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A pathway is proposed for the biosynthesis of the unique homodimeric terpene, halichonadin H. The proposed biosynthetic pathway involves two key Passerini reactions of eudesmane-type terpene isocyanides. The Passerini reaction of a model terpene isocyanide and formaldehyde afforded an α-hydroxy acetamide, which was further subjected to oxidation and a second Passerini reaction. This reaction sequence furnished an α-hydroxy malonamide connected with two identical terpene units which is the identical structural motif found in halichonadin H.

A new structural health monitoring (SHM) system is proposed for inspecting the failure characteristics of engineering materials, which are monitored remotely. This system is controlled using electrical devices with wireless networking system. A PZT ceramic transducer, an accelerometer and a strain gauge are used to understand the failure characteristics. These sensors are attached to the test sample and reveal its static and dynamic strain behaviour, i.e., information on the material failure (crack generation) and related strains. It is also designed that a dummy plate with a PZT material is attached to the test sample. The dummy plate is designed to fracture earlier than the test sample during the loading process, whereupon the SHM system is triggered via the piezoelectric effect. A compact prototype SHM system is created to evaluate the effectiveness of our system. The failure characteristics are monitored well using the prototype SHM system, which might be able to predict the lifetime of the test sample, i.e., the SHM system reveals the critical strain value and crack growth rate just before the final failure of the test samples.

In recent years, casualties was caused by the sudden appearance of a serious disaster (fire, terrorism, earthquake) in urban areas. Most of the casualties are caused by escape delay in these disasters. The most effective measures that provide disaster information and evacuation support information to evacuees are able to reduce the victim. To enable real-time evacuation support by such as a mobile terminal, we are developing Emergency Rescue Evacuation Support System (ERESS). This system aims to detect disaster automatically and support evacuation by collecting and sharing information of peripheral terminals. The authors have developed a prototype system of Pre-ERESS. In this paper we develop new prototype system using iOS terminals for evaluation and analysis of ERESS.

A lot of people have got injured and died by sudden disasters such as fires and terrorisms. We have proposed an Emergency Rescue Support System (ERESS) for the purpose of reducing victims at the time of disaster. This system operates under mobile ad-hoc networks (MANET). So ERESS uses handheld terminals (ERESS terminals) such as smartphones and tablets. The ERESS terminals have disaster detection algorithm and plural sensors (acceleration, angular velocity, and geomagnetism). The sensors are used for the behavior analysis of ERESS terminal holders. By using the results of the analysis, the system detects the disaster from the behavior of people. In this paper, we propose a new disaster detection method by performing the machine learning in the group using a support vector domain description (SVDD). We are able to detect the behavior that is different from normal state of people in the disaster by using this method. The results of the performance evaluation by disaster simulation experiments show the validity of the proposed method.