In this paper we present a survey about a series of works developed in the last 20 years, with our group, on chemical aggression of stone artifacts. Here we describe the modelling of different phenomena responsible for exterior and internal degradation of porous materials, such as the evolution of gypsum crust in marble stones, the sodium sulphate crystallization inside porous stone (masonry brick), or the effect of injection of consolidants in stones. For sulfation and other surface reactions we adapted our previous models to take into account more possible features, as for instance rugosity of stones and the possible interaction between chemical and mechanical damage, to evaluate the propagation of cracks in stones under stress. For the problem of salt crystallization, a new mathematical model describing the effect of protective products on sodium sulphate crystallization inside bricks has been proposed and tested against experiments. Finally, a mathematical model for evaluating the penetration and the ultimate depth of filtration of a consolidant product (ethyl silicate) on tuff was proposed and calibrated using experimental data. The proposed models were calibrated by tuning model parameters with numerical fitting procedures based on the comparison between simulation results and available experimental data. Since the obtained results were in qualitative and quantitative accordance with data, this confirmed the soundness of implemented procedures and the effectiveness of the proposed methods.

Real scientific contact between mathematical community and experts in cultural heritageResults of concrete collaboration projects are presentedMathematical models can provide an effective and non-invasive analysis tools in this field

The volume contains high quality articles in the framework of multiscale modelling including lab-on-chip framework It includes models of classification and tumour growth in patient-specific framework The present collection covers a large array of topical biomedical applications

Functionally graded materials (FGMs), possessing properties that vary smoothly from one region to another,have been receiving increasing attention in recent years, particularly in the aerospace, automotive andbiomedical sectors. However, they have yet to reach their full potential. In this paper, we explore the potentialof FGMs in the context of drug delivery, where the unique material characteristics offer the potential of finetuningdrug-release for the desired application. Specifically, we develop a mathematical model of drug releasefrom a thin film FGM, based upon a spatially-varying drug diffusivity. We demonstrate that, depending on thefunctional form of the diffusivity (related to the material properties) a wide range of drug release profilesmay be obtained. Interestingly, the shape of these release profiles are not, in general, achievable from ahomogeneous medium with a constant diffusivity.

We describe preliminary results from a multiobjectivegraph matching algorithm, in the coarsening step of anaggregation-based Algebraic MultiGrid (AMG) preconditioner,for solving large and sparse linear systems of equations on highendparallel computers. We have two objectives. First, we wishto improve the convergence behavior of the AMG method whenapplied to highly anisotropic problems. Second, we wish to extendthe parallel package PSCToolkit to exploit multi-threadedparallelism at the node level on multi-core processors. Ourmatching proposal balances the need to simultaneously computehigh weights and large cardinalities by a new formulation ofthe weighted matching problem combining both these objectivesusing a parameter ?. We compute the matching by a parallel2/3 - ?-approximation algorithm for maximum weight matchings.Results with the new matching algorithm show that for a suitablechoice of the parameter ? we compute effective preconditionersin the presence of anisotropy, i.e., smaller solve times, setup times,iterations counts, and operator complexity.

Vitamin D has been proven to be a strong stimulator of mechanisms associated with the elimination of pathogens. Because of its recognized effectiveness against viral infections, during SARS-CoV-2 infection, the effects of Vitamin D supplementation have been the object of debate. This study aims to contribute to this debate by the means of a qualitative phenomenological mathematical model in which the role of Vitamin D and its interactions with the innate immune system are explicitly considered. We show that Vitamin D influx and degradation can be considered as possible control parameters for the disease evaluation and recovery. By varying Vitamin D influx, three dynamical scenarios have been found with different modalities of recovery from the disease. Inside each scenario, Vitamin D degradation has been related to different degrees of severity in disease development. Interestingly, the emergence of hysteretic phenomenologies when Vitamin D influx is too low can be related to the onset of Long-COVID syndrome, confirming clinical evidence from recent studies on the topic.

We tackle the problem of coupling a spatiotemporal model for simulating the spread and control of an invasive alien species with data coming from image processing and expert knowledge. In this study, we implement a spatially explicit optimal control model based on a reaction-diffusion equation which includes an Holling II type functional response term for modeling the density control rate. The model takes into account the budget constraint related to the control program and searches for the optimal effort allocation for the minimization of the invasive alien species density. Remote sensing and expert knowledge have been assimilated in the model to estimate the initial species distribution and its habitat suitability, empirically extracted by a land cover map of the study area. The approach has been applied to the plant species Ailanthus altissima (Mill.) Swingle within the Alta Murgia National Park. This area is one of the Natura 2000 sites under the study of the ongoing National Biodiversity Future Center (NBFC) funded by the Italian National Recovery and Resilience Plan (NRRP), and pilot site of the finished H2020 project ECOPOTENTIAL, which aimed at the integration of modeling tools and Earth Observations for a sustainable management of protected areas. Both the initial density map and the land cover map have been generated by using very high resolution satellite images and validated by means of ground truth data provided by the EU Life Alta Murgia project (LIFE12 BIO/IT/000213), a project aimed at the eradication of Ailanthus altissima in the Alta Murgia National Park

A new fractional q-order variation of the RothC model for the dynamics of soil organic carbon is introduced. A computational method based on the discretization of the analytic solution along with the finite-difference technique are suggested and the stability results for the latter are given. The accuracy of the scheme, in terms of the temporal step size h, is confirmed through numerical testing of a constructed analytic solution. The effectiveness of the proposed discrete method is compared with that of the classical discrete RothC model. Results from real-world experiments show that, by adjusting the fractional order q and the multiplier term ?(t,q), a better match between simulated and actual data can be achieved compared to the traditional integer-order model.

To evaluate changes in the Soil Organic Carbon (SOC) index, one of the key indicators of land degradation neutrality, soil carbon modeling is of primary importance. In litera-ture, the analysis has been focused on the stability characterization of soil carbon steady states and in the calculation of the resilience of the stable equilibria. Neither stability nor resilience, however, provide any information about transient dynamics, and models with highly resilient equilibria can exhibit dramatic transient responses to perturbations. To trace how environmental changes affect the transient dynamics of SOC indicator, we use the concept of generalized reactivity (g-reactivity) to models belonging to two main classes: the first-order, linear and semilinear carbon transfer models and fully nonlinear microbe-explicit models. A novel formulation of a general two-dimensional model allows to deal with different functional forms and to perform a systematic analysis of both stabil-ity of soil carbon equilibria and SOC-reactivity. Using temperatures and Net Primary Pro-duction (NPP) data of Alta Murgia National Park, the RothC, MOMOS and the fully implicit dynamical planar system are compared in predicting the impact of increased temperatures in the years 2005-2019 on the asymptotic stability of carbon steady states and in increas-ing the SOC-reactivity.(c) 2023 Elsevier Inc. All rights reserved.

Tracking droplets in microfluidics is a challenging task. The difficulty arises in choosing a tool to analyze general microfluidic videos to infer physical quantities. The state-of-the-art object detector algorithm You Only Look Once (YOLO) and the object tracking algorithm Simple Online and Realtime Tracking with a Deep Association Metric (DeepSORT) are customizable for droplet identification and tracking. The customization includes training YOLO and DeepSORT networks to identify and track the objects of interest. We trained several YOLOv5 and YOLOv7 models and the DeepSORT network for droplet identification and tracking from microfluidic experimental videos. We compare the performance of the droplet tracking applications with YOLOv5 and YOLOv7 in terms of training time and time to analyze a given video across various hardware configurations. Despite the latest YOLOv7 being 10% faster, the real-time tracking is only achieved by lighter YOLO models on RTX 3070 Ti GPU machine due to additional significant droplet tracking costs arising from the DeepSORT algorithm. This work is a benchmark study for the YOLOv5 and YOLOv7 networks with DeepSORT in terms of the training time and inference time for a custom dataset of microfluidic droplets.

Mathematical models based on non-linear integral and integro-differential equations are gaining increasing attention in mathematical epidemiology due to their ability to incorporate the past infection dynamic into its current development. This property is particularly suitable to represent the evolution of diseases where the dependence of infectivity on the time since becoming infected plays a crucial role. These renewal equation models contain an integral term describing the contribution of the force of infection to the total infectivity and need, in general, numerical simulations for a complete understanding and quantitative description. For a general model which includes demographic effects [1, 2], we propose a non-standard approach [3] based on a non local discretization of the integral term characterizing the mathematical equations. We discuss classical problems related to the behaviour of this scheme and we prove the positivity invariance and the unconditional preservation of the stability nature of equilibria, with respect to the discretization parameter. These properties, together with the fact that the method can be put into an explicit form, actually make it a computationally attractive tool and, at the same time, a stand-alone discrete model describing the evolution of an epidemic. This is a joint work with Bruno Buonomo and Claudia Panico from University of Naples Federico II, and Antonia Vecchio from IAC-CNR, Naples.

We consider a discrete model of planar elasticity where the particles, in the reference configuration, sit on a regular triangular lattice and interact through nearest-neighbor pairwise potentials, with bonds modeled as linearized elastic springs. Within this framework, we introduce plastic slip fields, whose discrete circulation around each tri-angle detects the possible presence of an edge dislocation. We provide a gamma-convergence analysis, as the lattice spacing tends to zero, of the elastic energy induced by edge dislocations in the energy regime corresponding to a finite number of geometrically necessary dislocations.

This paper considers a Multiple Objective variant of the Critical Disruption Path problem to extend itssuitability in a range of security operations relying on path-based network interdiction, including flight patternoptimisation for surveillance. Given a pair of nodes s and t from the network to be monitored, the problemseeks for loopless s - t paths such that, within the induced subgraph obtained via deletion of the path, thesize of the largest connected component is minimised, the number of connected components is maximised,while concurrently reducing as much as possible the cost of such disruption path. These three objectives arepossibly in conflict with each other, and the scope of this work is to allow for an efficient and insightfulapproximation of the Pareto front, looking for a trade-off between costs and effectiveness to secure the mostconvenient paths for security and surveillance operations. We first introduce and formulate the Multi-ObjectiveCritical Disruption Path Problem (Multi-Objs-CDP) as a mixed integer programming formulation (MO-CDP),then we propose an original evolutionary metaheuristic algorithm hybridising modified-NSGA-II and VNS forfinding an approximation of the Pareto front, as well as a procedure securing the efficient generation of a highquality pool of initial solutions. The experimental performance of the proposed algorithm, as compared witha variety of competing approaches, proves to be fully satisfactory in terms of time efficiency and quality ofthe solutions obtained on a set of medium to large benchmark instances.

In this work we have studied in situ the formation and growth of calcium phosphate (CaP) nanoparticles (NPs) in the presence of three calcium-binding carboxylate molecules having different affinities for Ca2+ ions: citrate (Cit), hydroxycitrate (CitOH), and glutarate (Glr). The formation of CaP NPs at several reaction temperatures ranging from 25 °C to 80 °C was monitored in situ through simultaneous Small and Wide X-ray Scattering (SAXS/WAXS) using synchrotron light. SAXS was used to investigate the first stages of NP formation where a crystalline order is not yet formed. In this regard we have developed a new bivariate mesh data analysis method for identifying the SAXS curves associated with the most relevant timeframes for performing curve modeling. WAXS was used to study the formation of crystalline phases and their evolution over time. The combined SAXS/WAXS data allowed us to track NP nucleation, their size and morphology, and their evolution up to mature hydroxyapatite (HA) nanocrystals. We have assessed that in the first stages of reaction (80 seconds) amorphous, elongated primary NPs nucleate whose size and morphology depend on the temperature and type of carboxylate molecule. The temperature controls the release of Ca2+ ions from carboxylate molecules, and thus induces the formation of a higher amount of amorphous particles and increases their size and aspect ratio. As the reaction time progresses, amorphous particles evolve into crystalline ones, whose kinetics of crystal growth are controlled by temperature and carboxylate ions. Stronger Ca-binding carboxylates (CitOH > Cit > Glr) have a more pronounced inhibiting effect on HA crystallization, retarding the formation and growth of crystalline domains, while a rise of temperature promotes crystallization. This work allowed us to shed more light on the formation of HA in the presence of growth-controlling molecules, as well as present the potential of combined SAXS/WAXS for studying the formation of highly relevant NPs for different applications.

The appearance of AC72 foiling catamarans in the scenario of sailing yacht competitions in 2013 raised attention to this ship design concept, although not brand new in the yacht design history. The drastic drag reduction connected with the elevation of the ship hull outside the water is obtained by the use of a foil, or a system of foils, acting as the wings of a plane, providing a lift force balancing the weight of the ship. Since this lift is proportional (non-linearly) to the ship hull speed, the take-off speed of the hull cannot be low. As a result, since we are travelling in water at high speeds, the occurrence of the phenomenon of cavitation cannot be completely avoided, and the performance of the ship undergoes deterioration. Shaping of the foil profile must consider this peculiar situation, so the design tools commonly adopted for the aero-hydrodynamic hull design optimization are no longer adequate. In this paper, we are considering the optimization of the 2D profile of a foil in three different physical conditions: single fluid, two fluids and two fluids with cavitation. The first is typical of aeronautic wing design, the second of the appendages of a displacement ship, and the third of a foiling ship. Results give evidence of the different requirements for the three different conditions.

The importance of the immune system (IS) in tuberculosis (TB) drug development is often underestimated because of the intricate nature of experiments and the specialized knowledge needed. In vitro and animal studies fall short in replicating the intricate reactions of the human IS to drugs and infections. In this study, we present our initial efforts in employing an in silico approach to comprehend how an individual’s IS impacts the efficacy of therapy, particularly in managing mycobacterium tuberculosis (Mtb) infection and minimizing the risk of relapse. We employed a well-established agent-based IS simulator called C-IMMSIM. We conducted simulations to investigate the long-term outcomes of TB disease in a virtual cohort infected with Mtb over a 50-year period. Our simulations revealed that individuals with competent IS showed a high success rate in containing Mtb infection. Furthermore, to better understand the dynamic interactions between Mtb and the IS, we deliberately introduced specific IS deficiencies, thus successfully inducing short-term relapses and mortality. These results confirm the model’s ability to elucidate the mechanisms underlying the interactions between Mtb and the IS.

Algebraic Cryptanalysis is a widely used technique that tackles the problem of breaking ciphers mainly relying on the ability to express a cryptosystem as a solvable polynomial system. Each output bit/word can be expressed as a polynomial equation in the cipher’s inputs—namely the key and the plaintext or the initialisation vector bits/words. A part of research in this area consists in finding suitable algebraic structures where polynomial systems can be effectively solved, e.g., by computing Gröbner bases. In 2009, Dinur and Shamir proposed the cube attack, a chosen plaintext algebraic cryptanalysis technique for the offline acquisition of an equivalent system by means of monomial reduction; interpolation on cubes in the space of variables enables retrieving a linear polynomial system, hence making it exploitable in the online phase to recover the secret key. Since its introduction, this attack has received both many criticisms and endorsements from the crypto community; this work aims at providing, under a unified notation, a complete state-of-the-art review of recent developments by categorising contributions in five classes. We conclude the work with an in-depth description of the kite attack framework, a cipher-independent tool that implements cube attacks on GPUs. Mickey2.0 is adopted as a showcase.

We present a standalone Matlab software platform complete with visualization for the reconstruction of the neural activity in the brain from MEG or EEG data. The underlying inversion combines hierarchical Bayesian models and Krylov subspace iterative least squares solvers. The Bayesian framework of the underlying inversion algorithm allows to account for anatomical information and possible a priori belief about the focality of the reconstruction. The computational efficiency makes the software suitable for the reconstruction of lengthy time series on standard computing equipment. The algorithm requires minimal user provided input parameters, although the user can express the desired focality and accuracy of the solution. The code has been designed so as to favor the parallelization performed automatically by Matlab, according to the resources of the host computer. We demonstrate the flexibility of the platform by reconstructing activity patterns with supports of different sizes from MEG and EEG data. Moreover, we show that the software reconstructs well activity patches located either in the subcortical brain structures or on the cortex. The inverse solver and visualization modules can be used either individually or in combination. We also provide a version of the inverse solver that can be used within Brainstorm toolbox. All the software is available online by Github, including the Brainstorm plugin, with accompanying documentation and test data.

An experimental setup operating in transmission mode in the frequency range between 18 and 40 GHz is described. This study shows how the system is able to distinguish healthy and rotten hazelnuts. In addition, a Self-Organizing Map (SOM) trained with the Kohonen algorithm was used to classify the hazelnuts according to their quality.