Computation

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

Abstract

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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39 pages, 3397 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

Abstract

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)

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

Abstract

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

Abstract

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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Graphical abstract

23 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

Abstract

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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Graphical abstract

21 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

Abstract

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

Abstract

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

Abstract

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

Abstract

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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23 pages, 3141 KB

Open AccessArticle

Machine Learning-Assisted Cryptographic Security: A Novel ECC-ANN Framework for MQTT-Based IoT Device Communication

by Kalimu Karimunda, Jean de Dieu Marcel Ufitikirezi, Roman Bumbálek, Tomáš Zoubek, Petr Bartoš, Radim Kuneš, Sandra Nicole Umurungi, Anozie Chukwunyere, Mutagisha Norbelt and Gao Bo

Computation 2025, 13(10), 227; https://doi.org/10.3390/computation13100227 - 26 Sep 2025

Abstract

The Internet of Things (IoT) has surfaced as a revolutionary technology, enabling ubiquitous connectivity between devices and revolutionizing traditional lifestyles through smart automation. As IoT systems proliferate, securing device-to-device communication and server–client data exchange has become crucial. This paper presents a novel security [...] Read more.

The Internet of Things (IoT) has surfaced as a revolutionary technology, enabling ubiquitous connectivity between devices and revolutionizing traditional lifestyles through smart automation. As IoT systems proliferate, securing device-to-device communication and server–client data exchange has become crucial. This paper presents a novel security framework that integrates elliptic curve cryptography (ECC) with artificial neural networks (ANNs) to enhance the Message Queuing Telemetry Transport (MQTT) protocol. Our study evaluated multiple machine learning algorithms, with ANN demonstrating superior performance in anomaly detection and classification. The hybrid approach not only encrypts communications but also employs the optimized ANN model to detect and classify anomalous traffic patterns. The proposed model demonstrates robust security features, successfully identifying and categorizing various attack types with 90.38% accuracy while maintaining message confidentiality through ECC encryption. Notably, this framework retains the lightweight characteristics essential for IoT devices, making it especially relevant for environments where resources are constrained. To our knowledge, this represents the first implementation of an integrated ECC-ANN approach for securing MQTT-based IoT communications, offering a promising solution for next-generation IoT security requirements. Full article

(This article belongs to the Section Computational Engineering)

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11 pages, 256 KB

Open AccessArticle

Equations of Motion and Navier–Stokes Equations

by Dušan J. Simjanović, Ivana Djurišić, Aleksandra Penjišević, Marko Stefanović and Branislav M. Randjelović

Computation 2025, 13(9), 226; https://doi.org/10.3390/computation13090226 - 19 Sep 2025

Abstract

In this research, we present the analogies between variational calculations in cosmology and in classical mechanics. Our approach is based on the invariants for transformations of affine connections defined on N-dimensional manifolds (special cases are the 8-dimensional, 5-dimensional, and 4-dimensional manifolds used [...] Read more.

In this research, we present the analogies between variational calculations in cosmology and in classical mechanics. Our approach is based on the invariants for transformations of affine connections defined on N-dimensional manifolds (special cases are the 8-dimensional, 5-dimensional, and 4-dimensional manifolds used in cosmology and 2-dimensional manifolds used in classical mechanics). Any of these transformations represents a class of curves on initial manifolds, which transmits to an another class of curves on the current manifolds. The main results of this paper are general equations of motion, which are obtained from the invariants caused by the transformation rule of an initial affine connection to the current one and the corresponding Navier–Stokes equations, recognized in transformations of curves along which moves a fluid particle. Full article

(This article belongs to the Section Computational Engineering)

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Graphical abstract

15 pages, 4689 KB

Open AccessArticle

Mining Scraper Conveyors Chain Drive System Lightweight Design: Based on DEM and Topology Optimization

by Qiang Zhang, Wei Liu, Anhao Jia, Shouji Sun, Xin Li and Xiangjun Song

Computation 2025, 13(9), 225; https://doi.org/10.3390/computation13090225 - 17 Sep 2025

Abstract

For the issue of excessive mass in the chain drive system of long-distance scraper conveyors, this paper proposes a method to optimize the scraper chains by integrating discrete element simulation (DEM) with topological optimization. The aim is to reduce the system’s mass while [...] Read more.

For the issue of excessive mass in the chain drive system of long-distance scraper conveyors, this paper proposes a method to optimize the scraper chains by integrating discrete element simulation (DEM) with topological optimization. The aim is to reduce the system’s mass while maintaining its transportation capacity and structural integrity. The SGZ1000 model scraper conveyor with a length of 400 m was selected as the research object. Studies have demonstrated that for 56 × 187 mm scraper chains, a non-equally spaced configuration (6p-8p-6p, where p represents the chain link pitch) outperforms an equally spaced configuration (6p). While ensuring the conveying capacity of the scraper chains, the optimized configuration reduces the number of scrapers in chains of equal length by 11.11%. For a 400 m scraper conveyor, adopting the 6p-8p-6p scraper spacing reduces the number of scrapers by 72 and decreases the mass by 6357.6 kg. Additionally, utilizing topologically optimized scrapers further reduces the total mass by 10,131.4 kg. Compared to the original chain drive system, the optimized scraper chains have reduced the mass by 26.2%, significantly lowering the no-load energy consumption of the long-distance scraper conveyor. Full article

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

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7 pages, 178 KB

Open AccessEditorial

Computational Methods in Structural Engineering: Current Advances and Future Perspectives

by Vagelis Plevris, Manolis Georgioudakis and Mahdi Kioumarsi

Computation 2025, 13(9), 224; https://doi.org/10.3390/computation13090224 - 16 Sep 2025

Abstract

This brief editorial introduces the Special Issue “Computational Methods in Structural Engineering”. This Special Issue brings together recent advances in computational approaches—including finite element modeling, machine learning applications, stochastic analysis, and high-precision numerical methods— highlighting their increasing influence on the analysis, design, and [...] Read more.

This brief editorial introduces the Special Issue “Computational Methods in Structural Engineering”. This Special Issue brings together recent advances in computational approaches—including finite element modeling, machine learning applications, stochastic analysis, and high-precision numerical methods— highlighting their increasing influence on the analysis, design, and assessment of modern structural systems. The published contributions cover topics such as the nonlinear finite element method (FEM) for structural response under extreme loading, advanced plate and composite modeling, explainable AI for material characterization, machine learning for predictive performance modeling, data-driven signal processing for structural health monitoring, and stochastic analysis of dynamic inputs. Through this collection of studies, this Special Issue underscores both the opportunities and the challenges of applying advanced computational methods to enhance the resilience, efficiency, and understanding of structural engineering systems. Full article

(This article belongs to the Special Issue Computational Methods in Structural Engineering)

19 pages, 4586 KB

Open AccessArticle

Heat Losses in the Exhaust Manifold of a 4-Stoke DI Diesel Engine Subjected to Pulsating Flow

by Grigorios Spyrounakos and Georgios Mavropoulos

Computation 2025, 13(9), 223; https://doi.org/10.3390/computation13090223 - 15 Sep 2025

Abstract

This paper presents a study aiming to provide insight into the complex flow and heat transfer processes in the exhaust manifold of a four-stroke, compression ignition engine. An experimental system has been constructed capable of capturing temperature and heat flux high-frequency signals as [...] Read more.

This paper presents a study aiming to provide insight into the complex flow and heat transfer processes in the exhaust manifold of a four-stroke, compression ignition engine. An experimental system has been constructed capable of capturing temperature and heat flux high-frequency signals as they develop in the exhaust pipe wall during the engine cycle, under its steady-state operation. The values of the Heat Transfer Coefficient obtained by applying the classic convection relations have been correlated in the form of a Nusselt–Reynolds number relationship for local and spatially averaged steady-state heat transfer and compared with available experimental data obtained at the same position of the exhaust manifold. It has been shown that the use of conventional steady-state heat transfer relationships for fully developed steady-state turbulent flow in pipes underpredicts heat transfer rates when compared with those experimentally observed. Periodic flow of high frequency and geometrical effects at the exhaust entrance are expected to affect the validity of the application of the classic steady-state correlations for the exhaust manifold. To overcome this problem it is developed and presented a new correlation for the time-averaged heat transfer rates. To verify the heat transfer mechanism, the thermal field of the whole engine cylinder head, including the intake and exhaust manifolds, was analyzed using FEA (Finite Element Analysis), and the results are compared and verified with available experimental data. Full article

(This article belongs to the Special Issue Experiments/Process/System Modeling/Simulation/Optimization (IC-EPSMSO 2025))

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28 pages, 1812 KB

Open AccessArticle

An Integrated Hybrid Deep Learning Framework for Intrusion Detection in IoT and IIoT Networks Using CNN-LSTM-GRU Architecture

by Doaa Mohsin Abd Ali Afraji, Jaime Lloret and Lourdes Peñalver

Computation 2025, 13(9), 222; https://doi.org/10.3390/computation13090222 - 14 Sep 2025

Abstract

Intrusion detection systems (IDSs) are critical for securing modern networks, particularly in IoT and IIoT environments where traditional defenses such as firewalls and encryption are insufficient against evolving cyber threats. This paper proposes an enhanced hybrid deep learning model that integrates convolutional neural [...] Read more.

Intrusion detection systems (IDSs) are critical for securing modern networks, particularly in IoT and IIoT environments where traditional defenses such as firewalls and encryption are insufficient against evolving cyber threats. This paper proposes an enhanced hybrid deep learning model that integrates convolutional neural networks (CNNs), Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRU) in a multi-branch architecture designed to capture spatial and temporal dependencies while minimizing redundant computations. Unlike conventional hybrid approaches, the proposed parallel–sequential fusion framework leverages the strengths of each component independently before merging features, thereby improving detection granularity and learning efficiency. A rigorous preprocessing pipeline is employed to handle real-world data challenges: missing values are imputed using median filling, class imbalance is mitigated through SMOTE (Synthetic Minority Oversampling Technique), and feature scaling is performed with Min–Max normalization to ensure convergence consistency. The methodology is validated on the TON_IoT and CICIDS2017 dataset, chosen for its diversity and realism in IoT/IIoT attack scenarios. Three hybrid models—CNN-LSTM, CNN-GRU, and the proposed CNN-LSTM-GRU—are assessed for binary and multiclass intrusion detection. Experimental results demonstrate that the CNN-LSTM-GRU architecture achieves superior performance, attaining 100% accuracy in binary classification and 97% in multiclass detection, with balanced precision, recall, and F1-scores across all classes. Furthermore, evaluation on the CICIDS2017 dataset confirms the model’s generalization ability, achieving 99.49% accuracy with precision, recall, and F1-scores of 0.9954, 0.9943, and 0.9949, respectively, outperforming CNN-LSTM and CNN-GRU baselines. Compared to existing IDS models, our approach delivers higher robustness, scalability, and adaptability, making it a promising candidate for next-generation IoT/IIoT security. Full article

(This article belongs to the Section Computational Engineering)

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42 pages, 8013 KB

Open AccessArticle

Adaptive Neural Network System for Detecting Unauthorised Intrusions Based on Real-Time Traffic Analysis

by Serhii Vladov, Victoria Vysotska, Vasyl Lytvyn, Anatolii Komziuk, Oleksandr Prokudin and Andrii Ostapiuk

Computation 2025, 13(9), 221; https://doi.org/10.3390/computation13090221 - 11 Sep 2025

Abstract

This article solves the anomalies’ operational detection in the network traffic problem for cyber police units by developing an adaptive neural network platform combining a variational autoencoder with continuous stochastic dynamics of the latent space (integration according to the Euler–Maruyama scheme), a continuous–discrete [...] Read more.

This article solves the anomalies’ operational detection in the network traffic problem for cyber police units by developing an adaptive neural network platform combining a variational autoencoder with continuous stochastic dynamics of the latent space (integration according to the Euler–Maruyama scheme), a continuous–discrete Kalman filter for latent state estimation, and Hotelling’s T² statistical criterion for deviation detection. This paper implements an online learning mechanism (“on the fly”) via the Euler Euclidean gradient step. Verification includes variational autoencoder training and validation, ROC/PR and confusion matrix analysis, latent representation projections (PCA), and latency measurements during streaming processing. The model’s stable convergence and anomalies’ precise detection with the metrics precision is ≈0.83, recall is ≈0.83, the F1-score is ≈0.83, and the end-to-end delay of 1.5–6.5 ms under 100–1000 sessions/s load was demonstrated experimentally. The computational estimate for typical model parameters is ≈5152 operations for a forward pass and ≈38,944 operations, taking into account batch updating. At the same time, the main bottleneck, the O(m³) term in the Kalman step, was identified. The obtained results’ practical significance lies in the possibility of the developed adaptive neural network platform integrating into cyber police units (integration with Kafka, Spark, or Flink; exporting incidents to SIEM or SOAR; monitoring via Prometheus or Grafana) and in proposing applied optimisation paths for embedded and high-load systems. Full article

(This article belongs to the Section Computational Engineering)

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18 pages, 2934 KB

Open AccessArticle

A Method for Synthesizing Self-Checking Discrete Systems with Calculations Testing Based on Parity and Self-Duality of Calculated Functions

by Dmitry V. Efanov, Tatiana S. Pogodina, Nazirjan M. Aripov, Sunnatillo T. Boltayev, Asadulla R. Azizov, Elnara K. Ametova and Zohid B. Toshboyev

Computation 2025, 13(9), 220; https://doi.org/10.3390/computation13090220 - 11 Sep 2025

Abstract

Calculations testing can be effectively used in the construction of discrete self-checking devices. Calculations testing is based on the parity and self-duality of the calculated functions. This can be used for modern blocks and nodes of control systems for responsible technological processes. However, [...] Read more.

Calculations testing can be effectively used in the construction of discrete self-checking devices. Calculations testing is based on the parity and self-duality of the calculated functions. This can be used for modern blocks and nodes of control systems for responsible technological processes. However, its use has a number of features that must be considered when building concurrent error-detection circuits. The authors used methods of discrete mathematics and Boolean algebra as well as technical diagnostics of discrete systems to investigate the problem of ensuring the testability of the parity encoder. Theorems on the testability of convolution functions modulo 2 are proved. Considering these theorems allowed the authors of the article to propose a method for synthesizing CED circuits. This method increases the testability of the encoder for parity. This method is based on the use of two diagnostic signs at once. The first sign is that the code words belong to the parity code. The second is the self-dual control function in the concurrent error-detection circuit. This method is guaranteed to increase the testability of the parity coder compared to using one of the diagnostic signs for calculations testing. Experiments with testing discrete devices have shown the effectiveness of the organization structure of the concurrent error-detection circuit that we developed. The theorems that we proved form the basis of proof of similar provisions for the use of other linear codes in the synthesis of concurrent error-detection circuits. Our proposed solutions with calculations testing based on two diagnostic signs should be used in the synthesis of discrete systems. Discrete systems should be self-checking and have improved testability indicators. Full article

(This article belongs to the Section Computational Engineering)

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37 pages, 5162 KB

Open AccessArticle

Fourier–Gegenbauer Integral Galerkin Method for Solving the Advection–Diffusion Equation with Periodic Boundary Conditions

by Kareem T. Elgindy

Computation 2025, 13(9), 219; https://doi.org/10.3390/computation13090219 - 9 Sep 2025

Abstract

This study presents the Fourier–Gegenbauer integral Galerkin (FGIG) method, a new numerical framework that uniquely integrates Fourier series and Gegenbauer polynomials to solve the one-dimensional advection–diffusion (AD) equation with spatially symmetric periodic boundary conditions, achieving exponential convergence and reduced computational cost compared to [...] Read more.

This study presents the Fourier–Gegenbauer integral Galerkin (FGIG) method, a new numerical framework that uniquely integrates Fourier series and Gegenbauer polynomials to solve the one-dimensional advection–diffusion (AD) equation with spatially symmetric periodic boundary conditions, achieving exponential convergence and reduced computational cost compared to traditional methods. The FGIG method uniquely combines Fourier series for spatial periodicity and Gegenbauer polynomials for temporal integration within a Galerkin framework, resulting in highly accurate numerical and semi-analytical solutions. Unlike traditional approaches, this method eliminates the need for time-stepping procedures by reformulating the problem as a system of integral equations, reducing error accumulation over long-time simulations and improving computational efficiency. Key contributions include exponential convergence rates for smooth solutions, robustness under oscillatory conditions, and an inherently parallelizable structure, enabling scalable computation for large-scale problems. Additionally, the method introduces a barycentric formulation of the shifted Gegenbauer–Gauss (SGG) quadrature to ensure high accuracy and stability for relatively low Péclet numbers. This approach simplifies calculations of integrals, making the method faster and more reliable for diverse problems. Numerical experiments presented validate the method’s superior performance over traditional techniques, such as finite difference, finite element, and spline-based methods, achieving near-machine precision with significantly fewer mesh points. These results demonstrate its potential for extending to higher-dimensional problems and diverse applications in computational mathematics and engineering. The method’s fusion of spectral precision and integral reformulation marks a significant advancement in numerical PDE solvers, offering a scalable, high-fidelity alternative to conventional time-stepping techniques. Full article

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

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26 pages, 16767 KB

Open AccessArticle

Effect of Heated Wall Corrugation on Thermal Performance in an L-Shaped Vented Cavity Crossed by Metal Foam Saturated with Copper–Water Nanofluid

by Luma F. Ali, Hussein Togun and Abdellatif M. Sadeq

Computation 2025, 13(9), 218; https://doi.org/10.3390/computation13090218 - 6 Sep 2025

Abstract

Practical applications such as solar power energy systems, electronic cooling, and the convective drying of vented enclosures require continuous developments to enhance fluid and heat flow. Numerous studies have investigated the enhancement of heat transfer in L-formed vented cavities by inserting heat-generating components, [...] Read more.

Practical applications such as solar power energy systems, electronic cooling, and the convective drying of vented enclosures require continuous developments to enhance fluid and heat flow. Numerous studies have investigated the enhancement of heat transfer in L-formed vented cavities by inserting heat-generating components, filling the cavity with nanofluids, providing an inner rotating cylinder and a phase-change packed system, etc. Contemporary work has examined the thermal performance of L-shaped porous vented enclosures, which can be augmented by using metal foam, using nanofluids as a saturated fluid, and increasing the wall surface area by corrugating the cavity’s heating wall. These features are not discussed in published articles, and their exploration can be considered a novelty point in this work. In this study, a vented cavity was occupied by a copper metal foam with

P P I = 10

and saturated with a copper–water nanofluid. The cavity walls were well insulated except for the left wall, which was kept at a hot isothermal temperature and was either non-corrugated or corrugated with rectangular waves. The Darcy–Brinkman–Forchheimer model and local thermal non-equilibrium models were adopted in momentum and energy-governing equations and solved numerically by utilizing commercial software. The influences of various effective parameters, including the Reynolds number (

20 \leq R e \leq 1000

), the nanoparticle volume fraction (

0 % \leq φ \leq 20 %

), the inflow and outflow vent aspect ratios (

0.1 \leq D / H \leq 0.4

), the rectangular wave corrugation number (

N = 5

and

N = 10

), and the corrugation dimension ratio (

C R = 1

and

C R = 0.5

) were determined. The results indicate that the flow field and heat transfer were affected mainly by variations in

R e

,

D / H

, and

φ

for a non-corrugated left wall; they were additionally influenced by

N

and

C R

when the wall was corrugated. The fluid- and solid-phase temperatures of the metal foam increased with an increase in

R e

and

D / H

. The fluid-phase Nusselt number near the hot left sidewall increased with an increase in

φ

by

(25 - 60) %

, while the solid-phase Nusselt number decreased by

(10 - 30) %

, and these numbers rose by around

3.5

times when the Reynolds number increased from

20

to

1000

. For the corrugated hot wall, the Nusselt numbers of the two metal foam phases increased with an increase in

R e

and decreased with an increase in

D / H

,

C R

, or

N

by

10 %

,

19 %

, and

37 %

. The original aspect of this study is its use of a thermal, non-equilibrium, nanofluid-saturated metal foam in a corrugated L-shaped vented cavity. We aimed to investigate the thermal performance of this system in order to reinforce the viability of applying this material in thermal engineering systems. Full article

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

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