Documents published in Scipedia

• Matrix Algorithms Based on Jacobi and Romanovski-Jacobi Polynomials for Solving the FitzHugh-Nagumo Nonlinear Equation

  R. Hafez, S. Boulaaras, H. Khalifa

  Rev. int. métodos numér. cálc. diseño ing. (2025). Vol. 41, (1), 6

  
  

  Abstract

  The present paper develops and makes efficient, a new, state-of-theart numerical technique for solving the FitzHugh-Nagumo Nonlinear Equation (FH-NNE) with initial and boundary conditions, which represents perhaps the simplest mathematical model for discussing biological systems, including nerve signals and cardiac behavior. Which consists of operational matrices and spectral techniques based on Jacobi and Romanovski-Jacobi polynomials. It is ensured that the nonlinear system is modeled so accurately that it can be effectively solved to ensure the best accuracy combined with computational economy. Comparing the results with the respective numerical results, it is seen that the proposed techniques outdo the standard ones as respects accuracy and efficiency of computation.OPEN ACCESS Received: 11/01/2025 Accepted: 25/02/2025 Published: 07/04/2025

  Abstract
  The present paper develops and makes efficient, a new, state-of-theart numerical technique for solving the FitzHugh-Nagumo Nonlinear Equation (FH-NNE) with initial and boundary [...]

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• Boundary Element Analysis of a Semi-Infinite Anisotropic Plane Containing Inclusions/Holes

  Y. Shiah, C. Ko

  Rev. int. métodos numér. cálc. diseño ing. (2025). Vol. 41, (1), 5

  
  

  Abstract

  In this article, the fundamental solutions with Lekhnitskii-like formulations are implemented in the boundary element method (BEM) to calculate the Two-dimensional elastostatic field in a semi-infinite anisotropic plane containing inclusions/holes. In addition to the source point in the semi-infinite plane, four pseudo-sources are superposed to provide traction-free conditions on the plane surface. To avoid mesh modeling at infinity, the fundamental solutions are modified so that the displacements/stresses at the far field automatically vanish. For modeling problems in civil engineering, loading on the plane surface can be specified as part of the boundary condition. When embedded holes are present in the halfplane, loading on the holes can also be applied. Using the sub-region technique, the elastic field in the half-plane containing inclusions can also be investigated. Studies of a few examples indicate that the proposed BEM is a very efficient and versatile methodology to investigate many practical problems, especially in civil engineering.OPEN ACCESS Received: 11/10/2024 Accepted: 10/01/2025 Published: 07/04/2025

  Abstract
  In this article, the fundamental solutions with Lekhnitskii-like formulations are implemented in the boundary element method (BEM) to calculate the Two-dimensional elastostatic [...]

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• HDAug: Optimizing Medical Image Segmentation through Hyperparameter-Driven Brightness and Contrast Augmentation for Deep Learning Models

  Y. Yang, H. Shao, X. Deng, Y. Jiang

  Rev. int. métodos numér. cálc. diseño ing. (2025). Vol. 41, (1), 4

  
  

  Abstract

  Traditional data augmentation methods, which employ static hyperparameters, often lead to model overfitting. To address this limitation, a novel hyperparameter-driven data augmentation approach (HDAug) is introduced in this study. Training images that simulate a plethora of lighting and exposure conditions are synthesized by HDAug through the stochastic sampling of augmentation hyperparameters, within predefined ranges. Additionally, HDAug does not rely on prior knowledge of specific datasets, endowing it with superior generalization capabilities. The Dice coefficient was utilized as the primary evaluation metric. Experimental results demonstrate that HDAug achieves significant performance improvements in two challenging cross-modality medical image segmentation datasets, with average Dice coefficients of 86.77%, 88.08%, and 84.11%, respectively. The superiority of HDAug lies in its ability to substantially enhance model robustness across diverse imaging conditions while circumventing the overfitting issues inherent in conventional methods. Furthermore, HDAug is computationally efficient and is integrated into existing medical image segmentation workflows.OPEN ACCESS Received: 30/08/2024 Accepted: 22/10/2024 Published: 07/04/2025

  Abstract
  Traditional data augmentation methods, which employ static hyperparameters, often lead to model overfitting. To address this limitation, a novel hyperparameter-driven data [...]

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• Approximate Solutions for Fractional Gas Dynamics and Convection-Diffusion Equations via the Temimi-Ansari Method

  A. Almuneef, A. Hagag

  Rev. int. métodos numér. cálc. diseño ing. (2025). Vol. 41, (1), 3

  
  

  Abstract

  In this paper, a new analytical iterative method is used to obtain the fractional analytical solutions of the nonlinear gas dynamics and convectiondiffusion equations. The paper’s novelty appears in its specific application of the Caputo fractional operator to conventional equations while achieving highly accurate solutions. Numerical outcomes for various cases of the equations are represented via tables and graphs. The convergence analysis for the present approach was completed. The methodology is very capable of reducing the size of the analytical steps and is convenient and efficient for solving nonlinear fractional equations. The Temimi-Ansari method’s applicability across different types of fractional differential equations indicates its potential as a powerful tool in solving nonlinear fractional models in diverse scientific and technical fields.OPEN ACCESS Received: 19/07/2024 Accepted: 11/11/2024 Published: 07/04/2025

  Abstract
  In this paper, a new analytical iterative method is used to obtain the fractional analytical solutions of the nonlinear gas dynamics and convectiondiffusion equations. The [...]

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• Improved Modal Parameter Identification of Spillway Radial Gate under Discharge Excitation based on COV-SSI

  Y. Lu, Y. Liu*, Z. Cao, D. Zhang, C. Neng

  Rev. int. métodos numér. cálc. diseño ing. (2025). Vol. 41, (1), 2

  
  

  Abstract

  This paper aims to improve the accuracy of modal parameter identification for Spillway Radial Gate (SRG) under discharge excitation. An Improved Covariance-driven Stochastic Subspace Identification (COVISSI) method, enhanced by Singular Entropy Increment (SEI) and Potential-based Hierarchical Agglomerative (PHA) clustering, is proposed. This method is designed to effectively eliminate false poles in the identification process. The performance of COV-ISSI is compared against the original stochastic subspace identification method, demonstrating its superior capability in accurately identifying modal parameters. Additionally, both the Additional Mass Method (AMM) and Direct Coupling Method (DCM) are employed to model the fluid-structure interaction system of the SRG. The derived vibration frequencies are compared with those obtained from COV-ISSI and an improved peak-picking method. Results show that the DCM closely aligns with COV-ISSI, with a relative error within 3%, while AMM results show significant deviation from the DCM. The proposed COV-ISSI method provides a reliable and automatic approach for identifying the operating frequencies of SRG structures, offering significant improvements over traditional methods.OPEN ACCESS Received: 24/07/2024 Accepted: 27/09/2024 Published: 07/04/2025

  Abstract
  This paper aims to improve the accuracy of modal parameter identification for Spillway Radial Gate (SRG) under discharge excitation. An Improved Covariance-driven Stochastic [...]

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• Study on the Deformation Law of Foundation Pit with Shared Ground Connecting Walls

  Z. XU, X. Shao, J. He, Y. Chen, L. Wang

  Rev. int. métodos numér. cálc. diseño ing. (2025). Vol. 41, (1), 1

  
  

  Abstract

  This study comprehensively investigates the mechanical behavior and deformation patterns of adjacent foundation pits sharing a common ground connecting wall. Utilizing a combination of detailed field measurements and finite element modeling, the study simulates the excavation process to analyze the mutual interactions between two adjacent pits of equal depth. Results indicate that the excavation of a subsequent pit influences both surface settlement and the horizontal displacement of the retaining structure of the first-excavated pit. Specifically, the surface settlement outside the first-excavated pit increased by 0.08% H

  Abstract
  This study comprehensively investigates the mechanical behavior and deformation patterns of adjacent foundation pits sharing a common ground connecting wall. Utilizing a combination [...]

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• A Fractional Semi-Analytical Iterative Method for the Approximate Treatment of Fisher’s Equations_prueba_latex

  J. Sánchez Pinedo, A. Almuneef

  (Select a year).

  
  

  Abstract

  This study presents a novel fractional semi-analytical iterative approach for solving nonlinear fractional Fisher’s equations using the Caputo fractional operator. The primary objective is to provide a method that yields exact solutions to nonlinear fractional equations without requiring assumptions about nonlinear terms. By applying the Temimi-Ansari Method (TAM) with fractional calculus, this approach offers a robust solution to the time-fractional nonlinear Fisher’s equation, a model relevant in fields such as population dynamics, tumor growth, and gene propagation. In this work, tables and graphical illustrations show that the proposed method minimizes computational complexity and delivers significant accuracy across multiple cases of Fisher’s equations. The findings indicate that TAM with fractional order derivatives provides accurate, efficient approximations with reduced computational workload, showcasing the technique’s potential for addressing a wide range of nonlinear fractional differential equations.OPEN ACCESS Received: 19/07/2024 Accepted: 01/11/2024

  Abstract
  This study presents a novel fractional semi-analytical iterative approach for solving nonlinear fractional Fisher’s equations using the Caputo fractional operator. The [...]

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• Eddy Current Modeling and Simulations for the Characterization of Low Thickness Multilayer Materials_prueba_latex

  S. Bennoud, J. Sánchez Pinedo, E. Sari

  (Select a year).

  
  

  Abstract

  Measuring and determining the parameters and characteristics of multilayer structures have become an important subject for several recent studies. This importance is due to industry needs and structural health requirements. The eddy current inspection is considered an important practical tool that ensures the safety and efficiency of multilayer structures and responds to the above necessities. The diversity of multilayer structure characteristics is one of the principal problems that must be solved. These physical and electromagnetic parameters are not always available or provided by the suppliers. Another problem that arises in the development of different models related to these structures is the difficulty of obtaining a satisfactory diagnosis. This difficulty returns to the complexity of geometry, the presence of small dimensions and sizes, and the existence of various parameters. In this context, it is necessary to achieve a strategy for the development of software and hardware tools concerning the characterization of multilayer structures. These tools must be applied to surmount the above problems and improve the technical advantages of the eddy current inspection. The principal objective of this work is to investigate the efficacy of the eddy current method applied to aeronautical materials, particularly low thickness multilayer structures. The modeling was performed using the finite element method. A software program was developed to investigate changes in the coil impedance. Results are initially validated and compared against the analytical and computational results given for simple cases. They are very similar, and they present a good agreement for both situations. The error is 5% for the calculus of the induction magnetic B. It also varies from 0.17% to 5.32% for impedance responses that enable the application of the developed code to carry out simulations for complex geometries. For various values of parameters and a wide range of applications, the parameters and properties of the problem can easily be introduced into the code. This permits the analysis and calculation of changes in impedance versus the effect of any variation in parameters. The developed approach is sufficiently general. It can simulate various potential cases of defects in low thickness multilayer structures due to its adequate design. It can generate interpretable results for differentOPEN ACCESS Received: 14/07/2024 Accepted: 02/10/2024

  Abstract
  Measuring and determining the parameters and characteristics of multilayer structures have become an important subject for several recent studies. This importance is due to [...]

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• A Solar Irradiance Prediction Model with Recurrent Neural Networks and Computer Graphics Methods

  W. Shin, N. Baek

  Rev. int. métodos numér. cálc. diseño ing. (2024). Vol. 40, (4), 52

  
  

  Abstract

  This paper presents a computer simulation model for predicting solar irradiance in a three-dimensional (3D) environment. Solar irradiance prediction is critical for solar energy systems and related fields. Machine learning techniques, such as recurrent neural networks (RNNs), are employed for more accurate predictions. Integrating a 3D environmental simulation with the RNN models achieves accurate predictions with reasonable resolutions. The training model uses selected astronomical and atmospheric factors to train the RNN models. The proposed method allows the user to obtain the corresponding solar irradiance prediction values for arbitrary periods. Astronomical and atmospheric factors affect solar irradiance; hence, data from the Korea Meteorological Administration are used for training. The RNNs, including the long short-term memory (LSTM) and gated recurrent unit methods, are employed for the prediction. The LSTM layers outperformed other configurations, accurately predicting zero irradiation values. A set of solar irradiance models is presented using RNNs by configuring their layers, and the layout consisting of four LSTM layers performed best. This layout achieved reasonable error bounds, with relatively good root mean squared error and mean absolute error values. A computer graphics-based solar irradiance prediction model is proposed based on this prediction model, incorporating simulations of the surrounding environment. A case study is presented with surrounding buildings to analyze the solar irradiance over the year with a one-hour forecasting horizon to demonstrate its feasibility. Moreover, we plan to improve the results with other neural network models, such as the fuzzyembedded RNN.OPEN ACCESS Received: 19/09/2024 Accepted: 13/12/2024

  Abstract
  This paper presents a computer simulation model for predicting solar irradiance in a three-dimensional (3D) environment. Solar irradiance prediction is critical for solar [...]

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• Binary and Multi-Classification Models for Breast Cancer Diagnosis Using Automated Deep Learning and Mammography Images with Different Augmentation Cases

  N. Charim, N. Ruza, M. Arzmi, S. Hussain

  Rev. int. métodos numér. cálc. diseño ing. (2024). Vol. 40, (4), 53

  
  

  Abstract

  Mammography is a very efficient medical imaging procedure that is used to detect and diagnose breast cancer. However, the use of mammography for the early detection and identification of cancer is very complicated and represents a considerable workload for radiologists. Machine learning (ML) can help address these challenges by providing accurate, automated diagnosis, but traditional ML methods are complex and resourceintensive. Google AutoML Vision offers a simplified approach, enabling healthcare professionals with minimal programming skills to develop effective diagnostic models. The aim of this study was to evaluate the ability of automated deep learning using mammography images using Google AutoML with different augmentation cases. In this work, two models were created: one for binary classification and another for multiclassification. The binary classification model includes two scenarios: noncancerous and malignant, while the multi-classification approach includes three scenarios: normal, benign and malignant. The average accuracy of the two classifications was evaluated and compared. The average accuracy of the binary and multi-classification models was 77.98% and 79.29%, respectively. These results suggest that Google AutoML can simplify the use of ML models in the clinical setting and provide a reliable diagnostic tool that can reduce the workload of radiologists. This study shows that AutoML has the potential to streamline diagnostic workflows in healthcare and make machine learning more accessible and effective in medical practise.OPEN ACCESS Received: 01/08/2024 Accepted: 29/11/2024

  Abstract
  Mammography is a very efficient medical imaging procedure that is used to detect and diagnose breast cancer. However, the use of mammography for the early detection and identification [...]

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