检索结果-南通市图书馆

post processing of additive manufactured Mg alloys:Current status,challenges,and opportunities

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Journal of Magnesium and Alloys 2024年第4期12卷 1283-1310页

作者： Nooruddin Ansari Fatima Ghassan Alabtah Mohammad I.Albakri Marwan Khraisheh Mechanical Engineering program Texas A&M University at QatarP.O.Box 23874DohaQatar

Magnesium(Mg)and its alloys are emerging as a structural material for the aerospace,automobile,and electronics industries,driven by the imperative of weight reduction.They are also drawing notable attention in the medical industries owing to their biodegradability and a lower elastic modulus comparable to bone.The ability to manufacture near-net shape products featuring intricate geometries has sparked huge interest in additive manufacturing(AM)of Mg alloys,reflecting a transformation in the manufacturing sectors.However,AM of Mg alloys presents more formidable challenges due to inherent properties,particularly susceptibility to oxidation,gas trapping,high thermal expansion coefficient,and low solidification temperature.This leads to defects such as porosity,lack of fusion,cracking,delamination,residual stresses,and inhomogeneity,ultimately influencing the mechanical,corrosion,and surface properties of AM Mg alloys.To address these issues,post-processing of AM Mg alloys are often needed to make them suitable for application.The present article reviews all post-processing techniques adapted for AM Mg alloys to date,including heat treatment,hot isostatic pressing,friction stir processing,and surface peening.The utilization of these methods within the hybrid AM process,employing interlayer post-processing,is also discussed.optimal post-processing conditions are reported,and their influence on the microstructure,mechanical,and corrosion properties are detailed.Additionally,future prospects and research directions are proposed.

关键词： Magnesium alloy Additive manufacturing post-processing Heat treatment HIp

Enhancing pDF Malware Detection through Logistic Model Trees

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Computers, Materials & Continua 2024年第3期78卷 3645-3663页

作者： Muhammad Binsawad Department of Information Systems King Abdulaziz UniversityP.O.Box 80217Jeddah21589Saudi Arabia

Malware is an ever-present and dynamic threat to networks and computer systems in cybersecurity,and because of its complexity and evasiveness,it is challenging to identify using traditional signature-based detection approaches.The study article discusses the growing danger to cybersecurity that malware hidden in pDF files poses,highlighting the shortcomings of conventional detection techniques and the difficulties presented by adversarial methodologies.The article presents a new method that improves pDF virus detection by using document analysis and a Logistic Model Tree.Using a dataset from the Canadian Institute for Cybersecurity,a comparative analysis is carried out with well-known machine learning models,such as Credal Decision Tree,Naïve Bayes,Average one Dependency Estimator,Locally Weighted Learning,and Stochastic Gradient Descent.Beyond traditional structural and JavaScript-centric pDF analysis,the research makes a substantial contribution to the area by boosting precision and resilience in malware detection.The use of Logistic Model Tree,a thorough feature selection approach,and increased focus on pDF file attributes all contribute to the efficiency of pDF virus detection.The paper emphasizes Logistic Model Tree’s critical role in tackling increasing cybersecurity threats and proposes a viable answer to practical issues in the sector.The results reveal that the Logistic Model Tree is superior,with improved accuracy of 97.46%when compared to benchmark models,demonstrating its usefulness in addressing the ever-changing threat landscape.

关键词： Malware detection pDF files logistic model tree feature selection cybersecurity

Improving Channel Estimation in a NoMA Modulation Environment Based on Ensemble Learning

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Computer Modeling in Engineering & Sciences 2024年第8期140卷 1315-1337页

作者： Lassaad K.Smirani Leila Jamel Latifah Almuqren Elearning Deanship and Distance Education Umm Al-Qura UniversityP.O.Box 715MekkahSaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

This study presents a layered generalization ensemble model for next generation radio mobiles,focusing on supervised channel estimation approaches.Channel estimation typically involves the insertion of pilot symbols with a well-balanced rhythm and suitable layout.The model,called Stacked Generalization for Channel Estimation(SGCE),aims to enhance channel estimation performance by eliminating pilot insertion and improving throughput.The SGCE model incorporates six machine learning methods:random forest(RF),gradient boosting machine(GB),light gradient boosting machine(LGBM),support vector regression(SVR),extremely randomized tree(ERT),and extreme gradient boosting(XGB).By generating meta-data from five models(RF,GB,LGBM,SVR,and ERT),we ensure accurate channel coefficient predictions using the XGB model.To validate themodeling performance,we employ the leave-one-out cross-validation(LooCV)approach,where each observation serves as the validation set while the remaining observations act as the training set.SGCE performances’results demonstrate higher mean andmedian accuracy compared to the separatedmodel.SGCE achieves an average accuracy of 98.4%,precision of 98.1%,and the highest F1-score of 98.5%,accurately predicting channel coefficients.Furthermore,our proposedmethod outperforms prior traditional and intelligent techniques in terms of throughput and bit error rate.SGCE’s superior performance highlights its efficacy in optimizing channel estimation.It can effectively predict channel coefficients and contribute to enhancing the overall efficiency of radio mobile systems.Through extensive experimentation and evaluation,we demonstrate that SGCE improved performance in channel estimation,surpassing previous techniques.Accordingly,SGCE’s capabilities have significant implications for optimizing channel estimation in modern communication systems.

关键词： Stacked generalization ensemble learning Non-orthogonal Multiple Access(NoMA) channel estimation 5G

Matrix Assisted Laser Desorption Ionization Time of Flight(MALDI-ToF)-Mass Spectrometry and^(13)C-NMR-Identified New Compounds in paraberlinia bifoliolata(Ekop-Beli)Bark Tannins

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Journal of Renewable Materials 2024年第3期12卷 553-568页

作者： Liliane Nga Benoit Ndiwe Achille Bernard Biwolé Antonio pizzi Jean Jalin Eyinga Biwole Joseph Zobo Mfomo laboratory of Forest Resources and Wood Valorization Training Unit in Engineering SciencesPost Graduate School of Fundamental and Applied SciencesUniversity of DoualaP.O.Box 1872DoualaCameroun laboratory of Mechanics Training Unit in Engineering SciencesPost Graduate School of Fundamental and Applied SciencesUniversity of DoualaP.O.Box 2701DoualaCameroun laboratory of Studies and Research on Wood Material(LERMAB) University of LorraineNancyFrance

Extracts of plant origin,particularly tannins,are attracting growing interest for the sustainable development of materials in the industrial sector.The discovery of new tannins is therefore necessary.The aim of this work was to contribute to the understanding of the properties of paraberlinia bifoliolata tannin by Matrix Assisted Laser Desorption Ionization Time of Flight Mass Spectroscopy MALDI-ToF/MS and Carbon 13 Nuclear Magnetic Resonance(13C NMR).The chemical composition of tannin extracted from paraberlinia bifoliolata bark was determined,as was the mechanical strength of the resin hardened with Acacia nilotica extracts.Yield by successive water extraction was 35%.MALDI-ToF/MS analysis revealed the presence of three new compounds in this tannin,previously unknown in this family of extracts.These are 3-hydroxyproline acid,N-methyl-4-hydroxypipecolic acid and N-methyl-5-dihydroxypipecolic acid.The identification of the above molecules means that this tannin can be used for industrial applications,as a resin in the manufacture of particleboard and in the formulation of green corrosion inhibitors.This information is reinforced by 13C NMR spectrometry,which indicates the presence of several polyflavonoid units,confirming the condensed nature of the tannin.Thermomechanical analysis of the resin formed by the purified tannin of paraberlinia bifoliolata to which a vegetal biohardener has been added provided a Modulus of Elasticity(MoE)value of 4840 Mpa at 150℃,confirming its possible use as a binder resin in the manufacture of wood panels as well as for the formulation of a corrosion inhibitor.

关键词： Tannin paraberlinia bifoliolata Central African wood species MALDI-ToF/MS 13C NMR bark extracts thermomechanical behaviour wood-binder

Assessment of compressive strength of jet grouting by machine learning

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Journal of Rock Mechanics and Geotechnical Engineering 2024年第1期16卷 102-111页

作者： Esteban Diaz Edgar Leonardo Salamanca-Medina Roberto Tomas Departamento de Ingeniería Civil Escuela Politécnica SuperiorUniversidad de AlicanteP.O.Box 99E-03080AlicanteSpain

Jet grouting is one of the most popular soil improvement techniques,but its design usually involves great uncertainties that can lead to economic cost overruns in construction projects.The high dispersion in the properties of the improved material leads to designers assuming a conservative,arbitrary and unjustified strength,which is even sometimes subjected to the results of the test fields.The present paper presents an approach for prediction of the uniaxial compressive strength(UCS)of jet grouting columns based on the analysis of several machine learning algorithms on a database of 854 results mainly collected from different research papers.The selected machine learning model(extremely randomized trees)relates the soil type and various parameters of the technique to the value of the compressive strength.Despite the complex mechanism that surrounds the jet grouting process,evidenced by the high dispersion and low correlation of the variables studied,the trained model allows to optimally predict the values of compressive strength with a significant improvement with respect to the existing works.Consequently,this work proposes for the first time a reliable and easily applicable approach for estimation of the compressive strength of jet grouting columns.

关键词： Jet grouting Ground improvement Compressive strength Machine learning

The Impact of Network Topologies and Radio Duty Cycle Mechanisms on the RpL Routing protocol power Consumption

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Computers, Materials & Continua 2024年第5期79卷 1835-1854页

作者： Amal Hkiri Hamzah Faraj omar Ben Bahri Mouna Karmani Sami Alqurashi Mohsen Machhout physic Department Electronics and Micro-Electronics LaboratoryFaculty of Sciences of MonastirMonastir5000Tunisia Department of Science and Technology College of RanyahTaif UniversityP.O.Box 11099Taif21944Saudi Arabia

The Internet of Things(IoT)has witnessed a significant surge in adoption,particularly through the utilization of Wireless Sensor Networks(WSNs),which comprise small internet-connected devices.These deployments span various environments and offer a multitude of benefits.However,the widespread use of battery-powered devices introduces challenges due to their limited hardware resources and communication capabilities.In response to this,the Internet Engineering Task Force(IETF)has developed the Ipv6 Routing protocol for Low-power and Lossy Networks(RpL)to address the unique requirements of such networks.Recognizing the critical role of RpL in maintaining high performance,this paper proposes a novel approach to optimizing power consumption.Specifically,it introduces a developed sensor motes topology integrated with a Radio Duty Cycling(RDC)mechanism aimed at minimizing power usage.Through rigorous analysis,the paper evaluates the power efficiency of this approach through several simulations conducted across different network topologies,including random,linear,tree,and elliptical topologies.Additionally,three distinct RDC mechanisms—CXMAC,ContikiMAC,and NullRDC—are investigated to assess their impact on power consumption.The findings of the study,based on a comprehensive and deep analysis of the simulated results,highlight the efficiency of ContikiMAC in power conservation.This research contributes valuable insights into enhancing the energy efficiency of RpL-based IoT networks,ultimately facilitating their widespread deployment and usability in diverse environments.

关键词： WSN IoT radio duty cycles topologies

RpL-Based IoT Networks under Decreased Rank Attack:performance Analysis in Static and Mobile Environments

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Computers, Materials & Continua 2024年第1期78卷 227-247页

作者： Amal Hkiri Mouna Karmani omar Ben Bahri Ahmed Mohammed Murayr Fawaz Hassan Alasmari Mohsen Machhout physic Department Electronics and Micro-Electronics LaboratoryFaculty of Sciences of MonastirMonastir5000Tunisia Department of Science and Technology College of RanyahTaif UniversityP.O.Box 11099Taif21944Saudi Arabia

The RpL(Ipv6 Routing protocol for Low-power and Lossy Networks)protocol is essential for efficient communi-cation within the Internet of Things(IoT)ecosystem.Despite its significance,RpL’s susceptibility to attacks remains a concern.This paper presents a comprehensive simulation-based analysis of the RpL protocol’s vulnerability to the decreased rank attack in both static andmobilenetwork environments.We employ the Random Direction Mobility Model(RDM)for mobile scenarios within the Cooja simulator.our systematic evaluation focuses on critical performance metrics,including packet Delivery Ratio(pDR),Average End to End Delay(AE2ED),throughput,Expected Transmission Count(ETX),and Average power Consumption(ApC).our findings illuminate the disruptive impact of this attack on the routing hierarchy,resulting in decreased pDR and throughput,increased AE2ED,ETX,and ApC.These results underscore the urgent need for robust security measures to protect RpL-based IoT networks.Furthermore,our study emphasizes the exacerbated impact of the attack in mobile scenarios,highlighting the evolving security requirements of IoT networks.

关键词： RpL decreased rank attacks mobility random direction model

Improving prediction of Chronic Kidney Disease Using KNN Imputed SMoTE Features and TrioNet Model

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Computer Modeling in Engineering & Sciences 2024年第6期139卷 3513-3534页

作者： Nazik Alturki Abdulaziz Altamimi Muhammad Umer oumaima Saidani Amal Alshardan Shtwai Alsubai Marwan omar Imran Ashraf Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department College of Computer Science and Engineering University of Hafr Al-BatinHafarAl-Batin39524Saudi Arabia Department of Computer Science&Information Technology The Islamia University of BahawalpurP.O.Box 63100BahawalpurPakistan Department of Computer Science College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityP.O.Box 151Al-Kharj11942Saudi Arabia Information Technology and Management Illinois Institute of TechnologyChicagoIL 60616-3793USA Information and Communication Engineering Yeungnam UniversityGyeongsan38541Korea

Chronic kidney disease(CKD)is a major health concern today,requiring early and accurate diagnosis.Machine learning has emerged as a powerful tool for disease detection,and medical professionals are increasingly using ML classifier algorithms to identify CKD early.This study explores the application of advanced machine learning techniques on a CKD dataset obtained from the University of California,UC Irvine Machine Learning repository.The research introduces TrioNet,an ensemble model combining extreme gradient boosting,random forest,and extra tree classifier,which excels in providing highly accurate predictions for CKD.Furthermore,K nearest neighbor(KNN)imputer is utilized to deal withmissing values while synthetic minority oversampling(SMoTE)is used for class-imbalance problems.To ascertain the efficacy of the proposed model,a comprehensive comparative analysis is conducted with various machine learning models.The proposed TrioNet using KNN imputer and SMoTE outperformed other models with 98.97%accuracy for detectingCKD.This in-depth analysis demonstrates the model’s capabilities and underscores its potential as a valuable tool in the diagnosis of CKD.

关键词： precisionmedicine chronic kidney disease detection SMoTE missing values healthcare KNNimputer ensemble learning

Stepped-up development of accelerator mass spectrometry method for the detection of ^(60)Fe with the HI-13 tandem accelerator

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Nuclear Science and techniques 2024年第4期35卷 136-143页

作者： Yang Zhang Sheng-Quan Yan Ming He Qing-Zhang Zhao Wen-Hui Zhang Chao-Xin Kan Jian-Ming Zhou Kang-Ning Li Xiao-Fei Wang Jian-Cheng Liu Zhao-Hua peng Zhuo Liang Ai-Ling Li Jian Zheng Qi-Wen Fan Yun-Ju Li You-Bao Wang Zhi-Hong Li Yang-ping Shen Ding Nan Wei Nan Yu-Qiang Zhang Jia-Ying-Hao Li Jun-Wen Tian Jiang-Lin Hou Chang-Xin Guo Zhi-Cheng Zhang Ming-Hao Zhu Yu-Wen Chen Yu-Chen Jiang Tao Tian Jin-Long Ma Yi-Hui Liu Jing-Yu Dong Run-Long Liu Mei-Yue-Nan Ma Yong-Shou Chen Wei-ping Liu Bing Guo China Institute of Atomic Energy P.O.Box 275(10)Beijing 102413China College of Science Southern University of Science and TechnologyShenzhen 518055China School of physics Xi’an Jiaotong UniversityXi’an 710049China

The Moon provides a unique environment for investigating nearby astrophysical events such as supernovae.Lunar samples retain valuable information from these events,via detectable long-lived“fingerprint”radionuclides such as^(60)Fe.In this work,we stepped up the development of an accelerator mass spectrometry(AMS)method for detecting^(60)Fe using the HI-13tandem accelerator at the China Institute of Atomic Energy(CIAE).Since interferences could not be sufficiently removed solely with the existing magnetic systems of the tandem accelerator and the following Q3D magnetic spectrograph,a Wien filter with a maximum voltage of±60 kV and a maximum magnetic field of 0.3 T was installed after the accelerator magnetic systems to lower the detection background for the low abundance nuclide^(60)Fe.A 1μm thick Si_(3)N_(4) foil was installed in front of the Q3D as an energy degrader.For particle detection,a multi-anode gas ionization chamber was mounted at the center of the focal plane of the spectrograph.Finally,an^(60)Fe sample with an abundance of 1.125×10^(-10)was used to test the new AMS system.These results indicate that^(60)Fe can be clearly distinguished from the isobar^(60)Ni.The sensitivity was assessed to be better than 4.3×10^(-14)based on blank sample measurements lasting 5.8 h,and the sensitivity could,in principle,be expected to be approximately 2.5×10^(-15)when the data were accumulated for 100 h,which is feasible for future lunar sample measurements because the main contaminants were sufficiently separated.

关键词： Accelerator mass spectrometry Wien filter Isobar separation Supernovae Chang'e-5 lunar samples