This study proposes visualization of output results from agent log analysis in agent-based evacuation simulations. Although evacuation simulations are used for various purposes, their output results are mostly based on macro indicators such as the number of people who have completed evacuation and the time required to complete evacuation, and the behavior of individual evacuees is not adequately evaluated. Therefore, this study attempted to visualize the behavior of individual evacuees during the evacuation process, which is related to the success or failure of evacuation completion, by analyzing the log data of evacuation agents. This method is expected to provide more concrete information in evacuation planning and practice, and to contribute to the realization of efficient evacuation guidance.

The study acquires sensory evaluations of property floor plans as a form of tacit knowledge of experts and examines the impact of these evaluations on the price of condominium unit (Unit). The floor plans of Units are evaluated by the expert sensory with their ease of use and efficiency, but it has not been quantified to date. This study targets the floor plan images of the second-hand Units for sale, and these sensory evaluations acquired from experts. The multiple regression analysis for the Unit price is conducted by adding the sensory evaluation of the floor plan to the candidate factor variables such as building age and total exclusive area. The result of the multiple regression analysis indicates that the sensory evaluation of the floor plan was selected with the statistically significant level in the model. Therefore, it was suggested that the sensory evaluation of the floor plan has a certain impact on the Unit price.

In Bottom of the Pyramid (BOP) business, the supply of pharmaceuticals is a critical issue. This study analyzes the impact of news articles on market evaluations of companies facing pharmaceutical supply shortages or logistics delays in emerging markets. By examining these factors, the research aims to clarify the market's response to BOP business challenges and contribute to the improvement of corporate management strategies, while providing insights for investor decision-making. The findings of this research are expected to enhance understanding of sustainable corporate management and foster the development of new markets in emerging economies.

In recent years, information issued by companies has become more diversified, increasing corporate transparency to stakeholders. Especially, in CEO messages, which are a source of information indicating a company's future vision, changes over time are likely to emerge. Therefore, we collect CEO messages for each year as text data, vectorize them using natural language processing techniques, and then calculate the cosine similarity between messages for each year. Cosine similarity is a quantitative measure of similarity between documents by calculating the cosine of the angle between two vectors. Using this method, we quantitatively evaluate the similarities and differences between the CEO messages of each company for each year to identify trends in the real estate industry and the differences and characteristics of each company. As a result, it was observed that when social conditions undergo significant changes, the CEOs work to further promote the traditional businesses held by the companies by adapting the direction of the companies to the social conditions.

To stimulate the Japanese economy, various efforts have been made to nurture entrepreneurship, while any specific methodologies were fixed as the best ones in this regard. The author tries to reframe the conceptofinformationliteracybyregardingitasthecoreofentrepreneurshipeducation.Presentinganovel system flow chart based on partly making use of artificial intelligence, the author demonstrates illustrative outcomes of retrieval augmented generation (RAG) by large language model (LLM) with a corpus of text data from research and analysis of both political and economic issues in the global community, which can be effectively used to help entrepreneurs to make future scenarios by abduction in their brains.

This study examines the effectiveness of a community-based gas pricing plan. The community- based pricing system determines rates based on the overall usage within a community, making cooperation among community members a crucial element. Through agent-based modeling, this research investigates the potential for energy consumption reduction under a community-based gas pricing system. The paper specifically explores the effectiveness of this approach when there is a high degree of similarity in attributes within the community.

This study observes transitions in social interest in past large-scale earthquake disasters over time, based on time-series trends in the volume of newspaper coverage by newspaper page name. In this study, we conducted a trend analysis based on the number of newspaper articles and the number of words in each newspaper page name from 1995 to 2020 for the Great Hanshin-Awaji Earthquake and the Great East Japan Earthquake. The results showed that the volume of articles on the Great Hanshin-Awaji Earthquake declined sharply one month after the occurrence of the earthquake. The local and society pages showed similar trends of increase and decrease, eventually leveling off. On the other hand, the decrease in the volume of articles for the Great East Japan Earthquake was less variable and more gradual than that for the Great Hanshin-Awaji Earthquake, and a similar pattern of trends was observed for many page names In this presentation, details and discussion of each result are reported.

This study uses a large-scale language model to examine methods for preventing frailty. In Japan, one out of every 2.5 persons will be 65 or older in the first half of life in 2060. Compared to healthy elderly, frail elderly people are at about twice the risk of needing assistance or nursing care. This study examines prompts to prevent frailty, improve quality of life, and realize a sustainable society. Utilizing a large-scale language model, we confirmed the feasibility of several prompts and the challenges.

This paper presents the concept of a research to compare the causes and risk recognition of bicycle and motorcycle accidents based on road characteristics. The number of seriously injured people in traffic accidents in Osaka Prefecture is 1.5 times higher than that of the second highest prefecture in Japan, with bicycle and motorcycle accidents accounting for more than 70% of the total. To address this issue, this study focuses on the causes of accidents as seen from road characteristics, and furthermore on their relationship with risk recognition. As a research method, we first use a statistical model to calculate the accident occurrence rate for each road characteristic based on a digital road map. Next, we calculate the risk recognition rate for each road characteristic using questionnaire data. After that, we visualize the difference between the accident occurrence rate and risk recognition rate based on these and propose appropriate countermeasures.