Tricube Weighted Linear Regression and Interquartile for Cloud Infrastructural Resource Optimization

Cloud infrastructural resource optimization is the process of precisely selecting the allocating the correct resources either to a workload or application.When workload execution,accuracy,and cost are accurately stabilized in opposition to the best possible framework in real-time,efficiency is attained.In addition,every workload or application required for the framework is characteristic and these essentials change over time.But,the existing method was failed to ensure the high Quality of Service(QoS).In order to address this issue,a Tricube Weighted Linear Regression-based Inter Quartile(TWLR-IQ)for Cloud Infrastructural Resource Optimization is introduced.A Tricube Weighted Linear Regression is presented in the proposed method to estimate the resources(i.e.,CPU,RAM,and network bandwidth utilization)based on the usage history in each cloud server.Then,Inter Quartile Range is applied to efficiently predict the overload hosts for ensuring a smooth migration.Experimental results show that our proposed method is better than the approach in Cloudsim under various performance metrics.The results clearly showed that the proposed method can reduce the energy consumption and provide a high level of commitment with ensuring the minimum number of Virtual Machine(VM)Migrations as compared to the state-of-the-art methods.

On Efficient Monitoring of Process Dispersion using Interquartile Range

The presence of dispersion/variability in any process is understood and its careful monitoring may furnish the performance of any process. The interquartile range (IQR) is one of the dispersion measures based on lower and upper quartiles. For efficient monitoring of process dispersion, we have proposed auxiliary information based Shewhart-type IQR control charts (namely IQRr and IQRp charts) based on ratio and product estimators of lower and upper quartiles under bivariate normally distributed process. We have developed the control structures of proposed charts and compared their performances with the usual IQR chart in terms of detection ability of shift in process dispersion. For the said purpose power curves are constructed to demonstrate the performance of the three IQR charts under discussion in this article. We have also provided an illustrative example to justify theory and finally closed with concluding remarks.

Density and Seasonal Dynamics of Bemisia tabaci(Gennadius) Mediterranean on Common Crops and Weeds Around Cotton Fields in Northern China

The density seasonal dynamics of Bemisia tabaci MED were evaluated over two years in a cotton-growing area in Langfang, Hebei Province, northern China on cotton （Gossypium hirsutum L.） and six other co-occurring common plants, common ragweed （Ambrosia artemisiifolia L.）, piemarker （Abutilon theophrasti Medicus）, sunflower （Helianthus annuus L.）, sweet potato （Ipomoea batatas L.）, soybean （Glycine max L.）, and maize （Zea mays L.）. The whitefly species identity was repeatedly tested and confirmed; seasonal dynamics on the various host plants were standardized by the quartile method. B. tabaci MED appeared on weeds （the common ragweed and piemarker） about 10 days earlier than on cotton, or the other cultivated plants. The peak population densities were observed over a span of 2 to 3 weeks on cotton, starting in early （2010） or mid-August （2011）. The common ragweed growing adjacent to cotton supported the highest B. tabaci densities （no. on 100 cm2 leaf surface）, 12-22 fold higher than on cotton itself. Sunflower supported more B. tabaci than the other plants, and about 1.5-2 fold higher than cotton did, Our results indicate that weeds （esp. the common ragweed） around cotton fields could increase the population density of B. tabaci MED on cotton, while sunflower could act as a trap crop for decreasing pest pressure on cotton.

CiteScore与影响因子及其相关指标的对比分析

对比分析Scopus数据库新推出的期刊评价指标CiteScore及其相关指标CiteScore Percentile、CiteScore Percentile Quartile,与Web of Science常用期刊评价指标影响因子及其相关指标Journal Impact Factor Percentile(影响因子百分位)、影响因子分区的异同,并选取学科规模及影响力均有差异的4个学科进行实证研究,以深入了解CiteScore的文献计量学特征,为指标的进一步应用提供参考。

Estimation of Hydraulic Conductivity of Soils at a Watershed-Scale Using Porchet’s Method: Application in the Olezoa Waterhed, Yaounde, Cameroon

Contamination analysis of the unsaturated zone requires information on the spatial variability of hydraulic conductivity. Two types of hydraulic tests (variable and constant charge) were identified to estimate the spatial variability of the hydraulic conductivity of the surface portion of the unsaturated zone in the Olezoa watershed. These tests were performed on 100 holes at depths ranging between 50 and 90 cm, spread throughout the watershed. The hydraulic conductivity values obtained at 50 and 90 cm are close to the absolute value for each method. However, they show a difference of 10-1 m/s between the two types of test regardless of the depth of investigation. The representation of data in the graph indicates a staircase quartile distribution for the variable charge test. The test at constant charge, rather presents a log normal distribution which is also supported by the Kolmogorov-Smirnov test. Hydraulic conductivities have a random component and a spatial organization which results from soil and/or morphological factors. This organization thus permits the distinction of zones which could show high pollution risk.