Analysis of temperature time-series: embedding dynamics into the MDS method

Global warming and the associated climate changes are being the subject of intensive research due to their major impact on social, economic and health aspects of the human life. Surface temperature time-series characterise Earth as a slow dynamics spatiotemporal system, evidencing long memory behaviour, typical of fractional order systems. Such phenomena are difficult to model and analyse, demanding for alternative approaches. This paper studies the complex correlations between global temperature time-series using the Multidimensional scaling (MDS) approach. MDS provides a graphical representation of the pattern of climatic similarities between regions around the globe. The similarities are quantified through two mathematical indices that correlate the monthly average temperatures observed in meteorological stations, over a given period of time. Furthermore, time dynamics is analysed by performing the MDS analysis over slices sampling the time series. MDS generates maps describing the stations’ locus in the perspective that, if they are perceived to be similar to each other, then they are placed on the map forming clusters. We show that MDS provides an intuitive and useful visual representation of the complex relationships that are present among temperature time-series, which are not perceived on traditional geographic maps. Moreover, MDS avoids sensitivity to the irregular distribution density of the meteorological stations.

Analysis of financial data series using fractional Fourier transform and multidimensional scaling

The goal of this study is the analysis of the dynamical properties of financial data series from worldwide stock market indexes during the period 2000–2009. We analyze, under a regional criterium, ten main indexes at a daily time horizon. The methods and algorithms that have been explored for the description of dynamical phenomena become an effective background in the analysis of economical data. We start by applying the classical concepts of signal analysis, fractional Fourier transform, and methods of fractional calculus. In a second phase we adopt the multidimensional scaling approach. Stock market indexes are examples of complex interacting systems for which a huge amount of data exists. Therefore, these indexes, viewed from a different perspectives, lead to new classification patterns.

Cu2ZnSnS4 absorber layers obtained through sulphurization of metallic precursors: Graphite box versus sulphur flux

In this work we employed a hybrid method, combining RF-magnetron sputtering with evaporation, for the deposition of tailor made metallic precursors, with varying number of Zn/Sn/Cu (ZTC) periods and compared two approaches to sulphurization. Two series of samples with 1×, 2× and 4× ZTC periods have been prepared. One series of precursors was sulphurized in a tubular furnace directly exposed to a sulphur vapour and N2+5% H2 flux at a pressure of 5.0×10+4 Pa. A second series of identical precursors was sulphurized in the same furnace but inside a graphite box where sulphur pellets have been evaporated again in the presence of N2+5% H2 and at the same pressure as for the sulphur flux experiments. The morphological and chemical analyses revealed a small grain structure but good average composition for all three films sulphurized in the graphite box. As for the three films sulphurized in sulphur flux grain growth was seen with the increase of the number of ZTC periods whilst, in terms of composition, they were slightly Zn poor. The films' crystal structure showed that Cu2ZnSnS4 is the dominant phase. However, in the case of the sulphur flux films SnS2 was also detected. Photoluminescence spectroscopy studies showed an asymmetric broad band emission whichoccurs in the range of 1–1.5 eV. Clearly the radiative recombination efficiency is higher in the series of samples sulphurized in sulphur flux. We have found that sulphurization in sulphur flux leads to better film morphology than when the process is carried out in a graphite box in similar thermodynamic conditions. Solar cells have been prepared and characterized showing a correlation between improved film morphology and cell performance. The best cells achieved an efficiency of 2.4%.

An improved preemption delay upper bound for floating non-preemptive region

In embedded systems, the timing behaviour of the control mechanisms are sometimes of critical importance for the operational safety. These high criticality systems require strict compliance with the offline predicted task execution time. The execution of a task when subject to preemption may vary significantly in comparison to its non-preemptive execution. Hence, when preemptive scheduling is required to operate the workload, preemption delay estimation is of paramount importance. In this paper a preemption delay estimation method for floating non-preemptive scheduling policies is presented. This work builds on [1], extending the model and optimising it considerably. The preemption delay function is subject to a major tightness improvement, considering the WCET analysis context. Moreover more information is provided as well in the form of an extrinsic cache misses function, which enables the method to provide a solution in situations where the non-preemptive regions sizes are small. Finally experimental results from the implementation of the proposed solutions in Heptane are provided for real benchmarks which validate the significance of this work.

Analysis of financial indices by means of the windowed Fourier transform

The goal of this study is to analyze the dynamical properties of financial data series from nineteen worldwide stock market indices (SMI) during the period 1995–2009. SMI reveal a complex behavior that can be explored since it is available a considerable volume of data. In this paper is applied the window Fourier transform and methods of fractional calculus. The results reveal classification patterns typical of fractional order systems.

Dynamical Analysis and Visualization of Tornadoes Time Series

In this paper we analyze the behavior of tornado time-series in the U.S. from the perspective of dynamical systems. A tornado is a violently rotating column of air extending from a cumulonimbus cloud down to the ground. Such phenomena reveal features that are well described by power law functions and unveil characteristics found in systems with long range memory effects. Tornado time series are viewed as the output of a complex system and are interpreted as a manifestation of its dynamics. Tornadoes are modeled as sequences of Dirac impulses with amplitude proportional to the events size. First, a collection of time series involving 64 years is analyzed in the frequency domain by means of the Fourier transform. The amplitude spectra are approximated by power law functions and their parameters are read as an underlying signature of the system dynamics. Second, it is adopted the concept of circular time and the collective behavior of tornadoes analyzed. Clustering techniques are then adopted to identify and visualize the emerging patterns.