587 research outputs found
Control predictivo para seguimiento de sistemas no lineales. Aplicación a una planta piloto
No full textEste artículo trata el problema del diseño de un controlador predictivo para seguimiento de referencias cambiantes, en el caso de sistemas no lineales. Los controladores predictivos proveen leyes de control adecuadas para regular sistemas lineales o no lineales a un punto de equilibrio dado garantizando la satisfacción de restricciones y la estabilidad asintótica. Pero si este punto de equilibrio cambia, el controlador podría perder la estabilidad o incluso la factibilidad y por lo tanto sería incapaz de seguir la referencia deseada. En (Ferramosca et al., 2009a) se ha propuesto un controlador predictivo para seguimiento de referencias capaz de garantizar factibilidad y convergencia al punto de equilibrio a pesar de los cambios que este pueda sufrir. En este artículo, este controlador se utiliza para controlar en tiempo real una planta piloto de procesos. Los resultados obtenidos demuestran que el controlador predictivo para seguimiento es capaz de controlar plantas con dinámicas no lineales y restricciones. El experimento
demuestra cómo el controlador garantiza estabilidad, factibilidad y convergencia también en caso de referencias no alcanzables
Optimization approaches in distributed systems
No full textThe necessity of performing multiple complex tasks in short time in a typical Industry 4.0 environment has enlightened the need to improve computational and storage infrastructures and ensure efficient communication between machines in modern manufacturing plants.
Most of these processes are pure digital and can be delivered directly in the Cloud, thus ensuring more resilience and performance. When Cloud resources are delivered in distributed fashion, the global computational effort is spread across all the network, whereas when the process is also decentralized there’s not a single point of decision in system behaviour. As a result, the innate features of distributed and decentralized systems contribute to decreasing the execution time of the tasks and preventing single points of failure in the infrastructure.
Within the scope of these systems, a great variety of optimization problems can be implemented. Some of these problems pertain to classical linear or nonlinear approaches, whereas others to Machine Learning techniques, such as Artificial Neural Networks and Deep Reinforcement Learning (DRL), or Swarm Intelligence algorithms.
The main research direction of this thesis is the implementation of some common optimization problems in distributed systems. In detail, in part I, the application of the task assignment problem in Blockchain-based environments is proposed in a typical manufacturing environment. Two different original architectures, the first in Ethereum and the second in Hyperledger Fabric, are described with the scope of consuming multiple complex digital processes, such as data mining problems, heavy file format conversion, and 3D rendering, in a parallel fashion in multi agent environments.
In both proposed architectures, task delivery is orchestrated by a Smart Contract. The main goal is to find the agent that executes the required task in least time. To this end, a task runtime prediction algorithm is designed by the means of an Artificial Neural Network in the first case and a Deep Reinforcement Learning algorithm in the second case. While in the first case, the main contribution is the combined use of Blockchain with some popular cloud technologies, such as Docker containers and Cloud Storage, the main advantages of the second implementation are that the Smart Contract implements an auction and bidding scheme to deliver tasks, and that the agents learn how to make predictions as they collect new experiences.
In addition, a third Blockchain-based architecture is proposed in the context of energy management in which a penalty-reward optimization scheme for the users in a district is implemented based on their consumption.
Still in the context of manufacturing, a general model for mass production systems based on Timed Coloured Petri Net and Particle Swarm Intelligence is proposed in part II. In the model, the Flexible Job Shop Sequence Problem is approached by the means of a Particle Swarm Optimization algorithm that can be efficiently implemented in a distributed environment.
Finally, in part III, a cooperative and distributed-oriented multi-agent DRL scheme is proposed in the domain of autonomous driving. In detail, the problem of autonomous intersection managements at unsignalized intersections is investigated in a complex scenario in which different classes of vehicles are involved including Connected Autonomous Vehicles, Connected Only emergency vehicles with high priority and regular unconnected vehicles. The main goal of the proposed scheme is to optimize traffic flow, ensure priorities and prevent collisions. The main contributions of the proposed approach are the novel state representation of the intersection state, which is also partially observable, the structure of the global reward function and subsequent implementation of Proximal Policy Optimization (PPO) algorithm to determine the best policy
Da Abella Salernitana a EcoFoodFertility - GLI STUDI SUL SEME e la sua storia millenaria
No full textLe malattie mitocondriali
No full textLe malattie mitocondriali, conosciute anche come mitocondriopatie, sono un gruppo eterogeneo di patologie ereditarie che, pur pre- sentando una notevole variabilità clinica relativamente all’età di insorgenza, il tipo di evoluzione ed i tessuti coinvolti, sono caratterizzate dalla presenza di alterazioni nel funzionamento dei mitocondri. L’alterato funzionamento dei mitocondri determina, nei tessuti interessati, la comparsa di un deficit energetico, in quanto questi organelli sono responsabili della sintesi di ATP mediante il processo della fosforilazione ossidativa (OXPHOS). Ad essere maggiormente colpiti sono quindi i tessuti che presentano una maggiore richiesta energetica, come il muscolo ed il cervello. Per questo motivo, le malattie mitocondriali sono spesso definite “encefalomiopatie”
Cooperative MPC with Guaranteed Exponential Stability
No full textIn this chapter, a cooperative distributed MPC is presented. The main features of this control strategy are: constraints satisfaction; cooperation between agents to achieve an agreement; closed-loop stability that is always ensured, even in the case of just one iteration; achieved control actions that are plantwide Pareto optimal and equivalent to the centralized solution; Pareto optimality is achieved also in case of coupled constraints; a coordination layer is not needed. It is proved that cooperative MPC is a particular case of suboptimal MPC; exponential stability is then proved, based on exponential stability of suboptimal centralized MPC
Switched NMPC for epidemiological and social-economic control objectives in SIR-type systems
No full textSeveral optimal control strategies have recently been developed to minimize the infected peak prevalence or the epidemic final size. Although these two indexes are critical to assess any control policy tending to mitigate an epidemic by means of non-pharmaceutical measures, they are usually considered separately and, in general, no consensus has been reached about how to simultaneously handle them in a simple and realistic way (i.e., accounting for the limitations in the control actions, avoiding new cycles of infections or reboundings, considering side effects, etc.) Here, based on a theoretical dynamical analysis of SIR-type models, a realistic nonlinear model predictive control strategy is proposed. Apart from minimizing the epidemic final size and keeping the infected peak prevalence under an established value, the controller accounts for feedback uncertainty and different actuator constraints, such as a limited number of social distancing policies, which may remain active for a minimal and a maximal time interval. Several simulations considering different SIR-type models illustrate the benefits of the proposal
A worldwide empirical analysis of the accounting behaviour in the waste management sector
No full textDrawing on stakeholder theory, the premise in this manuscript is that moral and ethical behavior in terms of correct financial information contribute to higher sustainable performance that satisfies the wide range of stakeholders who are interested in the economic feasibility and environmental viability of waste management firms. On the basis of a scientific literature review and by using a balanced panel data set of 416 waste management firms worldwide over the period 2013–2016, the empirical evidence shows that ownership structures (e.g. governmental, institutional, corporate group, family, and concentrated) as well as corporate governance characteristics (e.g. size of the board, directors’ gender, nationality, and expertise) diversely affect waste management firms’ accounting behavior in terms of both discretionary accruals and earnings smoothness. The findings bring into focus the “black boxes” of ownership structures and corporate governance encouraging the policy makers to shape up laws that can constrain accounting misbehavior in waste management firms
Differences in psychopathology and behavioral characteristics of patients affected by conversion motor disorder and organic dystonia
No full textAdriana Pastore, Grazia Pierri, Giada Fabio, Silvia Ferramosca, Angelo Gigante, Maria Superbo, Roberta Pellicciari, Francesco Margari Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari “Aldo Moro”, Bari, Italy Purpose: Typically, the diagnosis of conversion motor disorder (CMD) is achieved by the exclusion of a wide range of organic illnesses rather than by applying positive criteria. New diagnostic criteria are highly needed in this scenario. The main aim of this study was to explore the use of behavioral features as an inclusion criterion for CMD, taking into account the relationship of the patients with physicians, and comparing the results with those from patients affected by organic dystonia (OD). Patients and methods: Patients from the outpatient Movement Disorder Service were assigned to either the CMD or the OD group based on Fahn and Williams criteria. Differences in sociodemographics, disease history, psychopathology, and degree of satisfaction about care received were assessed. Patient–neurologist agreement about the etiological nature of the disorder was also assessed using the k-statistic. A logistic regression analysis estimated the discordance status as a predictor to case/control status. Results: In this study, 31 CMD and 31 OD patients were included. CMD patients showed a longer illness life span, involvement of more body regions, higher comorbidity with anxiety, depression, and borderline personality disorder, as well as higher negative opinions about physicians’ delivering of proper care. Contrary to our expectations, CMD disagreement with neurologists about the etiological nature of the disorder was not statistically significant. Additional analysis showed that having at least one personality disorder was statistically associated with the discordance status. Conclusion: This study suggests that CMD patients show higher conflicting behavior toward physicians. Contrary to our expectations, they show awareness of their psychological needs, suggesting a possible lack of recognition of psychological distress in the neurological setting. Keywords: functional movement disorder, patient–doctor relationship, diagnosis, psychopatholog
Mitochondrial protein network: From biogenesis to bioenergetics in health and disease
No full textMitochondria are double membrane-bound organelles which are essential for the viability of eukaryotic cells, because they play a crucial role in bioenergetics, metabolism and signaling [...
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