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Fluctuation Theorems for Synchronization of Interacting Polya's urns
No full textWe consider a model of N two-colors urns in which the reinforcement of each urn depends also on the content of all the other urns. This interaction is of mean-field type and it is tuned by a parameter \alpha in [0,1]; in particular, for \alpha=0 the N urns behave as N independent Polya's urns. For \alpha>0 urns synchronize, in the sense that the fraction of balls of a given color converges a.s. to the same (random) limit in all urns. In this paper we study fluctuations around this synchronized regime. The scaling of these fluctuations depends on the parameter \alpha. In particular, the standard scaling t^{-1/2} appears only for \alpha>1/2. For \alpha\geq 1/2 we also determine the limit distribution of the rescaled fluctuations. We use the notion of stable convergence, which is stronger than convergence in distribution
Noise Reduction in Complex Biological Switches
No full textCells operate in noisy molecular environments via complex regulatory networks. It is possible to understand how molecular counts are related to noise in specific networks, but it is not generally clear how noise relates to network complexity, because different levels of complexity also imply different overall number of molecules. For a fixed function, does increased network complexity reduce noise, beyond the mere increase of overall molecular counts? If so, complexity could provide an advantage counteracting the costs involved in maintaining larger networks. For that purpose, we investigate how noise affects multistable systems, where a small amount of noise could lead to very different outcomes; thus we turn to biochemical switches. Our method for comparing networks of different structure and complexity is to place them in conditions where they produce exactly the same deterministic function. We are then in a good position to compare their noise characteristics relatively to their identical deterministic traces. We show that more complex networks are better at coping with both intrinsic and extrinsic noise. Intrinsic noise tends to decrease with complexity, and extrinsic noise tends to have less impact. Our findings suggest a new role for increased complexity in biological networks, at parity of function
(a cura di) Policies for Happiness
No full textIn recent years, debates on the economics of happiness have shown that, over the long-term, well-being is influenced more by social and personal relationships than by income. This evidence challenges the traditional economic policy paradigm that has emphasized income as the primary determinant of well-being. This volume brings together contributions from leading scholars to ask: What should be done to improve the quality of people's lives? Can economic and social changes be made which enhance well-being? What policies are required? How do policies for well-being differ from traditional ones targeted on redistribution, the correction of market inefficiencies, and growth? Are there dimensions of well-being that have been neglected by traditional policies? Is happiness a meaningful policy target? The volume presents reflections and proposals which constitute a first step towards answering these questions
Ricchezza aziendale e patrimonio intangibile: prospettive di osservazione, strumenti di misura, modelli di rappresentazione e analisi
No full textI capitolo tratta del processo di genesi del reddito e della lettura della dimensione qualitativo produttiva degli andamenti aziendali
Time-optimal race car driving using an online exact hessian based nonlinear MPC algorithm
No full textThis work presents an embedded nonlinear model predictive control (NMPC) strategy for autonomous vehicles under a minimum time objective. The time-optimal control problem is stated in a path-parametric formulation such that existing reliable numerical methods for real-time nonlinear MPC can be used. Building on previous work on timeoptimal driving, we present an approach based on a sequential quadratic programming type algorithm with online propagation of second order derivatives. As an illustration of our method, we provide closed-loop simulation results based on a vehicle model identified for small-scale electric race cars
(a cura di) Elementi di bilancio e di management. Vol. 1: Il bilancio di esercizio : principi, schemi e criteri di valutazione
No full textIl volume è interamente dedicato al bilancio di esercizio
Identità territoriale e radici familiari strategie di crescita
No full textI capitolo tratta delle rilevanza strategica del connubio fra area a forte vocazione produttiva e profondità delle radici dell'azienda familiare
Approximate reduction of heterogenous nonlinear models with differential hulls
No full textWe present a model reduction technique for a class of nonlinear ordinary differential equation (ODE) models of heterogeneous systems, where heterogeneity is expressed in terms of classes of state variables having the same dynamics structurally, but which are characterized by distinct parameters. To this end, we first build a system of differential inequalities that provides lower and upper bounds for each original state variable, but such that it is homogeneous in its parameters. Then, we use two methods for exact aggregation of ODEs to exploit this homogeneity, yielding a smaller model of size independent of the number of heterogeneous classes. We apply this technique to two case studies: a multiclass queuing network and a model of epidemics spread
Symbolic Computation of Differential Equivalences
No full textOrdinary differential equations (ODEs) are widespread in manynatural sciences including chemistry, ecology, and systems biology,and in disciplines such as control theory and electrical engineering. Building on the celebrated molecules-as-processes paradigm, they have become increasingly popular in computer science, with high-level languages and formal methods such as Petri nets, process algebra, and rule-based systems that are interpreted as ODEs. We consider the problem of comparing and minimizing ODEs automatically. Influenced by traditional approaches in the theory of programming, we propose differential equivalence relations. We study them for a basic intermediate language, for which we have decidability results, that can be targeted by a class of high-level specifications. An ODE implicitly represents an uncountable state space, hence reasoning techniques cannot be borrowed from established domains such as probabilistic programs with finite-state Markov chain semantics. We provide novel symbolic procedures to check an equivalence and compute the largest one via partition refinement algorithms that use satisfiability modulo theories. We illustrate the generality of our framework by showing that differential equivalences include (i) well-known notions for the minimization of continuous-time Markov chains (lumpability),(ii) bisimulations for chemical reaction networks recently proposedby Cardelli et al., and (iii) behavioral relations for process algebra with ODE semantics. With a prototype implementation we are able to detect equivalences in biochemical models from the literature thatcannot be reduced using competing automatic techniques