ABSTRACT
To study the otorhinolaryngological clinical characteristics of COVID-19 positive patients. A prospective cross sectional study on sixty five patients who were SARS-CoV-2 PCR positive, and completed 14 days of isolation period were surveyed with a questionnaire. The responses were evaluated and assessed. Sixty five SARS-Cov-2 PCR positive cases were included in the study. There were 57 (87.6%) males and 8 (12.3%) females. Thirty five (53.8%) were in home isolation, whereas, 30 (46.2%) were under institutional care. Forty five patients (72.6%) presented with mild symptoms, and 4 (6.4%) developed moderate symptoms. Thirteen (21%) were asymptomatic. Overall, 46 patients (70.7%) presented with upper airway symptoms with or without general symptoms. More than half of the patients experienced pharyngodynia or sorethroat, smell and taste dysfunction as common symptoms (66.7%, 61.4% and 50.7% respectively). Severe headache was noticed by eighteen (27.7%) patients. Other respiratory symptoms such as nasal congestion, rhinorrhoea, sneezing, facial pain, etc. were present with less frequency. In more than half of the patients (61.5%), all the symptoms recovered within 5 days, in 12 (18.5%) between 5 and 8 days, and in 9 (13.8%), between 9 and14 days. However, in four patients, symptoms lasted for 28-30 days. In seven patients (10.7%), symptoms recurred after the period of isolation, however, the retest was negative. Fever, cough and or shortness of breath are the commonly reported prominent symptoms of COVID-19, however, there is a changing trend of clinical presentation towards variable otorhinolaryngologic manifestations. Pharyngodynia, taste and smell dysfunctions are common in patients with COVID-19, and could represent potential characters.
ABSTRACT
The coronavirus outbreak dramatically changed the work culture in the software industry. Most software practitioners began working remotely, which significantly revolutionized the traditional software processes landscape. Software development organizations have begun thinking about automating software processes to cope with the challenges raised by remote work. This special issue presents papers describing soft computing solutions for improving traditional software processes and capabilities. This editorial introduces the accepted papers and reflects on their contributions.
ABSTRACT
Early and precise COVID-19 identification and analysis are pivotal in reducing the spread of COVID-19. Medical imaging techniques such as Chest X-ray or chest radiographs, Computed Tomography (CT) scan, and electrocardiogram (ECG) trace images are most widely known for early discovery and analysis of the Coronavirus disease (COVID-19). Deep learning (DL) frameworks for identifying COVID-19 positive patients in the literature are limited to one data format, either ECG or chest radiograph images. Moreover, using several data types to recover abnormal patterns caused by COVID-19 could potentially provide more information and restrict the spread of the virus. This study presents an effective COVID-19 detection and classification approach using the Shufflenet CNN by employing three types of images, i.e., chest radiograph, CT-scan, and ECG-trace images. For this purpose, we performed extensive classification experiments with the proposed approach using each type of image. With the chest radiograph dataset, we performed three classification experiments at different levels of granularity, i-e, binary, three-class, and four-class classifications. Also, we performed a binary classification experiment with the proposed approach by classifying CT-scan images into COVID-positive and normal. Finally, utilizing the ECG-trace images, we conducted three experiments at different levels of granularity, i-e, binary, three-class, and five-class classifications. We evaluated the proposed approach with the baseline COVID-19 radiography database, SARS-CoV-2 CT-scan, and ECG images dataset of cardiac and COVID-19 patients. The average accuracy of 99.98% for COVID-19 detection in the three-class classification scheme using chest radiographs, optimal accuracy of 100% for COVID-19 detection using CT scans, and average accuracy of 99.37% for five-class classification scheme using ECG trace images have proved the efficacy of our proposed method over the contemporary methods. The optimal accuracy of 100% for COVID-19 detection using CT scans and the accuracy gain of 1.54% (in the case of five-class classification using ECG trace images) from the previous approach, which utilized ECG images for the first time, has a major contribution to improving the COVID-19 prediction rate in early stages. Experimental findings demonstrate that the proposed framework outperforms contemporary models. For example, the proposed approach outperforms the state-of-the-art DL approaches such as Squeezenet, Alexnet, and Darknet19 by achieving the accuracy of 99.98 (proposed method), 98.29, 98.50, and 99.67, respectively.
Subject(s)
COVID-19ABSTRACT
The COVID-19 pandemic has impacted communities far and wide and put tremendous pressure on healthcare systems of countries across the globe. Understanding and monitoring the major influences on COVID-19 prevalence is essential to inform policy making and device appropriate packages of non-pharmaceutical interventions (NPIs). This study evaluates community level influences on COVID-19 incidence in England and their variations over time with specific focus on understanding the impact of working in so called high-risk industries such as care homes and warehouses. Analysis at community level allows accounting for interrelations between socioeconomic and demographic profile, land use, and mobility patterns including residents' self-selection and spatial sorting (where residents choose their residential locations based on their travel attitudes and preferences or social structure and inequality); this also helps understand the impact of policy interventions on distinct communities and areas given potential variations in their mobility, vaccination rates, behavioural responses, and health inequalities. Moreover, community level analysis can feed into more detailed epidemiological and individual models through tailoring and directing policy questions for further investigation. We have assembled a large set of static (socioeconomic and demographic profile and land use characteristics) and dynamic (mobility indicators, COVID-19 cases and COVID-19 vaccination uptake in real time) data for small area statistical geographies (Lower Layer Super Output Areas, LSOA) in England making the dataset, arguably, the most comprehensive set assembled in the UK for community level analysis of COVID-19 infection. The data are integrated from a wider range of sources including telecommunications companies, test and trace data, national travel survey, Census and Mid-Year estimates. To tackle methodological challenges specifically accounting for highly interrelated influences, we have augmented different statistical and machine learning techniques. We have adopted a two-stage modelling framework: a) Latent Cluster Analysis (LCA) to classify the country into distinct land use and travel patterns, and b) multivariate linear regression to evaluate influences at each distinct travel cluster. We have also segmented our data into different time periods based on changes in policies and evolvement in the course of pandemic (such as the emergence of a new variant of the virus). By segmenting and comparing influences across spaces and time, we examine more homogeneous behaviour and uniform distribution of infection risks which in turn increase the potential to make causal inferences and help understand variations across communities and over time. Our findings suggest that there exist significant spatial variations in risk influences with some being more consistent and persistent over time. Specifically, the analysis of industrial sectors shows that communities of workers in care homes and warehouses and to a lesser extent textile and ready meal industries tend to carry a higher risk of infection across all spatial clusters and over the whole period we modelled in this study. This demonstrates the key role that workplace risk has to play in COVID-19 risk of outbreak after accounting for the characteristics of workers' residential area (including socioeconomic and demographic profile and land use features), vaccination rate, and mobility patterns.
Subject(s)
COVID-19ABSTRACT
PurposeIn this work, a microstrip antenna array for wireless power transfer (WPT) application is reported. The proposed 4 × 4 antenna array operating at 16 GHz is designed using a flexible Kapton polyimide substrate for a far-field charging unit (FFCU).Design/methodology/approachThe proposed antenna is designed using the transmission line model on a flexible Kapton polyimide substrate. The finite element method (FEM) is used to perform the full-wave electromagnetic analysis of the proposed design.FindingsThe antenna offers −10 dB bandwidth of 240 MHz with beam width and broadside gain found to be 29.4° and 16.38 dB, respectively. Also, a very low cross-polarization level of −34.23 dB is achieved with a radiation efficiency of 36.67%. The array is capable of scanning −15° to +15° in both the elevation and azimuth planes.Originality/valueThe radiation characteristics achieved suggest that the flexible substrate antenna is suitable for wireless charging purposes.
ABSTRACT
BACKGROUND: To study, estimate and discuss the variations of the aerosol optical depth (AOD), black carbon, sulfate and organic matter, in the atmosphere in Blida City of Algeria, which was greatly affected by COVID-19 pandemic. METHODS: We analyzed the effects of changes in the total AOD, black carbon, sulfate, and organic matter in the atmosphere (λ = 550 nm) in the same period of 2019 and 2020, following the COVID-19 epidemic in Blida City, which was the most-affected city in Algeria. RESULTS: The quarantine that was enacted to limit the spread of COVID-19 resulted in side effects that were identifiable in the total AOD and in some of its atmospheric components. Comparing these variables in 2019 and 2020 (in the months during the quarantine) revealed that in April, the BCAOD values were much lower in 2020 than in 2019. CONCLUSION: Based on the effects of the emerging COVID-19, the research listed the changes received from the AOD, and is considered as a comparative study and represents a significant side effect of the quarantine that was mainly designed to limit COVID-19.
ABSTRACT
Most recently, an outbreak of severe pneumonia caused by the infection of 2019-nCoV, a novel coronavirus first identified in Wuhan, China, imposes serious threats to public health. Upon infecting host cells, coronaviruses assemble a multi-subunit RNA-synthesis complex of viral non-structural proteins (nsp) responsible for the replication and transcription of the viral genome. Therefore, the role and inhibition of nsp12 are indispensable. Since there is no crystallographic structure of RdRp is available, so, here, we present the 3-dimensional structure of the 2019-nCoV nsp12 polymerase using a computational approach. nsp12 of 2019-nCoV possesses an architecture common to all viral polymerases as well as a large N-terminal extension. This structure illuminates the assembly of the coronavirus core RNA-synthesis machinery, provides key insights into nsp12 polymerase catalysis and fidelity, and acts as a template for the design of novel antiviral therapeutics. Besides, the experimental structure could reveal the organization in a more sophisticated way. Furthermore, the ancestral state reconstruction suggests the possible evolution of nCoV in Wuhan China and its dispersal to the USA. The result of our analyses postulates the possible dispersal of nCoV from the USA and Shenzhen back to Wuhan. This disclosing of valuable knowledge regarding the 3D structure of 2019-nCoV nsp12 architecture, ancestral relation, and dispersion pattern could help to design effective therapeutic candidates against the coronaviruses and design robust preventive measurements.