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.
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Background: Tumor necrosis factor-alpha (TNF-α) and interleukins play important roles in the pathogenesis of rheumatoid arthritis (RA). Genetic research has been employed to find many of the missing connections between genetic risk variations and causal genetic components. Objective: The goal of this study is to look at the genetic variations of TNF-α and interleukins in Iraqi RA patients and see how they relate to disease severity or response to biological therapy. Method: Using specific keywords, the authors conducted a systematic and comprehensive search to identify relevant Iraqi studies examining the genetic variations of TNF-α and interleukins in Iraqi RA patients and how they relate to disease severity or response to biolo
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The study dealt with the design of tourist resting units in the Iraqi Marshes in contemporary design methods while preserving the heritage. The first chapter contained the research problem which is the important event of the inclusion of the Marshes in the World Heritage List. This research is important because it aims to develop the tourist reality of the Marshlands. The second chapter dealt with three sections the first of which is tourism in the marsh environment, the second one dealt with the structure of tourist space and the third dealt with the design methods of the tourist resting units in the Iraqi marshes.
The most important results of the research are the following: the sample models have taken desig
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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 MoreA 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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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 others
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