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Journal of Acute Disease 2024年第3期13卷 111-115页

作者： Elham Jafari Maskouni Samaneh Abbasi Elham Mousavi Zahra Najafimemar ali mohammad Arabzadeh Mehrdad Farrokhnia Saeedeh Ebrahimi Department of Medical Microbiology(Bacteriology&Virology) Afzalipour Faculty of MedicineKerman University of Medical SciencesKermanIran Abadan University of Medical Sciences AbadanIran Infectious Diseases Research Center Golestan University of Medical SciencesGorganIran Department of Internal Medicine Afzalipour School of MedicineKerman University of Medical SciencesKermanIran

Objective:To explore expression level of interferon-stimulated genes PKR,OAS1,MX1,and ISG15 in peripheral blood mononuclear cells of COVID-19 ***:In this study,changes in the expression of four interferon-stimulated genes(ISGs),including PKR,OAS1,MX1,and ISG15,in peripheral blood mononuclear cells of 45 COVID-19 patients with different severities were evaluated by real-time PCR ***:OAS1,MX1,PKR,and ISG15 were differently expressed in COVID-19 patients with different *** results showed that the expression of OAS1,MX1,PKR,and ISG15 genes was significantly(P=0.001)lower in severe ***:Weak and defective IFN response and subsequent disruption of ISGs may be associated with COVID-19 severity.

关键词： COVID-19 SARS-CoV-2 Interferon ISGs Severe COVID-19 Risk factors Interferon signaling

Decoding molecular mechanisms:brain aging and Alzheimer's disease

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Neural Regeneration Research 2025年第8期20卷 2279-2299页

作者： Mahnoor Hayat Rafay ali Syed Hammad Qaiser mohammad Uzair Khalid Al-Regaiey Roaa Khallaf Lubna Abdullah Mohammed Albassam Imdad Kaleem Xueyi Wang Ran Wang Mehwish SBhatti Shahid Bashir Atta-ur-Rahman School of Applied Biosciences(ASAB) National University of Sciences and Technology(NUST)IslamabadPakistan Department of Biotechnology Quaid-i-Azam UniversityIslamabadPakistan Department of Biological Sciences Faculty of Basic&Applied SciencesInternational Islamic University Islamabad(IIUI)IslamabadPakistan Department of Bioengineering King Fahd University of Petroleum and MineralsDhahranSaudi Arabia Department of Physiology College of MedicineKing Saud UniversityRiyadhSaudi Arabia Department of Neurology Neuroscience CenterKing Fahad Specialist HospitalDammamSaudi Arabia Department of Mental Health Neuroscience CenterKing Fahad Specialist HospitalDammamSaudi Arabia Department of Biosciences Commission on Science and Technology for Sustainable Development in the South(COMSATS University)IslamabadPakistan Department of Psychiatry The First Hospital of Hebei Medical UniversityShijiazhuangHebei ProvinceChina Mental Health Institute of Hebei Medical University ShijiazhuangHebei ProvinceChina Department of Neurobiology and Behavior University of CaliforniaIrvineCAUSA Neuroscience Center King Fahad Specialist Hospital DammamDammamSaudi Arabia

The complex morphological,anatomical,physiological,and chemical mechanisms within the aging brain have been the hot topic of research for *** aging process alters the brain structure that affects functions and cognitions,but the worsening of such processes contributes to the pathogenesis of neurodegenerative disorders,such as Alzheimer's *** these observable,mild morphological shifts,significant functional modifications in neurotransmission and neuronal activity critically influence the aging *** these changes is important for maintaining cognitive health,especially given the increasing prevalence of age-related conditions that affect *** review aims to explore the age-induced changes in brain plasticity and molecular processes,differentiating normal aging from the pathogenesis of Alzheimer's disease,thereby providing insights into predicting the risk of dementia,particularly Alzheimer's disease.

关键词： Alzheimer’s disease brain aging cognitive health dementia molecular mechanisms neuronal activity neuroplasticity neurotransmission

Facial Image-Based Autism Detection:A Comparative Study of Deep Neural Network Classifiers

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Computers, Materials & Continua 2024年第1期78卷 105-126页

作者： Tayyaba Farhat Sheeraz Akram Hatoon SAlSagri Zulfiqar ali Awais Ahmad Arfan Jaffar Faculty of Computer Science and Information Technology The Superior UniversityLahore54600Pakistan Intelligent Data Visual Computing Research(IDVCR) Faculty of Computer Science and Information TechnologyThe Superior UniversityLahore54600Pakistan Information Systems Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh12571Saudi Arabia School of Computer Science and Electronic Engineering(CSEE) University of EssexWivenhoe ParkColchesterCO43SQUK

Autism Spectrum Disorder(ASD)is a neurodevelopmental condition characterized by significant challenges in social interaction,communication,and repetitive *** and precise ASD detection is crucial,particularly in regions with limited diagnostic resources like *** study aims to conduct an extensive comparative analysis of various machine learning classifiers for ASD detection using facial images to identify an accurate and cost-effective solution tailored to the local *** research involves experimentation with VGG16 and MobileNet models,exploring different batch sizes,optimizers,and learning rate *** addition,the“Orange”machine learning tool is employed to evaluate classifier performance and automated image processing capabilities are utilized within the *** findings unequivocally establish VGG16 as the most effective classifier with a 5-fold cross-validation ***,VGG16,with a batch size of 2 and the Adam optimizer,trained for 100 epochs,achieves a remarkable validation accuracy of 99% and a testing accuracy of 87%.Furthermore,the model achieves an F1 score of 88%,precision of 85%,and recall of 90% on test *** validate the practical applicability of the VGG16 model with 5-fold cross-validation,the study conducts further testing on a dataset sourced fromautism centers in Pakistan,resulting in an accuracy rate of 85%.This reaffirms the model’s suitability for real-world ASD *** research offers valuable insights into classifier performance,emphasizing the potential of machine learning to deliver precise and accessible ASD diagnoses via facial image analysis.

关键词： Autism Autism Spectrum Disorder(ASD) disease segmentation features optimization deep learning models facial images classification

Oral candidiasis and potential risk factors among disabled and nondisabled in Al-Baha region, Saudi Arabia

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World Journal of Clinical Cases 2024年第27期12卷 6077-6086页

作者： Abdullah ali H Alzahrani Nagesh Bhat Pankaj Kukreja Eltayeb Mohammed Alhassan Abdallah Ibrahim A Mudawi Faisal A Alzahrani mohammad A Albanghali Department of Dental Health Faculty of Applied Medical SciencesAl-Baha UniversityAl Bahah 65731Saudi Arabia Department of Preventive Dental Sciences School of DentistryAlBaha UniversityAl Bahah 65731Saudi Arabia Department of Biomedical Dental Sciences School of DentistryAlBaha UniversityAl Bahah 65731Saudi Arabia Department of Medical Services General Administration Public SecurityThe Saudi Ministry of InteriorRiyadh 11235Saudi Arabia Department of Public Health Faculty of Applied Medical SciencesAl-Baha UniversityAl Bahah 65731Saudi Arabia

BACKGROUND Oral candidiasis(OC)is an oral health disease that could influence patients’oral health quality of *** To estimate prevalence of OC among disabled and non-disabled individuals and its potential risk factors in the Al-Baha region,Saudi *** An observational cross-sectional study was carried out among 148 disabled and non-disabled *** technique of concentrated oral rinse employing the Sabouraud Dextrose Agar medium accompanied with 0.05%chloramphenicol was conducted to assess and isolate *** examination using the World Health Organization guidelines was conducted to examine participants’oral hea-lth status.A pre-designed questionnaire was also used to evaluate sociodemo-graphic,medical history,and oral hygiene habits of the studied *** Out of 148 participants(n=57,38%)had colonized *** of the studied population had visible Candida ***,Candida was found in the oral rinses without the subject presenting any lesions or issues caused by Candida(asymptomatic colonization).The most common prevalent OC among participants were Candida albicans,Candida glabrata,Candida dubliniensis,Candida krusei,Candida tropicalis,and Candida parapsilosis(n=35,61%;n=8,14%;n=6,10%;n=5,9%;n=2,4%;and n=1,2%)***,smoking,poor plaque,and gingival status were key potential risk factors that significantly associated with candida’s density and presence(P=0.001,P=0.001,P=0.01,and P=0.01)*** status had no statistically significant effect on presence and density of *** The prevalence of OC is almost third of the studied population;thus,may provoke a need to develop preventive strategies to reduce the OC rate and establish solid treatment plans.

关键词： Oral health Candida Oral candidiasis Dental public health Disabled Disability Risk factors Epidemiology

Secrecy Outage Probability Minimization in Wireless-Powered Communications Using an Improved Biogeography-Based Optimization-Inspired Recurrent Neural Network

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Computers, Materials & Continua 2024年第3期78卷 3971-3991页

作者： mohammad Mehdi Sharifi Nevisi Elnaz Bashir Diego Martín Seyedkian Rezvanjou Farzaneh Shoushtari Ehsan Ghafourian Department of Industrial Engineering Iran University of Science and TechnologyNarmakTehran16846-13114Iran Department of Computer Science Iowa State UniversityAmesIowaIA50011USA ETSI de Telecomunicación Universidad Politécnica de MadridMadrid28040Spain Department of Engineering California State University East BayHaywardCalifornia94542USA Department of Industrial Engineering Bu-Ali Sina UniversityHamedan65178-38695Iran

This paper focuses on wireless-powered communication systems,which are increasingly relevant in the Internet of Things(IoT)due to their ability to extend the operational lifetime of devices with limited *** main contribution of the paper is a novel approach to minimize the secrecy outage probability(SOP)in these *** SOP is crucial for maintaining the confidentiality and integrity of data,especially in situations where the transmission of sensitive data is *** proposed method harnesses the power of an improved biogeography-based optimization(IBBO)to effectively train a recurrent neural network(RNN).The proposed IBBO introduces an innovative migration *** core advantage of IBBO lies in its adeptness at maintaining equilibrium between exploration and *** is accomplished by integrating tactics such as advancing towards a random habitat,adopting the crossover operator from genetic algorithms(GA),and utilizing the global best(Gbest)operator from particle swarm optimization(PSO)into the IBBO *** IBBO demonstrates its efficacy by enabling the RNN to optimize the system parameters,resulting in significant outage probability *** comprehensive simulations,we showcase the superiority of the IBBO-RNN over existing approaches,highlighting its capability to achieve remarkable gains in SOP *** paper compares nine methods for predicting outage probability in wireless-powered *** IBBO-RNN achieved the highest accuracy rate of 98.92%,showing a significant performance *** contrast,the standard RNN recorded lower accuracy rates of 91.27%.The IBBO-RNN maintains lower SOP values across the entire signal-to-noise ratio(SNR)spectrum tested,suggesting that the method is highly effective at optimizing system parameters for improved secrecy even at lower SNRs.

关键词： Wireless-powered communications secrecy outage probability improved biogeography-based optimization recurrent neural network

Empowering Diagnosis: Cutting-Edge Segmentation and Classification in Lung Cancer Analysis

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Computers, Materials & Continua 2024年第6期79卷 4963-4977页

作者： Iftikhar Naseer Tehreem Masood Sheeraz Akram Zulfiqar ali Awais Ahmad Shafiq Ur Rehman Arfan Jaffar Faculty of Computer Sciences&Information Technology The Superior UniversityLahore54000Pakistan Intelligent Data Visual Computing Research(IDVCR) Lahore54000Pakistan Information System Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia School of Computer Science and Electronic Engineering(CSEE) University of EssexWivenhoe ParkColchesterCO43SQUK

Lung cancer is a leading cause of global mortality *** detection of pulmonary tumors can significantly enhance the survival rate of ***,various Computer-Aided Diagnostic(CAD)methods have been developed to enhance the detection of pulmonary nodules with high ***,the existing method-ologies cannot obtain a high level of specificity and *** present study introduces a novel model for Lung Cancer Segmentation and Classification(LCSC),which incorporates two improved architectures,namely the improved U-Net architecture and the improved AlexNet *** LCSC model comprises two distinct *** first stage involves the utilization of an improved U-Net architecture to segment candidate nodules extracted from the lung ***,an improved AlexNet architecture is employed to classify lung *** the first stage,the proposed model demonstrates a dice accuracy of 0.855,a precision of 0.933,and a recall of 0.789 for the segmentation of candidate *** suggested improved AlexNet architecture attains 97.06%accuracy,a true positive rate of 96.36%,a true negative rate of 97.77%,a positive predictive value of 97.74%,and a negative predictive value of 96.41%for classifying pulmonary cancer as either benign or *** proposed LCSC model is tested and evaluated employing the publically available dataset furnished by the Lung Image Database Consortium and Image Database Resource Initiative(LIDC-IDRI).This proposed technique exhibits remarkable performance compared to the existing methods by using various evaluation parameters.

关键词： Lung cancer segmentation AlexNet U-Net classification

High-Performance Flow Classification of Big Data Using Hybrid CPU-GPU Clusters of Cloud Environments

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Tsinghua Science and Technology 2024年第4期29卷 1118-1137页

作者： Azam Fazel-Najafabadi Mahdi Abbasi Hani H.Attar Ayman Amer Amir Taherkordi Azad Shokrollahi mohammad R.Khosravi Ahmed A.Solyman Department of Computer Engineering Faculty of EngineeringBu-Ali Sina UniversityHamedan ***Iran Department of Energy Engineering Zarqa UniversityZarqa 13132Jordan Department of Informatics University of OsloOslo 0316Norway Department of Computer Science MalmöUniversityMalmö20506Sweden Shandong Provincial University Laboratory for Protected Horticulture Weifang University of Science and TechnologyWeifang 261100China Department of Electrical and Electronics Engineering NişantaşıUniversityIstanbul 34481742Türkiye

The network switches in the data plane of Software Defined Networking (SDN) are empowered by an elementary process, in which enormous number of packets which resemble big volumes of data are classified into specific flows by matching them against a set of dynamic rules. This basic process accelerates the processing of data, so that instead of processing singular packets repeatedly, corresponding actions are performed on corresponding flows of packets. In this paper, first, we address limitations on a typical packet classification algorithm like Tuple Space Search (TSS). Then, we present a set of different scenarios to parallelize it on different parallel processing platforms, including Graphics Processing Units (GPUs), clusters of Central Processing Units (CPUs), and hybrid clusters. Experimental results show that the hybrid cluster provides the best platform for parallelizing packet classification algorithms, which promises the average throughput rate of 4.2 Million packets per second (Mpps). That is, the hybrid cluster produced by the integration of Compute Unified Device Architecture (CUDA), Message Passing Interface (MPI), and OpenMP programming model could classify 0.24 million packets per second more than the GPU cluster scheme. Such a packet classifier satisfies the required processing speed in the programmable network systems that would be used to communicate big medical data.

关键词： OpenMP Compute Unified Device Architecture(CUDA) Message Passing Interface(MPI) packet classification medical data tuple space algorithm Graphics Processing Unit(GPU)cluster

Adaptive Cloud Intrusion Detection System Based on Pruned Exact Linear Time Technique

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Computers, Materials & Continua 2024年第6期79卷 3725-3756页

作者： Widad Elbakri Maheyzah Md.Siraj Bander ali Saleh Al-rimy Sultan Noman Qasem Tawfik Al-Hadhrami Faculty of Computing Universiti Teknologi MalaysiaJohor BahruJohor81310Malaysia Computer Science Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia School of Science and Technology Nottingham Trent UniversityNottinghamNG118NSUK

Cloud computing environments,characterized by dynamic scaling,distributed architectures,and complex work-loads,are increasingly targeted by malicious *** threats encompass unauthorized access,data breaches,denial-of-service attacks,and evolving malware *** security solutions often struggle with the dynamic nature of cloud environments,highlighting the need for robust Adaptive Cloud Intrusion Detection Systems(CIDS).Existing adaptive CIDS solutions,while offering improved detection capabilities,often face limitations such as reliance on approximations for change point detection,hindering their precision in identifying *** can lead to missed attacks or an abundance of false alarms,impacting overall security *** address these challenges,we propose ACIDS(Adaptive Cloud Intrusion Detection System)-*** novel Adaptive CIDS framework leverages the Pruned Exact Linear Time(PELT)algorithm and a Support Vector Machine(SVM)for enhanced accuracy and ***-PELT comprises four key components:(1)Feature Selection:Utilizing a hybrid harmony search algorithm and the symmetrical uncertainty filter(HSO-SU)to identify the most relevant features that effectively differentiate between normal and anomalous network traffic in the cloud environment.(2)Surveillance:Employing the PELT algorithm to detect change points within the network traffic data,enabling the identification of anomalies and potential security threats with improved precision compared to existing approaches.(3)Training Set:Labeled network traffic data forms the training set used to train the SVM classifier to distinguish between normal and anomalous behaviour patterns.(4)Testing Set:The testing set evaluates ACIDS-PELT’s performance by measuring its accuracy,precision,and recall in detecting security threats within the cloud *** evaluate the performance of ACIDS-PELT using the NSL-KDD benchmark *** results demonstrate that ACIDS-PELT outperf

关键词： Adaptive cloud IDS harmony search distributed denial of service(DDoS) PELT machine learning SVM ISOTCID NSL-KDD

Eco-friendly green synthesis of novel magnetic Fe3O4/SiO2/ZnO-Pr6O11 nanocomposites for photocatalytic degradation of organic pollutant

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Journal of Rare Earths 2020年第1期38卷 13-20页

作者： mohammad ali Ebrahimzadeh Sobhan Mortazavi-Derazkola mohammad ali Zazouli Pharmaceutical Sciences Research Center Hemoglobinopathy Institute and Department of Medicinal ChemistrySchool of PharmacyMazandaran University of Medical ScienceSariP.O.Box 48471-93698Iran Department of Environmental Health Engineering Health Science Research CenterFaculty of HealthMazandaran University of Medical ScienceSariP.O.Box 48471-16548Iran

This study reports the successful so nochemical synthesis of novel Fe3O4/SiO2/ZnO-Pr6O11(Fe/Si/Zn-Pr6O11) nanocomposites using fructose as a green capping *** influence of various parameters containing capping agent,power and time of ultrasound irradiation was investigated to reach optimum morphology and size *** products obtained were characterized by transmission electron microscopy(TEM),UV/Vis diffuse reflectance spectroscopy(DRS),Fourier transform infrared spectra(FT-IR),vibration sample magnetometer(VSM),scanning electron microscopy(SEM),energy dispersive X-ray microanalysis(EDS) and X-ray diffraction(XRD).The Fe3O4/SiO2/ZnO-Pr6O11 nanocomposites display remarkably enhanced photocatalytic activity towards rhodamine b degradation(89.6%)and Congo red(84.7%) under UV irradiation compared with the other *** results illustrate that the photocatalytic efficiency of magnetic nanocomposites is very much higher than pure *** photocatalyst still has good stability after five successive ***,these recyclable nanocomposites can play a role in the treatment of both industrial and domestic contaminated water.

关键词： Sonochemical synthesis Nanocomposite Photocatalysis Electron microscopy Rhodamine b Praseodymium oxide

A Galvanostatic Modeling for Preparation of Electrodeposited Nanocrystalline Coatings by Control of Current Density

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Journal of Materials Science & Technology 2012年第12期28卷 1071-1076页

作者： ali mohammad Rashidi School of Engineering Razi UniversityP.O. Box 67149-67346 Kermanshah Iran

The correlation between the grain size of electrodeposited coatings and the current densities was modeled by considering galvanostatic conditions. In order to test the model by experimental results, nanocrystalline （NC） nickel samples were deposited at different current densities using a Watts bath. The grain size of the deposits was evaluated by X-ray diffraction （XRD） technique. Model predictions were validated by finding a curve being the best-fit to the experimental results which were gathered from literature for different NC coatings in addition to those data measured in this research for NC nickel coatings. According to our model, the variation of grain size with the reciprocal of the current density follows a power law. A good agreement between the experimental results and model predictions was observed which indicated that the derived analytical model is applicable for producting the nanocrystalline electrodeposits with the desired grain size by controling current density.

关键词： Electrodeposition Galvanostatic modeling Grain size control Nanocrystalline