The SOC change index, defined as the normalized difference between the actual Soil Organic Carbon and the value assumed at an initial reference year, is here tailored to the RothC carbon model dynamics. It assumes as a baseline the value of the SOC equilibrium under constant environmental conditions. A sensitivity analysis is performed to evaluate the response of the model to changes in temperature, Net Primary Production (NPP), and land use soil class (forest, grassland, arable). A non-standard monthly time-stepping procedure has been proposed to approximate the SOC change index in the Alta Murgia National Park, a protected area in the Italian Apulia region, selected as a test site. The SOC change index exhibits negative trends for all the land use considered without fertilizers. The negative trend in the arable class can be inverted by a suitable organic fertilization program here proposed.

We analyze a second-order accurate implicit-symplectic (IMSP) scheme for reaction-diffusion systems modeling spatiotemporal dynamics of predator-prey populations. We prove stability and errors estimates of the semi-discrete-in-time approximations, under positivity assumptions. The numerical simulations confirm the theoretically derived rates of convergence and show an improved accuracy in the second-order IMSP in comparison with the first-order IMSP, at same computational cost.

We report the regularization technique used in the inversion of the radiative transfer equation. The talk is intended for a general public, and does not require specialistic notions.

We report the results of the regularization of the surface emissivity for the FORUM instrument.

Amino acids (AAs) are well known to be involved in the regulation of glucose metabolism and, in particular, of insulin secretion. However, the effects of different AAs on insulin release and kinetics have not been completely elucidated. The aim of this study was to propose a mathematical model that includes the effect of AAs on insulin kinetics during a mixed meal tolerance test. To this aim, five different models were proposed and compared. Validation was performed using average data, derived from the scientific literature, regarding subjects with normal glucose tolerance (CNT) and with type 2 diabetes (T2D). From the average data of the CNT and T2D people, data for two virtual populations (100 for each group) were generated for further model validation. Among the five proposed models, a simple model including one first-order differential equation showed the best results in terms of model performance (best compromise between model structure parsimony, estimated parameters plausibility, and data fit accuracy). With regard to the contribution of AAs to insulin appearance/disappearance (k model parameter), model analysis of the average data from the literature yielded 0.0247 (confidence interval, CI: 0.0168 - 0.0325) and -0.0048 (CI: -0.0281 - 0.0185) ?U·ml/(?mol·l·min), for CNT and T2D, respectively. This suggests a positive effect of AAs on insulin secretion in CNT, and negligible effect in T2D. In conclusion, a simple model, including single first-order differential equation, may help to describe the possible AAs effects on insulin kinetics during a physiological metabolic test, and provide parameters that can be assessed in the single individuals.

An unsupervised segmentation/clustering algorithm is a method for modeling the generation of directly observable visible variables from hidden sources. Each hidden source coop- erates in activating a subset of visible variables, or parts, which, in turn, additively generate the whole. This project aims at applying the versatility of such a method to the semantic analysis of text documents, namely patent applications.

Overview on hydrodynamic stability and wave turbulence

A hidden defect D affecting the interface in a layered specimen is evaluated from temperature maps collected on its top side. An explicit formula that approximates thermal conductance of the damaged interface is derived by means of first order Thin Plate Approximation. This formula is used to obtain a geometrical description of D from experimental data.

The dynamical response of a tethered semiflexible polymer with self-attractive interactions and subjected to an external force field is numerically investigated by varying stiffness and self-interaction strength. The chain is confined in two spatial dimensions and placed in contact with a heat bath described by the Brownian multi-particle collision method. For strong self-attraction the equilibrium conformations range from compact structures to double-stranded chains, and to rods when increasing the stiffness. Under the external field at small rigidities, the initial close-packed chain is continuously unwound by the force before being completely elongated. For double-stranded conformations the transition from the folded state to the open one is sharp being steeper for larger stiffnesses. The discontinuity in the transition appears in the force-extension relation, as well as in the probability distribution function of the gyration radius. The relative deformation with respect to the equilibrium case along the direction normal to the force is found to decay as the inverse of the applied force.

We develop a lumped parameter model to describe and predict the mass release of (absorption from) an arbitrary shaped body of any dimension in a large environment. Through the one-to-one analogy between diffusion-dominated mass transfer systems and electrical circuits we provide exact solutions in terms of averaged concentrations and mass released. An estimate of the equivalent resistance and of the release time is also given, and shown to be inversely proportional to the diffusivity. The proposed electrical analogue approach allows a time constant to be defined and provides an easy extension to a multi-layer and multi-phase cases in planar and spherical geometries. The simulation results are compared with those obtained from the solution of the corresponding analytical, numerical and experimental solutions, showing a satisfactory accuracy and a good agreement.

The prediction of drug dissolution profiles is crucial for elucidating the pharmacokinetic behaviour of drugs and the bioavailability of dosage forms. In this work, we develop a mathematical model to describe the dissolution process of irregularly shaped particles. We use a complete dissolution model that accounts for both surface kinetics and convective diffusion. The mechanistic relationship between the mass transfer coefficient and the local curvature is derived from the fundamental physical laws governing these processes. Our model theoretically shows that the dissolution rate depends nonlinearly on the surface curvature. The subsequent recrystallization process in the bulk fluid is also considered. The main result of this work is its simplicity, since only two coupled nonlinear ordinary differential equations are needed to describe the dissolution process. Another remarkable advantage is the possibility to determine the model parameters using common independent techniques, so that the importance of the wettability of solids on the dissolution process can be evaluated. Finally, the proposed model demonstrated the importance of particle shape in describing the experimental dissolution data of theophylline monohydrate.

Drug delivery carriers are considered an encouraging approach for the localized treatment of disease with minimum effect on the surrounding tissue. Particularly, layer-by-layer releasing particles have gained increasing interest for their ability to develop multifunctional systems able to control the release of one or more therapeutical drugs and biomolecules. Although experimental methods can offer the opportunity to establish cause and effect relationships, the data collection can be excessively expensive or/and time-consuming. For a better understanding of the impact of different design conditions on the drug-kinetics and release profile, properly designed mathematical models can be greatly beneficial. In this work, we develop a continuum-scale mathematical model to evaluate the transport and release of a drug from a microparticle based on an inner core covered by a polymeric shell. The present mathematical model includes the dissolution and diffusion of the drug and accounts for a mechanism that takes into consideration the drug biomolecules entrapped into the polymeric shell. We test a sensitivity analysis to evaluate the influence of changing the model conditions on the total system behavior. To prove the effectiveness of this proposed model, we consider the specific application of antibacterial treatment and calibrate the model against the data of the release profile for an antibiotic drug, metronidazole. The results of the numerical simulation show that ~85% of the drug is released in 230 h, and its release is characterized by two regimes where the drug dissolves, diffuses, and travels the external shell layer at a shorter time, while the drug is released from the shell to the surrounding medium at a longer time. Within the sensitivity analysis, the outer layer diffusivity is more significant than the value of diffusivity in the core, and the increase of the dissolution parameters causes an initial burst release of the drug. Finally, changing the shape of the particle to an ellipse produces an increased percentage of drugs released with an unchanged release time.

Drug-coated balloons (DCBs) are used commonly for delivering drug into diseased arteries. When applied on the inner surface of an artery, drug is transported from the balloon into the multilayer arterial wall through diffusion and advection, where it is ultimately absorbed through binding reactions. Mathematical modeling of these mass transport processes has the potential to help understand and optimize balloon-based drug delivery, thereby ensuring both safety and efficacy. The present work derives a closed-form solution for the multilayer cylindrical convection-diffusion-reaction (CDR) transport problem that occurs in balloon-based endovascular drug delivery. The model is presented for an arbitrary number of layers, and accounts for various transport processes in terms of relevant non-dimensional numbers. Quasi-orthogonality for this multilayer problem is derived. Closed-form expressions for the amounts of drug delivered by the balloon, bound in each arterial layer and lost from the external surfaces are derived. It is shown that only a small fraction of drug from the balloon is actually delivered into the artery during the short exposure time, which is influenced strongly by the diffusion coefficient of the inner-most layer. Further, binding of the drug is found to depend strongly on the reaction coefficient, expressed in terms of the Damköhler number. It is shown that boundary conditions on the inner and outer surfaces, expressed in terms of Sherwood numbers, play a role in drug uptake over a longer time period. The model is general enough to be applicable for a wide variety of scenarios and operational conditions, including an arbitrary number of layers. Results from this work provide fundamental insights into drug transport and uptake processes. In addition, these results may help improve the safety and efficacy of balloon-based drug delivery.

We survey some mathematical models for electro-magnetic emission due to electro-mechanically generated sources in heterogeneous materials. Because of the applications in geophysics, we focus our attention on parabolic approxima- tions of Maxwell's equations; also, we estimate under various assumptions the discrepancy with respect to the complete set of classical electrodynamics. Then, we introduce a related inverse problem

The traditional approach in medicine starts with investigating patients' symptoms to make a diagnosis. While with the advent of precision medicine, a diagnosis results from several factors that interact and need to be analyzed together. This added complexity asks for increased support for medical personnel in analyzing these data altogether. Our objective is to merge the traditional approach with network medicine to offer a tool to investigate together symptoms, anatomies, diseases, and genes to establish a diagnosis from different points of view. This paper aims to help the clinician with the typical workflow of disease analysis, proposing a Visual Analytics tool to ease this task. A use case demonstrates the benefits of the proposed solution.

The dynamics and rheology of a vesicle confined in a channel under shear flow are studied at finite temperature. The effect of finite temperature on vesicle motion and system viscosity is investigated. A two-dimensional numerical model, which includes thermal fluctuations and is based on a combination of molecular dynamics and mesoscopic hydrodynamics, is used to perform a detailed analysis in a wide range of the Peclet numbers (the ratio of the shear rate to the rotational diffusion coefficient). The suspension viscosity is found to be a monotonous increasing function of the viscosity contrast (the ratio of the viscosity of the encapsulated fluid to that of the surrounding fluid) both in the tank-treading and the tumbling regime due to the interplay of different temperature-depending mechanisms. Thermal effects induce shape and inclination fluctuations of the vesicle which also experiences Brownian diffusion across the channel increasing the viscosity. These effects reduce when increasing the Peclet number.

Background: The immune response to adenoviral COVID-19 vaccines is affected by the interval between doses. The optimal interval is unknown.Aim: We aim to explore in-silico the effect of the interval between vaccine administrations on immunogenicity and to analyze the contribution of pre-existing levels of antibodies, plasma cells, and memory B and T lymphocytes.Methods: We used a stochastic agent-based immune simulation platform to simulate two-dose and three-dose vaccination protocols with an adenoviral vaccine. We identified the model's parameters fitting anti-Spike antibody levels from individuals immunized with the COVID-19 vaccine AstraZeneca (ChAdOx1-S, Vaxzevria). We used several statistical methods, such as principal component analysis and binary classification, to analyze the correlation between pre-existing levels of antibodies, plasma cells, and memory B and T cells to the magnitude of the antibody response following a booster dose.Results and conclusions: We find that the magnitude of the antibody response to a booster depends on the number of pre-existing memory B cells, which, in turn, is highly correlated to the number of T helper cells and plasma cells, and the antibody titers. Pre-existing memory T cytotoxic cells and antibodies directly influence antigen availability hence limiting the magnitude of the immune response. The optimal immunogenicity of the third dose is achieved over a large time window, spanning from 6 to 16 months after the second dose. Interestingly, after any vaccine dose, individuals can be classified into two groups, sustainers and decayers, that differ in the kinetics of decline of their antibody titers due to differences in long-lived plasma cells. This suggests that the decayers may benefit from a tailored boosting schedule with a shorter interval to avoid the temporary loss of serological immunity.

This proof-of-concept (PoC) study presents a pipeline made by two blocks: 1. the identification of the network that generates interictal epileptic activity; and 2. the study of the time course of the electrical activity that it generates, called neurodynamics, and the study of its functional connectivity to the other parts of the brain. Network identification is achieved with the Functional Source Separation (FSS) algorithm applied to electroencephalographic (EEG) recordings, the neurodynamics quantified through signal complexity with the Higuchi Fractal Dimension (HFD), and functional connectivity with the Directed Transfer Function (DTF). This PoC is enhanced by the data collected before and after neuromodulation via transcranial Direct Current Stimulation (tDCS, both Real and Sham) in a single drug-resistant epileptic person. We observed that the signal complexity of the epileptogenic network, reduced in the pre-Real, pre-Sham, and post-Sham, reached the level of the rest of the brain post-Real tDCS. DTF changes post-Real tDCS were maintained after one month. The proposed approach can represent a valuable tool to enhance understanding of the relationship between brain neurodynamics characteristics, the effects of non-invasive brain stimulation, and epileptic symptoms.