Structural and entropic insights into the nature of the random-close-packing limit

Disordered packings of equal sized spheres cannot be generated above the limiting density (fraction of volume occupied by the spheres) of ??0.64 without introducing some partial crystallization. The nature of this “random-close-packing” limit (RCP) is investigated by using both geometrical and statistical mechanics tools applied to a large set of experiments and numerical simulations of equal-sized sphere packings. The study of the Delaunay simplexes decomposition reveals that the fraction of “quasiperfect tetrahedra” grows with the density up to a saturation fraction of ?30% reached at the RCP limit. At this limit the fraction of aggregate “polytetrahedral” structures (made of quasiperfect tetrahedra which share a common triangular face) reaches it maximal extension involving all the spheres. Above the RCP limit the polytetrahedral structure gets rapidly disassembled. The entropy of the disordered packings, calculated from the study of the local volume fluctuations, decreases uniformly and vanishes at the (extrapolated) limit ?K?0.66. Before such limit, and precisely in the range of densities between 0.646 and 0.66, a phase separated mixture of disordered and crystalline phases is observed

The topological structure of 2D disordered cellular systems

We analyze the structure of two dimensional disordered cellular systems generated by extensive computer simulations. These cellular structures are studied as topological trees rooted on a central cell or as closed shells arranged concentrically around a germ cell. We single out the most significant parameters that characterize statistically the organization of these patterns. Universality and specificity in disordered cellular structures are discussed.Comment: 18 Pages LaTeX, 16 Postscript figure

Correlation filtering in financial time series

We apply a method to filter relevant information from the correlation coefficient matrix by extracting a network of relevant interactions. This method succeeds to generate networks with the same hierarchical structure of the Minimum Spanning Tree but containing a larger amount of links resulting in a richer network topology allowing loops and cliques. In Tumminello et al. \cite{TumminielloPNAS05}, we have shown that this method, applied to a financial portfolio of 100 stocks in the USA equity markets, is pretty efficient in filtering relevant information about the clustering of the system and its hierarchical structure both on the whole system and within each cluster. In particular, we have found that triangular loops and 4 element cliques have important and significant relations with the market structure and properties. Here we apply this filtering procedure to the analysis of correlation in two different kind of interest rate time series (16 Eurodollars and 34 US interest rates).Comment: 10 pages 7 figure

Entropy Bound with Generalized Uncertainty Principle in General Dimensions

In this letter, the entropy bound for local quantum field theories (LQFT) is studies in a class of models of the generalized uncertainty principle(GUP) which predicts a minimal length as a reflection of the quantum gravity effects. Both bosonic and fermionic fields confined in arbitrary spatial dimension $d\geq4$ ball ${\cal B}^{d}$ are investigated. It is found that the GUP leads to the same scaling $A_{d-2}^{(d-3)/(d-2)}$ correction to the entropy bound for bosons and fermions, although the coefficients of this correction are different for each case. Based on our calculation, we conclude that the GUP effects can become manifest at the short distance scale. Some further implications and speculations of our results are also discussed.Comment: 8 pages, topos corrected and references adde

An invariant distribution in static granular media

We have discovered an invariant distribution for local packing configurations in static granular media. This distribution holds in experiments for packing fractions covering most of the range from random loose packed to random close packed, for beads packed both in air and in water. Assuming only that there exist elementary cells in which the system volume is subdivided, we derive from statistical mechanics a distribution that is in accord with the observations. This universal distribution function for granular media is analogous to the Maxwell-Boltzmann distribution for molecular gasses.Comment: 4 pages 3 figure

Clustering and hierarchy of financial markets data: advantages of the DBHT

We present a set of analyses aiming at quantifying the amount of information filtered by di↵erent hierarchical clustering methods on correlations between stock returns. In particular we apply, for the first time to financial data, a novel hierarchical clustering approach, the Directed Bubble Hierarchical Tree (DBHT), and we compare it with other methods including the Linkage and k-medoids. In particular by taking the industrial sector classification of stocks as a benchmark partition we evaluate how the di↵erent methods retrieve this classification. The results show that the Directed Bubble Hierarchical Tree outperforms the other methods, being able to retrieve more information with fewer clusters. Moreover, we show that the economic information is hidden at di↵erent levels of the hierarchical structures depending on the clustering method. The dynamical analysis also reveals that the di↵erent methods show di↵erent degrees of sensitivity to financial events, like crises. These results can be of interest for all the applications of clustering methods to portfolio optimization and risk hedging

Optimization concepts in district energy design and management – A case study

AbstractThe integration of optimization techniques in building and district energy design constitute an essential tool for reducing the global impact of energy services. Appropriate dynamic energy management systems must be employed too in order to maintain a high level of performance in the operational phase and to obtain better system knowledge. Therefore, in the strategic energy planning of districts, it is necessary to embody the main concepts of Smart Grid and virtual power plants frameworks. In the research presented, the preliminary results from a case study are illustrated with a reflection on energy consumption subdivision and load profiles for the sizing and operational strategy definition of distributed generation systems

Time-dependent scaling patterns in high frequency financial data

We measure the influence of different time-scales on the dynamics of financial market data. This is obtained by decomposing financial time series into simple oscillations associated with distinct time-scales. We propose two new time-varying measures: 1) an amplitude scaling exponent and 2) an entropy-like measure. We apply these measures to intraday, 30-second sampled prices of various stock indices. Our results reveal intraday trends where different time-horizons contribute with variable relative amplitudes over the course of the trading day. Our findings indicate that the time series we analysed have a non-stationary multifractal nature with predominantly persistent behaviour at the middle of the trading session and anti-persistent behaviour at the open and close. We demonstrate that these deviations are statistically significant and robust

Multi-commodity network flow models for dynamic energy management – Smart Grid applications

AbstractThe strong interconnection between human activities, energy use and pollution reduction strategies in contemporary society has determined the necessity of collecting scientific knowledge from different fields to provide useful methods and models to foster the transition towards more sustainable energy systems. This is a challenging task in particular for contemporary communities where an increasing demand for services is combined with rapidly changing lifestyles and habits. The Smart Grid concept is the result of a confluence of issues and a convergence of objectives, which include national energy security, climate change, pollution reduction, grid reliability, etc. While thinking about a paradigm shift in energy systems, drivers, characteristics, market segments, applications and other interconnected aspects must be taken into account simultaneously. In this context, the use of multi-commodity network flow models for dynamic energy management aims at finding a compromise between model usefulness, accuracy, flexibility, solvability and scalability in Smart Grid applications