A method to take account of inhomogeneity in mechanical component reliability calculations

YesThis paper proposes a method by which material inhomogeneity may be taken into account in a reliability calculation. The method employs Monte-Carlo simulation; and introduces a material strength index, and a standard deviation of material strength to model the variation in the strength of a component throughout its volume. The method is compared to conventional load-strength interference theory. The results are identical for the case of homogeneous material, but reliability is shown to reduce for the same load as the component volume increases. The case of a tensile bar is used to explore the variation of reliability with component volume

Recovery of Sparse Signals Using Multiple Orthogonal Least Squares

We study the problem of recovering sparse signals from compressed linear measurements. This problem, often referred to as sparse recovery or sparse reconstruction, has generated a great deal of interest in recent years. To recover the sparse signals, we propose a new method called multiple orthogonal least squares (MOLS), which extends the well-known orthogonal least squares (OLS) algorithm by allowing multiple $L$ indices to be chosen per iteration. Owing to inclusion of multiple support indices in each selection, the MOLS algorithm converges in much fewer iterations and improves the computational efficiency over the conventional OLS algorithm. Theoretical analysis shows that MOLS ($L > 1$) performs exact recovery of all $K$-sparse signals within $K$ iterations if the measurement matrix satisfies the restricted isometry property (RIP) with isometry constant $\delta_{LK} < \frac{\sqrt{L}}{\sqrt{K} + 2 \sqrt{L}}.$ The recovery performance of MOLS in the noisy scenario is also studied. It is shown that stable recovery of sparse signals can be achieved with the MOLS algorithm when the signal-to-noise ratio (SNR) scales linearly with the sparsity level of input signals

Pathogenetic role of tissue factor in graft-versus-host disease

Graft-versus-host disease (GVHD) is a serious complication after allogeneic stem cell transplantation, the mechanism of it is still not elucidated. Recent findings suggest that host endothelial cells are a target of alloreactive donor cytotoxic T lymphocytes in GVHD and tissue factor (TF) plays an important role not only in coagulation-inflammation cycle, but also in transplant immunology. We postulate TF expression in vascular endothelial cells(VEC) may play an pivotal role in the pathogenesis of GVHD. TF gene andprotein expression in target organs of GVHD in aGVHD mice was significantly elevated compared to that of controls as determined by real-time PCR and Western blotting. Allogeneic CD4^+^T cell and CD8^+^T cells enhanced TF, VCAM-1, TNF-[alpha], IFN-[gamma] and IL-6 expression in TNF-[alpha] prestimulated HUVECs compared to controls as determined by flowcytometry and real-time PCR. JNK and p38MAPK mediated allogeneic T cells-induced TF expression in HUVECs. These effects were largely prevented by monoclonal antibody against TF, SB203580 and SP600125. In concert, these data provide strong evidence that upregulated TF expression is related to tissue damage caused by GVHD, TF isthe key factor in GVHD mediated by endothelial cells and allogeneic T cells-induced TF and consecutive proinflammatory cytokines expression in VEC contribute to the pathogenesis of GVHD

Queue-Aware Energy-Efficient Joint Remote Radio Head Activation and Beamforming in Cloud Radio Access Networks

In this paper, we study the stochastic optimization of cloud radio access networks (C-RANs) by joint remote radio head (RRH) activation and beamforming in the downlink. Unlike most previous works that only consider a static optimization framework with full traffic buffers, we formulate a dynamic optimization problem by explicitly considering the effects of random traffic arrivals and time-varying channel fading. The stochastic formulation can quantify the tradeoff between power consumption and queuing delay. Leveraging on the Lyapunov optimization technique, the stochastic optimization problem can be transformed into a per-slot penalized weighted sum rate maximization problem, which is shown to be non-deterministic polynomial-time hard. Based on the equivalence between the penalized weighted sum rate maximization problem and the penalized weighted minimum mean square error (WMMSE) problem, the group sparse beamforming optimization based WMMSE algorithm and the relaxed integer programming based WMMSE algorithm are proposed to efficiently obtain the joint RRH activation and beamforming policy. Both algorithms can converge to a stationary solution with low-complexity and can be implemented in a parallel manner, thus they are highly scalable to large-scale C-RANs. In addition, these two proposed algorithms provide a flexible and efficient means to adjust the power-delay tradeoff on demand.Comment: Accepted by IEEE Transactions on Wireless Communications, 14 pages, 8 figure