Many problems are facing the installation of piles group in laboratory testing and the errors in results of load and settlement are measured experimentally may be happened due to select inadequate method of installation of piles group. There are three main methods of installation in-flight, pre-jacking and hammering methods. In order to find the correction factor between these methods the laboratory model tests were conducted on small-scale models. The parameters studied were the methods of installation (in-flight, pre-jacking and hammering method), the number of piles and in sandy soil in loose state. The results of experimental work show that the increase in the number of piles value led to increase in load carrying capacity of piled raft and decrease in settlement value for three methods of installation. The response of increases load capacity for hammering method is the same value of pre-jacking method at the number of piles less than (N=2), while when the number of piles are beyond (N=3 to 9). The load capacity of hammering method is more than pre-jacking method and the correction factor of method of installation depend on the type of method of installation and the piles number. The increase in carrying capacity by hammering method is due to mobilize the dynamic soil structure interaction (soil-pile and pile-pile interaction) and the change in properties for surrounding soil for loose state of sand is more effective than static soil structure interaction mobilize by pre-jacking method. The correction factor of increase in load capacity and the correction factor of the percentage of settlement reduction for pre-jacking and hammering methods are compared with in-flight method of installation are changed with the number of piles and these values are increased with increasing the number of piles.
Elastic magnetic M1 electron scattering form factor has been calculated for the ground state J,T=1/2-,1/2 of 13C. The single-particle model is used with harmonic oscillator wave function. The core-polarization effects are calculated in the first-order perturbation theory including excitations up to 5ħω, using the modified surface delta interaction (MSDI) as a residual interaction. No parameters are introduced in this work. The data are reasonably explained up to q~2.5fm-1 .
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<em>The aim of the research is to set a set of BioKinematic variables for the step of crossing barriers (3–6–9) in a 110-meter barrier for young runners. The researchers concluded the study by interpreting and discussing the results that the most important variables must be relied upon when training and selecting runners that got the best saturation on their factors: 1-The first factor which refers to the total distance of the plan to pass the third barrier + the total distance of the plan to pass the ninth barrier + the total distance Plan to cross the sixth barrier. 2-The second factor which refers to the total vertical speed before passing the third barrier + the total vertical speed before the sixth barrier + the total vertica
... Show MoreOsteoarthritis (OA) is a series of aggressive destructive inflammatory processes. Synovitis is common both at an early and a late phase. This disease may be uniquely singular in some site but phylogenetically related at some point in time to produce a common outcome of dysfunction, disability, socioeconomic destruction and sometimes socioeconomic failure. Articular cartilage, subchondral bone and synovial membrane are the site of major abnormalities in this disease process. Rheumatoid factor (RF) represents one of the routine laboratory tests that made for all patients have joint complaints.Chloroquine phosphate (CQP) is agent belong to disease modifying osteoathritic drugs (DMOADs). Chloroquine and their derivatives have been used for t
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Methods of estimating statistical distribution have attracted many researchers when it comes to fitting a specific distribution to data. However, when the data belong to more than one component, a popular distribution cannot be fitted to such data. To tackle this issue, mixture models are fitted by choosing the correct number of components that represent the data. This can be obvious in lifetime processes that are involved in a wide range of engineering applications as well as biological systems. In this paper, we introduce an application of estimating a finite mixture of Inverse Rayleigh distribution by the use of the Bayesian framework when considering the model as Markov chain Monte Carlo (MCMC). We employed the Gibbs sampler and
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A mixture model is used to model data that come from more than one component. In recent years, it became an effective tool in drawing inferences about the complex data that we might come across in real life. Moreover, it can represent a tremendous confirmatory tool in classification observations based on similarities amongst them. In this paper, several mixture regression-based methods were conducted under the assumption that the data come from a finite number of components. A comparison of these methods has been made according to their results in estimating component parameters. Also, observation membership has been inferred and assessed for these methods. The results showed that the flexible mixture model outperformed the
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