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Dynamic Instability of Straight Bars Subjected to Impulsive Axial Loads Using the DEM

Computer Modeling in Engineering & Sciences 2015年第2期104卷 87-104页

作者： Leticia Fleck fadel Miguel Leandro Fleck fadel Miguel Joao Kaminski Jr. Department of Mechanical Engineering UFRGSPorto AlegreRSBrazil Department of Civil Engineering UFSCFlorianopolisSCBrazil Department of Structures and Civil Construction UFSMSanta MariaRSBrazil

Since the half of the XX century,attention was given to the instability of structures under parametric excitation,especially under periodic *** the other hand,the instability of bars subjected to axial loads of impulsive type has been little studied,in spite of the practical importance of the ***,in Engineering Design it is frequently supposed,without tests or additional verifications,that an axial load of short duration can exceed the Euler critical load of the bar without inducing damage in the *** this context,this paper proposes the use of the truss-like Discrete Element Method(DEM)for determining the dynamic response of elastic straight bars subjected to axial loads defined by pulses of short *** proposed approach allows the consideration of initial imperfections,as well as large displacements and other non-linear *** influence of the pulse duration and other effects in the response of the bar are also ***,the performance of the proposed methodology is verified in static and dynamic instability problems of homogeneous bars without geometrical imperfections,by comparing the DEM results with analytical solutions available in the *** that,the DEM is employed to analyze more complex cases,including bars with initial imperfections and non-homogeneous bars,in which material properties,as Young’s modulus and mass density,are assumed to be correlated Gaussian random *** proposed methodology has proven to be a useful and easy tool for analysis of dynamic instability of bars and could therefore be used by designers for estimating the dynamic buckling load.

关键词： Dynamic Instability Critical Buckling Load Discrete Element Method Impulsive Load Stochastic Fields Mesh Imperfections

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Evolution,Extinction,Homology and Homoplasy of the Larger Benthic Foraminifera from the Carboniferous to the Present Day,as Exemplified by Planispiral-Fusiform and Discoidal Forms

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Journal of Earth Science 2022年第6期33卷 1348-1361页

作者： Marcelle K.BouDagher-fadel Office of the Vice-Provost University College LondonLondonWC1H 0BTUK

Examples of evolution,extinction and homoplasy of the larger benthic foraminifera(LBF)occur throughout their *** the Carboniferous,LBF have thrived in carbonate-rich tropical and subtropical shallow-marine shelf *** high abundance and diversity are due primarily to their extraordinary ability to inhabit a range of ecological niches and by hosting a variety of *** relatively large,centimetre-scale sizes,made some forms very specialized and vulnerable to rapid ecological *** this reason,some LBF have shown a tendency to suffer periodically during major extinctions,especially when environmental conditions have changed rapidly and/or ***,however,makes them valuable biostratigraphic microfossils and,in addition,gives invaluable insight into the spatial and temporal process of biological evolution,such as convergent/homoplasy and homology/iterative *** the evolutionary behavior of two important morphological types that occurred throughout the history of the LBF are discussed,namely the planispiral-fusiform test as typified by the fusulinids in the Late Paleozoic and the alveolinids in the Mid-Cretaceous and Neogene,and the three-layered discoid lenticular test as characterized by the orbitoids in the Mid-to Late Cretaceous,the orthophragminids in the Paleogene,and lepidocyclinids in the Oligocene to *** the propensity of these forms to convergent and iterative evolution,with the repeated re-occurrence of certain morphological features,is essential in understanding and constructing their phylogenetic relationships more generally within the main groups of the *** insights gained from the history of these LBF have wider implications,and provide a more general understanding of the impacts of climate and ecological changes as driving forces for biological evolution.

关键词： larger benthic foraminifera extinction homoplasy homology convergent/iterative evolution climate change

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Evaluation of Deep Learning Models for Person Authentication Based on Touch Gesture

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Computer Systems Science & Engineering 2022年第8期42卷 465-481页

作者： Asrar Bajaber Mai fadel Lamiaa Elrefaei Computer Science Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Electrical Engineering Department Faculty of Engineering at ShoubraBenha UniversityCairo11629Egypt

Touch gesture biometrics authentication system is the study of user's touching behavior on his touch device to identify *** features traditionally used in touch gesture authentication systems are extracted using hand-crafted feature extraction *** this work,we investigate the ability of Deep Learning(DL)to automatically discover useful features of touch gesture and use them to authenticate the *** different models are investigated Long-Short Term Memory(LSTM),Gated Recurrent Unit(GRU),Convolutional Neural Network(CNN)combined with LSTM(CNN-LSTM),and CNN combined with GRU(CNN-GRU).In addition,different regularization techniques are investigated such as Activity Regularizer,Batch Normalization(BN),Dropout,and *** deep networks were trained from scratch and tested using TouchAlytics and BioIdent datasets for dynamic touch *** result reported in terms of authentication accuracy,False Acceptance Rate(FAR),False Rejection Rate(FRR).The best result we have been obtained was 96.73%,96.07%and 96.08%for training,validation and testing accuracy respectively with dynamic touch authentication system on TouchAlytics dataset with CNN-GRU DL model,while the best result of FAR and FRR obtained on TouchAlytics dataset was with CNN-LSTM were FAR was 0.0009 and FRR was *** BioIdent dataset the best results have been obtained was 84.87%,78.28%and 78.35%for Training,validation and testing accuracy respectively with CNN-LSTM *** use of a learning based approach in touch authentication system has shown good results comparing with other state-of-the-art using TouchAlytics dataset.

关键词： Touch authentication system touch gestures behavioral biometric deep learning classification CNN RNN LSTM

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Connecting neurodevelopment to neurodegeneration:a spotlight on the role of kinesin superfamily protein 2A(KIF2A)

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Neural Regeneration Research 2024年第2期19卷 375-379页

作者： Nuria Ruiz-Reig Janne Hakanen fadel Tissir Universitécatholique de Louvain Institute of neuroscienceBrusselsBelgium College of Health and Life Sciences Hamad Bin Khalifa University(HBKU)DohaQatar

Microtubules play a central role in cytoskeletal changes during neuronal development and *** dynamics is essential to polarity and shape transitions underlying neural cell division,differentiation,motility,and *** superfamily protein 2A is a member of human kinesin 13 gene family of proteins that depolymerize and destabilize *** dividing cells,kinesin superfamily protein 2A is involved in mitotic progression,spindle assembly,and chromosome *** postmitotic neurons,it is required for axon/dendrite specification and extension,neuronal migration,connectivity,and *** with kinesin superfamily protein 2A mutations suffer from a variety of malformations of cortical development,epilepsy,autism spectrum disorder,and *** this review,we discuss how kinesin superfamily protein 2A regulates neuronal development and function,and how its deregulation causes neurodevelopmental and neurological disorders.

关键词： brain disorders cortical malformations kinesin microtubules neurodegeneration neurodevelopment

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An Efficient Path Planning Strategy in Mobile Sink Wireless Sensor Networks

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Computers, Materials & Continua 2022年第10期73卷 1237-1267页

作者： Najla Bagais Etimad fadel Amal Al-Mansour Department of Computer Science Faculty of Computing and Information TechnologyKing Abdul-Aziz UniversityJeddah21589Saudi Arabia

Wireless sensor networks (WSNs) are considered the backbone ofthe Internet of Things (IoT), which enables sensor nodes (SNs) to achieveapplications similarly to human intelligence. However, integrating a WSNwith the IoT is challenging and causes issues that require careful *** the lifetime of a network through appropriately utilising energyconsumption is among the essential challenges due to the limited resourcesof SNs. Thus, recent research has examined mobile sinks (MSs), which havebeen introduced to improve the overall efficiency of WSNs. MSs bear theburden of data collection instead of consuming energy at the routeing bySNs. In a network, some areas generate more data through SNs that containfrequent, urgent messages. These messages carry sensitive data that must bedelivered immediately to user applications. Collecting such messages via MSs,especially on a large scale, increases delays, which are not tolerable in some realapplications. This issue has not been studied much. Thus, the present studyutilises the advantages of the priority parameter to concentrate on these areasand proposes a new model named ‘energy efficient path planning of MS-basedarea priority’ (EEPP-BAP). This method involves non-urgent and urgentmessages. It is comprised of four procedures. Initially, after SNs are distributedrandomly in a wide monitoring field, the monitoring field is partitionedinto equal zones according to priority, either differently or equally. Next isclustering based on the cluster head (CH) selected to perform the particleswarm optimisation algorithm (PSO). Then, the MS moves first to the zoneswith higher priority and less distance to perform the brain storm optimisationalgorithm. Finally, for urgent messages from the other zones at which theMS continues, the proposed approach establishes a routeing technique usingmulti-hop communication based on the MS position and using PSO. The proposed solution is aimed at delivering urgent messages to MSs free of

关键词： Wireless sensor network priority urgent message swarm intelligence optimisation mobile sink clustering energy efficiency

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Influence of Cr, Mn and Mo Addition on Structure and Properties of V Microalloyed Medium Carbon Steels

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Journal of Materials Science & Technology 2012年第11期28卷 1053-1058页

作者： Abdunnaser fadel Dragomir Glii Nenad Radovi Djordje Drobnjak Department of Metallurgical Engineering Faculty of Technology and Metallurgy University of Belgrade

The effect of austenitizing temperature and Cr, Mo and Mn addition on microstructure and mechanical properties of V microalloyed medium carbon steel has been studied by means of metallography and mechanical testing. The addition of Cr, Mn and Mo leads to a decrease in yield strength （YS） by approximate 100 MPa in comparison to the base steel. It is assumed that Mn and Mo increase hardenability by promoting the formation of bainitic sheaves （BS）, i.e. by suppressing the formation of ferrite-pearlite and acicular ferrite （FP-AF）. Cr at the level used in this work is not that effective. Presence and packet size of bainitic sheaves decrease the Charpy V-notch impact energy at 20 ℃ （CVN20） in comparison to ferrite-pearlite and acicular ferrite microstructures.

关键词： Medium-carbon steels Chromium Molybdenum Manganese addition Bainite Acicular ferrite

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Multi-Task Learning Model with Data Augmentation for Arabic Aspect-Based Sentiment Analysis

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Computers, Materials & Continua 2023年第5期75卷 4419-4444页

作者： Arwa Saif fadel Osama Ahmed Abulnaja Mostafa Elsayed Saleh Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science Faculty of Computer Sciences and EngineeringHodeidah UniversityYemen

Aspect-based sentiment analysis(ABSA)is a fine-grained *** fundamental subtasks are aspect termextraction(ATE)and aspect polarity classification(APC),and these subtasks are dependent and closely ***,most existing works on Arabic ABSA content separately address them,assume that aspect terms are preidentified,or use a pipeline *** solutions design different models for each task,and the output from the ATE model is used as the input to the APC model,which may result in error propagation among different steps because APC is affected by ATE *** methods are impractical for real-world scenarios where the ATE task is the base task for APC,and its result impacts the accuracy of ***,in this study,we focused on a multi-task learning model for Arabic ATE and APC in which the model is jointly trained on two subtasks simultaneously in a *** paper integrates themulti-task model,namely Local Cotext Foucse-Aspect Term Extraction and Polarity classification(LCF-ATEPC)and Arabic Bidirectional Encoder Representation from Transformers(AraBERT)as a shred layer for Arabic contextual text *** LCF-ATEPC model is based on a multi-head selfattention and local context focus mechanism(LCF)to capture the interactive information between an aspect and its ***,data augmentation techniques are proposed based on state-of-the-art augmentation techniques(word embedding substitution with constraints and contextual embedding(AraBERT))to increase the diversity of the training *** paper examined the effect of data augmentation on the multi-task model for Arabic *** experiments were conducted on the original and combined datasets(merging the original and augmented datasets).Experimental results demonstrate that the proposed Multi-task model outperformed existing APC *** results were obtained by AraBERT and LCF-ATEPC with fusion layer(AR-LCF-ATEPC-Fusion)and the proposed data augmentation

关键词： Arabic aspect extraction arabic sentiment classification AraBERT multi-task learning data augmentation

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Hybrid Deep Learning Based Attack Detection for Imbalanced Data Classification

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Intelligent Automation & Soft Computing 2023年第1期35卷 297-320页

作者： Rasha Almarshdi Laila Nassef Etimad fadel Nahed Alowidi Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Computer Science Faculty of Computer Science and EngineeringUniversity of HailHailSaudi Arabia

Internet of Things(IoT)is the most widespread and fastest growing technology *** to the increasing of IoT devices connected to the Internet,the IoT is the most technology under security *** IoT devices are not designed with security because they are resource constrained ***,having an accurate IoT security system to detect security attacks is *** Detection Systems(IDSs)using machine learning and deep learning techniques can detect security attacks *** paper develops an IDS architecture based on Convolutional Neural Network(CNN)and Long Short-Term Memory(LSTM)deep learning *** implement our model on the UNSW-NB15 dataset which is a new network intrusion dataset that cate-gorizes the network traffic into normal and attacks *** this work,interpolation data preprocessing is used to compute the missing ***,the imbalanced data problem is solved using a synthetic data generation *** experiments have been implemented to compare the performance results of the proposed model(CNN+LSTM)with a basic model(CNN only)using both balanced and imbalanced ***,with some state-of-the-art machine learning classifiers(Decision Tree(DT)and Random Forest(RF))using both balanced and imbalanced *** results proved the impact of the balancing *** proposed hybrid model with the balance technique can classify the traffic into normal class and attack class with reasonable accuracy(92.10%)compared with the basic CNN model(89.90%)and the machine learning(DT 88.57%and RF 90.85%)***,comparing the proposed model results with the most related works shows that the proposed model gives good results compared with the related works that used the balance techniques.

关键词： IoT IDS deep learning machine learning CNN LSTM

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Ensemble Voting-Based Anomaly Detection for a Smart Grid Communication Infrastructure

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Intelligent Automation & Soft Computing 2023年第6期36卷 3257-3278页

作者： Hend Alshede Laila Nassef Nahed Alowidi Etimad fadel Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Self-Development Skills Department Common First Year DeanshipKing Saud UniversityRiyadh12211Saudi Arabia

Advanced Metering Infrastructure(AMI)is the metering network of the smart grid that enables bidirectional communications between each consumer’s premises and the provider’s control *** massive amount of data collected supports the real-time decision-making required for diverse *** communication infrastructure relies on different network types,including the *** makes the infrastructure vulnerable to various attacks,which could compromise security or have devastating ***,traditional machine learning solutions cannot adapt to the increasing complexity and diversity of *** objective of this paper is to develop an Anomaly Detection System(ADS)based on deep learning using the CIC-IDS2017 ***,this dataset is highly imbalanced;thus,a two-step sampling technique:random under-sampling and the Synthetic Minority Oversampling Technique(SMOTE),is proposed to balance the *** proposed system utilizes a multiple hidden layer Auto-encoder(AE)for feature extraction and dimensional *** addition,an ensemble voting based on both Random Forest(RF)and Convolu-tional Neural Network(CNN)is developed to classify the multiclass attack *** proposed system is evaluated and compared with six different state-of-the-art machine learning and deep learning algorithms:Random Forest(RF),Light Gradient Boosting Machine(LightGBM),eXtreme Gradient Boosting(XGboost),Convolutional Neural Network(CNN),Long Short-Term Memory(LSTM),and bidirectional LSTM(biLSTM).Experimental results show that the proposed model enhances the detection for each attack class compared with the other machine learning and deep learning models with overall accuracy(98.29%),precision(99%),recall(98%),F_(1) score(98%),and the UNDetection rate(UND)(8%).

关键词： Advanced metering infrastructure smart grid cyberattack ensemble voting anomaly detection system CICIDS2017

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A New Hybrid Model for Segmentation of the Skin Lesion Based on Residual Attention U-Net

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Computers, Materials & Continua 2023年第6期75卷 5177-5192页

作者： Saleh Naif Almuayqil Reham Arnous Noha Sakr Magdy M.fadel Department of Information Systems College of Computer and Information SciencesJouf UniversitySakakaSaudi Arabia Department of Communications and Electronics Delta Higher Institute for Engineering and Technology(DHIET)MansouraEgypt Computers Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansouraEgypt

Skin segmentation participates significantly in various biomedical applications,such as skin cancer identification and skin lesion *** paper presents a novel framework for segmenting the *** framework contains two main stages:The first stage is for removing different types of noises from the dermoscopic images,such as hair,speckle,and impulse noise,and the second stage is for segmentation of the dermoscopic images using an attention residual U-shaped Network(U-Net).The framework uses variational Autoencoders(VAEs)for removing the hair noises,the Generative Adversarial Denoising Network(DGAN-Net),the Denoising U-shaped U-Net(D-U-NET),and Batch Renormalization U-Net(Br-U-NET)for remov-ing the speckle noise,and the Laplacian Vector Median Filter(MLVMF)for removing the impulse *** the second main stage,the residual attention u-net was used for *** framework achieves(35.11,31.26,27.01,and 26.16),(36.34,33.23,31.32,and 28.65),and(36.33,32.21,28.54,and 27.11)for removing hair,speckle,and impulse noise,respectively,based on Peak Signal Noise Ratio(PSNR)at the level of(0.1,0.25,0.5,and 0.75)of *** framework also achieves an accuracy of nearly 94.26 in the dice score in the process of segmentation before removing noise and 95.22 after removing different types of *** experiments have shown the efficiency of the used model in removing noise according to the structural similarity index measure(SSIM)and PSNR and in the segmentation process as well.

关键词： Skin tumor speckle noise impulse noise hair noise deep learning segmentation

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