Journal of Techniques

The oil industry is, by virtue of being a resource of value addition, the engine of industrialisation in countries. When focusing on environmental concerns today, studying the connection between financial management and Building Information Modelling (BIM) in climate-friendly oil projects is highly recommended. This paper is dedicated to the role of sustainability and Iraq’s decisions in Accounting and Information Systems in Iraq’s oil sector. It aims to capture the relationship between everything that is based on sustainability metrics and economic actors who also play important roles in such undertakings. The research succeeded in collecting data by preparing a survey, which was conducted among leading oil companies in Iraq, with the aim of determining their information system procedures. The data was analysed using the methodology of Partial Least Squares Structural Equation Modelling (PLS-SEM). The results illustrate the positive help for BIM by including sustainability metrics. The information system type for the BIM also significantly influences the relationship between sustainability indicators and public sectors, with public sector participants being the more determinant group. This means that financial metrics, which may be interpreted as a performance factor for BIM, play a role in creating links that serve as the predicates of the sustainability indicators. Additionally, it rBIMes the concept of trade-off on the operational aspects between sustainability and economic gains in oil trading by adding a unique perspective to existing literature. The model created in this study is grounded on the PLS-SEM, complemented by the literature, and the hypotheses are tested. Through the inclusive coverage of the oil industry, the information can enlighten and guide effective strategies for collaborating with architectural work stakeholders that highlight the importance of oil sector proposals.

Heart disease lies among the top causes of death worldwide and accounts for a large number of deaths annually. Researchers are using artificial intelligence as a potent tool to construct cutting-edge healthcare applications in an effort to address this problem for the detection and avoidance of heart disease. This article presents the design and development of an artificial intelligence model using Python, TensorFlow, and Google Colab resources. Trained son simulation data with an 80:20 train/validation split and employing the Adam optimizer over 50 epochs, the model achieved an impressive 95% accuracy. Utilizing input data sumlation data from temperature, SpO2, heart rate and ECG signal the AI model predicts the individual\'s health state with a confidence level of 95%.

Diabetes is an illness that is widespread throughout the world and is considered a health concern, which requires work to explore advanced predictive techniques for early diagnosis of the illness. This paper discusses diabetes prediction by using the K-Nearest Neighbors (KNN) classifier, which is a widely used algorithm in machine learning. Most studies only dealt with investigating the optimal value of k in the KNN algorithm and did not address the best method to measure distance alone or together with the optimal value of k to improve the efficiency of diabetes prediction. This study simultaneously investigates both the optimal value of k and the optimal method for measuring distance to improve the performance of the KNN technique in predicting diabetes. By using and analyzing the Indian Diabetes PIMA dataset, this study seeks to discover the extent to which different parameters, especially the optimal value of K and distance metrics, affect the performance of the classifier. Through experiments that included applying different values for the K factor and using various distance measures, the study reached insights into maximizing the classifier\'s accuracy. The study shows that choosing the distance measure greatly affects the accuracy of classification and selecting the optimal K value helps eliminate problems of overfitting and underfitting, which is a feature of robust models for diabetes prediction. The research results showed that the best performance achieved was 80.5% when

Nowadays, the safest and most extensive type of transportation is air flights, and tens of thousands of flights are carried out from different airports every day around the world. However, many of these flights have short-term or long-term delays, which cause the passengers\' plans to change, and as a result, the airlines suffer losses. the first step to deal with these air delays is to predict them air delay. Regression functions and machine learning algorithms have a very good performance in predicting time series data. In this research, the optimized support vector regression (SVR) algorithm has been used to improve the accuracy of air delay prediction. The SVR algorithm is a machine learning algorithm that uses regression functions. The SVR algorithm has hyperparameters that play a key role in its performance and setting these parameters is very important. In this research, to improve the performance of the SVR algorithm in predicting air delays, the particle swarm optimization (PSO) algorithm has been used to adjust the hyperparameters of the SVR algorithm. The database used in this research was taken from the US Air Transport Fleet website and is an unbalanced database. Based on the results obtained from the simulations, our proposed method has improved with an average accuracy of 87.41% compared to other compared works.

The aim of this study is to measure residual stresses, specifically in wear protection coating, using the sin2ψ based on the X-ray diffraction technique. The combination of silicon carbide substrate and aluminium oxide (SiC-5wt.% Al2O3) is used as the wear protection coating, and AlNi alloy represents the bond coat, which is formed by the flame spraying method on the mild steel substrate. This in turn has enabled to reduce the thermal expansion constant between the substrate and composite ceramic layer. The diffraction angle, 2θ, is measured experimentally, and the lattice spacing is calculated using Bragg\\'s Law using the measured diffraction angle and the known X-ray wavelength. Interestingly, stress calculations for the samples demonstrate a linear relationship of a slop proportionate to stress same as to homogenous isotropic samples in a bi-axial stress. Thus, the relationship between dspacing and sin2ψ is demonstrated as a straight line with a slope proportional to stress. However, the oscillatory trend showed the existence of in-homogeneous stress distribution. To resolve this challenge, X-ray elastic parameters are deployed instead of Poisson ratio (ν) and Young\\'s modulus (E) values. The value of the residual stresses for these coatings calculated is compressive residual stresses of (-594.029 MPa).

Releasing inorganic ions from Ca/P biomaterials could provide an alternative approach to using growth factors to improve bone healing. Two types of hydroxyapatite HA fiber scaffolds, copper-doped (CuHA) and magnesium-doped (MgHA), were fabricated by using the sol-gel method. Two types of scaffolds were immersed in simulated body fluid SPF (i,e, Phosphate Buffered Saline PBS) for 30 days. The results after immersions indicated the amount of Cu and Mg ions released from scaffolds. A low amount of Mg and Cu ions could improve vascular endothelial growth factor (VEGF) and angiogenesis around bone, thus can hasten bone healing and regeneration.

Al-Razzaza Lake, which was established in 1969 to protect the southern regions from heavy rains, is a part of the western plateau and is characterized by a semi-arid climate, hot in summer and cold in winter. The lake water level decreased in the past ten years due to the lack of rain and high temperatures, which affected the water quality and quantity, so it was studied and monitored the decline of the water level and calculate the surface area of the lake by several researchers using remote sensing techniques integrated with the GIS environment. Several researches were reviewed in this study, which dealt with the use of satellite images taken at different periods with the help of Geographic Information Systems (GIS) to detect change in the areas covered by Al-Razzaza Lake. Studies have shown that the area of the lake gradually decreased from 1990 to 2018 at a rate of about 1285 km2, then recovered slightly since 2018 at a rate of 118 km2. The results of the studies also showed that there is a real threat to the environment of Al-Razzaza Lake as a result of the significant deterioration that affects the water quality and the environment in the region. Consequently, researchers are required to expand the study of this problem to provide recommendations and propose appropriate solutions to reduce the resulting risks.

Reversible plows are one of the important and efficient tools in primary tillage, which are affected by many dynamic loads. These tools are damaged in different working conditions, and they are damaged from the stem area. For this reason, in this research, a method based on the finite element method and genetic algorithm was presented to optimize the reversible plow shaft. In this research, two parameters of cross-sectional area and stem curvature were investigated as independent variables. A total of 24 different models for the plow shaft were designed in SolidWorks software, FEM software and used Iron Cow Stem. Then, the different designs of the stem in the environment of the abacus were loaded and stress free occurred in them and were eliminated. Then, using an artificial neural network, a model was presented to estimate the von Mises tension based on the information related to the cross-sectional area and stem curvature, and this model was able to estimate the maximum von Mises tension with an accuracy of 99%. Then the mentioned model was linked with the genetic algorithm and it was used to optimize the plow shaft. After selecting the optimized model through the genetic algorithm, the plow shaft was designed again and the tireless stress that occurred in it under the same loading conditions as the previous conditions was eliminated. The results showed that the amount of maximum stress in this model decreased by 6% compared to the previous models and the best stem designs is (Stress (MPa) VonMises=295.2).

The technology of reconfigurable optical networks (RONs) is growing to be a promising solution to effectively cater to the rapidly increasing traffic generated by the digital society. The present study offers an overview of the datacenter technology and optical datacenter with details of its concepts and technologies. First, the optical data center systems are described and the components that describe it are network operations. Then, we detail the specific technologies that are important to data center networks throughout wide-area networks, analyzing them deeply. Finally, we make a comprehensive between some related works with an analysis of the algorithmic challenges faced by these technologies. In addition, we examine the solutions that have been developed to address these challenges and explore the relevant systems and implementation considerations.

Presented in this paper is a direct model predictive control (MPC) for directly controlling the rotor speed and flux of an inverter-fed induction motor, which is also applicable to induction generators. Discrete dynamic model of the induction machine is applied in stationary reference frame with pre-set present and past reference values of the speed and rotor flux for generating the stator current references The uniqueness of the MPC proposed herein is that it does not require outer cascade proportional plus integral (PI) controllers used for regulating rotor speed and flux. Elimination of the outer loop cascade is made possible in this article by the introduction of a unique four-term cost function comprising of weighted magnitudes of the errors between four measured state variables and their reference commands. The four state variables in the unique cost function are the rotor speed, the rotor flux linkage, quadrature axis and direct axis stator currents. The MPC minimizes the cost function at every switching instance by selecting the switching states that gives the least cost function. Consequently, the selected optimal switching states are used to switch optimal inverter output voltages across the machine’s stator terminals. As a precursor for obtaining an optimal rotor flux command in the MPC for every torque output, another unique efficiency improvement scheme is developed, which uniquely determines the stator angular velocity and the rotor flux that minimizes the core and copper losses while maintaining a constant slip operation. Therefore, efficacy of the proposed direct model predictive control scheme is verified by comparing its results to results obtained from equivalent vector control on equivalent machine. Results presented show lower loss regime with MPC on optimal stator angular velocity and rotor flux than vector control.