This Special Issue of Mathematics and Computers in Simulation collects a selection of peer-reviewed original articles on research topics developed in connection with IMACS2023, the IMACS World Congress, held in Rome (Italy) at the Faculty of Engineering, Sapienza University of Rome on September 11 - 15, 2023, that we organized, in the role of Local Scientific Committee, together with Rosa Maria Spitaleri, Congress Chair.

In this editorial the historical premises of the world Congress IMACS2023 are delineated in order to appreciate the development of IMACS as a scientific association keeping up with the ultimate scientific aspirations of society in the fields of Applied Mathematics and Scientific Computing. The World Congress, IMACS2023, the last considered step, celebrates successfully such a prestigious story.

Editorial

Editorial

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

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.

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

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.

An X-ray diffraction pattern consists of relevant information (the signal) and noisy background. Under the assumption that they behave as the components of a two-dimensional mixture (bicomponent fluid) having slightly different physical properties related to the density gradients, a Lattice Boltzmann Method is applied to disentangle the two different diffusive dynamics. The solution is numerically stable, not computationally demanding, and, it also provides an efficient increase in the signal-to-noise ratio for patterns blurred by Poissonian noise and affected by collection data anomalies (fiber-like samples, experimental setup, etc.). The model is succesfully applied to different resolution images.

Droplet microfluidics has emerged as highly relevant technology in diverse fields such as nanomaterials synthesis, photonics, drug delivery, regenerative medicine, food science, cosmetics, and agriculture. While significant progress has been made in understanding the fundamental mechanisms underlying droplet generation in microchannels and in fabricating devices to produce droplets with varied functionality and high throughput, challenges persist along two important directions. On one side, the generalization of numerical results obtained by computational fluid dynamics would be important to deepen the comprehension of complex physical phenomena in droplet microfluidics, as well as the capability of predicting the device behavior. Conversely, truly three-dimensional architectures would enhance microfluidic platforms in terms of tailoring and enhancing droplet and flow properties. Recent advancements in artificial intelligence (AI) and additive manufacturing (AM) promise unequaled opportunities for simulating fluid behavior, precisely tracking individual droplets, and exploring innovative device designs. This review provides a comprehensive overview of recent progress in applying AI and AM to droplet microfluidics. The basic physical properties of multiphase flows and mechanisms for droplet production are discussed, and the current fabrication methods of related devices are introduced, together with their applications. Delving into the use of AI and AM technologies in droplet microfluidics, topics covered include AI-assisted simulations of droplet behavior, real-time tracking of droplets within microfluidic systems, and AM-fabrication of three-dimensional systems. The synergistic combination of AI and AM is expected to deepen the understanding of complex fluid dynamics and active matter behavior, expediting the transition toward fully digital microfluidic systems.

Dimensionality reduction is a hot research topic in data analysis today. Thanks to the advances in high performance computing technologies and in the engineering field, we entered in the so-called big-data era and an enormous quantity of data is available in every scientific area, ranging from social networking, economy and politics to e-health and life sciences. However, much of the data is highly redundant and can be efficiently brought down to a much smaller number of variables without a significant loss of information using different strategies.