Computation

21 pages, 329 KiB

Open AccessArticle

Subsequential Continuity in Neutrosophic Metric Space with Applications

by Vishal Gupta, Nitika Garg and Rahul Shukla

Computation 2025, 13(4), 87; https://doi.org/10.3390/computation13040087 (registering DOI) - 25 Mar 2025

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This paper introduces two concepts, subcompatibility and subsequential continuity, which are, respectively, weaker than the existing concepts of occasionally weak compatibility and reciprocal continuity. These concepts are studied within the framework of neutrosophic metric spaces. Using these ideas, a common fixed point theorem [...] Read more.

This paper introduces two concepts, subcompatibility and subsequential continuity, which are, respectively, weaker than the existing concepts of occasionally weak compatibility and reciprocal continuity. These concepts are studied within the framework of neutrosophic metric spaces. Using these ideas, a common fixed point theorem is developed for a system involving four maps. Furthermore, the results are applied to solve the Volterra integral equation, demonstrating the practical use of these findings in neutrosophic metric spaces. Full article

(This article belongs to the Special Issue Nonlinear System Modelling and Control)

22 pages, 1039 KiB

Open AccessArticle

A Machine Learning-Based Computational Methodology for Predicting Acute Respiratory Infections Using Social Media Data

by Jose Manuel Ramos-Varela, Juan C. Cuevas-Tello and Daniel E. Noyola

Computation 2025, 13(4), 86; https://doi.org/10.3390/computation13040086 - 25 Mar 2025

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Abstract

We study the relationship between tweets referencing Acute Respiratory Infections (ARI) or COVID-19 symptoms and confirmed cases of these diseases. Additionally, we propose a computational methodology for selecting and applying Machine Learning (ML) algorithms to predict public health indicators using social media data. [...] Read more.

We study the relationship between tweets referencing Acute Respiratory Infections (ARI) or COVID-19 symptoms and confirmed cases of these diseases. Additionally, we propose a computational methodology for selecting and applying Machine Learning (ML) algorithms to predict public health indicators using social media data. To achieve this, a novel pipeline was developed, integrating three distinct models to predict confirmed cases of ARI and COVID-19. The dataset contains tweets related to respiratory diseases, published between 2020 and 2022 in the state of San Luis Potosí, Mexico, obtained via the Twitter API (now X). The methodology is composed of three stages, and it involves tools such as Dataiku and Python with ML libraries. The first two stages focuses on identifying the best-performing predictive models, while the third stage includes Natural Language Processing (NLP) algorithms for tweet selection. One of our key findings is that tweets contributed to improved predictions of ARI confirmed cases but did not enhance COVID-19 time series predictions. The best-performing NLP approach is the combination of Word2Vec algorithm with the KMeans model for tweet selection. Furthermore, predictions for both time series improved by 3% in the second half of 2020 when tweets were included as a feature, where the best prediction algorithm is DeepAR. Full article

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

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18 pages, 15002 KiB

Open AccessArticle

Numerical Analysis of the Impact of Variable Borer Miner Operating Modes on the Microclimate in Potash Mine Working Areas

by Lev Levin, Mikhail Semin, Stanislav Maltsev, Roman Luzin and Andrey Sukhanov

Computation 2025, 13(4), 85; https://doi.org/10.3390/computation13040085 - 24 Mar 2025

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Abstract

This paper addresses the numerical simulation of unsteady, non-isothermal ventilation in a dead-end mine working of a potash mine excavated using a borer miner. During its operations, airflow can become unsteady due to the variable operating modes of the borer miner, the switching [...] Read more.

This paper addresses the numerical simulation of unsteady, non-isothermal ventilation in a dead-end mine working of a potash mine excavated using a borer miner. During its operations, airflow can become unsteady due to the variable operating modes of the borer miner, the switching on and off of its motor cooling fans, and the movement of a shuttle car transporting ore. While steady ventilation in a dead-end working with a borer miner has been previously studied, the specific features of air microclimate parameter distribution in more complex and realistic unsteady scenarios remain unexplored. Our experimental studies reveal that over time, air velocity and, particularly, air temperature experience significant fluctuations. In this study, we develop and parameterize a mathematical model and perform a series of numerical simulations of unsteady heat and mass transfer in a dead-end working. These simulations account for the switching on and off of the borer miner’s fans and the movement of the shuttle car. The numerical model is calibrated using data from our experiments conducted in a potash mine. The analysis of the first factor is carried out by examining two extreme scenarios under steady-state ventilation conditions, while the second factor is analyzed within a fully unsteady framework using a dynamic mesh approach in the ANSYS Fluent 2021 R2. The numerical results demonstrate that the borer miner’s operating mode notably impacts the velocity and temperature fields, with a twofold decrease in maximum velocity near the cabin after the shuttle car departed and a temperature difference of about 1–1.5 °C between extreme scenarios in the case of forcing ventilation. The unsteady simulations using the dynamic mesh approach revealed that temperature variations were primarily caused by the borer miner’s cooling system, while the moving shuttle car generated short-term aerodynamic oscillations. Full article

(This article belongs to the Special Issue Advances in Computational Methods for Fluid Flow)

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9 pages, 915 KiB

Open AccessArticle

Tree-Based Methods of Volatility Prediction for the S&P 500 Index

by Marin Lolic

Computation 2025, 13(4), 84; https://doi.org/10.3390/computation13040084 - 24 Mar 2025

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Abstract

Predicting asset return volatility is one of the central problems in quantitative finance. These predictions are used for portfolio construction, calculation of value at risk (VaR), and pricing of derivatives such as options. Classical methods of volatility prediction utilize historical returns data and [...] Read more.

Predicting asset return volatility is one of the central problems in quantitative finance. These predictions are used for portfolio construction, calculation of value at risk (VaR), and pricing of derivatives such as options. Classical methods of volatility prediction utilize historical returns data and include the exponentially weighted moving average (EWMA) and generalized autoregressive conditional heteroskedasticity (GARCH). These approaches have shown significantly higher rates of predictive accuracy than corresponding methods of return forecasting, but they still have vast room for improvement. In this paper, we propose and test several methods of volatility forecasting on the S&P 500 Index using tree ensembles from machine learning, namely random forest and gradient boosting. We show that these methods generally outperform the classical approaches across a variety of metrics on out-of-sample data. Finally, we use the unique properties of tree-based ensembles to assess what data can be particularly useful in predicting asset return volatility. Full article

(This article belongs to the Special Issue Quantitative Finance and Risk Management Research: 2nd Edition)

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19 pages, 1891 KiB

Open AccessArticle

A High-Order Hybrid Approach Integrating Neural Networks and Fast Poisson Solvers for Elliptic Interface Problems

by Yiming Ren and Shan Zhao

Computation 2025, 13(4), 83; https://doi.org/10.3390/computation13040083 - 23 Mar 2025

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Abstract

A new high-order hybrid method integrating neural networks and corrected finite differences is developed for solving elliptic equations with irregular interfaces and discontinuous solutions. Standard fourth-order finite difference discretization becomes invalid near such interfaces due to the discontinuities and requires corrections based on [...] Read more.

A new high-order hybrid method integrating neural networks and corrected finite differences is developed for solving elliptic equations with irregular interfaces and discontinuous solutions. Standard fourth-order finite difference discretization becomes invalid near such interfaces due to the discontinuities and requires corrections based on Cartesian derivative jumps. In traditional numerical methods, such as the augmented matched interface and boundary (AMIB) method, these derivative jumps can be reconstructed via additional approximations and are solved together with the unknown solution in an iterative procedure. Nontrivial developments have been carried out in the AMIB method in treating sharply curved interfaces, which, however, may not work for interfaces with geometric singularities. In this work, machine learning techniques are utilized to directly predict these Cartesian derivative jumps without involving the unknown solution. To this end, physics-informed neural networks (PINNs) are trained to satisfy the jump conditions for both closed and open interfaces with possible geometric singularities. The predicted Cartesian derivative jumps can then be integrated in the corrected finite differences. The resulting discrete Laplacian can be efficiently solved by fast Poisson solvers, such as fast Fourier transform (FFT) and geometric multigrid methods, over a rectangular domain with Dirichlet boundary conditions. This hybrid method is both easy to implement and efficient. Numerical experiments in two and three dimensions demonstrate that the method achieves fourth-order accuracy for the solution and its derivatives. Full article

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13 pages, 2265 KiB

Open AccessArticle

Numerical Simulation of Capture of Diffusing Particles in Porous Media

by Valeriy E. Arkhincheev, Bair V. Khabituev and Stanislav P. Maltsev

Computation 2025, 13(4), 82; https://doi.org/10.3390/computation13040082 - 22 Mar 2025

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Abstract

Numerical modeling was conducted to study the capture of particles diffusing in porous media with traps. The pores are cylindrical in shape, and the traps are randomly distributed along the cylindrical surfaces of the pores. The dynamics of particle capture by the traps [...] Read more.

Numerical modeling was conducted to study the capture of particles diffusing in porous media with traps. The pores are cylindrical in shape, and the traps are randomly distributed along the cylindrical surfaces of the pores. The dynamics of particle capture by the traps, as well as the filling of the traps, were investigated. In general, the decrease in the number of particles follows an exponential trend, with a characteristic time determined by the trap concentration. However, at longer times, extended plateaus emerge in the particle distribution function. Additionally, the dynamics of the interface boundary corresponding to the median trap filling (M = 0.5) were examined. This interface separates regions where traps are filled with a probability greater than 0.5 from regions where traps are filled with a probability less than 0.5. The motion of the interface over time was found to follow a logarithmic dependence. The influence of the radius of the pore on the capture on traps, which are placed on the internal surface of the cylinders, was investigated. The different dependencies of the extinction time on the number of traps were found at different radii of pores the first time. Full article

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16 pages, 347 KiB

Open AccessArticle

Introducing Monotone Enriched Nonexpansive Mappings for Fixed Point Approximation in Ordered CAT(0) Spaces

by Safeer Hussain Khan, Rizwan Anjum and Nimra Ismail

Computation 2025, 13(4), 81; https://doi.org/10.3390/computation13040081 - 21 Mar 2025

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Abstract

The aim of this paper is twofold: introducing the concept of monotone enriched nonexpansive mappings and a faster iterative process. Our examples illustrate the novelty of our newly introduced concepts. We investigate the iterative estimation of fixed points for such mappings for the [...] Read more.

The aim of this paper is twofold: introducing the concept of monotone enriched nonexpansive mappings and a faster iterative process. Our examples illustrate the novelty of our newly introduced concepts. We investigate the iterative estimation of fixed points for such mappings for the first time within an ordered CAT(0) space. It is done by proving some strong and

Δ

-convergence theorems. Additionally, numerical experiments are included to demonstrate the validity of our theoretical results and to establish the superiority of convergence behavior of our iterative process. As an application, we use our newly introduced concepts to find the solution of an integral equation. The outcomes of our study expand upon and enhance certain established findings in the current body of literature. Full article

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17 pages, 1513 KiB

Open AccessArticle

Cascade-Based Input-Doubling Classifier for Predicting Survival in Allogeneic Bone Marrow Transplants: Small Data Case

by Ivan Izonin, Roman Tkachenko, Nazarii Hovdysh, Oleh Berezsky, Kyrylo Yemets and Ivan Tsmots

Computation 2025, 13(4), 80; https://doi.org/10.3390/computation13040080 - 21 Mar 2025

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Abstract

In the field of transplantology, where medical decisions are heavily dependent on complex data analysis, the challenge of small data has become increasingly prominent. Transplantology, which focuses on the transplantation of organs and tissues, requires exceptional accuracy and precision in predicting outcomes, assessing [...] Read more.

In the field of transplantology, where medical decisions are heavily dependent on complex data analysis, the challenge of small data has become increasingly prominent. Transplantology, which focuses on the transplantation of organs and tissues, requires exceptional accuracy and precision in predicting outcomes, assessing risks, and tailoring treatment plans. However, the inherent limitations of small datasets present significant obstacles. This paper introduces an advanced input-doubling classifier designed to improve survival predictions for allogeneic bone marrow transplants. The approach utilizes two artificial intelligence tools: the first Probabilistic Neural Network generates output signals that expand the independent attributes of an augmented dataset, while the second machine learning algorithm performs the final classification. This method, based on the cascading principle, facilitates the development of novel algorithms for preparing and applying the enhanced input-doubling technique to classification tasks. The proposed method was tested on a small dataset within transplantology, focusing on binary classification. Optimal parameters for the method were identified using the Dual Annealing algorithm. Comparative analysis of the improved method against several existing approaches revealed a substantial improvement in accuracy across various performance metrics, underscoring its practical benefits Full article

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

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Computation, Volume 13, Issue 4 (April 2025) – 8 articles

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