A Mask Detection Algorithm Based on Improved Yolov5s

Aiming at the severe form of new coronavirus epidemic prevention and control, a target detection algorithm is proposed to detect whether masks are worn in public places. The Ghostnet and SElayer modules with fewer design parameters replace the BottleneckCSP part in the original Yolov5s network, which reduces the computational complexity of the model and improves the detection accuracy. The bounding box regression loss function DIOU is optimized, the DGIOU loss function is used for bounding box regression, and the center coordinate distance between the two bounding boxes is considered to achieve a better convergence effect. In the feature pyramid, the depthwise separable convolution DW is used to replace the ordinary convolution, which further reduces the amount of parameters and reduces the loss of feature information caused by multiple convolutions. The experimental results show that compared with the yolov5s algorithm, the proposed method improves the mAP by 4.6% and the detection rate by 10.7 frame/s in the mask wearing detection. Compared with other mainstream algorithms, the improved yolov5s algorithm has better generalization ability and practicability. © 2022 ACM.

The Spillover Effects of US Unconventional Monetary Policy on Inflation and Non-Inflation Targeting Emerging Markets

This study employs the panel vector autoregressive (PVAR) model to examine the spillover effect of US unconventional monetary policy on inflation and non-inflation targeting emerging markets post credit crunch and during COVID-19 from 2000Q1 to 2020Q4. Unlike other analyses, this paper adds to the existing body of knowledge by employing a dummy variable to represent the United States' quantitative easing. Other included control variables are equity prices, the federal reserve rate, the exchange rate, central bank assets and the short-term interest rate. This paper estimated two-panel VARs, Model one and Model two, for inflation and non-inflation targeting emerging markets, respectively. Model one consists of eight inflation-targeting markets, and Model two consists of four non-inflation-targeting countries. Other included control variables are equity prices, the federal reserve rate, the nominal effective exchange rate, and the central bank policy rate. According to the empirical results, the US unconventional monetary policy induces a surge in the exchange rate and a decrease in the central bank policy rate for both inflation and non-inflation targeting emerging markets. However, there was no significant impact on the equity prices. The empirical results are statistically significant, robust, and consistent with previous studies except for the response of equity prices. Unconventional monetary policy is effective in steering macroeconomic variables in developed economies. The monetary policymakers in emerging markets must also use the currency reserve to stabilise the macroeconomic variables in response to US unconventional monetary policy shocks.

Resilience of Retailing: From the Perspective of Chinese Daily Shopping Distance in Three Stages Related to the COVID-19 Pandemic

COVID-19 has a major impact on people's daily shopping. Geographers need to consider the characteristics of the commercial layout in order to be resilient when the pandemic comes. This chapter compares the Chinese people's daily shopping distances in three stages. Stage 1 is 1 year before the strict lockdown (January 23, 2019 to January 23, 2020). On January 23, 2020 the city of Wuhan temporarily closed its airport and railway station. Six departments, including China's National Health Commission, issued an announcement on the Prevention of COVID-19 by Strict Transport Control. Stage 2 is the period of strict pandemic control (January 23 to April 27, 2020). Stage 3 is the period of regular pandemic control (April 27, 2020 to February 18, 2021, the last day of this survey). During Stage 3, the lockdown has only been implemented in high-risk areas in China. We divide the study areas into four zones: old city, new city, suburban and rural areas. Three types of daily shopping were investigated: buying fresh foods, household supplies, and medical supplies. This study uses the change in shopping distance of sampled interviewees to measure the resilience of retail layout in response to the pandemic. The conclusions are that the retail layout in China has the resilience to deal with the pandemic in general and that resilience varies in the different zones. The resilience of retail layout in response to the pandemic can also be measured by more dimensions, which is what we need to explore in the future. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

An investigation of financial contagion between cryptocurrency and equity markets: Evidence from developed and emerging markets

The present study conducts a dynamic conditional cross-correlation and time–frequency correlation analyses between cryptocurrency and equity markets in both advanced and emerging economies. The purpose of the study is twofold. First, the study investigates the presence of the pure (narrow) form of financial contagion between cryptocurrency and stock markets in both advanced and emerging economies, during the black swan event of the COVID-19 crisis. Second, the study examines the hedging and safe-haven properties of cryptocurrencies against equity markets, before and during periods of financial upheaval triggered by the COVID-19 pandemic. Two econometric models are used: (1) the dynamic conditional correlation (DCC) GARCH and (2) the wavelet analysis models. Using the DCC GARCH model, the study found the evidence of high conditional correlations between cryptocurrency and equity markets. The high conditional correlation was mostly detected in periods of financial turmoil corresponding to the first quarter and the second quarter of 2020. The increase in conditional correlation during periods of financial upheaval (compared to a tranquil period) indicates the presence of the pure form of financial contagion. The wavelet cross-correlation analysis showed the evidence of positive cross-correlation between the Bitcoin and the equity markets during period of financial turmoil. The cross-correlation was identified in both short and long (coarse) scales. In short scales, the equity markets lead the cryptocurrency market, while the cryptocurrency market leads equity markets in coarse scales. The findings of the present study revealed that the degree of interdependence between cryptocurrency and equity markets has substantially increased during the COVID-19 period, and this has negated the safe-haven and hedging benefits of cryptocurrencies over equity markets. © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

Activated CD8 T-cell hepatitis in children: a single institution experience

Background: Pediatric acute liver failure is a rare and serious life-threatening situation, principally for the 30 to 50% of children in whom the etiology of their liver failure is unclear or indeterminate. Treating these patients is challenging, requiring constant assessment over time with regular evaluation for possible liver transplantation. Children with pediatric acute liver failure of undetermined etiology have lower spontaneous survival and higher rates of transplantation and death than other diagnostic groups. Emerging evidence suggests that a subgroup of patients with indeterminate pediatric acute liver failure have clinical, laboratory, and liver biopsy features of immune dysregulation with a dense infiltration of CD8 T cells. Method(s): In 2022, we received percutaneous liver biopsies from three children with acute hepatic dysfunction that showed an increased number of lymphocytes including CD8 T cells. For each case, routine H&E stains with levels, special stains and immunostains were performed. The first biopsy was from an 18-month-old male who presented with COVID infection, pancytopenia, elevated transaminases, and synthetic liver dysfunction (elevated INR). The second was from a 9-year-old female with a history of elevated liver enzymes with no clear cause. The third case was from a 2-year-old male with elevated liver enzymes, coagulopathy, and cholestasis. Result(s): The three cases showed similar histopathologic findings;an acute liver injury pattern with lobular architectural disarray, giant cell formation, reactive changes, single cell necrosis, cholestasis and marked mixed lymphocytic infiltrates. The infiltrates were predominantly composed of CD8-positive T-lymphocytes with scattered neutrophils, eosinophils and rare plasma cells. Portal areas were mildly expanded with mild bile ductular proliferation and mild to moderate lymphocytic infiltrates. Immunostains for CD8 demonstrated that the infiltrates were predominantly composed of CD8-positive T-lymphocytes. All three patients received steroids and responded to treatment evidenced by normalization of liver enzymes and function. Conclusion(s): Dense hepatic CD8 T-cell infiltration is a major finding inactivated CD8 T-cell hepatitis. However, the percentage distribution of lymphocyte subtypes in the setting of hepatitis is not well established, and CD8 T-cell infiltration has also been described in cases of drug-induced hypersensitivity reactions, viral hepatitis, hemophagocytic lymphohistiocytosis, and macrophage activation syndrome, as well as autoimmune hepatitis. Further investigation is needed to better understand the diagnostic criteria in this disease.

Nanopore single-molecule analysis of biomarkers: Providing possible clues to disease diagnosis

Biomarker detection has attracted increasing interest in recent years due to the minimally or non-invasive sampling process. Single entity analysis of biomarkers is expected to provide real-time and accurate biological information for early disease diagnosis and prognosis, which is critical to the effective disease treatment and is also important in personalized medicine. As an innovative single entity analysis method, nanopore sensing is a pioneering single-molecule detection technique that is widely used in analytical bioanalytical fields. In this review, we overview the recent progress of nanopore biomarker detection as new approaches to disease diagnosis. In highlighted studies, nanopore was focusing on detecting biomarkers of different categories of communicable and noncommunicable diseases, such as pandemic COVID-19, AIDS, cancers, neurologic diseases, etc. Various sensitive and selective nanopore detecting strategies for different types of biomarkers are summarized. In addition, the challenges, opportunities, and direction for future development of nanopore-based biomarker sensors are also discussed.Copyright © 2023 Elsevier B.V.

Assessment of respiratory disease infection risk and natural ventilation intervention countermeasures in teaching spaces: A campus case study

The risk of indoor respiratory disease transmission can be significantly reduced through interventions that target the built environment. Several studies have successfully developed theoretical models to calculate the effects of built environment parameters on infection rates. However, current studies have mainly focused on calculating infection rate values and comparing pre- and post-optimization values, lacking a discussion of safe baseline values for infection rates with risk class classification. The purpose of this paper is to explore the design of interventions in the built environment to improve the ability of buildings to prevent virus transmission, with a university campus as an example. The study integrates the Wells-Riley model and basic reproduction number to identify teaching spaces with high infection risk on campus and proposes targeted intervention countermeasures based on the analysis of critical parameters. The results showed that teaching buildings with a grid layout pattern had a higher potential risk of infection under natural ventilation. By a diversity of building environment interventions designed, the internal airflow field of classrooms can be effectively organized, and the indoor virus concentration can be reduced. We can find that after optimizing the building mentioned above and environment intervention countermeasures, the maximum indoor virus infection probability can be reduced by 22.88%, and the basic reproduction number can be reduced by 25.98%, finally reaching a safe level of less than 1.0. In this paper, we support university campuses' respiratory disease prevention and control programs by constructing theoretical models and developing parametric platforms. © 2023 Elsevier Ltd

A Social Distance Monitoring Method Based on Improved YOLOv4 for Surveillance Videos

Social distance monitoring is of great significance for public health in the era of COVID-19 pandemic. However, existing monitoring methods cannot effectively detect social distance in terms of efficiency, accuracy, and robustness. In this paper, we proposed a social distance monitoring method based on an improved YOLOv4 algorithm. Specifically, our method constructs and pre-processes a dataset. Afterwards, our method screens the valid samples and improves the K-means clustering algorithm based on the IoU distance. Then, our method detects the target pedestrians using a trained improved YOLOv4 algorithm and gets the pedestrian target detection frame location information. Finally, our method defines the observation depth parameters, generates the 3D feature space, and clusters the offending aggregation groups based on the L2 parametric distance to finally realize the pedestrian social distance monitoring of 2D video. Experiments show that the proposed social distance monitoring method based on improved YOLOv4 can accurately detect pedestrian target locations in video images, where the pre-processing operation and improved K-means algorithm can improve the pedestrian target detection accuracy. Our method can cluster the offending groups without going through calibration mapping transformation to realize the pedestrian social distance monitoring of 2D videos. © 2023 World Scientific Publishing Company.

Are government bonds still safe havens in the context of COVID-19?

This study analyzes whether government bonds can act as safe havens in the context of COVID-19. Using a panel fixed effect model, data were collected for both advanced and emerging market economies from March 11, 2020, to June 30, 2021. Robustness tests were used to add to the credibility of the findings. Our evidence supports that government bonds maintained their safe haven status during the COVID-19 pandemic. Hence, investors can still use government bonds to hedge financial market risks in the uncertain environment associated with this pandemic. Additionally, the negative effects of the COVID-19 pandemic on government bond yields in emerging economies are larger than in advanced economies. Therefore, policymakers' measures should focus on reducing COVID-19 cases to alleviate panic and diminish economic fluctuations, especially for emerging economies. Regulators can also use short-term interest rates to guide market capital flow to avoid a liquidity crisis, reducing financial stress and market uncertainty. © 2021 Informa UK Limited, trading as Taylor & Francis Group.

Oncology workload in a tertiary hospital during the COVID-19 pandemic

Introduction: Workload in oncology during a pandemic is expected to increase as manpower is shunted to other areas of need in combating the pandemic. This increased workload, coupled with the high care needs of cancer patients, can have negative effects on both healthcare providers and their patients. Method(s): This study aims to quantify the workload of medical oncologists compared to internal medicine physicians and general surgeons during the current COVID-19 pandemic, as well as the previous H1N1 pandemic in 2009. Result(s): Our data showed decrease in inpatient and outpatient workload across all three specialties, but the decrease was least in medical oncology (medical oncology -18.5% inpatient and -3.8% outpatient, internal medicine -5.7% inpatient and -24.4% outpatient, general surgery -17.6% inpatient, and -39.1% outpatient). The decrease in general surgery workload was statistically significant. The proportion of emergency department admissions to medical oncology increased during the COVID-19 pandemic. Furthermore, the study compared the workload during COVID-19 with the prior H1N1 pandemic in 2009 and showed a more drastic decrease in patient numbers across all three specialties during COVID-19. Discussion(s): We conclude that inpatient and outpatient workload in medical oncology remains high despite an ongoing COVID-19 pandemic. The inpatient medical oncology workload is largely contributed by the stable number of emergency department admissions, as patients who require urgent care will present to a healthcare facility, pandemic or not. Healthcare systems should maintain manpower in medical oncology to manage this vulnerable group of patients in light of the prolonged COVID-19 pandemic. Copyright © The Author(s) 2022.

Cardiac arrest: an interdisciplinary scoping review of clinical literature from 2021

The Interdisciplinary Cardiac Arrest Research Review (ICARE) group was formed in 2018 to conduct an annual search of peer-reviewed literature relevant to cardiac arrest. Now in its fourth year, the goals of this review are to highlight annual updates on clinically relevant and impactful clinical and population-level studies in the interdisciplinary world of cardiac arrest research from 2021. To achieve these goals, a search of PubMed using keywords related to clinical research in cardiac arrest was conducted. Titles and s were screened for relevance and sorted into seven categories: Epidemiology & Public Health;Prehospital Resuscitation;In-Hospital Resuscitation & Post-Arrest Care;Prognostication & Outcomes;Pediatrics;Interdisciplinary Guidelines;and Coronavirus disease 2019. Screened manuscripts underwent standardized scoring of methodological quality and impact by reviewer teams lead by a subject matter expert editor. Articles scoring higher than 99t h per-centile by category were selected for full critique. Systematic differences between editors' and reviewers' scores were assessed using Wilcoxon signed-rank test. A total of 4,730 articles were identified on initial search;of these, 1,677 were scored after screening for relevance and deduplication. Compared to the 2020 ICARE review, this represents a relative increase of 32% and 63%, respectively. Ultimately, 44 articles underwent full critique. The leading category was In-Hospital Resuscitation, representing 41% of fully reviewed articles, followed by Pre-hospital Resuscitation (20%) and Interdisciplinary Guidelines (16%). In conclusion, several clinically relevant studies in 2021 have added to the evidence base for the management of cardiac arrest patients including implementation and incorporation of resuscitation systems, technology, and quality improvement programs to improve resuscitation. © 2023 Tehran University of Medical Sciences.

Enhanced natural releases of mercury in response to the reduction in anthropogenic emissions during the COVID-19 lockdown by explainable machine learning

The wide spread of the coronavirus (COVID-19) has significantly impacted the global human activities. Compared to numerous studies on conventional air pollutants, atmospheric mercury that has matched sources from both anthropogenic and natural emissions is rarely investigated. At a regional site in eastern China, an intensive measurement was performed, showing obvious decreases in gaseous elemental mercury (GEM) during the COVID-19 lockdown, while it was not as significant as most of the other measured air pollutants. Before the lockdown, when anthropogenic emissions dominated, GEM showed no correlation with temperature and negative correlations with wind speed and the height of the boundary layer. In contrast, GEM showed significant correlation with temperature, while the relationship between GEM and the wind speed/boundary layer disappeared during the lockdown, suggesting the enhanced natural emissions of mercury. By applying a machine learning model and the SHAP (SHapley Additive exPlanations) approach, it was found that the mercury pollution episodes before the lockdown were driven by anthropogenic sources, while they were mainly driven by natural sources during and after the lockdown. Source apportionment results showed that the absolute contribution of natural surface emissions to GEM unexpectedly increased (44 %) during the lockdown. Throughout the whole study period, a significant negative correlation was observed between the absolute contribution of natural and anthropogenic sources to GEM. We conclude that the natural release of mercury could be stimulated to compensate for the significantly reduced anthropogenic GEM via the surface-air exchange in the balance of mercury.

Investigating the determinants of performance of artificial intelligence adoption in hospitality industry during COVID-19

PurposeDrawing on the technology-organization-environment (TOE) framework, this study aims to investigate determinants of performance of artificial intelligence (AI) adoption in hospitality industry during COVID-19 and identifies the relative importance of each determinant. Design/methodology/approachA two-stage approach that integrates partial least squares structural equation modeling (PLS-SEM) with artificial neural network (ANN) is used to analyze survey data from 290 managers in the hospitality industry. FindingsThe empirical results reveal that perceived AI risk, management support, innovativeness, competitive pressure and regulatory support significantly influence the performance of AI adoption. Additionally, the ANN results show that competitive pressure and management support are two of the strongest determinants. Practical implicationsThis research offers guidelines for hospitality managers to enhance the performance of AI adoption and presents policy-making insights to promote and support organizations to benefit from the adoption of AI technology. Originality/valueThis study conceptualizes the performance of AI adoption from both process and firm levels and examines its determinants based on the TOE framework. By adopting an innovative approach combining PLS-SEM and ANN, the authors not only identify the essential performance determinants of AI adoption but also determine their relative importance.

Government Interventions to Ameliorate Covid-19 Recession: The Case of Small, Micro, and Medium Firm's Survival in South Africa

This paper analyzes the impact of government economic interventions to ameliorate the COVID-19 pandemic on the survival of small, micro, and medium enterprises (SMMEs) in South Africa. We use the Cox Proportional Hazards approach and cross-sectional data from King Cetshwayo District Municipality covering 641 SMMEs. The study finds that tax relief was the most important intervention used to sustain SMMEs during the pandemic. Other interventions, such as cash grants and cheap credit, were also used during the period but had a small impact. Our findings support the interventions used by the South African government in mitigating the negative consequences of the pandemic-induced lockdown on small businesses. However, we also note that the magnitude at which the interventions were made could have been lower than what is optimal. The paper recommends the need to increase and have sustainable targeted expenditure during the difficult times to enhance the resilience of SMMEs to accelerate economic development and growth.

Regulation of innate and adaptive immunity using herbal medicine: benefits for the COVID-19 vaccination

Vaccination is a major achievement that has become an effective prevention strategy against infectious diseases and active control of emerging pathogens worldwide. In response to the coronavirus disease 2019 (COVID-19) pandemic, several diverse vaccines against severe acute respiratory syndrome coronavirus 2 have been developed and deployed for use in a large number of individuals, and have been reported to protect against symptomatic COVID-19 cases and deaths. However, the application of vaccines has a series of limitations, including protective failure for variants of concern, unavailability of individuals due to immune deficiency, and the disappearance of immune protection for increasing infections in vaccinated individuals. These aspects raise the question of how to modulate the immune system that contributes to the COVID-19 vaccine protective effects. Herbal medicines are widely used for their immune regulatory abilities in clinics. More attractively, herbal medicines have been well accepted for their positive role in the COVID-19 prevention and suppression through regulation of the immune system. This review presents a brief overview of the strategy of COVID-19 vaccination and the response of the immune system to vaccines, the regulatory effects and mechanisms of herbal medicine in immune-related macrophages, natural killer cells, dendritic cells, and lymphocytes T and B cells, and how they help vaccines work. Later in the article, the potential role and application of herbal medicines in the most recent COVID-19 vaccination are discussed. This article provides new insights into herbal medicines as promising alternative supplements that may benefit from COVID-19 vaccination.Graphical :: http://links.lww.com/AHM/A31.

DuDoTrans: Dual-Domain Transformer for Sparse-View CT Reconstruction

While Computed Tomography (CT) is necessary for clinical diagnosis, ionizing radiation in the imaging process induces irreversible injury, thereby driving researchers to study sparse-view CT reconstruction. Iterative models are proposed to alleviate the appeared artifacts in sparse-view CT images, but their computational cost is expensive. Deep-learning-based methods have gained prevalence due to the excellent reconstruction performances and computation efficiency. However, these methods ignore the mismatch between the CNN's local feature extraction capability and the sinogram's global characteristics. To overcome the problem, we propose Dual-Domain Transformer (DuDoTrans) to simultaneously restore informative sinograms via the long-range dependency modeling capability of Transformer and reconstruct CT image with both the enhanced and raw sinograms. With such a novel design, DuDoTrans even with fewer involved parameters is more effective and generalizes better than competing methods, which is confirmed by reconstruction performances on the NIH-AAPM and COVID-19 datasets. Finally, experiments also demonstrate its robustness to noise.

Manufacturing Process of SER-109, a Purified Investigational Microbiome Therapeutic, Reduces Risk of Coronavirus Transmission From Donor Stool.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) may present risk to patients treated with donor-derived microbiome therapies when appropriate manufacturing controls and inactivation processes are lacking. We report that the manufacturing steps for SER-109, a purified investigational microbiome therapeutic developed to reduce risk of Clostridioides difficile recurrence, inactivate porcine epidemic diarrhea virus, a model coronavirus for SARS-CoV-2.