MOX Reports
The preprint collection of the Laboratory for Modeling and Scientific Computation MOX. It mainly contains works on numerical
analysis and mathematical modeling applied to engineering problems. MOX web site is mox.polimi.it
Found 1288 products
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70/2025 - 11/24/2025
Greco, M.; Milan, G.; Ieva, F.; Secchi, P.
The Social Growth Index: Measuring Socioeconomic Resilience at the Municipal Level in Italy | Abstract | | This study introduces the Social Growth Index (SGI), an integrated framework for assessing socioeconomic resilience at the municipal level across Italy. The SGI builds on the idea that a territory is resilient if it can sustain long-term growth despite exposure to external shocks—both positive, such as large-scale public investments under the National Recovery and Resilience Plan (PNRR), and negative, such as the COVID-19 pandemic. These events represent structural tests for local systems, revealing their ability to adapt, recover, and convert temporary disturbances into lasting development trajectories.
Using harmonized data for 2010–2022, the SGI integrates three standardized variables—GDP density (GDP per m2), GDP per capita, and population density—that jointly capture productive intensity, individual prosperity, and demographic vitality within a consistent spatial structure.
To obtain the SGI, we design a methodological framework integrating Fixed Rank Kriging (FRK) for the spatial downscaling of Gross Domestic Product (GDP) with Copeland aggregation for multi-criteria ranking. FRK enables spatially coherent GDP estimates at fine resolution, while the Copeland method aggregates municipalities’ relative performance without imposing arbitrary weights.
Results reveal a persistent North–South divide, with higher resilience levels in Northern and Central Italy and lower values in Southern, Sicilian, and inland Sardinian areas. Temporal analysis indicates structural persistence in highly resilient urban and industrial systems alongside localized improvements around regional capitals. Comparison with ISTAT’s Municipal Fragility Index (IFC)exhibits consistency among indicators measuring intersecting economic aspects.
Future extensions include developing a spatio-temporal FRK model and incorporating additional drivers—such as employment, innovation, and environmental sustainability—to enhance temporal coherence and policy relevance. |
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69/2025 - 11/14/2025
Marino, F.; Guagliardi, O.; Di Stazio, F.; Mazza, E.; Paganoni, A.M.; Tanelli, M.
Measuring Academic Stress and Well-Being in Higher Education: A Psychometric Study | Abstract | | This study investigates the assessment of perceived academic stress and its impact on students’ mental health by employing advanced psychometric and statistical models. A dataset of 9,000 university students was analyzed using Item Response Theory (IRT), specifically the Four-parameter Nested Logistic Regression Model (4PLnRM), to estimate latent parameters that quantify well-being across nine stress-related domains. Linear and mixed-effects models were applied to identify the most relevant socio-demographic, psychosocial and academic risk factors, highlighting the strong influence of economic conditions, exam backlog, and academic self-perceptions. To further validate these measures, Random Forest classification models were trained to identify students at different levels of psychological vulnerability psychological risk, demonstrating that latent parameters are effective predictors of distress and well-being. Additionally, Exploratory Factor Analysis (EFA) on self-reported mental health symptoms psychological symptoms revealed four interpretable latent factors—anxiety, depression, motivational block, and somatization—used in subsequent clustering to classify students into low, medium, and high-risk groups. Across methods, consistent associations emerged between risk classes and demographic variables such as gender, age, academic performance, and economic satisfaction. The results emphasize the value of latent psychometric modeling for identifying stress mechanisms and developing targeted interventions aimed at improving the academic climate and supporting students’ mental health. |
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68/2025 - 11/13/2025
Leimer Saglio, C. B.; Corti, M.; Pagani, S.; Antonietti, P. F.
A novel mathematical and computational framework of amyloid-beta triggered seizure dynamics in Alzheimer's disease | Abstract | | The association of epileptic activity and Alzheimer's disease (AD) has been increasingly reported in both clinical and experimental studies, suggesting that amyloid-beta accumulation may directly affect neuronal excitability. Capturing these interactions requires a quantitative description that bridges the molecular alterations of AD with the fast electrophysiological dynamics of epilepsy. We introduce a novel mathematical model that extends the Barreto-Cressman ionic formulation by incorporating multiple mechanisms of calcium dysregulation induced by amyloid-beta, including formation of calcium-permeable pores, overactivation of voltage-gated calcium channels, and suppression of calcium-sensitive potassium currents. The resulting ionic model is coupled with the monodomain equation and discretized using a p-adaptive discontinuous Galerkin method on polytopal meshes, providing an effective balance between efficiency and accuracy in capturing the sharp spatiotemporal electrical wavefronts associated with epileptiform discharges. Numerical simulations performed on idealized and realistic brain geometries demonstrate that progressive amyloid-beta accumulation leads to severe alterations in calcium homeostasis, increased neuronal hyperexcitability, and pathological seizure propagation. Specifically, high amyloid-beta concentrations produce secondary epileptogenic sources and spatially heterogeneous wavefronts, indicating that biochemical inhomogeneities play a critical role in shaping seizure dynamics. These results illustrate how multiscale modeling provides new mechanistic insights into the interplay between neurodegeneration and epilepsy in Alzheimer's disease. |
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67/2025 - 11/10/2025
Antonietti, P.F.; Corti, M.; Gómez S.; Perugia, I.
Structure-preserving local discontinuous Galerkin discretization of conformational conversion systems | Abstract | | We investigate a two-state conformational conversion system and introduce a novel structure-preserving numerical scheme that couples a local discontinuous Galerkin space discretization with the backward Euler time-integration method. The model is first reformulated in terms of auxiliary variables involving suitable nonlinear transformations, which allow us to enforce positivity and boundedness at the numerical level. Then, we prove a discrete entropy-stability inequality, which we use to show the existence of discrete solutions, as well as to establish the convergence of the scheme by means of some discrete compactness arguments. As a by-product of the theoretical analysis, we also prove the existence of global weak solutions satisfying the system's physical bounds. Numerical results validate the theoretical results and assess the capabilities of the proposed method in practice. |
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66/2025 - 11/09/2025
Speroni, G.; Mondini, N.; Ferro, N.; Perotto, S.
A topology optimization framework for scaffold design in soilless cultivation | Abstract | | In this work, we customize the microSIMPATY algorithm, combining Solid Isotropic Material with Penalization (SIMP) topology optimization with anisotropic mesh adaptation, within a multi-physics framework to design cellular material scaffolds suitable for soilless cultivation systems.
The design of these novel substrates is driven by optimization criteria that balance mechanical and fluid-dynamic performance, with the aim of effectively supporting plant growth.
The numerical validation results and the in silico root growth simulations carried out in virtual vase-like containers confirm promising potential toward replacing conventional organic and inorganic substrates with an optimized, sustainable, and readily accessible alternative. |
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65/2025 - 10/27/2025
Pottier, A.; Gelardi, F.; Larcher, A.; Capitanio, U.; Rainone, P.; Moresco, R.M.; Tenace, N.; Colecchia, M.; Grassi, S.; Ponzoni, M.; Chiti, A.; Cavinato, L.
MOSAIK: A computational framework for theranostic digital twin in renal cell carcinoma | Abstract | | In nuclear oncology, radiopharmaceuticals (RP) emerged as theranostic tools able to bind specifically to cancer biomarkers and inflict subsequently systematic and irreparable damage to the DNA of the targeted cells. That is why radioisotopes-
based therapies entered the clinical practice to diagnose and treat tumours simultaneously and may potentially overcome
therapeutic resistance encountered in various cancers. Despite these advancements, tumoural heterogeneity and poor anti-
cancer drug penetration in solid tumours turns out to be overlooked pieces of the personalized oncology puzzle, leading to
treatment failure. In this study, we propose MOSAIK, an oncological digital twin framework to simulate the intra-tumour uptake of radiopharmaceutical agent, specifically [89Zr]Zr-girentuximab in clear cell Renal Cell Carcinoma (ccRCC). Our comprehensive approach integrates patient-based insights in space and time for reflecting the multi-faceted nature of RP uptake. We develop models to segment blood vessels and identify neoplastic regions, enabling the characterization of the
biological domain. To discuss the intra-tumour heterogeneity contribution to the drug diffusion process, we spatially correlate immunochemistry images-derived parameters with the baseline drug accumulation captured through micro PET
imaging. Additionally, the model is informed with temporal features leveraged from the compartmental model of the RP
agent. The presented Deep Learning (DL) framework incorporates interpretable spatial and temporal inputs stemming from
histopathology images. This work aims to provide a computational model with predictive capabilities in drug retention in
tissues to move beyond the one-size-fits-all paradigm in nuclear medicine. |
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64/2025 - 10/21/2025
Celora, S.; Tonini, A.; Regazzoni, F.; Dede', L. Parati, G.; Quarteroni, A.
Cardiocirculatory Computational Models for the Study of Hypertension | Abstract | | In this work, we develop patient-specific cardiocirculatory models with the aim of building Digital Twins for hypertension. In particular, in our pathophysiology-based framework, we consider both 0D cardiocirculatory models and a 3D-0D electromechanical model. The 0D model, which consists of an RLC circuit, is studied in two variants, with and without capillaries. The 3D--0D model consists of a three-dimensional electromechanical model of the left ventricle, coupled with a 0D model for the external blood circulation: this representation enables the assessment of additional quantities related to ventricular deformation and stress, and offers a more detailed representation compared to a fully 0D model. Sensitivity analysis is performed on the 0D model, with both a mono- and a multi-parametric approach, in order to identify the parameters that most influence the model outputs and guide the calibration process. We studied three different scenarios, corresponding to systemic, pulmonary and renovascular hypertension, each in three nuances of severity. To maintain a fair comparison among the models, a parameter calibration strategy is developed; the outputs of the 0D model with capillaries are utilized to enhance the 3D-0D model. The results demonstrate that the 3D-0D model yields an accurate representation of cardiocirculatory dynamics in the presence of hypertension; this model represents a powerful step toward digital twins for real-time hypertension control, providing refined and clinically meaningful insights beyond those achievable with 0D models alone. |
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63/2025 - 10/14/2025
Panzeri, S.; Clemente, A.; Arnone, E.; Mateu, J.; Sangalli, L.M.
Spatio-Temporal Intensity Estimation for Inhomogeneous Poisson Point Processes on Linear Networks: A Roughness Penalty Method | Abstract | | Nowadays, a vast amount of georeferenced data pertains to human and natural activities occurring in complex network-constrained regions, such as road or river networks. In this article, our research focuses on spatio-temporal point patterns evolving over time on linear networks, which we model as inhomogeneous Poisson point processes. Within this framework, we propose an innovative nonparametric method for intensity estimation that leverages penalized maximum likelihood with roughness penalties based on differential operators applied across space and time. We provide an efficient implementation of the proposed method, relying on advanced computational and numerical techniques that involve finite element discretizations on linear networks. We validate the method through simulation studies conducted across various scenarios, evaluating its performance compared to state-of-the-art competitors. Finally, we illustrate the method through an application to road accident data recorded in the municipality of Bergamo, Italy, during the years 2017–2019. |
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