Mathematical and Computational Applications

19 pages, 3279 KB

Open AccessArticle

Exploring Bifurcation Analysis, Conservation Laws and Soliton Dynamics for the Dual-Mode Nonlinear Schrödinger Equation with Applications

by Muhammad Arshad, Naila Nasreen, Evren Hincal, Mohamed Hafez and Muhammad Farman

Math. Comput. Appl. 2026, 31(3), 97; https://doi.org/10.3390/mca31030097 - 2 Jun 2026

Viewed by 90

Abstract

This study examines the dynamical behavior of the dual-mode nonlinear Schrödinger equation (d-mNLSE), which describes the interaction, amplification, and attenuation of two coexisting wave modes in nonlinear media. The model incorporates key physical parameters including the nonlinearity coefficient, interaction phase velocity, and dispersion [...] Read more.

This study examines the dynamical behavior of the dual-mode nonlinear Schrödinger equation (d-mNLSE), which describes the interaction, amplification, and attenuation of two coexisting wave modes in nonlinear media. The model incorporates key physical parameters including the nonlinearity coefficient, interaction phase velocity, and dispersion parameter, which significantly influence the evolution of nonlinear waves. By applying the modified Sardar sub-equation method (mSS-EM), a wide spectrum of exact analytical solutions is derived. These solutions include mixed trigonometric waves, shock-type structures, singular solutions, complex dark–bright solitons, multi-peak solitons, periodic and mixed-periodic waves, as well as mixed hyperbolic structures. The analytical findings provide useful insight into nonlinear wave propagation phenomena arising in fluid mechanics, water wave dynamics, ocean engineering, and related physical systems. Moreover, the conservation laws of the d-mNLSE are established, which leads to the conserved quantities of impulse power, momentum, and energy and describes the invariant characteristics of the soliton solutions during their propagation. The bifurcation analysis of the reduced dynamical model is carried out to explore the qualitative characteristics of the obtained solutions. The equilibrium points of the considered model are calculated, and their stability properties are analyzed systematically. To demonstrate the physical characteristics of the obtained solutions, different kinds of two-dimensional, three-dimensional, and contour plots are plotted using symbolic computations software. These findings confirm that the analytical method used to obtain the soliton solutions can be used to obtain a variety of soliton solutions of nonlinear evolution equations that appear in applied sciences and engineering. Full article

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29 pages, 8854 KB

Open AccessArticle

A Hybrid Ensemble Deep Learning Framework for Pediatric Pneumonia Classification Using Transfer Learning and Convolutional Neural Networks

by Arda Yunianta

Math. Comput. Appl. 2026, 31(3), 96; https://doi.org/10.3390/mca31030096 - 2 Jun 2026

Viewed by 153

Abstract

Accurate diagnosis of pediatric pneumonia remains a challenging task in clinical practice. The aim of this research is to propose a hybrid ensemble framework for pediatric pneumonia diagnosis that unites three fine-tuned pre-trained CNN models through feature fusion, EfficientNetB0, ResNet50, and MobileNetV2, to [...] Read more.

Accurate diagnosis of pediatric pneumonia remains a challenging task in clinical practice. The aim of this research is to propose a hybrid ensemble framework for pediatric pneumonia diagnosis that unites three fine-tuned pre-trained CNN models through feature fusion, EfficientNetB0, ResNet50, and MobileNetV2, to achieve better performance and results. This research experiment used the Chest X-Ray Images (Pneumonia) dataset, which contains 5863 high-resolution anterior–posterior (AP) chest radiographs sampled from children aged 1 to 5 years old. This study presents four key contributions. Firstly, we systematically evaluated five CNN (Convolutional Neural Network) combinations with seven different individual base models to identify the optimal ensemble configuration. Each base model was initialized with ImageNet pre-trained weights, with top classification layers replaced by global average pooling. Secondly, the proposed ensemble approach of MobileNetV2, ResNet50, and EfficientNetB0 achieved superior performance with accuracy: 96.1%, precision: 97.8%, recall: 96.7%, and F1-Score: 97.3%, outperforming all individual models and alternative ensemble combinations. Thirdly, this study compared the experiment results with several existing studies related to pneumonia classification. Fourthly, this study validated the proposed model on an external NIH pediatric dataset (94.73% accuracy) without fine-tuning, demonstrating true clinical transportability beyond benchmark dataset performance. Full article

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15 pages, 664 KB

Open AccessArticle

Mathematical Analysis of Non-Steady-State Immobilized Glucose Dehydrogenase Glucose and Oxygen-Driven Reactions in Spherical Microreactors

by Daniel Samuel, Mallikarjuna Mohanasundaraganesan and Senthamarai Rathinam

Math. Comput. Appl. 2026, 31(3), 95; https://doi.org/10.3390/mca31030095 - 2 Jun 2026

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Abstract

The governing reaction–diffusion model for carbohydrate oxidation catalyzed by an immobilized bienzyme system glucose dehydrogenase and laccase within a spherical porous microreactor is adapted from Baronas et al. and extended here to the non-steady-state regime. The model consists of coupled non-linear partial differential [...] Read more.

The governing reaction–diffusion model for carbohydrate oxidation catalyzed by an immobilized bienzyme system glucose dehydrogenase and laccase within a spherical porous microreactor is adapted from Baronas et al. and extended here to the non-steady-state regime. The model consists of coupled non-linear partial differential equations based on non-Michaelis–Menten kinetics. The principal novelty of this work lies in the derivation of closed-form semi-analytical expressions for transient and steady-state concentrations of the carbohydrate substrate, oxygen, and product, as well as for the effectiveness factor, using the Laplace Homotopy Perturbation Method (LHPM). The LHPM solutions are validated against MATLAB R2026a numerical simulations (maximum error

< 0.009 %

) and demonstrate superior accuracy compared to previously reported Adomian Decomposition Method (ADM) and Taylor Series Method (TSM) solutions. Parametric analysis reveals that the Thiele modulus, saturation parameters, and dimensionless time strongly influence the internal concentration profiles and reactor effectiveness. These analytical results provide rapid, closed-form predictive tools for optimizing catalyst particle size, enzyme loading, and operating conditions in immobilized enzyme microreactor systems. Full article

(This article belongs to the Section Engineering)

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27 pages, 2515 KB

Open AccessArticle

Learning Bi-Objective Bayesian Network Structure from Data Using Particle Swarm Optimization

by Vicente-Josué Aguilera-Rueda, Nicandro Cruz-Ramírez, Efrén Mezura-Montes and Ricardo Vilalta

Math. Comput. Appl. 2026, 31(3), 94; https://doi.org/10.3390/mca31030094 - 2 Jun 2026

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Abstract

This paper proposes a bi-objective approach to address the data-driven Bayesian network structure learning problem. The objectives considered for optimization are minimum description length (MDL) and misclassification. An algorithm based on the well-known multi-objective particle swarm optimization (MOPSO), called MOPSO-BN, is used to [...] Read more.

This paper proposes a bi-objective approach to address the data-driven Bayesian network structure learning problem. The objectives considered for optimization are minimum description length (MDL) and misclassification. An algorithm based on the well-known multi-objective particle swarm optimization (MOPSO), called MOPSO-BN, is used to tackle the bi-objective learning problem. Furthermore, a strategy for preference handling from the Pareto front that selects the nearest model to a reference point is proposed. Finally, this bi-objective approach is compared against a single-objective approach. Numerical results show how this multi-objective approach is highly efficient at competitive Bayesian networks with a balanced trade-off between MDL and misclassification. Full article

(This article belongs to the Special Issue New Trends in Computational Intelligence and Applications 2025)

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25 pages, 491 KB

Open AccessArticle

Stability Analysis via a Neurodynamic Approach with Time-Varying Coefficients for Solving Inverse Quasi-Variational Inequality Problems

by Vajahat Karim Khan, Md. Kalimuddin Ahmad and Adnène Arbi

Math. Comput. Appl. 2026, 31(3), 93; https://doi.org/10.3390/mca31030093 - 1 Jun 2026

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Abstract

This paper proposes finite-time (FT) and fixed-time (FXT) neurodynamic models with time-varying coefficients for solving inverse quasi-variational inequality problems (IQVIPs). Two projected models with time-dependent gains are developed to enhance convergence speed and transient performance. A nominal model establishes the equivalence between equilibrium [...] Read more.

This paper proposes finite-time (FT) and fixed-time (FXT) neurodynamic models with time-varying coefficients for solving inverse quasi-variational inequality problems (IQVIPs). Two projected models with time-dependent gains are developed to enhance convergence speed and transient performance. A nominal model establishes the equivalence between equilibrium points and IQVIP solutions. Under Lipschitz continuity and strong monotonicity assumptions, the existence, uniqueness, and global convergence of the proposed models are ensured. By employing Lyapunov stability theory, finite-time and fixed-time convergence of the continuous-time models are rigorously established, where explicit settling-time bounds independent of initial conditions are derived for the FXT case. Furthermore, the robustness of the proposed models under bounded disturbances is analyzed. To validate the theoretical findings, a discrete-time implementation based on the forward Euler method is developed. Numerical experiments demonstrate that all trajectories converge within a uniform upper bound, showing convergence behavior consistent with the fixed-time characteristics of the continuous-time model. Although the convergence time varies with initial conditions, it remains uniformly bounded, which is consistent with the fixed-time stability characteristics of the continuous-time model. The proposed framework provides a computationally efficient and scalable approach for solving IQVIPs, with potential applications in traffic equilibrium, communication networks, distributed control systems, and multi-agent coordination. Its adaptive structure and fixed-time convergence properties make it particularly suitable for real-time optimization in dynamic and uncertain environments. Full article

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27 pages, 3956 KB

Open AccessArticle

Development and Optimization of Cattaneo–Christov Carreau–Yasuda Tri-Hybrid Nanofluid Using Artificial Neural Networks

by Aqsa Zafar Abbasi, Mamoon Aamir, Ayesha Rafiq, Mohamed Omri, Walid Aich and Lioua Kolsi

Math. Comput. Appl. 2026, 31(3), 92; https://doi.org/10.3390/mca31030092 - 1 Jun 2026

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Abstract

An artificial neural network (ANN) prediction model based on the Levenberg–Marquardt (LM) algorithm has been developed to predict the nonlinear heat and mass transfer characteristics of Cattaneo–Christov Carreau–Yasuda tri-hybrid nanofluid (CCHMF–THNF) flow over a porous stretching sheet. A mathematical model of the phenomenon [...] Read more.

An artificial neural network (ANN) prediction model based on the Levenberg–Marquardt (LM) algorithm has been developed to predict the nonlinear heat and mass transfer characteristics of Cattaneo–Christov Carreau–Yasuda tri-hybrid nanofluid (CCHMF–THNF) flow over a porous stretching sheet. A mathematical model of the phenomenon was developed based on a number of elements, including the combined effect of magnetohydrodynamic forces, thermal and solutal relaxation and the influence of viscoelastic fluid behavior and is numerically analyzed utilizing MATLAB bvp4c software. A set of standard data was generated as a reference for developing the ANN-LM model with one hidden layer containing 10 neurons and log-sigmoid activation function, to achieve rapid predictions of velocity, temperature and concentration profiles from the identified data set. This study introduces a novel methodology to provide fast prediction capabilities for transport characteristics through integration of the ANN–LM model with the non-linear CCHMF-THNF model, producing computational savings by providing prediction accuracy of transport characteristics with MSE values on the order of

1.0 \times 10^{- 10}

using ANN–LM in place of repeated bvp4c solutions. Furthermore, the predictive capability of the developed ANN–LM framework may be beneficial in the areas of thermal management systems, polymer processing, energy transport applications, and magnetically controlled cooling technologies since they all share a need for fast access to transportation characteristic evaluation data. Full article

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25 pages, 2159 KB

Open AccessArticle

A Distributed Primal-Dual Framework for Composite Optimization with Nonseparable Coupled NonSmooth Function

by Zhe Li, Liang Ran, Jun Li and Lifeng Zheng

Math. Comput. Appl. 2026, 31(3), 91; https://doi.org/10.3390/mca31030091 - 1 Jun 2026

Viewed by 213

Abstract

This paper investigates a distributed convex optimization problem whose objective contains three terms: a local smooth convex function, a local nonsmooth function, and a globally shared, possibly nonsmooth, nonseparable coupling function. To solve this problem, a novel distributed primal-dual proximal gradient algorithm and [...] Read more.

This paper investigates a distributed convex optimization problem whose objective contains three terms: a local smooth convex function, a local nonsmooth function, and a globally shared, possibly nonsmooth, nonseparable coupling function. To solve this problem, a novel distributed primal-dual proximal gradient algorithm and its asynchronous version are proposed, designated as DPD-PG and AsynDPD-PG, respectively. Each agent communicates with its neighbors locally and updates iteratively with local step-sizes and local relaxation factors. By means of the operator splitting technique, the convergence of the algorithms is rigorously established under mild assumptions. Finally, numerical experiments demonstrate the efficiency of our algorithm, confirming its practical applicability and theoretical soundness. Full article

(This article belongs to the Section Engineering)

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23 pages, 1528 KB

Open AccessArticle

Distributional Coherence-Based Data Refinement for Early Detection in the Alzheimer’s Disease Spectrum Using SHAP-Guided Feature Selection

by Hany Hanafy Mahmoud Said

Math. Comput. Appl. 2026, 31(3), 90; https://doi.org/10.3390/mca31030090 - 1 Jun 2026

Viewed by 161

Abstract

Alzheimer’s disease (AD) is a major type of cognitive impairment. Most existing studies on cognitive state classification focus on the direct application of various machine learning (ML) algorithms. These studies typically assume consistency in patient features across assessments, without explicitly accounting for the [...] Read more.

Alzheimer’s disease (AD) is a major type of cognitive impairment. Most existing studies on cognitive state classification focus on the direct application of various machine learning (ML) algorithms. These studies typically assume consistency in patient features across assessments, without explicitly accounting for the variability introduced by subjective evaluation. While a limited number of studies have attempted to address such variability, their applicability remains constrained. This study addresses the identified gap by proposing a distribution–coherent data refinement method. First, it incorporates SHAP-based feature selection, then excludes a subset of distributionally atypical records. These include records from patients with a single visit that contribute to increased intra-class variance and reduced inter-class separability, as well as records from patients with multiple visits that exhibit inconsistent longitudinal trajectories. The method operates on the training data, and the resulting refined training data is fed to ML algorithms. The experimental results show that detecting a small number of records (1.285%) leads to a minor enhancement in data quality. The Fisher score and Cohen’s f are increased by on average 0.016 and 0.011, while the means for inter-class and intra-class are improved by 0.012 and 0.004, respectively. Furthermore, the refined training data enables ML models to achieve a higher joint correctness rate of up to 7.1% compared with the original data. Additionally, ML models trained on the refined data exhibit improved classification performance, as reflected by an increase in the

F_{1}

score. Therefore, the proposed method provides a potential data refinement strategy for the longitudinal restricted cognitive impairment spectrum, specifically for those data describing normal cognition, early mild cognitive impairment, and late mild cognitive impairment. Full article

(This article belongs to the Section Engineering)

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16 pages, 23893 KB

Open AccessArticle

Bio-Inspired Auxetic Bimodal Particle-Reinforced Composites with Chiral Confinement—A Computational Study

by Benjamin Alheit, Celal Soyarslan, Jongil Kim, Sang Ho Oh and Swantje Bargmann

Math. Comput. Appl. 2026, 31(3), 89; https://doi.org/10.3390/mca31030089 - 1 Jun 2026

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Abstract

Limpet teeth contain a desirable combination of material properties: they are auxetic (i.e., they have a negative Poisson’s ratio) and have high stiffness and strength. In contrast, synthetic auxetic structures presented in the literature to date are typically accompanied by low stiffness. Hence, [...] Read more.

Limpet teeth contain a desirable combination of material properties: they are auxetic (i.e., they have a negative Poisson’s ratio) and have high stiffness and strength. In contrast, synthetic auxetic structures presented in the literature to date are typically accompanied by low stiffness. Hence, limpet teeth microstructures are an attractive candidate for bio-inspired synthetic structures that have both auxeticity and high stiffness. The microstructure consists of iron oxide hydroxide crystal nanorods embedded in an amorphous hydrated silica matrix. Moreover, a portion of the nanorods are arranged into oriented bundles which are surrounded by a chiral arrangement of the remaining nanorods. Many synthetic structures achieve auxeticity by means of a similar chiral arrangement of rods. However, such structures are typically continuous, whereas the limpet teeth structures are not. In this work, a mechanistic description of such microstructural arrangements is provided by means of a computational parametric study of idealized microstructures comprising bimodal particle reinforcements with chiral confinement. The influence of microstructural geometry on the effective mechanical properties is investigated by varying parameters such as particle shape, bundle design, rod orientation, and others. It is shown that chiral rods that impinge on the central bundle are crucial for auxeticity and high material stiffness. Full article

(This article belongs to the Special Issue Current Problems and Advances in Computational and Applied Mechanics (AfriComp7))

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16 pages, 2266 KB

Open AccessArticle

Benchmarking Ethnic Hate Speech Detection in México

by Verónica Neri-Mendoza, Yulia Ledeneva, Jonathan Rojas-Simón, Muhammad Tayyab Zamir, René Arnulfo García-Hernández and Ángel Hernández-Castañeda

Math. Comput. Appl. 2026, 31(3), 88; https://doi.org/10.3390/mca31030088 - 1 Jun 2026

Viewed by 138

Abstract

Ethnic hate speech is a form of intersectional violence that affects Indigenous groups in México. Despite the severity of this social phenomenon, there is a lack of computational resources, specifically labeled datasets, to enable the development of automated tools for its detection. This [...] Read more.

Ethnic hate speech is a form of intersectional violence that affects Indigenous groups in México. Despite the severity of this social phenomenon, there is a lack of computational resources, specifically labeled datasets, to enable the development of automated tools for its detection. This paper presents a methodology for calculating benchmarks of the EthnoHate dataset, designed for the classification of ethnic hate speech in México, and focuses on establishing baselines using machine learning algorithms. Moreover, different levels of linguistic representation are evaluated. The results reveal that hate in the dataset is predominantly explicit, with a strong lexical component, enabling models such as BoW, and TF-IDF to achieve an F1-macro competitive. However, semantic techniques like BERT with a contextual classifier achieves the best overall performance in F1-macro, demonstrating that there is a significant proportion of implicit cases that require deep semantic understanding. Analysis of algorithms reveals that ethnic hate speech in México manifests through a recurring vocabulary with complex combinations, and that, while lexical approaches are highly effective, contextual models are necessary to capture the subtlety and diversity of hate expressions. This work establishes the feasibility of the task, validates the quality of the EthnoHate dataset, and lays the groundwork for future research employing more complex architectures. Full article

(This article belongs to the Special Issue New Trends in Computational Intelligence and Applications 2025)

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20 pages, 8797 KB

Open AccessArticle

Modeling Damage in Self-Regulated Transportation Networks with Tree-like Structure

by Clarissa Astuto and Daniele Francesco Santamaria

Math. Comput. Appl. 2026, 31(3), 87; https://doi.org/10.3390/mca31030087 - 1 Jun 2026

Viewed by 192

Abstract

Self-regulated transportation networks belong to the class of continuous network models and are widely used not only in biological applications such as vascular systems, neural networks or tissue regeneration, but also in urban infrastructure and in communication technologies. Their well-established tree structure prevents [...] Read more.

Self-regulated transportation networks belong to the class of continuous network models and are widely used not only in biological applications such as vascular systems, neural networks or tissue regeneration, but also in urban infrastructure and in communication technologies. Their well-established tree structure prevents the formation of loops, which limits their ability to capture an important feature observed in real systems: when a disruption or damage occurs, the network should be able to reorganize to restore transport pathways. In this work, we propose alternative modeling strategies to incorporate this capability. These approaches allow for the network to adapt to perturbations by modifying its structure and, in some cases, by creating alternative routes that compensate for damaged regions. Numerical results illustrate how the modified models can reproduce self-repair mechanisms that are not captured by standard formulations. Full article

(This article belongs to the Special Issue Computational Methods for Coupled Problems in Science and Engineering 2025)

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25 pages, 1639 KB

Open AccessArticle

Prior-Guided Diffusion Processes: A Unified Framework for Knowledge-Informed Generative Modeling with Theoretical Guarantees and Prognostic Case Studies

by Qing Liu, Yanqiang Di, Xianguo Meng, Zhiqiang Wang, Zhiying Xie, Haohao Cui and Tao Wang

Math. Comput. Appl. 2026, 31(3), 86; https://doi.org/10.3390/mca31030086 - 22 May 2026

Viewed by 156

Abstract

Diffusion probabilistic models are powerful generative tools but are purely data-driven, limiting their ability to incorporate domain knowledge—such as physical laws, degradation trends, or engineering priors—in scientific and engineering applications. We introduce Prior-Guided Diffusion Processes (PGDPs), a unified mathematical framework that integrates arbitrary [...] Read more.

Diffusion probabilistic models are powerful generative tools but are purely data-driven, limiting their ability to incorporate domain knowledge—such as physical laws, degradation trends, or engineering priors—in scientific and engineering applications. We introduce Prior-Guided Diffusion Processes (PGDPs), a unified mathematical framework that integrates arbitrary differentiable prior knowledge into the reverse diffusion dynamics by augmenting the score function with a guidance term derived from a prior potential

V (x, t)

and weighted by a time-dependent strength

γ_{t}

. This formulation subsumes existing mechanisms (classifier guidance, model-based diffusion, physics-informed corrections) as special cases. We analyze the guided path measures, providing an upper bound on the Kullback–Leibler divergence between guided and unguided marginals (Theorem 1), quantifying the inherent trade-off between data fidelity and prior satisfaction. Experiments on synthetic data confirm the predicted dependence on

γ_{t}

. On the NASA C-MAPSS turbofan benchmark, we enforce compressor-oriented physical constraints (e.g., speed–pressure consistency, monotonicity) within PGDP; remaining useful life scores are reported only as reference metrics under transparent protocols. A cross-domain study on the NASA IGBT accelerated aging dataset, using the same backbone with a replaced physics module, achieves a 99.98% reduction in monotonicity loss, demonstrating generality across distinct degradation mechanisms. PGDP provides a principled, extensible template for knowledge-informed generative modeling with theoretical guarantees and verifiable physical consistency. Full article

(This article belongs to the Section Engineering)

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Graphical abstract

29 pages, 14588 KB

Open AccessArticle

Using the LSTM Network for Gray-Box Dynamic Identification of Aircraft at Post-Stall Maneuvers

by Seyed Amin Bagherzadeh

Math. Comput. Appl. 2026, 31(3), 85; https://doi.org/10.3390/mca31030085 - 18 May 2026

Viewed by 143

Abstract

Accurate aerodynamic modeling of aircraft during post-stall maneuvers remains challenging due to massive flow separation, vortex breakdown, and unsteady hysteresis. This paper presents a gray-box system identification framework that integrates a Long Short-Term Memory (LSTM) network into the physical equations of aircraft motion. [...] Read more.

Accurate aerodynamic modeling of aircraft during post-stall maneuvers remains challenging due to massive flow separation, vortex breakdown, and unsteady hysteresis. This paper presents a gray-box system identification framework that integrates a Long Short-Term Memory (LSTM) network into the physical equations of aircraft motion. Unlike black-box methods that sacrifice interpretability, the proposed architecture preserves the rigid-body Newton-Euler equations while replacing empirical aerodynamic coefficient models with an LSTM network. The LSTM directly predicts the aerodynamic coefficients, which are transformed into forces and moments via exact physical laws, ensuring hard constraint satisfaction. Validation using real flight test data from a large-scale (3/8) fighter aircraft at angles of attack up to 80° demonstrates that the method achieves regression coefficients exceeding 0.96 for all coefficients on unseen data, with near-zero mean errors. Quantitative comparisons show that the proposed method reduces prediction error by 50–70% compared to black-box LSTM and PINN baselines. The framework offers a practical balance of accuracy, interpretability, and extrapolation reliability for post-stall aerodynamic identification. Full article

(This article belongs to the Section Engineering)

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19 pages, 3814 KB

Open AccessArticle

Robust Route–Speed Optimization for UAV Inspection Missions Under Wind Uncertainty

by Qin Li, Wei Zhang and Bingyun Zheng

Math. Comput. Appl. 2026, 31(3), 84; https://doi.org/10.3390/mca31030084 - 18 May 2026

Viewed by 324

Abstract

Unmanned aerial vehicles (UAVs) are widely used for inspection and monitoring tasks, where mission efficiency is strongly influenced by environmental conditions such as wind. In this work, we study a joint route–speed optimization problem for UAV inspection missions under uncertain wind conditions. The [...] Read more.

Unmanned aerial vehicles (UAVs) are widely used for inspection and monitoring tasks, where mission efficiency is strongly influenced by environmental conditions such as wind. In this work, we study a joint route–speed optimization problem for UAV inspection missions under uncertain wind conditions. The objective is to determine both the visiting sequence of inspection targets and the flight speeds along route segments in order to minimize worst-case energy consumption while satisfying mission duration constraints. We formulate the problem using a robust optimization framework that accounts for uncertainty in both wind speed and wind direction. The resulting model involves coupled discrete routing decisions and continuous speed control variables, which makes the problem computationally challenging. To address this difficulty, we propose a robust route–speed decomposition (RRSD) framework that alternates between route improvement and nonlinear speed optimization. Computational experiments on randomly generated instances, evaluated over eight random seeds per setting and compared against five baselines, including a simulated-annealing metaheuristic, demonstrate that RRSD consistently reduces worst-case energy consumption. A sensitivity analysis over the wind-uncertainty half-widths further shows that this advantage widens as the uncertainty set grows, and comparisons with exact enumeration on small instances confirm near-optimal solution quality at reasonable computational cost. These results highlight the importance of jointly optimizing routing decisions and speed control for energy-efficient UAV mission planning under uncertain environmental conditions. Full article

(This article belongs to the Special Issue Celebrate the 30th Anniversary of Mathematical and Computational Applications (MCA))

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37 pages, 1760 KB

Open AccessArticle

Chaotic Artificial Rabbits Optimization for Minimax Problems

by Amira A. Allam, Mohamed A. Tawhid and Mahmoud Owais

Math. Comput. Appl. 2026, 31(3), 83; https://doi.org/10.3390/mca31030083 - 17 May 2026

Viewed by 186

Abstract

Numerous engineering problems can be represented as minimax optimization problems, including machine learning, classification, robust optimal control, signal processing, game theory, and more. Typically, minimax problems are considered challenging, especially constrained ones. The recently introduced artificial rabbits optimization (ARO) is inspired by the [...] Read more.

Numerous engineering problems can be represented as minimax optimization problems, including machine learning, classification, robust optimal control, signal processing, game theory, and more. Typically, minimax problems are considered challenging, especially constrained ones. The recently introduced artificial rabbits optimization (ARO) is inspired by the natural behaviour of rabbits. ARO exhibits robust effectiveness in tackling optimization challenges. Despite its advantages, ARO converges early to local optima, especially in complex or multi-modal optimization problems, and it struggles to balance exploration and exploitation, often leading to premature convergence and reduced accuracy. In this paper, we present a chaotic ARO that employs five maps exhibiting randomization behaviour to refresh candidate solutions. We assess the performance of the suggested CARO by applying it to 46 benchmark functions (25 unconstrained and 21 non-smooth minimax) and 15 constrained test functions with diverse characteristics. We evaluate its performance against six swarm intelligence algorithms. Also, we employ the chaotic maps to ARO and the six compared algorithms, and we perform a non-parametric statistical test, the Friedman test, on all outcomes. The findings show that the proposed algorithm can solve both unconstrained and constrained minimax problems more effectively and efficiently than other swarm intelligence methods. Full article

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35 pages, 927 KB

Open AccessArticle

Evolutionary Linear Discriminant Projection for Sensory Analysis of Tortillas Fortified with Chilacayote Powder

by Adriana-Laura López-Lobato, Héctor-Gabriel Acosta-Mesa, Efrén Mezura-Montes, Jimena-Esther Alba-Jiménez, Amalia-Guadalupe Rodríguez-Gómez, Elia-Nora Aquino-Bolaños and Rosa-Hayde Alfaro-Rodríguez

Math. Comput. Appl. 2026, 31(3), 82; https://doi.org/10.3390/mca31030082 - 17 May 2026

Viewed by 181

Abstract

Chilacayote (Cucurbita ficifolia Bouché) is recognized as a rich source of nutrients and bioactive compounds, making it a promising ingredient for fortifying staple foods such as corn tortillas. While fortification can improve nutritional properties, it may also alter sensory characteristics that determine [...] Read more.

Chilacayote (Cucurbita ficifolia Bouché) is recognized as a rich source of nutrients and bioactive compounds, making it a promising ingredient for fortifying staple foods such as corn tortillas. While fortification can improve nutritional properties, it may also alter sensory characteristics that determine consumer acceptance. Therefore, a rigorous and structurally grounded assessment of these sensory modifications is required. In this study, sensory evaluations were conducted with regular tortilla consumers using Check-All-That-Apply (CATA) questionnaires to examine six attributes (color, smell, texture, taste, mouthfeel, and aftertaste) in tortillas made with nixtamalized dough and commercial flour, both with and without chilacayote powder. Then, a structured framework for dimensionality reduction and sensory profile identification of tortillas is proposed. In this framework, three classical feature extraction methods (Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and a combination of both (PCA+LDA)) were compared with an evolutionary discriminant approach (Differential Evolutionary Linear Discriminant Analysis for Feature Extraction and Visualization (DE-

{LDA}_{F E}

)). The projection quality of these methods was evaluated using a multi-scale separability index that integrates global, semi-global, and local metrics, and the experiments were conducted considering global and attribute-based analyses. Beyond quantitative discrimination, the optimized projections enabled a geometric interpretation that allows the identification of sensory profiles for the tortilla variants. The proposed methodology bridges evolutionary optimization, structural separability assessment, and interpretable sensory characterization, offering a robust and adaptable strategy for multivariate food analysis and other complex discrimination problems and insights into the sensory impact of chilacayote fortification for the development of nutritionally enhanced tortillas that preserve consumer appeal. Full article

(This article belongs to the Special Issue New Trends in Computational Intelligence and Applications 2025)

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40 pages, 1859 KB

Open AccessArticle

Nonlinear Analysis for Non-Newtonian Nanofluid Flow over a Shrinking Plate with Convective Boundary Conditions

by Mashael A. Aljohani and Mohamed Y. Abouzeid

Math. Comput. Appl. 2026, 31(3), 81; https://doi.org/10.3390/mca31030081 - 14 May 2026

Viewed by 188

Abstract

Significance: This study addresses critical industrial and biomedical applications including glass blowing (thermal management of shrinking sheets), polymer sheet extrusion (controlled cooling), magnetic drug delivery (nanoparticle targeting), and nuclear reactor cooling (enhanced heat transfer). Aim: We present a novel nonlinear analysis of magnetohydrodynamic [...] Read more.

Significance: This study addresses critical industrial and biomedical applications including glass blowing (thermal management of shrinking sheets), polymer sheet extrusion (controlled cooling), magnetic drug delivery (nanoparticle targeting), and nuclear reactor cooling (enhanced heat transfer). Aim: We present a novel nonlinear analysis of magnetohydrodynamic (MHD) boundary layer flow of a Jeffery Al₂O₃ nanofluid over a shrinking permeable plate with convective boundary conditions, uniquely integrating mixed convection, Ohmic dissipation, heat generation, Brownian motion, and thermophoresis within a non-Newtonian nanofluid framework. Methodology: The governing partial differential equations are transformed using similarity transformations and solved via the Adomian decomposition method (ADM). Comprehensive validation against RK4, RK45, and bvp4c demonstrates excellent agreement with maximum relative errors below

5 \times 10^{- 4}

. Key Contribution: (i) Normal velocity decreases by 15–25% as the Biot number increases from

B i = 0.4

to

0.6

; (ii) tangential velocity decreases by 20–30% as the magnetic parameter increases from

M = 5

to 15; (iii) temperature increases by 30–40% as the Eckert number increases from

E c = 0.5

to

2.5

; (iv) ADM converges within 12–15 terms with

L_{2}

errors

< 10^{- 5}

; (v) skin friction coefficient increases from

C_{f} = 3.02713

to

3.90082

as

Q_{0}

increases from 1 to 4; (vi) Nusselt number values:

N u / \sqrt{R e} = 0.4621

at

P r = 0.7

,

0.8954

at

P r = 2

,

3.2890

at

P r = 20

. These quantitative findings provide design guidelines for engineers in thermal management and biomedical applications. Full article

(This article belongs to the Special Issue Advances in Computational and Applied Mechanics (SACAM))

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28 pages, 3871 KB

Open AccessArticle

Simulated Annealing Applied to Alternative Assets in Mexican Stock Exchange

by Jose Luis Purata Aldaz, Juan Frausto Solís, Juan J. Gonzalez Barbosa, Guadalupe Castilla-Valdez and Juan Paulo Sánchez Hernández

Math. Comput. Appl. 2026, 31(3), 80; https://doi.org/10.3390/mca31030080 - 13 May 2026

Viewed by 174

Abstract

Accurate price forecasting and portfolio optimization in emerging financial markets remain challenging due to the non-stationary dynamics, structural breaks, and heterogeneous behavior of traded instruments. This paper proposes the Time-series Adaptive Forecast Ensemble (TAFE), a method that combines single popular forecasting methods, such [...] Read more.

Accurate price forecasting and portfolio optimization in emerging financial markets remain challenging due to the non-stationary dynamics, structural breaks, and heterogeneous behavior of traded instruments. This paper proposes the Time-series Adaptive Forecast Ensemble (TAFE), a method that combines single popular forecasting methods, such as ARIMA, by using an algorithm derived from both the simulated annealing (SA) and Threshold Accepting algorithms. The TAFE is applied to twenty-four weekly price series of Mexican exchange-traded funds (ETFs) and Real Estate Investment Trusts (FIBRAs) over the period 2020–2025. A top-K pre-selection strategy is used, mitigating the adverse cross-model interaction effect of some assets over others, in other words, reducing the propagation of errors from poorly performing base learners. In addition, the sample results show that the TAFE achieves the lowest mean SMAPE across the panel, with statistical superiority over the equal-weight benchmark and a Hybrid Model, confirmed by Diebold–Mariano and Harvey–Leybourne–Newbold tests. Out-of-sample evaluation over a 26-week horizon reveals a regime-shift-driven performance reversal consistent with the bias–variance tradeoff in adaptive combination schemes. Portfolio optimization using SA-generated forecasts yields with an expected return of 35.77%; thus, the model presents a slight overestimation of the return, with a variance of 2.4%. However, it has an acceptable level of risk. Full article

(This article belongs to the Special Issue Numerical and Evolutionary Optimization 2025)

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23 pages, 8367 KB

Open AccessArticle

Real-Time Urban Animal Monitoring Using Transfer Learning-Based Object Detection on Web Platforms

by Carlos Julio Fierro-Silva, Carlos A. Sánchez, Jorge S. Sánchez, Carolina Del-Valle-Soto, Nancy Velasco and José Varela-Aldás

Math. Comput. Appl. 2026, 31(3), 79; https://doi.org/10.3390/mca31030079 - 13 May 2026

Viewed by 385

Abstract

This study addresses the growing need for scalable solutions to monitor domestic and stray animals in urban environments. The objective is to develop and evaluate a real-time animal detection system using transfer learning and lightweight object detection models. The methodology includes the adaptation [...] Read more.

This study addresses the growing need for scalable solutions to monitor domestic and stray animals in urban environments. The objective is to develop and evaluate a real-time animal detection system using transfer learning and lightweight object detection models. The methodology includes the adaptation of a custom dataset with annotated images of cats and dogs under real-world conditions, followed by preprocessing, data augmentation, and model fine-tuning. Two architectures, SSD-MobileNet and YOLOv26s, were trained and evaluated using standard metrics such as precision, recall, F1-score, and mAP, as well as operational indicators like inference speed and system responsiveness. The best-performing model was integrated into a web-based platform with real-time detection, mobile access, and automated alerts. Results show that YOLOv26s outperforms SSD-MobileNet, achieving higher precision and recall while significantly reducing false positives and improving background discrimination. The system demonstrates near real-time performance suitable for monitoring applications and effective deployment across different input sources. The discussion findings highlight that integrating detection models with notification and visualization tools enhances practical applicability. Although SSD-MobileNet is suitable for low-resource environments, YOLOv26s provides a better balance between accuracy and reliability, making it more appropriate for real-world intelligent monitoring systems. Full article

(This article belongs to the Special Issue Celebrate the 30th Anniversary of Mathematical and Computational Applications (MCA))

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18 pages, 787 KB

Open AccessArticle

A Comparison Between Heuristic and Automatic Design in Variational Quantum Circuits for the MaxCut Problem Under Noise Effects

by Emmanuel Isaac Juárez Caballero, Horacio Tapia-McClung and Efrén Mezura-Montes

Math. Comput. Appl. 2026, 31(3), 78; https://doi.org/10.3390/mca31030078 - 7 May 2026

Viewed by 388

Abstract

The selection of the right topology (ansatz) for a Variational Quantum Algorithm (VQA) is a complex task that usually involves deep knowledge of a particular problem. The importance of the selection is greater when we consider the current state of quantum hardware, particularly [...] Read more.

The selection of the right topology (ansatz) for a Variational Quantum Algorithm (VQA) is a complex task that usually involves deep knowledge of a particular problem. The importance of the selection is greater when we consider the current state of quantum hardware, particularly the noise associated with the complexity of Variational Quantum Circuits (VQCs) that implement VQAs. Here, a comparison is presented between two confronted approaches for solving the MaxCut problem: QAOA, which has a theoretical proof of convergence, and the automatic design proposal (QNAS), which relies on evolutionary algorithms (NSGA-II) to discover efficient circuits. The comparison was made across 490 graph instances from different graph topologies and sizes (

n = 4

to

n = 16

), accounting for noise models such as depolarizing noise, gate errors, and readout noise. The results show that QAOA achieves an approximation ratio (

r_{A}

)

\approx 1

on complete graphs at the cost of being almost 12 times more complex than QNAS in ideal conditions while approaching the random noise floor (

r_{A} \approx 0.5)

. QNAS was capable of finding circuits less complex while maintaining

69 %

of the fidelity at a cost of having an

r_{A}

on the interval

0.7 \leq r_{A} \leq 0.8

. However, when the comparison is made across sparse graphs, performance is comparable, while QNAS is less complex. Full article

(This article belongs to the Special Issue New Trends in Computational Intelligence and Applications 2025)

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22 pages, 3217 KB

Open AccessArticle

From RVE Data to Auxetic Design Rules: Interpretable Feature Analysis and Machine Learning-Based Modeling of Microstructured Materials

by Alexander Hüls, Benjamin Alheit and Swantje Bargmann

Math. Comput. Appl. 2026, 31(3), 77; https://doi.org/10.3390/mca31030077 - 6 May 2026

Viewed by 433

Abstract

We study 2D RVEs based on microstructures inspired by limpet teeth with the objective of efficiently identifying auxetic designs and building surrogates for effective elastic response. The starting point is an unbalanced database; thus, we run a weighted random forest classifier and a [...] Read more.

We study 2D RVEs based on microstructures inspired by limpet teeth with the objective of efficiently identifying auxetic designs and building surrogates for effective elastic response. The starting point is an unbalanced database; thus, we run a weighted random forest classifier and a neural network classifier to balance it. The resulting feature importances provide an interpretable ranking of 18 geometric and material variables and guide importance-biased Monte Carlo sampling. Random forest and FCNN classifiers are used to prioritize candidates. Dataset rebalancing is achieved by adding newly FEM-confirmed auxetic samples and applying clustering-guided downsampling to the non-auxetic majority. On this final set, a multi-output FCNN regressor predicts nine targets: inclusion volume fractions and minima/means/maxima of Young’s modulus and Poisson’s ratio. Overall, the framework supports rapid, interpretable screening and property prediction for auxetic composite designs while reducing the need for repeated FEM evaluations. Full article

(This article belongs to the Special Issue Current Problems and Advances in Computational and Applied Mechanics (AfriComp7))

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29 pages, 8813 KB

Open AccessArticle

Medical Big Data-Driven Prenatal Risk Assessment and Testing-Time Optimization

by Can Jiang, Weicheng Li, Ziqian Geng, Hongmei Shang and Yan Li

Math. Comput. Appl. 2026, 31(3), 76; https://doi.org/10.3390/mca31030076 - 5 May 2026

Viewed by 256

Abstract

Medical big data derived from clinical records, laboratory tests, sequencing outputs, and quality-control indicators provides new opportunities for individualized prenatal risk assessment and optimized screening strategies. This study proposes an interpretable computational framework for prenatal risk assessment and testing-time optimization by integrating ensemble [...] Read more.

Medical big data derived from clinical records, laboratory tests, sequencing outputs, and quality-control indicators provides new opportunities for individualized prenatal risk assessment and optimized screening strategies. This study proposes an interpretable computational framework for prenatal risk assessment and testing-time optimization by integrating ensemble learning, BMI-stratified analysis, and uncertainty evaluation. For male-fetus samples, Y-chromosome-related measurements were used as biologically meaningful proxies for fetal signal. Linear regression, polynomial regression, random forest regression, and least-squares boosting were evaluated using cross-validated root mean squared error and coefficient of determination. BMI-stratified monotonic success-rate functions across gestational age were then estimated using a sliding-window procedure to identify practical sampling windows. Monte Carlo perturbation and bootstrap resampling were further applied to assess robustness against measurement noise and threshold variation. Least-squares boosting achieved the best overall predictive performance. The estimated optimal sampling ages were approximately 10 weeks, 14 weeks + 5 days, and 23 weeks + 3 days for the low-, medium-, and high-BMI strata, respectively, with greater instability observed in the high-BMI stratum. For female-fetus samples, aneuploidy screening was formulated as a binary classification task. Random forest substantially outperformed logistic regression, with an ROC-AUC of 0.884 versus 0.538 and an average precision of 0.668 versus 0.070, and supported a decision threshold of 0.1437. These findings suggest that medical big data-driven methods can improve prenatal risk assessment, testing-time optimization, and uncertainty-aware decision support in prenatal screening. Full article

(This article belongs to the Topic Health Monitoring in the Context of Medical Big Data)

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30 pages, 1617 KB

Open AccessArticle

ESIPO Methodology: An Ensemble Deep Learning and Metaheuristic Strategies for Stock Forecasting and Investment Portfolio Optimization

by Francisco Rivera Vargas, Juan Javier González Barbosa, Juan Frausto Solís, Mirna Ponce Flores, José Luis Purata Aldaz, Guadalupe Castilla-Valdez and Juan Paulo Sánchez Hernández

Math. Comput. Appl. 2026, 31(3), 75; https://doi.org/10.3390/mca31030075 - 4 May 2026

Viewed by 455

Abstract

An investment portfolio consists of a set of financial assets, such as stocks, fixed-income securities, mutual funds, and real estate, held to achieve diversification and to optimize returns. Accurate asset forecasting provides investors with valuable information to support decision-making. Although existing studies have [...] Read more.

An investment portfolio consists of a set of financial assets, such as stocks, fixed-income securities, mutual funds, and real estate, held to achieve diversification and to optimize returns. Accurate asset forecasting provides investors with valuable information to support decision-making. Although existing studies have proposed models for forecasting and portfolio optimization, most rely mainly on traditional techniques and metaheuristic approaches. This work introduces ESIPO (Ensemble Strategies for Investment Portfolio Optimization), a methodology that integrates deep learning and metaheuristic algorithms to perform asset forecasting and investment portfolio optimization. The dataset is obtained from the S&P 500 index, one of the main stock markets. To enhance forecasting accuracy, ESIPO combines five methods from the top-performing models of the international M4 competition: (a) ARIMA (AutoRegressive Integrated Moving Average) and ETS (the statistical exponential-smoothing state-space), which represent classical statistical approaches; (b) FFORMA (Feature-based FORecast Model Averaging) and JAGANATHAN, two ensemble-based methods; (c) CNN (Convolutional Neural Network), which is one of the most common deep learning models. ESIPO improves the forecast performance of the portfolio by applying the TAIPO (Threshold Accepting Investment Portfolio Optimization) metaheuristic to select the best assets and optimize portfolio composition. The results obtained 45% of improvement according to the Sharpe Ratio metric. Full article

(This article belongs to the Special Issue Numerical and Evolutionary Optimization 2025)

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30 pages, 2393 KB

Open AccessArticle

Airline Carbon Emission Efficiency Study: Static and Dynamic Perspectives

by Lianbin Zhou, Zhifeng Zhou, Peiwen Zhang and Lidan Li

Math. Comput. Appl. 2026, 31(3), 74; https://doi.org/10.3390/mca31030074 - 4 May 2026

Viewed by 248

Abstract

Amid the rapid growth of the aviation sector, carbon reduction presents a significant challenge for airlines. This study investigates the structural characteristics and dynamic evolution of carbon emission efficiency among 18 global airlines from 2015 to 2021 using a two-stage super-efficient slack-based measure [...] Read more.

Amid the rapid growth of the aviation sector, carbon reduction presents a significant challenge for airlines. This study investigates the structural characteristics and dynamic evolution of carbon emission efficiency among 18 global airlines from 2015 to 2021 using a two-stage super-efficient slack-based measure model (SBM) and an SBM-based Hicks–Moorsteen productivity index, incorporating absolute β-convergence tests. Key findings include the following: (1) The overall mean static efficiency of the airlines ranged from 0.225 (American Airlines) to 0.662 (Singapore Airlines), with an industry-wide average of 0.44. (2) Dynamic productivity change also exhibited significant variation: the overall mean superefficient SBM-based Hicks–Moorsteen (HM) productivity index was 0.962, but it dropped sharply to 0.526 in 2019–2020 due to the COVID-19 pandemic. After 2020, several airlines demonstrated significant recovery, with Emirates and Singapore Airlines achieving dynamic productivity change indices above 1.5. (3) In 16 out of 18 airlines, operational efficiency exceeded production efficiency, highlighting the importance of technological improvements in production. (4) Limited technological progress was identified as the main factor behind efficiency declines, while absolute β-convergence indicated that inefficient airlines are gradually catching up with efficient peers. These findings provide insights for airlines and policymakers in designing targeted carbon reduction strategies and promoting sustainable aviation development. The empirical scope of this study is limited to 18 major global airlines over the period 2015–2021. Due to data availability constraints, the sample does not fully cover all regions or low-cost carriers. The Hicks–Moorsteen index and its EC/TC components are used for interpretative and heuristic purposes only and should not be understood as a strict mathematical decomposition within the two-stage network SBM framework. Full article

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31 pages, 5537 KB

Open AccessArticle

Domain Decomposition of Large Neural Network Surrogate Models

by Timm Gödde, Eisso Hendrik Atzema and Bojana Rosić

Math. Comput. Appl. 2026, 31(3), 73; https://doi.org/10.3390/mca31030073 - 2 May 2026

Viewed by 295

Abstract

Data-driven neural networks (NNs) have gained significant attention across engineering disciplines, particularly in design optimization and experimental settings, where they are widely used to construct surrogate models for high-dimensional regression problems. Despite their power as global approximators, neural networks often struggle to accurately [...] Read more.

Data-driven neural networks (NNs) have gained significant attention across engineering disciplines, particularly in design optimization and experimental settings, where they are widely used to construct surrogate models for high-dimensional regression problems. Despite their power as global approximators, neural networks often struggle to accurately capture local features without relying on a large number of trainable parameters and training data points, resulting in increased training time. To address these limitations, in this paper we propose domain decomposition methods (DDMs), which divide the input feature space into multiple local subdomains, each modeled by a simpler NN, trained in parallel. Interface constraints are introduced in the local loss functions to enforce continuity between subdomains. They are enforced with two different approaches: by utilizing Lagrange multipliers or augmented Lagrange multiplier methods. Compared to unconstrained approximations, both methods significantly improve continuity across subdomain interfaces. For a 2D and a 3D problem, computational time and accuracy are investigated across varying numbers of subdomains to identify optimal partitioning strategies. The use of DDMs improves approximation accuracy in local regions with smaller number of parameters when compared to standard global NN training. In terms of convergence, the augmented Lagrange method outperforms the standard Lagrange formulation by converging faster due to lower convergence requirements, albeit with a slightly lower accuracy. Overall, these results highlight the augmented Lagrange method as a promising DDM approach for training efficient and scalable NN surrogate models. Full article

(This article belongs to the Special Issue Computational Methods for Coupled Problems in Science and Engineering 2025)

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22 pages, 2910 KB

Open AccessArticle

Multi-Strategy Improved Northern Goshawk Optimization for Wireless Sensor Network Coverage Enhancement

by Yiran Tian and Yuanjia Liu

Math. Comput. Appl. 2026, 31(3), 71; https://doi.org/10.3390/mca31030071 - 2 May 2026

Viewed by 305

Abstract

To address node redundancy and coverage holes in Wireless Sensor Network (WSN) deployment, this paper proposes an Improved Northern Goshawk Optimization (INGO) algorithm with multiple enhancements. It integrates a Diverse Chaotic Map Initialization Strategy (DCMIS) into the standard Northern Goshawk Optimization (NGO) for [...] Read more.

To address node redundancy and coverage holes in Wireless Sensor Network (WSN) deployment, this paper proposes an Improved Northern Goshawk Optimization (INGO) algorithm with multiple enhancements. It integrates a Diverse Chaotic Map Initialization Strategy (DCMIS) into the standard Northern Goshawk Optimization (NGO) for Diverse, uniform initial populations and improved global exploration. A Bidirectional Population Evolution Dynamics (BPED) mechanism follows the pursuit-and-evasion phase, applying asymmetric logic—elite guidance and selective replacement of weak individuals—to escape local optima and accelerate global convergence. Simulations reveal uniform grid topologies and an average coverage ratio of 91.90% with INGO, outperforming Northern Goshawk Optimization (NGO), Artificial Bee Colony (ABC), Improved Wild Horse Optimizer (IWHO), and the Firefly Algorithm (FA). INGO also achieves 100.00% connectivity, eliminating isolated nodes and ensuring reliable full-network communication. These results indicate that INGO achieves higher coverage and full connectivity under the studied simulation setting, demonstrating its effectiveness for WSN deployment optimization. Full article

(This article belongs to the Section Engineering)

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21 pages, 604 KB

Open AccessArticle

Security-Aware Task Offloading in IoT Edge Networks Using Software-Defined Networking

by Ahmed Raoof Tawfeeq Al-Hasani, Ali Broumandnia and Hamid Haj Seyyed Javadi

Math. Comput. Appl. 2026, 31(3), 72; https://doi.org/10.3390/mca31030072 - 1 May 2026

Viewed by 414

Abstract

The rapid proliferation of Internet of Things (IoT) devices increases the demand for task offloading mechanisms that satisfy strict latency constraints while limiting security exposure in edge computing environments. This paper proposes a security-aware task offloading framework for IoT edge networks, using Software-Defined [...] Read more.

The rapid proliferation of Internet of Things (IoT) devices increases the demand for task offloading mechanisms that satisfy strict latency constraints while limiting security exposure in edge computing environments. This paper proposes a security-aware task offloading framework for IoT edge networks, using Software-Defined Networking (SDN) as a centralized control plane. The SDN controller combines real-time monitoring, threat-aware risk estimation, and a lightweight heuristic decision engine to assign tasks to heterogeneous edge nodes according to latency constraints, resource availability, and task security sensitivity. To avoid optimistic scalability assumptions, the evaluation explicitly models contention through load-dependent queueing delay at edge nodes and reduced effective bandwidth on shared links. Simulation results with realistic IoT task parameters and heterogeneous edge capacities show that the proposed framework achieves an average latency of approximately

125 \pm 5

ms, a task completion ratio (TCR) of about

92 \pm 2

%, and a security success rate (SSR) near

95 \pm 1.5

%, compared to the considered baselines. These results indicate that incorporating risk assessment into SDN-based offloading decisions can improve security-related outcomes while maintaining practical performance under contention. Limitations include the use of an analytical risk model and a single-controller SDN setting; future work will investigate multi-controller deployments, attack-trace-driven evaluation, and energy-aware extensions. Full article

(This article belongs to the Special Issue Applied Optimization in Automatic Control and Systems Engineering)

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21 pages, 2795 KB

Open AccessArticle

Human Action Generation from Skeleton Sequences: A Comparative Study of Mathematical and Bio-Inspired Algorithms

by Sergio Hernandez-Mendez, Carolina Maldonado-Mendez, Sergio Fabian Ruiz-Paz, Hiram García-Lozano, Antonio Marin-Hernandez and Oscar Alonso-Ramirez

Math. Comput. Appl. 2026, 31(3), 70; https://doi.org/10.3390/mca31030070 - 1 May 2026

Viewed by 522

Abstract

In recent years, animation-based systems for human-computer interaction have attracted increasing attention. This work proposes a hybrid framework that combines mathematical modeling and bio-inspired optimization algorithms to generate motion sequences from skeletal data. The framework takes as input a complete skeletal sequence corresponding [...] Read more.

In recent years, animation-based systems for human-computer interaction have attracted increasing attention. This work proposes a hybrid framework that combines mathematical modeling and bio-inspired optimization algorithms to generate motion sequences from skeletal data. The framework takes as input a complete skeletal sequence corresponding to a given action and optimizes both the number of key poses and the parameters of a homotopy-based formulation to generate transitions between consecutive poses. A homotopy-based approach is used to compute transitions between selected key poses. The homotopy parameter

λ

serves as an indicator of the completeness of the transition between pairs of key poses. Four nature-inspired optimization algorithms: Genetic Algorithm, Micro Genetic Algorithm, Particle Swarm Optimization, and Ant Colony Optimization were evaluated to determine the number of key poses and homotopy parameters that enable feasible motion generation. Dynamic Time Warping (DTW) is used as an external metric to assess the similarity between generated and reference sequences. It is important to note that Dynamic Time Warping (DTW) should be considered as a sequence similarity measure, as it does not explicitly evaluate perceptual realism or biomechanical plausibility. The framework was evaluated on 18 action sequences, demonstrating its ability to generate feasible motion transitions in 16 of the 18 evaluated actions when using PSO and MicroGA. For each pair of key poses, a fixed number of intermediate frames is generated to provide a uniform temporal discretization of the motion. The results suggest that homotopy-based methods provide a feasible approach for animation-based interaction systems. Full article

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38 pages, 1956 KB

Open AccessArticle

Institutional Monitoring and Ledgers for Cooperative Human–AI Systems: A Framework with Pilot Evidence

by Saad Alqithami

Math. Comput. Appl. 2026, 31(3), 69; https://doi.org/10.3390/mca31030069 - 1 May 2026

Viewed by 330

Abstract

Human–AI systems often involve repeated interaction among users, organizations, and AI components rather than isolated model outputs. In such settings, cooperation can be pursued either by changing agent incentives or by adding an explicit accountability layer. We formalize the Institutional Monitoring and Ledger [...] Read more.

Human–AI systems often involve repeated interaction among users, organizations, and AI components rather than isolated model outputs. In such settings, cooperation can be pursued either by changing agent incentives or by adding an explicit accountability layer. We formalize the Institutional Monitoring and Ledger (IML) framework, which augments a Markov game with monitoring, evidence logging, delayed settlement, and review while leaving the base dynamics unchanged. We derive conservative incentive checks that clarify how detection quality, review accuracy, settlement delay, and sanction size jointly shape deterrence and wrongful-penalty risk. We then provide pilot evidence in two canonical sequential social dilemmas, Harvest and Cleanup, using five agents, PPO training, five training seeds per condition, and comparisons against PPO, inequity aversion, social influence, and IML ablations. In these settings, IML avoided some of the optimization instability observed in the representative internalization baselines tested here, made monitoring error directly visible through ledger records, and showed how false positives can accumulate into a persistent welfare cost. Agent-level analyses in these symmetric environments found nearly uniform measured enforcement burden, while temporal analyses showed that late-stage enforcement is increasingly dominated by residual false positives. These results do not establish legitimacy in human-facing settings or deployment readiness. They instead position IML as a framework with pilot evidence for studying accountability mechanisms in cooperative human–AI systems and highlight measurement error, review design, and due process as central design constraints. Full article

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20 pages, 3577 KB

Open AccessArticle

Analytical Integration for Logarithmic Spatial Singularities in the Time Domain Boundary Element Method

by Feng Zhao, Xiaokun Li, Juncheng Luo, Weidong Lei and Hongjun Li

Math. Comput. Appl. 2026, 31(3), 68; https://doi.org/10.3390/mca31030068 - 29 Apr 2026

Viewed by 237

Abstract

The treatment of logarithmic spatial singular integrals is a key challenge affecting the reliability of results when the time domain boundary element method (TD-BEM) is used to solve elastodynamic problems. To address this problem, this paper derives and establishes a set of analytically [...] Read more.

The treatment of logarithmic spatial singular integrals is a key challenge affecting the reliability of results when the time domain boundary element method (TD-BEM) is used to solve elastodynamic problems. To address this problem, this paper derives and establishes a set of analytically rigorous integration formulas for logarithmic spatial singularities based on the fundamental properties of the Heaviside function, which enables the direct spatiotemporal analytical solution of such singular integrals in TD-BEM. The formulas fill the research gap of the absence of direct analytical solutions for logarithmic spatial singular integrals in elastodynamic problems of TD-BEM, and enrich the theoretical system of the treatment of singular integrals for TD-BEM. Three typical elastodynamic engineering problems, including a fixed–fixed beam under a uniform sudden load, an infinite domain with a single cavity under a boundary blasting load, and a double tunnel beneath valley topography subjected to metro vibration load, are selected for numerical verification. The calculation results of the proposed method are compared with the reference solutions. It is shown that the calculation results of the proposed method are in good agreement with the reference solutions, which effectively verifies the correctness and engineering applicability of the analytical integration formulas. Full article

(This article belongs to the Special Issue Advances in Computational and Applied Mechanics (SACAM))

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