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Effects of high glucose and severe hypoxia on the biological behavior of mesenchymal stem cells at various passages

World Journal of Stem Cells 2024年第4期16卷 434-443页

作者： Fatimah Almahasneh Ejlal Abu-El-Rub Ramada R Khasawneh Rawan Almazari Department of Basic Medical Sciences Faculty of MedicineYarmouk UniversityIrbid 21163Jordan

BACKGROUND Mesenchymal stem cells(MSCs)have been extensively studied for therapeutic potential,due to their regenerative and immunomodulatory *** passage and stress factors may affect the biological characteristics of MSCs,but the details of these effects have not been recognized *** To investigate the effects of stress factors(high glucose and severe hypoxia)on the biological characteristics of MSCs at different passages,in order to optimize the therapeutic applications of *** In this study,we investigated the impact of two stress conditions;severe hypoxia and high glucose on human adipose-tissue derived MSCs(hAD-MSCs)at passages 6(P6),P8,and ***,senescence and apoptosis were evaluated measuring WST-1,senescence-associated beta-galactosidase,and annexin V,*** Cells at P6 showed decreased proliferation and increased apoptosis under conditions of high glucose and hypoxia compared to control,while the extent of senescence did not change significantly under stress *** P8 hAD-MSCs cultured in stress conditions had a significant decrease in proliferation and apoptosis and a significant increase in senescence compared to counterpart cells at *** cultured in high glucose at P10 had lower proliferation and higher senescence than their counterparts in the previous passage,while no change in apoptosis was *** the other hand,MSCs cultured under hypoxia showed decreased senescence,increased apoptosis and no significant change in proliferation when compared to the same conditions at *** These results indicate that stress factors had distinct effects on the biological processes of MSCs at different passages,and suggest that senescence may be a protective mechanism for MSCs to survive under stress conditions at higher passage numbers.

关键词： Mesenchymal stem cells High glucose Hypoxia Stressful microenvironment Serial passage Senescence Apoptosis

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Optimization Algorithms of PERT/CPM Network Diagrams in Linear Diophantine Fuzzy Environment

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Computer Modeling in Engineering & Sciences 2024年第4期139卷 1095-1118页

作者： Mani Parimala Karthikeyan Prakash Ashraf Al-Quran Muhammad Riaz Saeid Jafari Department of Mathematics Bannari Amman Institute of TechnologySathyamangalamTamil Nadu638401India Basic Sciences Department Preparatory Year DeanshipKing Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Mathematics University of the PunjabLahorePakistan College of Vestsjaelland South Herrestraede 11Slagelse4200Denmark

The idea of linear Diophantine fuzzy set(LDFS)theory with its control parameters is a strong model for machine learning and optimization under *** activity times in the critical path method(CPM)representation procedures approach are initially static,but in the Project Evaluation and Review Technique(PERT)approach,they are *** study proposes a novel way of project review and assessment methodology for a project network in a linear Diophantine fuzzy(LDF)*** LDF expected task time,LDF variance,LDF critical path,and LDF total expected time for determining the project network are all computed using LDF numbers as the time of each activity in the project *** primary premise of the LDF-PERT approach is to address ambiguities in project network activity timesmore simply than other approaches such as conventional PERT,Fuzzy PERT,and so *** LDF-PERT is an efficient approach to analyzing symmetries in fuzzy control systems to seek an optimal *** also present a new approach for locating LDF-CPM in a project network with uncertain and erroneous activity *** the available resources and activity times are imprecise and unpredictable,this strategy can help decision-makers make better judgments in a project.A comparison analysis of the proposed technique with the existing techniques has also been *** suggested techniques are demonstrated with two suitable numerical examples.

关键词： Linear Diophantine fuzzy graphs project management PERT CPM linear Diophantine fuzzy numbers score function accuracy function

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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 *** study introduces an innovative solution,the Gaussian Bare-Bones Levy Cheetah Optimizer(GBBLCO),addressing OPF challenges in power generation systems with stochastic *** 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 ***,a carbon tax is included in the objective function to reduce carbon *** scrutiny,using modified IEEE 30-bus and IEEE 118-bus systems,validates GBBLCO’s superior performance in achieving optimal *** 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 ***,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)*** research significantly contributes to advancing OPF,emphasizing GBBLCO’s improved global search capabilities and ability to address challenges related to local *** 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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Enhanced Steganalysis for Color Images Using Curvelet Features and Support Vector Machine

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Computers, Materials & Continua 2024年第1期78卷 1311-1328页

作者： Arslan Akram Imran Khan Javed Rashid Mubbashar Saddique Muhammad Idrees Yazeed Yasin Ghadi Abdulmohsen Algarni Faculty of Computer Science and Information Technology Department of Computer ScienceSuperior UniversityLahore54000Pakistan Machine Learning Code(MLC)Lab Meharban HouseHouse Number 209Zafar ColonyOkara56300Pakistan Information Technology Services University of OkaraOkara56310Pakistan Department of Computer Science&Engineering University of Engineering&Technology LahoreNarowal CampusNarowal51601Pakistan Department of Computer Science and Software Engineering Al Ain UniversityAl Ain11671Abu Dhabi Department of Computer Science King Khalid UniversityAbha61421Saudi Arabia

Algorithms for steganography are methods of hiding data transfers in media *** machine learning architectures have been presented recently to improve stego image identification performance by using spatial information,and these methods have made it feasible to handle a wide range of problems associated with image *** with little information or low payload are used by information embedding methods,but the goal of all contemporary research is to employ high-payload images for *** address the need for both low-and high-payload images,this work provides a machine-learning approach to steganography image classification that uses Curvelet transformation to efficiently extract characteristics from both type of *** Vector Machine(SVM),a commonplace classification technique,has been employed to determine whether the image is a stego or *** Wavelet Obtained Weights(WOW),Spatial Universal Wavelet Relative Distortion(S-UNIWARD),Highly Undetectable Steganography(HUGO),and Minimizing the Power of Optimal Detector(MiPOD)steganography techniques are used in a variety of experimental scenarios to evaluate the performance of the *** WOW at several payloads,the proposed approach proves its classification accuracy of 98.60%.It exhibits its superiority over SOTA methods.

关键词： Curvelets fast fourier transformation support vector machine high pass filters steganography

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Hybrid Gene Selection Methods for High-Dimensional Lung Cancer Data Using Improved Arithmetic Optimization Algorithm

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Computers, Materials & Continua 2024年第6期79卷 5175-5200页

作者： Mutasem K.Alsmadi Department of Management Information Systems College of Applied Studies and Community ServiceImam Abdulrahman Bin Faisal UniversityDammamSaudi Arabia

Lung cancer is among the most frequent cancers in the world,with over one million deaths per *** is required for lung cancer diagnosis and therapy to be effective,accurate,and *** expression microarrays have made it possible to find genetic biomarkers for cancer diagnosis and prediction in a high-throughput *** Learning(ML)has been widely used to diagnose and classify lung cancer where the performance of ML methods is evaluated to identify the appropriate *** and selecting the gene expression patterns can help in lung cancer diagnoses and ***,microarrays include several genes and may cause confusion or false ***,the Arithmetic Optimization Algorithm(AOA)is used to identify the optimal gene subset to reduce the number of selected *** can allow the classifiers to yield the best performance for lung cancer *** addition,we proposed a modified version of AOA which can work effectively on the high dimensional *** the modified AOA,the features are ranked by their weights and are used to initialize the AOA *** exploitation process of AOA is then enhanced by developing a local search algorithm based on two neighborhood ***,the efficiency of the proposed methods was evaluated on gene expression datasets related to Lung cancer using stratified 4-fold *** method’s efficacy in selecting the optimal gene subset is underscored by its ability to maintain feature proportions between 10%to 25%.Moreover,the approach significantly enhances lung cancer prediction *** instance,Lung_Harvard1 achieved an accuracy of 97.5%,Lung_Harvard2 and Lung_Michigan datasets both achieved 100%,Lung_Adenocarcinoma obtained an accuracy of 88.2%,and Lung_Ontario achieved an accuracy of 87.5%.In conclusion,the results indicate the potential promise of the proposed modified AOA approach in classifying microarray cancer data.

关键词： Lung cancer gene selection improved arithmetic optimization algorithm and machine learning

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Genetic Variability and Phenotypic Correlations Study among Grain Quality Traits and Mineral Elements Concentrations in Colored and Non-Colored Rice (Oryza sativa L.)

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Phyton-International Journal of Experimental Botany 2024年第7期93卷 1733-1748页

作者： Adel A.Rezk Mohamed M.El-Malky Heba I.Mohamed Hossam S.El-Beltagi Agricultural Biotechnology Department College of Agricultural and Food ScienceKing Faisal UniversityAl-Ahsa31982Saudi Arabia Plant Pathology research Institute Agriculture Research CenterGiza12619Egypt Rice research and Training Center Field Crop Research InstituteAgriculture Research Center33717Egypt Biological and Geological Sciences Department Faculty of EducationAin Shams UniversityCairo11511Egypt Biochemistry Department Faculty of AgricultureCairo UniversityGiza12613Egypt

Twenty-four rice genotypes were examined to assess genetic variability,heritability,and correlations for seven-grain quality traits,eight nutritional elements,and *** revealed signiﬁcant differences for the quality traits *** every trait under study,the phenotypic coefﬁcient of variation was higher than the correspon-dence genotypic coefﬁcient of *** in a broad sense varied from 29.75%for grain length to 98.31%for the elongation *** percentage recovery had a highly signiﬁcant positive correlation with milling and head rice ***,milling percentage had a highly positive correlation with head rice *** amylose percentage,all the genotypes belonged to low amylose except the Hassawi-1 variety,which had intermediate amylose *** nutrition contents of magnesium(Mg),sodium(Na),potas-sium(K),calcium(Ca),copper(Cu),manganese(Mn),zinc(Zn),iron(Fe),or protein percentage gave different variations for 24 rice genotypes under all the nutritional *** the 24 genotypes,ten rice genotypes–HighNutrient-1,HighNutrient-2,HighNutrient-9,HighNutrient-8,HighNutrient-3,Hassawi-2,HighNutrient-7,HighNutrient-6,Hassawi-1,and HighNutrient-4–had the highest heist value for all nutritional and protein con-tents,and could be used as a donor to improving new *** was a positive and signiﬁcant correlation between magnesium Mg,K,Zn and ***,K had a positive correlation with zinc Zn,Fe,and protein *** analysis was divided into two groups:theﬁrst group included all genotypes rich in nutri-ents,while the remaining genotypes with low nutritional content were included in the second group.

关键词： Amylose cluster analysis genotypes grain quality mineral nutrition rice

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Enhancing ChatGPT’s Querying Capability with Voice-Based Interaction and CNN-Based Impair Vision Detection Model

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Computers, Materials & Continua 2024年第3期78卷 3129-3150页

作者： Awais Ahmad Sohail Jabbar Sheeraz Akram Anand Paul Umar Raza Nuha Mohammed Alshuqayran College of Computer and Information Sciences Imam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudia Arabia 2 School of Computer Science and EngineeringKyungpook National UniversityDaegu41566South Korea 3 Department of EngineeringManchester Metropolitan UniversityManchesterM156BHUK

This paper presents an innovative approach to enhance the querying capability of ChatGPT,a conversational artificial intelligence model,by incorporating voice-based interaction and a convolutional neural network(CNN)-based impaired vision detection *** proposed system aims to improve user experience and accessibility by allowing users to interact with ChatGPT using voice ***,a CNN-based model is employed to detect impairments in user vision,enabling the system to adapt its responses and provide appropriate *** research tackles head-on the challenges of user experience and inclusivity in artificial intelligence(AI).It underscores our commitment to overcoming these obstacles,making ChatGPT more accessible and valuable for a broader *** integration of voice-based interaction and impaired vision detection represents a novel approach to conversational ***,this innovation transcends novelty;it carries the potential to profoundly impact the lives of users,particularly those with visual *** modular approach to system design ensures adaptability and scalability,critical for the practical implementation of these ***,the solution places the user at its *** responses for those with visual impairments demonstrates AI’s potential to not only understand but also accommodate individual needs and preferences.

关键词： Accessibility in conversational AI CNN-based impair vision detection ChatGPT voice-based interaction recommender system

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Bayesian and Non-Bayesian Analysis for the Sine Generalized Linear Exponential Model under Progressively Censored Data

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Computer Modeling in Engineering & Sciences 2024年第9期140卷 2795-2823页

作者： Naif Alotaibi A.S.Al-Moisheer Ibrahim Elbatal Mohammed Elgarhy Ehab M.Almetwally Department of Mathematics and Statistics Faculty of ScienceImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia Department of Mathematics College of ScienceJouf UniversityP.O.Box 848Sakaka72351Saudi Arabia Mathematics and Computer Science Department Faculty of ScienceBeni-Suef UniversityBeni-Suef62521Egypt Department of Basic Sciences Higher Institute of Administrative SciencesBelbeisEgypt Faculty of Business Administration Delta University for Science and TechnologyGamasa11152Egypt

This article introduces a novel variant of the generalized linear exponential(GLE)distribution,known as the sine generalized linear exponential(SGLE)*** SGLE distribution utilizes the sine transformation to enhance its *** updated distribution is very adaptable and may be efficiently used in the modeling of survival data and dependability *** suggested model incorporates a hazard rate function(HRF)that may display a rising,J-shaped,or bathtub form,depending on its unique *** model includes many well-known lifespan distributions as separate *** suggested model is accompanied with a range of statistical *** model parameters are examined using the techniques of maximum likelihood and Bayesian estimation using progressively censored *** order to evaluate the effectiveness of these techniques,we provide a set of simulated data for testing *** relevance of the newly presented model is shown via two real-world dataset applications,highlighting its superiority over other respected similar models.

关键词： Sine G family generalized linear failure rate progressively censored data moments maximum likelihood estimation Bayesian estimation simulation

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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

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Extended Deep Learning Algorithm for Improved Brain Tumor Diagnosis System

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Intelligent Automation & Soft Computing 2024年第1期39卷 33-55页

作者： M.Adimoolam K.Maithili N.M.Balamurugan R.Rajkumar S.Leelavathy Raju Kannadasan Mohd Anul Haq Ilyas Khan ElSayed M.Tag El Din Arfat Ahmad Khan Department of Computer Engineering Government Polytechnic CollegeSevvapetTiruvallurTamil Nadu602025India Department of Computer Science and Engineering KG Reddy College of Engineering and TechnologyHyderabadTelangana500075India Department of Computer Science and Engineering Sri Venkateswara College of EngineeringSriperumbudurChennai602117India Department of Computer Science and Engineering Annamalai UniversityChidambaramTamil Nadu608002India Department of Computer Science and Engineering Aarupadai Veedu Institute of TechnologyVinayaka Mission Research FoundationPaiyanurTamil Nadu603104India Department of Electrical and Electronics Engineering Sri Venkateswara College of EngineeringSriperumbudur602117India Department of Computer Science College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Mathematics College of Science Al-ZulfiMajmaah UniversityAl-Majmaah11952Saudi Arabia Electrical Engineering Department Faculty of Engineering&TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer Science College of ComputingKhon Kaen UniversityKhon Kaen40002Thailand

At present,the prediction of brain tumors is performed using Machine Learning(ML)and Deep Learning(DL)*** various ML and DL algorithms are adapted to predict brain tumors to some range,some concerns still need enhancement,particularly accuracy,sensitivity,false positive and false negative,to improve the brain tumor prediction system ***,this work proposed an Extended Deep Learning Algorithm(EDLA)to measure performance parameters such as accuracy,sensitivity,and false positive and false negative *** addition,these iterated measures were analyzed by comparing the EDLA method with the Convolutional Neural Network(CNN)way further using the SPSS tool,and respective graphical illustrations were *** results were that the mean performance measures for the proposed EDLA algorithm were calculated,and those measured were accuracy(97.665%),sensitivity(97.939%),false positive(3.012%),and false negative(3.182%)for ten *** in the case of the CNN,the algorithm means accuracy gained was 94.287%,mean sensitivity 95.612%,mean false positive 5.328%,and mean false negative 4.756%.These results show that the proposed EDLA method has outperformed existing algorithms,including CNN,and ensures symmetrically improved *** EDLA algorithm introduces novelty concerning its performance and particular activation *** proposed method will be utilized effectively in brain tumor detection in a precise and accurate *** algorithm would apply to brain tumor diagnosis and be involved in various medical diagnoses *** the quantity of dataset records is enormous,then themethod’s computation power has to be updated.

关键词： Brain tumor extended deep learning algorithm convolution neural network tumor detection deep learning

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