Search

This paper addresses the leader tracking control problem for heterogeneous uncertain nonlinear multi-agent systems sharing information via a communication network, modeled as a directed graph. To solve the problem, we propose a novel distributed PID-like control strategy which, enhanced with a Lyapunov-based adaption mechanism for the control parameters, is able to counteract the uncertainties acting on the agents dynamics. The stability analysis analytically proves the effectiveness of the proposed PID protocol in ensuring the leader-tracking as well as the boundedness of the adaptive control gains. Numerical simulations, involving both the synchronization control problem for nonlinear harmonic oscillators and the practical engineering problem of the cooperative driving for autonomous connected vehicles, confirm the theoretical derivation and disclose the capability of the proposed strategy in achieving the control objective.

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.

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 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.

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.

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.

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.

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.

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.

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.

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.

Background: Neonatal infections represent one of the six main types of healthcare-associated infections and have resulted in increasing mortality rates in recent years due to preterm births or problems arising from childbirth. Although advances in obstetrics and technologies have minimized the number of deaths related to birth, different challenges have emerged in identifying the main factors affecting mortality and morbidity. Dataset characterization: We investigated healthcare-associated infections in a cohort of 1203 patients at the level III Neonatal Intensive Care Unit (ICU) of the “Federico II” University Hospital in Naples from 2016 to 2020 (60 months). Methods: The present paper used statistical analyses and logistic regression to identify an association between healthcare-associated blood stream infection (HABSIs) and the available risk factors in neonates and prevent their spread. We designed a supervised approach to predict whether a patient suffered from HABSI using seven different artificial intelligence models. Results: We analyzed a cohort of 1203 patients and found that birthweight and central line catheterization days were the most important predictors of suffering from HABSI. Conclusions: Our statistical analyses showed that birthweight and central line catheterization days were significant predictors of suffering from HABSI. Patients suffering from HABSI had lower gestational age and birthweight, which led to longer hospitalization and umbilical and central line catheterization days than non-HABSI neonates. The predictive analysis achieved the highest Area Under Curve (AUC), accuracy and F1-macro score in the prediction of HABSIs using Logistic Regression (LR) and Multi-layer Perceptron (MLP) models, which better resolved the imbalanced dataset (65 infected and 1038 healthy).

The Green Light Optimal Speed Advisory (GLOSA) system is a Cooperative Intelligent Transportation System (C-ITS) that is supposed to reduce the energy consumption and travel time associated with a vehicle trip by providing an optimal speed profile to avoid unnecessary stops at an intersection, based on Traffic Light Signals (TLSs) information. It is expected that GLOSA solutions will be widely deployed on transportation networks, thanks to the rapid spread of increasingly effective and pervasive communication technologies. Thus, appropriate methodologies and tools should be adopted to test sustainability objectives and evaluate the effects of such systems on the performance of traffic networks. In this perspective, this paper proposes an enhanced testing approach of GLOSA services based on an Integrated Simulation Environment that combines custom models developed in Matlab/Simulink with the SUMO traffic simulation environment, allowing both the traffic environment and vehicle dynamics to be represented with a suitable level of detail. The proposed approach can be used to cover several aspects which, in the existing technical literature, are: (i) not considered (traffic signal phase condition); (ii) rarely considered (electric engine); (iii) considered in a non-integrated way (traffic condition, TLS cycle duration, communication distance and minimum speed). Indeed, the considered factors pertain to different subsystems of the mobility environment (network and vehicles), and the proposed testing framework allows an equally detailed simulation of all, thus enhancing the accuracy of the results. To show the validity of the proposed approach, this study considers one controlled vehicle, equipped with a GLOSA system, travelling along a single route through a city centre including many TLSs. The simulation analysis deals with different levels of considered factors, assessed through Key Performance Indicators (KPIs) related to mobility and environmental indicators. Numerical results confirm that the proposed Integrated Simulation Environment can give valuable insights and suggestions to design a GLOSA system aiming to enhance both mobility and environmental performance. They also show that the factors in question affect system performance differently.

Nowadays, Internet-of-Things (IoT), big data, and cloud computing technologies allow increasing the throughput and quality of manufacturing systems, bringing to the rise of the Industry 4.0 paradigm. The aim is to leverage the ICT technologies to achieve a flexible customised production with reduced time while avoiding resources waste. In this framework, Zero-Defect Manufacturing (ZDM) concept plays a crucial role in guaranteeing the minimisation of defects and errors in industry processes by trying to act at the first time properly. Due to its importance, this topic has received greater attention during the last two years by the technical literature. Given the increasing number of research works and the several approaches suggested by researcher interested into the topic, this study aims at providing a literature overview of the current trends in the ZDM field from 2018 to 2020. The focus of the work is to provide a state-of-the-art about ZDM strategies, i.e. Detection, Repair, Prediction and Prevention, by analyzing the related most significant works, thus providing a single-strategy analysis and the corresponding most frequently used methods. A brief bibliometric study corroborates the general research patterns and the relevant aspects emerging from each single-strategy analysis. Finally, based on the conducted study, we point out the shortcomings present in the current technical literature to target the future research directions in the ZDM field.

Thanks to resource elasticity, cloud systems allow to build high performance applications by dynamically adapting resources to workload dynamics. In this paper, we present a novel approach for horizontally scaling cloud resources. The approach is based on an optimized feedback control scheme that leverages fuzzy logic to self-adjust its parameters in order to cope with unpredictable and highly time-varying public-cloud operating conditions. The proposed approach takes as input heterogeneous monitoring metrics related to distinct aspects of interest (i.e., CPU and network load) merged through a fitness function. Therefore, it is able to accomplish the application needs from different viewpoints. The extensive experimental evaluation performed in the Amazon EC2 environment showed how the proposed approach is robust against a number of realistic workloads—also when VM failures happen— and that it is flexible, as being suitable for applications with different needs. Finally, it also achieves better performance when compared to previously proposed solutions.

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"

Objectives In the context of the gradual development of artificial intelligence in health care, the clinical decision support systems (CDSS) play an increasing crucial role in improving the quality of the therapeutic and diagnostic efficiency in health care. The fuzzy logic (FL) provides an effective means for dealing with uncertainties in the health decision‐making process; therefore, FL‐based CDSS becomes a very powerful tool for data and knowledge management, being able to think like an expert clinician. This work proposes an FL‐based CDSS for the evaluation of renal function in posttransplant patients. Method Based on the data provided by the Department of Nephrology of the University Hospital Federico II of Naples, a statistical sample is selected according to appropriate inclusion criteria. Four fuzzy inference systems are implemented monitoring the renal function by the level of proteinuria and the glomerular filtration rate (GFR). Results The systems show an accuracy of more than 90% and the outputs are provided through easy to read graphics, so that physicians can intuitively monitor the patient's clinical status, with the objective to improve drugs dosage and reduce medication errors. Conclusions We propose that the CDSSs for the assessment and follow‐up of kidney‐transplanted patients built in this study are applicable to clinical practice.

Modern power distribution systems require reliable, self-organizing and highly scalable voltage control systems, which should be able to promptly compensate the voltage fluctuations induced by intermittent and non-programmable generators. However, their deployment in realistic operation scenarios is still an open issue due, for example, to the presence of non-ideal and unreliable communication systems that allow each component within the power network to share information about its state. Indeed, due to technological constraints, time-delays in data acquisition and transmission are unavoidable and their effects have to be taken into account in the control design phase. To this aim, in this paper, we propose a fully distributed cooperative control protocol allowing the voltage control to be achieved despite the presence of heterogeneous time-varying latencies. The idea is to exploit the distributed intelligence along the network, so that it is possible to bring out an optimal global behavior via cooperative distributed control action that leverages both local and the outdated information shared among the devices within the power network. Detailed simulation results obtained on the realistic case study of the IEEE 30-bus test system are presented and discussed in order to prove the effectiveness of the proposed approach in the task of solving complex voltage control problems. Finally, a robustness analysis with respect to both loads variations and hard communication delays was also carried to disclose the efficiency of the approach.

The large-scale integration of renewable power generators in power grids may cause complex technical issues, which could hinder their hosting capacity. In this context, the mitigation of the grid voltage fluctuations represents one of the main issues to address. Although different control paradigms, based on both local and global computing, could be deployed for online voltage regulation in active power networks, the identification of the most effective approach, which is influenced by the available computing resources, and the required control performance, is still an open problem. To face this issue, in this paper, the mathematical backbone, the expected performance, and the architectural requirements of a novel decentralized control paradigm based on dynamic agents are analyzed. Detailed simulation results obtained in a realistic case study are presented and discussed to prove the effectiveness and the robustness of the proposed method.

In this paper a new dynamic simulation model for the building energy performance analysis of multi-enclosed thermal zones, where rigid air temperature and humidity conditions must be kept, is presented. The model was implemented in a suitable computer code (DETECt 2.3.1) developed for research scopes. Such simulation model allows the hygrothermal analysis of buildings with multi-enclosed thermal zones surrounded by larger ones (e.g. display glass cases with valuable artefacts in museum halls, neonatal intensive care units for premature and full-term newborn babies in hospitals wards, etc.). For this purpose, a novel control algorithm, based on a model reference adaptive control scheme, enabling the online adaptation of the control gains, is implemented. Rigid air temperature and humidity conditions can be guaranteed also in case of sudden and rapid variations of hygrothermal loads. Through such new tool innovative techniques and operative strategies for obtaining energy efficiency and indoor comfort of special building spaces can be studied. In order to show the capabilities of the tool and the robustness of the adaptive algorithm, as well as the potentiality of the proposed multidisciplinary approach to the energy-related behaviour in buildings (based on building energy modelling and simulation and control theory), two meaningful case studies have been developed. In particular, they refer to the museums and hospital indoor spaces where enclosed thermal zones have to be kept under stringent hygrothermal conditions.

This paper presents a fully distributed interaction protocol for connected self-driving cars negotiating the access a traffic junction. The vehicles coordination has been reformulated as an equivalent virtual platoon control problem. The desired vehicles spacing within the platoon is chosen so to avoid the side and rear-end collisions and is controlled by a cooperative non-linear control algorithm, based on potential functions. The asymptotic stability of the closed-loop system has been analytically proved by leveraging the LaSalle's Invariance Principle. The analysis of the convergence rate has resulted in an effective tuning tool ensuring that the desired formation is achieved before vehicles reach the intersection. Most notably, results from an in-vehicle experimental validation are presented. The experiments were carried out on three self-driving cars connected through a V2V communication infrastructure based on the IEEE 802.11p protocol. The experimental results confirm the theoretical analysis and reveal the effectiveness of the control approach for autonomously and safely negotiating a generic traffic junction.

Autonomous driving is a safety critical application of sensing and decision-making technologies. Communication technologies extend the awareness capabilities of vehicles, beyond what is achievable with the on-board systems only. Nonetheless, issues typically related to wireless networking must be taken into account when designing safe and reliable autonomous systems. The aim of this work is to present a control algorithm and a communication paradigm over 5G networks for negotiating traffic junctions in urban areas. The proposed control framework has been shown to converge in a finite time and the supporting communication software has been designed with the objective of minimising communication delays. At the same time, the underlying network guarantees reliability of the communication. The proposed framework has been successfully deployed and tested, in partnership with Ericsson AB, at the AstaZero proving ground in Goteborg, Sweden. In our experiments, three autonomous vehicles successfully drove through an intersection of 235 square meters in a urban scenario., Comment: 15 pages, 16 figures, submitted to journal

The superficial injection needling botulinum (SINB) technique is the dermal injection of microdoses of botulin toxin, not by traditional syringe but with needling technique that consists in multiple microdroplets by electrical device. The intention is to decrease sweat and sebaceous gland activity to improve skin texture and sheen and to target the superficial layer of muscles that find attachment to the undersurface of the dermis causing visible rhytides. The technique is for treatment of face and neck by the injection of the botulin toxin into the dermis or in subdermal plane to improve skin texture, smoothen horizontal creases, and decrease vertical banding of the neck as well as to achieve better apposition of the platysma to the jawline and neck, improving contouring of the cervicomental angle. The botox solution is hyperconcentrated when compared to traditional dilution or compared to microbotox or mesobotox. Furthermore, the injection technique is different because spreading superficial microdroplets are not performed, but small, homogeneous, and controlled amounts of solution are injected. Each 0.8-mL syringe contains 50 units of onabotulinumtoxinA. The solution is delivered intradermally, using an electrical needling pen and setting the depth penetration of the needles at 3 to 3.5 mm. The 2 conjugated techniques play a 2-fold action on the skin. The technique was applied to a group of 63 patients dealing with face, forehead, cheekbones, and neck.La technique d’injection superficielle de botuline à l’aiguille (SINB d’après l’acronyme anglais) consiste à procéder à l’injection dermique de microdoses de toxine botulique, non pas à l’aide de la seringue habituelle, mais d’une technique à l’aiguille constituée de multiples microgouttes injectées par un dispositif électrique. On vise ainsi à réduire l’activité sudoripare et sébacée pour améliorer la texture et la brillance de la peau et cibler la couche superficielle des muscles attachés à la face inférieure du derme, responsables de rhytides visibles. La technique vise le traitement de la face et du cou par l’injection de toxine botulique dans le derme ou le plan sous-cutané pour améliorer la texture de la peau, lisser les plis horizontaux, réduire les bandes verticales du cou, obtenir une meilleure apposition du muscle peaucier sur les maxillaires et le cou et ainsi améliorer le contour de l’angle cervicomentonnier. La solution de botox est hyperconcentrée par rapport à la dilution habituelle ou à la dilution au microbotox ou au mésobotox. De plus, la technique d’injection est différente puisqu’on n’étend pas de microgouttes superficielles, mais qu’on injecte de petites quantités de solutions homogènes et contrôlées. Chaque seringue de 0,8 mL contient 50 unités d’onabotulinum toxine A. La solution est administrée par voie intradermique au moyen d’un stylo injecteur électrique dont la profondeur d’insertion se situe entre 3,0 et 3,5 mm. Les deux techniques conjuguées ont une double action sur la peau. La technique a été utilisée dans un groupe de 63 patients, sur le visage, le front, les pommettes et le cou.

In this paper a novel optimal Model Reference Adaptive Control approach, developed to tame the thermohygrometric behaviour of buildings as well as to guarantee the indoor comfort, is presented. The main advantages of the proposed method are: (i) no-need of a priori knowledge of the specific building dynamics due to climate, occupants’ behaviour, building use, envelope features and utility rates; (ii) robustness with respect to a large class of perturbations, external disturbances, nonlinear unmodelled dynamics or parameters uncertainty; (iii) ability to impose desired optimal dynamics; (iv) accurate regulation and fast tracking of indoor air temperature and humidity in the case of stringent requirements in special building spaces (e.g. hospitals, museums, laboratories, etc.). In order to investigate the effectiveness of the proposed method, the developed strategy has been implemented in the new release of a white-modelling building energy performance simulation code, called DETECt 2.3. The tool enables multi-zone building simulation analyses and is capable to dynamically predict: (i) spaces sensible and latent heating and cooling demands and loads; (ii) indoor air temperatures and humidity, as well as building envelope internal and external temperatures; (iii) the performance of phase change materials (PCM) embedded in building enclosures and for any layer configuration; (iv) the thermohygrometric comfort of occupants. In order to analyse the effectiveness and robustness of the proposed control strategy, several case studies have been carried out. They refer to some reference buildings with different geometry, use and construction materials (also including PCM integrated into the building envelope) simulated in different weather conditions. For each case study, both continuous and intermittent control system regimes have been considered. Results confirm the ability of the developed approach to achieve the selected indoor air temperature and humidity conditions in order to guarantee indoor comfort in uncertain conditions. The numerical analysis is complemented with the rigorous analytical proof of asymptotic stability.

A novel dynamic simulation model, called DETECt 2.3.1, purposely developed for thermo-hygrometric energy performance analyses of multi-zone buildings, is presented. Relevant novelties vs. previous implemented code releases are discussed. They regard: i) the simulation model for building spaces with multi-enclosed thermal zones; ii) a novel temperature-humidity control algorithm based on a reference adaptive control scheme. Such algorithm enables the online adaptation of the control gains, in order to overcome the well-known problems of classical fixed gain control algorithms. With the aim to show the features and the potentialities of the simulation code coupled with this new control scheme, a suitable case study related to special indoor hospitals spaces including multiple infant-incubators is developed. The robustness of the designed control approach is confirmed through the good regulation performance obtained for both the indoor air temperature and humidity, simultaneously.