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Daily COVID-19-related morbidity and mortality rates are still high globally, and masking is considered a reliable method of preventing its infections. Yet, the rate of voluntary compliance with masking remains very low in most parts of the world, especially in developing countries. The authors hypothesize that the decision to wear a mask entails some benefit-cost analysis that involves time discounting. In addition, the authors surmise that feel-good benefits from pro-social behavior and from wearing fashionable masks are substantial.

The study is based on a survey of 900 fishermen and fish traders in Ghana. A simple experiment was designed to elicit individual rates of time preference. In addition, the fishers were asked questions about their political affiliation and knowledge of the COVID-19 pandemic, among others. A logit model is used to investigate the determinants of the decision to wear a mask.

The authors found that masking compliance increases in time discounting for fishmongers, suggesting that private benefits from pro-social behavior or feel-good benefits from wearing a mask are very strong. In addition, those who belonged to the ruling political party were more likely to wear a mask. Other factors increasing the likelihood of masking include affiliation with the ruling political party, knowledge of COVID-19 and knowledge of someone who lost his/her job due to COVID-19.

To the best of the authors’ knowledge, this is the first study to investigate the effect of time discounting on the voluntary compliance of a health safety measure, which could provide a direct utility. In addition, the study explores the effect of political affiliation on voluntary masking behavior.

The purpose of this paper is to highlight the sources of noise generation in a library and suggest the implementation of a sound masking system to provide acoustic comfort, maintain speech privacy and create an environment more engaging for the users.

Analyzing the existing literature and exploring the existing practices as observed in different libraries, the study gives an overview of the sound masking initiatives in libraries.

With practical examples of libraries, the study demonstrates how a sound masking system has been implemented to invoke better acoustic design in the library. The expansion of various activities in the library and a gradual shift from individual attention to a collaborative approach necessitates a strong focus on the acoustic design architecture of the library. The study showcases how the libraries adopt sound masking with the introduction of acoustic panels, dual panel partitions, sound-absorbent false ceilings, sound insulation, sound isolation and noise-dampening measures, installing furniture with sound containment features, adopting vibration control mechanism, mounting of white noise machines, etc., keeping the aesthetic quotient of the library alive.

The study attempts to show the current practices of the adoption of the sound masking system in libraries and promotes collaborative reading with the creation of an acoustic design-influenced library environment to control noise and reverberation and provide a comfortable reading environment.

The purpose of this paper is to provide maintenance personnel with a methodology for using masked field reliability data to determine the probability of each subassembly failure.

The paper compares an iterative maximum likelihood estimation method and a Bayesian methodology for handling masked data collected from 227 identical radar power supplies. The power supply consists of several subassemblies hereafter referred to as shop replaceable assemblies (SRAs).

The study examined two approaches for dealing with masking, an iterative maximum likelihood estimate procedure, IMLEP, and a Bayesian approach implemented with the application WinBUGS. It indicates that the performances of IMLEP and WinBUGS in estimating the parameters of the SRA distribution under no masking conditions are similar. IMLEP and WinBUGS also provide similar results under masking conditions. However, the study indicates that WinBUGS may perform better than IMLEP when the competing risk responsible for a failure represents a smaller total percentage of the total failures. Future study to confirm this conclusion by expanding the number of SRAs into which the item under study is organized is required.

If an item is considered to be comprised of various subassemblies and the failure of the first subassembly causes the item to fail, then the item is referred to as a series system in the literature. If the probability of a each subassembly failure is statistically independent then the item can be represented by a competing risk model and the probability distributions of the subassemblies can be ascertained from the item's failure data. When the item's cause of failure is not known, the data are referred to in the literature as being masked. Since competing risk theory requires a cause of failure and a time of failure, any masked data must be addressed in the competing risk model.

This study indicates that competing risk theory can be applied to the equipment field failure data to determine a SRA's probability of failure and thereby provide an efficient sequence of replacing suspect failed SRAs.

The analysis of masked failure data is an important area that has had only limited study in the literature due to the availability of failure data. This paper contributes to the research by providing the complete historical equipment usage data for the item under study gathered over a time frame of approximately seven years.

This paper asks how values and beliefs around gender influence social norms regarding masking. Specifically, the paper explores how the gendered meme “Karen” fits into social media discussions on support for and opposition to the wearing of masks to fight the spread of COVID-19.

The authors analyze tweets containing the hashtags #Masks4All and #NoMasks over a three-week period, using adjacent hashtag analysis to determine the terms most associated with Karen in the pro and anti-mask communities associated with these hashtags.

Anti-maskers reference Karen more often than pro-maskers, although she is presented in negative terms with gendered overtones by those on both sides of the masking debate.

The paper highlights how hypermasculinity rhetoric impedes social change that normalizes mask wearing.

The COVID-19 epidemic is still spreading globally and will not be completely over in a short time. Wearing a mask is an effective means to combat the spread of COVID-19. However, whether the public wear a mask for epidemic prevention and control will be affected by stochastic factors such as vaccination, cultural differences and irrational emotions, which bring a high degree of uncertainty to the prevention and control of the epidemic. The purpose of this study is to explore and analyze the epidemic prevention and control strategies of the public in an uncertain environment.

Based on the stochastic evolutionary game model of the Moran process, the study discusses the epidemic prevention and control strategies of the public under the conditions of the dominance of stochastic factors, expected benefits and super-expected benefits.

The research shows that the strategic evolution of the public mainly depends on stochastic factors, cost-benefit and the number of the public. When the stochastic factors are dominant, the greater the perceived benefit, the lower the cost and the greater the penalty for not wearing masks, the public will choose to wear a mask. Under the dominance of expected benefits and super-expected benefits, when the number of the public is greater than a certain threshold, the mask-wearing strategy will become an evolutionary stable strategy. From the evolutionary process, the government’s punishment measures will slow down the speed of the public choosing the strategy of not wearing masks. The speed of the public evolving to the stable strategy under the dominance of super-expected benefits is faster than that under the dominance of expected benefits.

The study considers the impact of stochastic factors on public prevention and control strategies and provides decision-making support and theoretical guidance for the scientific prevention of the normalized public.

To the best of the authors’ knowledge, no research has considered the impact of different stochastic interference intensities on public prevention and control strategies. Therefore, this paper can be seen as a valuable resource in this field.

The purpose of this paper is to present a novel approach for digital watermarking and steganography technique that uses neural networks. The performance of the proposed solution in terms of its capacity, transparency, and robustness is investigated.

The proposed technique trains a neural network to learn the perceptual masking model of the human vision system. Once trained, the neural network identifies pixels whose most significant alteration will be least perceptible to the human eye. The image is then altered based on the network recommendation to include the watermark or the covert data.

Experimental results demonstrate that the proposed technique offers excellent transparency and good capacity. In addition, since neural networks store their learned knowledge in a distributed fashion, steganalysis of the image without access to the network is very difficult, if not impossible. Results demonstrate good performance of the proposed solution in terms of its capacity, transparency, and robustness.

Use of neural networks in extracting and representing perceptual masking model of human vision system is interesting. Value added by the proposed approach is in its use of artificial neural networks to model the perceptual masking model of human vision system for injecting imperceptible data into most perceptually significant pits of an image. The proposed approach may be used in combination with most current and popular methods with little impact on perceptual quality of the resulting image.

This paper aims to identify data set entities in scientific literature. To address poor recognition caused by a lack of training corpora in existing studies, a distant supervised learning-based approach is proposed to identify data set entities automatically from large-scale scientific literature in an open domain.

Firstly, the authors use a dictionary combined with a bootstrapping strategy to create a labelled corpus to apply supervised learning. Secondly, a bidirectional encoder representation from transformers (BERT)-based neural model was applied to identify data set entities in the scientific literature automatically. Finally, two data augmentation techniques, entity replacement and entity masking, were introduced to enhance the model generalisability and improve the recognition of data set entities.

In the absence of training data, the proposed method can effectively identify data set entities in large-scale scientific papers. The BERT-based vectorised representation and data augmentation techniques enable significant improvements in the generality and robustness of named entity recognition models, especially in long-tailed data set entity recognition.

This paper provides a practical research method for automatically recognising data set entities in scientific literature. To the best of the authors’ knowledge, this is the first attempt to apply distant learning to the study of data set entity recognition. The authors introduce a robust vectorised representation and two data augmentation strategies (entity replacement and entity masking) to address the problem inherent in distant supervised learning methods, which the existing research has mostly ignored. The experimental results demonstrate that our approach effectively improves the recognition of data set entities, especially long-tailed data set entities.

With continuous scaling of digital circuit CMOS technology, the vulnerability of these circuits are significantly increasing against the soft errors. On the other hand, the effects of process variation in the electrical properties of nano-scale circuits, have introduced the statistical methods as an unavoidable choice for the soft error rate (SER) estimation. The purpose of this paper is to provide a statistical soft error rate (SSER) estimation approach for combinational circuits in the presence of process variation.

In this paper a new method is proposed for the SSER estimation of combinational circuits based on the Bayesian networks (BNs). This allows to factor the joint probability distributions over variables in a circuit graph. The distribution of the initial transient fault pulse is estimated by the pre-characterization tables. Timing signals are propagated by BN theory and the probability distribution of electrical and timing masking are calculated.

Simulation results for some benchmark circuits show that the proposed method is accurate with 3.7 percent difference with the Monte-Carlo SPICE simulation and with orders of magnitude improvement in runtime.

The proposed framework is the scheme giving the low estimation time with plausible accuracy compared to other schemes. The comparison exhibits that the designer can save its estimation time in terms of performance and complexity. The deterministic-based methods also are able to evaluate the SER of combinational circuit, yet in an unacceptable time.

The purpose of this paper is to explore the role that the emotional expressions (i.e. emotional masking and emotional sharing) of students play in fostering positive implicit abilities, as indicated by learning, interpersonal skills and the ability to acquire a supervisor’s support. By introducing a new theory of creative expressiveness, the authors have further examined whether college students’ creative thinking is significantly associated with their emotional expression.

This paper establishes a conceptual framework to map the relationships between students’ emotional expressions, their implicit abilities and their creative abilities. Scale measures of these constructs were built, and a total of 400 questionnaires were distributed at universities in Hefei and Nanjing. Finally, ordinary least squares estimations were conducted to provide quantitative estimations.

The empirical results show that emotional sharing is significantly positive for college students’ implicit and creative abilities, while emotional masking is negatively related to students’ implicit abilities and creativity. Moreover, the effects of emotional sharing by college students on their creative abilities are partially mediated by students’ implicit abilities.

It is necessary to emphasize emotional sharing in education and to create a friendly atmosphere for students, in which they can feel comfortable expressing themselves in class. Likewise, students should learn to improve their expressive abilities, particularly how to express and share their inner feelings and emotions, since this will contribute to their creative thinking.

It is increasingly being recognized in organizational science that emotions and the way they are experienced and expressed by employees in work environments have fundamental impacts on work-related outcomes. However, limited attention has been given to the impacts of emotional expression on students’ learning performances and creativity abilities, especially in the Chinese context where students are more reluctant to express their emotions and ideas. Thus, by introducing a new theory of creative expressiveness to examine the benefits of emotional expression for students’ implicit abilities and creative thinking, the authors have sought to extend prior research on the cultivation of college students’ creative abilities.

In order to manufacture scaffolds for tissue engineering, a dynamic mask rapid prototyping system to cure UV‐curable biodegradable material was developed. The aim of this paper is to document this system.

A digital micro‐mirror device (DMD) was used as the dynamic mask generator, with each layer's mask pattern dictated by our self‐developed software. To build the scaffolds, UV light reflected by the DMD was then focused onto the biodegradable material to cure it. The biodegradable material used in this experiment was a mixture of 85/15 PLGA, PEG‐HEMA, and a photo‐initiator.

The dynamic mask rapid prototyping system was successfully built and scaffolds made of UV‐curable biodegradable material were fabricated to verify the system capacity. The working exposure time for each layer was 45 s except for the first layer, which was 60 s. Scaffolds with 0°/90° and 60°/120° strips in alternating layers were fabricated and the pore size error in X and Y axes of 0°/90° design was found to be 7.33 and 2.13 percent, respectively. Preliminary cell culture tests indicate the fabricated scaffold is not harmful to MG‐63 cell growth.

Different scaffold designs and more UV‐curable biodegradable materials may be further implemented and tested through this system.

This research developed a novel system for tissue engineering scaffold fabrication which can process UV‐curable biodegradable material.