X-ray Temperatures, Luminosities, and Masses From XMM-Newton Follow-up of the First Shear-selected Galaxy Cluster Sample

We continue the study of the first sample of shear-selected clusters (Wittman et al. 2006) from the initial 8.6 square degrees of the Deep Lens Survey (DLS, Wittman et al. 2002); a sample with well-defined selection criteria corresponding to the highest ranked shear peaks in the survey area. We aim to characterize the weak lensing selection by examining the sample's X-ray properties. There are multiple X-ray clusters associated with nearly all the shear peaks: 14 X-ray clusters corresponding to seven DLS shear peaks. An additional three X-ray clusters cannot be definitively associated with shear peaks, mainly due to large positional offsets between the X-ray centroid and the shear peak. Here we report on the X-ray properties of the 17 X-ray clusters. The X-ray clusters display a wide range of luminosities and temperatures; the Lx-Tx relation we determine for the shear-associated X-ray clusters is consistent with X-ray cluster samples selected without regard to dynamical state, while it is inconsistent with self-similarity. For a subset of the sample, we measure X-ray masses using temperature as a proxy, and compare to weak lensing masses determined by the DLS team (Abate et al. 2009; Wittman et al. 2014). The resulting mass comparison is consistent with equality. The X-ray and weak lensing masses show considerable intrinsic scatter (~48%), which is consistent with X-ray selected samples when their X-ray and weak lensing masses are independently determined.Comment: 14 pages, 4 figure

M\"{o}ller and Bhabha scattering in the noncommutative standard model

We study the M\"{o}ller and Bhabha scattering in the noncommutative extension of the standard model(SM) using the Seiberg-Witten maps of this to first order of the noncommutative parameter $\theta_{\mu \nu}$. We look at the angular distribution $d\sigma/d\Omega$ to explore the noncommutativity of space-time at around $\Lambda_{NC} \sim$ TeV and find that the distribution deviates significantly from the one obtained from the commutative version of the standard model.Comment: 15 pages, 14 eps figures.Text is modified a little and version to appear in Phys.Rev.

Why my photos look sideways or upside down? Detecting Canonical Orientation of Images using Convolutional Neural Networks

Image orientation detection requires high-level scene understanding. Humans use object recognition and contextual scene information to correctly orient images. In literature, the problem of image orientation detection is mostly confronted by using low-level vision features, while some approaches incorporate few easily detectable semantic cues to gain minor improvements. The vast amount of semantic content in images makes orientation detection challenging, and therefore there is a large semantic gap between existing methods and human behavior. Also, existing methods in literature report highly discrepant detection rates, which is mainly due to large differences in datasets and limited variety of test images used for evaluation. In this work, for the first time, we leverage the power of deep learning and adapt pre-trained convolutional neural networks using largest training dataset to-date for the image orientation detection task. An extensive evaluation of our model on different public datasets shows that it remarkably generalizes to correctly orient a large set of unconstrained images; it also significantly outperforms the state-of-the-art and achieves accuracy very close to that of humans

New targets for resolution of airway remodeling in obstructive lung diseases.

Airway remodeling (AR) is a progressive pathological feature of the obstructive lung diseases, including asthma and chronic obstructive pulmonary disease (COPD). The pathology manifests itself in the form of significant, progressive, and (to date) seemingly irreversible changes to distinct respiratory structural compartments. Consequently, AR correlates with disease severity and the gradual decline in pulmonary function associated with asthma and COPD. Although current asthma/COPD drugs manage airway contraction and inflammation, none of these effectively prevent or reverse features of AR. In this review, we provide a brief overview of the features and putative mechanisms affecting AR. We further discuss recently proposed strategies with promise for deterring or treating AR

Effects of submergence and test startup conditions on local scour by plane turbulent wall jets.

Experiments were carried out to study the interaction of plane turbulent wall jets with cohesionless soils during scour. The jet of water having a constant mean velocity of 1.16 m/s and thickness of 25.4 mm at the nozzle exit was set to initially flow tangentially along the sand bed with a median grain size of 2.15 mm. Both scour profile measurements and velocity measurements were obtained for a range of submergences, defined by the ratio of the tailwater depth to the nozzle opening. The results confirm the presence of two distinct types of flow fields, one that occurs at lower submergences and the other at higher submergences of the jet. At low submergences, laser Doppler anemometer measurements indicate that the jet flow is initially close to the bed and then flicks towards the water surface, whereas at the higher submergences, no such flicking movement was noticed. At higher submergences, however, the jet impingement point on the sand bed was highly unsteady. Low pass filtering of the velocity data give further details of these processes. It is seen that the movement of the impingement point for high submergences is more rapid with decreasing submergence. Furthermore, variations in scour and velocity profiles were noted in the mound region across the flume cross-section, and were found to be dependent on the level of submergence. In an effort to clarify some of the differences noted in the scour characteristics in earlier studies, the test startup conditions were varied. This includes an instantaneous, a gradual and a stepwise startup condition. The velocity measurements indicate that the flow gradually evolves to a state that is independent of the startup conditions. However, the scour profiles appear to be dependent on the startup conditions for a longer period of time.Dept. of Civil and Environmental Engineering. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2004 .D47. Source: Masters Abstracts International, Volume: 43-03, page: 0915. Advisers: Ram Balachandar; K. Mazurek. Thesis (M.A.Sc.)--University of Windsor (Canada), 2004

MOLECULAR MODELING OF HIGH-PERFORMANCE THERMOSET POLYMER MATRIX COMPOSITES FOR AEROSPACE APPLICATIONS

The global efforts from major space agencies to transport humans to Mars will require a novel lightweight and ultra-high strength material for the spacecraft structure. Three decades of research with the carbon nanotubes (CNTs) have proved that the material can be an ideal candidate for the composite reinforcement if certain shortcomings are overcome. Also, the rapid development of the polymer resin industry has introduced a wide range of high-performance resins that show high compatibility with the graphitic surface of the CNTs. This research explores the computational design of these materials and evaluates their efficacy as the next generation of aerospace structural materials. Process-induced residual stresses are a commonly observed phenomenon in composite structures during the manufacturing process. These are generated because of resin shrinkage and relative thermal contraction between the resin and reinforcement during the curing process. Experimental or computational characterization of these stresses can be a challenge due to their complex nature. Predictive models of the curing process require detailed knowledge of the resin thermo-mechanical property evolution during the cure. Molecular Dynamics (MD) is implemented to predict the resin properties of EPON 828-Jeffamine D230 as a function of the crosslink density at room temperature. The molecular models are developed using the Reactive Interface Forcefield (IFF-R). The physical, mechanical, and thermal properties are validated experimentally and using the literature data. The predicted progression of resin properties indicates that each property evolves distinctively. The next generation of ultra-high strength composites for structural components of vehicles for crewed missions to deep space will incorporate flattened carbon nanotubes (flCNTs). With a wide range of high-performance polymers to choose from as the matrix component, efficient and accurate computational modeling can be used to efficiently down-select compatible resins, drive the design of these composites by predicting interface behavior, and provide critical physical insight into the flCNT/polymer interface. In this study, molecular dynamics simulation is used to predict the interaction energy, frictional sliding resistance, and mechanical binding of flCNT/polymer interfaces for a high-performance epoxy resin. The results, when compared to the sister studies, indicate that the BMI has stronger interfacial interaction and transverse tension binding with flCNT interfaces, while the benzoxazine demonstrates the strongest levels of interfacial friction resistance. Epoxy dwells in the “Goldilocks” zone with neither superior nor inferior properties. Comparison of these results indicate that BMI demonstrates the best overall compatibility with flCNTs for use in high-performance structural composites. One critical factor limiting the potential of carbon-based composites in aerospace applications is the poor load transferability between the reinforcement and the polymer matrix, which arises due to low interfacial shear strength at molecular scale. Molecular dynamics (MD) simulations have been employed in several studies that investigate the interface, such simulations are computationally expensive. To efficiently explore and optimize the interfacial design space with the goal of improving the mechanical performance, it is important to develop a machine learning (ML) approach that can be used to assist in the identification of optimal combinations of interface variables. In this study, a MD-ML workflow is proposed to predict optimal functionalization strategies for a bismaleimide (BMI) and three-layer graphene nanoplatelet (GNP) nanocomposite with maximized interfacial shear strength. In turn, these predictions of pull-out force will be used to identify optimal surface functionalizations that maximize the pull-out force. The details on the MD modeling and training data generation for the ML model are discussed in this work