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).

In the last decades, melanoma incidence has been rising in many countries, e.g., in Northern Europe, where rates are among the highest despite high latitudes. Using population incidence and demographic data from the NORDCAN database, we estimate the 5-year probability of melanoma diagnosis in 1973-1999. We consider individuals aged 30-54 years, grouped by calendar year and 5-year age group. We analyse temporal patterns to detect possible differences among countries and local deviations from the overall increasing trend in melanoma risk. Across nearly all age groups and Nordic countries, except Sweden, we observe localized peaks interrupting the monotonic increase pattern of risk over time. The locations of the peaks of different age groups are consistently associated with individuals born during or shortly after World War II. These findings suggest a possible long-term impact on melanoma risk from early-life body stressors, including dietary restrictions.

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.

: The FORUM (Far-infrared Outgoing Radiation Understanding and Monitoring) mission will provide, for the first time, systematic far-infrared spectral measurements of Earth's outgoing radiation, enabling improved understanding of atmospheric processes and the radiation budget. Retrieving atmospheric states from these observations constitutes a high-dimensional, ill-posed inverse problem, particularly under cloudy-sky conditions where multiple-scattering effects are present. In this work, we develop a data-driven, physics-aware inversion framework for FORUM all-sky retrievals based on latent twins: coupled autoencoders for atmospheric states and spectra, combined with bidirectional latent-space mappings. A lightweight model-consistency correction ensures physically plausible cloud variable reconstructions. The resulting framework demonstrates potential for retrievals of atmospheric, cloud and surface variables, providing information that can serve as a prior, initial guess, or surrogate for computationally expensive full-physics inversion methods. It also enables robust scene classification and near-instantaneous inference, making it suitable for operational near-real-time applications. We demonstrate its performance on synthetic FORUM-like data and discuss implications for future data assimilation and climate studies.

Invasive alien species pose major ecological and socio-economic challenges, especially when they exert strong predation pressure on native resources while offering opportunities for commercial use. Addressing this dual role requires models that integrate ecological dynamics, economic incentives, and institutional policies. This paper develops a general bioeconomic framework for a predator–prey system subject to human interventions. The native resource is harvested for economic purposes, while the invasive predator can be controlled through both commercial exploitation and institutional removal. The analysis combines equilibrium methods and optimal control over finite horizons, capturing the interaction between long-run ecological regimes and short-term management objectives. The results characterize threshold conditions for species coexistence and identify how harvesting and institutional removal affect the ecological regimes of the system. The analysis shows that moderate exploitation of the invasive predator can enlarge the coexistence region, whereas eradication requires removal efforts exceeding its intrinsic growth rate. The optimal control analysis highlights the role of coordinated management instruments, with numerical results indicating that mixed market and institutional strategies outperform single-instrument policies. The framework is inspired by the interaction between the invasive blue crab (Callinectes sapidus) and native bivalves in Mediterranean coastal systems, and points to policy strategies that integrate ecological conservation, economic valorization, and institutional containment.

This paper addresses the numerical modeling of isentropic gas dynamics on one-dimensional networks, focusing on the Euler equations with a novel class of transmission conditions at network junctions, termed the Jump Transmission Condition (JTC). Unlike traditional models that enforce continuity of density at junctions, the (JTC) allows for a density jump proportional to the flux, reflecting phenomena such as bottlenecks in biological or traffic networks. The authors propose a relaxation scheme based on the vector BGK approach, which ensures mass conservation and enforces the (JTC) without requiring the explicit solution of the Riemann problem at the junction. The scheme is analyzed for its mathematical properties, including entropy dissipation and positivity, and is compared with classical solvers based on the explicit solution of the Riemann problem. Numerical experiments on simple and complex networks demonstrate the accuracy, robustness, and flexibility of the proposed method, especially in handling both subsonic and supersonic regimes and in extending to general networks.

We introduce VCKNet (Variable-sized Convolutional Kernel Network), an adaptive, modular, and lightweight Convolutional Neural Network (CNN) with three kernel-scale branches, channel attention, and dynamic fusion. These components recalibrate scale-aware features to reduce redundancy and support discriminative learning. The proposed architectural design preserves computational efficiency while maintaining a clear structural organization. Experiments on standard benchmarks and in-the-wild datasets show that VCKNet effectively captures multiscale structure, achieving performance competitive with that of deeper state-of-the-art models. Statistical analysis across repeated runs further confirms VCKNet’s stability. Computational cost and deployment efficiency analyses further support its suitability for resource-constrained and production-oriented scenarios where flexibility, stability, and conceptual clarity are essential.

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.

We provide explicit upper bounds on some distances between the (law of the) output of a random Gaussian neural network and (the law of) a random Gaussian vector. Our main results concern deep random Gaussian neural networks, with a rather general activation function. The upper bounds show how the widths of the layers, the activation function and other architecture parameters affect the Gaussian approximation of the output. Our techniques, relying on Stein's method and integration by parts formulas for the Gaussian law, yield estimates on distances which are indeed integral probability metrics, and include the convex distance. This latter metric is defined by testing against indicator functions of measurable convex sets, and so allows for accurate estimates of the probability that the output is localized in some region of the space. Such estimates have a significant interest both from a practitioner's and a theorist's perspective.