Long memory analysis is one of the most active areas in econometrics and time series where various methods have been introduced to identify and estimate the long memory parameter in partially integrated time series. One of the most common models used to represent time series that have a long memory is the ARFIMA (Auto Regressive Fractional Integration Moving Average Model) which diffs are a fractional number called the fractional parameter. To analyze and determine the ARFIMA model, the fractal parameter must be estimated. There are many methods for fractional parameter estimation. In this research, the estimation methods were divided into indirect methods, where the Hurst parameter is estimated fir

Piracy on phonograms is now, rightly, the crime of the electronic age. Despite the protection sought by States to provide for such registrations, whether at the level of national legislation or international agreements and conventions, but piracy has been and continues to pose a significant threat to the rights of the producers of those recordings, especially as it is a profitable way for hackers to get a lot of money in a way Illegal, which is contrary to the rules of legitimate competition. Hence, this research highlights the legal protection of producers of phonograms in light of the Iraqi Copyright Protection Act No. (3) of 1971, as amended.

This study investigates the results of electrocoagulation (EC) using aluminum (Al) electrodes as anode and stainless steel (grade 316) as a cathode for removing silica, calcium, and magnesium ions from simulated cooling tower blowdown waters. The simulated water contains (50 mg/l silica, 508 mg/l calcium, and 292 mg/l magnesium). The influence of different experimental parameters, such as current density (0.5, 1, and 2 mA/cm²), initial pH(5,7, and 10), the temperature of the simulated solution(25⁰C and 35 ⁰C), and electrolysis time was studied. The highest removal efficiency of 80.183%, 99.21%, and 98.06% for calcium, silica, and magnesium ions, respectively, were obtained at a current de

With the increasing reliance on microgrids as flexible and sustainable solutions for energy distribution, securing decentralized electricity grids requires robust cybersecurity strategies tailored to microgrid-specific vulnerabilities. The research paper focuses on enhancing detection capabilities and response times in the face of coordinated cyber threats in microgrid systems by implementing advanced technologies, thereby supporting decentralized operations while maintaining robust system performance in the presence of attacks. It utilizes advanced power engineering techniques to strengthen cybersecurity in modern power grids. A real-world CPS testbed was utilized to simulate the smart grid environment and analyze the impact of cyberattack

One of the diseases on a global scale that causes the main reasons of death is lung cancer. It is considered one of the most lethal diseases in life. Early detection and diagnosis are essential for lung cancer and will provide effective therapy and achieve better outcomes for patients; in recent years, algorithms of Deep Learning have demonstrated crucial promise for their use in medical imaging analysis, especially in lung cancer identification. This paper includes a comparison between a number of different Deep Learning techniques-based models using Computed Tomograph image datasets with traditional Convolution Neural Networks and SequeezeNet models using X-ray data for the automated diagnosis of lung cancer. Although the simple details p

Over the past few decades, the surveying fieldworks were usually carried out based on classical positioning methods for establishing horizontal and vertical geodetic networks. However, these conventional positioning techniques have many drawbacks such as time-consuming, too costly, and require massive effort. Thus, the Global Navigation Satellite System (GNSS) has been invented to fulfill the quickness, increase the accuracy, and overcome all the difficulties inherent in almost every surveying fieldwork. This research assesses the accuracy of local geodetic networks using different Global Navigation Satellite System (GNSS) techniques, such as Static, Precise Point Positioning, Post Processing Kinematic, Session method, a

The paper generates a geological model of a giant Middle East oil reservoir, the model constructed based on the field data of 161 wells. The main aim of the paper was to recognize the value of the reservoir to investigate the feasibility of working on the reservoir modeling prior to the final decision of the investment for further development of this oilfield. Well log, deviation survey, 2D/3D interpreted seismic structural maps, facies, and core test were utilized to construct the developed geological model based on comprehensive interpretation and correlation processes using the PETREL platform. The geological model mainly aims to estimate stock-tank oil initially in place of the reservoir. In addition, three scenarios were applie

This paper presents a new transform method to solve partial differential equations, for finding suitable accurate solutions in a wider domain. It can be used to solve the problems without resorting to the frequency domain. The new transform is combined with the homotopy perturbation method in order to solve three dimensional second order partial differential equations with initial condition, and the convergence of the solution to the exact form is proved. The implementation of the suggested method demonstrates the usefulness in finding exact solutions. The practical implications show the effectiveness of approach and it is easily implemented in finding exact solutions.

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