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Fabrication of La,Ce co-doped ZnO nanorods for improving photodegradation of methylene blue

Journal of Rare Earths 2024年第1期42卷 76-83,I0003页

作者： Erfan Hakim Azmal Zaid Jin-Chung Sin Sze-Mun Lam abdul Rahman mohamed School of Chemical Engineering Universiti Sains Malaysia(USM)14300Nibong TebalPenangMalaysia College of Environmental Science and Engineering Guilin University of TechnologyGuilin541004China Guangxi Key Laboratory of Theory and Technology for Environmental Pollution Control Guilin University of TechnologyGuilin541004China Collaborative Innovation Center for Water Pollution Control and Water Safety in Karst Area Guilin University of TechnologyGuilin541004China Faculty of Engineering and Green Technology Universiti Tunku Abdul RahmanJalan UniversitiBandar Barat31900KamparPerakMalaysia

La,Ce co-doped ZnO nanorods(ZnLC)were synthesized through a one-step solvothermal route.The photocatalysts were characterized by X-ray diffraction,Raman spectroscopy,field-emission scanning electron microscopy,energy dispersive X-ray,transmission electron microscopy,UV-vis diffuse reflectance spectroscopy and photo luminescence spectroscopy.The La and Ce doping enhanced the visible light absorption ability of ZnLC and a red shift was detected for ZnLC.Under simulated solar light irradiation,the ZnO doped with 3 at%La and 1 at%Ce(ZnLC1)degrades methylene blue(MB)more effectively than those of pure ZnO,La-doped ZnO(ZnL)and commercially available ZnO.The improved photocatalytic performance of ZnLC1 can be attributed to the high charge separation efficiency as demonstrated by the photoluminescence spectra.Additionally,the photocatalytic experiments reveal that several parameters have their own impact on the MB degradation.Using a variety of radical scavengers,it is discovered that superoxide anion radical plays a crucial role in the degradation of MB.The ZnLC1 is also reused several times without noticeable decrease of photoactivity,indicating that it has a substantial potential for environmental remediation applications.

关键词： ZnO Lanthanum Cerium Co-doping Photodegradation Rare earths

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Gradient Optimizer Algorithm with Hybrid Deep Learning Based Failure Detection and Classification in the Industrial Environment

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Computer Modeling in Engineering & Sciences 2024年第2期138卷 1341-1364页

作者： mohamed Zarouan Ibrahim M.Mehedi Shaikh abdul Latif Md.Masud Rana Department of Electrical and Computer Engineering(ECE) King Abdulaziz UniversityJeddah21589Saudi Arabia Center of Excellence in Intelligent Engineering Systems(CEIES) King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Nuclear Engineering King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Electrical and Electronic Engineering Rajshahi University of Engineering and TechnologyRajshahiBangladesh

Failure detection is an essential task in industrial systems for preventing costly downtime and ensuring the seamlessoperation of the system. Current industrial processes are getting smarter with the emergence of Industry 4.0.Specifically, various modernized industrial processes have been equipped with quite a few sensors to collectprocess-based data to find faults arising or prevailing in processes along with monitoring the status of processes.Fault diagnosis of rotating machines serves a main role in the engineering field and industrial production. Dueto the disadvantages of existing fault, diagnosis approaches, which greatly depend on professional experienceand human knowledge, intellectual fault diagnosis based on deep learning (DL) has attracted the researcher’sinterest. DL reaches the desired fault classification and automatic feature learning. Therefore, this article designs a Gradient Optimizer Algorithm with Hybrid Deep Learning-based Failure Detection and Classification (GOAHDLFDC)in the industrial environment. The presented GOAHDL-FDC technique initially applies continuous wavelettransform (CWT) for preprocessing the actual vibrational signals of the rotating machinery. Next, the residualnetwork (ResNet18) model was exploited for the extraction of features from the vibration signals which are thenfed into theHDLmodel for automated fault detection. Finally, theGOA-based hyperparameter tuning is performedtoadjust the parameter valuesof theHDLmodel accurately.The experimental result analysis of the GOAHDL-FD Calgorithm takes place using a series of simulations and the experimentation outcomes highlight the better resultsof the GOAHDL-FDC technique under different aspects.

关键词： Fault detection Industry 4.0 gradient optimizer algorithm deep learning rotating machineries artificial intelligence

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Efficacy of larvicidal and pupicidal activity of Catharanthus roseus aqueous and solvent extracts against Anopheles stephensi Liston and Culex quinquefasciatus Say (Diptera: Culicidae)

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Asian Pacific Journal of Tropical Medicine 2013年第8期6卷 625-630页

作者： Selladurai Subarani Selvi Sabhanayakam Chinnaperumal Kamaraj Gandhi Elango mohamed abdul kadir Department of Zoology Annamalai University Post Graduate and Research Department of Zoology C. Abdul Hakeem College Department of Chemistry MSS Wake Board College

Objective: To investigate the larvicidal and pupicidal activities of aqueous, ethyl acetate and methanol extracts of Catharanthus roseus ( C. roseus ) against malaria and filariasis vectors. Methods: The larvicidal and pupicidal activities of C. roseus leaf extracts were tested against the fourth instar larvae and pupae of Anopheles stephensi ( An. stephensi ) and Culex quinquefasciatus ( Cx. quinquefasciatus ). The mortality was observed after 24 and 48 h post the treatment. The data were subjected to probit analysis to determine the lethal concentrations (LC 50 and LC 90 ) at which 50% and 90% of the treated larvae or pupae of the tested species were killed. Results: The larval and pupal mortality were observed after 24 and 48 h of exposure of aqueous, ethyl acetate and methanol extracts of C. roseus ; no mortality was observed in the control group. The LC 50 values against the fourth-instar larvae of An. stephensi were 68.62 and 72.04 mg/mL for the aqueous extract, 82.47 mg/mL for the ethyl acetate extract, and 78.80 and 86.64 mg/mL for the methanol extract, while the aqueous, ethyl acetate and methanol extracts had LC 50 values of 85.21, 76.84 and 94.20 mg/mL against the fourth-instar larvae of Cx. quinquefasciatus . The aqueous, ethyl acetate and methanol extracts had LC 50 values of 118.08, 182.47 and 143.80 mg/mL against the pupae of An. stephensi and 146.20, 226.84 and 156.62 mg/mL against the pupae of Cx. quinquefasciatus , respectively. Conclusions: The aqueous and methanol extracts of C. roseus leaves had an excellent potential to control the malarial vector An. stephensi and filariasis vector Cx. quinquefasciatus .

关键词： Catharanthus roseus Anopheles stephensi Culex quinquefasciatus Larvicidal activity Pupicidal activity Medicinal plant

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A Deep Learning Approach for Crowd Counting in Highly Congested Scene

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Computers, Materials & Continua 2022年第12期73卷 5825-5844页

作者： Akbar Khan Kushsairy abdul kadir Jawad Ali Shah Waleed Albattah Muhammad Saeed Haidawati Nasir Megat Norulazmi Megat mohamed Noor Muhammad Haris Kaka Khel Electrical Section Universiti Kuala Lumpur British Malaysian InstituteSelangor53100Malaysia Department of Electrical Engineering International Islamic UniversityIslamabad44000Pakistan Department of Information Technology College of ComputerQassim UniversityBuraydah51452Saudi Arabia Department of Electrical Engineering National University of Computer and Emerging SciencesIslamabad44000Pakistan Computer Engineering Technology Section Universiti Kuala Lumpur Malaysian Institute of Information TechnologyKuala Lumpur50250Malaysia

With the rapid progress of deep convolutional neural networks,several applications of crowd counting have been proposed and explored in the literature.In congested scene monitoring,a variety of crowd density estimating approaches has been developed.The understanding of highly congested scenes for crowd counting during Muslim gatherings of Hajj and Umrah is a challenging task,as a large number of individuals stand nearby and,it is hard for detection techniques to recognize them,as the crowd can vary from low density to high density.To deal with such highly congested scenes,we have proposed the Congested Scene Crowd Counting Network(CSCC-Net)using VGG-16 as a core network with its first ten layers due to its strong and robust transfer learning rate.A hole dilated convolutional neural network is used at the back end to widen the relevant field to extract a large range of information from the image without losing its original resolution.The dilated convolution neural network is mainly chosen to expand the kernel size without changing other parameters.Moreover,several loss functions have been applied to strengthen the evaluation accuracy of the model.Finally,the entire experiments have been evaluated using prominent data sets namely,ShanghaiTech parts A,B,UCF_CC_50,and UCF_QNRF.Our model has achieved remarkable results i.e.,68.0 and 9.0 MAE on ShanghaiTech parts A,B,199.1 MAE on UCF_CC_50,and 99.8 on UCF_QNRF data sets respectively.

关键词： Deep learning congested scene crowd counting fully convolutional neural network dilated convolution

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Analysing Various Control Techniques for Manipulator Robotic System (Robogymnast)

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Computers, Materials & Continua 2023年第6期75卷 4681-4696页

作者： Mahmoud mohamed Bdereddin abdul Samad Fatih Anayi Michael Packianather Khalid Yahya School of Engineering Cardiff UniversityCardiffCF243AAUK Faculty of Engineering University of ZawiaZawia16418Libya Electrical and Electronic Department Azahra Higher Institution of Sciences and TechnologyAzahraLibya Department of Electrical and Electronics Engineering Nisantasi UniversityIstanbul34398Turkey

The Robogymnast is a highly complex,three-link system based on the triple-inverted pendulum and is modelled on the human example of a gymnast suspended by their hands from the high bar and executing larger and larger upswings to eventually rotate fully.The links of the Robogymnast correspond respectively to the arms,trunk,and lower limbs of the gymnast,and from its three joints,one is under passive operation,while the remaining two are powered.The passive top joint poses severe challenges in attaining the smooth movement control needed to operate the Robogymnast effectively.This study assesses four types of controllers used for systems operation and identifies how far response stabilisation is achieved with each.The system is simulated using MATLAB Simulink,with findings generated regarding rising and settling time,as well as overshoot.The research primarily seeks to exam-ine the application of a linear quadratic regulator controller,proportional-integral-derivative controller,fuzzy linear quadratic regulator controller and linear quadratic regulator-proportional-integral-derivative controller for this type of system and comparisons between the different controllers to demon-strate successful performance,which highlights the claimed advantages of the proposed system.

关键词： Robot gymnast(Robogymnast) proportional-integral-derivative(PID) linear quadratic regulator(LQR) fuzzy linear quadratic regulator(FLQR) Acrobat Robot(Acrobat)

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Power Quality Improvement Using ANN Controller For Hybrid Power Distribution Systems

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Intelligent Automation & Soft Computing 2023年第6期36卷 3469-3486页

作者： abdul Quawi Y.mohamed Shuaib M.Manikandan B.S.Abdur Rahman Crescent Institute of Science and Technology A Deemed To Be UniversityChennaiTamil Nadu600048India Jyothishmathi Institute of Technology and Science KarimnagerTelangana505481India

In this work,an Artificial Neural Network(ANN)based technique is suggested for classifying the faults which occur in hybrid power distribution systems.Power,which is generated by the solar and wind energy-based hybrid system,is given to the grid at the Point of Common Coupling(PCC).A boost converter along with perturb and observe(P&O)algorithm is utilized in this system to obtain a constant link voltage.In contrast,the link voltage of the wind energy conversion system(WECS)is retained with the assistance of a Proportional Integral(PI)controller.The grid synchronization is tainted with the assis-tance of the d-q theory.For the analysis of faults like islanding,line-ground,and line-line fault,the ANN is utilized.The voltage signal is observed at the PCC,and the Discrete Wavelet Transform(DWT)is employed to obtain different features.Based on the collected features,the ANN classifies the faults in an effi-cient manner.The simulation is done in MATLAB and the results are also validated through the hardware implementation.Detailed fault analysis is carried out and the results are compared with the existing techniques.Finally,the Total harmonic distortion(THD)is lessened by 4.3%by using the proposed methodology.

关键词： Artificial neural network discrete wavelet transform hybrid power distribution system power quality power quality disturbances

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Autonomous Unmanned Aerial Vehicles Based Decision Support System for Weed Management

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Computers, Materials & Continua 2022年第10期73卷 899-915页

作者： Ashit Kumar Dutta Yasser Albagory abdul Rahaman Wahab Sait Ismail mohamed Keshta Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityAd DiriyahRiyadh13713Kingdom of Saudi Arabia Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Kingdom of Saudi Arabia Department of Archives and Communication King Faisal UniversityAl AhsaHofuf31982Kingdom of Saudi Arabia

Recently,autonomous systems become a hot research topic among industrialists and academicians due to their applicability in different domains such as healthcare,agriculture,industrial automation,etc.Among the interesting applications of autonomous systems,their applicability in agricultural sector becomes significant.Autonomous unmanned aerial vehicles(UAVs)can be used for suitable site-specific weed management(SSWM)to improve crop productivity.In spite of substantial advancements in UAV based data collection systems,automated weed detection still remains a tedious task owing to the high resemblance of weeds to the crops.The recently developed deep learning(DL)models have exhibited effective performance in several data classification problems.In this aspect,this paper focuses on the design of autonomous UAVs with decision support system for weed management(AUAV-DSSWM)technique.The proposed AUAV-DSSWM technique intends to identify the weeds by the use of UAV images acquired from the target area.Besides,the AUAV-DSSWM technique primarily performs image acquisition and image pre-processing stages.Moreover,the Adam optimizer with You Only Look Once Object Detector-(YOLOv3)model is applied for the detection of weeds.For the effective classification of weeds and crops,the poor and rich optimization(PRO)algorithm with softmax layer is applied.The design of Adam optimizer and PRO algorithm for the parameter tuning process results in enhanced weed detection performance.A wide range of simulations take place on UAV images and the experimental results exhibit the promising performance of the AUAV-DSSWM technique over the other recent techniques with the accy of 99.23%.

关键词： Autonomous systems object detection precision agriculture unmanned aerial vehicles deep learning parameter tuning

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A Hybrid Deep Learning-Based Unsupervised Anomaly Detection in High Dimensional Data

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Computers, Materials & Continua 2022年第3期70卷 5363-5381页

作者： Amgad Muneer Shakirah Mohd Taib Suliman mohamed Fati abdullateef O.Balogun Izzatdin abdul Aziz Department of Computer and Information Sciences Universiti Teknologi PETRONASSeri Iskandar32160Malaysia Centre for Research in Data Science(CERDAS) Universiti Teknologi PETRONASSeri Iskandar32610PerakMalaysia Information Systems Department Prince Sultan UniversityRiyadh11586Saudi Arabia

Anomaly detection in high dimensional data is a critical research issue with serious implication in the real-world problems.Many issues in this field still unsolved,so several modern anomaly detection methods struggle to maintain adequate accuracy due to the highly descriptive nature of big data.Such a phenomenon is referred to as the“curse of dimensionality”that affects traditional techniques in terms of both accuracy and performance.Thus,this research proposed a hybrid model based on Deep Autoencoder Neural Network(DANN)with five layers to reduce the difference between the input and output.The proposed model was applied to a real-world gas turbine(GT)dataset that contains 87620 columns and 56 rows.During the experiment,two issues have been investigated and solved to enhance the results.The first is the dataset class imbalance,which solved using SMOTE technique.The second issue is the poor performance,which can be solved using one of the optimization algorithms.Several optimization algorithms have been investigated and tested,including stochastic gradient descent(SGD),RMSprop,Adam and Adamax.However,Adamax optimization algorithm showed the best results when employed to train theDANNmodel.The experimental results show that our proposed model can detect the anomalies by efficiently reducing the high dimensionality of dataset with accuracy of 99.40%,F1-score of 0.9649,Area Under the Curve(AUC)rate of 0.9649,and a minimal loss function during the hybrid model training.

关键词： Anomaly detection outlier detection unsupervised learning autoencoder deep learning hybrid model

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Point-to-face contact heterojunctions:Interfacial design of 0D nanomaterials on 2D g-C_(3)N_(4)towards photocatalytic energy applications

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Carbon Energy 2022年第5期4卷 665-730页

作者： Xin-Quan Tan Sue-Faye Ng abdul Rahman mohamed Wee-Jun Ong School of Energy and Chemical Engineering Xiamen University MalaysiaSelangor Darul EhsanMalaysia Center of Excellence for NaNo Energy&Catalysis Technology(CONNECT) Xiamen University MalaysiaSelangor Darul EhsanMalaysia Low Carbon Economy(LCE)Research Group School of Chemical EngineeringUniversiti Sains MalaysiaNibong TebalPulau PinangMalaysia State Key Laboratory of Physical Chemistry of Solid Surfaces College of Chemistry and Chemical EngineeringXiamen UniversityXiamenFujianChina Shenzhen Research Institute of Xiamen University ShenzhenGuangdongChina

Green energy generation is an indispensable task to concurrently resolve fossil fuel depletion and environmental issues to align with the global goals of achieving carbon neutrality.Photocatalysis,a process that transforms solar energy into clean fuels through a photocatalyst,represents a felicitous direction toward sustainability.Eco-rich metal-free graphitic carbon nitride(g-C_(3)N_(4))is profiled as an attractive photocatalyst due to its fascinating properties,including excellent chemical and thermal stability,moderate band gap,visible light-active nature,and ease of fabrication.Nonetheless,the shortcomings of g-C_(3)N_(4)include fast charge recombination and limited surface-active sites,which adversely affect photocatalytic reactions.Among the modification strategies,point-to-face contact engineering of 2D g-C_(3)N_(4)with 0D nanomaterials represents an innovative and promising synergy owing to several intriguing attributes such as the high specific surface area,short effective charge-transfer pathways,and quantum confinement effects.This review introduces recent advances achieved in experimental and computational studies on the interfacial design of 0D nanostructures on 2D g-C_(3)N_(4)in the construction of point-to-face heterojunction interfaces.Notably,0D materials such as metals,metal oxides,metal sulfides,metal selenides,metal phosphides,and nonmetals on g-C_(3)N_(4)with different charge-transfer mechanisms are systematically discussed along with controllable synthesis strategies.The applications of 0D/2D g-C_(3)N_(4)-based photocatalysts are focused on solar-to-energy conversion via the hydrogen evolution reaction,the CO_(2)reduction reaction,and the N2 reduction reaction to evaluate the photocatalyst activity and elucidate reaction pathways.Finally,future perspectives for developing high-efficiency 0D/2D photocatalysts are proposed to explore potential emerging carbon nitride allotropes,large-scale production,machine learning integration,and multidisciplinary advances for technological breakthroughs.

关键词： 0D/2D heterojunction charge-transfer mechanisms g-C_(3)N_(4)nanosheets heterojunction interface photocatalysis solar-to-energy conversion

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A Compact 28 GHz Millimeter Wave Antenna for Future Wireless Communication

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Computers, Materials & Continua 2022年第7期72卷 301-314页

作者： Shahid Khan Adil Bashir Haider Ali abdul Rauf mohamed Marey Hala Mostafa Ikram Syed LCOMS ASECUniversity de LorraineMetz57070France Department of Electrical and Computer Engineering Comsats University IslamabadAbbottabad Campus22060Pakistan Electrical Engineering Department Bahauddin Zakariya UniversityMultan60800Pakistan Department of Electronics Technology University of TechnologyNowshera24100Pakistan Department of Electrical Engineering National University of Sciences and TechnologyH-12Islamabad44000Pakistan Smart Systems Engineering Laboratory College of EngineeringPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Computer Software Engineering Military College of SignalsNational University of Sciences and TechnologyIslamabad44000Pakistan

This article presents a novel modified chuck wagon dinner bell shaped millimeter wave(mm-wave)antenna at 28 GHz.The proposed design has ultra-thin Rogers 5880 substrate with relative permittivity of 2.2.The design consists of T shaped resonating elements and two open ended side stubs.The desired 28 GHz frequency response is achieved by careful parametric modeling of the proposed structure.The maximum achieved single element gain at the desired resonance frequency is 3.45 dBi.The efficiency of the proposed design over the operating band is more than 88%.The impedance bandwidth achieved for−10 dB reference value is nearly 2.9 GHz.The proposed antenna is transformed into four element linear array which increases the gain up to 10.5 dBi.The fabricated prototype is tested for the measured results.It is observed that measured results closely match the simulated results.By considering its simple structure and focused radiation patterns,the proposed design is well suited for IoT(Internet of Things),mmWave microwave sensing,5G and future RF(Radio Frequency)frontends.

关键词： RF front-ends mmWave partial ground plane 28 GHz IoT 5G antenna

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