We propose a three-dimensional non-hydrostatic shock-capturing numerical model for the simulation of wave propagation, transformation and breaking, which is based on an original integral formulation of the contravariant Navier-Stokes equations, devoid of Christoffel symbols, in general time-dependent curvilinear coordinates. A coordinate transformation maps the time-varying irregular physical domain that reproduces the complex geometries of coastal regions to a fixed uniform computational one. The advancing of the solution is performed by a second-order accurate strong stability preserving Runge-Kutta fractional-step method in which, at every stage of the method, a predictor velocity field is obtained by the shock-capturing scheme and a corrector velocity field is added to the previous one, to produce a non-hydrostatic divergence-free velocity field and update the water depth. The corrector velocity field is obtained by numerically solving a Poisson equation, expressed in integral contravariant form, by a multigrid technique which uses a four-colour Zebra Gauss-Seidel line-by-line method as smoother. Several test cases are used to verify the dispersion and shockcapturing properties of the proposed model in time-dependent curvilinear grids.

A numerical model that solves two-phase flow motion equations to reproduce turbidity currents that occur in reservoirs, is proposed. Three formalizations of the two-phase flow motion equations are presented: the first one can be adopted for high concentration values; the second one is valid under the hypothesis of diluted concentrations; the third one is based on the assumption that the particles are in translational equilibrium with the fluid flow. The proposed numerical model solves the latter formalization of two-phase flow motion equations, in order to simulate turbidity currents. The motion equations are presented in an integral form in time-dependent curvilinear coordinates, with the vertical coordinate that varies in order to follow the free surface movements. The proposed numerical model is validated against experimental data and is applied to a practical engineering case study of a reservoir, in order to evaluate the possibility of the formation of turbidity currents.

In this work a simulation model of aeroelastic phenomena for long-span bridges is presented. By the proposed model the aerodynamic field and the structural motion are simulated simultaneously and in a coupled manner. The structure is represented as a bidimensional rigid body with two degrees of freedom, having mass per unit length and mass moment of inertia per unit length equal to those of the deck. The aerodynamic fields are simulated by numerically integrating the Arbitrary Lagrangian-Eulerian (ALE) formulated Unsteady Reynolds-Averaged Navier-Stokes (URANS) equations with a finite volume scheme on moving grids which adapt themselves to the structural motion. The finite volume method is based on high order weighted essentially non-oscillatory (WENO) reconstructions. The time discretisation is performed by a five stage fourth order accurate strong stability preserving Runge-Kutta (SSPRK) method. The URANS equations are completed by the turbulent closure relations which are expressed as a function of the turbulent kinetic energy and the turbulent frequency according to the k- SST approach. The model validation is performed by the comparison between numerical and experimental results. The proposed model is utilised in order to identify the flutter critical wind velocity of the Forth Road Bridge deck, and the numerical results are compared with those of an experimental campaign.

Introduction. The heart rate variability (HRV) is based on measuring (time) intervals between R-peaks (of RR-intervals) of an electrocardiogram (ECG) and plotting a rhythmogram on their basis with its subsequent analysis by various mathematical methods which are classified as Time-Domain (TD), Frequency-Domain (FD) and Nonlinear [1, 2]. There are a number of popular Nonlinear methods used in HRV analysis, such as entropy-based measures that mostly applied for TD. Spectral Entropy (SE) is using for Frequency-Domain: it is defined to be the Shannon entropy of the power spectral density (PSD) of the data. An important characteristic of Frequency-Domain studies is sympatho-vagal balance, which has been overlooked by entropy-based analysis. This is due to the fact that good entropy analysis restricted the number of existing HRV data, which is shrinking in FD and also in total spectrum parts. Aim of the research. The goal of this paper is to provide a reliable formula for calculating entropy accurately for Frequency-domain of standard 5-min. HRV records and to show the advantages of such approach for analyzing of sympatho-vagal balance for healthy subjects (NSR), Congestive Heart Failure (CHF) and Atrial Fibrillation (AF) patients. Materials and Methods. We used MIT-BIH long-term HRV records for Normal Sinus Rhythm (NSR), Congestive Heart Failure (CHF) and Atrial Fibrillation (AF). The generalized form of the Robust Entropy Estimator (EnRE) for Frequency-domain of standard 5-min. HRV records was proposed and the key EnRE futures was shown. The difference between means of the two independent selections (NSR and CHF, before and after AF) has been determined by a t-test for independent samples; discriminant analysis and statistical calculations have been done by using the statistical package IBM SPSS 27. The results of the study. We calculate entropy for all valuable for HRV spectral interval, namely 0–0.4 Hz and to compare with existing results for Spectral Entropy: qualitatively we receive the same distribution number as [14] and significant difference (p < 0.001) between entropy averages for NSR and CHF or AF patients. We define low-frequencies (LF) power spectrum components in the range of 0.04–0.15 Hz and high-frequencies (HF) power spectrum components in the range of 0.15–0.4 Hz [1]. The sympatho-vagal balance is a simple ratio LF/HF [1]. Then, we define an entropy eLF of the LF power spectrum components, an entropy eHF of the HF power spectrum components and entropy based sympatho-vagal balance as a ratio eLF/eHF. The difference between NSR and CHF groups are significant in both cases LF/HF and eLF/eHF with p < 0.001, but in case of eLF/eHF the results are quite better (t = -4.8, compared to LF/HF where t = -4.4). The discriminant analysis shows total classification accuracy for eLF/eHF in 79.3 % (χ2 = 19.4, p < 0.001) and for LF/HF in 72.4 % (χ2 = 16.6, p < 0.001). We applied entropy-based Frequencies-domain analyzing for AF patients and showed that ratio eLF/eHF is significantly higher during AF than before AF (p < 0.001). This is opposite to ordinary LF/HF where difference is insignificant due to high variation of this ratio. Conclusion. Proposed in the article is generalized form for Robust Entropy Estimator EnRE for Frequencies-domain, which allows, for time series of a limited length (standard 5-min. records), to find entropy value of HRV power spectrum (total spectrum, low- and high- frequencies bands). Using the proposed EnRE for MIT-BIH database of HRV records, we show for standard 5 min. HRV records the usage of EnRE of HRV power spectrum and entropy-based sympatho-vagal balance of Normal Sinus Rhythm (NSR) and Congestive Heart Failure (CHF) cases. It is demonstrated, that, entropy-based Frequencies-domain analyzing is applicable for case of Atrial Fibrillation (AF) even during AF episodes. We showed the significant difference (p < 0.001) before and during AF for entropy of total spectrum, as well as for sympatho-vagal balance in form of eLF/eHF.

Introdution. Exercise can be defined as any structured and planned activity leading to an increase of energy expenditure, breathing and pulse rate. In the context of a correct lifestyle, a regular physical activity reduces the probability of cardiovascular events, diabetes and other possible related diseases. The aim of this study is to evaluate the neurovegetative cardiovascular regulation and the fluids distribution in healthy subjects undergoing dynamic and isometric training regimes. We have employed Heart Rate Variability (HRV) analysis by various mathematical methods that are classified as Time Domain (TD), Frequency Domain (FD) and Nonlinear (NM). We incorporated currently existing HRV indicators into a unified Fuzzy Logic (FL) methodology, which in turn will allow to integrally assessing each metric and HRV results as a whole. Objective. The goal of this study is to verify the response of the ANS before and after the execution of different training in the clearest view by our Fuzzy Logic approach to Heart Rate Variability series analysing. Our Fuzzy Logic algorithm incorporate into a single view of each metric, – Time Domain, Frequency Domain, Nonlinear Methods and HRV as a whole. Materials and methods. 24 young subjects aged between 20 and 30 (11 males and 13 females) have been enrolled. Exclusion criteria are: tobacco use; BMI > 25 kg/m2; cardiovascular diseases; blood pressure ≥ 140/90 mmHg; chronic pathologies; sport competition. Each of the examined subjects underwent four different tests and analyses: before the beginning of the isotonic training, which has been carried out by 30-minute run each day for a period of 20 days, and after the end of the training, both in upright and supine position; before the beginning of the isometric training, which has been carried out by lifting a 2-kg weight for 30 minutes per day for a period of 20 days, and after the end of the training, both in upright and supine position. Conclusion. HRV is a complex phenomenon, study of which requires various approaches and methods. However, a comprehensive view of HRV is only possible when there is a technology similar to Fuzzy Logic, one that allows combining all used methods and approaches into an integral assessment. In this article, we showed the Fuzzy Logic approach for series of Heart Rate Variability records and we can assert that: the training through exercises of dynamic type could reduce the cardiovascular risk, thus confirming the importance of a correct lifestyle; the isometric exercise generally produces an increase of the indexes of the sympathetic activity and then an increase of the cardiovascular risk with reduced cardioprotection; the Base state (before training) showing the biggest distance from abnormality because the Norm HRV values were defined for calm body state – before any training or disturbances; FL distances after Isometric training showing the worst distance from abnormality.