Documents published in Scipedia

• Artificial Neural Network Analysis of Magnetohydrodynamic Flow and Thermal Radiation in Hybrid Nanofluid with Bio-Convection over a Stretching Sheet

  M. Akmal, A. Zafar, S. Abdal, N. Shah

  Rev. int. métodos numér. cálc. diseño ing. (2026). Vol. 42, (3), 75

  
  

  Abstract

  This study investigates the flow and heat transfer of a hybrid nanofluid including alumina and titanium dioxide nanoparticles across a stretched sheet in the presence of a magnetic field, thermal radiation, and bioconvection. The problem is significant because hybrid nanofluids are increasingly employed in industrial cooling, biomedical transport, and energy systems that need accurate prediction of nonlinear thermal and solutal behavior. The governing nonlinear equations were transformed into ordinary differential form and computationally solved via a fourthorder Runge-Kutta method. In addition to this, an artificial neural network model was created to reproduce the numerical results and evaluate prediction abilities. The results show that stronger magnetic forces reduce velocity by about 18%, while higher radiation levels increase fluid temperature by nearly 22%. Increasing the Lewis number lowers nanoparticle concentration by roughly 15%, and higher bioconvection effects reduce microorganism density by about 12%. The neural network achieved excellent agreement with the numerical results, with regression values close to 1 and a mean squared error of order 10−10. The findings indicate that hybrid nano-fluids offer enhanced heat transfer and flow stability under MHD conditions, supporting potential improvements in fluid and thermal performance for applications requiring precise thermal management.OPEN ACCESS Received: 27/08/2025 Accepted: 05/11/2025 Published: 16/04/2026

  Abstract
  This study investigates the flow and heat transfer of a hybrid nanofluid including alumina and titanium dioxide nanoparticles across a stretched sheet in the presence of a [...]

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• Prioritizing Barriers to Total Quality Management Implementation for Sustainable Construction Using AHP–GDM

  D. Alqahtani, M. Awadh

  Rev. int. métodos numér. cálc. diseño ing. (2026). Vol. 42, (3), 85

  
  

  Abstract

  The construction industry is increasingly challenged by complex decisionmaking environments involving economic, technical, and sustainability related factors. This study proposes a structured multi-criteria decisionmaking (MCDM) framework to systematically evaluate and prioritize critical factors influencing construction project performance in Saudi Arabia. The methodology integrates objective weight determination with rankingbased MCDM techniques to assess multiple, often conflicting criteria using expert-driven and data-informed analysis. The results identify the most influential factors affecting construction efficiency and sustainability, providing a transparent and reproducible decision-support model. The main contribution of this study lies in offering a context-specific, analytically robust framework that advances MCDM applications in construction management, particularly within emerging economies. From a practical perspective, the findings support policymakers, project managers, and industry stakeholders in improving strategic planning, resource allocation, and risk-informed decision-making aligned with national development goals. Overall, the proposed framework enhances evidence-based decision-making and contributes to improving the resilience and performance of the construction industry.OPEN ACCESS Received: 13/11/2025 Accepted: 19/01/2026

  Abstract
  The construction industry is increasingly challenged by complex decisionmaking environments involving economic, technical, and sustainability related factors. This study proposes [...]

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• Proposal and Energy Analysis of a New Design Integrating the SCPP into Electricity Pylon Using CFD Numerical Method: Towards Ensuring Electric Needs of Rural Dwellings

  A. Mebarki, M. Titi

  Rev. int. métodos numér. cálc. diseño ing. (2026). Vol. 42, (3), 80

  
  

  Abstract

  Rural electrification is critical for increasing agricultural output, thereby reducing poverty and improving food security. Electricity pylons are steel structures that transport electricity at high voltages and are often the tallest objects in the countryside. The current study proposes a novel solar chimney power plant (SCPP) model in which the chimney is contained within the pylon’s vertical structure. The numerical methodology was carried out using Manzanares’ SCPP 3D axisymmetric CFD model, which was simulated with ANSYS Fluent software. The RNG k-ε turbulence model and a discrete ordinates non-grey radiation model were used. Following model validation, the results led to the identification of five turbine positions based on five collector radius intervals. To the best of our knowledge, this is the first time this has been observed in the SCPP system as a result of air velocity variations throughout the chimney. Equations based on collector radius and solar radiation are used to calculate the generated electric energy and the number of rural houses that could be electrified at each interval. Our findings confirmed the proposed model’s efficiency in generating electricity; for instance, one hour of 1000 W/m2 solar radiation using a collector radius of 200 m produces 14.82 kW that could supply 6.877 rural houses in India or 7.411 rural houses in China for 24 h. This study proposes a plan for electricity transmission companies to invest in infrastructure (pylons) to provide rural renewable electricity.OPEN ACCESS Received: 16/10/2025 Accepted: 27/11/2025

  Abstract
  Rural electrification is critical for increasing agricultural output, thereby reducing poverty and improving food security. Electricity pylons are steel structures that transport [...]

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• A Detail Enhancement Method for Weakly Illuminated Images Using Weighted Guided Filtering Technology

  Z. Qiu, X. Tang, Y. Zhou

  Rev. int. métodos numér. cálc. diseño ing. (2026). Vol. 42, (3), 71

  
  

  Abstract

  In order to optimize the visual effect of weakly illuminated images, this study designed a detail enhancement method for weakly illuminated images based on weighted guided filtering technology. Low-light images undergo a series of transformations that include, among others, the grayscale transformation and the multi-scale weighted guided filtering. Filtration improves both the basic and detailed layers. The proposed method is validated through experimental results to be capable of effectively increasing the visibility and clarity of dimly lit images without losing the delicate texture and edge information. More precisely, the output images register an average gradient of 17.547, which shows that the edges are sharper; information entropy that is always above 4.0, which means that the detail content is richer; and a peak signal-to-noise ratio of 42.85 to 48.37, which asserts that the image quality is high. The whole enhancement process is very fast, and it takes only 1 min to complete, indicating that this method effectively achieves the design expectation.OPEN ACCESS Received: 08/08/2025 Accepted: 29/01/2026

  Abstract
  In order to optimize the visual effect of weakly illuminated images, this study designed a detail enhancement method for weakly illuminated images based on weighted guided [...]

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• Numerical Simulation and Optimization of Grey Relational Analysis Models for Panel Data

  X. Liu, F. Jian, X. Tang

  Rev. int. métodos numér. cálc. diseño ing. (2025). Vol. 41, (2), 101

  
  

  Abstract

  The indicators-coupled grey relational analysis (ICGRA) models are important in clustering panel data with cross-sectional dependence. However, there is still little research on performance validation for the various ICGRA models. In this paper, we investigate the performance of the existing ICGRA models accounting for the reordering of indicators. Firstly, the robot execution failures (REF) dataset of the University of California Irvine (UCI) machine learning database is adopted to validate the robustness of four traditional ICGRA models. Then, we compared the grey relational orders for all arrangements of indicators in panel data. Simulation experiments showed that the four ICGRA models are not all robust against the grey relational order. To resolve this problem, we adopted the mean value theory and deep modeling to optimize the four models and compared them with the tetrahedral grey relational analysis (GRA) model that considers the coupling effect between indicators on the grey relational order, as well as with the k-nearest neighbor (KNN) algorithm. Results show that the classification accuracy of the averaged absolute GRA model was 97.73%, the other optimized ICGRA models and the k-nearest neighbor (KNN) method all achieved 100% accuracy, while the tetrahedral GRA model has an accuracy of 83.33%. Therefore, the average grey incidence degree for all arrangements of indicators and deep modeling significantly improves the stability of models and enhances the clustering accuracy in different cases.

  Abstract
  The indicators-coupled grey relational analysis (ICGRA) models are important in clustering panel data with cross-sectional dependence. However, there is still little research [...]

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• Optimized Multimodal Healthcare Image Fusion Using U2Net Restormer with Dilated Dense Encoder–Decoder and Haar-Based Feature Selection

  M. Shafiq, W. Obidallah, M. Albathan, T. Kamal

  Rev. int. métodos numér. cálc. diseño ing. (2026). Vol. 42, (3), 84

  
  

  Abstract

  Multimodal medical imaging plays a pivotal role in clinical diagnostics by integrating complementary anatomical and functional information from modalities such as Computed Tomography (CT), Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET), and Single-Photon Emission Computed Tomography (SPECT). Despite notable progress, existing fusion approaches continue to face persistent challenges. Convolutional Neural Network (CNN)-based methods often suffer from information loss due to convolutional down-sampling, while Transformer architectures, though effective at capturing global dependencies, incur high computational costs and rely on large-scale pretraining. Generative Adversarial Network (GAN)-based fusion models can generate visually realistic outputs but are prone to training instability and limited reproducibility. In addition, prior studies frequently adopt inconsistent evaluation metrics, with insufficient emphasis on clinical interpretability and robustness, hindering real-world deployment across heterogeneous datasets and institutions. To address these limitations, this study proposes a U-shaped Nested Network – Restoration Transformer (U2Net–Restormer) framework with a Dilated Dense Encoder–Decoder architecture for robust multimodal medical image fusion. The framework integrates hierarchical multiscale representation learning with residual global contextual refinement. To enhance discriminative capability, an optimized Haar-based feature selection strategy is introduced to preserve high-gradient structural and functional details while reducing feature redundancy. Furthermore, an attention-driven fusion mechanism adaptively weights modality-specific contributions, enabling effective integration of heterogeneous information. The proposed method is evaluated on the Augmented Alzheimer’s Neuroimaging Library (AANLIB) multimodal brain imaging dataset, covering CT-MRI, PET-MRI, and SPECT-MRI fusion tasks. Experimental results demonstrate consistent performance gains over state-of-the-art CNN-, Transformer-, and GANbased methods, achieving Structural Similarity Index Measure (SSIM) up to 0.963, Peak Signal-to-Noise Ratio (PSNR) of 42.1 dB, Feature Mutual Information (FMI) of 0.86, and Edge Preservation Index (EPI) of 0.91, with improvements of at least 4%–6% across modalities. Subjective evaluations by radiologists and neurologists report Likert scores up to 4.8/5 for structural visibility, functional fidelity, and diagnostic value. Robustness analysis under Gaussian noise (σ= 15%) further confirms the method’s resilience. Overall, the proposed framework delivers high-fidelity, clinically interpretable multimodal fusion suitable for diverse imaging scenarios.

  Abstract
  Multimodal medical imaging plays a pivotal role in clinical diagnostics by integrating complementary anatomical and functional information from modalities such as Computed [...]

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• Automatic Detection Algorithm of Typical Hidden Dangers in Substation Based on Dynamic Convolutional Feature Extraction

  W. Wu, M. Liang, X. Wu, W. Zhang, Y. Ruan

  Rev. int. métodos numér. cálc. diseño ing. (2026). Vol. 42, (3), 79

  
  

  Abstract

  With the advancement of smart grid construction, substation equipment, being perpetually exposed to complex environments, is prone to latent hazards such as meter malfunctions and insulator cracks. Additionally, personnel violations may trigger safety incidents, directly jeopardising grid reliability. However, traditional manual inspections suffer from low efficiency and high rates of missed defects, while existing detection methods struggle to accommodate irregular defect classifications and multi-type defect characteristics, failing to meet engineering demands for real-time response and precise identification. To address these challenges, this study proposes a substation hazard detection framework (YOLOv10_DSE) based on an enhanced YOLOv10 (You Only Look Once version 10) architecture, designed to tackle multi-type hazard detection within complex substation scenarios. Firstly, a dynamic feature extraction module (C2fDSC) was designed, employing dynamic snake convolutions to enhance adaptive sampling capabilities for small targets and irregular defects. Secondly, a self-integrated attention module head (SEAMHead) was introduced to decouple localisation and classification tasks, thereby improving multi-type hazard discrimination accuracy. Finally, a bounding box regression loss function (inner_CIoU) was adopted to optimise small target localisation and irregular shape fitting. Experiments demonstrate that on a substation dataset containing 17 defect types, this method achieves mAP@0.5 and mAP@0.95 of 73.3% and 48.2%, respectively, representing improvements of 2.6% and 1.6% over the YOLOv10 baseline. This provides an efficient and reliable solution for multi-type defect detection in substations, holding significant engineering value for ensuring the secure operation of power grids.OPEN ACCESS Received: 15/10/2025 Accepted: 17/12/2025

  Abstract
  With the advancement of smart grid construction, substation equipment, being perpetually exposed to complex environments, is prone to latent hazards such as meter malfunctions [...]

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• An Innovative Approach to Extracting and Classifying Non-Functional Requirements Using Machine Learning and NLP

  M. Shafiq, W. Obidallah, M. Albathan, T. Kamal

  Rev. int. métodos numér. cálc. diseño ing. (2026). Vol. 42, (3), 82

  
  

  Abstract

  This study presents a hybrid automated framework based on a combination of machine learning (ML) and natural language processing (NLP) approaches for the automatic categorization and extraction of nonfunctional requirements (NFRs) from free-text software development documents. Using the PROMISE dataset, this framework systematically integrates semantic representation learning, deep feature extraction, and kernel-based classification to improve the performance of NFR classification. Unlike current CNN-based approaches with end-to-end softmaxbased classification, our proposed method fundamentally decouples feature learning from decision making. The first approach is to use Word2Vec embeddings to capture semantic context, and then use Convolutional Neural Networks (CNNs) as high-level feature extractors. An Improved Support Vector Machine with a Radial Basis Function kernel (ISVM-RBF) is applied for final classification, enabling more discriminative decision boundaries to be drawn in the high-dimensional semantic feature space. We reveal a considerable performance improvement with the CNN– Word2Vec setup, achieving as high as a 90% precision, significantly outperforming standard ML classifiers. The study points to three main findings: (i) CNN-based feature extraction is an efficient approach for finding and classifying NFRs, (ii) the semantic representation provided by word embedding methods is clearly superior to other traditional methods used in NLP, and (iii) NLP preprocessing of text is crucial for enhancing classification accuracy. Finally, ISVM-RBF adapts kernel-based classification over features derived from CNN, which enhances the robustness of the model to semantic overlaps between NFR categories and alleviates challenges posed by potentially large textual datasets required to train such models. This hybrid CNN–ISVM-RBF design constitutes the methodological novelty of the proposed method and effectively distinguishes it from current state-of-the-art methods in the literature.OPEN ACCESS Received: 03/11/2025 Accepted: 22/01/2026

  Abstract
  This study presents a hybrid automated framework based on a combination of machine learning (ML) and natural language processing (NLP) approaches for the automatic categorization [...]

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• Study on Basic Machine Learning Techniques to Detect and Classify Cardiovascular Diseases

  Y. Rawash, H. Ting, K. Chee

  Rev. int. métodos numér. cálc. diseño ing. (2026). Vol. 42, (3), 78

  
  

  Abstract

  The heart is an essential organ required to maintain the general health of individuals. Cardiovascular diseases (CVDs) have become the leading cause of death globally, replacing cancer and diabetes. Computer-based techniques have made it easier for physicians to diagnose various cardiac conditions, including heart failure. We are currently in the “information age,” a period characterized by the generation of millions of bytes of data every day. By applying ML algorithms’ techniques, such as Random Forest (RF), XGBoost, KNN, and GaussianNB, we can evaluate and compare the performance of machine learning classifiers for heart disease prediction and transform these data into information for the estimation of heart disease. The World Health Organization has estimated that in 2019, cardiac disease was responsible for 32% of all deaths worldwide. In this paper, we use a public data set (Indicators of Heart Disease) and hyperparameters to develop four classifiers—the Random Forest, XGBoost, KNN, and GaussianNB—and compare their performance. Based on the trial data, XGBoost was the best, with an accuracy of 91.63%, a precision of 94.40%, a recall of 88.50%, an F1-score of 91.36%, a specificity of 94.75%, and an AUC score of 97.39%. This study showcases the accuracy of machine learning systems in predicting cardiac conditions and can serve as a foundation for developing a decision-support tool aimed at detecting and preventing heart disease in its early stages.OPEN ACCESS Received: 09/10/2025 Accepted: 20/01/2026

  Abstract
  The heart is an essential organ required to maintain the general health of individuals. Cardiovascular diseases (CVDs) have become the leading cause of death globally, replacing [...]

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• Modeling and Simulation of a Novel Permanent Magnet Motor for Enhanced Vehicle Steering Performance

  M. Assen, G. Geng

  Rev. int. métodos numér. cálc. diseño ing. (2026). Vol. 42, (3), 77

  
  

  Abstract

  This paper introduces a new design for a permanent magnet motor (PMM) tailored for automotive steering applications. By utilizing new permanent magnet topology, high-performance materials, and rotor designs, the newly proposed PMM design reduces torque ripple while boosting torque density. This design is integrated within a sophisticated steer-bywire (SBW) system, enabling seamless operation and reliable feedback, which are crucial for modern vehicles. The finite element analysis (FEA) substantiates the effectiveness of the proposed PMM, resulting in a 1.2% enhancement in magnetic flux density, a 4.65% increase in static torque, and a 4.4% reduction in torque ripple. This research addresses the issues in wheel steering technology and lays the groundwork for future automotive motor technologies. And also, it provides valuable insights into the development of automotive technologies, paving the way for enhanced performance and sustainability in the automotive sector. Furthermore, these improvements not only aim to enhance driver satisfaction but also align with the industry’s transition toward greener technologies.OPEN ACCESS Received: 05/10/2025 Accepted: 15/12/2025

  Abstract
  This paper introduces a new design for a permanent magnet motor (PMM) tailored for automotive steering applications. By utilizing new permanent magnet topology, high-performance [...]

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