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Introduction: Literature is lacking on the safety of storing contaminated PPE in paper bags for reuse, potentially increasing exposure to frontline healthcare workers (HCW) and patients. The aim of this study is to evaluate the effectiveness of paper bags as a barrier for fomite transmission of SARS-CoV-2 by storing face masks, respirators, and face shields. Methods: This quasi-experimental study evaluated the presence of SARS-CoV-2 on the interior and exterior surfaces of paper bags containing PPE that had aerosolized exposures in clinical and simulated settings. Between May and October 2020, 30 unique PPE items were collected from COVID-19 units at two urban hospitals. Exposed PPE, worn by either an infected patient or HCW during a SARS-CoV-2 aerosolizing event, were placed into an unused paper bag. Samples were tested at 30-minute and 12-hour intervals. Results: A total of 177 swabs were processed from 30 PPE samples. We found a 6.8% positivity rate among all samples across both collection sites. Highest positivity rates were associated with ventilator disconnection and exposure to respiratory droplets from coughing. Positivity rates differed between hospital units. Total positivity rates were similar between 30-minute (6.7%) and 12-hour (6.9%) sample testing time intervals. Control samples exposed to inactivated SARS-CoV-2 droplets had higher total viral counts than samples exposed to nebulized aerosols. Conclusions: Data suggests paper bags are not a significant fomite risk for SARS-CoV-2 transmission. However, controls demonstrated a risk with droplet exposure. Data can inform guidelines for storing and re-using PPE in situations of limited supplies during future pandemics. [ABSTRACT FROM AUTHOR]

Background Since the outbreak of COVID-19, how to reduce the risk of spreading viruses and other microorganisms while performing aerosol generating procedures (AGPs) has become a challenging question within the dental and dental hygiene communities. The purpose of this position paper is to summarize the existing evidence about the effectiveness of various mitigation methods used to reduce the risk of infection transmission during AGPs in dentistry. Methods The authors searched six databases, MEDLINE, EMBASE, Scopus, Web of Science, Cochrane Library, and Google Scholar, for relevant scientific evidence published in the last ten years (January 2012 to December 2022) to answer six research questions about the the aspects of risk of transmission, methods, devices, and personal protective equipment (PPE) used to reduce contact with microbial pathogens and limit the spread of aerosols. Results A total of 78 studies fulfilled the eligibility criteria. There was limited literature to indicate the risk of infection transmission of SARS-CoV-2 between dental hygienists and their patients. A number of mouthrinses are effective in reducing bacterial contaminations in aerosols; however, their effectiveness against SARS-CoV-2 was limited. The combined use of eyewear, masks, and face shields are effective for the prevention of contamination of the facial and nasal region, while performing AGPs. High volume evacuation with or without an intraoral suction, low volume evacuation, saliva ejector, and rubber dam (when appropriate) have shown effectiveness in reducing aerosol transmission beyond the generation site. Finally, the appropriate combination of ventilation and filtration in dental operatories are effective in limiting the spread of aerosols. Conclusion Aerosols produced during clinical procedures can potentially pose a risk of infection transmission between dental hygienists and their patients. The implementation of practices supported by available evidence are best practices to ensure patient and provider safety in oral health settings. More studies in dental clinical environment would shape future practices and protocols, ultimately to ensure safe clinical care delivery. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic forced use of face masks up to billions of masks per day globally. Though an important and necessary measure for control of the pandemic, use of masks also poses some inherent risks. One of those risks is inhalation of microplastics released from the mask materials. Since most of the mask materials are made from plastic/polymers, they always have the potential to expose the user to fragmented microplastics. To estimate the amount of inhalable microplastic exuded from masks, an experiment simulating real-life scenario of mask usage was performed. The study included collection of microplastics oozed out from the masks on to a filter paper followed by staining and fluorescence detection of the total number of microplastics using a microscope. Both used and new masks were studied. Based on the emission wavelength, the microplastics were found to be belonging to three different categories, namely blue, green and red emitting microplastics respectively. The number of microplastic particles emitted per mask over a period of usage of 8 h was about 5000 to 9000 for new masks and about 6500 to 15,000 for used masks respectively. The estimation of polymer type of plastic in the mask fabrics was also carried out using Raman and FTIR spectroscopy. [ABSTRACT FROM AUTHOR]

The diverse physicochemical properties of polymers make them perfect candidates for developing biosensor elements when combined with porous substrates. For example, microfluidic channels, valves, reservoirs and biorecognition elements have been proposed that rely on filling the cavities of porous cellulose with different polymers. Yet, highly concentrated polymers are often too viscous to dispense them with precision using conventional printing methods. This increases the manufacturing variability, which degrades the performance of the biosensors. Here this issue is solved with a new method for infusing porous materials (filter paper) with concentrated polymers (PSS) that circumvents the dropwise addition of highly viscous reagents. The resulting films contain homogenously distributed polymers, which reduces the intra- and inter-batch variabilities of biosensors based on dispensing nanoparticles from the reservoirs. The proposed method was utilized to develop immunosensors for detecting the cytokines IL-6 and IL-5 in aerosols, which were trapped by the polypropylene layer of a surgical face mask (limit of detection 10−2 pg·mL−1). Using this approach, we were able to detect elevated levels of airways cytokines when exacerbated COPD patients and eosinophilic asthma patients wore the face mask for 30 min. The results shown here pave the way for upscaling the manufacturing of paper-based nanoparticle biosensors, which is a crucial step towards their future commercialization. [Display omitted] • Nanoparticle reservoirs are fabricated by infusing filter paper with polystyrene sulfonate • The proposed method reduces the intra- and inter-batch fabrication variability • Biosensors against IL-6 and IL-5 in aerosols are developed • Non-invasive detection of airways cytokines in patient samples is demonstrated [ABSTRACT FROM AUTHOR]

Aim: To nuance the discussion on the suggested absence of the risk-compensation phenomenon in times of COVID-19. Subject and methods: In many countries, behavioral restrictions for COVID-19 prevention started with asking people to stay 1.5 m away from each other. To further prevent the spread of the virus, governments made it mandatory for people to wear facemasks in situations where keeping the 1.5-m distance was not possible. This triggered a discussion among behavioral scientists regarding whether making face-masks mandatory might lead to the undesirable side effect that people would be less compliant with the 1.5-m distance rule: risk compensation. Mantzari et al. (2020) published a paper claiming that risk compensation was a non-existing phenomenon. Czyprionka et al. (2020) repeated the same claim, referring to the same original publications. We performed a rapid review of the existing literature and identified 19 papers that presented original data on studies that identified the consequences of wearing a facemask. Results: The evidence for risk compensation is inconclusive. Risk compensation may occur with the encouraged use of face coverings. However, it may not always be present, especially when there are other social processes in play, such as respecting other people's choices. Conclusion: Inconclusive evidence doesn't mean that risk compensation should be ignored or that potential public health interventions shouldn't be implemented. The introduction of mandatory face masks should, therefore, always be accompanied by a theory- and evidence-based health promotion campaign to prevent the possible negative effects of distancing. Carefully applying social psychological insights to guide people in their decision to wear masks when needed and to do so correctly, may overcome potential risk compensation and thereby optimize COVID-19 preventive efforts. Highlights: The evidence for risk compensation – in this case, keeping less distance while wearing a mask for COVID-19 prevention – is inconclusive. Risk compensation should not be a reason to refrain from implementing an evidence-based health promotion intervention. The implementation of health promotion interventions should always be combined with additional interventions to reduce the chance that risk compensation will lessen the intended effect. [ABSTRACT FROM AUTHOR]

During the COVID-19 pandemic, concerns were raised that face covering use may elicit risk compensation; a false sense of security resulting in reduced adherence to other protective behaviours such as physical distancing. This systematic review aimed to investigate the effect of face covering use on adherence to other COVID-19 related protective behaviours. Medline, Embase, PsychInfo, EmCare, medRxiv preprints, Research Square and WHO COVID-19 Research Database were searched for all primary research studies published from 1st January 2020 to 17th May 2022 that investigated the effect of face covering use on adherence to other protective behaviours in public settings during the COVID-19 pandemic. Papers were selected and screened in accordance with the PRISMA framework. Backwards and forwards citation searches of included papers were also conducted on 16th September 2022, with eligible papers published between 1st January 2020 and that date being included. A quality appraisal including risk of bias was assessed using the Academy of Nutrition and Dietetics' Quality Criteria Checklist. This review is registered on PROSPERO, number CRD42022331961. 47 papers were included, with quality ranging from low to high. These papers investigated the effects of face covering use and face covering policies on adherence to six categories of behaviour: physical distancing; mobility; face-touching; hand hygiene; close contacts; and generalised protective behaviour. Results reveal no consistent evidence for or against risk compensation, with findings varying according to behaviour and across study types, and therefore confident conclusions cannot be made. Any policy decisions related to face coverings must consider the inconsistencies and caveats in this evidence base. [ABSTRACT FROM AUTHOR]

With the advancement of society, ensuring the safety of personnel involved in municipal construction projects, particularly in the context of pandemic control measures, has become a matter of utmost importance. This paper introduces a security measure for municipal engineering, combining deep learning with object detection technology. It proposes a lightweight artificial intelligence (AI) detection method capable of simultaneously identifying individuals wearing masks and safety helmets. The method primarily incorporates the ShuffleNetv2 feature extraction mechanism within the framework of the YOLOv5s network to reduce computational overhead. Additionally, it employs the ECA attention mechanism and optimized loss functions to generate feature maps with more comprehensive information, thereby enhancing the precision of target detection. Experimental results indicate that this algorithm improves the mean average precision (mAP) value by 4.3%. Furthermore, it reduces parameter and computational loads by 54.8% and 53.8%, respectively, effectively striking a balance between lightweight operation and precision. This study serves as a valuable reference for research pertaining to lightweight target detection in the realm of municipal construction safety measures. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

With the impact of the COVID‐19 epidemic, the demand for masked face recognition technology has increased. In the process of masked face recognition, some problems such as less feature information and poor robustness to the environment are obvious. The current masked face recognition model is not quantified enough for feature extraction, there are large errors for faces with high similarity, and the categories cannot be clustered during the detection process, resulting in poor classification of masks, which cannot be well adapted to changes in multiple environments. To solve current problems, this paper designs a new masked face recognition model, taking improved Single Shot Multibox Detector (SSD) model as a face detector, and replaces the input layer VGG16 of SSD with Deep Residual Network (ResNet) to increase the receptive field. In order to better adapt to the network, we adjust the convolution kernel size of ResNet. In addition, we fine‐tune the Xception network by designing a new fully connected layer, and reduce the training cycle. The weights of the three input samples including anchor, positive and negative are shared and clustered together with triplet network to improve recognition accuracy. Meanwhile, this paper adjusts alpha parameter in triplet loss. A higher value of alpha can improve the accuracy of model recognition. We further adopt a small trick to classify and predict face feature vectors using multi‐layer perceptron (MLP), and a total of 60 neural nodes are set in the three neural layers of MLP to get higher classification accuracy. Moreover, three datasets of MFDD, RMFRD and SMFRD are fused to obtain high‐quality images in different scenes, and we also add data augmentation and face alignment methods for processing, effectively reducing the interference of the external environment in the process of model recognition. According to the experimental results, the accuracy of masked face recognition reaches 98.3%, it achieves better results compared with other mainstream models. In addition, the hyper‐parameters tuning experiment is carried out to improve the utilization of computing resources, which shows better results than the indicators of different networks. [ABSTRACT FROM AUTHOR]

Coronavirus (COVID-19) is a highly infectious respiratory disease which spread can be effectively curbed by wearing facial masks, especially N95 and surgical masks. In this paper, we develop a stylised game-theoretical model to evaluate the impacts of producing and selling masks on supply chain profits, safety index and consumer and social welfare. Firstly, we find that as the infection probability without protection (IPWP) increases, both the retail price and demand for these masks will increase. When the IPWP is sufficiently low, those consumers who want to purchase masks are more likely to purchase N95 masks, but when the IPWP increases, surgical masks are more popular amongst consumers. Secondly, we develop a safety index that indicates the effectiveness of using masks in preventing respiratory disease infection. This index is especially crucial in cases where the IPWP is moderate; in other words, recommending to wear masks is particularly important when the IPWP is moderate. We also examine the impacts of government involvement in handling the outbreak of respiratory diseases. Providing consumer subsidies and promoting the social mask enterprise can effectively combat respiratory diseases under different conditions. Our results can be used for combating COVID-19 and preparing for future health crisess. [ABSTRACT FROM AUTHOR]

Aim: The aim of this study is to investigate the pesticide safety behaviours and related factors among cabbage farmers in Kalaw Township, Myanmar. Background: Unsafe chemical pesticide practices affect farmers' health and the environment. Understanding pesticide safety behaviours is necessary for healthcare providers to protect agriculture workers against unwanted adverse health effects. Methods: The cross‐sectional study was conducted among 195 cabbage farmers from March to May 2019. Safety behaviours and factors applied in the PRECEDE–PROCEDE model as a framework were assessed using questionnaires. Bivariate and multivariate logistic regression was applied to identify any associations. Results: Altogether, 72.1% of the farmers had a low level of safety behaviours. Goggles and masks were rarely used before and during pesticide applications. All of the farmers experienced adverse health effects after using pesticides. The attitudes toward pesticide poisoning; support from family, co‐workers and healthcare providers; and the availability of personal protective equipment were significantly associated with safety behaviours. Conclusion: Our data provide novel empirical evidence for an opportunity to design effective nursing interventions to promote pesticide safety behaviours among Myanmar agricultural workers. Nurses should pay more attention to promoting comprehensive interventions to reduce both exposure and health effects among agricultural workers through the involvement of supported stakeholders. Summary statement: What is already known about this topic? Farmers and agricultural workers have extensively used pesticides in all steps of cultivation and face a greater risk of pesticide exposure, which could result in adverse health effects.Owing to the lack of research on community health promotion conducted in Myanmar, the burden of the consequences and adverse effects of pesticides remains a significant health issue. What this paper adds? Findings reveal a clear framework for better understanding and assessing pesticide safety behaviours among agricultural workers and farmers in Myanmar.Findings contribute new knowledge for nursing community health promotion by enhancing understanding of multilevel factors, including international and external factors, associated with safety behaviours among agricultural workers and farmers. The implications of this paper: Nurses and other healthcare professionals should be aware of the importance of preventing adverse health effects among agricultural workers in the community and interventions aimed at promoting safety behaviours should be focused not only on individuals but also on the involvement of social supports—particularly family and co‐workers.Nursing policymakers should consider strengthening the nursing role in the recognition of pesticide risk exposure and health outcomes, management of pesticide poisoning and exposure and prevention of pesticide‐related health conditions both in nursing education and nursing practice.Local authorities and policymakers should focus their attention on health promotion and health services to promote safety behaviours when using pesticides, and treatment and extension services should be provided for cases of suspected pesticide poisoning. [ABSTRACT FROM AUTHOR]

The ongoing COVID-19 pandemic remains a significant threat, emphasizing the critical importance of mask-wearing to reduce infection risks. However, existing methods for mask detection encounter challenges such as identifying small targets and achieving high accuracy. In this paper, we present an enhanced YOLOv7 model tailored for mask-wearing detection. we employing a Generative Adversarial Network (GAN) to augment the original dataset, introducing the Convolutional Block Attention Module (CBAM) mechanism into the YOLOv7 model to enhance its small target detection capabilities, and replacing the model's activation function with Parametric Rectified Linear Unit (FReLU) to improve overall performance. Experimental validation on a dataset showcases an average precision of 97.8% and a real-time inference speed of 64 frames per second (fps), meeting the real-time mask-wearing detection requirements effectively. [ABSTRACT FROM AUTHOR]

Our faces provide information about our emotions, personal identity, trustworthiness, attractiveness, age, and sex. However, because of the COVID-19 pandemic, mask-wearing, which occludes 60% of the face, has become pervasive. Consequently, some clinicians and educators have expressed concern about the effect of masks on social, emotional, and cognitive health in adults and during child and adolescent development. This paper investigates the impact of mask-wearing on the ability of children, adolescents, and adults to identify emotions. In particular, this paper explores the typical development of emotion)reading abilities, emotion reading through masks, and emotion reading in specific populations such as those with disabilities and those from various cultural backgrounds. The paper ends with a framework for improving communication through masks. [ABSTRACT FROM AUTHOR]

This study investigates the role of linguistic and semiotic resources in the construction of exclusionary discourses amidst the COVID-19 semiotic landscape. Drawing on systematic observations of and engagement with Weibo and Twitter from February 2020 to April 2021, the paper adopts a geosemiotic perspective and analyses social media posts as publicly displayed language items. This study's findings suggest that exclusion is embedded in perpetual ideologies of distrust and xenophobia and constructed by languages and material objects drawn from both online and offline spaces. More importantly, politicised signage in the COVID-19 semiotic landscape, although ephemeral in nature, are configured to prohibit mobility and materialise playfulness. By focusing on the emplacement of exclusionary discourses at the interface between the online and the offline, this study moves beyond terrestrial semiotic landscapes to capture fleeting semiotic practices during the pandemic. The paper also argues for a semiotic assemblage perspective to account for the implications of resemiotisation in the online environment. [ABSTRACT FROM AUTHOR]

Covid-19 was declared a pandemic in March 2020, and the world has witnessed significant changes since then. Spain has been forced to go into extreme lockdown, cancelling all school classes and outdoor activities for children, which may have significant consequences on young people. This paper explores how young children have experienced lockdown as a consequence of the ongoing Covid-19 pandemic and what they think about their future lives after Covid-19. Data were collected from 73 students aged from 7 to 9 years old, using participant-produced drawings and short questions with children's and parents' descriptive comments. We used a children's rights perspective and the Freirean approach of a pedagogy of love and hope to analyse the data. Results suggest that participants have been through significant changes in their routines, and that what they miss most from their lives before Covid-19 is playing outdoors with their friends and visiting their grandparents. To our knowledge, this paper is the first of its kind in investigating how the Covid-19 pandemic has influenced the ways that children lived during pandemic and its possible implications for their futures. [ABSTRACT FROM AUTHOR]

In the latest issue's "Editor's Note" of EASTS, Wen-hua Kuo made a call to East Asian science studies scholars to commit to an archeology of the social and technical infrastructure of epidemics. Coincidently, ten historians and sociologists working on science, technology, medicine, and environment with a focus on China, Japan, Taiwan, Singapore, and South Korea had just begun a collective effort to understand how face masks had become the most important part of the current pandemic governance in East Asia. As its first step, a virtual workshop, "The Socio-Material History of Masked Societies in East Asia," was held at the Max-Planck Institute for the History of Science on 26 October 2020. This forum aims to introduce the virtual meeting's outcome to the wider EASTS community and encourages them to engage with the collaborative enterprise to investigate the history of masks. All papers focus on the socio-material dimension of masks while problematizing current culturalist explanatory narratives about "masked societies" in East Asia. By doing so, the papers show how mask use is closely linked to heterogenous but interconnected entanglements of environmental governance, political movements, and risk cultures in East Asian polities. It interrogates these relationships in the context of scientific controversies and quarantine regimes. [ABSTRACT FROM AUTHOR]

Based on the background of rapid transmission of novel coronavirus and various pneumonia, wearing masks becomes the best solution to effectively reduce the probability of transmission. For a series of problems arising from crowded public places and collective units, where face recognition is difficult to increase target density, a deep convolutional neural network is used for real-time mask detection and recognition. This paper presents the method based on YOLOv5 model for deep learning and mask detection in image recognition as well as a live camera to label the pedestrians without masks in time. This experiment will use LabelImg software to preprocess 5003 images and make lightweight improvements based on the original YOLOv5 model to generate the final face mask recognition model. The Mosaic method is added to merge the images effectively and process the images in batch, and secondly, the GIoU loss function is selected to calculate the bounding box regression loss by comparison, which improves the localization accuracy even more. According to the experimental detection results, analogized with the original model YOLOv5, the recall and accuracy are effectively improved. In this paper, YOLOv3, SSD, Fast-R-CNN detection algorithms are used for comparison, the detection results of this model have a high mAP value which is equal to 92.9, which are higher than the detection results of other models. Real-time target recognition based on this model combined with practical applications can be applied in hospitals and crowd-gathering places to achieve effective reduction of epidemic transmission probability in a short period of time. [ABSTRACT FROM AUTHOR]

In response to the COVID-19 pandemic, governments worldwide have implemented mandatory face mask regulations in crowded public spaces, making the development of automatic face mask detection systems critical. To achieve robust face mask detection performance, a high-quality and comprehensive face mask dataset is required. However, due to the difficulty in obtaining face samples with masks in the real-world, public face mask datasets are often imbalanced, leading to the data imbalance problem in model training and negatively impacting detection performance. To address this problem, this paper proposes a novel recursive model-training technique designed to improve detection accuracy on imbalanced datasets. The proposed method recursively splits and merges the dataset based on the attribute characteristics of different classes, enabling more balanced and effective model training. Our approach demonstrates that the carefully designed splitting and merging of datasets can significantly enhance model-training performance. This method was evaluated using two imbalanced datasets. The experimental results show that the proposed recursive learning technique achieves a percentage increase (PI) of 84.5% in mean average precision (mAP@0.5) on the Kaggle dataset and of 186.3% on the Eden dataset compared to traditional supervised learning. Additionally, when combined with existing oversampling techniques, the PI on the Kaggle dataset further increases to 88.9%, highlighting the potential of the proposed method for improving detection accuracy in highly imbalanced datasets. [ABSTRACT FROM AUTHOR]

After significant progress in stereo matching, the pursuit of robust and efficient ill-posed-region disparity refinement methods remains challenging. To further improve the performance of disparity refinement, in this paper, we propose the matchability and uncertainty-aware iterative disparity refinement neural network. Firstly, a new matchability and uncertainty decoder (MUD) is proposed to decode the matchability mask and disparity uncertainties, which are used to evaluate the reliability of feature matching and estimated disparity, thereby reducing the susceptibility to mismatched pixels. Then, based on the proposed MUD, we present two modules: the uncertainty-preferred disparity field initialization (UFI) and the masked hidden state global aggregation (MGA) modules. In the UFI, a multi-disparity window scan-and-select method is employed to provide a further initialized disparity field and more accurate initial disparity. In the MGA, the adaptive masked disparity field hidden state is globally aggregated to extend the propagation range per iteration, improving the refinement efficiency. Finally, the experimental results on public datasets show that the proposed model achieves a reduction up to 17.9% in disparity average error and 16.9% in occluded outlier proportion, respectively, demonstrating its more practical handling of ill-posed regions. [ABSTRACT FROM AUTHOR]

Due to the material properties of current ski face masks for adolescents, moisture in exhaled air can become trapped within the material fibers and freeze, leading to potential issues such as breathing difficulties and increased risk of facial frostbite after prolonged skiing. This paper proposes a research approach combining computational fluid dynamics (CFD) and ergonomics to address these issues and enhance the comfort of adolescent skiers. We developed head and face mask models based on the head dimensions of 15–17-year-old males. For enclosed cavities, ensuring the smooth expulsion of exhaled air to prevent re-inhalation is the primary challenge. Through fluid simulation of airflow characteristics within the cavity, we evaluated three different inlet configurations. The results indicate that the location of the air inlets significantly affects the airflow characteristics within the cavity. The side inlet design (type II) showed an average face temperature of 35.35 °C, a 38.5% reduction in average CO2 concentration within the cavity, and a smaller vortex area compared to the other two inlet configurations. Although the difference in airflow velocity within the cavity among the three configurations was minimal, the average exit velocity differed by up to 0.11 m/s. Thus, we conclude that the side inlet configuration offers minimal obstruction to airflow circulation and better thermal insulation when used in the design of fully enclosed helmets. This enhances the safety and comfort of adolescent wearers during physical activities in cold environments. Through this study, we aim to further promote the development of skiing education, enhance the overall quality of adolescents' skiing, and thus provide them with more opportunities for the future. [ABSTRACT FROM AUTHOR]

Wearing a mask is an important way of preventing COVID-19 transmission and infection. German researchers found that wearing masks can effectively reduce the infection rate of COVID-19 by 40%. However, the detection of face mask-wearing in the real world is affected by factors such as light, occlusion, and multi-object. The detection effect is poor, and the wearing of cotton masks, sponge masks, scarves and other items greatly reduces the personal protection effect. Therefore, this paper proposes a new algorithm for mask detection and classification that fuses transfer learning and deep learning. Firstly, this paper proposes a new algorithm for face mask detection that integrates transfer learning and Efficient-Yolov3, using EfficientNet as the backbone feature extraction network, and choosing CIoU as the loss function to reduce the number of network parameters and improve the accuracy of mask detection. Secondly, this paper divides the mask into two categories of qualified masks (N95 masks, disposable medical masks) and unqualified masks (cotton masks, sponge masks, scarves, etc.), creates a mask classification data set, and proposes a new mask classification algorithm that the combines transfer learning and MobileNet, enhances the generalization of the model and solves the problem of small data size and easy overfitting. Experiments on the public face mask detection data set show that the proposed algorithm has a better performance than existing algorithms. In addition, experiments are performed on the created mask classification data set. The mask classification accuracy of the proposed algorithm is 97.84%, which is better than other algorithms. [ABSTRACT FROM AUTHOR]

This paper investigates the role of short-term economic dynamism in responding to crisis induced supply shortages. We focus on the domestic manufacturing ramp-up of surgical masks, respirators, and their intermediary products in response to the COVID-19 pandemic. We develop a novel method for timely identification and validation of the evolving state of domestic manufacturing. To unpack the activities of domestic manufacturers and related institutions, we triangulate across 56 qualitative interviews, certifications, Thomasnet.com®, industry associations, and other public data. We find that while large manufacturers could rapidly scale up, onshore, or diversify production to enter into domestic production of critical medical supplies, these large manufacturers alone were insufficient to meet the spike in demand. In face of this shortage, small and medium enterprises (SME), who entered into mask and respirator production as de novo firms, spin-offs, and by diversifying, were important in increasing overall domestic capacity and serving markets unmet by large hospital distributors. These firms often had fewer competencies and resources compared to larger firms, and received less effective government support. Despite these disadvantages, a number of SMEs succeeded in entering into domestic production, and our interviews suggest this capacity could have been better integrated into the national response. We propose new theory for how and when federal and state governments should support short-term economic dynamism (firm entry into target products and/or markets) during crises to address supply shortages, and the types of market and network failures federal or state governments may be most effective at addressing. [ABSTRACT FROM AUTHOR]

This paper mainly reviews the fate of microplastics, released from used face masks, in the water environment. Through previous experiments, the amount of fiber microplastics released from used face masks into aqueous environments was not negligible, with the maximum microplastics releasing amount reaching 10,000 piece·day−1 for each mask. Microplastic derived from these masks often occurred in the shape of polymeric fibers that resulted from the breakage of the chemical bonds in the plastic fibers by the force of water flow. The potential contact forces between microplastics (originating from face masks) with other pollutants, primarily encompass hydrophobic and electrostatic interactions. This critical review paper briefly illustrates the fate of microplastics derived from disposable face masks, further devising effective strategies to mitigate the environmental impact of plastic particle release from the used personal protective equipment. [ABSTRACT FROM AUTHOR]

As COVID-19 spread the whole way across the world, a significant number of us got mindful of how significant face covers are. Medical services authorities and nearby foundations from one side of the planet to the other are encouraging individuals to wear masks, as it is the best way to forestall the transmission of the infection. Masks have without a doubt frustrated the facial-acknowledgment industry; the innovation has likewise adjusted. It might sound odd yet wearing a cover does not really prevent a PC from recognizing somebody. We are intending to prepare our model to recognize whether the pictures are genuine or fake one even though individuals are wearing face cover. In this paper, we intend to make a liveness detector equipped for spotting counterfeit faces. To make a liveness detector, we will prepare a deep learning neural network fit for recognizing genuine versus counterfeit appearances. It deals with two correlative spaces: RGB space and multi-scale Retinex (MSR) space. The RGB space contains the point-by-point facial surfaces, yet it is sensitive to illumination whereas the MSR pictures can adequately catch the high recurrence data, which is discriminative for face recognition. [ABSTRACT FROM AUTHOR]

The object detection task in the medical field is challenging in terms of classification and regression. Due to its crucial applications in computer-aided diagnosis and computer-aided detection techniques, an increasing number of researchers are transferring the object detection techniques to the medical field. However, in existing work on object detection, researchers do not consider the low resolution of medical images, the high amount of noise, and the small size of the objects to be detected. Based on this, this paper proposes a new algorithmic model called the MS Transformer, where a self-supervised learning approach is used to perform a random mask on the input image to reconstruct the input features, learn a richer feature vector, and filter out excessive noise. To focus the model on the small objects that are being detected, the hierarchical transformer model is introduced in this paper, and a sliding window with a local self-attention mechanism is used to give a higher attention score to the small objects to be detected. Finally, a single-stage object detection framework is used to predict the sequence of sets at the location of the bounding box and the class of objects to be detected. On the DeepLesion and BCDD benchmark dataset, the model proposed in this paper achieves better performance improvement on multiple evaluation metric categories. [ABSTRACT FROM AUTHOR]

Face mask-wearing detection is of great significance for safety protection during the epidemic. Aiming at the problem of low detection accuracy due to the problems of occlusion, complex illumination, and density in mask-wearing detection, this paper proposes a neural network model based on the loss function and attention mechanism for mask-wearing detection in complex environments. Based on YOLOv5s, we first introduce an attention mechanism in the feature fusion process to improve feature utilization, study the effect of different attention mechanisms (CBAM, SE, and CA) on improving deep network models, and then explore the influence of different bounding box loss functions (GIoU, CIoU, and DIoU) on mask-wearing recognition. CIoU is used as the frame regression loss function to improve the positioning accuracy. By collecting 7,958 mask-wearing images and a large number of images of people without masks as a dataset and using YOLOv5s as the benchmark model, the mAP of the model proposed in the paper reached 90.96% on the validation set, which is significantly better than the traditional deep learning method. Mask-wearing detection is carried out in a real environment, and the experimental results of the proposed method can meet the daily detection requirements. [ABSTRACT FROM AUTHOR]

This paper studies the problem of facility location in a hybrid uncertain environment with both randomness and fuzziness. We establish a data-driven two-stage fuzzy random mixed integer optimization model, by considering the uncertainty of transportation cost and customer demand. Given the complexity of the model, this paper based on particle swarm optimization (PSO), beetle antenna search algorithm (BAS) and interior point algorithm, a hybrid intelligent algorithm (HIA) is proposed to solve two-stage fuzzy random mixed integer optimization model, yielding the optimal facility location and maximal expected return of supply chain simultaneously. Finally, taking the supply chain of medical mask in Shanghai as an example, the influence of uncertainty on the location of processing factory was studied. We compare the HIA with hybrid PSO and hybrid genetic algorithm (GA), to validate the proposed algorithm based on the computational time and the convergence rate. [ABSTRACT FROM AUTHOR]

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The world has faced up with a significant healthcare challenge due to the COVID-19 pandemic. The main, convenient, and effective solution is wearing a certified mask, which can prevent approximately 80% of all respiratory infections. Therefore, many COVID-19 monitoring systems based on face mask detection have been proposed or commercially developed to provide effective supervision for public areas. This paper proposes a comprehensive in-browser face mask detection with stand-alone (server-less) and client-server architectures. They can be integrated into available real-world scenarios at public area entrances and CCTV surveillance systems. To find the best predictive face mask detection model to deploy on devices for real-time in-browser applications, we build four YOLO iterations, namely YOLOv4, YOLOv5 small, YOLOv5 nano, and YOLOX with a network size of 640 × 640. We consider CPU and GPU device deployment techniques to optimize the inference speed (FPS) with high accuracy. Experimental results present that deploying YOLOv5 small on the client-server and implementing YOLOv5 nano on the stand-alone satisfied our study's goal to balance between adequate accuracy and real-time detection speed. This works achieves accuracy mAP of 90.8% and 29.59 FPS on GPU with client-server architecture, and mAP of 89.40%, and speed of 33 FPS on stand-alone low computing CPU resource. [ABSTRACT FROM AUTHOR]

In order to prevent the spread of respiratory viruses like COVID-19 and influenza, an effective protection method is to wear facial masks in densely populated areas. This has led to a growing need for smart services that automatically detect facial masks and replace manual reminding. To address this challenging task and to contribute towards health safety, this paper introduces MedNetV2 system, which is an efficient deep learning-based facial mask detector with a low computational cost. In comparison with existing systems, the main specificities of the proposed system are: (1) the adoption of an effective deep learning-based framework to deal with both the large scale diversity and position variations of masked faces involved in natural scenes, (2) the interaction between face localization and facial mask detection modules to achieve the overall system goal, (3) the lightweight design and the real-time response well suited for real-world scenarios. Extensive experiments on public dataset and real-world video streaming are carried out to validate quantitatively and visually the effectiveness of the proposed system. Promising results, in terms of detection accuracy as well as time response, are achieved when compared it with other state-of-the-art systems. [ABSTRACT FROM AUTHOR]

Different kinds of occlusion have proven to disrupt the accuracy of face recognition systems, one of them being masks. The problem of masked faces has become even more apparent with the widespread of the COVID-19 virus, with most people wearing masks in public. This brings up the issue of existing face recognition systems been able to accurately recognize people even when part of their face and the major identifiers (such as the nose and mouth) are covered by a facemask. In addition, most of the databases that have been curated in different organizations, countries are majorly of non-masked faces, and masked databases are rarely stored or universally accepted compared with conventional face datasets. Therefore, this paper aim at the development of a Masked Face Recognition System using facial anthropometrics technique (FAT). FAT is the science of calculating the measurements, proportion and dimension of human face and their features. A dataset of faces with individual wearing medical face mask was curated. Using the Facial anthropometry based technique a Masked Face Recognition System developed. This system was implemented using Local Binary Patterns Histogram algorithms for recognition. On testing the developed system trained with unmasked dataset, show a high recognition performance of 94% and 96.8% for masked and non-masked face recognition respectively because of the Facial anthropometry based technique adapted. On deployment, users were been recognized when they are wearing a mask with part of their face covered in real-time. [ABSTRACT FROM AUTHOR]

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A new coronavirus has caused a pandemic crisis around the globe. According to the WHO, this is an infectious illness that spreads from person to person. Therefore, the only way to avoid this infection is to take precautions. Wearing a mask is the most critical COVID‐19 protection method because it prevents the virus from spreading from an infected person to a healthy one. This study reflects a deep learning method to create a system for detecting Face Masks. The paper proposes a unique FMDRT (Face Mask Dataset in Real‐Time) dataset to determine whether a person is wearing a mask or not. The RFMD and Face Mask datasets are also taken from the internet to evaluate the performance of the proposed method. The CLAHE preprocessing method is employed to enhance the image quality, then resizing and Image augmentation techniques are used to convert it into a standard format and increase the size of the dataset, respectively. The pretrained Caffe face detector model is used to detect the faces, and then the lightweight transfer learning‐based Xception model is applied for the feature extraction process. This paper recommended a novel model that is, CL‐SSDXcept to distinguish the Face Mask or no mask images. However, accession with the MobileNetV2, VGG16, VGG19, and InceptionV3 models with different hyperparameter settings has been tested on the FMDRT dataset. We have also compared the results of the synthesized dataset FMDRT to the existing Face Mask datasets. The experimental results attained 98% test accuracy on the suggested dataset 'FMDRT' using the CL‐SSDXcept method. The empirical findings have been reported at 50 iterations with tuned hyperparameter values with an average accuracy 98% and a loss of 0.05. [ABSTRACT FROM AUTHOR]