Mathematical and Computational Applications

20 pages, 5466 KiB

Open AccessArticle

Decoding Retail Commerce Patterns with Multisource Urban Knowledge

by Tianchu Xia, Yixue Chen, Fanru Gao, Yuk Ting Hester Chow, Jianjing Zhang and K. L. Keung

Math. Comput. Appl. 2025, 30(4), 75; https://doi.org/10.3390/mca30040075 - 17 Jul 2025

Urban commercial districts, with their unique characteristics, serve as a reflection of broader urban development patterns. However, only a handful of studies have harnessed point-of-interest (POI) data to model the intricate relationship between retail commercial space types and other factors. This paper endeavors [...] Read more.

Urban commercial districts, with their unique characteristics, serve as a reflection of broader urban development patterns. However, only a handful of studies have harnessed point-of-interest (POI) data to model the intricate relationship between retail commercial space types and other factors. This paper endeavors to bridge this gap, focusing on the influence of urban development factors on retail commerce districts through the lens of POI data. Our exploration underscores how commercial zones impact the density of residential neighborhoods and the coherence of pedestrian pathways. To facilitate our investigation, we propose an ensemble clustering technique for identifying and outlining urban commercial areas, including Kernel Density Analysis (KDE), Density-based Spatial Clustering of Applications with Noise (DBSCAN), Geographically Weighted Regression (GWR). Our research uses the city of Manchester as a case study, unearthing the relationship between commercial retail catchment areas and a range of factors (retail commercial space types, land use function, walking coverage). These include land use function, walking coverage, and green park within the specified areas. As we explore the multiple impacts of different urban development factors on retail commerce models, we hope this study acts as a springboard for further exploration of the untapped potential of POI data in urban business development and planning. Full article

(This article belongs to the Section Engineering)

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26 pages, 2178 KiB

Open AccessArticle

Testing Neural Architecture Search Efficient Evaluation Methods in DeepGA

by Jesús-Arnulfo Barradas-Palmeros, Carlos-Alberto López-Herrera, Efrén Mezura-Montes, Héctor-Gabriel Acosta-Mesa and Adriana-Laura López-Lobato

Math. Comput. Appl. 2025, 30(4), 74; https://doi.org/10.3390/mca30040074 - 17 Jul 2025

Abstract

Neural Architecture search (NAS) aims to automate the design process of Deep Neural Networks, reducing the Deep Learning (DL) expertise required and avoiding a trial-and-error process. Nonetheless, one of the main drawbacks of NAS is the high consumption of computational resources. Consequently, efficient [...] Read more.

Neural Architecture search (NAS) aims to automate the design process of Deep Neural Networks, reducing the Deep Learning (DL) expertise required and avoiding a trial-and-error process. Nonetheless, one of the main drawbacks of NAS is the high consumption of computational resources. Consequently, efficient evaluation methods (EEMs) to assess the quality of candidate architectures are an open research problem. This work tests various EEMs in the Deep Genetic Algorithm (DeepGA), including early stopping, population memory, and training-free proxies. The Fashion MNIST, CIFAR-10, and CIFAR-100 datasets were used for experimentation. The results show that population memory has a valuable impact on avoiding repeated evaluations. Additionally, early stopping achieved competitive performance while significantly reducing the computational cost of the search process. The training-free configurations using the Logsynflow and Linear Regions proxies, as well as a combination of both, were only partially competitive but dramatically reduced the search time. Finally, a comparison of the architectures and hyperparameters obtained with the different algorithm configurations is presented. The training-free search processes resulted in deeper architectures with more fully connected layers and skip connections than the ones obtained with accuracy-guided search configurations. Full article

(This article belongs to the Special Issue Feature Papers in Mathematical and Computational Applications 2025)

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23 pages, 10801 KiB

Open AccessArticle

Secure Communication of Electric Drive System Using Chaotic Systems Base on Disturbance Observer and Fuzzy Brain Emotional Learning Neural Network

by Huyen Chau Phan Thi, Nhat Quang Dang and Van Nam Giap

Math. Comput. Appl. 2025, 30(4), 73; https://doi.org/10.3390/mca30040073 - 14 Jul 2025

Abstract

This paper presents a novel wireless control framework for electric drive systems by employing a fuzzy brain emotional learning neural network (FBELNN) controller in conjunction with a Disturbance Observer (DO). The communication scheme uses chaotic system dynamics to ensure data confidentiality and robustness [...] Read more.

This paper presents a novel wireless control framework for electric drive systems by employing a fuzzy brain emotional learning neural network (FBELNN) controller in conjunction with a Disturbance Observer (DO). The communication scheme uses chaotic system dynamics to ensure data confidentiality and robustness against disturbance in wireless environments. To be applied to embedded microprocessors, the continuous-time chaotic system is discretized using the Grunwald–Letnikov approximation. To avoid the loss of generality of chaotic behavior, Lyapunov exponents are computed to validate the preservation of chaos in the discrete-time domain. The FBELNN controller is then developed to synchronize two non-identical chaotic systems under different initial conditions, enabling secure data encryption and decryption. Additionally, the DOB is introduced to estimate and mitigate the effects of bounded uncertainties and external disturbances, enhancing the system’s resilience to stealthy attacks. The proposed control structure is experimentally implemented on a wireless communication system utilizing ESP32 microcontrollers (Espressif Systems, Shanghai, China) based on the ESP-NOW protocol. Both control and feedback signals of the electric drive system are encrypted using chaotic states, and real-time decryption at the receiver confirms system integrity. Experimental results verify the effectiveness of the proposed method in achieving robust synchronization, accurate signal recovery, and a reliable wireless control system. The combination of FBELNN and DOB demonstrates significant potential for real-time, low-cost, and secure applications in smart electric drive systems and industrial automation. Full article

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

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21 pages, 7862 KiB

Open AccessArticle

Physics-Informed Neural Network for Nonlinear Bending Analysis of Nano-Beams: A Systematic Hyperparameter Optimization

by Saba Sadat Mirsadeghi Esfahani, Ali Fallah and Mohammad Mohammadi Aghdam

Math. Comput. Appl. 2025, 30(4), 72; https://doi.org/10.3390/mca30040072 - 14 Jul 2025

Abstract

This paper investigates the nonlinear bending analysis of nano-beams using the physics-informed neural network (PINN) method. The nonlinear governing equations for the bending of size-dependent nano-beams are derived from Hamilton’s principle, incorporating nonlocal strain gradient theory, and based on Euler–Bernoulli beam theory. In [...] Read more.

This paper investigates the nonlinear bending analysis of nano-beams using the physics-informed neural network (PINN) method. The nonlinear governing equations for the bending of size-dependent nano-beams are derived from Hamilton’s principle, incorporating nonlocal strain gradient theory, and based on Euler–Bernoulli beam theory. In the PINN method, the solution is approximated by a deep neural network, with network parameters determined by minimizing a loss function that consists of the governing equation and boundary conditions. Despite numerous reports demonstrating the applicability of the PINN method for solving various engineering problems, tuning the network hyperparameters remains challenging. In this study, a systematic approach is employed to fine-tune the hyperparameters using hyperparameter optimization (HPO) via Gaussian process-based Bayesian optimization. Comparison of the PINN results with available reference solutions shows that the PINN, with the optimized parameters, produces results with high accuracy. Finally, the impacts of boundary conditions, different loads, and the influence of nonlocal strain gradient parameters on the bending behavior of nano-beams are investigated. Full article

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

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20 pages, 981 KiB

Open AccessArticle

Permeability Prediction Using Vision Transformers

by Cenk Temizel, Uchenna Odi, Kehao Li, Lei Liu, Salih Tutun and Javier Santos

Math. Comput. Appl. 2025, 30(4), 71; https://doi.org/10.3390/mca30040071 - 8 Jul 2025

Abstract

Accurate permeability predictions remain pivotal for understanding fluid flow in porous media, influencing crucial operations across petroleum engineering, hydrogeology, and related fields. Traditional approaches, while robust, often grapple with the inherent heterogeneity of reservoir rocks. With the advent of deep learning, convolutional neural [...] Read more.

Accurate permeability predictions remain pivotal for understanding fluid flow in porous media, influencing crucial operations across petroleum engineering, hydrogeology, and related fields. Traditional approaches, while robust, often grapple with the inherent heterogeneity of reservoir rocks. With the advent of deep learning, convolutional neural networks (CNNs) have emerged as potent tools in image-based permeability estimation, capitalizing on micro-CT scans and digital rock imagery. This paper introduces a novel paradigm, employing vision transformers (ViTs)—a recent advancement in computer vision—for this crucial task. ViTs, which segment images into fixed-sized patches and process them through transformer architectures, present a promising alternative to CNNs. We present a methodology for implementing ViTs for permeability prediction, its results on diverse rock samples, and a comparison against conventional CNNs. The prediction results suggest that, with adequate training data, ViTs can match or surpass the predictive accuracy of CNNs, especially in rocks exhibiting significant heterogeneity. This study underscores the potential of ViTs as an innovative tool in permeability prediction, paving the way for further research and integration into mainstream reservoir characterization workflows. Full article

(This article belongs to the Special Issue Feature Papers in Mathematical and Computational Applications 2025)

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15 pages, 1031 KiB

Open AccessArticle

A Comparative Analysis of Numerical Methods for Mathematical Modelling of Intravascular Drug Concentrations Using a Two-Compartment Pharmacokinetic Model

by Kaniz Fatima, Basit Ali, Abdul Attayyab Khan, Sadique Ahmed, Abdelhamied Ashraf Ateya and Naveed Ahmad

Math. Comput. Appl. 2025, 30(4), 70; https://doi.org/10.3390/mca30040070 - 7 Jul 2025

Abstract

Pharmacokinetic modelling is extensively used in understanding drug behavior, distribution and optimizing dosing regimens. This study presents a two-compartment pharmacokinetic model developed using three numerical approaches that includes the Euler method, fourth-order Runge–Kutta method, and Adams–Bashforth–Moulton method. The model incorporates key parameters including [...] Read more.

Pharmacokinetic modelling is extensively used in understanding drug behavior, distribution and optimizing dosing regimens. This study presents a two-compartment pharmacokinetic model developed using three numerical approaches that includes the Euler method, fourth-order Runge–Kutta method, and Adams–Bashforth–Moulton method. The model incorporates key parameters including elimination, transfer rate constants, and compartment volumes. The numerical approaches are used to simulate the concentration of drug profiles, which are then compared to the exact solution. The results reveal that with an average error of 1.54%, the fourth-order Runge–Kutta technique provides optimized results compared to other methods when the overall average error is taken into account, which shows that the Runge–Kutta method is better in terms of accuracy and consistency for drug concentration estimates in the two-compartment model. This mathematical model may be used to optimize dosing procedures by providing a less complex method. Along with that, it also monitors therapeutic medication levels, which provides accurate analysis for drug distribution and elimination kinetics. Full article

(This article belongs to the Topic Numerical Methods for Partial Differential Equations)

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23 pages, 1090 KiB

Open AccessArticle

Air Pollution, Socioeconomic Status, and Avoidable Hospitalizations: A Multifaceted Analysis

by Carlos Minutti-Martinez, Miguel F. Mata-Rivera, Magali Arellano-Vazquez, Boris Escalante-Ramírez and Jimena Olveres

Math. Comput. Appl. 2025, 30(4), 69; https://doi.org/10.3390/mca30040069 - 30 Jun 2025

Abstract

This study investigates the combined effects of air pollution and socioeconomic factors on disease incidence and severity, addressing gaps in prior research that often analyzed these factors separately. Using data from 86,170 hospitalizations in Mexico City (2015–2019), we employed multivariate statistical methods (PCA [...] Read more.

This study investigates the combined effects of air pollution and socioeconomic factors on disease incidence and severity, addressing gaps in prior research that often analyzed these factors separately. Using data from 86,170 hospitalizations in Mexico City (2015–2019), we employed multivariate statistical methods (PCA and factor analysis) to construct composite measures of social and economic status and grouped correlated pollutants. Logistic and negative binomial regression models assessed their associations with hospitalization risk and frequency. Results showed that economic status significantly influenced diabetes complications, while social factors affected prenatal care-related diseases and hypertension. The

{PM}_{10}

–

{PM}_{2.5}

–CO group increased the incidence of asthma, influenza, and epilepsy, whereas

{NO}_{2}

–

{NO}_{x}

impacted diabetes complication severity and influenza. Nonlinear effects and interactions (e.g., age and weight) were also identified, highlighting the need for integrated analyses in environmental health research. Full article

(This article belongs to the Special Issue New Trends in Computational Intelligence and Applications 2024)

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17 pages, 4466 KiB

Open AccessArticle

Extracting Flow Characteristics from Single and Multi-Point Time Series Through Correlation Analysis

by Anup Saha and Harish Subramani

Math. Comput. Appl. 2025, 30(4), 68; https://doi.org/10.3390/mca30040068 - 30 Jun 2025

Abstract

Strongly driven fluid and combustion systems typically contain a few, nonlinearly coupled, major flow constituents. It is necessary to identify the flow constituents in order to establish the underlying dynamics and to control these complex flows. Due to non-trivial boundary condition in realistic [...] Read more.

Strongly driven fluid and combustion systems typically contain a few, nonlinearly coupled, major flow constituents. It is necessary to identify the flow constituents in order to establish the underlying dynamics and to control these complex flows. Due to non-trivial boundary condition in realistic systems and long-range coupling, it is often difficult to construct accurate models of large-scale reacting systems. The question then arises if these flow constituents can be identified and controlled through analysis of experimental data. The difficulties in such analyses originate in the presence of high levels of noise and irregularities in the flow. A typical time series contains high-frequency noise as well as low-frequency features originating from the near translational invariance of the underlying fluid systems. We propose a pair of approaches to study such data. The first is the use of auto and cross correlation functions. Auto-correlation functions of the time series from a single transducer can be used effectively to demonstrate the low dimensionality of the flow. Second, we show that multi-point time series from appropriately placed transducers can be used to establish spatial characteristics of these flow constituents. The novelty of the approaches lies in the establishment of geometric and dynamic features of the primary flow constituents based on sensor data only, without the need of expensive imaging tools. These methods can potentially identify changes in flow behavior within complex propulsion systems, such as aircraft engines, by utilizing data collected from embedded transducers. Full article

(This article belongs to the Section Engineering)

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30 pages, 2494 KiB

Open AccessArticle

A Novel Framework for Mental Illness Detection Leveraging TOPSIS-ModCHI-Based Feature-Driven Randomized Neural Networks

by Santosh Kumar Behera and Rajashree Dash

Math. Comput. Appl. 2025, 30(4), 67; https://doi.org/10.3390/mca30040067 - 30 Jun 2025

Abstract

Mental illness has emerged as a significant global health crisis, inflicting immense suffering and causing a notable decrease in productivity. Identifying mental health disorders at an early stage allows healthcare professionals to implement more targeted and impactful interventions, leading to a significant improvement [...] Read more.

Mental illness has emerged as a significant global health crisis, inflicting immense suffering and causing a notable decrease in productivity. Identifying mental health disorders at an early stage allows healthcare professionals to implement more targeted and impactful interventions, leading to a significant improvement in the overall well-being of the patient. Recent advances in Artificial Intelligence (AI) have opened new avenues for analyzing medical records and behavioral data of patients to assist mental health professionals in their decision-making processes. In this study performance of four Randomized Neural Networks (RandNNs) such as Board Learning System (BLS), Random Vector Functional Link Network (RVFLN), Kernelized RVFLN (KRVFLN), and Extreme Learning Machine (ELM) are explored for detecting the type of mental illness a user may have by analyzing the random text of the user posted on social media. To improve the performance of the RandNNs during handling the text documents with unbalanced class distributions, a hybrid feature selection (FS) technique named as TOPSIS-ModCHI is suggested in the preprocessing stage of the classification framework. The effectiveness of the suggested FS with all the four randomized networks is assessed over the publicly available Reddit Mental Health Dataset after experimenting on two benchmark multiclass unbalanced datasets. From the experimental results, it is inferred that detecting the mental illness using BLS with TOPSIS-ModCHI produces the highest precision value of 0.92, recall value of 0.66, f-measure value of 0.77, and Hamming loss value of 0.06 as compared to ELM, RVFLN, and KRVFLN with a minimum feature size of 900. Overall, utilizing BLS for mental health analysis can offer a promising avenue toward improved interventions and a better understanding of mental health issues, aiding in decision-making processes. Full article

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26 pages, 4215 KiB

Open AccessArticle

Classification of Common Bean Landraces of Three Species Using a Neuroevolutionary Approach with Probabilistic Color Characterization

by José-Luis Morales-Reyes, Elia-Nora Aquino-Bolaños, Héctor-Gabriel Acosta-Mesa, Nancy Pérez-Castro and José-Luis Chavez-Servia

Math. Comput. Appl. 2025, 30(3), 66; https://doi.org/10.3390/mca30030066 - 19 Jun 2025

Abstract

The common bean is a widely cultivated food source. Many domesticated species of common bean varieties, known as landraces, are cultivated in Mexico by local farmers, exhibiting various colorations and seed mixtures as part of agricultural practices. In this work, we propose a [...] Read more.

The common bean is a widely cultivated food source. Many domesticated species of common bean varieties, known as landraces, are cultivated in Mexico by local farmers, exhibiting various colorations and seed mixtures as part of agricultural practices. In this work, we propose a methodology for classifying bean landrace samples using three two-dimensional histograms with data in the CIE L*a*b* color space while additionally integrating chroma (C*) and hue (h°) to develop a new proposal from histograms, employing deep learning for the classification task. The results indicate that utilizing three histograms based on L*, C*, and h° brings an average accuracy of 85.74 ± 2.37 compared to three histograms using L*, a*, and b*, which reported an average accuracy of 82.22 ± 2.84. In conclusion, the new color characterization approach presents a viable solution for classifying common bean landraces of both homogeneous and heterogeneous colors. Full article

(This article belongs to the Special Issue Feature Papers in Mathematical and Computational Applications 2025)

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30 pages, 2368 KiB

Open AccessArticle

A Hybrid Approach for Reachability Analysis of Complex Software Systems Using Fuzzy Adaptive Particle Swarm Optimization Algorithm and Rule Composition

by Nahid Salimi, Seyfollah Soleimani, Vahid Rafe and Davood Khodadad

Math. Comput. Appl. 2025, 30(3), 65; https://doi.org/10.3390/mca30030065 - 10 Jun 2025

Abstract

Model checking has become a widely used and precise technique for verifying software systems. However, a major challenge in model checking is state space explosion, which occurs due to the exponential memory usage required by the model checker. To address this issue, meta-heuristic [...] Read more.

Model checking has become a widely used and precise technique for verifying software systems. However, a major challenge in model checking is state space explosion, which occurs due to the exponential memory usage required by the model checker. To address this issue, meta-heuristic and evolutionary algorithms offer a promising solution by searching for a state where a property is either satisfied or violated. Recently, various evolutionary algorithms, such as Genetic Algorithms and Particle Swarm Optimization, have been applied to detect deadlock states. While these approaches have been useful, they primarily focus on deadlock detection. This paper proposes a fuzzy algorithm to analyse reachability properties in systems specified through Graph Transformation Systems with large state spaces. To achieve this, the existing Particle Swarm Optimisation algorithm, which is typically used for deadlock detection, has been extended to analyse reachability properties. To further enhance accuracy, a Fuzzy Adaptive Particle Swarm Optimization algorithm is introduced to determine which states and paths should be explored at each step-in order to find the corresponding reachable state. Additionally, the proposed hybrid algorithm was applied to models generated through rule composition to assess the impact of rule composition on execution time and the number of explored states. These approaches were implemented within an open-source toolset called GROOVE, which is used for designing and model checking Graph Transformation Systems. Experimental results demonstrate that proposed hybrid algorithm reduced verification time by up to 49.86% compared to Particle Swarm Optimization and 65.17% compared to Genetic Algorithms in reachability analysis of complex models. Furthermore, it explored 32.7% fewer states on average than the hybrid method based on Particle Swarm Optimization and Gravitational Search Algorithms, and 57.4% fewer states compared to Genetic Algorithms, indicating improved search efficiency. The application of rule composition further reduced execution time by 35.7% and the number of explored states by 41.2% in large-scale models. These results confirm that proposed hybrid algorithm significantly enhances reachability analysis in the systems modelled via Graph Transformation, improving both computational efficiency and scalability. Full article

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36 pages, 5967 KiB

Open AccessArticle

Color Identification on Heterogeneous Bean Landrace Seeds Using Gaussian Mixture Models in CIE L*a*b* Color Space

by Adriana-Laura López-Lobato, Martha-Lorena Avendaño-Garrido, Héctor-Gabriel Acosta-Mesa, José-Luis Morales-Reyes and Elia-Nora Aquino-Bolaños

Math. Comput. Appl. 2025, 30(3), 64; https://doi.org/10.3390/mca30030064 - 6 Jun 2025

Abstract

The classification of bean landraces based on their coloration is of particular interest, as the color of these plants is associated with the nutritional components present in their seeds. In this paper, the authors propose a procedure to identify the colors of heterogeneous [...] Read more.

The classification of bean landraces based on their coloration is of particular interest, as the color of these plants is associated with the nutritional components present in their seeds. In this paper, the authors propose a procedure to identify the colors of heterogeneous color bean landraces based on the information from their digital images. The proposed methodology employs a three-dimensional histogram representation of the estimated color, expressed in the CIE L*a*b* color space, with an unsupervised learning method called the Gaussian Mixture Model. This approach facilitates the acquisition of representative information for the colors of a bean landrace, represented as points in the CIE L*a*b* color space. Furthermore, the K-

n n

method can be trained with these punctual representations to identify colors, yielding satisfactory results on landraces with homogeneous and heterogeneous seeds. Full article

(This article belongs to the Special Issue Feature Papers in Mathematical and Computational Applications 2025)

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13 pages, 384 KiB

Open AccessArticle

On the Study of Wealth Distribution with Non-Maxwellian Collision Kernels and Variable Trading Propensity

by Yaxue Liu, Miao Liu and Shaoyong Lai

Math. Comput. Appl. 2025, 30(3), 63; https://doi.org/10.3390/mca30030063 - 5 Jun 2025

Abstract

A class of dynamic equations containing a non-Maxwellian collision kernel is used to investigate the distribution of wealth. A trading rule, in which the trading propensity

γ

of agents is a function of wealth w (namely,

γ = γ (w)

), [...] Read more.

A class of dynamic equations containing a non-Maxwellian collision kernel is used to investigate the distribution of wealth. A trading rule, in which the trading propensity

γ

of agents is a function of wealth w (namely,

γ = γ (w)

), is considered. Two different trading propensity functions are discussed. One is that

γ (w)

increases with wealth. The other is that

γ (w)

decreases with the increase in wealth. In a single transaction, when the transaction tendency increases with the increase in wealth, the rich invest more in transactions. The gap between the rich and the poor in society is reduced under suitable conditions. Through numerical simulation, we conclude that an escalation in market risk intensifies the inequality in wealth distribution. Full article

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30 pages, 787 KiB

Open AccessArticle

A New Logistic Distribution and Its Properties, Applications and PORT-VaR Analysis for Extreme Financial Claims

by Piotr Sulewski, Morad Alizadeh, Jondeep Das, Gholamhossein G. Hamedani, Partha Jyoti Hazarika, Javier E. Contreras-Reyes and Haitham M. Yousof

Math. Comput. Appl. 2025, 30(3), 62; https://doi.org/10.3390/mca30030062 - 4 Jun 2025

Abstract

This paper introduces a new extension of exponentiated standard logistic distribution. Some important statistical properties of the novel family of distributions are discussed. A simulation study is also conducted to observe the behavior of the estimated parameter using several estimation methods. The adaptability [...] Read more.

This paper introduces a new extension of exponentiated standard logistic distribution. Some important statistical properties of the novel family of distributions are discussed. A simulation study is also conducted to observe the behavior of the estimated parameter using several estimation methods. The adaptability as well as the flexibility of the new model is checked through two real-life applications. A comprehensive financial risk assessment is conducted using multiple actuarial risk measures: Peaks Over Random Threshold Value-at-Risk, Value-at-Risk, Tail Value-at-Risk, the risk-adjusted return on capital and the Mean of Order P. These indicators offer a nuanced view of risk by capturing different aspects of tail behavior, which are critical in understanding potential extreme losses. These risk indicators are applied to analyze actuarial financial claims data, providing a robust framework for assessing financial stability and decision-making in the face of uncertainty. Full article

(This article belongs to the Section Social Sciences)

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5 pages, 166 KiB

Open AccessEditorial

Numerical and Evolutionary Optimization 2024

by Marcela Quiroz-Castellanos, Oliver Cuate, Leonardo Trujillo and Oliver Schütze

Math. Comput. Appl. 2025, 30(3), 61; https://doi.org/10.3390/mca30030061 - 1 Jun 2025

Abstract

This Special Issue was inspired by the 11th International Workshop on Numerical and Evolutionary Optimization (NEO 2024), held from 3 to 6 September 2024 in Mexico City, Mexico, and hosted by Cinvestav [...] Full article

(This article belongs to the Special Issue Numerical and Evolutionary Optimization 2024)

24 pages, 4242 KiB

Open AccessArticle

Numerical Simulation of Drilling Fluid-Wellbore Interactions in Permeable and Fractured Zones

by Diego A. Vargas Silva, Zuly H. Calderón, Darwin C. Mateus and Gustavo E. Ramírez

Math. Comput. Appl. 2025, 30(3), 60; https://doi.org/10.3390/mca30030060 - 30 May 2025

Abstract

In well drilling operations, interactions between drilling fluid water-based and the well-bore present significant challenges, often escalating project costs and timelines. Particularly, fractures (both induced and natural) and permeable zones at the wellbore can result in substantial mud loss or increased filtration. Addressing [...] Read more.

In well drilling operations, interactions between drilling fluid water-based and the well-bore present significant challenges, often escalating project costs and timelines. Particularly, fractures (both induced and natural) and permeable zones at the wellbore can result in substantial mud loss or increased filtration. Addressing these challenges, our research introduces a novel coupled numerical model designed to precisely calculate fluid losses in fractured and permeable zones. For the permeable zone, fundamental variables such as filtration velocity, filtrate concentration variations, permeability reduction, and fluid cake growth are calculated, all based on the law of continuity and convection-dispersion theory. For the fracture zone, the fluid velocity profile is determined using the momentum balance equation and both Newtonian and non-Newtonian rheology. The model was validated against laboratory data and physical models, and adapted for field applications. Our findings emphasize that factors like mud particle size, shear stress, and pressure differential are pivotal. Effectively managing these factors can significantly reduce fluid loss and mitigate formation damage caused by fluid invasion. Furthermore, the understanding gathered from studying mud behavior in both permeable and fractured zones equips drilling personnel with valuable information related to the optimal rheological properties according to field conditions. This knowledge is crucial for optimizing mud formulations and strategies, ultimately aiding in the reduction of non-productive time (NPT) associated with wellbore stability issues. Full article

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18 pages, 1166 KiB

Open AccessArticle

Hybrid Deep Learning Models for Predicting Student Academic Performance

by Kuburat Oyeranti Adefemi, Murimo Bethel Mutanga and Vikash Jugoo

Math. Comput. Appl. 2025, 30(3), 59; https://doi.org/10.3390/mca30030059 - 23 May 2025

Cited by 1

Abstract

Educational data mining (EDM) is instrumental in the early detection of students at risk of academic underperformance, enabling timely and targeted interventions. Given that many undergraduate students face challenges leading to high failure and dropout rates, utilizing EDM to analyze student data becomes [...] Read more.

Educational data mining (EDM) is instrumental in the early detection of students at risk of academic underperformance, enabling timely and targeted interventions. Given that many undergraduate students face challenges leading to high failure and dropout rates, utilizing EDM to analyze student data becomes crucial. By predicting academic success and identifying at-risk individuals, EDM provides a data-driven approach to enhance student performance. However, accurately predicting student performance is challenging, as it depends on multiple factors, including academic history, behavioral patterns, and health-related metrics. This study aims to bridge this gap by proposing a deep learning model to predict student academic performance with greater accuracy. The approach combines a convolutional neural network (CNN) and a bidirectional gated recurrent unit (BiGRU) network to enhance predictive capabilities. To improve the model’s performance, we address key data preprocessing challenges, including handling missing data, addressing class imbalance, and selecting relevant features. Additionally, we incorporate optimization techniques to fine-tune hyperparameters to determine the best model architecture. Using key performance metrics such as accuracy, precision, recall, and F-score, our experimental results show that our proposed model achieves improved prediction accuracy of 97.48%, 90.90%, and 95.97% across the three datasets. Full article

(This article belongs to the Special Issue New Trends in Computational Intelligence and Applications 2024)

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23 pages, 1322 KiB

Open AccessArticle

Comparative Analysis of ALE Method Implementation in Time Integration Schemes for Pile Penetration Modeling

by Ihab Bendida Bourokba, Abdelmadjid Berga, Patrick Staubach and Nazihe Terfaya

Math. Comput. Appl. 2025, 30(3), 58; https://doi.org/10.3390/mca30030058 - 22 May 2025

Abstract

This study investigates the full penetration simulation of piles from the ground surface, focusing on frictional contact modeling without mesh distortion. To overcome issues related to mesh distortion and improve solution convergence, the Arbitrary Lagrangian–Eulerian (ALE) adaptive mesh technique was implemented within both [...] Read more.

This study investigates the full penetration simulation of piles from the ground surface, focusing on frictional contact modeling without mesh distortion. To overcome issues related to mesh distortion and improve solution convergence, the Arbitrary Lagrangian–Eulerian (ALE) adaptive mesh technique was implemented within both explicit and implicit time integration schemes. The numerical model was validated against field experiments conducted at Bothkennar, Scotland, using the Imperial College instrumented displacement pile (ICP) in soft clay, where the soil behavior was effectively represented using the modified Cam-Clay model and the Mohr–Coulomb model. The primary objectives of this study are to evaluate the ALE method performance in handling mesh distortion; analyze the effects of soil–pile interface friction, pile dimensions, and various dilation angles on pile resistance; and compare the effectiveness of explicit and implicit time integration schemes in terms of stability, computational efficiency, and solution accuracy. The ALE method effectively modeled pile penetration in Bothkennar clay, validating the numerical model against field experiments. Comparative analysis revealed the explicit time integration method as more robust and computationally efficient, particularly for complex soil–pile interactions with higher friction coefficients. Full article

(This article belongs to the Topic Numerical Methods for Partial Differential Equations)

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18 pages, 1692 KiB

Open AccessArticle

Multiple-Feature Construction for Image Segmentation Based on Genetic Programming

by David Herrera-Sánchez, José-Antonio Fuentes-Tomás, Héctor-Gabriel Acosta-Mesa, Efrén Mezura-Montes and José-Luis Morales-Reyes

Math. Comput. Appl. 2025, 30(3), 57; https://doi.org/10.3390/mca30030057 - 21 May 2025

Abstract

Within the medical field, computer vision has an important role in different tasks, such as health anomaly detection, diagnosis, treatment, and monitoring medical conditions. Image segmentation is one of the most used techniques for medical support to identify regions of interest in different [...] Read more.

Within the medical field, computer vision has an important role in different tasks, such as health anomaly detection, diagnosis, treatment, and monitoring medical conditions. Image segmentation is one of the most used techniques for medical support to identify regions of interest in different organs. However, performing accurate segmentation is difficult due to image variations. In this way, this work proposes an automated multiple-feature construction approach for image segmentation, working with magnetic resonance images, computed tomography, and RGB digital images. Genetic programming is used to automatically create and construct pipelines to extract meaningful features for segmentation tasks. Additionally, a co-evolution strategy is proposed within the evolution process to increase diversity without affecting segmentation performance. The segmentation is addressed as a pixel classification task; in this way, a wrapper approach is used, and the classification model’s segmentation performance determines the fitness. To validate the effectiveness of the proposed method, four datasets were used to measure the capability of the proposal to deal with different types of medical images. The results demonstrate that the proposal achieves values of the DICE similarity coefficient of more than 0.6 in MRI and C.T. images. Additionally, the proposal is compared with SOTA GP-based methods and the convolutional neural networks used within the medical field. The method proposed outperforms these methods, achieving improvements greater than

20 %

in DICE, specificity, and sensitivity. Additionally, the qualitative results demonstrate that the proposal accurately identifies the region of interest. Full article

(This article belongs to the Special Issue Feature Papers in Mathematical and Computational Applications 2025)

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