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Computation

Computation is a peer-reviewed journal of computational science and engineering published monthly online by MDPI.

Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
High Visibility: indexed within Scopus, ESCI (Web of Science), CAPlus / SciFinder, Inspec, dblp, and other databases.
Journal Rank: JCR - Q2 (Mathematics, Interdisciplinary Applications) / CiteScore - Q2 (Applied Mathematics)
Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 18.6 days after submission; acceptance to publication is undertaken in 4.2 days (median values for papers published in this journal in the second half of 2024).
Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.

Impact Factor: 1.9 (2023); 5-Year Impact Factor: 2.0 (2023)

Imprint Information Journal Flyer Open Access ISSN: 2079-3197

Latest Articles

29 pages, 1949 KiB

Open AccessArticle

An Explainable Framework Integrating Local Biplots and Gaussian Processes for Unemployment Rate Prediction in Colombia

by Diego Armando Pérez-Rosero, Diego Alejandro Manrique-Cabezas, Jennifer Carolina Triana-Martinez, Andrés Marino Álvarez-Meza and German Castellanos-Dominguez

Computation 2025, 13(5), 116; https://doi.org/10.3390/computation13050116 (registering DOI) - 10 May 2025

Addressing unemployment is essential for formulating effective public policies. In particular, socioeconomic and monetary variables serve as essential indicators for anticipating labor market trends, given their strong influence on employment dynamics and economic stability. However, effective unemployment rate prediction requires addressing the non-stationary [...] Read more.

Addressing unemployment is essential for formulating effective public policies. In particular, socioeconomic and monetary variables serve as essential indicators for anticipating labor market trends, given their strong influence on employment dynamics and economic stability. However, effective unemployment rate prediction requires addressing the non-stationary and non-linear characteristics of labor data. Equally important is the preservation of interpretability in both samples and features to ensure that forecasts can meaningfully inform public decision-making. Here, we provide an explainable framework integrating unsupervised and supervised machine learning to enhance unemployment rate prediction and interpretability. Our approach is threefold: (i) we gather a dataset for Colombian unemployment rate prediction including monetary and socioeconomic variables. (ii) Then, we used a Local Biplot technique from the widely recognized Uniform Manifold Approximation and Projection (UMAP) method along with local affine transformations as an unsupervised representation of non-stationary and non-linear data patterns in a simplified and comprehensible manner. (iii) A Gaussian Processes regressor with kernel-based feature relevance analysis is coupled as a supervised counterpart for both unemployment rate prediction and input feature importance analysis. We demonstrated the effectiveness of our proposed approach through a series of experiments conducted on our customized database focused on unemployment indicators in Colombia. Furthermore, we carried out a comparative analysis between traditional statistical techniques and modern machine learning methods. The results revealed that our framework significantly enhances both clustering and predictive performance, while also emphasizing the importance of input samples and feature selection in driving accurate outcomes. Full article

(This article belongs to the Special Issue Applications of Machine Learning and Data Science Methods in Social Sciences)

27 pages, 6623 KiB

Open AccessArticle

Three-Dimensional and Multiple Image Encryption Algorithm Using a Fractional-Order Chaotic System

by Ghader Ghasemi, Reza Parvaz and Yavar Khedmati Yengejeh

Computation 2025, 13(5), 115; https://doi.org/10.3390/computation13050115 (registering DOI) - 10 May 2025

The rapid development of communication in the last decade has heightened the necessity to create a secure platform for transferring data, including images, more than in previous years. One of the methods of secure image transmission is the encryption method. In this work, [...] Read more.

The rapid development of communication in the last decade has heightened the necessity to create a secure platform for transferring data, including images, more than in previous years. One of the methods of secure image transmission is the encryption method. In this work, an encryption algorithm for multiple images is introduced. In the first step of the proposed algorithm, a key generation algorithm based on a chaotic system and wavelet transform is introduced, and in the next step, the encryption algorithm is developed by introducing rearrange and shift functions based on a chaotic system. One of the most important tools used in the proposed algorithm is the hybrid chaotic system, which is obtained by fractional derivatives and the Cat map. Different types of tests used to study the behavior of this system demonstrate the efficiency of the proposed hybrid system. In the last step of the proposed method, various statistical and security tests, including histogram analysis, correlation coefficient analysis, data loss and noise attack simulations, have been performed on the proposed algorithm. The results show that the proposed algorithm performs well in secure transmission. Full article

20 pages, 3650 KiB

Open AccessArticle

Battery Sizing Method for Microgrids—A Colombian Application Case

by Andres Felipe Zamora-Muñoz, Martha Lucia Orozco-Gutierrez, Dany Mauricio Lopez-Santiago, Jhoan Alejandro Montenegro-Oviedo and Carlos Andres Ramos-Paja

Computation 2025, 13(5), 114; https://doi.org/10.3390/computation13050114 (registering DOI) - 10 May 2025

The introduction of renewable energy sources in microgrids increases energy reliability, especially in small communities that operate disconnected from the main power grid. A battery energy storage system (BESS) plays an important role in microgrids because it helps mitigate the problems caused by [...] Read more.

The introduction of renewable energy sources in microgrids increases energy reliability, especially in small communities that operate disconnected from the main power grid. A battery energy storage system (BESS) plays an important role in microgrids because it helps mitigate the problems caused by the variability of renewable energy sources, such as unattended demand and voltage instability. However, a BESS increases the cost of a microgrid due to the initial investment and maintenance, requiring a cost–benefit analysis to determine its size for each application. This paper addresses this problem by formulating a method that combines economic and technical approaches to provide favorable relations between costs and performances. Mixed integer linear programming (MILP) is used as optimization algorithm to size BESS, which is applied to an isolated community in Colombia located at Isla Múcura. The results indicate that the optimal BESS requires a maximum power of 17.6 kW and a capacity of 76.61 kWh, which is significantly smaller than the existing 480 kWh system. Thus, a reduction of 83.33% in the number of batteries is obtained. This optimized size reduces operational costs while maintaining technical reliability. The proposed method aims to solve an important problem concerning state policy and the universalization of electrical services, providing more opportunities to decision makers in minimizing the costs and efforts in the implementation of energy storage systems for isolated microgrids. Full article

(This article belongs to the Section Computational Engineering)

15 pages, 1375 KiB

Open AccessArticle

How Re-Infections and Newborns Can Impact Visible and Hidden Epidemic Dynamics?

by Igor Nesteruk

Computation 2025, 13(5), 113; https://doi.org/10.3390/computation13050113 - 9 May 2025

Mathematical modeling allows taking into account registered and hidden infections to make correct predictions of epidemic dynamics and develop recommendations that can reduce the negative impact on public health and the economy. A model for visible and hidden epidemic dynamics (published by the [...] Read more.

Mathematical modeling allows taking into account registered and hidden infections to make correct predictions of epidemic dynamics and develop recommendations that can reduce the negative impact on public health and the economy. A model for visible and hidden epidemic dynamics (published by the author in February 2025) has been generalized to account for the effects of re-infection and newborns. An analysis of the equilibrium points, examples of numerical solutions, and comparisons with the dynamics of real epidemics are provided. A stable quasi-equilibrium for the particular case of almost completely hidden epidemics was also revealed. Numerical results and comparisons with the COVID-19 epidemic dynamics in Austria and South Korea showed that re-infections, newborns, and hidden cases make epidemics endless. Newborns can cause repeated epidemic waves even without re-infections. In particular, the next epidemic peak of pertussis in England is expected to occur in 2031. With the use of effective algorithms for parameter identification, the proposed approach can ensure effective predictions of visible and hidden numbers of cases and infectious and removed patients. Full article

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

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21 pages, 2407 KiB

Open AccessArticle

A Novel Algorithm for the Decomposition of Non-Stationary Multidimensional and Multivariate Signals

by Roberto Cavassi, Antonio Cicone, Enza Pellegrino and Haomin Zhou

Computation 2025, 13(5), 112; https://doi.org/10.3390/computation13050112 - 8 May 2025

The decomposition of a signal is a fundamental tool in many fields of research, including signal processing, geophysics, astrophysics, engineering, medicine, and many more. By breaking down complex signals into simpler oscillatory components, we can enhance the understanding and processing of the data, [...] Read more.

The decomposition of a signal is a fundamental tool in many fields of research, including signal processing, geophysics, astrophysics, engineering, medicine, and many more. By breaking down complex signals into simpler oscillatory components, we can enhance the understanding and processing of the data, unveiling hidden information contained in them. Traditional methods, such as Fourier analysis and wavelet transforms, which are effective in handling mono-dimensional stationary signals, struggle with non-stationary datasets and they require the selection of predefined basis functions. In contrast, the empirical mode decomposition (EMD) method and its variants, such as Iterative Filtering (IF), have emerged as effective non-linear approaches, adapting to signals without any need for a priori assumptions. To accelerate these methods, the Fast Iterative Filtering (FIF) algorithm was developed, and further extensions, such as Multivariate FIF (MvFIF) and Multidimensional FIF (FIF2), have been proposed to handle higher-dimensional data. In this work, we introduce the Multidimensional and Multivariate Fast Iterative Filtering (MdMvFIF) technique, an innovative method that extends FIF to handle data that varies simultaneously in space and time, like the ones sampled using sensor arrays. This new algorithm is capable of extracting Intrinsic Mode Functions (IMFs) from complex signals that vary in both space and time, overcoming limitations found in prior methods. The potentiality of the proposed method is demonstrated through applications to artificial and real-life signals, highlighting its versatility and effectiveness in decomposing multidimensional and multivariate non-stationary signals. The MdMvFIF method offers a powerful tool for advanced signal analysis across many scientific and engineering disciplines. Full article

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17 pages, 2664 KiB

Open AccessArticle

Exploring the Chemical and Pharmaceutical Potential of Kapakahines A–G Using Conceptual Density Functional Theory-Based Computational Peptidology

by Norma Flores-Holguín, Juan Frau and Daniel Glossman-Mitnik

Computation 2025, 13(5), 111; https://doi.org/10.3390/computation13050111 - 7 May 2025

Kapakahines A–G are natural products isolated from the marine sponge Carteriospongia sp., characterized by complex molecular architectures composed of fused rings and diverse functional groups. Preliminary studies have indicated that some of these peptides may exhibit cytotoxic and antitumor activities, which has prompted [...] Read more.

Kapakahines A–G are natural products isolated from the marine sponge Carteriospongia sp., characterized by complex molecular architectures composed of fused rings and diverse functional groups. Preliminary studies have indicated that some of these peptides may exhibit cytotoxic and antitumor activities, which has prompted interest in further exploring their chemical and pharmacokinetic properties. Computational chemistry—particularly Conceptual Density Functional Theory (CDFT)-based Computational Peptidology (CP)—offers a valuable framework for investigating such compounds. In this study, the CDFT-CP approach is applied to analyze the structural and electronic properties of Kapakahines A–G. Alongside the calculation of global and local reactivity descriptors, predicted ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) profiles and pharmacokinetic parameters, including pKa and LogP, are evaluated. The integrated computational analysis provides insights into the stability, reactivity, and potential drug-like behavior of these marine-derived cyclopeptides and contributes to the theoretical groundwork for future studies aimed at optimizing their bioactivity and safety profiles. Full article

(This article belongs to the Section Computational Chemistry)

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27 pages, 561 KiB

Open AccessArticle

An Algorithm Based on Connectivity Properties for Finding Cycles and Paths on Kidney Exchange Compatibility Graphs

by Roger Z. Ríos-Mercado, L. Carolina Riascos-Álvarez and Jonathan F. Bard

Computation 2025, 13(5), 110; https://doi.org/10.3390/computation13050110 - 6 May 2025

Kidney-paired donation programs assist patients in need of a kidney to swap their incompatible donor with another incompatible patient–donor pair for a suitable kidney in return. The kidney exchange problem (KEP) is a mathematical optimization problem that consists of finding the maximum set [...] Read more.

Kidney-paired donation programs assist patients in need of a kidney to swap their incompatible donor with another incompatible patient–donor pair for a suitable kidney in return. The kidney exchange problem (KEP) is a mathematical optimization problem that consists of finding the maximum set of matches in a directed graph representing the pool of incompatible pairs. Depending on the specific framework, these matches can come in the form of (bounded) directed cycles or directed paths. This gives rise to a family of KEP models that have been studied over the past few years. Several of these models require an exponential number of constraints to eliminate cycles and chains that exceed a given length. In this paper, we present enhancements to a subset of existing models that exploit the connectivity properties of the underlying graphs, thereby rendering more compact and tractable models in both cycle-only and cycle-and-chain versions. In addition, an efficient algorithm is developed for detecting violated constraints and solving the problem. To assess the value of our enhanced models and algorithm, an extensive computational study was carried out comparing with existing formulations. The results demonstrated the effectiveness of the proposed approach. For example, among the main findings for edge-based cycle-only models, the proposed (*PRE(i)) model uses a new set of constraints and a small subset of the full set of length-k paths that are included in the edge formulation. The proposed model was observed to achieve a more than 98% reduction in the number of such paths among all tested instances. With respect to cycle-and-chain formulations, the proposed (*ReSPLIT) model outperformed Anderson’s arc-based (AA) formulation and the path constrained-TSP formulation on all instances that we tested. In particular, when tested on a difficult sets of instances from the literature, the proposed (*ReSPLIT) model provided the best results compared to the AA and PC-based models. Full article

(This article belongs to the Section Computational Social Science)

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18 pages, 591 KiB

Open AccessArticle

Research on a Method for Identifying Key Fault Information in Substations

by Pan Zhang, Lei Guo, Zhicheng Huang, Zhoupeng Rao, Ying Zhang, Zhi Sun, Rui Xu and Deng Li

Computation 2025, 13(5), 109; https://doi.org/10.3390/computation13050109 - 6 May 2025

The identification of critical fault information plays a crucial role in ensuring the reliability and stability of power systems. However, existing fault-identification technologies heavily rely on high-dimensional sensor data, which often contain redundant and noisy information. Moreover, conventional data preprocessing approaches typically employ [...] Read more.

The identification of critical fault information plays a crucial role in ensuring the reliability and stability of power systems. However, existing fault-identification technologies heavily rely on high-dimensional sensor data, which often contain redundant and noisy information. Moreover, conventional data preprocessing approaches typically employ fixed time windows, neglecting variations in fault characteristics under different system states. This limitation may lead to incomplete feature selection and ineffective dimensionality reduction, ultimately affecting the accuracy of fault classification. To address these challenges, this study proposes a method of critical fault information identification that integrates a scalable time window with Principal Component Analysis (PCA). The proposed method dynamically adjusts the time window size based on real-time system conditions, ensuring more flexible data capture under diverse fault scenarios. Simultaneously, PCA is employed to reduce dimensionality, extract representative features, and remove redundant noise, thereby enhancing the quality of the extracted fault information. Furthermore, this approach lays a solid foundation for the subsequent application of deep learning-based fault-diagnosis techniques. By improving feature extraction and reducing computational complexity, the proposed method effectively alleviates the workload of operation and maintenance personnel while enhancing fault classification accuracy. Our experimental results demonstrate that the proposed method significantly improves the precision and robustness of fault identification in power systems. Full article

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41 pages, 6681 KiB

Open AccessArticle

AraEyebility: Eye-Tracking Data for Arabic Text Readability

by Ibtehal Baazeem, Hend Al-Khalifa and Abdulmalik Al-Salman

Computation 2025, 13(5), 108; https://doi.org/10.3390/computation13050108 - 5 May 2025

Assessing text readability is important for helping language learners and readers select texts that match their proficiency levels. Research in cognitive psychology, which uses behavioral data such as eye-tracking and electroencephalogram signals, has shown its effectiveness in detecting cognitive activities that correlate with [...] Read more.

Assessing text readability is important for helping language learners and readers select texts that match their proficiency levels. Research in cognitive psychology, which uses behavioral data such as eye-tracking and electroencephalogram signals, has shown its effectiveness in detecting cognitive activities that correlate with text difficulty during reading. However, Arabic, with its distinctive linguistic characteristics, presents unique challenges in readability assessment using cognitive data. While behavioral data have been employed in readability assessments, their full potential, particularly in Arabic contexts, remains underexplored. This paper presents the development of the first Arabic eye-tracking corpus, comprising eye movement data collected from Arabic-speaking participants, with a total of 57,617 words. Subsequently, this corpus can be utilized to evaluate a broad spectrum of text-based and gaze-based features, employing machine learning and deep learning methods to improve Arabic readability assessments by integrating cognitive data into the readability assessment process. Full article

(This article belongs to the Special Issue Recent Advances on Computational Linguistics and Natural Language Processing)

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15 pages, 1720 KiB

Open AccessArticle

State Observer for Deflections in Rectangular Flat Plates Simply Supported Subjected to Uniform and Hydrostatic Pressure

by Juan P. Cardona, José U. Castellanos and Luis C. Gutiérrez

Computation 2025, 13(5), 107; https://doi.org/10.3390/computation13050107 - 30 Apr 2025

The present work aims to validate the computational simulation model that determines the static deflection experienced by rectangular flat plates along the longest edge when subjected to uniform and hydrostatic pressures, proposed as a state observer for active control. The plates are isotropic [...] Read more.

The present work aims to validate the computational simulation model that determines the static deflection experienced by rectangular flat plates along the longest edge when subjected to uniform and hydrostatic pressures, proposed as a state observer for active control. The plates are isotropic and simply supported on their four edges. The pressures do not exceed the plate material’s elastic limit. The solutions in the analytical form of the partial differential equation of flat plates established by Kirchoff theory are first determined by Fourier double series. On the other hand, simulations are performed using the Finite Element Computational Model (MEF) using ANSYS Workbench17 software. Full article

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38 pages, 2327 KiB

Open AccessArticle

Supervised Machine Learning Insights into Social and Linguistic Influences on Cesarean Rates in Luxembourg

by Prasad Adhav and María Bélen Farias

Computation 2025, 13(5), 106; https://doi.org/10.3390/computation13050106 - 30 Apr 2025

Cesarean sections (CSs) are essential in certain medical contexts but, when overused, can carry risks for both the mother and child. In the unique multilingual landscape of Luxembourg, this study explores whether non-medical factors—such as the language spoken—affect CS rates. Through a survey [...] Read more.

Cesarean sections (CSs) are essential in certain medical contexts but, when overused, can carry risks for both the mother and child. In the unique multilingual landscape of Luxembourg, this study explores whether non-medical factors—such as the language spoken—affect CS rates. Through a survey conducted with women in Luxembourg, we first applied statistical methods to investigate the influence of various social and linguistic parameters on CS. Additionally, we explored how these factors relate to the feelings of happiness and respect women experience during childbirth. Subsequently, we employed four machine learning models to predict CS based on the survey data. Our findings reveal that women who speak Spanish have a statistically higher likelihood of undergoing a CS than women that do not report speaking that language. Furthermore, those who had CS report feeling less happy and respected compared to those with vaginal births. With both limited and augmented data, our models achieve an average accuracy of approximately 81% in predicting CS. While this study serves as an initial exploration into the social aspects of childbirth, it underscores the need for larger-scale studies to deepen our understanding and to inform policy-makers and health practitioners that support women during their pregnancies and births. This preliminary research advocates for further investigation to address this complex social issue comprehensively. Full article

(This article belongs to the Section Computational Social Science)

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19 pages, 3751 KiB

Open AccessArticle

A Novel Methodology for Scrutinizing Periodic Solutions of Some Physical Highly Nonlinear Oscillators

by Gamal M. Ismail, Galal M. Moatimid, Stylianos V. Kontomaris and Livija Cveticanin

Computation 2025, 13(5), 105; https://doi.org/10.3390/computation13050105 - 28 Apr 2025

The study offers a comprehensive investigation of periodic solutions in highly nonlinear oscillator systems, employing advanced analytical and numerical techniques. The motivation stems from the urgent need to understand complex dynamical behaviors in physics and engineering, where traditional linear approximations fall short. This [...] Read more.

The study offers a comprehensive investigation of periodic solutions in highly nonlinear oscillator systems, employing advanced analytical and numerical techniques. The motivation stems from the urgent need to understand complex dynamical behaviors in physics and engineering, where traditional linear approximations fall short. This work precisely applies He’s Frequency Formula (HFF) to provide theoretical insights into certain classes of strongly nonlinear oscillators, as illustrated through five broad examples drawn from various scientific and engineering disciplines. Additionally, the novelty of the present work lies in reducing the required time compared to the classical perturbation techniques that are widely employed in this field. The proposed non-perturbative approach (NPA) effectively converts nonlinear ordinary differential equations (ODEs) into linear ones, equivalent to simple harmonic motion. This method yields a new frequency approximation that aligns closely with the numerical results, often outperforming existing approximation techniques in terms of accuracy. To aid readers, the NPA is thoroughly explained, and its theoretical predictions are validated through numerical simulations using Mathematica Software (MS). An excellent agreement between the theoretical and numerical responses highlights the robustness of this method. Furthermore, the NPA enables a detailed stability analysis, an area where traditional methods frequently underperform. Due to its flexibility and effectiveness, the NPA presents a powerful and efficient tool for analyzing highly nonlinear oscillators across various fields of engineering and applied science. Full article

(This article belongs to the Special Issue Numerical Simulation of Nanofluid Flow in Porous Media)

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24 pages, 3375 KiB

Open AccessArticle

Fractional-Order Modeling of Sediment Transport and Coastal Erosion Mitigation in Shorelines Under Extreme Climate Conditions: A Case Study in Iraq

by Ibtisam Aldawish and Rabha W. Ibrahim

Computation 2025, 13(5), 104; https://doi.org/10.3390/computation13050104 - 27 Apr 2025

Coastal erosion and sediment transport dynamics in Iraq’s shoreline are increasingly affected by extreme climate conditions, including rising sea levels and intensified storms. This study introduces a novel fractional-order sediment transport model, incorporating a modified gamma function-based differential operator to accurately describe erosion [...] Read more.

Coastal erosion and sediment transport dynamics in Iraq’s shoreline are increasingly affected by extreme climate conditions, including rising sea levels and intensified storms. This study introduces a novel fractional-order sediment transport model, incorporating a modified gamma function-based differential operator to accurately describe erosion rates and stabilization effects. The proposed model evaluates two key stabilization approaches: artificial stabilization (breakwaters and artificial reefs) and bio-engineering solutions (coral reefs, sea-grass, and salt marshes). Numerical simulations reveal that the proposed structures provide moderate sediment retention but degrade over time, leading to diminishing effectiveness. In contrast, bio-engineering solutions demonstrate higher long-term resilience, as natural ecosystems self-repair and adapt to changing environmental conditions. Under extreme climate scenarios, enhanced bio-engineering retains 55% more sediment than no intervention, compared to 35% retention with artificial stabilization.The findings highlight the potential of hybrid coastal protection strategies combining artificial and bio-based stabilization. Future work includes optimizing intervention designs, incorporating localized field data from Iraq’s coastal zones, and assessing cost-effectiveness for large-scale implementation. Full article

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31 pages, 4116 KiB

Open AccessArticle

Parallel Simulation Using Reactive Streams: Graph-Based Approach for Dynamic Modeling and Optimization

by Oleksii Sirotkin, Arsentii Prymushko, Ivan Puchko, Hryhoriy Kravtsov, Mykola Yaroshynskyi and Volodymyr Artemchuk

Computation 2025, 13(5), 103; https://doi.org/10.3390/computation13050103 - 26 Apr 2025

Modern computational models tend to become more and more complex, especially in fields like computational biology, physical modeling, social simulation, and others. With the increasing complexity of simulations, modern computational architectures demand efficient parallel execution strategies. This paper proposes a novel approach leveraging [...] Read more.

Modern computational models tend to become more and more complex, especially in fields like computational biology, physical modeling, social simulation, and others. With the increasing complexity of simulations, modern computational architectures demand efficient parallel execution strategies. This paper proposes a novel approach leveraging the reactive stream paradigm as a general-purpose synchronization protocol for parallel simulation. We introduce a method to construct simulation graphs from predefined transition functions, ensuring modularity and reusability. Additionally, we outline strategies for graph optimization and interactive simulation through push and pull patterns. The resulting computational graph, implemented using reactive streams, offers a scalable framework for parallel computation. Through theoretical analysis and practical implementation, we demonstrate the feasibility of this approach, highlighting its advantages over traditional parallel simulation methods. Finally, we discuss future challenges, including automatic graph construction, fault tolerance, and optimization strategies, as key areas for further research. Full article

(This article belongs to the Section Computational Engineering)

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15 pages, 1206 KiB

Open AccessArticle

A Simplified Fish School Search Algorithm for Continuous Single-Objective Optimization

by Elliackin Figueiredo, Clodomir Santana, Hugo Valadares Siqueira, Mariana Macedo, Attilio Converti, Anu Gokhale and Carmelo Bastos-Filho

Computation 2025, 13(5), 102; https://doi.org/10.3390/computation13050102 - 25 Apr 2025

The Fish School Search (FSS) algorithm is a metaheuristic known for its distinctive exploration and exploitation operators and cumulative success representation approach. Despite its success across various problem domains, the FSS presents issues due to its high number of parameters, making its performance [...] Read more.

The Fish School Search (FSS) algorithm is a metaheuristic known for its distinctive exploration and exploitation operators and cumulative success representation approach. Despite its success across various problem domains, the FSS presents issues due to its high number of parameters, making its performance susceptible to improper parameterization. Additionally, the interplay between its operators requires a sequential execution in a specific order, requiring two fitness evaluations per iteration for each individual. This operator’s intricacy and the number of fitness evaluations pose the issue of costly fitness functions and inhibit parallelization. To address these challenges, this paper proposes a Simplified Fish School Search (SFSS) algorithm that preserves the core features of the original FSS while redesigning the fish movement operators and introducing a new turbulence mechanism to enhance population diversity and robustness against stagnation. The SFSS also reduces the number of fitness evaluations per iteration and minimizes the algorithm’s parameter set. Computational experiments were conducted using a benchmark suite from the CEC 2017 competition to compare the SFSS with the traditional FSS and five other well-known metaheuristics. The SFSS outperformed the FSS in 84% of the problems and achieved the best results among all algorithms in 10 of the 26 problems. Full article

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15 pages, 16764 KiB

Open AccessArticle

Computational Analysis of Tandem Micro-Vortex Generators for Supersonic Boundary Layer Flow Control

by Caixia Chen, Yong Yang and Yonghua Yan

Computation 2025, 13(4), 101; https://doi.org/10.3390/computation13040101 - 19 Apr 2025

Micro-vortex generators (MVGs) are widely utilized as passive devices to control flow separation in supersonic boundary layers by generating ring-like vortices that mitigate shock-induced effects. This study employs large eddy simulation (LES) to investigate the flow structures in a supersonic boundary layer (Mach [...] Read more.

Micro-vortex generators (MVGs) are widely utilized as passive devices to control flow separation in supersonic boundary layers by generating ring-like vortices that mitigate shock-induced effects. This study employs large eddy simulation (LES) to investigate the flow structures in a supersonic boundary layer (Mach 2.5, Re = 5760) controlled by two MVGs installed in tandem, with spacings varying from 11.75 h to 18.75 h (h = MVG height), alongside a single-MVG reference case. A fifth-order WENO scheme and third-order TVD Runge–Kutta method were used to solve the unfiltered Navier–Stokes equations, with the Liutex method applied to visualize vortex structures. Results reveal that tandem MVGs produce complex vortex interactions, with spanwise and streamwise vortices merging extensively, leading to a significant reduction in vortex intensity due to mutual cancellation. A momentum deficit forms behind the second MVG, weakening that from the first, while the boundary layer energy thickness doubles compared to the single-MVG case, indicating increased energy loss. Streamwise vorticity distributions and instantaneous streamlines highlight intensified interactions with closer spacings, yet this complexity diminishes overall flow control effectiveness. Contrary to expectations, the tandem configuration does not enhance boundary layer control but instead weakens it, as evidenced by reduced vortex strength and amplified energy dissipation. These findings underscore a critical trade-off in tandem MVG deployment, suggesting that while vortex interactions enrich flow complexity, they may compromise the intended control benefits in supersonic flows, with implications for optimizing MVG arrangements in practical applications. Full article

(This article belongs to the Section Computational Engineering)

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27 pages, 431 KiB

Open AccessArticle

On the Generalized Inverse Gaussian Volatility in the Continuous Ho–Lee Model

by Roman V. Ivanov

Computation 2025, 13(4), 100; https://doi.org/10.3390/computation13040100 - 19 Apr 2025

This paper presents a new model of the term structure of interest rates that is based on the continuous Ho–Lee one. In this model, we suggest that the drift and volatility coefficients depend additionally on a generalized inverse Gaussian (GIG) distribution. Analytical expressions [...] Read more.

This paper presents a new model of the term structure of interest rates that is based on the continuous Ho–Lee one. In this model, we suggest that the drift and volatility coefficients depend additionally on a generalized inverse Gaussian (GIG) distribution. Analytical expressions for the bond price and its moments are found in the new GIG continuous Ho–Lee model. Also, we compute in this model the prices of European call and put options written on bond. The obtained formulas are determined by the values of the Humbert confluent hypergeometric function of two variables. A numerical experiment shows that the third and fourth moments of the bond prices differentiate substantially in the continuous Ho–Lee and GIG continuous Ho–Lee models. Full article

(This article belongs to the Section Computational Social Science)

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36 pages, 14909 KiB

Open AccessArticle

Enhanced Efficient 3D Poisson Solver Supporting Dirichlet, Neumann, and Periodic Boundary Conditions

by Chieh-Hsun Wu

Computation 2025, 13(4), 99; https://doi.org/10.3390/computation13040099 - 18 Apr 2025

This paper generalizes the efficient matrix decomposition method for solving the finite-difference (FD) discretized three-dimensional (3D) Poisson’s equation using symmetric 27-point, 4th-order accurate stencils to adapt more boundary conditions (BCs), i.e., Dirichlet, Neumann, and Periodic BCs. It employs equivalent Dirichlet nodes to streamline [...] Read more.

This paper generalizes the efficient matrix decomposition method for solving the finite-difference (FD) discretized three-dimensional (3D) Poisson’s equation using symmetric 27-point, 4th-order accurate stencils to adapt more boundary conditions (BCs), i.e., Dirichlet, Neumann, and Periodic BCs. It employs equivalent Dirichlet nodes to streamline source term computation due to BCs. A generalized eigenvalue formulation is presented to accommodate the flexible 4th-order stencil weights. The proposed method significantly enhances computational speed by reducing the 3D problem to a set of independent 1D problems. As compared to the typical matrix inversion technique, it results in a speed-up ratio proportional to

n^{4}

, where

n

is the number of nodes along one side of the cubic domain. Accuracy is validated using Gaussian and sinusoidal source fields, showing 4th-order convergence for Dirichlet and Periodic boundaries, and 2nd-order convergence for Neumann boundaries due to extrapolation limitations—though with lower errors than traditional 2nd-order schemes. The method is also applied to vortex-in-cell flow simulations, demonstrating its capability to handle outer boundaries efficiently and its compatibility with immersed boundary techniques for internal solid obstacles. Full article

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

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28 pages, 2783 KiB

Open AccessArticle

Blockchain-Enhanced Security for 5G Edge Computing in IoT

by Manuel J. C. S. Reis

Computation 2025, 13(4), 98; https://doi.org/10.3390/computation13040098 - 18 Apr 2025

The rapid expansion of 5G networks and edge computing has amplified security challenges in Internet of Things (IoT) environments, including unauthorized access, data tampering, and DDoS attacks. This paper introduces EdgeChainGuard, a hybrid blockchain-based authentication framework designed to secure 5G-enabled IoT systems through [...] Read more.

The rapid expansion of 5G networks and edge computing has amplified security challenges in Internet of Things (IoT) environments, including unauthorized access, data tampering, and DDoS attacks. This paper introduces EdgeChainGuard, a hybrid blockchain-based authentication framework designed to secure 5G-enabled IoT systems through decentralized identity management, smart contract-based access control, and AI-driven anomaly detection. By combining permissioned and permissionless blockchain layers with Layer-2 scaling solutions and adaptive consensus mechanisms, the framework enhances both security and scalability while maintaining computational efficiency. Using synthetic datasets that simulate real-world adversarial behaviour, our evaluation shows an average authentication latency of 172.50 s and a 50% reduction in gas fees compared to traditional Ethereum-based implementations. The results demonstrate that EdgeChainGuard effectively enforces tamper-resistant authentication, reduces unauthorized access, and adapts to dynamic network conditions. Future research will focus on integrating zero-knowledge proofs (ZKPs) for privacy preservation, federated learning for decentralized AI retraining, and lightweight anomaly detection models to enable secure, low-latency authentication in resource-constrained IoT deployments. Full article

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9 pages, 243 KiB

Open AccessCommunication

Pareto Efficiency in Euclidean Spaces and Its Applications in Economics

by Christos Kountzakis and Vasileia Tsachouridou-Papadatou

Computation 2025, 13(4), 97; https://doi.org/10.3390/computation13040097 - 14 Apr 2025

The aim of the first part of this paper is to show whether a set of Proper Efficient Points and a set of Pareto Efficient Points coincide in Euclidean spaces. In the second part of the paper, we show that supporting prices, which [...] Read more.

The aim of the first part of this paper is to show whether a set of Proper Efficient Points and a set of Pareto Efficient Points coincide in Euclidean spaces. In the second part of the paper, we show that supporting prices, which are actually strictly positive, do exist for a large class of exchange economies. A consequence of this result is a generalized form of the Second Welfare theorem. The properties of the cones’ bases are significant for this purpose. Full article

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