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。     

用于高速列车移动网络的资源分配实时算法

误码率高、频繁中断的高速列车移动场景宜采用多宿主容迟网络。但其资源分配计算复杂,实时性较差。基于效用差分法设计初始化过程资源分配算法,以保证过程中每一步的效用单调不减,算法时间复杂度降至O(n)。     

A meta-analysis of mobile commerce adoption and the moderating effect of culture

This research aims to discover the general factors that influence mobile commerce adoption. A meta-analysis of previous empirical studies on individuals’ mobile commerce adoption behavior was conducted and the proposed mobile commerce adoption model was tested using structural equation modeling. Furthermore, moderator analysis was carried out using partition tests by dividing context into subgroups of eastern and western culture, to investigate the moderating effect of culture. Results confirm the proposed model, and moderator analysis indicates that culture does have specific moderating effects on mobile commerce adoption. The findings of this paper will yield theoretical and managerial implications for the future.     

Estimating the Natural Illumination Conditions from a Single Outdoor Image

Given a single outdoor image, we present a method for estimating the likely illumination conditions of the scene. In particular, we compute the probability distribution over the sun position and visibility. The method relies on a combination of weak cues that can be extracted from different portions of the image: the sky, the vertical surfaces, the ground, and the convex objects in the image. While no single cue can reliably estimate illumination by itself, each one can reinforce the others to yield a more robust estimate. This is combined with a data-driven prior computed over a dataset of 6 million photos. We present quantitative results on a webcam dataset with annotated sun positions, as well as quantitative and qualitative results on consumer-grade photographs downloaded from Internet. Based on the estimated illumination, we show how to realistically insert synthetic 3-D objects into the scene, and how to transfer appearance across images while keeping the illumination consistent.     

A flexible edge matching technique for object detection in dynamic environment

Considering the robustness, stability and reduced volume of data, researchers have focused on using edge information in various video processing applications including moving object detection, tracking and target recognition. Though the edge information is more robust compared to intensity, it also exhibits variations in different frames due to illumination change and noise. In addition to this, the amount of variation varies from edge to edge. Thus, without making use of this variability information, it is difficult to obtain an optimal performance during edge matching. However, traditional edge pixel-based methods do not keep structural information of edges and thus they are not suitable to extract and hold this variability information. To achieve this, we represent edges as segments that make use of the structural and relational information of edges to allow extraction of this variability information. During edge matching, existing algorithms do not handle the size, positional and rotational variations to deal with edges of arbitrary shapes. In this paper, we propose a knowledge-based flexible edge matching algorithm where knowledge is obtained from the statistics on the environmental dynamics, and flexibility is to deal with the arbitrary shape and the geometric variations of edges by making use of this knowledge. In this paper, we detailed the effectiveness of the proposed matching algorithm in moving object detection and also indicated its suitability in other applications like target detection and tracking.     

Generalized Adjusted Rand Indices for cluster ensembles

In this paper, Adjusted Rand Index (ARI) is generalized to two new measures based on matrix comparison: (i) Adjusted Rand Index between a similarity matrix and a cluster partition (ARImp), to evaluate the consistency of a set of clustering solutions with their corresponding consensus matrix in a cluster ensemble, and (ii) Adjusted Rand Index between similarity matrices (ARImm), to evaluate the consistency between two similarity matrices. Desirable properties of ARI are preserved in the two new measures, and new properties are discussed. These properties include: (i) detection of uncorrelatedness; (ii) computation of ARImp/ARImm in a distributed environment; and (iii) characterization of the degree of uncertainty of a consensus matrix. All of these properties are investigated from both the perspectives of theoretical analysis and experimental validation. We have also performed a number of experiments to show the usefulness and effectiveness of the two proposed measures in practical applications.     

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.