1,721,445 research outputs found
From Complex Systems to Complex Systems Technology and Return: A Tale of an Endless Loop
No full textSoft Sensors and Artificial Intelligence for Nuclear Fusion Applications
No full textSoft sensors are mathematical models able to estimate process variables. They can work in parallel with hardware sensors, and can be implemented at a low-cost on existing hardware. They are useful for back-up of measuring devices, reduction of measuring hardware requirements, real-time estimation for monitoring and control, sensor validation, fault detection and diagnosis, what-if analysis. In industrial applications, data-driven approaches, especially based on soft-computing techniques, are very promising. In this paper we review important issues in soft sensor design and applications, especially concerning the applications in the field of nuclear fusion
Assessing Reactive Responses and Gathering Restrictions for Eradicating Epidemic Diseases on Networks with Higher-Order Interactions
Full text linkWe characterize the dynamics of epidemic disease propagation on realistic temporal networks, where individuals participate in both pairwise and higher-order interactions. The latter captures large gatherings that may lead to superspreading events. We introduce an analytically tractable mathematical model for these temporal networks, based on continuous-time activity-driven networks, and study a susceptible–infected–susceptible (SIS) model spreading on its fabric. Utilizing a mean-field approach, we derive a system of ordinary differential equations (ODEs) that dictate the mean dynamics of the SIS process. By analyzing these ODEs, we identify the epidemic threshold of the model-revealing a phase transition between a regime where trajectories converge to a disease-free equilibrium and one where they stabilize at an endemic equilibrium (EE)-and delineate the unique EE for homogeneous networks. Subsequently, we integrate two distinct control measures into the model: i) restricting gatherings and ii) promoting a reactive behavioral response. We evaluate the efficacy of these control measures by computing the epidemic threshold of the controlled SIS model and employing various tools, including sensitivity analysis, mathematical optimization, and numerical simulations, to quantitatively assess how the control measures elevate the threshold, thus aiding in disease eradication, and how to amalgamate them for designing an optimal control policy
On a Susceptible-Infected-Susceptible Epidemic Model with Reactive Behavioral Response on Higher-Order Temporal Networks
Full text linkWe characterize the spread of epidemic diseases on higher-order temporal networks to shed light on the impact of large gatherings, where superspreading events occur and pairwise interactions are not sufficient to model the dynamics of infection. We propose a novel analytically-tractable continuous-time formalism for higher-order temporal networks based on the paradigm of activity-driven networks and we study a susceptible–infected–susceptible model spreading on top of it. By using a mean-field approach, we compute the epidemic threshold, characterizing a phase transition between a regime where the system converges to a disease-free equilibrium and one in which all trajectories converge to an endemic equilibrium. Using such a threshold, we quantify the role of higher-order interactions in favoring the spread of epidemic diseases, providing analytical support to restricting large gatherings during an epidemic outbreak. Finally, we incorporate a reactive behavioral response in the network formation process
A Neuro-Inspired Control Architecture to Enhance Robot Self-Preservation and Adaptation in Autonomous Navigation Tasks
Full text linkEnsuring survival and self-preservation is essential to design intelligent robots that adapt to dynamic and unfamiliar environments. Inspired by the dual-pathway model from neuroscience, we introduce a control architecture designed to ensure the adaptability of robotic behavior during navigation. This approach parallels the neuroscientific "Low Road" paradigm by incorporating constructs resembling the thalamus, implemented as a nonlinear filter; the amygdala, modeled as a Soft Actor-Critic (SAC) reinforcement learning agent; and the brainstem-cerebellum connection, represented by a Nonlinear Model Predictive Controller (NMPC). Our findings indicate superior adaptiveness, generalizability, and computational efficiency compared to standard NMPCs and Artificial Potential Fields in both static and dynamic environments with obstacles of varying risk levels
Misura della contaminazione dell’aria
No full textL'Istituto di Radioprotezione ENEA (IRP) assicura la sorveglianza fisica di radioprotezione per tutte le attività con rischi da radiazioni ionizzanti all'interno dei centri di ricerca ENEA, con lo scopo principale di tutelare la salute dei lavoratori, della popolazione e dell'ambiente.
Per garantire il migliore servizio nell'affrontare tutte le problematiche connesse alla misura della radioattività è stato elaborato, a cura degli Esperti Qualificati, un approfondimento che riguarda le tecniche di misura della contaminazione aeriforme con campionamento retrospettivo, a scopo di indagine.
Questo documento di sintesi fa riferimento a documenti nazionali ed internazionali aggiornati e referenziabili, e applica le norme tecniche in vigore.
Il documento può essere un valido supporto alle attività di sorveglianza fisica della radioprotezione di responsabilità dell'Esperto Qualificato e riassume molte informazioni utili anche per il tecnico della radioprotezione.
Il presente documento è stato redatto tenendo conto dei commenti e dei suggerimenti di Luca Ciciani, Lorenzo Florita e Sandro Sandri
Soft Sensors and Artificial Intelligence with Applications to Nuclear Fusion Experiments
No full textGame theoretical trajectory planning enhances social acceptability of robots by humans
Full text linkSince humans and robots are increasingly sharing portions of their operational spaces, experimental evidence is needed to ascertain the safety and social acceptability of robots in human-populated environments. Although several studies have aimed at devising strategies for robot trajectory planning to perform safe motion in populated environments, a few efforts have measured to what extent a robot trajectory is accepted by humans. Here, we present a navigation system for autonomous robots that ensures safety and social acceptability of robotic trajectories. We overcome the typical reactive nature of state-of-the-art trajectory planners by leveraging non-cooperative game theory to design a planner that encapsulates human-like features of preservation of a personal space, recognition of groups, sequential and strategized decision making, and smooth obstacle avoidance. Social acceptability is measured through a variation of the Turing test administered in the form of a survey questionnaire to a pool of 691 participants. Comparison terms for our tests are a state-of-the-art navigation algorithm (Enhanced Vector Field Histogram, VFH) and purely human trajectories. While all participants easily recognized the non-human nature of VFH-generated trajectories, the distinction between game-theoretical trajectories and human ones were hardly revealed. Our results mark a strong milestone toward the full integration of robots in social environments.Team Bart De SchutterTeam Sergio Grammatic
A Neural System for Radiation Discrimination in Nuclear Fusion Applications
No full textThis work presents an approach to discriminate between neutrons and /spl gamma/-rays in nuclear fusion applications, based on a neural network able to analyze the shape of light pulses produced by these ionizing particles in an organic liquid scintillator. Such an approach is particularly promising especially for the possibility of classifying correctly (either as neutrons or as /spl gamma/-rays) fast superimposed events (pile-ups). Satisfactory experimental results were obtained at the Frascati Tokamak Upgrade, ENEA-Frascati, Italy
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