Documents published in Scipedia

• Mother Algorithm Optimized Controller for Multi-Area Deregulated Power System Modelled with Solar/Wind/EV Sources: A Novel Approach

  S. Sriramula, B. Reddy

  Rev. int. métodos numér. cálc. diseño ing. (2026). Vol. 42, (5), 148

  
  

  Abstract

  Due to increased system uncertainty, nonlinear dynamics, and marketdriven power exchanges, modern deregulated multi-area power systems with high penetration of renewable energy sources (RES) like wind and solar, and electric vehicle (EV) charging stations present serious challenges to automatic generation control (AGC). Degraded frequency regulation and tie-line power control under deregulated environments result from the limited robustness, slow dynamic response, poor handling of stochastic RES/EV variations, and susceptibility to local optimal solutions of both conventional PI/PID controllers and recently reported optimizationbased AGC schemes. However, with a view to addressing these issues, this study focuses on reducing area control errors (ACE) during different operational shifts, such as frequency fluctuation (f) and tie line variations (Ptie). The main objective of this study is to determine the optimal gain settings for the Fractional Order Proportional-Integral-Derivative controller (FOPIDC) using the mother optimization algorithm (MOA). The proposed control strategy looks at how generators 3-AMS behave in a deregulated environment and emphasises the significance of FOPIDC optimization in preserving system stability with the goal of reducing integral time and absolute error (ITAE). Furthermore, the effectiveness of the proposed method is verified by comparing it with the Walrus Optimization Algorithm (WOA). As case studies, the effectiveness of the suggested strategy is also evaluated under Poolco, bilateral agreements, stability, and sensitivity analysis. In terms of generator outputs, tie-line power variations, and frequencies across different locations, comparative data unequivocally demonstrate that the suggested MOA-adjusted FOPIDC performs better than alternative approaches. In case 1, by implementing the MOA optimized controller, the settling time is 8.5 s, and the value of the objective ITAE for the transient responses is 0.0005292. However, these values are less than the values obtained by WOA. Similarly, in case 2, the settling time is 11.5 s, and ITAE is 0.000395 less

  Abstract
  Due to increased system uncertainty, nonlinear dynamics, and marketdriven power exchanges, modern deregulated multi-area power systems with high penetration of renewable energy [...]

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• Isogeometric Boundary Element Analysis for Two-Dimensional Transient Heat Conduction Problems Based on the Radial Integration Method and the Application of Polynomial Chaos Expansion

  X. Jiang, L. Huang, Y. Wang, J. Xuan, Y. Xu

  Rev. int. métodos numér. cálc. diseño ing. (2026). Vol. 42, (5), 147

  
  

  Abstract

  This study introduces a novel coupled computational framework that seamlessly fuses the Isogeometric Boundary Element Method (IGABEM) with the Polynomial Chaos Expansion (PCE) to address transient heat conduction problems under parametric uncertainty. The proposed approach transcends traditional numerical paradigms by constructing a unified formulation that intertwines geometric precision, computational efficiency, and probabilistic rigor. At the heart of the framework lie three tightly integrated representations. First, Non-Uniform Rational B-Splines (NURBS) basis functions serve a dual purpose—they simultaneously embody geometric modeling and field discretization. This duality allows for the direct transformation of ComputerAided Design (CAD)-level geometric fidelity into the numerical domain, preserving intricate boundary characteristics while discretizing the governing boundary integral equations with remarkable exactness. Second, through the introduction of Bézier extraction, the computational overhead associated with NURBS basis evaluations is dramatically reduced. This operation reformulates complex NURBS structures into more manageable Bézier entities, unlocking substantial efficiency gains without compromising the inherent smoothness or precision of the geometric representation. Third, the framework incorporates Polynomial Chaos Expansion to systematically quantify and propagate uncertainties in material parameters. By coupling this stochastic representation with an advanced radial integration scheme, the method elegantly circumvents the need for conventional domain meshing while maintaining high accuracy in evaluating domain integrals. The result is a streamlined yet robust uncertainty quantification process embedded directly within the boundary element context. A series of numerical experiments corroborates the effectiveness of the proposed hybrid strategy. The findings reveal that the method not only preserves the celebrated advantages of IGABEM—such as geometric exactness and reduced-dimensional computation—but also extends its capability into the stochastic domain, enabling a comprehensive evaluation of how parameter uncertainties influence transient thermal responses. In essence, this integrated IGABEM–PCE framework bridges the gap between deterministic modeling and probabilistic analysis, yielding a unified, highfidelity numerical tool. Its capacity to deliver both geometric accuracy and uncertainty quantification positions it as an indispensable technique for complex engineering scenarios, particularly in thermal management, energy conversion, and high-precision design systems, where reliability under uncertainty is paramount.

  Abstract
  This study introduces a novel coupled computational framework that seamlessly fuses the Isogeometric Boundary Element Method (IGABEM) with the Polynomial Chaos Expansion (PCE) [...]

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• MAM-YOLO: A Real-Time Algorithm for Detecting Defects in Steel Surfaces Using Multi-Scale Feature Enhancement

  Y. Wang, C. Mao, F. Jiang, L. Zou, J. Liu

  Rev. int. métodos numér. cálc. diseño ing. (2026). Vol. 42, (5), 146

  
  

  Abstract

  Traditional methods for detecting surface defects in steel typically rely on manual visual inspection, eddy current testing, magnetic particle inspection, and machine vision. These methods often struggle to adapt to defects of varying scales and complex shapes, resulting in insufficient feature extraction, false detections, missed detections, and low detection accuracy. To address these issues, we propose an enhanced YOLOv8n model named MAM-YOLO, which aims to optimize defect detection on steel surfaces through multi-scale feature enhancement. By redesigning the convolution modules, the model improves accuracy in identifying surface defects, crazing, scratches, and patches. Furthermore, the design of a new module enhances processing capabilities for diverse surface features. A new loss function optimization is also applied to better identify crack characteristics. Experimental results on the NEU-DET dataset demonstrate that MAM-YOLO increases mAP by 5.4% and improves accuracy by 5.9% compared to the original YOLOv8n. Additionally, the model’s generalization capability was verified on the KolektorSDD dataset, achieving performance superior to the YOLOv8n baseline. Therefore, the proposed MAM-YOLO model offers significant advantages in precision and robust feature extraction for identifying steel surface defects.OPEN ACCESS Received: 25/11/2025 Accepted: 27/01/2026 Published: 21/07/2026

  Abstract
  Traditional methods for detecting surface defects in steel typically rely on manual visual inspection, eddy current testing, magnetic particle inspection, and machine vision. [...]

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• CAM-YOLO: A Framework for Non-Salient Object Detection by Exploiting Contextual Interdependencies

  C. Dong, Y. Ding, J. Hu

  Rev. int. métodos numér. cálc. diseño ing. (2026). Vol. 42, (5), 144

  
  

  Abstract

  Real-time object detection requires identifying objects in video streams or consecutive images with minimal latency, yet it continues to struggle with non-salient objects—those that are small, occluded, or otherwise inconspicuous. To address this limitation, this paper proposes CAM-YOLO, an enhanced architecture based on YOLOv8. First, to mitigate the baseline model’s limited representational capacity for non-salient targets, we introduce a Multi-Scale Aggregation Module (MSAM) into the feature fusion process, enabling the backbone network to extract more discriminative fine-grained features. Second, to better capture global contextual relationships associated with such objects, we design a Contextual Association Module (CAM) that explicitly models long-range spatial dependencies. Furthermore, we integrate a Dual-Branch Attention Mechanism (DBAM) to refine the feature processing flow, thereby strengthening the contextual feature representations crucial for detecting non-salient instances. Extensive experiments on two large-scale public benchmarks, Microsoft Common Objects in Context 2017 (MS COCO 2017) and PASCAL Visual Object Classes (PASCAL VOC), demonstrate that CAM-YOLO achieves highly competitive performance compared to several widely-adopted realtime detectors.OPEN ACCESS Received: 19/11/2025 Accepted: 15/01/2026 Published: 21/07/2026

  Abstract
  Real-time object detection requires identifying objects in video streams or consecutive images with minimal latency, yet it continues to struggle with non-salient objects—those [...]

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• An Innovative Distribution that Incorporates Actuarial Parameters and Applications, Real Data in a Variety of Domains

  H. Aljohani

  Rev. int. métodos numér. cálc. diseño ing. (2026). Vol. 42, (5), 145

  
  

  Abstract

  The complexity of data necessitates the development of novel distributions. The introduction of new models allows us to enhance this data and remain contemporary. In this paper, we proposed and explored a new flexible distribution, referred to as the extended inverse Weibull model. This study presents a novel probability model, the extended inverse Weibull (E-IW) distribution. The recently introduced distribution combines the E-X approach with the inverse Weibull distribution. The novel distribution facilitates the evaluation of real-world data due to its analytical feasibility and applicability. The suggested distribution may accommodate several types of datasets. Numerous statistical characteristics of the proposed model were acquired. The features include quantile functions, ordinary moments, order statistics, and moment-generating functions. functions. The estimated parameters of the new model are determined using several estimation techniques, including maximum likelihood, least squares, weighted least squares, maximum product spacing, and the Bayesian method under the square error loss function. The simulation analysis performed on the suggested model validated the dependability and consistency of its parameters. Furthermore, actuarial measures were computed, along with simulation study for these actuarial metrics was executed. Five real data sets were taken from several sectors to illustrate the importance and usefulness of the proposed model. Our empirical findings underscore the significance of the recommended model as a flexible and reliable tool for statistical modeling, with implications for improving data-driven analyses and integrating parametric modeling into modern applications.OPEN ACCESS Received: 10/11/2025 Accepted: 27/01/2026 Published: 21/07/2026

  Abstract
  The complexity of data necessitates the development of novel distributions. The introduction of new models allows us to enhance this data and remain contemporary. In this [...]

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• Bayesian and Classical Survival Inference for the Nadarajah–Haghighi Model under Unified Type-I Progressive Hybrid Censoring with Random Binomial Removals

  R. Alotaibi, M. Nassar, Z. Khan, A. Elshahhat

  Rev. int. métodos numér. cálc. diseño ing. (2026). Vol. 42, (5), 143

  
  

  Abstract

  A highly adaptable censoring framework, known as the unified TypeI progressive hybrid censoring scheme, has been recently introduced as an enhancement to the unified hybrid censoring approach. However, this censoring method has a significant limitation: it assumes that the removal pattern is fixed and established prior to conducting the experiment, which may lack practical applicability. This study presents, for the first time, the unified Type-I progressive hybrid censoring scheme incorporating binomial removal, which enables the random withdrawal of surviving units following each failure, making it more suitable for survival studies. Assuming that the underlying distribution of the test units follows the Nadarajah–Haghighi distribution, we examine both point and interval estimation problems for the model parameters, binomial parameter, and two key survival measures. Maximum likelihood estimation serves as the classical methodology for obtaining point estimates and approximate confidence intervals. For Bayesian estimation, we employ the squared error loss function in conjunction with Markov Chain Monte Carlo sampling techniques. A comprehensive simulation analysis is conducted to assess the performance of the various point and interval estimators. In addition, to demonstrate the practical relevance of the proposed methodologies in survival analysis, a real-world data application involving the death times of a group of 45 gastric cancer patients is investigated.OPEN ACCESS Received: 02/11/2025 Accepted: 11/12/2025 Published: 21/07/2026

  Abstract
  A highly adaptable censoring framework, known as the unified TypeI progressive hybrid censoring scheme, has been recently introduced as an enhancement to the unified hybrid [...]

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• Reliability Analysis of Modified Kies– Rayleigh Distribution with Optimal Progressive First-Failure Sampling Plans with Application to Turbocharged Diesel Engines

  R. Alotaibi, M. Nassar, Z. Khan, A. Elshahhat

  Rev. int. métodos numér. cálc. diseño ing. (2026). Vol. 42, (5), 142

  
  

  Abstract

  This study investigates the utility of the newly modified Kies–Rayleigh distribution for analyzing progressively first-failure censored samples. We develop both classical and Bayesian estimators for the distribution’s parameters and derived reliability measures, including the reliability function and hazard rate. Interval estimation is addressed via approximate confidence intervals and Bayesian credible intervals. Classical estimates are obtained numerically by solving the likelihood equations, whereas Bayesian inference is conducted through Markov chain Monte Carlo sampling from the posterior distribution. To assess and compare the performance of the classical and Bayesian procedures, we carry out a comprehensive simulation study under multiple experimental scenarios. We also consider the design problem of choosing an optimal progressive sampling plan and evaluate competing plans using four standard optimality criteria. We analyze a dataset comprising time-to-failure observations for turbocharged engines from a single model of diesel engine. The results highlight the flexibility of the modified Kies–Rayleigh model and provide guidance on estimation and design choices for progressively censored reliability data.OPEN ACCESS Received: 21/09/2025 Accepted: 07/11/2025 Published: 21/07/2026

  Abstract
  This study investigates the utility of the newly modified Kies–Rayleigh distribution for analyzing progressively first-failure censored samples. We develop both classical [...]

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• Existence and Uniqueness Results for Weighted Fractional Stochastic Integral Equations

  A. Alanzi, R. Fakhfakh, G. Alomani, A. Makhlouf

  Rev. int. métodos numér. cálc. diseño ing. (2026). Vol. 42, (5), 141

  
  

  Abstract

  This study explores the existence, uniqueness (EU), and Hyers-Ulam stability (HUS) for a class of weighted fractional Itô-Doob stochastic integral equations (WFIDSIEs). To establish these crucial properties, we utilize the Banach fixed-point theorem (BFPT) as a foundational tool, alongside a variety of essential mathematical inequalities. These inequalities offer valuable insights into the underlying structure and behavior of WFIDSIEs, particularly in the context of fractional and stochastic dynamics. By leveraging these techniques, we demonstrate the conditions under which solutions to these equations exist and are unique, as well as how small perturbations affect the stability of these solutions in the Hyers-Ulam sense. Our results contribute to a deeper understanding of the stability and solvability of WFIDSIEs, which are important in modeling complex systems exhibiting both stochasticity and memory effects. Additionally, these findings open avenues for further research into the robustness of solutions in fractional stochastic models across various applied disciplines, such as finance, engineering, and biology.OPEN ACCESS Received: 12/09/2025 Accepted: 11/11/2025 Published: 21/07/2026

  Abstract
  This study explores the existence, uniqueness (EU), and Hyers-Ulam stability (HUS) for a class of weighted fractional Itô-Doob stochastic integral equations (WFIDSIEs). [...]

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• Rainfall Trend Analysis and One-Dimensional Hydrodynamic Assessment of the Ganga River Basin Using HEC-RAS 6.3

  G. Goswami, M. Alsubih, S. Islam, S. Basack, P. Borah

  Rev. int. métodos numér. cálc. diseño ing. (2026). Vol. Online First, 140

  
  

  Abstract

  This study integrates long-term rainfall trend analysis with one-dimensional hydrodynamic modelling to assess hydro-climatic variability and flood vulnerability along the selected stretch of the Ganga River near Farakka Barrage, eastern India. Daily gridded rainfall data for 1991–2025 were analyzed to identify trends and potential change points. The Mann–Kendall test, Sen’s slope estimator, Bias-Corrected Pre-Whitening, and Trend-Free Pre-Whitening were applied to account for serial correlation. The rainfall series showed an overall decreasing tendency, although statistical significance varied among the applied trend tests after pre-whitening. HEC-RAS 6.3 simulations showed increasing water surface elevation, velocity, and discharge under 25-, 50-, and 100-year design-flood scenarios. The findings indicate that flood risk in the study reach is shaped by both hydro-climatic variability and local hydraulic controls.

  Abstract
  This study integrates long-term rainfall trend analysis with one-dimensional hydrodynamic modelling to assess hydro-climatic variability and flood vulnerability along the [...]

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• Dynamic Multi-Objective Collaborative Optimization of Cement Combined Grinding Based on Cooperative Game and Temporal Awareness

  D. Ju, X. Ma, R. Zhang, Q. Zhang, X. Wang, Z. Liu

  Rev. int. métodos numér. cálc. diseño ing. (2026). Vol. 42, (5), 139

  
  

  Abstract

  To address the multi-objective collaborative optimization of quality, energy consumption, and yield under dynamic conditions in the Portland cement combined grinding process, this paper proposes a novel algorithm, CGDS-LTL, based on cooperative game theory and temporal perception. First, a hybrid temporal model combining Linformer, TCN, and LSTM was developed to dynamically track process conditions in the Portland cement combined grinding process. Second, an optimization objective function was established, and a cooperative game theory framework was introduced to address the challenge of achieving multi-objective optimization, which could not be effectively solved with a single paretooptimal solution. Meanwhile, volatility metrics were used to quantify the adjustment range of operational variables, allowing for the dynamic optimization of decision constraints. This approach mitigated the deviation of the pareto front from current decision settings caused by high population randomness, ultimately identifying the optimal solution for the multiobjective collaborative optimization problem. Finally, experiments using real production data from a Portland cement plant demonstrated that, compared with NSGA-II and C-TAEA, the proposed method improved the hypervolume indicator by 95% and 33.3%, respectively, indicating a more uniform solution distribution and better convergence. This demonstrated the interpretability and effectiveness of the proposed framework for dynamic multi-objective optimization in Portland cement combined grinding.OPEN ACCESS Received: 14/04/2026 Accepted: 21/05/2026

  Abstract
  To address the multi-objective collaborative optimization of quality, energy consumption, and yield under dynamic conditions in the Portland cement combined grinding process, [...]

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