Statistical Analysis of the Wave Runup at Walls in a Changing Climate by Means of Image Clustering

This contribution builds on an existing methodology of image clustering analysis, conceived for modelling the wave overtopping at dikes from video records of laboratory experiments. It presents new procedures and algorithms developed to extend this methodology to the representation of the wave runup at crown walls on top of smooth berms. The upgraded methodology overcomes the perspective distortion of the native images and deals with the unsteady, turbulent and bi-phase flow dynamics characterizing the wave impacts at the walls. It accurately reconstructs the free surface along the whole structure profile and allows for a statistical analysis of the wave runup in the time and spatial domain. The effects of different structural configurations are investigated to provide key information for the design of coastal defences. In particular, the effects of increased sea levels in climate change scenarios are analysed. Innovative results, such as profiling of the envelopes of the runup along the wall cross and front sections, and the evidencing of 3D effects on the runup are presented. The extreme runup is estimated for the definition of the design conditions, while the envelopes of the average and minimum runup heights are calculated to assess the normal exercise conditions of existing structures

Tuning of subspace predictive controls

Data-driven predictive control has recently gained increasing attention, as it makes it possible to design constrained controls directly from a set of data, without requiring an intermediate identification step. In this paper, we focus on a Subspace Predictive Control (SPC) scheme, with the aim of clarifying the sensitivity of the final closed-loop performance to its main hyperparameters, namely the length of the past horizon and the regularization penalties. Moreover, by delving deep into the structural properties of the control problem formulation, we provide a set of guidelines for the choice of such hyperparameters. The effectiveness of the resulting overall tuning strategy is assessed on two benchmark examples.</p

Heart Rate Turbulence Predicts Survival Independently From Severity of Liver Dysfunction in Patients With Cirrhosis

Background: Reduced heart rate variability (HRV) is an independent predictor of mortality in patients with cirrhosis. However, conventional HRV indices can only be interpreted in individuals with normal sinus rhythm. In patients with recurrent premature ventricular complexes (PVCs), the predictive capacity of conventional HRV indices is compromised. Heart Rate Turbulence (HRT) represents the biphasic change of the heart rate after PVCs. This study was aimed to define whether HRT parameters could predict mortality in cirrhotic patients. Materials and Methods: 24 h electrocardiogram recordings were collected from 40 cirrhotic patients. Turbulence Onset was calculated as HRT indices. The enrolled patients were followed up for 12 months after the recruitment in relation to survival and/or transplantation. Results: During the follow-up period, 21 patients (52.5%) survived, 12 patients (30%) died and 7 patients (17.5%) had liver transplantation. Turbulence Onset was found to be strongly linked with mortality on Cox regression (Hazard ratio = 1.351, p < 0.05). Moreover, Turbulence Onset predicted mortality independently of MELD and Child-Pugh's Score. Conclusion: This study provides further evidence of autonomic dysfunction in cirrhosis and suggests that HRT is reliable alternative to HRV in patients with PVCs

The Alarm Clock Against the Sun: Trends in Google Trends Search Activity Across the Transitions to and from Daylight Saving Time

The human circadian timing system depends on the light/dark cycle as its main cue to synchronize with the environment, and thus with solar time. However, human activities depend also on social time, i.e. the set of time conventions and restrictions dictated by society, including Daylight Saving Time (DST), which adds an hour to any degree of desynchrony between social and solar time. Here, we used Google Trends as a data source to analyze diurnal variation, if any, and the daily peak in the relative search volume of 26 Google search queries in relation to the transitions to/from DST in Italy from 2015 to 2020. Our search queries of interest fell into three categories: sleep/health-related, medication and random non sleep/health-related. After initial rhythm and phase analysis, 11 words were selected to compare the average phase of the 15 days before and after the transition to/from DST. We observed an average phase advance after the transition to DST, and a phase delay after the transition to civil time, ranging from 25 to 60 minutes. Advances or delays shorter than 60 minutes, which were primarily observed in the sleep/ health-related category, may suggest that search timing for these queries is at least partially driven by the endogenous circadian rhythm. Finally, a significant trend in phase anticipation over the years was observed for virtually all words. This is most likely related to an increase in age, and thus in earlier chronotypes, amongst Google users

MicroMotility: State of the art, recent accomplishments and perspectives on the mathematical modeling of bio-motility at microscopic scales

Mathematical modeling and quantitative study of biological motility (in particular, of motility at microscopic scales) is producing new biophysical insight and is offering opportunities for new discoveries at the level of both fundamental science and technology. These range from the explanation of how complex behavior at the level of a single organism emerges from body architecture, to the understanding of collective phenomena in groups of organisms and tissues, and of how these forms of swarm intelligence can be controlled and harnessed in engineering applications, to the elucidation of processes of fundamental biological relevance at the cellular and sub-cellular level. In this paper, some of the most exciting new developments in the fields of locomotion of unicellular organisms, of soft adhesive locomotion across scales, of the study of pore translocation properties of knotted DNA, of the development of synthetic active solid sheets, of the mechanics of the unjamming transition in dense cell collectives, of the mechanics of cell sheet folding in volvocalean algae, and of the self-propulsion of topological defects in active matter are discussed. For each of these topics, we provide a brief state of the art, an example of recent achievements, and some directions for future research

Structure Selection of Noise Covariance Matrices for Linear Kalman Filter Design

In state reconstruction problems, the statistics of the noise affecting the state equations is often supposed to be known. Since an incorrect description of the model stochastic properties may have detrimental effects on the final filtering performance, many algorithms have been proposed to estimate the noise covariance matrices together with the unknown state. Due to the high computational load, a typical practical assumption is that the process noise covariance can be parameterized as a diagonal matrix. In this paper, we show by counterexamples that this is not always the best compromise between computational complexity and tracking accuracy. Furthermore, a combinatorial optimization algorithm originally employed for model structure selection in nonlinear identification applications is here adapted to the task of selecting the structure of the process noise covariance matrices. The effectiveness of the proposed approach is illustrated by means of some numerical examples

Learning-based hierarchical control of water reservoir systems

The optimal control of a water reservoir system represents a challenging problem, due to uncertain hydrologic inputs and the need to adapt to changing environment and varying control objectives. In this work, we propose a real-time learning-based control strategy based on a hierarchical predictive control architecture. Two control loops are implemented: the inner loop is aimed to make the overall dynamics similar to an assigned linear model through data-driven control design, then the outer economic model-predictive controller compensates for model mismatches, enforces suitable constraints, and boosts the tracking performance. The effectiveness of the proposed approach is illustrated on an accurate simulator of the Hoa Binh reservoir in Vietnam. Results show that the proposed approach outperforms stochastic dynamic programming