Mutations in genes encoding proteins necessary for detoxifying oxidative stress products have been predicted to increase susceptibility to lung cancer (LC). Despite this, the association between waterpipe tobacco smoking (WP), genetic polymorphisms, and LC risk remains poorly understood. This is the first study to explore the relationship between WP tobacco smoking and these genetic factors. Previously, we investigated the association of GSTP1 SNPs (rs1695-A/G and rs1138272-C/T) with LC in Iraqi males who smoke WP. Here, we expanded our analysis to include GSTM1 (active/null) and GSTT1 (active/null) genotypes, both individually and in combination with GSTP1 SNPs. Multiplex PCR and RFLP-PCR assays were utilized to determine the genotypes of

 

This work implements the face recognition system based on two stages, the first stage is feature extraction stage and the second stage is the classification stage. The feature extraction stage consists of Self-Organizing Maps (SOM) in a hierarchical format in conjunction with Gabor Filters and local image sampling. Different types of SOM’s were used and a comparison between the results from these SOM’s was given.

The next stage is the classification stage, and consists of self-organizing map neural network; the goal of this stage is to find the similar image to the input image. The proposal method algorithm implemented by using C++ packages, this work is successful classifier for a face database consist of 20

"The aim of the research is to identify the availability of the dimensions of the research variables represented by organizational symmetry and the quality of work-life at the University of Information and Communications Technology, which is one of the formations of the Ministry of Higher Education and Scientific Research in Baghdad, in addition to knowing the relationship and influence between them. The research relied on the descriptive analytical approach based on peer description. The research was analyzed and the research sample consisted of (148) individuals, the sample was chosen using the comprehensive inventory method, data was obtained by relying on the questionnaire which was prepared from ready-made m

The success of any institution must be based on means to protect its resources and assets from the waste, loss, misuse and the availability of accurate and reliable data by accounting reports to increase its operational efficiency, namely, that the internal control system is considered as a safety valve for top management in any economic unit. The problem is represented by the need for an efficient system, so to ensure its success, there must exist external parties which monitor and evaluate the performance because of its importance by following clear criteria. So, the research problem came to address performance evaluation indicators which are set by the Federal Board of Supreme Audit (FBSA) and identify the extent of its contribution t

The shear strength of soil is one of the most important soil properties that should be identified before any foundation design. The presence of gypseous soil exacerbates foundation problems. In this research, an approach to forecasting shear strength parameters of gypseous soils based on basic soil properties was created using Artificial Neural Networks. Two models were built to forecast the cohesion and the angle of internal friction. Nine basic soil properties were used as inputs to both models for they were considered to have the most significant impact on soil shear strength, namely: depth, gypsum content, passing sieve no.200, liquid limit, plastic limit, plasticity index, water content, dry unit weight, and initial

Using the Neural network as a type of associative memory will be introduced in this paper through the problem of mobile position estimation where mobile estimate its location depending on the signal strength reach to it from several around base stations where the neural network can be implemented inside the mobile. Traditional methods of time of arrival (TOA) and received signal strength (RSS) are used and compared with two analytical methods, optimal positioning method and average positioning method. The data that are used for training are ideal since they can be obtained based on geometry of CDMA cell topology. The test of the two methods TOA and RSS take many cases through a nonlinear path that MS can move through that region. The result

Using the Neural network as a type of associative memory will be introduced in this paper through the problem of mobile position estimation where mobile estimate its location depending on the signal strength reach to it from several around base stations where the neural network can be implemented inside the mobile. Traditional methods of time of arrival (TOA) and received signal strength (RSS) are used and compared with two analytical methods, optimal positioning method and average positioning method. The data that are used for training are ideal since they can be obtained based on geometry of CDMA cell topology. The test of the two methods TOA and RSS take many cases through a nonlinear path that MS can move through tha

In this work, a joint quadrature for numerical solution of the double integral is presented. This method is based on combining two rules of the same precision level to form a higher level of precision. Numerical results of the present method with a lower level of precision are presented and compared with those performed by the existing high-precision Gauss-Legendre five-point rule in two variables, which has the same functional evaluation. The efficiency of the proposed method is justified with numerical examples. From an application point of view, the determination of the center of gravity is a special consideration for the present scheme. Convergence analysis is demonstrated to validate the current method.

Due to the huge variety of 5G services, Network slicing is promising mechanism for dividing the physical network resources in to multiple logical network slices according to the requirements of each user. Highly accurate and fast traffic classification algorithm is required to ensure better Quality of Service (QoS) and effective network slicing. Fine-grained resource allocation can be realized by Software Defined Networking (SDN) with centralized controlling of network resources. However, the relevant research activities have concentrated on the deep learning systems which consume enormous computation and storage requirements of SDN controller that results in limitations of speed and accuracy of traffic classification mechanism. To fill thi