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Numerical Treatments for Crossover Cancer Model of Hybrid Variable-Order Fractional Derivatives

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Computer Modeling in Engineering & Sciences 2024年第8期140卷 1619-1645页

作者： Nasser Sweilam Seham Al-Mekhlafi Aya ahmed Ahoud Alsheri Emad Abo-Eldahab Mathematics Department Faculty of ScienceCairo UniversityGiza12613Egypt Mathematics Department Faculty of EducationSana’a UniversitySana’a1247Yemen Mathematics Department Faculty of ScienceHelwan UniversityCairo11795Egypt Department of Mathematics College of ScienceUniversity of BishaAl Namas67398-5644Saudi Arabia

In this paper,two crossover hybrid variable-order derivatives of the cancer model are ***ünwald-Letnikov approximation is used to approximate the hybrid fractional and variable-order fractional *** existence,uniqueness,and stability of the proposed model are *** Bashfourth’s fifth-step method with a hybrid variable-order fractional operator is developed to study the proposed *** studies with generalized fifth-order Runge-Kutta method are *** examples and comparative studies to verify the applicability of the used methods and to demonstrate the simplicity of these approximations are *** have showcased the efficiency of the proposed method and garnered robust empirical support for our theoretical findings.

关键词： Cancer diseases hybrid variable-order fractional derivatives adams bashfourth fifth step generalized fifth order Runge-Kutta method

Artificial Intelligence Prediction of One-Part Geopolymer Compressive Strength for Sustainable Concrete

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Computer Modeling in Engineering & Sciences 2024年第10期141卷 525-543页

作者： Mohamed Abdel-Mongy Mudassir Iqbal M.Farag ahmed.M.Yosri Fahad Alsharari Saif Eldeen A.S.Yousef Department of Civil Engineering College of EngineeringJouf UniversitySakaka72388Saudi Arabia Department of Civil Engineering University of Engineering and TechnologyPeshawar54890Pakistan Civil Engineering Department Faculty of EngineeringAl-Azhar UniversityCairo11884Egypt Civil Engineering Department Faculty of EngineeringAswan UniversityAswan81528Egypt

Alkali-activated materials/geopolymer(AAMs),due to their low carbon emission content,have been the focus of recent studies on ecological *** terms of performance,fly ash and slag are preferredmaterials for precursors for developing a one-part ***,determining the optimum content of the input parameters to obtain adequate performance is quite challenging and scarcely ***,in this study,machine learning methods such as artificial neural networks(ANN)and gene expression programming(GEP)models were developed usingMATLAB and GeneXprotools,respectively,for the prediction of compressive strength under variable input materials and content for fly ash and slag-based one-part *** database for this study contains 171 points extracted from literature with input parameters:fly ash concentration,slag content,calcium hydroxide content,sodium oxide dose,water binder ratio,and curing *** performance of the two models was evaluated under various statistical indices,namely correlation coefficient(R),mean absolute error(MAE),and rootmean square error(RMSE).In terms of the strength prediction efficacy of a one-part geopolymer,ANN outperformed *** and parametric analysis were also performed to identify the significant contributor to *** to a sensitivity analysis,the activator and slag contents had the most effects on the compressive strength at 28 *** water binder ratio was shown to be directly connected to activator percentage,slag percentage,and calcium hydroxide percentage and inversely related to compressive strength at 28 days and curing temperature.

关键词： Artificial intelligence techniques one-part geopolymer artificial neural network gene expression modelling sustainable construction polymers

A Hybrid Classification and Identification of Pneumonia Using African Buffalo Optimization and CNN from Chest X-Ray Images

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Computer Modeling in Engineering & Sciences 2024年第3期138卷 2497-2517页

作者： Nasser Alalwan ahmed I.Taloba Amr Abozeid ahmed Ibrahim Alzahrani Ali H.Al-Bayatti Computer Science Department Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia Information System Department Faculty of Computers and InformationAssiut UniversityAssiutEgypt Mathematics Department Faculty of ScienceAl-Azhar UniversityCairoEgypt Cyber Technology Institute(CTI) De Montfort UniversityLeicesterUK

An illness known as pneumonia causes inflammation in the *** there is so much information available fromvarious X-ray images,diagnosing pneumonia has typically proven *** improve image quality and speed up the diagnosis of pneumonia,numerous approaches have been *** date,several methods have been employed to identify *** Convolutional Neural Network(CNN)has achieved outstanding success in identifying and diagnosing diseases in the fields of medicine and ***,these methods are complex,inefficient,and imprecise to analyze a big number of *** this paper,a new hybrid method for the automatic classification and identification of Pneumonia from chest X-ray images is *** proposed method(ABOCNN)utilized theAfrican BuffaloOptimization(ABO)algorithmto enhanceCNNperformance and *** Weinmed filter is employed for pre-processing to eliminate unwanted noises from chest X-ray images,followed by feature extraction using the Grey Level Co-Occurrence Matrix(GLCM)*** features are then selected from the dataset using the ABO algorithm,and ultimately,high-performance deep learning using the CNN approach is introduced for the classification and identification of *** results on various datasets showed that,when contrasted to other approaches,the ABO-CNN outperforms them all for the classification *** proposed method exhibits superior values like 96.95%,88%,86%,and 86%for accuracy,precision,recall,and F1-score,respectively.

关键词： African buffalo optimization convolutional neural network pneumonia X-ray

IoT Task Offloading in Edge Computing Using Non-Cooperative Game Theory for Healthcare Systems

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Computer Modeling in Engineering & Sciences 2024年第5期139卷 1487-1503页

作者： Dinesh Mavaluru Chettupally Anil Carie ahmed I.Alutaibi Satish Anamalamudi Bayapa Reddy Narapureddy Murali Krishna Enduri Md Ezaz ahmed School of Computing and Informatics Saudi Electronic UniversityRiyadhSaudi Arabia Department of Computer Engineering SRM University APGunturIndia College of Computer and Information Sciences Majmaah UniversityAl Majma’ahSaudi Arabia Department of Public Health College of Applied Medical SciencesKing Khalid UniversityAbhaSaudi Arabia

In this paper,we present a comprehensive system model for Industrial Internet of Things(IIoT)networks empowered by Non-Orthogonal Multiple Access(NOMA)and Mobile Edge Computing(MEC)*** network comprises essential components such as base stations,edge servers,and numerous IIoT devices characterized by limited energy and computing *** central challenge addressed is the optimization of resource allocation and task distribution while adhering to stringent queueing delay constraints and minimizing overall energy *** system operates in discrete time slots and employs a quasi-static approach,with a specific focus on the complexities of task partitioning and the management of constrained resources within the IIoT *** study makes valuable contributions to the field by enhancing the understanding of resourceefficient management and task allocation,particularly relevant in real-time industrial *** results indicate that our proposed algorithmsignificantly outperforms existing approaches,reducing queue backlog by 45.32% and 17.25% compared to SMRA and ACRA while achieving a 27.31% and 74.12% improvement in Qn ***,the algorithmeffectively balances complexity and network performance,as demonstratedwhen reducing the number of devices in each group(Ng)from 200 to 50,resulting in a 97.21% reduction in complexity with only a 7.35% increase in energy *** research offers a practical solution for optimizing IIoT networks in real-time industrial settings.

关键词： Internet of Things edge computing offloading NOMA

Boosted photocatalytic removal of NO using direct Z-scheme UiO-66-NH_(2)/Bi_(2)MoO_(6) nanoflowers heterojunction:mechanism insight and humidity effect

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Rare Metals 2024年第8期43卷 3758-3770页

作者： Jiang-Yu-Shan Liang Hai-Tao Ren Abdelkader Labidi Qiu-Hui Zhu Qi-Bing Dong ahmed A.Allam ahmed Rady Chuan-Yi Wang School of Environmental Science and Engineering Shaanxi University of Science and TechnologyXi'an 710021China Zoology Department Faculty of ScienceBeni-Suef UniversityBeni-Suef 6251lEgypt Department of Zoology College of ScienceKing Saud University11451 RiyadhSaudi Arabia

In practical applications,relative humidity in the air is a key factor affecting the photocatalytic removal of NO,which is often overlooked in previous ***,we developed a direct Z-scheme UiO-66-NH_(2)/Bi_(2)MoO_(6)heterojunction with a nanoflower-like structure to systematically investigate the effect of relative humidity on photocatalytic removal of *** optimized heterojunction for the removal efficiency of NO was 71.6%at1.07 mg·m^(-3)NO concentration(relative humidity=10%),and the generation of NO_(2) was only 1.1%.Interestingly,with the increase in relative humidity,it showed a higher inhibition effect on NO_(2),while the removal of NO decreased slightly(8%),which might be attributed to the affinity effect of NO_(2) with water molecules and the competitive adsorption of H_(2)O and NO on the surface of the heterojunction ***,the reaction pathways of NO removal at the developed heterojunctions were revealed by in situ DRIFTS *** work provides a novel vision for the development of direct Z-scheme heterojunction photocatalysts to effectively remove NO and inhibit the formation of toxic intermediate NO_(2) under different humidities.

关键词： NO removal Relative humidity Direct Z-scheme Bismuth molybdate Visible-light photocatalysis

Intelligent Fractional-Order Controller for SMES Systems in Renewable Energy-Based Microgrid

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Computer Modeling in Engineering & Sciences 2024年第8期140卷 1807-1830页

作者： Aadel M.Alatwi Abualkasim Bakeer Sherif A.Zaid Ibrahem E.Atawi Hani Albalawi ahmed M.Kassem Electrical Engineering Department Faculty of EngineeringUniversity of TabukTabuk47913Saudi Arabia Industrial Innovation and Robotic Center(IIRC) University of TabukTabuk47731Saudi Arabia Department of Electrical Engineering Faculty of EngineeringAswan UniversityAswan81542Egypt Renewable Energy and Environmental Technology Centre University of TabukTabuk47913Saudi Arabia Electrical Engineering Department Faculty of EngineeringSohag UniversitySohag82524Egypt

An autonomous microgrid that runs on renewable energy sources is presented in this *** has a supercon-ducting magnetic energy storage(SMES)device,wind energy-producing devices,and an energy storage ***,because such microgrids are nonlinear and the energy they create varies with time,controlling and managing the energy inside them is a difficult ***-order proportional integral(FOPI)controller is recommended for the current research to enhance a standalone microgrid’s energy management and *** suggested dedicated control for the SMES comprises two loops:the outer loop,which uses the FOPI to regulate the DC-link voltage,and the inner loop,responsible for regulating the SMES current,is constructed using the intelligent FOPI(iFOPI).The FOPI+iFOPI parameters are best developed using the dandelion optimizer(DO)approach to achieve the optimum *** suggested FOPI+iFOPI controller’s performance is contrasted with a conventional PI controller for variations in wind speed and microgrid *** optimal FOPI+iFOPI controller manages the voltage and frequency of the *** behavior of the microgrid as a reaction to step changes in load and wind speed was measured using the proposed *** simulations were used to evaluate the recommended system’s *** results of the simulations showed that throughout all interruptions,the recommended microgrid provided the load with AC power with a constant amplitude and *** addition,the required load demand was accurately ***,the microgrid functioned incredibly well despite SMES and varying wind *** obtained under identical conditions were compared with and without the best FOPI+iFOPI *** utilizing the optimal FOPI+iFOPI controller with SMES,it was found that the microgrid performed better than the microgrid without SMES.

关键词： Fractional-order proportional integral(FOPI) intelligent controller renewable energy resources superconducting magnetic energy storage optimization

Contrast Normalization Strategies in Brain Tumor Imaging:From Preprocessing to Classification

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Computer Modeling in Engineering & Sciences 2024年第8期140卷 1539-1562页

作者： Samar M.Alqhtani Toufique A.Soomro Faisal Bin Ubaid ahmed Ali Muhammad Irfan Abdullah A.Asiri Department of Information Systems College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Electronic Engineering The University of LarkanoLarkanaSindh75660Pakistan Computer Science Department Sukkur IBA UniversitySukkurSindh65200Pakistan Eletrical Engineering Department Sukkur IBA UniversitySukkurSindh65200Pakistan Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia Radiological Sciences Department College of Applied Medical SciencesNajran UniversityNajran61441Saudi Arabia

Cancer-related to the nervous system and brain tumors is a leading cause of mortality in various *** resonance imaging(MRI)and computed tomography(CT)are utilized to capture brain *** plays a crucial role in the diagnosis of brain tumors and the examination of other brain ***,manual assessment of MRI images by radiologists or experts is performed to identify brain tumors and abnormalities in the early stages for timely ***,early diagnosis of brain tumors is intricate,necessitating the use of computerized *** research introduces an innovative approach for the automated segmentation of brain tumors and a framework for classifying different regions of brain *** proposed methods consist of a pipeline with several stages:preprocessing of brain images with noise removal based on Wiener Filtering,enhancing the brain using Principal Component Analysis(PCA)to obtain well-enhanced images,and then segmenting the region of interest using the Fuzzy C-Means(FCM)clustering technique in the third *** final step involves classification using the Support Vector Machine(SVM)*** classifier is applied to various types of brain tumors,such as meningioma and pituitary tumors,utilizing the Contrast-Enhanced Magnetic Resonance Imaging(CE-MRI)*** proposed method demonstrates significantly improved contrast and validates the effectiveness of the classification framework,achieving an average sensitivity of 0.974,specificity of 0.976,accuracy of 0.979,and a Dice Score(DSC)of ***,this method exhibits a shorter processing time of 0.44 s compared to existing *** performance of this method emphasizes its significance when compared to state-of-the-art methods in terms of sensitivity,specificity,accuracy,and *** enhance the method further in the future,it is feasible to standardize the approach by incorporating a set of classifiers to increase the robustness of the brain classi

关键词： Brain tumor magnetic resonance imaging principal component analysis fuzzy c-clustering support vector machine

Analysis of cerium substitution in Co-Sr based spinel ferrites for high frequency application

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Journal of Rare Earths 2024年第9期42卷 1755-1763页

作者： Laura Khudaysh Inas A.ahmed Syeda Rabia Ejaz Sajjad Ahmad Khan Yasir A.Haleem Mohamed M.Ibrahim Mahmood Ali Hafiz Muhammad Tahir Farid Zeinhom M.El-Bahy Department of Physics Faculty of ScienceJazan University Department of Chemistry Faculty of ScienceKing Khalid University Department of Physics The Government Sadiq College Women University Institute of Physics Khwaja Fareed University of Engineering and Information Technology Department of Chemistry College of ScienceTaif University Centre of Excellence in Solid State Physics University of the Punjab Department of Physics Government College Department of Chemistry Faculty of ScienceAl-Azhar University

Spinel ferrites exhibit exceptional magnetic properties,making them a distinctive class of magnetic *** sol-gel technique was utilized for the synthesis of spinel ferrites with the chemical formula Co0.6Sr0.4CexFe2-xO4. Following that,a comprehensive X-ray diffraction analysis unveiled the crystalline cubic structure of the synthesized *** the utilization of the M-H loop approach,the ferromagnetic attributes of ferrites were assessed,and the assimilation of rare earth elements led to substantial enhancements in saturation magnetization,remanence,and *** ferrites with a high concentration of rare earth elements have improved direct current resistivity and activation *** logarithm of a material's resistance increased from 5.29 to 8.12 Ω·cm as cerium is *** a change in the amount of cerium,the activation energy goes up from 0.19 to *** changing the frequency from 5.5 to 9.5 GHz,the dielectric characteristics were *** the frequency goes up,the dielectric constant goes *** ferrites that have been made better in every way can be used in high-frequency applications.

关键词： Activation energy DC electrical resistance Remanence magnetization Cobalt strontium spinel ferrites High frequency application Rare earths

Mental Health Consequences among Sudanese Due to the Armed Conflicts and Civil Unrest of 2023: One Year Later

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Journal of Biosciences and Medicines 2024年第4期12卷 21-37页

作者： Sahar Moawia Balla Elnour Nahla Yousif Osman Mohammed Saeed Awadia Babiker Mohammed Osman Abeer Alawad Hassan Eman Mohamed Nour Hamad Alneel Mustafa Rowa Abdelmonem Sidig Hamato Malaz Mohamed Khair Alsafi Mohamed Amna Alnaim Mohamed ahmed Osman Awab Abdelbagi Mustafa ahmed Jalal Abdelrasool Hassan Eltaib Department of Family Medicine Health Gates Riyadh Saudi Arabia Family Medicine Department Ultracare Home Health Care Riyadh Saudi Arabia Psychiatic Department Erada Hospital and Mental Health Alkharj Saudi Arabia Family Medicine Department Ahmed Gasim Diabetic Center Portsudan Sudan QCHP Qatar Qatar Primary Care Najran Health Cluster Najran Saudi Arabia Emergency Department Best Care Hospital Khartoum Sudan Family Medicine Department Mouwasat Hospital Khobar Saudi Arabia

Aim: To investigate depression, anxiety, PTSD, and insomnia levels among Sudanese citizens after a year has passed since the start of Armed Forces conflict in Sudan. Methods: An online survey was distributed. It is composed of five parts, covering the following areas: 1) sociodemographic data;2) depression assessment;3) generalized anxiety disorder assessment;4) post-traumatic stress disorder assessment;and 5) insomnia assessment. Statistical Package for Social Sciences version 27 was used for data analysis;frequency and percentage were used to describe the qualitative variables. Spearman’s correlation analysis and Chi-square test were used for correlation and association analysis;a P-value equal to or less than 0.05 was considered statistically significant. Results: The study included 283 participants, primarily female (76.0%), with a mean age of 35.26 ± 6.96 years. The majority of participants were married (50.5%) and had a university level of education (50.2%). At the start of the war, 70.3% of participants were inside the war zones, and at the time of data collection, 73.5% were outside Sudan. The length of stay in the war zone ranged from 1 to 300 days, with a mean of 48.59 ± 70.284 days. Additionally, 63.3% of participants stated that they did not receive any form of mental health intervention, and the remaining did not provide an answer. There was a high prevalence of moderate-severe depression (50.5%), moderate anxiety (35.3%), PTSD (56.5%), and sub-threshold insomnia (53.4%) among the participants. Correlation analyses revealed associations between demographic factors such as sex, age, marital status, and education and mental health issues. Notably, female participants showed a higher prevalence of PTSD (53.0%) compared to male participants (67.6%). Conclusions: There is a substantial impact of war-related trauma on mental health, revealing a high prevalence of moderate-severe depression, moderate anxiety, PTSD, and sub-threshold insomnia among the particip

关键词： Sudan War Conflict Mental Health