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This paper describes a study about the impact of earthquakes on debris flows with a focus on the Great Wenchuan Earthquake 2008 in China. The land form, precipitation, and source material are the three key factors for debris flow initiation in the Wenchuan surrounding area. Classifications and examples of four types of debris flow initiation triggering (gully triggering, slope triggering, liquefaction triggering, and gully erosion triggering) have been presented. The initiation mechanisms are attributed to hydraulic and geomechanical aspects. The actual debris flow cases linked with the Great Wenchuan Earthquake and other earthquakes in China have been used to illustrate the increased magnitudes of debris flows due to a large amount of loose materials created by the seismic actions. The critical precipitation for debris flows is reduced by the earthquake. It is predicted that the impact of the Great Wenchuan Earthquake on the local debris flows would be significant in the next 5–6 years, and much less in the following years (up to 20 years). Finally, the debris flow system will reach a relative stable stage. This prediction is based on the historical observations at other earthquake areas and the qualitative analysis on debris flow initiation mechanisms.

The classification of multivariate time-varying data finds application in several fields, such as economics, finance, marketing research, psychometrics, bioinformatics, medicine, signal processing, pattern recognition, etc. In this paper, by considering an exploratory formalization, we propose different unsupervised clustering models for multivariate data time arrays (objects×quantitative variables×times). These models can be classified in two different approaches: the cross sectional and the longitudinal approach. In the first case, after the objects, observed at each time, have been classified, comparison among the classifications made in different time instants will be done. In the second approach, we cluster the time trajectories of the objects; then, we obtain only one classification by comparing the instantaneous and evolutive features of the trajectories of the objects. In particular, in this work, the second approach is analyzed in detail, with reference to the so-called single and double step procedures. Geometric, correlative, instantaneous, evolutive and trend characteristics of the multivariate time arrays are taken into account in the different proposed clustering models. Furthermore, the fuzzy approach, that is particularly suitable in the dynamic classification problem, has been considered. Extensions of a cluster-validity criterion for the proposed fuzzy dynamic clustering models are also suggested. A socio-economic example concludes the paper.

The Detrended Fluctuation Analysis (DFA) and its extensions (MF-DFA) have been proposed as robust techniques to determine possible long-range correlations in self-affine signals. However, many studies have reported the susceptibility of DFA to trends which give rise to spurious crossovers and prevent reliable estimations of the scaling exponents. Lately, several modifications of the DFA method have been reported with many different techniques for eliminating the monotonous and periodic trends. In this study, a smoothing algorithm based on the Orthogonal V-system (OVS) is proposed to minimize the effect of power-law trends, periodic trends, assembled trends and piecewise function trends. The effectiveness of the new method is demonstrated on monofractal data and multifractal data corrupted with different trends.

A key element in the design of a repeated sample survey is the rotation pattern, which affects the variability of the time series of survey estimates and the seasonally adjusted and trend estimates produced from them. This paper considers the choice of rotation pattern for seasonally adjusted and trend estimates obtained from a repeated survey, using X11 based methods.

The amplitudes of R and T waves of the electrocardiogram (ECG) recorded during the exercise test show both large inter- and intra-individual variability in response to stress. We analyze a dataset of 65 normal subjects undergoing ambulatory test. We model the dataset of R and T series in the framework of functional data, assuming that the individual series are realizations of a non-stationary process, centered at the population trend. We test the time variability of this trend computing a simultaneous confidence band and the zero crossing of its derivative. The analysis shows that the amplitudes of the R and T waves have opposite responses to stress, consisting respectively in a bump and a dip at the early recovery stage. Our findings support the existence of a relationship between R and T wave amplitudes and respectively diastolic and systolic ventricular volumes.

Due to the fact that rainfall may hamper signal availability, rainfall variability and its resultant effect on environment and communication have become of global concern. In this study, we investigate the trend and variability of rainfall in southwestern Nigeria and examine its effect on radio communication. Our results reveal a steady increasing in rainfall and slightly unstable in volume variation in southwestern Nigeria. The study also reveals that the tendency of higher attenuation in years was caused by the increasing trend but showing variability with frequency. With the rising trend in view, there is therefore the likelihood that radio communication infrastructures will experience increasing outage and more signal loss in the future years. This outcome should serve as useful tools in optimizing satellite link budget and better utilization of available bandwidth.