Documents published in Scipedia

• On Nonlinear Monge-Ampère Equations and Their Symmetry Classifications

  Z. Izgi, Ö. Avit, D. Baleanu, M. Yousif, M. Alharthi, P. Mohammed

  
  

  Abstract

  The Monge-Ampère equation (MAE) plays a pivotal role across a broad spectrum of theoretical and applied sciences, with its solutions being essential for advancing various fields. This study explores all forms of the fully nonlinear MAE using Lie group transformations to reduce the equation into solvable forms. Analytical solutions are derived using ansatzbased methods, yielding novel and generalized results that enhance the existing body of knowledge. In particular, solutions for cases with diverse source functions and boundary conditions are obtained, addressing gaps in the literature. Stability of the solutions is studied through both analytical and numerical approaches. Comparisons with existing solutions demonstrate the efficiency and generality of the proposed methods. The results presented in this work are poised to impact numerous applications, providing a robust framework for further research on MAEs.OPEN ACCESS Received: 24/05/2025 Accepted: 07/08/2025 Published: 27/10/2025

  Abstract
  The Monge-Ampère equation (MAE) plays a pivotal role across a broad spectrum of theoretical and applied sciences, with its solutions being essential for advancing various [...]

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• Integration of Monte Carlo Simulation and Linear Programming in Multi-Product Multi-Stage Supply Chains: A Theoretical Framework for Decision-Making under Uncertainty

  K. Abuhasel

  
  

  Abstract

  This study outlines a hybrid Monte Carlo Simulation-Linear Programming framework to increase the level of operational efficiency of a multistage supply chain. This model is an integration of probabilistic simulation and deterministic optimization to take into account the effects of demand variability, lead-time variability, and capacity variability on profitability and the overall service delivery performance. The suggested framework is tested with the help of a multi-product, multi-stage supply chain case study that is implemented on the basis of a publicly available dataset containing around 9000 transactional records. Monte Carlo simulation generates random uncertainty scenarios, whereas linear programming finds the ideal decisions related to production, distribution, and inventory levels for each iteration. The results suggest that reducing the amount of demand variability and better capacity planning resulted in a good performance with an expected profit of $328,100.16, a profit variance of 3.72× 109, and a service level of 94.3%. The result of the sensitivity analysis shows that demand variability and lead times have a negative effect on profit, while optimal capacity planning enhances operational flexibility. The MCS-LP provides an advantage to the use of stochastic and deterministic methods for risk-aware decision making and has the potential to be computationally scalable and efficient for uncertaintydriven supply chain design. The general approach provides a decisionsupport tool for managers considering how to balance costs, risk, service quality, and uncertainty in ever-changing industrial contexts.OPEN ACCESS Received: 14/11/2025 Accepted: 13/01/2026 Published: 20/03/2026

  Abstract
  This study outlines a hybrid Monte Carlo Simulation-Linear Programming framework to increase the level of operational efficiency of a multistage supply chain. This model is [...]

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• AI-Driven Predictive Analytics for Demand Forecasting in Transportation Logistics to Enhance Supply Chain Agility

  K. Abuhasel

  
  

  Abstract

  The logistics and transportation sectors are struggling with major issues like demand variations, disruptions, and inefficiencies, which ultimately undermine the agility and efficiency of the entire supply chain. Most of the time, traditional forecasting models are not entirely accurate in response to life-changing factors like weather, traffic, and inventory levels. The present research intends to build an AI-powered predictive model that can seamlessly enhance not only demand forecasting and logistics but also by the integration of real-time data. The framework incorporates several Machine Learning (ML) models, which are Light GBM for demand forecasting, Random Forest for disruption prediction, Linear Regression for shipping cost estimation, and Support Vector Regression for delivery time deviation prediction. A thorough dataset containing historical demand, weather conditions, traffic, and stock levels was used for the model’s training and evaluation, and its performance was monitored using MAE, MSE, RMSE, and MAPE metrics. The findings indicate that the suggested framework is a lot better than the existing ones, with Light GBM getting the lowest MAE (0.056), MSE (0.005), RMSE (0.072), and MAPE (0.142). This means that the new system can predict much better than before, thus making it possible for the company to take the right decision at the right time and consequently improving the overall supply chain efficiency. The research paper reveals the future possibilities of AI-based solutions for optimising logistics operations and building supply chain resilience.OPEN ACCESS Received: 20/10/2025 Accepted: 25/12/2025 Published: 03/02/2026

  Abstract
  The logistics and transportation sectors are struggling with major issues like demand variations, disruptions, and inefficiencies, which ultimately undermine the agility and [...]

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• An Integrated IVHFS and DEMATEL-ANP Framework for Competitive Intelligence Evaluation in Smart Factories

  W. Lo, M. Li, D. Huang, C. Yang, W. Deng, S. Li

  
  

  Abstract

  In the era of big data, the ability to evaluate high-quality and actionable competitive intelligence (CI) has become essential for smart factories to support data-driven decision-making and maintain technological and operational advantages. However, the highly dynamic and complex nature of the smart manufacturing environment introduces considerable uncertainty, hesitation, and interdependencies among evaluation indicators, posing significant challenges to traditional decision-making frameworks. To address these issues, this study proposes an integrated framework that combines interval-valued hesitant fuzzy sets (IVHFS) with the decision-making trial and evaluation laboratory-analytic network process (DEMATEL-ANP). IVHFS is employed to capture the ambiguity and hesitation inherent in expert judgments, enabling a more flexible and realistic representation of evaluation inputs. Subsequently, the DEMATEL-ANP approach is used to uncover the causal relationships among CI indicators and to construct a network-based weighting structure that reflects their interdependencies. A case study in a smart factory is conducted to validate the practicality and effectiveness of the proposed framework, and a sensitivity analysis confirmed its stability.OPEN ACCESS Received: 04/08/2025 Accepted: 28/10/2025 Published: 23/01/2026

  Abstract
  In the era of big data, the ability to evaluate high-quality and actionable competitive intelligence (CI) has become essential for smart factories to support data-driven decision-making [...]

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• Thermomechanical Uncertainty Analysis of Steel Partition Walls Using Direct FE2 and Polynomial Chaos Expansion

  S. Yang, Y. Xu, L. Huang, L. Liu

  
  

  Abstract

  Steel partition walls are essential components in modern civil engineering, providing both structural support and spatial separation. These walls are frequently exposed to combined thermal and mechanical loads, particularly in specialized environments such as high-temperature workshops or fire scenarios, where their thermo-mechanical coupling behavior is critical to building safety and functionality. This study integrates the direct finite element squared (Direct FE2) method with generalized polynomial chaos expansion (PCE) to quantify the uncertainties in key material propertiesnamely, the elastic modulus and the coefficient of thermal expansionand to evaluate their effects on the thermo-mechanical performance of steel partition walls. The proposed approach enables efficient simulation of material uncertainties and their influence on structural behavior under coupled thermal-mechanical conditions. Case studies demonstrate both the accuracy and computational efficiency of the method, while sensitivity analysis highlights the most influential uncertainty factors. The integration of Direct FE2and PCE thus offers a robust framework for assessing the reliability of steel partition walls under uncertain conditions, providing valuable insights for design optimization and enhancing the safety and efficiency of building structures in practical applications.OPEN ACCESS Received: 05/07/2025 Accepted: 17/09/2025 Published: 23/01/2026

  Abstract
  Steel partition walls are essential components in modern civil engineering, providing both structural support and spatial separation. These walls are frequently exposed to [...]

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

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

  
  

  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 Simulation Analysis of Influence of Wind and Temperature on Deformation of Constructed Zhaidi River High Bridge Pier

  M. Tian, J. Sun, X. Chen, F. Xiong

  
  

  Abstract

  Bridges are key projects of high-grade Expressways in mountainous areas. The verticality of bridge piers with a height of more than 100 m is crucial to ensure the safety and stability of bridge projects. When a pier construction is completed but the upper beam structure has not yet been connected (socalled after construction or constructed), the verticality of the pier is most likely to vary due to some factors. Based on the Zhaidi River Bridge project of Yunnan Zhenhe Expressway, considering the natural environment of the bridge site, three wind force intensity levels (Beaufort Scale 8, 10, and 12) and two climate conditions (high temperature and high radiation in summer, and low temperature and low radiation in winter) were identified; and the wind-induced deformation, temperature-induced deformation, and wind-temperature coupled deformation of the constructed main pier of the Zhaidi River Bridge with a height of 112.60 m were simulated with ANSYS Workbench numerical simulation platform. The simulation results show that: the influence of wind on pier deformation is much greater than that of ambient temperature variation; the influence of solar radiation on temperature-induced deformation of the bridge pier is much greater than that of air temperature variation; the temperature-induced deformation of the pier body under low temperature and low radiation condition in winter is greater than that under high temperature and high radiation condition in summer; the directional effect of the superposition of wind-induced deformation and temperature-induced deformation is more significant under low temperature and low radiation condition in winter.OPEN ACCESS Received: 11/04/2025 Accepted: 16/10/2025 Published: 23/01/2026

  Abstract
  Bridges are key projects of high-grade Expressways in mountainous areas. The verticality of bridge piers with a height of more than 100 m is crucial to ensure the safety and [...]

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• A Deep Learning-Based Time-Depth Conversion Method for Continuous Wellbore Temperature Profile Construction Using a Micro-Measurer

  M. Li, Y. Wang, X. Song, S. Duan, H. Wang, W. Hao, J. Wang

  Rev. int. métodos numér. cálc. diseño ing. (2026). Vol. 42, (5), 120

  
  

  Abstract

  Real-time monitoring of oil-gas wellbore temperature profiles provides important support for drilling strategy adjustment and model parameter calibration. MEMS-based micro-measurers can collect fullwellbore temperature data by flowing with drilling fluid circulation, but converting their time-series records to well depth relies on idealized steady-motion assumptions that neglect wall collisions, sticking, and local flow disturbances, introducing systematic positioning errors. In this study, a deep learning-based time-depth conversion method is proposed. A gated recurrent unit (GRU)-based temporal neural network is developed to extract motion-state features from six-axis dynamic signals, and a bounded velocity correction mechanism is introduced to compensate deviations from the idealized terminal velocity. The results show that: (1) The proposed model effectively learns motion-state deviations from dynamic response data and provides stable velocity correction under complex downhole conditions. (2) The corrected time-velocity curve exhibits transient fluctuations relative to the idealized terminal velocity, accurately capturing non-ideal behaviors while preserving directional stability. (3) During model training, the loss function decreases progressively and stabilizes, indicating good convergence and training stability. (4) Comparative analysis shows that the proposed GRU-Full method achieves a mean absolute anchor-point error of 1.23 m (0.24% of the well depth), outperforming the MLP and LSTM alternatives. Ablation experiments confirm that both the physical constraints and regularization terms contribute substantially to the model accuracy. This study enhances the spatial mapping accuracy of temperature data acquired by micro-measurers under complex downhole dynamic conditions. The established physically constrained deep learning framework provides a new technical pathway for refined wellbore thermal-field characterization and intelligent drilling decision-making.

  Abstract
  Real-time monitoring of oil-gas wellbore temperature profiles provides important support for drilling strategy adjustment and model parameter calibration. MEMS-based micro-measurers [...]

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• A Point Cloud Registration Method for Ship Plates Based on Density Denoising and Anderson-Accelerated Iterative Closest Point

  Z. Wang, X. Liu, D. Liu, Y. Zhang, P. Hou, W. Sun, Y. Li, H. Yuan

  Rev. int. métodos numér. cálc. diseño ing. (2026). Vol. 42, (4), 119

  
  

  Abstract

  Point cloud registration is essential for closed-loop digital forming of ship plates, where CAD-toscan alignment is required for surface error evaluation and compensation. Industrial ship-plate point clouds acquired by structured-light or laser sensing are often corrupted by boundary-related structural outliers and are feature-sparse, causing classical ICP to be sensitive to mismatches and slow to converge. We propose a training-free, deployment-oriented pipeline that combines density-based outlier removal with an Andersonaccelerated ICP (A-ICP) formulated in the SE(3) Lie algebra. Experiments on three representative plate geometries show 37.7%–39.5% lower registration error and indicate improved convergence behavior relative to classical ICP variants. The method is further validated on an SKWB-2500 machine-in-the-loop workflow, achieving MAE of 0.68 mm (sail-shaped) and 0.38 mm (saddle-shaped), with corresponding RMSE of 0.7301 mm and 0.4141 mm. Learning-based baselines are not benchmarked due to the lack of a fair in-domain dataset and retraining protocol under proprietary sensing conditions.

  Abstract
  Point cloud registration is essential for closed-loop digital forming of ship plates, where CAD-toscan alignment is required for surface error evaluation and compensation. [...]

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• Optimal FOPID Controller Design for DC Motor Speed Control Using Ant Lion Optimization Algorithm

  S. Waseem, M. Irfan, A. Amin, S. Rahman, H. Alwadie, S. dawsari

  Rev. int. métodos numér. cálc. diseño ing. (2026). Vol. 42, (5), 118

  
  

  Abstract

  DC motors are frequently utilized in industrial and automation applications where accurate speed control is crucial. Although conventional Proportional-Integral-and Derivative (PID) controllers are widely utilized, their constant gain values make them less effective in managing dynamic loads and disturbances. It’s difficult to get optimal transient and steady-state performance with traditional PID tuning methods. To overcome these limitations, more adaptable and dependable control systems are needed. This study introduces a novel control strategy by optimizing a Fractional-Order PID (FOPID) controller using the Ant Lion Optimization (ALO) method. Mathematical modeling is used to determine the DC motor’s transfer function. An ALO, a metaheuristic algorithm, is then implemented to improve five FOPID parameters using Integral Timeweighted Absolute Error (ITAE). The simulation is done in MATLAB-Simulink software. According to the findings, the enhanced ALO-FOPID controller decreased settling time (0.0728 s) and rising time (0.0455 s) when compared to the PID controller, which is taken as a reference. It is noted that the proposed ALO-tuned FOPID demonstrated enhanced response over the conventional methods, demonstrating the usefulness of bioinspired algorithms for precision control applications. The comparison of the proposed methodology is also done with other studies during different operating conditions. The results show that intelligent optimizationbased control in industrial systems is feasible, which aids in the creation of reliable and flexible automation solutions.

  Abstract
  DC motors are frequently utilized in industrial and automation applications where accurate speed control is crucial. Although conventional Proportional-Integral-and Derivative [...]

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