Workers living with mental health illnesses are increasing due to job stress. Previous studies have identified intra-organizational communication as one of the job stressors. This study examined the correlation between intra-organizational communication and mental health illnesses in the workplace. Participants were recruited from Corporation A (N=283). Team Communication Interface Questionnaire (TCIQ) and Kessler 6 Scale (K6) were used to measure intra-organizational communication and mental health illnesses, respectively. TCIQ was used due to having good psychometric properties and for its unique ability to gauge each member’s impressions about their team. Results of the Pearson correlation coefficient indicated that there was a significant negative association between K6 score and total TCIQ score (R=-0.321, P<0.01). The correlation coefficients also reported significant negative associations (P<0.01) between K6 score and each item of TCIQ: mission recognition (-.280), information transmission (-.275), schedule management (-.284), and evaluation awareness (-.227). The results show that intra-organizational communication is related to mental health illnesses. Examining intra-organizational communication through TCIQ in the workplace may promote mental health and help achieve organizational goals more effectively.

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The purpose of this study was to clarify the Career Decision-Making Self-Efficacy of the 4th year students of Nursing University and its influential factors. We conducted a questionnaire survey of 4th grade students of Nursing University and analyzed valid responses from 85 people. As a result, Career Decision-Making Self-Efficacy was found to be related to the total learning motivation and the subfactors [independent learning behavior], [expectations for practical training / exercises], and [appropriateness for small group learning]. Career Decision-Making Self-Efficacy tended to be higher for those with higher learning motivation. In addition, the Career Decision-Making Self-Efficacy tended to be higher when there were many friends outside the university. In order to enhance the self-efficacy of career choices as a nurse, it is necessary to educate the 4th year students of the Nursing University, considering that learning motivation and friends outside the university are influential factors.

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There is a lot of previous research on strategies in role-playing games(RPG). The strategies can be used in a beginner support function that recommends appropriate actions to beginners. By comparing the strategy and subjects’ playing histories, it is expected that the game setting that creates the enjoyment of RPG will be discovered. The sophistication of the enemies’ strategies is expected to increase the satisfaction of improved players. But in the previous research, enemies have only simple strategies. In this research, a strategy in RPG with adaptive actions of enemies is studied. In the proposed method, actions are selected based on the max-min criterion. The enemy minimizes the player’s expected total rewards. The player maximizes the minimum expected total rewards. Markov decision processes is used in modeling. In the proposed method, dynamic programming is used. The effectiveness of the proposed method is shown by some computational examples. Since the RPG of the proposed method is more difficult than the RPG of a comparison target with an enemy’s simple strategy, the expected total rewards of the proposed method is smaller than that of the comparison target.

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Discounts on food in supermarkets, based on the number of days remaining until expiration date are considered a kind of dynamic programming. Purchasing strategy with dynamic programming has been already studied. But in the previous study, the expiration date is not considered. In this study, a new purchasing strategy with dynamic programming is proposed under the condition that expiration date is considered. The proposed method maximizes the expected profit based on statistical decision theory using Markov decision processes as the previous study. In the proposed method, the expected profit is maximized by dynamic programming. The effectiveness of the proposed method is shown by some computational examples. The expected profit of the proposed method is greater than that of the comparison target. The adaptive selections are confirmed in the results of the proposed method. This study is a basic study, and future extended study is required.

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This study proposes three-second rule intelligence that simulates the human behavior selection. This method first calculates the appearance probability of an action candidate, and it selects an action that has appearance probability exceeds the preset threshold. As a previous study, there is a study that evaluates the environment using deep learning and makes an action selection, but it is essentially different from this selection method. there is no study that calculates the appearance probability of each action candidate and selects actions that exceed the preset threshold. And as far as I have investigated, this method is unique and innovative. By sequentially calculating and evaluating the time-series appearance probabilities of action candidates, it may be possible to give an empirical explanation for the "fluctuations" hidden in human behavior selection. This study applied this method to patient wait-and-see behavior of medical staff in radiation therapy, and by subdividing the factors to be observed and evaluating the appearance probability of action candidates by three-second rule intelligence, it clinically showed this method was effective. And this study succeeded in showing the feasibility of automating patient wait-and-see behavior of medical staff in radiation therapy.

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