Teamwork plays an important role in achieving a team’s performance objectives. When demonstrating teamwork, team communication becomes a key component. This study examined the correlation between impressions of team communication and superior's evaluation score for performance. The impressions of team communication was measured using the Team Communication Interface questionnaire (TCIQ). The subjects of this study were 92 people from corporation A who came from a total of 15 departments. The values of the Pearson correlation coefficient between each department’s average value of the five interfaces of TCIQ (including the entire TCIQ score) and each department’s average value of the superior’s evaluation score for performance were: Mission Recognition .478, Information Transmission .237, Schedule Management .453, Evaluation Awareness .366, entire TCIQ .411. All the values showed significant positive correlations, which suggest that impressions of team communication affect the degree to which a team achieves their objectives. The TCIQ can be applied to organizational improvements aimed at achieving performance objectives.

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This study aims to understand the current status of oral health behaviors during pregnancy and to explore issues necessary to maintain good oral health for pregnant women and their fetuses. The need for oral health care during pregnancy is listed in the Maternal and Child Health Handbook with a grant certificate, but it is not fully utilized. However, because oral health management of children begins in the fetal period, it is necessary to raise awareness of oral health behaviors during pregnancy, and to continue efforts until the postpartum period and child-rearing period. Therefore, a self-administered questionnaire survey was conducted to understand the current status of oral health behaviors during pregnancy, and data from 32 valid responses were obtained and analyzed. The results showed that there was a significant difference between the presence or absence of a family dentist and the presence or absence of a family dentist in the items of habit of regular dental checkups before pregnancy, use of supplementary cleaning tools, and experience of dental caries and periodontal disease prevention guidance. In other words, the importance of having a family dentist and practicing dental checkups before childbirth became clear. In addition, in order to promote the attendance of pregnant women for dental health examinations, it is necessary to collaborate with multiple professions, such as by holding seminars on the importance of oral health behavior for obstetricians and midwives, with whom pregnant women are most closely involved.

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In this research, healthcare software for personal health management is studied. Healthcare software gives appropriate advice based on personal health data. In previous research, probability of staying in objective states is maximized under the condition that true health state is known. But it is not always possible to determine the true health state from the observed data. In this research, a new method which maximizes probability of staying in objective states under the condition that true health state is unknown is proposed. Markov decision processes with unknown state is used in the modeling. Dynamic programming is used in the proposed method. The effectiveness of the proposed method is shown by some computational examples. In the results, probability of staying in objective states by the proposed method is greater than that of the comparison target. This research is a basic research, and future extended research is required.

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There is a lot of previous research on profit maximization in agriculture. In a previous research crop rotation problem and cultivation management problem are solved as one integrated management problem in order to maximize the profit in agriculture. In the previous research the profit is maximized under the condition that probabilities are known. Sufficient historical data is required to estimate probabilities. The previous method cannot be applied to areas where there is no historical data. In this research, a new integrated management method for areas where there is no historical data is proposed. Integrated management is modeled by Markov decision processes with unknown probabilities. Historical data of neighboring areas is used. The proposed method maximizes profit with reference to a Bayes criterion by dynamic programming. In the numerical calculation results, adaptive crop selection and cultivation action selection according to posterior probabilities for neighboring areas are confirmed. This research is a basic research, and future extended research is required.

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Magnetic Particle Testing (MT) is a method for determining the presence or absence of defects in a test object by using a ferromagnetic test object that is expected to have defects as a magnet. This method is used to determine the presence or absence of defects by having a skilled technician evaluate the magnetic powder. It allows inspection without destroying the test object. However, there are some problems with Magnetic Particle testing, such as the possibility of overlooking defects. To solve these problems, this paper develops a deep learning based defect image classification method for automated Magnetic Particle Testing. The proposed method first performs segmentation based on the structure of Eff-UNet, which can perform segmentation with high accuracy even for small defects. Then, an algorithm that combines the result of segmentation with the last part of the encoder of Eff-UNet is used to determine whether a defect is present or not. Using this method, defects were classified from the images obtained from Magnetic Particle Testing. The results showed that Accuracy of 92.4%, TPR of 89.2%, and FPR of 7.62%.

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 The care provided by nurses is supported by evidence with knowledge and repeated implementation that leads to confidence in the care. Nurses providing care on the patient's side give little importance, inadequate knowledge, and insufficient observation of oral care. The authors used a 0%–100% visual analog scale and self-administered questionnaire to clarify the relationship between knowledge and perception and confidence in oral care.
 Results revealed that nurses scored ≥70% in their knowledge of the underpinnings of oral care routinely performed by nurses, 50%–70% for content performed in specific departments, and <50% for content in which nurses are not the primary providers. In addition, the awareness of the importance of the subject is high, but this is not linked to knowledge or confidence.
 Therefore, nurses who do not combine "knowledge," "awareness," and "confidence" in oral care may result in care ambiguity, but with a significant impact on the "quality" of care.

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The purpose of this study is to develop a scale for measuring motivation to learn about oral care among nursing students. A scale composed of 16 items and 2 factors was created from the results of factor analysis (maximum likelihood method, varimax method rotation, cumulative contribution rate 66.8%) of the data of 349 nursing students. The created scale had a Cronbach's alpha coefficient of 0.961 and was judged to have internal consistency. Cronbach's α coefficient for each factor was α = 0.941 for the 10 items for the first factor [motivation to learn related to basic oral care acquisition] and 6 items for the second factor [motivation to learn related to the acquisition of oral care according to the patient]. α = 0.931, indicating that there is internal consistency. In addition, we examined the external criteria of the created scale, and set up the “Measuring Motivation Scale for Nursing Students for Oral Care,” which can be used to self-evaluate the motivation for learning about oral care that nursing students have. A correlation with the external reference scale (r = 0.324, p< 0.01) was observed, confirming the validity of the reference. Based on the above results, the reliability and validity of the developed scale were generally secured, and it was considered to be a practical scale.

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