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High-frequency parametric approximation of the Floquet-Bloch spectrum for anti-tetrachiral materials
No full textThe class of anti-tetrachiral cellular materials is phenomenologically characterized by a strong auxeticity of the elastic macroscopic response. The auxetic behavior is activated by rolling-up deformation mechanisms developed by the material microstructure, composed by a periodic pattern of stiff rings connected by flexible ligaments. A linear beam lattice model is formulated to describe the free dynamic response of the periodic cell, in the absence of a soft matrix. After a static condensation of the passive degrees-of-freedom, a general procedure is applied to analyze the wave propagation in the low-dimensional space of the active degrees-of-freedom. The exact dispersion functions are compared with explicit – although approximate – dispersion relations, obtained from asymptotic perturbation solutions of the eigenproblem governing the Floquet–Bloch theory. A general hierarchical scheme is outlined to formulate and solve the perturbation equations, taking into account the dimension of the perturbation vector. Original recursive formulas are presented to achieve any desired order of asymptotic approximation. For the anti-tetrachiral material, the fourth-order asymptotic solutions are found to approximate the dispersion curves with fine agreement over wide regions of the parameter space. The asymptotic eigensolutions allow an accurate sensitivity analysis of the material spectrum under variation of the key physical parameters, including the cell aspect ratio, the ligament slenderness and the spatial ring density. Finally, the explicit dependence of the dispersion functions on the mechanical parameters may facilitate the custom design of specific spectral properties, such as the wave velocities and band gap amplitudes
Overall thermomechanical properties of layered materials for energy devices applications
No full textThis paper is concerned with the analysis of effective thermomechanical properties of multi- layered materials of interest for solid oxide fuel cells (SOFC) and lithium ions batteries fabrication. The recently developed asymptotic homogenization procedure is applied in order to express the overall thermoelastic constants of the first order equivalent continuum in terms of microfluctuations functions, and these functions are obtained by the solution of the corresponding recursive cell problems. The effects of thermal stresses on periodic multi-layered thermoelastic composite reproducing the characteristics of solid oxide fuel cells (SOFC-like) are studied assuming periodic body forces and heat sources, and the solution derived by means of the asymptotic homogenization approach is compared with the results obtained by finite elements analysis of the associate heterogeneous material
Overall thermomechanical properties of layered materials for energy devices applications
No full textThis paper is concerned with the analysis of effective thermomechanical properties of multi-layered materials of interest for solid oxide fuel cells (SOFC) and lithium ions batteries fabrication. The recently developed asymptotic homogenization procedure is applied in order to express the overall thermoelastic constants of the first order equivalent continuum in terms of microfluctuations functions, and these functions are obtained by the solution of the corresponding recursive cell problems. The effects of thermal stresses on periodic multi-layered thermoelastic composite reproducing the characteristics of solid oxide fuel cells (SOFC-like) are studied assuming periodic body forces and heat sources, and the solution derived by means of the asymptotic homogenization approach is compared with the results obtained by finite elements analysis of the associate heterogeneous material
Supervised and semi-supervised classifiers for the detection of flood-prone areas
No full textSupervised and semi-supervised machine-learning techniques are applied and compared for the recognition of the flood hazard. The learning goal consists in distinguishing between flood-exposed and marginal-risk areas. Kernel-based binary classifiers using six quantitative morphological features, derived from data stored in digital elevation models, are trained to model the relationship between morphology and the flood hazard. According to the experimental outcomes, such classifiers are appropriate tools when one is interested in performing an initial low-cost detection of flood-exposed areas, to be possibly refined in successive steps by more time-consuming and costly investigations by experts. The use of these automatic classification techniques is valuable, e.g., in insurance applications, where one is interested in estimating the flood hazard of areas for which limited labeled information is available. The proposed machine-learning techniques are applied to the basin of the Italian Tanaro River. The experimental results show that for this case study, semi-supervised methods outperform supervised ones when—the number of labeled examples being the same for the two cases—only a few labeled examples are used, together with a much larger number of unsupervised ones
Ereditare il futuro: dilemmi sul patrimonio culturale
No full textIl libro ricostruisce le regole, le prassi e i problemi della gestione del patrimonio culturale, soffermandosi su quattro dilemmi che segnano le politiche di questo settore: pubblico vs privato (cosa è davvero la valorizzazione?); «retenzione» vs esportazione (come circolano le opere d’arte?); in-house vs outsourcing (chi progetta le mostre?); natura vs cultura (qual è il rapporto tra ambiente, paesaggio e cultura?). I dilemmi si intrecciano con l’assetto delle istituzioni chiamate a risolverli, con l’emergere di interessi globali e con la necessità di adottare misure oltre il presente: il patrimonio culturale non è solo memoria del passato, ma anche eredità del futuro
Syncronization and functional central limit theorems for interacting reinforced random walks
No full textWe obtain Central Limit Theorems in Functional form for a class of time-inhomogeneous interacting random walks on the simplex of probability measures over a finite set. Due to a reinforcement mechanism, the increments of the walks are correlated, forcing their convergence to the same, possibly random, limit. Random walks of this form have been introduced in the context of urn models and in stochastic approximation. We also propose an application to opinion dynamics in a random network evolving via preferential attachment. We study, in particular, random walks interacting through a mean-field rule and compare the rate they converge to their limit with the rate of synchronization, i.e. the rate at which their mutual distances converge to zero. Under certain conditions, synchronization is faster than convergence
Symmetric and antisymmetric properties of solutions to kernel-based machine learning problems
No full textA cortical and sub-cortical parcellation clustering by intrinsic functional connectivity
No full textNetwork analysis of resting-state fMRI (rsfMRI) has been widely utilized to investigate the functional architecture of the whole brain. Here we propose a robust parcellation method that first divides cortical and sub-cortical regions into sub-regions by clustering the rsfMRI data for each subject independently, and then merges those individual parcellations to obtain a global whole brain parcellation. To do so our method relies on majority voting (to merge parcellations of multiple subjects) and enforces spatial constraints within a hierarchical agglomerative clustering framework to define parcels that are spatially homogeneous
A Network Model characterized by a Latent Attribute Structure with Competition
No full textThe quest for a model that is able to explain, describe, analyze and simulate real-world complex networks is of uttermost practical, as well as theoretical, interest. In fact, networks can be a natural way to represent many phenomena; often, they arise from a complex interweaving of some features of the nodes. For example, in a co-authorship network, a link stems more easily between authors with similar interests; similarly, in a genetic regulatory network, links are affected by the different biological functions of the regulators.
In this paper we introduce and study a novel network model that is based on a latent attribute structure: this model, inspired by a generalization of the Indian Buffet process, is simple and contains a small number of parameters, with a clear and intuitive role. Each node is characterized by a number of features and the probability of the existence of an edge between two nodes depends on the features they share; the number of possible features is not fixed a priori and can grow indefinitely. Moreover, a random fitness parameter is introduced for each node in order to determine its ability to transmit its own features to other nodes; this behavior is added on top of a process of Indian-Buffet type. Because of the fitness property, a node’s connectivity does not depend on its age alone, so that also
“young but fit” nodes are able to compete and succeed in propagating their features and acquiring links. We also show how, considering the resulting bipartite node-attribute network, it is possible to gain some insight about which nodes were originally the most “fit”.
Our model for this bipartite network depends on few parameters, that are characterized by their straightforward interpretation and by the availability of proper estimators. Even if the parameters are easy to interpret and tune, the model is general enough to represent
complex phenomena—e.g., homophily, heterophily, or any interplay between features. We provide some theoretical as well as experimental results regarding the power-law behavior of the model and the proposed tools for the estimation of the parameters. We also show, through a number of experiments, how the proposed model naturally captures most local and global properties (e.g., degree distributions, connectivity and distance distributions)
real networks exhibit
Economic cycles and their synchronization: a comparison of cyclic modes in three European countries
No full textThe present work applies singular spectrum analysis (SSA) to the study of
macroeconomic
uctuations in three European countries: Italy, The Netherlands,
and the United Kingdom. This advanced spectral method provides valuable
spatial and frequency information for multivariate data sets and goes far
beyond the classical forms of time domain analysis. In particular, SSA enables
us to identify dominant cycles that characterize the deterministic behavior of
each time series separately, as well as their shared behavior. We demonstrate its
usefulness by analyzing several fundamental indicators of the three countries'
real aggregate economy in a univariate, as well as a multivariate setting. Since
business cycles are international phenomena, which show common characteristics
across countries, our aim is to uncover supranational behavior within the
set of representative European economies selected herein. Finally, the analysis
is extended to include several indicators from the U.S. economy, in order to
examine its in
uence on the European economies under study and their interrelationships