Maria Luca, M. Di Mauro, Marco Di Mauro, Antonina Luca,
Gut microbiota consists of over 100 trillion microorganisms including at least 1000 different species of bacteria and is crucially involved in physiological and pathophysiological processes occurring in the host. An imbalanced gastrointestinal ecosystem (dysbiosis) seems to be a contributor to the development and maintenance of several diseases, such as Alzheimer’s disease, depression, and type 2 diabetes mellitus. Interestingly, the three disorders are frequently associated as demonstrated by the ...
Tópico(s): Diet and metabolism studies
2019 - Hindawi Publishing Corporation | Oxidative Medicine and Cellular Longevity
Vincenzo Carfora, Giorgio Spiniello, Riccardo Ricciolino, Marco Di Mauro, Marco Giuseppe Migliaccio, Filiberto Fausto Mottola, Nicoletta Verde, Nicola Coppola, Nicola Coppola, Caterina Sagnelli, Stefania De Pascalis, Maria Stanzione, Gianfranca Stornaiuolo, Angela Cascone, Salvatore Martini, Margherita Macera, Caterina Monari, Federica Calò, Andrea Bianco, Antonio Russo, Valeria Gentile, Clarissa Camaioni, Giulia De Angelis, Giulia Marino, Roberta Astorri, Ilario de Sio, Marco Niosi, Serena Borrelli, Benito Celia, M Ceparano, Salvatore Cirillo, María De Luca, Grazia Mazzeo, Giorgio Paoli, Maria Giovanna Russo, Vincenzo Carfora, Marco Di Mauro, Marco Giuseppe Migliaccio, Filiberto Fausto Mottola, Riccardo Ricciolino, Giorgio Spiniello, Nicoletta Verde,
Abstract The actual Coronavirus Disease (COVID 19) pandemic is due to Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a member of the coronavirus family. Besides the respiratory involvement, COVID 19 patients frequently develop a pro-coagulative state caused by virus-induced endothelial dysfunction, cytokine storm and complement cascade hyperactivation. It is common to observe diffuse microvascular thrombi in multiple organs, mostly in pulmonary microvessels. Thrombotic risk seems to ...
Tópico(s): Long-Term Effects of COVID-19
2020 - Springer Science+Business Media | Journal of Thrombosis and Thrombolysis
Mauro Di Marco, Mauro Forti, Luca Pancioni, Giacomo Innocenti, A. Tesi,
This article introduces a new class of memristor neural networks (NNs) for solving, in real-time, quadratic programming (QP) and linear programming (LP) problems. The networks, which are called memristor programming NNs (MPNNs), use a set of filamentary-type memristors with sharp memristance transitions for constraint satisfaction and an additional set of memristors with smooth memristance transitions for memorizing the result of a computation. The nonlinear dynamics and global optimization capabilities ...
Tópico(s): Neural Networks and Applications
2020 - Institute of Electrical and Electronics Engineers | IEEE Transactions on Cybernetics
Giacomo Innocenti, Mauro Di Marco, Mauro Forti, A. Tesi,
The paper studies bifurcations and complex dynamics in a class of nonautonomous oscillatory circuits with a flux-controlled memristor and harmonic forcing term. It is first shown that, as in the autonomous case, the state space of any memristor circuit of the class can be decomposed in invariant manifolds. It turns out that the memristor circuit dynamics is given by the collection of the dynamics of a family of circuits, with a nonlinear resistor in place of the memristor, which is parameterized ...
Tópico(s): Neural Networks Stability and Synchronization
2019 - Springer Science+Business Media | Nonlinear Dynamics
Fernando Corinto, Mauro Di Marco, Mauro Forti, Leon O. Chua,
Nonlinear dynamic memory elements, as memristors, memcapacitors, and meminductors (also known as mem-elements), are of paramount importance in conceiving the neural networks, mem-computing machines, and reservoir computing systems with advanced computational primitives. This paper aims to develop a systematic methodology for analyzing complex dynamics in nonlinear networks with such emerging nanoscale mem-elements. The technique extends the flux-charge analysis method (FCAM) for nonlinear circuits ...
Tópico(s): Neural dynamics and brain function
2019 - Institute of Electrical and Electronics Engineers | IEEE Transactions on Cybernetics
Mauro Di Marco, Mauro Forti, Luca Pancioni,
The paper considers a class of neural networks where flux-controlled dynamic memristors are used in the neurons and finite concentrated delays are accounted for in the interconnections. Goal of the paper is to thoroughly analyze the nonlinear dynamics both in the flux-charge domain and in the current-voltage domain. In particular, a condition that is expressed in the form of a linear matrix inequality, and involves the interconnection matrix, the delayed interconnection matrix, and the memristor ...
Tópico(s): stochastic dynamics and bifurcation
2018 - Elsevier BV | Journal of the Franklin Institute
Mauro Di Marco, Mauro Forti, Luca Pancioni,
The paper introduces a class of memristor neural networks (NNs) that are characterized by the following salient features. (a) The processing of signals takes place in the flux–charge domain and is based on the time evolution of memristor charges. The processing result is given by the constant asymptotic values of charges that are stored in the memristors acting as non-volatile memories in steady state. (b) The dynamic equations describing the memristor NNs in the flux–charge domain are analogous to ...
Tópico(s): Neural dynamics and brain function
2017 - Elsevier BV | Neural Networks
Mauro Di Marco, Mauro Forti, Giacomo Innocenti, A. Tesi,
Summary The paper studies nonlinear dynamics and bifurcations of a class of memristor oscillatory circuits obtained by replacing the nonlinear resistor of a Chua's oscillator with a flux‐controlled memristor. A recently developed technique, named flux‐charge analysis method, has shown that the state space of such circuits can be decomposed in invariant manifolds, where each manifold is characterized by a different dynamics and different attractors. Goal of the paper is to investigate the use of ...
Tópico(s): stochastic dynamics and bifurcation
2017 - Wiley | International Journal of Circuit Theory and Applications
Mauro Di Marco, Mauro Forti, Luca Pancioni,
Recent papers in the literature introduced a class of neural networks (NNs) with memristors, named dynamic-memristor (DM) NNs, such that the analog processing takes place in the charge-flux domain, instead of the typical current-voltage domain as it happens for Hopfield NNs and standard cellular NNs. One key advantage is that, when a steady state is reached, all currents, voltages, and power of a DM-NN drop off, whereas the memristors act as nonvolatile memories that store the processing result. Previous ...
Tópico(s): stochastic dynamics and bifurcation
2017 - Institute of Electrical and Electronics Engineers | IEEE Transactions on Neural Networks and Learning Systems
Mauro Di Marco, Mauro Forti, Luca Pancioni,
Recent work has considered a class of cellular neural networks (CNNs) where each cell contains an ideal capacitor and an ideal flux-controlled memristor. One main feature is that during the analog computation the memristor is assumed to be a dynamic element, hence each cell is second-order with state variables given by the capacitor voltage and the memristor flux. Such CNNs, named dynamic memristor (DM)-CNNs, were proved to be convergent when a symmetry condition for the cell interconnections is satisfied. ...
Tópico(s): stochastic dynamics and bifurcation
2016 - Institute of Electrical and Electronics Engineers | IEEE Transactions on Cybernetics
Mauro Di Marco, Mauro Forti, Luca Pancioni,
Summary The paper considers a feedback cellular neural network (CNN) obtained by interconnecting elementary cells with an ideal capacitor and an ideal flux‐controlled memristor. It is supposed that during the analogue computation of the CNN the memristors behave as dynamic elements, so that each dynamic memristor (DM)‐CNN cell is described by a second‐order differential system in the state variables given by the capacitor voltage and the memristor flux. The proposed networks are called DM‐CNNs, that ...
Tópico(s): stochastic dynamics and bifurcation
2016 - Wiley | International Journal of Circuit Theory and Applications
Mauro Di Marco, Mauro Forti, Paolo Nistri, Luca Pancioni,
This paper considers a class of nonsmooth neural networks with discontinuous hard-limiter (signum) neuron activations for solving time-dependent (TD) systems of algebraic linear equations (ALEs). The networks are defined by the subdifferential with respect to the state variables of an energy function given by the L 1 norm of the error between the state and the TD-ALE solution. It is shown that when the penalty parameter exceeds a quantitatively estimated threshold the networks are able to reach in ...
Tópico(s): Control Systems and Identification
2015 - Institute of Electrical and Electronics Engineers | IEEE Transactions on Cybernetics
Mauro Di Marco, Mauro Forti, Massimo Grazzini, Luca Pancioni,
The paper considers nonsmooth neural networks described by a class of differential inclusions termed differential variational inequalities (DVIs). The DVIs include the relevant class of neural networks, introduced by Li, Michel and Porod, described by linear systems evolving in a closed hypercube of Rn. The main result in the paper is a necessary and sufficient condition for multistability of DVIs with nonsymmetric and cooperative (nonnegative) interconnections between neurons. The condition is easily ...
Tópico(s): Topology Optimization in Engineering
2014 - Elsevier BV | Neural Networks
Mauro Di Marco, Mauro Forti, Massimiliano Grazzini, Luca Pancioni,
Recent papers have pointed out the interest to study convergence in the presence of multiple equilibrium points (EPs) (multistability) for neural networks (NNs) with nonsymmetric cooperative (nonnegative) interconnections and neuron activations modeled by piecewise linear (PL) functions. One basic difficulty is that the semiflows generated by such NNs are monotone but, due to the horizontal segments in the PL functions, are not eventually strongly monotone (ESM). This notwithstanding, it has been shown ...
Tópico(s): Neural dynamics and brain function
2012 - Institute of Electrical and Electronics Engineers | IEEE Transactions on Neural Networks and Learning Systems
Mauro Di Marco, Mauro Forti, Massimo Grazzini, Luca Pancioni,
This paper considers a nonsymmetric standard (S) cellular neural network (CNN) array with cooperative (nonnegative) interconnections between neurons and a typical three-segment piecewise-linear (PL) neuron activation. The CNN is defined by a one-dimensional cell-linking (irreducible) cloning template with nearest-neighbor interconnections and has periodic boundary conditions. The flow generated by the SCNN is monotone but, due to the squashing effect of the horizontal segments in the PL activations, ...
Tópico(s): Nonlinear Dynamics and Pattern Formation
2011 - Institute of Electrical and Electronics Engineers | IEEE Transactions on Circuits and Systems I Regular Papers
Mauro Di Marco, Mauro Forti, Massimo Grazzini, Luca Pancioni,
This paper considers a class of nonsymmetric cooperative neural networks (NNs) where the neurons are fully interconnected and the neuron activations are modeled by piecewise linear (PL) functions. The solution semiflow generated by cooperative PLNNs is monotone but, due to the horizontal segments in the neuron activations, is not eventually strongly monotone (ESM). The main result in this paper is that it is possible to prove a peculiar form of the Limit Set Dichotomy for this class of cooperative ...
Tópico(s): Advanced Memory and Neural Computing
2010 - Institute of Electrical and Electronics Engineers | IEEE Transactions on Circuits and Systems I Regular Papers
Mauro Di Marco, Mauro Forti, Massimo Grazzini, Luca Pancioni,
The paper analyzes some fundamental properties of the solution semiflow of nonsymmetric cooperative standard (S) cellular neural networks (CNNs) with a typical three-segment piecewise-linear (pwl) neuron activation. Two relevant subclasses of SCNNs, corresponding to one-dimensional circular SCNNs with two-sided or single-sided positive interconnections between nearest neighboring neurons only, are considered. For these subclasses it is shown that the associated solution semiflow satisfies the fundamental ...
Tópico(s): Advanced Memory and Neural Computing
2010 - World Scientific | International Journal of Bifurcation and Chaos
Mauro Di Marco, Mauro Forti, Massimiliano Grazzini, Paolo Nistri, Luca Pancioni,
This paper develops a Lyapunov approach for studying convergence and stability of a class of differential inclusions termed differential variational inequalities (DVIs). The DVIs describe the dynamics of a general system evolving in a compact convex subset of the state space. In particular, they include the dynamics of the full-range (FR) model of cellular neural networks (CNNs), which is characterized by hard-limiter nonlinearities with vertical segments in the i-v characteristic. The approach is ...
Tópico(s): Gene Regulatory Network Analysis
2008 - Institute of Electrical and Electronics Engineers | IEEE Transactions on Circuits and Systems I Regular Papers
Mauro Di Marco, Mauro Forti, Massimo Grazzini, Luca Pancioni,
Abstract This paper compares the dynamical behaviour of the standard (S) cellular neural networks (CNNs) and the full‐range (FR) CNNs, when the two CNN models are characterized by the same set of parameters (interconnections and inputs). The FR‐CNNs are assumed to be characterized by ideal hard‐limiter nonlinearities with two vertical segments in the i – v characteristic. The main result is that some basic conditions ensuring global exponential stability (GES) of the unique equilibrium point of S‐CNNs, ...
Tópico(s): Nonlinear Dynamics and Pattern Formation
2007 - Wiley | International Journal of Circuit Theory and Applications
Mauro Di Marco, Mauro Forti, A. Tesi,
This paper further investigates a basic issue that has received attention in the recent literature, namely, the robustness of complete stability of standard Cellular Neural Networks (CNNs) with respect to small perturbations of the nominal symmetric interconnections. More specifically, a class of third-order CNNs with a nominal symmetric interconnection matrix is considered, and the Harmonic Balance (HB) method is exploited for addressing the possible existence of period-doubling bifurcations, and ...
Tópico(s): Nonlinear Dynamics and Pattern Formation
2003 - World Scientific | Journal of Circuits Systems and Computers
Mauro Di Marco, Mauro Forti, A. Tesi,
It is known that additive neural networks with a symmetric interconnection matrix are completely stable, i.e., each trajectory converges toward some equilibrium point. This paper addresses the fundamental question of robustness of complete stability of additive neural networks with respect to small perturbations of the nominal symmetric interconnections. It is shown that in the general case, complete stability is not robust. More precisely, the paper considers a class of neural networks, and gives ...
Tópico(s): Advanced Memory and Neural Computing
2002 - Institute of Electrical and Electronics Engineers | IEEE Transactions on Circuits and Systems I Fundamental Theory and Applications
Mauro Di Marco, Mauro Forti, Paolo Nistri, Luca Pancioni,
This paper introduces a nonsmooth (NS) neural network that is able to operate in a time-dependent (TD) context and is potentially useful for solving some classes of NS-TD problems. The proposed network is named nonsmooth time-dependent network (NTN) and is an extension to a TD setting of a previous NS neural network for programming problems. Suppose C(t), t ≥ 0, is a nonempty TD convex feasibility set defined by TD inequality constraints. The constraints are in general NS (nondifferentiable) functions ...
Tópico(s): Control Systems and Identification
2015 - Institute of Electrical and Electronics Engineers | IEEE Transactions on Neural Networks and Learning Systems
Mauro Di Marco, Mauro Forti, Massimo Grazzini,
Abstract A recent work has introduced a class of neural networks for solving linear programming problems, where all trajectories converge toward the global optimal solution in finite time. In this paper, it is shown that global convergence in finite time is robust with respect to tolerances in the electronic implementation, and an estimate of the allowed perturbations preserving convergence is obtained. Copyright © 2006 John Wiley & Sons, Ltd.
Tópico(s): Machine Learning and ELM
2006 - Wiley | International Journal of Circuit Theory and Applications
Mauro Di Marco, Mauro Forti, A. Tesi,
The paper introduces a class of third-order nonsymmetric Cellular Neural Networks (CNNs), and shows through computer simulations that they undergo a cascade of period doubling bifurcations which leads to the birth of a large-size complex attractor. A major point is that these bifurcations and complex dynamics happen in a small neighborhood of a particular CNN with a symmetric interconnection matrix.
Tópico(s): Neural Networks and Applications
2002 - World Scientific | International Journal of Bifurcation and Chaos
Giacomo Innocenti, Mauro Di Marco, A. Tesi, Mauro Forti,
Since the introduction of memristors, it has been widely recognized that they can be successfully employed as synapses in neuromorphic circuits. This paper focuses on showing that memristor circuits can be also used for mimicking some features of the dynamics exhibited by neurons in response to an external stimulus. The proposed approach relies on exploiting multistability of memristor circuits, i.e., the coexistence of infinitely many attractors, and employing a suitable pulse-programmed input ...
Tópico(s): stochastic dynamics and bifurcation
2021 - Frontiers Media | Frontiers in Neuroscience
Mauro Di Marco, A. Tesi, Mauro Forti,
When the neuron interconnection matrix is symmetric, the standard Cellular Neural Networks (CNN's) introduced by Chua and Yang [1988a] are known to be completely stable, that is, each trajectory converges towards some stationary state. In this paper it is shown that the interconnection symmetry, though ensuring complete stability, is not in the general case sufficient to guarantee that complete stability is robust with respect to sufficiently small perturbations of the interconnections. To this end, ...
Tópico(s): Neural dynamics and brain function
2000 - World Scientific | International Journal of Bifurcation and Chaos
Mauro Di Marco, Mauro Forti, Fernando Corinto, Leon O. Chua,
The paper considers a relevant class of networks containing memristors and (possibly) nonlinear capacitors and inductors. The goal is to unfold the nonlinear dynamics of these networks by highlighting some main features that are potentially useful for real-time signal processing and in-memory computing. In particular, an analytic treatment is provided for dynamic phenomena as the presence of invariant manifolds, the coexistence of different regimes, complex dynamics and attractors and the phenomenon ...
Tópico(s): stochastic dynamics and bifurcation
2020 - Institute of Electrical and Electronics Engineers | IEEE Transactions on Circuits and Systems I Regular Papers
Tommaso Addabbo, Mauro Di Marco, Ada Fort, Elia Landi, Marco Mugnaini, Valerio Vignoli, Gianluca Ferretti,
In this paper, a system for the measurement of the instantaneous speed of rotating parts in turbines is proposed and modeled. The system is based on traditional and widely used variable reluctance sensors often mounted on-board for other purposes. By coupling this sensors with ad hoc rotating target (novel design) and front-end circuit, it is possible to extend the application of this sensor to accurate instantaneous angular speed monitoring, and to exploit it for torsional vibration monitoring or, ...
Tópico(s): Structural Health Monitoring Techniques
2018 - Institute of Electrical and Electronics Engineers | IEEE Transactions on Instrumentation and Measurement
Pierfrancesco Fusco, Vincenza Cofini, Emiliano Petrucci, Paolo Scimia, Giuseppe Paladini, Astrid Ursula Behr, Fabio Gobbi, Tullio Pozone, G. Danelli, Mauro Di Marco, Roberto Vicentini, Stefano Necozione, Franco Marinangeli,
Inguinal herniorrhaphy is a common surgical procedure. The aim of this investigation was to determine whether unilateral paravertebral block could provide better control of postoperative pain syndrome compared with unilateral subarachnoid block (SAB). A randomized controlled study was conducted using 50 patients with unilateral inguinal hernias. The patients were randomized to receive either paravertebral block (S group) or SAB (C group). Paravertebral block was performed by injecting a total of 20 ...
Tópico(s): Intraoperative Neuromonitoring and Anesthetic Effects
2016 - Lippincott Williams & Wilkins | Pain
Tommaso Addabbo, Ada Fort, Mauro Di Marco, Luca Pancioni, Valerio Vignoli,
We propose the design of Physically Unclonable Functions (PUFs) exploiting the nonlinear behavior of Cellular Neural Networks (CNNs). Our work derives from some theoretical results achieved within the theory of CNNs, adapted to a simpler case. The theoretical analysis discussed in this work has a general validity, whereas the presented basic hardware solution (i.e., the PUF electronic implementation) has to be understood as a reference demonstrating circuit to be further optimized and developed for ...
Tópico(s): Neuroscience and Neural Engineering
2013 - Institute of Electrical and Electronics Engineers | IEEE Transactions on Circuits and Systems I Regular Papers