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Institution

Polytechnic University of Turin

EducationTurin, Piemonte, Italy
About: Polytechnic University of Turin is a education organization based out in Turin, Piemonte, Italy. It is known for research contribution in the topics: Finite element method & Computer science. The organization has 11553 authors who have published 41395 publications receiving 789320 citations. The organization is also known as: POLITO & Politecnico di Torino.


Papers
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Journal ArticleDOI
TL;DR: This paper presents the fundamental properties of causality, stability, and passivity that electrical interconnect models must satisfy in order to be physically consistent and interpret several common situations where either model derivation or model use in a computer-aided design environment fails dramatically.
Abstract: Modern packaging design requires extensive signal integrity simulations in order to assess the electrical performance of the system. The feasibility of such simulations is granted only when accurate and efficient models are available for all system parts and components having a significant influence on the signals. Unfortunately, model derivation is still a challenging task, despite the extensive research that has been devoted to this topic. In fact, it is a common experience that modeling or simulation tasks sometimes fail, often without a clear understanding of the main reason. This paper presents the fundamental properties of causality, stability, and passivity that electrical interconnect models must satisfy in order to be physically consistent. All basic definitions are reviewed in time domain, Laplace domain, and frequency domain, and all significant interrelations between these properties are outlined. This background material is used to interpret several common situations where either model derivation or model use in a computer-aided design environment fails dramatically. We show that the root cause for these difficulties can always be traced back to the lack of stability, causality, or passivity in the data providing the structure characterization and/or in the model itself.

268 citations

Journal ArticleDOI
TL;DR: In this article, the role of poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS) in the degradation of polymer:PCBM ((6,6)-phenyl C61-butyric acid methyl ester) solar cells was elucidated.

267 citations

Journal ArticleDOI
TL;DR: In this article, the eigenfunctions of the Dirac operator on spheres and real hyperbolic spaces of arbitrary dimension are computed by separating variables in geodesic polar coordinates.

267 citations

Journal ArticleDOI
TL;DR: An innovative procedure is presented that allows the method of moments (MoM) analysis of large and complex antenna and scattering problems at a reduced memory and CPU cost, bounded within the resources provided by a standard (32 bit) personal computer.
Abstract: An innovative procedure is presented that allows the method of moments (MoM) analysis of large and complex antenna and scattering problems at a reduced memory and CPU cost, bounded within the resources provided by a standard (32 bit) personal computer. The method is based on the separation of the overall geometry into smaller portions, called blocks, and on the degrees of freedom of the field. The blocks need not be electrically unconnected. On each block, basis functions are generated with support on the entire block, that are subsequently used as basis functions for the analysis of the complete structure. Only a small number of these functions is required to obtain an accurate solution; therefore, the overall number of unknowns is drastically reduced with consequent impact on storage and solution time. These entire-domain basis functions are called synthetic functions; they are generated from the solution of the electromagnetic problem for the block in isolation, under excitation by suitably defined sources. The synthetic functions are obtained from the responses to all sources via a procedure based on the singular-value decomposition. Because of the strong reduction of the global number of unknowns, one can store the MoM matrix and afford a direct solution. The method is kernel-free, and can be implemented on existing MoM codes.

267 citations

Journal ArticleDOI
TL;DR: It is reported that LKB1 loss results in marked silencing of stimulator of interferon genes (STING) expression and insensitivity to cytoplasmic double-strand DNA (dsDNA) sensing, which is mediated at least in part by hyperactivation of DNMT1 and EZH2 activity related to elevated S-adenylmethionine levels and reinforced byDNMT1 upregulation.
Abstract: KRAS-driven lung cancers frequently inactivate TP53 and/or STK11/LKB1, defining tumor subclasses with emerging clinical relevance. Specifically, KRAS-LKB1 (KL) mutant lung cancers are particularly aggressive, lack PD-L1, and respond poorly to immune checkpoint blockade (ICB). The mechanistic basis for this impaired immunogenicity, despite the overall high mutational load of KRAS mutant lung cancers, remains obscure. Here we report that LKB1 loss results in marked silencing of STING expression and insensitivity to cytoplasmic double strand DNA (dsDNA) sensing. This effect is mediated at least in part by hyperactivation of DNMT1 and EZH2 activity related to elevated S-adenylmethionine (SAM) levels, and reinforced by DNMT1 upregulation. Ectopic expression of STING in KL cells engages IRF3 and STAT1 signaling downstream of TBK1 and impairs cellular fitness, due to the pathologic accumulation of cytoplasmic mitochondrial dsDNA associated with mitochondrial dysfunction. Thus, silencing of STING avoids these negative consequences of LKB1 inactivation, while facilitating immune escape.

267 citations


Authors

Showing all 11854 results

NameH-indexPapersCitations
Rodney S. Ruoff164666194902
Silvia Bordiga10749841413
Sergio Ferrara10572644507
Enrico Rossi10360641255
Stefano Passerini10277139119
James Barber10264242397
Markus J. Buehler9560933054
Dario Farina9483232786
Gabriel G. Katul9150634088
M. De Laurentis8427554727
Giuseppe Caire8282540344
Christophe Fraser7626429250
Erasmo Carrera7582923981
Andrea Califano7530531348
Massimo Inguscio7442721507
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023210
2022487
20212,789
20202,969
20192,779
20182,509