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Mitigation of tribocorrosion of metals in aqueous solutions by potential-enhanced adsorption of surfactants

Friction 2023年第5期11卷 801-819页

作者： Chenxu LIU Yu TIAN zulfiqar a.khan Yonggang MENG State Key Laboratory of Tribology in Advanced Equipment Tsinghua UniversityBeijing 100084China NanoCorr Energy&Modelling Research GroupBournemouth UniversityBournemouthUK

Corrosion and corrosive wear occur commonly on metals surface in aqueous solutions.External electric field is usually considered as one of the factors to accelerate corrosion or corrosive wear of materials in the presence of conventional electrolytes.This work aims to reposition widely believed perspective by experimental justification which have been conducted in aqueous solutions containing surfactants.Electric potential of metal surfaces was modulated externally within the electrochemical potential window of the metal electrode-solution-counter electrode system,which actively regulated the adsorption or desorption of surfactant molecule in the aqueous solution over the electrodes to form a molecular barrier of electron transportation across the electrode–electrolyte interface.The advantage of the approach over the anodic passivation is negligible redox reactions on the protected electrode surface while a better lubricious and wear resistant film than oxide is maintained in the meantime.Tribopairs of several metal/metal and metal/ceramic were tested by employing a ball-on-disc tribometer with anionic and cationic surfactants solutions.For anionic surfactant as the modifier,positive surface potential enables coefficient of friction to be decreased by promoting the formation of adsorption film on metal surface in aqueous solutions.For cationic surfactant,negative surface potential plays a role in decreasing the coefficient of friction.Phase diagrams of friction and wear in wide ranges of surfactant concentration and surface potential were plotted for the tested metal/metal and metal/ceramic tribopairs.These results indicate that the adsorption behavior of molecules or ions at the metal–aqueous interface can be well regulated when an external electric field is present without inducing corrosion or corrosive wear.

关键词： corrosive wear metal electric field surfactant adsorption

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AMachine Learning Approach to User Profiling for Data Annotation of Online Behavior

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Computers, Materials & Continua 2024年第2期78卷 2419-2440页

作者： Moona Kanwal Najeed Akhan Aftab a.khan Computer Science and Information Technology Department NED University of Engineering and TechnologyKarachiPakistan Computer Engineering Department Sir Syed University of Engineering and TechnologyKarachiPakistan College of Education Health and Human ServicesLongwood UniversityFarmvilleVAUSA

The user’s intent to seek online information has been an active area of research in user profiling.User profiling considers user characteristics,behaviors,activities,and preferences to sketch user intentions,interests,and motivations.Determining user characteristics can help capture implicit and explicit preferences and intentions for effective user-centric and customized content presentation.The user’s complete online experience in seeking information is a blend of activities such as searching,verifying,and sharing it on social platforms.However,a combination of multiple behaviors in profiling users has yet to be considered.This research takes a novel approach and explores user intent types based on multidimensional online behavior in information acquisition.This research explores information search,verification,and dissemination behavior and identifies diverse types of users based on their online engagement using machine learning.The research proposes a generic user profile template that explains the user characteristics based on the internet experience and uses it as ground truth for data annotation.User feedback is based on online behavior and practices collected by using a survey method.The participants include both males and females from different occupation sectors and different ages.The data collected is subject to feature engineering,and the significant features are presented to unsupervised machine learning methods to identify user intent classes or profiles and their characteristics.Different techniques are evaluated,and the K-Mean clustering method successfully generates five user groups observing different user characteristics with an average silhouette of 0.36 and a distortion score of 1136.Feature average is computed to identify user intent type characteristics.The user intent classes are then further generalized to create a user intent template with an Inter-Rater Reliability of 75%.This research successfully extracts different user types based on their preferences in online content,platforms,crit

关键词： User intent cluster user profile online search information sharing user behavior search reasons

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A new class of generalized quasi-variational inequalities with applications to Oseen problems under nonsmooth boundary conditions

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Science China Mathematics 2024年第2期67卷 315-338页

作者： Shengda Zeng Akhtar a.khan Stanislaw Migórski Guangxi Colleges and Universities Key Laboratory of Complex System Optimization and Big Data Processing Yulin Normal UniversityYulin 537000China Faculty of Mathematics and Computer Science Jagiellonian University in Krak´owKrak´ow 30348Poland Center for Applied and Computational Mathematics School of Mathematical SciencesRochester Institute of TechnologyRochesterNY 14623USA College of Sciences Beibu Gulf UniversityQinzhou 535000China

In this paper, we study a generalized quasi-variational inequality (GQVI for short) with twomultivalued operators and two bifunctions in a Banach space setting. A coupling of the Tychonov fixedpoint principle and the Katutani-Ky Fan theorem for multivalued maps is employed to prove a new existencetheorem for the GQVI. We also study a nonlinear optimal control problem driven by the GQVI and givesufficient conditions ensuring the existence of an optimal control. Finally, we illustrate the applicability of thetheoretical results in the study of a complicated Oseen problem for non-Newtonian fluids with a nonmonotone andmultivalued slip boundary condition (i.e., a generalized friction constitutive law), a generalized leak boundarycondition, a unilateral contact condition of Signorini’s type and an implicit obstacle effect, in which themultivalued slip boundary condition is described by the generalized Clarke subgradient, and the leak boundarycondition is formulated by the convex subdifferential operator for a convex superpotential.

关键词： generalized quasi-variational inequality existence theorem optimal control Kakutani-Ky Fan theorem Oseen problem non-Newtonian fluid nonmonotone slip boundary condition

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A Systematic Literature Review of Machine Learning and Deep Learning Approaches for Spectral Image Classification in Agricultural Applications Using Aerial Photography

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Computers, Materials & Continua 2024年第3期78卷 2967-3000页

作者： Usman khan Muhammad Khalid khan Muhammad Ayub Latif Muhammad Naveed Muhammad Mansoor Alam Salman a.khan Mazliham Mohd Su’ud College of Computing and Information Sciences Karachi Institute of Economics and TechnologyKarachi75190Pakistan Faculty of Computing and Informatics(FCI) Multimedia UniversityCyberjaya63100Malaysia Faculty of Computing Riphah International UniversityIslamabad46000Pakistan Faculty of Engineering and Information Technology School of Computer ScienceUniversity of Technology SydneySydneyAustralia

Recently,there has been a notable surge of interest in scientific research regarding spectral images.The potential of these images to revolutionize the digital photography industry,like aerial photography through Unmanned Aerial Vehicles(UAVs),has captured considerable attention.One encouraging aspect is their combination with machine learning and deep learning algorithms,which have demonstrated remarkable outcomes in image classification.As a result of this powerful amalgamation,the adoption of spectral images has experienced exponential growth across various domains,with agriculture being one of the prominent beneficiaries.This paper presents an extensive survey encompassing multispectral and hyperspectral images,focusing on their applications for classification challenges in diverse agricultural areas,including plants,grains,fruits,and vegetables.By meticulously examining primary studies,we delve into the specific agricultural domains where multispectral and hyperspectral images have found practical use.Additionally,our attention is directed towards utilizing machine learning techniques for effectively classifying hyperspectral images within the agricultural context.The findings of our investigation reveal that deep learning and support vector machines have emerged as widely employed methods for hyperspectral image classification in agriculture.Nevertheless,we also shed light on the various issues and limitations of working with spectral images.This comprehensive analysis aims to provide valuable insights into the current state of spectral imaging in agriculture and its potential for future advancements.

关键词： Machine learning deep learning unmanned aerial vehicles multi-spectral images image recognition object detection hyperspectral images aerial photography

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A review of friction models in interacting joints for durability design

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Friction 2017年第1期5卷 1-22页

作者： zulfiqar a.khan Vivek CHACKO Hammad NAZIR NanoCorr Energy & Modelling(NCEM)Research Department of Design & Engineering Bournemouth University Bournemouth United Kingdom

This paper presents a comprehensive review of friction modelling to provide an understanding of design for durability within interacting systems. Friction is a complex phenomenon and occurs at the interface of two components in relative motion. Over the last several decades, the effects of friction and its modelling techniques have been of significant interests in terms of industrial applications. There is however a need to develop a unified mathematical model for friction to inform design for durability within the context of varying operational conditions. Classical dynamic mechanisms model for the design of control systems has not incorporated friction phenomena due to non-linearity behaviour. Therefore, the tribological performance concurrently with the joint dynamics of a manipulator joint applied in hazardous environments needs to be fully analysed. Previously the dynamics and impact models used in mechanical joints with clearance have also been examined. The inclusion of reliability and durability during the design phase is very important for manipulators which are deployed in harsh environmental and operational conditions. The revolute joint is susceptible to failures such as in heavy manipulators these revolute joints can be represented by lubricated conformal sliding surfaces. The presence of pollutants such as debris and corrosive constituents has the potential to alter the contacting surfaces, would in turn affect the performance of revolute joints, and puts both reliability and durability of the systems at greater risks of failure. Key literature is identified and a review on the latest developments of the science of friction modelling is presented here. This review is based on a large volume of knowledge. Gaps in the relevant field have been identified to capitalise on for future developments. Therefore, this review will bring significant benefits to researchers, academics and industry professionals.

关键词： friction dynamics joint clearance numerical models impact durability

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Clean production of lactic acid by selective carbon-carbon bond cleavage of biomass feedstock over a novel carbon-bismuth oxychloride photocatalyst

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Frontiers of Chemical Science and Engineering 2024年第2期18卷 69-81页

作者： zulfiqar Ali Jiliang Ma Dongnv Jin Rui Cui Runcang Sun Liaoning Key Lab of Lignocellulose Chemistry and Biomaterials Liaoning Collaborative Innovation Center for Lignocellulosic BiorefineryCollege of Light Industry and Chemical EngineeringDalian Polytechnic UniversityDalian 116034China

The use of functional materials such as carbon-bismuth oxyhalides in integrated photorefineries for the clean production of fine chemicals requires restructuring.A facile biomass-assisted solvothermal fabrication of carbon/bismuth oxychloride nanocomposites(C/BiOCl)was achieved at various temperatures.Compared with BiOCl and C/BiOCl-120,C/BiOCl-180 exhibited higher crystallinity,wider visible light absorption,and a faster migration/separation rate of photoinduced carriers.For the selective C–C bond cleavage of biomass-based feedstocks photocatalyzed by C/BiOCl-180,the xylose conversion and lactic acid yield were 100%and 92.5%,respectively.C/BiOCl-180 efficiently converted different biomass-based monosaccharides to lactic acid,and the efficiency of pentoses was higher than that of hexoses.Moreover,lactic acid synthesis was favored by all active radicals including superoxide ion(·O_(2)^(−)),holes(h^(+)),hydroxyl radical(·OH),and singlet oxygen(^(1)O_(2)),with·O_(2)^(−)playing a key role.The fabricated photocatalyst was stable,economical,and recyclable.The use of biomass-derived monosaccharides for the clean production of lactic acid via the C/BiOCl-180 photocatalyst has opened new research horizons for the investigation and application of C–C bond cleavage in biomass-based feedstocks.

关键词： carbon-carbon bond cleavage biomass reforming C/BiOCl lactic acid photocatalysis

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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 behaviors.Timely and precise ASD detection is crucial,particularly in regions with limited diagnostic resources like Pakistan.This 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 context.The research involves experimentation with VGG16 and MobileNet models,exploring different batch sizes,optimizers,and learning rate schedulers.In addition,the“Orange”machine learning tool is employed to evaluate classifier performance and automated image processing capabilities are utilized within the tool.The findings unequivocally establish VGG16 as the most effective classifier with a 5-fold cross-validation approach.Specifically,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 images.To 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 detection.This 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

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Can NogoA Be a Suitable Target to Treat Ischemic Stroke?

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Neuroscience Bulletin 2024年第4期40卷 557-560页

作者： Suliman khan Abdul Nasir Medical Research Center The Second Affiliated Hospital of Zhengzhou UniversityZhengzhou540014China

Treating stroke or restoring brain function after stroke remains challenging for clinicians[1,2].Researchers have largely focused on developing neuroprotective strategies and translating these discoveries from rodent experiments to the clinical setting;however,these have been typified by several failures[2].It is known that astrocytes play a crucial role in the pathogenesis of ischemic stroke[3,4].They are activated in response to stimuli arising from injury,characterized by drastic changes to their morphology and function-associated gene expression.Reactive astrocytes exacerbate secondary neurodegeneration(repair of nervous tissue by the generation of new neurons,glial cells,axons,myelin,or synapses)[5]as well as provide neuroprotection during CNS pathologies[6].It is worth noting that myelin-related neurite growth inhibitors induce the suppression of axon regeneration after stroke[7].

关键词： protective NogoA clinical

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Sentiment Analysis of Low-Resource Language Literature Using Data Processing and Deep Learning

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Computers, Materials & Continua 2024年第4期79卷 713-733页

作者： Aizaz Ali Maqbool khan Khalil khan Rehan Ullah khan Abdulrahman Aloraini Department of IT and Computer Science Pak-Austria Fachhochschule:Institute of Applied Sciences and TechnologyHaripur22620Pakistan Software Competence Center Hagenberg Softwarepark 32aHagenberg4232Austria Department of Computer Science School of Engineering and Digital SciencesNazarbayev UniversityAstana010000Kazakhstan Department of Information Technology College of ComputerQassim UniversityP.O.Box 1162BuraydahSaudi Arabia

Sentiment analysis, a crucial task in discerning emotional tones within the text, plays a pivotal role in understandingpublic opinion and user sentiment across diverse languages.While numerous scholars conduct sentiment analysisin widely spoken languages such as English, Chinese, Arabic, Roman Arabic, and more, we come to grapplingwith resource-poor languages like Urdu literature which becomes a challenge. Urdu is a uniquely crafted language,characterized by a script that amalgamates elements from diverse languages, including Arabic, Parsi, Pashtu,Turkish, Punjabi, Saraiki, and more. As Urdu literature, characterized by distinct character sets and linguisticfeatures, presents an additional hurdle due to the lack of accessible datasets, rendering sentiment analysis aformidable undertaking. The limited availability of resources has fueled increased interest among researchers,prompting a deeper exploration into Urdu sentiment analysis. This research is dedicated to Urdu languagesentiment analysis, employing sophisticated deep learning models on an extensive dataset categorized into fivelabels: Positive, Negative, Neutral, Mixed, and Ambiguous. The primary objective is to discern sentiments andemotions within the Urdu language, despite the absence of well-curated datasets. To tackle this challenge, theinitial step involves the creation of a comprehensive Urdu dataset by aggregating data from various sources such asnewspapers, articles, and socialmedia comments. Subsequent to this data collection, a thorough process of cleaningand preprocessing is implemented to ensure the quality of the data. The study leverages two well-known deeplearningmodels, namely Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), for bothtraining and evaluating sentiment analysis performance. Additionally, the study explores hyperparameter tuning tooptimize the models’ efficacy. Evaluation metrics such as precision, recall, and the F1-score are employed to assessthe effectiveness of the models. The research findings revea

关键词： Urdu sentiment analysis convolutional neural networks recurrent neural network deep learning natural language processing neural networks

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A novel fractional case study of nonlinear dynamics via analytical approach

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Applied Mathematics(A Journal of Chinese Universities) 2024年第2期39卷 276-290页

作者： Hassan khan Adnan khan Rasool Shah Dumitru Baleanu Department of Mathematics Abdul Wali khan UniversityMardan 23200Pakistan Department of Mathematics Faculty of Arts and SciencesCankaya UniversityAnkara 06530Turkey Institute of Space Sciences Magurele-BucharestRomania

The present work describes the fractional view analysis of Newell-Whitehead-Segal equations,using an innovative technique.The work is carried with the help of the Caputo operator of fractional derivative.The analytical solutions of some numerical examples are presented to confirm the reliability of the proposed method.The derived results are very consistent with the actual solutions to the problems.A graphical representation has been done for the solution of the problems at various fractional-order derivatives.Moreover,the solution in series form has the desired rate of convergence and provides the closed-form solutions.It is noted that the procedure can be modified in other directions for fractional order problems.

关键词： Homotopy Perturbation method Shehu transform Newell-Whitehead-Segel Equation Caputo operator

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