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One-step hydrothermal synthesis of Sn-doped α-Fe_(2)O_(3) nanoparticles for enhanced photocatalytic degradation of Congo red

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Journal of Semiconductors 2022年第12期43卷 22-31页

作者： Van Nang Lam Thi Bich Vu Quang Dat Do Thi Thanh Xuan Le Tien Dai Nguyen T-Thanh-Bao Nguyen Hoang Tung Do Thi Tu Oanh Nguyen Department of Natural Sciences Hoa Lu UniversityNinh NhatNinh Binh CityViet Nam Institute of Theoretical and Applied Research Duy Tan UniversityHanoi 100000Viet Nam Faculty of Natural Sciences Duy Tan UniversityDa Nang 550000Vietnam Institute of Physics Vietnam Academy of Science and Technology10 Dao TanBa DinhHanoiVietnam Institute of Materials Science Vietnam Academy of Science and Technology18 Hoang Quoc VietCaugiayHanoiVietnam

We report on the synthesis of Sn-doped hematite nanoparticles(Sn-α-Fe_(2)O_(3) NPs)by the hydrothermal *** prepared Sn-α-Fe_(2)O_(3) NPs had a highly pure and well crystalline rhombohedral phase with an average particle size of 41.4 *** optical properties of as-synthesizedα-Fe_(2)O_(3) NPs show a higher bandgap energy(2.40-2.57 eV)than that of pure bulkα-Fe_(2)O_(3)(2.1 eV).By doping Sn intoα-Fe_(2)O_(3) NPs,the Sn-doped hematite was observed a redshift toward a long wavelength with in-creasing Sn concentration from 0%to 4.0%.The photocatalytic activity of Sn-dopedα-Fe_(2)O_(3) NPs was evaluated by Congo red(CR)dye *** degradation efficiency of CR dye using Sn-α-Fe_(2)O_(3) NPs catalyst is higher than that of pureα-Fe_(2)O_(3) *** highest degradation efficiency of CR dye was 97.8%using 2.5%Sn-dopedα-Fe_(2)O_(3) NPs catalyst under visible-light *** results suggest that the synthesized Sn-dopedα-Fe_(2)O_(3) nanoparticles might be a suitable approach to develop a photocatalytic degradation of toxic inorganic dye in wastewater.

关键词： α-Fe_(2)O_(3)nanoparticles Sn Congo red photocatalytic properties photodegradation

Predicting Resource Availability in Local Mobile Crowd Computing Using Convolutional GRU

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Computers, Materials & Continua 2022年第3期70卷 5199-5212页

作者： Pijush Kanti Dutta Pramanik Nilanjan Sinhababu Anand Nayyar Mehedi Masud Prasenjit Choudhury Department of Computer Science&Engineering National Institute of TechnologyDurgapurIndia Reliability Engineering Centre Indian Institute of Technology KharagpurIndia Graduate School Duy Tan UniversityDa Nang550000Vietnam Faculty of Information Technology Duy Tan UniversityDa Nang550000Vietnam Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia

In mobile crowd computing(MCC),people’s smart mobile devices(SMDs)are utilized as computing *** the ever-growing computing capabilities of today’s SMDs,a collection of them can offer significantly high-performance computing *** a localMCC,the SMDs are typically connected to a local Wi-Fi *** and institutions can leverage the SMDs available within the campus to form local MCCs to cater to their computing needs without any financial and operational *** it offers an economical and sustainable computing solution,users’mobility poses a serious issue in the QoS of *** address this,before submitting a job to an SMD,we suggest estimating that particular SMD’s availability in the network until the job is *** this,we propose a convolutional GRU-based prediction model to assess how long an SMD is likely to be available in the network from any given point of *** experimental purposes,we collected real users’mobility data(in-time and outtime)with respect to a Wi-Fi access *** build the prediction model,we presented a novel feature extraction method to be applied to the time-series *** experimental results prove that the proposed convolutional GRU model outperforms the conventional GRU model.

关键词： Resource selection resource availability mobile grid mobile cloud ad-hoc cloud crowd computing deep learning GRU CNN RNN

Multiparticle^(nat)Sr(γ,xny p)reactions induced with bremsstrahlung end-point energies of 55,60,and 65 MeV

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Chinese Physics C 2022年第9期46卷 185-196页

作者： Nguyen Van Do Nguyen Thanh Luan Nguyen Thi Xuan Kim Tien Thanh Nguyen Thi Hien Guinyun Kim Institute of Theoretical and Applied Research Duy Tan UniversityHanoi 100000Vietnam Faculty of Natural Sciences Duy Tan UniversityDa Nang 550000Vietnam Institute of Physics Vietnam Academy of Science and Technology10 Dao TanHanoi 10000Vietnam Department of Physics Kyungpook National UniversityDaegu 415661Republic of Korea Graduate School of Science and Technology Vietnam Academy of Science and Technology18 Hoang Quoc VietHanoiVietnam

This study measured the yields of the radionuclides^(82)Sr,^(83(m+g))Sr,^(85m)Sr,^(85g)Sr,^(87m)Sr,^(81(g+0.976m))Rb,^(82m)Rb,^(83g)Rb,^(84(m+g))Rb,and^(86(m+g))Rb produced in^(nat)Sr(γ,xnyp)multiparticle reactions with bremsstrahlung end-point energies of 55,60,and 65 *** bremsstrahlung radiation was generated using the 100-MeV electron linear accelerator at the Pohang Accelerator Laboratory,Korea,and the reaction yields were derived from the induced activitiesmeasured using off-lineγ-ray *** obtain accurate experimental results,we performed the necessaryγ-ray interference *** experimental results were compared with the theoretical predictions obtained using the TALYS-1.95 statistical nuclear model *** calculations were performed using six different level-density models to ascertain which model best fitted the experimental *** dependence of the reaction yield on the incident bremsstrahlung energy and on the number of nucleons emitted by the photonuclear reactions was also investigated.

关键词： reaction yield activation method ^(nat)Sr(γ xnyp)reactions γ-ray spectrometry 55- 60- and 65-MeV bremsstrahlung.

Mutation Prediction for Coronaviruses Using Genome Sequence and Recurrent Neural Networks

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Computers, Materials & Continua 2022年第10期73卷 1601-1619页

作者： Pranav Pushkar Christo Ananth Preeti Nagrath Jehad F.Al-Amri Vividha Anand Nayyar Bharati Vidyapeeth’s College of Engineering New Delhi110063India Department of Natural and Exact Sciences Samarkand State UniversitySamarkandUzbekistan Graduate School Duy Tan UniversityDa Nang550000Viet Nam Faculty of Information Technology Duy Tan UniversityDa Nang550000Viet Nam Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia

The study of viruses and their genetics has been an opportunity as well as a challenge for the scientific *** recent ongoing SARSCov2(Severe Acute Respiratory Syndrome)pandemic proved the unpreparedness for these *** only the countermeasures for the effect caused by virus need to be tackled but the mutation taking place in the very genome of the virus is needed to be kept in check *** major way to find out more information about such pathogens is by extracting the genetic data of such *** genetic data of viruses have been cultured and stored as well as isolated in form of their genome sequences,there is still limited methods on what new viruses can be generated in future due to *** research proposes a deep learning model to predict the genome sequences of the SARS-Cov2 virus using only the previous viruses of the coronaviridae family with the help of RNN-LSTM(Recurrent Neural Network-Long ShortTerm Memory)and RNN-GRU(Gated Recurrent Unit)so that in the future,several counter measures can be taken by predicting possible changes in the genome with the help of existing mutations in the *** the process of testing the model,the F1-recall came out to be more than *** mutation detection’s accuracy of both the models come out about 98.5%which shows the capability of the recurrent neural network to predict future changes in the genome of virus.

关键词： COVID-19 genome sequence coronaviridae RNN-LSTM RNN-GRU

Free Vibration Analysis of FG-CNTRC Cylindrical Pressure Vessels Resting on Pasternak Foundation with Various Boundary Conditions

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Computers, Materials & Continua 2020年第3期62卷 1001-1023页

作者： Mohammad Arefi Masoud Mohammadi Ali Tabatabaeian Timon Rabczuk Department of Solid Mechanic Faculty of Mechanical EngineeringUniversity of KashanKashan 87317-51167Iran duy tan university Institute of Research&Development3 Quang TrungDanangVietnam

This study focuses on vibration analysis of cylindrical pressure vessels constructed by functionally graded carbon nanotube reinforced composites(FG-CNTRC).The vessel is under internal pressure and surrounded by a Pasternak *** investigation was founded based on two-dimensional elastic analysis and used Hamilton’s principle to drive the governing *** deformations and effective-mechanical properties of the reinforced structure were elicited from the first-order shear theory(FSDT)and rule of mixture,*** main goal of this study is to show the effects of various design parameters such as boundary conditions,reinforcement distribution,foundation parameters,and aspect ratio on the free vibration characteristics of the structure.

关键词： FG-CNTRC cylindrical pressure vessel first-ordershear deformation theory free vibration Pasternak’s foundation rule of mixture

Modeling viscosity of methane,nitrogen,and hydrocarbon gas mixtures at ultra-high pressures and temperatures using group method of data handling and gene expression programming techniques

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Chinese Journal of Chemical Engineering 2021年第4期34卷 431-445页

作者： Farzaneh Rezaei Saeed Jafari Abdolhossein Hemmati-Sarapardeh Amir H.Mohammadi Department of Petroleum Engineering Shahid Bahonar University of KermanKermanIran Institute of Research and Development Duy Tan UniversityDa Nang 550000Vietnam Faculty of Environment and Chemical Engineering Duy Tan UniversityDa Nang 550000Vietnam Institut de Recherche en Genie Chimique et Petrolier(IRGCP) Paris CedexFrance Discipline of Chemical Engineering School of EngineeringUniversity of KwaZulu-NatalHoward College CampusKing George V AvenueDurban 4041South Africa

Accurate gas viscosity determination is an important issue in the oil and gas *** approaches for gas viscosity measurement are timeconsuming,expensive and hardly possible at high pressures and high temperatures(HPHT).In this study,a number of correlations were developed to estimate gas viscosity by the use of group method of data handling(GMDH)type neural network and gene expression programming(GEP)techniques using a large data set containing more than 3000 experimental data points for methane,nitrogen,and hydrocarbon gas *** is worth mentioning that unlike many of viscosity correlations,the proposed ones in this study could compute gas viscosity at pressures ranging between 34 and 172 MPa and temperatures between 310 and 1300 ***,a comparison was performed between the results of these established models and the results of ten wellknown models reported in the *** absolute relative errors of GMDH models were obtained 4.23%,0.64%,and 0.61%for hydrocarbon gas mixtures,methane,and nitrogen,*** addition,graphical analyses indicate that the GMDH can predict gas viscosity with higher accuracy than GEP at HPHT ***,using leverage technique,valid,suspected and outlier data points were ***,trends of gas viscosity models at different conditions were evaluated.

关键词： Gas Viscosity High pressure high temperature Group method of data handling Gene expression programming

Medical Image Analysis Using Deep Learning and Distribution Pattern Matching Algorithm

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Computers, Materials & Continua 2022年第8期72卷 2175-2190页

作者： Mustafa Musa Jaber Salman Yussof Amer S.Elameer Leong Yeng Weng Sura Khalil Abd Anand Nayyar Institute of Informatics and Computing in Energy Universiti Tenaga NasionalMalaysia Department of Computer Science Dijlah University CollageBaghdad10021Iraq Biomedical Informatics College University of Information Technology and CommunicationsBaghdadIraq Faculty of Information Technology Duy Tan UniversityDa Nang550000Viet Nam Graduate School Duy Tan UniversityDa Nang550000Viet Nam Department of Computer Science Al-turath University CollegeBaghdadIraq

Artificial intelligence plays an essential role in the medical and health *** convolution networks offer valuable services and help create automated systems to perform medical image ***,convolution networks examine medical images effectively;such systems require high computational complexity when recognizing the same disease-affected ***,an optimized deep convolution network is utilized for analyzing disease-affected regions in this *** disease-relatedmedical images are selected and examined pixel by pixel;this analysis uses the gray wolf optimized deep learning *** method identifies affected pixels by the gray wolf hunting *** convolution network uses an automatic learning function that predicts the disease affected by previous imaging *** optimized algorithm-based selected regions are further examined using the distribution pattern-matching *** pattern-matching process recognizes the disease effectively,and the system’s efficiency is evaluated using theMATLAB implementation *** process ensures high accuracy of up to 99.02%to 99.37%and reduces computational complexity.

关键词： Artificial intelligence medical field gray wolf-optimized deep convolution networks distribution pattern-matching rule

Enhanced Marathi Speech Recognition Facilitated by Grasshopper Optimisation-Based Recurrent Neural Network

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Computer Systems Science & Engineering 2022年第11期43卷 439-454页

作者： Ravindra Parshuram Bachate Ashok Sharma Amar Singh Ayman AAly Abdulaziz HAlghtani Dac-Nhuong Le School of Computer Science and Engineering Lovely Professional UniversityPunjab144001India Department of Computer Science and IT University of JammuJammu and Kashmir180006India School of Computer Applications Lovely Professional UniversityPunjab144001India Department of Mechanical Engineering College of EngineeringTaif UniversityTaif21944Saudi Arabia School of Computer Science Duy Tan UniversityDanang550000Vietnam Institute of Research and Development Duy Tan UniversityDanang550000Vietnam

Communication is a significant part of being human and living in the *** kinds of languages and their variations are there;thus,one person can speak any language and cannot effectively communicate with one who speaks that language in a different *** application fields such as education,mobility,smart systems,security,and health care systems utilize the speech or voice recognition models ***,various studies are focused on the Arabic or Asian and English languages by ignoring other significant languages like Marathi that leads to the broader research motivations in regional *** is necessary to understand the speech recognition field,in which the major concentrated stages are feature extraction and *** paper emphasis developing a Speech Recognition model for the Marathi language by optimizing Recurrent Neural Network(RNN).Here,the preprocessing of the input signal is performed by smoothing and median *** preprocessing the feature extraction is carried out using MFCC and Spectral features to get precise features from the input Marathi Speech *** optimized RNN classifier is used for speech recognition after completing the feature extraction task,where the optimization of hidden neurons in RNN is performed by the Grasshopper Optimization Algorithm(GOA).Finally,the comparison with the conventional techniques has shown that the proposed model outperforms most competing models on a benchmark dataset.

关键词： Deep learning grasshopper optimization algorithm recurrent neural network speech recognition word error rate

Deep Learning Based Optimal Multimodal Fusion Framework for Intrusion Detection Systems for Healthcare Data

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Computers, Materials & Continua 2021年第3期66卷 2555-2571页

作者： Phong Thanh Nguyen Vy Dang Bich Huynh Khoa Dang Vo Phuong Thanh Phan Mohamed Elhoseny Dac-Nhuong Le Department of Project Management Ho Chi Minh City Open UniversityHo Chi Minh City700000Vietnam Department of Learning Material Ho Chi Minh City Open UniversityHo Chi Minh City700000Vietnam Faculty of Computers and Information Mansoura UniversityDakahlia Governorate35516Egypt Institute of Research and Development Duy Tan UniversityDanang550000Vietnam Faculty of Information Technology Duy Tan UniversityDanang550000Vietnam

Data fusion is a multidisciplinary research area that involves different *** is used to attain minimum detection error probability and maximum reliability with the help of data retrieved from multiple healthcare *** generation of huge quantity of data from medical devices resulted in the formation of big data during which data fusion techniques become *** medical data is a crucial issue of exponentially-pacing computing world and can be achieved by Intrusion Detection Systems(IDS).In this regard,since singularmodality is not adequate to attain high detection rate,there is a need exists to merge diverse techniques using decision-based multimodal fusion *** this view,this research article presents a new multimodal fusion-based IDS to secure the healthcare data using *** proposed model involves decision-based fusion model which has different processes such as initialization,pre-processing,Feature Selection(FS)and multimodal classification for effective detection of *** FS process,a chaotic Butterfly Optimization(BO)algorithmcalled CBOA is *** the classic BO algorithm offers effective exploration,it fails in achieving faster *** order to overcome this,i.e.,to improve the convergence rate,this research work modifies the required parameters of BO algorithm using chaos ***,to detect intrusions,multimodal classifier is applied by incorporating three Deep Learning(DL)-based classification ***,the concepts like Hadoop MapReduce and Spark were also utilized in this study to achieve faster computation of big data in parallel computation *** validate the outcome of the presented model,a series of experimentations was performed using the benchmark NSLKDDCup99 Dataset *** proposed model demonstrated its effective results on the applied dataset by offering the maximum accuracy of 99.21%,precision of 98.93%and detection rate of 99.59%.The results assured the betterm

关键词： Big data data fusion deep learning intrusion detection bio-inspired algorithm spark