Investigation of metal flow in bridge die extrusion of Alloy 6063 and subsequent effect on surface quality and weld seam integrity

This paper describes a detailed study of tube extrusion by simulation using finite element method (FEM). The finite element model used one-sixth of symmetry. The extrusion load, emperature evolution and metal flow were predicted. Innovative methods, combining both grid and surface tools, were used to define in detail the flow of material. These showed clearly the inner and outer surface formation mechanisms of the tube extrusion. The seam weld, an important quality indicator, was also evaluated by selecting an appropriate criterion

Failure Probabilities and Tough-Brittle Crossover of Heterogeneous Materials with Continuous Disorder

The failure probabilities or the strength distributions of heterogeneous 1D systems with continuous local strength distribution and local load sharing have been studied using a simple, exact, recursive method. The fracture behavior depends on the local bond-strength distribution, the system size, and the applied stress, and crossovers occur as system size or stress changes. In the brittle region, systems with continuous disorders have a failure probability of the modified-Gumbel form, similar to that for systems with percolation disorder. The modified-Gumbel form is of special significance in weak-stress situations. This new recursive method has also been generalized to calculate exactly the failure probabilities under various boundary conditions, thereby illustrating the important effect of surfaces in the fracture process.Comment: 9 pages, revtex, 7 figure

Remineralization of demineralized dentin using a dual analog system.

ObjectiveImproved methods are needed to remineralize dentin caries in order to promote conservation of dentin tissue and minimize the surgical interventions that are currently required for clinical treatment. Here, we test the hypothesis that bulk substrates can be effectively mineralized via a dual analog system proposed by others, using a tripolyphosphate (TPP) "templating analog" and a poly(acrylic acid) (PAA) or poly(aspartic acid) (pAsp) "sequestration analog," the latter of which generates the polymer-induced liquid-precursor (PILP) mineralization process studied in our laboratory.Material & methodsDemineralized human dentin slices were remineralized with and without pre-treatment with TPP, using either PAA or pAsp as the PILP process-directing agent. A control experiment with no polymer present was used for comparison.ResultsNo mineralization was observed in any of the PAA groups. In both the pAsp and no polymer groups, TPP inhibited mineralization on the surfaces of the specimens but promoted mineralization within the interiors. Pre-treatment with TPP enhanced overall mineralization of the pAsp group. However, when analysed via TEM, regions with little mineral were still present.ConclusionPoly(acrylic acid) was unable to remineralize demineralized dentin slices under the conditions employed, even when pre-treated with TPP. However, pre-treatment with TPP enhanced overall mineralization of specimens that were PILP-remineralized using pAsp

Ab initio calculation of intrinsic spin Hall effect in semiconductors

Relativistic band theoretical calculations reveal that intrinsic spin Hall conductivity in hole-doped archetypical semiconductors Ge, GaAs and AlAs is large $[\sim 100 (\hbar/e)(\Omega cm)^{-1}]$, showing the possibility of spin Hall effect beyond the four band Luttinger Hamiltonian. The calculated orbital-angular-momentum (orbital) Hall conductivity is one order of magnitude smaller, indicating no cancellation between the spin and orbital Hall effects in bulk semiconductors. Furthermore, it is found that the spin Hall effect can be strongly manipulated by strains, and that the $ac$ spin Hall conductivity in the semiconductors is large in pure as well as doped semiconductors.Comment: Phys. Rev. Lett. (accepted

Kernel Density Estimation for Undirected Dyadic Data

We study nonparametric estimation of density functions for undirected dyadic random variables (i.e., random variables defined for all n\overset{def}{\equiv}\tbinom{N}{2} unordered pairs of agents/nodes in a weighted network of order N). These random variables satisfy a local dependence property: any random variables in the network that share one or two indices may be dependent, while those sharing no indices in common are independent. In this setting, we show that density functions may be estimated by an application of the kernel estimation method of Rosenblatt (1956) and Parzen (1962). We suggest an estimate of their asymptotic variances inspired by a combination of (i) Newey's (1994) method of variance estimation for kernel estimators in the "monadic" setting and (ii) a variance estimator for the (estimated) density of a simple network first suggested by Holland and Leinhardt (1976). More unusual are the rates of convergence and asymptotic (normal) distributions of our dyadic density estimates. Specifically, we show that they converge at the same rate as the (unconditional) dyadic sample mean: the square root of the number, N, of nodes. This differs from the results for nonparametric estimation of densities and regression functions for monadic data, which generally have a slower rate of convergence than their corresponding sample mean.Comment: 31 page

Minimax Risk and Uniform Convergence Rates for Nonparametric Dyadic Regression

Let $i=1,\ldots,N$ index a simple random sample of units drawn from some large population. For each unit we observe the vector of regressors $X_{i}$ and, for each of the $N\left(N-1\right)$ ordered pairs of units, an outcome $Y_{ij}$. The outcomes $Y_{ij}$ and $Y_{kl}$ are independent if their indices are disjoint, but dependent otherwise (i.e., "dyadically dependent"). Let $W_{ij}=\left(X_{i}',X_{j}'\right)'$; using the sampled data we seek to construct a nonparametric estimate of the mean regression function $g\left(W_{ij}\right)\overset{def}{\equiv}\mathbb{E}\left[\left.Y_{ij}\right|X_{i},X_{j}\right].$ We present two sets of results. First, we calculate lower bounds on the minimax risk for estimating the regression function at (i) a point and (ii) under the infinity norm. Second, we calculate (i) pointwise and (ii) uniform convergence rates for the dyadic analog of the familiar Nadaraya-Watson (NW) kernel regression estimator. We show that the NW kernel regression estimator achieves the optimal rates suggested by our risk bounds when an appropriate bandwidth sequence is chosen. This optimal rate differs from the one available under iid data: the effective sample size is smaller and $d_W=\mathrm{dim}(W_{ij})$ influences the rate differently.Comment: 28 page

Concept coupling learning for improving concept lattice-based document retrieval

© 2017 Elsevier Ltd The semantic information in any document collection is critical for query understanding in information retrieval. Existing concept lattice-based retrieval systems mainly rely on the partial order relation of formal concepts to index documents. However, the methods used by these systems often ignore the explicit semantic information between the formal concepts extracted from the collection. In this paper, a concept coupling relationship analysis model is proposed to learn and aggregate the intra- and inter-concept coupling relationships. The intra-concept coupling relationship employs the common terms of formal concepts to describe the explicit semantics of formal concepts. The inter-concept coupling relationship adopts the partial order relation of formal concepts to capture the implicit dependency of formal concepts. Based on the concept coupling relationship analysis model, we propose a concept lattice-based retrieval framework. This framework represents user queries and documents in a concept space based on fuzzy formal concept analysis, utilizes a concept lattice as a semantic index to organize documents, and ranks documents with respect to the learned concept coupling relationships. Experiments are performed on the text collections acquired from the SMART information retrieval system. Compared with classic concept lattice-based retrieval methods, our proposed method achieves at least 9%, 8% and 15% improvement in terms of average MAP, IAP@11 and P@10 respectively on all the collections

Quantized Adiabatic Charge Transport in a Carbon Nanotube

The coupling of a metallic Carbon nanotube to a surface acoustic wave (SAW) is proposed as a vehicle to realize quantized adiabatic charge transport in a Luttinger liquid system. We demonstrate that electron backscattering by a periodic SAW potential, which results in miniband formation, can be achieved at energies near the Fermi level. Electron interaction, treated in a Luttinger liquid framework, is shown to enhance minigaps and thereby improve current quantization. Quantized SAW induced current, as a function of electron density, changes sign at half-filling.Comment: 5 pages, 2 figure