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The purpose of this paper is to intelligibly demonstrate the effectiveness of face mask wearing as a means to prevent COVID-19 transmission. Through understanding the benefits of wearing masks, it is hoped to facilitate the change of societal behavior and more people are willing to wear face mask.

The paper investigates the 50 states in the United States of America (U.S.) and Washington, D.C. that implemented the mask mandates before September 30, 2020, which are divided into four groups: (1) those implemented the statewide mask mandates before June 5, 2020 when World Health Organization (WHO) recommended mask wearing; (2) those implemented statewide mask mandates after June 5, 2020; (3) those implemented partial mandates affecting 30 percent or more of the state’s population; and (4) those implemented partial mandates affecting less than 30 percent. Simple descriptive statistics are analyzed.

For the 50 U.S. states and Washington, D.C., the higher the mask wearing rate, the lower the number of COVID-19 cases (correlation coefficient: −0.69 (p<0.001)). For the 23 states with mobility reduction of less than 15 percent, the higher the proportion of population required to wear masks, the lower the number of cases. This can be seen from the difference in the number of cases among the four groups by ANOVA (p = 0.013).

The positive effect of wearing masks is shown based on simple descriptive statistics for intuitive and intelligible understanding, which may lead people to comprehend the importance of wearing masks, and break through their custom, culture, and norms, and wear masks.

To provide a brief survey of the literature directed towards the analysis of warranty claim data.

For convenience, this survey of the analysis of warranty claims data is somewhat arbitrarily be classified by topics as follows: age‐based claims analysis, aggregated warranty claims analysis, marginal counts of claims analysis, warranty claims analysis by using covariates, estimation of lifetime distribution using supplementary data, two‐dimensional warranty, warranty costs analysis, sales lag and reporting lag analysis, and forecasts of warranty claims.

Emphasis is placed on a discussion of different kinds of warranty claims data selected from reviews and on a comparison of the statistical models and methods used to analyze such data.

Since the literature on product warranty data is vast, more work on this problem is needed.

This review points out why warranty claims data is important and gives a survey of the literature pertaining to the analysis of such data. The emphasis is on the analysis of minimal databases of real warranty data, constructed by combining information from different sources, which can be collected economically and efficiently through service networks. The research is applicable for those responsible for product reliability, product design decisions and warranty management in manufacturing industries.

The paper reviews different statistical models and methods used to analyze warranty claims data. The statistical models and methods presented are be valuable and meaningful tools for product reliability and warranty management and analysis.

This research investigated the optimal structure of a discrete‐time Markov deterioration system monitored by multiple non‐independent monitors. The purpose is to obtain a sufficient condition with which the optimal policy is given by a control limit policy.

The model of this research is formulated as a partially observable Markov decision process. The problem is to obtain an optimal policy which can minimize the expected total discounted cost over an infinite horizon.

The research found that the expected optimal cost function over an infinite horizon has a property of control limit policy given the conditions that a transition probability having a property of totally positive of order 2 and a conditional probability of the monitors having a property of weak multivariate monotone likelihood ratio. Furthermore, we showed that the optimal policy has only four action regions at most.

If the optimum policy can be limited to a control limit policy, the tremendous amount of calculation time required to find the optimum procedure can be reduced. This enables the best decision to be identified in a much shorter period of time.

A deterioration system monitored incompletely by one monitor has been studied in the previous research. This research considered the case of a multiple number monitors whose observations were not independent.

This study aims to develop a self-reflection scale useful for teachers to improve their skills and to clarify the Japanese teachers’ characteristics during mathematics lesson observation (MLO). In MLO, it is important to understand the lesson plan in advance to clarify observation points, and we aim to develop a scale including these points.

Based on the pre-questionnaire survey, nine perspectives and two situations for MLO were extracted. From these, a questionnaire for MLO was created. The results obtained from 161 teachers were examined, and exploratory factor analysis was conducted. ANOVA was conducted to analyze the effect of differences across the duration of teaching experience on the identified factors.

We developed a self-reflection scale consisting of 14 items with three factors: [B1] focus on instructional techniques and evaluation, [B2] focus on proactive problem-solving lesson development and [B3] focus on the mathematical background of the learning content. While duration of teaching experience showed no effect, three factors of the self-reflection scale for MLO showed a significant effect. Further multiple comparisons revealed the degree of focus was [B2]>[B1]>[B3].

Teachers who use this developed scale may grasp the strengths and weaknesses of their own MLO, which leads to self-improvement. The perspectives emphasized in lesson observation are the same when creating lesson plans and implementing lessons, leading to lesson improvement. Furthermore, based on the characteristics of teachers revealed, new training programs regarding MLO can lead to higher-quality lesson studies.

As the university–industry collaboration (UIC) gradually attracts the attention of various national governments, the number of studies on UIC has increased substantially. Past UIC studies have mostly focused on investigating the incentives and the motivation for UIC, forms of UIC and performance output of UIC. However, they have not actively identified the key technologies and technology distribution that are conductive to the commercialization of UIC outcomes. Therefore, this study aims to adopt the licensed UIC patents as the basis for analysis and to construct a patent licensing technology network.

This study focused on licensed patents because past studies have indicated that such patents usually have higher value. Moreover, patent licensing can be seen as the final step for the commercialization of UIC outcomes. Finally, past studies have rarely explored patent examiners’ views on key technologies. However, during the substantive examination of patents, patent examiners often use their background knowledge regarding the technology to include citations to other patented technologies that they consider valuable or indispensable. Therefore, this study focused on investigating the patents recognized and cited by patent examiners and conducted key technology identification.

The results indicated that past key technologies in UIC focused on surveying, medicine, biochemistry and electric digital data processing; these fields are crucial to the commercialization of key UIC technologies. Finally, the USA, Japan, Sweden and Germany play critical roles in the network of global university–industry cooperation and technology licensing.

Patent examiners’ perspectives were adopted to establish a patent licensing technology network, through which the key technologies that could promote UIC patent licensing were mined. This study can also serve as a reference for resource allocation in university research and development and for governments to promote new technologies.