The current research aims to verify the relationship and impact of internal control activities as an independent variable in its dimensions (efficiency, training, independence) in making administrative decisions as a responsive variable and its dimensions (identifying and diagnosing the problem, searching for alternatives, evaluating alternatives, choosing the appropriate alternative, implementing the decision, following up Decision and evaluation) in the Kirkuk Water Directorate, as well as to identify the levels of their practices, and then try to come up with a number of recommendations contribute to the strengthening of internal control activities and the process of making administrative decisions of the researched Directorat

Seven fish species were collected from the drainage network at Al-Madaen region, south of
Baghdad with the aid of a cast net during the period from March to August 1993. These fishes
were infected with 22 parasite species (seven sporozoans, three ciliated protozoans, seven
monogeneans, two nematodes, one acanthocephalan and two crustaceans) and one fungus
species. Among such parasites, Chloromyxum wardi and Cystidicola sp. are reported here for
the first time in Iraq. In addition, 11 new host records are added to the list of parasites of
fishes of Iraq.

Deep submicron technologies continue to develop according to Moore’s law allowing hundreds of processing elements and memory modules to be integrated on a single chip forming multi/many-processor systems-on-chip (MPSoCs). Network on chip (NoC) arose as an interconnection for this large number of processing modules. However, the aggressive scaling of transistors makes NoC more vulnerable to both permanent and transient faults. Permanent faults persistently affect the circuit functionality from the time of their occurrence. The router represents the heart of the NoC. Thus, this research focuses on tolerating permanent faults in the router’s input buffer component, particularly the virtual channel state fields. These fields track packets f

In this paper we used frequentist and Bayesian approaches for the linear regression model to predict future observations for unemployment rates in Iraq. Parameters are estimated using the ordinary least squares method and for the Bayesian approach using the Markov Chain Monte Carlo (MCMC) method. Calculations are done using the R program. The analysis showed that the linear regression model using the Bayesian approach is better and can be used as an alternative to the frequentist approach. Two criteria, the root mean square error (RMSE) and the median absolute deviation (MAD) were used to compare the performance of the estimates. The results obtained showed that the unemployment rates will continue to increase in the next two decade

The charge transfer at C23H17F8N8O2PRu, C44H30BF4N5O4Ru, C56H52CL5N5OOsP2 and C76H88F80N24O11P10Ru4 nitrosyl complexes are investigation and studies theoretically using the quantum consideration. Charge transfer behavior largely rely to the electric properties of nitrosyl complexes system whose depending on the main important parameters for the transmission rate constant such that: orientation transition energy, overlapping coupling coefficient, driving force energy, height barrier and Temperature T (K). Data results have been evaluated using a MATLAB program. Results show that rate of charge transfer increases due to increases the orientation transition energy.

   This research  studyies  wear  rate  of  composite  materials  by using Epoxy Resin and Polyurethane Rubber as a matrix  of weigt percentage (90:10) (Ep/Pu) and reinforced by PVC fibers and Aluminum fibers two dimension knitted mat with fractional volume(15 %), in different conditions like: lab conditions and after submerge the samples in water for different periods of time. . four kinds of materials were prepared: (Ep+pu), (Ep+Pu+PVC), (Ep+Pu+Al.F), (Ep+Pu+PVC+Al. F) .And the results have shown that the best wear resistance are for the hybrid composite material    (Ep + Pu+ PVC + Al. F) and wear rate of  all samples increased when it was submerged in water

In recent years, the migration of the computational workload to computational clouds has attracted intruders to target and exploit cloud networks internally and externally. The investigation of such hazardous network attacks in the cloud network requires comprehensive network forensics methods (NFM) to identify the source of the attack. However, cloud computing lacks NFM to identify the network attacks that affect various cloud resources by disseminating through cloud networks. In this paper, the study is motivated by the need to find the applicability of current (C-NFMs) for cloud networks of the cloud computing. The applicability is evaluated based on strengths, weaknesses, opportunities, and threats (SWOT) to outlook the cloud network. T

At the level of both individuals and companies, Wireless Sensor Networks (WSNs) get a wide range of applications and uses. Sensors are used in a wide range of industries, including agriculture, transportation, health, and many more. Many technologies, such as wireless communication protocols, the Internet of Things, cloud computing, mobile computing, and other emerging technologies, are connected to the usage of sensors. In many circumstances, this contact necessitates the transmission of crucial data, necessitating the need to protect that data from potential threats. However, as the WSN components often have constrained computation and power capabilities, protecting the communication in WSNs comes at a significant performance pena

In this paper, we derive and prove the stability bounds of the momentum coefficient µ and the learning rate ? of the back propagation updating rule in Artificial Neural Networks .The theoretical upper bound of learning rate ? is derived and its practical approximation is obtained

The speech recognition system has been widely used by many researchers using different
methods to fulfill a fast and accurate system. Speech signal recognition is a typical
classification problem, which generally includes two main parts: feature extraction and
classification. In this paper, a new approach to achieve speech recognition task is proposed by
using transformation techniques for feature extraction methods; namely, slantlet transform
(SLT), discrete wavelet transforms (DWT) type Daubechies Db1 and Db4. Furthermore, a
modified artificial neural network (ANN) with dynamic time warping (DTW) algorithm is
developed to train a speech recognition system to be used for classification and recognition
purposes. T