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Author Driouich, Y.; Parente, M.; Tronci, E. pdf  doi
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  Title A methodology for a complete simulation of Cyber-Physical Energy Systems Type Conference Article
  Year 2018 Publication EESMS 2018 – Environmental, Energy, and Structural Monitoring Systems, Proceedings Abbreviated Journal  
  Volume Issue Pages 1-5  
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  Notes Approved (up) no  
  Call Number MCLab @ davi @ Driouich20181 Serial 169  
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Author Mancini, T.; Mari, F.; Melatti, I.; Salvo, I.; Tronci, E.; Gruber, J.; Hayes, B.; Prodanovic, M.; Elmegaard, L. pdf  doi
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  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) Abbreviated Journal  
  Volume Issue Pages 1-6  
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  Notes Approved (up) no  
  Call Number MCLab @ davi @ mancini-etal:2018:smartgridcomm Serial 170  
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Author Driouich, Y.; Parente, M.; Tronci, E. pdf  doi
openurl 
  Title Model Checking Cyber-Physical Energy Systems Type Conference Article
  Year 2018 Publication Proceedings of 2017 International Renewable and Sustainable Energy Conference, IRSEC 2017 Abbreviated Journal  
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  Publisher Institute of Electrical and Electronics Engineers Inc. Place of Publication Editor  
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  Notes Approved (up) no  
  Call Number MCLab @ davi @ Driouich2018 Serial 177  
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Author Mancini, T.; Tronci, E.; Scialanca, A.; Lanciotti, F.; Finzi, A.; Guarneri, R.; Di Pompeo, S. pdf  doi
openurl 
  Title Optimal Fault-Tolerant Placement of Relay Nodes in a Mission Critical Wireless Network Type Conference Article
  Year 2018 Publication 25th RCRA International Workshop on “Experimental Evaluation of Algorithms for Solving Problems with Combinatorial Explosion” (RCRA 2018) Abbreviated Journal  
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  Notes Approved (up) no  
  Call Number MCLab @ davi @ Serial 174  
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Author Mancini, T.; Mari, F.; Massini, A.; Melatti, I.; Salvo, I.; Sinisi, S.; Tronci, E.; Ehrig, R.; Röblitz, S.; Leeners, B. pdf  doi
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  Title Computing Personalised Treatments through In Silico Clinical Trials. A Case Study on Downregulation in Assisted Reproduction Type Conference Article
  Year 2018 Publication 25th RCRA International Workshop on “Experimental Evaluation of Algorithms for Solving Problems with Combinatorial Explosion” (RCRA 2018) Abbreviated Journal  
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  Notes Approved (up) no  
  Call Number MCLab @ davi @ Serial 175  
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Author Tortora, L.; Meynen, G.; Bijlsma, J.; Tronci, E.; Ferracuti, S. pdf  url
doi  openurl
  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  
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  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 (up) no  
  Call Number MCLab @ davi @ Neuroprediction-2020 Serial 180  
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Author Pappagallo, A.; Massini, A.; Tronci, E. pdf  doi
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  Title Monte Carlo Based Statistical Model Checking of Cyber-Physical Systems: A Review Type Journal Article
  Year 2020 Publication Information Abbreviated Journal  
  Volume 11 Issue 558 Pages  
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  Call Number MCLab @ davi @ Serial 181  
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Author Sinisi, S.; Alimguzhin, V.; Mancini, T.; Tronci, E.; Leeners, B. pdf  url
doi  openurl
  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  
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  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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  ISSN 1367-4803 ISBN Medium  
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  Call Number MCLab @ davi @ ref10.1093/bioinformatics/btaa1026 Serial 182  
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Author Mancini, T.; Mari, F.; Massini, A.; Melatti, I.; Tronci, E. pdf  url
doi  openurl
  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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  Call Number MCLab @ davi @ Mancini2021100640 Serial 183  
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Author Leeners, B.; Krueger, T.; Geraedts, K.; Tronci, E.; Mancini, T.; Ille, F.; Egli, M.; Roeblitz, S.; Wunder, D.; Saleh, L.; Schippert, C.; Hengartner, M.P. pdf  url
doi  openurl
  Title Cognitive function in association with high estradiol levels resulting from fertility treatment Type Journal Article
  Year 2021 Publication Hormones and Behavior Abbreviated Journal  
  Volume 130 Issue Pages 104951  
  Keywords Cognition, Estrogen, Estradiol, Fertility treatment, Attention, Cognitive bias  
  Abstract The putative association between hormones and cognitive performance is controversial. While there is evidence that estradiol plays a neuroprotective role, hormone treatment has not been shown to improve cognitive performance. Current research is flawed by the evaluation of combined hormonal effects throughout the menstrual cycle or in the menopausal transition. The stimulation phase of a fertility treatment offers a unique model to study the effect of estradiol on cognitive function. This quasi-experimental observational study is based on data from 44 women receiving IVF in Zurich, Switzerland. We assessed visuospatial working memory, attention, cognitive bias, and hormone levels at the beginning and at the end of the stimulation phase of ovarian superstimulation as part of a fertility treatment. In addition to inter-individual differences, we examined intra-individual change over time (within-subject effects). The substantial increases in estradiol levels resulting from fertility treatment did not relate to any considerable change in cognitive functioning. As the tests applied represent a broad variety of cognitive functions on different levels of complexity and with various brain regions involved, we can conclude that estradiol does not show a significant short-term effect on cognitive function.  
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  ISSN 0018-506x ISBN Medium  
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  Call Number MCLab @ davi @ Leeners2021104951 Serial 185  
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