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The new Coronavirus disease (COVID-19) has seriously affected the world. By the end of November 2020, the global number of new coronavirus cases had already exceeded 60 million and the number of deaths 1,410,378 according to information from the World Health Organization (WHO). To limit the spread of the disease, mandatory face-mask rules are now becoming common in public settings around the world. Additionally, many public service providers require customers to wear face-masks in accordance with predefined rules (e.g., covering both mouth and nose) when using public services. These developments inspired research into automatic (computer-vision-based) techniques for face-mask detection that can help monitor public behavior and contribute towards constraining the COVID-19 pandemic. Although existing research in this area resulted in efficient techniques for face-mask detection, these usually operate under the assumption that modern face detectors provide perfect detection performance (even for masked faces) and that the main goal of the techniques is to detect the presence of face-masks only. In this study, we revisit these common assumptions and explore the following research questions: (i) How well do existing face detectors perform with masked-face images? (ii) Is it possible to detect a proper (regulation-compliant) placement of facial masks? and iii) How useful are existing face-mask detection techniques for monitoring applications during the COVID-19 pandemic? To answer these and related questions we conduct a comprehensive experimental evaluation of several recent face detectors for their performance with masked-face images. Furthermore, we investigate the usefulness of multiple off-the-shelf deep-learning models for recognizing correct face-mask placement. Finally, we design a complete pipeline for recognizing whether face-masks are worn correctly or not and compare the performance of the pipeline with standard face-mask detection models from the literature. To facilitate the study, we compile a large dataset of facial images from the publicly available MAFA and Wider Face datasets and annotate it with compliant and non-compliant labels. The annotation dataset, called Face-Mask-Label Dataset (FMLD), is made publicly available to the research community.

Background Acne is a common skin issue that typically occurs during adolescence. It causes long‐lasting redness and swelling in the skin. An alternative approach to treating acne could involve using a cosmetic facial mask containing herbal ingredients such as Curcumin and Rosa Damascena extract for its antibacterial properties. Aims This study aims to create and try out a peel‐off mask gel made from Curcumin and R. Damascena extract. This gel is intended to have the ability to kill bacteria such as Staphylococcus aureus, Escherichia coli, and Propionibacterium acnes and remove dead cells from the skin surface. Methods The peel‐off mask was made using polyvinyl alcohol (PVA) in 8% and 10% as solidifier. The evaluation of peel‐off masks comprises the examination of physiochemical and mechanical aspects. Furthermore, their longevity, effectiveness, and antibacterial properties are also considered. Results The white color, pleasant smell, and soft texture were the defining features of the peel‐off gel mask. The changes in PVA affect the pH level, thickness, and how quickly the peel‐off mask dries. The stability test found that the peel‐off mask had no significant physical changes when exposed to freezing and thawing. However, there were some differences in color and separation when using the real‐time method. A prepared peel‐off mask containing 10% PVA and curcumin works best against P. acne. The amount of PVA in the formula affected the physical and chemical qualities, but it did not impact on the antibacterial abilities of the peel‐off mask gel. The best formula that gives the best results uses 10% PVA + curcumin. Conclusions Using the Curcumin and R. Damascena extract in the creation of the peel‐off mask gel ensures its efficacy and safety for skin application.

Masked face recognition (MFR) has emerged as a critical domain in biometric identification, especially with the global COVID-19 pandemic, which introduced widespread face masks. This survey paper presents a comprehensive analysis of the challenges and advancements in recognizing and detecting individuals with masked faces, which has seen innovative shifts due to the necessity of adapting to new societal norms. Advanced through deep learning techniques, MFR, along with face mask recognition (FMR) and face unmasking (FU), represents significant areas of focus. These methods address unique challenges posed by obscured facial features, from fully to partially covered faces. Our comprehensive review explores the various deep learning-based methodologies developed for MFR, FMR, and FU, highlighting their distinctive challenges and the solutions proposed to overcome them. Additionally, we explore benchmark datasets and evaluation metrics specifically tailored for assessing performance in MFR research. The survey also discusses the substantial obstacles still facing researchers in this field and proposes future directions for the ongoing development of more robust and effective masked face recognition systems. This paper serves as an invaluable resource for researchers and practitioners, offering insights into the evolving landscape of face recognition technologies in the face of global health crises and beyond.

•The paper predicts the COVID-19 epidemic in the US with different vaccine effectiveness and coverage.•The required vaccine effectiveness and coverage to suppress the epidemic are calculated.•The degree to relax social distancing and face mask use depends on the vaccine effectiveness and coverage. Multiple candidates of COVID-19 vaccines have entered Phase III clinical trials in the United States (US). There is growing optimism that social distancing restrictions and face mask requirements could be eased with widespread vaccine adoption soon. We developed a dynamic compartmental model of COVID-19 transmission for the four most severely affected states (New York, Texas, Florida, and California). We evaluated the vaccine effectiveness and coverage required to suppress the COVID-19 epidemic in scenarios when social contact was to return to pre-pandemic levels and face mask use was reduced. Daily and cumulative COVID-19 infection and death cases from 26th January to 15th September 2020 were obtained from the Johns Hopkins University Coronavirus resource center and used for model calibration. Without a vaccine (scenario 1), the spread of COVID-19 could be suppressed in these states by maintaining strict social distancing measures and face mask use levels. But relaxing social distancing restrictions to the pre-pandemic level without changing the current face mask use would lead to a new COVID-19 outbreak, resulting in 0.8–4 million infections and 15,000–240,000 deaths across these four states over the next 12 months. Under this circumstance, introducing a vaccine (scenario 2) would partially offset this negative impact even if the vaccine effectiveness and coverage are relatively low. However, if face mask use is reduced by 50% (scenario 3), a vaccine that is only 50% effective (weak vaccine) would require coverage of 55–94% to suppress the epidemic in these states. A vaccine that is 80% effective (moderate vaccine) would only require 32–57% coverage to suppress the epidemic. In contrast, if face mask usage stops completely (scenario 4), a weak vaccine would not suppress the epidemic, and further major outbreaks would occur. A moderate vaccine with coverage of 48–78% or a strong vaccine (100% effective) with coverage of 33–58% would be required to suppress the epidemic. Delaying vaccination rollout for 1–2 months would not substantially alter the epidemic trend if the current non-pharmaceutical interventions are maintained. The degree to which the US population can relax social distancing restrictions and face mask use will depend greatly on the effectiveness and coverage of a potential COVID-19 vaccine if future epidemics are to be prevented. Only a highly effective vaccine will enable the US population to return to life as it was before the pandemic.

Background The use of face masks has become ubiquitous in Taiwan during the early COVID-19 pandemic. A name-based rationing system was established to enable the population of Taiwan to purchase face masks. This study is to assess the extent and fairness of face mask supply to the public in Taiwan. Methods The weekly face marks supplies were collected from name-based rationing system administrative statistics included national health insurance card and e-Mask selling record. National registered population statistics by age, gender, and district were collected from department of statistics ministry of the interior. The number of COVID-19 non-imported cases of Taiwan was collected from Taiwan centers of disease control. Results A total of 146,831,844 person times purchase records from February 6, 2020, to July 19, 2020, the weekly average face mask supply is 0.5 mask (per person) at the start of name-based rationing system, and gradually expanded to the maximum 5.1 masks (per person). Comparing the highest weekly total face mask supply (from Apr 9, 2020, to Apr 15, 2020) in aged 0–9 -, 10–19 -, 20–29 -, 30–39 -, 40–49 -, 50–59 -, 60–69 -,70–79 -, 80–89 -, 90–99, and > 100 years to the register population showed similar distribution between mask supplied people and total population (all standardized difference < 0.1). Conclusion The masks supply strategies has gradually escalated the number of face masks for the public, it not only has dominant decreased the barrier of acquiring face mask, but a fair supply for total population use of Taiwan.

Face masking proved essential to reduce transmission of COVID-19 and other respiratory infections in indoor environments, but standards and literature do not provide simple quantitative methods for quantifying air leakage at the face seal. This study reports an original method to quantify outward leakage and how wearing style impacts on leaks and filtration efficiency. The amount of air leakage was evaluated on four medical masks and four barrier face coverings, exploiting a theoretical model and an instrumented dummy head in a range of airflows between 30 and 160 L/min. The fraction of air leaking at the face seal of the medical masks and barrier face coverings ranged from 43% to 95% of exhaled air at 30 L/min and reduced to 10–85% at 160 L/min. Filter breathability was the main driver affecting both leak fraction and total filtration efficiency that varied from 5% to 53% and from 15% to 84% at 30 and 160 L/min, respectively. Minor changes were related to wearing style, supporting indications on the correct mask use. The fraction of air leaking from medical masks and barrier face coverings during exhalation is relevant and varies according to design and wearing style. The use of highly breathable filter materials reduces air leaks and improve total filtration efficiency.

The balanced amelioration of mechanical characteristics of fat clay with an additive refers to the attainment of high strength without compromising ductility, which is unattainable by solitary usage of a cementing additive. For this purpose, an amalgamated binary admixture (ABA) is proposed by assimilating shredded face mask (FM) waste, which is posing serious environmental concerns these days, with a cementitious waste material, i.e., silica fume (SF). However, for such ABA, the optimization of mix design is desirable because an excessive amount of one component could disturb the required balance. To address this issue, response surface methodology (RSM) is used in the current study, which is a strong technique used during the process of production to develop, improve, and optimize product inputs. Several experiments are designed and conducted to evaluate mechanical responses, i.e., unconfined compressive strength ( q u ), brittleness index ( I B ), deformability index ( I D ), and California bearing ratio ( CBR ) value, of treated fat clay by varying mix designs of ABA. Based on the test results, mathematical models are developed which are found to be statistically valid to predict the subjected responses using SF and FM as inputs. Afterward, an optimized mix design is determined by integrating developed models with a desirability function model and setting maximization of strength and ductility as the optimization goals. An ABA having 7.9% SF and 1.2% FM is observed to provide the highest strength and ductility for multiple applications, i.e., road and buildings, with desirability factor close to unity; responses of which are also validated by performing tests. Furthermore, analysis of cleaning aspect shows that the use of optimized ABA in place of cement for subgrade improvement of 1 km two-lane road could avoid CO 2 emission of around 79,032 kg of C, save 42,720 kWh and 1174.8 GJ of electrical and thermal energy, respectively, and clean 43 Mg of FM waste; however, astute protocols of COVID-19 FM waste handling and disinfection are needed to be established and followed.

The spread of COVID-19 resulted in the utilization of preventive measures, such as face masks. However, there is currently a lack of attitude measures available to examine an individual's face mask attitudes. The purpose of this study was to develop an instrument to measure face mask wearing attitudes. Participants (N = 447) responded to an item pool of 173 positive and negative statements about face masks. Factor analysis resulted in a one-factor 16-item scale solution. Analyses also revealed significant group differences in attitudes. Caucasians had significantly less positive attitudes toward face masks than African Americans. Republicans had significantly less positive attitudes toward face masks than Democrats, those identifying with neither party, and those identifying as having no political affiliation. Heterosexuals had significantly less positive attitudes toward face masks than LGBTQ+ identifying individuals. Implications for research are discussed.

In response to the Coronavirus Disease 2019 (COVID-19) pandemic, current modeling supports the use of masks in community settings to reduce the transmission of SARS-CoV-2. However, concerns have been raised regarding the global shortage of medical grade masks and the limited evidence on the efficacy of fabric masks. This study used a standard mask testing method (ASTM F2101-14) and a model virus (bacteriophage MS2) to test the viral filtration efficiency (VFE) of fabric masks compared with commercially available disposable, surgical, and N95 masks. Five different types of fabric masks were purchased from the ecommerce website Etsy to represent a range of different fabric mask designs and materials currently available. One mask included a pocket for a filter; which was tested without a filter, with a dried baby wipe, and a section of a vacuum cleaner bag. A sixth fabric mask was also made according to the Victorian Department of Health and Human Services (DHHS) guidelines (Australia). Three masks of each type were tested. This study found that all the fabric masks had a VFE of at least 50% when tested against aerosols with an average size of 6.0 µm (VFE(6.0 µm)). The minimum VFE of fabric masks improved (to 63%) when the larger aerosols were excluded to give and average aerosol size of 2.6 µm (VFE(2.6 µm)), which better represents inhaled aerosols that can reach the lower respiratory system. The best performing fabric masks were the cotton mask with a section of vacuum cleaner bag (VFE(6.0 µm) = 99.5%, VFE(2.6 µm) = 98.8%) or a dried baby wipe (VFE(6.0 µm) = 98.5%, VFE(2.6 µm) = 97.6%) in the pocket designed for a disposable filter, the mask made using the Victorian DHHS design (VFE(6.0 µm) = 98.6%, VFE(2.6 µm) =99.1%) and one made from a layer of 100% hemp, a layer of poly membrane, and a layer of cheesecloth (VFE(6.0 µm) = 93.6%, VFE(2.6 µm) = 89.0%). The VFE of two surgical masks (VFE(6.0 µm) = 99.9% and 99.6%, VFE(2.6 µm) = 99.5% and 98.5%) and a N95 masks (VFE(6.0 µm) = 99.9%, VFE(2.6 µm) = 99.3%) were comparable to their advertised bacterial filtration efficacy. This research supports the use of fabric masks in the community to prevent the spread of SARS-CoV-2; however, future research is needed to explore the optimum design in ensuring proper fit. There is also a need for mass education campaigns to disseminate this information, along with guidelines around the proper usage and washing of fabric masks.

Personal protective equipment (PPE) detection plays a crucial role in ensuring safety in various settings such as factories, hospitals, and disease prevention measures. However, manually checking individuals for proper PPE usage in public can be a challenging task. This paper focuses on the detection of face mask usage and aims to develop a robust system that can identify individuals who are not wearing masks or are not wearing them correctly. Here, we present an enhanced face mask detection method based on YOLOv4. Currently, there is a shortage of a comprehensive and diverse dataset that can be used to accurately evaluate the correct use of masks, mainly due to limited samples of incorrect mask wearing. To address this issue, we propose a pipeline to generate a simulated face mask dataset derived from the original dataset. This approach allows us to enhance the performance of the face mask detection model without requiring additional data samples. Additionally, we introduce a modified face mask detection model called MaskYOLO, which includes improvements in the original YOLOv4 network structure. In the feature extraction network, a global context block is incorporated between the backbone and neck of YOLOv4 to obtain a more comprehensive understanding of the scene. Furthermore, the prediction network incorporates an improved structure to achieve a more efficient network. The effectiveness and accuracy of the proposed method are demonstrated through statistical analyses of the experimental results. Our method outperforms the YOLOv4 baseline by 3.1% in mean Average Precision (mAP).