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Deterministic and Stochastic Fractional-Order Hastings-Powell Food Chain Model

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Computers, Materials & Continua 2022年第2期70卷 2277-2296页

作者： moustafa el-shahed Asmaa M.Al-Dububan Department of Mathematics Unaizah College of Sciences and ArtsQassim UniversityUnaizah51911Saudi Arabia Department of Mathematics Buraydah College of Sciences and ArtsQassim UniversityBuraydah51431Saudi Arabia

In this paper,a deterministic and stochastic fractional-order model of the tri-trophic food chain model incorporating harvesting is proposed and analysed.The interaction between prey,middle predator and top predator population is investigated.In order to clarify the characteristics of the proposed model,the analysis of existence,uniqueness,non-negativity and boundedness of the solutions of the proposed model are examined.Some sufficient conditions that ensure the local and global stability of equilibrium points are obtained.By using stability analysis of the fractional-order system,it is proved that if the basic reproduction number R_(0)elp of Sotomayor’s theorem.Some numerical examples are given to illustrate the theoretical findings.The impact of harvesting on prey and themiddle predator is studied.We conclude that harvesting parameters can control the dynamics of the middle predator.A numerical approximation method is developed for the proposed stochastic fractional-order model.

关键词： Food chain model global dynamics stability stochastic Hastings-Powell model harvesting

Real-Time Multi-Class Infection Classification for Respiratory Diseases

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Computers, Materials & Continua 2022年第11期73卷 4157-4177页

作者： Ahmed el.Shafee Walid el-Shafai Abdulaziz Alarifi Mohammed Amoon Aman Singh moustafa H.Aly Department of electrical Engineering Faculty of EngineeringAhram Canadian UniversityGizaEgypt Department of electronics and electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Computer Science Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia Higher Polytechnic School Universidad Europea del AtlánticoSantander39011Spain electronics and Communications Engineering Department College of Engineering and TechnologyArab Academy for ScienceTechnology and Maritime TransportAlexandria1029Egypt

Real-time disease prediction has emerged as the main focus of study in the field of computerized medicine.Intelligent disease identification framework can assist medical practitioners in diagnosing disease in a way that is reliable,consistent,and timely,successfully lowering mortality rates,particularly during endemics and pandemics.To prevent this pandemic’s rapid and widespread,it is vital to quickly identify,confine,and treat affected individuals.The need for auxiliary computer-aided diagnostic(CAD)systems has grown.Numerous recent studies have indicated that radiological pictures contained critical information regarding the COVID-19 virus.Utilizing advanced convolutional neural network(CNN)architectures in conjunction with radiological imaging makes it possible to provide rapid,accurate,and extremely useful susceptible classifications.This research work proposes a methodology for real-time detection of COVID-19 infections caused by the Corona Virus.The purpose of this study is to offer a two-way COVID-19(2WCD)diagnosis prediction deep learning system that is built on Transfer Learning Methodologies(TLM)and features customized fine-tuning on top of fully connected layered pre-trained CNN architectures.2WCD has applied modifications to pre-trained models for better performance.It is designed and implemented to improve the generalization ability of the classifier for binary and multi-class models.Along with the ability to differentiate COVID-19 and No-Patient in the binary class model and COVID-19,No-Patient,and Pneumonia in the multi-class model,our framework is augmented with a critical add-on for visually demonstrating infection in any tested radiological image by highlighting the affected region in the patient’s lung in a recognizable color pattern.The proposed system is shown to be extremely robust and reliable for real-time COVID-19 diagnostic prediction.It can also be used to forecast other lung-related disorders.As the system can assist medical practitioners in diagnosing the greatest number of patients

关键词： COVID-19 real-time computerized disease prediction intelligent disease identification framework CAD systems X-rays CT-scans CNN real-time detection of COVID-19 infections

Hybrid Single Image Super-Resolution Algorithm for Medical Images

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Computers, Materials & Continua 2022年第9期72卷 4879-4896页

作者： Walid el-Shafai Ehab Mahmoud Mohamed Medien Zeghid Anas MAli moustafa H.Aly Department of electronics and electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Lab Computer Science DepartmentPrince Sultan UniversityRiyadh11586Saudi Arabia electrical Engineering Department College of EngineeringPrince Sattam Bin Abdulaziz UniversityWadi Addwasir11991Saudi Arabia electrical Engineering Department Aswan UniversityAswan81542Egypt electronics and Micro-electronics Laboratory(E.μ.E.L) Faculty of SciencesUniversity of MonastirMonastir5000Tunisia Alexandria Higher Institute of Engineering&Technology(AIET) AlexandriaEgypt electronics and Communications Engineering Department College of Engineering and TechnologyArab Academy for ScienceTechnology and Maritime TransportAlexandria1029Egypt

High-quality medical microscopic images used for diseases detection are expensive and difficult to store.Therefore,low-resolution images are favorable due to their low storage space and ease of sharing,where the images can be enlarged when needed using Super-Resolution(SR)techniques.However,it is important to maintain the shape and size of the medical images while enlarging them.One of the problems facing SR is that the performance of medical image diagnosis is very poor due to the deterioration of the reconstructed image resolution.Consequently,this paper suggests a multi-SR and classification framework based on Generative Adversarial Network(GAN)to generate high-resolution images with higher quality and finer details to reduce blurring.The proposed framework comprises five GAN models:Enhanced SR Generative Adversarial Networks(ESRGAN),Enhanced deep SR GAN(EDSRGAN),Sub-Pixel-GAN,SRGAN,and Efficient Wider Activation-B GAN(WDSR-b-GAN).To train the proposed models,we have employed images from the famous BreakHis dataset and enlarged them by 4×and 16×upscale factors with the ground truth of the size of 256×256×3.Moreover,several evaluation metrics like Peak Signal-to-Noise Ratio(PSNR),Mean Squared Error(MSE),Structural Similarity Index(SSIM),Multiscale Structural Similarity Index(MS-SSIM),and histogram are applied to make comprehensive and objective comparisons to determine the best methods in terms of efficiency,training time,and storage space.The obtained results reveal the superiority of the proposed models over traditional and benchmark models in terms of color and texture restoration and detection by achieving an accuracy of 99.7433%.

关键词： GAN medical images SSIM MS-SSIM PSNR SISR

Antimicrobial activities of three seaweeds extract against some human viral and bacterial pathogens

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BIOCelL 2022年第1期46卷 247-261页

作者： JelAN MOFEED MOHAMED DEYAB ABDelNASER MOHAMED MAHMOUD moustafa SALLY NEGM EMAD el-BILAWY Faculty of Science King Salman International UniversitySinaiEgypt Faculty of Fish Resources Suez UniversitySuezEgypt Faculty of Science Damietta UniversityDamiettaEgypt Department of Biology College of ScienceKing Khalid UniversityAbha9004Saudi Arabia Department of Botany and Microbiology Faculty of ScienceSouth Valley UniversityQenaEgypt Life Sciences Department Faculty of Science and ArtKing Khalid UniversityMahayel Aseer 61421Saudi Arabia Unit of Food Bacteriology Central Laboratory of Food HygieneMinistry of HealthSharkia44516Egypt

Microbial infections cause complicated health influences along with bad economic impacts.In the present investigation,three dominant seaweeds namely,Amphiroa anceps,Corallina officinalis and Sargassum filipendula were collected from different Egyptian sites at the Red Sea and Mediterranean Sea during autumn 2019.Organic extracts of the three algae were screened for their antibacterial activity against three pathogenic bacteria Salmonella typhiimurium,Staphylococcus aureus and Escherichia coli,in addition to in vitro antiviral activity against Rotavirus(RV),and Coxsackie virus B3(CVB3)that cause severe diseases in human.Organic extract of A.anceps,C.officinalis and S.filipendula inhibit E.coli cells by 57.1%,85.7%,and 91.4%,respectively.The highest level of concentration of the C.officinalis extract(100μg/mL)inhibits 100%of Staphylococcus aureus cells followed by S.filipendula and A.anceps extract which inhibit 82.5%and 75%of S.aureus.Similarly,the highest concentration of C.officinalis extract inhibits S.typhiimurium by 80%.The extract of A.anceps exhibited a high antiviral effect against RV infection with TI=22 and virus titers lessened by 2.75 log TCID_(50) followed by extractions of C.officinalis with TI=18.3 and virus titers reduced by 2.5 log TCID_(50).Against CVB3 infection,the extract of A.anceps causes the highest antiviral activity with TI=15 and reduce the viral titers by 2.5 log TCID_(50),followed by extractions of C.officinalis with TI=8.8 and inhibition of virus titers by 1.75 log TCID_(50).Extract of S.filipendula displayed the lowest antiviral effects against RV and CVB3 infection with TI=2.4 and 1.4,respectively.The obtained results clarified that the extract of three marine seaweeds maintains a potent antimicrobial activity,making them a future promising source of new antimicrobial drugs.

关键词： Seaweeds Antimicrobial Antibacterial Cytotoxicity Antiviral

Secondary antiviral metabolites from fungi with special reference to coronaviruses

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BIOCelL 2022年第8期46卷 1979-1988页

作者： MOHAMED SALEM MOHAMMAD el-METWALLY WESAMelDIN SABER SALLY NEGM ATTALLA el-KOTT YASSER MAZROUA ABEER MAKHLOUF MAHMOUD moustafa Department of Chemistry Faculty of Science and ArtKing Khalid UniversityMohailAssirSaudi Arabia Department of Chemistry Faculty of ScienceAl-Azhar UniversityCairoEgypt Botany and Microbiology Department Faculty of ScienceDamanhour UniversityEl-BeheraEgypt Microbial Activity Unit Department of MicrobiologySoilsWater and Environment Research InstituteAgricultural Research CenterGizaEgypt Life Sciences Department College of Science and ArtKing Khalid UniversityMohailAseerSaudi Arabia Unit of Food Bacteriology Central Laboratory of Food HygieneMinistry of HealthSharkiaEgypt Department of Biology College of ScienceKing Khalid UniversityAbhaSaudi Arabia Zoology Department Faculty of ScienceDamanhour UniversityEl-BeheraEgypt Department of Business Administration Community CollegeKing Khalid UniversityAbhaSaudi Arabia Department of Agricultural Economics Faculty of AgricultureTanta UniversityTantaEgypt Department of Botany Faculty of AgricultureMenoufia UniversitySheben El-KomMenoufiaEgypt Botany and Microbiology Department Faculty of ScienceSouth Valley UniversityQenaEgypt

Profound inspection of the life forms on the earth teaches how to be the complexity of interrelationships among the various systems.Because of the emergence of novel viruses all the time and the inadequate of vaccines and antivirals,viral contagions are amongst the most causative diseases affecting people worldwide.Fungi exemplify a massive source of bioactive molecules as,many fungal secondary metabolities like Oxoglyantrypine,Carneic acid F,Scedapin C,Asteltoxin E,Phomanolide,Norquinadoline A and Quinadoline B have antiviral activity.This review deals with how secondary metabolites of fungi can help in the war against viruses in general and especially Coronaviruses moreover several pieces of literature pointed out that many clusters of fungi in different biotopes are waiting to be exploited.

关键词： COVID-19 SARS-CoV-2 Antibiotics Virus Fungi

Secure and Robust Optical Multi-Stage Medical Image Cryptosystem

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Computers, Materials & Continua 2022年第1期70卷 895-913页

作者： Walid el-Shafai moustafa H.Aly Abeer D.Algarni Fathi E.Abd el-Samie Naglaa F.Soliman Department electronics and electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of electronics and Communications Engineering College of Engineering and TechnologyArab Academy for ScienceTechnology and Maritime TransportAlexandria1029Egypt Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh84428Saudi Arabia Department of electronics and Communications Faculty of EngineeringZagazig UniversityZagazig44519Egypt

Due to the rapid growth of telemedicine and healthcare services,color medical image security applications have been expanded precipitously.In this paper,an asymmetric PTFrFT(Phase Truncated Fractional Fourier Transform)-based color medical image cryptosystem is suggested.Two different phases in the fractional Fourier and output planes are provided as deciphering keys.Accordingly,the ciphering keys will not be employed for the deciphering procedure.Thus,the introduced PTFrFT algorithm comprises asymmetric ciphering and deciphering processes in contrast to the traditional optical symmetric OSH(Optical Scanning Holography)and DRPE(Double Random Phase Encoding)algorithms.One of the principal impacts of the introduced asymmetric cryptosystem is that it eliminates the onedimensionality aspects of the related symmetric cryptosystems due to its remarkable feature of phase nonlinear truncation components.More comparisons on various colormedical images are examined and analyzed to substantiate the cryptosystem efficacy.The achieved experimental outcomes ensure that the introduced cryptosystem is robust and secure.It has terrific cryptography performance compared to conventional cryptography algorithms,even in the presence of noise and severe channel attacks.

关键词： Optical encryption medical image security symmetric and asymmetric encryption OSH DRPE PTFrFT

Shadow Extraction and elimination of Moving Vehicles for Tracking Vehicles

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Computers, Materials & Continua 2023年第11期77卷 2009-2030页

作者： Kalpesh Jadav Vishal Sorathiya Walid el-Shafai Torki Altameem moustafa HAly Vipul Vekariya Kawsar Ahmed Francis MBui Department of electronics&Communication Engineering Faculty of Engineering and TechnologyParul Institute of Engineering and TechnologyParul UniversityVadodaraGujarat391760India Department electronics and electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Computer Science Department Community CollegeKing Saud UniversityRiyadh11362Saudi Arabia electronics and Communications Engineering Department College of Engineering and TechnologyArab Academy for ScienceTechnology and Maritime TransportAlexandria1029Egypt Department of Computer Engineering Faculty of Engineering and TechnologyParul Institute of Engineering and TechnologyParul UniversityVadodaraGujarat391760India Department of electrical and Computer Engineering University of Saskatchewan57 Campus DriveSaskatoonSK S7N5A9Canada

Shadow extraction and elimination is essential for intelligent transportation systems(ITS)in vehicle tracking application.The shadow is the source of error for vehicle detection,which causes misclassification of vehicles and a high false alarm rate in the research of vehicle counting,vehicle detection,vehicle tracking,and classification.Most of the existing research is on shadow extraction of moving vehicles in high intensity and on standard datasets,but the process of extracting shadows from moving vehicles in low light of real scenes is difficult.The real scenes of vehicles dataset are generated by self on the Vadodara–Mumbai highway during periods of poor illumination for shadow extraction of moving vehicles to address the above problem.This paper offers a robust shadow extraction of moving vehicles and its elimination for vehicle tracking.The method is distributed into two phases:In the first phase,we extract foreground regions using a mixture of Gaussian model,and then in the second phase,with the help of the Gamma correction,intensity ratio,negative transformation,and a combination of Gaussian filters,we locate and remove the shadow region from the foreground areas.Compared to the outcomes proposed method with outcomes of an existing method,the suggested method achieves an average true negative rate of above 90%,a shadow detection rate SDR(η%),and a shadow discrimination rate SDR(ξ%)of 80%.Hence,the suggested method is more appropriate for moving shadow detection in real scenes.

关键词： Change illuminations ImageJ software intelligent traffic systems mixture of Gaussian model National Institute of Health vehicle tracking

Using nanotechnology to prevent fines migration while production

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Petroleum 2021年第2期7卷 168-177页

作者： Mohamed Mansour Mohamed eleraki Ahmed Noah el-Abbas moustafa Faculty of Science Zagazig UniversityEgypt Egyptian Petroleum Research Institute(EPRI) Egypt The British University Egypt

Formation damage due to fines migration is a major reason for well productivity decline for oil and gas wells.Formation fines are small enough to pass through pore throats causing pore plugging and permeability decline.Different factors affect fines migration such as flow rate,salinity,pH value,reservoir temperature and oil polarity,as well as changes in chemical environment induced by Enhanced Oil Recovery(EOR)agents.This paper focuses on the effect of flow rates on fines detachment from the grain surfaces,which causing permeability reduction.As the fluid inside the reservoir moves towards the wellbore,the fluid velocity increases,when the fluid reaches the critical flow rate these fines can be picked up into the fluid.These fines captured by thinner pore throats causing pore plugging and permeability reduction.Different concentrations of nanoparticles were used to fix these fines on their sources and prevent their mobilization at high flow rates.The unique technique used in this study is changing the potential surfaces between fines and grain surfaces to prevent fines movement above the critical flow rate.SiO_(2)and MgO NPs used in this study can be adsorbed on the pore surfaces and reduce the repulsion forces between fines and pore surfaces.SiO_(2) and MgO nanoparticles at different concentrations(0.25,0.50 and 0.75 g/L)were used on treating the Abu-Rawash sandstone reservoir using Formation Damage System Cell FDS-350.The experimental studies showed that using MgO NPs would prevent fines detachment from the pore surfaces and decrease the reduction of permeability at high flow rates more than SiO_(2) NPs.The optimum concentration of MgO NPs was at 0.5 g/L as the permeability remediation at this concentration reaches to 64.83%.

关键词： Formation damage Fines migration Nanoparticles Zeta potential Permeability reduction Fluid flow rate

Simple identification approach for trace heteroatom-containing compounds in petroleum fraction by Automated Mass Spectral Deconvolution and Identification System

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色谱 2016年第9期34卷 925-932页

作者： moustafa Nagy E Mahmoud Kout el-Kloub Fars Institute of Inorganic and Analytical Chemistry University of Munster Germany Munster 48149 Department of Chemistry Women's College for Arts Science and Education Ain Shams University Egypt Cairo 11566

Here we present a simple yet effective gas chromatography-mass spectrometry (GC-MS) identification approach for the detection of heteroatom-containing compounds (HACCs) in petroleum fractions. The MS/AMDIS (Automated Mass Spectral Deconvolution and Identification System) program was used to identify parts per million (ppm) HACC concentrations in petroleum fractions in place of traditional techniques (extraction and standard injection). Polycyclic aromatic sulfur heterocycles (S-PAHs) were used as model compounds to confirm the validity of the AMDIS identifiers, which were compared with extracted results using the off-line X-calibur software. AMDIS was able to identify ppm concentrations of S-PAHs in oil condensate. There was good agreement between experimental and AMDIS identification results for S-PAHs in oil condensate. AMDIS was also used to detect nitrogen-containing compounds (NCCs) and alkylphenols in oil condensate. Our results confirmed the presence of 2-methylbenzothiazole, carbazole, and 2,4-ditertbutyl phenol. In a crude oil sample, AMDIS identification of m/z=191 biomarkers was consistent with empirical results. Therefore, AMDIS can help to reduce the number of experimental steps in identification protocols.

关键词： Automated Mass Spectral Deconvolution and Identification System (AMDIS) heteroatom-containing compounds petroleum identification