vaccination status, patients undergoing hemodialysis, the elderly, and sex-informed analyses such as comparison of women and men, or among pregnant and breastfeeding women. • Cardiovascular events, mainly thrombosis, have been widely reported in COVID-19. • Recent event rates are unknown, considering vaccination or COVID-19 therapies. • Limited data exist in vulnerable subgroups such as pregnant or breastfeeding patients. • The CORONA-VTE-Network study seeks to address several of such knowledge gaps. • This paper provides an evidence summary of prior studies and the current registry. [ABSTRACT FROM AUTHOR]

• The first evidence of solar box cooker cum dryer working above 100 °C. • SBCD's dual mode operation helps understating design for similar large-scale systems. • COR enables TPP-based performance evaluation of dual-mode systems effectively. • A realistic and inexpensive technique allows effective moisture removal from SBDC. • LCCM provides vital insights into the economics of SBCD and dual-mode operation. In the background of the COVID 2019 pandemic, the importance of developing realistic and efficient decentralized energy solutions is one of the essential requirements. This paper presents the performance of a small-scale solar box cooker cum dryer (SBCD) for decentralized communities and domestic scale applications. The drying process in SBCD uses a simple and effective method for moisture removal. It enables de-moisturization of the cooker interior, allowing efficient use of SBCD for the simultaneous dual-mode operation of cooking and drying. Cooker Opto-thermal Ratio (COR) as a thermal performance parameter and glycerin as a test load enable cooking process analysis. COR-based objective parameters (OPs) realistically comment on the cooker performance in the dual-mode operation. Drying kinetic studies describe the drying performance of the device. The levelized cost of cooking meals (LCCM) allows understating of the economics of SBCD. The mean value of COR for the cooker is 0.104 ± 0.0028 (m2·°C)/W with a percentage standard deviation of 2.69%. The experimental values of OPs, reference cooking time, and maximum achievable load temperature varies between 74 and 86 min and 103–111 °C, respectively. Thus, SBCD cooks in approximately 80–90 min and dries ∼ 100 g of food products simultaneously with 70–80 % moisture removal. The LCCM for SBCD is $ 0.0174 per meal. Thus, SBCD depicts a realistic solution for ensuring self-sustainability in decentralized communities. [ABSTRACT FROM AUTHOR]

Adolescents' mental health was severely compromised during the COVID-19 pandemic. Longitudinal real-world studies on changes in the mental health of adolescents during the later phase of the pandemic are limited. We aimed to quantify the effect of COVID-19 pandemic on adolescents' mental health outcomes based on electronic health records. This was a retrospective cohort study using the computerized database of a 2.5 million members, state-mandated health organization in Israel. Rates of mental health diagnoses and psychiatric drug dispensations were measured among adolescents 12 to 17 years of age with and without pre-existing mental history, for the years 2017 to 2021. Relative risks were computed between the years, and interrupted time series (ITS) analyses evaluated changes in monthly incidence rates of psychiatric outcomes. The average population size was 218,146 in 2021. During the COVID-19 period, a 36% increase was observed in the incidence of depression (95% CI = 25-47), 31% in anxiety (95% CI = 23-39), 20% in stress (95% CI = 13-27), 50% in eating disorders (95% CI = 35-67), 25% in antidepressant use (95% CI = 25-33), and 28% in antipsychotic use (95% CI = 18-40). A decreased rate of 26% (95% CI = 0.80-0.88) was observed in ADHD diagnoses. The increase of the examined outcomes was most prominent among youth without psychiatric history, female youth, general secular Jewish population, youth with medium−high socioeconomic status, and those 14 to 15 years of age. ITS analysis confirmed a significantly higher growth in the incidence of psychiatric outcomes during the COVID-19 period, compared to those in previous years. This real-world study highlights the deterioration of adolescents' mental health during the COVID-19 pandemic and suggests that youth mental health should be considered during health policy decision making. We worked to ensure sex and gender balance in the recruitment of human participants. We worked to ensure race, ethnic, and/or other types of diversity in the recruitment of human participants. We actively worked to promote sex and gender balance in our author group. The author list of this paper includes contributors from the location and/or community where the research was conducted who participated in the data collection, design, analysis, and/or interpretation of the work. [ABSTRACT FROM AUTHOR]

This systematic review aims to understand the problems associated with lockdown and the various conditions of Covid-19 infection and to help prepare athletes and exercise enthusiasts for the safe resumption of sport in a manner that promotes wellness, healthy competition, and a sports industry that survives the current situation. This systematic review was carried out, following the recommendations of the currently pre-established reporting elements for systematic reviews and meta-analyses. The following databases were consulted: ISI Web of Science (WOS), Scopus and Google Scholar. This review includes a total of 19 articles. The results are presented based on three predominant themes: (1) psychological impact produced by SARS-CoV-2; (2) post-Covid-19 cardiac abnormalities and (3) protocols for an adequate return to physical practice. Most of the protocols shown by the various papers are consistent in terms of duration and number of phases. Based on this, the safe return to practice protocol can be divided into four stages of seven days in each phase in order to progress according to the symptoms perceived. In each phase, the physiological demand and efforts necessary to cover the proposed activities are increased until optimal physical condition is recovered. [ABSTRACT FROM AUTHOR]

The end of smallpox in 1980 and the subsequent stopping of vaccination against smallpox was followed by the emergence of monkeypox (mpox), a viral disease of animal origin, meaning that it is transmitted from animal to human. The symptoms of mpox are similar to smallpox, except that they are less severe in terms of clinical features. In the case of public health, the mpox virus is one of the most important orthopoxviruses (such as variola, cowpox, and vaccinia) that come from the family Poxviridae. Mpox occurs mostly in central Africa and sometimes in tropical rainforests or some urban areas. Also, there are threats other than COVID-19, that must be addressed and prevented from spreading, as there has been an outbreak of mpox cases since May 7, 2022, throughout the USA, Europe, Australia, and part of Africa. In this review, we will discuss mpox between the past, the present and during the COVID-19 pandemic. Also, it offers an updated summary of the taxonomy, etiology, transmission, and epidemiology of mpox illness. In addition, the current review aims to highlight the importance of emerging pandemics in the same era such as mpox and COVID-19. A literature search was done for the study using online sources like PubMed and Google Scholar. Publications in English were included. Data for study variables were extracted. After the duplicate articles were eliminated, full-text screening was performed on the papers' titles and abstracts. The evaluation included a series documenting mpox virus outbreaks, and both prospective and retrospectiveinvestigations. monkeypox is a viral disease caused by the monkeypox virus (MPXV), which is primarily found in central and western Africa. The disease is transmitted from animals to humans and presents symptoms similar to those of smallpox, including fever, headache, muscle aches, and a rash. Monkeypox can lead to complications such as secondary integument infection, bronchopneumonia, sepsis, and encephalitis, as well as corneal infection that can result in blindness. There is no specific clinically proven treatment for monkeypox, and treatment is primarily supportive. However, antiviral drugs and vaccines are available for cross-protection against the virus, and strict infection control measures and vaccination of close contacts of affected individuals can help prevent and control outbreaks. [ABSTRACT FROM AUTHOR]

• Significant growth in most teleactivities (remote online activities) due to COVID-19. • Telework, online learning, telehealth, and virtual meetings increased during COVID-19. • Neighborhood density associated with more frequent telework and virtual meetings with friends and family during COVID-19. • More local facilities associated with more frequent online learning during COVID-19. • Short and long-term implications for mobility are discussed. This paper presents new evidence on changes in a broad range of teleactivities due to the Coronavirus disease (COVID-19) pandemic and investigates how the built environment relates to these changes. The paper relies on survey and geospatial data from Oslo and its surrounding Viken region in Norway. Findings suggest that most teleactivities increased due to COVID-19. Telework, teleconferencing, online learning, telehealth, and virtual meetings with friends and family all increased during COVID-19 compared to the pre-COVID-19 period. The next step in the analysis examined relationships between built environment characteristics and teleactivities before and during COVID-19. Telework and virtual meetings increased to a greater extent in denser neighborhoods than in lower-density neighborhoods. A larger increase in online learning was associated with lower neighborhood density, lower accessibility to public transport, and more local facilities. Numerous local facilities were associated with more frequent telework and virtual meetings both before and during COVID-19. The substantial COVID-19-induced increase in teleactivities found in the study highlights the potential of information and communications technology (ICT) for replacing travel for various activities. [ABSTRACT FROM AUTHOR]

• COVID-19 reveals radiological signatures that can be detected using chest X-rays. • The evaluation of radiological signatures is a time-consuming and error-prone task. • Therefore, there is a need to automate the analysis of chest X-rays. • An automatic analysis of chest X-rays is achieved using deep learning models. The most widely used novel coronavirus (COVID-19) detection technique is a real-time polymerase chain reaction (RT-PCR). However, RT-PCR kits are costly and take 6-9 hours to confirm infection in the patient. Due to less sensitivity of RT-PCR, it provides high false-negative results. To resolve this problem, radiological imaging techniques such as chest X-rays and computed tomography (CT) are used to detect and diagnose COVID-19. In this paper, chest X-rays is preferred over CT scan. The reason behind this is that X-rays machines are available in most of the hospitals. X-rays machines are cheaper than the CT scan machine. Besides this, X-rays has low ionizing radiations than CT scan. COVID-19 reveals some radiological signatures that can be easily detected through chest X-rays. For this, radiologists are required to analyze these signatures. However, it is a time-consuming and error-prone task. Hence, there is a need to automate the analysis of chest X-rays. The automatic analysis of chest X-rays can be done through deep learning-based approaches, which may accelerate the analysis time. These approaches can train the weights of networks on large datasets as well as fine-tuning the weights of pre-trained networks on small datasets. However, these approaches applied to chest X-rays are very limited. Hence, the main objective of this paper is to develop an automated deep transfer learning-based approach for detection of COVID-19 infection in chest X-rays by using the extreme version of the Inception (Xception) model. Extensive comparative analyses show that the proposed model performs significantly better as compared to the existing models. [ABSTRACT FROM AUTHOR]

Two years have passed since the outbreak of the COVID-19 pandemic. The entire world is still struggling with the virus and its mutations, and governments have been imposing various restrictions, including social distancing and quarantine. This paper leans on the Conservation of Resources (COR) theoretical model in comparing the impact of COVID-19 with that of an ongoing threat of terror attacks, allowing us to understand the similarities and differences between them. Such exploration could result in an understanding that may guide devising psycho-social interventions to minimize the negative psychological consequences of a continuous life-threatening situation. By applying the extensive available literature on the aftermath of terrorist attacks to the little-known long-run consequences of the COVID-19 pandemic, this paper comes up with several guidelines such as increasing online social support and enhancing adaptive coping with stress, that are applicable on the individual, community, and societal levels. [ABSTRACT FROM AUTHOR]

This paper analyzes the impact of COVID-19 on China's coal import trade. First , this paper makes a qualitative analysis of the status quo of China's coal import scale before and after COVID-19. Second, the impact of the pandemic on China's coal import was estimated by panel VAR model based on the confirmed global COVID-19 data and China's coal import data with Johns Hopkins University Center for Systems Science and Engineering database and Wind database from January 2019 to June 2021. The result shows that the pandemic has a significant impact on both the supply and demand of China's coal industry in short term. There is a rebound in coal demand due to manufacturing industries having resumed work and production under the successful pandemic control in China. Finally, this paper analyzes the measures of China's coal import process during the COVID-19 and suggests how to deal with the global public crisis in the future. [ABSTRACT FROM AUTHOR]

The key aim of this paper is to construct a modified version of the SEIQR essential disease dynamics model for the COVID-19 emergence. The modified SEIQR pandemic model takes a groundbreaking approach to evaluate and monitor the COVID-19 epidemic. The complex studies presented in this paper are based on real-world data from Saudi Arabia. A reproduction number and a systematic stability analysis are included in the new version of SEIQR model dynamics. Using the Jacobian linearization process, we can obtain the domain of the solution and the state of equilibrium based on the modified SEIQR model. The equilibrium and its importance have been identified, and the disease-free stability of the equilibrium has been investigated. The reproduction number was calculated using internal metrics, and the global stability of the current model's equilibrium was demonstrated using Lyapunov's stability theorem. To see how well the SEIQR proposed model went, it was compared to real COVID-19 spread data in Saudi Arabia. According to the results, the new SEIQR proposed model is a good match for researching the spread of epidemics like COVID-19. In the end, we presented an optimal protocol to prevent the dissemination of COVID-19. Staying at home and transporting sick people as far as possible to a safe region is the most effective strategy to prevent COVID-19 spread. It is critical to offer infected people safe and effective treatment, as well as antibiotics and nutrients to non-affected people. To detect confirmed infections, we must provide more effective and reliable diagnostic methods. Furthermore, increasing understanding of how to recognize the disease, its symptoms, and how to confirm the infection. [ABSTRACT FROM AUTHOR]

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Under the influence of COVID-19, the global economic and social development is facing great challenges. With the increase of government financial pressure and the decrease of debt paying ability, the problem of debt risk of local governments in China is attracting wide attention. In order to measure the level of China's local government debt risk under the influence of COVID-19, this paper takes China's Sichuan Province as an example, collects the core indicators data of measuring local government debt risk in 2017-2020 years, and uses AHP-TOPSIS method to make a comprehensive analysis of the local government debt risk situation in different periods before and after COVID-19. It is found that the local government debt risk in Sichuan Province is generally controllable. However, influenced by COVID-19, in 2020, the overall level of local government debt risk in Sichuan province expanded by 22.1% compared with 2019, this is mainly due to the further expansion of debt scale and slower economic growth. This paper suggests that the Chinese government should speed up the construction of comprehensive early warning and supervision system of local government debt risk, and prevent and resolve the debt risk of local government in advance. [ABSTRACT FROM AUTHOR]

This paper studied the impact of COVID-19 on China's capital market and major industry sectors via an improved ICSS algorithm, a time series model with the exogenous variable and a non-parametric conditional probability estimation. Through the empirical analysis, it is found that the epidemic has no significant impact on the return of the stock and bond markets, but it has increased the market volatility and the impact on the stock market volatility is gradual and more obvious. There are significant differences in the significance, direction and duration of the epidemic on different sectors. In addition, the impact of COVID-19 has been gradual in some industries and rapid in others. Different industries show different sensitivities in their response to COVID-19. Based on the analysis of the impact, this paper put forward the corresponding suggestions for investment strategies and macro-control decisions. [ABSTRACT FROM AUTHOR]

• Average weekly WFH frequency may rise from 0.69 (pre-COVID) to 1.29 (post-COVID). • Theory of Planned Behaviour used to examine intention to increase WFH post-COVID. • Intention influenced by subjective norms and perceived behavioural control. • Job type; access to tech, materials; employer and family support key determinants. • Determinants differ based on pre-COVID WFH experience and work location. COVID related lockdowns have forced many workers around the world into work from home (WFH) arrangements. While the proportion of people who work from home has typically been very low (4.5% in Melbourne in 2016), it is worth considering how trends might change once the world emerges out of COVID lockdowns. This paper aims to examine (1) the long-term impacts of COVID on WFH and (2) the psycho-social factors which will impact workers' intention to increase WFH post-COVID. The paper uses the Theory of Planned Behaviour as a lens to examine these psycho-social determinants. The study uses the survey responses of 1,364 workers which were collected in June-August 2020 as part of a larger survey (n = 2,158), which aimed to capture a representative population of Greater Melbourne. Quasi-longitudinal investigation of self-reported WFH frequency revealed that WFH as a share of average weekday employment increased 310% during COVID lockdown. In the longer term, when COVID is no longer an issue, WFH can be expected to be 75% higher than pre-COVID levels. Structural Equation Modelling was applied to the Theory of Planned Behaviour to understand motivators for and barriers to increasing WFH post-COVID. Perceived Behavioural Control (e.g. job type, technology, access to materials) and Subjective Norms (e.g. employer and family support) will be crucial determinants of the intention to work from home post-COVID. Attitudes regarding WFH will only have a weak impact on future intention to work from home, a finding which contrasts with previous research. [ABSTRACT FROM AUTHOR]

• Vaporized hydrogen peroxide has the most data supporting its use for decontamination. • Hydrogen peroxide gas plasma can cause a degradation in filtration efficiency. • Respirator fit failure may limit reuse independent of decontamination method. • Consistent standards for testing microbial inactivation should be developed. Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has led to over 170?million cases worldwide with over 33.2?million cases and 594,000 deaths in the US alone as of May 31st, 2021. The pandemic has also created severe shortages of personal protective equipment, particularly of filtering facepiece respirators (FFRs). The Centers for Disease Control and Prevention (CDC) has issued recommendations to help conserve FFRs, as well as crisis standards, including four criteria required for decontamination of the traditionally single use respirators. This review is designed to provide an overview of the current literature on vaporized hydrogen peroxide (vHP), hydrogen peroxide gas plasma (HPGP), and aerosolized hydrogen peroxide (aHP) with respect to each of the four CDC decontamination criteria. PubMed and Medrxiv were queried for relevant articles. All articles underwent a title and abstract screen as well as subsequent full text screen by two blinded reviewers if indicated. Searches yielded 195 papers, of which, 79 were found to be relevant. Of those, 23 papers presented unique findings and 8 additional articles and technical papers were added to provide a comprehensive review. Overall, while there are potential concerns for all 3 decontamination methods, we found that vHP has the most evidence supporting its use in FFR decontamination consistent with CDC recommendation. Future research is recommended to evaluate biological inactivation and real world fit failures after FFR reuse. [ABSTRACT FROM AUTHOR]

In the article, the analysis of researches was carried out concerning consideration of a question of forecasting of strains of COVID where based on indicators of patients it would be possible to predict with a specific error existence at them of COVID. Therefore, this work is focused on refuting COVID forecasting, as well as the disclosure of new approaches to the application of traditional methods to solve the problem of forecasting. The paper presents the results of forecasting. The preprocessing of medical data was performed, emissions were taken from them, categorical data were converted with the help of one-hot encoder. This paper proposes a system for automatic detection of COVID in a patient during his treatment. To solve this problem, classifiers were used, and the results of using the principles of their work were obtained. [ABSTRACT FROM AUTHOR]

This paper analyses the requirements of a blockchain-based data governance model for COVID-19 digital health certificates. Recognizing a gap in the existing literature, this paper aims to answer the research question "To what extent does a blockchain-based governance model for COVID-19 digital health certificates in the EU meet the relevant legal, technical, ethical and security requirements?" This paper identifies the required standards and develops a novel framework to determine the viability of blockchain as a governance model. The results of our evaluation indicate that while a private permissioned blockchain can meet the requirements to some degree, the governance element is key to legal compliance; legal risks and ethical implications remain unresolved with the use of blockchain. The paper also found that this model comes with the loss of the main advantages of blockchain – decentralization and anonymity. This evaluation framework may be used in other contexts and for assessing other technologies. [ABSTRACT FROM AUTHOR]

Airborne transmissibility of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has highlighted the urgent need for aerosol monitoring of SARS-CoV-2 to prevent sporadic outbreaks of COVID-19. The inadequate sensitivity of conventional methods and the lack of an on-site detection system limited the practical SARS-CoV-2 monitoring of aerosols in public spaces. We have developed a novel SARS-CoV-2-in-aerosol monitoring system (SIAMs) which consists of multiple portable cyclone samplers for collecting aerosols from several venues and a sensitive "sample-to-answer" microsystem employing an integrated cartridge for the analysis of SARS-CoV-2 in aerosols (iCASA) near the sampling site. By seamlessly combining viral RNA extraction based on a chitosan-modified quartz filter and " in situ " tetra-primer recombinase polymerase amplification (tpRPA) into an integrated microfluidic cartridge, iCASA can provide an ultra-high sensitivity of 20 copies/mL, which is nearly one order of magnitude greater than that of the commercial kit, and a short turnaround time of 25 min. By testing various clinical samples of nasopharyngeal swabs, saliva, and exhaled breath condensates obtained from 23 COVID-19 patients, we demonstrate that the positive rate of our system was 3.3 times higher than those of the conventional method. Combining with multiple portable cyclone samplers, we detected 52.2% (12/23) of the aerosol samples, six times higher than that of the commercial kit, collected from the isolation wards of COVID-19 patients, demonstrating the excellent performance of our system for SARS-CoV-2-in-aerosol monitoring. We envision the broad application of our microsystem in aerosol monitoring for fighting the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

In March 2020, the World Health Organization (WHO) declared COVID-19 a global epidemic, caused by the SARS-CoV-2 virus. Initially, COVID-19 was diagnosed using real-time reverse transcription–polymerase chain reaction (RT-PCR) tests with a turnaround time of 2–3 days. To enhance diagnostic accuracy, medical professionals use medical imaging alongside RT-PCR. A positive result on both RT-PCR and medical imaging confirms a COVID-19 diagnosis. Imaging modalities like chest X-ray (CXR), computed tomography (CT) scans, and ultrasound are widely utilized for rapid and precise COVID-19 diagnoses. However, interpreting COVID-19 from these images is time-consuming and susceptible to human error. Therefore, leveraging artificial intelligence (AI) methods, particularly deep learning (DL) models, can deliver consistent, high-performance results. Unlike conventional machine learning (ML), DL models automate all stages of feature extraction, selection, and classification. This paper presents a comprehensive review of using DL techniques for diagnosing COVID-19 from medical imaging. The introduction provides an overview of diagnosing the coronavirus using medical imaging, highlighting associated challenges. Subsequently, the paper delves into key aspects of Computer-Aided Diagnosis Systems (CADS) based on DL methods for diagnosing COVID-19, covering segmentation, classification, explainable AI (XAI), and predictive research. Additionally, it reviews the rehabilitation systems such as the Internet of Medical Things (IoMT) in the context of COVID-19. In another section, uncertainty quantification (UQ) research is showcased, focusing on DL models for the diagnosis of Covid-19. Crucial challenges and future research directions are outlined in another section. Finally, discussion and conclusion sections are also provided at the end of the paper. [ABSTRACT FROM AUTHOR]

• Audience interaction at livestreamed music concerts differ according to genre. • Online, ritualised activities take on a symbolic position, memorialised in emojis. • Context collapse online leads to the hyper ritualization of genre conventions. • Virtual large-scale interactions are characterised by totem-defining behaviour. This paper examines audience engagement at livestreamed concerts, a form of mediatised cultural consumption that saw an immense growth in popularity during the COVID-19 pandemic. Concerts, as events that draw large groups of people with similar intentions, are the perfect location for the establishment of large-scale interaction rituals – moments of group behaviour characterised by a highly intense collective emotion. Furthermore, as social occasions, concerts are organised around a set of routine interactions that construct and define the collective experience. We argue that in moving online, the definition of the (concert) situation is highly impaired due to a context collapse. In comparing two distinct audiences (classical and Dutch popular music), the first aim of this research is to explore how these differing audiences adapt their cultural behaviour to the virtual sphere. Secondly, by adopting a microsociological perspective, we aim to broaden the theoretical understanding of virtual large-scale interaction rituals, an area becoming increasingly important due to the growth in online communication. This paper uses discourse analysis of the synchronised comments, left on livestreamed concerts on Facebook Live (n = 2,075), to examine the interaction between audience members. We find that both classical and Dutch popular music audiences use a form of hyper-ritualised interaction. In an attempt to combat the plurality of meanings online, they explicitly refer back to the central conventions of the face-to-face concert. This emphasises not only the significance of genre conventions, but also presents a form of virtual interaction distinct form interpersonal interaction. [ABSTRACT FROM AUTHOR]

Deep learning methods have ushered in an unprecedented transformation in medical image segmentation by automating the segmentation of computed tomography (CT) slices. However, challenges persist in the application of these deep learning methods, including models with a high number of training parameters which hinder their clinical deployment and practical use. Furthermore, acquiring a large volume of high-quality labels from radiologists for training is prohibitively expensive. In light of these challenges, this paper introduces a weakly supervised method aimed at addressing the scarcity of high-quality labels by annotating only a small subset of pixels within regions of interest. This strategy expedites the labeling process, requiring just a few seconds to complete. Additionally, we present a lightweight segmentation model designed to minimize computational complexity. By incorporating parallel dilated convolutions and depthwise separable convolutions, this model consists of 11.03M parameters and can segment lesions in lung CT slices in just 0.05 s, showcasing high segmentation efficiency. To capture shape and size information from point labels, we propose a Convex-Hull-based Segmentation (CHS) loss function. This loss function empowers our weakly supervised model to approximate the performance of a fully supervised model when evaluated against point labels. We conduct a comprehensive evaluation, comparing our proposed method with other state-of-the-art methods using multiple metrics on public datasets. The experimental results demonstrate a significant improvement in performance compared to current state-of-the-art (SOTA) point label segmentation methods, with an increase of 19.08% in Intersection over Union (IoU), 20.54% in Dice Similarity Coefficient (DSC), 2.60% in sensitivity, and 1.33% in specificity. In comparison to the SOTA fully supervised methods, our approach exhibits a minimal IoU difference of just 2.1%. Further ablation experiments validate the effectiveness of the key methodologies introduced in this work. • This paper designs a lightweight weakly supervised convex-hull-based D-block network (CHDNet) to segment the lesions of COVID-19 rapidly and efficiently. • To obtain the shape and size of lesions from point labels, this paper proposes a convex-hull based segmentation (CHS) loss function. • To address the difficulty of manually labeling public datasets, this paper proposes a method that marks only a few points in each lesion area. [ABSTRACT FROM AUTHOR]

The coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been ongoing for more than three years and urgently needs to be addressed. Traditional Chinese medicine (TCM) prescriptions have played an important role in the clinical treatment of patients with COVID-19 in China. However, it is difficult to uncover the potential molecular mechanisms of the active ingredients in these TCM prescriptions. In this paper, we developed a new approach by integrating the experimental assay, virtual screening, and the experimental verification, exploring the rapid discovery of active ingredients from TCM prescriptions. To achieve this goal, 4 TCM prescriptions in clinical use for different indications were selected to find the antiviral active ingredients in TCMs. The 3-chymotrypsin-like protease (3CLpro), an important target for fighting COVID-19, was utilized to determine the inhibitory activity of the TCM prescriptions and single herb. It was found that 10 single herbs had better inhibitory activity than other herbs by using a fluorescence resonance energy transfer (FRET) - based enzymatic assay of SARS-CoV-2 3CLpro. The ingredients contained in 10 herbs were thus virtually screened and the predicted active ingredients were experimentally validated. Thus, such a research strategy firstly removed many single herbs with no inhibitory activity against SARS-CoV-2 3CLpro at the very beginning by FRET-based assay, making our subsequent virtual screening more effective. Finally, 4 active components were found to have stronger inhibitory effects on SARS-CoV-2 3CLpro, and their inhibitory mechanism was subsequently investigated. Among of them, methyl rosmarinate as an allosteric inhibitor of SARS-CoV-2 3CLpro was confirmed and its ability to inhibit viral replication was demonstrated by the SARS-CoV-2 replicon system. To validate the binding mode via docking, the mutation experiment, circular dichroism (CD), enzymatic inhibition and surface plasmon resonance (SPR) assay were performed, demonstrating that methyl rosmarinate bound to the allosteric site of SARS-CoV-2 3CLpro. In conclusion, this paper provides the new ideas for the rapid discovery of active ingredients in TCM prescriptions based on a specific target, and methyl rosmarinate has the potential to be developed as an antiviral therapeutic candidate against SARS-CoV-2 infection. • This paper explores the rapid discovery of the active ingredients from TCMs. • TCM prescriptions were selected to find anti-SARS-CoV-2 3CLpro active ingredients. • Methyl rosmarinate is an allosteric inhibitor of SARS-CoV-2 3CLpro. [ABSTRACT FROM AUTHOR]

Populism has emerged as a central explanation employed by both media outlets and scholars for the mishandling of the COVID-19 crisis. Nonetheless, the relationship between public health and populism extends before and beyond the pandemic. This paper offers a comprehensive overview of existing evidence and theoretical conceptualisations on the intersection of populism, health emergencies, and contrarian scientific positions, drawing from a diverse range of disciplines. I conducted a scoping review of 283 original studies, analysing their analytical framework, geographic focuses, and methodological approaches. Employing quantitative text analysis, I summarised the research field into 18 common topics, organised into five coherent categories: citizen's perspective, political elites, political communication, pandemic consequences, and non-COVID-related issues. While the scholarly interest in this area has surged since the onset of the pandemic, it has predominantly concentrated on specific cases, such as Brazil and the US, often conflating different policy types. The evidence summary elucidates that populism assumes varying roles within distinct contexts, and there is no linear relationship between political populism and specific approaches to health crises and science. I further compare definitions of populism within the context of health and scientific positions. I propose that future research should employ a policy typology for health emergency responses, assessing political positions based on policy arenas. This paper contributes to the understanding of the complex interplay between political populism, contrarian scientific perspectives, and public health. • Descriptive summary of multiple disciplines and theoretical frameworks. • Research interest in this theme surged after the COVID-19 pandemic. • Geographic focus and generalisation of specific cases such as Brazil and the US. • Lack of linear and universal association between populism and health emergencies. • Adoption of a common policy typology to guide future research. [ABSTRACT FROM AUTHOR]

• This paper investigates the impact of air pollution on excess executives' perks. • To establish causality, we exploit an exogenous event (i.e., COVID-19), which restrained executives' perk consumption due to social distancing and lockdown policies. • We find that air pollution positively affects excess perks. • External monitors and managerial monetary compensation can curb excess perk consumption. This paper investigates the impact of air pollution on excess executives' perks. To establish causality, we exploit an exogenous event (i.e., COVID-19), which restrained executives' entertainment activities and travelling due to social distancing and lockdown policies. Using a difference-in-difference model and a sample of listed firms in China from 2016 to 2022, we find that excess perks of companies located in polluted cities reduce more due to COVID-19 than those of companies in cities with good air, indicating that air pollution positively affects excess perks. Further analyses demonstrate that external monitors and managerial monetary compensation can curb excess perk consumption. [ABSTRACT FROM AUTHOR]

This paper first further clarifies economic operating state resilience from the concept of regional economic resilience and uses it to explore what factors influence the resilience during the pandemic. We find that the impact of different factors on regional economic resilience and economic operating state resilience is not entirely consistent in the early stages of the pandemic. The results reveal that industry specialization and unrelated diversity have positive effects at different stages, with opposite roles for human capital. High shares of secondary and tertiary industries can impair resilience in the recovery phase. However, the roles of government and diversity are not consistent across stages. Going beyond the determinants of regional economic resilience in previous economic cycles of liquidity crises, this article highlights the role of the public governance and industrial structure at different stages of the pandemic shock and argues that enhancing regional economic resilience in the reshaping of the 'face-to-face' pattern of economic activity is a great challenge in the post-pandemic era. This paper provides new insights for policymakers and researchers in responding to major public health events, understanding resilience process and promoting sustainable development in the post-pandemic era. [ABSTRACT FROM AUTHOR]

Amidst the current global inflationary challenges, the concurrent rise of energy and agricultural commodity prices, which constitute the primary components of consumer prices, has emerged as a matter of significant interest among both scholars and policymakers. To this end, this study examines the dynamic interlinkages between food and energy commodity indexes from 2005:1 to 2023:3, covering turmoil episodes such as the Global Financial Crisis (GFC), the COVID-19 pandemic, and the Russia-Ukraine Conflict (RUC). Additionally, following Broadstock et al. (2022), we perform dynamic portfolio analyses to determine portfolio performances under 3 different portfolio construction approaches. The empirical results presented in this paper allow for a number of important findings. First, both the time and frequency-domain connectedness indexes associate with major financial/geopolitical stress events. Second, the fuel energy and the crude oil price indexes are the largest propagators and recipients of spillovers Third, the cumulative portfolio returns exhibit significant growth during the early phase of the COVID-19, declining during the RUC, and a notable upswing during the GFC. Finally, our findings for frequency-dependent connectedness networks indicate that the market is particularly susceptible to short-term shocks. This paper has significant ramifications for investors, market players, and policymakers. • Fuel energy and crude oil prices are key transmitters of return shocks. • Time-varying interlinkages associate with major financial/geopolitical stress events. • The market is particularly susceptible to short-term shocks. • Cumulative portfolio returns exhibit a sharp increase during the early phase of the COVID-19. • Portfolio performances witnessed a significant decrease between mid-2014 and early-2016. [ABSTRACT FROM AUTHOR]

In their 2010 book, Lorna Weir and Eric Mykhalovskiy conceptualised the role of vigilance in unknown and emerging infectious disease threats. Theirs is a macro-level account which draws on empirical data to describe vigilance as a set of technical and political arrangements that govern collection, analysis, interpretation and communication of data as it pertains to unknown threats. In this paper we expand their work to detail a conceptual analysis of the role of vigilance at the micro-level during periods of high infectious disease threat. Our data are daily press conferences and associated non-discursive tools in New South Wales (NSW), Australia during times of heightened COVID-19 risk. This paper is a conceptual analysis that draws on theories of vigilance and related concepts to show how a key aspect of vigilance is making previously unseen threats visible or present. Communications formulated and encouraged three types of vigilance as a set of governing relations: institutional or authority-based; individual outward-facing; and individual inward-facing. We also describe the relationship between vigilance and related concepts that are used in response to anticipated public threats. Authority based vigilance involved contact tracing and policing of movement and behaviours. In individual outward facing vigilance people were asked to be alert to, analyse, and react to risk in their immediate environment. Inward facing vigilance required people to gather and react to information about their own behaviours and within their own bodies. There was a relationship between different types of vigilance; as risk increased and authority-based vigilance was less successful in containing the spread of infection, individual vigilance had a stronger role to play. This extension of vigilance at the micro-level sees some of the same unintended consequences as Weir and Mykhalovskiy describe at the global level, particularly in how burdens are inequitably distributed and experienced. • Citizens became an arm of vigilance apparatus during COVID-19 pandemic in Australia. • Vigilance was authority-based; individual outward facing; individual inward facing. • Disadvantaged populations carried higher vigilance burden. • Earlier conceptions of vigilance in infectious disease emergencies are extended. [ABSTRACT FROM AUTHOR]

Rising demand for emergency food during the last couple of decades in the UK has led to a great deal of academic interest in food aid provision, and food banking in particular. Efforts have also been made to examine food poverty and responses to it in more critical terms, which has entailed moving beyond a focus on emergency food support to engage with 'more than food aid' approaches. In this paper, I discuss how these latter approaches are beginning to be mobilised by national organisations, local authorities and place-based food partnerships in the UK. An important catalyst for this shift was the Covid-19 pandemic, which provided the crisis conditions that encouraged public and third-sector actors to think about, and act upon, food poverty in different ways. Drawing on an analysis of submissions to a Covid-19 food inquiry, place-based food initiatives implemented during the pandemic period and more recent initiatives instigated by national food support and anti-hunger groups, the paper examines how a diverse range of organisations are becoming more critical of existing (food aid) responses to food poverty and are seeking to develop more supportive local foodscapes based on a 'more than food aid' approach. The paper concludes with a discussion of the implications of this shift for future research on food poverty. [ABSTRACT FROM AUTHOR]

Airport management is a complex and multifarious activity, involving many operators including airlines, retailers and ground handlers, and processes. The presence of wildlife at airports poses a safety risk to aircraft operations and as such managing wildlife hazards is a mandatory legal responsibility. This is important not only from a safety perspective but also from the fact that safety incidents can impact the operational efficiency and the reputation of an airport. Airport operators are required to devise and enact site-specific Wildlife Hazard Management Plans (WHMP) to reduce the risk of aircraft-wildlife interaction under normal airport operating conditions. The COVID-19 pandemic, however, led to an unprecedented reduction in commercial air traffic and the partial or total suspension of flights at some airports. The aim of this paper is to examine the impact of COVID-19 related flight reductions on bird prevalence and behaviour and the potential implications for airport management. Drawing on an empirical dataset of wildlife observations at Manchester Airport, UK, in 2019 and 2020, this paper details the airfield ornithology before and during the pandemic and examines the impact of COVID-19 related flight reductions on bird prevalence and behaviour. The findings reveal variations in the frequency and apparency of individual species as well as changes in the spatial location of bird sightings on the airfield. The paper concludes by discussing the implications of these findings for post-pandemic operations and for the formulation of future airport wildlife hazard management policies. • Identifies the business and management implications of wildlife hazard management at airports. • Describes the aerodrome ornithology at Manchester Airport, UK. • Compares bird prevalence and behaviour at Manchester Airport before and during the COVID-19 pandemic. • Differences in bird prevalence and behaviour were found. • Makes recommendations for wildlife management at airports. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to compare changes in air passenger's perceptions of airline services before and during COVID-19. To conduct the comparative analysis, two approaches have been employed, topic modeling and sentimental analysis. Firstly, influential topics that reflect passenger's opinions are extracted from online reviews using the Latent Dirichlet Allocation (LDA) model. These topics are then distributed into the five dimensions of the SERVQUAL model to analyze the changes in importance of each service quality dimension. Moreover, online reviews are classified as negative or positive based on the review score. Using logistics regression with negative reviews, this paper analyzes which keywords affect passenger dissatisfaction with airline services and compares the changes in negative keywords before and during COVID-19. The results show that the reliability of airlines was the most significant dimension before COVID-19, while the responsiveness of airlines has become the most crucial dimension during COVID-19. From the results of the sentimental analysis, words such as 'pay', 'refund', 'book', and 'cancelled' can be identified as keywords that bring about negative emotions, indicating that airline companies need to focus upon refund and reservation services to enhance their service level. Based upon this analysis, the paper makes several suggestions for improving airline service quality in light of the changes in air passenger perception. • The responsiveness dimension of SERVQUAL, which includes refund services and reaction time to changes, has become the most crucial during the COVID-19 period. • Factors affecting passenger dissatisfaction with airline services are investigated by a machine learning approach. • The findings and implications proposed in this study could benefit airline companies seeking to improve their service quality. [ABSTRACT FROM AUTHOR]

The aim of the study was to discuss the catastrophic consequences of inequitable vaccine distribution and analyze the main challenges to address it, helping to guide efforts to address inequities in vaccine coverage. All published papers written in English were searched through PubMed, Web of Science, and Google Scholar with the combination of relevant terms of COVID-19 vaccine inequity. In this paper, we first outlined the scope of inequitable vaccine distribution and identify its truly catastrophic consequences. Next, from the perspectives of political will, free markets, and profit-driven enterprises based on patent and intellectual property protection, we analyzed in depth the root causes of why this phenomenon is so difficult to combat. In addition, some specific and crucial solutions that should be undertaken in the long term were also put forward in order to provide a useful reference for the authorities, stakeholders, and researchers involved in addressing this worldwide crisis and the next one. Achieving COVID-19 vaccine equity faces funding gaps, vaccine nationalism, and barriers to access to intellectual property and technology. Thus, the scope of global vaccine inequity is immense, and its repercussions will continue to be felt worldwide, especially among the world's most vulnerable residents, both adults and children. Beyond fundamental issues, the growing vaccine hesitancy and unreliable distribution in low-income countries must be addressed. [ABSTRACT FROM AUTHOR]

The outbreak of COVID-19 raised critical questions about the functionalities of urban infrastructures, and in particular, practices of food provision, consumption and distribution. This paper focuses on the actions of non-commercial collaborative food sharing practices in Dublin city, incorporating surplus food redistribution initiatives, community kitchens, and community gardens, during the first wave of the pandemic in 2020. Whilst initially curtailed under conditions of strict lockdowns, many of these food sharing initiatives became characterised as essential services as the crisis unfolded, offering infrastructures of mobility, accessibility and care. Focusing on urban food infrastructures, qualitative content analysis of online media debates around food sharing in Dublin during this period was conducted to explore how non-commercial food sharing initiatives have been affected by, and responded to, the COVID-19 outbreak in Dublin. The paper analyses forms of improvisation, institionalisation and impact that emerged through food sharing actions and reflects on whether food sharing initiatives prefigure more sustainable urban food systems. • Demonstrates how food sharing initiatives have been affected by, and responded to, the COVID-19 outbreak in Dublin, Ireland • Contributes to the research on critical urban infrastructures and food infrastructures in cities. [ABSTRACT FROM AUTHOR]

This paper describes the participatory modeling experiences of five discrete teams across the U.S. working to develop models of food systems to identify leverage points and policies to induce food system transformation. Collaboration between academic and community partners within these individual modeling processes enables teams to address food systems complexity, integrate scientific evidence and local knowledge into models, while improving a model's credibility and accessibility for policymaking. While tools for facilitating participatory modeling are becoming more available, there is scant discussion on the practicalities of community engagement processes, including how teams respond to the needs of partners, navigate challenges that arise during projects, and communicate results. Synthesizing results from five independent teams in Albany, New York; Austin, Texas; Cleveland, Ohio; Denver, Colorado; and Flint, Michigan, this paper provides an overview of each team's approach to community engagement for participatory modeling of food systems. Analysis of engagement strategies across these five teams revealed four essential components to successful participatory modeling projects: 1) building research in collaboration with partners from the onset, 2) developing awareness of the challenges of community-researcher partnerships, 3) supporting transparent communication, and 4) promoting justice and trust through accessible dissemination processes. We emphasize that there is no single best approach to participatory modeling with community partners, rather that researchers need to understand and respond to various stakeholder needs. While each team faced challenges to the engagement process, including responding to the COVID-19 pandemic, our findings reveal important considerations for research in participatory modeling for food system policy. • Participatory modeling is useful to bridge academic knowledge and policy-making. • Immediate food access and lasting climate impacts of food systems challenges engaged modeling. • We synthesize lessons from five independent projects on participatory modeling of food systems. • Researchers must be responsive to community assets to produce policy relevant results. [ABSTRACT FROM AUTHOR]