Determinants of COVID-19 Face Mask Litter in Coastal Urban Parking Lots: Implications for Source Modelling of Potential Microplastic Pollution

Despite cities being recognized as being potential sources of microplastic pollution to the wider environment, most surveys of COVID-19 plastic-based litter have been undertaken through linear transects of marine beaches. For the far fewer number of studies conducted on inland and urban locations, the site-specific focus has primarily been surveys along the length of streets. The present study is the first to specifically assess the standing stock (i.e., moment-in-time) of littered face masks for the entire surface area of urban parking lots. The density of face masks in 50 parking lots in a Canadian coastal town (0.00054 m2 ± 0.00051 m2) was found to be significantly greater than the background level of littering of town streets. Face mask density was significantly related to visitation “usage” of parking lots as gauged by the areal size of the lots and of their onsite buildings, as well as the number of vehicles present. Neither parking lot typology nor estimates of inferred export (various measures of wind exposure) and entrapment (various metrics of obstruction) of face masks had a significant influence on the extent of whole-lot littering. In consequence, modelling of the potential input of mask-derived microplastics to the marine environment from coastal communities can use the areal density of face masks found here in association with the total surface area of lots for individual municipalities as determined through GIS analysis.

Advanced Face Mask Detection Model Using Hybrid Dilation Convolution Based Method

A face-mask object detection model incorporating hybrid dilation convolutional network termed ResNet Hybrid-dilation-convolution Face-mask-detector (RHF) is proposed in this paper. Furthermore, a lightweight face-mask dataset named Light Masked Face Dataset (LMFD) and a medium-sized face-mask dataset named Masked Face Dataset (MFD) with data augmentation methods applied is also constructed in this paper. The hybrid dilation convolutional network is able to expand the perception of the convolutional kernel without concern about the discontinuity of image information during the convolution process. For the given two datasets being constructed above, the trained models are significantly optimized in terms of detection performance, training time, and other related metrics. By using the MFD dataset of 55,905 images, the RHF model requires roughly 10 hours less training time compared to ResNet50 with better detection results with mAP of 93.45%.

Deep Learning Based Face Mask Detection in Religious Mass Gathering During COVID-19 Pandemic

Notwithstanding the religious intention of billions of devotees,the religious mass gathering increased major public health concerns since it likely became a huge super spreading event for the severe acute respiratory syndrome coronavirus 2(SARS-CoV-2).Most attendees ignored preventive measures,namely maintaining physical distance,practising hand hygiene,and wearing facemasks.Wearing a face mask in public areas protects people from spreading COVID-19.Artificial intelligence(AI)based on deep learning(DL)and machine learning(ML)could assist in fighting covid-19 in several ways.This study introduces a new deep learning-based Face Mask Detection in Religious Mass Gathering(DLFMD-RMG)technique during the COVID-19 pandemic.The DLFMD-RMG technique focuses mainly on detecting face masks in a religious mass gathering.To accomplish this,the presented DLFMD-RMG technique undergoes two pre-processing levels:Bilateral Filtering(BF)and Contrast Enhancement.For face detection,the DLFMD-RMG technique uses YOLOv5 with a ResNet-50 detector.In addition,the face detection performance can be improved by the seeker optimization algorithm(SOA)for tuning the hyperparameter of the ResNet-50 module,showing the novelty of the work.At last,the faces with and without masks are classified using the Fuzzy Neural Network(FNN)model.The stimulation study of the DLFMD-RMG algorithm is examined on a benchmark dataset.The results highlighted the remarkable performance of the DLFMD-RMG model algorithm in other recent approaches.

Fast and Accurate Detection of Masked Faces Using CNNs and LBPs

Face mask detection has several applications,including real-time surveillance,biometrics,etc.Identifying face masks is also helpful for crowd control and ensuring people wear them publicly.With monitoring personnel,it is impossible to ensure that people wear face masks;automated systems are a much superior option for face mask detection and monitoring.This paper introduces a simple and efficient approach for masked face detection.The architecture of the proposed approach is very straightforward;it combines deep learning and local binary patterns to extract features and classify themasmasked or unmasked.The proposed systemrequires hardware withminimal power consumption compared to state-of-the-art deep learning algorithms.Our proposed system maintains two steps.At first,this work extracted the local features of an image by using a local binary pattern descriptor,and then we used deep learning to extract global features.The proposed approach has achieved excellent accuracy and high performance.The performance of the proposed method was tested on three benchmark datasets:the realworld masked faces dataset(RMFD),the simulated masked faces dataset(SMFD),and labeled faces in the wild(LFW).Performancemetrics for the proposed technique weremeasured in terms of accuracy,precision,recall,and F1-score.Results indicated the efficiency of the proposed technique,providing accuracies of 99.86%,99.98%,and 100%for RMFD,SMFD,and LFW,respectively.Moreover,the proposed method outperformed state-of-the-art deep learning methods in the recent bibliography for the same problem under study and on the same evaluation datasets.

Face Mask and Social Distance Monitoring via Computer Vision and Deployable System Architecture

The coronavirus(COVID-19)is a lethal virus causing a rapidly infec-tious disease throughout the globe.Spreading awareness,taking preventive mea-sures,imposing strict restrictions on public gatherings,wearing facial masks,and maintaining safe social distancing have become crucial factors in keeping the virus at bay.Even though the world has spent a whole year preventing and curing the disease caused by the COVID-19 virus,the statistics show that the virus can cause an outbreak at any time on a large scale if thorough preventive measures are not maintained accordingly.Tofight the spread of this virus,technologically developed systems have become very useful.However,the implementation of an automatic,robust,continuous,and lightweight monitoring system that can be efficiently deployed on an embedded device still has not become prevalent in the mass community.This paper aims to develop an automatic system to simul-taneously detect social distance and face mask violation in real-time that has been deployed in an embedded system.A modified version of a convolutional neural network,the ResNet50 model,has been utilized to identify masked faces in peo-ple.You Only Look Once(YOLOv3)approach is applied for object detection and the DeepSORT technique is used to measure the social distance.The efficiency of the proposed model is tested on real-time video sequences taken from a video streaming source from an embedded system,Jetson Nano edge computing device,and smartphones,Android and iOS applications.Empirical results show that the implemented model can efficiently detect facial masks and social distance viola-tions with acceptable accuracy and precision scores.

A Real-Time Integrated Face Mask Detector to Curtail Spread of Coronavirus

Effective strategies to control COVID-19 pandemic need high attention to mitigate negatively impacted communal health and global economy,with the brim-full horizon yet to unfold.In the absence of effective antiviral and limited medical resources,many measures are recommended by WHO to control the infection rate and avoid exhausting the limited medical resources.Wearing mask is among the non-pharmaceutical intervention measures that can be used as barrier to primary route of SARS-CoV2 droplets expelled by presymptomatic or asymptomatic individuals.Regardless of discourse on medical resources and diversities in masks,all countries are mandating coverings over nose and mouth in public areas.Towards contribution of public health,the aim of the paper is to devise a real-time technique that can efficiently detect non mask faces in public and thus enforce to wear mask.The proposed technique is ensemble of one stage and two stage detectors to achieve low inference time and high accuracy.We took ResNet50 as a baseline model and applied the concept of transfer learning to fuse high level semantic information in multiple feature maps.In addition,we also propose a bounding box transformation to improve localization performance during mask detection.The experiments are conducted with three popular baseline models namely ResNet50,AlexNet and MobileNet.We explored the possibility of these models to plug-in with the proposed model,so that highly accurate results can be achieved in less inference time.It is observed that the proposed technique can achieve high accuracy(98.2%)when implemented with ResNet50.Besides,the proposed model can generate 11.07%and 6.44%higher precision and recall respectively in mask detection when compared to RetinaFaceMask detector.

Face mask detection algorithm based on HSV+HOG features and SVM

To automatically detecting whether a person is wearing mask properly,we propose a face mask detection algorithm based on hue-saturation-value(HSV)+histogram of oriented gradient(HOG)features and support vector machines(SVM).Firstly,human face and five feature points are detected with RetinaFace face detection algorithm.The feature points are used to locate to mouth and nose region,and HSV+HOG features of this region are extracted and input to SVM for training to realize detection of wearing masks or not.Secondly,RetinaFace is used to locate to nasal tip area of face,and YCrCb elliptical skin tone model is used to detect the exposure of skin in the nasal tip area,and the optimal classification threshold can be found to determine whether the wear is properly according to experimental results.Experiments show that the accuracy of detecting whether mask is worn can reach 97.9%,and the accuracy of detecting whether mask is worn correctly can reach 87.55%,which verifies the feasibility of the algorithm.

Comparative Study of Polypropylene Non-Woven Surgical Mask and Locally Manufactured Woven and Knitted Fabrics Facial Masks

The outbreak of coronavirus has led to an increase in the demand for facemasks globally. Unavailability of appropriate polypropylene non-woven fabrics face masks as a result of inadequate supply to satisfy the growing population has brought about the manufacturing of locally fabrics masks to augment or substitute standard medical class facemasks. The study aims at analyzing airflow of these locally manufactured fabrics to determine possible means of transmitting the virus as well as establish comfort of the user of these masks. Standard polypropylene non-woven, woven and knitted fabrics were considered for the study. Air permeability test was conducted on these fabrics using Frazier Air permeability tester. Depending on the property significant variation in the textile fabrics, polypropylene non-woven is widely accepted for facial masks. Nevertheless, this study illustrates that woven and knitted fabrics have more open structures, which allow a high rate of air penetration and so may require two or three layers to prevent antimicrobial or antiviral potential.

Effect of an E-Educational Poster on Improving the Knowledge, Attitude, and Practice on the Proper Use of Face Masks among School Students

Introduction: Two spread methods of Covid-19, namely airborne and respiratory droplets, can be prevented by proper use of face masks. However, it has been reported an inadequate knowledge attitude and practice of proper use of face masks among school students. Therefore, the knowledge, attitude, and practice of school students should be improved. Different approaches are used to improve knowledge, attitude, and practice. However, e-posters are rare, and the effect of e-posters on improving the knowledge, attitude, and practice of school students on the proper use of face masks has not been studied. Objectives: The objective of this study was to determine the effect of an e-educational poster on knowledge, attitude, and practice of the proper use of face masks among school students. Method: This study was conducted as a pre-test and post-test design. The sample was 364 grade 11 students of the Gampaha educational division, Sri Lanka. Data were collected using self-administered questionnaires distributed pre and post to the interventional e-education poster. Data analyses were conducted by using SPSS Software. Results: Results show no significant demographic difference (p = 0.446) between the pre and post-test groups. A significant increase was observed between the pre and post-test mean scores of knowledge (p ≤ 0.05), attitude (p ≤ 0.05), and practice (p ≤ 0.05) on the proper use of face masks. In pre-group knowledge (p = 0.155), attitude (p = 0.258) and practice (p = 0.211) shows no significant difference due to gender. Also post group knowledge (p = 0.079), attitude (p = 0.835) and practice (p = 0.435) shows no significant difference due to gender. Conclusions: The results suggest that e-educational posters may be useful to improve the knowledge, attitude, and practice on the proper use of face masks among school students. The improvement of knowledge, attitude, and practice on the proper use of face masks by e-educational posters among school students occurs irrespective of gender.

Running with Face Masks or Respirators Can Be Detrimental to the Respiratory and Cardiovascular Systems

Since the fi rst cases of coronavirus disease 2019(COVID-19)caused by severe acute respiratory syndrome coro-navirus 2(SARS-CoV-2)were reported at the end of 2019,this infection has spread around the globe,becoming a pandemic.The use of face masks and respirators is an important public health measure to reduce or prevent transmission of SARS-CoV-2.Here we discuss the hypothetical mechanisms by which exercise with face masks or respirators can induce detrimental effects on the cardiovascular system,potentially explaining adverse events such as cardiac arrhythmias and spontaneous pneumothorax.Although sudden death associated with the wearing of a face mask during running is a rare event,the risk is higher especially in those with existing cardiac comorbidities.In such cases,a mask designed specifi cally for runners with no or few side effects of oxygen defi ciency should be considered instead.

An Automated Real-Time Face Mask Detection System Using Transfer Learning with Faster-RCNN in the Era of the COVID-19 Pandemic

Today,due to the pandemic of COVID-19 the entire world is facing a serious health crisis.According to the World Health Organization(WHO),people in public places should wear a face mask to control the rapid transmission of COVID-19.The governmental bodies of different countries imposed that wearing a face mask is compulsory in public places.Therefore,it is very difficult to manually monitor people in overcrowded areas.This research focuses on providing a solution to enforce one of the important preventative measures of COVID-19 in public places,by presenting an automated system that automatically localizes masked and unmasked human faces within an image or video of an area which assist in this outbreak of COVID-19.This paper demonstrates a transfer learning approach with the Faster-RCNN model to detect faces that are masked or unmasked.The proposed framework is built by fine-tuning the state-of-the-art deep learning model,Faster-RCNN,and has been validated on a publicly available dataset named Face Mask Dataset(FMD)and achieving the highest average precision(AP)of 81%and highest average Recall(AR)of 84%.This shows the strong robustness and capabilities of the Faster-RCNN model to detect individuals with masked and un-masked faces.Moreover,this work applies to real-time and can be implemented in any public service area.

Effect of face masks on speech understanding: A clinical perspective during speech audiometry

The objective was to measure the effect of various face masks on speech recognition threshold and the word recognition score in the presence of varying background noise levels.20 normal-hearing adult subjects(a total of 40 ears)participated.Pure tone audiometry followed by speech recognition threshold and word recognition score at the most comfortable level in varying signal-to-noise ratios(SNR0,SNR10,and SNR15)using surgical,pleated cloth,and N95 masks.Using surgical,cloth,and N95 masks,speech recognition thresholds increased by 1.8 dB,4.4 dB,and 5.05 dB,respectively.Word recognition scores decreased by 32%without a mask,43.7%in a surgical mask,46.3%in a cloth mask,and 46.7%in N95 mask conditions,between SNR15 and SNR0.The speech recognition threshold was negatively affected with cloth and N95 masks.Surgical masks do not affect the word recognition scores at lower background noise levels.However,as the signal-to-noise ratio decreased,even the surgical,cloth,and N95 masks significantly impacted the word recognition score even in normal-hearing individuals.

Face Mask Recognition for Covid-19 Prevention

In recent years,the COVID-19 pandemic has negatively impacted all aspects of social life.Due to ease in the infected method,i.e.,through small liquid particles from the mouth or the nose when people cough,sneeze,speak,sing,or breathe,the virus can quickly spread and create severe problems for people’s health.According to some research as well as World Health Organization(WHO)recommendation,one of the most economical and effective methods to prevent the spread of the pandemic is to ask people to wear the face mask in the public space.A face mask will help prevent the droplet and aerosol from person to person to reduce the risk of virus infection.This simple method can reduce up to 95%of the spread of the particles.However,this solution depends heavily on social consciousness,which is sometimes unstable.In order to improve the effectiveness of wearing face masks in public spaces,this research proposes an approach for detecting and warning a person who does not wear or misuse the face mask.The approach uses the deep learning technique that relies on GoogleNet,AlexNet,and VGG16 models.The results are synthesized by an ensemble method,i.e.,the bagging technique.From the experimental results,the approach represents a more than 95%accuracy of face mask recognition.

The bonding bite plate combined with the face mask protracting maxilla to treat skeletal crossbite in the early mixed dentition

Objective： To evaluate the orthopedic effects of a new method to treat skeletal crossbite in the early mixed dentition. Methods ：Twenty cases （5.8-7. 5 years old ） with skeletal crossbite were treated by bonding the bite plate combined with the face mask protracting the upper jaw. Cephalometric radiographs were taken and analyzed before and after treatments. Results： On average, in all 20 cases the maxilla was moved by 2.06 mm forwards the mandible was turned 2.45° downward and backwards the skeletal crossbites were corrected and the facial profiles were improved satisfactorily. The period of treatment was conducted for 1.7 months on average. Conclusion： A good effect can be achieved by bonding bite plate combined with the face mask protracting the upper jaw to treat skeletal crossbite in the early mixed dentition, which will benefit the craniofacial growth and the development of young children.

Conductive Polymer Composites Fabricated by Disposable Face Masks and Multi-Walled Carbon Nanotubes: Crystalline Structure and Enhancement Effect

Influenced by recent COVID-19,wearing face masks to block the spread of the epidemic has become the simplest and most effective way.However,after the people wear masks,thousands of tons of medical waste by used dis-posable masks will be generated every day in the world,causing great pressure on the environment.Herein,con-ductive polymer composites are fabricated by simple melt blending of mask fragments(mask polypropylene,short for mPP)and multi-walled carbon nanotubes(MWNTs).MWNTs were used as modifiers for composites because of their high strength and high conductivity.The crystalline structure,mechanical,electrical and thermal enhancement effect of the composites were investigated.MWNTs with high thermal stability acted the role of promoting the crystallisation of mPP by expediting the crystalline nucleation,leading to the improvement of amount for crystalline nucleus.MWNTs fibers interpenetrate with each other in mPP matrix to form conducting network.With 2.0 wt% MWNTs loading,the tensile strength and electrical conductivity of the composites were increased by 809% and 7 orders of magnitude.MWNTs fibers interpenetrate with each other in mPP matrix to form conducting network.Thus,more conducting paths were constructed to transport carriers.The findings may open a way for high value utilization of the disposable masks.

The Impact of Face Mask Inks and Dyes on the Environment and Human Health

To reduce the impact of the novel SARS-CoV-2 virus, popularly known as the Coronavirus, many public health-related rules have been established around the world. Along with social distancing and lockdowns, most countries have mandatory wearing of face masks in public areas to limit the spread of the virus during the COVID-19 pandemic. However, because people are free to choose any method to make their masks, some are being fabricated from materials that can be toxic to the environment and human health. This paper discusses how inks and dyes used in face masks are causing major environmental degradation and health issues in industry workers and the general mask-wearing public. The goal fixed for the present study is to raise the alarm with authorities and decision-makers regarding the toxic nature of some colors (dyes and inks) and fabrics in the masks being worn every day.

Use of the Appropriate Fabric for Cloth Face Mask: A Necessity in the Fight against COVID-19

The outbreak of coronavirus disease (COVID-19) has created a global health crisis that has had a deep impact on the way we perceive our world and our everyday lives. The call for the wearing of face masks as one of the ways of curbing the disease has resulted in the proliferation of cloth face masks on our markets. In the desperation to cash in on the season and make money at all costs, some manufacturers use inferior fabrics to produce face masks. Some of these fabrics do not meet the basic performance requirements of cloth face masks. This study was therefore carried out to research into the appropriate fabrics that will be suitable for the production of cloth face masks in terms of comfort, breathability and protection. To do this, 1225 participants were conveniently drawn for the study. The main research instrument employed for the study was the survey approach in which well-structured questionnaires were administered to solicit information from the participants. To determine the reliability and validity of data, the Cronbach’s Alpha test was conducted. Data were analyzed using the Stata statistical software to perform a multinomial logistic regression to estimate Odds Ratios (ORs) with 95% CIs. A multinomial logit model was constructed to determine the nominal variables. A major finding of the study was that people’s choice of fabric for cloth face masks is determined to a larger extent by their professions. The study also revealed that cotton, silk and linen possess good properties for the production of cloth face masks. Based on the findings, the study concludes that cloth face masks made from two-layered fabrics or three-layered fabrics are the best in terms of comfort and full protection of the wearer. It is recommended that the outer layer should be made from cotton and the inner layer made from linen, cotton-polyester blend or silk.

基于改进Mask R-CNN的牛脸目标检测算法

针对牛脸检测时,存在的检测精度不高、牛脸较小被漏检或误检等问题,提出一种改进的Mask R-CNN+MResNet模型。首先,在ResNet101网络的基础上提出一种MResNet网络,通过对ResNet101网络的改进,提高了模型检测精度。其次,对模型的RPN网络的锚框尺寸进行调整,提高了模型对较小目标的牛脸检测能力。实验结果表明,MResNet网络对牛脸检测精度相比较原始的网络模型,提高了12.6%;改进后的模型对于小目标检测能力平均精度较原始模型提高了2.4%。说明该模型能有效的实现小目标牛脸的检测,具有实际应用价值。

Re-designing Conventional Face Mask by Using Triz(Teoriya Resheniya Izobreatatelskikh Zadatch)Method

New Normal becomes the way out to keep Indonesia's weakened economy because of covid-19 pandemic stays in balance.Of course,New Normal must be well-balanced with the application of strict protocols to reduce transmission rates and anticipate a surge in new cases.From a few points of pandemic protocols,using face mask is considered as the most-effective basic need.Re-designing the mask is aiming at eliminating the worsening feature and developing the improving feature.This research is developing the widely used 1-ply mask,which doesn't meet WHO standard regarding cloth-material mask that must be made in 3-ply.The method used in this research is TRIZ(Teoriya Resheniya Izobreatatelskikh Zadatch),an inventive problem solving theory to the issues regarding ideas,products,or concepts,in this case is face mask redesigning concept.In this research,the data are collected by doing library research to obtain secondary data that will be processed in qualitative and quantitative methods.The results show the aspects that should be eliminated from cloth-material face mask are brittle for being easily damaged,short period of usage,and non-reusable.After the redesigning concept is carried out using the principles and features of TRIZ method,some aspects such as weight,material,design,and functionality are developed.The result is a relatively light weight face mask because the using of PLA+filament-based material with good durability and environmentally friendly.This design has a longer period of usage,and the user only needs to replace the additional inserted tissue every 4 hours.The researchers hope that the new design can be further developed by government to deal with New Normal and other possibilities in the future.

Non-Contact Physiological Measurement System for Wearing Masks During the Epidemic

Physiological signals indicate a person’s physical and mental state at any given time.Accordingly,many studies extract physiological signals from the human body with non-contact methods,and most of them require facial feature points.However,under COVID-19,wearing a mask has become a must in many places,so how non-contact physiological information measurements can still be performed correctly even when a mask covers the facial information has become a focus of research.In this study,RGB and thermal infrared cameras were used to execute non-contact physiological information measurement systems for heart rate,blood pressure,respiratory rate,and forehead temperature for peoplewearing masks due to the pandemic.Using the green(G)minus red(R)signal in the RGB image,the region of interest(ROI)is established in the forehead and nose bridge regions.The photoplethysmography(PPG)waveforms of the two regions are obtained after the acquired PPG signal is subjected to the optical flow method,baseline drift calibration,normalization,and bandpass filtering.The relevant parameters in Deep Neural Networks(DNN)for the regression model can correctly predict the heartbeat and blood pressure.In addition,the temperature change in the ROI of the mask after thermal image processing and filtering can be used to correctly determine the number of breaths.Meanwhile,the thermal image can be used to read the temperature average of the ROI of the forehead,and the forehead temperature can be obtained smoothly.The experimental results show that the above-mentioned physiological signals of a subject can be obtained in 6-s images with the error for both heart rate and blood pressure within 2%∼3%and the error of forehead temperature within±0.5°C.