1,721,142 research outputs found
BDC-Decomposition for global influence analysis
No full textIn biochemical networks, the steady-state input-output influence is the sign of the output steady-state variation due to a persistent positive input perturbation; if the sign does not depend on the value of the strictly positive system parameters, the influence is structural. As recently shown for small perturbations, when the linearized system approximation is valid, steady-state input-output influences can be structurally assessed, for biochemical networks with m unknown parameters, by means of a vertex algorithm with complexity 2m. This letter shows that the structural input-output influence of a biochemical network is a global property, which does not require any small-perturbation assumption. It also shows that, using a new algorithm, the complexity can be reduced down to 2m-n , where n is the system order, thus drastically reducing the computation time. Finally, when the uncertain parameters belong to known intervals, non-conservative bounds are given for the steady-state ratio between output and input, allowing for sensitivity analysis.Accepted Author ManuscriptTeam Tamas Keviczk
Quote in trust e diritto di voto a un soggetto diverso dal trustee
No full textIl contributo analizza l'istituto del trust applicato alla disciplina di quote di s.r.l., verificando i limiti e le potenzialità dei corpus normativi in materia, con particolare riferimento alla scissione tra diritti patrimoniali e diritti amministrativi delle partecipazioni sociali
Structural Analysis and Control of Dynamical Networks
Full text linkA dynamical network is comprised of a finite number of subsystems, each having
its own dynamics, which interact according to a given interconnection topology.
Dynamical networks are a powerful modelling tool to represent a large number of systems in different contexts, ranging from natural to man-made systems, and have a peculiar feature: the global behaviour is the outcome of an ensemble of local interactions. Hence, dynamical networks can be analysed so as to understand
how local events can lead to global consequences and can be controlled by acting locally so as to achieve the desired global behaviour. The analysis and the control of dynamical networks are structural when they are grounded on the topology of the interconnection graph, along with qualitative, parameter-free specifications.
Structural analysis aims at assessing properties for a whole family of systems
having the same structure and is particularly suited for natural systems, which can exhibit an extraordinary robustness in spite of large uncertainties and intrinsic
variability. In this thesis, results and procedures are presented to structurally assess relevant properties, such as stability, boundedness and the sign of steady-state
input-output influences, for a wide class of systems whose Jacobian admits the so-called BDC-decomposition, which embodies the sum of the effects of single local interactions. A structural classification is also proposed, to discriminate between systems that can possibly or exclusively admit instability related to oscillations or to multistationarity, for systems with a sign-definite Jacobian and for systems composed of the interconnection of stable monotone subsystems; a graph-based classification is
given and applied to examples of artificial biomolecular networks.
In a dynamical network described by a graph, subsystems are associated with
nodes and interactions with arcs. When the interactions are not given, they can be decided by a control system. In particular, network-decentralised control aims at governing the global behaviour of a dynamical network through controllers that are
associated with the arcs of the interconnection graph, hence act locally and have access to local information only. Despite the restricted information constraint, a
large class of systems can be always stabilised resorting to a network-decentralised controller. Both linear systems composed of independent subsystems, connected by the control action, and nonlinear compartmental systems are considered; the robustness and optimality properties of the devised network-decentralised control are investigated and several application examples are proposed, spanning from traffic control and data transmission to synchronisation and vehicle platooning. Network-decentralised estimation is also considered, for systems composed of identical agents; a robustness result is provided, exploiting the smallest eigenvalue of the generalised
Laplacian matrix associated with the interaction graph. Structural analysis and network-decentralised control synthesis are presented in this work as complementary facets of the same approach, which can streamline each
other. Structural analysis can help explain the robustness of natural systems, so that the clever resources of nature can be mimicked to improve the control strategies designed for man-made systems; at the same time, local interactions can be engineered
in biomolecular systems, as is done for artificial systems, to obtain the desired global behaviour. This virtuous circle will hopefully result in innovative approaches for biotechnologies and large-scale network engineering, aimed at improving the quality of our daily life
Piecewise-linear Lyapunov functions for structural stability of biochemical networks
No full textWe consider the problem of assessing structural stability of biochemical reaction networks with monotone reaction rates, namely of establishing if all the networks with a certain structure are stable regardless of specific parameter values. We investigate stability by absorbing the network equations in a linear differential inclusion and seeking for a polyhedral Lyapunov function proper to the considered network structure. A numerical recursive procedure is devised to test stability. For a wide class of mono- and bimolecular reaction networks, which we name unitary, the procedure is shown to be very efficient since, due to the particular structure of the problem, it requires iterations in the space of integer-valued matrices. We also consider a similar, less conservative procedure that allows us to test, even when the Lyapunov function cannot be found, whether the system evolution is structurally bounded. In this case, we absorb the equations in a positive linear differential inclusion. To show the effectiveness of the proposed procedure, we report the outcomes of both a stability and a boundedness test, for many non-trivial biochemical reaction networks, and we analyze well established models in the literature
Current goals of neuroimaging for mental disorders: a report by the WPA Section on Neuroimaging in Psychiatry
No full textDe-localizzazione e a-territorialità
No full textAnalisi di alcuni punti nodali della regolamentazione del Fintech negli ambiti del diritto bancario, dei mercati finanziari e delle assicurazion
A robust saturated strategy for n-player prisoner's dilemma
No full textWe study diffusion of cooperation in an n-population game in continuous time. At each instant, the game involves n random individuals, one from each population. The game has the structure of a prisoner's dilemma, where each player can choose a continuous decision variable associated with the probability of cooperating or defecting. We turn the game into a positive dynamical system. Then, we propose a novel strategy that is the saturation of a polynomial function. The strategy requires to each player exclusively the knowledge of her/his own current average payoff, along with her/his own payoffs in the cooperative and noncooperative equilibria; no information about other players' payoffs is required. The proposed strategy guarantees local stability of the cooperative equilibrium if the degree p of the polynomial is greater than or equal to 2. Conversely, the noncooperative equilibrium becomes unstable, for p large enough, if and only if a certain Metzler matrix depending on the payoffs has a positive Frobenius eigenvalue. We prove that the n-dimensional box of all payoffs between the noncooperative and the cooperative ones is positively invariant. Finally we show that, for p large, the domain of attraction of the cooperative equilibrium inside this box becomes arbitrarily close to the box itself.Team Tamas Keviczk
A Structural Classification of Candidate Oscillatory and Multistationary Biochemical Systems
No full textMolecular systems are uncertain: The variability of reaction parameters and the presence of unknown interactions can weaken the predictive capacity of solid mathematical models. However, strong conclusions on the admissible dynamic behaviors of a model can often be achieved without detailed knowledge of its specific parameters. In systems with a sign-definite Jacobian, for instance, cycle-based criteria related to the famous Thomas’ conjectures have been largely used to characterize oscillatory and multistationary dynamic outcomes. We build on the rich literature focused on the identification of potential oscillatory and multistationary behaviors using parameter-free criteria. We propose a classification for sign-definite non-autocatalytic biochemical networks, which summarizes several existing results in the literature. We call weak (strong) candidate oscillators systems which can possibly (exclusively) transition to instability due to the presence of a complex pair of eigenvalues, while we call weak (strong) candidate multistationary systems those which can possibly (exclusively) transition to instability due to the presence of a real eigenvalue. For each category, we provide a characterization based on the exclusive or simultaneous presence of positive and negative cycles in the associated sign graph. Most realistic examples of biochemical networks fall in the gray area of systems in which both positive and negative cycles are present: Therefore, both oscillatory and bistable behaviors are in principle possible. However, many canonical example circuits exhibiting oscillations or bistability fall in the categories of strong candidate oscillators/multistationary systems, in agreement with our results
Polyhedral Lyapunov functions for structural stability of biochemical systems in concentration and reaction coordinates
No full textStructural properties, independent of specific parameter values, can explain the robustness of biochemical systems. In this paper we consider the framework previously proposed by the authors to assess structural stability of biochemical reaction networks with monotone reaction rates, which considers systems in concentration coordinates, and we show that the results can be applied to systems in reaction coordinates (whose stability was first investigated by Al-Radhawi and Angeli): the same numerical test can be employed to find a polyhedral Lyapunov function and thus certify stability. Under suitable assumptions on the rank of structural matrices, we prove the equivalence between the test performed for the system in concentration coordinates and in reaction coordinates. We finally illustrate the approach by examples
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