Blockchain has garnered the most attention as the most important new technology that supports recent digital transactions via e-government. The most critical challenge for public e-government systems is reducing bureaucracy and increasing the efficiency and performance of administrative processes in these systems since blockchain technology can play a role in a decentralized environment and execute a high level of security transactions and transparency. So, the main objectives of this work are to survey different proposed models for e-government system architecture based on blockchain technology implementation and how these models are validated.  This work studies and analyzes some research trends focused on blockchain

Abstract:

        The research sought to identify the crises that occurred during the research period and their reflection on the performance of the hotel Research sample as well as to identify the reality of auditing the hotel Research sample and the preparation of a performance audit program can be adopted in auditing the performance of hotels in light of crises, and the problem of the research lies in the lack of a program to audit the performance of hotels that takes into account the crises experienced by the hotel sector, The research was based on solving its problems on three hypotheses, the first is that the performance audit in light of the Covid-19 pand

In this study, the kinematic analysis including the forward and inverse kinematic developed for a 5 degree of freedom robotic arm. The forward kinematic is elaborated using Denavit-Hartenberg (DH) convention. Inverse Kinematic is established using Deep Neural Network (DNN) model with five hidden layer each contain 50 neurons fully connected using ReLu activation. A data of inputs and outputs are created and trained. The inputs are the end-effector position and orientation. The outputs are the joint angles of the manipulator. The data is generated by the forward kinematics, where a set of joint angles that limited by their corresponding ranges are inserted to the forward kinematic equations to result the end-effector positions and or

Abstract: In recent times, global attention has increasingly focused on the critical issue of environmental sustainability, owing to escalating environmental degradation exacerbated by the utilization of green spaces and technological innovation. This phenomenon necessitates thorough examination, prompting the present study to scrutinize the impact of various factors, namely green spaces, technological innovation, environmental taxes, renewable energy consumption (REC), inflation, and economic growth (EG), on environmental sustainability within the context of Iraq. Secondary data extracted from the World Development Indicators (WDI) spanning the period from 1991 to 2022 served as the foundation for this investigation. Methodologically, the

Several correlations have been proposed for bubble point pressure, however, the correlations could not predict bubble point pressure accurately over the wide range of operating conditions. This study presents Artificial Neural Network (ANN) model for predicting the bubble point pressure especially for oil fields in Iraq. The most affecting parameters were used as the input layer to the network. Those were reservoir temperature, oil gravity, solution gas-oil ratio and gas relative density. The model was developed using 104 real data points collected from Iraqi reservoirs. The data was divided into two groups: the first was used to train the ANN model, and the second was used to test the model to evaluate their accuracy and trend stability

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

An application of neural network technique was introduced in modeling the point efficiency of sieve tray, based on a
data bank of around 33l data points collected from the open literature.Two models proposed,using back-propagation
algorithm, the first model network consists: volumetric liquid flow rate (QL), F foctor for gas (FS), liquid density (pL),
gas density (pg), liquid viscosity (pL), gas viscosity (pg), hole diameter (dH), weir height (hw), pressure (P) and surface
tension between liquid phase and gas phase (o). In the second network, there are six parameters as dimensionless
group: Flowfactor (F), Reynolds number for liquid (ReL), Reynolds number for gas through hole (Reg), ratio of weir
height to hole diqmeter

A cost-effective and efficient detector was created to conduct thorough turbidimetric measurements by reaction of Co (II) ion with calcium ferro cyanide to form bright green particulate, using the method of continuous flow injection analysis, the use of NAG-5SX1-1D-SSP Analyzer in determining cobalt (II) ion in a test for the validity of the new design. The NAG-5SX1-1D-SSP Analyzer is composed of five irradiation sources of white snow leds having the diameter of 10 mm with one solar cell of 55 mm length, 13.5 mm width. Using a selector switch to select the optimum voltage to be used which was 2.7 VDC. Under conditions of optimization, cobalt (II) ion was determined at 0.005–20 mmol. L–1(n = 23) while linearity dynamic range 0.005–7 mm