In the present work, pattern recognition is carried out by the contrast and relative variance of clouds. The K-mean clustering process is then applied to classify the cloud type; also, texture analysis being adopted to extract the textural features and using them in cloud classification process. The test image used in the classification process is the Meteosat-7 image for the D3 region.The K-mean method is adopted as an unsupervised classification. This method depends on the initial chosen seeds of cluster. Since, the initial seeds are chosen randomly, the user supply a set of means, or cluster centers in the n-dimensional space.The K-mean cluster has been applied on two bands (IR2 band) and (water vapour band).The textural analysis is used

Background: Treatment of invasive prolactinoma, which has several characteristics including invasive growth into cavernous sinuses and formation of giant adenomas compressing adjacent neural structures, resulting in neurological dysfunction, has been very challenging. There are relatively few reports available describing long-term treatment outcome.

Aims of the study: In this study we evaluate the results of cabergoline administration as initial treatment during 4 years follow up period.

Methods: We prospectively categorized 36 patients into four groups according to the results of 3 months of cabergoline treatment: group 1, tumor volume reduction (TVR) ˃25% with normaliz

The inverse problem is important method in the design of electrostatic lenses which is used in this work, with new technique by suggesting an axial electrostatic potential distribution using polynomial functions of the third order. The paraxial-ray equation is solved to obtain the trajectory of particles that satisfy the suggested potential function.In this work design of immersion electrostatic lens operated under zero magnification condition. The electrode shape of sthe electrostatic lens was the dermined from the solution of laplace equation and plotted in two deimensions . The results showed low values of spherical and chromatic aberrations , which are considered as good criteria for good desigh.

Self-compacting concrete (SCC) is an innovative concrete that does not require vibration for placing and compaction. It is able to flow under its own weight, completely filling formwork and achieving full compaction, even in the presence of congested reinforcement. The effect of external sulfate attack was studied-Es (very sever exposure SO4>10000ppm) according to ACI 318-11. The mix design method of SCC used is according to EFNARC 2002, and then must satisfy the criteria of filling ability, passing ability and segregation resistance. The experimental program focuses to study two different chemical composition of sulfate resistance Portland cement with different percentage of silica fume replacement by weight of cement and W/cm (0.3 and 0.3

This paper presents the results of experimental investigations to predict the bearing capacity of square footing on geogrid-reinforced loose sand by performing model tests. The effects of several parameters were studied in order to study the general behavior of improving the soil by using the geogrid. These parameters include the eccentricity value, depth of first layer of reinforcement, and vertical spacing of reinforcement layers. The results of the experimental work indicated that there was an optimum reinforcement embedment depth at which the bearing capacity was the highest when single-layer reinforcement was used. The increase of (z/B) (vertical spacing of reinforcement layer/width of footing) above 1.5 has no effect on the re

Maximum likelihood estimation method, uniformly minimum variance unbiased estimation method and minimum mean square error estimation, as classical estimation procedures, are frequently used for parameter estimation in statistics, which assuming the parameter is constant , while Bayes method assuming the parameter is random variable and hence the Bayes estimator is an estimator which minimize the Bayes risk for each value the random observable and for square error lose function the Bayes estimator is the posterior mean. It is well known that the Bayesian estimation is hardly used as a parameter estimation technique due to some difficulties to finding a prior distribution.

The interest of this paper is that