An isolated bacterium that converted unsaturated fatty acids to hydroxy fatty acids was identified as Stenotrophomonas nitritireducens by API analysis, cellular fatty acids compositions, sequencing the full 16S ribosomal ribonucleic acid, and evaluating its nitrite reduction ability. S. nitritireducens has unique regio-specificity for C16 and C18 cis-9 unsaturated fatty acids. These fatty acids are converted to their 10-hydroxy fatty acids without detectable byproducts. Among the cis-9-unsaturated fatty acids, S. nitritireducens showed the highest specificity for linoleic acid. The cells converted 20 mM linoleic acid to 13.5 mM 10-hydroxy-12(Z)-octadecenoic acid at 30 degrees C and pH 7.5 with a yield of 67.5% (mol/mol).

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The smart house under consideration is a service-integrated complex system to assist older persons and/or people with disabilities. The primary goal of the system is to achieve independent living by various robotic devices and systems. Such a system is treated as a human-in-the loop system in which humanrobot interaction takes place intensely and frequently. Based on our experiences of having designed and implemented a smart house environment, called Intelligent Sweet Home (ISH), we present a framework of realizing human-friendly HRI (human-robot interaction) module with various effective techniques of computational intelligence. More specifically, we partition the robotic tasks of HRI module into three groups in consideration of the level of specificity, fuzziness or uncertainty of the context of the system, and present effective interaction method for each case. We first show a task planning algorithm and its architecture to deal with well-structured tasks autonomously by a simplified set of commands of the user instead of inconvenient manual operations. To provide with capability of interacting in a human-friendly way in a fuzzy context, it is proposed that the robot should make use of human bio-signals as input of the HRI module as shown in a hand gesture recognition system, called a soft remote control system. Finally we discuss a probabilistic fuzzy rule-based lifelong learning system, equipped with intention reading capability by learning human behavioral patterns, which is introduced as a solution in uncertain and time-varying situations.

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