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Synergistic effect of adsorption and photocatalytic degradation of oilfield-produced water by electrospun photocatalytic fibers of Polystyrene/Nanorod-Graphitic carbon nitride

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Journal of Environmental Sciences 2024年第7期141卷 287-303页

作者： Parisa Azmoon Mehrdad Farhadian Alireza Pendashteh Amir H.Navarchian department of Chemical engineering Faculty of EngineeringUniversity of IsfahanIsfahanIran department of Chemical engineering Faculty of EngineeringUniversity of GuilanRashtIran department of Water and Environmental engineering Caspian Sea Basin Research CenterUniversity of GuilanRashtIran

Graphitic carbon nitride with nanorod structure(Nr-GCN)was synthesized using melamine as a precursor without any other reagents by hydrothermal pretreatment ***,FTIR,SEM,N_(2)adsorption-desorption from BET,UV-Vis DRS spectroscopy,and photoluminescence were used to characterize the prepared ***,the photoelectrochemical behavior of nanoparticles was studied by photocurrent transient response and cyclic voltammetry ***(PS)fibrous mat was fabricated by electrospinning technique and used as a support for the stabilization of the *** performance of the synthesized nanoparticles and photocatalytic fibers(PS/Nr-GCN)was evaluated in oilfield-produced water treatment under visible light *** this process,oil contaminants were adsorbed by hydrophobic polystyrene fibers and simultaneously degraded by *** removal efficiency of chemical oxygen demand(COD)has been obtained 96.6%and 98.4%by Nr-GCN and PS/Nr-GCN,respectively,at the optimum conditions of pH4,photocatalyst dosage 0.5 g/L,COD initial concentration 550 mg/L,and illumination time 150 *** gas chromatography-mass spectroscopy analysis results showed 99.3%removal of total petroleum hydrocarbons using photocatalytic fibers of PS/*** results demonstrated that the GCN has outstanding features like controllable morphology,visible-light-driven,and showing high potential in oily wastewater ***,the synergistic effect of adsorption and photocatalytic degradation is an effective technique in oilfield-produced water treatment.

关键词： Graphitic carbon nitride(GCN) Nanorod structure Polystyrene fibrous mat Photocatalytic fibers Oilfield-produced water(OPW)

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Underwater inertial error rectification with limited acoustic observations

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Satellite Navigation 2024年第1期5卷 207-223页

作者： Hongqiong Tang Hongyang He Fangneng Li Jiangning Xu department of Navigation engineering Naval University of EngineeringWuhan430000China

Underwater inertial navigation is particularly difficult for the long-durance operations as many navigation systems such global satellite navigation systems are *** acoustic signal is a marvelous choice for underwater inertial error rectification due to its underwater penetration ***,the traditional Acoustic Positioning Systems(APS)are expensive and incapable of positioning with limited acoustic *** novel underwater inertial error rectification algorithms with limited acoustic observations are *** first one is the single acoustic-beacon Range-only Matching Aided Navigation(RMAN)method,which is inspired by matching navigation without reference maps and presented for the first *** second is the improved single acoustic-beacon Virtual Long Baseline(VLBL)method,which considers the impact of indicated relative position increments on virtual beacon *** RMAN and improved VLBL are further developed when multi acoustic-beacons are available,named mAB-RMAN and *** comprehensive simulations and field investigations were *** results demonstrated that the proposed methods achieved excellent accuracy and stability compared to the baseline,specifically,the mAB-RMAN and mAB-VLBL can reduce the inertial error by more than 90%and 98%when using single and double acoustic-beacons,*** proposed techniques will provide new perspectives for underwater positioning,navigation,and timing.

关键词： Underwater PNT Range-only matching aided navigation Virtual long baseline Inertial error rectification

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Artificial Intelligence Prediction of One-Part Geopolymer Compressive Strength for Sustainable Concrete

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Computer Modeling in engineering & Sciences 2024年第10期141卷 525-543页

作者： Mohamed Abdel-Mongy Mudassir Iqbal M.Farag Ahmed.M.Yosri Fahad Alsharari Saif Eldeen A.S.Yousef department of Civil engineering College of EngineeringJouf UniversitySakaka72388Saudi Arabia department of Civil engineering University of Engineering and TechnologyPeshawar54890Pakistan Civil engineering department Faculty of EngineeringAl-Azhar UniversityCairo11884Egypt Civil engineering department Faculty of EngineeringAswan UniversityAswan81528Egypt

Alkali-activated materials/geopolymer(AAMs),due to their low carbon emission content,have been the focus of recent studies on ecological *** terms of performance,fly ash and slag are preferredmaterials for precursors for developing a one-part ***,determining the optimum content of the input parameters to obtain adequate performance is quite challenging and scarcely ***,in this study,machine learning methods such as artificial neural networks(ANN)and gene expression programming(GEP)models were developed usingMATLAB and GeneXprotools,respectively,for the prediction of compressive strength under variable input materials and content for fly ash and slag-based one-part *** database for this study contains 171 points extracted from literature with input parameters:fly ash concentration,slag content,calcium hydroxide content,sodium oxide dose,water binder ratio,and curing *** performance of the two models was evaluated under various statistical indices,namely correlation coefficient(R),mean absolute error(MAE),and rootmean square error(RMSE).In terms of the strength prediction efficacy of a one-part geopolymer,ANN outperformed *** and parametric analysis were also performed to identify the significant contributor to *** to a sensitivity analysis,the activator and slag contents had the most effects on the compressive strength at 28 *** water binder ratio was shown to be directly connected to activator percentage,slag percentage,and calcium hydroxide percentage and inversely related to compressive strength at 28 days and curing temperature.

关键词： Artificial intelligence techniques one-part geopolymer artificial neural network gene expression modelling sustainable construction polymers

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ASLP-DL—A Novel Approach Employing Lightweight Deep Learning Framework for Optimizing Accident Severity Level Prediction

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Computers, Materials & Continua 2024年第2期78卷 2535-2555页

作者： Saba Awan Zahid Mehmood department of Software engineering University of Engineering and TechnologyTaxila47050Pakistan department of Computer engineering University of Engineering and TechnologyTaxila47050Pakistan

Highway safety researchers focus on crash injury severity,utilizing deep learning—specifically,deep neural networks(DNN),deep convolutional neural networks(D-CNN),and deep recurrent neural networks(D-RNN)—as the preferred method for modeling accident *** learning’s strength lies in handling intricate relation-ships within extensive datasets,making it popular for accident severity level(ASL)prediction and *** prior success,there is a need for an efficient system recognizing ASL in diverse road *** address this,we present an innovative Accident Severity Level Prediction Deep Learning(ASLP-DL)framework,incorporating DNN,D-CNN,and D-RNN models fine-tuned through iterative hyperparameter selection with Stochastic Gradient *** framework optimizes hidden layers and integrates data augmentation,Gaussian noise,and dropout regularization for improved *** and factor contribution analyses identify influential *** on three diverse crash record databases—NCDB 2018–2019,UK 2015–2020,and US 2016–2021—the D-RNN model excels with an ACC score of 89.0281%,a Roc Area of 0.751,an F-estimate of 0.941,and a Kappa score of 0.0629 over the NCDB *** proposed framework consistently outperforms traditional methods,existing machine learning,and deep learning techniques.

关键词： Injury severity prediction deep learning feature

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Statistical analysis and interpretation on lightning impulse breakdown voltages of ester liquids under varying field uniformity

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High Voltage 2024年第1期9卷 22-34页

作者： Haichuan Yu Qiang Liu Zhongdong Wang department of Electrical and Electronic engineering The University of ManchesterManchesterUK department of engineering University of ExeterExeterUK

Ester liquids(ELs),as the alternative transformer liquids to the conventional mineral oils(MOs),have been increasingly used in power transformers with high voltage *** various properties,the lightning impulse breakdown voltages(LIBDVs)have been studied elaborately in the past two decades under different electric field *** electric field uniformities do have an effect on the breakdown characteristics,for example,breakdown mechanisms,polarity effects,and differences of LIBDV between ELs and MOs,which is quantitatively investigated based on the LIBDVs collected from the *** breakdown mechanism transition phenomenon between streamer initiation dominated breakdown and streamer propagation dominated breakdown has been reviewed ***,the transition field factor boundaries to observe the polarity effect on LIBDVs are taken at around 20 for ELs and 10 for ***,the transition field factor boundaries to observe the LIBDV distinctions between ELs and MOs are about 109 and 14 under positive and negative lightning impulse,*** systematic analysis and interpretation on the transition field factor boundaries among different phenomena can provide insights on insulation design of ELs filled power transformers.

关键词： field lightning breakdown

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Formation adaptation in obstacle-cluttered environments via MPC-based trajectory planning

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Science China(Information Sciences) 2024年第7期67卷 321-323页

作者： Yuda CHEN Zhongkui LI department of Mechanics and engineering Science College of Engineering Peking University

Formation is a typical collective behavior in multiagent systems utilized in areas such as inspection, surveillance, and transportation. Commonly-used approaches to achieve formation include virtual structure [1], con...

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An Electrical Impedance Imaging System Towards Edge Intelligence for Non-Destructive Testing of Concrete

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Tsinghua Science and Technology 2024年第3期29卷 883-896页

作者： Abolfazl Roshanpanah Mahdi Abbasi Hani Attar Ayman Amer Mohammad R.Khosravi Ahmed A.Solyman department of Electrical engineering Islamic Azad UniversityScience and Research BranceTehran ***Iran department of Computer engineering Bu-Ali Sina UniversityHamedan ***Iran department of Energy engineering Zarqa UniversityZarqa 13110Jordan department of Computer engineering Persian Gulf UniversityBushehr ***Iran department of Electrical and Electronics engineering Faculty of Engineering and ArchitectureNişantaşıUniversityIstanbul 34398Turkey

In the construction industry,to prevent accidents,non-destructive tests are necessary and *** impedance tomography is a new technology in non-invasive imaging in which the image of the inner part of conductive bodies is reconstructed by the arrays of external electrodes that are connected on the periphery of the *** equipment is cheap,fast,and edge *** this imaging method,the image of electrical conductivity distribution(or its opposite;electrical impedance)of the internal parts of the target object is *** image reconstruction process is performed by injecting a precise electric current to the peripheral boundaries of the object,measuring the peripheral voltages induced from it and processing the collected *** an electrical impedance tomography system,the voltages measured in the peripheral boundaries have a non-linear equation with the electrical conductivity *** paper presents a cheap Electrical Impedance Tomography(EIT)instrument for detecting impurities in the concrete.A voltage-controlled current source,a micro-controller,a set of multiplexers,a set of electrodes,and a personal computer constitute the structure of the *** conducted tests on concrete with impurities show that the designed EIT system can reveal impurities with a good accuracy in a reasonable time.

关键词： non-destructive testing system concrete electrical impedance tomography

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Prediction of bearing capacity of pile foundation using deep learning approaches

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Frontiers of Structural and Civil engineering 2024年第6期18卷 870-886页

作者： Manish KUMAR Divesh Ranjan KUMAR Jitendra KHATTI Pijush SAMUI Kamaldeep Singh GROVER department of Civil engineering SRM Institute of Science and Technology Tiruchirappalli CampusTrichy 621105India department of Civil engineering National Institute of Technology PatnaBihar 800005India department of Civil engineering Faculty of EngineeringThammasat School of EngineeringThammasat UniversityBangkok10200Thailand department of Civil engineering Rajasthan Technical UniversityKota 324010India

The accurate prediction of bearing capacity is crucial in ensuring the structural integrity and safety of pile *** research compares the Deep Neural Networks(DNN),Convolutional Neural Networks(CNN),Recurrent Neural Networks(RNN),Long Short-Term Memory(LSTM),and Bidirectional LSTM(BiLSTM)algorithms utilizing a data set of 257 dynamic pile load tests for the first ***,this research illustrates the multicollinearity effect on DNN,CNN,RNN,LSTM,and BiLSTM models’performance and accuracy for the first time.A comprehensive comparative analysis is conducted,employing various statistical performance parameters,rank analysis,and error matrix to evaluate the performance of these *** performance is further validated using external validation,and visual interpretation is provided using the regression error characteristics(REC)curve and Taylor *** from the comparative analysis reveal that the DNN(Coefficient of determination(R^(2))_(training(TR))=0.97,root mean squared error(RMSE)_(TR)=0.0413;R^(2)_(testing(TS))=0.9,RMSE_(TS)=0.08)followed by BiLSTM(R^(2)_(TR)=0.91,RMSE_(TR)=0.782;R^(2)_(TS)=0.89,RMSE_(TS)=0.0862)model demonstrates the highest performance *** is noted that the BiLSTM model is better than LSTM because the BiLSTM model,which increases the amount of information for the network,is a sequence processing model made up of two LSTMs,one of which takes the input in a forward manner,and the other in a backward *** prediction of pile-bearing capacity is strongly influenced by ram weight(having a considerable multicollinearity level),and the effect of the considerable multicollinearity level has been determined for the model based on the recurrent neural network *** this study,the recurrent neural network model has the least performance and accuracy in predicting the pile-bearing capacity.

关键词： deep learning algorithms high-strain dynamic pile test bearing capacity of the pile

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Atomically Adjustable Rhodium Catalyst Synthesis with Outstanding Mass Activity via Surface-Limited Cation Exchange

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Energy & Environmental Materials 2024年第2期7卷 87-96页

作者： Hak Hyeon Lee Dong Su Kim Swagotom Sarker Ji Hoon Choi Ho Seong Lee Hyung Koun Cho department of Advanced Materials Science & engineering Sungkyunkwan University department of Advanced Materials Science & engineering Kyungpook National University

Rh has been widely studied as a catalyst for the promising hydrazine oxidation reaction that can replace oxygen evolution reactions for boosting hydrogen production from hydrazine-containing wastewater. Despite Rh being expensive, only a few studies have examined its electrocatalytic mass activity. Herein, surface-limited cation exchange and electrochemical activation processes are designed to remarkably enhance the mass activity of Rh. Rh atoms were readily replaced at the Ni sites on the surface of NiOOH electrodes by cation exchange, and the resulting RhOOH compounds were activated by the electrochemical reduction process. The cation exchangederived Rh catalysts exhibited particle sizes not exceeding 2 nm without agglomeration, indicating a decrease in the number of inactive inner Rh atoms. Consequently, an improved mass activity of 30 A mgRh-1was achieved at 0.4 V versus reversible hydrogen electrode. Furthermore, the two-electrode system employing the same CE-derived Rh electrodes achieved overall hydrazine splitting over 36 h at a stable low voltage. The proposed surface-limited CE process is an effective method for reducing inactive atoms of expensive noble metal catalysts.

关键词： cation exchange synthesis electrochemical metallization hydrazine oxidation reaction mass activity rhodium catalyst

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Advances in Manta Ray Foraging Optimization:A Comprehensive Survey

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Journal of Bionic engineering 2024年第2期21卷 953-990页

作者： Farhad Soleimanian Gharehchopogh Shafi Ghafouri Mohammad Namazi Bahman Arasteh department of Computer engineering Urmia BranchIslamic Azad UniversityUrmiaIran department of Computer engineering Tabriz BranchIslamic Azad UniversityTabrizIran department of Computer engineering Maybod BranchIslamic Azad UniversityMaybodIran department of Software engineering Faculty of Engineering and Natural ScienceIstinye UniversityIstanbulTurkey

This paper comprehensively analyzes the Manta Ray Foraging Optimization(MRFO)algorithm and its integration into diverse academic *** in 2020,the MRFO stands as a novel metaheuristic algorithm,drawing inspiration from manta rays’unique foraging behaviors—specifically cyclone,chain,and somersault *** biologically inspired strategies allow for effective solutions to intricate physical *** its potent exploitation and exploration capabilities,MRFO has emerged as a promising solution for complex optimization *** utility and benefits have found traction in numerous academic *** its inception in 2020,a plethora of MRFO-based research has been featured in esteemed international journals such as IEEE,Wiley,Elsevier,Springer,MDPI,Hindawi,and Taylor&Francis,as well as at international conference *** paper consolidates the available literature on MRFO applications,covering various adaptations like hybridized,improved,and other MRFO variants,alongside optimization *** trends indicate that 12%,31%,8%,and 49%of MRFO studies are distributed across these four categories respectively.

关键词： Manta ray foraging optimization Metaheuristic algorithms Hybridization Improved Optimization

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