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Objective to analyze scientific evidence on the efficacy of cloth masks in preventing COVID-19 and other respiratory infections. Method integrative literature review based on the following guiding question: What is the efficacy of cloth face masks in absorbing particles that cause respiratory infection? The search was conducted in eight electronic databases, without any restriction in terms of language or period. Results low coverage cloth face masks made of 100% cotton, scarf, pillowcase, antimicrobial pillowcase, silk, linen, tea towel, or vacuum bag, present marginal/reasonable protection against particles while high coverage cloth masks provide high protection. Conclusion cloth face masks are a preventive measure with moderate efficacy in preventing the dissemination of respiratory infections caused by particles with the same size or smaller than those of SARS-CoV-2. The type of fabric used, number of layers and frequency of washings influence the efficacy of the barrier against droplets. Objetivo analisar as evidências científicas sobre a eficácia de máscaras de tecido para a prevenção da COVID-19 e outras infecções respiratórias. Método revisão integrativa da literatura a partir da questão norteadora: “Qual a eficácia das máscaras de tecido na absorção de partículas causadoras de infecção respiratória?”. A busca ocorreu em oito bases de dados eletrônicas sem restrição de tempo e idioma. Resultados as máscaras de tecido de baixa cobertura, 100% algodão, cachecol, fronha, fronha antimicrobiana, linho, seda, toalha de chá e saco de aspirador apresentam proteção marginal/razoável na absorção de partículas, enquanto as máscaras de tecido com alta cobertura têm elevada proteção. Conclusão as máscaras de tecido representam medida preventiva com eficácia moderada na disseminação de infecções respiratórias ocasionadas por partículas de tamanho igual e/ou menor ao SARS-CoV-2. A eficácia da barreira contra as gotículas é influenciada, principalmente, pelo tipo de tecido, quantidade de camadas e frequência de lavagens. Objetivo analizar las evidencias científicas sobre la eficacia de las máscaras de tejido para la prevención de COVID-19 y otras infecciones respiratorias. Método revisión integradora de la literatura a partir de la pregunta orientadora: ¿qué eficacia tienen las máscaras de tejido en la absorción de partículas que causan infección respiratoria? La búsqueda fue llevada a cabo en ocho bases de datos electrónicas, sin restricciones de tiempo e idioma. Resultados las máscaras de tejido de baja cobertura, 100% algodón, bufanda, funda, funda antimicrobiana, lino, seda, toalla de té y bolsa de aspiradora demuestran una protección marginal/razonable en la absorción de partículas, mientras que las máscaras de tejido de alta cobertura tienen una protección elevada. Conclusión las máscaras de tejido representan una medida preventiva de eficacia moderada en la propagación de infecciones respiratorias causadas por partículas de tamaño igual o menor al SARS-CoV-2. La eficacia de la barrera contra las gotitas se ve influida principalmente por el tipo de tejido, la cantidad de capas y la frecuencia de lavado.

Objective: Our goal is to evaluate the effects of heat and ultraviolet (UV) irradiation on P3 facial respirator microstructure. Intervention: P3 facial filters were exposed to dry heat and UV sterilization procedures. Methods: P3 facial filter samples underwent a standardized sterilization process based on dry heat and UV irradiation techniques. We analyzed critical parameters of internal microstructure, such as fiber thickness and porosity, before and after sterilization, using 3D data obtained with synchrotron radiation-based X-ray computed microtomography (micro-CT). The analyzed filter has two inner layers called the “finer” and “coarser” layers. The “finer” layer consists of a dense fiber network, while the “coarser” layer has a less compact fiber network. Results: Analysis of 3D images showed no statistically significant differences between the P3 filter of the controls and the dry heat/UV sterilized samples. In particular, averages fiber thickness in the finer layer of the control and the 60° dry heated and UV-irradiated sample groups was almost identical. Average fiber thickness for the coarser layer of the control and the 60° dry heated and UV-irradiated sample groups was very similar, measuring 19.33 µm (±0.47), 18.33 µm (±0.47), and 18.66 µm (±0.47), respectively. There was no substantial difference in maximum fiber thickness in the finer layers and coarser layers. For the control group samples, maximum thickness was on average 11.43 µm (±1.24) in the finer layer and 59.33 µm (±6.79) in the coarser layer. Similarly, the 60° dry heated group samples were thickened 12.2 µm (±0.21) in the finer layer and 57.33 µm (±1.24) in the coarser layer, while for the UV-irradiated group, the mean max thickness was 12.23 µm (±0.90) in the finer layer and 58.00 µm (±6.68) in the coarser layer. Theoretical porosity analysis resulted in 74% and 88% for the finer and coarser layers. The finer layers’ theoretical porosity tended to decrease in dry heat and UV-irradiated samples compared with the respective control samples. Conclusions: Dry heat and UV sterilization processes do not substantially alter the morphometry of the P3 filter samples’ internal microstructure, as studied with micro-CT. The current study suggests that safe P3 filter facepiece reusability is theoretically feasible and should be further investigated.

Avian feathers are remarkably diverse in both form and function. Coloration is among the most studied feature of feathers, but we know relatively little about melanin‐based black coloration. Despite many crows ( Corvus sp.) and other corvids having black feathers that appear uniform to human perception, their feather coloration could play an important role in social communication. We therefore tested whether the coloration of American crow Corvus brachyrhynchos feathers varied by age class and sex, two socially relevant variables. Using a visual modeling approach that accounts for the visual system of American crows, we measured the coloration of American crow feathers from museum specimens. We found that feather coloration varied by age class but not sex. Older individuals had feathers with different hue and more ultraviolet than younger crows. Discriminant function analyses correctly categorized individuals into age classes based on feather coloration with high classification success. The coloration of American crow feathers did not vary based on the time since the last molt and replacement, but did vary with the time since the specimen was collected. The visual modeling approach suggests that crows can discriminate among different feather regions. One region with particularly distinctive coloration properties was a facial mask, which could potentially function to minimize eye glare. Our results suggest that feather coloration in American crows (and potentially other seemingly monomorphic corvids) could reflect underlying qualities of those individuals that are important for social communication.

Owing to the availability of a wide range of emotion recognition applications in our lives, such as for mental status calculation, the demand for high-performance emotion recognition approaches remains uncertain. Nevertheless, the wearing of facial masks has been indispensable during the COVID-19 pandemic. In this study, we propose a graph-based emotion recognition method that adopts landmarks on the upper part of the face. Based on the proposed approach, several pre-processing steps were applied. After pre-processing, facial expression features need to be extracted from facial key points. The main steps of emotion recognition on masked faces include face detection by using Haar–Cascade, landmark implementation through a media-pipe face mesh model, and model training on seven emotional classes. The FER-2013 dataset was used for model training. An emotion detection model was developed for non-masked faces. Thereafter, landmarks were applied to the upper part of the face. After the detection of faces and landmark locations were extracted, we captured coordinates of emotional class landmarks and exported to a comma-separated values (csv) file. After that, model weights were transferred to the emotional classes. Finally, a landmark-based emotion recognition model for the upper facial parts was tested both on images and in real time using a web camera application. The results showed that the proposed model achieved an overall accuracy of 91.2% for seven emotional classes in the case of an image application. Image based emotion detection of the proposed model accuracy showed relatively higher results than the real-time emotion detection.

In the skin care field, bacterial nanocellulose (BNC), a versatile polysaccharide produced by non-pathogenic acetic acid bacteria, has received increased attention as a promising candidate to replace synthetic polymers (e.g., nylon, polyethylene, polyacrylamides) commonly used in cosmetics. The applicability of BNC in cosmetics has been mainly investigated as a carrier of active ingredients or as a structuring agent of cosmetic formulations. However, with the sustainability issues that are underway in the highly innovative cosmetic industry and with the growth prospects for the market of bio-based products, a much more prominent role is envisioned for BNC in this field. Thus, this review provides a comprehensive overview of the most recent (last 5 years) and relevant developments and challenges in the research of BNC applied to cosmetic, aiming at inspiring future research to go beyond in the applicability of this exceptional biotechnological material in such a promising area.

Aiming to address the challenges of reduced detection accuracy in face mask applications due to mutual occlusion, lighting variations, and detection distance, this paper proposes a face mask detection algorithm tailored for complex environments. First, we construct a comprehensive face mask dataset. Then, based on the YOLOv8 architecture, we enhance the C2f module in the backbone network by incorporating depth-separable convolutions to better capture the color and texture features of the target. We also integrate the SENet attention mechanism to further optimize feature extraction efficiency. To improve the transmission of fine-grained face mask features within the network, we introduce context-aware convolutions in the Neck module, which facilitates the integration of contextual semantic information and enriches the feature details of small targets. Building on this, we design an enhanced detection head, DAM-Head, which amplifies target saliency and improves both target recognition and localization accuracy. Experimental results demonstrate that the proposed algorithm achieves a mean Average Precision (mAP) of 98.11% and a Frames Per Second (FPS) rate of 135.61 on the constructed dataset, outperforming other mainstream algorithms in both accuracy and real-time performance.

Interactions with talkers wearing face masks have become part of our daily routine since the beginning of the COVID-19 pandemic. Using an on-line experiment resembling a video conference, we examined the impact of face masks on speech comprehension. Typical-hearing listeners performed a speech-in-noise task while seeing talkers with visible lips, talkers wearing a surgical mask, or just the name of the talker displayed on screen. The target voice was masked by concurrent distracting talkers. We measured performance, confidence and listening effort scores, as well as meta-cognitive monitoring (the ability to adapt self-judgments to actual performance). Hiding the talkers behind a screen or concealing their lips via a face mask led to lower performance, lower confidence scores, and increased perceived effort. Moreover, meta-cognitive monitoring was worse when listening in these conditions compared with listening to an unmasked talker. These findings have implications on everyday communication for typical-hearing individuals and for hearing-impaired populations.

In response to the COVID-19 pandemic, single-use disposable masks saw a dramatic rise in production. Facial masks that are not properly disposed of will expose the environment to a form of non-biodegradable plastic waste that will take hundreds of years to degrade. Therefore, recycling such waste in an eco-friendly manner is imperative. Fibered or shredded waste masks can be used to make green concrete that is an environmentally friendly solution to dispose the facial masks. This study prepared six classes of concrete samples, three of which contained fibers from masks and three of which contained shredded masks at the ages of seven days and 28 days. The results show that in the seven-day and 28-day samples, mask fiber added to the mixes resulted in increased compressive strength. For seven-day and 28-day samples, the compressive strength increased by 7.2% and 10%, respectively. Despite that, the results of the shredded mask addition to concrete indicate that the increase in shredded mask volume has a minor impact on the compressive strength of the seven-day samples. An increase in shredded mask from 0.75 to 1% increased 28-day compressive strength by 14%. However, the compressive strength of the mask fiber decreased by 8 after 1% volume. According to a thermal analysis of 28-day concrete samples, as the fiber percentage increases, the mass loss percentage increases. The mass loss rate for samples containing fibers is higher than that for samples containing shredded mask pieces. In general, based on the results mentioned above, the use of fiber in concrete in its fiber state enhances its strength properties. As a result, using shredded mask pieces in concrete leads to better curing due to the reduction of residual capillary pore water loss in construction materials.

Wearing a facial mask is indispensable in the COVID-19 pandemic; however, it has tremendous effects on the performance of existing facial emotion recognition approaches. In this paper, we propose a feature vector technique comprising three main steps to recognize emotions from facial mask images. First, a synthetic mask is used to cover the facial input image. With only the upper part of the image showing, and including only the eyes, eyebrows, a portion of the bridge of the nose, and the forehead, the boundary and regional representation technique is applied. Second, a feature extraction technique based on our proposed rapid landmark detection method employing the infinity shape is utilized to flexibly extract a set of feature vectors that can effectively indicate the characteristics of the partially occluded masked face. Finally, those features, including the location of the detected landmarks and the Histograms of the Oriented Gradients, are brought into the classification process by adopting CNN and LSTM; the experimental results are then evaluated using images from the CK+ and RAF-DB data sets. As the result, our proposed method outperforms existing cutting-edge approaches and demonstrates better performance, achieving 99.30% and 95.58% accuracy on CK+ and RAF-DB, respectively.

Mass customization is the concept of integrating advanced designing and fabrication approaches to produce personalized products while ensuring efficiency and low cost over the entire customization cycle. The escalation of COVID-19 in the past few years has stimulated massive demand for personal protective equipment (PPE), including surgery masks, oxygen respirators, and other wearable equipment that could prevent the virus from spreading. Both performance and comfort are affected by the fit of facial masks; thus, implementing mass customization on PPE could improve user comfort and enhance air impermeability while maintaining low costs during design and fabrication. Current mass customization methods for surgery masks, oxygen respirators, or other general purpose medical masks mostly use a 3D scanner or integrated smartphone applications to acquire the patient’s facial data profile. However, these methods are manual and either require a high-precision scanner or cameras with the depth mapping function. To fill these gaps, this paper proposed a video-based method for supporting automated facial mask design customization. More specifically, the proposed method records a short facial video as the primary input, where facial data is extracted from each video frame and forms a cross-validated dataset with facial variations. The dataset further supports the generative design of customized facial masks based on defined regions of interest. Finally, design considerations of additive manufacturing are integrated into the fabrication of customized masks. Preliminary user feedback has shown promising results for the proposed method.