In order for children to grow in today’s educational environment, they need to receive not only support from schools but also from their local community. Furthermore, improvement in information and communication technology (ICT) literacy is becoming a pressing issue that needs to be addressed. The purpose of this study is to create and consider the possibilities of a venue for non-formal education through a privately-run program utilizing ICT. Content of the non-formal education consisted of observance of rules as law-abiding citizens and topics related to local lifestyles and occupations in the community. Five members from the local area were designated as instructors for the classes. A total of 18 participants were recruited through flyer distribution. The program adapted a web version of Key Words Meeting (KWM), an education and training support system that can produce a visualization from the preparatory stages of the classes to the learning process after the classes. The effectiveness of the classes was evaluated based on the proportion of each keyword that was retained by the participants, as measured by KWM. Classes were held five times from September through November 2017 with 17 children participating in the first class, 15 in the second, 12 in the third, 14 in the fourth, and 7 in the fifth. The results indicated that there were capable members from the local area who could serve as instructors in the classes. Efforts to recruit the participants were affected by factors such as cooperation from local schools, the weather, and club activity schedules. In the classes, the proportions of each keyword retained by the participants came close to or exceeded 75%. The study presents the possibility of sustainable non-formal education through the development of various topics that are specific to the regional characteristics of the local community, as evidenced by the visualized data obtained from KWM.

Much research in recent years has focused on stock price prediction. In previous research learning data of a prediction target company is used. However, there is not learning data for the prediction target company often in the real world. In this research existing data of other companies is used as learning data based on Bayesian statistics. In previous research deep learning by fully connected neural networks is applied to predict stock price. In this research deep learning by fully connected neural networks based on Bayesian statistics is applied to predict stock price. This research is one of the extended research of the previous research from the viewpoint of Bayesian statistics. A new prediction method is proposed. Results of some stock price prediction experiments and investment simulations are shown. The effectiveness of the proposed method is shown by the results of the investment simulations.

The mechanism of the generation of air pollutants such as fine particulate matter is extremely complicated because it depends on transboundary pollution from the continent, urban and living air pollution, and even meteorological data. The prediction method has not been established yet. At present, prediction and analysis using simulations with numerical models are common, but require advanced equipment and expertise, and are limited to wide-area predictions. Therefore, they are not suitable for alerting local governments. Therefore, establishment of a simple statistical prediction method is required. In this paper, we verified a low-cost and simple time series prediction without requiring technical knowledge using a self-organizing maps, which is one of the analysis methods of multidimensional data. By actually preparing a self-organizing maps using air pollutant concentration and meteorological data in Yonago City, Tottori Prefecture, and analyzing the tendency of air pollutant concentration and predicting the practical time series concentration, the air pollutant concentration close to the region was predicted.

Human immunodeficiency virus type 1 (HIV-1) infection progresses to acquired immunodeficiency syndrome (AIDS), destroying the immune systems protecting humans from infections and malignant tumors over the years. Recently many types of anti-HIV-1 drugs that can prevent the infection from progressing to AIDS have been prescribed to HIV-1 infected individuals. Rapid and mutant-prone replication during HIV-1 infection causes quasispecies (populations of closely related virus variants), which is considered to contribute to escape from the immune systems and drug resistance. In this paper, a new quasi-steady state multiscale model of HIV-1 infection is presented. The model can exhibit the evolutionary dynamics of quasispecies better than a previous multiscale model by the author. Quasi-steady state, used in enzyme kinetics, is considered to be useful for modeling viral infection.

In order to build a business system quickly, more efficient project management is needed. However, uncertain and subjective factors in requirements from customer cause cost overruns or schedule delays in the project. Furthermore, uncertain and subjective factors can lead to misunderstandings and false estimates when converting requirements into specifications or scheduling within a project. Thus this paper proposes cost share rate and probabilistic risk evaluation method with Bayesian estimation for project management to accurately assess the project risk. Cost share rate is defined as the percentage of total cost assigned to each requirement. This paper proposes cost share rate to evaluate risks of requirements. Also the attention must be paid to the fact that here are plus risks and minus risks in project and that the estimation usually takes only minus risks into account. In addition, the estimation has subjective factors, for example productivity and skills of the programmer. Thus this paper did the research using the cost share rate and Bayesian estimation to predict costs and show the potential for project risk management. If this method would work well, efficient project management would be able to realize and there would be better relationships between customers and system developers.

In this paper, we compared the changes in the blood oxygen saturation in the bilateral prefrontal cortex before and after the occurrence of the evaluation behavior and the thinking process of the evaluators when the class was evaluated by watching a VTR of the actual class. The results showed that there was no significant difference in the total number of evaluative behaviors among the participants, but there was a characteristic response to the fNIRS measure during evaluative behaviors between those with teaching experience and those with teaching experience. In addition, it is suggested that teaching experience students tend to take a top-down approach to evaluating the professional skills of others based on their own past experiences, while teaching learners tend to take a bottom-up approach depending on the situation. We believe that these findings may contribute to the way of professional training and post-graduate education in medical, educational, and welfare fields by adding further studies in the future.