Scipedia: Revista Internacional de Métodos Numéricos para Cálculo y Diseño en Ingeniería

Adaptive Power Splitting Strategies for Smart Microgrids with Enhancing Energy Efficiency and Resilience through Dynamic Load Management

Jesús Sánchez Pinedo — Wed, 28 Jan 2026 12:02:43 +0100

The integration of a renewable energy distributed generation into microgrids poses a significant constraint in the way power is managed, further so due to the natural variability in renewable generation and the variability in the load demands. To address these issues, this paper introduces a novel approach to the Spider Swarm Optimization (SSO) algorithm, the Dynamic LoadAdaptive Power Splitting (DLAPS) strategy, to enable real-time adaptive power sharing and enhance system resilience. Unlike the classical methods of power allocation that are static, according to which the power is divided between sources of renewable energy and storage systems, and between these sources and critical loads, the DLAPS-SSO applies the idea of a machine learning based predictive model to predict the power and dynamically optimize power allocation between the sources of renewable energy and storage systems and the sources and the critical loads. The model provides a multi-objective optimization framework that aims to minimize power losses and grid frequency variations, and to maximize the system’s resilience to disturbances, including disconnection from the grid, component malfunctions, and the availability of renewable energy sources. The comparison of simulation results with those of the Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) methods shows that the energy efficiency of the DLAPS-SSO increases by 15%–20%, and the amount of power losses across various load profiles decreases by 30%–35%. Moreover, the proposed solution offers 60% faster recovery time in case of grid disconnection, maintains 65.9% of the critical load in case of component failure, and provides 40%–50% less resilience than state-of-the-art techniques. The analysis of seasons and real data shows that there is stability of the behavior with the increase of efficiency (18%–22% during winter, and 23%–25% during summer), and the ability of the suggested approach to be robust when changing plant configuration/operation. Integration of optimization of dynamic load management and adaptive power splitting will spur microgrid control strategies and offer a viable strategy to stabilize the grid, reduce operation costs, and enable sustainable changes in energy transformations. The results demonstrate the essential role of bio-inspired optimization and reactivity in the next generation of smart grids.

Advanced Computational Study of Nonlinear Time-Fractional Newell-Whitehead-Segel Equation with Caputo-Fabrizio Derivative Using B-Spline Techniques

Jesús Sánchez Pinedo — Wed, 28 Jan 2026 12:03:33 +0100

This study presents numerical solutions for the time-fractional NewellWhitehead-Segel (NWS) equation with a Caputo-Fabrizio derivative. Spatial derivatives are discretized using three B-splines-cubic, cubic trigonometric and extended cubic B-splines-while temporal discretization is handled by a finite difference scheme. The proposed schemes are rigorously analyzed for stability and convergence. Their performance is evaluated in terms of accuracy and computational efficiency. Numerical experiments confirm the effectiveness of these techniques in capturing the dynamics of the fractional NWS equation. Each B-spline variant demonstrates unique strengths, highlighting the flexibility of B-spline approaches for solving fractional differential equations with nonlocal, memory-dependent operators. These results affirm the reliability and robustness of B-spline-based methods for such problems, paving the way for future advancements in this area.OPEN ACCESS Received: 23/08/2025 Accepted: 08/12/2025

On a Class of Hybrid Langevin Inclusions Involving Two Generalized Fractional Derivatives

Scipedia content — Wed, 18 Feb 2026 10:47:03 +0100

In this paper, we study the existence of solutions for a hybrid Langevin inclusion involving a combination of φ-Hilfer and φ-Caputo fractional derivatives. To this end, we construct a new operator derived from the integral solution of the given boundary value inclusion problem and subsequently apply the hypotheses of Dhage’s fixed point theorem to this fractional operator. Finally, to support our theoretical findings, an illustrative example is provided.OPEN ACCESS Received: 13/11/2025 Accepted: 20/01/2026

Advancing Fractional Calculus: A Fixed-Point Strategy for Nonlinear Caputo-Hadamard Equations

Scipedia content — Fri, 06 Mar 2026 10:52:43 +0100

This paper investigates the existence, uniqueness, and Ulam–Hyers stability of solutions for a coupled system of nonlinear Caputo–Hadamard fractional differential equations in Banach spaces. By reformulating the boundary value problem into an equivalent integral system via the Hadamard fractional integral operator, sufficient conditions for existence and uniqueness are established using Krasnoselskii’s and Banach’s fixedpoint theorems. Within the same functional framework, Ulam–Hyers stability results are derived for the proposed system. The theoretical analysis provides a consistent and unified approach for studying nonlinear coupled fractional systems with nonlocal operators, and the validity of the assumptions is illustrated through representative examples.

Garhy distribution with Different Estimation Methods and Applications to Engineering and Medical Data

Scipedia content — Fri, 06 Mar 2026 10:53:33 +0100

In this article, we introduce and investigate a new one-parameter mixture distribution called the “Garhy distribution”. The probability density function is very adaptable, as it may take on right skewed, unimodal, and heavy tailed patterns. In addition, the hazard rate function indicates that data with increasing shaped failure rates may be adapted by the Garhy distribution. Several fundamental statistical and mathematical properties are calculated including mode, quantile function, moments, mean, variance, skewness, kurtosis, moment-generating function, incomplete moments, inequality measures, order statistics, and extropy measures. The scale parameter of the Garhy distribution is estimated employing twelve different estimation approaches, maximum likelihood, maximum product of spacings, least-squares, weighted least-squares, Anderson darling, right-tail Anderson darling, left-tail Anderson darling, Cramér von-Misses, and the least-squares method. The effectiveness of these strategies is evaluated using a detailed simulation study. Furthermore, we used the Garhy distribution to examine two real-world data sets, demonstrating its superior performance compared to specific competitors.

An Improved ORB-Based Multi-View Stereo Matching Algorithm for Accurate Visual SLAM

Scipedia content — Mon, 23 Mar 2026 10:22:43 +0100

This study addresses image matching in visual Simultaneous Localization and Mapping (SLAM) to enhance synchronous positioning and mapping using a multi-view stereo matching algorithm. Utilizing a vision odometer and a binocular camera, multi-view stereo images are captured. An improved ORB (Oriented FAST and Rotated BRIEF) algorithm extracts feature points and establishes binary descriptors, with similarity computed using Euclidean distance to enable stereo matching. Mismatches are filtered using parallax constraints and corrected through triangulation. Loopback detection minimizes cumulative drift in camera position, improving spatial perception. The back-end optimization employs graph optimization theory to refine the position and pose of the binocular camera and landmarks, addressing random noise and errors. Experimental results indicate effective feature extraction, with mismatches limited to three, overall positioning error under 100 mm, and directional error within 2°.OPEN ACCESS Received: 11/08/2025 Accepted: 14/01/2026

Least Square Support Vector Machine Framework for Meshless and Accurate Solution of Higher Order Boundary Value Problems with Comparative Analysis of Machine Learning Techniques

Scipedia content — Mon, 23 Mar 2026 10:23:35 +0100

This paper presents an enhanced Least Squares Support Vector Machine (LS-SVM) approach for meshless and accurate solution of higher-order boundary value problems (BVPs) that commonly arise in structural mechanics, fluid dynamics, and other engineering fields. The discussed method formulates thirdand fourth-order linear and nonlinear ordinary differential equations (ODEs) as data-driven optimization problems, eliminating the need for traditional mesh-based discretization. Leveraging a Radial Basis Function (RBF) kernel and regularization-based control of model complexity, the LS-SVM captures complex solution behaviour while maintaining stability and smoothness. The meshless nature of the model ensures geometry-independence, making it suitable for irregular or multi-point boundary conditions. A comparative analysis with established machine learning techniques, including Ridge Regression (RR), classical SVM, Random Forest (RF), and Extreme Gradient Boosting (XGB), demonstrates the competitive accuracy, robustness, and efficiency of LSSVM. The results highlight its potential as a promising solver for nonlinear and multi-point problems where meshless methods are advantageous. The results highlight its potential as a promising solver for simulation-based workflows in computational mechanics and scientific computing, where adaptability, generalization, and reliability are critical.OPEN ACCESS Received: 25/08/2025 Accepted: 21/10/2025

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

Scipedia content — Mon, 23 Mar 2026 10:25:26 +0100

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

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

Scipedia content — Mon, 23 Mar 2026 10:44:33 +0100

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

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

Scipedia content — Mon, 23 Mar 2026 10:47:14 +0100

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

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

Scipedia content — Mon, 23 Mar 2026 10:46:14 +0100

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

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

Scipedia content — Mon, 23 Mar 2026 10:47:19 +0100

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.

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

Scipedia content — Mon, 30 Mar 2026 09:29:34 +0200

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

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

Scipedia content — Mon, 30 Mar 2026 09:31:03 +0200

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

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

Scipedia content — Mon, 30 Mar 2026 09:36:34 +0200

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