The nonequilibrium structural and dynamical properties of semiflexible active polar polymers subject to linear flow are studied using numerical simulations. Filaments are confined in two dimensions and immersed in a fluid described by the Brownian multiparticle collision dynamics approach. The applied shear flow causes conformational changes in a polymer, aligns it along the flow direction, and induces a tumbling motion at high flow rates. In an intermediate, activity-dependent shear-rate regime, a characteristic scaling exponent for the mean-square end-to-end distance along the gradient direction is observed. This exponent appears to be determined by the semiflexibility of the polymer. The tumbling dynamics exhibits a characteristic time, with a stronger dependence on the Weissenberg number than that of flexible active or passive polymers. Activity strongly impacts the rheological properties of semiflexible polymers and even implies a negative viscosity for weak flows. At very large values of the shear rate, shear dominates over activity, and passive-polymer behavior is assumed.

In this work, we characterize the water absorption properties of selected porous materials through a combined approach that integrates laboratory experiments and mathematical modeling. Specifically, experimental data from imbibition tests on marble, travertine, wackestone and mortar mock-ups are used to inform and validate the mathematical and simulation frameworks. First, a monotonicity-preserving fitting procedure is developed to preprocess the measurements, aiming to reduce noise and mitigate instrumental errors. The imbibition process is then simulated through a partial differential equation model, with parameters calibrated against rough and smoothed data. The proposed procedure appears particularly effective to characterize absorption properties of different materials and it represents a reliable tool for the study and preservation of cultural heritage.

: Color information plays a crucial role in digital image processing [...].

Accurate electron temperature (Te) measurements are critical for future reactors such as ITER, CFETR, and DEMO, where core T e is expected to exceed 25 keV [1-3]. However, in current tokamaks, core electron temperature measurements become increasingly challenging at high values (typically above 6–7 keV), where discrepancies frequently arise between diagnostics such as Thomson Scattering (TS) and Electron cyclotron emission (ECE). These discrepancies highlight both a diagnostic challenge and an opportunity to deepen the understanding of core plasma physics. Recent studies have provided further insights into these phenomena, clarifying key physical aspects, and yielding more substantial results [4-8]. Nevertheless, a broader experimental database remains essential to validate and support the physical hypotheses developed in recent years. This contribution reports on preliminary results obtained from the analysis of the entire JET-DTE3 dataset, providing a status update on our ongoing research. Specifically, we focus on the methodological advancements and the analytical tools recently developed to manage the unprecedented volume of data within the DTE3 database. This framework enables a deep investigation into the T e discrepancy, marking the first time this phenomenon has been systematically studied across such an extensive and statistically significant dataset. This work is conducted within the framework of the International Tokamak Physics Activity (ITPA) JEX#17 on `High Electron Temperature Measurements', which aims to compare data collected across multiple fusion devices to systematically identify the origin of the observed Te discrepancy.

The Divertor Tokamak Test facility (DTT) is a fusion device under construction at the ENEA Research Centre in Frascati, Italy. DTT’s primary mission is to explore and test the physics and technology of concepts for the exhaust of the plasma thermal power, especially in the divertor region, in support to ITER and DEMO design. From this perspective, careful control of the total radiation emission will be essential for the operation of these next generation devices. This work focuses on the DTT bolometry system, which is currently in the design phase. Commercial foil bolometers have been selected to provide line-integrated measurements and enable tomographic reconstructions at this stage. The mechanical layout and integration into the machine have been defined, and the line-of-sights (LoS) configuration has been validated. However, preliminary thermo-mechanical analyses have revealed that the initial design did not fully meet all specifications. To address this, an actively cooled protective housing has been included to withstand the high thermal loads from the plasma, approximately 0.5 MW/m2 for about 100 s in DTT. In this study, the equatorial bolometric camera simulations have been further refined, and the protective housing has been designed and fully integrated in the diagnostic port. A parametric thermal analysis has been performed, and the final design has been validated through finite elements simulations.

In this paper, we propose a numerical study of macroscopic models for collective cell migration, focusing on a multidimensional pressureless Euler-type model with nonlocal interactions coupled with chemotaxis, rigorously derived from microscopic dynamics. Different mechanical interactions are investigated, including attraction-repulsion effects. Moreover, the model is extended to the case of different populations of interacting cells. The validity of such a macroscopic model and its agreement with the microscopic dynamics is finally assessed through a parameter estimation analysis in a specific setting.

Hyperspectral sensors provide researchers and governmental authorities with a wealth of information due to their fine spectral resolution, numerous bands, and wide spectral range. These sensors are used in various fields, including agriculture, environmental and forestry monitoring, geology, biology, medicine, and food quality assessment, among others. Generally, they measure across the visible and infrared parts of the electromagnetic spectrum, but they cannot penetrate thick cloud layers, which makes observations unusable under cloudy conditions. Also, the presence of thin and very thin clouds is a problem for the accurate retrieval of surface and atmospheric parameters. The PRecursore IperSpettrale della Missione Applicativa (PRISMA) is a medium-resolution hyperspectral imaging satellite, developed, owned, and operated by Agenzia Spaziale Italiana, launched in orbit on the 22 March 2019. PRISMA carries two sensor instruments, the HYC Hyperspectral Camera module and the panchromatic camera module. In this article, we present the results we obtained by testing some machine learning techniques for cloud detection on Top of Atmosphere (TOA) reflectance data. In particular, we focused on k-nearest neighbors, random forest, and extreme gradient boosting trained on a dataset of manually annotated images by the authors, after transforming the L1 TOA radiance in reflectance data. We also provide numerical comparison with the Cloud detection in hyperspectral images with atmospheric column water vapor method.

Precision medicine is facing a critical transition driven by the growing complexity of biological data and the insufficient ability of current models to translate such data into clinically meaningful information. Linear, single-gene approaches are no longer adequate to explain the multifactorial nature of most modern diseases, whose phenotypes emerge from combinations of genetic, molecular, and environmental factors. Network-based precision medicine addresses this by providing a systemic framework capable of integrating heterogeneous omics data, interactomes, and clinical information to identify disease modules and novel therapeutic opportunities. The distinct novelty of this review is its focus on the potential of “network language” as the primary driver for realizing precision medicine through professional collaboration. We argue that networks are not merely tools that achieve precision “per se”; rather, their transformative power lies in their ability to serve as a shared and interpretable interface grounded in network theory. By offering this common conceptual ground, the paradigm bridges the deep cultural and methodological gaps between clinicians and data analysts, enabling effective cooperation between figures with fundamentally different, and often divergent, backgrounds. Practical tools—such as biological network analysis and Molecular Tumor Boards—demonstrate how computational modeling and clinical expertise can be successfully combined to generate actionable insights. Ultimately, network-based precision medicine represents a decisive step toward reconstructing the patient’s complexity and promoting a genuinely personalized clinical approach in which quantitative analysis and medical reasoning act synergistically through multidisciplinary integration.

Because of the complexity of data sets in practice, there has been much interest in developing statistical analysis tools for problems involving high-dimensional covariates. Examples of these models include partial linear additive models (PLAMs) and single-index models (SIMs). A common feature of these models is that they achieve dimension reduction to circumvent the “curse of dimensionality” while retaining the flexibility of the nonparametric regression. In the statistical and machine learning literature, fitting the additive parts in PLAM models and the link function in SIM models by nonparametric methods usually requires smooth additive components and regular link functions, and it is usually achieved using kernel methods or spline smoothing. In this work, we present a novel intrinsically interpretable combination of these two models with competitive predictive performance. We relax the smoothness assumptions and develop a nonparametric estimation procedure of the additive components and the link function that uses wavelet bases expansions adapted to non-equispaced designs. Simulation studies and real data analyses are employed to demonstrate the usefulness of the approach. Computer codes are provided as Supporting Information.

We investigate the effect of a dispersed bubble phase on forced homogeneous and isotropic turbulence using high-resolution high-performance simulations based on the lattice Boltzmann method. While the classical Kolmogorov energy cascade is largely preserved when considering the system as a whole, a phase-specific analysis reveals striking deviations from the classical turbulence scaling. In particular, the gas phase exhibits significant departures from Kolmogorov’s predictions, whereas the continuous liquid phase retains a turbulence structure consistent with classical expectations up to 24% in gas volume fractions. These findings suggest that, despite the presence of a dispersed phase, the global energy transfer remains close to a universal behavior. At the same time, phase-specific interactions are shown to introduce modifications to the turbulent dynamics at small scales. In particular, the gas phase exhibits a nearly flat spectrum at low wave numbers followed by a k−3 scaling at intermediate scales pointing to the presence of patterns of localized bursts uniformly distributed between two finite wavelengths. Our results aim at deepening the understanding of multiphase turbulence, particularly in the context of energy transfer mechanisms and phase interactions in bubble-laden flows. This study provides a framework for future investigations into the fundamental properties of multiphase turbulence and its implications for environmental, atmospheric, and industrial flows.

We numerically study the dynamics of a three-dimensional contractile fluid droplet in the bulk and under confinement. We show that varying activity leads to a variety of shapes and motile regimes whose motion is driven by an interplay between spontaneous flows and elasticity. In the bulk the droplet self-propels unidirectionally, acquiring either an almost spherical shape at intermediate activity or a peanut-like geometry for larger values. Under confinement, the droplet exhibits a previously unreported oscillating dynamics characterized by periodic hits against opposite walls of a microchannel while moving forward. These results could be of interest for the study of artificial microswimmers and their biological analogs, such as living cells.

: Fluctuating lattice Boltzmann solvers are widely employed to model mesoscopic fluid behavior in soft-matter systems, including colloidal suspensions and dilute polymer solutions. Despite their utility, these methods can lose accuracy and stability when non-hydrodynamic modes interfere with the dynamics, especially in single-relaxation-time schemes. Here, we introduce a ghost-mode filtered fluctuating lattice Boltzmann method (GMF-FLBM) for the D3Q27 lattice, obtained by selectively eliminating the propagation of the ghost deterministic content while preserving the necessary stochastic forcing. We show, over a broad range of relaxation times, that GMF-FLBM recovers the amplitudes of equilibrium fluctuations with a comparable accuracy to a fully regularized high-order formulation, while requiring only minor adjustments to the conventional BGK collision framework.

The TEXTAROSSA project aims to bridge the technology gaps that exascale computing systems are currently facing and will be key in the near future to overcome performance and energy efficiency challenges. This project provides solutions for improved energy efficiency by using state-of-the-art cooling and thermal control, seamless integration of heterogeneous accelerators in HPC multi-node platforms, and new arithmetic methods tailored to heterogeneous hardware platforms. Challenges are tackled through a co-design approach to heterogeneous HPC solutions, supported by the integration and extension of HW and SW IPs, programming models, and tools derived from European research.

We study LDPs of NN

We study stability and rate of convergence to stationarity of nonlinear INAR time series

We prove that random matrices with entries the q-chaos on the Wiener space are approximate isometries

We investigate magnetic active matter in confined geometries using both experiments with magnetic toy robots, Hexbugs, and simulations of elongated magnetic active Brownian particles in circular domains. Standard active particles tend to accumulate at boundaries, forming clusters even at relatively low densities. In the presence of magnetic interactions, we provide evidence for a fluidization effect that inhibits clustering and shifts its onset to higher packing fractions. Moreover, magnetic dipolar interactions give rise to collective behaviors such as train-like formations, rotating pairs, and rotating clusters.

In this paper, we carry out a computational study of a novel microscopic follow-the-leader model for traffic flow on road networks. We assume that each driver has his or her own origin and destination, and wants to complete his or her journey in the minimal time. We also assume that each driver is able to take rational decisions at junctions and can change the route while moving depending on the traffic conditions. The main novelty of the model is that vehicles can automatically and anonymously share information about their position, destination, and planned path when they are close to each other within a certain distance. The pieces of information acquired during the journey are used to optimize the route itself. In the limit case of an infinite communication range, we recover the classical Reactive User Equilibrium (RUE) and Dynamic User Equilibrium (DUE).

We analyze a cosmological solution to the field equations of a modified gravity model where curvature and matter are nonminimally coupled. The current Universe's accelerated expansion is driven by a cosmological constant while the impact of the nonminimal coupling on the expansion history is recast as an effective equation of state for evolving dark energy. The model is analyzed under a tracking solution that follows the minimum of the effective potential for a scalar field that captures the modified theory's effects. We determine the conditions for the existence of this minimum and for the validity of the tracking solution. Cosmological constraints on the parameters of the model are obtained by resorting to recent outcomes of data from the DESI collaboration in combination with the Pantheon+ and Dark Energy Survey supernovae compilations, which give compatible results that point to the presence of a dynamical behavior for dark energy. The gravity model violates the equivalence principle since it gives rise to a fifth force that implies the Earth and Moon fall differently towards the Sun. The cosmological constraints are intersected with limits resulting from a test of the equivalence principle in the Earth-Moon system based on lunar laser ranging data. We find that a variety of model parameters are consistent with both of these constraints, all while producing a dynamical evolution of dark energy with similarities to that found in recent DESI results.

In this paper, a multidisciplinary design optimization algorithm, the Normal Boundary Intersection (NBI) method, is applied to the design of some devices of a sailing yacht. The full Pareto front is identified for two different design problems, and the optimal configurations are compared with standard devices. The great efficiency of the optimization algorithm is demonstrated by the wideness and density of the identified Pareto front.