The aim of this study is to highlight on the concept of joint arrangements and projects in accordance with IFRS 11, in addition, the study also focuses on accounting standards and IFRSs which are relevant to this standard.  With a description of the legislative and accounting challenges in the Iraqi environment facing the application of IFRS 11, as well as studying the reality of accounting in such economic activity of companies operating in this sector.

In order to achieve the study objectives, the researcher conducted a comparative analysis between IFRS 11 (Joint Arrangements) and the Iraqi Unified Accounting System, In the second step, IFRS 11 is applied to the Basrah Gas Company's (research sampl

This study investigated the applicability of iron oxide (Fe₂O₃) nanoparticles for the removal of cadmium metal from sewage water by using batch scale experiments. The iron oxide nanoparticles of 27.7nm were synthesized using a biological method and characterized by Atomic Force Microscope (AFM).  The Box-Wilson design was used to conduct experiments with three parameters such as pH (2-6), time of adsorption (6-120min) and adsorbent dosage (5-25mg/L). The best conditions occurred at pH: 5.5; contact time: 95.8 min; and iron oxide nanoparticle dosage: 20.77 mg/L for maximum cadmium removal of (96.9%).

Liquid membrane electrodes for the determination iron(III) were constructed based on chloramphenicol sodium succinate and iron(III) CPSS-Fe(III) as ion pair complex, with four plasticizers Di-butyl phosphate (DBP); Di-butyl phthalate (DBPH); Di-octyl phthalate (DOP); Tri-butyl phosphate (TBP); in PVC matrix . These electrodes give Nernstian and sub-Nernstian slopes (19.79, 24.60, 16.01 and 13.82mV/decade) and linear ranges from (1x10-5-1x10-2 M, 1x10-5-1x10-2 M, 1x10-6-1x10-2 M and 1x10-5-1x10-2 M) respectively. The best electrode was based on DBP plasticizer which gave a slope 19.79 mV/decade, correlation coefficient 0.9999, detection limit of 9×10-6 M, lifetime 37 day displayed good stability and reproducibility and used to determine

In this research the results of applying Artificial Neural Networks with modified activation function to
perform the online and offline identification of four Degrees of Freedom (4-DOF) Selective Compliance
Assembly Robot Arm (SCARA) manipulator robot will be described. The proposed model of
identification strategy consists of a feed-forward neural network with a modified activation function that
operates in parallel with the SCARA robot model. Feed-Forward Neural Networks (FFNN) which have
been trained online and offline have been used, without requiring any previous knowledge about the
system to be identified. The activation function that is used in the hidden layer in FFNN is a modified
version of the wavelet func

In this research the results of applying Artificial Neural Networks with modified activation function to perform the online and offline identification of four Degrees of Freedom (4-DOF) Selective Compliance Assembly Robot Arm (SCARA) manipulator robot will be described. The proposed model of identification strategy consists of a feed-forward neural network with a modified activation function that operates in parallel with the SCARA robot model. Feed-Forward Neural Networks (FFNN) which have been trained online and offline have been used, without requiring any previous knowledge about the system to be identified. The activation function that is used in the hidden layer in FFNN is a modified version of the wavelet function. This approach ha

The electrode in the microbial fuel cell has a significant effect on cell performance. The treatment of the electrode is a crucial step to make the electrode surface more habitable for bacteria growth, thus, increases the power production as well as waste treatment. In the current study, two graphite electrodes were treated by a microwave. The first electrode was treated with 100W microwave energy, while the second one was treated with 600W microwave energy. There is a significant enhancement in the surface of the graphite anode after the pretreatment process. The results show an increase in the power density from 10 mW/m² to 15 mW/m² with 100w treatment and to 13.47 mW/m² with 600w treatment. An organic

Support Vector Machines (SVMs) are supervised learning models used to examine data sets in order to classify or predict dependent variables. SVM is typically used for classification by determining the best hyperplane between two classes. However, working with huge datasets can lead to a number of problems, including time-consuming and inefficient solutions. This research updates the SVM by employing a stochastic gradient descent method. The new approach, the extended stochastic gradient descent SVM (ESGD-SVM), was tested on two simulation datasets. The proposed method was compared with other classification approaches such as logistic regression, naive model, K Nearest Neighbors and Random Forest. The results show that the ESGD-SVM has a

summary

In this search, we examined the factorial experiments and the study of the significance of the main effects, the interaction of the factors and their simple effects by the F test (ANOVA) for analyze the data of the factorial experience. It is also known that the analysis of variance requires several assumptions to achieve them, Therefore, in case of violation of one of these conditions we conduct a transform to the data in order to match or achieve the conditions of analysis of variance, but it was noted that these transfers do not produce accurate results, so we resort to tests or non-parametric methods that work as a solution or alternative to the parametric tests , these method