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Weight Prediction Using the Hybrid Stacked-LSTM Food Selection Model

Computer Systems Science & Engineering 2023年第7期46卷 765-781页

作者： Ahmed M.elshewey Mahmoud Y.Shams Zahraa Tarek mohamed Megahed el-Sayed M.el-kenawy mohamed a.el-dosuky Faculty of Computers and Information Computer Science DepartmentSuez UniversitySuezEgypt Faculty of Artificial Intelligence Kafrelsheikh UniversityKafrelsheikh33511Egypt Faculty of Computers and Information Computer Science DepartmentMansoura University35561Egypt Faculty of Physical Education Track and Field Competitions DepartmentArish UniversityEgypt Delta Higher Institute of Engineering and Technology Mansoura35111Egypt Department of Computer Science Arab East CollegesRiyadh13544Saudi Arabia

Food choice motives(i.e.,mood,health,natural content,convenience,sensory appeal,price,familiarities,ethical concerns,and weight control)have an important role in transforming the current food system to ensure the healthiness of people and the sustainability of the *** from several domains have presented several models addressing issues influencing food choice over the ***,a multidisciplinary approach is required to better understand how various aspects interact with one another during the decision-making *** this paper,four Deep Learning(DL)models and one Machine Learning(ML)model are utilized to predict the weight in pounds based on food *** Long Short-Term Memory(LSTM)model,stacked-LSTM model,Conventional Neural Network(CNN)model,and CNN-LSTM model are the used deep learning *** the applied ML model is the K-Nearest Neighbor(KNN)*** efficiency of the proposed model was determined based on the error rate obtained from the experimental *** findings indicated that Mean Absolute Error(MAE)is 0.0087,the Mean Square Error(MSE)is 0.00011,the Median Absolute Error(MedAE)is 0.006,the Root Mean Square Error(RMSE)is 0.011,and the Mean Absolute Percentage Error(MAPE)is ***,the results demonstrated that the stacked LSTM achieved improved results compared with the LSTM,CNN,CNN-LSTM,and KNN regressor.

关键词： Weight prediction machine learning deep learning LSTM CNN KNN

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Wind Power Prediction Based on Machine Learning and Deep Learning Models

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Computers, Materials & Continua 2023年第1期74卷 715-732页

作者： Zahraa Tarek Mahmoud Y.Shams Ahmed M.elshewey el-Sayed M.el-kenawy Abdelhameed Ibrahim Abdelaziz A.Abdelhamid mohamed a.el-dosuky Faculty of Computers and Information Computer Science DepartmentMansoura UniversityMansoura35561Egypt Faculty of Artificial Intelligence Kafrelsheikh UniversityKafrelsheikh33511Egypt Faculty of Computers and Information Computer Science DepartmentSuez UniversitySuezEgypt Department of Communications and electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Department of Computer Science Arab East CollegesRiyadh13544Saudi Arabia

Wind power is one of the sustainable ways to generate renewable *** recent years,some countries have set renewables to meet future energy needs,with the primary goal of reducing emissions and promoting sustainable growth,primarily the use of wind and solar *** achieve the prediction of wind power generation,several deep and machine learning models are constructed in this article as base *** regression models are Deep neural network(DNN),k-nearest neighbor(KNN)regressor,long short-term memory(LSTM),averaging model,random forest(RF)regressor,bagging regressor,and gradient boosting(GB)*** addition,data cleaning and data preprocessing were performed to the *** dataset used in this study includes 4 features and 50530 *** accura.ely predict the wind power values,we propose in this paper a new optimization technique based on stochastic fractal search and particle swarm optimization(SFSPSO)to optimize the parameters of LSTM *** evaluation criteria were utilized to estimate the efficiency of the regression models,na.ely,mean absolute error(MAE),Nash Sutcliffe Efficiency(NSE),mean square error(MSE),coefficient of determination(R2),root mean squared error(RMSE).The experimental results illustrated that the proposed optimization of LSTM using SFS-PSO model achieved the best results with R2 equals 99.99%in predicting the wind power values.

关键词： Prediction of wind power data preprocessing performance evaluation

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Optimized system for combined production of electricity/green hydrogen for multiple energy pathways:a case study of Egypt

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Clean Energy 2024年第4期8卷 219-236页

作者： Reda Ragab mohamed HamdiHafez a.el Salmawy mohamed A.Ismail Mechanical Power Engineering Department Zagazig UniversityZagazig 44519Egypt Mechanical Engineering Department College of EngineeringKing Faisal UniversityAl-Ahsa 31982Saudi Arabia

By optimal sizing of a wind/photovoltaic hybrid renewable-energy(RE)system,trimming the surplus capacity to reduce the fluctuations in the electricity supplied to the grid,and using it to produce green hydrogen through electrolysis,a stable output with maximum possible capacity factor(CF)is generated to maintain the electricity grid ***,the trimmed energy is used in a secondary conversion path that minimizes the weighted average cost of the energy generated from the entire *** surplus power-to-gas conversion allows the use of green hydrogen to produce electricity,methanol,or ammonia subject to the resource availability,site characteristics,and financial *** on robust site selection criteria,the best performance is obtained at two sites:Ras Ghareb and Minya,achieving the lowest energy cost with some variance in their *** the Ras Ghareb site,the optimally sized RE plant provided the grid with a quasi-steady capacity of 423 MW with a CF of 80.04%and was capable of injecting 2965.8648 GWh throughout the year with the lowest cost of 2.4355¢/kWh.A surplus of 3.9%of the total energy produced from the plant was directed to produce 1922-ton H_(2)/year,achieving the lowest cost of hydrogen production of$1.9745/kg H_(2).For the other selected site,Minya,the clipped energy is used to produce 3330.47-ton H_(2)/year with an optimized lowest cost of$3.5268/kg H_(2).The difference in hydrogen costs was attributed to the number of full operating hours of the electrolyser in both *** cost is mainly affected by the electricity price and the electrolyser *** both tending to decrease,future forecasts show hydrogen cost reductions.

关键词： hybrid renewable-energy system(HRES) green hydrogen electrolysis power-to-gas methanol ammonia

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Mafic and felsic magmatism in the Wadi Kalalat area, South Eastern Desert, Egypt: mineralogy, geochemistry and geodynamic evolution during the Neoproterozoic in the Nubian Shield

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Acta Geochimica 2024年第1期43卷 150-173页

作者： a.el A.Surour Ahmed A.Madani mohamed a.el-Sobky Faculty of Science Galala UniversityNew Galala CitySuez 43511Egypt Department of Geology Faculty of ScienceCairo UniversityGiza 12613Egypt Egyptian Mineral Resources Authority P.O.Box 11517AbbasiyaCairoEgypt

In the south Eastern Desert of Egypt,two contrasting types of magmatism(mafic and felsic) are recorded in the Wadi Kalalat area,and form the Gabal el Motaghiarat and Gabal Batuga intrusions,*** two intrusions post-dates ophiolitic and arc associations represented by serpentinite and metagabbro-diorite,*** mafic intrusion has a basal ultramafic member represented by fresh peridotite,which is followed upward by olivine gabbro and anorthositic or *** mafic intrusion pertains to the Alaskan-type mafic-ultramafic intrusions in the Arabian-Nubian Shield(ANS)being of tholeiitic nature and emplaced in a typical arc *** the other hand,the Gabal Batuga intrusion comprises three varieties of fresh A-type granites of high K-calc alkaline nature,which is peraluminous and garnetbearing in parts.A narrow thermal aureole in the olivine gabbro of the mafic intrusion was developed due to the intrusion of the Batuga *** results in the development of a hornfelsic melagabbro variety in which the composition changed from tholeiitic to a calc-alkaline composition due to the addition of S_(i)O_(2),Al_(2)O_(3),alkalis,lithosphile elements(LILEs) such as Rb(70 ppm) and Y(28 ppm) from the felsic *** the thermal aureole,Rb amounts 2-8 ppm and Y lies in the range elieved that the Gabal Batuga felsic intrusion started to emplace during the waning stage of an arc system,with transition from the pre-collisional(i.e.,arc setting) to post-collisional and within plate *** from which the Gabal Batuga granites were fractionated is high-K calc-alkaline giving rise to a typical post-collisional A-type granite(A_(2)-subtype) indicating an origin from an underplating crustal ***,it is stressed here that the younger granites in the ANS are not exclusively post-collisional and within-plate but most likely they started to develop before closure of the arc *** possible source(s) of mafic magmas

关键词： Wadi Kalalat Gabal el Motaghairat mafic intrusion Gabal Batuga felsic intrusion Arc setting Post-collision Within-plate

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Comparative study between three carbonaceous nanoblades and nanodarts for antimicrobial applications

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Journal of Environmental Sciences 2024年第2期136卷 594-605页

作者： mohamed S.Selim Ahmed M.Azzam mohamed A.Shenashen Shimaa A.Higazy Bayaumy B.Mostafa Sherif a.el-Safty National Institute for Materials Science(NIMS) 1-2-1 SengenTsukuba-ShiIbaraki-Ken 305-0047Japan Petroleum Application Department Egyptian Petroleum Research InstituteNasr City 11727Egypt Department of Environmental Research Theodor Bilharz Research InstituteGizaEgypt

The design of nanostructured materials occupies a privileged position in the development and management of affordable and effective technology in the antibacterial ***,we discuss the antimicrobial properties of three carbonaceous nanoblades and nanodarts materials of graphene oxide(GO),reduced graphene oxide(RGO),and single-wall carbon nanotubes(SWCNTs)that have a mechano-bactericidal effect,and the ability to piercing or slicing bacterial *** demonstrate the significance of size,morphology and composition on the antibacterial activity mechanism,the designed nanomaterials have been *** minimum inhibitory concentration(MIC),standard agar well diffusion,and transmission electron microscopy were utilized to evaluate the antibacterial activity of GO,RGO,and *** on the evidence obtained,the three carbonaceous materials exhibit activity against all microbial strains tested by completely encapsulating bacterial cells and causing morphological disruption by degrading the microbial cell membrane in the order of RGO>GO>*** of the external cell wall structure and outer membrane proteins,the synthesized carbonaceous nanomaterials exhibited higher antibacterial activity against Gram-positive bacterial strains than Gram-negative and fungal *** had the lowest MIC values(0.062,0.125,and 0.25 mg/mL against ***,***,and ***,respectively),as well as minimum fungal concentrations(0.5 mg/mL for both *** and ***).At 12 hr,the cell viability values against tested microbial strains were completely *** lysis and death occurred as a result of severe membrane damage caused by microorganisms perched on RGO *** work gives an insight into the design of effective graphene-based antimicrobial materials for water treatment and remediation.

关键词： Antimicrobial properties Nanoblades Mechano-bactericidal effect RGO nanosheets Gram-positive bacteria Cell viability

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Multi-Objective Optimization Algorithm for Grouping Decision Variables Based on Extreme Point Pareto Frontier

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Computers, Materials & Continua 2024年第4期79卷 1281-1299页

作者： JunWang Linxi Zhang Hao Zhang Funan Peng Mohammed a.el-Meligy mohamed Sharaf Qiang Fu College of Computer Science and Technology Shenyang University of Chemical TechnologyShenyang110142China Key Laboratory of Intelligent Technology for Chemical Process Industry of Liaoning Province Shenyang University of Chemical TechnologyShenyang110142China Industrial Engineering Department College of EngineeringKing Saud UniversityP.O.Box 800Riyadh11421Saudi Arabia

The existing algorithms for solving multi-objective optimization problems fall into three main categories:Decomposition-based,dominance-based,and *** multi-objective optimization problemsmainly focus on objectives,treating decision variables as a total variable to solve the problem without consideringthe critical role of decision variables in objective *** seen,a variety of decision variable groupingalgorithms have been ***,these algorithms are relatively broad for the changes of most decisionvariables in the evolution process and are time-consuming in the process of finding the Pareto *** solvethese problems,a multi-objective optimization algorithm for grouping decision variables based on extreme pointPareto frontier(MOEA-DV/EPF)is *** algorithm adopts a preprocessing rule to solve the Paretooptimal solution set of extreme points generated by simultaneous evolution in various target directions,obtainsthe basic Pareto front surface to determine the convergence effect,and analyzes the convergence and distributioneffects of decision *** the later stages of algorithm optimization,different mutation strategies are adoptedaccording to the nature of the decision variables to speed up the rate of evolution to obtain excellent individuals,thusenhancing the performance of the *** validation of the test functions shows that this algorithmcan solve the multi-objective optimization problem more efficiently.

关键词： Multi-objective evolutionary optimization algorithm decision variables grouping extreme point pareto frontier

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Strain engineering in electrocatalysis:Strategies,characterization,and insights

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Nano Research 2024年第5期17卷 3603-3621页

作者： Qibo Deng Peng Xu Hassanien Gomaa mohamed A.Shenashen Sherif a.el-Safty Cuihua An Li-Hua Shao Ning Hu School of Mechanical Engineering Hebei University of TechnologyTianjin 300401China Department of Chemistry Faculty of ScienceAl-Azhar University71524AssiutEgypt Department of Petrochemical Egyptian Petroleum Research Institute(EPRI)Nasr City11727CairoEgypt Center for Functional Materials National Institute for Materials Science(NIMS)1-2-1 SengenTsukubaIbaraki-ken305-0047Japan State Key Laboratory of Reliability and Intelligence electrical Equipment Key Laboratory of Advanced Intelligent Protective Equipment TechnologyMinistry of Educationand School of Mechanical EngineeringHebei University of TechnologyTianjin 300401China National Key Laboratory of Strength and Structural Integrity Institute of Solid MechanicsSchool of Aeronautic Science and EngineeringBeihang UniversityBeijing100191China Advanced Equipment Research Institute Co. Ltd.of HEBUTTianjin 300401China

Strain engineering,as a cutting-edge method for modulating the electronic structure of catalysts,plays a crucial role in regulating the interaction between the catalytic surface and the adsorbed *** electrocatalytic performance is influenced by the electronic structure,which can be achieved by introducing the external forces or stresses to adjust interatomic spacing between surface *** challenges in strain engineering research lie in accura.ely understanding the mechanical impact of strain on *** paper first introduces the basic strategy for generating the strain,summarizes the different strain generation forms and their advantages and *** progress in researching the characterization means for the lattice strains and their applications in the field of electrocatalysis is also ***,the challenges of strain engineering are introduced,and an outlook on the future research directions is provided.

关键词： strain electrocatalysis characterization chemical reaction kinetics lattice mismatch

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Comparison between seismic analysis of twisting and regular 52-story towers considering soil-structure interaction

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Earthquake Engineering and Engineering Vibration 2024年第3期23卷 663-675页

作者： mohamed Naguib Abouelsaad Mohammed Shaaban Salah el Bagalaty mohamed E.el Madawy Structural Engineering Department Faculty of EngineeringMansura UniversityMansoura 35511Egypt Faculty of Engineering Delta University for Science and TechnologyGamasa 35712Egypt

A dynamic analysis of both twisting and regular towers is carried out to determine the results of considering soil-structure interaction(SSI)on high-rise *** addition,the difference between the seismic performance of using twisting towers over regular ones is *** twisting tower is a simulation of the Evolution Tower(Moscow).The towers’skeletons consist of RC elements and rest on a reinforced concrete piled-raft *** soil model is considered as multi-layered with the same soil properties as the zone chosen for the analysis(New Mansoura City,Egypt).The only difference between both towers is their shape in *** whole system is modelled and analyzed in a single step as one full 3D model,which is known as the direct approach in *** analyses are carried out using finite-element software(Midas GTS NX).Dynamic output responses due to three records of seismic loads are proposed and presented in some *** on the results,it is concluded that SSI has a considerable effect on the dynamic response of tall buildings mainly because of the foundation flexibility,as it leads to lengthening the vibration period,increasing the story drift and the base shear for both cases.

关键词： soil-structure interaction seismic analysis twisting towers base shear story drift finite element method Midas GTS NX

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A review on the multi-scaled structures and mechanical/thermal properties of tool steels fabricated by laser powder bed fusion additive manufacturing

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International Journal of Minerals,Metallurgy and Materials 2024年第5期31卷 1048-1071页

作者： Huajing Zong Nan Kang Zehao Qin mohamed el Mansori Arts et Metiers Institute of Technology MSMPHESAM UniversitéChâlons-en-Champagne F-51006France Texas A&M Engineering Experiment Station Institute for Manufacturing SystemsCollege Station TX77843USA

The laser powder bed fusion(LPBF) process can integrally form geometrically complex and high-performance metallic parts that have attracted much interest,especially in the molds *** appearance of the LPBF makes it possible to design and produce complex conformal cooling channel systems in ***,LPBF-processed tool steels have attracted more and more *** complex thermal history in the LPBF process makes the microstructural characteristics and properties different from those of conventional manufactured tool *** paper provides an overview of LPBF-processed tool steels by describing the physical phenomena,the microstructural characteristics,and the mechanical/thermal properties,including tensile properties,wear resistance,and thermal *** microstructural characteristics are presented through a multiscale perspective,ranging from densification,meso-structure,microstructure,substructure in grains,to ***,a summary of tool steels and their challenges and outlooks are introduced.

关键词： additive manufacturing laser powder bed fusion tool steel multi-scaled structure mechanical properties thermal properties

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Machine Learning and Data Fusion Approach for elastic Rock Properties Estimation and Fracturability Evaluation

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Energy and AI 2024年第2期16卷 1-13页

作者： Yiwen Gong Ilham el-Monier mohamed Mehana The Ohio State University SimTech LLC Los Alamos National Laboratory

Accurate rock elastic property determination is vital for effective hydraulic fracturing,particularly Young’s modulus due to its link to rock *** study integrates interdisciplinary data for better predictions of elastic modulus,combining data mining,experiments,and calibrated *** used the microstructural insights extracted from rock images for geomechanical facies ***,the petrophysical data and well logs were correlated with shear wave velocity(Vs)and Young’s *** developed a machine-learning workflow to predict Young’s modulus and assess rock fracturability,considering mineral composition,geomechanics,and *** findings indicate that artificial neural networks effectively predict Young’s modulus,while K-Means clustering and hierarchical support vector machines excel in identifying rock and geomechanical *** Microscale thin section analysis in conjunction with fracture modeling enhances our understanding of fracture geometries and facilitates fracturability ***,fracturability is controlled by specific geomechanical facies during initiation and propagation and influenced by continuity of geomechanical facies in small depth *** conclusion,this study demonstrates data mining and machine learning potential for predicting rock properties and assessing fracturability,aiding hydraulic fracturing design optimization through diverse data and advanced methods.

关键词： Young’s modulus prediction fracability evaluation landing point selection rock mechanics machine learning

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