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PRISMA is a hyperspectral pushbroom sensor, launched by the Italian Space Agency in 2019. PRISMA collects the reflected Earth signal from VNIR to the SWIR with 230 spectral bands with a variable FWHM according to the prism dispersion element. This work intends to develop a procedure suitable to monitor the consistency of photon and thermal noise components across a times series of L1 radiance images collected on different Mediterranean scenarios (i.e. rural and coastal). To improve the retrieval of the useful signal and the random noise on PRISMA images the spatial variability of the scenes has been considered in the new version of the HYperspectral Noise Parameters Estimation (HYNPE) algorithm. The procedure, tested on two PRISMA time series, has assessed quite stable and coherent values for the retrieved noise coefficients, not significantly affected by seasonal radiance variations and scene characteristics

In this paper we study a general class of hybrid mathematical models of collective motions of cells under the influence of chemical stimuli. The models are hybrid in the sense that cells are discrete entities given by ODEs, while the chemoattractant is considered as a continuous signal which solves a diffusive equation. For this model we prove the mean-field limit in the Wasserstein distance to a system given by the coupling of a Vlasov-type equation with the chemoattractant equation. Our approach and results are not based on empirical measures but rather on marginals of a large number of individuals densities, and we show the limit with explicit bounds by proving also existence and uniqueness for the limit system. In the monokinetic case we derive a new pressureless nonlocal Euler-type model with chemotaxis.

Wound healing (WH) is a complex phenomenon recollecting the ability of the body to preserve homeostasis. Its description ranges from the very minute details on the progression of the molecular and cellular events (bench) occurring locally to a wound, to the very general, and almost colloquial description of how injuries recover more or less quickly and smoothly in an individual or a patient (bed).The connection between the two representations of WH is far from clear and rarely discussed from an overarching and theoretical perspective in biological, computational and medical terms that represent three fundamental components of modern PPPM. Importantly, understanding WH and its eliciting/modulating factors (including physical stimuli) as a continuum between molecular (local) and clinical (systemic) events is of particular relevance for advancing in the management of chronic inflammation, a hallmark of non-communicable diseases (NCDs). The ambition of this chapter is to make the necessity for such continuum explicit. This evidence will be supported with the first integrated overview on the scattered basic knowledge existing about WH's neglected eliciting factors: physical stimuli. Further, an exemplar translational process using rheumatoid arthritis (RA), proceeding from experimental data in animal models to a pilot clinical study, will cover from bench to bedside the relevance of WH as a systemic anti-inflammatory phenomenon, and will be discussed in the frame of PPPM for its therapeutic potential.

Image resizing (IR) has a crucial role in remote sensing (RS), since an image's level of detail depends on the spatial resolution of the acquisition sensor; its design limitations; and other factors such as (a) the weather conditions, (b) the lighting, and (c) the distance between the satellite platform and the ground targets. In this paper, we assessed some recent IR methods for RS applications (RSAs) by proposing a useful open framework to study, develop, and compare them. The proposed framework could manage any kind of color image and was instantiated as a Matlab package made freely available on Github. Here, we employed it to perform extensive experiments across multiple public RS image datasets and two new datasets included in the framework to evaluate, qualitatively and quantitatively, the performance of each method in terms of image quality and statistical measures.

Resolution Approximation Methods (RAM) play a crucial role in many real-world applications where preserving the original image quality is essential. Depending on the specific applicative field, the approximation may focus on spatial and/or color (intensity) information [7], [6]. Over the years, several methods have been proposed for color (gray) images, and multiple research directions have been pursued to enhance the performance and robustness of RAM [1],[2], [3], [4] and [5]. This contribution explores some approaches for both spatial and color (intensity) resolution approximation, providing a comprehensive analysis of their benefits, drawbacks, and potential future advancements.

Smart farming is becoming an active and interdisciplinary research field as it requires to solve interesting and challenging research issues to respond concretely to the demands of specific use-cases. One of the most delicate tasks is the automatic yield estimation, as for example in vineyards [1]. Computer vision methods that implement the rules of the human visual system can contribute to task accomplishment as they simulate what winemakers make manually [2]. An automatic artificial-intelligence method for grape bunch detection from RGB images is presented. It properly defines the input of a Convolutional Neural Network whose task is the segmentation of grape bunches [3]. The network input consists of pointwise visual contrast-based measurements that allow us to discriminate and detect grape bunches even in uncontrolled acquisition conditions and with limited computational load. The latter property makesthe proposed method implementable on smart devices and appropriate for onsite and real-time applications.

Switchable and adaptive substrates emerged as valuable tools for controlling wetting and actuation of droplet motion. Here, we report a computational study of the dynamics of an unstable thin liquid film deposited on a switchable substrate, modeled with a space- and time-varying contact angle. For a sufficiently large rate of wettability variation, a topological transition appears. Instead of breaking up into droplets, as expected for a substrate with multiple wetting minima, a metastable rivulet state emerges. A criterion discriminating whether or not rivulets occur is identified in terms of a single dimensionless parameter. Finally, we show and derive theoretically how the film rupture times, droplet shape, and rivulet lifetime depend on the pattern wavelength and speed.

Active turbulence is a paradigmatic and fascinating example of self-organized motion at large scales occurring in active matter. We employ massive hydrodynamic simulations of suspensions of resolved model microswimmers to tackle the phenomenon in semi-diluted conditions at a mesoscopic level. We measure the kinetic energy spectrum and find that it decays as k-3 over a range of interme- diate wavenumbers. The velocity distributions are of L ́evy type, a distinct difference with inertial turbulence. Furthermore, we propose a reduced order dynamical deterministic model for active turbulence, inspired to shell models for classical turbulence, whose numerical and analytical study confirms the spectrum power-law observed in the simulations and reveals hints of a non-Gaussian, intermittent, physics of active turbulence. Direct numerical simulations and modelling also agree in pointing to a phenomenological picture whereby, in the absence of an energy cascade `a la Richardson forbidden by the low Reynolds number regime, it is the coupling between fluid velocity gradients and bacterial orientation that gives rise to a multiscale dynamics.

We numerically study the dynamics of a passive fluid droplet confined within a microchannel whose walls are covered with a thin layer of active gel. The latter represents a fluid of extensile material modelling, for example, a suspension of cytoskeletal filaments and molecular motors. Our results show that the layer is capable of producing a spontaneous flow triggering a rectilinear motion of the passive droplet. For a hybrid design (a single wall covered by the active layer), at the steady state the droplet attains an elliptical shape, resulting from an asymmetric saw-toothed structure of the velocity field. In contrast, if the active gel covers both walls, the velocity field exhibits a fully symmetric pattern considerably mitigating morphological deformations. We further show that the structure of the spontaneous flow in the microchannel can be controlled by the anchoring conditions of the active gel at the wall. These findings are also confirmed by selected 3D simulations. Our results may stimulate further research addressed to design novel microfludic devices whose functioning relies on the collective properties of active gels.

Active fluid droplets are a class of soft materials exhibiting autonomous motion sustained by an energy supply. Such systems have been shown to capture motility regimes typical of biological cells and are ideal candidates as building-block for the fabrication of soft biomimetic materials of interest in pharmacology, tissue engineering and lab on chip devices. While their behavior is well established in unconstrained environments, much less is known about their dynamics under strong confinement. Here, we numerically study the physics of a droplet of active polar fluid migrating within a microchannel hosting a constriction with adhesive properties, and report evidence of a striking variety of dynamic regimes and morphological features, whose properties crucially depend upon droplet speed and elasticity, degree of confinement within the constriction and adhesiveness to the pore. Our results suggest that non-uniform adhesion forces are instrumental in enabling the crossing through narrow orifices, in contrast to larger gaps where a careful balance between speed and elasticity is sufficient to guarantee the transition. These observations may be useful for improving the design of artificial micro-swimmers, of interest in material science and pharmaceutics, and potentially for cell sorting in microfluidic devices.

A regularized version of the lattice Boltzmann method for efficient simulation of soft materials is introduced. Unlike standard approaches, this method reconstructs the distribution functions from available hydrodynamic variables (density, momentum, and pressure tensor) without storing the full set of discrete populations. This scheme shows significantly lower memory requirements and data access costs. A series of benchmark tests of relevance to soft matter, such as collisions of fluid droplets, is discussed to validate the method. The results can be of particular interest for high-performance simulations of soft matter systems on future exascale computers.

We present thread-safe, highly-optimized lattice Boltzmann implementations, specifically aimed at exploiting the high memory bandwidth of GPU-based architectures. At variance with standard approaches to LB coding, the proposed strategy, based on the reconstruction of the post-collision distribution via Hermite projection, enforces data locality and avoids the onset of memory dependencies, which may arise during the propagation step, with no need to resort to more complex streaming strategies. The thread-safe lattice Boltzmann achieves peak performances, both in two and three dimensions and it allows to reduce significantly the memory footprint (tens of GigaBytes for order billions lattice nodes simulations) by retaining the algorithmic simplicity of standard LB computing. Our findings open attractive prospects for high-performance simulations of complex flows on GPU-based architectures.

Clathrate hydrates are crystalline inclusion compounds wherein a water framework encages small guest atoms/molecules within its cavities. Among the others, methane clathrates are the largest fossil fuel resource still available. They can also be used to safely transport gases and can also form spontaneously under suitable conditions plugging pipelines. Understanding the crystallization mechanism is very important, and given the impossibility of experimentally identifying the atomistic path, simulations played an important role in this field. Given the large computational cost of these simulations, in addition to all-atom force fields, scientists considered coarse-grained water models. Here, we have investigated the effect of coarse-graining, as implemented in the water model mW, on the crystallization characteristics of methane clathrate in comparison with the all-atom TIP4P force field. Our analyses revealed that although the characteristics directly depending on the energetics of the water models are well reproduced, dynamical properties are off by the orders of magnitude. Being crystallization a non-equilibrium process, the altered kinetics of the process results in different characteristics of crystalline nuclei. Both TIP4P and mW water models produce methane clathrate nuclei with some amount of the less stable (in the given thermodynamic conditions) structure II phase and an excess of pentagonal dodecahedral cages over the tetrakaidecahedral ones regarding the ideal ratio in structure I. However, the dependence of this excess on the methane concentration in solution is higher with the former water model, whereas with the latter, the methane concentration in solution dependence is reduced and within the statistical error.

Physiological solvent flows surround biological structures triggering therein collective motions. Notable examples are virus/host-cell interactions and solvent-mediated allosteric regulation. The present work describes a multiscale approach joining the Lattice Boltzmann fluid dynamics (for solvent flows) with the all-atom atomistic molecular dynamics (for proteins) to model functional interactions between flows and molecules. We present, as an applicative scenario, the study of the SARS-CoV-2 virus spike glycoprotein protein interacting with the surrounding solvent, modeled as a mesoscopic fluid. The equilibrium properties of the wild-type spike and of the Alpha variant in implicit solvent are described by suitable observables. The mesoscopic solvent description is critically compared to the all-atom solvent model, to quantify the advantages and limitations of the mesoscopic fluid description.

In the last decades kinetic theory has developed into a very elegant and effective framework to handle a broad spectrum of problems involving complex states of flowing matter, far beyond the original realm of rarefied gas dynamics. In this paper, we present recent applications of the lattice Boltzmann method to the computational design of soft mesoscale materials, including soft flowing crystals, dense multicore emulsions, as well as Petascale simulations of deep-sea glassy sponges. This manuscript is a tribute to the groundbreaking work of Carlo Cercignani and his undiminished impact on modern non-equilibrium statistical physics.

Preface to DSFD 2021, the 30th edition of the discrete simulation of fluid dynamics at the University of Tuscia, Viterbo, Italy, on September 13-17, 2021.

Active droplets are artificial microswimmers built from a liquid dispersion by microfluidic tools and showing self-propelled motion. These systems hold particular interest for mimicking biological phenomena, such as some aspects of cell locomotion and collective behaviors of bacterial colonies, as well as for the design of droplet-based biologically inspired materials, such as engineered tissues. Growing evidence suggests that geometrical confinement crucially affects their morphology and motility, but the driving physical mechanisms are still poorly understood. Here, we study the effect of activity on a droplet containing a contractile polar fluid confined within microfluidic channels of various sizes. We find a surprising wealth of shapes and dynamic regimes, whose mechanics is regulated by a subtle interplay between contractile stress, droplet elasticity, and microchannel width. They range from worm-like and cell-like shaped droplets displaying an oscillating behavior within wider channels to bullet-shaped droplets exhibiting rectilinear motion in narrower slits. Our findings support the view that geometrical confinement can provide a viable strategy to control and predict the propulsion direction of active droplets. It would be of interest to look for analogs of these motility modes in biological cells or in synthetic active matter.

By combining experiments and numerical simulations which model the dynamics of shaken atomic Bose-Einstein condensates, we reveal the surprising nature of quantum turbulence in these systems. Unlike the tangles of vortex lines described in the superfluid helium literature, we find that our turbulent atomic condensate contains a mixture of strong fragmented density fluctuations and small random vortex loops which are not homogeneously distributed. This unusual form of turbulence, with its own properties and scaling behavior, which we call strong quantum turbulence, is significantly different from the turbulence which is observed in either classical or other quantum systems, thus posing a new challenge in turbulence research.

Drain vortices are among the most common vortices observed in everyday life, yet their physics is complex due to the competition of vorticity's transport and diffusion, and the presence of viscous layers and a free surface. Recently, it has become possible to study experimentally drain vortices in liquid helium II, a quantum fluid whose physics is characterised by the absence of viscosity and the quantisation of the circulation in the superfluid component. Using the Gross-Pitaevskii equation, we make a simple model of the problem which captures the essential physics ingredients, showing that the drain vortex of a pure superfluid consists of a bundle of vortex lines which, in the presence of a radial drain, twist, thus strengthening the axial flow into the drain.