Covid the Board System

The dark cloud of the vigorously spreading pandemic is still seen hovering over our heads. Though the situation presently seems under control, a continuation of the same is not guaranteed. The massive disturbance to the environmental assets leading to the melting of polar ice caps, ozone depletion, global warming, etc., poses a towering threat to every surviving species. The most recently faced devastation by the people is the 'Corona' virus;a ginormous pandemic, which has affected people living in almost every nook and corner of the world. For which the primary steps were to wear a mask, maintain social distancing and sanitize almost every single thing that comes in physical contact with more than one human. This brings in a crucial technical catastrophe, the physical barrier to be maintained, and the face masks covering the face, making fingerprint and face recognition systems totally impractical. This demands a grave need for a situation-friendly technical solution. © 2022 IEEE.

Determination of the Morphological Characteristics of Eight Types of Protective Face Masks and Respirators

Respiratory infections have arisen as a public health concern. The regulation of COVID-19 is based on knowledge of its transmission mechanism. Masks and respirators act as a physical barrier against respiratory droplets that enter through the nose and mouth, as well as droplets spat by sick persons. Textile masks (including 'do-it-yourself'), surgical (medical) masks and respirators are the three basic types of personal protection devices, covering the human face. The purpose of our work is to give a study on the morphological features of masks and respirators, which are widely accessible in Bulgarian shops and pharmacies, revealing their structure and differences between them. The results will be further used for the assessment of heat and mass transfer abilities of the masks/respirators, which are largely preconditioned by the masks' morphology. © 2022 IEEE.

Lower Face Inpainting Aiming at Face Recognition under Occlusions

Although both face recognition and object inpainting have become promising approaches through the use of deep learning, the COVID-19 pandemic has created a tremendous challenge to their further development. Masks, which people have become accustomed to as an effective sanitary measure to prevent infection of COVID-19, have also become an undeniable physical barrier between devices applying face recognition authentication and the faces to be recognized. Therefore, methods that can overcome this dilemma are urgently needed. This study proposes a method that applies a generative model to recognize masked faces based on face inpainting. We introduced a newly proposed identity loss term to conform to the identity information. The reconstructed face will be fed into a face recognition network to extract the feature embeddings for a distance comparison. Taking a naive generative model without an identity loss term introduced as the baseline, the model with an identity loss term improves the recognition accuracy by more than 4%. © 2022 IEEE.

The (Augmented) World Is Our Campus

Just prior to the spread of the COVID-19 pandemic to the United States, an augmented reality-based exhibit was built and pilot tested with a group of students and faculty. The exhibit strives to utilize key educational affordances of this teaching medium (e.g., removing physical barriers of time and space) to increase patron engagement with content and help them learn. This unique application of augmented reality offers many opportunities to enhance learning about our world and provides students with greater exposure to this emerging technology. It also may serve as a model of how the technology can be utilized in the classroom to teach principles and other challenging conceptual knowledge for which the affordances of this technology are suited. This paper is a report of ongoing efforts to further develop and improve this exhibit in an effort to provide background and ideas for those interested in creating AR experiences to enhance learning. It discusses key development considerations and summarizes several practical applications for this technology in a library setting. © American Society for Engineering Education, 2021

Characteristics of SARS-CoV-2 Transmission among Meat Processing Workers in Nebraska, USA, and Effectiveness of Risk Mitigation Measures.

The coronavirus disease (COVID-19) pandemic has severely impacted the meat processing industry in the United States. We sought to detail demographics and outcomes of severe acute respiratory syndrome coronavirus 2 infections among workers in Nebraska meat processing facilities and determine the effects of initiating universal mask policies and installing physical barriers at 13 meat processing facilities. During April 1-July 31, 2020, COVID-19 was diagnosed in 5,002 Nebraska meat processing workers (attack rate 19%). After initiating both universal masking and physical barrier interventions, 8/13 facilities showed a statistically significant reduction in COVID-19 incidence in <10 days. Characteristics and incidence of confirmed cases aligned with many nationwide trends becoming apparent during this pandemic: specifically, high attack rates among meat processing industry workers, disproportionately high risk of adverse outcomes among ethnic and racial minority groups and men, and effectiveness of using multiple prevention and control interventions to reduce disease transmission.