On March 11, 2011, a massive earthquake (magnitude 9.0) and accompanying tsunami hit the Tohoku region of eastern Japan. Since then, the Fukushima Daiichi Nuclear Power Plants have been facing a crisis due to the loss of all power that resulted from the meltdown accidents. Three buildings housing nuclear reactors were seriously damaged from hydrogen explosions, and, in one building, the nuclear reactions became out of control. It was too dangerous for humans to enter the buildings to inspect the damage because radioactive materials were also being released. In response to this crisis, it was decided that mobile rescue robots would be used to carry out surveillance missions. The mobile rescue robots needed could not be delivered to the Tokyo Electric Power Company (TEPCO) until various technical issues were resolved. Those issues involved hardware reliability, communication functions, and the ability of the robots' electronic components to withstand radiation. Additional sensors and functionality that would enable the robots to respond effectively to the crisis were also needed. Available robots were therefore retrofitted for the disaster reponse missions. First, the radiation tolerance of the electronic componenets was checked by means of gamma ray irradiation tests, which were conducted using the facilities of the Japan Atomic Energy Agency (JAEA). The commercial electronic devices used in the original robot systems operated long enough (more than 100 h at a 10% safety margin) in the assumed environment (100 mGy/h). Next, the usability of wireless communication in the target environment was assessed. Such tests were not possible in the target environment itself, so they were performed at the Hamaoka Daiichi Nuclear Power Plants, which are similar to the target environment. As previously predicted, the test results indicated that robust wireless communication would not be possible in the reactor buildings. It was therefore determined that a wired communication device would need to be installed. After TEPCO's official urgent mission proposal was received, the team mounted additional devices to facilitate the installation of a water gauge in the basement of the reactor buildings to determine flooding levels. While these preparations were taking place, prospective robot operators from TEPCO trained in a laboratory environment. Finally, one of the robots was delivered to the Fukushima Daiichi Nuclear Power Plants on June 20, 2011, where it performed a number of important missions inside the buildings. In this paper, the requirements for the exploration mission in the Fukushima Daiichi Nuclear Power Plants are presented, the implementation is discussed, and the results of the mission are reported.

We report recent results from field experiments conducted with a team of ground and aerial robots engaged in the collaborative mapping of an earthquake‐damaged building. The goal of the experimental exercise is the generation of three‐dimensional maps that capture the layout of a multifloor environment. The experiments took place in the top three floors of a structurally compromised building at Tohoku University in Sendai, Japan that was damaged during the 2011 Tohoku earthquake. We provide details of the approach to the collaborative mapping and report results from the experiments in the form of maps generated by the individual robots and as a team. We conclude by discussing observations from the experiments and future research topics. © 2012 Wiley Periodicals, Inc.

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A gene expression module (module for short) is a set of genes with shared expression behavior under certain experimental conditions. Discovering of modules enables us to uncover the function of uncharacterized genes or genetic networks. In recent years, several biclustering methods have been suggested to discover modules from gene expression data matrices, where a bicluster is defined as a subset of genes that exhibit a highly correlated expression pattern over a subset of conditions. Biclustering however involves combinatorial optimization in selecting the rows and columns composing modules. Hence most existing algorithms are based on heuristic or stochastic approaches and produce possibly sub-optimal solutions. In this paper, we propose a novel biclustering method, BiModule, based on a closed itemset enumeration algorithm. By exhaustive enumeration of such biclusters, it is possible to select only biclusters satisfying certain criteria such as a user-specified bicluster size, an enrichment of functional annotation terms, etc. We performed comparative experiments to existing salient biclustering methods to test the validity of biclusters extracted by BiModule using synthetic data and real expression data. We show that BiModule provides high performance compared to the other methods in extracting artificially-embedded modules as well as modules strongly related to GO annotations, protein-protein interactions and metabolic pathways.