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Voltage Profile Enhancement and Power Loss Reduction with Economic feasibility Using Small Capacity Distribution Transformers

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Energy Engineering 2022年第6期119卷 2447-2467页

作者： Rasool m.Imran mohammed R.Saeed mohammed Amer mohammed Osama A.Suhry Ihsan H.Abdulqadder Hasan Wahhab Salih mohammed R.Almallah firas m.f.flaih Department of Electric Power Techniques Al-Hussain University CollegeKarbala56001Iraq School of Artificial Intelligence Wuchang University of TechnologyWuhan430223China State Company of the North Distribution Electricity Ministry of ElectricityBaghdad10013Iraq Department of Informatics and Software Engineering Cihan University-ErbilErbilKurdistan44001Iraq Najaf Gas Turbine Power Plant General Company of the Electricity ProductionMinistry of ElectricityBaghdad10013Iraq General Company of the Electricity Transmission-North Region Ministry of ElectricityBaghdad10013Iraq

Usually,rural areas can be electrified via three-phase distribution transformers with relatively large *** such areas,low voltage lines are used for long distances,which cause power losses and voltage drop for different types of *** losses and improving voltage profiles in rural distribution networks are significant challenges for electricity distribution *** different solutions were proposed in the literature to overcome these challenges,most of them face difficulties when applied in the conventional distribution *** address the above issues,an applicable solution is proposed in this paper by installing a number of small-capacity distribution transformers instead of every single large-capacity transformer in rural *** proposed approach is implemented in the branch network of Al-Hoqool village,which belongs to the Nineveh distribution *** network has been inspected on-site,drawn,and analyzed using the electrical systems analysis program(ETAP).The analysis showed that using the single-phase pole-mounted transformers can improve the voltage in the network’s end by 29%and enhance the voltage profile for all *** analysis has also demonstrated that the modification can reduce the total power losses by 78%compared to the existing *** the economic aspect,the payback period for the proposed network is assigned to be 20 months.

关键词： Single-phase pole-mounted transformer three-phase transformer power losses voltage profile distribution networks

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Complement-dependent neuroinflammation in spinal cord injury:from pathology to therapeutic implications

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Neural Regeneration Research 2025年第5期20卷 1324-1335页

作者： Hassan Saad Bachar El Baba Ali Tfaily firas Kobeissy Juanmarco Gutierrez Gonzalez Daniel Refai Gerald R.Rodts Christian mustroph David Gimbel Jonathan Grossberg Daniel L.Barrow matthew f.Gary Ali m.Alawieh Department of Neurosurgery Emory University School of MedicineAtlantaGAUSA Department of Neurology Yale UniversityNew HavenCTUSA Center for Neurotrauma Multiomics&Biomarkers(CNMB)Morehouse School of MedicineAtlantaGAUSA

Spinal cord injury remains a major cause of disability in young adults,and beyond acute decompression and rehabilitation,there are no pharmacological treatments to limit the progression of injury and optimize recovery in this *** the thorough investigation of the complement system in triggering and propagating cerebral neuroinflammation,a similar role for complement in spinal neuroinflammation is a focus of ongoing *** this work,we survey the current literature investigating the role of complement in spinal cord injury including the sources of complement proteins,triggers of complement activation,and role of effector functions in the *** study relevant data demonstrating the different triggers of complement activation after spinal cord injury including direct binding to cellular debris,and or activation via antibody binding to damage-associated molecular *** effector functions of complement have been implicated in spinal cord injury,and we critically evaluate recent studies on the dual role of complement anaphylatoxins in spinal cord injury while emphasizing the lack of pathophysiological understanding of the role of opsonins in spinal cord *** this pathophysiological review,we systematically review the different translational approaches used in preclinical models of spinal cord injury and discuss the challenges for future translation into human *** review emphasizes the need for future studies to dissect the roles of different complement pathways in the pathology of spinal cord injury,to evaluate the phases of involvement of opsonins and anaphylatoxins,and to study the role of complement in white matter degeneration and regeneration using translational strategies to supplement genetic models.

关键词： complement neuroinflammation neuroplasticity regeneration spinal cord injury targeted therapy

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Sports Prediction model through Cloud Computing and Big Data Based on Artificial Intelligence method

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Journal of Intelligent Learning Systems and Applications 2024年第2期16卷 53-79页

作者： Aws I. Abu Eid Achraf Ben miled Ahlem fatnassi majid A. Nawaz Ashraf f. A. mahmoud faroug A. Abdalla Chams Jabnoun Aida Dhibi firas m. Allan mohammed Ahmed Elhossiny Salem Belhaj Imen Ben mohamed faculty of Computing Studies Arab Open University Amman Jordan Computer Science Department Science College Northern Border University Arar Kingdom of Saudi Arabia Artificial Intelligence and Data Engineering Laboratory LR21ES23 Faculty of Sciences of Bizerte University of Carthage Tunis Tunisia Applied College Northern Border University Arar Kingdom of Saudi Arabia faculty of Specific Education Mansoura University Mansoura Egypt

This article delves into the intricate relationship between big data, cloud computing, and artificial intelligence, shedding light on their fundamental attributes and interdependence. It explores the seamless amalgamation of AI methodologies within cloud computing and big data analytics, encompassing the development of a cloud computing framework built on the robust foundation of the Hadoop platform, enriched by AI learning algorithms. Additionally, it examines the creation of a predictive model empowered by tailored artificial intelligence techniques. Rigorous simulations are conducted to extract valuable insights, facilitating method evaluation and performance assessment, all within the dynamic Hadoop environment, thereby reaffirming the precision of the proposed approach. The results and analysis section reveals compelling findings derived from comprehensive simulations within the Hadoop environment. These outcomes demonstrate the efficacy of the Sport AI model (SAIm) framework in enhancing the accuracy of sports-related outcome predictions. Through meticulous mathematical analyses and performance assessments, integrating AI with big data emerges as a powerful tool for optimizing decision-making in sports. The discussion section extends the implications of these results, highlighting the potential for SAIm to revolutionize sports forecasting, strategic planning, and performance optimization for players and coaches. The combination of big data, cloud computing, and AI offers a promising avenue for future advancements in sports analytics. This research underscores the synergy between these technologies and paves the way for innovative approaches to sports-related decision-making and performance enhancement.

关键词： Artificial Intelligence machine Learning Spark Apache Big Data SAIm

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Assessment of Nanoparticle-Enriched Solvents for Oil Recovery Enhancement

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fluid Dynamics & materials Processing 2023年第11期19卷 2827-2835页

作者： muayad m.Hasan firas K.Al-Zuhairi Anfal H.Sadeq Rana A.Azeez Department of Petroleum Technology University of Technology-IraqBaghdadIraq Department of Chemical Engineering University of Technology-IraqBaghdadIraq

Solvents are generally used to reduce the viscosity of heavy crude oil and ultimately enhance oil ***,a new method has been introduced where nanoparticles(NPs)are exploited to induce enhanced oil recovery owing to their ability to improve the mobility ratio,dampen the interfacial tension,and alter rock *** study investigated the integration of nano-alumina(Al_(2)O_(3))NPs with an n-hexane *** parti-cular,a Brookfield viscometer has been used to measure the crude oil viscosity and it has been found that NPs can effectively lead to a significant decrease in the overall oil viscosity(70 cp using the solvent only,45 cp when NPs are added).

关键词： Nanotechnology viscosity reduction organic solvents degree of viscosity reduction(DVR) enhanced oil recovery(EOR)

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Optimal management of Energy Storage Systems for Peak Shaving in a Smart Grid

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Computers, materials & Continua 2023年第5期75卷 3317-3337页

作者： firas m.makahleh Ayman Amer Ahmad A.manasrah Hani Attar Ahmed A.A.Solyman mehrdad Ahmadi Kamarposhti Phatiphat Thounthong Department of mechanical Engineering Al-Zaytoonah University of JordanAmman11733Jordan Department of Energy Engineering Zarqa UniversityZarqa13133Jordan Department of Electrical and Electronics Engineering Faculty of Engineering and ArchitecturesNisantasi UniversityIstanbul34398Turkey Department of Electrical Engineering Jouybar BranchIslamic Azad UniversityJouybarIran Renewable Energy Research Centre(RERC) Department of Teacher Training in Electrical EngineeringFaculty of Technical EducationKing Mongkut’s University of Technology North Bangkok1518Pracharat 1 RoadBangsueBangkok10800Thailand

In this paper,the installation of energy storage systems(EES)and their role in grid peak load shaving in two echelons,their distribution and generation are ***,the optimal placement and capacity of the energy storage is taken into consideration,then,the charge-discharge strategy for this equipment is ***,Genetic Algorithm(GA)and Particle Swarm Optimization(PSO)are used to calculate the minimum and maximum load in the network with the presence of energy storage *** energy storage systems were utilized in a distribution system with the aid of a peak load shaving ***,the battery charge-discharge is managed at any time during the day,considering the load consumption at each *** results depict that the load curve reached a constant state by managing charge-discharge with no significant *** shows the significance of such matters in terms of economy and technicality.

关键词： Cost energy storage particle swarm optimization(PSO) peak load smart grid

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Computational Intelligence Driven Secure Unmanned Aerial Vehicle Image Classification in Smart City Environment

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Computer Systems Science & Engineering 2023年第12期47卷 3127-3144页

作者： firas Abedi Hayder m.A.Ghanimi Abeer D.Algarni Naglaa f.Soliman Walid El-Shafai Ali Hashim Abbas Zahraa H.Kareem Hussein muhi Hariz Ahmed Alkhayyat Department of mathematics College of EducationAl-Zahraa University forWomenKarbalaIraq Biomedical Engineering Department College of EngineeringUniversity ofWarith Al-AnbiyaaKarbalaIraq Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Security Engineering Lab Computer Science DepartmentPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt College of Information Technology Imam Jaafar Al-Sadiq UniversityAl-Muthanna66002Iraq Department of medical Instrumentation Techniques Engineering Al-Mustaqbal University CollegeHillah51001Iraq Computer Engineering Department Mazaya University CollegeDhi QarIraq College of Technical Engineering The Islamic UniversityNajafIraq

Computational intelligence(CI)is a group of nature-simulated computationalmodels and processes for addressing difficult real-life *** CI is useful in the UAV domain as it produces efficient,precise,and rapid ***,unmanned aerial vehicles(UAV)developed a hot research topic in the smart city *** the benefits of UAVs,security remains a major challenging *** addition,deep learning(DL)enabled image classification is useful for several applications such as land cover classification,smart buildings,*** paper proposes novel meta-heuristics with a deep learning-driven secure UAV image classification(mDLS-UAVIC)model in a smart city *** purpose of the mDLS-UAVIC algorithm is to securely encrypt the images and classify them into distinct class *** proposedmDLS-UAVIC model follows a two-stage process:encryption and image *** encryption technique for image encryption effectively encrypts the UAV ***,the image classification process involves anXception-based deep convolutional neural network for the feature extraction ***,shuffled shepherd optimization(SSO)with a recurrent neural network(RNN)model is applied for UAV image classification,showing the novelty of the *** experimental validation of the mDLS-UAVIC approach is tested utilizing a benchmark dataset,and the outcomes are examined in various *** achieved a high accuracy of 98%.

关键词： Computational intelligence unmanned aerial vehicles deep learning metaheuristics smart city image encryption image classification

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Chimp Optimization Algorithm Based feature Selection with machine Learning for medical Data Classification

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Computer Systems Science & Engineering 2023年第12期47卷 2791-2814页

作者： firas Abedi Hayder m.A.Ghanimi Abeer D.Algarni Naglaa f.Soliman Walid El-Shafai Ali Hashim Abbas Zahraa H.Kareem Hussein muhi Hariz Ahmed Alkhayyat Department of mathematics College of EducationAl-Zahraa University for WomenKarbalaIraq Biomedical Engineering Department College of EngineeringUniversity ofWarith Al-AnbiyaaKarbalaIraq Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Computer Science Department Security Engineering LabPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt College of Information Technology Imam Jaafar Al-Sadiq UniversityAl-Muthanna66002Iraq Department of medical Instrumentation Techniques Engineering Al-Mustaqbal University CollegeHillah51001Iraq Computer Engineering Department Mazaya University CollegeDhi QarIraq College of Technical Engineering the Islamic UniversityNajafIraq

Datamining plays a crucial role in extractingmeaningful knowledge fromlarge-scale data repositories,such as data warehouses and *** rule mining,a fundamental process in data mining,involves discovering correlations,patterns,and causal structures within *** the healthcare domain,association rules offer valuable opportunities for building knowledge bases,enabling intelligent diagnoses,and extracting invaluable information *** paper presents a novel approach called the machine Learning based Association Rule mining and Classification for Healthcare Data management System(mLARmC-HDmS).The mLARmC-HDmS technique integrates classification and association rule mining(ARm)***,the chimp optimization algorithm-based feature selection(COAfS)technique is employed within mLARmC-HDmS to select relevant *** by the foraging behavior of chimpanzees,the COA algorithm mimics their search strategy for ***,the classification process utilizes stochastic gradient descent with a multilayer perceptron(SGD-mLP)model,while the Apriori algorithm determines attribute *** propose a COA-based feature selection approach for medical data classification using machine learning *** approach involves selecting pertinent features from medical datasets through COA and training machine learning models using the reduced feature *** evaluate the performance of our approach on various medical datasets employing diverse machine learning *** results demonstrate that our proposed approach surpasses alternative feature selection methods,achieving higher accuracy and precision rates in medical data classification *** study showcases the effectiveness and efficiency of the COA-based feature selection approach in identifying relevant features,thereby enhancing the diagnosis and treatment of various *** provide further validation,we conduct detailed experiments on a benchmark

关键词： Association rule mining data classification healthcare data machine learning parameter tuning data mining feature selection mLARmC-HDmS COA stochastic gradient descent Apriori algorithm

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Hybrid Deep Learning Enabled Load Prediction for Energy Storage Systems

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Computers, materials & Continua 2023年第5期75卷 3359-3374页

作者： firas Abedi Hayder m.A.Ghanimi mohammed A.m.Sadeeq Ahmed Alkhayyat Zahraa H.Kareem Sarmad Nozad mahmood Ali Hashim Abbas Ali S.Abosinnee Waleed Khaild Al-Azzawi mustafa musa Jaber mohammed Dauwed Department of mathematics College of EducationAl-Zahraa University for WomenKarbalaIraq Biomedical Engineering Department College of EngineeringUniversity of Warith Al-AnbiyaaKarbalaIraq ITm Department Technical College of AdministrationDuhok Polytechnic UniversityDuhokIraq College of Technical Engineering The Islamic UniversityNajafIraq Department of medical Instrumentation Techniques Engineering Al-Mustaqbal University CollegeHillah51001Iraq Computer Technology Engineering College of Engineering TechnologyAl-Kitab UniversityKirkuk36013Iraq College of Information Technology Imam Ja’afar Al-Sadiq UniversityAl-Muthanna66002Iraq Altoosi University College NajafIraq Department of medical Instruments Engineering Techniques Al-Farahidi UniversityBagdadIraq Department of medical Instruments Engineering Techniques Al-Turath University CollegeBaghdad10021Iraq Department of medical Instruments Engineering Techniques Al-Farahidi UniversityBaghdad10021Iraq Department of medical Instrumentations Techniques Engineering Dijlah University CollegeBaghdadIraq

Recent economic growth and development have considerably raised energy consumption over the *** load prediction approaches become essential for effective planning,decision-making,and contract evaluation of the power *** order to achieve effective forecasting outcomes with minimumcomputation time,this study develops an improved whale optimization with deep learning enabled load prediction(IWO-DLELP)scheme for energy storage systems(ESS)in smart grid *** major intention of the IWO-DLELP technique is to effectually forecast the electric load in SG environment for designing proficient *** proposed IWO-DLELP model initially undergoes pre-processing in two stages namely min-max normalization and feature ***,partition clustering approach is applied for the decomposition of data into distinct clusters with respect to distance and objective ***,IWO with bidirectional gated recurrent unit(BiGRU)model is applied for the prediction of load and the hyperparameters are tuned by the use of IWO *** experiment analysis reported the enhanced results of the IWO-DLELP model over the recent methods interms of distinct evaluation measures.

关键词： Load forecasting smart grid energy storage system electricity load forecasting artificial intelligence clustering

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Assessment of rapid impact compaction in ground improvement from in-situ testing

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Journal of Central South University 2013年第3期20卷 786-790页

作者： m.m.mohammed H.Roslan S.firas Department of Civil Engineering Faculty of EngineeringUniversity of MalayaKuala Lumpur 50603Malaysia

Ground improvement has been used on many construction sites to densify granular materials,in other word,to improve soil properties and reduce potential *** work presents a case study of ground improvement using rapid impact compaction(RIC).The research site comprises the construction of workshop and depots as part of railway development project at Batu Gajah-Ipoh,***-situ testing results show that the subsurface soil comprises mainly of sand and silty sand through the investigated depth extended to *** is approximately 0.5m below the ground *** of improvement was based on the results of pre-and post-improvement cone penetration test(CPT).Interpretation software has been used to infer soil *** test was conducted to estimate soil *** is found that the technique succeeds in improving soil properties namely the relative density increases from 45% to 70%,the friction angle of soil is increased by an average of 3o,and the soil settlement is reduced by 50%.The technique succeeds in improving soil properties to approximately 5.0m in depth depending on soil uniformity with depth.

关键词：冲击压实测试评估地面土壤理化性质原位土壤性质粒状物料建筑工地

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Severity Based Light-Weight Encryption model for Secure medical Information System

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Computers, materials & Continua 2023年第3期74卷 5691-5704页

作者： firas Abedi Subhi R.m.Zeebaree Zainab Salih Ageed Hayder m.A.Ghanimi Ahmed Alkhayyat mohammed A.m.Sadeeq Sarmad Nozad mahmood Ali S.Abosinnee Zahraa H.Kareem Ali Hashim Abbas Waleed Khaild Al-Azzawi mustafa musa Jaber mohammed Dauwed Department of mathematics College of EducationAl-Zahraa University for WomenKarbalaIraq Energy Eng.Department Technical College of EngineeringDuhok Polytechnic UniversityDuhokIraq Computer Science Department College of ScienceNawroz UniversityDuhokIraq Biomedical Engineering Department College of EngineeringUniversity of Warith Al-AnbiyaaKarbalaIraq College of Technical Engineering The Islamic UniversityNajafIraq ITm Department Technical College of AdministrationDuhok Polytechnic UniversityDuhokIraq Computer Technology Engineering College of Engineering TechnologyAl-Kitab UniversityIraq Altoosi University College NajafIraq Department of medical Instrumentation Techniques Engineering Al-Mustaqbal University CollegeHillah51001Iraq College of Information Technology Imam Ja’afar Al-Sadiq UniversityAl-Muthanna66002Iraq Department of medical Instruments Engineering Techniques Al-Farahidi UniversityBagdadIraq Department of medical Instruments Engineering Techniques Al-Turath University CollegeBaghdad10021Iraq Department of medical Instruments Engineering Techniques Al-Farahidi UniversityBaghdad10021Iraq Department of medical Instrumentations Techniques Engineering Dijlah University CollegeBaghdadIraq

As the amount of medical images transmitted over networks and kept on online servers continues to rise,the need to protect those images digitally is becoming increasingly ***,due to the massive amounts of multimedia and medical pictures being exchanged,low computational complexity techniques have been *** commonly used algorithms offer very little security and require a great deal of communication,all of which add to the high processing costs associated with using ***,a deep learning classifier is used to classify records according to the degree of concealment they *** images that aren’t needed can be saved by using this method,which cuts down on security *** is one of the most effective methods for protecting medical images after this *** and dispersion are two fundamental encryption processes.A new encryption algorithm for very sensitive data is developed in this *** splitting with image blocks is nowdeveloped by using Zigzag patterns,rotation of the image blocks,and random permutation for scrambling the *** that,this research suggests a Region of Interest(ROI)technique based on selective picture *** the first step,we use an active contour picture segmentation to separate the ROI from the Region of Background(ROB).Permutation and diffusion are then carried out using a Hilbert curve and a Skew Tent *** all of the blocks have been encrypted,they are combined to create encrypted *** investigational analysis is carried out to test the competence of the projected ideal with existing techniques.

关键词： Deep learning encryption medical images scrambling security skew tent map rotation zigzag pattern

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