| Records |
| Author |
Hayes, B. P. ; Melatti, I.; Mancini, T.; Prodanovic, M.; Tronci, E. |
| Title |
Residential Demand Management using Individualised Demand Aware Price Policies |
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Journal Article |
| Year |
2017 |
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IEEE Transactions On Smart Grid |
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| Volume |
8 |
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3 |
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1284-1294 |
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MCLab @ davi @ |
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157 |
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| Author |
Mancini, T. |
| Title |
Now or Never: Negotiating Efficiently with Unknown or Untrusted Counterparts |
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Journal Article |
| Year |
2016 |
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Fundamenta Informaticae |
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149 |
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1-2 |
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61-100 |
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MCLab @ davi @ DBLP:journals/fuin/Mancini16 |
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161 |
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| Author |
Mancini, T.; Mari, F.; Massini, A.; Melatti, I.; Tronci, E. |
| Title |
SyLVaaS: System Level Formal Verification as a Service |
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Journal Article |
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2016 |
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Fundamenta Informaticae |
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149 |
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1-2 |
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101-132 |
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MCLab @ davi @ DBLP:journals/fuin/ManciniMMMT16 |
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160 |
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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 |
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Journal Article |
| Year |
2019 |
Publication |
Frontiers in Psychology |
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| Volume |
10 |
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1296 |
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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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1664-1078 |
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MCLab @ davi @ ref10.3389/fpsyg.2019.01296 |
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178 |
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Driouich, Y.; Parente, M.; Tronci, E. |
| Title |
Modeling cyber-physical systems for automatic verification |
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Conference Article |
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2017 |
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14th International Conference on Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design (SMACD 2017) |
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1-4 |
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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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MCLab @ davi @ ref7981621 |
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168 |
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Driouich, Y.; Parente, M.; Tronci, E. |
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A methodology for a complete simulation of Cyber-Physical Energy Systems |
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Conference Article |
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2018 |
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EESMS 2018 – Environmental, Energy, and Structural Monitoring Systems, Proceedings |
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1-5 |
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MCLab @ davi @ Driouich20181 |
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169 |
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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 |
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Conference Article |
| Year |
2018 |
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2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm) |
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1-6 |
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MCLab @ davi @ mancini-etal:2018:smartgridcomm |
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170 |
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Mancini, T.; Tronci, E.; Scialanca, A.; Lanciotti, F.; Finzi, A.; Guarneri, R.; Di Pompeo, S. |
| Title |
Optimal Fault-Tolerant Placement of Relay Nodes in a Mission Critical Wireless Network |
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Conference Article |
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2018 |
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25th RCRA International Workshop on “Experimental Evaluation of Algorithms for Solving Problems with Combinatorial Explosion” (RCRA 2018) |
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no |
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MCLab @ davi @ |
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174 |
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Mancini, T.; Mari, F.; Massini, A.; Melatti, I.; Salvo, I.; Sinisi, S.; Tronci, E.; Ehrig, R.; Röblitz, S.; Leeners, B. |
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Computing Personalised Treatments through In Silico Clinical Trials. A Case Study on Downregulation in Assisted Reproduction |
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Conference Article |
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2018 |
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25th RCRA International Workshop on “Experimental Evaluation of Algorithms for Solving Problems with Combinatorial Explosion” (RCRA 2018) |
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MCLab @ davi @ |
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175 |
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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 |
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2020 |
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Frontiers in Psychology |
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11 |
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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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