Documents published in Scipedia

• Classical and Bayesian Inference of Engineering and Disability Data: Using the Kavya Manoharan Power Chris-Jerry Distribution under Hybrid Censoring

  A. Ahmadini, A. El-Saeed, E. Almetwally, M. Elgarhy

  Rev. int. métodos numér. cálc. diseño ing. (2025). Vol. 41, (4), 80

  
  

  Abstract

  In this article, we study and introduce the Kavya-Manoharan power ChrisJerry distribution (KMPCJD) which is a new generation of the power Chris-Jerry distribution (PCJD) which is suitable for engineering and disability data. The probability density curves of KMPCJD demonstrate that it has practical applications in analyzing engineering and disability data in Saudi Arabia. Researchers have a lot of flexibility when developing statistical models for research on disability issues, since the hazard rate function (HRF) for KMPCJD can exhibit J-shaped, increasing, and decreasing trends. In addition, several significant KMPCJD features are calculated, including moments, reliability metrics, moment-generating function, and order statistics. Using data on engineering and disability difficulties, we estimate the parameters of KMPCJD and use classical and Bayesian techniques to assess their reliability and HRF under hybrid censored schemes. Asymptotic confidence/credible intervals are calculated. The numerical results show that when the sample size n increases while keeping other factors like r and T constant, the estimators for δ and λ show improved performance in terms of reduced Bias, mean square error (MSE), and narrower confidence intervals. Also, the Bayesian method also produces shorter credible intervals (LCCI) compared to the traditional confidence intervals (LACI) from ML and MPS methods, suggesting higher precision. To show the utility of the suggested distribution, it was tested in five datasets related to engineering and disability issues in Saudi Arabia. The KMPCJD performed better in terms of goodness of fit than a number of models, including the Kavya Manoharan Rayleigh inverted Weibull distribution, Kavya Manoharan Burr X distribution, exponentiated generalized power Lindley distribution, Weibull power Lindley distribution, power Lindley distribution, Kavya Manoharan generalized exponential distribution, power XLindley distribution, Kavya Manoharan unit exponentiated half logistic distribution, and PCJD. Due to its superior fit capabilities, the KMPCJD is suggested for data modeling in disciplines including engineering and disability difficulties.OPEN ACCESS Received: 27/08/2025 Accepted: 19/09/2025 Published: 27/11/2025

  Abstract
  In this article, we study and introduce the Kavya-Manoharan power ChrisJerry distribution (KMPCJD) which is a new generation of the power Chris-Jerry distribution (PCJD) which [...]

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• F-Q-LiTO: A Federated Q-Learning-Based Lightweight Intelligent Task Orchestrator for Multi-Tenant Container Clusters

  V. Veeramani, M. Balamurugan, M. Oberoi

  Rev. int. métodos numér. cálc. diseño ing. (2025). Vol. 41, (4), 78

  
  

  Abstract

  The demand for intelligent, scalable, and energy-conscious container orchestration has increased due to the growth of microservice-based designs and multi-tenant workloads. A novel federated reinforcement learning framework for adaptive task scheduling in heterogeneous container clusters, F-Q-LiTO (Federated Q-Learning-Based Lightweight Intelligent Task Orchestrator), is proposed in this research. In contrast to traditional orchestrators, F-Q-LiTO uses federated Q-learning to decentralise decision-making, guaranteeing convergence across dispersed nodes while maintaining data locality and minimising synchronisation overhead. The system has several lightweight components, including energy-conscious placement penalties, XOR filters for secure container fingerprinting, Count-Min Sketches (CMS) for constant-space resource estimation, and workload forecasting based on the Long Short-Term Model (LSTM) for proactive migration. In comparison to DeepPlace, F-Q-LiTO reduced task deadline misses by around 34% and achieved an average SLA satisfaction of 96.8% when tested on simulated multitenant workloads with over 1000 tasks. Ablation studies confirm that federated coordination and predictive migration materially improve performance. Global Q-values converged within six episodes, and SHAPbased explanations identify CPU forecast, SLA urgency, and node energy state as dominant decision factors. F-Q-LiTO demonstrates practical, interpretable, and low-latency orchestration suitable for dynamic edge– cloud deployments.OPEN ACCESS Received: 01/08/2025 Accepted: 09/10/2025 Published: 27/11/2025

  Abstract
  The demand for intelligent, scalable, and energy-conscious container orchestration has increased due to the growth of microservice-based designs and multi-tenant workloads. [...]

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• AI-Enhanced and Other Load Modelling in Modern Power Systems: A Comprehensive Review of Advances, Challenges, and Future Directions

  S. Singh, M. Singh, A. Singh, S. Kumar

  Rev. int. métodos numér. cálc. diseño ing. (2025). Vol. 41, (4), 77

  
  

  Abstract

  Load modelling is a crucial element of power system study that significantly affects the field’s planning, operation, and control methods. With the increasing penetration of renewable energy sources, electric vehicles, demand-side management, and distributed generation (DG), the traditional static and dynamic load model approaches are being replaced. This paper reviews extensively the existing load modelling techniques, namely, component-based load modelling, measurement-based load modelling, and hybrid methods. In addition, advancements tuned by artificial intelligence (AI) and machine learning (ML) are critically reviewed, emphasizing improving the accuracy, flexibility, and real-time adaptability of load models. For instance, Long Short-Term Memory (LSTM) networks have demonstrated significant improvements in forecasting accuracy, while Reinforcement Learning (RL) techniques enable adaptive and real-time control of load dynamics. Special focus is laid on load modelling in conditions of imbalance, dynamic parameter identification, and integration with smart grids and active distribution networks (ADNs). The review also discusses the importance of uncertainty embedded in probabilistic and data-driven models, customer behaviour, and the stochastic nature of distributed energy resources (DERs). The areas of future study emphasized AI-assisted adaptive architectures, hybrid frameworks, and digital twin applications for resilient and intelligent load modelling.OPEN ACCESS Received: 01/08/2025 Accepted: 26/09/2025 Published: 27/11/2025

  Abstract
  Load modelling is a crucial element of power system study that significantly affects the field’s planning, operation, and control methods. With the increasing penetration [...]

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• Generalized Convergence of Sequences of Fuzzy Numbers by Means of Modulus Functions

  I. Ibrahim, D. Baleanu, M. Yousif, P. Mohammed, T. Abdeljawad

  Rev. int. métodos numér. cálc. diseño ing. (2025). Vol. 41, (4), 76

  
  

  Abstract

  In this paper, we extend the concepts of statistical convergence and strong summability for the sequences of fuzzy numbers using modulus functions. By introducing appropriate conditions on the modulus functions, we generalize and refine existing notions of convergence within the fuzzy setting. Additionally, we establish several interrelationships between these extended concepts, thereby contributing to the deeper understanding of summability and convergence behavior in the sequences of fuzzy numbers.OPEN ACCESS Received: 16/06/2025 Accepted: 20/08/2025 Published: 27/11/2025

  Abstract
  In this paper, we extend the concepts of statistical convergence and strong summability for the sequences of fuzzy numbers using modulus functions. By introducing appropriate [...]

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• Modeling and Thermal Analysis of a Ground Cooling System for Drilling Fluids in Ultra-Deep Wells

  J. Wang, Z. Song, Y. Chen, B. Zheng, S. Deng, K. Xu, B. Wang, Q. Ren

  Rev. int. métodos numér. cálc. diseño ing. (2025). Vol. 41, (4), 74

  
  

  Abstract

  Elevated downhole temperatures in ultra-deep wells (>8000 m) accelerate thermal degradation of drilling fluids and tools, reducing operational safety and efficiency. The reduction of wellbore temperature is an important issue. In this paper, a cooling system model for a drilling fluid was designed. Additionally, a comprehensive analysis of the heat transfer behavior within ultra-deep wells was conducted. A miniaturized heat exchange cooling device was used to simulate various conditions, including fluid media, flow rate, drilling fluid/coolant temperature, and heat exchanger structure. This analysis elucidates the impact of various factors on cooling efficiency. The experimental results show that the cooling effect is best at a medium flow rate, with a refrigerant temperature of−10°C reducing the temperature of pure water from 60°C to 32°C. The experiment also found that the higher the temperature of the pure water and the lower the temperature of the coolant, the better the heat transfer efficiency. For water-based drilling fluid, the optimum cooling flow rate is around 0.52 m/s, with an average Reynolds number of 4966, and the maximum cooling range can exceed 30°C. Furthermore, the coil heat exchanger significantly improves the cooling rate compared to the straight-tube heat exchanger, although the pressure difference also increases. The cooling rate of oil-based drilling fluid at high flow rates is greater than that of water-based drilling fluid, and the pressure difference in the coil heat exchanger for oil-based drilling fluid, which has higher viscosity, increases significantly. This research provides an experimental basis for the design and optimization of drilling fluid surface cooling systems, which is crucial for improving the safety and efficiency of deep and ultra-deep wells drilling.OPEN ACCESS Received: 10/06/2025 Accepted: 15/08/2025 Published: 27/11/2025

  Abstract
  Elevated downhole temperatures in ultra-deep wells (>8000 m) accelerate thermal degradation of drilling fluids and tools, reducing operational safety and efficiency. The [...]

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• Multi-Dimensional Mechanical Properties Approach to Analyzing Thin UHPFRC Decks

  M. Liu, X. Li, A. Auwal, F. Cui

  Rev. int. métodos numér. cálc. diseño ing. (2025). Vol. 41, (4), 73

  
  

  Abstract

  This study evaluates the flexural behavior of an Ultra high performance fiber-reinforced concrete (UHPFRC) slab through experimental and Finite Element Method (FEM) analytical investigations. A full-size U-UHPFRC bridge deck specimen serves as a reference for the research. A nonlinear FEM is put forward to link material characteristics, failure mode, and bearing capacity of U-UHPFRC decks, considering the failure behavior with different impact parameters of reinforcement ratio, thickness and side ratio. The flexural performance calculation formula for UHPFRC slabs was derived using three failure modes. The results indicate that this method can effectively predict the load transfer and distribution patterns of UHPFRC thin slabs, providing a reference range for the reinforcement ratio, thickness and long-short side ratio in UHPFRC one-way or two-way slabs. These research results can optimize the crack resistance and toughness of thin UHPFRC decks, improve durability, and appropriately reduce carbon emissions. It is suitable for bridges or special structures with higher load requirements and provides theoretical support for the full-life operation and development of UHPFRC components.OPEN ACCESS Received: 31/05/2025 Accepted: 10/07/2025 Published: 27/11/2025

  Abstract
  This study evaluates the flexural behavior of an Ultra high performance fiber-reinforced concrete (UHPFRC) slab through experimental and Finite Element Method (FEM) analytical [...]

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• Numerical Analysis of Geometry and Hole Effects on Fatigue Crack Growth and Component Lifespan

  E. Kakavand, A. Parsania, S. Hosseini

  Rev. int. métodos numér. cálc. diseño ing. (2025). Vol. 41, (4), 72

  
  

  Abstract

  Mechanical failures have caused significant damage and financial losses. However, compared to the vast number of successful designs for mechanical components and structures, mechanical failures are relatively rare. Mechanical failures involve a highly complex interaction between time, load, and the environment, with the environment comprising two factors: temperature and corrosion. The load can be uniform, steady, variable, uniaxial, or multiaxial. Undesirable effects of cyclic loads lead to crack initiation and growth, ultimately resulting in component failure. In this study, the focus is on increasing the life of components under cyclic loading by investigating crack behavior and development in the presence of stop holes. The investigation was conducted on aluminum 7075. Initially, the arrangement of circular holes in the standard CT sample was obtained using Design Expert software to assess this effect. By performing simulations in Abaqus software, the life of each sample was determined. In the next stage, based on these results, the samples are subjected to examination in the presence of key-shaped notches.OPEN ACCESS Received: 22/03/2025 Accepted: 27/05/2025 Published: 27/11/2025

  Abstract
  Mechanical failures have caused significant damage and financial losses. However, compared to the vast number of successful designs for mechanical components and structures, [...]

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• Comparison with Different Bayesian and non-Bayesian Estimation Techniques for the Compound Rayleigh Exponential Distribution with Actuarial Measures and Applications

  I. Alkhairy, H. Alsuhabi

  Rev. int. métodos numér. cálc. diseño ing. (2025). Vol. 41, (4), 75

  
  

  Abstract

  Many lifetime analysis, such as engineering, biology, survival, actuarial and medical sciences, heavily rely on the two-parameter compound Rayleigh exponential distribution, which is well-known in statistical theory. Because of their ability to successfully handle small sample sizes and involve prior knowledge, Bayesian techniques are crucial for estimating the parameters of the compound Rayleigh exponential distribution. This study presents the estimation of the compound Rayleigh exponential distribution unknown parameters using Bayesian and non-Bayesian estimation techniques, including maximum likelihood estimation, maximum product spacing, least square estimator, weighted least square estimator, Cramer-Von-Mise estimator, Anderson-Darling estimator, and Bayesian techniques with informative and non-informative priors based on different loss functions. Additionally, we used several methods to obtain the confidence intervals for the unknown parameters, such as the approximate and Bootstrap methods. The effectiveness of these estimators is evaluated through a Monte Carlo simulation study. Furthermore, we are committed to investigating three widely recognized risk metrics: the value at risk, the tail value at risk, and the tail variance premium. These findings are helpful for actuarial risk researchers who depend on risk measurement fitting when evaluating Bayesian tools for effectively modeling actuarial sciences. Finally, different applications taken from several areas are examined to illustrate the practical usefulness of the compound Rayleigh exponential distribution. Using various model selection criteria, the introduced model is contrasted with that of several well-known distributions. Our empirical findings indicate that the suggested model has superior goodness-of-fit to the other models examined.

  Abstract
  Many lifetime analysis, such as engineering, biology, survival, actuarial and medical sciences, heavily rely on the two-parameter compound Rayleigh exponential distribution, [...]

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• On Observer-Based Control of One-Sided Lipschitz ω-Tempered Fractional-Order Systems under Input Saturation

  S. Afli, A. Jmal, O. Naifar

  Rev. int. métodos numér. cálc. diseño ing. (2025). Vol. 41, (4), 71

  
  

  Abstract

  This paper addresses the critical challenge of observer-based control for one-sided Lipschitz (OSL) nonlinear systems governed by tempered fractional-order dynamics under input saturation constraints. We introduce a novel stability framework combining ω-tempered Caputo derivatives with Mittag-Leffler stability theory, enabling significantly faster exponential error decay compared to classical fractional operators. The proposed methodology features three key innovations: (1) a tempered Mittag-Leffler stability theorem incorporating sector-bounded saturation nonlinearities, (2) linear matrix inequality (LMI) conditions accommodating arbitrary OSL constants (ρ∈ R), and (3) a Cone Complementary Linearization (CCL) algorithm resolving gain synthesis challenges in tempered fractional systems. Numerical validation demonstrates 62% faster convergence than classical fractional approaches while maintaining saturation constraints. The CCL algorithm provides guaranteed convergence with computational efficiency, overcoming initialization sensitivity through regularization techniques.OPEN ACCESS Received: 05/06/2025 Accepted: 15/08/2025 Published: 27/10/2025

  Abstract
  This paper addresses the critical challenge of observer-based control for one-sided Lipschitz (OSL) nonlinear systems governed by tempered fractional-order dynamics under [...]

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• Numerical Simulation, Physical Modeling and Plant Trial on the Slag Entrapment Behavior in a Four-Strand Tundish

  B. Qin, J. Zhang, J. Chen, C. Gao, X. Zuo, Q. Liu

  Rev. int. métodos numér. cálc. diseño ing. (2025). Vol. 41, (4), 70

  
  

  Abstract

  The continuous casting process yields more than 96% crude steel worldwide. Numerical simulation and physical modeling were employed to investigate the slag entrainment behavior during the final casting stage in a four-strand continuous casting tundish. It is demonstrated that the numerical and physical modelling results agree well. Vortex formation in the final casting stage can be divided into four distinct stages: surface rotation, vortex generation, vortex development, and vortex penetration, with significant variations in critical slag entrainment depths observed among different outlets. Increasing casting speed increases slag entrainment risks, while employing vortex inhibitors could reduce the critical slag entrainment height by up to 29%. Through optimized casting termination procedures in a plant trial, the tundish residual depth decreased from 400 to 200 mm; The tail-end billet cleanliness significantly improved to reach a normal billet quality level in the end billet range of 2–3 m. This study provides theoretical and technical support for reducing slag entrainment defects and improving steel cleanliness in continuous casting.OPEN ACCESS Received: 15/05/2025 Accepted: 10/07/2025 Published: 27/10/2025

  Abstract
  The continuous casting process yields more than 96% crude steel worldwide. Numerical simulation and physical modeling were employed to investigate the slag entrainment behavior [...]

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