scholarly journals A Geometric Framework for the Subfield Problem of Generic Polynomials Via Tschirnhausen Transformation

2009 ◽  
pp. 65-104 ◽  
Author(s):  
Akinari Hoshi ◽  
Katsuya Miyake
Automatica ◽  
2021 ◽  
Vol 128 ◽  
pp. 109494
Author(s):  
Yujendra Bharathi Mitikiri ◽  
Kamran Mohseni

Author(s):  
Benjamin Wassermann ◽  
Nina Korshunova ◽  
Stefan Kollmannsberger ◽  
Ernst Rank ◽  
Gershon Elber

AbstractThis paper proposes an extension of the finite cell method (FCM) to V-rep models, a novel geometric framework for volumetric representations. This combination of an embedded domain approach (FCM) and a new modeling framework (V-rep) forms the basis for an efficient and accurate simulation of mechanical artifacts, which are not only characterized by complex shapes but also by their non-standard interior structure. These types of objects gain more and more interest in the context of the new design opportunities opened by additive manufacturing, in particular when graded or micro-structured material is applied. Two different types of functionally graded materials (FGM) are considered: The first one, multi-material FGM is described using the inherent property of V-rep models to assign different properties throughout the interior of a domain. The second, single-material FGM—which is heterogeneously micro-structured—characterizes the effective material behavior of representative volume elements by homogenization and performs large-scale simulations using the embedded domain approach.


2021 ◽  
Vol 225 (10) ◽  
pp. 106717
Author(s):  
Pierre Dèbes ◽  
Joachim König ◽  
François Legrand ◽  
Danny Neftin
Keyword(s):  

Author(s):  
Ching-An Cheng ◽  
Mustafa Mukadam ◽  
Jan Issac ◽  
Stan Birchfield ◽  
Dieter Fox ◽  
...  
Keyword(s):  

Author(s):  
T. N. Palmer

A new law of physics is proposed, defined on the cosmological scale but with significant implications for the microscale. Motivated by nonlinear dynamical systems theory and black-hole thermodynamics, the Invariant Set Postulate proposes that cosmological states of physical reality belong to a non-computable fractal state-space geometry I , invariant under the action of some subordinate deterministic causal dynamics D I . An exploratory analysis is made of a possible causal realistic framework for quantum physics based on key properties of I . For example, sparseness is used to relate generic counterfactual states to points p ∉ I of unreality, thus providing a geometric basis for the essential contextuality of quantum physics and the role of the abstract Hilbert Space in quantum theory. Also, self-similarity, described in a symbolic setting, provides a possible realistic perspective on the essential role of complex numbers and quaternions in quantum theory. A new interpretation is given to the standard ‘mysteries’ of quantum theory: superposition, measurement, non-locality, emergence of classicality and so on. It is proposed that heterogeneities in the fractal geometry of I are manifestations of the phenomenon of gravity. Since quantum theory is inherently blind to the existence of such state-space geometries, the analysis here suggests that attempts to formulate unified theories of physics within a conventional quantum-theoretic framework are misguided, and that a successful quantum theory of gravity should unify the causal non-Euclidean geometry of space–time with the atemporal fractal geometry of state space. The task is not to make sense of the quantum axioms by heaping more structure, more definitions, more science fiction imagery on top of them, but to throw them away wholesale and start afresh. We should be relentless in asking ourselves: From what deep physical principles might we derive this exquisite structure? These principles should be crisp, they should be compelling. They should stir the soul. Chris Fuchs ( Gilder 2008 , p. 335)


1983 ◽  
Vol 84 (2) ◽  
pp. 441-448 ◽  
Author(s):  
Frank R DeMeyer
Keyword(s):  

Author(s):  
Nicholas A Koemel ◽  
Alistair M Senior ◽  
Hasthi U Dissanayake ◽  
Jason Ross ◽  
Rowena L McMullan ◽  
...  

Abstract Background Maternal nutrition is associated with epigenetic and cardiometabolic risk factors in offspring. Research in humans has primarily focused on assessing the impact of individual nutrients. Objective We sought to assess the collective impact of maternal dietary monounsaturated (MUFA), polyunsaturated (PUFA), and saturated fat (SFA) on epigenetic aging and cardiometabolic risk markers in healthy newborn infants using a geometric framework approach. Design Body fatness (n = 162), aortic intima-media thickness (n = 131), heart rate variability (n = 118), and epigenetic age acceleration (n = 124) were assessed in newborn infants. Maternal dietary intake was cross-sectionally assessed in the immediate postpartum period via a validated 80-item self-administered food-frequency questionnaire. Generalized additive models were used to explore interactive associations of nutrient intake, with results visualized as response surfaces. Results After adjustment for total energy intake, maternal age, gestational age, and sex there was a 3-way interactive association of MUFA, PUFA, and SFA (P = 0.001) with newborn epigenetic aging. This suggests that the nature of each fat class association depends upon one another. Response surfaces revealed MUFA was positively associated with newborn epigenetic age acceleration only at proportionately lower intakes of SFA or PUFA. We also demonstrate a potential beneficial association of omega-3 PUFA with newborn epigenetic age acceleration (P = 0.008). There was no significant association of fat class with newborn aortic intima-media thickness, heart rate variability, or body fatness. Conclusions In this study, we demonstrate an association between maternal dietary fat class composition and epigenetic aging in newborns. Future research should consider other characteristics such as the source of maternal dietary fatty acids.


Sensors ◽  
2018 ◽  
Vol 18 (7) ◽  
pp. 2379
Author(s):  
Ibrahim Salim ◽  
A. Hamza

We present a geometric framework for surface denoising using graph signal processing, which is an emerging field that aims to develop new tools for processing and analyzing graph-structured data. The proposed approach is formulated as a constrained optimization problem whose objective function consists of a fidelity term specified by a noise model and a regularization term associated with prior data. Both terms are weighted by a normalized mesh Laplacian, which is defined in terms of a data-adaptive kernel similarity matrix in conjunction with matrix balancing. Minimizing the objective function reduces it to iteratively solve a sparse system of linear equations via the conjugate gradient method. Extensive experiments on noisy carpal bone surfaces demonstrate the effectiveness of our approach in comparison with existing methods. We perform both qualitative and quantitative comparisons using various evaluation metrics.


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