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Author Mancini, T.; Mari, F.; Massini, A.; Melatti, I.; Tronci, E.
Title On Checking Equivalence of Simulation Scripts Type Journal Article
Year 2021 Publication Journal of Logical and Algebraic Methods in Programming Abbreviated Journal
Volume Issue Pages 100640
Keywords Formal verification, Simulation based formal verification, Formal Verification of cyber-physical systems, System-level formal verification
Abstract To support Model Based Design of Cyber-Physical Systems (CPSs) many simulation based approaches to System Level Formal Verification (SLFV) have been devised. Basically, these are Bounded Model Checking approaches (since simulation horizon is of course bounded) relying on simulators to compute the system dynamics and thereby verify the given system properties. The main obstacle to simulation based SLFV is the large number of simulation scenarios to be considered and thus the huge amount of simulation time needed to complete the verification task. To save on computation time, simulation based SLFV approaches exploit the capability of simulators to save and restore simulation states. Essentially, such a time saving is obtained by optimising the simulation script defining the simulation activity needed to carry out the verification task. Although such approaches aim to (bounded) formal verification, as a matter of fact, the proof of correctness of the methods to optimise simulation scripts basically relies on an intuitive semantics for simulation scripting languages. This hampers the possibility of formally showing that the optimisations introduced to speed up the simulation activity do not actually omit checking of relevant behaviours for the system under verification. The aim of this paper is to fill the above gap by presenting an operational semantics for simulation scripting languages and by proving soundness and completeness properties for it. This, in turn, enables formal proofs of equivalence between unoptimised and optimised simulation scripts.
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ISSN 2352-2208 ISBN Medium
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Notes Approved no
Call Number (up) MCLab @ davi @ Mancini2021100640 Serial 183
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Author Tortora, L.; Meynen, G.; Bijlsma, J.; Tronci, E.; Ferracuti, S.
Title Neuroprediction and A.I. in Forensic Psychiatry and Criminal Justice: A Neurolaw Perspective Type Journal Article
Year 2020 Publication Frontiers in Psychology Abbreviated Journal
Volume 11 Issue Pages 220
Keywords
Abstract 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 ÃâA.I. neuroprediction,Ãâ 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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ISSN 1664-1078 ISBN Medium
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Notes Approved no
Call Number (up) MCLab @ davi @ Neuroprediction-2020 Serial 180
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Author Sinisi, S.; Alimguzhin, V.; Mancini, T.; Tronci, E.; Leeners, B.
Title Complete populations of virtual patients for in silico clinical trials Type Journal Article
Year 2021 Publication Bioinformatics Abbreviated Journal
Volume Issue Pages 1-8
Keywords
Abstract Model-based approaches to safety and efficacy assessment of pharmacological drugs, treatment strategies, or medical devices (In Silico Clinical Trial, ISCT) aim to decrease time and cost for the needed experimentations, reduce animal and human testing, and enable precision medicine. Unfortunately, in presence of non-identifiable models (e.g., reaction networks), parameter estimation is not enough to generate complete populations of Virtual Patient (VPs), i.e., populations guaranteed to show the entire spectrum of model behaviours (phenotypes), thus ensuring representativeness of the trial.We present methods and software based on global search driven by statistical model checking that, starting from a (non-identifiable) quantitative model of the human physiology (plus drugs PK/PD) and suitable biological and medical knowledge elicited from experts, compute a population of VPs whose behaviours are representative of the whole spectrum of phenotypes entailed by the model (completeness) and pairwise distinguishable according to user-provided criteria. This enables full granularity control on the size of the population to employ in an ISCT, guaranteeing representativeness while avoiding over-representation of behaviours.We proved the effectiveness of our algorithm on a non-identifiable ODE-based model of the female Hypothalamic-Pituitary-Gonadal axis, by generating a population of 4 830 264 VPs stratified into 7 levels (at different granularity of behaviours), and assessed its representativeness against 86 retrospective health records from Pfizer, Hannover Medical School and University Hospital of Lausanne. The datasets are respectively covered by our VPs within Average Normalised Mean Absolute Error of 15%, 20%, and 35% (90% of the latter dataset is covered within 20% error).
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Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1367-4803 ISBN Medium
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Notes Approved no
Call Number (up) MCLab @ davi @ ref10.1093/bioinformatics/btaa1026 Serial 182
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Author Maggioli, F.; Mancini, T.; Tronci, E.
Title SBML2Modelica: Integrating biochemical models within open-standard simulation ecosystems Type Journal Article
Year 2019 Publication Bioinformatics Abbreviated Journal
Volume 36 Issue 7 Pages 2165â2172
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Abstract SBML is the most widespread language for the definition of biochemical models. Although dozens of SBML simulators are available, there is a general lack of support to the integration of SBML models within open-standard general-purpose simulation ecosystems. This hinders co-simulation and integration of SBML models within larger model networks, in order to, e.g., enable in-silico clinical trials of drugs, pharmacological protocols, or engineering artefacts such as biomedical devices against Virtual Physiological Human models.Modelica is one of the most popular existing open-standard general-purpose simulation languages, supported by many simulators. Modelica models are especially suited for the definition of complex networks of heterogeneous models from virtually all application domains. Models written in Modelica (and in 100+ other languages) can be readily exported into black-box Functional Mock-Up Units (FMUs), and seamlessly co-simulated and integrated into larger model networks within open-standard language-independent simulation ecosystems.In order to enable SBML model integration within heterogeneous model networks, we present SBML2Modelica, a software system translating SBML models into well-structured, user-intelligible, easily modifiable Modelica models. SBML2Modelica is SBML Level 3 Version 2 -compliant and succeeds on 96.47% of the SBML Test Suite Core (with a few rare, intricate, and easily avoidable combinations of constructs unsupported and cleanly signalled to the user). Our experimental campaign on 613 models from the BioModels database (with up to 5438 variables) shows that the major open-source (general-purpose) Modelica and FMU simulators achieve performance comparable to state-of-the-art specialised SBML simulators.SBML2Modelica is written in Java and is freely available for non-commercial use at https://bitbucket.org/mclab/sbml2modelica
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Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1367-4803 ISBN Medium
Area Expedition Conference
Notes Approved no
Call Number (up) MCLab @ davi @ ref10.1093/bioinformatics/btz860 Serial 179
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Author 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 Type Journal Article
Year 2021 Publication Frontiers in Endocrinology Abbreviated Journal
Volume 12 Issue Pages 117
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Abstract 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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Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1664-2392 ISBN Medium
Area Expedition Conference
Notes Approved no
Call Number (up) MCLab @ davi @ ref10.3389/fendo.2021.613048 Serial 189
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Author Leeners, B.; Krueger, T.H.C.; Geraedts, K.; Tronci, E.; Mancini, T.; Egli, M.; Roeblitz, S.; Saleh, L.; Spanaus, K.; Schippert, C.; Zhang, Y.; Ille, F.
Title Associations Between Natural Physiological and Supraphysiological Estradiol Levels and Stress Perception Type Journal Article
Year 2019 Publication Frontiers in Psychology Abbreviated Journal
Volume 10 Issue Pages 1296
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Abstract Stress is a risk factor for impaired general, mental and reproductive health. The role of physiological and supraphysiological estradiol concentrations in stress perception and stress processing is less well understood. We therefore, conducted a prospective observational study to investigate the association between estradiol, stress perception and stress-related cognitive performance within serial measurements either during the natural menstrual cycle or during fertility treatment, where estradiol levels are strongly above the physiological level of a natural cycle and consequently, represent a good model to study dose-dependent effects of estradiol. Data from 44 women receiving in vitro fertilization at the Department of Reproductive Endocrinology in Zurich, Switzerland was compared to data from 88 women with measurements during their natural menstrual cycle. The german version of the Perceived Stress Questionnaire (PSQ) and the Cognitive Bias Test (CBT), in which cognitive performance is tested under time stress were used to evaluate subjective and functional aspects of stress. Estradiol levels were investigated at four different time points during the menstrual cycle and at two different time points during a fertility treatment. Cycle phase were associated with PSQ worry and cognitive bias in normally cycling women, but different phases of fertility treatment were not associated with subjectively perceived stress and stress-related cognitive bias. PSQ lack of joy and PSQ demands related to CBT in women receiving fertility treatment but not in women with a normal menstrual cycle. Only strong changes of the estradiol level during fertility treatment were weakly associated with CBT, but not with subjectively experienced stress. Our research emphasises the multidimensional character of stress and the necessity to adjust stress research to the complex nature of stress perception and processing. Infertility is associated with an increased psychological burden in patients. However, not all phases of the process to overcome infertility do significantly increase patient stress levels. Also, research on the psychological burden of infertility should consider that stress may vary during the different phases of fertility treatment.
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Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1664-1078 ISBN Medium
Area Expedition Conference
Notes Approved no
Call Number (up) MCLab @ davi @ ref10.3389/fpsyg.2019.01296 Serial 178
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Author Driouich, Y.; Parente, M.; Tronci, E.
Title Modeling cyber-physical systems for automatic verification Type Conference Article
Year 2017 Publication 14th International Conference on Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design (SMACD 2017) Abbreviated Journal
Volume Issue Pages 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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Call Number (up) MCLab @ davi @ ref7981621 Serial 168
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Author Melatti, I.; Mari, F.; Mancini, T.; Prodanovic, M.; Tronci, E.
Title A Two-Layer Near-Optimal Strategy for Substation Constraint Management via Home Batteries Type Journal Article
Year 2021 Publication IEEE Transactions on Industrial Electronics Abbreviated Journal
Volume Issue Pages 1-1
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Abstract Within electrical distribution networks, substation constraints management requires that aggregated power demand from residential users is kept within suitable bounds. Efficiency of substation constraints management can be measured as the reduction of constraints violations w.r.t. unmanaged demand. Home batteries hold the promise of enabling efficient and user-oblivious substation constraints management. Centralized control of home batteries would achieve optimal efficiency. However, it is hardly acceptable by users, since service providers (e.g., utilities or aggregators) would directly control batteries at user premises. Unfortunately, devising efficient hierarchical control strategies, thus overcoming the above problem, is far from easy. We present a novel two-layer control strategy for home batteries that avoids direct control of home devices by the service provider and at the same time yields near-optimal substation constraints management efficiency. Our simulation results on field data from 62 households in Denmark show that the substation constraints management efficiency achieved with our approach is at least 82% of the one obtained with a theoretical optimal centralized strategy.
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Notes To appear Approved no
Call Number (up) MCLab @ davi @ ref9513535 Serial 190
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Author Mancini, T.; Melatti, I.; Tronci, E.
Title Any-horizon uniform random sampling and enumeration of constrained scenarios for simulation-based formal verification Type Journal Article
Year 2021 Publication IEEE Transactions on Software Engineering Abbreviated Journal
Volume Issue Pages 1-1
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Abstract Model-based approaches to the verification of non-terminating Cyber-Physical Systems (CPSs) usually rely on numerical simulation of the System Under Verification (SUV) model under input scenarios of possibly varying duration, chosen among those satisfying given constraints. Such constraints typically stem from requirements (or assumptions) on the SUV inputs and its operational environment as well as from the enforcement of additional conditions aiming at, e.g., prioritising the (often extremely long) verification activity, by, e.g., focusing on scenarios explicitly exercising selected requirements, or avoiding </i>vacuity</i> in their satisfaction. In this setting, the possibility to efficiently sample at random (with a known distribution, e.g., uniformly) within, or to efficiently enumerate (possibly in a uniformly random order) scenarios among those satisfying all the given constraints is a key enabler for the practical viability of the verification process, e.g., via simulation-based statistical model checking. Unfortunately, in case of non-trivial combinations of constraints, iterative approaches like Markovian random walks in the space of sequences of inputs in general fail in extracting scenarios according to a given distribution (e.g., uniformly), and can be very inefficient to produce at all scenarios that are both legal (with respect to SUV assumptions) and of interest (with respect to the additional constraints). For example, in our case studies, up to 91% of the scenarios generated using such iterative approaches would need to be neglected. In this article, we show how, given a set of constraints on the input scenarios succinctly defined by multiple finite memory monitors, a data structure (scenario generator) can be synthesised, from which any-horizon scenarios satisfying the input constraints can be efficiently extracted by (possibly uniform) random sampling or (randomised) enumeration. Our approach enables seamless support to virtually all simulation-based approaches to CPS verification, ranging from simple random testing to statistical model checking and formal (i.e., exhaustive) verification, when a suitable bound on the horizon or an iterative horizon enlargement strategy is defined, as in the spirit of bounded model checking.
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Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1939-3520 ISBN Medium
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Notes To appear Approved no
Call Number (up) MCLab @ davi @ ref9527998 Serial 191
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Author Sinisi, S.; Alimguzhin, V.; Mancini, T.; Tronci, E.
Title Reconciling interoperability with efficient Verification and Validation within open source simulation environments Type Journal Article
Year 2021 Publication Simulation Modelling Practice and Theory Abbreviated Journal
Volume Issue Pages 102277
Keywords Simulation, Verification and Validation, Interoperability, FMI/FMU, Model Exchange, Cyber-Physical Systems
Abstract A Cyber-Physical System (CPS) comprises physical as well as software subsystems. Simulation-based approaches are typically used to support design and Verification and Validation (V&V) of CPSs in several domains such as: aerospace, defence, automotive, smart grid and healthcare. Accordingly, many simulation-based tools are available to support CPS design. This, on one side, enables designers to choose the toolchain that best suits their needs, on the other side poses huge interoperability challenges when one needs to simulate CPSs whose subsystems have been designed and modelled using different toolchains. To overcome such an interoperability problem, in 2010 the Functional Mock-up Interface (FMI) has been proposed as an open standard to support both Model Exchange (ME) and Co-Simulation (CS) of simulation models created with different toolchains. FMI has been adopted by several modelling and simulation environments. Models adhering to such a standard are called Functional Mock-up Units (FMUs). Indeed FMUs play an essential role in defining complex CPSs through, e.g., the System Structure and Parametrization (SSP) standard. Simulation-based V&V of CPSs typically requires exploring different simulation scenarios (i.e., exogenous input sequences to the CPS under design). Many such scenarios have a shared prefix. Accordingly, to avoid simulating many times such shared prefixes, the simulator state at the end of a shared prefix is saved and then restored and used as a start state for the simulation of the next scenario. In this context, an important FMI feature is the capability to save and restore the internal FMU state on demand. This is crucial to increase efficiency of simulation-based V&V. Unfortunately, the implementation of this feature is not mandatory and it is available only within some commercial software. As a result, the interoperability enabled by the FMI standard cannot be fully exploited for V&V when using open-source simulation environments. This motivates developing such a feature for open-source CPS simulation environments. Accordingly, in this paper, we focus on JModelica, an open-source modelling and simulation environment for CPSs based on an open standard modelling language, namely Modelica. We describe how we have endowed JModelica with our open-source implementation of the FMI 2.0 functions needed to save and restore internal states of FMUs for ME. Furthermore, we present experimental results evaluating, through 934 benchmark models, correctness and efficiency of our extended JModelica. Our experimental results show that simulation-based V&V is, on average, 22 times faster with our get/set functionality than without it.
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Series Editor Series Title Abbreviated Series Title
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ISSN 1569-190x ISBN Medium
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Notes Approved no
Call Number (up) MCLab @ davi @ Sinisi2021102277 Serial 186
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