| Records |
| Author |
Mancini, T.; Mari, F.; Melatti, I.; Salvo, I.; Tronci, E.; Gruber, J.; Hayes, B.; Prodanovic, M.; Elmegaard, L. |
| Title |
Parallel Statistical Model Checking for Safety Verification in Smart Grids |
Type |
Conference Article |
| Year |
2018 |
Publication |
2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm) |
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1-6 |
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no |
| Call Number |
MCLab @ davi @ mancini-etal:2018:smartgridcomm |
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170 |
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| Author |
Driouich, Y.; Parente, M.; Tronci, E. |
| Title |
A methodology for a complete simulation of Cyber-Physical Energy Systems |
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Conference Article |
| Year |
2018 |
Publication |
EESMS 2018 – Environmental, Energy, and Structural Monitoring Systems, Proceedings |
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1-5 |
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no |
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MCLab @ davi @ Driouich20181 |
Serial |
169 |
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| Author |
Driouich, Y.; Parente, M.; Tronci, E. |
| Title |
Modeling cyber-physical systems for automatic verification |
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Conference Article |
| Year |
2017 |
Publication |
14th International Conference on Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design (SMACD 2017) |
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1-4 |
| Keywords |
cyber-physical systems;formal verification;maximum power point trackers;power engineering computing;Modelica;automatic verification;complex power electronics systems;cyber-physical systems modeling;distributed maximum power point tracking system;open standard modeling language;Computational modeling;Control systems;Integrated circuit modeling;Mathematical model;Maximum power point trackers;Object oriented modeling;Radiation effects;Automatic Formal Verification;Cyber-Physical Systems;DMPPT;Modeling;Photovoltaic systems;Simulation;System Analysis and Design |
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no |
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MCLab @ davi @ ref7981621 |
Serial |
168 |
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| Author |
Cesta, Amedeo; Fratini, Simone; Orlandini, Andrea; Finzi, Alberto; Tronci, Enrico |
| Title |
Flexible Plan Verification: Feasibility Results |
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Journal Article |
| Year |
2011 |
Publication |
Fundamenta Informaticae |
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107 |
Issue |
2 |
Pages |
111-137 |
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yes |
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Sapienza @ mari @ fi11 |
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15 |
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Fischer, S.; Ehrig, R.; Schaefer, S.; Tronci, E.; Mancini, T.; Egli, M.; Ille, F.; Krueger, T.H.C.; Leeners, B.; Roeblitz, S. |
| Title |
Mathematical Modeling and Simulation Provides Evidence for New Strategies of Ovarian Stimulation |
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Journal Article |
| Year |
2021 |
Publication |
Frontiers in Endocrinology |
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| Volume |
12 |
Issue |
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Pages |
117 |
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New approaches to ovarian stimulation protocols, such as luteal start, random start or double stimulation, allow for flexibility in ovarian stimulation at different phases of the menstrual cycle. It has been proposed that the success of these methods is based on the continuous growth of multiple cohorts (“waves”) of follicles throughout the menstrual cycle which leads to the availability of ovarian follicles for ovarian controlled stimulation at several time points. Though several preliminary studies have been published, their scientific evidence has not been considered as being strong enough to integrate these results into routine clinical practice. This work aims at adding further scientific evidence about the efficiency of variable-start protocols and underpinning the theory of follicular waves by using mathematical modeling and numerical simulations. For this purpose, we have modified and coupled two previously published models, one describing the time course of hormones and one describing competitive follicular growth in a normal menstrual cycle. The coupled model is used to test ovarian stimulation protocols in silico. Simulation results show the occurrence of follicles in a wave-like manner during a normal menstrual cycle and qualitatively predict the outcome of ovarian stimulation initiated at different time points of the menstrual cycle. |
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1664-2392 |
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no |
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MCLab @ davi @ ref10.3389/fendo.2021.613048 |
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189 |
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Tortora, L.; Meynen, G.; Bijlsma, J.; Tronci, E.; Ferracuti, S. |
| Title |
Neuroprediction and A.I. in Forensic Psychiatry and Criminal Justice: A Neurolaw Perspective |
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Journal Article |
| Year |
2020 |
Publication |
Frontiers in Psychology |
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11 |
Issue |
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220 |
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Advances in the use of neuroimaging in combination with A.I., and specifically the use of machine learning techniques, have led to the development of brain-reading technologies which, in the nearby future, could have many applications, such as lie detection, neuromarketing or brain-computer interfaces. Some of these could, in principle, also be used in forensic psychiatry. The application of these methods in forensic psychiatry could, for instance, be helpful to increase the accuracy of risk assessment and to identify possible interventions. This technique could be referred to as ‘A.I. neuroprediction,’ and involves identifying potential neurocognitive markers for the prediction of recidivism. However, the future implications of this technique and the role of neuroscience and A.I. in violence risk assessment remain to be established. In this paper, we review and analyze the literature concerning the use of brain-reading A.I. for neuroprediction of violence and rearrest to identify possibilities and challenges in the future use of these techniques in the fields of forensic psychiatry and criminal justice, considering legal implications and ethical issues. The analysis suggests that additional research is required on A.I. neuroprediction techniques, and there is still a great need to understand how they can be implemented in risk assessment in the field of forensic psychiatry. Besides the alluring potential of A.I. neuroprediction, we argue that its use in criminal justice and forensic psychiatry should be subjected to thorough harms/benefits analyses not only when these technologies will be fully available, but also while they are being researched and developed. |
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1664-1078 |
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MCLab @ davi @ Neuroprediction-2020 |
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180 |
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Chen, Q.M.; Finzi, A.; Mancini, T.; Melatti, I.; Tronci, E. |
| Title |
MILP, Pseudo-Boolean, and OMT Solvers for Optimal Fault-Tolerant Placements of Relay Nodes in Mission Critical Wireless Networks |
Type |
Journal Article |
| Year |
2020 |
Publication |
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Abbreviated Journal |
Fundamenta Informaticae |
| Volume |
174 |
Issue |
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229-258 |
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In critical infrastructures like airports, much care has to be devoted in protecting radio communication networks from external electromagnetic interference. Protection of such mission-critical radio communication networks is usually tackled by exploiting radiogoniometers: at least three suitably deployed radiogoniometers, and a gateway gathering information from them, permit to monitor and localise sources of electromagnetic emissions that are not supposed to be present in the monitored area. Typically, radiogoniometers are connected to the gateway through relay nodes . As a result, some degree of fault-tolerance for the network of relay nodes is essential in order to offer a reliable monitoring. On the other hand, deployment of relay nodes is typically quite expensive. As a result, we have two conflicting requirements: minimise costs while guaranteeing a given fault-tolerance. In this paper, we address the problem of computing a deployment for relay nodes that minimises the overall cost while at the same time guaranteeing proper working of the network even when some of the relay nodes (up to a given maximum number) become faulty (fault-tolerance ). We show that, by means of a computation-intensive pre-processing on a HPC infrastructure, the above optimisation problem can be encoded as a 0/1 Linear Program, becoming suitable to be approached with standard Artificial Intelligence reasoners like MILP, PB-SAT, and SMT/OMT solvers. Our problem formulation enables us to present experimental results comparing the performance of these three solving technologies on a real case study of a relay node network deployment in areas of the Leonardo da Vinci Airport in Rome, Italy. |
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IOS Press |
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1875-8681 |
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no |
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MCLab @ davi @ |
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188 |
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| Author |
Sinisi, S.; Alimguzhin, V.; Mancini, T.; Tronci, E.; Mari, F.; Leeners, B. |
| Title |
Optimal Personalised Treatment Computation through In Silico Clinical Trials on Patient Digital Twins |
Type |
Journal Article |
| Year |
2020 |
Publication |
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Abbreviated Journal |
Fundamenta Informaticae |
| Volume |
174 |
Issue |
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Pages |
283-310 |
| Keywords |
Artificial Intelligence; Virtual Physiological Human; In Silico Clinical Trials; Simulation; Personalised Medicine; In Silico Treatment Optimisation |
| Abstract |
In Silico Clinical Trials (ISCT), i.e. clinical experimental campaigns carried out by means of computer simulations, hold the promise to decrease time and cost for the safety and efficacy assessment of pharmacological treatments, reduce the need for animal and human testing, and enable precision medicine. In this paper we present methods and an algorithm that, by means of extensive computer simulation-based experimental campaigns (ISCT) guided by intelligent search, optimise a pharmacological treatment for an individual patient (precision medicine ). We show the effectiveness of our approach on a case study involving a real pharmacological treatment, namely the downregulation phase of a complex clinical protocol for assisted reproduction in humans. |
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IOS Press |
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1875-8681 |
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no |
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MCLab @ davi @ |
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187 |
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Mancini, T.; Mari, F.; Melatti, I.; Salvo, I.; Tronci, E. |
| Title |
An Efficient Algorithm for Network Vulnerability Analysis Under Malicious Attacks |
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Conference Article |
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2018 |
Publication |
Foundations of Intelligent Systems – 24th International Symposium, ISMIS 2018, Limassol, Cyprus, October 29-31, 2018, Proceedings |
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302-312 |
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Best Paper |
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no |
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MCLab @ davi @ DBLP:conf/ismis/ManciniMMST18 |
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176 |
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Martinelli, Marco; Tronci, Enrico; Dipoppa, Giovanni; Balducelli, Claudio |
| Title |
Electric Power System Anomaly Detection Using Neural Networks |
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Conference Article |
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2004 |
Publication |
8th International Conference on: Knowledge-Based Intelligent Information and Engineering Systems (KES) |
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1242-1248 |
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The aim of this work is to propose an approach to monitor and protect Electric Power System by learning normal system behaviour at substations level, and raising an alarm signal when an abnormal status is detected; the problem is addressed by the use of autoassociative neural networks, reading substation measures. Experimental results show that, through the proposed approach, neural networks can be used to learn parameters underlaying system behaviour, and their output processed to detecting anomalies due to hijacking of measures, changes in the power network topology (i.e. transmission lines breaking) and unexpected power demand trend. |
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Springer |
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Wellington, New Zealand |
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Negoita, M.G.; Howlett, R.J.; Jain, L.C. |
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Lecture Notes in Computer Science |
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3213 |
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3-540-23318-0 |
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Approved |
yes |
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Sapienza @ mari @ kes04 |
Serial |
35 |
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