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A. Pappagallo, A. Massini, and E. Tronci. "Monte Carlo Based Statistical Model Checking of Cyber-Physical Systems: A Review." Information 11, no. 558 (2020). DOI: 10.3390/info11120588.
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T. Mancini, F. Mari, A. Massini, I. Melatti, I. Salvo, S. Sinisi, E. Tronci, R. Ehrig, S. Röblitz, and B. Leeners. "Computing Personalised Treatments through In Silico Clinical Trials. A Case Study on Downregulation in Assisted Reproduction." In 25th RCRA International Workshop on “Experimental Evaluation of Algorithms for Solving Problems with Combinatorial Explosion” (RCRA 2018)., 2018. DOI: 10.29007/g864.
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Q. M. Chen, A. Finzi, T. Mancini, I. Melatti, and E. Tronci. "MILP, Pseudo-Boolean, and OMT Solvers for Optimal Fault-Tolerant Placements of Relay Nodes in Mission Critical Wireless Networks." Fundamenta Informaticae 174 (2020): 229–258. IOS Press. ISSN: 1875-8681. DOI: 10.3233/FI-2020-1941.
Abstract: In critical infrastructures like airports, much care has to be devoted in protecting radio communication networks from external electromagnetic interference. Protection of such mission-critical radio communication networks is usually tackled by exploiting radiogoniometers: at least three suitably deployed radiogoniometers, and a gateway gathering information from them, permit to monitor and localise sources of electromagnetic emissions that are not supposed to be present in the monitored area. Typically, radiogoniometers are connected to the gateway through relay nodes . As a result, some degree of fault-tolerance for the network of relay nodes is essential in order to offer a reliable monitoring. On the other hand, deployment of relay nodes is typically quite expensive. As a result, we have two conflicting requirements: minimise costs while guaranteeing a given fault-tolerance. In this paper, we address the problem of computing a deployment for relay nodes that minimises the overall cost while at the same time guaranteeing proper working of the network even when some of the relay nodes (up to a given maximum number) become faulty (fault-tolerance ). We show that, by means of a computation-intensive pre-processing on a HPC infrastructure, the above optimisation problem can be encoded as a 0/1 Linear Program, becoming suitable to be approached with standard Artificial Intelligence reasoners like MILP, PB-SAT, and SMT/OMT solvers. Our problem formulation enables us to present experimental results comparing the performance of these three solving technologies on a real case study of a relay node network deployment in areas of the Leonardo da Vinci Airport in Rome, Italy.
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S. Sinisi, V. Alimguzhin, T. Mancini, E. Tronci, F. Mari, and B. Leeners. "Optimal Personalised Treatment Computation through In Silico Clinical Trials on Patient Digital Twins." Fundamenta Informaticae 174 (2020): 283–310. IOS Press. ISSN: 1875-8681. DOI: 10.3233/FI-2020-1943.
Abstract: In Silico Clinical Trials (ISCT), i.e. clinical experimental campaigns carried out by means of computer simulations, hold the promise to decrease time and cost for the safety and efficacy assessment of pharmacological treatments, reduce the need for animal and human testing, and enable precision medicine. In this paper we present methods and an algorithm that, by means of extensive computer simulation-based experimental campaigns (ISCT) guided by intelligent search, optimise a pharmacological treatment for an individual patient (precision medicine ). We show the effectiveness of our approach on a case study involving a real pharmacological treatment, namely the downregulation phase of a complex clinical protocol for assisted reproduction in humans.
Keywords: Artificial Intelligence; Virtual Physiological Human; In Silico Clinical Trials; Simulation; Personalised Medicine; In Silico Treatment Optimisation
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T. Mancini, E. Tronci, A. Scialanca, F. Lanciotti, A. Finzi, R. Guarneri, and S. Di Pompeo. "Optimal Fault-Tolerant Placement of Relay Nodes in a Mission Critical Wireless Network." In 25th RCRA International Workshop on “Experimental Evaluation of Algorithms for Solving Problems with Combinatorial Explosion” (RCRA 2018)., 2018. DOI: 10.29007/grw9.
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T. Mancini, F. Mari, I. Melatti, I. Salvo, and E. Tronci. "An Efficient Algorithm for Network Vulnerability Analysis Under Malicious Attacks." In Foundations of Intelligent Systems – 24th International Symposium, ISMIS 2018, Limassol, Cyprus, October 29-31, 2018, Proceedings, 302–312., 2018. Notes: Best Paper. DOI: 10.1007/978-3-030-01851-1_29.
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Y. Driouich, M. Parente, and E. Tronci. "Model Checking Cyber-Physical Energy Systems." In Proceedings of 2017 International Renewable and Sustainable Energy Conference, IRSEC 2017. Institute of Electrical and Electronics Engineers Inc., 2018. DOI: 10.1109/IRSEC.2017.8477334.
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Y. Driouich, M. Parente, and E. Tronci. "A methodology for a complete simulation of Cyber-Physical Energy Systems." In EESMS 2018 – Environmental, Energy, and Structural Monitoring Systems, Proceedings, 1–5., 2018. DOI: 10.1109/EESMS.2018.8405826.
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B. Leeners, T. Krueger, K. Geraedts, E. Tronci, T. Mancini, F. Ille, M. Egli, S. Roeblitz, D. Wunder, L. Saleh et al. "Cognitive function in association with high estradiol levels resulting from fertility treatment." Hormones and Behavior 130 (2021): 104951. ISSN: 0018-506x. DOI: 10.1016/j.yhbeh.2021.104951.
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.
Keywords: Cognition, Estrogen, Estradiol, Fertility treatment, Attention, Cognitive bias
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T. Mancini, F. Mari, I. Melatti, I. Salvo, E. Tronci, J. Gruber, B. Hayes, M. Prodanovic, and L. Elmegaard. "Parallel Statistical Model Checking for Safety Verification in Smart Grids." In 2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm), 1–6., 2018. DOI: 10.1109/SmartGridComm.2018.8587416.
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