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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 - Q1 (Applied Mathematics)
Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 16.7 days after submission; acceptance to publication is undertaken in 5.6 days (median values for papers published in this journal in the first half of 2025).
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.
Journal Cluster of Mathematics and Its Applications: AppliedMath, Axioms, Computation, Fractal and Fractional, Geometry, International Journal of Topology, Logics, Mathematics and Symmetry.

Impact Factor: 1.9 (2024); 5-Year Impact Factor: 1.9 (2024)

Imprint Information Journal Flyer Open Access ISSN: 2079-3197

Latest Articles

21 pages, 3046 KB

Open AccessArticle

A Finite-Time Extended State Observer with Prediction Error Compensation for PMSM Control

by Lihua Gao, Guangming Zhang, Xiaodong Lv, Yan Wang and Zhihan Shi

Computation 2025, 13(10), 247; https://doi.org/10.3390/computation13100247 - 20 Oct 2025

This paper proposes a finite-time extended state observer (FTESO) integrated with model predictive control (MPC) for high-performance control of permanent magnet synchronous motors (PMSMs). A disturbance-aware predictive model is constructed by incorporating lumped disturbances into the PMSM current equations, addressing load fluctuations and [...] Read more.

This paper proposes a finite-time extended state observer (FTESO) integrated with model predictive control (MPC) for high-performance control of permanent magnet synchronous motors (PMSMs). A disturbance-aware predictive model is constructed by incorporating lumped disturbances into the PMSM current equations, addressing load fluctuations and parameter uncertainties. The FTESO, designed with nonlinear gains and Lyapunov stability, ensures rapid disturbance estimation and is embedded into a feedforward-compensated MPC with a composite cost function considering current error and voltage increment. Simulations show that under sudden load disturbances, FTESO-MPC achieves faster recovery and a smaller steady-state error than LESO-MPC; when inductance triples, FTESO-MPC maintains smooth convergence, whereas LESO-MPC exhibits oscillations with d-axis current peaks near 200 A. Under resistance or flux variations, FTESO-MPC sustains stable regulation with less ripple, confirming its superior tracking accuracy and robustness compared with LESO-MPC. Full article

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

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15 pages, 1168 KB

Open AccessArticle

Stability of the DuFort–Frankel Scheme on Unstructured Grids

by Nikolay Yavich, Evgeny Burnaev and Vladimir Vanovskiy

Computation 2025, 13(10), 246; https://doi.org/10.3390/computation13100246 (registering DOI) - 20 Oct 2025

The DuFort–Frankel scheme was introduced in the 1950s to solve parabolic equations, and has been widely used ever since due to its stability and explicit nature. However, for over seven decades, its application has been limited to Cartesian grids. In this work, we [...] Read more.

The DuFort–Frankel scheme was introduced in the 1950s to solve parabolic equations, and has been widely used ever since due to its stability and explicit nature. However, for over seven decades, its application has been limited to Cartesian grids. In this work, we propose a generalization of the DuFort–Frankel scheme that could be applied to arbitrary unstructured grids. Specifically, we focus on Voronoi grids in both 2D and 3D, and use the finite volume method for spatial discretization. Additionally, we present a proof of its stability based on the analysis of the spectrum of the amplification matrix, along with numerical examples. Full article

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

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29 pages, 2348 KB

Open AccessArticle

Advanced Dynamic Responses of Thick FGM Spherical Shells Analyzed Using TSDT Under Thermal Vibration

by Chih-Chiang Hong

Computation 2025, 13(10), 245; https://doi.org/10.3390/computation13100245 - 20 Oct 2025

The effect of third-order shear deformation theory (TSDT) on thick functionally graded material (FGM) spherical shells under sinusoidal thermal vibration is investigated by using the generalized differential quadrature (GDQ) numerical method. The TSDT displacement field and an advanced nonlinear shear correction coefficient are [...] Read more.

The effect of third-order shear deformation theory (TSDT) on thick functionally graded material (FGM) spherical shells under sinusoidal thermal vibration is investigated by using the generalized differential quadrature (GDQ) numerical method. The TSDT displacement field and an advanced nonlinear shear correction coefficient are used to derive the equations of motion for FGM spherical shells. The simple stiffness of FGM spherical shells under a temperature difference along the linear vs. z-axis direction is considered in the heat conduction equation. The dynamic GDQ discrete equations of motion subjected to thermal load and inertia terms can be expressed in matrix form. A parametric study of environmental temperature, FGM power-law index, and advanced nonlinear shear correction on thermal stress and displacement is conducted under the vibration frequency of a simply homogeneous equation and applied heat flux frequency. This is a novel method for obtaining the numerical GDQ results, comparing cases with linear and advanced nonlinear shear correction. The novelty of the present work is that an advanced varied-value type of shear correction coefficient can be successfully used in the thick-walled structure of FGM spherical shells subject to thermal vibration while considering the nonlinear term of TSDT displacements. The purpose of the present work is to investigate the numerical thermal vibration data for a two-material thick FGM spherical shell. Full article

(This article belongs to the Section Computational Engineering)

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14 pages, 2443 KB

Open AccessArticle

Numerical Study on Infrared Radiation Signatures of Debris During Projectile Impact Damage Process

by Wenqiang Gao, Teng Zhang and Qinglin Niu

Computation 2025, 13(10), 244; https://doi.org/10.3390/computation13100244 - 19 Oct 2025

High-speed impact is a critical mechanism for structural damage. The infrared signatures generated during fragment formation provide essential data for damage assessment, protective system design, and target identification. This study investigated an aluminum alloy blunt projectile penetrating a target plate by employing smoothed [...] Read more.

High-speed impact is a critical mechanism for structural damage. The infrared signatures generated during fragment formation provide essential data for damage assessment, protective system design, and target identification. This study investigated an aluminum alloy blunt projectile penetrating a target plate by employing smoothed particle hydrodynamics to simulate the debris ejection thermal and infrared properties. The infrared signatures of the debris clouds were calculated using Mie scattering theory under a spherical particle approximation. The reverse Monte Carlo methodology was applied to solve the radiative transfer equations and quantify the infrared emission characteristics. The infrared radiation characteristics of the debris cloud were investigated for projectile impact velocities of 800, 1000, and 1200 m/s. The results showed that the anterior debris regions reached peak temperatures of approximately 1200 K, with a temperature rise of 150–200 K per 200 m/s velocity increase behind the target. The medium-wave (3–5 μm) infrared intensity of the debris cloud was higher than the long-wave (8–12 μm) infrared intensity. The development of debris clouds enhanced the dispersion effect and slowed the increase in infrared radiation intensity in the same time interval. This study provides theoretical foundations for the dynamic infrared radiation characteristics of fragments generated by high-velocity projectile impacts. The infrared radiation characteristics within typical spectral bands can be utilized to assess hit probability and kill effectiveness. Full article

(This article belongs to the Section Computational Engineering)

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17 pages, 2401 KB

Open AccessArticle

Thermal Rectification in One-Dimensional Atomic Chains with Mass Asymmetry and Nonlinear Interactions

by Arseny M. Kazakov, Elvir Z. Karimov, Galiia F. Korznikova and Elena A. Korznikova

Computation 2025, 13(10), 243; https://doi.org/10.3390/computation13100243 - 17 Oct 2025

Understanding and controlling thermal rectification is pivotal for designing phononic devices that guide heat flow in a preferential direction. This study investigates one-dimensional atomic chains with binary mass asymmetry and nonlinear interatomic potentials, focusing on how energy propagates under thermal and wave excitation. [...] Read more.

Understanding and controlling thermal rectification is pivotal for designing phononic devices that guide heat flow in a preferential direction. This study investigates one-dimensional atomic chains with binary mass asymmetry and nonlinear interatomic potentials, focusing on how energy propagates under thermal and wave excitation. Two potential models—the β-FPU and Morse potentials—were employed to examine the role of nonlinearity and bond softness in energy transport. Simulations reveal strong directional energy transport governed by the interplay of mass distribution, nonlinearity, and excitation type. In FPU chains, pronounced rectification occurs: under “cold-heavy” conditions, energy in the left segment increases from ~1% to over 63%, while reverse (“hot-heavy”) cases show less than 4% net transfer. For wave-driven excitation, the rectification coefficient reaches ~0.58 at 100:1. In contrast, Morse-based systems exhibit weaker rectification (∆E < 1%) and structural instabilities at high asymmetry due to bond breaking. A comprehensive summary and heatmap visualization highlight how system parameters govern rectification efficiency. These findings provide mechanistic insights into nonreciprocal energy transport in nonlinear lattices and offer design principles for nanoscale thermal management strategies based on controlled asymmetry and potential engineering. Full article

(This article belongs to the Section Computational Chemistry)

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26 pages, 16140 KB

Open AccessArticle

A Multiphysics Framework for Fatigue Life Prediction and Optimization of Rocker Arm Gears in a Large-Mining-Height Shearer

by Chunxiang Shi, Xiangkun Song, Weipeng Xu, Ying Tian, Jinchuan Zhang, Xiangwei Dong and Qiang Zhang

Computation 2025, 13(10), 242; https://doi.org/10.3390/computation13100242 - 15 Oct 2025

This study investigates premature fatigue failure in rocker arm gears of large-mining-height shearers operating at alternating ±45° working angles, where insufficient lubrication generates non-uniform thermal -stress fields. In this study, an integrated multiphysics framework combining transient thermal–fluid–structure coupling simulations with fatigue life prediction [...] Read more.

This study investigates premature fatigue failure in rocker arm gears of large-mining-height shearers operating at alternating ±45° working angles, where insufficient lubrication generates non-uniform thermal -stress fields. In this study, an integrated multiphysics framework combining transient thermal–fluid–structure coupling simulations with fatigue life prediction is proposed. Transient thermo-mechanical coupling analysis simulated dry friction conditions, capturing temperature and stress fields under varying speeds. Fluid–thermal–solid coupling analysis modeled wet lubrication scenarios, incorporating multiphase flow to track oil distribution, and calculated convective heat transfer coefficients at different immersion depths (25%, 50%, 75%). These coupled simulations provided the critical time-varying temperature and thermal stress distributions acting on the gears (Z6 and Z7). Subsequently, these simulated thermo-mechanical loads were directly imported into ANSYS 2024R1 nCode DesignLife to perform fatigue life prediction. Simulations demonstrate that dry friction induces extreme operating conditions, with Z6 gear temperatures reaching over 800 °C and thermal stresses peaking at 803.86 MPa under 900 rpm, both escalating linearly with rotational speed. Lubrication depth critically regulates heat dissipation, where 50% oil immersion optimizes convective heat transfer at 8880 W/m²·K for Z6 and 11,300 W/m²·K for Z7, while 25% immersion exacerbates thermal gradients. Fatigue life exhibits an inverse relationship with speed but improves significantly with cooling. Z6 sustains a lower lifespan, exemplified by 25+ days at 900 rpm without cooling versus 50+ days for Z7, attributable to higher stress concentrations. Based on the multiphysics analysis results, two physics-informed engineering optimizations are proposed to reduce thermal stress and extend gear fatigue life: a staged cooling system using spiral copper tubes and an intelligent lubrication strategy with gear-pump-driven dynamic oil supply and thermal feedback control. These strategies collectively enhance gear longevity, validated via multiphysics-driven topology optimization. Full article

(This article belongs to the Section Computational Engineering)

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21 pages, 604 KB

Open AccessArticle

A Fourth-Order Parametric Iterative Approach for Solving Systems of Nonlinear Equations

by Sonia Bhalla, Gurjeet Singh, Higinio Ramos, Ramandeep Behl and Hashim Alshehri

Computation 2025, 13(10), 241; https://doi.org/10.3390/computation13100241 - 14 Oct 2025

In this paper, we present a novel one-parameter family with fourth-order convergence to solve nonlinear systems, along with its convergence analysis. Several numerical experiments including a Bratu problem, a mixed Hammerstein integral equation, and nonlinear optimization problems (namely, the Broyden banded function and [...] Read more.

In this paper, we present a novel one-parameter family with fourth-order convergence to solve nonlinear systems, along with its convergence analysis. Several numerical experiments including a Bratu problem, a mixed Hammerstein integral equation, and nonlinear optimization problems (namely, the Broyden banded function and the Broyden tridiagonal function) as well as applications of differential equations, are analyzed using the proposed schemes to demonstrate their effectiveness. The results indicate that these methods produce more accurate approximations and exhibit greater efficiency compared to existing approaches. Full article

(This article belongs to the Section Computational Engineering)

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18 pages, 1475 KB

Open AccessArticle

Sentiment Analysis of Tourist Reviews About Kazakhstan Using a Hybrid Stacking Ensemble Approach

by Aslanbek Murzakhmetov, Maxatbek Satymbekov, Arseniy Bapanov and Nurbol Beisov

Computation 2025, 13(10), 240; https://doi.org/10.3390/computation13100240 - 13 Oct 2025

Tourist reviews provide essential insights into travellers experiences and public perceptions of destinations. In Kazakhstan, however, sentiment analysis, particularly using ensemble learning, remains underexplored for evaluating such reviews. This study proposes a hybrid stacking ensemble for sentiment analysis of English-language tourist reviews about [...] Read more.

Tourist reviews provide essential insights into travellers experiences and public perceptions of destinations. In Kazakhstan, however, sentiment analysis, particularly using ensemble learning, remains underexplored for evaluating such reviews. This study proposes a hybrid stacking ensemble for sentiment analysis of English-language tourist reviews about Kazakhstan, integrating four complementary approaches: VADER, TextBlob, Stanza, and Local Context Focus Mechanism with Bidirectional Encoder Representations from Transformers (LCF-BERT). Each model contributes distinct analytical capabilities, including lexicon-based polarity detection, rule-based subjectivity evaluation, generalised star-rating estimation, and contextual aspect-oriented sentiment classification. The evaluation utilised a cleaned dataset of 11,454 TripAdvisor reviews collected between February 2022 and June 2025. The ensemble aggregates model outputs through majority and weighted voting strategies to enhance robustness. Experimental results (accuracy 0.891, precision 0.838, recall 0.891, and F1-score 0.852) demonstrate that the proposed method KazSATR outperforms individual models in overall classification accuracy and exhibits superior capacity for aspect-level sentiment detection. These findings underscore the potential of the hybrid ensemble as a practical and scalable tool for the tourism sector in Kazakhstan. By leveraging multiple analytical paradigms, the model enables tourism professionals and policymakers to better understand traveller preferences, identify service strengths and weaknesses, and inform strategic decision-making. The proposed approach contributes to advancing sentiment analysis applications in tourism research, particularly in underrepresented geographic contexts. Full article

(This article belongs to the Section Computational Social Science)

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18 pages, 8879 KB

Open AccessArticle

Energy-Conscious Lightweight LiDAR SLAM with 2D Range Projection and Multi-Stage Outlier Filtering for Intelligent Driving

by Chun Wei, Tianjing Li and Xuemin Hu

Computation 2025, 13(10), 239; https://doi.org/10.3390/computation13100239 - 10 Oct 2025

To meet the increasing demands of energy efficiency and real-time performance in autonomous driving systems, this paper presents a lightweight and robust LiDAR SLAM framework designed with power-aware considerations. The proposed system introduces three core innovations. First, it replaces traditional ordered point cloud [...] Read more.

To meet the increasing demands of energy efficiency and real-time performance in autonomous driving systems, this paper presents a lightweight and robust LiDAR SLAM framework designed with power-aware considerations. The proposed system introduces three core innovations. First, it replaces traditional ordered point cloud indexing with a 2D range image projection, significantly reducing memory usage and enabling efficient feature extraction with curvature-based criteria. Second, a multi-stage outlier rejection mechanism is employed to enhance feature robustness by adaptively filtering occluded and noisy points. Third, we propose a dynamically filtered local mapping strategy that adjusts keyframe density in real time, ensuring geometric constraint sufficiency while minimizing redundant computation. These components collectively contribute to a SLAM system that achieves high localization accuracy with reduced computational load and energy consumption. Experimental results on representative autonomous driving datasets demonstrate that our method outperforms existing approaches in both efficiency and robustness, making it well-suited for deployment in low-power and real-time scenarios within intelligent transportation systems. Full article

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

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17 pages, 1112 KB

Open AccessArticle

Management of Severe COVID-19 Diagnosis Using Machine Learning

by Larysa Sydorchuk, Maksym Sokolenko, Miroslav Škoda, Daniel Lajcin, Yaroslav Vyklyuk, Ruslan Sydorchuk, Alina Sokolenko and Dmytro Martjanov

Computation 2025, 13(10), 238; https://doi.org/10.3390/computation13100238 - 9 Oct 2025

COVID-19 remains a global health challenge, with severe cases often leading to complications and fatalities. The objective of this study was to assess supervised machine learning algorithms for predicting severe COVID-19 based on demographic, clinical, biochemical, and genetic variables, with the aim of [...] Read more.

COVID-19 remains a global health challenge, with severe cases often leading to complications and fatalities. The objective of this study was to assess supervised machine learning algorithms for predicting severe COVID-19 based on demographic, clinical, biochemical, and genetic variables, with the aim of identifying the most informative prognostic markers. For Machine Learning (ML) analysis, we utilized a dataset comprising 226 observations with 68 clinical, biochemical, and genetic features collected from 226 patients with confirmed COVID-19 (54—moderate, 142—severe and 30 with mild disease). The target variable was disease severity (mild, moderate, severe). The feature set included demographic variables (age, sex), genetic markers (single-nucleotide polymorphisms (SNPs) in FGB (rs1800790), NOS3 (rs2070744), and TMPRSS2 (rs12329760)), biochemical indicators (IL-6, endothelin-1, D-dimer, fibrinogen, among others), and clinical parameters (blood pressure, body mass index, comorbidities). The target variable was disease severity. To identify the most effective predictive models for COVID-19 severity, we systematically evaluated multiple supervised learning algorithms, including logistic regression, k-nearest neighbors, decision trees, random forest, gradient boosting, bagging, naïve Bayes, and support vector machines. Model performance was assessed using accuracy and the area under the receiver operating characteristic curve (AUC-ROC). Among the predictors, IL-6, presence of depression/pneumonia, LDL cholesterol, AST, platelet count, lymphocyte count, and ALT showed the strongest correlations with severity. The highest predictive accuracy, with negligible error rates, was achieved by ensemble-based models such as ExtraTreesClassifier, HistGradientBoostingClassifier, BaggingClassifier, and GradientBoostingClassifier. Notably, decision tree models demonstrated high classification precision at terminal nodes, many of which yielded a 100% probability for a specific severity class. Full article

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

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16 pages, 1750 KB

Open AccessArticle

Rigorous Asymptotic Perturbation Bounds for Hermitian Matrix Eigendecompositions

by Mihail Konstantinov and Petko Hristov Petkov

Computation 2025, 13(10), 237; https://doi.org/10.3390/computation13100237 - 7 Oct 2025

In this paper, we present rigorous asymptotic componentwise perturbation bounds for regular Hermitian indefinite matrix eigendecompositions, obtained via the method of splitting operators. The asymptotic bounds are derived from exact nonlinear expressions for the perturbations and allow each entry of every matrix eigenvector [...] Read more.

In this paper, we present rigorous asymptotic componentwise perturbation bounds for regular Hermitian indefinite matrix eigendecompositions, obtained via the method of splitting operators. The asymptotic bounds are derived from exact nonlinear expressions for the perturbations and allow each entry of every matrix eigenvector to be bounded in the case of distinct eigenvalues. In contrast to the perturbation analysis of the Schur form of a nonsymmetric matrix, the bounds obtained here do not rely on the Kronecker product, which significantly reduces both memory requirements and computational cost. This enables efficient sensitivity analysis of high-order problems. The eigenvector perturbation bounds are further applied to estimate the angles between perturbed and unperturbed one-dimensional invariant subspaces spanned by the corresponding eigenvectors. To reduce conservatism in the case of high-order problems, we propose the use of probabilistic perturbation bounds based on the Markov inequality. The analysis is illustrated by two numerical experiments of order 5000. Full article

(This article belongs to the Section Computational Engineering)

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18 pages, 7182 KB

Open AccessArticle

Mechanical Evaluation of Topologically Optimized Shin Pads with Advanced Composite Materials: Assessment of the Impact Properties Utilizing Finite Element Analysis

by Ioannis Filippos Kyriakidis, Nikolaos Kladovasilakis, Eleftheria Maria Pechlivani and Konstantinos Tsongas

Computation 2025, 13(10), 236; https://doi.org/10.3390/computation13100236 - 5 Oct 2025

In this paper, the evaluation of the mechanical performance of novel, designed topologically optimized shin pads with advanced materials will be conducted with the aid of Finite Element Analysis (FEA) to assess the endurance of the final structure on impact phenomena extracted from [...] Read more.

In this paper, the evaluation of the mechanical performance of novel, designed topologically optimized shin pads with advanced materials will be conducted with the aid of Finite Element Analysis (FEA) to assess the endurance of the final structure on impact phenomena extracted from actual real-life data acquired from contact sports. The main focus of the developed prototype is to have high-enough energy absorption capabilities and vibration isolation properties, crucial for the development of trustworthy protective equipment. The insertion of advanced materials with controlled weight fractions and lattice geometries aims to strategically improve those properties and provide tailored characteristics similar to the actual human skeleton. The final design is expected to be used as standalone protective equipment for athletes or as a protective shield for the development of human lower limb prosthetics. In this context, computational investigation of the dynamic mechanical response was conducted by replicating a real-life phenomenon of the impact during a contact sport in a median condition of a stud kick impact and an extreme case scenario to assess the dynamic response under shock-absorption conditions and the final design’s structural integrity by taking into consideration the injury prevention capabilities. The results demonstrate that the proposed lattice geometries positively influence the injury prevention capabilities by converting a severe injury to light one, especially in the gyroid structure where the prototype presented a unified pattern of stress distribution and a higher reduction in the transmitted force. The incorporation of the PA-12 matrix reinforced with the reused ground tire rubber results in a structure with high enough overall strength and crucial modifications on the absorption and damping capabilities vital for the integrity under dynamic conditions. Full article

(This article belongs to the Special Issue Advanced Topology Optimization: Methods and Applications)

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34 pages, 6219 KB

Open AccessArticle

Decision-Making and Data Sharing in Smart Catering: An Evolutionary Game Approach

by Jiping Xu, Shuaishuai Cao, Zhaoyang Wang, Chongchong Yu and Minzhang Zheng

Computation 2025, 13(10), 235; https://doi.org/10.3390/computation13100235 - 5 Oct 2025

With the rapid advancement of the Internet and big data, data sharing has become pivotal for enhancing operational efficiency and user experience across industries. In the restaurant sector, the emergence of smart kitchens has accelerated digital transformation, underscoring the critical importance of data [...] Read more.

With the rapid advancement of the Internet and big data, data sharing has become pivotal for enhancing operational efficiency and user experience across industries. In the restaurant sector, the emergence of smart kitchens has accelerated digital transformation, underscoring the critical importance of data sharing. In this study, we investigate the evolutionary dynamics among four key stakeholders in the smart kitchen ecosystem: data providers, data-sharing platforms, data consumers, and regulators. We develop a four-party evolutionary game model to analyze the strategic interactions and behavioral evolution of each participant, applying replicator dynamics and Lyapunov stability theory. Our findings reveal that (1) data providers’ willingness to supply high-quality data is strongly influenced by platform incentives; (2) platforms’ adoption of data governance mechanisms depends on associated governance costs; (3) regulatory subsidies contribute significantly to system stability; and (4) increased financial support for regulators promotes favorable system evolution. This work offers both theoretical insights and practical guidance for data sharing in smart kitchens, providing a novel perspective on digital transformation within the restaurant industry. Full article

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

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25 pages, 6100 KB

Open AccessArticle

UAV Image Denoising and Its Impact on Performance of Object Localization and Classification in UAV Images

by Rostyslav Tsekhmystro, Vladimir Lukin and Dmytro Krytskyi

Computation 2025, 13(10), 234; https://doi.org/10.3390/computation13100234 - 3 Oct 2025

Unmanned aerial vehicles (UAVs) have become a tool for solving numerous practical tasks. UAV sensors provide images and videos for on-line or off-line data processing for object localization, classification, and tracking due to the use of trained convolutional neural networks (CNNs) and artificial [...] Read more.

Unmanned aerial vehicles (UAVs) have become a tool for solving numerous practical tasks. UAV sensors provide images and videos for on-line or off-line data processing for object localization, classification, and tracking due to the use of trained convolutional neural networks (CNNs) and artificial intelligence. However, quality of images acquired by UAV-based sensors is not always perfect due to many factors. One of them could be noise arising because of several reasons. Its presence, especially if noise is intensive, can make significantly worse the performance characteristics of CNN-based techniques of object localization and classification. We analyze such degradation for a set of eleven modern CNNs for additive white Gaussian noise model and study when (for what noise intensity and for what CNN) the performance reduction becomes essential and, thus, special means to improve it become desired. Representatives of two most popular families, namely the block matching 3-dimensional (BM3D) filter and DRUNet denoiser, are employed to enhance images under condition of a priori known noise properties. It is shown that, due to preliminary denoising, the CNN performance characteristics can be significantly improved up to almost the same level as for the noise-free images without CNN retraining. Performance is analyzed using several criteria typical for image denoising, object localization and classification. Examples of object localization and classification are presented demonstrating possible object missing due to noise. Computational efficiency is also taken into account. Using a large set of test data, it is demonstrated that: (1) the best results are usually provided for SSD Mobilenet V2 and VGG16 networks; (2) the performance characteristics for cases of applying BM3D filter and DRUNet denoiser are similar but the use of DRUNet is preferable since it provides slightly better results. Full article

(This article belongs to the Section Computational Engineering)

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32 pages, 4520 KB

Open AccessArticle

Beyond the Gold Standard: Linear Regression and Poisson GLM Yield Identical Mortality Trends and Deaths Counts for COVID-19 in Italy: 2021–2025

by Marco Roccetti and Giuseppe Cacciapuoti

Computation 2025, 13(10), 233; https://doi.org/10.3390/computation13100233 - 3 Oct 2025

While it is undisputed that Poisson GLMs represent the gold standard for counting COVID-19 deaths, recent studies have analyzed the seasonal growth and decline trends of these deaths in Italy using a simple segmented linear regression. They found that, despite an overall decreasing [...] Read more.

While it is undisputed that Poisson GLMs represent the gold standard for counting COVID-19 deaths, recent studies have analyzed the seasonal growth and decline trends of these deaths in Italy using a simple segmented linear regression. They found that, despite an overall decreasing trend throughout the entire period analyzed (2021–2025), rising mortality trends from COVID-19 emerged in all summers and winters of the period, though they were more pronounced in winter. The technical reasons for the general unsuitability of using linear regression for the precise counting of deaths are well-known. Nevertheless, the question remains whether, under certain circumstances, the use of linear regression can provide a valid and useful tool in a specific context, for example, to highlight the slopes of seasonal growth/decline in deaths more quickly and clearly. Given this background, this paper presents a comparison between the use of linear regression and a Poisson GLM with the aforementioned death data, leading to the following conclusions. Appropriate statistical hypothesis testing procedures have demonstrated that the conditions of a normal distribution of residuals, their homoscedasticity, and the lack of autocorrelation were essentially guaranteed in this particular Italian case (weekly COVID-19 deaths in Italy, from 2021 to 2025) with very rare exceptions, thus ensuring the acceptable performance of linear regression. Furthermore, the development of a Poisson GLM definitively confirmed a strong agreement between the two models in identifying COVID-19 mortality trends. This was supported by a Kolmogorov–Smirnov test, which found no statistically significant difference between the slopes calculated by the two models. Both the Poisson and the linear model also demonstrated a comparably high accuracy in counting COVID-19 deaths, with MAE values of 62.76 and a comparable 88.60, respectively. Based on an average of approximately 6300 deaths per period, this translated to a percentage error of just 1.15% for the Poisson and only a slightly higher 1.48% for the linear model. Full article

(This article belongs to the Section Computational Biology)

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21 pages, 2260 KB

Open AccessArticle

Computation of the Radius of Curvature R in Any Avian Egg and Identification of the Location of Potential Load Application That Forms Its Unique Asymmetric Shape: A Theoretical Hypothesis

by Valeriy G. Narushin, Michael N. Romanov and Darren K. Griffin

Computation 2025, 13(10), 232; https://doi.org/10.3390/computation13100232 - 1 Oct 2025

In avian biology, the radius of curvature, or R, has hardly ever been used to study the mechanics of birds’ egg shape formation. However, it is essential for introducing important details about the form, function, and performance of an object, which is [...] Read more.

In avian biology, the radius of curvature, or R, has hardly ever been used to study the mechanics of birds’ egg shape formation. However, it is essential for introducing important details about the form, function, and performance of an object, which is useful in biomedicine, manufacturing, and precision design. In order to determine a possible biological mechanism and the location of load application that creates the distinctive asymmetric egg shape in nature, the goal of this study was to develop a formula for computing R at any point over an egg contour. We derived a relatively simple means of computing R and identified the location that muscular compression is carried out to give the egg its characteristic form. This location (x/L), the angle (α) of compression and the relative magnitude of the load proportional to R can help identify a specific section of the oviduct and the squeezing muscle involved. Novel equations for computing R, x/L and α were proposed, based on standard geometric parameters. Our findings demonstrate how the theoretical knowledge of physical, mechanical and mathematical processes can contribute to the solution of biological problems and resonates with the fields of egg-inspired engineering. Full article

(This article belongs to the Section Computational Biology)

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19 pages, 1415 KB

Open AccessArticle

An Energy Saving MTPA-Based Model Predictive Control Strategy for PMSM in Electric Vehicles Under Variable Load Conditions

by Lihua Gao, Xiaodong Lv, Kai Ma and Zhihan Shi

Computation 2025, 13(10), 231; https://doi.org/10.3390/computation13100231 - 1 Oct 2025

To promote energy efficiency and support sustainable electric transportation, this study addresses the challenge of real-time and energy-optimal control of permanent magnet synchronous motors (PMSMs) in electric vehicles operating under variable load conditions, proposing a novel Laguerre-based model predictive control (MPC) strategy integrated [...] Read more.

To promote energy efficiency and support sustainable electric transportation, this study addresses the challenge of real-time and energy-optimal control of permanent magnet synchronous motors (PMSMs) in electric vehicles operating under variable load conditions, proposing a novel Laguerre-based model predictive control (MPC) strategy integrated with maximum torque per ampere (MTPA) operation. Traditional MPC methods often suffer from limited prediction horizons and high computational burden when handling strong coupling and time-varying loads, compromising real-time performance. To overcome these limitations, a Laguerre function approximation is employed to model the dynamic evolution of control increments using a set of orthogonal basis functions, effectively reducing the control dimensionality while accelerating convergence. Furthermore, to enhance energy efficiency, the MTPA strategy is embedded by reformulating the current allocation process using d- and q-axis current variables and deriving equivalent reference currents to simplify the optimization structure. A cost function is designed to simultaneously ensure current accuracy and achieve maximum torque per unit current. Simulation results under typical electric vehicle conditions demonstrate that the proposed Laguerre-MTPA MPC controller significantly improves steady-state performance, reduces energy consumption, and ensures faster response to load disturbances compared to traditional MTPA-based control schemes. This work provides a practical and scalable control framework for energy-saving applications in sustainable electric transportation systems. Full article

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

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31 pages, 3644 KB

Open AccessArticle

Machine Learning for Basketball Game Outcomes: NBA and WNBA Leagues

by João M. Alves and Ramiro S. Barbosa

Computation 2025, 13(10), 230; https://doi.org/10.3390/computation13100230 - 1 Oct 2025

Artificial intelligence has become crucial in sports, leveraging its analytical capabilities to enhance the understanding and prediction of complex events. Machine learning algorithms in sports, especially basketball, are transforming performance analysis by identifying patterns and trends invisible to traditional methods. This technology provides [...] Read more.

Artificial intelligence has become crucial in sports, leveraging its analytical capabilities to enhance the understanding and prediction of complex events. Machine learning algorithms in sports, especially basketball, are transforming performance analysis by identifying patterns and trends invisible to traditional methods. This technology provides in-depth insights into individual and team performance, enabling precise evaluation of strategies and tactics. Consequently, the detailed analysis of every aspect of a team’s routine can significantly elevate the level of competition in the sport. This study investigates a range of machine learning models, including Logistic Regression (LR), Ridge Regression Classifier (RR), Random Forest (RF), Naive Bayes (NB), K-Nearest Neighbors (KNNs), Support Vector Machine (SVM), Stacking Classifier (STACK), Bagging Classifier (BAG), Multi-Layer Perceptron (MLP), AdaBoost (AB), and XGBoost (XGB), as well as deep learning architectures such as Long Short-Term Memory (LSTM) networks and Convolutional Neural Networks (CNNs), to compare their effectiveness in predicting game outcomes in the NBA and WNBA leagues. The results show highly acceptable prediction accuracies of 65.50% for the NBA and 67.48% for the WNBA. This study allows us to understand the impact that artificial intelligence can have on the world of basketball and its current state in relation to previous studies. It can provide valuable insights for coaches, performance analysts, team managers, and sports strategists by using machine learning and deep learning models to predict NBA and WNBA outcomes, enabling informed decisions and enhancing competitive performance. Full article

(This article belongs to the Section Computational Engineering)

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24 pages, 6313 KB

Open AccessArticle

Research on the Internal Force Solution for Statically Indeterminate Structures Under a Local Trapezoidal Load

by Pengyun Wei, Shunjun Hong, Lin Li, Junhong Hu and Haizhong Man

Computation 2025, 13(10), 229; https://doi.org/10.3390/computation13100229 - 1 Oct 2025

The calculation of internal forces is a critical aspect in the design of statically indeterminate structures. Local trapezoidal loads, as a common loading configuration in practical engineering (e.g., earth pressure, uneven surcharge), make it essential to investigate how to compute the internal forces [...] Read more.

The calculation of internal forces is a critical aspect in the design of statically indeterminate structures. Local trapezoidal loads, as a common loading configuration in practical engineering (e.g., earth pressure, uneven surcharge), make it essential to investigate how to compute the internal forces of statically indeterminate structures under such loads by using the displacement method. The key to displacement-based analysis lies in deriving the fixed-end moment formulas for local trapezoidal loads. Traditional methods, such as the force method, virtual beam method, or integral method, often involve complex computations. Therefore, this study aims to derive a general formula for fixed-end moments in statically indeterminate beams subjected to local trapezoidal loads by using the integral method, providing a more efficient and clear theoretical tool for engineering practice while addressing the limitations of existing educational and applied methodologies. The integral method is employed to derive fixed-end moment expressions for three types of statically indeterminate beams: (1) a beam fixed at both ends, (2) an an-end-fixed another-end-simple-support beam, and (3) a beam fixed at one end and sliding at the other. This approach eliminates the redundant equations of the traditional force method or the indirect transformations of the virtual beam method, directly linking boundary conditions through integral operations on load distributions, thereby significantly simplifying the solving process. Three representative numerical examples validate the correctness and universality of the derived formulas. The results demonstrate that the solutions obtained via the integral method align with software-calculated results, yet the proposed method yields analytical expressions for structural internal forces. Comparative analysis shows that the integral method surpasses traditional approaches (e.g., force method, virtual beam method) in terms of conceptual clarity and computational efficiency, making it particularly suitable for instructional demonstrations and rapid engineering calculations. The proposed integral method provides a systematic analytical framework for the internal force analysis of statically indeterminate structures under local trapezoidal loads, combining mathematical rigor with engineering practicality. The derived formulas can be directly applied to real-world designs, substantially reducing computational complexity. Moreover, this method offers a more intuitive theoretical case for structural mechanics education, enhancing students’ understanding of the mathematical–mechanical relationship between loads and internal forces. The research outcomes hold both theoretical significance and practical engineering value, establishing a solving paradigm for the displacement-based analysis of statically indeterminate structures under complex local trapezoidal loading conditions. Full article

(This article belongs to the Section Computational Engineering)

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25 pages, 20183 KB

Open AccessArticle

Dual Adaptive Neural Network for Solving Free-Flow Coupled Porous Media Models Under Unique Continuation Problem

by Kunhao Liu and Jibing Wu

Computation 2025, 13(10), 228; https://doi.org/10.3390/computation13100228 - 1 Oct 2025

The core challenge of the Unique Continuity (UC) problem lies in inferring solutions across an entire domain using limited observational data, holding significant practical implications for multiphysics coupled models. Recently, physics-informed neural networks (PINNs) have shown considerable promise in addressing the UC problem. [...] Read more.

The core challenge of the Unique Continuity (UC) problem lies in inferring solutions across an entire domain using limited observational data, holding significant practical implications for multiphysics coupled models. Recently, physics-informed neural networks (PINNs) have shown considerable promise in addressing the UC problem. However, the reliance on a fixed activation function and a fixed weighted loss function prevents PINNs from adequately representing the multiphysics characteristics embedded in coupled models. To overcome these limitations, we propose a novel dual adaptive neural network (DANN) algorithm. This approach integrates trainable adaptive activation functions and an adaptively weighted loss scheme, enabling the network to dynamically balance the observational data and governing physics. Our method is applicable not only to the UC problem but also to general forward problems governed by partial differential equations. Furthermore, we provide a theoretical foundation for the algorithm by deriving a generalization error estimate, discussing the potential causes of neural networks solving this problem. Extensive numerical experiments including 3D demonstrate the superior accuracy and effectiveness of the proposed DANN framework in solving the UC problem compared to standard PINNs. Full article

(This article belongs to the Section Computational Engineering)

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