FRC板力学性能分析和材性结构设计

本文采用预测FRC板的复合材料模型,从表面层到夹芯层进行力学性能分析,并根据工程使用的关键力学性能—抗弯强度进行材性结构设计。     

A novel augmented Lagrangian‐based formulation for upper‐bound limit analysis

This paper describes a novel upper‐bound formulation of limit analysis. This formulation is an innovative variant of an existing two‐field mixed formulation based on the augmented Lagrangian method also developed by the authors. A natural approach is used to describe the deformation of each finite element. Furthermore, and in contrast to the previous formulation, two independent field approximations are now both used to define the velocity field, defined globally and at element level. It is shown that this feature allows a governing system of uncoupled linear equations to be obtained. Some numerical examples in plane strain conditions are presented in order to illustrate the current model performance. In conclusion, the potential and advantages of this new approach are discussed. Copyright © 2011 John Wiley & Sons, Ltd.     

Flow and heat transfer of a Giesekus fluid in plane and 3D ducts

This study aims to investigate the Graetz problem of Newtonian and viscoelastic fluid obeying Giesekus model using ANSYS Polyflow solver. The non‐isothermal flow in straight ducts of circular and noncircular cross‐sections under the constant heat flux boundary conditions is considered. The effect of the mobility parameter (α), fluid elasticity defined by Weissenberg number (We) and Reynolds number (Re) on the flow field, secondary flows, and the fully developed and developing Nusselt number along the ducts length are investigated for all geometries. The obtained results are of great importance for practical application in the polymer industries such as polymer melt.     

GPU accelerated MCMC for modeling terrorist activity

The use of graphical processing unit (GPU) parallel processing is becoming a part of mainstream statistical practice. The reliance of Bayesian statistics on Markov Chain Monte Carlo (MCMC) methods makes the applicability of parallel processing not immediately obvious. It is illustrated that there are substantial gains in improved computational time for MCMC and other methods of evaluation by computing the likelihood using GPU parallel processing. Examples use data from the Global Terrorism Database to model terrorist activity in Colombia from 2000 through 2010 and a likelihood based on the explicit convolution of two negative-binomial processes. Results show decreases in computational time by a factor of over 200. Factors influencing these improvements and guidelines for programming parallel implementations of the likelihood are discussed.     

Comparisons of adaptive fault‐tolerant insensitive control methods for a class of linear systems

In this paper, the problem of fault‐tolerant insensitive control is addressed for a class of linear time‐invariant continuous‐time systems against bounded time‐varying actuator faults and controller gain variations. Adaptive mechanisms are developed to adjust controller gains in order to compensate for the detrimental effects of partial loss of control effectiveness and bias‐actuator faults. Variations of controller gains arise from time‐varying and bounded perturbations that are supposed to always exist in adaptive mechanisms. Based on the disturbed outputs of adaptive mechanisms, three different adaptive control strategies are constructed to achieve bounded stability results of the closed‐loop adaptive fault‐tolerant control systems in the presence of actuator faults and controller gain variations. Furthermore, comparisons of convergence boundaries of states and limits of control inputs among adaptive strategies are developed in this paper. The efficiency of the proposed adaptive control strategies and their comparisons are demonstrated by a rocket fairing structural‐acoustic model.     

The provably good parallel seeding algorithms for the k-means problem with penalties

As a classic NP-hard problem in machine learning and computational geometry, the k-means problem aims to partition the given points into k sets to minimize the within-cluster sum of squares. The k-means problem with penalties (k-MPWP), as a generalizing problem of the k-means problem, allows a point that can be either clustered or penalized with some positive cost. In this paper, we mainly apply the parallel seeding algorithm to the k-MPWP, and show sufficient analysis to bound the expected solution quality in the case where both the number of iterations and the number of points sampled in each iteration can be given arbitrarily. The approximate guarantee can be obtained as , where is a polynomial function involving the maximal ratio M between the penalties. On one hand, this result can be viewed as a further improvement on the parallel algorithm for k-MPWP given by Li et al., where the number of iterations depends on other factors. On the other hand, our result also generalizes the one solving the k-means problem presented by Bachem et al., because k-MPWP is a variant of the k-means problem. Moreover, we present a numerical experiment to show the effectiveness of the parallel algorithm for k-means with penalties.     

Parallel‐series connected rectenna array using frequency selective surface (FSS) for power harvesting applications at 5.8 GHz

This article presents a frequency selective surface (FSS) and rectenna array in parallel‐series connection to harvest power of wireless power systems. To improve the basic antenna parameters, a frequency selective surface was mounted on the reverse side of the substrate. According to the results, the proposed method showed significant improvement in comparison to other methods in both single and rectenna array. Moreover, the conversion efficiency of the presented technique was increased to 76%. The output voltage of 4.5 and 9 V and the current of 60 and 120 mA were resulted for 5 and 10 rectenna units, respectively. The proposed arrays can be expanded to large scale integrated array without any significant degradation in conversion efficiency.     

Smart dispatching for energy internet with complex cyber‐physical‐social systems: A parallel dispatch perspective

Energy internet (EI) is a complex coupled multienergy system; it is essential to investigate its multienergy dispatching optimization issues. To this end, this paper first proposes a novel conception of smart dispatching for EI with a complex cyber‐physical‐social system (CPSS) network from the perspective of parallel dispatch, called parallel dispatching robot (PDR), and investigates the implementations of PDR based on smart artificial society (SAS) modeling. First, we introduce EI and describe the dispatching issues of EI. Second, we discuss several important concepts supporting the parallel dispatch conception of EI, including knowledge automation (KA), CPSS, and parallel machine learning (PML). On the basis of these, we elaborate the concept of parallel dispatch. Moreover, we construct a large closed‐loop feedback control framework of parallel dispatch for EI integrating a CPSS network based on KA and PML. Third, we establish an experimental platform for PDR research based on the proposed parallel dispatch framework. Fourth, we develop the PML‐based SAS models of a single PDR in centralized dispatching modes and group PDRs in decentralized dispatching modes to achieve crowd wisdom emergence and performance improvement in current cyber‐physical system frameworks of EI. Moreover, we design an external global closed loop for PDR to evaluate its operation stability. Lastly, we conduct a detailed discussion on PDR and offer some prospects for its engineering implementations. The biggest innovation of this paper lies in systematically proposing the smart dispatching concept and framework for complex CPSS‐based EI from the perspective of parallel dispatch and thoroughly investigating how to use SAS modeling to implement parallel dispatching and control for EI considering human and social factors, which is a major extension and theoretical improvement to existing single smart wide area robot concept and a preliminary attempt in investigating a shift from Energy 4.0 to Energy 5.0 in China.     

Vitamin E and its anticancer effects

Abstract

Vitamin E is a lipid soluble vitamin comprising of eight natural isoforms, namely, α, β, δ, γ isoforms of tocopherol and α, β, δ, γ isoforms of tocotrienol. Many studies have been performed to elucidate its role in cancer. Until last decade, major focus was on alpha tocopherol and its anticancer effects. However, major clinical trials using alpha-tocopherol like SELECT trial and ATBC trial did not yield meaningful results. Hence there was a shift of focus to gamma-tocopherol, delta-tocopherol and tocotrienol. Unlike alpha-tocopherol, gamma-tocopherol and delta-tocopherol can scavenge reactive nitrogen species in addition to reactive oxygen species. Antiangiogenic effect, inhibition of HMG CoA reductase enzyme and inhibition of NF-κB pathway make the anti-cancer effects of tocotrienols unique compared to other vitamin E isoforms. Preclinical research on non-alpha tocopherol isoforms of vitamin E showed promising data on their anticancer effects. In this review, we deal with the current understanding on the potential mechanisms involved in the anticancer effects of vitamin E and the controversies in this field over last three decades. We also highlight the need to conduct further research on the anticancer effects of non-alpha-tocopherol isoforms in larger population and clinical setting.