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Assessing the Performance of a Dynamical Downscaling Simulation Driven by a Bias-Corrected CMIP6 Dataset for Asian Climate

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Advances in Atmospheric Sciences 2024年第5期41卷 974-988页

作者： Zhongfeng XU Ying HAN Meng-Zhuo ZHANG Chi-Yung TAM Zong-Liang YANG ahmed M.EL kenawy Congbin FU Institute of Atmospheric Physics Chinese Academy of SciencesBeijing 100029China School of Atmospheric Sciences Nanjing UniversityNanjing 210093China Earth System Science Programme The Chinese University of Hong KongHong KongChina Department of Geological Sciences Jackson School of GeosciencesThe University of Texas at AustinAustinTexasUSA Instituto Pirenaico de Ecología Consejo Superior de Investigaciones Científicas(CSIC)Campus de Aula Dei1005Zaragoza 50059Spain Department of Geography Mansoura UniversityMansoura 35516Egypt

In this study,we aim to assess dynamical downscaling simulations by utilizing a novel bias-corrected global climate model(GCM)data to drive a regional climate model(RCM)over the Asia-western North Pacific *** simulations were conducted with a 25-km grid spacing for the period 1980–*** first simulation(WRF_ERA5)was driven by the European Centre for Medium-Range Weather Forecasts Reanalysis 5(ERA5)dataset and served as the validation *** original GCM dataset(MPI-ESM1-2-HR model)was used to drive the second simulation(WRF_GCM),while the third simulation(WRF_GCMbc)was driven by the bias-corrected GCM *** bias-corrected GCM data has an ERA5-based mean and interannual variance and long-term trends derived from the ensemble mean of 18 CMIP6 *** demonstrate that the WRF_GCMbc significantly reduced the root-mean-square errors(RMSEs)of the climatological mean of downscaled variables,including temperature,precipitation,snow,wind,relative humidity,and planetary boundary layer height by 50%–90%compared to the WRF_***,the RMSEs of interannual-tointerdecadal variances of downscaled variables were reduced by 30%–60%.Furthermore,the WRF_GCMbc better captured the annual cycle of the monsoon circulation and intraseasonal and day-to-day *** leading empirical orthogonal function(EOF)shows a monopole precipitation mode in the WRF_*** contrast,the WRF_GCMbc successfully reproduced the observed tri-pole mode of summer precipitation over eastern *** improvement could be attributed to a better-simulated location of the western North Pacific subtropical high in the WRF_GCMbc after GCM bias correction.

关键词： bias correction multi-model ensemble mean dynamical downscaling interannual variability day-to-day variability validation

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Performance of WSNs under the Effect of Collisions and Interference

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Wireless Sensor Network 2014年第6期6卷 93-103页

作者： Mark Onsy Ramy Salah Michael Makar Ghada Badawi ahmed kenawy Hassan H. Halawa Tarek K. Refaat Ramez M. Daoud Hassanein H. Amer Hany M. ElSayed Magdy El Soudani Electronics and Communication Department Cairo University Giza Egypt Electronics Engineering Department American University in Cairo Cairo Egypt School of Electrical Engineering and Computer Science University of Ottawa Ottawa Canada

This paper proposes a methodology for calculating the energy consumed by a Wireless Sensor Network as well as its throughput under the effect of a jamming node modelling interference to account for collisions and retransmissions. Accordingly, the proposed methodology takes into consideration retransmissions and data dropped due to interference and collisions simultaneously. Simulations are conducted using OPNET to model various scenarios utilizing off-the-shelf wireless communication standards, namely ZigBee, Wi-Fi and Low Power Wi-Fi. A figure of merit is developed to offer more representative results for applications with different requirements. In achieving different requirements for a given application, there is a clear trade-off between energy consumption and throughput.

关键词： WSN Collisions Interference Goodput IEEE 802.11 IEEE 802.15.4 Low Power Wi-Fi ZigBee Energy Consumption

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Data-Driven Models for Predicting Solar Radiation in Semi-Arid Regions

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Computers, Materials & Continua 2023年第1期74卷 1625-1640页

作者： Mehdi Jamei Nadjem Bailek Kada Bouchouicha Muhammed A.Hassan ahmed Elbeltagi Alban Kuriqi Nadhir Al-Ansar Javier Almorox El-Sayed M.El-kenawy Engineering Faculty Shohadaye Hoveizeh Campus of TechnologyShahid Chamran University of AhvazDashte AzadeganIran Energies and Materials Research Laboratory Department of Matter SciencesFaculty of Sciences and TechnologyUniversity of TamanghassetTamanghassetAlgeria Unitéde Recherche en Energies Renouvelables en Milieu Saharien(URERMS) Centre de Développement des Energies Renouvelables(CDER)01000AdrarAlgeria Mechanical Power Engineering Department Faculty of EngineeringCairo UniversityGiza12613GizaEgypt Agricultural Engineering Department Faculty of AgricultureMansoura UniversityMansoura35516Egypt CERIS Instituto Superior TécnicoUniversidade de LisboaLisbonPortugal Department of Civil Environmental and Natural Resources EngineeringLulea University of Technology97187LuleaSweden Universidad Politécnica de Madrid UPMAvd.Puerta de Hierro28040MadridSpain Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt

Solar energy represents one of themost important renewable energy sources contributing to the energy transition *** that the observation of daily global solar radiation(GSR)is not affordable in some parts of the globe,there is an imperative need to develop alternative ways to predict ***,the main objective of this study is to evaluate the performance of different hybrid data-driven techniques in predicting daily GSR in semi-arid regions,such as the majority of Spanish ***,four ensemble-based hybrid models were developed by hybridizing Additive Regression(AR)with Random Forest(RF),Locally Weighted Linear Regression(LWLR),Random Subspace(RS),and *** base algorithms of the developed models are scarcely applied in previous studies to predict solar *** testing phase outcomes demonstrated that the ARRF models outperform all other hybrid *** provided models were validated by statisticalmetrics,such as the correlation coefficient(R)and root mean square error(RMSE).The results proved that Scenario#6,utilizing extraterrestrial solar radiation,relative humidity,wind speed,and mean,maximum,and minimum ambient air temperatures as the model inputs,leads to the most accurate predictions among all scenarios(R=0.968–0.988 and RMSE=1.274–1.403 MJ/m^(2)・d).Also,Scenario#3 stood in the next rank of accuracy for predicting the solar radiation in both validating *** AD-RF model was the best predictive,followed by AD-RS and ***,this study recommends new effective methods to predict GSR in semiarid regions.

关键词： Solar radiation prediction random forest locally-weighted linear regression additive regression

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Weight Prediction Using the Hybrid Stacked-LSTM Food Selection Model

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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 *** accurately 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,namely,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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Assessing the Efficacy of Improved Learning in Hourly Global Irradiance Prediction

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Computers, Materials & Continua 2023年第11期77卷 2579-2594页

作者： Abdennasser Dahmani Yamina Ammi Nadjem Bailek Alban Kuriqi Nadhir Al-Ansari Salah Hanini Ilhami Colak Laith Abualigah El-Sayed M.El-kenawy Department of Mechanical Engineering Faculty of Science and TechnologyGIDD Industrial Engineering and Sustainable Development LaboratoryUniversity of RelizaneBourmadiaRelizane48000Algeria Laboratory of Biomaterials and Transport Phenomena(LBMPT) University of MedeaUrban PoleMedea26000Algeria Energies and Materials Research Laboratory Faculty of Sciences and TechnologyUniversity of TamanghassetTamanrasset10034Algeria Sustainable Development and Computer Science Laboratory Faculty of Sciences and TechnologyAhmed Draia University of AdrarAdrarAlgeria CERIS Instituto Superior TécnicoUniversidade de LisboaLisbon1049-001Portugal Civil Engineering Department University for Business and TechnologyPristina10000Kosovo Department of Civil Environmental and Natural Resources EngineeringLulea University of TechnologyLulea97187Sweden Engineering and Architecture Faculty Nisantasi UniversityIstanbulTurkey Computer Science Department Prince Hussein Bin Abdullah Faculty for Information TechnologyAl al-Bayt UniversityMafraq25113Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman UniversityAmman19328Jordan MEU Research Unit Middle East UniversityAmman11831Jordan Applied Science Research Center Applied Science Private UniversityAmman11931Jordan School of Computer Sciences Universiti Sains MalaysiaPulau Pinang11800Malaysia Department of Computing and Information Systems School of Engineering and TechnologySunway University MalaysiaPetaling Jaya27500Malaysia Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt

Increasing global energy consumption has become an urgent problem as natural energy sources such as oil,gas,and uranium are rapidly running *** into renewable energy sources such as solar energy is being pursued to counter *** energy is one of the most promising renewable energy sources,as it has the potential to meet the world’s energy needs *** study aims to develop and evaluate artificial intelligence(AI)models for predicting hourly global *** hyperparameters were optimized using the Broyden-FletcherGoldfarb-Shanno(BFGS)quasi-Newton training algorithm and STATISTICA *** from two stations in Algeria with different climatic zones were used to develop the *** error measurements were used to determine the accuracy of the prediction models,including the correlation coefficient,the mean absolute error,and the root mean square error(RMSE).The optimal support vector machine(SVM)model showed exceptional efficiency during the training phase,with a high correlation coefficient(R=0.99)and a low mean absolute error(MAE=26.5741 Wh/m^(2)),as well as an RMSE of 38.7045 Wh/m^(2) across all ***,this study highlights the importance of accurate prediction models in the renewable energy,which can contribute to better energy management and planning.

关键词： Renewable energy energy prediction global irradiation artificial intelligence BFGS quasi-Newton training algorithm

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Endoscopic Transsphenoidal Surgery for Pituitary Adenoma. Early Experience in Sohag University Hospitals

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Open Journal of Modern Neurosurgery 2021年第3期11卷 194-203页

作者： Karam kenawy ahmed Ismail Ibrahim Rezk Abdin K. Kasim Department of Neurosurgery Sohag University Sohag Egypt Department of E.N.T Sohag University Sohag Egypt

Objective: To report the results of our first series of patients undergoing a purely endoscopic endonasal approach for pituitary surgery in Sohag University Hospitals. Patients and Methods: We reviewed 20 consecutive patients during the period from January 2017 to January 2020 with pituitary adenoma who underwent purely endoscopic transsphenoidal resection of their lesions. The patients’ clinical outcomes, degrees of tumor removal, and complications were reported. Results: There were 18 primary and 2 recurrent adenomas (19 nonfunctioning and 1 functioning GH secreting adenoma). The average degree of gross total removal for tumors was 70%, C.S.F leak occurred in 20%, diabetes insipidus in 10% and sellar hemorrhage in 5%. There was no post-operative mortality, vascular injury, sphenoiditis, septal perforation, epistaxis or general complication. Conclusion: A purely endoscopic approach for pituitary adenoma treatment is a safe and effective alternative to the traditional microscopic procedure.

关键词： Endoscopic Surgery Endonasal Pituitary Adenoma Transsphenoidal Outcome Complications

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