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Institution

Polytechnic University of Milan

EducationMilan, Italy
About: Polytechnic University of Milan is a education organization based out in Milan, Italy. It is known for research contribution in the topics: Computer science & Finite element method. The organization has 18231 authors who have published 58416 publications receiving 1229711 citations. The organization is also known as: PoliMi & L-NESS.


Papers
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Journal ArticleDOI
TL;DR: In this article, a detailed balance definition for quantum dynamical semigroups is given, and its close connection with the KMS condition is investigated, and detailed balance is discussed.
Abstract: A definition of detailed balance for quantum dynamical semigroups is given, and its close connection with the KMS condition is investigated.

196 citations

Journal ArticleDOI
TL;DR: A systematic literature review on existing Circular Business Models and their classification methods, by selecting the most promising ones to support both companies, researchers and governments in updating the current knowledge on CBMs and make them adoptable for practitioners from different industrial contexts.

196 citations

Journal ArticleDOI
TL;DR: Single-atom exchange series are introduced to extract the individual contributions of halogen bonds and anion–π interactions to the transport of anions across lipid bilayer membranes.
Abstract: Single-atom exchange series are introduced to extract the individual contributions of halogen bonds and anion–π interactions to the transport of anions across lipid bilayer membranes (see picture). Known cation binding sites are used for counterion activation of the neutral calix[4]arene transporters. The experimental evidence for anion transport with halogen bonds is unprecedented.

196 citations

Journal ArticleDOI
TL;DR: In this study the finite element method (FEM) was applied to a new generation coronary stent and matching between the different measurements was quite satisfactory even if some discrepancies were present due to the absence of the balloon in the numerical model.

196 citations

Journal ArticleDOI
TL;DR: The hemodynamics within the aorta of five healthy humans were investigated and group analysis suggested that aortic helical blood flow dynamics is an emerging behavior that is common to normal individuals, and the results suggest that helical flow might be caused by natural optimization of fluid transport processes in the cardiovascular system.
Abstract: The hemodynamics within the aorta of five healthy humans were investigated to gain insight into the complex helical flow patterns that arise from the existence of asymmetries in the aortic region. The adopted approach is aimed at (1) overcoming the relative paucity of quantitative data regarding helical blood flow dynamics in the human aorta and (2) identifying common characteristics in physiological aortic flow topology, in terms of its helical content. Four-dimensional phase-contrast magnetic resonance imaging (4D PC MRI) was combined with algorithms for the calculation of advanced fluid dynamics in this study. These algorithms allowed us to obtain a 4D representation of intra-aortic flow fields and to quantify the aortic helical flow. For our purposes, helicity was used as a measure of the alignment of the velocity and the vorticity. There were two key findings of our study: (1) intra-individual analysis revealed a statistically significant difference in the helical content at different phases of systole and (2) group analysis suggested that aortic helical blood flow dynamics is an emerging behavior that is common to normal individuals. Our results also suggest that helical flow might be caused by natural optimization of fluid transport processes in the cardiovascular system, aimed at obtaining efficient perfusion. The approach here applied to assess in vivo helical blood flow could be the starting point to elucidate the role played by helicity in the generation and decay of rotating flows in the thoracic aorta.

196 citations


Authors

Showing all 18743 results

NameH-indexPapersCitations
Alex J. Barker132127384746
Pierluigi Zotto128119778259
Andrea C. Ferrari126636124533
Marco Dorigo10565791418
Marcello Giroletti10355841565
Luciano Gattinoni10361048055
Luca Benini101145347862
Alberto Sangiovanni-Vincentelli9993445201
Surendra P. Shah9971032832
X. Sunney Xie9822544104
Peter Nijkamp97240750826
Nicola Neri92112241986
Ursula Keller9293433229
A. Rizzi9165340038
Martin J. Blunt8948529225
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023302
2022813
20214,152
20204,301
20193,831
20183,767