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.

The volume collects the long abstracts of the 79 contributions presented during the fourth edition of the “Young Applied Mathematicians Conference” (YAMC, www.yamc.it). Organized in Rome under the sponsorship of the Institute for Applied Mathematics (IAC) of the CNR and the Department of Mathematics at Sapienza, University of Rome, the conference took place from September 16 to 20, 2024, and brought together primarily young researchers (students, PhD candidates, post-docs, etc.) from 37 universities and research centers across 8 countries. This volume is intended to promote the communication of the research presented in the field of applied mathematics, with a primary focus on numerical analysis, artificial intelligence, statistics, and mathematical modeling. Il volume raccoglie i long abstracts dei 79 contributi presentati durante la quarta edizione del convegno "Young Applied Mathematicians Conference" (YAMC, www.yamc.it). Organizzato a Roma sotto il patrocinato dell'Istituto per le Applicazioni del Calcolo (IAC) del CNR e del dipartimento di Matematica di Sapienza, Università di Roma, il convegno si è svolto nelle giornate 16--20 settembre 2024 ed ha riunito principalmente giovani ricercatori (studenti, dottorandi, post-doc, ...) provenienti da 37 fra università e centri di ricerca di 8 nazioni. Il presente volume è indirizzato a favorire la comunicazione delle ricerche presentate nel panorama della matematica applicata, con principale attenzione in analisi numerica, intelligenza artificiale, statistica e modellistica matematica.

Personalized medicine strategies are gaining momentum nowadays, enabling the introduction of targeted treatments based on individual differences that can lead to greater therapeutic efficacy by reducing adverse effects. Despite its crucial role, studying the contribution of the immune system (IS) in this context is difficult because of the intricate interplay between host, pathogen, therapy, and other external stimuli. To address this problem, a multidisciplinary approach involving in silico models can be of great help. In this perspective, we will discuss the use of a well-established agent-based model of the immune response, C-ImmSim, to study the relationship between long-lasting diseases and the combined effects of IS, drug therapies and exogenous factors such as physical activity and dietary habits.

The volume collects the long abstracts of the 79 contributions presented during the fourth edition of the “Young Applied Mathematicians Conference” (YAMC, www.yamc.it). Organized in Rome under the sponsorship of the Institute for Applied Mathematics (IAC) of the CNR and the Department of Mathematics at Sapienza, University of Rome, the conference took place from September 16 to 20, 2024, and brought together primarily young researchers (students, PhD candidates, post-docs, etc.) from 37 universities and research centers across 8 countries. This volume is intended to promote the communication of the research presented in the field of applied mathematics, with a primary focus on numerical analysis, artificial intelligence, statistics, and mathematical modeling.

In a subsoil bioremediation intervention air or oxygen is injected in the polluted region and then a model for unsaturated porous media it is required, based on the theory of the dynamics of multiphase fluids in porous media. In order to optmize the costs of the intervention it is useful to consider the gas as compressible and this fact introduces nonlinearity in the mathematical model. The physical problem is described by a system of equations and the unknowns are: pollutant; bacteria concentration; oxygen saturation and oxygen pressure. Then, by algebraic manipulations, the model is reduced a to a nonlinear system of partial differential equations describing: oxygen saturation, oxygen density and bacteria concentration. For the proposed model, the results of some simulation experiments performed using COMSOL Multiphysics will be shown.

This booklet contains all the abstracts of the results which are going to be presented at the IMACS World Congress taking place in Rome at theEngineering Faculty of University 'La Sapienza', September 11-15, 2023.This is the 21st one in a series of World Conferences whose complete list goes back to 1955 and covered the whole continents. The subsequent World Conferences, usually, take place every three years. Unfortunately, due to the COVID pandemics, IMACS2023, initially scheduled in 2020, had to be postponed also in order to follow the spirit of the IMACS World Conference which prescribes to gather scientists in presence from all over the world. So, in the present occasion, the whole participants are expected to convene in Rome for exchanging their works, ideas and experiences.This Book of Abstracts, reflecting the Congress structure, is organized in sections: Keynote Lectures, General Session, Mini-symposia, Special Sessions and Posters. According to the IMACS philosophy, different aspects of applied mathematics are represented with a special interest towards the numerical methods and solutions.

This contribution outlines current research aimed at developing models for personalized type 2 diabetes mellitus (T2D) prevention in the framework of the European project PRAESIIDIUM (Physics Informed Machine Learn-ing-Based Prediction and Reversion of Impaired Fasting Glucose Management) aimed at building a digital twin for preventing T2D in patients at risk. Specifically, the modelling approaches include both a multiscale, hybrid computational model of the human metaflammatory (metabolic and inflammatory) status, and data-driven models of the risk of developing T2D able to generate personalized recommendations for mitigating the individ-ual risk. The prediction algorithm will draw on a rich set of information for training, derived from prior clinical data, the individual's family history, and prospective clinical trials including clinical variables, wearable sensors, and a tracking mobile app (for diet, physical activity, and lifestyle). The models developed within the project will be the basis for building a platform for healthcare professionals and patients to estimate and monitor the indi-vidual risk of T2D in real time, thus potentially supporting personalized prevention and patient engagement.

In this work, we propose and explore a novel network-constraint survival methodology considering the Weibull accelerated failure time (AFT) model combined with a penalized likelihood approach for variable selection and estimation [2]. Our estimator explicitly incorporates the correlation patterns among predictors using a double penalty that promotes both sparsity and the grouping effect. In or- der to solve the structured sparse regression problems we present an efficient iterative computational algorithm based on proximal gradient descent method [1]. We establish the theoretical consistency of the proposed estimator and moreover, we evaluate its performance both on synthetic and real data examples.

Background: The oral food challenge test (OFC) represents the gold standard for the diagnosis of food allergy (FA). It is also necessary for the safe reintroduction of the allergen into the diet of the patient. However, it is burdened with serious side effects. We aimed to determine the role of Skin Prick Tests (SPT) and of component resolved diagnosis (CRD) in the decision to perform OFC in patients aged between 0 and 18 years with FA towards tree nuts, peanuts and seeds. Material and Methods: We enrolled 22 patients (mean age of 10.91±4.22 years; 12 males) with a diagnosis of FA towards nuts, peanuts and seeds. A total of 29 OFCs were performed (6 of these patients were tested for more than one allergen at different times) with the following allergens: peanut, hazelnut, walnut, almond, pistachio and sesame. For each patient, were performed SPT and IgE levels through CRDs towards the culprit allergens (ImmunoCAP). In particular, for the first two variables we considered the diameter of the wheels for the allergen tested for OFC (Prick-OFC) and the mean diameter of the wheals for the other untested allergens (Prick-non OFC). For the other two variables, we focused on the IgE towards the CRDs characterizing the allergens of which they were diagnosed and tested at the OFC (CRD-OFC) and the two most cross-reactive CRDs with the first chosen CRD (CRD-cross). We conducted an inferential statistical analysis with the aim of answering the question: is it possible to predict OFC outcome using the values of the four covariates CRD- OFC, CRD-cross, Prick-OFC and Prick-non OFC? The discriminative power of each of the four covariates was first analyzed independently of the others, by performing a T-test on the two groups for each of them (Y=1, OFC positive result and Y=0, OFC negative result), figure 1. Results: The most indicative variables of a possible positive reaction to OFC are the values of the CRD-cross (p =0.01957) and of the Prick-OFC (p=0.046936), while the prick-non OFC (p=0.30857) and CRD-OFC(P=0.24193) variables do not seem to have discriminatory power. Multivariate logistic regression analysis indicates that none of the four variables considered is statistically predictive of test result. Conclusions: These results are unable to quantify the likelihood that a patient will have a positive OFC based on their SPT and IgE assays. However, we can qualitatively say what to expect by considering its CRD-cross and Prick-OFC values rather than those of CRD-OFC and Prick-non OFC.

Presents several mathematical problems for 3D and 3D printing plus a survey that gives the links between the two areas Contains contributions from highly reputed academic and industrial researchers with a long experience Shows several techniques, applications and benchmarks that can be useful for young researchers approaching the field

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