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Hybrid Model of Power Transformer Fault Classification Using C-set and MFCM – MCSVM

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CSEE Journal of Power and Energy Systems 2024年第2期10卷 672-685页

作者： Ali Abdo Hongshun Liu Yousif mahmoud Hongru Zhang Ying Sun Qingquan Li Jian Guo Shandong Provincial Key Laboratory of UHV Transmission Technology and Equipment School of Electrical EngineeringShandong UniversityJinan 250061China

This paper aims to increase the diagnosis accuracy of the fault classification of power transformers by introducing a new off-line hybrid model based on a combination subset of the et method(C-set)&modified fuzzy C-mean algorithm(MFCM)and the optimizable multiclass-SVM(MCSVM).The innovation in this paper is shown in terms of solving the predicaments of outliers,boundary proportion,and unequal data existing in both traditional and intelligence *** into consideration the closeness of dissolved gas analysis(DGA)data,the C-set method is implemented to subset the DGA data samples based on their type of faults within unrepeated ***,the MFCM is used for removing outliers from DGA samples by combining highly similar data for every subset within the same cluster to obtain the optimized training data(OTD)*** is also used to minimize dimensionality of DGA samples and the uncertainty of transformer condition *** that,the optimized MCSVM is trained by using the(OTD).The proposed model diagnosis accuracy is 93.3%.The obtained results indicate that our model significantly improves the fault identification accuracy in power transformers when compared with other conventional and intelligence models.

关键词： Combination subset of set(C-set)method modified fuzzy C-means(MFCM) optimizable multiclass-SVM(MCSVM) optimized training data(OTD)

Electrospun Aloe Vera Extract Loaded Polycaprolactone Scaffold for Biomedical Applications:A Promising Candidate for Corneal Stromal Regeneration

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Journal of Bionic Engineering 2024年第4期21卷 1949-1959页

作者： Amin Orash mahmoud Salehi Mohammad Rafienia Narsimha Mamidi Saeed Heidari Keshel Alireza Baradaran-Rafii Department of Chemistry and Nanotechnology School of Engineering and ScienceTecnologico de MonterreyMonterreyNuevo Leon64849Mexico Biosensor Research Center Isfahan University of Medical SciencesIsfahan81746-73461Iran Wisconsin Center for NanoBioSystems School of PharmacyUniversity of Wisconsin-MadisonMadisonWisconsin53705USA Medical Nanotechnology Research Center Shahid Beheshti University of Medical SciencesTehran19857-17443Iran Department of Ophthalmology Ophthalmic Research CenterLabbafinejad Medical CenterShahid Beheshti University of Medical SciencesTehran19395-4631Iran Department of Ophthalmology Morsani College of MedicineUniversity of South FloridaTampaFL33612USA Department of Tissue Engineering and Applied Cell Science School of Advanced Technologies in MedicineShahid Beheshti University of Medical SciencesTehran19857-17443Iran

Corneal diseases,the second leading cause of global vision loss affecting over 10.5 million people,underscores the unmet demand for corneal tissue *** the scarcity of fresh donor corneas and the associated risks of immune rejection,corneal tissue engineering becomes *** nanofibrous scaffolds that mimic the natural corneal structure is crucial for creating transparent and mechanically robust corneal equivalents in tissue ***,Aloe Vera Extract(AVE)/Polycaprolactone(PCL)nanofibrous scaffolds were primed using *** electrospun AVE/PCL fibers exhibit a smooth,bead-free morphology with a mean diameter of approximately 340±95 nm and appropriate light *** measurements reveal Young’s modulus and ultimate tensile strength values of around 3.34 MPa and 4.58 MPa,respectively,within the range of stromal *** addition,cell viability of AVE/PCL fibers was measured against Human Stromal Keratocyte Cells(HSKCs),and improved cell viability was *** cell-fiber interactions were investigated using scanning electron *** conclusion,the incorporation of Aloe Vera Extract enhances the mechanical,optical,hydrophilic,and biological properties of PCL fibers,positioning PCL/AVE fiber scaffolds as promising candidates for corneal stromal regeneration.

关键词： Corneal stromal regeneration Aloe vera extract Polycaprolactone Electrospinning

Source Camera Identification Algorithm Based on Multi-Scale Feature Fusion

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Computers, Materials & Continua 2024年第8期80卷 3047-3065页

作者： Jianfeng Lu Caijin Li Xiangye Huang Chen Cui mahmoud Emam School of Computer Science and Technology Hangzhou Dianzi UniversityHangzhou310018China Shangyu Institute of Science and Engineering Hangzhou Dianzi UniversityShaoxing312300China Key Laboratory of Public Security Information Application Based on Big-Data Architecture Ministry of Public SecurityZhejiang Police CollegeHangzhou310000China Faculty of Artificial Intelligence Menoufia UniversityShebin El-Koom32511Egypt

The widespread availability of digital multimedia data has led to a new challenge in digital *** source camera identification algorithms usually rely on various traces in the capturing ***,these traces have become increasingly difficult to extract due to wide availability of various image processing *** Neural Networks(CNN)-based algorithms have demonstrated good discriminative capabilities for different brands and even different models of camera ***,their performances is not ideal in case of distinguishing between individual devices of the same model,because cameras of the same model typically use the same optical lens,image sensor,and image processing algorithms,that result in minimal overall *** this paper,we propose a camera forensics algorithm based on multi-scale feature fusion to address these *** proposed algorithm extracts different local features from feature maps of different scales and then fuses them to obtain a comprehensive feature *** representation is then fed into a subsequent camera fingerprint classification *** upon the Swin-T network,we utilize Transformer Blocks and Graph Convolutional Network(GCN)modules to fuse multi-scale features from different stages of the backbone ***,we conduct experiments on established datasets to demonstrate the feasibility and effectiveness of the proposed approach.

关键词： Source camera identification camera forensics convolutional neural network feature fusion transformer block graph convolutional network

Predicting Energy Consumption Using Stacked LSTM Snapshot Ensemble

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Big Data Mining and Analytics 2024年第2期7卷 247-270页

作者： Mona Ahamd Alghamdi Abdullah S.A.L-Malaise AL-Ghamdi mahmoud Ragab Information Systems Department Faculty of Computing and Information Technology(FCIT)King Abdulaziz University(KAU)Jeddah 21589Kingdom of Saudi Arabia Information Systems Department FCITKAUJeddah 21589Kingdom of Saudi Arabiaand also with School of EngineeringComputing and DesignDar Al-Hekma UniversityJeddah 22246Kingdom of Saudi Arabia Information Technology Department FCITKAUJeddah 21589Kingdom of Saudi Arabiaand also with Mathematics DepartmentFaculty of ScienceAl-Azhar UniversityNaser City 11884Egypt

The ability to make accurate energy predictions while considering all related energy factors allows production plants,regulatory bodies,and governments to meet energy demand and assess the effects of energy-saving *** energy consumption falls within normal parameters,it will be possible to use the developed model to predict energy consumption and develop improvements and mitigating measures for energy *** objective of this model is to accurately predict energy consumption without data limitations and provide results that are easily *** proposed model is an implementation of the stacked Long Short-Term Memory(LSTM)snapshot ensemble combined with the Fast Fourier Transform(FFT)and *** and Berard’s Individual Household Electric-Power Consumption(IHEPC)dataset incorporated with weather data are used to analyse the model’s accuracy with predicting energy *** model is trained,and the results measured using Root Mean Square Error(RMSE),Mean Absolute Error(MAE),Mean Absolute Percentage Error(MAPE),and coefficient of determination(R^(2))metrics are 0.020,0.013,0.017,and 0.999,*** stacked LSTM snapshot ensemble performs better than the compared models based on prediction accuracy and minimized *** results of this study show that prediction accuracy is high,and the model’s stability is high as *** model shows that high levels of accuracy prove accurate predictive ability,and together with high levels of stability,the model has good interpretability,which is not typically accounted for in ***,this study shows that it can be inferred.

关键词： energy consumption prediction Artificial Intelligence(AI) Deep Learning(DL) snapshot ensemble

Bone marrow microRNA-34a is a good indicator for response to treatment in acute myeloid leukemia

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Oncology Research 2024年第3期32卷 577-584页

作者： MONA SA BDELLATEIF NAGLAA MHASSAN mahmoud MKAMEL YOMNA MEL-MELIGUI Department of Cancer Biology Medical Biochemistry and Molecular BiologyNational Cancer InstituteCairo UniversityCairo11976Egypt Department of Cinical Pathology National Cancer InstituteCairo UniversityCairo11976Egypt

Background:microRNA 34a(miR 34a)had been reported to have a diagnostic role in acute myeloid leukemia(AML).However,its value in the bone marrow(BM)of AML patients,in addition to its role in response to therapy is still *** current study was designed to assess the diagnostic,prognostic,and predictive significance of miR 34a in the BM of AML ***:The miR.34a was assed in BM aspirate of 82 AML patients in relation to 12 normal control subjects using *** data were assessed for correlation with the relevant dinical critenia,response to therapy,disease-free survival(DFS),and overall survival(OS)***:miR.34a was significantly downregulated in AML patients[0.005(3.3×10^(-6)-1.32)],compared to the control subjects[0.108(3.2× 10^(-4)-1.64),p=0.021].*** relative quantification(RQ)of miR-34a was 0.106(range;0-32.12).The specifaity,sensitivity,and area under the curve(AUC)for the diagnosis of AML were(58.3%,69.5%,0.707,respectively,p=0.021).patients with upregulated miR-34a showed decreased platelets count<34.5 × 10^(9)/L,and achieved early complete remission(CR,p=0.031,p=0.044,respectively).Similarly,patients who were refractory to therapy showed decreased miR 34a levels in comparison to those who achieved CR[0.002(0-0.01)and 0.12(0-32.12),respectively,p=0.002].Therefore,miR 34a could significantly identify patients with CR with a specificity of 75%and sensitivity of 100%at a cut-off of 0.014(AUC=0.927,p=0.005).There was no considerable association between miR-34a expression and survival rates of the induded AML ***:miR-34a could be a beneficial diagnostic biomarker for AML *** addition,it serves as a good indicator for response to therapy,which could possibly identify patients who are refractory to treatment with 100%sensitivity and 75%specificity.

关键词： AML miR 34a microRNA Leukemia

Unveiling the intrinsic properties of single NiZnFeO_x entity for promoting electrocatalytic oxygen evolution

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Chinese Chemical Letters 2024年第4期35卷 181-186页

作者： Zhihao Gu Jiabo Le Hehe Wei Zehui Sun mahmoud Elsayed Hafez Wei Ma Key Laboratory for Advanced Materials and Joint International Research Laboratory of Precision Chemistry and Molecular Engineering Feringa Nobel Prize Scientist Joint Research CenterFrontiers Science Center for Materiobiology and Dynamic ChemistrySchool of Chemistry and Molecular EngineeringEast China University of Science and TechnologyShanghai 200237China Ningbo Institute of Materials Technology and Engineering Chinese Academy of SciencesNingbo 315201China Department of Chemistry Faculty of ScienceBeni-Suef UniversityBeni-Suef 62511Egypt

Although considerable research efforts have been devoted to the design and development of non-noble electrocatalysts for oxygen evolution reaction(OER), substantial enhancement of OER performance with commercial-scale water electrolysis remains a big challenge. This could result from the difficulties in detecting the intrinsic properties and overlooking the assembly process for electrochemical OER process. Here, we employ a microjet collision method to investigate the intrinsic OER activities of individual NiZnFeO_x entities with and without a moderate magnetic field. Our results demonstrate that single NiZnFeO_x nanoparticles(NPs) show the excellent OER performance with a lowest onset potential(~1.35 V vs. RHE) and a greatest magnetic enhancement(~118%) among bulk materials, single agglomerations and NPs. Furthermore, we explore the utility of theoretical investigation by density functional theory(DFT)calculations for studying OER process on NiZnFeO_x surfaces without and with spin alignment, indicating monodispersed NiZnFeO_xNPs with totally spin alignment facilitates the OER process under the external magnetic field. It is found that the well-dispersion of NiZnFeO_x NPs would increase the electrical conductivity and the surface spin state, resulting in promoting their OER activities. This work provides a test for uncovering the essential roles of NPs assembly to a significant promotion of their magnet-assisted OER.

关键词： Single entity OER Magnetic enhancement Electrocatalysis Well-dispersion

维普期刊数据库

Search for production of dark fermion candidates in association with heavy neutral gauge boson decaying to dimuon in proton-proton collisions at√s=8 TeV using CMS open data

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Chinese Physics C 2024年第4期48卷 6-13页

作者： Y.mahmoud H.Abdallah M.T.Hussein S.Elgammal Centre for theoretical physics The British University in EgyptEgypt Physics Department Faculty of ScienceCairo UniversityEgypt

In this study,we conducted a search for dark matter using a part of the data recorded by the CMS experiment during run-I of the LHC in 2012 with a center of mass energy of 8 TeV and an integrated luminosity of 11.6 fb−*** data were gathered from the CMS open *** matter,in the framework of the simplified model(mono-Z′),can be produced from proton-proton collisions in association with a new hypothetical gauge boson,Z′.Thus,the search was conducted in the dimuon plus large missing transverse momentum *** benchmark scenario of mono-Z′,which is known as light vector,was used for interpreting the CMS open *** evidence of dark matter was observed,and exclusion limits were set on the masses of dark matter and Z′at 95%confidence level.

关键词： dark matter large hadron collider CMS open data search for new physics dark fermion

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Glucagon-like peptide-1 receptor agonists as a possible intervention to delay the onset of type 1 diabetes:A new horizon

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World Journal of Diabetes 2024年第2期15卷 133-136页

作者： mahmoud Nassar Ajay Chaudhuri Husam Ghanim Paresh Dandona Department of Internal Medicine Division of EndocrinologyDiabetes and MetabolismJacobs School of Medicine and Biomedical SciencesUniversity of BuffaloBuffaloNY 14221United States

Type 1 diabetes(T1D)is a chronic autoimmune condition that destroys insulinproducing beta cells in the pancreas,leading to insulin deficiency and *** management of T1D primarily focuses on exogenous insulin replacement to control blood glucose ***,this approach does not address the underlying autoimmune process or prevent the progressive loss of beta *** research has explored the potential of glucagon-like peptide-1 receptor agonists(GLP-1RAs)as a novel intervention to modify the disease course and delay the onset of ***-1RAs are medications initially developed for treating type 2 *** exert their effects by enhancing glucose-dependent insulin secretion,suppressing glucagon secretion,and slowing gastric *** evidence suggests that GLP-1RAs may also benefit the treatment of newly diagnosed patients with *** article aims to highlight the potential of GLP-1RAs as an intervention to delay the onset of T1D,possibly through their potential immunomodulatory and anti-inflammatory effects and preservation of *** article aims to explore the potential of shifting the paradigm of T1D management from reactive insulin replacement to proactive disease modification,which should open new avenues for preventing and treating T1D,improving the quality of life and long-term outcomes for individuals at risk of T1D.

关键词： Type 1 diabetes Semaglutide Glucagon-like peptide-1 receptor agonists Insulin therapy Autoimmune response Blood glucose monitoring Β-cell preservation Early screening Teplizumab Randomized controlled trials

Applications of artificial intelligence in geothermal resource exploration: A review

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Deep Underground Science and Engineering 2024年第3期3卷 269-285页

作者： mahmoud AlGaiar Mamdud Hossain Andrei Petrovski Aref Lashin Nadimul Faisal School of Engineering Robert Gordon UniversityAberdeenUK National Subsea Centre AberdeenUK Petroleum and Natural Gas Engineering Department College of EngineeringKing Saud UniversityRiyadhSaudi Arabia

Artificial intelligence (AI) has become increasingly important in geothermal exploration,significantly improving the efficiency of resource *** review examines current AI applications,focusing on the algorithms used,the challenges addressed,and the opportunities *** addition,the review highlights the growth of machine learning applications in geothermal exploration over the past decade,demonstrating how AI has improved the analysis of subsurface data to identify potential *** techniques such as neural networks,support vector machines,and decision trees are used to estimate subsurface temperatures,predict rock and fluid properties,and identify optimal drilling *** particular,neural networks are the most widely used technique,further contributing to improved exploration ***,the widespread adoption of AI in geothermal exploration is hindered by challenges,such as data accessibility,data quality,and the need for tailored data science training for industry ***,the review emphasizes the importance of data engineering methodologies,data scaling,and standardization to enable the development of accurate and generalizable AI models for geothermal *** is concluded that the integration of AI into geothermal exploration holds great promise for accelerating the development of geothermal energy *** effectively addressing key challenges and leveraging AI technologies,the geothermal industry can unlock cost‐effective and sustainable power generation opportunities.

关键词： artificial intelligence geothermal energy geothermal exploration geothermometry hidden/blind geothermal resources machine learning

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