The bodies responsible for the organization of accounting in the world seek to keep abreast of repaid development, by provide the information required by users, which they need to make efficient decision that return them to the desired benefits, and avoid the risks they could face if they made their decision based on misleading information, or insufficient, or not accurate, Hence, the IASB has undertaken to review the standards, and make the necessary adjustment and clarifications to remove the ambiguities that some of the paragraphs may have in IFRS issued.

And the Iraqi Central Bank obliges banks to convert from local accounting standards to apply IFRS only a step towards keeping pace with developments

  Abstract    

In this study, the effect of carboxylic methyl cellulose (CMC), and sodium dodcyl benzene sulfonate (SDBS) as an aqueous solution on the drag reduction was investigated. Different concentrations of (CMC) and (SDBS) such as (50, 100, 150, 200, 250, 300, 350, 400, 450, and 500 ppm) were used to analyze the aqueous solution properties, including surface tension, conductivity, and shear viscosity. The optimum four concentrations (i.e., 50, 100, 200, and 300 ppm) of fluid properties were utilized to find their effect on the drag reduction. Two different PVC pipe diameters (i.e., 1" and 3/4") were used in this work. The  results showed that blending CMC with SDBS gives

Many of accurate inertial guided missilc systems need to use more complex mathematical calculations and require a high speed processing to ensure the real-time opreation. This will give rise to the need of developing an effcint

بهذا البحث نقارن معاييرالمعلومات التقليدية (AIC , SIC, HQ , FPE ) مع معيارمعلومات الانحراف المحور (MDIC) المستعملة لتحديد رتبة انموذج الانحدارالذاتي (AR) للعملية التي تولد البيانات,باستعمال المحاكاة وذلك بتوليد بيانات من عدة نماذج للأنحدارالذاتي,عندما خضوع حد الخطأ للتوزيع الطبيعي بقيم مختلفة لمعلماته

The current study examines the combined impacts of ultrasonic waves and nano silica (NS) on reducing the viscosity Sharqy Baghdad heavy crude oil with an API gravity of 20.32. NS of an average particle size of 59.93 nm and 563.23 m²/g surface area were produced utilizing the sol-gel technique from Iraqi sand. The XRD analysis indicates the existence of an amorphous silica, the SEM analysis showed that NS tends to agglomerate, and the FTIR spectra exhibited the presence of siloxane and silanol groups. In addition, the TGA analysis demonstrated a total weight loss of 15.62%, validating the thermal stability of the NS. The experiments included a study of the impact of ultrasonic power, exposure time, duty cycle, temperature, and the c

The problem of Bi-level programming is to reduce or maximize the function of the target by having another target function within the constraints. This problem has received a great deal of attention in the programming community due to the proliferation of applications and the use of evolutionary algorithms in addressing this kind of problem. Two non-linear bi-level programming methods are used in this paper. The goal is to achieve the optimal solution through the simulation method using the Monte Carlo method using different small and large sample sizes. The research reached the Branch Bound algorithm was preferred in solving the problem of non-linear two-level programming this is because the results were better.

This paper presents a cognition path planning with control algorithm design for a nonholonomic wheeled mobile robot based on Particle Swarm Optimization (PSO) algorithm. The aim of this work is to propose the circular roadmap (CRM) method to plan and generate optimal path with free navigation as well as to propose a nonlinear MIMO-PID-MENN controller in order to track the wheeled mobile robot on the reference path. The PSO is used to find an online tune the control parameters of the proposed controller to get the best torques actions for the wheeled mobile robot. The numerical simulation results based on the Matlab package show that the proposed structure has a precise and highly accurate distance of the generated refere

A three-stage learning algorithm for deep multilayer perceptron (DMLP) with effective weight initialisation based on sparse auto-encoder is proposed in this paper, which aims to overcome difficulties in training deep neural networks with limited training data in high-dimensional feature space. At the first stage, unsupervised learning is adopted using sparse auto-encoder to obtain the initial weights of the feature extraction layers of the DMLP. At the second stage, error back-propagation is used to train the DMLP by fixing the weights obtained at the first stage for its feature extraction layers. At the third stage, all the weights of the DMLP obtained at the second stage are refined by error back-propagation. Network structures an