Comparison of popular video conferencing apps using client-side measurements on different backhaul networks

Video conferencing platforms have been appropriated during the COVID-19 pandemic for different purposes, including classroom teaching. However, the platforms are not designed for many of these objectives. When users, like educationists, select a platform, it is unclear which platform will perform better given the same network and hardware resources to meet the required Quality of Experience (QoE). Similarly, when developers design a new video conferencing platform, they do not have clear guidelines for making design choices given the QoE requirements. In this paper, we provide a set of networks and systems measurements, and quantitative user studies to measure the performance of video conferencing apps in terms of both, Quality of Service (QoS) and QoE. Using those metrics, we measure the performance of Google Meet, Microsoft Teams, and Zoom, which are three popular platforms in education and business. We find a substantial difference in how the three apps treat video and audio streams. Our quantitative user studies confirm the findings of our quantitative measurements. While each platform has its benefits, we find that no app is ideal. A user can choose a suitable platform depending on which of the following, audio, video, or network bandwidth, matters more. © 2022 ACM.

WE-Net: An Ensemble Deep Learning Model for Covid-19 Detection in Chest X-ray Images Using Segmentation and Classification

Amidst the increasing surge of Covid-19 infections worldwide, chest X-ray (CXR) imaging data have been found incredibly helpful for the fast screening of COVID-19 patients. This has been particularly helpful in resolving the overcapacity situation in the urgent care center and emergency department. An accurate Covid-19 detection algorithm can further aid this effort to reduce the disease burden. As part of this study, we put forward WE-Net, an ensemble deep learning (DL) framework for detecting pulmonary manifestations of COVID-19 from CXRs. We incorporated lung segmentation using U-Net to identify the thoracic Region of Interest (RoI), which was further utilized to train DL models to learn from relevant features. ImageNet based pre-trained DL models were fine-tuned, trained, and evaluated on the publicly available CXR collections. Ensemble methods like stacked generalization, voting, averaging, and the weighted average were used to combine predictions from best-performing models. The purpose of incorporating ensemble techniques is to overcome some of the challenges, such as generalization errors encountered due to noise and training on a small number of data sets. Experimental evaluations concluded on significant improvement in performance using the deep fusion neural network, i.e., the WE-Net model, which led to 99.02% accuracy and 0.989 area under the curve (AUC) in detecting COVID-19 from CXRs. The combined use of image segmentation, pre-trained DL models, and ensemble learning (EL) boosted the prediction results. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Does COVID-19 pandemic have any impact on use of behavior management strategies in paediatric dentistry? A cross sectional study

Objective: To analyse the effects of the COVID-19 outbreak on behavior management strategies in paediatric dentistry. Study Design: For this cross sectional research, before the COVID-19 pandemic and after the lift of lockdown during COVID-19 pandemic, a standardized dose-ended set of 26 questions on behaviour management and paediatric dental practise was developed and forwarded to pedodontist in India. The data of their responses was collected and put into a worksheet in Excel, then analysed statistically and inferences were drawn. Results : The preference for non-pharmacological and pharmacological behavior management techniques has been changed;before COVID-19, non-pharmacological behaviour management techniques were widely prevalent but after the lift of lockdown the preference for pharmacological behavior management techniques have noticeably increased. Conclusion: Because of the threat of cross-infection in the COVID-19 pandemic, use of strategies for behavior management has be changed. So the paediatric dentist should cope-up with the situations such as the COVID-19 outbreak, adapt to changes in behavior management strategies and become competent enough to effectively perform treatment in paediatric patients.

Perspective of ocular manifestations in coronavirus infection: A review

Coronavirus (COVID-19), the disease has become pandemic within a short span of time. Transmission of this disease occurs with the secretions from infected individuals to another directly. The secretions can be inhaling of droplets, saliva, or droplets of the infected person. It is a controversial statement if this pandemic can be transmitted through tears. However, this affects various parts of the body, and there exist associated ocular findings. Considering the reported studies on the ocular signs and symptoms of the disease, it has certainly drawn attention to the eye’s anterior segment. These findings include conjunctivitis, epiphora, and chemosis. This narrative review aims to understand the impact of coronavirus on the retina as there are numer-ous families of viruses that contribute to retinopathy. This review focuses on the imaging techniques that can be implemented to assess the affected patients’ retina. The pandemic has re-enforced health systems to evolve with technology;hence artificial intelligence can be considered to evaluate the ocular signs in patients who report with COVID-19. © IJCRR.