•A meta-modeling approach is formulated for frequency response variation prediction.•New composite neural network is constructed to facilitate multi-fidelity data fusion.•Low fidelity data is ...integrated with high-fidelity data to reduce cost.•Case studies demonstrate the high accuracy and efficiency of the new approach.
Uncertainties in a structure is inevitable, which generally lead to variation in dynamic response predictions. For a complex structure, brute force Monte Carlo simulation for response variation analysis is infeasible since one single run may already be computationally costly. Data driven meta-modeling approaches have thus been explored to facilitate efficient emulation and statistical inference. The performance of a meta-model hinges upon both the quality and quantity of training dataset. In actual practice, however, high-fidelity data acquired from high-dimensional finite element simulation or experiment are generally scarce, which poses significant challenge to meta-model establishment. In this research, we take advantage of the multi-level response prediction opportunity in structural dynamic analysis, i.e., acquiring rapidly a large amount of low-fidelity data from reduced-order modeling, and acquiring accurately a small amount of high-fidelity data from full-scale finite element analysis. Specifically, we formulate a composite neural network fusion approach that can fully utilize the multi-level, heterogeneous datasets obtained. It implicitly identifies the correlation of the low- and high-fidelity datasets, which yields improved accuracy when compared with the state-of-the-art. Comprehensive investigations using frequency response variation characterization as case example are carried out to demonstrate the performance.
Aims/hypothesis Our recent studies suggest that activation of the wingless-type MMTV integration site (WNT) pathway plays pathogenic roles in diabetic retinopathy and age-related macular ...degeneration. Here we investigated the causative role of oxidative stress in retinal WNT pathway activation in an experimental model of diabetes. Methods Cultured retinal pigment epithelial cells and retinal capillary endothelial cells were treated with a lipid peroxidation product, 4-hydroxynonenal (HNE), and an antioxidant, N-acetyl-cysteine (NAC). In vivo, rats with streptozotocin-induced diabetes were treated by NAC for 8 weeks. Activation of the canonical WNT pathway was measured by TOPFLASH assay and by western blot analysis of WNT pathway components and a WNT target gene, Ctgf. Oxidative stress in the retina was evaluated by immunostaining of HNE and 3-nitrotyrosine. Results Levels of phosphorylated and total LDL receptor-related protein (LRP)6, and cytosolic β-catenin, as well as transcriptional activity of T cell factor (TCF)/β-catenin were significantly increased by HNE. The production of connective tissue growth factor (CTGF) was also upregulated by HNE. NAC blocked the WNT pathway activation induced by HNE. Furthermore, LRP6 stability was increased by HNE and decreased by NAC. Retinal levels of HNE and 3-nitrotyrosine were significantly increased in diabetic rats, compared with those in non-diabetic rats. In the same diabetic rat retinas, levels of LRP6, cytosolic β-catenin and CTGF were significantly increased. NAC treatment reduced HNE and 3-nitrotyrosine levels and attenuated the upregulation of LRP6, β-catenin and CTGF in diabetic rat retina. Conclusions/interpretation Lipid peroxidation products activate the canonical WNT pathway through oxidative stress, which plays an important role in the development of retinal diseases.
A probabilistic framework for efficient uncertainty quantification in structural dynamic analysis is presented. This framework is built upon the combination of two-level Gaussian processes emulator ...and Bayesian inference technique. The underlying idea is to employ the two-level Gaussian processes emulator to integrate together small amount of high-fidelity data from full-scale finite element analysis and large amount of low-fidelity data from order-reduced analysis to improve the response variation prediction. As component mode synthesis (CMS) is adopted in order-reduced modeling, we then utilize the improved response variation prediction on modal characteristics to update the CMS model to facilitate the efficient probabilistic analysis of any responses of concern. The effectiveness of this framework is demonstrated through systematic case studies.
Abstract
We present different methods of unsupervised learning which can be used for outlier detection in high energy nuclear collisions. This method is of particular interest for heavy ion ...collisions where a direct comparison of experimental data to model simulations is often ambiguous and it is not easy to determine whether an observation is due to new physics, an incomplete understanding of the known physics or an experimental artefact. The UrQMD model is used to generate the bulk background of events as well as different variants of outlier events which may result from misidentified centrality or detector malfunctions. The methods presented here can be generalized to different and novel physics effects. To detect the outliers, dimensional reduction algorithms are implemented, speciftically the Principle Component Analysis (PCA) and Autoencoders (AEN). We find that mainly the reconstruction error is a good measure to distinguish outliers from background. The performance of the algorithms is compared using a ROC curve. It is shown that the number of reduced (encoded) dimensions to describe a single event contributes significantly to the performance of the outlier detection task. We find that the model which is best suited to separate outlier events requires a good performance in reconstructing events and at the same time a small number of parameters.
In this trial in patients with relapsed CLL, progression-free survival at 2 years was 78% with zanubrutinib and 66% with ibrutinib. Infections were common with both; cardiac events were less frequent ...with zanubrutinib.
While piezoelectric impedance/admittance measurements have been used for fault detection and identification, the actual identification of fault location and severity remains to be a challenging ...topic. On one hand, the approach that uses these measurements entertains high detection sensitivity owing to the high-frequency actuation/sensing nature. On the other hand, high-frequency analysis requires high dimensionality in the model and the subsequent inverse analysis contains a very large number of unknowns which often renders the identification problem under-determined. A new fault identification algorithm is developed in this research for piezoelectric impedance/admittance based measurement. Taking advantage of the algebraic relation between the sensitivity matrix and the admittance change measurement, we devise a pre-screening scheme that can rank the likelihoods of fault locations with estimated fault severity levels, which drastically reduces the fault parameter space. A Bayesian inference approach is then incorporated to pinpoint the fault location and severity with high computational efficiency. The proposed approach is examined and validated through case studies.
When viewed as an elementary particle, the electron has spin and charge. When binding to the atomic nucleus, it also acquires an angular momentum quantum number corresponding to the quantized atomic ...orbital it occupies. Even if electrons in solids form bands and delocalize from the nuclei, in Mott insulators they retain their three fundamental quantum numbers: spin, charge and orbital. The hallmark of one-dimensional physics is a breaking up of the elementary electron into its separate degrees of freedom. The separation of the electron into independent quasi-particles that carry either spin (spinons) or charge (holons) was first observed fifteen years ago. Here we report observation of the separation of the orbital degree of freedom (orbiton) using resonant inelastic X-ray scattering on the one-dimensional Mott insulator Sr2CuO3. We resolve an orbiton separating itself from spinons and propagating through the lattice as a distinct quasi-particle with a substantial dispersion in energy over momentum, of about 0.2 electronvolts, over nearly one Brillouin zone.
Celotno besedilo
Dostopno za:
DOBA, IJS, IZUM, KILJ, KISLJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Obesity and extracellular matrix (ECM) density are considered independent risk and prognostic factors for breast cancer. Whether they are functionally linked is uncertain. We investigated the ...hypothesis that obesity enhances local myofibroblast content in mammary adipose tissue and that these stromal changes increase malignant potential by enhancing interstitial ECM stiffness. Indeed, mammary fat of both diet- and genetically induced mouse models of obesity were enriched for myofibroblasts and stiffness-promoting ECM components. These differences were related to varied adipose stromal cell (ASC) characteristics because ASCs isolated from obese mice contained more myofibroblasts and deposited denser and stiffer ECMs relative to ASCs from lean control mice. Accordingly, decellularized matrices from obese ASCs stimulated mechanosignaling and thereby the malignant potential of breast cancer cells. Finally, the clinical relevance and translational potential of our findings were supported by analysis of patient specimens and the observation that caloric restriction in a mouse model reduces myofibroblast content in mammary fat. Collectively, these findings suggest that obesity-induced interstitial fibrosis promotes breast tumorigenesis by altering mammary ECM mechanics with important potential implications for anticancer therapies.
The crystal plane of ceria plays an essential role in determining its catalytic oxidation properties. In this study, single-crystalline CeO
2 nanorods with well-defined crystal planes have been ...synthesized by a facile solution-based hydrothermal method. HRTEM studies reveal that the predominantly exposed planes are the unusually reactive {001} and {110} in the CeO
2 nanorods rather than the stable {111} in the irregular nanoparticles. Consequently, it is demonstrated that the CeO
2 nanorods are more reactive for CO oxidation than their counterparts, irregular nanoparticles. The present results indicate that catalysts with well-defined reactive sites may be “designed” because of the recent development of morphology-controlled synthesis of nanostructured materials.
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