Scopus Research — Sara Hakem Al-Khafaji

The journal Energies retracts the article “Investigation and Evaluation of the Hybrid System of Energy Storage for Renewable Energies” [1] cited above. Following publication, concerns were brought to the attention of the publisher regarding overlap with a previously published manuscript [2] with a different authorship group and published in another language. Adhering to our complaint procedure, an investigation was conducted by the Editorial Office, members of the Editorial Board, and the Editor-in-Chief that confirmed a significant overlap. This article [1] is therefore retracted. This retraction was approved by the Editor-in-Chief of Energies. Maytham T. Qasim agrees to this retraction. Luis Andres Barboza-Arenas, Rosario Mireya Romero-Parra, Zbigniew Leonowicz and Radomir Gono disagree to this retraction. The remaining authors did not provide a comment on this decision. © 2024 by the authors.

Epilepsy is a central nervous system (CNS) disorder that affects nerve cells in the brain and produces seizures in which consciousness is lost. People with epilepsy have frequent seizures as a result of increased brain electrical activity, which disrupts the message system between brain cells, making epilepsy a serious condition that must be treated. Correct diagnosis of this disease can save the patient from death and complications caused by the disease. The recording of electroencephalogram (EEG) signals is an effective method for determining the electrical activity of the brain and learning a great deal about how the brain function. The Fast Fourier Transform (FFT), the Discrete Wavelet Transform (DWT), and a pattern recognition network are used in this study to offer a method for analyzing EEG signals. This study aimed to provide a multi-step algorithm for extracting signal features and diagnosing epilepsy. Wavelet transform is used to remove signal noise. For this purpose, the optimal wavelet mother (Dabichiz 8) was used. Then, signal features are extracted using the Fourier transform, and generate the EEG matrix. The features are regarded as the Pattern Recognition Network's input. There were 5% of test data and 80% of training data. 15% of the data were left over for validation. The architecture was thought to consist of one input layer (14 neurons = number of selected features), one hidden layer (6 neurons), and two output layers (2 neurons = number of sleep stages). The result of this output was compared with other classifiers, such as multilayer perceptron (MLP) neural network. The results show that using a Pattern Recognition Network can classify the features with 92.5% accuracy. Using this method can process the signal with high accuracy while being simple. In fact, a new method based on the architecture of a new network was presented for AC signal processing. Hence, the proposed method can diagnose epilepsy with high accuracy while removing signal noise. © 2023 Elsevier Ltd

The natural frequency of a clamped–clamped functionally graded porous (FGP) nanobeam is predicted in this study. Material distribution is considered based on monotonous, symmetric, and non-symmetric patterns in the thickness direction. This paper deals with governing equations of nanobeams based on third-order shear deformation beam theory in conjunction with nonlocal strain gradient theory (NSGT) and surface effects. Artificial neural network (ANN) is utilized to predict the effect of eight parameters including temperature gradient, residual surface stress, porosity distribution pattern, porosity parameter, nonlocal and material length scale parameters, and elastic and shear coefficients of Pasternak foundation on the fundamental frequency of FGP nanobeam. Different training methods are selected to simulate input and output dependency. Results show that the dependency of the natural frequency is inverse to the temperature gradient and nonlocal parameter in the sense that increasing these factors will decrease the natural frequency. Also, increasing the material length scale parameter grows the effect of the nonlocal parameter. Residual surface stress, material length scale, and Pasternak foundation parameters have a direct effect on the output and among them; the material length scale parameter has a more noticeable effect. Finally, it was found that by increasing the porosity parameter value, the diversity of natural frequency levels up drastically © 2023 Elsevier Ltd

The system presented in this paper can change the energy storage landscape by having the advantages of a compressed air storage system and pump storage, as well as minimizing the disadvantages of these two systems. One of the advantages of this system compared to similar systems is the lack of combustion of natural gas. Correspondingly, for construction, it does not require specific specifications for the executive site, and control of the energy and heat of the system (due to the use of water as an operational fluid) is easier than similar systems. In addition, this system is very scalable and can be designed in low capacities to high capacities, energy analysis of this research to identify the basic and effective parameters of the system and determine the limitations and relationships between them. The amount of energy saved in the current research system compared to previous research is significant, and 92% efficiency can be achieved. The energy analysis of this research determined the effect of the parameters on each other and their limitations so that the path of its feasibility design was paved. © 2023 by the authors.