In this work, we present accLB, a high-performance Fortran-based lattice Boltzmann (LB) solver tailored to multiphase turbulent flows on multi-GPU architectures. The code couples a conservative phase-field formulation of the Allen–Cahn equation with a thread-safe, regularized LB method to capture complex interface dynamics. Designed from the ground up for HPC environments, accLB employs MPI for distributed memory parallelism and OpenACC for GPU acceleration, achieving excellent portability and scalability on leading pre-exascale systems such as Leonardo and LUMI. Benchmark tests demonstrate strong and weak scaling efficiencies on multiple GPUs. Physical validation includes direct numerical simulations of homogeneous isotropic turbulence (HIT). Further, we examine bubble-laden HIT and observe a transition to a -3 energy scaling, as in experiments and theoretical predictions, due to bubble-induced dissipation, along with enhanced small-scale intermittency. These results highlight accLB as a robust and scalable platform for the simulation of multiphase turbulence in extreme computational regimes.

We introduce a regularized fluctuating lattice Boltzmann model (Reg-FLBM) for the D3Q27 lattice, which incorporates thermal fluctuations through Hermite-based projections to ensure compliance with the fluctuation-dissipation theorem. By leveraging the recursive regularization framework, the model achieves thermodynamic consistency for both hydrodynamic and ghost modes. Compared to the conventional single-relaxation-time BGK-FLBM, the Reg-FLBM provides improved stability and a more accurate description of thermal fluctuations. The implementation is optimized for large-scale parallel simulations on graphics processing unit-accelerated architectures, enabling systematic investigation of fluctuation-driven phenomena in mesoscale and nanoscale fluid systems.

This study presents a high-order, thread-safe version of the lattice Boltzmann method, incorporating an interface-capturing equation, based on the conservative Allen-Cahn equation, to simulate incompressible two-component systems with high-density and viscosity contrasts. The method utilizes a recently proposed thread-safe implementation optimized for shared-memory architectures, and it is employed to reproduce the dynamics of droplets and bubbles in several test cases with results in agreement with experiments and other numerical simulations from the literature. The proposed approach offers promising opportunities for high-performance computing simulations of realistic fluid systems with high-density and viscosity contrasts for advanced applications in environmental, atmospheric, and meteorological flows, all the way down to microfluidic and biological systems, particularly on graphic processing unit-based architectures.

Accurate prediction of rarefied gas dynamics is crucial for optimizing flows through microelectromechanical systems, air filtration devices, and shale gas extraction. Traditional methods, such as discrete velocity and direct simulation Monte Carlo (DSMC), demand intensive memory and computation, especially for microflows in non-convex domains. Recently, physics-informed neural networks (PINNs) emerged as a meshless and adaptable alternative for solving non-linear partial differential equations. We trained a PINN using a limited number of DSMC-generated rarefied gas microflows in the transition regime (0.1<3), incorporating continuity and Cauchy momentum exchange equations in the loss function. The PINN achieved under 2 % error on these residuals and effectively filtered DSMC's intrinsic statistical noise. Predictions remained strong for a tested flow field with Kn=0.7, and showed limited extrapolation performance on a flow field with Kn=5 with a local overshoot of about 20 %, while maintaining physical consistency. Notably, each DSMC field required ∼20 hours on 4 graphics processing units (GPU), while the PINN training took <2 hours on one GPU, with evaluations under 2 seconds.

Recent experiments of fluid transport in nano-channels have shown evidence of a coupling between charge-fluctuations in polar fluids and electronic excitations in graphene solids, which may lead to a significant reduction of friction a phenomenon dubbed “negative quantum friction.” In this paper, we present a semi-classical mesoscale Boltzmann-Wigner lattice kinetic model of quantum-nanoscale transport and perform a numerical study of the effects of the quantum interactions on the evolution of a one-dimensional nano-fluid subject to a periodic external potential. It is shown that the effects of quantum fluctuations become visible once the quantum length scale (Fermi wavelength) of the quasiparticles becomes comparable to the lengthscale of the external potential. Under such conditions, quantum fluctuations are mostly felt on the odd kinetic moments, while the even ones remain nearly unaffected because they are “protected” by thermal fluctuations. It is hoped that the present Boltzmann-Wigner lattice model and extensions thereof may offer a useful tool for the computer simulation of quantum-nanofluidic transport phenomena at scales of engineering relevance.

We study pattern formation in a chemotaxis model of bacteria and soil carbon dynamics as an example system where transient dynamics can give rise to pattern formation outside of Turing unstable regimes. We use a detailed analysis of the reactivity of the non-spatial and spatial dynamics, stability analyses, and numerical continuation to uncover detailed aspects of this system’s pattern-forming potential. In addition to patterning in Turing unstable parameter regimes, reactivity of the spatial system can itself lead to a range of parameters where a spatially uniform state is asymptotically stable, but exhibits transient growth that can induce pattern formation. We show that this occurs in the bistable region of a subcritical Turing bifurcation. Intriguingly, such bistable regions appear in two spatial dimensions, but not in a one-dimensional domain, suggesting important interplays between geometry, transient growth, and the emergence of multistable patterns. We discuss the implications of our analysis for the bacterial soil organic carbon system, as well as for reaction-transport modeling more generally.

In this work we propose a bi-objective variant of the well-known 0/1 Knapsack Problem, that finds application in cases in which some item pairs may be seen as mutually conflicting. Previous variants considered in this scenario proposed to either avoid all conflicts, or to deal with them by considering the payment of appropriate penalty costs. We propose a different approach where the maximization of the profit and the minimization of the accepted conflicts are considered two different objective functions. We aim at identifying all Pareto-optimal solutions, so that a decision maker may choose a posteriori the optimal trade-off. We propose an exact resolution method based on the epsilon-constraint approach. Computational results on a wide set of instances show that our approach can be used in practice to identify and analyze their Pareto front.

We report a numerical study addressing the dynamics of compound vesicles confined in a channel under shear flow. The system comprises a smaller vesicle embedded within a larger one and can be used to mimic, for example, leukocytes or nucleate cells. A two-dimensional model, which combines molecular dynamics and mesoscopic hydrodynamics including thermal fluctuations, is adopted to perform an extended investigation. We are able to vary independently the swelling degree and the relative size of vesicles, the viscosities of fluids internal and external to vesicles, and the Capillary number, so to observe a rich dynamical phenomenology which goes well beyond what observed for single vesicles, matching quantitatively with experimental findings. Tank-treading, tumbling, and trembling motions are enriched by dynamical states where inner and outer vesicles can perform different motions. We show that thermal fluctuations are crucial during trembling and swinging dynamics, as observed in experiments. Undulating motion of the external vesicle, characterized by periodic oscillation of the inclination and buckling of the membrane, is observed at high filling fractions. This latter state exhibits features that are shown to depend on the relative size, the swelling degree of both vesicles as well as on thermal noise lacking in previous analytical and numerical studies.

Scaling properties of active ring polymers

In this work, we propose a novel Biased Random-Key Genetic Algorithm (BRKGA) to solve the Maximum Flow with Minimum Number of Labels (MF-ML) problem, a challenging NP-Complete variant of the classical Maximum Flow problem defined on graphs in which arcs have both capacities and labels assigned. Labels give a qualitative characterization of each connection, in contexts where a solution that is as homogeneous as possible is sought. The MF-ML problem aims to maximize the flow from a source to a sink on a capacitated network while minimizing the number of distinct arc labels used, a modeling framework with applications such as water purification in distribution systems. Our proposed algorithm encodes solutions as random-key vectors, which are decoded into feasible solutions. The BRKGA demonstrates superior performance when compared to a Skewed Variable Neighborhood Search (VNS) approach previously proposed to solve MF-ML. In particular, on the largest considered graphs, BRKGA-MFML outperformed VNS in 55 out of 81 scenarios, with an average improvement per scenario that reaches 7.18%.

The ability of network users to influence other users’ behavior has deep implications for fields such as marketing, epidemiology, misinformation control, and cybersecurity. While traditional models have extensively studied the cascading effects of influence without considering the speed of propagation, this work highlights methodologies that account for this aspect, reviewing the recent advancements in the study of rapid influence (i.e., influence propagation limited to a fixed number of hops or within a minimal time frame from the initial seed set). We provide a comprehensive review of the different variants of this problem studied in the literature, discussing both the theoretical aspects and practical implications, as well as the proposed solution approaches.

In this contribution, we provide an overview of the EMM (Earth and Mars Research Network) research infrastructure, outlining its main components and the potential scientific products that can be derived through its use. The presentation delves into selected aspects in greater detail, particularly where they resonate with the multiple scientific and technological challenges associated with the FORUM mission.

Group 3 (G3) is one of the most common and aggressive subtypes of the paediatric cerebellar tumour Medulloblastoma (MB), primarily driven by the MYC oncogene. The challenging targeting of MYC, coupled with gaps in understanding G3 MB molecular bases, has hindered the development of targeted therapies. The unconventional oncogenic roles of long noncoding RNAs (lncRNAs) offer opportunities to address this complexity, to provide insights and to identify novel targets. Using -omics approaches and molecular/cellular assays, we elucidate the mode-of-action of lncMB3, a MYC-dependent, anti-apoptotic lncRNA in G3 MB. LncMB3 regulates the TGF-beta pathway, critically altered in G3 medulloblastomagenesis, via direct binding and translational inhibition of the mRNA for the epigenetic factor HMGN5. This regulatory axis affects apoptosis through photoreceptor lineage genes, including the G3 driver OTX2. The synergistic effects between lncMB3 targeting and cisplatin treatment underscore the relevance of this network. Additionally, we propose novel ferritin-based nanocarriers for the efficient delivery of antisense oligonucleotides against lncMB3. LncMB3 crucially links MYC amplification and apoptosis inhibition through a circuit involving RNA-based mechanisms, G3 MB key determinants and underexplored factors. This integrated framework deepens the understanding of G3 MB landscape and supports the potential for translating lncRNA research into future applications.

Supporto 3D

Il presente volume raccoglie i long abstracts dei contributi presentati durante la quinta edizione della “Young Applied Mathematicians Conference” (YAMC, www.yamc.it). Ospitato dal Dipartimento di Ingegneria Civile, Edile e Ambientale (DICEA) dell’Università di Padova, in collaborazione con il Dipartimento di Matematica “Tullio Levi-Civita”, il convegno si è svolto dal 22 al 26 settembre 2025. L’edizione ha riunito 108 partecipanti provenienti da 52 università e centri di ricerca di 12 Paesi, coinvolgendo principalmente giovani ricercatori, tra dottorandi e post-doc.

We present a comprehensive and automated methodology for processing two-dimensional X-ray diffraction (2D-XRD) patterns. The proposed workflow involves three sequential stages: (i) precise localization of the diffraction center, (ii) removal of high-frequency noise, and (iii) suppression of non-physical background signals. This method enables improved data quality for subsequent quantitative analysis such as radial integration, phase identification, and structural refinement. Application to experimental datasets from both the Synchrotron Radiation Facility and a table-top X-ray diffractometer demonstrates the method’s robustness, accuracy, and computational efficiency.

We report a numerical study addressing the dynamics of compound vesicles confined in a channel under shear flow. The system comprises a smaller vesicle embedded within a larger one and can be used to mimic, for example, leukocytes or nucleate cells. A two-dimensional model, which combines molecular dynamics and mesoscopic hydrodynamics including thermal fluctuations, is adopted to perform an extended investigation. We are able to vary independently the swelling degree and the relative size of vesicles, the viscosities of fluids internal and external to vesicles, and the Capillary number, so to observe a rich dynamical phenomenology which goes well beyond what observed for single vesicles, matching quantitatively with experimental findings. Tank-treading, tumbling, and trembling motions are enriched by dynamical states where inner and outer vesicles can perform different motions. We show that thermal fluctuations are crucial during trembling and swinging dynamics, as observed in experiments. Undulating motion of the external vesicle, characterized by periodic oscillation of the inclination and buckling of the membrane, is observed at high filling fractions. This latter state exhibits features that are shown to depend on the relative size, the swelling degree of both vesicles as well as on thermal noise lacking in previous analytical and numerical studies.

Background: The expression of imprinted genes, which depends on their gamete of origin, is regulated by DNA sequences characterized by differential methylation between the maternal and paternal alleles (also known as germline differentially methylated regions or gDMRs). The most common molecular defect associated with Beckwith-Wiedemann syndrome (BWS), a condition linked to overgrowth and tumours, is the loss of methylation of the KCNQ1OT1-TSS gDMR located on chromosome 11p15.5 (also known as IC2 LoM). Approximately one-third of BWS patients with IC2 LoM exhibit multi-locus imprinting disturbances (MLID). While maternal-effect variants in proteins of the oocyte subcortical maternal complex (SCMC) have been linked to MLID, the underlying mechanisms and health impact of this epigenetic disturbance remain unclear. Results: We used the Infinium EPIC methylation array to investigate whole-genome CpG methylation in 64 BWS patients with IC2 LoM and 37 control subjects. We distinguished two patient groups, one with a variable methylation level of 24 gDMRs and the other with single-locus IC2 LoM. We observed that the mothers of the former patient group carried more variants in maternal-effect genes than those of the latter group, and 50% of them, but none of the latter group had variants in the SCMC genes. Additionally, in the former group, the mothers were older at the time of pregnancy, and the patients showed higher variation in methylation levels of thousands of CpGs located in non-imprinted loci, including protochaderins and cancer-associated genes. We found no differences in clinical features or in the incidence of assisted reproductive technology between the two patient groups. However, multiple affected siblings and recurrent miscarriages were observed only among cases with biallelic maternal-effect SCMC gene variants. Conclusions: This study demonstrates that the BWS patients with MLID exhibit highly variable methylation changes that affect both imprinted and non-imprinted loci in a seemingly stochastic manner throughout the genome. These findings support the hypothesis that MLID results from the interaction of maternal-effect genes and environmental factors in aged oocytes, leading to disordered DNA methylation in the whole genome. Future research should investigate whether and how these epimutations impact the health of affected individuals, particularly in adulthood.

The immune regulatory defects that promote neuroinflammation in multiple sclerosis (MS) remain unclear. We show that a specific regulatory T (Treg) cell subpopulation expressing Notch3 was increased in individuals with MS and in mice with experimental autoimmune encephalomyelitis (EAE). Notch3+ Treg cells were induced by the gut microbiota via Toll-like receptor (TLR)-dependent mechanisms. They then translocated to the central nervous system (CNS) in EAE where they promoted disease severity. Notch3 interacted with delta-like ligand 1 (DLL1) on microglia to subvert Treg cells into T helper 17 (Th17) cells. Notch3 deletion in Treg cells prevented EAE onset by stabilizing Treg cells and by simultaneously promoting the expansion of a tissue-resident Treg cell population that expressed neuropeptide Y receptor 1 (NPY1R) and which suppressed pathogenic IFN-γ+ and GM-CSF+ T cells. Our studies thus identify altered Treg cell population dynamics as a fundamental pathogenic mechanism in autoimmune neuroinflammation.

Background: Genomic imprinting is required for normal development, and abnormal methylation of differentially methylated regions (iDMRs) controlling the parent of origin-dependent expression of the imprinted genes has been found in congenital disorders affecting growth, metabolism, neurobehavior, and in cancer. In most of these cases the cause of the imprinting abnormalities is unknown. Also, these studies have generally been performed on a limited number of CpGs, and a systematic investigation of iDMR methylation in the general population is lacking. Results: By analysing a vast number of either in-house generated or online available whole-genome methylation array datasets of unaffected individuals, and patients with complex and rare disorders, we determined the most common iDMR methylation profiles in a large population and identified many genetic and non-genetic factors contributing to their variability in blood DNA. We found that methylation variability was not homogeneous within the iDMRs and that the CpGs closer to the ZFP57 binding sites are less susceptible to methylation changes. We demonstrated the methylation polymorphism of three iDMRs and the atypical behaviour of several others, and reported the association of 25 disease- and 47 non-disease-complex traits as well as 15 Mendelian and chromosomal disorders with iDMR methylation changes. The most significantly associated complex traits included ageing, intracytoplasmic sperm injection, African versus European ancestry, female sex, pre- and postnatal exposure to pollutants and blood cell type compositions, while the associated genetic diseases included Down syndrome and the developmental disorders with molecular defects in the DNA methyltransferases DNMT1 and DNMT3B, H3K36 methyltransferase SETD2, chromatin remodelers SRCAP and SMARCA4 and transcription factor ADNP. Conclusions: These findings identify several genetic and non-genetic factors including new genes associated with genomic imprinting maintenance in humans, which may have a role in the aetiology of the diseases with imprinting abnormalities and have clear implications in molecular diagnostics.