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اثر تغير سعر الصرف للعملة على التضخم والنمو الاقتصادي والتوازن الخارجيللمدة (2003 – 2023 )
No full textيهدف البحث إثبات فيما إذا كان سعر الصرف يحقق اهداف السياسة النقدية لبلوغ الأهداف النهائية للمربع السحري. عالج البحث مشكلة " هل من الممكن ان يؤدي تغير قيمة العملة تأثيرا ايجابيا في تحقيق المربع السحري في العراق . وغطت الدراسة المدة 2003-2023م وذلك لمحاولة فهم العلاقة السببية بين هذه المتغيرات فيما بينها ثم مع سعر الصرف حيث استخدمنا اختبار السببية و نماذج الخطأ من أجل تقدير نماذج قياسية تشرح هذه العلاقة في المدى الطويل. وتوصلت البحث الى ان يعتبر المربع السحري مفهوما بعيد المنال بالنسبة للسلطات النقدية في العراق ، كما ان الاقتصاد في العراق يتصف بضعف هياكله و تركيبته التي تعاني الاختلالات الهيكليـة مما انعكس على قيمة العملة المحلية الخارجية وارتفاع معدلات التضخم والبطالة، و تميز الاقتصاد العراقي بعدم المرونة في الإنتاج والتصدير والاستيراد مـن خلال اعتماده على تصدير منتوج واحد، وانخفاض وتذبذب أسعار النفط العالمية، إضافة إلى السوق الموازية التي أنهكت خزينة الدولة وساهمت في تدهور قيمة الدينار العراقي واوصى البحث تشجيع الاستثمار في قطاع التصدير خارج القطاع النفطي . التخفيض من الاستيراد الاستهلاكي لتجنيب ميزانية الدولة أعباء تغيرات الأسعار في الأسواق الدولية وضبط حركة الاستيراد والتصدير منعا لتهريب العملة الاجنبية
Efficient Modeling of UPFC Controlling in 500 kV / 100 MVA Transmission Line Power Flow
No full textThis paper presents the unified power flow controller (UPFC) in 500 kV / 100 MVA transmission line which is a converter of voltage sources depend on supple AC systems for series and shunt improvement between multi substation transmission. Full model comprises the 48 pulses gate thyristors which is constructed to become digitalized forms of simulations system to investigate the dynamics operations of control design. Two series and shunt voltage source controller for reactive and active power improvements with voltage stabilizing in electric grids networks. The comprehensive digital simulations of voltage source operation in shunt condition is statically synchronizes the compensator of STATCOM controller of the voltage at the buses and series operation as static series capacitors SSSC control inject voltages. Kept injected voltages in quadrature and current inside power systems is achieved in MATLAB/SIMULINK block sets by means of power systems blocks. The structure of UPFC controller and electric grids networks has been modeled through specific block from power systems. The controlling of shunt with 2-series voltage sources remain in this work depend on the decouple currents controller scenarios. The model scheme behavioral connects to 500 kV grid to examine and evaluated the suggested techniques. The results of simulation provide important increasing in power flow speed compared with other approaches which reveal the UPFC devices has excellent capability to enhance the reactive and real power flow
Corrosion Behavior of Friction Stir Welded Joints in Aluminum Alloys (AA2000, AA5000, AA7000, AA8000)
No full textFriction Stir Welding (FSW) demonstrates superior corrosion resistance in aluminum alloys compared to conventional techniques like arc and laser welding, owing to reduced heat-affected zones (3–5 mm) and refined microstructures. Environmental conditions significantly influence corrosion behavior, with marine (3.5% NaCl) and acidic (pH 3) environments accelerating pitting and intergranular corrosion in AA5000 and AA2000 alloys, respectively. Alloy composition plays a critical role: AA6061-T6 (AA6000 series) exhibits lower pitting rates (0.18 mm/year) than copper-rich AA2024 (0.34 mm/year), while optimized Li/Cu ratios in AA8090 (AA8000 series) reduce exfoliation corrosion by 25%. Mechanical-corrosion synergy reveals residual stresses and microstructural features, such as low-angle grain boundaries, directly impact stress corrosion cracking (SCC) and pitting resistance,Industrial applications in aerospace, marine, and infrastructure highlight FSW’s advantages, including 30% lower SCC risk in AA7000 fuselage panels and 50% extended service life for AA5083 ship hulls. Enhanced predictive modeling, integrating environmental variables, aligns with experimental data, supporting optimized welding parameters and post-weld treatments like laser peening and anodizing,This study underscores the interplay between alloy design, welding parameters, and environmental adaptation to enhance durability in critical engineering applications.
Leveraging Machine Learning for Proactive Network Security Threat Detection: Techniques, Challenges, and Future Directions
No full textThe escalating sophistication and volume of cyber threats necessitate a paradigm shift from traditional, reactive network security measures towards proactive, intelligent detection systems. This paper investigates the application of Machine Learning (ML) techniques for enhancing network security threat detection. The core research problem addressed is the inadequacy of conventional signature-based and rule-based systems in identifying novel, zero-day, and polymorphic attacks effectively. We explore the fundamental ML paradigms – supervised, unsupervised, and reinforcement learning – detailing their applicability to various network security tasks such as intrusion detection, malware analysis, and anomaly identification. Key aspects of the ML pipeline, including feature selection, data preprocessing, and robust model evaluation metrics, are discussed. The paper reviews significant implementations and case studies, highlighting the performance of different ML algorithms using benchmark datasets like NSL-KDD, CIC-IDS, and UNSW-NB15. Despite promising results demonstrating ML\u27s capability to improve detection accuracy and reduce false alarms, significant challenges remain. These include the high dimensionality of network data, the need for large-scale labeled datasets, the persistent issue of false positives/negatives, vulnerability to adversarial attacks, data privacy concerns, computational overhead, and the inherent difficulty in interpreting complex models (explainability). Future directions point towards the development of explainable AI (XAI), federated learning for privacy preservation, advanced reinforcement learning for autonomous response, hybrid modeling approaches, and strategies to counter adversarial manipulations. This research concludes that while ML offers powerful tools for bolstering network defenses, continuous research and development are crucial to overcome existing limitations and stay ahead of the evolving threat landscape
Application of Binomial Theory in Determining the Relationship Between Importance and Impact of Risks in Hospital Projects
No full textThis research deals with the topic of risk management through the application of binomial theory and risk matrix to analyze the relationship between the importance of risks and their impact on hospital construction projects, with the aim of developing an effective mechanism to prioritize dealing with risks and ensuring the success of the project. To achieve this goal, a questionnaire based on the five-point Likert scale was prepared, and the stability coefficient (Cronbach\u27s alpha) was 0.891, confirming the reliability of the tool. The questionnaire was distributed to engineers and specialists in the implementation of hospital projects, and the data were analyzed using accurate statistical tools. The results showed that cybersecurity risks came in first place with a score of 18.11, followed by financing risks, power outages, and unexpected costs. The data also showed that most of the risks fall into the high-risk category, which requires continuous monitoring and immediate interventions. The study concluded that the Risk Matrix is a practical and flexible tool for managing complex risks in health projects. The study recommends that information security should be given top priority from the early stages of the project, to protect digital infrastructure and ensure business continuity
Impact of using Smart Batteries on Improving Efficiency Of Storage Renewable Energy Systems
No full textThe rise of the smart era has turbocharged the growth of connected devices—and with it, the demand for better power sources. Lithium-ion batteries are widely used today, but their performance and flexibility are still constrained by inherent material limits and the complexity of new technologies. As we move deeper into Industry 4.0, driven by information technology and AI, disruptive materials and methods are opening the door to new batteries with stronger electrochemical performance and greater reliability. Just as important, “intelligence” is increasingly being built into how batteries are designed and managed.
This review clarifies what we mean by “smart batteries,” proposes a three-generation typology based on intelligence and functionality, and lay out how they work and where they can be applied. We also offer practical recommendations to address real-world development challenges and support the long-term sustainability of battery technologies
Wavelet-Based Collocation Techniques for Fractional Equations with Multiple Terms and Boundaries Restrictions for Optimized Approach
No full textThis paper discusses the limitations of initial value problems in relation to the stability, uniqueness, and accuracy of boundary value problems involving fractional polynomial differential equations. The objective is to establish a robust framework for addressing complex boundary value problems through wavelet-based numerical techniques. The wavelet collocation method uses Taylor wavelets to figure out mixed partial derivatives while also taking into account the boundary conditions. The method tries to find solutions to problems with boundary values that are related to the Benjamin Ona Mahony equation. We look at the single solutions for two-dimensional and three-dimensional corner domains, as well as smooth domains with localized forcing terms that come from polynomial exponential Laplacian expressions. The study demonstrates that, unlike initial value problems, the geometry of the domain and the magnitude of the eigenvalues influence the stability of boundary value problems and Green\u27s functions. The wavelet collocation method provides a highly accurate numerical solution for challenging domains in physics and engineering by effectively capturing pronounced peaks and managing the boundary layers. This is not the same as how things are in problems with initial values
Numerical Methods for Solving Large Sparse Linear Systems
No full textOften used in engineering, scientific computing, and optimization, the study investigates numerical approaches for managing big sparse linear systems. Regarding memory Many of whose entries are zeros, sparse matrices will benefit greatly in large systems using both computational speed and use. Resolving such systems, however, creates difficulties in memory usage and computational complexity, and numerical stability. The essay contrasts direct and iterative methods along with their respective advantages and disadvantages. Direct techniques such LU decomposition yield right results, for larger systems, however, they are rather hard. Iterative methods such as the Conjugate Gradient (CG) method become useful for badly conditioned system (when combined with preconditioning), and can be very beneficial when convergence is accelerated. This article describes the method of solving large sparse linear systems that exist in the real-world using parallelization and preconditioning in combination with iterative methods.
Enhancement Accuracy of UTM Grid by Redesigning to 3° Zones
No full textGeographers and surveyors attempted to determine each\u27s precise shape to be accurately shown on a flat surface. Geographers and surveyors tried to determine each\u27s precise shape to be accurately shown on a flat surface. The Earth\u27s shape was known as "spheroid" or "flattened" until it was demonstrated that it was the consequence of an ellipsoidal shape rotating about its semi-major axis. This search aims to investigate the current topographic map system (UTM 6-degree zone) and contrast it with the proposed system (UTM 3-degree zone). The case will be discussed and searched in the following headings: the contrast between zone 3° and zone 6°. As part of our research, we rely on the accompanying index (1/1000000). The Ellipsoidal Transverse Mercator is the foundation for the UTM projection, a worldwide coordinate system adopted by the U.S. Army in 1947. From the 180th meridian eastward, it splits the Earth between 84°N and 80°S into 60 zones, each 6° broad in longitude and numbered 1 through 60. The research demonstrates that using a 3° UTM zone significantly reduces projection distortion, as seen by the smaller error margins between scale-corrected and raw lengths. The results underline the effectiveness of smaller zones in precision-critical geodetic work and confirm the significance of zone width selection in geospatial analysis. This research shows that azimuthal distortions can affect the accuracy of geodetic readings even within a 6° UTM zone. Even though these distortions are minor, they must be corrected for high-accuracy applications like exact engineering layouts, geodetic control networks, and cadastral surveys. Applying the scale factor to azimuth values makes directional data more reliable and consistent with the geodetic datum
Artificial Intelligence Applications in Improving Electric Load Management: Case Studies
No full textAs the globe faces serious climate and energy concerns, Artificial Intelligence has shown to be a transformative force in the advancement of renewable energy technologies. This paper examines the existing and prospective applications of artificial intelligence in renewable energy. This work focuses on the transformative role of Artificial Intelligence (AI) in making renewable energy systems more efficient, reliable, and scalable. AI technology - from machine learning and deep learning to learning to learning - optimization of energy production, forecasts demand, future maintenance and management of decentralized energy networks.
Looking ahead, emerging fields like quantum machine learning and AI-powered augmented reality offer exciting possibilities, with the potential to fundamentally reshape energy infrastructures.
The survey highlights major innovations across wind and solar power, energy storage, and smart grids, emphasizing how AI helps address persistent challenges such as intermittency and variability. Equally important are the supporting technologies—big data, the Internet of Things (IoT), and real-time analytics—that drive the development of more advanced AI models. We also explore how AI is shaping energy policy and market modeling, paving the way for broader renewable energy adoption.
Real-world applications bring these ideas to life. For instance, Google\u27s collaboration with DeepMind has enhanced wind power generation using wiser forecasting, while Australia\u27s National Electricity Market has looked to AI in order to enhance grid stability. These cases demonstrate that AI’s role in renewable energy is not just theoretical—it is already delivering measurable results and redefining what’s possible in the global energy landscape.
. In order to optimize AI\u27s potential for advancing sustainable energy and combating climate change, this study identifies the obstacles preventing its implementation in renewable energy systems and makes suggestions for enhancing current technology