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MCA
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Feature Papers in Mathematical and Computational Applications 2025
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Feature Papers in Mathematical and Computational Applications 2025

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

Deadline for manuscript submissions: 31 December 2025 | Viewed by 2901

Special Issue 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
Depto 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,

We are pleased to announce that the journal Mathematical and Computational Applications is presently compiling a collection of papers submitted exclusively by our Editorial Board Members (EBMs) and outstanding scholars in this research field.

The purpose of this collection is to publish a set of papers that typify the most insightful and influential original articles or reviews in which our EBMs discuss key topics in the field. We expect these papers to be widely read and highly influential. All papers in this collection will be collected into a printed book edition that will be extensively promoted.

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 100 words) can be sent to the Editorial Office for announcement on this website.

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 1400 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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  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • 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 (5 papers)

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Research

35 pages, 3870 KiB  
Article
Induction of Convolutional Decision Trees for Semantic Segmentation of Color Images Using Differential Evolution and Time and Memory Reduction Techniques
by Adriana-Laura López-Lobato, Héctor-Gabriel Acosta-Mesa and Efrén Mezura-Montes
Math. Comput. Appl. 2025, 30(3), 53; https://doi.org/10.3390/mca30030053 - 10 May 2025
Viewed by 165
Abstract
Convolutional Decision Trees (CDTs) are machine learning models utilized as interpretable methods for image segmentation. Their graphical structure enables a relatively simple interpretation of how the tree successively divides the image pixels into two classes, distinguishing between objects of interest and the image [...] Read more.
Convolutional Decision Trees (CDTs) are machine learning models utilized as interpretable methods for image segmentation. Their graphical structure enables a relatively simple interpretation of how the tree successively divides the image pixels into two classes, distinguishing between objects of interest and the image background. Several techniques have been proposed to induce CDTs. However, they have primarily been focused on analyzing grayscale images due to the computational cost of the Differential Evolution (DE) algorithm, which is employed in these techniques. This paper proposes a generalization of the induction process of a CDT with the DE algorithm using color images, implementing two techniques to reduce the computational time and memory employed in the induction process: the median selection technique and a memory of previously evaluated solutions. The first technique is applied to select a representative sample of pixels from an image for the model’s training process, and the second technique is implemented to reduce the number of evaluations in the fitness function considered in the DE process. The efficacy of these techniques was evaluated using the Weizmann Horse and DRIVE datasets, resulting in favorable outcomes in terms of the segmentation performance of the induced CDTs, and the processing time and memory required for the induction process. Full article
(This article belongs to the Special Issue Feature Papers in Mathematical and Computational Applications 2025)
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15 pages, 1443 KiB  
Article
Characterization of the Appointment’s Reasons for “P—Psychological” on the ICPC-2 Scale in Primary Health Care Services
by Filipa Rocha, Cristiana J. Silva, Sofia J. Pinheiro, Vera Afreixo, Rui Pedro Leitão and Miguel Felgueiras
Math. Comput. Appl. 2025, 30(2), 28; https://doi.org/10.3390/mca30020028 - 14 Mar 2025
Viewed by 453
Abstract
(1) Background: Mental health significantly impacts personal relationships and societal integration. Portugal faces a high prevalence of psychiatric illnesses and psychological distress, which the COVID-19 pandemic might have exacerbated. Therefore, this study aims to study risk factors that lead to psychological problems, using [...] Read more.
(1) Background: Mental health significantly impacts personal relationships and societal integration. Portugal faces a high prevalence of psychiatric illnesses and psychological distress, which the COVID-19 pandemic might have exacerbated. Therefore, this study aims to study risk factors that lead to psychological problems, using data available in the primary health care centers of the region of Aveiro. (2) Methods: This observational and retrospective study analyzes data from 2009 to 2022 on psychological consultations in the Aveiro municipalities. Variables considered are municipality, International Classification of Primary Care problem, sex, and comorbidities (cancer, obesity, and diabetes). Summary statistics and graphs were employed for data understanding, with R software used for analysis. Regression models, odds ratios, and association tests were calculated. Also, cluster analysis was performed on municipalities. (3) Results: A new, significant increase in the appointment growth rate was observed in 2021 and 2022. Anxiety and depressive disorders contribute to the identified growth. Women reported more problems than men. Cancer was the most present comorbidity. (4) Conclusions: The study reveals increased mental health problems, with primary health care users in Aveiro experiencing worsened psychosocial health, resulting in more medical consultations for psychological reasons. Risk factors included being female and having chronic conditions such as cancer. The findings provide insights into the burden of mental health issues in the region, highlighting the need for effective mental health interventions and resources to address health inequalities and support at-risk groups. Full article
(This article belongs to the Special Issue Feature Papers in Mathematical and Computational Applications 2025)
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24 pages, 399 KiB  
Article
Market Regime Identification and Variable Annuity Pricing: Analysis of COVID-19-Induced Regime Shifts in the Indian Stock Market
by Mohammad Sarfraz, Guglielmo D’Amico and Dharmaraja Selvamuthu
Math. Comput. Appl. 2025, 30(2), 23; https://doi.org/10.3390/mca30020023 - 27 Feb 2025
Viewed by 510
Abstract
Understanding how crises like the COVID-19 pandemic affect variable annuity pricing is crucial, especially in emerging markets like India. The motivation is that financial stability and risk management in these markets depend heavily on accurate pricing models. While prior research has primarily focused [...] Read more.
Understanding how crises like the COVID-19 pandemic affect variable annuity pricing is crucial, especially in emerging markets like India. The motivation is that financial stability and risk management in these markets depend heavily on accurate pricing models. While prior research has primarily focused on Western markets, there is a significant gap in analyzing the impact of extreme volatility and regime-dependent dynamics on variable annuities in emerging economies. This study investigates how regime shifts during the COVID-19 pandemic influence variable annuity pricing in the Indian stock market, specifically using the Nifty 50 Index data from 7 September 2017 until 7 September 2023. Advanced methodologies, including regime-switching hidden Markov models, artificial neural networks, and Monte Carlo simulations, were applied to analyze pre- and post-COVID-19 market behavior. The regime-switching hidden Markov models effectively capture latent market regimes and their transitions, which traditional models often overlook, while neural networks provide flexible functional approximations that enhance pricing accuracy in highly non-linear environments. The Expectation–Maximization (EM) algorithm was employed to achieve robust calibration and enhance pricing accuracy. The analysis showed significant pricing variations across market regimes, with heightened volatility observed during the pandemic. The findings highlight the effectiveness of regime-switching models in capturing market dynamics, particularly during periods of economic uncertainty and turbulence. This research contributes to the understanding of variable annuity pricing under regime-dependent dynamics in emerging markets and offers practical implications for improved risk management and policy formulation. Full article
(This article belongs to the Special Issue Feature Papers in Mathematical and Computational Applications 2025)
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15 pages, 1815 KiB  
Article
Predicting Red Blood Cell Transfusion in Elective Cardiac Surgery: A Machine Learning Approach
by Beatriz Lau, Daniel Ramos, Vera Afreixo, Luís M. Silva, Ana Helena Tavares, Miguel Martins Felgueiras, Diana Castro Paupério and João Firmino-Machado
Math. Comput. Appl. 2025, 30(2), 22; https://doi.org/10.3390/mca30020022 - 24 Feb 2025
Viewed by 675
Abstract
The benefits of Patient Blood Management can vary depending on a patient’s risk profile for requiring a blood transfusion. The objective of this study is to develop and analyse machine learning models that can identify patients at risk of requiring red blood cell [...] Read more.
The benefits of Patient Blood Management can vary depending on a patient’s risk profile for requiring a blood transfusion. The objective of this study is to develop and analyse machine learning models that can identify patients at risk of requiring red blood cell transfusion. This retrospective cohort study was conducted at a tertiary northern Portuguese hospital between 2018 and 2023. Two machine learning algorithms, extreme gradient boosting and neural networks, were employed due to their efficiency in handling complex feature interactions. Shapley additive explanations values were analysed to assess the contribution of each feature to the predictions generated by the models. The neural network achieved an accuracy of 0.735 and an area under the receiver operating characteristic curve of 0.798 (95% CI 0.747 to 0.849). The extreme gradient boosting model achieved an accuracy of 0.700 and an area under the receiver operating characteristic curve of 0.762 (95% CI 0.707 to 0.817). An analysis of Shapley additive explanations values revealed that the most important variable was preoperative haemoglobin levels, which can be optimised through the Patient Blood Management approach. These machine learning models demonstrate the potential to improve the accuracy of transfusion prediction at hospital admission, despite the absence of key variables such as surgeon identity and anaemia diagnosis. Full article
(This article belongs to the Special Issue Feature Papers in Mathematical and Computational Applications 2025)
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19 pages, 4016 KiB  
Article
Impact Loading on a Patient-Specific Head Model: The Significance of Brain Constitutive Models and Loading Location
by Amirhossein Gandomirouzbahani, Hadi Taghizadeh, Iman Z. Oskui and Fábio A. O. Fernandes
Math. Comput. Appl. 2025, 30(2), 21; https://doi.org/10.3390/mca30020021 - 21 Feb 2025
Viewed by 580
Abstract
Head impacts are common incidents that may cause traumatic brain injury (TBI), which imposes significant economic and social burdens. This study developed a patient-specific head model to address the significance of the brain’s constitutive model and loading location on head impact. Two hyperelastic [...] Read more.
Head impacts are common incidents that may cause traumatic brain injury (TBI), which imposes significant economic and social burdens. This study developed a patient-specific head model to address the significance of the brain’s constitutive model and loading location on head impact. Two hyperelastic (Model I and Model II) constitutive models and one hyper-viscoelastic (Model III) constitutive model for the brain tissue were developed. In Models II and III, white and gray matter heterogeneities were included. Respective volumetric and deviatoric responses were compared for a frontal head impact. Then, the load was applied to the head’s frontal, lateral, and posterior regions to report location-wise outcomes. The findings indicated that Model I, which was based on almost quasi-static experiments, underestimated the deviatoric responses. Although the pressure contours were similar for Models II and III, the latter included viscous effects and provided more accurate deviatoric responses. Lateral loading indicated a significantly higher risk of TBI. Interestingly, the deviatoric responses and strain energy density of the brain did not decay with relaxation of the impact load. Hence, the incidence of TBI should be explored after load relaxation. Full article
(This article belongs to the Special Issue Feature Papers in Mathematical and Computational Applications 2025)
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