This study aimed to improve the effectiveness of the active drainage method and drain management technique by the nurse. A total of 48 patients who underwent total knee arthroplasty were divided into two groups according to the drain management method performed by the nurse: conventional group and active group (comparison of four groups). The attributes of “whether tranexamic acid was used” and “number of drains left in place” were combined, and hemorrhage volume was determined. The results showed that the amount of bleeding in the drain bag increased with the active drainage method regardless of the use of tranexamic acid, which is effective in suppressing bleeding, and was higher with either one or two drains than in the conventional group, regardless of the number of drains. The active drainage method can intentionally direct drainage to the tip of the drain, and it is considered that the drainage bag is properly drained under negative pressure control. If it can be confirmed macroscopically that there is severe bleeding, preventive measures can be easily conducted, and assessment can be easily performed. This study reported that the active drainage method performed by the nurse greatly affected the quality of life (QOL) and safety of the patient.

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Food loss is a big problem all over the world. Many recommendation methods for cooking recipes are proposed in previous research. But the total cost of food loss and additional purchase is not minimized in the previous research. In this research dynamic programming is applied to minimize the total cost of food loss and additional purchase in recommendation and purchase method considering expiration date. A new recommendation and purchase method is proposed. The effectiveness of the proposed method is shown by some experiments. In the experiments the total cost of the proposed method is confirmed to be smaller than the total cost of the empirical recommendation and purchase method. The proposed method is expected to contribute to the reduction of food loss in households. The optimal solution of the proposed method is also a theoretical limit. The theoretical limit can be used to evaluate other recommendation and purchase methods. The application of the proposed method to healthcare is a topic of the future.

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Increasing profit is desired in Japanese agriculture. There is many previous research on profit maximization in agriculture. In previous studies crop rotation and cultivation management have been considered independently. But in order to maximize the profit in agriculture, crop rotation problem and cultivation management problem are solved as one problem. In this research crop rotation problem and cultivation management problem are solved as one problem using Markov decision processes. The profit is maximized by dynamic programming. The effectiveness of the proposed method is shown by some simulation examples. The profit of the proposed method is greater than that of the comparison target. In the results of the proposed method the adaptive selection according to the fluctuating crop price was confirmed. The optimal solution of the proposed method is also a theoretical limit. The theoretical limit can be used to evaluate other methods. The proposed method is expected to contribute to the automation of decision making in smart agriculture.

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The development of technology is rapidly advancing. Self-driving car is one of them. One of its important technologies is “Extracting of the car location on the traffic lane” and it is also the essential information for the safe driving by human. The current research work was planned to develop an algorithm to get such important information using a commercially available drive recorder. In this paper, (1) the extraction of the white line region, (2) the extraction of the relative location of the drive recorder on the traffic lane and (3) the parallelism of the car and the white lines are mentioned. To get the image of the road environment, the drive recorder DC-DR410 produced COMTEC Co. and a car named “Rush” produced Toyota Co. are used. The road used in order to get data is “TOMEI” highway. The maximum error of the distance between the white line and the drive recorder was about 1.2cm.

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The professional career of female medical doctors is influenced by various life events that can lead to early retirement: marriage, childbirth, childcare, nursing, mammy track etc. In recent years, efforts have been launched to have female doctors resume their clinical practice. In order to find clues to solve the problem, we used survey data from doctors who became mothers and identified their motivation using the spherical SOM and DIM mode methods.

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The computer vision involves many modeling problems for preventing noise caused by sensing units such as cameras. In order to improve computer vision system performance, a robust modeling technique must be developed for essential models in the system. The RANSAC and least median of squares (LMedS) algorithm have been widely applied in such issues. However, the performance deteriorates as the outlier noise ratio increases and the modeling time for algorithms tends to increase in industrial applications. As a promising technique, we proposed a new fuzzy LMedS method based on reinforcement learning concept for robust modeling. In this study, we investigated selecting strategies as an indispensable concept of reinforcement learning through modeling synthetic data by fuzzy modeling and camera homography experiments. Their results found the proposed ε-roulette strategy to be promising in improving calculation time, model optimality, and robustness in modeling performance.

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In many studies, optimists are considered as adaptive and pessimists are considered as maladaptive. However, the Defensive Pessimist, who achieve success by perceiving their past performance affirmatively, as well as thinking in a “bad way” when consider their future, has become known. The present study was designed to examine the relationship of cognitive strategy adoption orientation (Defensive Pessimism, Realistic Pessimism, Strategic Optimism, control group) to identity and self-acceptance. A survey was conducted in October, 2020. Subjects, 225 Japanese university students (84 male, 141 female) in Tohoku area, are asked to response to an anonymous self-administered questionnaire. Excluding missing data, answers of 210 of the subjects (84 male, 126 female, 20.63 ± 1.67 years old in average) were analyzed. A K-means cluster analysis was performed, in order to sort the subjects into four target strategy groups, by the scores of subfactors of the JDPI factors which are "pessimism," "past performance," "positive contemplation," and "effort". Next, a one-way ANOVA test was conducted with the JDPI cluster group as the independent variable, the total score of the Multidimensional Ego Identity Scale, along with scores of the four subfactors ("self-sameness, continuity", "self-identity", "interpersonal identity"", "psychological identity"), total score of the Self-Acceptance scale ("lifestyle", "personality", "home", "school", "physical ability"), and score of Active Class Attitude Scale as dependent variables. As a result, the Strategic Optimist group report higher level of psychological self-identity and self-acceptance than other groups. On the contrary, the Defensive Pessimist group had lower level of psychological self-identity than other groups, and showed higher level of self-acceptance than the Realistic Pessimism group. Results suggest that the cognitive strategy adoption orientation is related to identity and self-acceptance.

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Smartphone was equipped with the function equal to a PC and has become an indispensable tool in the information society. In particular, many people are using it as entertainment tools such as video viewing, communication applications, and game applications while moving. There are many people using smartphone while walking on a platform or a street because they are enthusiastic about the application and cannot be interrupted. For this reason, many accidents have occurred due to the use smartphone while walking, and have been alerted not to use smartphone while walking. However, smartphone use while walking does not decrease at all. In this report, we examined the risk of using a smartphone while walking, based on changes in perception. In the experiment, we measured the effective visual field when standing without using a smartphone, standing with a smartphone, walking without using a smartphone, and walking with a smartphone. As the result, it was shown that the effective visual field was 45-56% narrower when using a smartphone than when not using a smartphone in both standing and walking. Thus, when using a smartphone, it is meant that it isn't possible to notice the changes in surrounding circumstances. Therefore, it was shown that using a smartphone while walking was dangerous.

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This study is an attempt to automatically determine sleep stages using deep learning with deep neural network (DNN). Using overnight sleeping data on five male college students for 32 days, deep learning and evaluation were conducted. Input data are the extracted values for eight items drawn from the polygraph data; labeled training data are composed of seven items evaluated by one inspector according to Rechtshaffen and Kales(R&K) method. As a result, the concordance rates between the estimation of sleep stage data by deep learning and the evaluation by the inspector were as follows: SW (57.7%), SREM (75.8%), S1 (7.1%), S2 (79.2%), S3 (62.8%), S4 (80.9%), and MT (1.8%), and overall, it was 72.1% (κ = 0.57, P<0.01). In this study, deep learning with DNN was conducted, extracting and using values of eight items from polysomnography data; this method could be applicable for the automatic determination of sleep stages.

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