Previous Issue
Volume 30, October
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
2.1
Journals
MCA
Volume 30
Issue 6
share announcement

Math. Comput. Appl., Volume 30, Issue 6 (December 2025) – 18 articles

  • Issues are regarded as officially published after their release is announced to the table of contents alert mailing list.
  • You may sign up for e-mail alerts to receive table of contents of newly released issues.
  • PDF is the official format for papers published in both, html and pdf forms. To view the papers in pdf format, click on the "PDF Full-text" link, and use the free Adobe Reader to open them.
Order results
Result details
Section
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
24 pages, 13778 KB  
Article
Reinforcement Learning-Driven Evolutionary Stackelberg Game Model for Adaptive Breast Cancer Therapy
by Fatemeh Tavakoli, Davud Mohammadpur, Javad Salimi Sartakhti and Mohammad Hossein Manshaei
Math. Comput. Appl. 2025, 30(6), 134; https://doi.org/10.3390/mca30060134 - 5 Dec 2025
Abstract
In this paper, we present an integrative framework based on Evolutionary Stackelberg Game Theory to model the strategic interaction between a physician, acting as a rational leader, and a heterogeneous population of treatment-sensitive and treatment-resistant breast cancer cells. The model incorporates ecological competition, [...] Read more.
In this paper, we present an integrative framework based on Evolutionary Stackelberg Game Theory to model the strategic interaction between a physician, acting as a rational leader, and a heterogeneous population of treatment-sensitive and treatment-resistant breast cancer cells. The model incorporates ecological competition, evolutionary adaptation, and spatial heterogeneity, enabling prediction of tumor progression under clinically relevant treatment protocols. Using tumor volume data obtained from breast cancer-bearing mice treated with Capecitabine and Gemcitabine, we estimated treatment and subject-specific parameters via the GEKKO optimization package in Python. Benchmarking against classical tumor growth models (Exponential, Logistic, and Gompertz) showed that while classical models capture monotonic growth, they fail to reproduce complex, non-monotonic behaviors such as treatment-induced regression, rebound, and phenotypic switching. The game-theoretic approach achieved superior alignment with experimental data across Maximum Tolerated Dose, Dose-Modulation Adaptive Therapy, and Intermittent Adaptive Therapy protocols. To enhance adaptability, we integrated reinforcement learning (RL) for both single-agent and combination chemotherapy. The RL agent learned dosing policies that maximized tumor regression while minimizing cumulative drug exposure and resistance, with combination therapy exploiting dose diversification to improve control without exceeding total dose budgets. Incorporating reaction diffusion equations allowed the model to capture spatial dispersal of sensitive (cooperative) and resistant (defector) phenotypes, revealing that spatially aware adaptive strategies more effectively suppress resistant clones than non-spatial approaches. These results demonstrate that evolutionarily informed, spatially explicit, and computationally optimized strategies can outperform conventional fixed-dose regimens in reducing resistance, lowering toxicity, and improving efficacy. This framework offers a biologically interpretable tool for guiding evolution-aware, patient-tailored cancer therapies toward improved long-term outcomes. Full article
(This article belongs to the Special Issue Feature Papers in Mathematical and Computational Applications 2025)
20 pages, 3456 KB  
Article
RBF-Based Meshless Collocation Method for Time-Fractional Interface Problems with Highly Discontinuous Coefficients
by Faisal Bilal, Muhammad Asif, Mehnaz Shakeel and Ioan-Lucian Popa
Math. Comput. Appl. 2025, 30(6), 133; https://doi.org/10.3390/mca30060133 - 5 Dec 2025
Abstract
Time-fractional interface problems arise in systems where interacting materials exhibit memory effects or anomalous diffusion. These models provide a more realistic description of physical processes than classical formulations and appear in heat conduction, fluid flow, porous media diffusion, and electromagnetic wave propagation. However, [...] Read more.
Time-fractional interface problems arise in systems where interacting materials exhibit memory effects or anomalous diffusion. These models provide a more realistic description of physical processes than classical formulations and appear in heat conduction, fluid flow, porous media diffusion, and electromagnetic wave propagation. However, the presence of complex interfaces and the nonlocal nature of fractional derivatives makes their numerical treatment challenging. This article presents a numerical scheme that combines radial basis functions (RBFs) with the finite difference method (FDM) to solve time-fractional partial differential equations involving interfaces. The proposed approach applies to both linear and nonlinear models with constant or variable coefficients. Spatial derivatives are approximated using RBFs, while the Caputo definition is employed for the time-fractional term. First-order time derivatives are discretized using the FDM. Linear systems are solved via Gaussian elimination, and for nonlinear problems, two linearization strategies, a quasi-Newton method and a splitting technique, are implemented to improve efficiency and accuracy. The method’s performance is assessed using maximum absolute and root mean square errors across various grid resolutions. Numerical experiments demonstrate that the scheme effectively resolves sharp gradients and discontinuities while maintaining stability. Overall, the results confirm the robustness, accuracy, and broad applicability of the proposed technique. Full article
(This article belongs to the Special Issue Radial Basis Functions)
►▼ Show Figures

Figure 1

17 pages, 1722 KB  
Article
Detection in Road Crack Images Based on Sparse Convolution
by Yang Li, Xinhang Li, Ke Shen, Yacong Li, Dong Sui and Maozu Guo
Math. Comput. Appl. 2025, 30(6), 132; https://doi.org/10.3390/mca30060132 - 3 Dec 2025
Viewed by 121
Abstract
Ensuring the structural integrity of road infrastructure is vital for transportation safety and long-term sustainability. This study presents a lightweight and accurate pavement crack detection framework named SpcNet, which integrates a Sparse Encoding Module, ConvNeXt V2-based decoder, and a Binary Attention Module (BAM) [...] Read more.
Ensuring the structural integrity of road infrastructure is vital for transportation safety and long-term sustainability. This study presents a lightweight and accurate pavement crack detection framework named SpcNet, which integrates a Sparse Encoding Module, ConvNeXt V2-based decoder, and a Binary Attention Module (BAM) within an asymmetric encoder–decoder architecture. The proposed method first applies a random masking strategy to generate sparse pixel inputs and employs sparse convolution to enhance computational efficiency. A ConvNeXt V2 decoder with Global Response Normalization (GRN) and GELU activation further stabilizes feature extraction, while the BAM, in conjunction with Channel and Spatial Attention Bridge (CAB/SAB) modules, strengthens global dependency modeling and multi-scale feature fusion. Comprehensive experiments on four public datasets demonstrate that SpcNet achieves state-of-the-art performance with significantly fewer parameters and lower computational cost. On the Crack500 dataset, the method achieves a precision of 91.0%, recall of 85.1%, F1 score of 88.0%, and mIoU of 79.8%, surpassing existing deep-learning-based approaches. These results confirm that SpcNet effectively balances detection accuracy and efficiency, making it well-suited for real-world pavement condition monitoring. Full article
►▼ Show Figures

Figure 1

45 pages, 2785 KB  
Article
The Algebraic Theory of Operator Matrix Polynomials with Applications to Aeroelasticity in Flight Dynamics and Control
by Belkacem Bekhiti, Kamel Hariche, Vasilii Zaitsev, Guangren R. Duan and Abdel-Nasser Sharkawy
Math. Comput. Appl. 2025, 30(6), 131; https://doi.org/10.3390/mca30060131 - 29 Nov 2025
Viewed by 182
Abstract
This paper develops an algebraic framework for operator matrix polynomials and demonstrates its application to control-design problems in aeroservoelastic systems. We present constructive spectral-factorization and linearization tools (block spectral divisors, companion forms and realization algorithms) that enable systematic block-pole assignment for large-scale MIMO [...] Read more.
This paper develops an algebraic framework for operator matrix polynomials and demonstrates its application to control-design problems in aeroservoelastic systems. We present constructive spectral-factorization and linearization tools (block spectral divisors, companion forms and realization algorithms) that enable systematic block-pole assignment for large-scale MIMO models. Building on this theory, an adaptive block-pole placement strategy is proposed and cast in a practical implementation that augments a nominal state-feedback law with a compact neural-network compensator (single hidden layer) to handle un-modeled nonlinearities and uncertainty. The method requires state feedback and the system’s nominal model and admits Laplace-domain analysis and straightforward implementation for a two-degree-of-freedom aeroelastic wing with cubic stiffness nonlinearity and Roger aerodynamic lag is validated in MATLAB R2023a. Comprehensive simulations (Runge–Kutta 4) for different excitations and step disturbances demonstrate the approach’s advantages: compared with Eigenstructure assignment, LQR and H2-control, the proposed method achieves markedly better robustness and transient performance (e.g., closed-loop Hiω2 ≈ 4.64, condition number χ ≈ 11.19, and reduced control efforts μ ≈ 0.41, while delivering faster transients and tighter regulation (rise time ≈ 0.35 s, settling time ≈ 1.10 s, overshoot ≈ 6.2%, steady-state error ≈ 0.9%, disturbance-rejection ≈ 92%). These results confirm that algebraic operator-polynomial techniques, combined with a compact adaptive NN augmentation, provide a well-conditioned, low-effort solution for robust control of aeroelastic systems. Full article
►▼ Show Figures

Figure 1

18 pages, 2060 KB  
Article
A Context-Aware Representation-Learning-Based Model for Detecting Human-Written and AI-Generated Cryptocurrency Tweets Across Large Language Models
by Muhammad Asad Arshed, Ştefan Cristian Gherghina, Iqra Khalil, Hasnain Muavia, Anum Saleem and Hajran Saleem
Math. Comput. Appl. 2025, 30(6), 130; https://doi.org/10.3390/mca30060130 - 29 Nov 2025
Viewed by 202
Abstract
The extensive use of large language models (LLMs), particularly in the finance sector, raises concerns about the authenticity and reliability of generated text. Developing a robust method for distinguishing between human-written and AI-generated financial content is therefore essential. This study addressed this challenge [...] Read more.
The extensive use of large language models (LLMs), particularly in the finance sector, raises concerns about the authenticity and reliability of generated text. Developing a robust method for distinguishing between human-written and AI-generated financial content is therefore essential. This study addressed this challenge by constructing a dataset based on financial tweets, where original financial tweet texts were regenerated using six LLMs, resulting in seven distinct classes: human-authored text, LLaMA3.2, Phi3.5, Gemma2, Qwen2.5, Mistral, and LLaVA. A context-aware representation-learning-based model, namely DeBERTa, was extensively fine-tuned for this task. Its performance was compared to that of other transformer variants (DistilBERT, BERT Base Uncased, ELECTRA, and ALBERT Base V1) as well as traditional machine learning models (logistic regression, naive Bayes, random forest, decision trees, XGBoost, AdaBoost, and voting (AdaBoost, GradientBoosting, XGBoost)) using Word2Vec embeddings. The proposed DeBERTa-based model achieved an impressive test accuracy, precision, recall, and F1-score, all reaching 94%. In contrast, competing transformer models achieved test accuracies ranging from 0.78 to 0.80, while traditional machine learning models yielded a significantly lower performance (0.39–0.80). These results highlight the effectiveness of context-aware representation learning in distinguishing between human-written and AI-generated financial text, with significant implications for text authentication, authorship verification, and financial information security. Full article
(This article belongs to the Special Issue Innovative Deep Transfer Learning Techniques and Their Use in Real-World Applications)
►▼ Show Figures

Figure 1

20 pages, 4781 KB  
Article
Optimization for Sustainability: A Comparative Analysis of Evolutionary Crossover Operators for the Traveling Salesman Problem (TSP) with a Case Study on Croatia
by Petar Curkovic
Math. Comput. Appl. 2025, 30(6), 129; https://doi.org/10.3390/mca30060129 - 29 Nov 2025
Viewed by 166
Abstract
This study presents a systematic comparison of five crossover operators used in genetic algorithms (GA) for the Traveling Salesman Problem (TSP). Partially Mapped Crossover (PMX), Order Crossover (OX), Cycle Crossover (CX), Edge Recombination (ERX), and Alternating Edges (AEX) are evaluated within an identical [...] Read more.
This study presents a systematic comparison of five crossover operators used in genetic algorithms (GA) for the Traveling Salesman Problem (TSP). Partially Mapped Crossover (PMX), Order Crossover (OX), Cycle Crossover (CX), Edge Recombination (ERX), and Alternating Edges (AEX) are evaluated within an identical GA framework using tournament selection, inversion mutation, generational replacement, and elitism. Experiments were conducted on seven datasets, including three TSPLIB benchmarks, a clustered synthetic instance, a uniformly random instance, and two real-world Croatian city sets of 50 and 100 cities. Thirty independent GA runs per operator were analyzed using the Friedman test followed by Holm-corrected Wilcoxon pairwise comparisons. The Friedman test shows highly significant global performance differences. After applying Holm correction, the top four operators (PMX, OX, CX, and ERX) are statistically comparable on most datasets, as the correction eliminates most pairwise differences among them. All pairwise comparisons involving AEX remain significant across every dataset, confirming its consistently inferior performance. OX achieves the best average ranks across all datasets consistently, while PMX, CX, and ERX exhibit comparable mid-range performance. To illustrate practical relevance, optimized routes for Croatian instances were used to estimate fuel consumption and CO2 emissions for petrol, diesel, and electric vehicles. The results demonstrate meaningful sustainability benefits achievable through optimized routing. Full article
(This article belongs to the Section Engineering)
►▼ Show Figures

Figure 1

28 pages, 4503 KB  
Article
Comparative Performance Analysis of Machine Learning Models for Compressive Strength Prediction in Concrete Mix Design
by Junyu Liu, Dayou Guan and Xi Liu
Math. Comput. Appl. 2025, 30(6), 128; https://doi.org/10.3390/mca30060128 - 27 Nov 2025
Viewed by 310
Abstract
Recycled aggregate concrete (RAC) is a sustainable alternative to conventional concrete, reducing environmental hazards and conserving resources. Accurate compressive strength (CS) prediction is critical for its broader acceptance. This study uses machine learning (ML) models (elastic net regression, KNN, ANN, SVR, RF, XGBoost, [...] Read more.
Recycled aggregate concrete (RAC) is a sustainable alternative to conventional concrete, reducing environmental hazards and conserving resources. Accurate compressive strength (CS) prediction is critical for its broader acceptance. This study uses machine learning (ML) models (elastic net regression, KNN, ANN, SVR, RF, XGBoost, CatBoost, symbolic regression, stacking) trained on 1030 conventional concrete mixtures from UCI to support RAC’s CS prediction. The best model achieved R2 = 0.92; performance order: CatBoost > XGBoost > RF > SVR > ANN > symbolic regression > KNN > elastic net regression. Stacking improved RMSE by 6% over CatBoost. During the testing, sensitivity analysis revealed that CS exhibits pronounced sensitivity to the cement (C) content and testing age (TA). This aligns with existing experimental research. External validation, which is often neglected by prediction model research, was performed, from which a high-quality evaluating model was used for generalizability and reliability, enhancing the heterogenicity of its usefulness. Lastly, a user-friendly graphical interface was developed that allows users to input custom parameters to obtain sustainable RAC mixtures. This study offers insights into optimizing concrete mix designs for RAC, improving its performance and sustainability. It also advances the knowledge of cementitious materials, aligning with industrial and environmental objectives. Full article
►▼ Show Figures

Figure 1

26 pages, 1869 KB  
Article
Error Estimates and Generalized Trial Constructions for Solving ODEs Using Physics-Informed Neural Networks
by Atmane Babni, Ismail Jamiai and José Alberto Rodrigues
Math. Comput. Appl. 2025, 30(6), 127; https://doi.org/10.3390/mca30060127 - 24 Nov 2025
Viewed by 398
Abstract
In this paper, we address the challenge of solving differential equations using physics-informed neural networks (PINNs), an innovative approach that integrates known physical laws into neural network training. The PINN approach involves three main steps: constructing a neural-network-based solution ansatz, defining a suitable [...] Read more.
In this paper, we address the challenge of solving differential equations using physics-informed neural networks (PINNs), an innovative approach that integrates known physical laws into neural network training. The PINN approach involves three main steps: constructing a neural-network-based solution ansatz, defining a suitable loss function, and minimizing this loss via gradient-based optimization. We review two primary PINN formulations: the standard PINN I and an enhanced PINN II. The latter explicitly incorporates initial, final, or boundary conditions. Focusing on first-order differential equations, PINN II methods typically express the approximate solution as u˜(x,θ)=P(x)+Q(x)N(x,θ), where N(x,θ) is the neural network output with parameters θ, and P(x) and Q(x) are polynomial functions. We generalize this formulation by replacing the polynomial Q(x) with a more flexible function ϕ(x). We demonstrate that this generalized form yields a uniform approximation of the true solution, based on Cybenko’s universal approximation theorem. We further show that the approximation error diminishes as the loss function converges. Numerical experiments validate our theoretical findings and illustrate the advantages of the proposed choice of ϕ(x). Finally, we outline how this framework can be extended to higher-order or other classes of differential equations. Full article
(This article belongs to the Special Issue Numerical and Symbolic Computation: Developments and Applications 2025)
►▼ Show Figures

Figure 1

36 pages, 25371 KB  
Article
Performance Evaluation of Various Nanofluids in MHD Natural Convection Within a Wavy Trapezoidal Cavity Containing Heated Square Obstacles
by Sree Pradip Kumer Sarker and Md. Mahmud Alam
Math. Comput. Appl. 2025, 30(6), 126; https://doi.org/10.3390/mca30060126 - 18 Nov 2025
Viewed by 243
Abstract
Natural convection enhanced by magnetic fields and nanofluids has broad applications in thermal management systems. This study investigates magnetohydrodynamic (MHD) natural convection in a wavy trapezoidal cavity containing centrally located heated square obstacles, filled with various nanofluids Cu–H2O, Fe3O [...] Read more.
Natural convection enhanced by magnetic fields and nanofluids has broad applications in thermal management systems. This study investigates magnetohydrodynamic (MHD) natural convection in a wavy trapezoidal cavity containing centrally located heated square obstacles, filled with various nanofluids Cu–H2O, Fe3O4–H2O, and Al2O3–H2O. A uniform magnetic field is applied horizontally, and the effects of key parameters such as Rayleigh number, Ra (103–106), Hartmann number, Ha (0–50), and nanoparticle volume fraction, φ (0.00, 0.02, 0.04) are analyzed. The numerical simulations are performed using the finite element method, incorporating a wavy upper boundary and slanted sidewalls to model realistic enclosures. Results show that an increasing Rayleigh number enhances heat transfer, while a stronger magnetic field reduces convective flow. Among the nanofluids, Cu–H2O demonstrates the highest Nusselt number and ecological coefficient of performance (ECOP), whereas Fe3O4–H2O exhibits superior performance under stronger magnetic fields due to its magnetic nature. Entropy generation, ST decreases with increasing Ra and φ, indicating reduced thermodynamic irreversibility. These results provide insights into designing energy-efficient enclosures using nanofluids under magnetic control. Full article
►▼ Show Figures

Figure 1

20 pages, 711 KB  
Article
A Mean-Risk Multi-Period Optimization Model for Cooperative Risk in the Shipbuilding Supply Chain
by Ziquan Xiang, Muhammad Hamza Naseem, Xiuqian Pan and Fatima Sayeeda Ahmad
Math. Comput. Appl. 2025, 30(6), 125; https://doi.org/10.3390/mca30060125 - 13 Nov 2025
Viewed by 234
Abstract
This study addresses the cooperative development problem between shipbuilding enterprises and suppliers under supply risk by improving and optimizing the Markowitz model. A mean-risk multi-period linear programming decision model and a nonlinear programming decision model are constructed under uncertain conditions. First, the connotation [...] Read more.
This study addresses the cooperative development problem between shipbuilding enterprises and suppliers under supply risk by improving and optimizing the Markowitz model. A mean-risk multi-period linear programming decision model and a nonlinear programming decision model are constructed under uncertain conditions. First, the connotation of supply chain cooperation risks in shipbuilding enterprises is analyzed, and key risk characteristics are identified. Then, based on lifecycle theory, the cooperation process is examined, and corresponding risk prevention strategies are proposed. Finally, an enhanced mean-risk multi-period linear programming model is developed, and a nonlinear programming decision model is introduced. Numerical experiments and sensitivity analysis using real-world shipbuilding enterprise data validate the correctness and effectiveness of the proposed models. The results demonstrate that, compared to linear programming models, nonlinear programming models achieve a lower risk for equivalent returns. The findings suggest that the proposed approach can effectively optimize cooperative decision-making under uncertainty, providing valuable insights for risk management in the shipbuilding supply chain. Full article
►▼ Show Figures

Figure 1

25 pages, 2450 KB  
Systematic Review
The Preference Selection Index (PSI) in Multi-Criteria Decision-Making: A Systematic and Critical Review of Applications, Integrations, and Future Directions
by Mohammed Said Obeidat, Hala Al Sliti and Abdullah Obeidat
Math. Comput. Appl. 2025, 30(6), 124; https://doi.org/10.3390/mca30060124 - 13 Nov 2025
Viewed by 494
Abstract
This paper presents a systematic review of the performance of the Preference Selection Index (PSI) application in multi-criteria decision-making (MCDM) problems based on PRISMA. This work extensively reviewed more than 100 studies to investigate the methodological bases of the PSI and its synergistic [...] Read more.
This paper presents a systematic review of the performance of the Preference Selection Index (PSI) application in multi-criteria decision-making (MCDM) problems based on PRISMA. This work extensively reviewed more than 100 studies to investigate the methodological bases of the PSI and its synergistic combination with other decision-making methodologies. Interestingly, the PSI is highly commended as one of the most straightforward applications with low computational effort, which implies that the PSI in this context is receiving wide attention for complex decisions and sensitive judgments, where assigning criteria weights is challenging. However, in some circumstances, the PSI mechanism in assigning weights becomes a drawback when the accuracy of the decision is crucial. However, despite the increased use of the PSI, there is still a lack of systematic evaluation of its methodological sensitivity of weighting assumptions, consistency, and comparative performance in the hybrid MCDM problems. Addressing these gaps will help make the PSI more accurate in the evolving landscape of decision-making techniques. This review underscores the wide use of the PSI, encouraging further research in terms of its applications and methodology enhancement, ensuring that the PSI remains a relevant option that evolves the complexity and sensitivity of decision-making in various areas. Full article
(This article belongs to the Special Issue Applied Optimization in Automatic Control and Systems Engineering)
►▼ Show Figures

Figure 1

26 pages, 3049 KB  
Article
Numerical Aggregation and Evaluation of High-Dimensional Multi-Expert Decisions Based on Triangular Intuitionistic Fuzzy Modeling
by Yanshan Qian, Junda Qiu, Jiali Tang, Chuanan Li and Senyuan Chen
Math. Comput. Appl. 2025, 30(6), 123; https://doi.org/10.3390/mca30060123 - 6 Nov 2025
Viewed by 281
Abstract
To address the challenges of high-dimensional complexity and increasing heterogeneity in expert opinions, this study proposes a novel numerical aggregation model for multi-expert decision making based on triangular intuitionistic fuzzy numbers (TIFNs) and the Plant Growth Simulation Algorithm (PGSA). The proposed framework transforms [...] Read more.
To address the challenges of high-dimensional complexity and increasing heterogeneity in expert opinions, this study proposes a novel numerical aggregation model for multi-expert decision making based on triangular intuitionistic fuzzy numbers (TIFNs) and the Plant Growth Simulation Algorithm (PGSA). The proposed framework transforms experts’ fuzzy preference information into five-dimensional geometric vectors and employs the PGSA to perform global optimization, thereby yielding an optimized collective decision matrix. To comprehensively evaluate the aggregation performance, several quantitative indicators—such as weighted Hamming distance, correlation sum, information intuition energy, and weighted correlation coefficient—are introduced to assess the results from the perspectives of consensus, stability, and informational strength. Extensive numerical experiments and comparative analyses demonstrate that the proposed method significantly improves expert consensus reliability and aggregation robustness, achieving higher decision accuracy than conventional approaches. This framework provides a scalable and generalizable tool for high-dimensional fuzzy group decision making, offering promising potential for complex real-world applications. Full article
►▼ Show Figures

Figure 1

30 pages, 877 KB  
Article
Fractional Optimal Control of Anthroponotic Cutaneous Leishmaniasis with Behavioral and Epidemiological Extensions
by Asiyeh Ebrahimzadeh, Amin Jajarmi and Mehmet Yavuz
Math. Comput. Appl. 2025, 30(6), 122; https://doi.org/10.3390/mca30060122 - 6 Nov 2025
Viewed by 220
Abstract
Sandflies spread the neglected vector-borne disease anthroponotic cutaneous leishmaniasis (ACL), which only affects humans. Despite decades of control, asymptomatic carriers, vector pesticide resistance, and low public awareness prevent eradication. This study proposes a fractional-order optimal control model that integrates biological and behavioral aspects [...] Read more.
Sandflies spread the neglected vector-borne disease anthroponotic cutaneous leishmaniasis (ACL), which only affects humans. Despite decades of control, asymptomatic carriers, vector pesticide resistance, and low public awareness prevent eradication. This study proposes a fractional-order optimal control model that integrates biological and behavioral aspects of ACL transmission to better understand its complex dynamics and intervention responses. We model asymptomatic human illnesses, insecticide-resistant sandflies, and a dynamic awareness function under public health campaigns and collective behavioral memory. Four time-dependent control variables—symptomatic treatment, pesticide spraying, bed net use, and awareness promotion—are introduced under a shared budget constraint to reflect public health resource constraints. In addition, Caputo fractional derivatives incorporate memory-dependent processes and hereditary effects, allowing for epidemic and behavioral states to depend on prior infections and interventions; on the other hand, standard integer-order frameworks miss temporal smoothness, delayed responses, and persistence effects from this memory feature, which affect optimal control trajectories. Next, we determine the optimality conditions for fractional-order systems using a generalized Pontryagin’s maximum principle, then solve the state–adjoint equations numerically with an efficient forward–backward sweep approach. Simulations show that fractional (memory-based) dynamics capture behavioral inertia and cumulative public response, improving awareness and treatment efforts. Furthermore, sensitivity tests indicate that integer-order models do not predict the optimal allocation of limited resources, highlighting memory effects in epidemiological decision-making. Consequently, the proposed method provides a realistic and flexible mathematical basis for cost-effective and sustainable ACL control plans in endemic settings, revealing how memory-dependent dynamics may affect disease development and intervention efficiency. Full article
(This article belongs to the Special Issue Mathematics and Applied Data Science)
►▼ Show Figures

Figure 1

15 pages, 4417 KB  
Article
Efficient Biomedical Image Recognition Using Radial Basis Function Neural Networks and Quaternion Legendre Moments
by Kamal Okba, Amal Hjouji, Omar El Ogri, Jaouad El-Mekkaoui, Karim El Moutaouakil, Asmae Blilat and Mohamed Benslimane
Math. Comput. Appl. 2025, 30(6), 121; https://doi.org/10.3390/mca30060121 - 6 Nov 2025
Viewed by 322
Abstract
Biomedical images, whether acquired by techniques such as magnetic resonance imaging (MRI), computed tomography (CT), ultrasound, X-ray, or other methods, are commonly obtained and permanently stored for diagnostic purposes. Therefore, leveraging this large number of images has become essential for the development of [...] Read more.
Biomedical images, whether acquired by techniques such as magnetic resonance imaging (MRI), computed tomography (CT), ultrasound, X-ray, or other methods, are commonly obtained and permanently stored for diagnostic purposes. Therefore, leveraging this large number of images has become essential for the development of intelligent medical diagnostic systems. In this work, we propose a new biomedical image recognition in two stages: the first stage is to introduce a new image feature extraction technique using quaternion Legendre orthogonal moments (QLOMs) to extract features from biomedical images. The second stage is to use radial basis function (RBF) neural networks for image classification to know the type of disease. To evaluate our computer-aided medical diagnosis system, we present a series of experiments were conducted. Based on the results of a comparative study with recent approaches, we conclude that our method is very promising for the detection and recognition of dangerous diseases. Full article
►▼ Show Figures

Figure 1

14 pages, 470 KB  
Article
A Fav-Jerry Distribution Under Joint Type-II Censoring: Quantifying Cross-Cultural Differences in Autism Knowledge
by Asmaa S. Al-Moisheer, Khalaf S. Sultan and Mahmoud M. M. Mansour
Math. Comput. Appl. 2025, 30(6), 120; https://doi.org/10.3390/mca30060120 - 1 Nov 2025
Viewed by 367
Abstract
The given paper proposes a new statistical framework based on the combination of the Fav-Jerry distribution (FJD) and a joint type-II censoring scheme (JT-II-CS) to examine heterogeneous and censored data. The FJD offers tractability in analysis by using its closed form of the [...] Read more.
The given paper proposes a new statistical framework based on the combination of the Fav-Jerry distribution (FJD) and a joint type-II censoring scheme (JT-II-CS) to examine heterogeneous and censored data. The FJD offers tractability in analysis by using its closed form of the quantile function, whereas with missing or incomplete data, the JT-II-CS offers multi-sample comparisons. Bayesian estimation is based on Markov chain Monte Carlo procedures, while the maximum likelihood estimation is obtained via a Newton–Raphson method. Both estimation strategies provide estimates of the parameters along with corresponding measures of uncertainty. The proposed methodology is also used on coded survey data on the knowledge of autism in both Hong Kong and Canada, which illustrates its potential in the measurement of cultural variance. In addition to this use, the framework highlights the potential for integrating more complex distributional modeling with censoring methods for general applications in engineering, natural sciences, and social sciences. Full article
►▼ Show Figures

Figure 1

17 pages, 942 KB  
Article
Mathematical Modeling and Analysis of Tumor Growth Models Integrating Treatment Therapy
by Mohsin Kamran, Johari Yap Abdullah, Afaf Syahira Ahmad Satmi, Maya Genisa, Abdul Majeed and Tayyaba Nadeem
Math. Comput. Appl. 2025, 30(6), 119; https://doi.org/10.3390/mca30060119 - 30 Oct 2025
Viewed by 1037
Abstract
This study presents a comparative analysis of tumor growth models based on logistic, exponential, and Gompertz formulations. Their response to therapeutic intervention is examined to identify which model shows better behavior with minimal decline of immune cells. The framework incorporates three main cell [...] Read more.
This study presents a comparative analysis of tumor growth models based on logistic, exponential, and Gompertz formulations. Their response to therapeutic intervention is examined to identify which model shows better behavior with minimal decline of immune cells. The framework incorporates three main cell populations as follows: natural killer cells, cytotoxic T cells, and tumor cells, along with treatment effects. Dynamical properties such as positive invariance, existence, boundedness, and equilibrium stability are investigated. Numerical simulations indicate that the logistic model gives more favorable treatment outcomes compared to the exponential and Gompertz models. The results also show a faster decline of immune cell populations in the exponential and Gompertz models than in the logistic model under varying drug flux. Full article
(This article belongs to the Special Issue Feature Papers in Mathematical and Computational Applications 2025)
►▼ Show Figures

Figure 1

4 pages, 1994 KB  
Correction
Correction: Phan Thi et al. Secure Communication of Electric Drive System Using Chaotic Systems Base on Disturbance Observer and Fuzzy Brain Emotional Learning Neural Network. Math. Comput. Appl. 2025, 30, 73
by Huyen Chau Phan Thi, Nhat Quang Dang and Van Nam Giap
Math. Comput. Appl. 2025, 30(6), 118; https://doi.org/10.3390/mca30060118 - 30 Oct 2025
Viewed by 248
Abstract
In the original publication [...] Full article
►▼ Show Figures

Figure 2

25 pages, 1507 KB  
Article
Type-2 Backstepping T-S Fuzzy Control Based on Niche Situation
by Yang Cai, Yunli Hao and Yongfang Qi
Math. Comput. Appl. 2025, 30(6), 117; https://doi.org/10.3390/mca30060117 - 22 Oct 2025
Viewed by 340
Abstract
The niche situation can reflect the advantages and disadvantages of biological individuals in the ecosystem environment as well as the overall operational status of the ecosystem. However, higher-order niche systems generally exhibit complex nonlinearities and parameter uncertainties, making it difficult for traditional Type-1 [...] Read more.
The niche situation can reflect the advantages and disadvantages of biological individuals in the ecosystem environment as well as the overall operational status of the ecosystem. However, higher-order niche systems generally exhibit complex nonlinearities and parameter uncertainties, making it difficult for traditional Type-1 fuzzy control to accurately handle their inherent fuzziness and environmental disturbances in complex environments. To address this, this paper introduces the backstepping control method based on Type-2 T-S fuzzy control, incorporating the niche situation function as the consequent of the T-S backstepping fuzzy control. The stability analysis of the system is completed by constructing a Lyapunov function, and the adaptive law for the parameters of the niche situation function is derived. This design reflects the tendency of biological individuals to always develop in a direction beneficial to themselves, highlighting the bio-inspired intelligent characteristics of the proposed method. The results of case simulations show that the Type-2 backstepping T-S fuzzy control has significantly superior comprehensive performance in dealing with the complexity and uncertainty of high-order niche situation systems compared with the traditional Type-1 control and Type-2 T-S adaptive fuzzy control. These results not only verify the adaptive and self-development capabilities of biological individuals, as well as their efficiency in environmental utilization, but also endow this control method with a solid practical foundation. Full article
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-18
Previous Issue
Back to TopTop