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Prediction of vertical displacement for a buried pipeline subjected to normal fault using a hybrid FEM-ANN approach

Frontiers of Structural and Civil Engineering 2024年第3期18卷 428-443页

作者： hedye jalali Reza YEGANEH KHAKSAR Danial MOHAMMADZADEH S. Nader KARBALLAEEZADEH Amir H.GANDOMI Department of Civil Engineering Architecture and UrbanismSadjad University of TechnologyPO Box ***MashhadIran Department of Civil Engineering Faculty of EngineeringEqbal Lahoori Institute of Higher EducationPO Box ***MashhadIran Faculty of Civil Engineering Architectureand ArtScience and Research Branch of Islamic Azad UniversityPO Box ***TehranIran Faculty of Engineering&Information Technology University of Technology SydneyUltimoNSW 2007Australia University Research and Innovation Center(EKIK) Óbuda UniversityBudapest 1034Hungary

Fault movement during earthquakes is a geotechnical phenomenon threatening buried pipelines and with the potential to cause severe damage to critical infrastructures.Therefore,effective prediction of pipe displacement is crucial for preventive management strategies.This study aims to develop a fast,hybrid model for predicting vertical displacement of pipe networks when they experience faulting.In this study,the complex behavior of soil and a buried pipeline system subjected to a normal fault is analyzed by using an artificial neural network(ANN)to generate predictions the behavior of the soil when different parameters of it are changed.For this purpose,a finite element model is developed for a pipeline subjected to normal fault displacements.The data bank used for training the ANN includes all the critical soil parameters(cohesion,internal friction angle,Young’s modulus,and faulting).Furthermore,a mathematical formula is presented,based on biases and weights of the ANN model.Experimental results show that the maximum error of the presented formula is 2.03%,which makes the proposed technique efficiently predict the vertical displacement of buried pipelines and hence,helps to optimize the upcoming pipeline projects.

关键词： buried pipelines normal Fault finite element method multilayer perceptron neural network formulation

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Federated Learning Security and Privacy-Preserving Algorithm and Experiments Research Under Internet of Things Critical Infrastructure

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Tsinghua Science and Technology 2024年第2期29卷 400-414页

作者： Nasir Ahmad jalali Hongsong Chen Department of Computer Science University of Science and Technology Beijing(USTB)Beijing 100083China

The widespread use of the Internet of Things(IoTs)and the rapid development of artificial intelligence technologies have enabled applications to cross commercial and industrial band settings.Within such systems,all participants related to commercial and industrial systems must communicate and generate data.However,due to the small storage capacities of IoT devices,they are required to store and transfer the generated data to third-party entity called“cloud”,which creates one single point to store their data.However,as the number of participants increases,the size of generated data also increases.Therefore,such a centralized mechanism for data collection and exchange between participants is likely to face numerous challenges in terms of security,privacy,and performance.To address these challenges,Federated Learning(FL)has been proposed as a reasonable decentralizing approach,in which clients no longer need to transfer and store real data in the central server.Instead,they only share updated training models that are trained over their private datasets.At the same time,FL enables clients in distributed systems to share their machine learning models collaboratively without their training data,thus reducing data privacy and security challeges.However,slow model training and the execution of additional unnecessary communication rounds may hinder FL applications from operating properly in a distributed system.Furthermore,these unnecessary communication rounds make the system vulnerable to security and privacy issues,because irrelevant model updates are sent between clients and servers.Thus,in this work,we propose an algorithm for fully homomorphic encryption called Cheon-Kim-Kim-Song(CKKS)to encrypt model parameters for their local information privacy-preserving function.The proposed solution uses the impetus term to speed up model convergence during the model training process.Furthermore,it establishes a secure communication channel between IoT devices and the server.We also use a lightweight secure transport protocol

关键词： Federated Learning(FL) Internet of Things(IoTs) lightweight Transport Layer Security(iTLS) Cheon-Kim-Kim-Song(CKKS)

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用机械涂层技术在Ti-6Al-4V基体表面沉积HA/Ti复合涂层与功能梯度涂层的表征、磨损行为和生物相容性

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Transactions of Nonferrous Metals Society of China 2024年第2期34卷 547-559页

作者： Mahmood jalali BIDAKHAVIDI Hamid OMIDVAR Ali ZAMANIAN Jamshid AGHAZADEH MOHANDESI Hamid jalali Department of Materials and Metallurgical Engineering Amirkabir University of Technology(Tehran Polytechnic)Hafez AveP.O.Box 15875-4413TehranIran Department of Nanotechnology and Advanced Materials Materials and Energy Research CenterKarajIran School of Dentistry Tehran University of Medical SciencesTehranIran

采用机械涂层技术在Ti-6Al-4V基体表面沉积HA/Ti复合涂层与功能梯度涂层(FGC),对涂层进行表征,对比其磨损行为和生物相容性。制备复合涂层所用初始粉末为50%Ti(质量分数)和50%HA(质量分数)的混合粉末,而FGC分为两层,分别为75%Ti+25%HA和... 详细信息

采用机械涂层技术在Ti-6Al-4V基体表面沉积HA/Ti复合涂层与功能梯度涂层(FGC),对涂层进行表征,对比其磨损行为和生物相容性。制备复合涂层所用初始粉末为50%Ti(质量分数)和50%HA(质量分数)的混合粉末,而FGC分为两层,分别为75%Ti+25%HA和25%Ti+75%HA(质量分数)。XRD结果表明在沉积过程中没有发生相变。此外,由于原始混合粉末中存在Ti颗粒,在涂层和基体之间形成了结合良好的连续界面,特别是在功能梯度涂层样品中。磨损实验结果显示,非梯度复合涂层的摩擦因数约为0.37,在界面处发生突变,而功能梯度涂层的摩擦因数从0.32增加到0.5,在界面处发生平缓变化。磨损表面的SEM像表明涂层的主要磨损机制为磨粒磨损。生物相容性评价结果显示,FGC样品具有更好的细胞黏附和增殖能力。

关键词：机械涂层技术羟基磷灰石 Ti-6Al-4V基体功能梯度涂层摩擦因数

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Erratum to:Exciton recycling via InP quantum dot funnels for luminescent solar concentrators

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Nano Research 2024年第6期17卷 5756-5756页

作者： Houman Bahmani jalali Sadra Sadeghi Isinsu Baylam Mertcan Han Cleva W.Ow-Yang Alphan Sennaroglu Sedat Nizamoglu Graduate School of Biomedical Sciences and Engineering Koc UniversityIstanbul 34450Turkey Graduate School of Material Science and Engineering Koc UniversityIstanbul 34450Turkey Koc University Surface Science and Technology Center(KUYTAM) Koc UniversityIstanbul 34450Turkey Laser Research Laboratory Koc UniversityIstanbul 34450Turkey Department of Electrical and Electronics Engineering Koc UniversityIstanbul 34450Turkey Department of Material Science and Nano Engineering Sabanci UniversityIstanbul 34956Turkey

The article“Exciton recycling via InP quantum dot funnels for luminescent solar concentrators”written by Houman Bahmani jalali,Sadra Sadeghi,Isinsu Baylam,Mertcan Han,Cleva W.Ow-Yang,Alphan Sennaroglu,and Sedat Nizamoglu,was originally published Online First without Open Access.After publication online first,the author decided to opt for Open Choice and to make the article an Open Access publication.Therefore,the copyright of the article has been changed to©The Author(s)2020 and the article is forthwith distributed under the terms of the Creative Commons Attribution 4.0 International License(http://***/licenses/by/4.0/),which permits use,duplication,adaptation,distribution and reproduction in any medium or format,as long as you give appropriate credit to the original author(s)and the source,provide a link to the Creative Commons license,and indicate if changes were made.The original article has been corrected.

关键词： quantum Open license

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An Efficient Hybrid Model Based on Modified Whale Optimization Algorithm and Multilayer Perceptron Neural Network for Medical Classification Problems

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Journal of Bionic Engineering 2022年第5期19卷 1504-1521页

作者： Saeid Raziani Sajad Ahmadian Seyed Mohammad Jafar jalali Abdolah Chalechale Department of Computer Engineering Razi UniversityKermanshah ***Iran Faculty of Information Technology Kermanshah University of TechnologyKermanshah ***Iran Institute for Intelligent Systems Research and Innovation(IISRI) Deakin UniversityWaurn PondsMelbourne 3217Australia

Feedforward Neural Network(FNN)is one of the most popular neural network models that is utilized to solve a wide range of nonlinear and complex problems.Several models such as stochastic gradient descent have been developed to train FNNs.However,they mainly suffer from falling into local optima leading to reduce the accuracy of FNNs.Moreover,the convergence speed of training process depends on the initial values of weights and biases in FNNs.Generally,these values are randomly determined by most of the training models.To deal with these issues,in this paper,we develop a novel evolutionary algorithm by modifying the original version of Whale Optimization Algorithm(WOA).To this end,a nonlinear function is introduced to improve the exploration and exploitation phases in the search process of WOA.Then,the modified WOA is applied to automatically obtain the initial values of weights and biases in FNN leading to reduce the probability of falling into local optima.In addition,the FNN model trained by the modified WOA is used to develop a classification approach for medical diagnosis problems.Ten medical diagnosis datasets are utilized to evaluate the efficiency of the proposed method.Also,four evaluation metrics including accuracy,AUC,specificity,and sensitivity are used in the experiments to compare the performance of classification models.The experimental results demonstrate that the proposed method is better than other competing classification models due to achieving higher values of accuracy,AUC,specificity,and sensitivity metrics for the used datasets.

关键词： Feed forward neural network Meta-heuristic algorithm Whale optimization algorithm Optimization Classification Bionic algorithm

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Ductility improvement of GFRP-RC beams using precast confined concrete block in compression zone

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Frontiers of Structural and Civil Engineering 2023年第10期17卷 1585-1598页

作者： Nooshin G.AMIRABAD Farshid J.ALAEE Meysam jalali Civil Engineering Department Shahrood University of TechnologyShahrood ***Iran

Fiber-reinforced polymers(FRPs)have received considerable research attention because of their high strength,corrosion resistance,and low weight.However,owing to the lack of ductility in this material and the quasi-brittle behavior of concrete,FRP-reinforced concrete(FRP-RC)beams,even with flexural failure,do not fail in a ductile manner.Because the limited deformation capacity of FRP-RC beams depends on the ductility of their compression zones,the present study proposes using a precast confined concrete block(PCCB)in the compression zone to improve the ductility of the beams.A control beam and four beams with different PCCBs were cast and tested under four-point bending conditions.The control beam failed due to shear,and the PCCBs exhibited different confinements and perforations.The goal was to find an appropriate PCCB for use in the compression zone of the beams,which not only improved the ductility but also changed the failure mode of the beams from shear to flexural.Among the employed blocks,a ductile PCCB with low equivalent compressive strength increased the ductility ratio of the beam to twice that of the control beam.The beam failed in pure flexure with considerable deformation capacity and without significant stiffness reduction.

关键词： ductility four-point bending test glass fiber-reinforced polymer precast confined concrete block

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VEViD:Vision Enhancement via Virtual diffraction and coherent Detection

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eLight 2022年第1期2卷 301-316页

作者： Bahram jalali Callen MacPhee Electrical and Computer Engineering Department UCLALos AngelesUSA

The history of computing started with analog computers consisting of physical devices performing specialized functions such as predicting the position of astronomical bodies and the trajectory of cannon balls.In modern times,this idea has been extended,for example,to ultrafast nonlinear optics serving as a surrogate analog computer to probe the behavior of complex phenomena such as rogue waves.Here we discuss a new paradigm where physical phenomena coded as an algorithm perform computational imaging tasks.Specifically,diffraction followed by coherent detection becomes an image enhancement tool.Vision Enhancement via Virtual diffraction and coherent Detection(VEViD)reimagines a digital image as a spatially varying metaphoric“lightfield”and then subjects the field to the physical processes akin to diffraction and coherent detection.The term“Virtual”captures the deviation from the physical world.The light field is pixelated and the propagation imparts a phase with dependence on frequency which is different from the monotonically-increasing behavior of physical diffraction.Temporal frequencies exist in three bands corresponding to the RGB color channels of a digital image.The phase of the output,not the intensity,represents the output image.VEViD is a high-performance low-light-level and color enhancement tool that emerges from this paradigm.The algorithm is extremely fast,interpretable,and reduces to a compact and intuitively-appealing mathematical expression.We demonstrate image enhancement of 4k video at over 200 frames per second and show the utility of this physical algorithm in improving the accuracy of object detection in low-light conditions by neural networks.The application of VEViD to color enhancement is also demonstrated.

关键词： Physics-inspired computer vision Natural algorithms Low-light image enhancement Color enhancement

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Improving TC drill bit's efficiency and resistance to wear by graphene coating

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Petroleum Research 2022年第4期7卷 430-436页

作者： Reza Taheri Mohsen jalali Ahmed Al-Yaseri George Yabesh Petroleum Engineering Department University of WyomingWYUSA Petroleum Engineering Department Tehran Azad UniversityTehranIran Petroleum Engineering Discipline Engineering SchoolEdith Cowan UniversityAustralia

Displaying a two-dimensional pure crystal carbon structure,Graphene is the strongest,yet thinnest substance discovered by scientists.Coating tungsten carbide(TC)drill bits with graphene to evaluate the effect of graphene on the wear,as well as the rate of penetration of the drilling bit was examined in this research.Two evaluation approaches were employed:one with employing ANSYS Software and the second by employing atomic pressure chemical vapor deposition(APCVD synthesis)in the laboratory to produce a monolayer graphene coating.The simultaneous software-based and lab-based testing were performed to increase the credibility of the results and minimize the potential errors.Conducting the simulation using ANSYS,the maximum shear elastic strain,equivalent elastic strain,equivalent(von mises)stress,total deformation and maximum shear stress were investigated prior and after the gra-phene coating was applied on TC simulated bit.Total deformation was only slightly increased,while the maximum shear elastic strain was almost doubled,reflecting that the bit's wear was significantly reduced after the coating.Lab-based APCVD synthesis results showed 34%increase in compressive strength of the coated bit,in comparison to the uncoated one.The failure occurred for uncoated bit at 35 MPa,where the coated bit experienced failure at 46.9 MPa.The Von Mises stress test conducted on the coated and uncoated samples also indicated that this stress was 41%less for the coated bit,in comparison to the uncoated one.Finally,two small-scale drilling operations,one using a 1inch graphene-coated TC bit and the other using a 1inch non-coated TC bit,were performed on a granite block,to evaluate the performance of the graphene-coated bit in practice.In a chosen 120-min time frame,27 consecutive holes could be drilled by the graphene-coated TC bit,while 19 consecutive holes could be drilled by the uncoated TC bit,in identical drilling conditions.This implies a 42%increase in ROP.

关键词： Drill bit's efficiency Graphene-coated ANSYS APCVD Wear reduction Penetration rate

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Past,present and future of indium phosphide quantum dots

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Nano Research 2022年第5期15卷 4468-4489页

作者： Houman Bahmani jalali Sadra Sadeghi Itir Bakis Dogru Yuksel Asim Onal Sedat Nizamoglu Graduate School of Biomedical Science and Engineering KoçUniversityIstanbul 34450Turkey Department of Electric and Electronics Engineering KoçUniversityIstanbul 34450Turkey Department of Material Science and Engineering KoçUniversityIstanbul 34450Turkey Present address:Photonic Nanomaterials Group Istituto Italiano di TecnologiaGenova 16124Italy Present address:Department of Electrical and Computer Engineering Queen's UniversityKingston K7L 3N6Canada Present address:Nanophotonics Group Debye Institute for Nanomaterials ScienceUtrecht UniversityUtrecht 3508 TAThe Netherlands

Indium phosphide(InP)colloidal quantum dots(QDs)have been drawn significant attention as a potentially less toxic alternative to cadmium-based QDs over the past two decades.The advances in their colloidal synthesis methods have allowed for the synthesis of a wide variety of compositions,heterojunctions,dopants,and ligands that enabled spectral tunability from blue to near-infrared,narrow emission linewidths,and perfect quantum yields approaching unity.Furthermore,it has higher covalency compared to cadmium chalcogenides leading to improved optical stability.The state-of-the-art InP QDs with appealing optical and electronic properties have excelled in many applications such as light-emitting diodes,luminescent solar concentrators(LSCs),and solar cells with high potential for commercialization.This review focuses on the history,recent development,and future aspect of synthesis and application of colloidal InP QDs.

关键词： indium phosphide InP quantum dot nanocrystal synthesis colloidal doping

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L-arginine alleviates postmenopausal complications in female rats by stimulating ovarian dopamine beta hydroxylase

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Asian pacific Journal of Reproduction 2022年第6期11卷 276-284页

作者： Fatemeh Lakzaei Manizheh Karami Mohammadreza jalali Nadoushan Department of Biology Faculty of Basic SciencesShahed UniversityTehranIran Department of Pathology Faculty of MedicineShahed UniversityTehranIran

Objective:To evaluate the levels of estrogen,albumin and gonadotropins(luteinizing hormone,follicle-stimulating hormone)as well as the activity of dopamine beta hydroxylase(DAβH)in aged female rats treated with nitric oxide precursor L-arginine and neuronal nitric oxide synthase antagonist L-NAME.Methods:A total of 224 Wistar rats(36 weeks old,weighing 250 g)based on a random sampling were divided into the control and experimental groups after Pap smear test.The control group received only saline(1 mL/kg)intraperitoneally(i.p.).The experiential groups were treated with L-arginine(5,25 and 50 mg/kg,i.p.)and L-NAME(5 and 25 mg/kg,i.p.)for 3 to 21 days,once a day.Blood samples were taken from the rats and the levels of estrogen and albumin and gonadotropins in the serum were monitored by enzyme-linked immunosorbent assay kit,and the ovaries were examined immunohistopathologically for DAβH activity.Results:L-arginine(5 mg/kg)significantly increased estrogen level(P<0.05),which was associated with DAβH activation in the ovaries.L-NAME reduced this effect when administered prior to L-arginine dose.L-arginine caused no significant change in the levels of luteinizing hormone and follicle-stimulating hormone.Except for the lowest dose of L-arginine in the shortest period,albumin levels significantly decreased in other treatments compared to the control group(P<0.05).Conclusions:L-arginine is likely to reduce postmenopausal problems due to an increased nitric oxide level.

关键词： Postmenopausal Estrogen Albumin Dopamine beta-hydroxlase L-arginine Nitric oxide

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