Computation

21 pages, 1681 KiB

Open AccessArticle

Scalable Clustering of Complex ECG Health Data: Big Data Clustering Analysis with UMAP and HDBSCAN

by Vladislav Kaverinskiy, Illya Chaikovsky, Anton Mnevets, Tatiana Ryzhenko, Mykhailo Bocharov and Kyrylo Malakhov

Computation 2025, 13(6), 144; https://doi.org/10.3390/computation13060144 - 10 Jun 2025

Abstract

This study explores the potential of unsupervised machine learning algorithms to identify latent cardiac risk profiles by analyzing ECG-derived parameters from two general groups: clinically healthy individuals (Norm dataset, n = 14,863) and patients hospitalized with heart failure (patients’ dataset, n = 8220). [...] Read more.

This study explores the potential of unsupervised machine learning algorithms to identify latent cardiac risk profiles by analyzing ECG-derived parameters from two general groups: clinically healthy individuals (Norm dataset, n = 14,863) and patients hospitalized with heart failure (patients’ dataset, n = 8220). Each dataset includes 153 ECG and heart rate variability (HRV) features, including both conventional and novel diagnostic parameters obtained using a Universal Scoring System. The study aims to apply unsupervised clustering algorithms to ECG data to detect latent risk profiles related to heart failure, based on distinctive ECG features. The focus is on identifying patterns that correlate with cardiac health risks, potentially aiding in early detection and personalized care. We applied a combination of Uniform Manifold Approximation and Projection (UMAP) for dimensionality reduction and Hierarchical Density-Based Spatial Clustering (HDBSCAN) for unsupervised clustering. Models trained on one dataset were applied to the other to explore structural differences and detect latent predispositions to cardiac disorders. Both Euclidean and Manhattan distance metrics were evaluated. Features such as the QRS angle in the frontal plane, Detrended Fluctuation Analysis (DFA), High-Frequency power (HF), and others were analyzed for their ability to distinguish different patient clusters. In the Norm dataset, Euclidean distance clustering identified two main clusters, with Cluster 0 indicating a lower risk of heart failure. Key discriminative features included the “ALPHA QRS ANGLE IN THE FRONTAL PLANE” and DFA. In the patients’ dataset, three clusters emerged, with Cluster 1 identified as potentially high-risk. Manhattan distance clustering provided additional insights, highlighting features like “ST DISLOCATION” and “T AMP NORMALIZED” as significant for distinguishing between clusters. The analysis revealed distinct clusters that correspond to varying levels of heart failure risk. In the Norm dataset, two main clusters were identified, with one associated with a lower risk profile. In the patients’ dataset, a three-cluster structure emerged, with one subgroup displaying markedly elevated risk indicators such as high-frequency power (HF) and altered QRS angle values. Cross-dataset clustering confirmed consistent feature shifts between groups. These findings demonstrate the feasibility of ECG-based unsupervised clustering for early risk stratification. The results offer a non-invasive tool for personalized cardiac monitoring and merit further clinical validation. These findings emphasize the potential for clustering techniques to contribute to early heart failure detection and personalized monitoring. Future research should aim to validate these results in other populations and integrate these methods into clinical decision-making frameworks. Full article

(This article belongs to the Special Issue Artificial Intelligence Applications in Public Health: 2nd Edition)

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15 pages, 2557 KiB

Open AccessArticle

Precision-Driven Semantic Segmentation of Pipe Gallery Diseases Using PipeU-NetX: A Depthwise Separable Convolution Approach

by Wenbin Song, Hanqian Wu and Chunlin Pu

Computation 2025, 13(6), 143; https://doi.org/10.3390/computation13060143 - 10 Jun 2025

Abstract

Aiming at the problems of high labor cost, low detection efficiency, and insufficient detection accuracy of traditional pipe gallery disease detection methods, this paper proposes a pipe gallery disease segmentation model, PipeU-NetX, based on deep learning technology. By introducing the innovative down-sampling module [...] Read more.

Aiming at the problems of high labor cost, low detection efficiency, and insufficient detection accuracy of traditional pipe gallery disease detection methods, this paper proposes a pipe gallery disease segmentation model, PipeU-NetX, based on deep learning technology. By introducing the innovative down-sampling module MD-U, up-sampling module SC-U, and feature fusion module FFM, the model optimizes the feature extraction and fusion process, reduces the loss of feature information, and realizes the accurate segmentation of the pipe gallery disease image. In comparison with U-Net, FCN, and Deeplabv3+ models, PipeU-NetX achieved the best PA, MPA, FWIoU, and MIoU, which were 99.15%, 92.66%, 98.34%, and 87.63%, respectively. Compared with the benchmark model U-Net, the MIoU and MPA of the PipeU-NetX model increased by 4.64% and 3.92%, respectively, and the number of parameters decreased by 23.71%. The detection speed increased by 22.1%. The PipeU-NetX model proposed in this paper shows the powerful ability of multi-scale feature extraction and defect area adaptive recognition and provides an effective solution for the intelligent monitoring of the pipe gallery environment and accurate disease segmentation. Full article

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67 pages, 3117 KiB

Open AccessArticle

Stability Analysis and Local Convergence of a New Fourth-Order Optimal Jarratt-Type Iterative Scheme

by Eulalia Martínez, José A. Reyes, Alicia Cordero and Juan R. Torregrosa

Computation 2025, 13(6), 142; https://doi.org/10.3390/computation13060142 - 9 Jun 2025

Abstract

In this work, using the weight function technique, we introduce a new family of fourth-order iterative methods optimal in the sense of Kung and Traub for scalar equations, generalizing Jarratt’s method. Through Taylor series expansions, we confirm that all members of this family [...] Read more.

In this work, using the weight function technique, we introduce a new family of fourth-order iterative methods optimal in the sense of Kung and Traub for scalar equations, generalizing Jarratt’s method. Through Taylor series expansions, we confirm that all members of this family achieve fourth-order convergence when derivatives up to the fourth order are bounded. Additionally, a stability analysis is performed on quadratic polynomials using complex discrete dynamics, enabling differentiation among the methods based on their stability. To demonstrate practical applicability, a numerical example illustrates the effectiveness of the proposed family. Extending our findings to Banach spaces, we conduct local convergence analyses on a specific subfamily containing Jarratt’s method, requiring only boundedness of the first derivative. This significantly broadens the method’s applicability to more general spaces and reduces constraints on higher-order derivatives. Finally, additional examples validate the existence and uniqueness of approximate solutions in Banach spaces, provided the initial estimate lies within the locally determined convergence radius obtained using majorizing functions. Full article

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29 pages, 5696 KiB

Open AccessArticle

Sliding Window-Based Randomized K-Fold Dynamic ANN for Next-Day Stock Trend Forecasting

by Jaykumar Ishvarbhai Prajapati and Raja Das

Computation 2025, 13(6), 141; https://doi.org/10.3390/computation13060141 - 8 Jun 2025

Abstract

The integration of machine learning and stock forecasting is attracting increased curiosity owing to its growing significance. This paper presents two main areas of study: predicting pattern trends for the next day and forecasting opening and closing prices using a new method that [...] Read more.

The integration of machine learning and stock forecasting is attracting increased curiosity owing to its growing significance. This paper presents two main areas of study: predicting pattern trends for the next day and forecasting opening and closing prices using a new method that adds a dynamic hidden layer to artificial neural networks and employs a unique random k-fold cross-validation to enhance prediction accuracy and improve training. To validate the model, we are considering APPLE, GOOGLE, and AMAZON stock data. As a result, low root mean squared error (1.7208) and mean absolute error (0.9892) in both training and validation phases demonstrate the robust predictive performance of the dynamic ANN model. Furthermore, high R-values indicated a strong correlation between the experimental data and proposed model estimates. Full article

(This article belongs to the Special Issue Modern Applications for Computational Methods in Applied Economics and Business Engineering)

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19 pages, 5766 KiB

Open AccessArticle

Early Detection of Inter-Turn Short Circuits in Induction Motors Using the Derivative of Stator Current and a Lightweight 1D-ResNet

by Carlos Javier Morales-Perez, David Camarena-Martinez, Juan Pablo Amezquita-Sanchez, Jose de Jesus Rangel-Magdaleno, Edwards Ernesto Sánchez Ramírez and Martin Valtierra-Rodriguez

Computation 2025, 13(6), 140; https://doi.org/10.3390/computation13060140 - 4 Jun 2025

Abstract

This work presents a lightweight and practical methodology for detecting inter-turn short-circuit faults in squirrel-cage induction motors under different mechanical load conditions. The proposed approach utilizes a one-dimensional convolutional neural network (1D-CNN) enhanced with residual blocks and trained on differentiated stator current signals [...] Read more.

This work presents a lightweight and practical methodology for detecting inter-turn short-circuit faults in squirrel-cage induction motors under different mechanical load conditions. The proposed approach utilizes a one-dimensional convolutional neural network (1D-CNN) enhanced with residual blocks and trained on differentiated stator current signals obtained under different load mechanical conditions. This preprocessing step enhances fault-related features, enabling improved learning while maintaining the simplicity of a lightweight CNN. The model achieved classification accuracies above 99.16% across all folds in five-fold cross-validation and demonstrated the ability to detect faults involving as few as three short-circuited turns. Comparative experiments with the Multi-Scale 1D-ResNet demonstrate that the proposed method achieves similar or superior performance while significantly reducing training time. These results highlight the model’s suitability for real-time fault detection in embedded and resource-constrained industrial environments. Full article

(This article belongs to the Special Issue Diagnosing Faults with Machine Learning)

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20 pages, 3472 KiB

Open AccessArticle

Risk Assessment of Mud Cake on Shield Cutter Head Based on Modified Analytic Hierarchy Process

by Wen Cao, Shoubao Xue, Yujia Xu, Huanyu Lin, Hui Li, Shengjun Deng, Lin Li and Yun Bai

Computation 2025, 13(6), 139; https://doi.org/10.3390/computation13060139 - 4 Jun 2025

Abstract

When the shield machines are constructed in soft soil, excavation may be impeded by the accumulation of cutter head mud. Geological conditions and shield construction are identified as the main factors for cutter head mud formation, based on analysis of its mechanism. In [...] Read more.

When the shield machines are constructed in soft soil, excavation may be impeded by the accumulation of cutter head mud. Geological conditions and shield construction are identified as the main factors for cutter head mud formation, based on analysis of its mechanism. In addition to traditional metrics, the imperforation area in the cutter head center is incorporated into the analysis of shield construction factors. The Analytic Hierarchy Process (AHP) is utilized to establish a risk assessment model for shield cutter head mud cake, determining the weight of each sub-factor and enabling a preliminary risk assessment of mud cake occurrence. This study applies Analytic Hierarchy Process (AHP) to classify the factors affecting shield mud by using the Mawan cross-sea channel construction project (Moon Bay Avenue along the Yangtze River) as a case study. Each factor is scored and weighted according to established scoring criteria and evaluation formulas, and then the results of the risk of shield mud cake in the Mawan tunnel are obtained. Moreover, field observations validate the proposed risk model, with the derived risk index demonstrating strong alignment with actual data. Full article

(This article belongs to the Section Computational Engineering)

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14 pages, 2188 KiB

Open AccessArticle

CrystalShift: A Versatile Command-Line Tool for Crystallographic Structural Data Analysis, Modification, and Format Conversion Prior to Solid-State DFT Calculations of Organic Crystals

by Ilona A. Isupova and Denis A. Rychkov

Computation 2025, 13(6), 138; https://doi.org/10.3390/computation13060138 - 4 Jun 2025

Abstract

CrystalShift is an open-source computational tool tailored for the analysis, transformation, and conversion of crystallographic data, with a particular emphasis on organic crystal structures. It offers a comprehensive suite of features valuable for the computational study of solids: format conversion, crystallographic basis transformation, [...] Read more.

CrystalShift is an open-source computational tool tailored for the analysis, transformation, and conversion of crystallographic data, with a particular emphasis on organic crystal structures. It offers a comprehensive suite of features valuable for the computational study of solids: format conversion, crystallographic basis transformation, atomic coordinate editing, and molecular layer analysis. These options are especially valuable for studying the mechanical properties of molecular crystals with potential applications in organic materials science. Written in the C programming language, CrystalShift offers computational efficiency and compatibility with widely used crystallographic formats such as CIF, POSCAR, and XYZ. It provides a command-line interface, enabling seamless integration into research workflows while addressing specific challenges in crystallography, such as handling non-standard file formats and robust error correction. CrystalShift may be applied for both in-depth study of particular crystal structure origins and the high-throughput conversion of crystallographic datasets prior to DFT calculations with periodic boundary conditions using VASP code. Full article

(This article belongs to the Special Issue Feature Papers in Computational Chemistry)

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25 pages, 727 KiB

Open AccessArticle

Data Fusion and Dimensionality Reduction for Pest Management in Pitahaya Cultivation

by Wilson Chango, Mónica Mazón-Fierro, Juan Erazo, Guido Mazón-Fierro, Santiago Logroño, Pedro Peñafiel and Jaime Sayago

Computation 2025, 13(6), 137; https://doi.org/10.3390/computation13060137 - 3 Jun 2025

Abstract

This study addresses the critical need for effective data fusion strategies in pest prediction for pitahaya (dragon fruit) cultivation in the Ecuadorian Amazon, where heterogeneous data sources—such as environmental sensors and chlorophyll measurements—offer complementary but fragmented insights. Current agricultural monitoring systems often fail [...] Read more.

This study addresses the critical need for effective data fusion strategies in pest prediction for pitahaya (dragon fruit) cultivation in the Ecuadorian Amazon, where heterogeneous data sources—such as environmental sensors and chlorophyll measurements—offer complementary but fragmented insights. Current agricultural monitoring systems often fail to integrate these data streams, limiting early pest detection accuracy. To overcome this, we compared early and late fusion approaches using comprehensive experiments. Multidimensionality is a central challenge: the datasets span temporal (hourly sensor readings), spatial (plot-level chlorophyll samples), and spectral (chlorophyll reflectance) dimensions. We applied dimensionality reduction techniques—PCA, KPCA (linear, polynomial, RBF), t-SNE, and UMAP—to preserve relevant structure and enhance interpretability. Evaluation metrics included the proportion of information retained (score) and cluster separability (silhouette score). Our results demonstrate that early fusion yields superior integrated representations, with PCA and KPCA-linear achieving the highest scores (0.96 vs. 0.94), and KPCA-poly achieving the best cluster definition (silhouette: 0.32 vs. 0.31). Statistical validation using the Friedman test (

χ^{2}

= 12.00, p = 0.02) and Nemenyi post hoc comparisons (p < 0.05) confirmed significant performance differences. KPCA-RBF performed poorly (score: 0.83; silhouette: 0.05), and although t-SNE and UMAP offered visual insights, they underperformed in clustering (silhouette < 0.12). These findings make three key contributions. First, early fusion better captures cross-domain interactions before dimensionality reduction, improving prediction robustness. Second, KPCA-poly offers an effective non-linear mapping suitable for tropical agroecosystem complexity. Third, our framework, when deployed in Joya de los Sachas, improved pest prediction accuracy by 12.60% over manual inspection, leading to more targeted pesticide use. This contributes to precision agriculture by providing low-cost, scalable strategies for smallholder farmers. Future work will explore hybrid fusion pipelines and sensor-agnostic models to extend generalizability. Full article

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20 pages, 21534 KiB

Open AccessArticle

Smoothing Techniques for Improving COVID-19 Time Series Forecasting Across Countries

by Uliana Zbezhkhovska and Dmytro Chumachenko

Computation 2025, 13(6), 136; https://doi.org/10.3390/computation13060136 - 3 Jun 2025

Abstract

Accurate forecasting of COVID-19 case numbers is critical for timely and effective public health interventions. However, epidemiological data’s irregular and noisy nature often undermines the predictive performance. This study examines the influence of four smoothing techniques—the rolling mean, the exponentially weighted moving average, [...] Read more.

Accurate forecasting of COVID-19 case numbers is critical for timely and effective public health interventions. However, epidemiological data’s irregular and noisy nature often undermines the predictive performance. This study examines the influence of four smoothing techniques—the rolling mean, the exponentially weighted moving average, a Kalman filter, and seasonal–trend decomposition using Loess (STL)—on the forecasting accuracy of four models: LSTM, the Temporal Fusion Transformer (TFT), XGBoost, and LightGBM. Weekly case data from Ukraine, Bulgaria, Slovenia, and Greece were used to assess the models’ performance over short- (3-month) and medium-term (6-month) horizons. The results demonstrate that smoothing enhanced the models’ stability, particularly for neural architectures, and the model selection emerged as the primary driver of predictive accuracy. The LSTM and TFT models, when paired with STL or the rolling mean, outperformed the others in their short-term forecasts, while XGBoost exhibited greater robustness over longer horizons in selected countries. An ANOVA confirmed the statistically significant influence of the model type on the MAPE (p = 0.008), whereas the smoothing method alone showed no significant effect. These findings offer practical guidance for designing context-specific forecasting pipelines adapted to epidemic dynamics and variations in data quality. Full article

(This article belongs to the Special Issue Artificial Intelligence Applications in Public Health: 2nd Edition)

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31 pages, 1796 KiB

Open AccessArticle

Optimal Control Strategies for Dual-Strain SARS-CoV-2 Dynamics with Cost-Effectiveness Analysis

by Oke I. Idisi, Tajudeen T. Yusuf, Kolade M. Owolabi and Kayode Oshinubi

Computation 2025, 13(6), 135; https://doi.org/10.3390/computation13060135 - 3 Jun 2025

Abstract

This study investigates optimal intervention strategies for controlling the spread of two co-circulating strains of SARS-CoV-2 within the Nigerian population. A newly formulated epidemiological model captures the transmission dynamics of the dual-strain system and incorporates three key control mechanisms: vaccination, non-pharmaceutical interventions (NPIs), [...] Read more.

This study investigates optimal intervention strategies for controlling the spread of two co-circulating strains of SARS-CoV-2 within the Nigerian population. A newly formulated epidemiological model captures the transmission dynamics of the dual-strain system and incorporates three key control mechanisms: vaccination, non-pharmaceutical interventions (NPIs), and therapeutic treatment. To identify the most effective approach, Pontryagin’s Maximum Principle is employed, enabling the derivation of an optimal control function that minimizes both infection rates and associated implementation costs. Through numerical simulations, this study evaluates the performance of individual, paired, and combined intervention strategies. Additionally, a cost-effectiveness assessment based on the Incremental Cost-Effectiveness Ratio (ICER) framework highlights the most economically viable option, while results suggest that the combined application of vaccination and treatment strategies offers superior control over dual-strain transmission and implementing all three strategies together ensures the most robust suppression of the outbreak. Full article

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31 pages, 2442 KiB

Open AccessArticle

Performance-Enhancing Market Risk Calculation Through Gaussian Process Regression and Multi-Fidelity Modeling

by N. Lehdili, P. Oswald and H. D. Nguyen

Computation 2025, 13(6), 134; https://doi.org/10.3390/computation13060134 - 3 Jun 2025

Abstract

The market risk measurement of a trading portfolio in banks, specifically the practical implementation of the value-at-risk (VaR) and expected shortfall (ES) models, involves intensive recalls of the pricing engine. Machine learning algorithms may offer a solution to this challenge. In this study, [...] Read more.

The market risk measurement of a trading portfolio in banks, specifically the practical implementation of the value-at-risk (VaR) and expected shortfall (ES) models, involves intensive recalls of the pricing engine. Machine learning algorithms may offer a solution to this challenge. In this study, we investigate the application of the Gaussian process (GP) regression and multi-fidelity modeling technique as approximation for the pricing engine. More precisely, multi-fidelity modeling combines models of different fidelity levels, defined as the degree of detail and precision offered by a predictive model or simulation, to achieve rapid yet precise prediction. We use the regression models to predict the prices of mono- and multi-asset equity option portfolios. In our numerical experiments, conducted with data limitation, we observe that both the standard GP model and multi-fidelity GP model outperform both the traditional approaches used in banks and the well-known neural network model in term of pricing accuracy as well as risk calculation efficiency. Full article

(This article belongs to the Special Issue Applications of Intelligent Computing and Modeling in Construction Engineering)

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24 pages, 2351 KiB

Open AccessReview

Advancing Object Detection in Transportation with Multimodal Large Language Models (MLLMs): A Comprehensive Review and Empirical Testing

by Huthaifa I. Ashqar, Ahmed Jaber, Taqwa I. Alhadidi and Mohammed Elhenawy

Computation 2025, 13(6), 133; https://doi.org/10.3390/computation13060133 - 3 Jun 2025

Abstract

This study aims to comprehensively review and empirically evaluate the application of multimodal large language models (MLLMs) and Large Vision Models (VLMs) in object detection for transportation systems. In the first fold, we provide a background about the potential benefits of MLLMs in [...] Read more.

This study aims to comprehensively review and empirically evaluate the application of multimodal large language models (MLLMs) and Large Vision Models (VLMs) in object detection for transportation systems. In the first fold, we provide a background about the potential benefits of MLLMs in transportation applications and conduct a comprehensive review of current MLLM technologies in previous studies. We highlight their effectiveness and limitations in object detection within various transportation scenarios. The second fold involves providing an overview of the taxonomy of end-to-end object detection in transportation applications and future directions. Building on this, we proposed empirical analysis for testing MLLMs on three real-world transportation problems that include object detection tasks, namely, road safety attribute extraction, safety-critical event detection, and visual reasoning of thermal images. Our findings provide a detailed assessment of MLLM performance, uncovering both strengths and areas for improvement. Finally, we discuss practical limitations and challenges of MLLMs in enhancing object detection in transportation, thereby offering a roadmap for future research and development in this critical area. Full article

(This article belongs to the Special Issue Object Detection Models for Transportation Systems)

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21 pages, 2970 KiB

Open AccessReview

Revolutionizing Sperm Analysis with AI: A Review of Computer-Aided Sperm Analysis Systems

by Francisco J. Baldán, Diego García-Gil and Carlos Fernandez-Basso

Computation 2025, 13(6), 132; https://doi.org/10.3390/computation13060132 - 2 Jun 2025

Abstract

Advances in artificial intelligence (AI) are transforming assisted reproductive technologies by significantly enhancing fertility diagnostics. This review focuses on integrating AI with Computer-Aided Sperm Analysis (CASA) systems to improve assessments of sperm motility, morphology, and DNA integrity. By employing a spectrum of techniques, [...] Read more.

Advances in artificial intelligence (AI) are transforming assisted reproductive technologies by significantly enhancing fertility diagnostics. This review focuses on integrating AI with Computer-Aided Sperm Analysis (CASA) systems to improve assessments of sperm motility, morphology, and DNA integrity. By employing a spectrum of techniques, from classic machine learning (ML), often valued for its interpretability and efficiency with structured data, to deep learning (DL), which excels at extracting intricate features directly from image and video data, the field now achieves more accurate, automated, and high-throughput evaluations. These advanced systems offer significant advantages, including enhanced objectivity, improved consistency over manual methods, and the ability to detect subtle predictive patterns not discernible by human observation. The emergence of extensive open datasets and big data analytics has enabled the development of more robust models. However, limitations persist, such as the dependency on large, high-quality annotated datasets for training DL models, potential challenges in model generalizability across diverse clinical settings, and the “black-box” nature of some complex algorithms, alongside crucial needs for rigorous clinical validation, data standardization, and ethical management of sensitive information. Despite promising progress, these challenges must be addressed. Overall, this review outlines current innovations and future research directions essential for advancing personalized, efficient, and accessible fertility care. Full article

(This article belongs to the Section Computational Engineering)

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19 pages, 1612 KiB

Open AccessArticle

Exploring Dynamic Behavior in a Competition Duopoly Game Based on Corporate Social Responsibility

by A. A. Elsadany, Abdullah M. Adawi and A. M. Awad

Computation 2025, 13(6), 131; https://doi.org/10.3390/computation13060131 - 2 Jun 2025

Abstract

This study investigates dynamic behaviors within a competition Cournot duopoly framework incorporating consumer surplus, and social welfare through the bounded rationality method. The distinctive aspect of the competition game is the incorporation of discrete difference equations into the players’ optimization problems. Both rivals [...] Read more.

This study investigates dynamic behaviors within a competition Cournot duopoly framework incorporating consumer surplus, and social welfare through the bounded rationality method. The distinctive aspect of the competition game is the incorporation of discrete difference equations into the players’ optimization problems. Both rivals seek to achieve optimal quantity outcomes by maximizing their respective objective functions. The first firm seeks to enhance the average between consumer surplus and its profit, while the second firm focuses on its profit optimization with a social welfare component. The game map features four fixed points, with one being the Nash equilibrium point at the intersection of marginal objective functions. Our analysis explores equilibrium stability, dynamic complexities, basins of attraction, and the emergence of chaos through double routes via flip bifurcation and Neimark-Sacker bifurcations. We observe that increased adjustment speeds can destabilize the system, leading to a richness of dynamic complexity. Full article

(This article belongs to the Special Issue Computational Social Science and Complex Systems—2nd Edition)

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23 pages, 5783 KiB

Open AccessArticle

A Computational Methodology Based on Maximum Overlap Discrete Wavelet Transform and Autoencoders for Early Prediction of Sudden Cardiac Death

by Manuel A. Centeno-Bautista, Andrea V. Perez-Sanchez, Juan P. Amezquita-Sanchez, David Camarena-Martinez and Martin Valtierra-Rodriguez

Computation 2025, 13(6), 130; https://doi.org/10.3390/computation13060130 - 1 Jun 2025

Abstract

Cardiovascular diseases are among the major global health problems. For example, sudden cardiac death (SCD) accounts for approximately 4 million deaths worldwide. In particular, an SCD event can subtly change the electrocardiogram (ECG) signal before onset, which is generally undetectable by the patient. [...] Read more.

Cardiovascular diseases are among the major global health problems. For example, sudden cardiac death (SCD) accounts for approximately 4 million deaths worldwide. In particular, an SCD event can subtly change the electrocardiogram (ECG) signal before onset, which is generally undetectable by the patient. Hence, timely detection of these changes in ECG signals could help develop a tool to anticipate an SCD event and respond appropriately in patient care. In this sense, this work proposes a novel computational methodology that combines the maximal overlap discrete wavelet packet transform (MODWPT) with stacked autoencoders (SAEs) to discover suitable features in ECG signals and associate them with SCD prediction. The proposed method efficiently predicts an SCD event with an accuracy of 98.94% up to 30 min before the onset, making it a reliable tool for early detection while providing sufficient time for medical intervention and increasing the chances of preventing fatal outcomes, demonstrating the potential of integrating signal processing and deep learning techniques within computational biology to address life-critical health problems. Full article

(This article belongs to the Special Issue Feature Papers in Computational Biology)

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26 pages, 8542 KiB

Open AccessArticle

Solution of Coupled Systems of Reaction–Diffusion Equations Using Explicit Numerical Methods with Outstanding Stability Properties

by Husniddin Khayrullaev, Andicha Zain and Endre Kovács

Computation 2025, 13(6), 129; https://doi.org/10.3390/computation13060129 - 1 Jun 2025

Abstract

Recently, new and nontrivial analytical solutions that contain the Kummer functions have been found for an equation system of two diffusion–reaction equations. The equations are coupled by two different types of linear reaction terms which have explicit time-dependence. We first make some corrections [...] Read more.

Recently, new and nontrivial analytical solutions that contain the Kummer functions have been found for an equation system of two diffusion–reaction equations. The equations are coupled by two different types of linear reaction terms which have explicit time-dependence. We first make some corrections to these solutions in the case of two different reaction terms. Then, we collect eight efficient explicit numerical schemes which are unconditionally stable if the reaction terms are missing, and apply them to the system of equations. We show that they severely outperform the standard explicit methods when low or medium accuracy is required. Using parameter sweeps, we thoroughly investigate how the performance of the methods depends on the coefficients and parameters such as the length of the examined time interval. We obtained that, similarly to the single-equation case, the leapfrog–hopscotch method is usually the most efficient to solve these problems. Full article

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24 pages, 5645 KiB

Open AccessArticle

Dynamics of an Intraguild Predation Food Web Cooperation Model Under the Influence of Fear and Hunting

by Alyaa Hussain Naser and Dahlia Khaled Bahlool

Computation 2025, 13(6), 128; https://doi.org/10.3390/computation13060128 - 22 May 2025

Abstract

This study examines the impact of fear effects and cooperative hunting strategies in the context of intraguild predation food webs. The presented model includes a shared prey species with logistic growth and assumes that both the intraguild prey and intraguild predator draw their [...] Read more.

This study examines the impact of fear effects and cooperative hunting strategies in the context of intraguild predation food webs. The presented model includes a shared prey species with logistic growth and assumes that both the intraguild prey and intraguild predator draw their sustenance from the same resource. Using a Lyapunov function enables the system’s global stability to be proven. The impacts of key parameters on system stability are determined through bifurcation analysis. Numerical simulations show that even slight increases in the intensity of fear have drastic impacts on intraguild prey populations and, at higher levels, populations may go extinct. In addition, shifts in the parameter of cooperative hunting have a profound impact on the survival of the intraguild prey. Full article

(This article belongs to the Section Computational Biology)

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22 pages, 3362 KiB

Open AccessArticle

Assessing the Validity of k-Fold Cross-Validation for Model Selection: Evidence from Bankruptcy Prediction Using Random Forest and XGBoost

by Vlad Teodorescu and Laura Obreja Brașoveanu

Computation 2025, 13(5), 127; https://doi.org/10.3390/computation13050127 - 21 May 2025

Abstract

Predicting corporate bankruptcy is a key task in financial risk management, and selecting a machine learning model with superior generalization performance is crucial for prediction accuracy. This study evaluates the effectiveness of k-fold cross-validation as a model selection strategy for random forest and [...] Read more.

Predicting corporate bankruptcy is a key task in financial risk management, and selecting a machine learning model with superior generalization performance is crucial for prediction accuracy. This study evaluates the effectiveness of k-fold cross-validation as a model selection strategy for random forest and XGBoost classifiers using a publicly available dataset of Taiwanese listed companies. We employ a nested cross-validation framework to assess the relationship between cross-validation (CV) and out-of-sample (OOS) performance on 40 different train/test data partitions. On average, we find k-fold cross-validation to be a valid selection technique when applied within a model class; however, k-fold cross-validation may fail for specific train/test splits. We find that 67% of model selection regret variability is explained by the particular train/test split, highlighting an irreducible uncertainty real world practitioners must contend with. Our study extensively explores hyperparameter tuning for both classifiers and highlights key insights. Additionally, we investigate practical implementation choices in k-fold cross-validation—such as the value of k or prediction strategies. We conclude that k-fold cross-validation is effective for model selection within a model class and on average, but it can be unreliable in specific cases or when comparing models from different classes—this latter issue warranting further investigation. Full article

(This article belongs to the Special Issue Computational Approaches in Corporate Finance, Risk Management and Financial Markets)

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17 pages, 4010 KiB

Open AccessArticle

Simulation and Fitting of a PID Fuzzy Control in a Virtual Prototype of a Knee Orthosis

by Agustín Barrera Sánchez, Rafael Campos Amezcua, Héctor R. Azcaray Rivera, Arturo Martínez Mata, Andrés Blanco Ortega, Cuauhtémoc Mazón Valadez and César Humberto Guzmán Valdivia

Computation 2025, 13(5), 126; https://doi.org/10.3390/computation13050126 - 21 May 2025

Abstract

Nowadays, the use of biomechanical devices in medical processes and industrial applications allows us to perform tasks in a simpler and faster way. In the medical field, these devices are becoming more and more common, especially in therapeutic applications. In the design and [...] Read more.

Nowadays, the use of biomechanical devices in medical processes and industrial applications allows us to perform tasks in a simpler and faster way. In the medical field, these devices are becoming more and more common, especially in therapeutic applications. In the design and development of orthopedic devices, it is essential to consider the limbs’ kinematic, kinetic, and anthropometric conditions, as well as the implementation of control strategies (robust, PID, fuzzy, and impedance, among others). This work presents a virtual prototype of a knee orthosis and the implementation of a control system to follow a desired trajectory. Results are presented with the virtual prototype through a co-simulation between MSC Adams and MATLAB Simulink with fuzzy control, virtually replicating the gait cycle. Full article

(This article belongs to the Special Issue Kinematics, Dynamics and Control for Rehabilitation Robotics and Prostheses)

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