Publications by authors named "Mohamed El Shimy"

This study introduces an optimized hybrid deep learning approach that leverages meteorological data to improve short-term wind energy forecasting in desert regions. Over a year, various machine learning and deep learning models have been tested across different wind speed categories, with multiple performance metrics used for evaluation. Hyperparameter optimization for the LSTM and Conv-Dual Attention Long Short-Term Memory (Conv-DA-LSTM) architectures was performed.

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Global warming is a vital problem that many researchers tried to solve with so many solutions such as: reducing electricity production with conventional generators by using renewable energy resources and using hydrogen as an alternative to fossil fuels. The universal Economic crisis came across to cut off certain quantities at scheduled times. This, directly, affects renewable energy sources connected to the grid due to voltage and frequency variations.

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The transition to sustainable power infrastructure necessitates integrating various renewable energy sources efficiently. Our study introduces the deterministic balanced method (DBM) for optimizing hybrid energy systems, with a particular focus on using hydrogen for energy balance. The DBM translates the sizing optimization problem into a deterministic one, significantly reducing the number of iterations compared to state-of-the-art methods.

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Background: Cerebrospinal fluid (CSF) infection is one of the most common and serious complications of shunt placement. The CSF shunt infections are preferably treated with intravenous antibiotics, infected shunt removal, repeated tapping (infants) or placement of an external ventricular drainage (EVD) device, and placement of a new shunt once the CSF is sterile. The tapping through the anterior fontanelle is commonly used instead of the EVD in developing countries to manage CSF infection in open anterior fontanelle patients.

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