Documents published in Scipedia

• CARE-FSP: A Cost-Aware and Resource-Efficient Hybrid RSO–SFO Framework for Fog-Based IoT Service Placement

  H. Merouani, S. Bendib, H. Moumen, L. Guezouli

  Rev. int. métodos numér. cálc. diseño ing. (2026). Vol. 42, (5), 116

  
  

  Abstract

  sup>Efficient service placement is a critical challenge in large-scale Internet of Things (IoT) environments, where fog computing must balance deployment cost and resource utilization under heterogeneous and dynamic conditions. To address this challenge, this paper proposes a hybrid metaheuristic approach that combines Rat Swarm Optimization (RSO) and Sunflower Optimization (SFO), leveraging the strong global exploration capability of RSO and the efficient local exploitation behavior of SFO. The proposed RSO–SFO framework integrates both strategies within a unified fitness function designed to minimize deployment cost while ensuring efficient allocation of fog resources. Extensive simulation results demonstrate that the proposed hybrid algorithm consistently outperforms state-of-the-art optimization techniques, including Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO), and the standalone RSO and SFO methods. Specifically, the RSO–SFO approach achieves a fitness improvement of 45.38%, reduces deployment costs by 43.71%, and maintains a high average resource utilization of 78.83%. These results confirm the effectiveness and robustness of the proposed hybrid strategy for optimal service placement in fog-based IoT environments.

  Abstract
  sup>Efficient service placement is a critical challenge in large-scale Internet of Things (IoT) environments, where fog computing must balance deployment cost and resource [...]

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• IMA: An Interpretable Machine Learning Framework Based on Large-Scale Anonymous Evaluations to Decode the Black Box of Student-Supervisor Mentorships

  Y. Gao, W. Jin, Y. Nan, Y. Ma, B. Zhao, Z. Zhang

  Rev. int. métodos numér. cálc. diseño ing. (2026). Vol. 42, (5), 115

  
  

  Abstract

  Student-supervisor relationships (SSR) play a central role in postgraduate training, academic development, and research well-being. In the context of the rapid expansion of Chinese graduate education, understanding mentorship quality has become increasingly important for both educational governance and student development. However, prior SSR studies have often relied on small-scale surveys, context-specific qualitative evidence, or linear analytical approaches, which limits their ability to capture heterogeneous, non-linear, and system-level patterns across institutions and disciplines. To address this gap, we propose Interpretable Mentorship Analytics (IMA), a scalable analytical framework built on large-scale anonymous student evaluations. First, we construct a multi-platform dataset of anonymous supervisor evaluations and transform unstructured review text into structured mentorship indicators through a preprocessing and LLM-assisted feature engineering pipeline. Second, we employ gradient-boosting models, including XGBoost, LightGBM, and Gradient Boosting, to model the relationship between mentorship-related features and overall evaluation outcomes. Third, we apply explainable machine learning methods, particularly SHAP and LIME, to identify global feature importance, local decision patterns, and non-linear interactions among mentorship dimensions. The results show that IMA can effectively uncover the key drivers of mentorship satisfaction, especially the central role of teacher-student relationship quality, while also revealing substantial heterogeneity across regions, institution types, and disciplines. By combining large-scale anonymous evaluations with interpretable predictive modeling, this study provides a transparent and data-driven framework for evaluations with interpretable predictive modeling, this study provides a transparent and data-driven framework for understanding SSR and offers empirical evidence for improving postgraduate supervision and educational policy.

  Abstract
  Student-supervisor relationships (SSR) play a central role in postgraduate training, academic development, and research well-being. In the context of the rapid expansion of [...]

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• Dynamic Variational Autoencoder Regressor for Soft Sensor Modeling

  H. Wu, P. Zhang, S. Tian, Z. Chen

  Rev. int. métodos numér. cálc. diseño ing. (2026). Vol. 42, (5), 114

  
  

  Abstract

  Due to the dynamics of industrial production, it is a great challenge to learn robust feature representations from industrial process data for building an accurate soft sensor model. The traditional variational autoencoder (VAE) can learn robust features that can adapt to the dynamic process better, but cannot be directly applied in soft sensor modeling. Therefore, a novel regression-based dynamic VAE (REGDVAE) is introduced in this paper. Firstly, the encoder of the DVAE, which is constructed by the graph attention and the convolutional neural networks, is utilized to obtain the robust spatio-temporal features. The decoder of the DVAE is responsible for reconstructing the input data via transposed convolution. Secondly, the Transformer is employed to capture the dynamic associations between the robust spatio-temporal features and corresponding outputs. Moreover, with the purpose of guaranteeing the confidence of the proposed method, the Gaussian loss function is used as the optimization target of the REG-DVAE to enhance the confidence of each predicted value. The proposed REG-DVAE is implemented in a real-world melt index modeling of the polypropylene production process. The results show that compared with other baseline methods, the REG-DVAE not only can achieve the best performance, but also can provide a confidence interval, which greatly enhances the credibility of the prediction results.

  Abstract
  Due to the dynamics of industrial production, it is a great challenge to learn robust feature representations from industrial process data for building an accurate soft sensor [...]

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• Flexible Power Supply and Distribution Based on FDN for Photovoltaic Tobacco Logistics Warehouses

  T. Gui, G. Zhang, K. Zhang, J. Cui

  Rev. int. métodos numér. cálc. diseño ing. (2026). Vol. 42, (4), 113

  
  

  Abstract

  Industrial plants should implement sophisticated renewable-energy-based distribution systems to guarantee a continuous and reliable power supply and enhanced operational efficiency under varying loading conditions. A Flexible Power Supply and Distribution System (FPSDS) utilize the concept of Flexible Distribution Networks (FDN) for photovoltaic-contracted tobacco warehouse facilities. The integrated power system connects PV panels to a multi-port bidirectional DC-DC converter, which operates with a BESS and a full-bridge PWM inverter to provide bi-directional energy management and increased power reliability. The system achieves enhancement by integrating three control mechanisms, which include DLD for real-time energy distribution, IPR for loss minimization and Multi-Agent Deep Reinforcement Learning (MADRL) for adaptive system optimisation. The system manages dynamic voltage and power regimes and current movements through predictive behaviour prediction, which utilizes Model Predictive Control (MPC). Simulation results demonstrate enhanced electric power management that leads to better voltage systems alongside lower transmission losses and superior peak demand control. Such a solution enables both real-time capabilities and scalability benefits while enhancing operational durability and suits warehouses with energetic systems that function dynamically. The research creates a smart solution to incorporate renewable power sources within logistics operations that builds sustainable energy frameworks for decentralizing power systems. The proposed system introduces an integrated control framework combining Dynamic Load Distribution, Intelligent Power Routing, and Multi-Agent Deep Reinforcement Learning within an FDN for photovoltaic-integrated warehouses. The approach demonstrates a 15–20% reduction in transmission losses, a 12–18% increase in overall energy efficiency, and improved voltage stability by maintaining deviations within 5%. These results confirm the novelty of this research in achieving adaptive, intelligent, and scalable power distribution for logistics facilities.

  Abstract
  Industrial plants should implement sophisticated renewable-energy-based distribution systems to guarantee a continuous and reliable power supply and enhanced operational efficiency [...]

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• Hierarchical Convolutional Neural Network for Emotion Recognition Using EEG and Facial Expressions

  M. Shafiq, S. Awan, T. Kamal

  Rev. int. métodos numér. cálc. diseño ing. (2026). Vol. Online First, 112

  
  

  Abstract

  Emotion recognition is crucial for advancing human–computer interaction (HCI) by enabling systems to interpret complex affective states. While Electroencephalogram (EEG) signals provide direct insights into neural activity, facial expressions offer external emotional cues. However, unimodal systems often struggle with robustness and generalization across diverse subjects. This study presents a Hierarchical Convolutional Neural Network (HCNN) framework that integrates EEG and facial expressions through multi-level convolutional feature extraction and featurelevel fusion. The proposed model combines deep hierarchical representations with handcrafted temporal–frequency and texture-based descriptors to form a unified feature vector. Experiments on the MAHNOB-HCI and DEAP datasets show that the HCNN achieves accuracies of 91.40% and 88.09%, outperforming CNN-, LSTM-, and SVM-based methods. The results demonstrate the model’s ability to effectively capture complementary cross-modal correlations while reducing feature redundancy and computational complexity. The HCNN framework shows great promise for real-time emotion recognition applications, offering a scalable, interpretable, and data-efficient solution for multimodal emotion recognition in next-generation HCI systems.

  Abstract
  Emotion recognition is crucial for advancing human–computer interaction (HCI) by enabling systems to interpret complex affective states. While Electroencephalogram (EEG) [...]

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• Computational Study of Thermal Performance of Shape-Dependent CNT/ Titanium Dioxide/Aluminum OxideSodium Alginate Hybrid Nanofluids for Solar Annular Collectors

  M. Bhatti, T. Abbas, O. Bég, T. Bég

  Rev. int. métodos numér. cálc. diseño ing. (2026). Vol. 42, (4), 110

  
  

  Abstract

  Solar direct absorber collectors are increasingly deploying more complex working nanofluids to enhance thermal performance. Hybrid nanofluids with non-Newtonian base fluids offer great promise in this regard. Motivated by these developments, the present article examines theoretically and numerically the thermal convection in a ternary hybrid nanofluid comprising an incompressible non-Newtonian sodium alginate base fluid in the annular gap between a pair of infinite concentric cylinders, as a model of a solar annular collector system. Sodium alginate (C6H9NaO6)n exhibits distinctive thermophysical characteristics, including enhanced versatility and viscoelasticity, rendering it appropriate for solar energy applications when integrated with hybrid nanoparticles. The Reiner-Rivlin third-grade viscoelastic model is therefore deployed to simulate the non-Newtonian characteristics. Three categories of nanoparticles are featured in the hybrid nanofluid: Carbon nanotubes (CNTs), Titanium dioxide (TiO2), and Aluminum oxide (Al2O3). The nanoparticles are categorized by their specific shapes: Carbon nanotubes exhibit a cylindrical form, Titanium dioxide exhibits a spherical configuration, and Aluminum oxide takes on a platelet shape. The presence of hybrid nanofluids influences both the internal transport characteristics and the heat flux towards the curved boundary and the behavior of nanoparticles with varying shapes is also a critical factor. Both linear and quadratic convection, along with viscous dissipation and heat generation/absorption, are also taken into account. The transformed boundary value problem is solved numerically with a finite difference method. Validation of the computational scheme with previous studies is included. Graphical results are provided for the impact of all emerging parameters on transport characteristics. Nusselt number is observed to be elevated with higher Grashof number (thermal buoyancy parameter) and heat generation parameter, whereas this trend is reversed with larger volume fractions of nanoparticles (CNTs, TiO2, Al2O3) and greater values of the quadratic convection parameter. The larger volume fraction of CNTs, TiO2, and Al2O3 nanoparticles strongly modifies viscosity and suppresses velocity magnitudes. The skin friction profile shows an increasing trend, which is greatly influenced by the Grash of number, heat generation parameter, and third-grade fluid parameter. In contrast, the quadratic convection parameter and the introduction of nanoparticles (CNTs, TiO2, Al2O3) tend to reduce skin friction magnitudes

  Abstract
  Solar direct absorber collectors are increasingly deploying more complex working nanofluids to enhance thermal performance. Hybrid nanofluids with non-Newtonian base fluids [...]

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• River Water Temperature Prediction Based on Boosting Algorithms and Ensemble Empirical Mode Decomposition: CatBoost, XGBoost, LightGBM versus AdaBoost

  K. Merabet, F. Nunno, F. Granata, O. Kisi, S. Heddam, S. Kim, M. Ptak, M. Sojka, C. Kulls, M. Zounemat-Kermani

  Rev. int. métodos numér. cálc. diseño ing. (2026). Vol. 42, (6), 155

  
  

  Abstract

  This study presents a hybrid modelling framework for predicting river water temperature (Tw) by combining advanced boosting algorithms with signal decomposition techniques. Four models were compared: (i) categorical boosting (CatBoost), (ii) extreme gradient boosting (XGBoost), (iii) adaptive boosting (AdaBoost), and (iv) light gradient boosting machine (LightGBM). Each model was developed using air temperature (Ta) as input, both with and without signal decomposition. Three algorithms, empirical mode decomposition (EMD), ensemble EMD (EEMD), and complete EEMD with adaptive noise (CEEMDAN), were applied to decompose Ta into intrinsic mode functions (IMFs), which served as additional predictors. The models were trained and validated using data from four Polish river stations, and their performance was evaluated using root mean squared error (RMSE), mean absolute error (MAE), correlation coefficient (R), and Nash–Sutcliffe efficiency (NSE). Tw was modelled as an instantaneous function of Ta, without incorporating a forecasting horizon, to assess the ability of decomposition-boosting hybrids to reproduce observed thermal dynamics at the same time step rather than perform multi-step predictions. Unlike previous single-hybrid studies, this work systematically compares multiple boosting-decomposition combinations under identical settings, isolating the decomposition effect and assessing accuracy, efficiency, and robustness. Results show that integrating signal decomposition substantially improves prediction accuracy. The best performance was achieved by XGBoost_EEMD (RMSE = 0.804, MAE = 0.598, R = 0.993, NSE = 0.987), reflecting a ∼64% improvement over the standalone model. The proposed hybrid modelling framework offers methodological innovation and practical potential for real-time, datadriven river temperature prediction in support of climate-resilient water management.

  Abstract
  This study presents a hybrid modelling framework for predicting river water temperature (Tw) by combining advanced boosting algorithms with signal decomposition techniques. [...]

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• Integration of Renewable Energy Sources into Electrical Power Systems and Its Impact on Grid Stability

  M. KAMAL, M. Kashif, A. Ahmad, H. Samkari, M. Allehyani, H. Mohamed

  Rev. int. métodos numér. cálc. diseño ing. (2026). Vol. 42, (4), 109

  
  

  Abstract

  The integration of renewable energy sources (RES) into electrical power systems introduces critical challenges to grid stability, including frequency deviation, voltage fluctuation, and reduced transient performance. This study investigates the impact of increasing RES penetration (15%, 30%, 45%, and 60%) on grid stability using the IEEE 39-bus benchmark system. A coordinated mitigation framework integrating battery energy storage systems (BESS), synthetic inertia, and advanced inverter-based controls is proposed and evaluated. Simulation results demonstrate that at 60% RES penetration without mitigation, the frequency nadir declines to 49.32 Hz, the rate of change of frequency (RoCoF) increases to 1.82 Hz/s, voltage deviations exceed 9.3%, and the critical clearing time (CCT) reduces to 180 ms, indicating significant stability deterioration. The proposed mitigation strategy improves these metrics to 49.76 Hz, 0.94 Hz/s, 4.6%, and 260 ms, respectively, representing improvements of+0.44 Hz, –48.4%, –50.5%, and+44.4%. Benchmarking against recent literature confirms the superior performance of the coordinated approach. These findings provide quantitative guidance for grid planners and operators to maintain reliable operation under high renewable penetration scenarios.OPEN ACCESS Received: 19/01/2026 Accepted: 28/02/2026

  Abstract
  The integration of renewable energy sources (RES) into electrical power systems introduces critical challenges to grid stability, including frequency deviation, voltage fluctuation, [...]

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• Robust Adaptive Consensus Control of Fractional-Order Multi-Agent Systems Using BBO-Optimized Type-2 Fuzzy Neural Networks

  P. Yu, H. Rauf, M. Arab, S. Trabelsi

  Rev. int. métodos numér. cálc. diseño ing. (2026). Vol. 42, (4), 108

  
  

  Abstract

  The consensus control problem to be discussed in this paper is concerned with nonlinear multi-agent systems with unknown dynamics, unknown fractions, time-varying delays, and input saturation limits. In order to address these difficulties, an adaptive control system is established, incorporating a recurrent general type-2 fuzzy neural network (RGT2FNN) with a biogeography-based optimization (BBO) algorithm. The RGT2FNN is used to model nonlinear functions that are not known offline, and the BBO algorithm also optimizes the parameters of the fuzzy network and performs offline identification of the fractional order by minimizing multi-step model prediction error. In order to make the model more resistant to modeling uncertainties, time-varying delays, and actuator saturation effects, a LMI-based compensator is proposed to ensure the stability of the closed-loop. Lyapunov analysis guarantees the boundedness of consensus errors. The simulation findings prove that the suggested methodology can attain an accurate consensus tracking and strong performance when the uncertainties are harsh, the delays may change with time, and the limits of input saturation are taken into account.OPEN ACCESS Received: 06/01/2026 Accepted: 16/03/2026

  Abstract
  The consensus control problem to be discussed in this paper is concerned with nonlinear multi-agent systems with unknown dynamics, unknown fractions, time-varying delays, [...]

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• Mechanical Properties and Constitutive Modelling of UHPC with Ultra Fine Waste Glass Powder (UFWGP), Micro Silica, Fly Ash and Steel and Polypropylene Fibres

  A. Ayub, A. Khan, S. Fareed

  Rev. int. métodos numér. cálc. diseño ing. (2026). Vol. 42, (4), 111

  
  

  Abstract

  Ultra-high-performance concrete (UHPC) offers superior mechanical performance but remains limited by high cement content, sustainability concerns, and the lack of reliable constitutive models capable of capturing its full stress–strain response including pre-peak and post-peak behaviour. This study presents an integrated experimental and constitutive modelling investigation of UHPC incorporating ultra-fine waste glass powder, micro silica, fly ash, and different fibre reinforcement. Two UHPC mix designs with a water-to-cementitious material ratio of 0.20 were developed, yielding 24 independent mixtures. Cement was partially replaced with 20% ultra-fine waste glass powder and combined with either micro silica or fly ash. Fibre reinforcement was introduced independently using 2% straight steel fibres and 1.5% crimped polypropylene fibres to evaluate flowability, viscosity, uniaxial compressive strength, modulus of elasticity, splitting tensile strength, flexural strength and drying shrinkage behaviour. Experimental results demonstrate that UHPC with micro silica, UFWGP and steel fibres, prepared following the ACI 239 R-18 mix design, achieved the highest 28 days compressive, splitting tensile and flexural strength of 144.8, 12.8 and 36.1 MPa, respectively. Steel fibre-reinforced mixes exhibited up to a 42% increase in compressive strength and significantly enhanced post-peak ductility compared to the control mix (without fibre). Crimped Polypropylene fibre-reinforced UHPC attained compressive strength up to 129.7 MPa, provided improved strain capacity, and effectively reduced drying shrinkage, highlighting their suitability for crack control and deformation mitigation. The incorporation of UFWGP consistently improved flowability, reduced viscosity, and enhanced mechanical performance, while fly ash improved rheological behaviour but resulted in lower earlyage strength compared to micro silica-based mixes. To capture the compressive response of UHPC, an elasto-damage model previously proposed by Khan and Zahra was reformulated by recalibrating the compression damage parameter (β) using experimentally derived and literature based compressive strength and elastic modulus. The proposed model reproduces both pre-peak and post-peak stress–strain behaviour, with prediction errors generally within ±7% of experimental results ranging from 72.4 to 148.5 MPa. The findings provide robust experimental evidence and a validated constitutive framework for the sustainable design and structural application of UHPC

  Abstract
  Ultra-high-performance concrete (UHPC) offers superior mechanical performance but remains limited by high cement content, sustainability concerns, and the lack of reliable [...]

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