Identification of coastal water quality by multivariate statistical techniques in two typical bays of northern Zhejiang Province,East China Sea

The Hangzhou Bay（HZB） and Xiangshan Bay（XSB）, in northern Zhejiang Province and connect to the East China Sea（ECS） were considerably affected by the consequence of water quality degradation. In this study, we analyzed physical and biogeochemical properties of water quality via multivariate statistical techniques. Hierarchical cluster analysis（HCA） grouped HZB and XSB into two subareas of different pollution sources based on similar physical and biogeochemical properties. Principal component analysis（PCA） identified three latent pollution sources in HZB and XSB respectively and emphasized the importance of terrestrial inputs, coastal industries as well as natural processes in determining the water quality of the two bays. Therefore, proper measurement for the protection of aquatic ecoenvironment in HZB and XSB were of great urgency.

Evaluation of Sorghum (Sorghum bicolor) Landraces for Drought Tolerance Using Morphological and Yield Characters under Rainfed Conditions of Sub Region Hagaz, Eritrea

Sorghum is an important food crop in Eritrea where it is widely grown in the mid and low lands,of semi-arid regions.Eritrea being the center of ori­gin of sorghum,a large variability exist in its landraces being grown by the farmers since generations.In order to improve the productivity of sorghum under moisture stress conditions,it is imperative to evaluate these landraces for drought tolerant characteristics and their use for further crop improve­ment programmes.Therefore,a field study was conducted in a randomized complete block design with three replications to estimate the extent of genetic variability of 20 sorghum genotypes for moisture stress tolerance using various morphological,phenological,yield and yield related parame­ters under rainfed conditions at Hagaz Research Station.Significant differ­ence was observed for almost all the characters in the individual analysis of variance suggesting that these sorghum accessions were highly variable.Accessions EG 537,EG 1257,EG 849,EG 791,EG 783 and EG 813 showed promising results for post flowering drought tolerance,grain yield and stay green traits.Higher PCV and GCV were also obtained in parame­ters like plant height,leaf area,biomass,peduncle exertion,panicle length,and grain yield and panicle weight.The genotypes also exhibited varying degrees of heritability estimates.Characters such as plant height,panicle length,days to flowering and maturity showed higher heritability.Cluster analysis revealed that sorghum landraces were grouped on the basis of their morphological traits and geographical sites.77.3%of the total variation of sorghum landraces was contributed by the first four principal components analysis having Eigen value>1.Overall,the current study confirmed that EG 537,EG 849,EG 1257,EG 791,and EG 813 are drought tolerant sor­ghum landraces during post flowering stage.

Improving Scalability of Cloud Monitoring Through PCA-Based Clustering of Virtual Machines

Cloud computing has recently emerged as a leading paradigm to allow customers to run their applications in virtualized large-scale data centers. Existing solutions for monitoring and management of these infrastructures consider virtual machines （VMs） as independent entities with their own characteristics. However, these approaches suffer from scalability issues due to the increasing number of VMs in modern cloud data centers. We claim that scalability issues can bc addressed by leveraging the similarity among VMs behavior in terms of resource usage patterns. In this paper we propose an automated methodology to cluster VMs starting from the usage of multiple resources, assuming no knowledge of the services executed on them. The innovative contribution of the proposed methodology is the use of the statistical technique known as principal component analysis （PCA） to automatically select the most relevant information to cluster similar VMs. We apply the methodology to two case studies, a virtualized testbed and a real enterprise data center. In both case studies, the automatic data selection based on PCA allows us to achieve high performance, with a percentage of correctly clustered VMs between 80% and 100% even for short time series （1 day） of monitored data. Furthermore, we estimate the potential reduction in the amount of collected data to demonstrate how our proposal may address the scalability issues related to monitoring and management in cloud computing data centers.