Microscopic Abrams-Strogatz model of language competition

The differential equations of Abrams and Strogatz for the competition between two languages are compared with agent-based Monte Carlo simulations for fully connected networks as well as for lattices in one, two and three dimensions, with up to 10^9 agents.Comment: 10 pages, 7 figure

Le briofite della Grotta dell\u2019Orso (33-7VG, Carso triestino, NE Italia)

Vengono presentati i risultati di uno studio sulla flora briologica della Grotta dell\u2019Orso (33-7VG), situata sul Carso triestino. Sono state rinvenute 42 specie di briofite (33 specie di muschi e 9 epatiche); per ogni specie sono indicati l\u2019elemento corologico, la distribuzione nell\u2019area e note ecologico-stazionali. Aspetti floristici, biogeografici e conservazionistici della componente briologica dell\u2019area vengono discussi. Viene riportata una nuova specie per la Regione Friuli Venezia Giulia, l\u2019epatica Cololejeunea rossettiana; 2 altre specie di muschi sono segnalate come nuove per il Carso. La flora briologica aggiornata della cavit\ue0 comprende 46 specie

Modeling the Yield Curve of BRICS Countries: Parametric vs. Machine Learning Techniques

We compare parametric and machine learning techniques (namely: Neural Networks) for in\u2013sample modeling of the yield curve of the BRICS countries (Brazil, Russia, India, China, South Africa). To such aim, we applied the Dynamic De Rezende\u2013Ferreira five\u2013factor model with time\u2013varying decay parameters and a Feed\u2013Forward Neural Network to the bond market data of the BRICS countries. To enhance the flexibility of the parametric model, we also introduce a new procedure to estimate the time varying parameters that significantly improve its performance. Our contribution spans towards two directions. First, we offer a comprehensive investigation of the bond market in the BRICS countries examined both by time and maturity; working on five countries at once we also ensure that our results are not specific to a particular data\u2013set; second we make recommendations concerning modelling and estimation choices of the yield curve. In this respect, although comparing highly flexible estimation methods, we highlight superior in\u2013sample capabilities of the neural network in all the examined markets and then suggest that machine learning techniques can be a valid alternative to more traditional methods also in presence of marked turbulence

2016 field monitoring report

Compiled June 2019.Colorado Forestry Best Management Practices

2018 field monitoring report

Compiled August 2020.Colorado Forestry Best Management Practices

Second-order odd-harmonic repetitive control and its application to active filter control

High order repetitive control has been introduced toovercomeperformance decay of repetitive control systems undervarying frequency of the signals to be tracked/rejected orimproving the interhamonic behavior. However, most highorder repetitive internal models used to improve frequencyuncertainty are unstable, as a consequence practicalimplementations are more difficult. In this work a stable,second order odd-harmonic repetitive control system ispresented and studied.The proposed internal model has been implemented andvalidated in a shunt active filter current controller. Thishigh order controller allows dealing with the gridfrequency variations without using adaptive schemes

Finite-size scaling as a way to probe near-criticality in natural swarms

Collective behaviour in biological systems is often accompanied by strong correlations. The question has therefore arisen of whether correlation is amplified by the vicinity to some critical point in the parameters space. Biological systems, though, are typically quite far from the thermodynamic limit, so that the value of the control parameter at which correlation and susceptibility peak depend on size. Hence, a system would need to readjust its control parameter according to its size in order to be maximally correlated. This readjustment, though, has never been observed experimentally. By gathering three-dimensional data on swarms of midges in the field we find that swarms tune their control parameter and size so as to maintain a scaling behaviour of the correlation function. As a consequence, correlation length and susceptibility scale with the system's size and swarms exhibit a near-maximal degree of correlation at all sizes.Comment: Selected for Viewpoint in Physics; PRL Editor's Suggestio