Modeling the evolution of the melt front under gravity in the presence of a horizontal thermal gradient is a challenging issue, hitherto tackled exclusively with the concepts and tools of computational continuum thermomechanics, too phenomenologically driven to have satisfactory predictive capabilities. Here, we show that this complex phenomenon is amenable to treatment by the methods and tools of Non-Equilibrium Molecular Dynamics (NEMD). To do so, we addressed all the difficulties caused by the necessity of applying suitable boundary conditions and minimizing surface effects so that the bulk behavior of the system in non-equilibrium conditions can be detected. Sufficient adiabatic separation of the time scales permits us to use macroscopically relatively short-but microscopically long enough-time averages to get the macroscopic bulk behavior of the system accurately. To get an adequate signal-to-noise ratio, we had to use an unphysically large value of the gravity. However, we know from NEMD simulations in transport studies that the phenomena produced are stable over many orders of magnitude. In conclusion, our work proves that molecular simulation can be a good tool to study this family of non-equilibrium phenomena, although further work is needed to achieve quantitative predictive capabilities.

Models of soil organic carbon (SOC) frequently overlook the effects of spatial dimensions and microbiological activities. In this paper, we focus on two reaction-diffusion chemotaxis models for SOC dynamics, both supporting chemotaxis-driven instability and exhibiting a variety of spatial patterns as stripes, spots and hexagons when the microbial chemotactic sensitivity is above a critical threshold. We use symplectic techniques to numerically approximate chemotaxis-driven spatial patterns and explore the effectiveness of the piecewise Dynamic Mode Decomposition (pDMD) to reconstruct them. Moreover, we analyse the predictive performance of the pDMD for moderate time horizons. Our findings show that pDMD is effective at precisely recreating and predicting chemotaxis-driven spatial patterns, therefore broadening the range of application of the method to classes of solutions different than Turing patterns. By validating its efficacy across a wider range of models, this research lays the groundwork for applying pDMD to experimental spatiotemporal data, advancing predictions crucial for soil microbial ecology and agricultural sustainability.

Convolutional Neural Networks (CNNs) have become indispensable tools in skin cancer classification, aiding clinical experts to achieve earlier and more accurate diagnoses, improving treatment outcomes, and driving advancements in medical research. Despite their pivotal role, the most popular CNN architecture families exhibit a critical issue related to the distribution and quantity of available data, potentially compromising their performance and generalization abilities. This challenge is commonly overlooked in most skin lesion classification papers, which mainly rely on weighted classification techniques. Directly using appropriately dataset balancing or Transfer Learning (TL) methods, as suggested in recent studies, has the potential to deliver more satisfactory results, providing a more effective approach to addressing this issue. In the effort to tackle this problem, we provide a comprehensive quantitative evaluation aimed at identifying the most critical new emerging computational aspects and the related effective techniques. Specifically, we propose twelve Computational Models (CMs) based on four prominent CNN models with increasing structural complexity. We assess their effectiveness in both pretrained and unpretrained versions, incorporating TL strategies as well. Our experiments focus on the ISIC 2018 image dataset, a benchmark widely recognized for its extensive application in skin cancer research yet challenged by significant class imbalance issues. To mitigate this, we also randomly extracted a balanced image subset from ISIC 2018 for evaluation purposes. The experimental results, regarding four different scenarios, provide valuable insights into the design and utilization of CNNs for skin lesion classification, laying the groundwork for further investigations.

Emulsions are paramount in various interdisciplinary topical areas, yet a satisfactory understanding of their behavior in buoyancy-driven thermal flows has not been established. In the present work, we unravel the dynamical regimes of thermal convection in emulsions by leveraging a large set of mesoscale numerical simulations. Emulsions are prepared with a given volume fraction of the initially dispersed phase, φ, ranging from dilute (low values of φ) to jammed emulsions (high values of φ), resulting in different rheological responses of the emulsion, i.e., from Newtonian to non-Newtonian yield-stress behaviors, respectively. We then characterize the dynamics of the emulsions in the paradigmatic setup of the Rayleigh-Bénard convection, i.e., when confined between two parallel walls at different temperatures under the effect of buoyancy forces, the latter encoded in the dimensionless Rayleigh number Ra. We thoroughly investigated the dynamics of the emulsion in the changing of φ and Ra. For a given φ, at increasing Ra, we observe that the emulsion exhibits convection states, where structural changes may appear (i.e., droplet breakup, coalescence, or phase inversion), which inevitably impact the emulsion rheology. For sufficiently high values of Ra, two states of convection are observed: for low/moderate values of φ (Newtonian emulsions), we observe breakup-dominated dynamics, whereas for high values of φ (non-Newtonian emulsions), we observe phase-inverted states. For both scenarios, the droplet size distribution depends on Ra, and scaling laws for the average droplet size are analyzed and quantified. Our results offer insights into the rich dynamics of emulsions under thermal convection, offering a detailed characterization of the various dynamical regimes to be expected and their relation with structural changes occurring in such complex fluids.

Questo rapporto tecnico illustra la metodologia e gli strumenti utilizzati per il monitoraggio integrato di vari progetti PRIN finanziati dal PNRR nel corso del 2022 e attivi presso l’IAC-CNR. La struttura proposta ha l’obiettivo di tenere sotto controllo la documentazione amministrativa, la rendicontazione delle risorse umane impiegate e la gestione finanziaria, inclusa la proiezione di chiusura e l’analisi degli scostamenti per ciascun progetto. Vengono presentati esempi specifici che evidenziano l’efficacia del metodo applicato e indicazioni utili per garantire una gestione ottimale delle risorse.

In this work an optimized multicomponent lattice Boltzmann (LB) model is deployed to simulate axisymmetric turbulent jets of a fluid evolving in a quiescent, immiscible environment over a wide range of dynamic regimes. The implementation of the multicomponent LB code achieves peak performance on graphic processing units (GPUs) with a significant reduction of the memory footprint, retains the algorithmic simplicity inherent to standard LB computing, and, being based on a high-order extension of the thread-safe LB algorithm, allows us to perform stable simulations at vanishingly low viscosities. The proposed approach opens attractive prospects for high-performance computing simulations of realistic turbulent flows with interfaces on GPU-based architectures.

Cyclic nucleotide-gated (CNG) ion channels are crucial to the intracellular calcium dynamics in neurons and other sensory cells, in several organisms. Mutations in CNG genes are linked to various dysfunctions and diseases. In this work, we propose a theoretical investigation of the structural and functional properties of wild-type TAX-4, a non-selective CNG ion channel, expressed in various sensory neurons of Caenorhabditis elegans, and involved in the permeation of monovalent and multivalent cations. Using a recent cryo-electron microscopy structure of the open state of the channel as the starting conformation, we conduct all-atom molecular dynamics simulations of the full-length channel in a membrane/water/ions system, both in the cGMP-bound and unbound conformations. Several channel structural descriptors are examined and a first-level functional annotation is carried out, on the microsecond time scale. A comparison with the available experimental data on TAX-4 and human homologues allows us to assign the simulated bound and unbound models as the pre-open and closed conformations of TAX-4, respectively. Comparisons between the bound and unbound conformations enable us to suggest key conformational changes underlying the binding-to-gating transition.

We present a mathematical framework to include quantum interfacial interactions, provided by Keldysh nonequilibrium quantum transport formalism, bottom-up coupled to a nanoscale lattice Boltzmann method. As an applicative scenario, we simulate a two-dimensional water flow between two parallel solid plates hosting electrons and phonons in the solid bottom wall. The corresponding tool may prove useful for the computational design of quantum-engineered nanofluidic devices, showing its capability to explore the effects of the interfacial quantum transport phenomena at scales of experimental relevance.

A key step to optimize the tests of semiconductors during the production process is to improve the prediction of the final yield from the defects detected on the wafers during the production process. This study investigates the link between the defects detected by a Scanning Electron Microscope (SEM) and the electrical failure of the final semiconductors, with two main objectives: (a) to identify the best layers to inspect by SEM; (b) to develop a model that predicts electrical failures of the semiconductors from the detected defects. The first objective has been reached by a model based on Odds Ratio that gave a (ranked) list of the layers that best predict the final yield. This allows process engineers to concentrate inspections on a few important layers. For the second objective, a regression/classification model based on Gradient Boosting has been developed. As a by-product, this latter model confirmed the results obtained by Odds Ratio analysis. Both models take account of the high lacunarity of the data and have been validated on two distinct datasets from STMicroelectronics.

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.

In this paper we design high-order positivity-preserving approximation schemes for an integro-differential model describing photochemical reactions. Specifically, we introduce and analyze three classes of dynamically consistent methods, encompassing non-standard finite difference schemes, direct quadrature techniques and predictor- corrector approaches. The proposed discretizations guarantee the positivity, monotonicity and boundedness of the solution regardless of the temporal, spatial and frequency stepsizes. Comprehensive numerical experiments confirm the theoretical findings and demonstrate the efficacy of the proposed methods in simulating realistic photochemical phenomena.

We explicitly demonstrate the universality of critical dynamics through unprecedented large-scale Graphics Processing Units (GPU)-based simulations of two out-of-equilibrium processes, comparing the behavior of spin-1/2 Ising and spin-1 Blume-Capel models on a square lattice. In the first protocol, a completely disordered system is instantaneously brought into contact with a thermal bath at the critical temperature, allowing it to evolve until the coherence length exceeds 103 lattice spacings. Finite-size effects are negligible due to the mesoscopic scale of the lattice sizes studied, with linear dimensions up to L=222 and 219 for the Ising and Blume-Capel models, respectively. Our numerical data, and the subsequent analysis, demonstrate a strong dynamic universality between the two models and provide the most precise estimate to date of the dynamic critical exponent for this universality class, z=2.1676⁢(1). In the second protocol, we corroborate the role of the universal ratio of dynamic and static length scales in achieving an exponential acceleration in the approach to equilibrium just above the critical temperature, through a time-dependent variation of the thermal bath temperature. The results presented in this work leverage our Compute Unified Device Architecture (CUDA)-based numerical code, breaking the world record for the simulation speed of the Ising model.

We present high-performance implementations of the two-dimensional Ising and Blume-Capel models for large-scale, multi-GPU simulations. Our approach takes full advantage of the NVIDIA GB200 NVL72 system, which features up to 72 GPUs interconnected via high-bandwidth NVLink, enabling direct GPU-to-GPU memory access across multiple nodes. By utilizing Fabric Memory and an optimized Monte Carlo kernel for the Ising model, our implementation supports simulations of systems with linear sizes up to L=223, corresponding to approximately 70 trillion spins. This allows for a peak processing rate of nearly 1.15×105 lattice updates per nanosecond—setting a new performance benchmark for Ising model simulations. Additionally, we introduce a custom protocol for computing correlation functions, which strikes an optimal balance between computational efficiency and statistical accuracy. This protocol enables large-scale simulations without incurring prohibitive runtime costs. Benchmark results show near-perfect strong and weak scaling up to 64 GPUs, demonstrating the effectiveness of our approach for large-scale statistical physics simulations. Program summary: Program title: cuIsing (optimized) CPC Library link to program files: https://doi.org/10.17632/ppkwwmcpwg.1 Licensing provisions: MIT license Programming languages: CUDA C Nature of problem: Comparative studies of the critical dynamics of the Ising and Blume-Capel models are essential for gaining deeper insights into phase transitions, enhancing computational methods, and developing more accurate models for complex physical systems. To minimize finite-size effects and optimize the statistical quality of simulations, large-scale simulations over extended time scales are necessary. To support this, we provide two high-performance codes capable of running simulations with up to 70 trillion spins. Solution method: We present updated versions of our multi-GPU code for Monte Carlo simulations, implementing both the Ising and Blume-Capel models. These codes take full advantage of multi-node NVLink systems, such as the NVIDIA GB200 NVL72, enabling scaling across GPUs connected across different nodes within the same NVLink domain. Communication between GPUs is handled seamlessly via Fabric Memory–a novel memory allocation technique that facilitates direct memory access between GPUs within the same domain, eliminating the need for explicit data transfers. By employing highly optimized CUDA kernels for the Metropolis algorithm and a custom protocol that reduces the computational overhead of the correlation function, our implementation achieves the highest recorded performance to date.

In this work we present three classes of unconditionally positive numerical methods for a photochemical model governed by non-local integro-differential equations. Specifically, we design and compare dynamically-consistent approximation schemes based on non-standard finite differences discretizations, predictor-corrector approaches and direct quadrature integrators. A rigorous analysis is performed to establish the preservation of key physical properties, i.e. positivity, monotonicity and boundedness, regardless of the temporal, spatial and frequency stepsizes. Furthermore, theoretical results are provided to establish the high-order consistency and convergence of the methods. Comprehensive numerical experiments confirm the theoretical findings and allow for a detailed comparison of the performance and computational efficiency of the proposed discretizations. Applications to two case studies of interest, photoactivation of serotonin in left-right brain patterning and photodegradation of cadmium pigments in historical paintings, demonstrate the practical relevance of the proposed model and simulation techniques in addressing complex phenomena in photochemistry.

A Cournot triopoly is a type of oligopoly market involving three firms that produce and sell homogeneous or similar products without cooperating with one another. In Cournot models, firms' decisions about production levels play a crucial role in determining overall market output. Compared to duopoly models, oligopolies with more than two firms have received relatively less attention in the literature. Nevertheless, triopoly models are more reflective of real-world market conditions, even though analyzing their dynamics remains a complex challenge. A reaction-diffusion system of PDEs generalizing a nonlinear triopoly model describing a master-slave Cournot game is introduced. The effect of diffusion on the stability of Nash equilibrium is investigated. Self-diffusion alone cannot induce Turing pattern formation. In fact, linear stability analysis shows that cross-diffusion is the key mechanism for the formation of spatial patterns. The conditions for the onset of cross-diffusion-driven instability are obtained via linear stability analysis, and the formation of several Turing patterns is investigated through numerical simulations.

Morphological Characterization of figs leaves

Phase separation in the presence of external forces has attracted considerable attention since the initial works for solid mixtures. Despite this, only very few studies are available which address the segregation process of liquid-vapor systems under gravity. We present here an extensive study which takes into account both hydrodynamic and gravitational effects on the coarsening dynamics. An isothermal formulation of a lattice Boltzmann model for a liquid-vapor system with the van der Waals equation of state is adopted. In the absence of gravity, the growth of domains follows a power law with the exponent 2/3 of the inertial regime. The external force deeply affects the observed morphology accelerating the coarsening of domains and favoring the liquid accumulation at the bottom of the system. Along the force direction, the growth exponent is found to increase with the gravity strength still preserving sharp interfaces since the Porod's law is found to be verified. The time evolution of the average thickness L of the layers of accumulated material at confining walls shows a transition from an initial regime where L≃t2/3 (t: time) to a late-time regime L≃gt5/3 with g the gravitational acceleration. The final steady state, made of two overlapped layers of liquid and vapor, shows a density profile in agreement with theoretical predictions.

This paper broaches the peridynamic inverse problem of determining the horizon size of the kernel function in a one-dimensional model of a linear microelastic material. We explore different kernel functions, including V-shaped, distributed, and tent kernels. The paper presents numerical experiments using PINNs to learn the horizon parameter for problems in one and two spatial dimensions. The results demonstrate the effectiveness of PINNs in solving the peridynamic inverse problem, even in the presence of challenging kernel functions. We observe and prove a one-sided convergence behavior of the Stochastic Gradient Descent method towards a global minimum of the loss function, suggesting that the true value of the horizon parameter is an unstable equilibrium point for the PINN's gradient flow dynamics.

Most vaccines require multiple doses, the first to induce recognition and antibody production and subsequent doses to boost the primary response and achieve optimal protection. We show that properly prioritizing the administration of first and second doses can shift the epidemic threshold, separating the disease-free from the endemic state and potentially preventing widespread outbreaks. Assuming homogeneous mixing, we prove that at a low vaccination rate, the best strategy is to give absolute priority to first doses. In contrast, for high vaccination rates, we propose a scheduling that outperforms a first-come first-served approach. We identify the threshold that separates these two scenarios and derive the optimal prioritization scheme and interdose interval. Agent-based simulations on real and synthetic contact networks validate our findings. We provide specific guidelines for effective resource allocation, showing that adjusting the timing between the primer and booster significantly impacts epidemic outcomes and can determine whether the disease persists or disappears.

Far-infrared Outgoing Radiation Understanding and Monitoring (FORUM) was selected in 2019 as the ninth Earth Explorer mission by the European Space Agency. Its primary objective is to collect interferometric measurements in the far-infrared (FIR) spectral range, which accounts for 50% of Earth's outgoing longwave radiation emitted into space, and will be observed from space for the first time. Accurate measurements of the FIR at the top of the atmosphere are crucial for improving climate models. Current instruments are insufficient, necessitating the development of advanced computational techniques. FORUM will provide unprecedented insights into key atmospheric parameters, such as surface emissivity, water vapor, and ice cloud properties, through the use of a Fourier transform spectrometer. To ensure the quality of the mission's data, an end-to-end simulator was developed to simulate the measurement process and evaluate the effects of instrument characteristics and environmental factors. The core challenge of the mission is solving the retrieval problem, which involves estimating atmospheric properties from the radiance spectra observed by the satellite. This problem is ill-posed and regularization techniques are necessary to stabilize the solution. In this work, we present a data-driven approach to approximate the inverse mapping in the retrieval problem, aiming to achieve a solution that is both computationally efficient and accurate. In the first phase, we generate an initial approximation of the inverse mapping using only simulated FORUM data. In the second phase, we improve this approximation by introducing climatological data as a priori information and using a neural network to estimate the optimal regularization parameters during the retrieval process. While our approach does not match the precision of full-physics retrieval methods, its key advantage is the ability to deliver results almost instantaneously, making it highly suitable for real-time applications. Furthermore, the proposed method can provide more accurate a priori estimates for full-physics methods, thereby improving the overall accuracy of the retrieved atmospheric profiles.