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Energy and Life Cycle Assessment of Zinc/Water Nanofluid Based Photovoltaic Thermal System

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Energy Engineering 2022年第2期119卷 827-846页

作者： Junaid Khan Rashid ali Muzammil zubair Syed Mohd Yahya SEARL Laboratory Department of Mechancial EngineeringAligarh Muslim UniversityAligarh202002India

Cooling the PV surface in a Photovoltaic Thermal system is a pivotal operational aspect to be taken into account to achieve optimized values of performance parameters in a Photovoltaic Thermal *** experimental design used in this study facilitates the flow of varying concentrations of Zn-water nanofluid in serpentine copper tubing installed at the rear of the PV panel thereby preventing the PV surface temperature from increasing beyond the threshold value at which a decrease in electrical efficiency starts to *** fusion of solar thermal with PV devices leads to better electrical and thermal efficiency values resulting in decreased cell degradation over time and maximization of the lifespan of the PV module and the energy output from the PV *** to the superior thermal heat properties of nanofluids,their usage in such systems has become increasingly *** cycle metrics which include Energy Payback period,Energy Production Factor and life cycle conversion efficiency were evaluated for the PVT system by exhaustively chalking fundamental parameters such as embodied energy of the PVT setup and the total energy output from the PVT *** research aims to be a major milestone in the evolutionary journey of Photovoltaic Thermal modules by guiding the engineers working on the theory,design and implementation of PVT systems towards its economic feasibility,environmental impact and energy sustainability.

关键词： Photovoltaic thermal system nanofluids performance parameters life cycle assessment

Implications of twinning on the microstructure development,crystallographic texture and mechanical performance of Mg alloys-a critical review

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Journal of Magnesium and alloys 2023年第11期11卷 4146-4165页

作者： Umer Masood Chaudry Hafiz Muhammad Rehan Tariq Muhammad zubair Nooruddin Ansari Tea-Sung Jun Department of Mechanical Engineering Incheon National UniversityIncheon 22012Republic of Korea Research Institute for Engineering and Technology Incheon National UniversityIncheon 22012Republic of Korea Department of Metallurgical&Materials Engineering Faculty of ChemicalMetallurgical&Materials EngineeringUniversity of Engineering&Technology(UET)LahorePakistan Department of Materials Science&Engineering Chungnam National UniversityDaejeon 34134Republic of Korea

Deformation twinning is profusely activated in the Mg alloys due to lower critical resolved shear stress(CRSS) compared to the non-basal slip systems(prismatic and pyramidal ) and plays a significant role in texture reorientation, grain refinement and enhancement of mechanical performance. Twinning is a sequential process comprising twin nucleation, twin propagation and twin growth, hence several intrinsic and extrinsic parameters that facilitate or suppress the process have been critically reviewed. The dependence of twinning on the grain size, deformation temperature, favorable grain orientation and shear strain have been thoroughly discussed in the context of published literature and an attempt has been made to provide a benchmark conclusive finding based on the majority of works. Furthermore, the subsequent effect of twinning on the mechanical performance of Mg alloys, including ductility, formability and tension-compression asymmetry has been discussed in detail. Lastly, the stability of twins, including stress and thermal stability, is summarized and critical issues related to pertinent bottlenecks have been addressed.

关键词： Magnesium Twinning Twinning variant selection EBSD Texture Ductility

Fault Detection and Identification Using Deep Learning algorithms in Induction Motors

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Computer Modeling in Engineering & Sciences 2022年第11期133卷 435-470页

作者： Majid Hussain Tayab Din Memon Imtiaz Hussain zubair Ahmed Memon Dileep Kumar Department of Electronic Engineering Quaid-e-Awam University of Engineering Science and TechnologyNawabshah67480Pakistan NCRA Condition Monitoring Systems Lab Mehran University of Engineering and TechnologyJamshoro76062Pakistan Department of Electronic Engineering Mehran University of Engineering and TechnologyJamshoro76062Pakistan Design and Creative Technology Torrens University AustraliaMelbourneAustralia Department of Electronics and Power Engineering Pakistan Navy Engineering College NUSTKarachi75350Pakistan

Owing to the 4.0 industrial revolution condition monitoring maintenance is widely accepted as a useful approach to avoiding plant disturbances and ***,Motor Current Signature Analysis(MCSA)is widely reported as a condition monitoring technique in the detection and identification of individual andmultiple Induction Motor(IM)***,checking the fault detection and classification with deep learning models and its comparison among them selves or conventional approaches is rarely reported in the ***,in this work,wepresent the detection and identification of induction motor faults with MCSA and three Deep Learning(DL)models namely MLP,LSTM,and ***,we have developed the model of Squirrel Cage induction motor in MATLAB and simulated it for single phasing and stator winding faults(SWF)using Fast Fourier Transform(FFT),Short Time Fourier Transform(STFT),and Continuous Wavelet Transform(CWT)to detect and identify the healthy and unhealthy conditions with phase to ground,single phasing and in multiple fault conditions using Motor Current Signature *** faults impact on stator current is presented in the time and frequency domain(i.e.,power spectrum).The simulation results show that the scalogram has shown good results in time-frequency analysis for fault and showing its impact on the energy of current during individual fault and multiple fault *** is further investigated with three deep learning models(i.e.,MLP,LSTM,and 1D-CNN)for checking the fault detection and identification(i.e.,classification)improvement in a three-phase induction *** simulating the three-phase induction motor in various healthy and unhealthy conditions in MATLAB,we have collected current signature data in the time domain,labeled them accordingly and created the 50 thousand samples dataset for DL *** the DL models are trained and validated with a suitable number of architecture *** simulation,the multiclass confusion

关键词： Condition monitoring motor fault diagnosis stator winding faults deep learning signal processing

Energy Theft Detection in Smart Grids with Genetic algorithm-Based Feature Selection

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Computers, Materials & Continua 2023年第3期74卷 5431-5446页

作者： Muhammad Umair Zafar Saeed Faisal Saeed Hiba Ishtiaq Muhammad zubair Hala Abdel Hameed Faculty of Information Technology University of Central PunjabLahore54590Pakistan DAAI Research Group Department of Computing and Data ScienceSchool of Computing and Digital TechnologyBirmingham City UniversityBirmingham B47XGUK Faculty of Computer and Information Systems Fayoum University63514Egypt Khaybar Applied College Taibah UniversitySaudi Arabia

As big data,its technologies,and application continue to advance,the Smart Grid(SG)has become one of the most successful pervasive and fixed computing platforms that efficiently uses a data-driven approach and employs efficient information and communication technology(ICT)and cloud *** a result of the complicated architecture of cloud computing,the distinctive working of advanced metering infrastructures(AMI),and the use of sensitive data,it has become challenging tomake the SG *** of the SG are categorized into two main categories,Technical Losses(TLs)and Non-Technical Losses(NTLs).Hardware failure,communication issues,ohmic losses,and energy burnout during transmission and propagation of energy are ***’s are human-induced errors for malicious purposes such as attacking sensitive data and electricity theft,along with tampering with AMI for bill reduction by fraudulent *** research proposes a data-driven methodology based on principles of computational intelligence as well as big data analysis to identify fraudulent customers based on their load *** our proposed methodology,a hybrid Genetic algorithm and Support Vector Machine(GA-SVM)model has been used to extract the relevant subset of feature data from a large and unsupervised public smart grid project dataset in London,UK,for theft detection.A subset of 26 out of 71 features is obtained with a classification accuracy of 96.6%,compared to studies conducted on small and limited datasets.

关键词： Big data data analysis feature engineering genetic algorithm machine learning

Optimizing Storage for Energy Conservation in Tracking Wireless Sensor Network Objects

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Computer Systems Science & Engineering 2023年第5期45卷 1211-1231页

作者： Vineet Sharma Mohammad zubair Khan Shivani Batra Abdullah alsaeedi Prakash Srivastava Department of Computer Science and Engineering KIET Group of InstitutionsDelhi-NCRGhaziabad201206India Department of Computer Science and Information Taibah UniversityMedina42353Saudi Arabia Department of Computer Science Taibah UniversityMedina42353Saudi Arabia Department of Computer Science and Engineering Graphic Era(Deemed to be University)Dehradun248002India

The amount of needed control messages in wireless sensor networks(WSN)is affected by the storage strategy of detected *** broadcasting superfluous control messages consumes excess energy,the network lifespan can be extended if the quantity of control messages is *** this study,an optimized storage technique having low control overhead for tracking the objects in WSN is *** basic concept is to retain observed events in internal memory and preserve the relationship between sensed information and sensor nodes using a novel inexpensive data structure entitled Ordered Binary Linked List(OBLL).Whenever an object passes over the sensor area,the recognizing sensor can immediately produce an OBLL along the object’s *** retrieve the entire information,the OBLL can be traversed with logarithmic complexity which is much less than the traversing complexity of existing linked list *** evaluation and simulations were carried out to ensure that the suggested technique minimizes the number of messages and thus saving energy and extending the network life.

关键词： Energy conservation linked list object tracking wireless sensor networks

SRC:Superior Robustness of COVID-19 Detection from Noisy Cough Data Using GFCC

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Computer Systems Science & Engineering 2023年第8期46卷 2337-2349页

作者： Basanta Kumar Swain Mohammad zubair Khan Chiranji Lal Chowdhary Abdullah alsaeedi Department of Computer Science&Engineering Government College of EngineeringKalahandiBhawanipatna766002India Department of Computer Science and Information Taibah UniversityMedina42353Saudi Arabia School of Information Technology and Engineering VIT University Vellore632014Tamil NaduIndia Computer Science Department College of Computer Science and EngineeringTaibah UniversityMedina42353Saudi Arabia

This research is focused on a highly effective and untapped feature called gammatone frequency cepstral coefficients(GFCC)for the detection of COVID-19 by using the nature-inspired meta-heuristic algorithm of deer hunting optimization and artificial neural network(DHO-ANN).The noisy crowdsourced cough datasets were collected from the public *** research work claimed that the GFCC yielded better results in terms of COVID-19 detection as compared to the widely used Mel-frequency cepstral coefficient in noisy crowdsourced speech *** proposed algorithm's performance for detecting COVID-19 disease is rigorously validated using statistical measures,F1 score,confusion matrix,specificity,and sensitivity ***,it is found that the proposed algorithm using GFCC performs well in terms of detecting the COVID-19 disease from the noisy crowdsourced cough dataset,***,the proposed algorithm and undertaken feature parameters have improved the detection of COVID-19 by 5%compared to the existing methods.

关键词： COVID-19 GFCC DHO-ANN cough data

Regulation effect of osteoblasts towards osteocytes by silk fibroin encapsulation

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Frontiers of Materials Science 2022年第4期16卷 1-13页

作者： Dandan LUO Rui ZHANG Shibo WANG M.zubair IQBal Ruibo ZHAO Xiangdong KONG Institute of Smart Biomedical Materials School of Materials Science and EngineeringZhejiang Sci-Tech UniversityHangzhou 310018China Zhejiang-Mauritius Joint Research Centerfor Biomaterials and Tissue Engineering Zhejiang Sci-Tech UniversityHangzhou 310018China School of Textile Science and Engineering Zhejiang Sci-Tech UniversityHangzhou 310018China

Herein,the rational design micromilieus involved silk fibroin(SF)-based materials have been used to encapsulate the osteoblasts,forming an extracellular coated shell on the cells,which exhibited the high potential to shift the regulation of osteoblasts to osteocytes by encapsulation *** coating treated cells showed a change in cell morphology from osteoblasts-like to osteocytes-like shape comparedwith untreated ***,the expression of alkaline phosphatase(alP),collagen I(Col I)and osteocalcin(OcN)further indicated a potential approach for inducingosteoblasts regulation,which typically accelerates calcium deposition and cell calcification,presenting a key role for the SF encapsulation in controlling osteoblasts *** discovery showed that SF-based cell encapsulation could be used for osteoblasts behavior regulation,which offers a great potential to modulate mammalian cells'phenotype involvingalternatingsurrounding cues.

关键词： Cell encapsulation silk fibroin osteoblasts modulation cell differentiation cell calcification

Performance Enhancement of Adaptive Neural Networks Based on Learning Rate

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Computers, Materials & Continua 2023年第1期74卷 2005-2019页

作者： Swaleha zubair Anjani Kumar Singha Nitish Pathak Neelam Sharma Shabana Urooj Samia Rabeh Larguech Department of Computer Science Aligarh Muslim UniversityAligarh202002Uttar PradeshIndia Department of Information Technology Bhagwan Parshuram Institute of Technology(BPIT)GGSIPUNew Delhi110089India Department of Computer Science and Engineering Maharaja Agrasen Institute of Technology(MAIT)GGSIPUNew DelhiIndia Department of Electrical Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

Deep learning is the process of determining parameters that reduce the cost function derived from the *** optimization in neural networks at the time is known as the optimal *** solve optimization,it initialize the parameters during the optimization *** should be no variation in the cost function parameters at the global *** momentum technique is a parameters optimization approach;however,it has difficulties stopping the parameter when the cost function value fulfills the global minimum(non-stop problem).Moreover,existing approaches use techniques;the learning rate is reduced during the iteration *** techniques are monotonically reducing at a steady rate over time;our goal is to make the learning rate *** present a method for determining the best parameters that adjust the learning rate in response to the cost function *** a result,after the cost function has been optimized,the process of the rate Schedule is *** approach is shown to ensure convergence to the optimal *** indicates that our strategy minimizes the cost function(or effective learning).The momentum approach is used in the proposed *** solve the Momentum approach non-stop problem,we use the cost function of the parameter in our proposed *** a result,this learning technique reduces the quantity of the parameter due to the impact of the cost function *** verify that the learning works to test the strategy,we employed proof of convergence and empirical tests using current methods and the results are obtained using Python.

关键词： Deep learning optimization convergence stochastic gradient methods

Chemotherapeutic nanomaterials in tumor boundary delineation:Prospects for effective tumor treatment

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Acta Pharmaceutica Sinica B 2022年第6期12卷 2640-2657页

作者： Ozioma Udochukwu Akakuru Zhoujing Zhang M.zubair Iqbal Chengjie Zhu Yewei Zhang Aiguo Wu Cixi Institute of Biomedical Engineering International Cooperation Base of Biomedical Materials Technology and ApplicationChinese Academy of Sciences(CAS)Key Laboratory of Magnetic Materials and DevicesZhejiang Engineering Research Center for Biomedical MaterialsNingbo Institute of Materials Technology and EngineeringCASNingbo 315201China School of Medicine Southeast UniversityNanjing 210009China School of Materials Science and Engineering Zhejiang Sci-Tech UniversityHangzhou 310018China

Accurately delineating tumor boundaries is key to predicting survival rates of cancer patients and assessing response of tumor microenvironment to various therapeutic techniques such as chemotherapy and *** review discusses various strategies that have been deployed to accurately delineate tumor boundaries with particular emphasis on the potential of chemotherapeutic nanomaterials in tumor boundary *** also compiles the types of tumors that have been successfully delineated by currently available ***,the challenges that still abound in accurate tumor boundary delineation are presented alongside possible perspective strategies to either ameliorate or solve the *** is expected that the information communicated herein will form the first compendious baseline information on tumor boundary delineation with chemotherapeutic nanomaterials and provide useful insights into future possible paths to advancing current available tumor boundary delineation approaches to achieve efficacious tumor therapy.

关键词： Bioimaging Boundary delineation Tumor Tumor imaging Tumor therapy Surgery