202 research outputs found
Life Extension Factor Klotho Prevents Mortality and Enhances Cognition in hAPP Transgenic Mice
No full textAging is the principal demographic risk factor for Alzheimer disease (AD), the most common neurodegenerative disorder. Klotho is a key modulator of the aging process and, when overexpressed, extends mammalian lifespan, increases synaptic plasticity, and enhances cognition. Whether klotho can counteract deficits related to neurodegenerative diseases, such as AD, is unknown. Here we show that elevating klotho expression decreases premature mortality and network dysfunction in human amyloid precursor protein (hAPP) transgenic mice, which simulate key aspects of AD. Increasing klotho levels prevented depletion of NMDA receptor (NMDAR) subunits in the hippocampus and enhanced spatial learning and memory in hAPP mice. Klotho elevation in hAPP mice increased the abundance of the GluN2B subunit of NMDAR in postsynaptic densities and NMDAR-dependent long-term potentiation, which is critical for learning and memory. Thus, increasing wild-type klotho levels or activities improves synaptic and cognitive functions, and may be of therapeutic benefit in AD and other cognitive disorders
Numerical Evaluation and Optimization of the Mechanical Properties of Particle Reinforced Composites
No full textIn today’s world, composite materials form the bases of many products, ranging from simple’ packaging material to the parts of a wind turbine. Even though computational methods have entered the modern product development cycle, material development has largely stayed unchanged with the use of personal and energy intensive trial and error approaches. This traditional method necessitates the manufacturing of multiple specimens for physical testing, resulting in a long development time while also generating a lot of waste in the process. Numerical methods such as Finite Element Analysis (FEA) in combination with optimization methods can act as an alternative pathway to shrink the development time of a novel composite and reduce the wastage of materials, time and money.
The goal of this work was to develop a numerical method for particle reinforced composites that generates microstructures with targeted effective material properties intended for a specific application. These requirements stated during the development process alongside with a set of constraints are utilized for the optimization algorithm to generate an optimized microstructure, drawing upon a data bank for the individual material phases.
Digital twins of particles encountered during the research are obtained by use of analytical functions such as Spherical Harmonics, Super Ellipsoids or other equations, which are in the following called ‘exact’ particles. Numerical studies based on FEA were conducted on representative volume elements (RVE) of particle reinforced composites to obtain effective elastic properties The results of the FEA calculations for spherical particles were then compared with results obtained with the use of micromechanical models, such as the Mori-Tanaka scheme, Dilute inclusion. Evaluations of the effect of the particle distribution on the elastic properties of the composite were studied, comparing a homogeneous particle distribution with two distinctly different particle cluster distributions. A numerical surrogate model was developed to approximate the effective elastic properties of the composite with ‘exact’ particles. This method is intended to reduce the computational effort such as calculation time and RAM requirements of evaluating the composites effective elastic material properties in comparison to RVE containing the surrogates’ ‘exact’ particle counterpart. This simplification makes it viable to explore different combinations of particle shapes for different matrix materials. Heuristic optimization methods such as Simulated Annealing, Genetic Algorithm and Particle Swarm Optimization (PSO) were explored for finding an optimal material combination to achieve the targeted effective material properties of the composite. For this a function was derived to obtain the optimal material mixture of the composite, which achieves the targeted effective material properties of the compound. The different heuristic methods were compared according to their numerical stability during optimization and the PSO method was chosen. Numerical methods to generate and evaluate conductive particle reinforced polymer matrix composites were explored, which can utilize a material mixture of two particle shapes such as disc-shaped and line-shaped in a polymer matrix. Lastly the application of machine learning methods such as feed forward neural networks were explored to enable a swift quantification of all the possible solutions with regards to different particle and matrix materials and provide a material mixture which achieves the targeted effective elastic properties
ASYMPTOTICS OF TWISTED ALEXANDER POLYNOMIALS AND HYPERBOLIC VOLUME
No full textFor a hyperbolic knot and a natural number n, we consider the Alexander polynomial twisted by the n-th symmetric power of a lift of the holonomy. We establish the asymptotic behavior of these twisted Alexander polynomials evaluated at unit complex numbers, yielding the volume of the knot exterior. More generally, we prove the asymptotic behavior for cusped hyperbolic manifolds of finite volume. The proof relies on results of Müller, and Menal-Ferrer and the last author. Using the uniformity of the convergence, we also deduce a similar asymptotic result for the Mahler measures of those polynomials
Correlation between surface-to-volume ratio of the particle shape and elastic properties of the particulate composites
No full textThis work is motivated by real-world particle shapes, observed using a scanning electron microscopy. The focus of the presented studies was to understand the influence of the particle shapes on the effective elastic properties of the two-phase composites. For this, particles with polyhedral, undulated and other shapes were numerically modeled using analytical functions. Creation of some shapes, like polyhedral, are known from the literature but Laplace’s spherical harmonics, as well as the Goursat’s surface and some others, were used for the first time to create novel particle shapes. Elastic properties of the composites with different particle shapes were calculated using the finite element analysis. The obtained results show good agreement with mean-field homogenization methods such like Mori-Tanaka and Lielens as well as other numerical results available in the literature. Further, the dependence of the effective Young’s moduli of the composite on the shape and the corresponding surface-to-volume ratio of the particles was studied. It was observed that the effective Young’s moduli increase with the surface-to-volume ratio of the particles in the case where particles are stiffer in comparison to the matrix. It was also remarked that, in the case of particles of similar shapes, the particle surface-to-volume ratio and the effective Young’s moduli differ significantly with the surface curvature and the edge sharpness of the particles
Measurement of the D+/- production asymmetry in 7 TeV pp collisions
No full textThe asymmetry in the production cross-section \sigma of D+/- mesons, A_P = (\sigma(D+) - \sigma(D-))/(\sigma(D+) + \sigma(D-)), is measured in bins of pseudorapidity \eta and transverse momentum p_T within the acceptance of the LHCb detector. The result is obtained with a sample of D+ -> K_S pi+ decays corresponding to an integrated luminosity of 1.0 fb^-1, collected in pp collisions at a centre of mass energy of 7 TeV at the Large Hadron Collider. When integrated over the kinematic range 2.0 K_S pi+ decay is negligible. No significant dependence on \eta or p_T is observed
Measurement of the time-dependent CP asymmetry in B0 -> J/ψ KS0 decays
No full textThis Letter reports a measurement of the CP violation observables SJ/ψK0S and CJ/ψK0S in the decay channel B0→J/ψK0S performed with 1.0 fb−1 of pp collisions at s√=7 TeV collected by the LHCb experiment. The fit to the data yields SJ/ψK0S=0.73±0.07(stat)±0.04(syst) and CJ/ψK0S=0.03±0.09(stat)±0.01(syst). Both values are consistent with the current world averages and within
expectations from the Standard Model
On application of a surrogate model to numerical evaluation of effective elastic properties of composites with 3D rotationally symmetric particles
No full textMicromechanical modelling of particulate composites with non-ellipsoidal particle shapes presents significant challenges because analytical approaches based on the fundamental results of Eshelby cannot be used. On the other side, direct numerical evaluations by finite element analysis can involve high computational cost in the case when particle features have small radius of curvature, sharp edges and require extremely fine meshes. This paper proposes substituting the exact particle shape with a surrogate model producing approximately the same contribution to the effective elastic moduli. We illustrate our approach by considering rotationally symmetric 3D particle shapes with the external surface defined by the Laplace's spherical harmonics. In this case, spherical layered surrogates offer good accuracy of approximation, especially when the material parameters of each layer are determined by the particle swarm optimization algorithm. The proposed approach is presented by considering several highly undulated particle shapes and comparing the surrogate model results with direct finite element simulations of the original microstructure
First observation of Bs → J/ψf0(980) decays
No full textUsing data collected with the LHCb detector in proton–proton collisions at a centre-of-mass energy of 7 TeV, the hadronic decay is observed. This CP eigenstate mode could be used to measure mixing-induced CP violation in the system. Using a fit to the π+π− mass spectrum with interfering resonances gives . In the interval ±90 MeV around 980 MeV, corresponding to approximately two full f0 widths we also find , where in both cases the uncertainties are statistical and systematic, respectively
On the Calogero-Moser space associated with dihedral groups
No full textInternational audienceUsing the geometry of the associated Calogero-Moser space, R. Rouquier and the author have attached to any finite complex reflection group several notions (Calogero-Moser left, right or two-sided cells, Calogero-Moser cellular characters), completing the notion of Calogero-Moser families defined by Gordon. If moreover is a Coxeter group, they conjectured that these notions coincide with the analogous notions defined using the Hecke algebra by Kazhdan and Lusztig (or Lusztig in the unequal parameters case). In the present paper, we aim to investigate these conjectures whenever is a dihedral group
Semi-Supervised Image Classification based on a Multi-Feature Image Query Language
No full textThe area of Content-Based Image Retrieval (CBIR) deals with a wide range of research disciplines. Being closely related to text retrieval and pattern recognition, the probably most serious issue to be solved is the so-called \semantic gap". Except for very restricted use-cases, machines are not able to recognize the semantic content of digital images as well as humans.
This thesis identifies the requirements for a crucial part of CBIR user interfaces, a multimedia-enabled query language. Such a language must be able to capture the user's
intentions and translate them into a machine-understandable format. An approach to tackle this translation problem is to express high-level semantics by merging low-level image features. Two related methods are improved for either fast (retrieval) or accurate(categorization) merging.
A query language has previously been developed by the author of this thesis. It allows the formation of nested Boolean queries. Each query term may be text- or content-based and the system merges them into a single result set. The language is extensible by arbitrary new feature vector plug-ins and thus use-case independent.
This query language should be capable of mapping semantics to features by applying machine learning techniques; this capability is explored. A supervised learning algorithm based on decision trees is used to build category descriptors from a training set. Each resulting \query descriptor" is a feature-based description of a concept which is comprehensible and modifiable. These descriptors could be used as a normal query and return a result set with a high CBIR based precision/recall of the desired category. Additionally, a method for normalizing the similarity profiles of feature vectors has been
developed which is essential to perform categorization tasks.
To prove the capabilities of such queries, the outcome of a semi-supervised training session with \leave-one-object-out" cross validation is compared to a reference system. Recent work indicates that the discriminative power of the query-based descriptors is similar and is likely to be improved further by implementing more recent feature vectors
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