Angelo Facchini

Energy and material flows of megacities

Significance Our quantification of energy and material flows for the world’s 27 megacities is a major undertaking, not previously achieved. The sheer magnitude of these flows (e.g., 9% of global electricity, 10% of gasoline; 13% of solid waste) shows the importance of megacities in addressing global environmental challenges. In aggregate the resource flows through megacities are consistent with scaling laws for cities. Statistical relations are established for electricity use, heating/industrial fuels, ground transportation, water consumption, waste generation, and steel production in terms of heating-degree days, urban form, economic activity, and population growth. Analysis at the microscale shows that electricity use is strongly correlated with building floor area, explaining the macroscale correlation between per capita electricity use and urbanized area per capita. Understanding the drivers of energy and material flows of cities is important for addressing global environmental challenges. Accessing, sharing, and managing energy and material resources is particularly critical for megacities, which face enormous social stresses because of their sheer size and complexity. Here we quantify the energy and material flows through the world’s 27 megacities with populations greater than 10 million people as of 2010. Collectively the resource flows through megacities are largely consistent with scaling laws established in the emerging science of cities. Correlations are established for electricity consumption, heating and industrial fuel use, ground transportation energy use, water consumption, waste generation, and steel production in terms of heating-degree-days, urban form, economic activity, and population growth. The results help identify megacities exhibiting high and low levels of consumption and those making efficient use of resources. The correlation between per capita electricity use and urbanized area per capita is shown to be a consequence of gross building floor area per capita, which is found to increase for lower-density cities. Many of the megacities are growing rapidly in population but are growing even faster in terms of gross domestic product (GDP) and energy use. In the decade from 2001–2011, electricity use and ground transportation fuel use in megacities grew at approximately half the rate of GDP growth.

Effect of mental imagery on the development of skilled motor actions.

To test the effect of imagery in the training of skilled movements, an experiment was designed in which athletes learned a new motor action and trained themselves for a month either by overt action or by mental imagery of the action. The experiment was carried out with 30 male karateka (M age = 35 yr., SD = 8.7; M years of practice = 6, SD = 3) instructed to perform an action (Ura-Shuto-Uchi) that they had not previously learned. The athletes were divided into three groups: Untrained (10 subjects who did not perform any training), Action Trained (10 subjects who performed Ura-Shuto-Uchi training daily for 16 minutes), and Mental Imagery (10 subjects who performed mental imagery training of Ura-Shuto-Uchi daily for 16 minutes). The subjects were tested five times, once every 7 days. During each test, they performed a series of 60 motor action trials. In Tests 1, 3, and 5, they also performed a series of 60 mental imagery trials. During the trials, an electroencephalogram (EEG), electromyography (EMG), muscle strength and power, and other physiological parameters were recorded. The results differed by group. Untrained subjects did not show significant effects. In the Action Trained group, training had an effect on reactivity and movement speed, with a reduction of EMG activation and reaction times. Moreover, muscle strength, power, and work increased significantly. The Mental Imagery group showed the same effects on muscle strength, power, and work, but changes in reactivity were not observed. In the Mental Imagery group, the study of Movement Related Brain Macropotentials indicated a progressive modification of the profile of the waves from Test 1 to Test 5 during imagery, showing significant variations of the amplitude of the waves related to the premotor and motor execution periods. Results show that motor imagery can influence muscular abilities such as strength and power and can modify Movement Related Brain Macropotentials, the profile of which potentially could be used to verify the effectiveness of motor imagery training.

Load forecasting for active distribution networks

This paper addresses the problem of electric load forecasting for distribution networks with Active Demand (AD), a new concept in smart-grids introduced within the EU project ADDRESS. By changing the typical consumption pattern of the consumers, AD adds a new dimension to the problem of load forecasting, and therefore makes currently available load forecasting techniques no more suitable. A new approach to load forecasting in the presence of AD is therefore proposed. The approach is based on a decomposition of the load into its components, namely the base load (representing different seasonal patterns), and a residual term depending both on stochastic fluctuations and AD effects. The performance of the proposed approach is illustrated through a numerical example. Since data sets including AD are not yet available, in the numerical example AD effects are simulated and added to real measurements representing the aggregated load of about 60 consumers from an Italian LV network.

Comparison of recurrence quantification methods for the analysis of temporal and spatial chaos

A comparative study of the recurrence properties of time series and two-dimensional spatial data is performed by means of Recurrence Quantification Analysis. The recent extension to distributed data of methods based on recurrences reveals new insights improving the performances of the approach for the analysis of complex spatial patterns. Indeed, the measures determinism and entropy provide significant information about the small and large scale characterization of the patterns allowing for a better connection to the physical properties of the spatial system under investigation.

Identifying the dynamics of complex spatio-temporal systems by spatial recurrence properties

Complex spatio-temporal systems may exhibit irregular behaviors when driven far from equilibrium. Reaction-diffusion systems often lead to the formation of patterns and spatio-temporal chaos. When a limited number of observations is available, the reconstruction and identification of complex dynamical regimes become challenging problems. A method based on spatial recurrence properties is proposed to deal with this problem: generalized recurrence plots and generalized recurrence quantification analysis are exploited to show that detection of structural changes in spatially distributed systems can be performed by setting up appropriate diagrams accounting for different spatial recurrences. The method has been tested on two prototypical systems forming complex patterns: the complex Ginzburg–Landau equation and the Schnakenberg system. This work allowed us to identify changes in the stability of spiral wave solutions in the former system and to analyze the Turing bifurcations in the latter.

Characterization of chaotic dynamics in the vocalization of Cervus elaphus corsicanus (L)

Chaos, oscillations, instabilities, and intermittency represent only some nonlinear examples apparent in the natural world. These phenomena appear in any field of study, and advances in complex and nonlinear dynamic techniques bring about opportunities to better understand animal signals. In this work an analysis method is suggested based on the characterization of the vocal-fold dynamics by means of the nonlinear time-series analysis, and by the computations of the parameters typical of chaotic oscillations: Attractor reconstruction, spectrum of Lyapunov exponents, and maximum Lyapunov exponent were used to reconstruct the dynamic of the vocal folds. Identifying a sort of vocal fingerprint can be useful in biodiversity monitoring and understanding the health status of a given animal. This method was applied to the vocalization of the Cervus elaphus corsicanus, the Sardinian red deer.

Multifractal fluctuations in the survival probability of an open quantum system

We predict a multifractal behavior of transport in the deep quantum regime for the opened δ-kicked rotor model. Our analysis focuses on intermediate and large scale correlations in the transport signal and generalizes previously found parametric mono-fractal fluctuations in the quantum survival probability on small scales.

Recurrence Methods for the Identification of Morphogenetic Patterns

This paper addresses the problem of identifying the parameters involved in the formation of spatial patterns in nonlinear two dimensional systems. To this aim, we perform numerical experiments on a prototypical model generating morphogenetic Turing patterns, by changing both the spatial frequency and shape of the patterns. The features of the patterns and their relationship with the model parameters are characterized by means of the Generalized Recurrence Quantification measures. We show that the recurrence measures Determinism and Recurrence Entropy, as well as the distribution of the line lengths, allow for a full characterization of the patterns in terms of power law decay with respect to the parameters involved in the determination of their spatial frequency and shape. A comparison with the standard two dimensional Fourier transform is performed and the results show a better performance of the recurrence indicators in identifying a reliable connection with the spatial frequency of the patterns. Finally, in order to evaluate the robustness of the estimation of the power low decay, extensive simulations have been performed by adding different levels of noise to the patterns.

FILLING GAPS IN ECOLOGICAL TIME SERIES BY MEANS OF TWIN SURROGATES

This paper addresses the problem of reconstructing missing data in time series by the twin surrogates method. We consider a set of time series, collected by a sensor network, representing the same state variable measured at different locations of a spatially distributed system. Assuming the presence of coherent dynamics among the available data, the method of twins is applied as an input–output model using the information provided by the complete time series for filling the incomplete recordings. The method is applied to the measurements of Dissolved Oxygen concentrations collected in the lagoon of Orbetello (Italy) and to time series obtained from the logistic chaotic map. Both cases show satisfactory performances in the reconstruction of missing data.

Identification of bifurcations of distributed systems using Generalized Recurrence Quantification Analysis

Abstract In this paper the Generalized Recurrence Plot and Generalized Recurrence Quantification Analysis are exploited to investigate spatially distributed systems characterized by a Hopf bifurcation. Specifically, the Complex Ginzburg- Landau equation is chosen as a prototypical example. Steady state spatial pattern evolution is studied by computing the recurrence quantification parameters Determinism (DET) and Entropy (ENT) of the images representing the equation solutions and plotting them on the DET-ENT plane. A point in the DET-ENT plane identifies the signature of the dynamic system generating the spatial patterns. Such patterns consist of stable or unstable waves, depending on the value of certain physical parameters. According to the different values of these parameters, the images cluster in the DET-ENT diagram quite neatly. This allows one to reconstruct the bifurcation curve separating stable and unstable spirals in the DET-ENT plane.