Stronger than partisanship and motivated reasoning: news exposure and news frames predicting US state-level preventive behaviours against COVID-19

The COVID-19 pandemic has become a partisan issue rather than an independent public health issue in the US. This study examined the behavioural consequences of motivated reasoning and framing by investigating the impacts of COVID-19 news exposure and news frames, as apparent through a Latent Dirichlet topic modelling analysis of local news coverage, on state-level preventive behaviours as understood through a nationally representative survey. Findings suggested that the media effects on various preventive behaviours differed. The overall exposure rate to all COVID-19 news articles increased mask-wearing but did not significantly impact other preventive behaviours. Four news frames significantly increased avoiding contact or avoiding public or crowded places. However, news articles discussing anxiety and stay at home order triggered resistance and countereffects and led to risky behaviours. ‘Solid Republican' state residents were less likely to avoid contact, avoid public or crowded places, and wear masks. However, partisan leanings did not interfere with the impact of differing local COVID-19 news frames on reported preventive behaviours. Plus, statements regarding pre-existing trust in Trump did not correlate with reported preventive behaviour. Attention to effect sizes revealed that news exposure and news frames could have a bigger impact on health behaviours than motivated reasoning.

Social Distance and Face Mask Detection using vision-based sensors and IP cameras

Healthcare systems all over the world are strained as the COVID-19 pandemic's spread becomes more widespread. The only realistic strategy to avoid asymptomatic transmission is to monitor social distance, as there are no viable medical therapies or vaccinations for it. A unique computer vision-based framework that uses deep learning is to analyze the images that are needed to measure social distance. This technique uses the key point regressor to identify the important feature points utilizing the Visual Geometry Group (VGG19) which is a standard Convolutional Neural Network (CNN) architecture having multiple layers, MobileNetV2 which is a computer vision network that advances the-state-of-art for mobile visual identification, including semantic segmentation, classification and object identification. VGG19 and MobileNetV2 were trained on the Kaggle dataset. The border boxes for the item may be seen as well as the crowd is sizeable, and red identified faces are then analyzed by MobileNetV2 to detect whether the person is wearing a mask or not. The distance between the observed people has been calculated using the Euclidian distance. Pretrained models like (You only look once) YOLOV3 which is a real-time object detection system, RCNN, and Resnet50 are used in our embedded vision system environment to identify social distance on images. The framework YOLOV3 performs an overall accuracy of 95% using transfer learning technique runs in 22ms which is four times fast than other predefined models. In the proposed model we achieved an accuracy of 96.67% using VGG19 and 98.38% using MobileNetV2, this beats all other models in its ability to estimate social distance and face mask. © 2023 IEEE.

Perception and Practice Towards Hand Hygiene during Pandemic among Patients Visiting OPD in A Private Institution

One of the most significant and well-publicized prevention practises for Covid 19 is hand cleanliness. Face masks and social withdrawal are useless without good hand hygiene. The healthcare professionals can only intervene and raise awareness to enhance the public's hand hygiene practises after they are aware of the public's perceptions of and barriers to hand hygiene. A private dental facility had 150 outpatients participate in this cross-sectional questionnaire survey. Ten questions addressing various facets of hand hygiene and perceived obstacles made up the survey. The information from Google Forms was then imported into SPSS Version 15 using Excel. Data were presented as frequencies and percentages after the chi square test, and a p value of 0.05 or less was regarded as statistically significant.. In our study, 92.62 percent of outpatients at a private facility said that they continue to take measures against COVID19. 83.89% of our patients agreed that good hand hygiene habits are crucial for preventing COVID19. Whereas 38.26% of outpatients claimed to only wash their hands for 30 seconds, 33.56% of outpatients claimed to wash their hands for a full minute. In contrast to the 48.32 percent who said hand sanitizer is best and important for hand hygiene, 51.68 percent of outpatients said soap and water is best and essential for hand hygiene. According to the study's findings, the participants had a reasonable understanding of hand hygiene and its significance. Yet, there is a need for greater awareness of the finishing details on touch surfaces. Thus, it is advised that media-based propaganda and awareness campaigns have a positive impact and should be kept up, with a stronger focus on the finer points. © 2023 IEEE.

Masked Student Dataset of Expressions

Facial expression recognition (FER) algorithms work well in constrained environments with little or no occlusion of the face. However, real-world face occlusion is prevalent, most notably with the need to use a face mask in the current Covid-19 scenario. While there are works on the problem of occlusion in FER, little has been done before on the particular face mask scenario. Moreover, the few works in this area largely use synthetically created masked FER datasets. Motivated by these challenges posed by the pandemic to FER, we present a novel dataset, the Masked Student Dataset of Expressions or MSD-E, consisting of 1,960 real-world non-masked and masked facial expression images collected from 142 individuals. Along with the issue of obfuscated facial features, we illustrate how other subtler issues in masked FER are represented in our dataset. We then provide baseline results using ResNet-18, finding that its performance dips in the non-masked case when trained for FER in the presence of masks. To tackle this, we test two training paradigms: contrastive learning and knowledge distillation, and find that they increase the model's performance in the masked scenario while maintaining its non-masked performance. We further visualise our results using t-SNE plots and Grad-CAM, demonstrating that these paradigms capitalise on the limited features available in the masked scenario. Finally, we benchmark SOTA methods on MSD-E. The dataset is available at https://github.com/SridharSola/MSD-E. © 2022 ACM.

Face Mask Detector for Raspberry Pi Based on Computer Vision and Edge Computing

The pandemic of the coronavirus disease 2019 has shown weakness and threats in various fields of human activity. In turn, the World Health Organization has recommended different preventive measures to decrease the spreading of coronavirus. Nonetheless, the world community ought to be ready for worldwide pandemics in the closest future. One of the most productive approaches to prevent spreading the virus is still using a face mask. This case has required staff who would verify visitors in public areas to wear masks. The aim of this paper was to identify persons remotely who wore masks or not, and also inform the personnel about the status through the message queuing telemetry transport as soon as possible using the edge computing paradigm. To solve this problem, we proposed to use the Raspberry Pi with a camera as an edge device, as well as the TensorFlow framework for pre-processing data at the edge. The offered system is developed as a system that could be introduced into the entrance of public areas. Experimental results have shown that the proposed approach was able to optimize network traffic and detect persons without masks. This study can be applied to various closed and public areas for monitoring situations. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

A Project to Distribute Personal Protective Equipment to Homecare Agencies during the COVID-19 Pandemic: Its Association with the Sense of Security Improvement among Homecare Nurses/Helpers

During the early period of COVID-19 pandemic, there was a serious shortage of personal protective equipments (PPEs), which caused difficulty in homecare agencies to make home visits to those (possible) positive COVID-19 cases. An organization with the help of several foundations started a special program to distribute PPEs to those agencies in which there was a possible case or those cases that had close contact with the positive cases. This study examined whether this voluntary activity contributed to increasing the sense of security in providing care among homecare workers. We conducted a survey with homecare agencies that received PPEs from the program between July 2020 and February 2021. The participants were agency managers who applied for PPEs. We conducted the survey twice, before and after receiving PPEs. In the questionnaire, we asked about the overall sense of security in providing care for those infected with COVID-19, reasons for applying for PPE, symptoms of the client or his/her family who caused the PPE request, and the agency's and clients' characteristics. We analyzed the data from 802 responses. Before PPE distribution, the sense of security was associated with the focal client having a cognitive impairment (β = −0.096), having cough (β = −0.088), fatigue (β = −0.085), or headache (β = −0.078). Agencies that did not visits those (possibly) positive cases (β = −0.123) had lower sense of security. Overall, the mean sense of security increased after receiving PPE. Factors that contributed to the increase in sense of security included a lower sense of security before the application (β = −0.529), visiting clients without dyspnoea (β = −0.109), the agency that did not visit positive cases before the application (β = −0.089), and with higher satisfaction with the days of PPEs received (β = 0.144). These results underline the benefit of the special PPsE distribution program.

An Exploratory Study of Masked Face Recognition with Machine Learning Algorithms

Automated face recognition is a widely adopted machine learning technology for contactless identification of people in various processes such as automated border control, secure login to electronic devices, community surveillance, tracking school attendance, workplace clock in and clock out. Using face masks have become crucial in our daily life with the recent world-wide COVID-19 pandemic. The use of face masks causes the performance of conventional face recognition technologies to degrade considerably. The effect of mask-wearing in face recognition is yet an understudied issue. In this paper, we address this issue by evaluating the performance of a number of face recognition models which are tested by identifying masked and unmasked face images. We use six conventional machine learning algorithms, which are SVC, KNN, LDA, DT, LR and NB, to find out the ones which perform best, besides the ones which poorly perform, in the presence of masked face images. Local Binary Pattern (LBP) is utilized as the feature extraction operator. We generated and used synthesized masked face images. We prepared unmasked, masked, and half-masked training datasets and evaluated the face recognition performance against both masked and unmasked images to present a broad view of this crucial problem. We believe that our study is unique in elaborating the mask-aware facial recognition with almost all possible scenarios including half_masked-to-masked and half_masked-to-unmasked besides evaluating a larger number of conventional machine learning algorithms compared the other studies in the literature. © 2023 IEEE.

MASK UP! Supporting Mask Wearing of Children With Extensive Support Needs

Since the reopening of schools after COVID-19, students and staff have been required to wear face masks. Some students, including those with extensive support needs (ESN), may have difficulty in wearing these novel protective garments. Fortunately, researchers have demonstrated the effectiveness of several procedures for supporting the wearing of similar equipment (e.g., glasses, hearing aids). In this paper, we draw upon the available research to propose a protocol for teaching students with ESN to put on and maintain the use of face masks.

COVID – 19: A Critical Ontology of the present1

COVID-19 has crowned a number of other disasters (wildfires in Australia, Desert Locusts in Kenya, an imminent WWIII merging Iran and the US), causing panic to click into place and horror to become our global predicament, making us realize that we live in the illusion of the permanence of things, of mastery, and of immortality. People's turning to social media for trans-local news on COVID-19 has stirred great ire in the world. This led to the proliferation of dark images that associate the viral catastrophe with the end as we know it. To problematize the idea of the apocalypse (or the end) this paper speaks of three moments of survival in human existence: the beneath, the behind and the beyond. We argue that the apocalyptic nature of the pandemic and its global horrorism are part of a congeries of apocalyptic simulations that have always been part of the narrative with which we try to define ourexistence on earth. This paper masks itself against perfunctory examinations of the term apocalypse, and offers instead an understanding that runs along the lines of its Greek etymological sense as apokalyptein (revelation). It offers what Foucault calls an ontology of the present, that interrogates the history of COVID -19 with an emphasis neither on its origin nor on its telos. As beyondists, the COVID-19 catastrophe has revealed to us that 1) we have ‘access to knowledge beyond knowledge' (see Gumpert 2012), and therefore that 2) our modern predicament is not very modern. The end, (not) to be sure, has been lived and relived in the boundary between reality and simulation. After all, the end of something comprises the beginning (in reverse) of that which "endeth”, throwing the beyond, behind and beneath in the Ferris wheel of epistemological and existential entanglement.

Face Mask detection using Mask R-CNN to control the spread of Covid-19

Business executives are developing cutting-edge digital solutions as the virus outbreak spreads. A face mask detection system is one of them, and it can be used to spot people wearing them. Face mask identification software and applications have already been released by a few businesses, and others have promised to do the same for the service. The proposed work examines face mask detection accuracy using CNN networks. Mask wear is now required in many developed and developing countries worldwide when leaving the house or entering public spaces. It will be difficult to maintain touchless access control in buildings while recognising faces wearing masks on any surveillance systems. Masks covering faces has made face detection algorithms and performance difficult. The proposed work detect face mask labeled no mask or mask with detection accuracy. The work train the system to click images of a face and provide labeled data. The work is classified using Convolution Neural Network (CNN), a Deep learning technique, to classify the input image with the help of the classification algorithm MobileNetV2. The trained system shows whether a person in the video frame is wearing a mask or not. © 2023 IEEE.

CATALYTIC PYROLYSIS OF DISCARDED COVID-19 MASKS OVER SEPIOLITE

This research aims to develop a new strategy to valorize wasted COVID-19 masks based on pyrolysis to convert them into useful products. First, surgical and FFP2 masks were thermally pyrolyzed at temperatures of 450–550 ºC with the purpose of determining gas, liquid (oil) and solid (char) yields. At low temperatures, solid yield was high, while at high temperatures the gas product was enhanced. The highest yield of liquid was found at an operating temperature of 500 ºC in both surgical and FPP2 masks pyrolysis. The liquid product yields were 59.08% and 58.86%, respectively. Then, the volatiles generated during thermal pyrolysis of residual masks were cracked over sepiolite as catalyst at a temperature of 500 ºC. The catalytic pyrolysis increased the yield of gas product (43.89% against 39.52% for surgical masks and 50.53% against 39.41% for FFP2 masks) and decreased the viscosity of the liquid product. Finally, the effect of sepiolite regeneration and reuse in consecutive pyrolysis tests was examined. Results showed that, with the higher regeneration-reuse of sepiolite, the catalyst was degraded obtaining a liquid product with higher molecular mass. This effect was hardly noticeable in the case of FFP2 masks. © 2022 International Multidisciplinary Scientific Geoconference. All rights reserved.

Masked Face Recognition System using Extended VGG-19

The scientific community has observed several issues as a result of COVID-19, both directly and indirectly. The use of face mask for health protection is crucial in the current COVID-19 scenario. Besides, ensuring the security of all people, from individuals to the state system, financial resources, diverse establishments, government, and non-government entities, is an essential component of contemporary life. Face recognition system is one of the most widely used security technology in modern life. In the presence of face masks, the performance of the current face recognition systems is not satisfactory. In this paper, we investigate a flexible solution that could be employed to recognize masked faces effectively. To do this, we develop a unique dataset to recognize the masked face, consisting of a frontal and lateral face with a mask. We propose an extended VGG19 deep model to improve the accuracy of the masked face recognition system. Then, we compare the accuracy of the proposed framework to that of well-known deep learning techniques, such as the standard Convolutional Neural Network (CNN) and the original VGG19. The experimental results demonstrate that the proposed extended VGG19 outperforms the investigated approaches. Quantitatively, the proposed model recognizes the frontal face with the mask with high accuracy of 96%. © 2022 IEEE.

Face Mask And Body Temperature Detection In Sensor Enabled Automated Door Control System

Developing an automatic door-opening system that can recognize masks and gauge body temperature is the aim of this project. The new Corona Virus (COVID-19) is an unimaginable pandemic that presents the medical industry with a serious worldwide issue in the twenty-first century. How individuals conduct their lives has substantially changed as a result. Individuals are reluctant to seek out even the most basic healthcare services because of the rising number of sick people who pass away, instilling an unshakable terror in their thoughts.This paper is about the Automatic Health Machine (AHM). In this dire situation, the government provided the people with a lot of directions and information. Apart from the government, everyone is accountable for his or her own health. The most common symptom of corona infection is an uncontrollable rise in body temperature. In this project, we create a novel device to monitor people's body temperatures using components such as an IR sensor and temperature sensor. © 2023 IEEE.

Bacteria Filtration Efficiency for Different Types of Protective Face Masks

Emerging infectious diseases that we are witnessing in the modern age are among the leading public health concerns. They most often occur in the form of epidemics or pandemics, and they have not been sufficiently researched. Owing to the current coronavirus disease 2019 (COVID-19) pandemic, the World Health Organization has published various recommendations to prevent the spread of this communicable disease, including a recommendation to wear protective facial masks. Therefore, this study aimed to determine the filtration effectiveness of bacteria, yeasts, and molds on three different commonly and commercially available masks used in children's educational institutions. In addition, the bacterial content of indoor air bioaerosols was identified. The genera Staphylococcus and Micrococcus were dominant in all samples, whereas bacteria of the genera Bacillus, Acinetobacter, and Corynebacterium were identified at a significantly smaller number. Bacterial, yeast, and mold filtering effectiveness increased from the single-layer cloth mask, which proved to be the least effective, to the surgical mask, to the filtering facepiece type 2 (FFP2) mask. Furthermore, surveys are needed to study the effectiveness of protective measures.

The semiotics of the anti-COVID-19 mask

The pandemic spreading of the COVID-19 virus has led to the global need to introduce, often by law, the medical face mask, which can undoubtedly be considered as "the object of 2020.” In a few months, most human faces around the world in the public space, but also often in the private space, have been covered with various kinds of protective masks. Very soon, these objects have become the centre of several discursive productions, going from medical reports to media coverage, from artistic representations to ironic memes. The medical face mask was not totally new in the west, where it was already present in special circumstances, like dentists' studios or emergency rooms, and was quite familiar in the east, especially in Japan, China, and Korea. Yet such massive introduction changed the meaning of the medical face mask in every context. Old habits were reconfigured or clashed with the new ones, giving rise to a novel syntax, semantics, and pragmatics of the human face in conjunction with this device and in the context of the global pandemic. The present paper offers an introduction to a semiotic mapping of such radical cultural change and its likely consequences.

Covid-19: Patients should self-isolate for two weeks before elective care, trusts are told

Patients should be admitted to hospital for planned or elective care only if they have self-isolated for 14 days beforehand and tested negative for covid-19, says new guidance for trusts in England designed to increase the number of routine operations and treatments.1 People who require urgent and emergency care should be tested on arrival and streamed accordingly, with services split to make the risk of picking up the virus in hospital as low as possible, said NHS England. Patients who stay in hospital should be monitored for symptoms and retested for infection every five and seven days after admission, and those who are being discharged to a care home should be tested up to 48 hours before they are due to leave. On 15 May the government announced that more than 70 million face masks would be manufactured by a private company, Honeywell, in Scotland from July, with 4.5 million FFP2 and FFP3 masks being made each month for the next 18 months.

What is behind the mask? Social representation of mask wearing

Background: During the COVID-19 pandemic, a preventive and widely mandatory use of face masks was a dominant segment of the infection prevention and control of the epidemic. Covering about 60-70% of the facial surface, face masks dramatically affect social interactions-especially emotion recognition, expression and mentalization. Difficulties in communication in the doctor-patient relationship become of paramount importance to the effectiveness of the healing work. This becomes even more critical when the patient suffers from a disorder characterized by a mentalization deficit. In our study, we use the theory of social representations to examine the contents with which mask wearing has become part of our everyday knowledge. Objectives: We aimed to explore the social representations of mask wearing considering its impact on interpersonal communication, in groups where the effectiveness of mutual understanding is critical. Methods: In our study, carried out during the second and third waves of the coronavirus epidemic in Hungary, we gave a free association task to the target word mask-wearing" in a group of medical doctors, and hospitalized somatic and psychiatric patients and healthy controls (total of 81 subjects, mean age 43.1 [13.83] years), then used the obtained associations to form semantic categories and to map the structure of social representations within the groups using a rank-frequency method. Results: The positive experience of safety and the negative experience of physiological discomfort caused by the facemasks were consistently central to the social representations of mask-wearing in all study groups. Differences were found between groups in terms of more mature elaborative categories, as well as anxiety, aggression, helplessness, damaged dependency needs, and forced conformity. Conclusions: The analysis of the social representations revealed ambivalent meanings of the mask wearing. Although there were significant differences in the structure of mask-related social representations, the mask was recognized as an "inconvenient but necessary" health protection measure in most of the groups studied. Based on the results, each group may be at risk in a different way or deal differently with the pandemic based on their specific representations. (PsycInfo Database Record (c) 2023 APA, all rights reserved) (Hungarian) Elmeleti hatter: A COVID-19-pandemia idejen a jarvanyugyi intezkedesek meghatarozo reszeve valt az arcmaszkok viselesenek preventiv es szeles koru alkalmazasa. Az arcmaszkok az arcfelulet mintegy 60-70%-at lefedve jelentosen befolyasoljak a szocialis interakciokat - kulonosen az erzelemfelismerest, erzelemkifejezest es mentalizalast. A kommunikacioban fellepo nehezsegek a gyogyito munka hatekonysaga szempontjabol kiemelt jelentoseguve valnak az orvos-beteg kapcsolatban. Ennek meg kritikusabb esetei azok a helyzetek, amikor a paciens mentalizacios deficittel jellemezheto zavarban szenved. Tanulmanyunkban a szocialis reprezentaciok elmeletet hasznaljuk annak vizsgalatara, hogy a maszkviseles milyen tartalmakkal valt a kozos tudas reszeve. Celkituzes: Vizsgalatunkban a maszkviseles szocialis reprezentaciojanak felterkepezeset tuztuk ki celul, figyelembe veve annak interperszonalis kommunikaciora gyakorolt hatasat, olyan csoportokban, ahol a kolcsonos megertes hatekonysaga kiemelt jelentoseggel bir. Modszerek: Kutatasunkban a koronavirus-jarvany masodik es harmadik magyarorszagi hullama idejen, orvos, szomatikus es pszichiatriai beteg csoportban, valamint kontrollcsoportban (osszesen 81 fo, atlageletkor 43,1 [SD = 13,83] ev) szabad asszociacios feladatot adtunk a maszkviseles" hivoszora. A nyert adatokbol szemantikus kategoriakat kepeztunk, majd ranggyakorisag-eljarassal felterkepeztuk a szocialis reprezentaciok szerkezetet az egyes csoportokon belul. Eredmenyek: A vizsgalati csoportok maszkhasznalathoz kapcsolodo szocialis reprezentaciojaban egysegesen kozponti elemkent jelent meg a maszkviseles altal nyujtott biztonsagelmeny, valamint a maszk zavaro testerzetet kelto hatasa. Kulonbseget talaltunk az egyes csoportok kozott elaborativ kategoriak megjelenese, illetve szorongas, agresszio, tehetetlenseg, serult dependenciaszukseglet, valamint a kenyszeru alkalmazkodas tekinteteben. Kovetkeztetesek: A maszkviseles szocialis reprezentaciojanak elemzese alapjan a maszkviseles ambivalens jelentestartalmakat hordoz. Bar a maszkviseleshez kapcsolodo szocialis reprezentaciok strukturajaban szamottevo kulonbsegek is mutatkoztak, ugyanakkor a legtobb vizsgalt csoportban a maszk a virusvedelem szempontjabol kenyelmetlen, de szukseges" eszkozkent kerult felismeresre. Az eredmenyek alapjan az egyes csoportok sajatos reprezentacioik alapjan eltero modokon lehetnek veszelyeztetettek, illetve kuzdhetnek meg a pandemia idejen kialakult helyzettel. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

Detection of Real Time Face Mask using Convolutional Neural Network

In recent years, the global pandemic like COVID - 19 has changed the lifestyle of people. Wearing face mask is must in order to stay safe and healthy. This paper presents a real-time face mask detector which identifies whether a human is wearing a mask or not. Moreover, this system can also recognize the person wearing a face mask inappropriately or wear other things except a face mask. The proposed algorithm for face mask detection in this system utilizes Haar cascade classifier to detect the face and Convolutional Neural Networks to detect the mask. The whole system has been demonstrated in a practical application for checking people wearing face mask. © 2023 IEEE.

Security Vulnerability in Face Mask Monitoring System

The COVID-19 pandemic has increased demand for face mask detection systems that utilize deep learning and machine learning algorithms. However, these systems are susceptible to adversarial attacks, where an attacker can manipulate the system to make incorrect predictions. This study aimed to test the vulnerability of a deep learning-based face mask detection model to a specific type of attack called a black box adversarial attack in which the attacker possesses only partial information about the target model. The study's findings showed that the attack successfully reduced the model's accuracy from 96.48% to 49.25%. This emphasizes the need for more robust defense mechanisms in face mask detection systems to ensure their reliability. © 2023 Bharati Vidyapeeth, New Delhi.

Protect our healthcare workers

[...]there is real and justified fear about personal safety, fuelled by a scandalous and widespread lack of personal protective equipment (PPE). Anna Sayburn's summary of PPE guidance from the World Health Organization, Public Health England, and specialist societies highlights the confusing variation (doi:10.1136/bmj.m1297), which in some cases is leading to inappropriate overuse and wastage. Lack of testing and PPE are redolent of the Ebola outbreak, say Megan Diamond and Liana Woskie on BMJ Opinion (https://bit.ly/2UOMSQS), showing that vital lessons have not been learnt.