A new temperature measurement procedure using phase mapping was developed that makes use of the temperature dependence of the water proton chemical shift. Highly accurate and fast measurements were obtained during phantom and in vivo experiments. In the pure water phantom experiments, an accuracy of more than +/- 0.5 degrees C was obtained within a few seconds/slice using a field echo pulse sequence (TR/TE = 115/13 ms, matrix = 128 x 128, number of slices = 5). The temperature dependence of the water proton chemical shift was found to be almost the same for different materials with a chemical composition similar to living tissues (water, glucide, protein). Using this method, the temperature change inside a cat's brain was obtained with an accuracy of more than +/- 1 degree C and an in-plane resolution of 0.6 x 0.6 mm. The temperature measurement error was affected by several factors in the living system (B0 shifts caused by position shifts of the sample, blood flow, etc.), the position shift effect being the most serious.

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Abstract. Closeness centrality is an important concept in social network analysis. In a graph representing a social network, closeness centrality measures how close a vertex is to all other vertices in the graph. In this paper, we combine existing methods on calculating exact values and approximate values of closeness centrality and present new algorithms to rank the top-k vertices with the highest closeness centrality. We show that under certain conditions, our algorithm is more efficient than the algorithm that calculates the closeness-centralities of all vertices.

Smartphones are very common devices in daily life that have a built-in tri-axial accelerometer. Similar to previously developed accelerometers, smartphones can be used to assess gait patterns. However, few gait analyses have been performed using smartphones, and their reliability and validity have not been evaluated yet. The purpose of this study was to evaluate the reliability and validity of a smartphone accelerometer. Thirty healthy young adults participated in this study. They walked 20 m at their preferred speeds, and their trunk accelerations were measured using a smartphone and a tri-axial accelerometer that was secured over the L3 spinous process. We developed a gait analysis application and installed it in the smartphone to measure the acceleration. After signal processing, we calculated the gait parameters of each measurement terminal: peak frequency (PF), root mean square (RMS), autocorrelation peak (AC), and coefficient of variance (CV) of the acceleration peak intervals. Remarkable consistency was observed in the test-retest reliability of all the gait parameter results obtained by the smartphone (p<0.001). All the gait parameter results obtained by the smartphone showed statistically significant and considerable correlations with the same parameter results obtained by the tri-axial accelerometer (PF r=0.99, RMS r=0.89, AC r=0.85, CV r=0.82; p<0.01). Our study indicates that the smartphone with gait analysis application used in this study has the capacity to quantify gait parameters with a degree of accuracy that is comparable to that of the tri-axial accelerometer.