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Beyond p-y method:A review of artificial intelligence approaches for predicting lateral capacity of drilled shafts in clayey soils

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Journal of Rock Mechanics and Geotechnical Engineering 2024年第9期16卷 3812-3840页

作者： M.E.Al-Atroush A.E.Aboelela ezz el-din hemdan Civil and Environmental Engineering Program Engineering Management DepartmentCollege of EngineeringPrince Sultan UniversityRiyadhSaudi Arabia Department of Structural Engineering Faculty of EngineeringAin Shams UniversityCairoEgypt Structure and Materials Research Lab Prince Sultan UniversityRiyadhSaudi Arabia Computer Science and Engineering Department Faculty of Electronic EngineeringMenoufia UniversityMenoufiaEgypt

In 2023,pivotal advancements in artificial intelligence(AI)have significantly *** that in mind,traditional methodologies,notably the p-y approach,have struggled to accurately model the complex,nonlinear soil-structure interactions of laterally loaded large-diameter drilled *** study undertakes a rigorous evaluation of machine learning(ML)and deep learning(DL)techniques,offering a comprehensive review of their application in addressing this geotechnical challenge.A thorough review and comparative analysis have been carried out to investigate various AI models such as artificial neural networks(ANNs),relevance vector machines(RVMs),and least squares support vector machines(LSSVMs).It was found that despite ML approaches outperforming classic methods in predicting the lateral behavior of piles,their‘black box'nature and reliance only on a data-driven approach made their results showcase statistical robustness rather than clear geotechnical insights,a fact underscored by the mathematical equations derived from these ***,the research identified a gap in the availability of drilled shaft datasets,limiting the extendibility of current findings to large-diameter *** extensive dataset,compiled from a series of lateral loading tests on free-head drilled shaft with varying properties and geometries,was introduced to bridge this *** paper concluded with a direction for future research,proposes the integration of physics-informed neural networks(PINNs),combining data-driven models with fundamental geotechnical principles to improve both the interpretability and predictive accuracy of AI applications in geotechnical engineering,marking a novel contribution to the field.

关键词： Laterally loaded drilled shaft load transfer and failure mechanisms Physics-informed neural networks(PINNs) P-y curves Artificial intelligence(AI) Dataset

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Care4U: Integrated healthcare systems based on blockchain

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Blockchain(Research and Applications) 2023年第4期4卷 2-13页

作者： Randa Kamal ezz el-din hemdan Nawal el-Fishway Department of Computer Science and Engineering Faculty of Electronic EngineeringMenoufia UniversityEgypt

During the COVID-19 crisis, the need to stay at home has increased dramatically. In addition, the number of sickpeople, especially elderly persons, has increased exponentially. In such a scenario, home monitoring of patientscan ensure remote healthcare at home using advanced technologies such as the Internet of Medical Things (IoMT).The IoMT can monitor and transmit sensitive health data;however, it may be vulnerable to various attacks. In thispaper, an efficient healthcare security system is proposed for IoMT applications. In the proposed system, themedical sensors can transmit sensed encrypted health data via a mobile application to the doctor for ***, three consortium blockchains are constructed for load balancing of transactions and reducing transactionlatency. They store the credentials of system entities, doctors' prescriptions and recommendations according to thedata transmitted via mobile applications, and the medical treatment process. Besides, cancelable biometrics areused for providing authentication and increasing the security of the proposed medical system. The investigationalresults show that the proposed system outperforms existing work where the proposed model consumed lessprocessing time by values of 18%, 22%, and 40%, and less energy for processing a 200 KB file by values of 9%,13%, and 17%. Finally, the proposed model consumed less memory usage by values of 7%, 7%, and 18.75%. Fromthese results, it is clear that the proposed system gives a very reliable and secure performance for efficientlysecuring medical applications.

关键词： Healthcare Blockchain Cyber security Internet of things Internet of Medical Things Cancelable biometrics

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Digital Twin-Based Automated Fault Diagnosis in Industrial IoT Applications

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Computers, Materials & Continua 2023年第4期75卷 183-196页

作者： Samah Alshathri ezz el-din hemdan Walid el-Shafai Amged Sayed Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science and Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Computer Science Department Security Engineering LabPrince Sultan UniversityRiyadh11586Saudi Arabia Department of electronics and electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Industrial electronics and Control Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of electrical Energy Engineering College of Engineering&TechnologyArab Academy for Science Technology&Maritime TransportSmart Village CampusEgypt

In recent years, Digital Twin (DT) has gained significant interestfrom academia and industry due to the advanced in information technology,communication systems, Artificial Intelligence (AI), Cloud Computing (CC),and Industrial Internet of Things (IIoT). The main concept of the DT isto provide a comprehensive tangible, and operational explanation of anyelement, asset, or system. However, it is an extremely dynamic taxonomydeveloping in complexity during the life cycle that produces a massive amountof engendered data and information. Likewise, with the development of AI,digital twins can be redefined and could be a crucial approach to aid theInternet of Things (IoT)-based DT applications for transferring the data andvalue onto the Internet with better decision-making. Therefore, this paperintroduces an efficient DT-based fault diagnosis model based on machinelearning (ML) tools. In this framework, the DT model of the machine isconstructed by creating the simulation model. In the proposed framework,the Genetic algorithm (GA) is used for the optimization task to improvethe classification accuracy. Furthermore, we evaluate the proposed faultdiagnosis framework using performance metrics such as precision, accuracy,F-measure, and recall. The proposed framework is comprehensively examinedusing the triplex pump fault diagnosis. The experimental results demonstratedthat the hybrid GA-ML method gives outstanding results compared to MLmethods like LogisticRegression (LR), Na飗e Bayes (NB), and SupportVectorMachine (SVM). The suggested framework achieves the highest accuracyof 95% for the employed hybrid GA-SVM. The proposed framework willeffectively help industrial operators make an appropriate decision concerningthe fault analysis for IIoT applications in the context of Industry 4.0.

关键词： Automated fault diagnosis control system ML AI CC IIoT digital twins genetic algorithm GA-ML technique

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An Efficient Intrusion Detection Framework for Industrial Internet of Things Security

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Computer Systems Science & Engineering 2023年第7期46卷 819-834页

作者： Samah Alshathri Ayman el-Sayed Walid el-Shafai ezz el-din hemdan Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science and Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Lab Computer Science DepartmentPrince Sultan UniversityRiyadh11586Saudi Arabia Department of electronics and electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt

Recently,the Internet of Things(IoT)has been used in various applications such as manufacturing,transportation,agriculture,and healthcare that can enhance efficiency and productivity via an intelligent management console *** the increased use of Industrial IoT(IIoT)applications,the risk of brutal cyber-attacks also *** leads researchers worldwide to work on developing effective Intrusion Detection Systems(IDS)for IoT infrastructure against any malicious ***,this paper provides effective IDS to detect and classify unpredicted and unpredictable severe attacks in contradiction to the IoT infrastructure.A comprehensive evaluation examined on a new available benchmark TON_IoT dataset is *** data-driven IoT/IIoT dataset incorporates a label feature indicating classes of normal and attack-targeting IoT/IIoT ***,this data involves IoT/IIoT services-based telemetry data that involves operating systems logs and IoT-based traffic networks collected from a realistic medium-scale IoT *** is to classify and recognize the intrusion activity and provide the intrusion detection objectives in IoT environments in an efficient ***,several machine learning algorithms such as Logistic Regression(LR),Linear Discriminant Analysis(LDA),K-Nearest Neighbors(KNN),Gaussian Naive Bayes(NB),Classification and Regression Tree(CART),Random Forest(RF),and AdaBoost(AB)are used for the detection intent on thirteen different intrusion *** performance metrics like accuracy,precision,recall,and F1-score are used to estimate the proposed *** experimental results show that the CART surpasses the other algorithms with the highest accuracy values like 0.97,1.00,0.99,0.99,1.00,1.00,and 1.00 for effectively detecting the intrusion activities on the IoT/IIoT infrastructure on most of the employed *** addition,the proposed work accomplishes high performance compared to other recent rela

关键词： Attacks intrusion detection machine learning deep learning industrial IoT TON_IoT dataset

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An Efficient Intrusion Detection Framework in Software-Defined Networking for Cybersecurity Applications

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Computers, Materials & Continua 2022年第8期72卷 3529-3548页

作者： Ghalib H.Alshammri Amani K.Samha ezz el-din hemdan Mohammed Amoon Walid el-Shafai Department of Computer Science Community CollegeKing Saud UniversityRiyadh28095Saudi Arabia Deanship of Scientific Research Saudi Electronic UniversityRiyadhSaudi Arabia Management Information System Department College of Business AdministrationKing Saud UniversityRiyadh28095Saudi Arabia Department of Computer Science and Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Lab Computer Science DepartmentPrince Sultan UniversityRiyadh11586Saudi Arabia electronics and electrical Communications Eng.Depart. Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt

Network management and multimedia data mining techniques have a great interest in analyzing and improving the network traffic *** recent times,the most complex task in Software Defined Network(SDN)is security,which is based on a centralized,programmable ***,monitoring network traffic is significant for identifying and revealing intrusion abnormalities in the SDN ***,this paper provides an extensive analysis and investigation of the NSL-KDD dataset using five different clustering algorithms:K-means,Farthest First,Canopy,Density-based algorithm,and Exception-maximization(EM),using the Waikato Environment for Knowledge Analysis(WEKA)software to compare extensively between these five ***,this paper presents an SDN-based intrusion detection system using a deep learning(DL)model with the KDD(Knowledge Discovery in Databases)***,the utilized dataset is clustered into normal and four major attack categories via the clustering ***,a deep learning method is projected for building an efficient SDN-based intrusion detection *** results provide a comprehensive analysis and a flawless reasonable study of different kinds of attacks incorporated in the KDD ***,the outcomes reveal that the proposed deep learning method provides efficient intrusion detection performance compared to existing *** example,the proposed method achieves a detection accuracy of 94.21%for the examined dataset.

关键词： Deep neural network DL WEKA network traffic intrusion and anomaly detection SDN clustering and classification KDD dataset

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Hematobiochemical and histopathological alterations in Nile Tilapia(Oreochromis niloticus)exposed to ethidium bromide:The protective role of Spirulina platensis

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Aquaculture and Fisheries 2024年第1期9卷 93-103页

作者： Sabreen Abdullah Mervat Naguib Alaa el-din Salah el-din Alaa el-din H.Sayed Zoology Department Faculty of ScienceAswan UniversityAswan81528Egypt Zoology Department Faculty of ScienceAssuit UniversityAssiut71516Egypt

Ethidium bromide(EtBr)is one of the contaminants recorded in aquatic environments whose effects have been investigated;however,there is still limited knowledge about its *** study examined the potential protective effects of Spirulina platensis(SP)against the effects of EtBr toxicity in the Nile tilapia(Oreochromis niloticus)*** were divided to five groups,viz.,a control and four treatment groups of low-dose EtBr(10μg/L),low-dose EtBr with SP(10μg/L EtBr+200 mg/L SP),high-dose EtBr(100μg/L),and high-dose EtBr with SP(100μg/L EtBr+200 mg/L SP);the exposure period was 2 *** and high doses of EtBr induced alterations in some hematological,biochemical,and histopathological *** hepatocytes,degenerated area,vacuolated hepatocytes,pyknotic nuclei,constricted and dilated blood sinusoids,and infiltration of inflammatory cells were *** peroxidation concentration was not significantly different in groups exposed to low doses of EtBr and EtBr with SP,but it was increased in groups exposed to high doses of EtBr and EtBr with SP,compared with the control *** feeding with SP,most histological and histochemical parameters restored to normal ***,SP may possess the ability to preserve the structural integrity of the hepatic and renal membranes.

关键词： Ethidium bromide Spirulina platensis Oreochromis niloticus Liver Lipid peroxidation

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Jellyfish-inspired alginate composite hydrogel filter prepared by macro-micro double bionic strategy for efficient water purification

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Frontiers of Environmental Science & Engineering 2024年第4期18卷 141-157页

作者： Huiting Peng Yan Chen Jiaopan Lin Chelsea Benally Mohamed Gamal el-din Junkai Gao School of Naval Architecture and Marinetime Zhejiang Ocean UniversityZhoushan 316022China Department of Civil and Environmental Engineering University of AlbertaEdmontonAB T6G 1H9Canada

Recently,research on hydrogel materials with a porous structure and superior water absorption capabilities significantly ***,the hydrogel under gravity-driven separation conditions often exhibit an unstable pore structure,poor mechanical properties,and limited *** this end,this work presents a novel approach that combines a macro-micro double bionic strategy with a triple crosslinking method to develop a multifunctional alginate composite hydrogel filter(2%-SA-κCG-PVA-Ca^(2+),2%-SKP-Ca^(2+)for short)with a stable pore structure and superior mechanical properties,which possessed an umbrella-shaped structure resembling that of *** 2%-SKPCa^(2+)filter was synthesized using polyvinyl alcohol(PVA)as a stable structure-directing agent,and sodium alginate(SA)andκ-carrageenan(κ-CG)as polymer *** distinctive umbrellashaped hydrogel of 2%-SKP-Ca^(2+)filter,formed through the triple crosslinking method,overcomes the limitations of unstable pore structure and poor durability seen in hydrogels prepared by traditional crosslinking ***,the utilization of the 2%-SKP-Ca^(2+)filter in water treatment demonstrates its good selective permeability,excellent resistance to fouling,and extended longevity,which enables it to simultaneously achieve the multifunctional water purification and the coating of multi-substrate anti-fouling ***,not only does this research provide an efficient,multifunctional,highly pollution-resistant preparation method for designing a new filter,but it also confirms the application prospect of the macro-micro dual bionic strategy developed in this study in complex water treatment.

关键词： Triple crosslinking method Alginate Umbrella-shaped Jellyfish Water purification

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Experimental and theoretical insight into carbamazepine degradation by chlorine-based advanced oxidation processes:Efficiency,energy consumption,mechanism and DBPs formation

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Journal of Environmental Sciences 2024年第5期139卷 72-83页

作者： Chunwei Liu Zuo Tong How Yue Ju Li Feng Xuefeng Ren Mohamed Gamal el-din Key Laboratory ofCoal Processing and Efficient Utilization of Ministry of Education School of Chemical Engineering and TechnologyChina University of Mining and TechnologyJiangsu 221116China Department of Civil and Environmental Engineering University of AlbertaEdmonton T6G 1H9AlbertaCanada School of Environment and Spatial Informatics China University of Mining and TechnologyJiangsu 221116China Key Laboratory of Agro-Forestry Environmental Processes and Ecological Regulation ofHainan Province School of Ecology and EnvironmentHainan UniversityHainan 570228China

Chlorine has been widely used in different advanced oxidation processes(AOPs)for micropollutants *** this study,different chlorine-based AOPs,namely medium pressure(MP)UV/chlorine,low pressure(LP)UV/chlorine,and in-situ chlorination,were compared for carbamazepine(CBZ)removal efficiency,energy consumption,and disinfection by-products(DBPs)*** three processes could achieve nearly 100%CBZ removal,while the reaction time needed by in-situ chlorination was double the time required by UV/chlorine *** energy consumed per magnitude of CBZ removed(EE/O)of MP UV/chlorine was 13 times higher than that of LP UV/chlorine,and relative to that of in-situ chlorination ***,MP and LP UV/chlorine processes generated one to two orders of magnitude more hydroxyl radicals(^(·)OH)and reactive chlorine species(RCS)than in-situ ***,RCS were the dominant reactive species,contributing to 78.3%,75.6%,and 71.6% of CBZ removal in MP,LP UV/chlorine,and in-situ chlorination,*** to the Gibbs free energy barriers between CBZ and RCS/^(·)OH calculated based on density functional theory(DFT),RCS had more reaction routes with CBZ and showed lower energy barrier in the main CBZ degradation pathways like epoxidation and formation of *** applied to secondary wastewater effluent,UV/chlorine and in-situ chlorination produced overall DBPs ranging from 104.77 to 135.41μg/***,the production of chlorate during UV/chlorine processes was 15 times higher than that during in-situ chlorination.

关键词： UV/chlorine In-situ chlorination Energy efficiency Role of reactive species DFT calculation Disinfection by-products

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A NOVel STOCHASTIC HEPATITIS B VIRUS EPIDEMIC MODel WITH SECOND-ORDER MULTIPLICATIVE α-STABLE NOISE AND REAL DATA

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Acta Mathematica Scientia 2024年第2期44卷 752-788页

作者： Anwarud din Yassine SABBAR 吴鹏 Department of Mathematics Sun Yat-Sen UniversityGuangzhou510275China MAIS Laboratory MAMCS GroupFST ErrachidiaMoulay Ismail University of MeknesP.O.Box 509Errachidia52000Morocco School of Science Hangzhou Dianzi UniversityHangzhou310018China School of Data Sciences Zhejiang University of Finance and EconomicsHangzhou310018China

This work presents an advanced and detailed analysis of the mechanisms of hepatitis B virus(HBV)propagation in an environment characterized by variability and *** on some biological features of the virus and the assumptions,the corresponding deterministic model is formulated,which takes into consideration the effect of *** deterministic model is extended to a stochastic framework by considering a new form of disturbance which makes it possible to simulate strong and significant *** long-term behaviors of the virus are predicted by using stochastic differential equations with second-order multiplicative α-stable *** developing the assumptions and employing the novel theoretical tools,the threshold parameter responsible for ergodicity(persistence)and extinction is *** theoretical results of the current study are validated by numerical simulations and parameters estimation is also ***,we obtain the following new interesting findings:(a)in each class,the average time depends on the value ofα;(b)the second-order noise has an inverse effect on the spread of the virus;(c)the shapes of population densities at stationary level quickly changes at certain values of α.The last three conclusions can provide a solid research base for further investigation in the field of biological and ecological modeling.

关键词： HBV model nonlinear perturbation probabilistic bifurcation long-run forecast numerical simulation

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Limits of Depth: Over-Smoothing and Over-Squashing in GNNs

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Big Data Mining and Analytics 2024年第1期7卷 205-216页

作者： Aafaq Mohi ud din Shaima Qureshi Department of Computer Science and Engineering National Institute of Technology SrinagarSrinagar 190006India

Graph Neural Networks(GNNs)have become a widely used tool for learning and analyzing data on graph structures,largely due to their ability to preserve graph structure and properties via graph representation ***,the effect of depth on the performance of GNNs,particularly isotropic and anisotropic models,remains an active area of *** study presents a comprehensive exploration of the impact of depth on GNNs,with a focus on the phenomena of over-smoothing and the bottleneck effect in deep graph neural *** research investigates the tradeoff between depth and performance,revealing that increasing depth can lead to over-smoothing and a decrease in performance due to the bottleneck *** also examine the impact of node degrees on classification accuracy,finding that nodes with low degrees can pose challenges for accurate *** experiments use several benchmark datasets and a range of evaluation metrics to compare isotropic and anisotropic GNNs of varying depths,also explore the scalability of these *** findings provide valuable insights into the design of deep GNNs and offer potential avenues for future research to improve their performance.

关键词： Graph Neural Networks(GNNs) learning on graphs over-smoothing over-squashing isotropic-GNNs anisotropic-GNNs

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