Exposure to SARS-CoV-2 Aerosols in U.S. Residences: Sample analysis Using Two Detection Criteria

Uniform practices and quality control methods are needed to detect and quantify airborne viruses across sampling and analysis platforms. We compared detection of airborne SARSCoV-2 RNA in residences of individuals with COVID-19 using two commonly used criteria: environmental (at least one SARS-CoV-2-specific gene and internal control amplified by PCR with Ct ≤ 40) and clinical (at least two SARS-CoV-2-specific genes and internal control amplified with Ct ≤ 37). 24-hr total aerosol samples were collected in a self-isolation room and an additional room without manipulating subjects' behavior/activities. Under the environmental criterion, 7/16 samples in primary rooms and 7/15 samples in secondary rooms were positive. Comparable but lower positive sample proportions were observed using the more rigorous clinical criterion: 6/16 primary rooms and 5/15 secondary rooms. A consensus SARS-CoV-2 environmental sampling and analysis framework is needed for comparisons between studies. © 2022 17th International Conference on Indoor Air Quality and Climate, INDOOR AIR 2022. All rights reserved.

Diagnosis of COVID-19 from CT Images and Respiratory Sound Signals Using Deep Learning Strategies

COVID-19 has been a major issue among various countries, and it has already affected millions of people across the world and caused nearly 4 million deaths. Various precautionary measures should be taken to bring the cases under control, and the easiest way for diagnosing the diseases should also be identified. An accurate analysis of CT has to be done for the treatment of COVID-19 infection, and this process is complex and it needs much attention from the specialist. It is also proved that the covid infection can be identified with the breathing sounds of the patient. A new framework was proposed for diagnosing COVID-19 using CT images and breathing sounds. The entire network is designed to predict the class as normal, COVID-19, bacterial pneumonia, and viral pneumonia using the multiclass classification network MLP. The proposed framework has two modules: (i) respiratory sound analysis framework and (ii) CT image analysis framework. These modules exhibit the workflow for data gathering, data preprocessing, and the development of the deep learning model (deep CNN + MLP). In respiratory sound analysis framework, the gathered audio signals are converted to spectrogram video using FFT analyzer. Features like MFCCs, ZCR, log energies, and Kurtosis are needed to be extracted for identifying dry/wet coughs, variability present in the signal, prevalence of higher amplitudes, and for increasing the performance in audio classification. All these features are extracted with the deep CNN architecture with the series of convolution, pooling, and ReLU (rectified linear unit) layers. Finally, the classification is done with a multilayer perceptron (MLP) classifier. In parallel to this, the diagnosis of the disease is improved by analyzing the CT images. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

UAE e-learning sentiment analysis framework

This research project predicts and infers real-time insights on public mental health relevant to education during and after the COVID-19 pandemic by modeling, deploying, and testing an end-to-end spatiotemporal sentiment analysis framework. Moreover, the project aims to analyze the sentiments and emotions of the public;from Twitter, toward the current context of the e-learning process factored by aspects and emotions. The framework consists of four predictive models based on statistical analysis and machine learning to analyze the UAE education-related Twitter dataset. The first analytics is spatiotemporal analytics, which describes an event at a specific time and specific location. Spatiotemporal analytics is used as the base for the remaining three analytics: Aspect-based Sentiment Analysis, sentiment analysis, and emotion analysis. Aspectbased Sentiment Analysis considers the words/terms related to relevant aspects and then identify the sentiment associated with them. Sentiment Analysis is used to extract the sentiment in a specific text. Emotion Analysis identifies the type of emotion felt by users in their tweets. All the analytics will be visualized into a responsive website that provides a prompt understanding of the public opinions and their feedback towards the e-learning process. As a result, a group of recommendations is generated based on the analytics' resulting emotion to enhance the mental health. © 2021 Association for Computing Machinery.