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The landslide activity in the area of Bolshoy Sochi (Big Sochi) situated at the Black Sea coast of the Great Caucasus has been studied using the StaMPS PS-InSAR method. We incorporated three sets of radar images from the satellites with different wavelengths ALOS, Envisat and Terra-SAR-X from both ascending and descending tracks which cover the time period from January 2007 to September 2012. Comparative investigation of surface displacements obtained from all the data sets is presented. Areas where high surface displacement rates have been located on the base of the satellite data coincide well with zones of high landslide activity according to ground observations. We constructed time series for the two landslides: in the Baranovka and Mamaika villages where considerable surface movements had been observed during the time of acquisitions. Analysis of the time series made it possible to determine periods of activity and relative stability of the landslides and compare them with ground observations. [ABSTRACT FROM AUTHOR]

During the last years a lot of papers have appeared dealing with the applications to physiological time series of some parameters originally found in Dynamical Systems. In this work, we looked for the Largest Lyapunov Exponents (LLE) in heart rate time series of healthy men in order to verify if it was possible to find the Anaerobic Threshold (AT) in a non-invasive way using just the time series and the LLE extracted from them. The work was undertaken in a group of 10 healthy individuals using a conventional electrocardiogram. The series of RR intervals, lasting for twelve minutes, were obtained in rest (supine and seated positions) and in a discontinuous protocol of dynamic exercise in seated position. The LLE were computed using the TISEAN system. The results were compared to those obtained by the measurement of AT using an autoregressive integrated moving-average model (ARIMA) and using the Kolmogorov-Sinai Entropy (KS). The Spearman test (rs) with α = 5% applied to these three methods gave: a) for KS × ARIMA: p < 0.01, rs = 0.93; b) LLE × KS: p < 0.01, rs = 0.94 and c) LLE × ARIMA: p < 0.01, rs = 0.93. These results are very expressive concerning the use of LLE to find the AT. [ABSTRACT FROM PUBLISHER]

For the single cyclist performance parameters such as power, speed, or heart rate can be monitored during training and competition. Although cycling is primarily a single sport, riding in groups is a very common in training. An ambient intelligence system has been developed for the training of a group of cyclists. The objective of this system is to improve team training such that each cyclist is as close to his individual exercise intensity as possible. Besides physiological and biomechanical data, subjective sensations are also considered. The focus of this paper is on feedback training. Based on the comparison of dynamically collected status data and set values, the feedback training system adjusts training parameters, for instance by advising the group to change the order or the formation, to increase or decrease the speed, or to split the group. In a final version, the system should run under outdoor conditions. As an intermediate step, a prototype for indoor training was established. [ABSTRACT FROM AUTHOR]