This study introduces an online disaster prevention course using STEAM education tailored for engineering students at the National Institute of Technology, addressing a current gap in the standard curriculum. The course guides students through understanding the mechanisms of earthquakes, floods, and tsunamis, exploring corresponding mitigation strategies and their spatial representation using GIS. The iterative process of problem identification and resolution is emphasized throughout. Interestingly, students majoring in fields like architecture or civil engineering reported lower comprehension levels compared to their non-major students in critical areas. These findings underscore the potential of this course to engage diverse student populations in comprehensive disaster risk management strategies. Future includes adapting these insights to develop educational modules for elementary and secondary school students.

Tsunami, floods, and other disasters occur frequently in Japan, but people do not have a high awareness of disasters as their own personal concerns. Therefore, in order to increase people’s awareness of their own personal involvement in disasters, we developed a "Smart Hazard Map" (SHM) for smartphones that uses location information to provide information on tsunami and river flooding depth, house damage, time until flooding, and evacuation routes and times from the current location, by individual evacuation method. SHM was tested on 339 students in 2023-2024, 159 of whom responded to four questions; 1) where the app was tested?, 2) Is this app easy to use to make disasters personal?, 3) Which functions are particularly effective? and 4) fHow should the feature be improved? These four questions assess the degree of stakeholder engagement with disasters. The four indicators were: 1) the app was tested at students’ homes more than 60% of the time; 2) the ease of use was rated positively by about 90% of the respondents; 3) the ability to check the new tsunami depth at the current location was rated highly for each function; 4) functional improvements were organized into 11 items. Then the respondents were especially interested in the display of tsunami information. We would like to verify whether more detailed tsunami risk information and multiple evacuation route information would increase the sense of ownership of disasters. We also plan to conduct a questionnaire survey of the elderly and physically-challenged to improve the functionality of the Smart Hazard Map as an application that can be used by everyone.

We describe a distance lecture support system using two cameras, which are a fixed view camera for image processing, and an active view camera for remote students, and propose a fuzzy camera control based on the behavior recognition of a lecturer. First, the silhouette of the lecturer is extracted from the input image of a fixed view camera. The parameters for behavior recognition of the lecturer such as face, body and hand are determined from the lecturer's silhouette. A fuzzy reasoning system recognizes the lecturer's behavior using the parameters. The active view area such as the portrait of the lecturer and the character strings on the blackboard is controlled by the fuzzy system automatically. The accuracy of the behavior recognition (70.4% for fuzzy reasoning and 55.2% for crisp reasoning) was obtained for the scenes of five lecturers.

This paper presents algorithms for approximating a set of n points by a linear function, or a line, that minimizes the L1 norm of vertical and orthogonal distances. The algorithms find exact solutions based upon geometric properties of the problems as opposed to approximate solutions based upon existing numerical techniques. The algorithmic complexity of these problems appears not to have been investigated before our work in [9], although Ο(n3) naive algorithms can be easily obtained based on some simple characteristics of optimal L1 solutions. In this paper, an Ο(n) optimal time algorithm for the weighted vertical L1 problem is presented. The algorithm is based upon a modified multi-dimensional search technique which extends the applicability of the basic technique to a wider class of problems. An Ο(n1.5 log2 n) algorithm is presented for the unweighted orthogonal problem, and an Ο(n2) algorithm is presented for the weighted problem. An Ω(n log n) lower bound for the orthogonal L1 problem is shown under a certain model of computation. Also, the complexity of solving the orthogonal L1 problem is related to the construction of the k-belt of an arrangement of lines.