Axial-flexural coupled vibration and buckling of composite beams using sinusoidal shear deformation theory

A finite element model based on sinusoidal shear deformation theory is developed to study vibration and buckling analysis of composite beams with arbitrary lay-ups. This theory satisfies the zero traction boundary conditions on the top and bottom surfaces of beam without using shear correction factors. Besides, it has strong similarity with Euler–Bernoulli beam theory in some aspects such as governing equations, boundary conditions, and stress resultant expressions. By using Hamilton’s principle, governing equations of motion are derived. A displacement-based one-dimensional finite element model is developed to solve the problem. Numerical results for cross-ply and angle-ply composite beams are obtained as special cases and are compared with other solutions available in the literature. A variety of parametric studies are conducted to demonstrate the effect of fiber orientation and modulus ratio on the natural frequencies, critical buckling loads, and load-frequency curves as well as corresponding mode shapes of composite beams

Imaging and Treatment of Patients with Acute Stroke: An Evidence-Based Review

Evidence-based medicine has emerged as a valuable tool to guide clinical decision-making, by summarizing the best possible evidence for both diagnostic and treatment strategies. Imaging plays a critical role in the evaluation and treatment of patients with acute ischemic stroke, especially those who are being considered for thrombolytic or endovascular therapy. Time from stroke-symptom onset to treatment is a strong predictor of long-term functional outcome after stroke. Therefore, imaging and treatment decisions must occur rapidly in this setting, while minimizing unnecessary delays in treatment. The aim of this review was to summarize the best available evidence for the diagnostic and therapeutic management of patients with acute ischemic stroke

The costs of traumatic brain injury due to motorcycle accidents in Hanoi, Vietnam

Background: Road traffic accidents are the leading cause of fatal and non-fatal injuries in Vietnam. The purpose of this study is to estimate the costs, in the first year post-injury, of non-fatal traumatic brain injury (TBI) in motorcycle users not wearing helmets in Hanoi, Vietnam. The costs are calculated from the perspective of the injured patients and their families, and include quantification of direct, indirect and intangible costs, using years lost due to disability as a proxy. Methods: The study was a retrospective cross-sectional study. Data on treatment and rehabilitation costs, employment and support were obtained from patients and their families using a structured questionnaire and The European Quality of Life instrument (EQ6D). Results: Thirty-five patients and their families were interviewed. On average, patients with severe, moderate and minor TBI incurred direct costs at USD 2,365, USD 1,390 and USD 849, with time lost for normal activities averaging 54 weeks, 26 weeks and 17 weeks and years lived with disability (YLD) of 0.46, 0.25 and 0.15 year, respectively. Conclusion: All three component costs of TBI were high; the direct cost accounted for the largest proportion, with costs rising with the severity of TBI. The results suggest that the burden of TBI can be catastrophic for families because of high direct costs, significant time off work for patients and caregivers, and impact on health-related quality of life. Further research is warranted to explore the actual social and economic benefits of mandatory helmet use

Second cohomology for finite groups of Lie type

Let $G$ be a simple, simply-connected algebraic group defined over $\mathbb{F}_p$. Given a power $q = p^r$ of $p$, let $G(\mathbb{F}_q) \subset G$ be the subgroup of $\mathbb{F}_q$-rational points. Let $L(\lambda)$ be the simple rational $G$-module of highest weight $\lambda$. In this paper we establish sufficient criteria for the restriction map in second cohomology $H^2(G,L(\lambda)) \rightarrow H^2(G(\mathbb{F}_q),L(\lambda))$ to be an isomorphism. In particular, the restriction map is an isomorphism under very mild conditions on $p$ and $q$ provided $\lambda$ is less than or equal to a fundamental dominant weight. Even when the restriction map is not an isomorphism, we are often able to describe $H^2(G(\mathbb{F}_q),L(\lambda))$ in terms of rational cohomology for $G$. We apply our techniques to compute $H^2(G(\mathbb{F}_q),L(\lambda))$ in a wide range of cases, and obtain new examples of nonzero second cohomology for finite groups of Lie type.Comment: 29 pages, GAP code included as an ancillary file. Rewritten to include the adjoint representation in types An, B2, and Cn. Corrections made to Theorem 3.1.3 and subsequent dependent results in Sections 3-4. Additional minor corrections and improvements also implemente

First cohomology for finite groups of Lie type: simple modules with small dominant weights

Let $k$ be an algebraically closed field of characteristic $p > 0$, and let $G$ be a simple, simply connected algebraic group defined over $\mathbb{F}_p$. Given $r \geq 1$, set $q=p^r$, and let $G(\mathbb{F}_q)$ be the corresponding finite Chevalley group. In this paper we investigate the structure of the first cohomology group $H^1(G(\mathbb{F}_q),L(\lambda))$ where $L(\lambda)$ is the simple $G$-module of highest weight $\lambda$. Under certain very mild conditions on $p$ and $q$, we are able to completely describe the first cohomology group when $\lambda$ is less than or equal to a fundamental dominant weight. In particular, in the cases we consider, we show that the first cohomology group has dimension at most one. Our calculations significantly extend, and provide new proofs for, earlier results of Cline, Parshall, Scott, and Jones, who considered the special case when $\lambda$ is a minimal nonzero dominant weight.Comment: 24 pages, 5 figures, 6 tables. Typos corrected and some proofs streamlined over previous versio

Deep Memory Networks for Attitude Identification

We consider the task of identifying attitudes towards a given set of entities from text. Conventionally, this task is decomposed into two separate subtasks: target detection that identifies whether each entity is mentioned in the text, either explicitly or implicitly, and polarity classification that classifies the exact sentiment towards an identified entity (the target) into positive, negative, or neutral. Instead, we show that attitude identification can be solved with an end-to-end machine learning architecture, in which the two subtasks are interleaved by a deep memory network. In this way, signals produced in target detection provide clues for polarity classification, and reversely, the predicted polarity provides feedback to the identification of targets. Moreover, the treatments for the set of targets also influence each other -- the learned representations may share the same semantics for some targets but vary for others. The proposed deep memory network, the AttNet, outperforms methods that do not consider the interactions between the subtasks or those among the targets, including conventional machine learning methods and the state-of-the-art deep learning models.Comment: Accepted to WSDM'1

Extracellular dsRNA induces a type I interferon response mediated via class A scavenger receptors in a novel Chinook salmon derived spleen cell line

The final publication is available at Elsevier via https://dx.doi.org/10.1016/j.dci.2018.08.010 © 2018. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/Despite increased global interest in Chinook salmon aquaculture, little is known of their viral immune defenses. This study describes the establishment and characterization of a continuous cell line derived from Chinook salmon spleen, CHSS, and its use in innate immune studies. Optimal growth was seen at 14–18 °C when grown in Leibovitz's L-15 media with 20% fetal bovine serum. DNA analyses confirmed that CHSS was Chinook salmon and genetically different from the only other available Chinook salmon cell line, CHSE-214. Unlike CHSE-214, CHSS could bind extracellular dsRNA, resulting in the rapid and robust expression of antiviral genes. Receptor/ligand blocking assays confirmed that class A scavenger receptors (SR-A) facilitated dsRNA binding and subsequent gene expression. Although both cell lines expressed three SR-A genes: SCARA3, SCARA4, and SCARA5, only CHSS appeared to have functional cell-surface SR-As for dsRNA. Collectively, CHSS is an excellent cell model to study dsRNA-mediated innate immunity in Chinook salmon.Natural Sciences and Engineering Research Council of CanadaCanada Research Counci

Synthesis of Single Phase Hg-1223 High Tc Superconducting Films With Multistep Electrolytic Process

We report the multistep electrolytic process for the synthesis of high Tc single phase HgBa2Ca2Cu3O8+ (Hg-1223) superconducting films. The process includes : i) deposition of BaCaCu precursor alloy, ii) oxidation of BaCaCu films, iii) electrolytic intercalation of Hg in precursor BaCaCuO films and iv) electrochemical oxidation and annealing of Hg-intercalated BaCaCuO films to convert into Hg1Ba2Ca2Cu3O8+ (Hg-1223). Films were characterized by thermo-gravimetric analysis (TGA) and differential thermal analysis (DTA), X-ray diffraction (XRD) and scanning electron microscopy (SEM). The electrolytic intercalation of Hg in BaCaCuO precursor is proved to be a novel alternative to high temperature-high pressure mercuration process. The films are single phase Hg-1223 with Tc = 121.5 K and Jc = 4.3 x 104 A/cm2.Comment: 17 Pages, 10 Figures. Submitted to Superconductor Science and Technolog

Tracking Target Signal Strengths on a Grid using Sparsity

Multi-target tracking is mainly challenged by the nonlinearity present in the measurement equation, and the difficulty in fast and accurate data association. To overcome these challenges, the present paper introduces a grid-based model in which the state captures target signal strengths on a known spatial grid (TSSG). This model leads to \emph{linear} state and measurement equations, which bypass data association and can afford state estimation via sparsity-aware Kalman filtering (KF). Leveraging the grid-induced sparsity of the novel model, two types of sparsity-cognizant TSSG-KF trackers are developed: one effects sparsity through $\ell_1$-norm regularization, and the other invokes sparsity as an extra measurement. Iterative extended KF and Gauss-Newton algorithms are developed for reduced-complexity tracking, along with accurate error covariance updates for assessing performance of the resultant sparsity-aware state estimators. Based on TSSG state estimates, more informative target position and track estimates can be obtained in a follow-up step, ensuring that track association and position estimation errors do not propagate back into TSSG state estimates. The novel TSSG trackers do not require knowing the number of targets or their signal strengths, and exhibit considerably lower complexity than the benchmark hidden Markov model filter, especially for a large number of targets. Numerical simulations demonstrate that sparsity-cognizant trackers enjoy improved root mean-square error performance at reduced complexity when compared to their sparsity-agnostic counterparts.Comment: Submitted to IEEE Trans. on Signal Processin