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Author Mancini, T.; Mari, F.; Melatti, I.; Salvo, I.; Tronci, E.
Title An Efficient Algorithm for Network Vulnerability Analysis Under Malicious Attacks Type Conference Article
Year 2018 Publication Foundations of Intelligent Systems – 24th International Symposium, ISMIS 2018, Limassol, Cyprus, October 29-31, 2018, Proceedings Abbreviated Journal
Volume Issue Pages 302-312
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Call Number MCLab @ davi @ DBLP:conf/ismis/ManciniMMST18 Serial 176
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Author Mancini, T.; Mari, F.; Melatti, I.; Salvo, I.; Tronci, E.; Gruber, J.K.; Hayes, B.; Prodanovic, M.; Elmegaard, L.
Title User Flexibility Aware Price Policy Synthesis for Smart Grids Type Conference Article
Year 2015 Publication Digital System Design (DSD), 2015 Euromicro Conference on Abbreviated Journal
Volume Issue Pages 478-485
Keywords Contracts; Current measurement; Load management; Power demand; Power measurement; State estimation; Substations; Grid State Estimation; Peak Shaving; Policy Robustness Verification; Price Policy Synthesis
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Call Number Sapienza @ preissler @ Mancini_etal2015_3 Serial 136
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Author Leeners, B.; Kruger, T.H.C.; Geraedts, K.; Tronci, E.; Mancini, T.; Ille, F.; Egli, M.; Röblitz, S.; Saleh, L.; Spanaus, K.; Schippert, C.; Zhang, Y.; Hengartner, M.P.
Title Lack of Associations between Female Hormone Levels and Visuospatial Working Memory, Divided Attention and Cognitive Bias across Two Consecutive Menstrual Cycles Type Journal Article
Year 2017 Publication Frontiers in Behavioral Neuroscience Abbreviated Journal
Volume 11 Issue Pages 120
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Abstract Background: Interpretation of observational studies on associations between prefrontal cognitive functioning and hormone levels across the female menstrual cycle is complicated due to small sample sizes and poor replicability. Methods: This observational multisite study comprised data of n=88 menstruating women from Hannover, Germany, and Zurich, Switzerland, assessed during a first cycle and n=68 re-assessed during a second cycle to rule out practice effects and false-positive chance findings. We assessed visuospatial working memory, attention, cognitive bias and hormone levels at four consecutive time-points across both cycles. In addition to inter-individual differences we examined intra-individual change over time (i.e., within-subject effects). Results: Oestrogen, progesterone and testosterone did not relate to inter-individual differences in cognitive functioning. There was a significant negative association between intra-individual change in progesterone and change in working memory from pre-ovulatory to mid-luteal phase during the first cycle, but that association did not replicate in the second cycle. Intra-individual change in testosterone related negatively to change in cognitive bias from menstrual to pre-ovulatory as well as from pre-ovulatory to mid-luteal phase in the first cycle, but these associations did not replicate in the second cycle. Conclusions: There is no consistent association between women's hormone levels, in particular oestrogen and progesterone, and attention, working memory and cognitive bias. That is, anecdotal findings observed during the first cycle did not replicate in the second cycle, suggesting that these are false-positives attributable to random variation and systematic biases such as practice effects. Due to methodological limitations, positive findings in the published literature must be interpreted with reservation.
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ISSN 1662-5153 ISBN Medium
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Call Number Sapienza @ mari @ ref10.3389/fnbeh.2017.00120 Serial 167
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Author Mancini, T. ; Mari, F.; Massini, A.; Melatti, I.; Salvo, I.; Tronci, E.
Title On minimising the maximum expected verification time Type Journal Article
Year 2017 Publication Information Processing Letters Abbreviated Journal
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Call Number Sapienza @ mari @ Serial 163
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Author Hengartner, M. P.; Kruger, T. H. C.; Geraedts, K.; Tronci, E.; Mancini, T.; Ille, F.; Egli, M.; Röblitz, S.; Ehrig, R.; Saleh, L.; Spanaus, K.; Schippert, C.; Zhang, Y.; Leeners, B.
Title Negative affect is unrelated to fluctuations in hormone levels across the menstrual cycle: Evidence from a multisite observational study across two successive cycles Type Journal Article
Year 2017 Publication Journal of Psychosomatic Research Abbreviated Journal
Volume 99 Issue Pages 21-27
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Call Number MCLab @ davi @ Serial 165
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Author Mancini, T.; Massini, A.; Tronci, E.
Title Parallelization of Cycle-Based Logic Simulation Type Journal Article
Year 2017 Publication Parallel Processing Letters Abbreviated Journal
Volume 27 Issue 02 Pages
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Call Number MCLab @ davi @ Serial 166
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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 2165–2172
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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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ISSN 1367-4803 ISBN Medium
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Call Number MCLab @ davi @ ref10.1093/bioinformatics/btz860 Serial 179
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Author Ehrig, R.; Dierkes, T.; Schaefer, S.; Roeblitz, S.; Tronci, E.; Mancini, T.; Salvo, I.; Alimguzhin, V.; Mari, F.; Melatti, I.; Massini, A.; Leeners, B.; Krueger, T.H.C.; Egli, M.; Ille, F.
Title An integrative approach for model driven computation of treatments in reproductive medicine Type Conference Article
Year 2015 Publication Proceedings of the 15th International Symposium on Mathematical and Computational Biology (BIOMAT 2015), Rorkee, India Abbreviated Journal
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Call Number Sapienza @ preissler @ Ehrig_etal2015 Serial 144
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Author Hayes, B. P. ; Melatti, I.; Mancini, T.; Prodanovic, M.; Tronci, E.
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.
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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