Statistics of small scale vortex filaments in turbulence

We study the statistical properties of coherent, small-scales, filamentary-like structures in Turbulence. In order to follow in time such complex spatial structures, we integrate Lagrangian and Eulerian measurements by seeding the flow with light particles. We show that light particles preferentially concentrate in small filamentary regions of high persistent vorticity (vortex filaments). We measure the fractal dimension of the attracting set and the probability that two particles do not separate for long time lapses. We fortify the signal-to-noise ratio by exploiting multi-particles correlations on the dynamics of bunches of particles. In doing that, we are able to give a first quantitative estimation of the vortex-filaments life-times, showing the presence of events as long as the integral correlation time. The same technique introduced here could be used in experiments as long as one is capable to track clouds of bubbles in turbulence for a relatively long period of time, at high Reynolds numbers; shading light on the dynamics of small-scale vorticity in realistic turbulent flows.Comment: 5 pages, 5 figure

Law of the wall in an unstably stratified turbulent channel flow

We perform direct numerical simulations of an unstably stratified turbulent channel flow to address the effects of buoyancy on the boundary layer dynamics and mean field quantities. We systematically span a range of parameters in the space of friction Reynolds number ($Re_{\tau}$) and Rayleigh number ($Ra$). Our focus is on deviations from the logarithmic law of the wall due to buoyant motion. The effects of convection in the relevant ranges are discussed providing measurements of mean profiles of velocity, temperature and Reynolds stresses as well as of the friction coefficient. A phenomenological model is proposed and shown to capture the observed deviations of the velocity profile in the log-law region from the non-convective case

Heat flux scaling in turbulent Rayleigh-B\'enard convection with an imposed longitudinal wind

We present a numerical study of Rayleigh-B\'enard convection disturbed by a longitudinal wind. Our results show that under the action of the wind, the vertical heat flux through the cell initially decreases, due to the mechanism of plumes-sweeping, and then increases again when turbulent forced convection dominates over the buoyancy. As a result, the Nusselt number is a non-monotonic function of the shear Reynolds number. We provide a simple model that captures with good accuracy all the dynamical regimes observed. We expect that our findings can lead the way to a more fundamental understanding of the of the complex interplay between mean-wind and plumes ejection in the Rayleigh-B\'enard phenomenology.Comment: 5 pages, 4 figure

Lattice Boltzmann Methods for thermal flows: continuum limit and applications to compressible Rayleigh-Taylor systems

We compute the continuum thermo-hydrodynamical limit of a new formulation of lattice kinetic equations for thermal compressible flows, recently proposed in [Sbragaglia et al., J. Fluid Mech. 628 299 (2009)]. We show that the hydrodynamical manifold is given by the correct compressible Fourier- Navier-Stokes equations for a perfect fluid. We validate the numerical algorithm by means of exact results for transition to convection in Rayleigh-B\'enard compressible systems and against direct comparison with finite-difference schemes. The method is stable and reliable up to temperature jumps between top and bottom walls of the order of 50% the averaged bulk temperature. We use this method to study Rayleigh-Taylor instability for compressible stratified flows and we determine the growth of the mixing layer at changing Atwood numbers up to At ~ 0.4. We highlight the role played by the adiabatic gradient in stopping the mixing layer growth in presence of high stratification and we quantify the asymmetric growth rate for spikes and bubbles for two dimensional Rayleigh- Taylor systems with resolution up to Lx \times Lz = 1664 \times 4400 and with Rayleigh numbers up to Ra ~ 2 \times 10^10.Comment: 26 pages, 13 figure

On the measurement of vortex filament lifetime statistics in turbulence

A numerical study of turbulence seeded with light particles is presented. We analyze the statistical properties of coherent, small-scale structures by looking at the trapping events of light particles inside vortex filaments. We study the properties of particles attracting set, measuring its fractal dimension and the probability that the separation between two particles remains within the dissipative scale, even for time lapses as long as the large-scale correlation time, TL . We show how to estimate the vortex lifetime by studying the moment of inertia of bunches of particles, showing the presence of an exponential lifetime distribution, with events up to

A blend of selected botanicals maintains intestinal epithelial integrity and reduces susceptibility to Escherichia coli F4 infection by modulating acute and chronic inflammation in vitro

In the pig production cycle, the most delicate phase is weaning, a sudden and early change that requires a quick adaptation, at the cost of developing inflammation and oxidation, especially at the intestinal level. In this period, pathogens like enterotoxigenic Escherichia coli (ETEC) contribute to the establishment of diarrhea, with long-lasting detrimental effects. Botanicals and their single bioactive components represent sustainable well-recognized tools in animal nutrition thanks to their wide-ranging beneficial functions. The aim of this study was to investigate the in vitro mechanism of action of a blend of botanicals (BOT), composed of thymol, grapeseed extract, and capsicum oleoresin, in supporting intestinal cell health during inflammatory challenges and ETEC infections. To reach this, we performed inflammatory and ETEC challenges on Caco-2 cells treated with BOT, measuring epithelial integrity, cellular oxidative stress, bacterial translocation and adhesion, gene expression levels, and examining tight junction distribution. BOT protected enterocytes against acute inflammation: while the challenge reduced epithelial tightness by 40%, BOT significantly limited its drop to 30%, also allowing faster recovery rates. In the case of chronic inflammation, BOT systematically improved by an average of 25% the integrity of challenged cells (p < 0.05). Moreover, when cells were infected with ETEC, BOT maintained epithelial integrity at the same level as an effective antibiotic and significantly reduced bacterial translocation by 1 log average. The mode of action of BOT was strictly related to the modulation of the inflammatory response, protecting tight junctions’ expression and structure. In addition, BOT influenced ETEC adhesion to intestinal cells (−4%, p < 0.05), also thanks to the reduction of enterocytes’ susceptibility to pathogens. Finally, BOT effectively scavenged reactive oxygen species generated by inflammatory and H2O2 challenges, thus alleviating oxidative stress by 40% compared to challenge (p < 0.05). These results support the employment of BOT in piglets at weaning to help manage bacterial infections and relieve transient or prolonged stressful states thanks to the modulation of host-pathogen interaction and the fine-tuning activity on the inflammatory tone

Beyond Multilayer Perceptrons: Investigating Complex Topologies in Neural Networks

In this study, we explore the impact of network topology on the approximation capabilities of artificial neural networks (ANNs), with a particular focus on complex topologies. We propose a novel methodology for constructing complex ANNs based on various topologies, including Barab\'asi-Albert, Erd\H{o}s-R\'enyi, Watts-Strogatz, and multilayer perceptrons (MLPs). The constructed networks are evaluated on synthetic datasets generated from manifold learning generators, with varying levels of task difficulty and noise. Our findings reveal that complex topologies lead to superior performance in high-difficulty regimes compared to traditional MLPs. This performance advantage is attributed to the ability of complex networks to exploit the compositionality of the underlying target function. However, this benefit comes at the cost of increased forward-pass computation time and reduced robustness to graph damage. Additionally, we investigate the relationship between various topological attributes and model performance. Our analysis shows that no single attribute can account for the observed performance differences, suggesting that the influence of network topology on approximation capabilities may be more intricate than a simple correlation with individual topological attributes. Our study sheds light on the potential of complex topologies for enhancing the performance of ANNs and provides a foundation for future research exploring the interplay between multiple topological attributes and their impact on model performance

4Ward: A relayering strategy for efficient training of arbitrarily complex directed acyclic graphs

thanks to their ease of implementation, multilayer perceptrons (MLPs) have become ubiquitous in deep learning applications. the graph underlying an MLP is indeed multipartite, i.e. each layer of neurons only connects to neurons belonging to the adjacent layer. In contrast, in vivo brain connectomes at the level of individual synapses suggest that biological neuronal networks are characterized by scale-free degree distributions or exponentially truncated power law strength distributions, hinting at potentially novel avenues for the exploitation of evolution-derived neuronal networks. In this paper, we present "4Ward", a method and python library capable of generating flexible and efficient neural networks (NNs) from arbitrarily complex directed acyclic graphs. 4Ward is inspired by layering algorithms drawn from the graph drawing discipline to implement efficient forward passes, and provides significant time gains in computational experiments with various Erdos-Renyi graphs. 4Ward not only overcomes the sequential nature of the learning matrix method, by parallelizing the computation of activations, but also addresses the scalability issues encountered in the current state-of-the-art and provides the designer with freedom to customize weight initialization and activation functions. Our algorithm can be of aid for any investigator seeking to exploit complex topologies in a NN design framework at the microscale

Lagrangian statistics of concentrated emulsions

The dynamics of stabilised concentrated emulsions presents a rich phenomenology including chaotic emulsification, non-Newtonian rheology and ageing dynamics at rest. Macroscopic rheology results from the complex droplet microdynamics and, in turn, droplet dynamics is influenced by macroscopic flows via the competing action of hydrodynamic and interfacial stresses, giving rise to a complex tangle of elastoplastic effects, diffusion, breakups and coalescence events. This tight multiscale coupling, together with the daunting challenge of experimentally investigating droplets under flow, has hindered the understanding of concentrated emulsions dynamics. We present results from three-dimensional numerical simulations of emulsions that resolve the shape and dynamics of individual droplets, along with the macroscopic flows. We investigate droplet dispersion statistics, measuring probability density functions (p.d.f.s) of droplet displacements and velocities, changing the concentration, in the stirred and ageing regimes. We provide the first measurements, in concentrated emulsions, of the relative droplet–droplet separations p.d.f. and of the droplet acceleration p.d.f., which becomes strongly non-Gaussian as the volume fraction is increased above the jamming point. Cooperative effects, arising when droplets are in contact, are argued to be responsible of the anomalous superdiffusive behaviour of the mean square displacement and of the pair separation at long times, in both the stirred and in the ageing regimes. This superdiffusive behaviour is reflected in a non-Gaussian pair separation p.d.f., whose analytical form is investigated, in the ageing regime, by means of theoretical arguments. This work paves the way to developing a connection between Lagrangian dynamics and rheology in concentrated emulsions

4Ward: a Relayering Strategy for Efficient Training of Arbitrarily Complex Directed Acyclic Graphs

Thanks to their ease of implementation, multilayer perceptrons (MLPs) have become ubiquitous in deep learning applications. The graph underlying an MLP is indeed multipartite, i.e. each layer of neurons only connects to neurons belonging to the adjacent layer. In contrast, in vivo brain connectomes at the level of individual synapses suggest that biological neuronal networks are characterized by scale-free degree distributions or exponentially truncated power law strength distributions, hinting at potentially novel avenues for the exploitation of evolution-derived neuronal networks. In this paper, we present ``4Ward'', a method and Python library capable of generating flexible and efficient neural networks (NNs) from arbitrarily complex directed acyclic graphs. 4Ward is inspired by layering algorithms drawn from the graph drawing discipline to implement efficient forward passes, and provides significant time gains in computational experiments with various Erd\H{o}s-R\'enyi graphs. 4Ward not only overcomes the sequential nature of the learning matrix method, by parallelizing the computation of activations, but also addresses the scalability issues encountered in the current state-of-the-art and provides the designer with freedom to customize weight initialization and activation functions. Our algorithm can be of aid for any investigator seeking to exploit complex topologies in a NN design framework at the microscale