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Brownian motion and thermophoresis effects on unsteady stagnation point flow of Eyring-Powell nanofluid: a Galerkin approach

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Communications in Theoretical Physics 2020年第12期72卷 71-82页

作者： Z H Khan M usman T Zubair M Hamid r u haq State Key Laboratory of Hydraulics and Mountain river Engineering College of Water Resource and HydropowerSichuan UniversityChengdu610065China BIC-ESAT College of EngineeringPeking UniversityBeijing 100871China State Key Laboratory for Turbulence and Complex Systems Department of Mechanics and Engineering SciencePeking UniversityBeijing 100871China Institute of Ocean research Peking UniversityBeijing 100871China School of Mathematical Sciences Peking UniversityBeijing 100871China Department of Mechanics and Engineering Science Fudan UniversityShanghai 200433China Department of Electrical Engineering Bahria UniversityIslamabad CampusIslamabad 44000Pakistan

This article concerns the analysis of an unsteady stagnation point flow of Eyring–Powell nanofluid over a stretching *** influence of thermophoresis and Brownian motion is also considered in transport *** nonlinear ODE set is obtained from the governing nonlinear equations via suitable *** numerical experiments are performed using the Galerkin scheme.A tabular form comparison analysis of outcomes attained via the Galerkin approach and numerical scheme(rK-4)is available to show the credibility of the Galerkin *** numerical exploration is carried out for various governing parameters,namely,Brownian motion,steadiness,thermophoresis,stretching ratio,velocity slip,concentration slip,thermal slip,and fluid parameters,and Hartmann,Prandtl and Schmidt *** velocity of fluid enhances with an increase in fluid and magnetic parameters for the case of opposing,but the behavior is reversed for assisting *** Brownian motion and thermophoresis parameters cause an increase in temperature for both cases(assisting and opposing).The Brownian motion parameter provides a drop-in concentration while an increase is noticed for the thermophoresis *** the outcomes and the behavior of emerging parameters are illustrated *** comparison analysis and graphical plots endorse the appropriateness of the Galerkin *** is concluded that said method could be extended to other problems of a complex nature.

关键词： Eyring-Powell fluid stagnation point flow Brownian motion and thermophoresis Galerkin method

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Wavelet analysis of stagnation point flow of non-Newtonian nanofluid

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Applied Mathematics and Mechanics(English Edition) 2019年第8期40卷 1211-1226页

作者： M.HAMID M.uSMAN r.u.haq4 Z.H.KHAN Wei WANG School of Mathematical Sciences Peking UniversityBeijing 100871China BIC-ESAT College of EngineeringPeking UniversityBeijing 100871China State Key Laboratory for Turbulence and Complex Systems Department of Mechanics and Engineering SciencePeking UniversityBeijing 100871China Department of Electrical Engineering Bahria UniversityIslamabad 44000Pakistan State Key Laboratory of Hydraulics and Mountain river Engineering College of Water Resource & HydropowerSichuan UniversityChengdu 610065China Key Laboratory of Advanced reactor Engineering and Safety Ministry of Education Tsinghua UniversityBeijing 100084China

The wavelet approach is introduced to study the influence of the natural convection stagnation point flow of the Williamson fluid in the presence of thermophysical and Brownian motion effects. The thermal radiation effects are considered along a permeable stretching surface. The nonlinear problem is simulated numerically by using a novel algorithm based upon the Chebyshev wavelets. It is noticed that the velocity of the Williamson fluid increases for assisting flow cases while decreases for opposing flow cases when the unsteadiness and suction parameters increase, and the magnetic effect on the velocity increases for opposing flow cases while decreases for assisting flow cases. When the thermal radiation parameter, the Dufour number, and Williamson’s fluid parameter increase, the temperature increases for both assisting and opposing flow cases. Meanwhile, the temperature decreases when the Prandtl number increases. The concentration decreases when the Soret parameter increases, while increases when the Schmidt number increases. It is perceived that the assisting force decreases more than the opposing force. The findings endorse the credibility of the proposed algorithm, and could be extended to other nonlinear problems with complex nature.

关键词： Williamson nanofluid heat and mass transfer stagnation point flow assisting and opposing flow Chebyshev wavelet method

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Dual solutions in MHD stagnation-point flow of Prandtl fluid impinging on shrinking sheet

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Applied Mathematics and Mechanics(English Edition) 2014年第7期35卷 813-820页

作者： N.S.AKBAr Z.H.KHAN r.u.haq S.NADEEM Department of Basic Science & Humanities College of Electrical & Mechanical Engineering(CEME) National University of Sciences and Technology Islamabad 46000 Pakistan School of Mathematical Sciences Peking University Department of Mathematics University of Malakand Khyber Pakhtunktwo 18800 Pakistan Department of Mathematics Quaid-i-Azam University Islamabad 44000 Pakistan

The present article investigates the dual nature of the solution of the magneto- hydrodynamic （MHD） stagnation-point flow of a Prandtl fluid model towards a shrinking surface. The self-similar nonlinear ordinary differential equations are solved numerically by the shooting： method. It is found that the dual solutions of the flow exist for cer- tain values of tile velocity ratio parameter. The special case of the first branch solutions （the classical Newtonian fluid model） is compared with the present numerical results of stretching flow. The results are found to be in good agreement. It is also shown that the boundary layer thickness for the second solution is thicker than that for the first solution.

关键词： stagnation-point flow shrinking sheet Prandtl fluid magnetohydrody-namic (MHD) dual solution shooting method

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Fuzzy and IrLNC-based routing approach to improve data storage and system reliability in IoT

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Intelligent and Converged Networks 2024年第1期5卷 68-80页

作者： u.Indumathi A.r.Arunachalam the Department of Computer Science and Engineering Dr.M.G.R.Educational and Research InstituteChennai 600095India

Internet of Things(IoT)based sensor network is largely utilized in various field for transmitting huge amount of data due to their ease and cheaper *** performing this entire process,there is a high possibility for data corruption in the mid of *** the other hand,the network performance is also affected due to various *** address these issues,an efficient algorithm that jointly offers improved data storage and reliable routing is ***,after the deployment of sensor nodes,the election of the storage node is achieved based on a fuzzy expert *** random Linear Network Coding(IrLNC)is used to create an encoded *** encoded packet from the source and neighboring nodes is transmitted to the storage ***,to transmit the encoded packet from the storage node to the destination shortest path is found using the Destination Sequenced Distance Vector(DSDV)*** analysis of the proposed work is carried out by evaluating some of the statistical *** residual energy,packet delivery ratio,compression ratio and storage time achieved for the proposed work are 8.8%,0.92%,0.82%,and 69 *** on this analysis,it is revealed that better data storage system and system reliability is attained using this proposed work.

关键词： Internet of Things(IoT) data storage management fuzzy system improved random linear network coding energy utilization system reliability

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Predicting potential invasion risks of Leucaena leucocephala(Lam.)de Wit in the arid area of Saudi Arabia

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Journal of Arid Land 2024年第7期16卷 983-999页

作者： haq S MArIFATuL Darwish MOHAMMED Waheed MuHAMMAD Kumar MANOJ Siddiqui H MANZEr Bussmann W rAINEr Department of Ethnobotany Institute of BotanyIlia State UniversityTbilisi 0162Georgia The Centre of Excellence on Sustainable Land Management(CoE-SLM) Indian Council of Forestry Research and EducationDehradun 248006India Department of Botany and Microbiology College of ScienceKing Saud UniversityRiyadh 11451Saudi Arabia Department of Botany State Museum for Natural HistoryKarlsruhe 76133Germany

The presence of invasive plant species poses a substantial ecological impact,thus comprehensive evaluation of their potential range and risk under the influence of climate change is *** study uses maximum entropy(MaxEnt)modeling to forecast the likelihood of Leucaena leucocephala(Lam.)de Wit invasion in Saudi Arabia under present and future climate change *** the MaxEnt modeling,we integrated climatic and soil data to predict habitat suitability for the invasive *** conducted a detailed analysis of the distribution patterns of the species,using climate variables and ecological *** focused on the important influence of temperature seasonality,temperature annual range,and precipitation *** distribution modeling used robust measures of area under the curve(AuC)and receiver-operator characteristic(rOC)curves,to map the invasion extent,which has a high level of accuracy in identifying appropriate *** complex interaction that influenced the invasion of *** was highlighted by the environmental parameters using Jackknife ***,the actual geographic area where *** was found in Saudi Arabia was considerably smaller than the theoretical maximum range,suggesting that it had the capacity to expand *** MaxEnt model exhibited excellent prediction accuracy and produced reliable results based on the data from the rOC *** and temperature were the primary factors influencing the potential distribution of ***,an estimated area of 216,342 km^(2)in Saudi Arabia was at a high probability of invasion by *** investigated the potential for increased invasion hazards in the future due to climate change scenarios(Shared Socioeconomic Pathways(SSPs)245 and 585).The analysis of key climatic variables,including temperature seasonality and annual range,along with soil properties such as clay composition and nitrogen content,unveiled their substanti

关键词： area under the curve invasive species invasion risks climate change MaxEnt model

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Deep Learning Based Efficient Crowd Counting System

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Computers, Materials & Continua 2024年第6期79卷 4001-4020页

作者： Waleed Khalid Al-Ghanem Emad ul haq Qazi Muhammad Hamza Faheem Syed Shah Amanullah Quadri Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh12372Saudi Arabia Centre of Excellence in Cybercrime and Digital Forensics Naif Arab University for Security SciencesRiyadh14812Saudi Arabia Center of Excellence for Information Assurance(COEIA) King Saud UniversityRiyadh12372Saudi Arabia

Estimation of crowd count is becoming crucial nowadays,as it can help in security surveillance,crowd monitoring,and management for different *** is challenging to determine the approximate crowd size from an image of the crowd’s *** in this research study,we proposed a multi-headed convolutional neural network architecture-based model for crowd counting,where we divided our proposed model into two main components:(i)the convolutional neural network,which extracts the feature across the whole image that is given to it as an input,and(ii)the multi-headed layers,which make it easier to evaluate density maps to estimate the number of people in the input image and determine their number in the *** employed the available public benchmark crowd-counting datasets uCF CC 50 and ShanghaiTech parts A and B for model training and testing to validate the model’s *** analyze the results,we used two metrics Mean Absolute Error(MAE)and Mean Square Error(MSE),and compared the results of the proposed systems with the state-of-art models of crowd *** results show the superiority of the proposed system.

关键词： Crowd counting EfficientNet multi-head attention convolutional neural network transfer learning

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CapsNet-Fr: Capsule Networks for Improved recognition of Facial Features

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Computers, Materials & Continua 2024年第5期79卷 2169-2186页

作者： Mahmood ul haq Muhammad Athar Javed Sethi Najib Ben Aoun Ala Saleh Alluhaidan Sadique Ahmad Zahid farid Department of Computer System Engineering University of Engineering&TechnologyPeshawar25000Pakistan College of Computer Science and Information Technology Al-Baha UniversityAlaqiq65779-7738Saudi Arabia rEGIM-Lab:research Groups in Intelligent Machines National School of Engineers of Sfax(ENIS)University of SfaxSfax3038Tunisia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia EIAS:Data Science and Blockchain Laboratory College of Computer and Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Computer Sciences Bahria University Karachi CampusKarachi541004Pakistan Department of Electrical Engineering Abasyn UniversityPeshawar25000Pakistan

Face recognition (Fr) technology has numerous applications in artificial intelligence including biometrics, security,authentication, law enforcement, and surveillance. Deep learning (DL) models, notably convolutional neuralnetworks (CNNs), have shown promising results in the field of Fr. However CNNs are easily fooled since theydo not encode position and orientation correlations between features. Hinton et al. envisioned Capsule Networksas a more robust design capable of retaining pose information and spatial correlations to recognize objects morelike the brain does. Lower-level capsules hold 8-dimensional vectors of attributes like position, hue, texture, andso on, which are routed to higher-level capsules via a new routing by agreement algorithm. This provides capsulenetworks with viewpoint invariance, which has previously evaded CNNs. This research presents a Fr model basedon capsule networks that was tested using the LFW dataset, COMSATS face dataset, and own acquired photos usingcameras measuring 128 × 128 pixels, 40 × 40 pixels, and 30 × 30 pixels. The trained model outperforms state-ofthe-art algorithms, achieving 95.82% test accuracy and performing well on unseen faces that have been blurred orrotated. Additionally, the suggested model outperformed the recently released approaches on the COMSATS facedataset, achieving a high accuracy of 92.47%. Based on the results of this research as well as previous results, capsulenetworks perform better than deeper CNNs on unobserved altered data because of their special equivarianceproperties.

关键词： CapsNet face recognition artificial intelligence

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Identification of Software Bugs by Analyzing Natural Language-Based requirements using Optimized Deep Learning Features

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Computers, Materials & Continua 2024年第3期78卷 4379-4397页

作者： Qazi Mazhar ul haq Fahim Arif Khursheed Aurangzeb Noor ul Ain Javed Ali Khan Saddaf rubab Muhammad Shahid Anwar Department of Computer Science and Engineering and International Bachelor’s Program in Informatics Yuan Ze UniversityZhongliTaoyuan320315Taiwan Department of Computer Science National University of PakistanRawalpindiPunjab46000Pakistan Department of Computer Software Engineering MCSNational University of Sciences and TechnologyIslamabad44000Pakistan Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia Department of Computer Science School of PhysicsEngineering and Computer ScienceUniversity of HertfordshireHatfieldAL109ABUK Department of Computer Engineering College of Computing and InformaticsUniversity of SharjahSharjah27272United Arab Emirates Department of AI and Software Gachon UniversitySeongnam-SI13120South Korea

Software project outcomes heavily depend on natural language requirements,often causing diverse interpretations and issues like ambiguities and incomplete or faulty *** are exploring machine learning to predict software bugs,but a more precise and general approach is *** bug prediction is crucial for software evolution and user training,prompting an investigation into deep and ensemble learning ***,these studies are not generalized and efficient when extended to other ***,this paper proposed a hybrid approach combining multiple techniques to explore their effectiveness on bug identification *** methods involved feature selection,which is used to reduce the dimensionality and redundancy of features and select only the relevant ones;transfer learning is used to train and test the model on different datasets to analyze how much of the learning is passed to other datasets,and ensemble method is utilized to explore the increase in performance upon combining multiple classifiers in a *** National Aeronautics and Space Administration(NASA)and four Promise datasets are used in the study,showing an increase in the model’s performance by providing better Area under the receiver Operating Characteristic Curve(AuC-rOC)values when different classifiers were *** reveals that using an amalgam of techniques such as those used in this study,feature selection,transfer learning,and ensemble methods prove helpful in optimizing the software bug prediction models and providing high-performing,useful end mode.

关键词： Natural language processing software bug prediction transfer learning ensemble learning feature selection

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Design of a Lightweight Compressed Video Stream-Based Patient Activity Monitoring System

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Computers, Materials & Continua 2024年第1期78卷 1253-1274页

作者： Sangeeta Yadav Preeti Gulia Nasib Singh Gill Piyush Kumar Shukla Arfat Ahmad Khan Sultan Alharby Ahmed Alhussen Mohd Anul haq Department of Computer Science&Applications Maharshi Dayanand UniversityRohtak124001India university Institute of Technology Rajiv Gandhi Proudyogiki VishwavidyalayaBhopal462033India Department of Computer Science College of ComputingKhon Kaen UniversityKhon Kaen40002Thailand Department of Computer Science College of Computer and Information SciencesMajmaah UniversityAl Majmaah11952Kingdom of Saudi Arabia Department of Computer Engineering College of Computer and Information SciencesMajmaah UniversityAl Majmaah11952Kingdom of Saudi Arabia

Inpatient falls from beds in hospitals are a common *** falls may result in severe *** problem can be addressed by continuous monitoring of patients using *** advancements in deep learning-based video analytics have made this task of fall detection more effective and *** with fall detection,monitoring of different activities of the patients is also of significant concern to assess the improvement in their *** computation-intensive models are required to monitor every action of the patient *** requirement limits the applicability of such ***,to keep the model lightweight,the already designed fall detection networks can be extended to monitor the general activities of the patients along with the fall *** by the same notion,we propose a novel,lightweight,and efficient patient activity monitoring system that broadly classifies the patients’activities into fall,activity,and rest classes based on their *** whole network comprises three sub-networks,namely a Convolutional Neural Networks(CNN)based video compression network,a Lightweight Pose Network(LPN)and a residual Network(resNet)Mixer block-based activity recognition *** compression network compresses the video streams using deep learning networks for efficient storage and retrieval;after that,LPN estimates human ***,the activity recognition network classifies the patients’activities based on their *** proposed system shows an overall accuracy of approx.99.7% over a standard dataset with 99.63% fall detection accuracy and efficiently monitors different events,which may help monitor the falls and improve the inpatients’health.

关键词： Fall detection activity recognition human pose estimation accuracy

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Suppressive effects of exercise-conditioned serum on cancer cells:A narrative review of the influence of exercise mode,volume,and intensity

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Journal of Sport and Health Science 2024年第4期13卷 484-498,F0003页

作者： Francesco Bettariga Dennis r.Taaffe Daniel A.Galvao Chris Bishop Jin-Soo Kim robert u.Newton Exercise Medicine research Institute Edith Cowan UniversityJoondalupWA 6027Australia School of Medical and Health Sciences Edith Cowan UniversityJoondalupWA 6027Australia London Sport Institute School of Science and TechnologyMiddlesex UniversityLondonNW44BTUK School of Human Movement and Nutrition Sciences University of QueenslandSt.LuciaQLD 4072Australia

Cancer is a major cause of morbidity and mortality worldwide,and the incidence is increasing,highlighting the need for effective strategies to treat this *** has emerged as fundamental therapeutic medicine in the management of cancer,associated with a lower risk of recur-rence and increased *** avenues of research demonstrate reduction in growth,proliferation,and increased apoptosis of cancer cells,including breast,prostate,colorectal,and lung cancer,when cultured by serum collected after exercise in vitro(i.e.,the cultivation of cancer cell lines in an experimental setting,which simpliﬁes the biological system and provides mechanistic insight into cell responses).The underlying mechanisms of exercise-induced cancer suppressive effects may be attributed to the alteration in circulating factors,such as skeletal muscle-induced cytokines(i.e.,myokines)and ***,exercise-induced tumor suppressive effects and detailed information about training interventions are not well investigated,constraining more precise application of exercise medicine within clinical *** date,it remains unclear what role different training modes(i.e.,resistance and aerobic training)as well as volume and intensity have on exercise-condi-tioned serum and its effects on cancer ***,the available evidence is that a single bout of aerobic training at moderate to vigorous intensity has cancer suppressive effects,while for chronic training interventions,exercise volume appears to be an inﬂuential candidate driving cancer inhibitory effects regardless of training *** for future research investigating training modes,volume and intensity are provided to further our understanding of the effects of exercise-conditioned serum on cancer cells.

关键词： Cancer cells High intensity interval training Moderate intensity continuous training resistance training Myokines

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