A Parallel Dual Fast Gradient Method for MPC Applications

We propose a parallel adaptive constraint-tightening approach to solve a linear model predictive control problem for discrete-time systems, based on inexact numerical optimization algorithms and operator splitting methods. The underlying algorithm first splits the original problem in as many independent subproblems as the length of the prediction horizon. Then, our algorithm computes a solution for these subproblems in parallel by exploiting auxiliary tightened subproblems in order to certify the control law in terms of suboptimality and recursive feasibility, along with closed-loop stability of the controlled system. Compared to prior approaches based on constraint tightening, our algorithm computes the tightening parameter for each subproblem to handle the propagation of errors introduced by the parallelization of the original problem. Our simulations show the computational benefits of the parallelization with positive impacts on performance and numerical conditioning when compared with a recent nonparallel adaptive tightening scheme.Comment: This technical report is an extended version of the paper "A Parallel Dual Fast Gradient Method for MPC Applications" by the same authors submitted to the 54th IEEE Conference on Decision and Contro

Geology of the Shakespeare quadrangle (H03), Mercury

By using images acquired by the Mercury dual imaging system (MDIS) on-board the MESSENGER spacecraft during 2008–2015 and available DTMs, a new 1:3,000,000-scale geological map of the Shakespeare quadrangle of Mercury has been compiled. The quadrangle is located between latitudes 22.5°–65.0°N and longitudes 270.0°–180.0°E and covers an area of about 5 million km2. The mapping was based on photo-interpretation performed on a reference monochromatic basemap of reflectance at 166 m/pixel resolution. The geological features were digitized within a geographic information system with a variable mapping scale between 1:300,000 and 1:600,000. This quadrangle is characterized by the occurrence of three main types of plains materials and four basin materials (pertaining to the Caloris basin), whose geologic boundaries have been here redefined compared to the previous map of the quadrangle. The stratigraphic relationships between the craters were based on three main degradation morphologies. Furthermore, previously unmapped tectonic landforms were detected and interpreted as thrusts or wrinkle ridges

The influence of the stratospheric state on North Atlantic weather regimes

Stratosphere-troposphere coupling is often viewed from the perspective of the annular modes and their dynamics. Despite the obvious benefits of this approach, recent work has emphasised the greater tropospheric sensitivity to stratospheric variability in the Atlantic basin than in the Pacific basin. In this study, a new approach to understanding stratosphere-troposphere coupling is proposed, with a focus on the influence of the stratospheric state on North Atlantic weather regimes (during extended winter, November to March). The influence of the strength of the lower stratospheric vortex on four commonly used tropospheric weather regimes is quantified. The negative phase of the North Atlantic Oscillation is most sensitive to the stratospheric state, occurring on 33% of days following weak vortex conditions but on only 5% of days following strong vortex conditions. An opposite and slightly weaker sensitivity is found for the positive phase of the North Atlantic Oscillation and the Atlantic Ridge regime. For the North Atlantic Oscillation regimes, stratospheric conditions change both the probability of remaining in each regime and the probability of transitioning to that regime from others. A logistic regression model is developed to further quantify the sensitivity of tropospheric weather regimes to the lower stratospheric state. The logistic regression model predicts an increase of 40-60% in the probability of transition to the negative phase of the North Atlantic Oscillation for a one standard deviation reduction in the strength of the stratospheric vortex. Similarly it predicts a 10-30% increase in the probability of transition to the positive phase of the North Atlantic Oscillation for a one standard deviation increase in the strength of the stratospheric vortex. The stratosphere-troposphere coupling in the European Centre for Medium Range Weather Forecasts, Integrated Forecasting System model is found to be consistent with the re- analysis data by fitting the same logistic regression model

PERINATAL DEPRESSION: A STUDY OF PREVALENCE AND OF RISK AND PROTECTIVE FACTORS

Background: International literature has shown that Postpartum Depression (PPD) has a significant social and relational impact on mothers and their partners, on the interaction between mother and child, as well as on the cognitive and emotional development of the child. The goal of this study is to increase the epidemiological knowledge of PPD and to evaluate both risk and protective factors. Subjects and methods: Our study is based on the administration of three tests, Paykel’s Life Events Scale, EPDS and MMPI-2, at three distinct time point (during the third trimester, 72 hours after delivery, and three months after delivery, respectively) to a sample of women recruited in the Prenatal Medicine Clinic at the Hospital of Perugia. The data collected was statistically analyzed. Results: The prevalence of PPD 72 hours after delivery was 11%, while the prevalence of PPD three months after delivery was 16.7%. Antepartum Depression (APD), measured using EPDS cut-offs scores of 9 and 14, was found to be a statistically significant risk factor for the development of PPD, while desired life-events during pregnancy can represent a protective factor. Conclusions: The prevalence of PPD that we measured, in agreement with that found in the literature, demonstrates that despite the fact that the diagnostic criteria of the DSM-IV refer to PPD only if it develops within 4 weeks after delivery, PPD can also develop after this period. Furthermore, it appears that monitoring APD and encouraging a psycho-socially serene pregnancy are important for prevention of PPD. In the case of APD it was shown that monitoring women with even light depressive symptoms is important, because these women are more likely to then develop PPD

PERINATAL DEPRESSION: A STUDY OF PREVALENCE AND OF RISK AND PROTECTIVE FACTORS

Background: International literature has shown that Postpartum Depression (PPD) has a significant social and relational impact on mothers and their partners, on the interaction between mother and child, as well as on the cognitive and emotional development of the child. The goal of this study is to increase the epidemiological knowledge of PPD and to evaluate both risk and protective factors. Subjects and methods: Our study is based on the administration of three tests, Paykel’s Life Events Scale, EPDS and MMPI-2, at three distinct time point (during the third trimester, 72 hours after delivery, and three months after delivery, respectively) to a sample of women recruited in the Prenatal Medicine Clinic at the Hospital of Perugia. The data collected was statistically analyzed. Results: The prevalence of PPD 72 hours after delivery was 11%, while the prevalence of PPD three months after delivery was 16.7%. Antepartum Depression (APD), measured using EPDS cut-offs scores of 9 and 14, was found to be a statistically significant risk factor for the development of PPD, while desired life-events during pregnancy can represent a protective factor. Conclusions: The prevalence of PPD that we measured, in agreement with that found in the literature, demonstrates that despite the fact that the diagnostic criteria of the DSM-IV refer to PPD only if it develops within 4 weeks after delivery, PPD can also develop after this period. Furthermore, it appears that monitoring APD and encouraging a psycho-socially serene pregnancy are important for prevention of PPD. In the case of APD it was shown that monitoring women with even light depressive symptoms is important, because these women are more likely to then develop PPD

Optimization-based Fault Mitigation for Safe Automated Driving

With increased developments and interest in cooperative driving and higher levels of automation (SAE level 3+), the need for safety systems that are capable to monitor system health and maintain safe operations in faulty scenarios is increasing. A variety of faults or failures could occur, and there exists a high variety of ways to respond to such events. Once a fault or failure is detected, there is a need to classify its severity and decide on appropriate and safe mitigating actions. To provide a solution to this mitigation challenge, in this paper a functional-safety architecture is proposed and an optimization-based mitigation algorithm is introduced. This algorithm uses nonlinear model predictive control (NMPC) to bring a vehicle, suffering from a severe fault, such as a power steering failure, to a safe-state. The internal model of the NMPC uses the information from the fault detection, isolation and identification to optimize the tracking performance of the controller, showcasing the need of the proposed architecture. Given a string of ACC vehicles, our results demonstrate a variety of tactical decision-making approaches that a fault-affected vehicle could employ to manage any faults. Furthermore, we show the potential for improving the safety of the affected vehicle as well as the effect of these approaches on the duration of the manoeuvre.Comment: Accepted for the 2023 IFAC World Conferenc

Assessing causal dependencies in climatic indices

We evaluate causal dependencies between thirteen indices that represent large-scale climate patterns (El Nino/Southern Oscillation, the North Atlantic Oscillation, the Pacifc Decadal Oscillation, etc.) using a recently proposed approach based on a linear approximation of the transfer entropy. We demonstrate that this methodology identifes causal relations that are well-known, as well as it uncovers some relations which, to the best of our knowledge, have not yet been reported in the literature. We also identify signifcant changes in causal dependencies that have occurred in the last three decades.Open Access funding provided thanks to the CRUECSIC agreement with Springer Nature. This work received funding from the European Union’s Horizon 2020 research and innovation program under Grant Agreement No 813844. C.M. also acknowledges funding by the ICREA ACADEMIA program of Generalitat de Catalunya and Ministerio de Ciencia e Innovacion, Spain, project PID2021-123994NB-C21.Peer ReviewedPostprint (published version

EValueAction: a proposal for policy evaluation in simulation to support interactive imitation learning

The up-and-coming concept of Industry 5.0 foresees human-centric flexible production lines, where collaborative robots support human workforce. In order to allow a seamless collaboration between intelligent robots and human workers, designing solutions for non-expert users is crucial. Learning from demonstration emerged as the enabling approach to address such a problem. However, more focus should be put on finding safe solutions which optimize the cost associated with the demonstrations collection process. This paper introduces a preliminary outline of a system, namely EValueAction (EVA), designed to assist the human in the process of collecting interactive demonstrations taking advantage of simulation to safely avoid failures. A policy is pre-trained with human-demonstrations and, where needed, new informative data are interactively gathered and aggregated to iteratively improve the initial policy. A trial case study further reinforces the relevance of the work by demonstrating the crucial role of informative demonstrations for generalization