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Author Hayes, B. P. ; Melatti, I.; Mancini, T.; Prodanovic, M.; Tronci, E. pdf  url
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  Title Residential Demand Management using Individualised Demand Aware Price Policies Type Journal Article
  Year 2017 Publication IEEE Transactions On Smart Grid Abbreviated Journal  
  Volume 8 Issue 3 Pages 1284-1294  
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  Call Number MCLab @ davi @ Serial 157  
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Author Mancini, T. pdf  doi
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  Title Now or Never: Negotiating Efficiently with Unknown or Untrusted Counterparts Type Journal Article
  Year 2016 Publication Fundamenta Informaticae Abbreviated Journal  
  Volume 149 Issue 1-2 Pages 61-100  
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  Call Number MCLab @ davi @ DBLP:journals/fuin/Mancini16 Serial 161  
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Author Mancini, T.; Mari, F.; Massini, A.; Melatti, I.; Tronci, E. pdf  doi
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  Title SyLVaaS: System Level Formal Verification as a Service Type Journal Article
  Year 2016 Publication Fundamenta Informaticae Abbreviated Journal  
  Volume 149 Issue 1-2 Pages 101-132  
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  Call Number MCLab @ davi @ DBLP:journals/fuin/ManciniMMMT16 Serial 160  
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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. pdf  url
doi  openurl
  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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  ISSN 1664-1078 ISBN Medium  
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  Call Number MCLab @ davi @ ref10.3389/fpsyg.2019.01296 Serial 178  
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Author Driouich, Y.; Parente, M.; Tronci, E. pdf  doi
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  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 MCLab @ davi @ ref7981621 Serial 168  
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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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  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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  Call Number MCLab @ davi @ mancini-etal:2018:smartgridcomm Serial 170  
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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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  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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  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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  Call Number MCLab @ davi @ Neuroprediction-2020 Serial 180  
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