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Explainable Conformer Network for Detection of COVID-19 Pneumonia from Chest CT scan: From Concepts toward Clinical Explainability

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Computers, Materials & Continua 2024年第1期78卷 1171-1187页

作者： mohamed abdel-Basset Hossam Hawash mohamed Abouhawwash s.s.Askar Alshaimaa A.Tantawy Faculty of Computers and Informatics Zagazig UniversityZagazig44519Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast LansingMI48824USA Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of statistics and Operations Research College of ScienceKing Saud UniversityP.O.Box 2455Riyadh11451Saudi Arabia

The early implementation of treatment therapies necessitates the swift and precise identification of COVID-19 pneumonia by the analysis of chest CT scans.This study aims to investigate the indispensable need for precise and interpretable diagnostic tools for improving clinical decision-making for COVID-19 diagnosis.This paper proposes a novel deep learning approach,called Conformer Network,for explainable discrimination of viral pneumonia depending on the lung Region of Infections(ROI)within a single modality radiographic CT scan.Firstly,an efficient U-shaped transformer network is integrated for lung image segmentation.Then,a robust transfer learning technique is introduced to design a robust feature extractor based on pre-trained lightweight Big Transfer(BiT-L)and finetuned on medical data to effectively learn the patterns of infection in the input image.secondly,this work presents a visual explanation method to guarantee clinical explainability for decisions made by Conformer Network.Experimental evaluation of real-world CT data demonstrated that the diagnostic accuracy of ourmodel outperforms cutting-edge studies with statistical significance.The Conformer Network achieves 97.40% of detection accuracy under cross-validation settings.Our model not only achieves high sensitivity and specificity but also affords visualizations of salient features contributing to each classification decision,enhancing the overall transparency and trustworthiness of our model.The findings provide obvious implications for the ability of our model to empower clinical staff by generating transparent intuitions about the features driving diagnostic decisions.

关键词： Deep learning COVID-19 multi-modal medical image fusion diagnostic image fusion

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Geochemistry of island arc assemblage in the Eastern Desert of Egypt and the role of Pan-African magmatism in crustal growth of the Arabian–Nubian shield:A review

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Acta Geochimica 2024年第3期43卷 588-622页

作者： abdel-Aal Mabdel-Karim mohamed MHamdy Adel A.surour Geology Department Faculty of ScienceZagazig UniversityZagazig 44519Egypt Geology Department Faculty of ScienceTanta UniversityTanta 31527Egypt Department of Geological sciences Faculty of ScienceNew Galala City 43511Egypt Geology Department Faculty of ScienceCairo UniversityGiza 12613Egypt

Neoproterozoic island arc assemblage of the Arabian–Nubian shield(ANs)in the Eastern Desert(ED)of Egypt comprises juvenile suites of metavolcanics(MV),large amounts of meta-sedimentary rocks(Ms),and voluminous metagabbros-diorites(MGD)and syn-tectonic intrusions of older granitoids(OG).We report here the updates of these four rock units in terms of classification,distribution,chemical characteristics,geodynamic evolution,metamorphism,and ages.In addition,we discuss these integrated data to elucidate a reasonable and reliable model for crustal evolution in the ANs.The main features of these rock units indicate their relation to each other and the geodynamic environment dominated by early immature oceanic island arcs to primitive continental arcs.Integrated information of the island arc metavolcanic and plutonic rocks(gabbros,diorites,tonalites,and granodiorites)furnish evidence of the genetic relationships.These include proximity and a coeval nature in the field;all protolith magmas are subalkaline in nature following calc-alkaline series with minor tholeiitic affinities;common geochemical signature of the arc rocks and subduction-related magmatism;their similar enrichment in LREEs;and similar major element compositions with mafic melts derived from metasomatized mantle wedge.The volcano-sedimentary and the OG rocks underwent multiphase deformation events whereas the MGD complexes deformed slightly.Based on the magmatic,sedimentological,and metamorphic evolutions constrained by geochronological data as well as the progressive evolutionary trend from extensional to compressional regimes,a possible gradual decrease in the subducted slab dip angle is the most infl uential in any geodynamic model for arc assemblage in the ED of Egypt.

关键词： Island arc assemblage Magmatism Crustal growth Neoproterozoic Arabian–Nubian shield Eastern Desert Egypt

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General exact solution of the fin problem with variable thermal conductivity

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Propulsion and Power Research 2016年第1期5卷 63-69页

作者： Abass H.abdel Kader mohamed s.abdel latif Hamed M.Nour Engineering Mathematics and Physics Department Faculty of EngineeringMansoura UniversityMansoura 35516Egypt

In this paper,Lie point symmetry method is used to obtain the general exact solution of the second order nonlinear ordinary differential equation which goveming heat transfer in rectangular fin with variable thermal conductivity.some new forms of thermal conductivity are introduced and the associated exact solution is obtained in each case.The general relation among the fin efficiency,thermal conductivity and thermo-geometric parameter is obtained.

关键词： Exact solution Fin Thermal conductivity Heat transfer Lie point symmetry

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Gradient Optimizer Algorithm with Hybrid Deep Learning Based Failure Detection and Classification in the Industrial Environment

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Computer Modeling in Engineering & sciences 2024年第2期138卷 1341-1364页

作者： mohamed Zarouan Ibrahim M.Mehedi shaikh Abdul latif Md.Masud Rana Department of Electrical and Computer Engineering(ECE) King Abdulaziz UniversityJeddah21589Saudi Arabia Center of Excellence in Intelligent Engineering systems(CEIEs) King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Nuclear Engineering King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Electrical and Electronic Engineering Rajshahi University of Engineering and TechnologyRajshahiBangladesh

Failure detection is an essential task in industrial systems for preventing costly downtime and ensuring the seamlessoperation of the system. Current industrial processes are getting smarter with the emergence of Industry 4.0.specifically, various modernized industrial processes have been equipped with quite a few sensors to collectprocess-based data to find faults arising or prevailing in processes along with monitoring the status of processes.Fault diagnosis of rotating machines serves a main role in the engineering field and industrial production. Dueto the disadvantages of existing fault, diagnosis approaches, which greatly depend on professional experienceand human knowledge, intellectual fault diagnosis based on deep learning (DL) has attracted the researcher’sinterest. DL reaches the desired fault classification and automatic feature learning. Therefore, this article designs a Gradient Optimizer Algorithm with Hybrid Deep Learning-based Failure Detection and Classification (GOAHDLFDC)in the industrial environment. The presented GOAHDL-FDC technique initially applies continuous wavelettransform (CWT) for preprocessing the actual vibrational signals of the rotating machinery. Next, the residualnetwork (ResNet18) model was exploited for the extraction of features from the vibration signals which are thenfed into theHDLmodel for automated fault detection. Finally, theGOA-based hyperparameter tuning is performedtoadjust the parameter valuesof theHDLmodel accurately.The experimental result analysis of the GOAHDL-FD Calgorithm takes place using a series of simulations and the experimentation outcomes highlight the better resultsof the GOAHDL-FDC technique under different aspects.

关键词： Fault detection Industry 4.0 gradient optimizer algorithm deep learning rotating machineries artificial intelligence

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A new technique for fault diagnosis in transformer insulating oil based on infrared spectroscopy measurements

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High Voltage 2024年第2期9卷 319-335页

作者： mohamed M.F.Darwish mohamed H.A.Hassan Nagat M.K.abdel-Gawad Matti Lehtonen Diaa-Eldin A.Mansour Department of Electrical Engineering Faculty of Engineering at ShoubraBenha UniversityCairoEgypt Department of Electrical Engineering and Automation School of Electrical EngineeringAalto UniversityEspooFinland Department of Electrical Power Engineering Egypt-Japan University of Science and Technology(E-JUST)New Borg El-Arab CityEgypt Department of Electrical Power and Machines Engineering Faculty of EngineeringTanta UniversityTantaEgypt

Condition monitoring of the insulating system within power transformers has a massive importance according to the electrical utilities.Dissolved gas analysis(DGA)is frequently used for this purpose.However,DGA lacks the necessary level of accuracy to identify all equipment faults,particularly in their initial stages of degradation.Also,it does not have the capability for real-time monitoring and relies on manual sampling and laboratory testing,causing potential delays in fault identification.Additionally,the interpretation of DGA data necessitates specialised expertise,which may pose difficulties for smaller entities that have limited access to resources.Therefore,the contribution of this research is to use infrared spectroscopy measurements as a new effective technique substituting the DGA method for fault diagnosis in insulating oil.The inception faults that were considered in this study were the electrical fault(discharges of high energy)and the thermal fault(300°Cst cells were crafted especially for serving the simulation processes inside the laboratory for both types of inception faults.subsequently,six samples of pure paraffinic mineral oil were taken to be degraded in the laboratory.Following that,all of them besides another sample that were not subjected to any kind of faults were taken to be examined by Fourier transform infrared(FTIR)spectroscopy to obtain an overview of the oil's behaviour in each fault case.After that,the FTIR analysis was initially verified utilising the DGA method.Then,for further affirmation,the dielectric dissipation factor(DDF)for all samples was measured.In the final analysis,the verification tests provide experimental evidence about the outperformance of this new optical technique in detecting the transformer's inception faults in addition to proving its potential for being a superior alternative to the well-known traditional diagnostic techniques.

关键词： fault diagnosis spectroscopy

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Improving Thyroid Disorder Diagnosis via Ensemble stacking and Bidirectional Feature selection

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Computers, Materials & Continua 2024年第3期78卷 4225-4241页

作者： Muhammad Armghan latif Zohaib Mushtaq saad Arif sara Rehman Muhammad Farrukh Qureshi Nagwan abdel samee Maali Alabdulhafith Yeong Hyeon Gu Mohammed A.Al-masni Department of Computer and Information system Cleveland State UniversityOhio44115USA Department of Electrical Electronics and Computer SystemsCollege of Engineering and TechnologyUniversity of SargodhaSargodha40100Pakistan Department of Mechanical Engineering College of EngineeringKing Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Biomedical Engineering Riphah International UniversityIslamabad44000Pakistan Department of Electrical Engineering Riphah International UniversityIslamabad44000Pakistan Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Artificial Intelligence College of Software and Convergence TechnologySejong UniversitySeoul05006Korea

Thyroid disorders represent a significant global health challenge with hypothyroidism and hyperthyroidism as two common conditions arising from dysfunction in the thyroid gland.Accurate and timely diagnosis of these disorders is crucial for effective treatment and patient care.This research introduces a comprehensive approach to improve the accuracy of thyroid disorder diagnosis through the integration of ensemble stacking and advanced feature selection techniques.sequential forward feature selection,sequential backward feature elimination,and bidirectional feature elimination are investigated in this study.In ensemble learning,random forest,adaptive boosting,and bagging classifiers are employed.The effectiveness of these techniques is evaluated using two different datasets obtained from the University of California Irvine-Machine Learning Repository,both of which undergo preprocessing steps,including outlier removal,addressing missing data,data cleansing,and feature reduction.Extensive experimentation demonstrates the remarkable success of proposed ensemble stacking and bidirectional feature elimination achieving 100%and 99.86%accuracy in identifying hyperthyroidism and hypothyroidism,respectively.Beyond enhancing detection accuracy,the ensemble stacking model also demonstrated a streamlined computational complexity which is pivotal for practical medical applications.It significantly outperformed existing studies with similar objectives underscoring the viability and effectiveness of the proposed scheme.This research offers an innovative perspective and sets the platform for improved thyroid disorder diagnosis with broader implications for healthcare and patient well-being.

关键词： Ensemble learning random forests boosting dimensionality reduction machine learning smart healthcare computer aided diagnosis

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Comparative study between three carbonaceous nanoblades and nanodarts for antimicrobial applications

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Journal of Environmental sciences 2024年第2期136卷 594-605页

作者： mohamed s.selim Ahmed M.Azzam mohamed A.shenashen shimaa A.Higazy Bayaumy B.Mostafa sherif A.El-safty National Institute for Materials science(NIMs) 1-2-1 SengenTsukuba-ShiIbaraki-Ken 305-0047Japan Petroleum Application Department Egyptian Petroleum Research InstituteNasr City 11727Egypt Department of Environmental Research Theodor Bilharz Research InstituteGizaEgypt

The design of nanostructured materials occupies a privileged position in the development and management of affordable and effective technology in the antibacterial sector.Here,we discuss the antimicrobial properties of three carbonaceous nanoblades and nanodarts materials of graphene oxide(GO),reduced graphene oxide(RGO),and single-wall carbon nanotubes(sWCNTs)that have a mechano-bactericidal effect,and the ability to piercing or slicing bacterial membranes.To demonstrate the significance of size,morphology and composition on the antibacterial activity mechanism,the designed nanomaterials have been characterized.The minimum inhibitory concentration(MIC),standard agar well diffusion,and transmission electron microscopy were utilized to evaluate the antibacterial activity of GO,RGO,and sWCNTs.Based on the evidence obtained,the three carbonaceous materials exhibit activity against all microbial strains tested by completely encapsulating bacterial cells and causing morphological disruption by degrading the microbial cell membrane in the order of RGO>GO>sWCNTs.Because of the external cell wall structure and outer membrane proteins,the synthesized carbonaceous nanomaterials exhibited higher antibacterial activity against Gram-positive bacterial strains than Gram-negative and fungal microorganisms.RGO had the lowest MIC values(0.062,0.125,and 0.25 mg/mL against B.subtilis,s.aureus,and E.coli,respectively),as well as minimum fungal concentrations(0.5 mg/mL for both A.fumigatus and C.albicans).At 12 hr,the cell viability values against tested microbial strains were completely suppressed.Cell lysis and death occurred as a result of severe membrane damage caused by microorganisms perched on RGO nanoblades.Our work gives an insight into the design of effective graphene-based antimicrobial materials for water treatment and remediation.

关键词： Antimicrobial properties Nanoblades Mechano-bactericidal effect RGO nanosheets Gram-positive bacteria Cell viability

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Enhancing Renewable Energy Integration:A Gaussian-Bare-Bones Levy Cheetah Optimization Approach to Optimal Power Flow in Electrical Networks

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Computer Modeling in Engineering & sciences 2024年第8期140卷 1339-1370页

作者： Ali s.Alghamdi mohamed A.Zohdy saad Aldoihi Department of Electrical Engineering College of EngineeringMajmaah UniversityAl-Majmaah11952Saudi Arabia Electrical and Computer Engineering Department Oakland UniversityRochesterMI48309USA Institute of Earth and space science King Abdulaziz City for Science and Technology(KACST)Riyadh11442Saudi Arabia Computer science and systems Engineering Institute Polytechnique de ParisPalaiseau CedexFrance

In the contemporary era,the global expansion of electrical grids is propelled by various renewable energy sources(REss).Efficient integration of stochastic REss and optimal power flow(OPF)management are critical for network optimization.This study introduces an innovative solution,the Gaussian Bare-Bones Levy Cheetah Optimizer(GBBLCO),addressing OPF challenges in power generation systems with stochastic REss.The primary objective is to minimize the total operating costs of REss,considering four functions:overall operating costs,voltage deviation management,emissions reduction,voltage stability index(VsI)and power loss mitigation.Additionally,a carbon tax is included in the objective function to reduce carbon emissions.Thorough scrutiny,using modified IEEE 30-bus and IEEE 118-bus systems,validates GBBLCO’s superior performance in achieving optimal solutions.simulation results demonstrate GBBLCO’s efficacy in six optimization scenarios:total cost with valve point effects,total cost with emission and carbon tax,total cost with prohibited operating zones,active power loss optimization,voltage deviation optimization and enhancing voltage stability index(VsI).GBBLCO outperforms conventional techniques in each scenario,showcasing rapid convergence and superior solution quality.Notably,GBBLCO navigates complexities introduced by valve point effects,adapts to environmental constraints,optimizes costs while considering prohibited operating zones,minimizes active power losses,and optimizes voltage deviation by enhancing the voltage stability index(VsI)effectively.This research significantly contributes to advancing OPF,emphasizing GBBLCO’s improved global search capabilities and ability to address challenges related to local minima.GBBLCO emerges as a versatile and robust optimization tool for diverse challenges in power systems,offering a promising solution for the evolving needs of renewable energy-integrated power grids.

关键词： Renewable energy integration optimal power flow stochastic renewable energy sources gaussian-bare-bones levy cheetah optimizer electrical network optimization carbon tax optimization

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Microbial,immune and antioxidant responses of Nile tilapia with dietary nano-curcumin supplements under chronic low temperatures

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Aquaculture and Fisheries 2024年第1期9卷 57-65页

作者： Mohammed F.El Basuini mohamed A.A.Zaki abdelaziz M.El-Hais mohamed G.Elhanafy Emad H.El-Bilawy Amr I.Zaineldin mohamed F.A.abdel-Aziz Ibrahim A.Abouelsaad Ibrahim TEl-Ratel Kumbukani Mzengereza Ronick s.shadrack Islam I.Teiba Faculty of Agriculture Tanta UniversityTanta31527Egypt King salman International University South Sinai46618Egypt Faculty of Agriculture Alexandria UniversityAlexandria21526Egypt Animal Health Research Institute(AHRI-DOKI) Agriculture Research CenterGiza12619Egypt Fish Rearing Lab. Aquaculture DivisionNational Institute of Oceanography and FisheriesAlexandria21556Egypt Horticulture Department Faculty of AgricultureDamanhour UniversityDamanhur22511Egypt Department of Poultry Production Faculty of AgricultureDamietta UniversityDamietta34517Egypt Laboratory of Aquatic Animal Nutrition Faculty of FisheriesKagoshima UniversityKagoshima890-0065Japan Department of Aquatic and Fisheries science Mzuzu UniversityMzuzuPrivate Bag 201Malawi Faculty of Aquaculture and Marine Fisheries Arish UniversityArish45516Egypt

A 56-day feeding period was performed to investigate the possible impacts of dietary nano curcumin(0,50,100,150,and 200 ppm)on the growth,nutrient utilization,non-specific immune parameters,antioxidants in Nile tilapia under chronic low temperature(21.02±0.11◦C).Fishes(n=225;Initial weight=4.39±0.08 g/fish)were randomly stocked at 15 fish/tank for five experimental groups in triplicates.Under low-temperature circumstances,dietary curcumin in nano form showed no notable alteration in growth variable,nutrient efficiency,digestive enzymes efficiency,biometric indices,survival rates,and hematological components.Meanwhile,the serum of fishes with nano curcumin diets under low-temperature stress displayed higher total protein as well as lower glucose,cortisol,and total cholesterol compared with the control group.Moreover,fish fed nano curcumin diets displayed higher lysozyme and bactericidal activities compared to the control group and the best performance was found at dietary nano curcumin level of≥100 ppm.Also,groups fed the basal diet demonstrated the poorest antioxidant capacity,and the best superoxide dismutase(sOD)and glutathione peroxidase(GPx)existed in fish with nano curcumin diets while the best catalase(CAT)efficiency occurred at higher nano curcumin levels≥100 ppm.In addition,higher counts of intestinal microbiota in terms of total bacterial count(TBC),total yeast and molds count(TYMC),and coliform were noticed in fish consumed the basal diet compared to groups fed on nano curcumin diets.In conclusion,incorporating nano curcumin at a level of≥100 mg/kg diet(particularly at 150 mg/kg)improved a non-specific immune response,antioxidant,and healthier gastrointestinal microbiota in Nile tilapia under chronic low-temperature stress.

关键词： Nano curcumin Gastrointestinal microbiota Antioxidants Non-specific immune responses Temperature stress

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Insulin therapy in type 2 diabetes: Insights into clinical efficacy, patient-reported outcomes, and adherence challenges

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World Journal of Diabetes 2024年第5期15卷 828-852页

作者： Mahmoud Emad-Eldin Gehan F Balata Eman A Elshorbagy Mona s Hamed mohamed s Attia Department of Pharmacy Practice Faculty of PharmacyZagazig UniversityZagazig HFQM+872Al-Sharqia GovernorateEgypt Department of Pharmacy Practice Faculty of PharmacyHeliopolis UniversityCairo 44519Egypt Department of Pharmaceutics Faculty of PharmacyZagazig UniversityZagazig 44519Al-Sharqia GovernorateEgypt Department of Internal Medicine Faculty of MedicineZagazig UniversityZagazig 44519Al-Sharqia GovernorateEgypt Department of Community at Faculty of Medicine Zagazig UniversityZagazig 44519Al-Sharqia GovernorateEgypt

Insulin therapy plays a crucial role in the management of type 2 diabetes as the disease progresses.Over the past century,insulin formulations have undergone significant modifications and bioengineering,resulting in a diverse range of available insulin products.These products show distinct pharmacokinetic and pharmacodynamic profiles.Consequently,various insulin regimens have em-erged for the management of type 2 diabetes,including premixed formulations and combinations of basal and bolus insulins.The utilization of different insulin regimens yields disparate clinical outcomes,adverse events,and,notably,patient-reported outcomes(PROs).PROs provide valuable insights from the patient’s perspective,serving as a valuable mine of information for enhancing healthcare and informing clinical decisions.Adherence to insulin therapy,a critical patient-reported outcome,significantly affects clinical outcomes and is influenced by multiple factors.This review provides insights into the clinical effectiveness of various insulin preparations,PROs,and factors impacting insulin therapy adherence,with the aim of enhancing healthcare practices and informing clinical decisions for individuals with type 2 diabetes.

关键词： Adherence Diabetes Insulin Patient-reported outcomes Pharmacokinetic Pharmacodynamic

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