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Effects of Moringa oleifera aqueous extract on the growth performance,blood characteristics,and histological features of gills and livers in Nile tilapia

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Aquaculture and Fisheries 2024年第1期9卷 85-92页

作者： Mahamoud A.Emam Ramy M.Shourbela Waleed N.El-Hawarry Seham Y.Abo-Kora Fatma Abdel-Monem Gad Ashraf M.Abd El-latif Mahmoud A.O.Dawood Histology Department Faculty of Veterinary MedicineBenha UniversityBenha13511Egypt Department of Animal Husbandry and Animal Wealth Development Faculty of Veterinary MedicineAlexandria UniversityAlexandria22758Egypt Pharmacology Department Faculty of Veterinary MedicineBenha UniversityBenha13511Egypt Clinical Pathology Department Faculty of Veterinary MedicineBenha UniversityBenha13511Egypt Department of Aquatic Animal Diseases and Management Faculty of Veterinary MedicineBenha UniversityBenha13511Egypt Animal Production Department Faculty of AgricultureKafrelsheikh UniversityKafr El-Sheikh33511Egypt The Center for Applied Research on the Environment and Sustainability The American University in CairoCairo11835Egypt

Moringa oleifera is well known as a highly nutritious plant and a water purifier in fish culture.Tilapia fish has many impressive qualities making them very suitable for aquaculture.Therefore,this study aimed to evaluate the effect of supplementation of Moringa oleifera aqueous extract(MOAE)at varying concentrations(0,100,200,and 400 mg/kg diet for 90 days)on growth performance,hematological,and some biochemical parameters of Nile tilapia.Additionally,histological changes and immunohistochemical expression of caspase 3 in gills and livers were evaluated.Significant enhancements(P<0.05)in the final weight,weight gain,specific growth rate,red blood cells,hemoglobin,packed cell volume,white blood cells,and total serum protein were observed in fish fed 200 mg MOAE/kg diet compared with other groups.However,a significant reduction(P<0.05)of liver and kidney function tests and non-significant changes in lipid profile were recorded.On the other hand,fish fed 400 mg MOAE/kg diet revealed disturbance in the corresponding parameters.Severe histological degenerative changes were detected in the gills and liver of fish fed 400 mg MOAE/kg diet.In comparison,fish fed 200 mg MOAE/kg diet showed the best histo-architectures of these organs compared with other groups.Concomitant immune expressions of caspase 3 were determined where gills and liver of fish fed 400 mg MOAE/kg diet revealed extensive number of positive caspase 3 cells indicating severe apoptotic features.Accordingly,a dietary MOAE at 200 mg/kg diet is recommended to improve the growth performance,hematological parameters,liver and kidney functions in Nile tilapia.Meanwhile,caution must be considered when using MOAE at a concentration over 200 mg/kg diet.

关键词： Growth performance Liver Gills Moringa oleifera Nile tilapia

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Group solution for an unsteady non-Newtonian Hiemenz flow with variable fluid properties and suction/injection

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Chinese Physics B 2014年第9期23卷 43-53页

作者： H.M.El-Hawary Mostafa A.A.Mahmoud Reda G.Abdel-Rahman Abeer S.Elfeshawey Department of Mathematics Faculty of ScienceAssuit University Department of Mathematics Faculty of ScienceBenha University

The theoretic transformation group approach is applied to address the problem of unsteady boundary layer flow of a non-Newtonian fluid near a stagnation point with variable viscosity and thermal conductivity. The application of a two- parameter group method reduces the number of independent variables by two, and consequently the governing partial differential equations with the boundary conditions transformed into a system of ordinary differential equations with the appropriate corresponding conditions. Two systems of ordinary differential equations have been solved numerically using a fourth-order Runge-Kutta algorithm with a shooting technique. The effects of various parameters governing the problem are investigated.

关键词： non-Newtonian fluid stagnation point two-parameter group method variable viscosity

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The Lambert-G Family:Properties,Inference,and Applications

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Computer Modeling in Engineering & Sciences 2024年第7期140卷 513-536页

作者： Jamal N.Al Abbasi Ahmed Z.Afify Badr Alnssyan Mustafa S.Shama Accounting Department Ashur UniversityBaghdad10011Iraq Department of Statistics Mathematics and InsuranceBenha UniversityBenha13511Egypt Department of Management Information Systems and Production Management College of Business and EconomicsQassim UniversityBuraydah51452Saudi Arabia Department of Basic Sciences Common First Year(CFY)King Saud UniversityRiyadh11362Saudi Arabia Department of Mathematics and Statistics Osim Higher Institute of Administrative ScienceOsim12961Egypt

This study proposes a new flexible family of distributions called the Lambert-G family.The Lambert family is very flexible and exhibits desirable properties.Its three-parameter special sub-models provide all significantmonotonic and non-monotonic failure rates.A special sub-model of the Lambert family called the Lambert-Lomax(LL)distribution is investigated.General expressions for the LL statistical properties are established.Characterizations of the LL distribution are addressed mathematically based on its hazard function.The estimation of the LL parameters is discussed using six estimation methods.The performance of this estimation method is explored through simulation experiments.The usefulness and flexibility of the LL distribution are demonstrated empirically using two real-life data sets.The LL model better fits the exponentiated Lomax,inverse power Lomax,Lomax-Rayleigh,power Lomax,and Lomax distributions.

关键词： Lambert function Lomax distribution maximum likelihood hazard function statistical model simulation

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A new technique for fault diagnosis in transformer insulating oil based on infrared spectroscopy measurements

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High Voltage 2024年第2期9卷 319-335页

作者： Mohamed M.F.Darwish Mohamed H.A.Hassan Nagat M.K.Abdel-Gawad Matti Lehtonen Diaa-Eldin A.Mansour Department of Electrical Engineering Faculty of Engineering at ShoubraBenha UniversityCairoEgypt Department of Electrical Engineering and Automation School of Electrical EngineeringAalto UniversityEspooFinland Department of Electrical Power Engineering Egypt-Japan University of Science and Technology(E-JUST)New Borg El-Arab CityEgypt Department of Electrical Power and Machines Engineering Faculty of EngineeringTanta UniversityTantaEgypt

Condition monitoring of the insulating system within power transformers has a massive importance according to the electrical utilities.Dissolved gas analysis(DGA)is frequently used for this purpose.However,DGA lacks the necessary level of accuracy to identify all equipment faults,particularly in their initial stages of degradation.Also,it does not have the capability for real-time monitoring and relies on manual sampling and laboratory testing,causing potential delays in fault identification.Additionally,the interpretation of DGA data necessitates specialised expertise,which may pose difficulties for smaller entities that have limited access to resources.Therefore,the contribution of this research is to use infrared spectroscopy measurements as a new effective technique substituting the DGA method for fault diagnosis in insulating oil.The inception faults that were considered in this study were the electrical fault(discharges of high energy)and the thermal fault(300°C

关键词： fault diagnosis spectroscopy

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SwinVid:Enhancing Video Object Detection Using Swin Transformer

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Computer Systems Science & Engineering 2024年第2期48卷 305-320页

作者： Abdelrahman Maharek Amr Abozeid Rasha Orban Kamal ElDahshan Computer Science Department Faculty of Artificial Intelligence and InformaticsBenhaEgypt Mathematics Department Faculty of SciencesAl-Azhar UniversityCairoEgypt Department of Computer Science College of Science and Arts in QurayyatJouf UniversitySakakaSaudi Arabia

What causes object detection in video to be less accurate than it is in still images?Because some video frames have degraded in appearance from fast movement,out-of-focus camera shots,and changes in posture.These reasons have made video object detection(VID)a growing area of research in recent years.Video object detection can be used for various healthcare applications,such as detecting and tracking tumors in medical imaging,monitoring the movement of patients in hospitals and long-term care facilities,and analyzing videos of surgeries to improve technique and training.Additionally,it can be used in telemedicine to help diagnose and monitor patients remotely.Existing VID techniques are based on recurrent neural networks or optical flow for feature aggregation to produce reliable features which can be used for detection.Some of those methods aggregate features on the full-sequence level or from nearby frames.To create feature maps,existing VID techniques frequently use Convolutional Neural Networks(CNNs)as the backbone network.On the other hand,Vision Transformers have outperformed CNNs in various vision tasks,including object detection in still images and image classification.We propose in this research to use Swin-Transformer,a state-of-the-art Vision Transformer,as an alternative to CNN-based backbone networks for object detection in videos.The proposed architecture enhances the accuracy of existing VID methods.The ImageNet VID and EPIC KITCHENS datasets are used to evaluate the suggested methodology.We have demonstrated that our proposed method is efficient by achieving 84.3%mean average precision(mAP)on ImageNet VID using less memory in comparison to other leading VID techniques.The source code is available on the website https://***/amaharek/SwinVid.

关键词： Video object detection vision transformers convolutional neural networks deep learning

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Multi-timescale Modeling and Dynamic Stability Analysis for Sustainable Microgrids:State-of-the-art and Perspectives

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Protection and Control of Modern Power Systems 2024年第3期9卷 1-35页

作者： Mingyue Zhang Yang Han Yuxiang Liu Amr S.Zalhaf Ensheng Zhao Karar Mahmoud Mohamed M.F.Darwish Frede Blaabjerg the School of Mechanical and Electrical Engineering University of Electronic Science and Technology of ChinaChengdu 611731China IEEE 不详 the university of Electronic Science and Technology of China(UESTC) ChengduChina Aswan university the School of Electrical Engineering Aalto UniversityFinland the Department of Electrical Engineering Faculty of Engineering at ShoubraBenha University the Department of EEA School of Electrical EngineeringAalto University

The increasing trend for integrating renewable energy sources into the grid to achieve a cleaner energy system is one of the main reasons for the development of sustainable microgrid(MG)technologies.As typical power-electronized power systems,MGs make extensive use of power electronics converters,which are highly controllable and flexible but lead to a profound impact on the dynamic performance of the whole system.Compared with traditional large-capacity power systems,MGs are less resistant to perturbations,and various dynamic variables are coupled with each other on multiple timescales,resulting in a more complex system instability mechanism.To meet the technical and economic challenges,such as active and reactive power-sharing,voltage,and frequency deviations,and imbalances between power supply and demand,the concept of hierarchical control has been introduced into MGs,allowing systems to control and manage the high capacity of renewable energy sources and loads.However,as the capacity and scale of the MG system increase,along with a multi-timescale control loop design,the multi-timescale interactions in the system may become more significant,posing a serious threat to its safe and stable operation.To investigate the multi-timescale behaviors and instability mechanisms under dynamic inter-actions for AC MGs,existing coordinated control strategies are discussed,and the dynamic stability of the system is defined and classified in this paper.Then,the modeling and assessment methods for the stability analysis of multi-timescale systems are also summarized.Finally,an outlook and discussion of future research directions for AC MGs are also presented.

关键词： Sustainable microgrid hierarchical control modeling model simplification multi-timescale dynamic stability analysis timescale decomposition.

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Development of Data Mining Models Based on Features Ranks Voting (FRV)

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Computers, Materials & Continua 2022年第11期73卷 2947-2966页

作者： Mofreh A.Hogo Department of Electrical Engineering Benha Faculty of EngineeringBenha UniversityBenha13518Egypt

Data size plays a significant role in the design and the performance of data mining models.A good feature selection algorithm reduces the problems of big data size and noise due to data redundancy.Features selection algorithms aim at selecting the best features and eliminating unnecessary ones,which in turn simplifies the structure of the data mining model as well as increases its performance.This paper introduces a robust features selection algorithm,named Features Ranking Voting Algorithm FRV.It merges the benefits of the different features selection algorithms to specify the features ranks in the dataset correctly and robustly;based on the feature ranks and voting algorithm.The FRV comprises of three different proposed techniques to select the minimum best feature set,the forward voting technique to select the best high ranks features,the backward voting technique,which drops the low ranks features(low importance feature),and the third technique merges the outputs from the forward and backward techniques to maximize the robustness of the selected features set.Different data mining models were built using obtained selected features sets from applying the proposed FVR on different datasets;to evaluate the success behavior of the proposed FRV.The high performance of these data mining models reflects the success of the proposed FRV algorithm.The FRV performance is compared with other features selection algorithms.It successes to develop data mining models for the Hungarian CAD dataset with Acc.of 96.8%,and with Acc.of 96%for the Z-Alizadeh Sani CAD dataset compared with 83.94%and 92.56%respectively in[48].

关键词： Evaluator features selection data mining forward backward voting feature rank

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Hybrid Sensor Selection Technique for Lifetime Extension of Wireless Sensor Networks

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Computers, Materials & Continua 2022年第3期70卷 4965-4985页

作者： Khaled M.Fouad Basma M.Hassan Omar M.Salim Information Systems Department Faculty of Computers and Artificial IntelligenceBenha UniversityBenha13518Egypt Faculty of Artificial Intelligence Kafrelsheikh UniversityKafrelsheikh33516Egypt Electrical Engineering Department Benha Faculty of EngineeringBenha UniversityBenha13518Egypt

Energy conservation is a crucial issue to extend the lifetime of wireless sensor networks(WSNs)where the battery capacity and energy sources are very restricted.Intelligent energy-saving techniques can help designers overcome this issue by reducing the number of selected sensors that report environmental measurements by eliminating all replicated and unrelated features.This paper suggests a Hybrid Sensor Selection(HSS)technique that combines filter-wrappermethod to acquire a rich-informational subset of sensors in a reasonable time.HSS aims to increase the lifetime of WSNs by using the optimal number of sensors.At the same time,HSS maintains the desired level of accuracy and manages sensor failures with the most suitable number of sensors without compromising the accuracy.The evaluation of the HSS technique has adopted four experiments by using four different datasets.These experiments show that HSS can extend the WSNs lifetime and increase the accuracy using a sufficient number of sensors without affecting theWSNfunctionality.Furthermore,to ensure HSS credibility and reliability,the proposed HSS technique has been compared to other corresponding methodologies and shows its superiority in energy conservation at premium accuracy measures.

关键词： Energy conservation WSNs intelligent techniques sensor selection

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Swarm-Based Extreme Learning Machine Models for Global Optimization

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Computers, Materials & Continua 2022年第3期70卷 6339-6363页

作者： Mustafa Abdul Salam Ahmad Taher Azar Rana Hussien Artificial Intelligence Department Faculty of Computers and Artificial IntelligenceBenha UniversityBenha13518Egypt Scientific Computing Department Faculty of Computers and Artificial IntelligenceBenha UniversityBenha13518Egypt

Extreme Learning Machine(ELM)is popular in batch learning,sequential learning,and progressive learning,due to its speed,easy integration,and generalization ability.While,Traditional ELM cannot train massive data rapidly and efficiently due to its memory residence,high time and space complexity.In ELM,the hidden layer typically necessitates a huge number of nodes.Furthermore,there is no certainty that the arrangement of weights and biases within the hidden layer is optimal.To solve this problem,the traditional ELM has been hybridized with swarm intelligence optimization techniques.This paper displays five proposed hybrid Algorithms“Salp Swarm Algorithm(SSA-ELM),Grasshopper Algorithm(GOA-ELM),Grey Wolf Algorithm(GWO-ELM),Whale optimizationAlgorithm(WOA-ELM)andMoth Flame Optimization(MFO-ELM)”.These five optimizers are hybridized with standard ELM methodology for resolving the tumor type classification using gene expression data.The proposed models applied to the predication of electricity loading data,that describes the energy use of a single residence over a fouryear period.In the hidden layer,Swarm algorithms are used to pick a smaller number of nodes to speed up the execution of ELM.The best weights and preferences were calculated by these algorithms for the hidden layer.Experimental results demonstrated that the proposed MFO-ELM achieved 98.13%accuracy and this is the highest model in accuracy in tumor type classification gene expression data.While in predication,the proposed GOA-ELM achieved 0.397which is least RMSE compared to the other models.

关键词： Extreme learning machine salp swarm optimization algorithm grasshopper optimization algorithm grey wolf optimization algorithm moth flame optimization algorithm bio-inspired optimization classification model and whale optimization algorithm

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Hypo-Driver: A Multiview Driver Fatigue and Distraction Level Detection System

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Computers, Materials & Continua 2022年第4期71卷 1999-2017页

作者： Qaisar Abbas Mostafa EAIbrahim Shakir Khan Abdul Rauf Baig College of Computer and Information Sciences Imam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia Department of Electrical Engineering Benha Faculty of EngineeringBenha UniversityQalubiaBenha13518Egypt

Traffic accidents are caused by driver fatigue or distraction in many cases.To prevent accidents,several low-cost hypovigilance(hypo-V)systems were developed in the past based on a multimodal-hybrid(physiological and behavioral)feature set.Similarly in this paper,real-time driver inattention and fatigue(Hypo-Driver)detection system is proposed through multi-view cameras and biosignal sensors to extract hybrid features.The considered features are derived from non-intrusive sensors that are related to the changes in driving behavior and visual facial expressions.To get enhanced visual facial features in uncontrolled environment,three cameras are deployed on multiview points(0◦,45◦,and 90◦)of the drivers.To develop a Hypo-Driver system,the physiological signals(electroencephalography(EEG),electrocardiography(ECG),electro-myography(sEMG),and electrooculography(EOG))and behavioral information(PERCLOS70-80-90%,mouth aspect ratio(MAR),eye aspect ratio(EAR),blinking frequency(BF),head-titled ratio(HT-R))are collected and pre-processed,then followed by feature selection and fusion techniques.The driver behaviors are classified into five stages such as normal,fatigue,visual inattention,cognitive inattention,and drowsy.This improved hypo-Driver system utilized trained behavioral features by a convolutional neural network(CNNs),recurrent neural network and long short-term memory(RNN-LSTM)model is used to extract physiological features.After fusion of these features,the Hypo-Driver system is classified hypo-V into five stages based on trained layers and dropout-layer in the deep-residual neural network(DRNN)model.To test the performance of a hypo-Driver system,data from 20 drivers are acquired.The results of Hypo-Driver compared to state-of-theart methods are presented.Compared to the state-of-the-art Hypo-V system,on average,the Hypo-Driver system achieved a detection accuracy(AC)of 96.5%.The obtained results indicate that the Hypo-Driver system based on multimodal and multiview features outperforms other state-of-the-art dr

关键词： Internet of things(IoT) intelligent transportation sensors multiview points transfer learning convolutional neural network recurrent neural network residual neural network multimodal features

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