Topology optimization of multiple anisotropic materials, with application to self-assembling diblock copolymers

We propose a solution strategy for a multimaterial minimum compliance topology optimization problem, which consists in finding the optimal allocation of a finite number of candidate (possibly anisotropic) materials inside a reference domain, with the aim of maximizing the stiffness of the body. As a relevant and novel application we consider the optimization of self-assembled structures obtained by means of diblock copolymers. Such polymers are a class of self-assembling materials which spontaneously synthesize periodic microstructures at the nanoscale, whose anisotropic features can be exploited to build structures with optimal elastic response, resembling biological tissues exhibiting microstructures, such as bones and wood. For this purpose we present a new generalization of the classical Optimality Criteria algorithm to encompass a wider class of problems, where multiple candidate materials are considered, the orientation of the anisotropic materials is optimized, and the elastic properties of the materials are assumed to depend on a scalar parameter, which is optimized simultaneously to the material allocation and orientation. Well-posedness of the optimization problem and well-definition of the presented algorithm are narrowly treated and proved. The capabilities of the proposed method are assessed through several numerical tests

Optimal control in ink-jet printing via instantaneous control

This paper concerns the optimal control of a free surface flow with moving contact line, inspired by an application in ink-jet printing. Surface tension, contact angle and wall friction are taken into account by means of the generalized Navier boundary condition. The time-dependent differential system is discretized by an arbitrary Lagrangian-Eulerian finite element method, and a control problem is addressed by an instantaneous control approach, based on the time discretization of the flow equations. The resulting control procedure is computationally highly efficient and its assessment by numerical tests show its effectiveness in deadening the natural oscillations that occur inside the nozzle and reducing significantly the duration of the transient preceding the attainment of the equilibrium configuration

Structure-preserving neural networks in data-driven rheological models

In this paper we address the importance and the impact of employing structure preserving neural networks as surrogate of the analytical physics-based models typically employed to describe the rheology of non-Newtonian fluids in Stokes flows. In particular, we propose and test on real-world scenarios a novel strategy to build data-driven rheological models based on the use of Input-Output Convex Neural Networks (ICNNs), a special class of feedforward neural network scalar valued functions that are convex with respect to their inputs. Moreover, we show, through a detailed campaign of numerical experiments, that the use of ICNNs is of paramount importance to guarantee the well-posedness of the associated non-Newtonian Stokes differential problem. Finally, building upon a novel perturbation result for non-Newtonian Stokes problems, we study the impact of our data-driven ICNN based rheological model on the accuracy of the finite element approximation.Comment: Submitted for publication in the SIAM Journal on Scientific Computing, 22 pages, 7 figures, 7 table

TEEN-IMMIGRANTS EXPLORE A MATH MOBILE APP

We present the pilot phase of the project "Teenagers Experience Empowerment by Numbers" (TEEN), which is funded by Politecnico di Milano through the Polisocial Award 2018 and concerns the development of a mobile app to teach essential mathematics to young immigrants. The project aims at preparing them for living in a conscious, autonomous way in a Western country, increasing their ability to deal with everyday tasks that require some mathematical understanding. We present the app, some materials and an activity with the learners who have interacted with that. The set of tasks, tested in small groups, is rooted in daily activities, such as shopping at the supermarket, choosing a mobile internet plan, planning a trip. Our theoretical background is related to existing research findings on teaching to immigrants, Rabardel’s instrumental orchestration and feedback

Level set-fitted polytopal meshes with application to structural topology optimization

We propose a method to modify a polygonal mesh in order to fit the zero-isoline of a level set function by extending a standard body-fitted strategy to a tessellation with arbitrarily-shaped elements. The novel level set-fitted approach, in combination with a Discontinuous Galerkin finite element approximation, provides an ideal setting to model physical problems characterized by embedded or evolving complex geometries, since it allows skipping any mesh post-processing in terms of grid quality. The proposed methodology is firstly assessed on the linear elasticity equation, by verifying the approximation capability of the level set-fitted approach when dealing with configurations with heterogeneous material properties. Successively, we combine the level set-fitted methodology with a minimum compliance topology optimization technique, in order to deliver optimized layouts exhibiting crisp boundaries and reliable mechanical performances. An extensive numerical test campaign confirms the effectiveness of the proposed method

Polystyrene microplastics exposure modulated the content and the profile of fatty acids in the Cladoceran Daphnia magna

A growing number of studies has shown that the exposure to microplastics (MPs) of different polymeric compositions can induce diverse adverse effects towards several aquatic species. The vast majority of such studies has been focused on the effects induced by the administration of MPs made by polystyrene (PS; hereafter PS-MPs). However, despite the increase in the knowledge on the potential toxicity of PS-MPs, there is a dearth of information concerning their role in affecting energy resources and/or their allocation. The present study aimed at exploring the impact of 21-days expo sure to three concentrations (0.125, 1.25 and 12.5 μg mL−1 ) of PS-MPs of different sizes (1 and 10 μm) on fatty acids (FAs) profile of the freshwater Cladoceran Daphnia magna. The exposure to the highest tested concentration of PS-MPs induced an overall decrease in D. magna total FAs content, independently of the particle size. Moreover, a change in the accumulation of essential FAs by the diet was noted, with an enhanced synthesis of monounsaturated FAs-rich storage lipids. However, a sort of adaptation to counteract the adverse effects and to re-establish the FAs homeostasis was ob served in individuals treated with high PS-MPs concentration, independently of their size. These results indicate that the exposure to PS-MPs could alter the allocation or induce changes in FAs composition in D. magna, with potential long-term consequences on life-history traits of this zooplanktonic species.info:eu-repo/semantics/publishedVersio

Link Prediction in Criminal Networks: A Tool for Criminal Intelligence Analysis

The problem of link prediction has recently received increasing attention from scholars in network science. In social network analysis, one of its aims is to recover missing links, namely connections among actors which are likely to exist but have not been reported because data are incomplete or subject to various types of uncertainty. In the field of criminal investigations, problems of incomplete information are encountered almost by definition, given the obvious anti-detection strategies set up by criminals and the limited investigative resources. In this paper, we work on a specific dataset obtained from a real investigation, and we propose a strategy to identify missing links in a criminal network on the basis of the topological analysis of the links classified as marginal, i.e. removed during the investigation procedure. The main assumption is that missing links should have opposite features with respect to marginal ones. Measures of node similarity turn out to provide the best characterization in this sense. The inspection of the judicial source documents confirms that the predicted links, in most instances, do relate actors with large likelihood of co-participation in illicit activitie

SUIHTER: A new mathematical model for COVID-19. Application to the analysis of the second epidemic outbreak in Italy

The COVID-19 epidemic is the last of a long list of pandemics that have affected humankind in the last century. In this paper, we propose a novel mathematical epidemiological model named SUIHTER from the names of the seven compartments that it comprises: susceptible uninfected individuals (S), undetected (both asymptomatic and symptomatic) infected (U), isolated (I), hospitalized (H), threatened (T), extinct (E), and recovered (R). A suitable parameter calibration that is based on the combined use of least squares method and Markov Chain Monte Carlo (MCMC) method is proposed with the aim of reproducing the past history of the epidemic in Italy, surfaced in late February and still ongoing to date, and of validating SUIHTER in terms of its predicting capabilities. A distinctive feature of the new model is that it allows a one-to-one calibration strategy between the model compartments and the data that are daily made available from the Italian Civil Protection. The new model is then applied to the analysis of the Italian epidemic with emphasis on the second outbreak emerged in Fall 2020. In particular, we show that the epidemiological model SUIHTER can be suitably used in a predictive manner to perform scenario analysis at national level.Comment: 25 page

Phospho-proteomic analysis of mantle cell lymphoma cells suggests a pro-survival role of B-cell receptor signaling

BACKGROUND: Mantle cell lymphoma (MCL) is currently an incurable entity, and new therapeutic approaches are needed. We have applied a high-throughput phospho-proteomic technique to MCL cell lines to identify activated pathways and we have then validated our data in both cell lines and tumor tissues. METHODS: PhosphoScan analysis was performed on MCL cell lines. Results were validated by flow cytometry and western blotting. Functional validation was performed by blocking the most active pathway in MCL cell lines. RESULTS: PhosphoScan identified more than 300 tyrosine-phosporylated proteins, among which many protein kinases. The most abundant peptides belonged to proteins connected with B-cell receptor (BCR) signaling. Active BCR signaling was demonstrated by flow cytometry in MCL cells and by western blotting in MCL tumor tissues. Blocking BCR signaling by Syk inhibitor piceatannol induced dose/time-dependent apoptosis in MCL cell lines, as well as several modifications in the phosphorylation status of BCR pathway members and a collapse of cyclin D1 protein levels. CONCLUSION: Our data support a pro-survival role of BCR signaling in MCL and suggest that this pathway might be a candidate for therapy. Our findings also suggest that Syk activation patterns might be different in MCL compared to other lymphoma subtypes