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In this paper, the containment control problem of heterogeneous uncertain high-order linear Multi-Agent Systems (MASs) is addressed and solved via a novel fully-Distributed Model Reference Adaptive Control (DMRAC) approach, where each follower computes its adaptive control action on the basis of local measurements, information shared with neighbors (within the communication range) and the matching errors w.r.t. its own reference model, without requiring any previous knowledge of the global directed communication topology structure. The approach inherits the robustness of the direct model reference adaptive control (MRAC) scheme and allows all agents converging towards the convex hull spanned by leaders while fulfilling at the same time local additional performance requirements at single-agent level, such as prescribed settling time, overshoot, etc. The asymptotic stability of the whole closed-loop network is analytically derived by exploiting the Lyapunov theory and the Barbalat lemma, hence proving that each follower converges to the convex hull spanned by the leaders, as well as the boundedness of the adaptive gains. Extensive numerical analysis for heterogeneous MAS composed of stable, unstable and oscillating agent dynamics are presented to validate the theoretical framework and to confirm the effectiveness of the proposed approach.

This paper addresses the leader-tracking and the containment control problems for heterogeneous high-order Multi-Agent Systems (MASs) sharing information through a directed communication topology. To solve both the control problems, a fully-distributed Proportional-Integral-Derivative (PID) control strategy is proposed, whose stability is analytically proven by exploiting the regulator equations and the Static Output Feedback (SOF) procedure adapted to the MASs framework. The application of SOF allows recasting the PID control design problem into a state-feedback control design one and, hence, finding the proper values of the proportional, integral and derivative actions via classical state-feedback approaches, such as the Linear-Quadratic-Regulator (LQR) strategy. Numerical simulations confirm the effectiveness of the proposed approach in guaranteeing that each follower tracks the leader behavior in the case of leader-tracking and that each follower converges to the convex hull spanned by the multiple leaders in the case of containment control.

Skin regeneration is a multidisciplinary topic that involves plastic surgery, aesthetics, and dermatology. In the last few years, several molecules have been used to prevent or delay the skin aging process. Recently, promising investigations on novel β-1,3/1,6-glucan-based treatments, associated with sodium hyaluronate, have demonstrated the induction of a naive biological stimulus in soft tissues. Serum Revitalizing Face (Lot 00118a0051 of 11/19/2018 approved by the Italian Ministry of Health as a Medical Device-Class I) was administered mainly on the face and forearms of the enrolled patients. The Serum Revitalizing Face was administered at T0 (first week), T1 (second week), and T2 (third week). Patients were asked to apply 1.5 mL of the serum on their skin until the product was completely absorbed. All patients were also examined by clinical assessments at different time points. The results showed that a daily application of the β-1,3/1,6-glucan induced a statistically significant increase in the elasticity of the skin.

In this paper we present a distributed PI-based control law that guarantees the platoon formation to track leader velocity with a pre-fixed inter-vehicular distance, when vehicles are heterogeneous in both parameters and nonlinear drivetrain dynamics. Moreover, differently from most of the approaches in the literature, the proposed PI protocol intrinsically compensates for the nonlinear, heterogeneous and uncertain drivetrain dynamics without requiring any feedforward control action. We formulate sufficient conditions for closed-loop heterogeneous non-linear vehicular network stability that can be used to tune the PI control parameters. Simulation results confirm the effectiveness of the proposed PI controller in both nominal and uncertain platooning scenario.

The accurate analysis of Electrocardiogram waveform plays a crucial role for supporting cardiologist in detecting and diagnosing the heartbeat disorders. To improve their detection accuracy, this work is devoted to the design of a novel classification algorithm which is composed of a cascade of two convolutional neural network (CNN), i.e a Binary CNN allowing the detection of the arrhythmic heartbeat and a Multiclass CNN able to recognize the specific disorder. Moreover, by combining the cascade architecture solution with a rule-based data splitting, which leverages the subject-exclusive and balances among the classes criteria, it is possible predicting the health status of unseen patients. Numerical results, carried out considering Massachusetts Institute of Technology-Beth Israel Hospital arrhythmia database, disclose a classification accuracy of $$96.2\%$$ 96.2 % . Finally, a cross-database performance evaluation and a comparison analysis w.r.t. the current state-of-art further disclose the effectiveness and the efficiency of the proposed solution, as well as its benefits in terms of patient health status prediction.

This paper presents a test rig developed for the experimental investigations on a vibration isolation system, based on the use of magnetorheological elastomers (MRE). These intelligent materials allow to adapt their stiffness to the needs of the isolation as the material stiffness depends on the intensity of the magnetic field in which it is immersed; it is possible to modify the material stiffness, quickly and reversibly, by means of coils. The paper describes an isolation system, based on MRE pads, and the test rig developed for the experimental tests. The rig allows to experimentally study the effectiveness of different control algorithms adopted to adjust the material stiffness, considering that the stiffness of the MRE pads can only be increased by activating the material.

Internet of Vehicle (IoV) has become a hot topic in the last years due to its significant benefits in transportation systems in terms of quality, costs, and traffic management capability. Indeed, accurate and real-time information acquisition and diffusion are required to reach an efficient traffic management system according to the smart city paradigm. In this context, this article provides a novel IoV-based distributed double layer control architecture to address the coordination control problem of connected autonomous vehicles (CAVs) multiple platoons negotiating the access to a road section restriction in the presence of heterogeneous communication time-varying delays. To this aim, leveraging the IoV paradigm and the equivalent virtual formation concept, we propose a novel distributed diffusive longitudinal protocol and a distributed lateral potential-function-based control strategy, where both of them are able to cope with communication delays and ensuring the multiplatoon coordination in finite time, i.e., before accessing the road section restrictions, so to avoid vehicles collisions. The finite-time stability of both the cooperative controllers is analytically proved by exploiting the Lyapunov-Krasovskii theory and the derived delay-dependent stability conditions, expressed as a set of linear matrix inequalities (LMIs), allows the proper tuning of the controllers gains. Numerical analysis confirms the theoretical derivation and discloses the effectiveness and the robustness of the proposed cooperative multiplatoon control in ensuring the safe crossing of the road section restriction in finite time.

This technical note addresses the formation stabilization problem for Multi-Agent Systems composed of dynamical agents moving within an obstacle-cluttered environment and sharing information via non ideal wireless communication networks. A novel distributed cooperative Navigation Functions-based control strategy is proposed, that drives the MAS to a desired formation without any collision, while counteracting the presence of unavoidable communication impairments originated by the wireless network. By recasting the formation stabilization problem into a consensus one and by combining the Lyapunov stability theory with Halanay's Lemma, Uniformly Ultimately Bounded stability of the whole delayed closed-loop system is proved. In the special case of an obstacle-free environment, our approach guarantees exponential stability of the closed-loop networked system. The stability analysis also provides an estimation of the delay-upper bound and allows to evaluate the stability margins with respect to the latencies that can be observed in practical application scenarios. Theoretical derivations are verified through non-trivial simulations.

This work presents an ACC-like longitudinal controller for an autonomous electric vehicle, named Ego-Vehicle, based on a Deep Deterministic Reinforcement Learning algorithm. More specifically, the designed algorithm exploits the use of the Deep Deterministic Policy Gradient (DDPG) agent and the reward function explicitly takes into account both the speed and position error of the Ego-Vehicle w.r.t. the preceding one. After properly training the DDPG agent, the control ACC-like strategy is validated considering a realistic driving cycle for the preceding vehicle. Numerical results confirm the effectiveness of the designed strategy.

This paper addresses the problem of computing an eco-driving speed profile for an autonomous electric vehicle traveling along curvy roads while ensuring path following/car following functionalities w.r.t. possible preceding vehicles ahead. To solve it, we propose a double-layer control architecture combining the classical Adaptive Cruise Control with a Nonlinear Model Predictive Control. This latter is designed so to drive the autonomous vehicle along a predefined path while guaranteeing the maintenance of a safe distance w.r.t. a predecessor vehicle ahead and ensuring energy-saving consumption. The appraised control-oriented design model is non-linear and the energy consumption one explicitly accounts for the cornering effects. Numerical results confirm the effectiveness of the proposed control architecture and disclose its ability in guaranteeing energy saving.

This study addresses the leader-tracking problem for linear multi-agent systems in the presence of both parameter model uncertainties and time-varying communication delays. To solve the robust output consensus problem, a delayed distributed proportional–integral–derivative control is proposed and the overall closed-loop stability is proven by exploiting the Lyapunov–Krasovskii theory. Delay-dependent robust stability conditions are given via linear matrix inequalities which allow the proper tuning of robust control gains. The effectiveness of the theoretical derivation is confirmed through a numerical analysis in the practical application domain of cooperative driving for connected vehicles.

Magneto-rheological elastomers (MRE), consisting of elastomeric matrix containing ferromagnetic particles, are a kind of smart material, whose mechanical properties are controllable via applied magnetic fields. In this paper, the possibility of adopting these materials to realize vibration isolators for lightweight structures is evaluated. Such isolators must be stiff enough in the vertical direction, to support the structure weight, while they must have a low horizontal stiffness to isolate ground vibrations originated by different sources. To meet these requirements, an isolation system, constituted by MRE pads and ball transfer units (BTU), is proposed. The BTUs support the structure weight allowing it to move in any horizontal direction, while the MRE pads provide a controllable horizontal restoring force. Therefore, the pad stiffness may be chosen considering only the horizontal isolation characteristics, regardless of the vertical ones. The paper describes the isolator layout, the criteria followed to make up a set of MRE specimens and the experimental set-up adopted to characterize them. The dynamic behaviour of the isolated structure and the isolator performances are described by means of numerical simulations. The analytical description of the isolator restoring force was deduced adopting a generalized Maxwell model in parallel with a Bouc–Wen element whose coefficients were identified from the experimental test results.

Longitudinal control for platoons of connected autonomous vehicles is a hot research topic in the Cooperative Intelligent Transport Systems (C-ITSs) domain. Most of the existing results solve the platooning problem asymptotically, without ensuring that the consensus could be achieved in a finite settling time. To this aim, in this work we address the problem of guaranteeing the leader-tracking for heterogeneous vehicles platoons in a fixed time despite the presence of external disturbances. To solve this problem, by exploiting the integral sliding mode (ISM) approach and the Lyapunov theory, we propose a distributed control strategy able to ensure the leader-tracking in a finite settling time which is independent from any vehicles initial conditions. The simulation analysis, carried out in two different driving scenarios, confirms the effectiveness of the theoretical derivation.

Drugs injected intradermally spread slowly into the underlying tissues and produce a drug-saving effect. The Italian society of mesotherapy suggested that intradermal therapy obtains analgesic effect on localized pain, with a lower risk of systemic drug interactions. We report a case of post-herpetic pain successfully treated by this technique. This case confirms that the intradermal administration technique (mesotherapy), which is based on the pathophysiology of the disorder, according to the recommendations, can contribute to the management of patients who do not tolerate standard therapies.

In this paper, the robust finite-time leader-following consensus problem for homogeneous uncertain linear Multi-Agent Systems (MASs) in the presence of time-varying communication delays is addressed and solved via a distributed time-delay PID-like control strategy. To analytically prove the robust finite-time stability of the resulting delayed neutral-type closed-loop MAS, we leverage both the descriptor transformation and the Lyapunov–Krasovskii theory. Delay-dependent finite-time stability conditions are expressed as a set of Linear Matrix Inequalities (LMIs), whose solutions allows obtaining both the weighted ℒ 2 gain and the state trajectories bound. Numerical results confirm the theoretical derivation and the effectiveness of the proposed approach in guaranteeing that each agent converges towards the leader reference behavior in a finite-time interval despite the presence of both time-varying delays and external disturbances.

This paper addresses and solves the secondary voltage regulation control problem in inverter-based islanded Microgrids (MGs) via a fully distributed delayed sampled-data PID controller, whose derivative action is approximated using finite difference. By choosing a small enough sampling period and leveraging artificial delays approach, the proposed strategy ensures the secondary voltage regulation with closed-loop performances similar to ones achievable via a continuous-time PID controller, but with a significant reduction of the communication burden, while improving the efficiency of the entire MG. Exponential stability of the closed-loop MG network is analytically proved via Lyapunov-Krasovskii theory and the derived sampling-dependent stability conditions are expressed as a set of LMIs, whose solution allows finding the weighted L2 gain. Finally, a detailed simulation analysis confirms the effectiveness and the robustness of the proposed approach.

The development of Connected and Autonomous Vehicles (CAVs) has the potential to improve the energy efficiency of the transportation system. Since the inter-vehicle distance plays a crucial role for energy-saving purposes, this paper proposes a novel optimization algorithm to compute the optimal gap distance in a heterogeneous platoon of electric CAVs by exploiting a distance-dependent air drag coefficient formulation. Specifically, the proposed method exploits the leader speed and acceleration profiles information, as well as road slope one, to compute the optimal inter-vehicle distance w.r.t. the preceding vehicle so as to reduce the air-drag coefficient and, hence, reduce energy consumption. The proposed algorithm exploits Nonlinear Programming method, taking into account safety constraints in order to avoid collisions among vehicles. The effectiveness of the approach is evaluated via the Matlab/Simulink simulation platform by considering two driving scenarios, namely base scenario, where the Constant Time Headway spacing policy is adopted, and optimized scenario, where the proposed algorithm is exploited to compute the optimal inter-vehicle distances. Numerical results confirm the effectiveness of the proposed optimization strategy in reducing energy consumption w.r.t. base scenario.

In this paper we consider the problem of restoring the voltage for stand-alone inverter-based Microgrids despite the effects of the time-delays arising with the information exchange among the electrical busses. To guarantee that all Distributed Generators (DGs) reach in a finite-time and maintain the voltage set-point, as imposed by a virtual DG acting as a leader, we suggest a novel robust networked-based control protocol that is also able to counteract both the time-varying communication delays and natural fluctuations caused by the primary controllers. The finite-time stability of the whole Microgrid is analytically proven by exploiting Lyapunov-Krasovskii theory and finite-time stability mathematical tools. In so doing, delay-dependent stability conditions are derived as a set of Linear Matrix Inequalities (LMIs), whose solution allows the proper tuning of the control gains such that the control objective is achieved with required transient and steady-state performances. A thorough numerical analysis is carried out on the IEEE 14-bus test system. Simulation results corroborate the analytical derivation and reveal both the effectiveness and the robustness of the suggested controller in ensuring the voltage restoration in finite-time in spite of the effects of time-varying communication delays.

This paper deals with the exponential leader-tracking consensus control problem of high-order Multi-Agent Systems (MASs) sharing information via a non-ideal communication network. To emulate a more realistic environment, a specific time-varying delay has been associated with each communication link within the network, whose value, at each time instant, depends on the real conditions of the communication channel. To solve this problem, a fully-distributed delayed Proportional-Integral (PI) control protocol able to guarantee the exponential stability of the entire delayed closed-loop MAS is proposed. The stability of this latter is analytically proved by exploiting Lyapunov-Krasovskii theory combined with Halanay Inequality, thus obtaining exponential stability conditions expressed as a feasible Linear Matrix Inequality (LMI) problem. Exemplary numerical simulations corroborate the effectiveness of the theoretical derivation.

In this paper, we suggest a novel Ecological Adaptive Cruise Control (Eco-ACC) system for an autonomous electric vehicle able to drive its motion while minimizing as much as possible its energy consumption. To this aim, we consider a Nonlinear Model Predictive Control (NMPC) method enhanced with an off-line Computational-intelligence (CI)-based optimization algorithm, i,e. the Improved-Grey Wolf Optimizer (I-GWO). Specifically, since the control performances strongly depend on the proper selection of the NMPC cost function, we propose the I-GWO algorithm to help the control designer find the sub-optimal weighting factors of the dynamic cost function optimized via the NMPC. An extensive numerical analysis involving realistic vehicle dynamics and a real-life Italian road network route confirm the effectiveness of the proposed approach in guaranteeing the ACC control objectives while ensuring energy saving.

In this paper we show how off-the-shelf tools can be used for the rapid prototyping of electronic fuel injection and ignition control systems that rely on a middleware developed by STMicroelectronics for its Electronic Control Units (ECUs). This middleware abstracts the functionalities of the ECU, whose architecture is based on the Generic Timer Module and on the L9177A component, by exposing to the developers a set of high-level services that are agnostic from their low-level implementation. By exploiting the Mathworks Legacy Code Tool, the middleware Application Programming Interfaces (APIs) have been included in the Simulink® environment. Moreover, thanks to the Embedded Coder®, automatic code generation for rapid prototyping of the control software has been enabled. Therefore, in the proposed framework, the designers can focus on the design and validation of the control algorithms in a model-based design environment, by ignoring the details of the target hardware. Automatic code generation is then exploited to deploy the control algorithm on the target ECU, to perform further hardware-in-the-loop validation of the control algorithms. The paper gives an overview of the middleware and of its integration within the Simulink® environment. Moreover, the effectiveness of the rapid prototyping capabilities are discussed by means of two examples.

Intradermal therapy, known as mesotherapy, is a technique used to inject a drug into the surface layer of the skin. In particular, it involves the use of a short needle to deposit the drug in the dermis. The intradermal microdeposit modulates the drug’s kinetics, slowing absorption and prolonging the local mechanism of action. It is successfully applied in the treatment of some forms of localized pain syndromes and other local clinical conditions. It could be suggested when a systemic drug-sparing effect is useful, when other therapies have failed (or cannot be used), and when it can synergize with other pharmacological or nonpharmacological therapies. Despite the lack of randomized clinical trials in some fields of application, a general consensus is also reached in nonpharmacological mechanism of action, the technique execution modalities, the scientific rationale to apply it in some indications, and the usefulness of the informed consent. The Italian Mesotherapy Society proposes this position paper to apply intradermal therapy based on scientific evidence and no longer on personal bias.

The development of autonomous connected vehicles, moving as a platoon formation, is a hot topic in the intelligent transportation system (ITS) research field. It is on the road and deployment requires the design of distributed control strategies, leveraging secure vehicular ad-hoc networks (VANETs). Indeed, wireless communication networks can be affected by various security vulnerabilities and cyberattacks leading to dangerous implications for cooperative driving safety. Control design can play an important role in providing both resilience and robustness to vehicular networks. To this aim, in this article, we tackle and solve the problem of cyber-secure tracking for a platoon that moves as a cohesive formation along a single lane undergoing different kinds of cyber threats, that is, application layer and network layer attacks, as well as network induced phenomena. The proposed cooperative approach leverages an adaptive synchronization-based control algorithm that embeds a distributed mitigation mechanism of malicious information. The closed-loop stability is analytically demonstrated by using the Lyapunov-Krasovskii theory, while its effectiveness in coping with the most relevant type of cyber threats is disclosed by using PLEXE, a high fidelity simulator which provides a realistic simulation of cooperative driving systems.

Publisher's version (útgefin grein), Coronary artery disease is one of the most prevalent chronic pathologies in the modern world, leading to the deaths of thousands of people, both in the United States and in Europe. This article reports the use of data mining techniques to analyse a population of 10,265 people who were evaluated by the Department of Advanced Biomedical Sciences for myocardial ischaemia. Overall, 22 features are extracted, and linear discriminant analysis is implemented twice through both the Knime analytics platform and R statistical programming language to classify patients as either normal or pathological. The former of these analyses includes only classification, while the latter method includes principal component analysis before classification to create new features. The classification accuracies obtained for these methods were 84.5 and 86.0 per cent, respectively, with a specificity over 97 per cent and a sensitivity between 62 and 66 per cent. This article presents a practical implementation of traditional data mining techniques that can be used to help clinicians in decision-making; moreover, principal component analysis is used as an algorithm for feature reduction., The authors wish to thank Alec Shawn for his contribute as regards "grammar and spell check". This work has been realized thanks to the collaboration of the Department of Advanced Biomedical Sciences of the University Hospital "Federico II" of Naples. The authors wish to thank Sabrina De Vita, Francesca D'Agostino, Giuseppina Toscano and Tania Di Monda for their valuable contribution to the implementation of data mining algorithm during their MS thesis. The work has been partially carried out under TablHealth [CUP B49J17000720008] project and AK12 s.r.l.", "Peer Reviewed"

The development of Intelligent Transportation Systems (ITS) aims to improve the performance of transport systems in terms of efficiency, safety and energy saving. Among these services, the Variable Speed Limit (VSL) systems are widely used to mitigate the problem of traffic congestion by changing the road speed limits in response to traffic, incidents, and/or weather conditions. This paper investigates the effects of a VSL system on a freeway road segment of the city of Naples. The control strategy leverages volume, occupancy and average speed data, provided by loop detectors located along the road, for adapting dynamically the road speed limit. Numerical simulations were carried out by exploiting VISSIM microscopic traffic simulator, which embedded the control strategy, for two different scenarios: with and without the VSL system. Results disclose the effectiveness of the VSL control strategy in improving road efficiency by reducing speed variance and increasing both average speed and volume of vehicles. Moreover, the VSL system has also proved to have environmental benefits, since it allows reducing fuel consumption.

Railway infrastructure operators need to push their network capacity up to their limits in high-traffic corridors. Virtual coupling is considered among the most relevant innovations to be studied within the European Horizon 2020 Shift2Rail Joint Undertaking as it can drastically reduce headways and thus increase the line capacity by allowing to dynamically connect two or more trains in a single convoy. This paper provides a proof of concept of Virtual coupling by introducing a specific operating mode within the European rail traffic management system/European train control system (ERTMS/ETCS) standard specification, and by defining a coupling control algorithm accounting for time-varying delays affecting the communication links. To that aim, we define one ploy to enrich the ERTMS/ETCS with Virtual coupling without changing its working principles and we borrow a numerical analysis methodology used to study platooning in the automotive field. The numerical analysis is also provided to support the proof of concept with quantitative results in a case-study simulation scenario.