Biochemical responses of the copepod Centropages tenuiremis to CO(2)-driven acidified seawater

An ecophysiological experiment was conducted to examine the biochemical effects of acidified seawater containing elevated concentration of CO(2) (C(CO2) 0.08, 0.20, 0.50 and 1.00%) on the copepod Centropages tenuiremis. AchE, ATPase, SOD, GPx, GST, GSH level and GSH/GSSG ratio of the copepod were analyzed. The results showed that elevated C(CO2) and the duration of culture time significantly influenced several biochemical indices in C. tenuiremis (ATPase, GPx, GST, GSH and SOD). Furthermore, the principal component analysis results indicated that 72.32% of the overall variance was explained by the first three principal components (GPx, SOD and GSH). Changes in GPx and GSH levels may play a significant role in the antioxidant defense of copepods against seawater acidification. The long-term response of copepods to seawater acidification and the synergistic effects of acidification with other environmental factors, such as temperature, salinity and trace metal need further investigation.     

中国西南易旱地区识别的探讨

利用中国西南地区13个气象站56年以上的长系列月降雨量资料,统计分析了降雨的多年变化、季节变化特征,以及连续少雨时间段事件,探讨了易旱区识别的定性定量分析方法,初步建立了气象易旱区识别指标及标准,将5个地区识别为易旱地区,5个地区识别为较易旱地区,3个地区识别为不易旱地区,将6个地区识别为易发长时间干旱地区,并对识别方法进行了讨论,为进一步的易旱区综合识别研究提出了建议。     

基于改进免疫遗传算法的电网故障诊断研究

针对电网故障的特点,应用一种改进的免疫遗传算法对电网故障进行研究。该算法能够较好地解决传统遗传算法的不足。通过引入新的交叉和变异率,更多考虑了种群的全局特征,采用动态自适应方式提取疫苗,避免了传统遗传算法收敛速度较慢的缺点。改进的算法本着优胜劣汰的思想,删除适值较低的抗体群,取而代之的是随机生成的部分新抗体,保持种群的多样性。建立一个新的目标函数,通过对一个电网的分析,验证了方法的有效性。     

Hybrid Well-balanced WENO Schemes with Different Indicators for Shallow Water Equations

In (J. Comput. Phys. 229: 8105–8129, 2010), Li and Qiu investigated the hybrid weighted essentially non-oscillatory (WENO) schemes with different indicators for Euler equations of gas dynamics. In this continuation paper, we extend the method to solve the one- and two-dimensional shallow water equations with source term due to the non-flat bottom topography, with a goal of obtaining the same advantages of the schemes for the Euler equations, such as the saving computational cost, essentially non-oscillatory property for general solution with discontinuities, and the sharp shock transition. Extensive simulations in one- and two-dimensions are provided to illustrate the behavior of this procedure.     

Optimal configuration of a multicore server processor for managing the power and performance tradeoff

We consider the problem of power and performance management for a multicore server processor in a cloud computing environment by optimal server configuration for a specific application environment. The motivation of the study is that such optimal virtual server configuration is important for dynamic resource provision in a cloud computing environment to optimize the power and performance tradeoff for certain specific type of applications. Our strategy is to treat a multicore server processor as an M/M/m queueing system with multiple servers. The system performance measures are the average task response time and the average power consumption. Two core speed and power consumption models are considered, namely, the idle-speed model and the constant-speed model. Our investigation includes justification of centralized management of computing resources, server speed constrained optimization, power constrained performance optimization, and performance constrained power optimization. Our main results are (1) cores should be managed in a centralized way to provide the highest performance without consumption of more energy in cloud computing; (2) for a given server speed constraint, fewer high-speed cores perform better than more low-speed cores; furthermore, there is an optimal selection of server size and core speed which can be obtained analytically, such that a multicore server processor consumes the minimum power; (3) for a given power consumption constraint, there is an optimal selection of server size and core speed which can be obtained numerically, such that the best performance can be achieved, i.e., the average task response time is minimized; (4) for a given task response time constraint, there is an optimal selection of server size and core speed which can be obtained numerically, such that minimum power consumption can be achieved while the given performance guarantee is maintained.     

Cytoplasm and nucleus segmentation in cervical smear images using Radiating GVF Snake

A Radiating Gradient Vector Flow (RGVF) Snake aiming at accurate extraction of both the nucleus and cytoplasm from a single-cell cervical smear image is proposed. After preprocessing, the areas in the image are roughly clustered into nucleus, cytoplasm and the background by a spatial K-means clustering algorithm. After initial contours are extracted, the image is segmented using RGVF. RGVF involves a new edge map computation method and a stack-based refinement, and is thus robust to contaminations and can effectively locate the obscure boundaries. The boundaries can also be correctly traced even if there are interferences near the cytoplasm and nucleus regions. Experiments performed on the Herlev dataset, which contains 917 images show the effectiveness of the proposed algorithm.     

Node trust evaluation in mobile ad hoc networks based on multi-dimensional fuzzy and Markov SCGM(1, 1) model

Due to the nature of distribution and self-organization, mobile ad hoc networks rely on cooperation between nodes to transfer information. One of the key factors to ensure high communication quality is an efficient assessment scheme for risks and trust of choosing next potential cooperative nodes. Trust model, an abstract psychological cognitive process, is one of the most complex concepts in social relationships, involving factors such as assumptions, expectations and behaviors. All of the above make it difficult to quantify and forecast trust accurately. In this paper, based on the theories of fuzzy recognition with feedback, SCGM(1, 1) model and Markov chain, we present a pattern of prediction making. The analysis and experimental computation show that this scheme is efficient in trust prediction for ad hoc networks.     

All-hexahedral mesh generation via inside-out advancing front based on harmonic fields

The generation of hexahedral meshes is an open problem that has undergone significant research. This paper deals with a novel inside-out advancing front method to generate unstructured all-hexahedral meshes for given volumes. Two orthogonal harmonic fields, principal and radial harmonic fields, are generated to guide the inside-out advancing front process based on a few user interactions. Starting from an initial hexahedral mesh inside the given volume, we advance the boundary quadrilateral mesh along the streamlines of radial field and construct layers of hexahedral elements. To ensure high quality and uniform size of the hexahedral mesh, quadrilateral elements are decomposed in such a way that no non-hexahedral element is produced. For complex volume with branch structures, we segment the complex volume into simple sub-volumes that are suitable for our method. Experimental results show that our method generates high quality all-hexahedral meshes for the given volumes.     

Vanishing point detection using cascaded 1D Hough Transform from single images

Vanishing point detection algorithms based on 2D histogramming techniques have been employed in a variety of computer vision systems. Previous algorithms achieved some good results but still failed to maintain a balanced performance in both accuracy and time. Recent research (Li et al., 2010) shows that, vanishing point detection could be converted to a 1D histogram search problem, which largely accelerates the procedure. In this paper, we further improve this idea and propose a complete scheme for vanishing point detection from images of the so called “Manhattan world”. We test our algorithm and some commonly used vanishing point detection methods on public database YorkUrbanDB and our own implemented database PKUCampusDB. Our algorithm shows significant performance improvements.