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MCA
Special Issues
Celebrate the 30th Anniversary of Mathematical and Computational Applications (MCA)
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Celebrate the 30th Anniversary of Mathematical and Computational Applications (MCA)

A special issue of Mathematical and Computational Applications (ISSN 2297-8747).

Deadline for manuscript submissions: 31 December 2026 | Viewed by 5873

Editors

Prof. Dr. Gianluigi Rozza

E-Mail Website
Guest Editor
SISSA MathLab, International School for Advanced Studies, Office A-435, Via Bonomea 265, 34136 Trieste, Italy
Interests: numerical analysis and scientific computing; reduced order modelling and methods; efficient reduced-basis methods for parametrized PDEs and a posteriori error estimation; computational fluid dynamics: aero-naval-mechanical engineering; blood flows (haemodynamics); environmental fluid dynamics; multi-physics; software in computational science and engineering
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Oliver Schütze

E-Mail Website
Guest Editor
Departamento de Computacion, Cinvestav, Mexico City 07360, Mexico
Interests: multi-objective optimization; evolutionary computation (genetic algorithms and evolution strategies); numerical analysis; engineering applications
Special Issues, Collections and Topics in MDPI journals
Dr. Nicholas Fantuzzi

E-Mail Website
Guest Editor
Department of Civil, Chemical, Environmental, and Materials Engineering, University of Bologna, Viale del Risorgimento 2, 40136 Bologna, Italy
Interests: modeling of offshore structures and offshore structural components; structural theories of plates and applied mathematical modeling; mechanics of solids and structures; study of composite laminated structures and advanced composite materials; fracture mechanics and crack propagation and initiation; applied numerical methods such as finite element method and mesh-free element method
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In 2026, we will proudly mark the 30th Anniversary of Mathematical and Computational Applications (MCA) (ISSN: 2297-8747), a journal committed to applications of mathematical and/or computational techniques. Over the 30 years, MCA has become an important forum for studies related to applied mathematics and computational techniques.

To celebrate this milestone, we are curating a Special Issue that reflects on the progress of mathematical and/or computational techniques over the past 30 years. Papers may be theoretical, where mathematics is used in a nontrivial way, computational, or a combination of both. The included research may be in the fields of engineering (electrical, mechanical, civil, industrial, aeronautical, nuclear, etc.) or natural sciences (physics, mathematics, chemistry, biology, etc.), among others.

We warmly invite researchers to contribute original research articles, comprehensive reviews, and short communications. Submissions should fit the scope of the journal and be of high quality. The journal has no restrictions regarding the maximum length of papers. Authors are encouraged to publish their experimental and theoretical results in as much detail as possible so that the results may be reproduced. Manuscripts concerning new and innovative research proposals and ideas are particularly welcome; software, datasets, or instructive videos complementing the research may be uploaded as supplementary materials.

Prof. Dr. Gianluigi Rozza
Prof. Dr. Oliver Schütze
Dr. Nicholas Fantuzzi
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematical and Computational Applications is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • applied mathematics
  • classical mechanics
  • computational fluid dynamics
  • computational techniques
  • differential equations
  • dynamical systems
  • evolutionary algorithms
  • finite element methods
  • machine learning and data mining
  • mathematical modelling
  • neural networks
  • numerical analysis
  • numerical simulation
  • optimization and control
  • statistics

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  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
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  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (6 papers)

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Research

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19 pages, 3814 KB  
Article
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 367
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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23 pages, 8367 KB  
Article
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 734
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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21 pages, 498 KB  
Article
An Evaluation of Supervised Machine Learning Pipelines for the Identification of Distributed Denial-of-Service Attacks Using Conventional and Computational Performance Metrics
by Adrian Kwiecien and Waddah Saeed
Math. Comput. Appl. 2026, 31(2), 62; https://doi.org/10.3390/mca31020062 - 13 Apr 2026
Viewed by 708
Abstract
Distributed denial-of-service (DDoS) attacks, a type of Denial-of-Service (DoS) attack in which the targeted server, service or network is overloaded with malicious traffic originating from various different sources with the aim of making such targets inaccessible for legitimate users, continue to pose a [...] Read more.
Distributed denial-of-service (DDoS) attacks, a type of Denial-of-Service (DoS) attack in which the targeted server, service or network is overloaded with malicious traffic originating from various different sources with the aim of making such targets inaccessible for legitimate users, continue to pose a pertinent threat to the availability and integrity of organisational digital assets. While many studies have shown that machine learning models can provide high predictive accuracy in detecting such attacks, they often fail to evaluate the practicality of deploying such models to production. This study aims to address this gap by evaluating a considerable amount of pipelines based on five popular supervised classifiers for detecting DDoS attacks using the CICDDoS2019 dataset. The study employs a comprehensive methodology that combines both manual feature removal with automated encoding, scaling and feature selection integrated within pipelines. A total of 210 pipelines formed of five classifiers, three features selectors, two hyperparameter tuners and seven train–test splits were initially evaluated. Pipeline performance was assessed using both conventional and computational performance metrics. To identify the champion pipeline, a two-step approach was employed: composite scoring for shortlisting and statistical testing using Friedman and post hoc Nemenyi tests. The champion pipeline was shown to be Decision Tree coupled with Recursive Feature Elimination (with 20 features selected) and Grid Search hyperparameter tuning with a 90-10 train–test split. It achieved the most optimal balance of predictive capabilities and computational overheads, achieving an MCC of 0.993±0.024, training time of 0.194±0.001 s, inference time of 0.000998±0.00008 s, CPU time of 0.194±0.008 s and average memory usage of 15,167 ± 322 kilobytes across training and inference. The findings highlight the importance of a holistic and more nuanced approach when selecting a champion pipeline that is not only effective but also feasible for deployment in resource-constrained environments. Full article
(This article belongs to the Special Issue Celebrate the 30th Anniversary of Mathematical and Computational Applications (MCA))
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23 pages, 2014 KB  
Article
A Machine Learning Framework for Interpreting Composition-Dependent Weathering in Heritage Glass
by Hailu Wan, Zhuo Jin, Gengqiang Huang and Shuang Li
Math. Comput. Appl. 2026, 31(2), 54; https://doi.org/10.3390/mca31020054 - 3 Apr 2026
Viewed by 667
Abstract
Glass artworks represent a significant component of cultural heritage, yet their surfaces are highly vulnerable to physicochemical weathering resulting from composition-dependent interactions with environmental factors. Understanding the complex and nonlinear relationships between glass composition and deterioration remains challenging using conventional, often invasive, analytical [...] Read more.
Glass artworks represent a significant component of cultural heritage, yet their surfaces are highly vulnerable to physicochemical weathering resulting from composition-dependent interactions with environmental factors. Understanding the complex and nonlinear relationships between glass composition and deterioration remains challenging using conventional, often invasive, analytical techniques. To address this issue, this study proposes an interpretable and non-destructive computational framework to analyze weathering patterns in historical glass based on oxide composition data. The framework combines statistical hypothesis testing (Chi-squared analysis), metric-based machine learning (Prototypical Networks), probabilistic modeling (Gaussian Mixture Models), multivariate statistical analysis (orthogonal partial least squares discriminant analysis), and information-theoretic methods (mutual information analysis) to identify key compositional features and inter-elemental relationships associated with surface degradation. The results show that lead-barium glass exhibits a higher susceptibility to weathering compared with high-potassium glass, with PbO, BaO, and SiO2 identified as the most discriminative components. The Prototypical Network achieved 100% accuracy on most specific data partitions within the analyzed dataset, demonstrating its effectiveness in small-sample compositional classification. Meanwhile, mutual information network analysis revealed the complex interrelationships among chemical components involved in surface weathering behavior. These findings indicate that interpretable machine learning and statistical modeling can provide meaningful insights into composition-dependent patterns and support reproducible analysis for the sustainable conservation of cultural heritage glass. Full article
(This article belongs to the Special Issue Celebrate the 30th Anniversary of Mathematical and Computational Applications (MCA))
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32 pages, 5960 KB  
Article
Complex Double Interface Dynamics in Time-Fractional Models: Computational Analysis of Meshless and Multi-Resolution Techniques
by Faisal Bilal, Muhammad Asif, Mehnaz Shakeel and Ioan-Lucian Popa
Math. Comput. Appl. 2026, 31(2), 44; https://doi.org/10.3390/mca31020044 - 7 Mar 2026
Viewed by 487
Abstract
Time-fractional interface problems, found in heat transfer with discontinuous conductivities and fluid flows with surface tension forces, are challenging due to irregular interfaces and the history-dependent nature of fractional derivatives. This paper presents two numerical methods for simulating time-fractional double interface problems. The [...] Read more.
Time-fractional interface problems, found in heat transfer with discontinuous conductivities and fluid flows with surface tension forces, are challenging due to irregular interfaces and the history-dependent nature of fractional derivatives. This paper presents two numerical methods for simulating time-fractional double interface problems. The first method uses the Haar wavelet collocation technique, while the second relies on a meshless approach with radial basis functions. The fractional derivatives are replaced with the Caputo sense, the resulting first-order time derivatives are handled using the finite difference method, and the spatial operator is approximated using the two proposed methods. Gauss elimination is used to solve linear problems. Quasi-Newton linearization method is used for nonlinear problems. Both methods accommodate constant and variable coefficients, handling discontinuities and singularities in both solutions and coefficients. To evaluate the effectiveness of the proposed methods, numerical experiments are carried out. The accuracy of each method is quantified using the L∞ error norm, and a comparative analysis highlights the validity and advantages of the approaches. Moreover, the proposed schemes are rigorously analyzed to establish their stability, and the existence and uniqueness of the solutions. Full article
(This article belongs to the Special Issue Celebrate the 30th Anniversary of Mathematical and Computational Applications (MCA))
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41 pages, 2638 KB  
Systematic Review
ML-Based Autoscaling for Elastic Cloud Applications: Taxonomy, Frameworks, and Evaluation
by Vishwanath Srikanth Machiraju, Vijay Kumar and Sahil Sharma
Math. Comput. Appl. 2026, 31(2), 49; https://doi.org/10.3390/mca31020049 - 16 Mar 2026
Cited by 1 | Viewed by 2177
Abstract
Elastic cloud systems are increasingly employing machine learning (ML) to automate resource scaling in response to variable workloads and stringent service-level objectives. However, current ML-based autoscalers are fragmented across different platforms, objectives, and evaluation frameworks. This survey examines 60 primary studies conducted between [...] Read more.
Elastic cloud systems are increasingly employing machine learning (ML) to automate resource scaling in response to variable workloads and stringent service-level objectives. However, current ML-based autoscalers are fragmented across different platforms, objectives, and evaluation frameworks. This survey examines 60 primary studies conducted between 2015 and 2025, categorising them according to a five-dimensional taxonomy that includes goal, decision logic, scaling mode, control scope, and deployment. This study classifies supervised, unsupervised, and reinforcement learning approaches and analyzes their integration into practical frameworks, including Kubernetes-based controllers and cloud provider services. This paper summarizes the application of machine learning to workload prediction, proactive and hybrid horizontal–vertical scaling, and adaptive policy optimization. Additionally, it synthesises common evaluation practices, encompassing workloads, metrics, and benchmarks. The analysis identifies ongoing challenges: actuation delays and telemetry lag, the intricacies of hybrid scaling, coordination across multi-service and edge-cloud deployments, and the constrained joint consideration of cost, SLO, and energy objectives. The identified gaps necessitate additional research on unified machine learning-driven orchestration, multi-agent and federated control, standardised benchmarks, and sustainability-aware autoscaling. Full article
(This article belongs to the Special Issue Celebrate the 30th Anniversary of Mathematical and Computational Applications (MCA))
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