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Rare earth based MgPm_(2)X_(4)(X=S,Se) spinel chalcogenides for spintronic and thermoelectric applications

Journal of Rare Earths 2024年第8期42卷 1577-1585,I0006页

作者： Tariq M.Al-Daraghmeh Omar Zayed Ghulam M.Mustafa Taharh Zelai Bisma Younas Hind Albalawi S.Bouzgarrou Othman Hakami Q.Mahmood khaild i.huss.i. Basic Sciences Department King Abdullah Ⅱ School of EngineeringPrincess Sumaya University For TechnologyP.O.Box 1438Al-jubaiha11941AmmanJordan Department of Physics Division of Science&TechnologyUniversity of EducationLahorePakistan Department of Physics Faculty of ScienceJazan University JazanSaudi Arabia Department of Physics The University of LahoreLahorePakistan Department of Physics College of SciencesPrincess Nourah bint Abdulrahman University(PNU)P.O.Box 84428Riyadh11671Saudi Arabia Department of Physics College of ScienceQassim UniversityPo.64BuraidahSaudi Arabia Laboratoire de Microelectronique et instrumentation(UR 03/13-04) Faculte des Sciences de MonastirAvenue de I'Environnement 5000MonastirTunisia Department of Chemistry Faculty of ScienceJazan University JazanSaudi Arabia Basic&Applied Scientific Research Center Department of PhysicsCollege of ScienceImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam 31441Saudi Arabia Department of Radiological Sciences College of Applied Medical SciencesKing Khalid UniversityAbha61421Saudi Arabia

in current report,the structural,magnetic,and thermoelectric properties of RE doped MgPm_(2)X_(4)(X=S,Se) spinels were *** energy difference in ferromagnetic and antiferromagnetic states reveals the stability of MgPm_(2)(S/Se)_(4) in the ferromagnetic *** co mputation of enthalpy of formation also ascertains thermodynamic stability of crystal ***-dependent band structure and density of states analysis reveal ferromagnetic semiconducting character showing different electronic behavior in both spin *** room temperature ferromagnetism,spin polarization and Curie temperature are estimated from exchange energies *** addition,exchange constants(N_(0)α and N_(0)β),exchange energy Δ_(x)(pd),crystal ifeld energy,and double exchange mechanism were studied to explore the magnetic ***,the electrical conductivity,thermal conductivity,Seebeck co-efficient,and power factor show effect on electrons spin and their potential for thermoelectric devices.

关键词： Rare earth elements Spintronic applications Thermoelectric effects Exchange energies

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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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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.