Computation

15 pages, 2875 KiB

Open AccessArticle

A Frugal Approach Toward Modeling of Defects in Metal 3D Printing Through Statistical Methods in Finite Element Analysis

by Antonio Martínez Raya, Matías Braun, Cristina Carrasco-Garrido and Vicente F. González-Albuixech

Computation 2025, 13(2), 35; https://doi.org/10.3390/computation13020035 (registering DOI) - 3 Feb 2025

Abstract

Metal additive manufacturing has emerged as a revolutionary technology for the fabrication of high-complexity components. However, this technique presents unique challenges related to the structural integrity and final strength of the parts produced due to inherent defects, such as porosity, cracks, and geometric [...] Read more.

Metal additive manufacturing has emerged as a revolutionary technology for the fabrication of high-complexity components. However, this technique presents unique challenges related to the structural integrity and final strength of the parts produced due to inherent defects, such as porosity, cracks, and geometric deviations. These defects significantly impact the fatigue life of the material by acting as stress concentrators that accelerate failure under cyclic loading. On the one hand, this type of model is very complicated in its approach, since, even with encouraging results, the complexity of the calculation with these variables makes it difficult to obtain a simple result that allows for a generalized interpretation. On the other hand, using more familiar methods, it is possible to qualitatively guess the behavior that helps obtain results with better applicability, even at limited levels of precision. This paper presents a simplified finite element method combined with a statistical approach to model the presence of porosity in metal components produced by additive manufacturing. The proposed model considers a two-dimensional square plate subjected to tensile stress, with randomly introduced defects characterized by size, shape, and orientation. The percentage of porosity that affects each aspect determines the adjustment of the mechanical properties of finite elements. A series of simulations were performed to generate multiple models with random defect distributions to estimate maximum stress values. This approach demonstrates that complex models are not always necessary for a preliminary practical estimate of the effects of new manufacturing techniques. Furthermore, it demonstrates the potential for the extension of frugal computational techniques, which aim to minimize computational and experimental costs in the engineering field. The article discusses future research directions, particularly those related to potential business applications, including commercial uses. This follows a discussion of the existing limitations of this study. Full article

(This article belongs to the Special Issue Computational Approaches for Manufacturing)

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24 pages, 12317 KiB

Open AccessArticle

Combining Subtractive Genomics with Computer-Aided Drug Discovery Techniques to Effectively Target S. sputigena in Periodontitis

by Mallari Praveen, Chendruru Geya Sree, Simone Brogi, Vincenzo Calderone and Kamakshya Prasad Kanchan Prava Dalei

Computation 2025, 13(2), 34; https://doi.org/10.3390/computation13020034 (registering DOI) - 1 Feb 2025

Abstract

This study aimed to provide an inclusive in silico investigation for the identification of novel drug targets that can be exploited to develop drug candidates for treating oral infections caused by S. sputigena. By coupling subtractive genomics with an in silico drug [...] Read more.

This study aimed to provide an inclusive in silico investigation for the identification of novel drug targets that can be exploited to develop drug candidates for treating oral infections caused by S. sputigena. By coupling subtractive genomics with an in silico drug discovery approach, we identified dTDP-4-dehydrorhamnose 3,5-epimerase (UniProt ID: C9LUR0), UTP-glucose-1-phosphate uridyltransferase (UniProt ID: C9LRH1), and imidazole glycerol phosphate synthase (UniProt ID: C9LTU7) as three unique proteins crucial for the S. sputigena life cycle with no substantial similarity to human proteins. These potential drug targets served as the starting point for screening bioactive phytochemicals (1090 compounds) from the Indian Medicinal Plants, Phytochemistry and Therapeutics (IMPPAT) database. Among the screened natural products, cubebin (IMPHY001912) showed a higher affinity for two of the three selected targets, as evidenced by molecular docking and molecular dynamics studies. Given its favorable drug-like profile and possible multitargeting behavior, cubebin could be further exploited as an antibacterial agent for treating S. sputigena-mediated oral infections. It is worth nothing that cubebin could be the active ingredient of appropriate formulations such as mouthwash and/or toothpaste to treat S. sputigena-induced periodontitis, with the advantage of limiting the adverse effects that could affect the use of current drugs. Full article

(This article belongs to the Section Computational Biology)

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25 pages, 10129 KiB

Open AccessArticle

A Novel ConvXGBoost Method for Detection and Identification of Cyberattacks on Grid-Connected Photovoltaic (PV) Inverter System

by Sai Nikhil Vodapally and Mohd. Hasan Ali

Computation 2025, 13(2), 33; https://doi.org/10.3390/computation13020033 (registering DOI) - 1 Feb 2025

Abstract

The integration of solar Photovoltaic (PV) systems into the AC grid poses stability challenges, especially with increasing inverter-based resources. For an efficient operation of the system, smart grid-forming inverters need to communicate with the Supervisory Control and Data Acquisition (SCADA) system. However, Internet-of-Things [...] Read more.

The integration of solar Photovoltaic (PV) systems into the AC grid poses stability challenges, especially with increasing inverter-based resources. For an efficient operation of the system, smart grid-forming inverters need to communicate with the Supervisory Control and Data Acquisition (SCADA) system. However, Internet-of-Things devices that communicate with SCADA make these systems vulnerable. Though many researchers proposed Artificial-Intelligence-based detection strategies, identification of the location of the attack is not considered by these strategies. To overcome this drawback, this paper proposes a novel Convolution extreme gradient boosting (ConvXGBoost) method for not only detecting Denial of Service (DoS) and False Data Injection (FDI) attacks but also identifying the location and component of the system that was compromised. The proposed model is compared with the existing Convolution Neural Network (CNN) and decision tree (DT) strategies. Simulation results demonstrate the effectiveness of the proposed method for both the smart PV and PV fuel cell (PV-FC) systems. For example, the proposed model is efficient with an accuracy of 99.25% compared to the 97.76% of CNN and 99.12% of DT during a DoS attack on a smart PV system. Moreover, the proposed method can detect and identify the attack location faster than other models. Full article

(This article belongs to the Special Issue Applications of Statistics and Machine Learning in Electronics)

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16 pages, 1166 KiB

Open AccessArticle

A Novel Zeroing Neural Network for the Effective Solution of Supply Chain Inventory Balance Problems

by Xinwei Cao, Penglei Li and Ameer Tamoor Khan

Computation 2025, 13(2), 32; https://doi.org/10.3390/computation13020032 (registering DOI) - 1 Feb 2025

Abstract

The issue of inventory balance in supply chain management represents a classic problem within the realms of management and logistics. It can be modeled using a mixture of equality and inequality constraints, encompassing specific considerations such as production, transportation, and inventory limitations. A [...] Read more.

The issue of inventory balance in supply chain management represents a classic problem within the realms of management and logistics. It can be modeled using a mixture of equality and inequality constraints, encompassing specific considerations such as production, transportation, and inventory limitations. A Zeroing Neural Network (ZNN) model for time-varying linear equations and inequality systems is presented in this manuscript. In order to convert these systems into a mixed nonlinear framework, the method entails adding a non-negative slack variable. The ZNN model uses an exponential decay formula to obtain the desired solution and is built on the specification of an indefinite error function. The suggested ZNN model’s convergence is shown by the theoretical results. The results of the simulation confirm how well the ZNN handles inventory balance issues in limited circumstances. Full article

(This article belongs to the Section Computational Social Science)

25 pages, 3685 KiB

Open AccessArticle

Blind Source Separation Using Time-Delayed Dynamic Mode Decomposition

by Gyurhan Nedzhibov

Computation 2025, 13(2), 31; https://doi.org/10.3390/computation13020031 (registering DOI) - 1 Feb 2025

Abstract

Blind Source Separation (BSS) is a significant field of study in signal processing, with many applications in various fields such as audio processing, speech recognition, biomedical signal analysis, image processing and communication systems. Traditional methods, such as Independent Component Analysis (ICA), often rely [...] Read more.

Blind Source Separation (BSS) is a significant field of study in signal processing, with many applications in various fields such as audio processing, speech recognition, biomedical signal analysis, image processing and communication systems. Traditional methods, such as Independent Component Analysis (ICA), often rely on statistical independence assumptions, which may limit their performance in systems with significant temporal dynamics. This paper introduces an extension of the dynamic mode decomposition (DMD) approach by using time-delayed coordinates to implement BSS. Time-delay embedding enhances the capability of the method to handle complex, nonstationary signals by incorporating their temporal dependencies. We validate the approach through numerical experiments and applications, including audio signal separation, image separation and EEG artifact removal. The results demonstrate that modification achieves superior performance compared to conventional techniques, particularly in scenarios where sources exhibit dynamic coupling or non-stationary behavior. Full article

(This article belongs to the Special Issue Mathematical Modeling and Study of Nonlinear Dynamic Processes)

59 pages, 1509 KiB

Open AccessArticle

From Vulnerability to Defense: The Role of Large Language Models in Enhancing Cybersecurity

by Wafaa Kasri, Yassine Himeur, Hamzah Ali Alkhazaleh, Saed Tarapiah, Shadi Atalla, Wathiq Mansoor and Hussain Al-Ahmad

Computation 2025, 13(2), 30; https://doi.org/10.3390/computation13020030 - 29 Jan 2025

Abstract

The escalating complexity of cyber threats, coupled with the rapid evolution of digital landscapes, poses significant challenges to traditional cybersecurity mechanisms. This review explores the transformative role of LLMs in addressing critical challenges in cybersecurity. With the rapid evolution of digital landscapes and [...] Read more.

The escalating complexity of cyber threats, coupled with the rapid evolution of digital landscapes, poses significant challenges to traditional cybersecurity mechanisms. This review explores the transformative role of LLMs in addressing critical challenges in cybersecurity. With the rapid evolution of digital landscapes and the increasing sophistication of cyber threats, traditional security mechanisms often fall short in detecting, mitigating, and responding to complex risks. LLMs, such as GPT, BERT, and PaLM, demonstrate unparalleled capabilities in natural language processing, enabling them to parse vast datasets, identify vulnerabilities, and automate threat detection. Their applications extend to phishing detection, malware analysis, drafting security policies, and even incident response. By leveraging advanced features like context awareness and real-time adaptability, LLMs enhance organizational resilience against cyberattacks while also facilitating more informed decision-making. However, deploying LLMs in cybersecurity is not without challenges, including issues of interpretability, scalability, ethical concerns, and susceptibility to adversarial attacks. This review critically examines the foundational elements, real-world applications, and limitations of LLMs in cybersecurity while also highlighting key advancements in their integration into security frameworks. Through detailed analysis and case studies, this paper identifies emerging trends and proposes future research directions, such as improving robustness, addressing privacy concerns, and automating incident management. The study concludes by emphasizing the potential of LLMs to redefine cybersecurity, driving innovation and enhancing digital security ecosystems. Full article

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27 pages, 1666 KiB

Open AccessArticle

Bioinformatics-Driven Structural and Pharmacological Analysis of SLITRK1 in Tourette Syndrome: Impact of S656M Mutation Using Molecular Dynamics, Docking, and Reinforcement Learning

by Emre Aktaş, Alirıza İslim, Kevser Kübra Kırboğa, Derya Yıldız, Nehir Özdemir Özgentürk, Mithun Rudrapal, Johra Khan, Raghu Ram Achar, Ekaterina Silina, Natalia Manturova and Victor Stupin

Computation 2025, 13(2), 29; https://doi.org/10.3390/computation13020029 - 27 Jan 2025

Abstract

Abstract: SLITRK1 is a critical protein involved in neural development and is associated with various neurological disorders, including Tourette Syndrome. This study investigates the structural dynamics, intrinsic disorder propensity, and pharmacological interactions of SLITRK1, with a particular focus on amino acid substitutions and [...] Read more.

Abstract: SLITRK1 is a critical protein involved in neural development and is associated with various neurological disorders, including Tourette Syndrome. This study investigates the structural dynamics, intrinsic disorder propensity, and pharmacological interactions of SLITRK1, with a particular focus on amino acid substitutions and their pathological implications. A comprehensive computational framework was employed, including intrinsic disorder region analysis, transmembrane topology predictions, and stability assessments of SLITRK1 variants. Integrated with reinforcement learning (RL), molecular docking and dynamics simulations were used to evaluate the pharmacotherapeutic potential of drugs commonly prescribed for Tourette Syndrome, such as Pimozide, Aripiprazole, Risperidone, and Haloperidol. Structural analyses revealed that the S656M mutation significantly alters SLITRK1’s 3D conformation, biological functions, and drug binding profiles. Among the tested drugs, Aripiprazole exhibited the highest binding affinity across various SLITRK1 variants, with reinforcement learning highlighting a notable interaction with the S659K mutation. These findings were supported by Ramachandran plot and molecular dynamics analyses, which identified mutation-induced structural and dynamic changes. This study provides an integrative analysis of SLITRK1, offering insights into its role in Tourette Syndrome and laying a foundation for targeted therapeutic strategies to mitigate SLITRK1-related neurological disorders. Full article

(This article belongs to the Section Computational Biology)

15 pages, 1964 KiB

Open AccessArticle

Theoretical Investigation of Bond Dissociation Energies of exo-Polyhedral B–H and B–F Bonds of closo-Borate Anions [B_nH_n−1X]²⁻ (n = 6, 10, 12; X = H, F)

by Ilya N. Klyukin, Anastasia V. Kolbunova, Alexander S. Novikov, Konstantin Yu. Zhizhin and Nikolay T. Kuznetsov

Computation 2025, 13(2), 28; https://doi.org/10.3390/computation13020028 - 25 Jan 2025

Abstract

This paper reports on a theoretical investigation of the bond dissociation energies of B–H and B–F interactions of closo-borate anions [B_nH_n−1X]²⁻ (n = 6, 10 and 12; X = H and F), in which homolytic and heterolytic [...] Read more.

This paper reports on a theoretical investigation of the bond dissociation energies of B–H and B–F interactions of closo-borate anions [B_nH_n−1X]²⁻ (n = 6, 10 and 12; X = H and F), in which homolytic and heterolytic bond breaking cases were considered, and the main trends in bond dissociation energy values were analysed. The wB97X-D3/TZVPP level of theory was applied for geometry optimisation of the molecular species under consideration. DLPNO-CCSDT/CBS single-point calculations were made to ensure an accurate estimation of the target systems’ electronic energy. The correlations between the value of the bond dissociation energy and variables such as electron density descriptors of B–H and B–F interactions and frontier orbital energies (HOMO, SOMO and LUMO) were established. Full article

(This article belongs to the Section Computational Chemistry)

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13 pages, 524 KiB

Open AccessArticle

A Simple Model of Turbine Control Under Stochastic Fluctuations of Internal Parameters

by Sergei V. Borzunov, Mikhail E. Semenov, Eugene Y. Zybin, Sergey Y. Zheltov, Vladislav V. Kosyanchuk and Andrey I. Barsukov

Computation 2025, 13(2), 27; https://doi.org/10.3390/computation13020027 - 24 Jan 2025

Abstract

This article considers a model of a wind power generation system. It is assumed that the wind torque is transmitted to the generator via a gear. At the same time, the gear itself can have backlash with stochastic parameters. This kind of nonlinearity [...] Read more.

This article considers a model of a wind power generation system. It is assumed that the wind torque is transmitted to the generator via a gear. At the same time, the gear itself can have backlash with stochastic parameters. This kind of nonlinearity simulates an inevitable aging and wear of the mechanical parts of wind power generation systems over time. The purpose of the study was to identify a control system that would allow for establishing and maintaining the stability of the desired characteristics. The control system is formalized in the form of a second-order linear system. Numerical experiments demonstrated that the suggested control system is robust to stochastic perturbations resulting from both external and internal factors. Full article

(This article belongs to the Special Issue Mathematical Modeling and Study of Nonlinear Dynamic Processes)

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19 pages, 7529 KiB

Open AccessArticle

Numerical Estimation of the Structural Integrity in an Existing Pipeline Network for the Transportation of Hydrogen Mixture in the Future

by Clio Vossou and Dimitrios Koulocheris

Computation 2025, 13(2), 26; https://doi.org/10.3390/computation13020026 - 24 Jan 2025

Abstract

Hydrogen is gaining attention due to its potential to address key challenges in the sectors of energy, transportation and industry, since it is a much cleaner energy source when compared to fossil fuels. The transportation of hydrogen from the point of its production [...] Read more.

Hydrogen is gaining attention due to its potential to address key challenges in the sectors of energy, transportation and industry, since it is a much cleaner energy source when compared to fossil fuels. The transportation of hydrogen from the point of its production to the point of use can be performed by road, rail, sea, pipeline networks or a combination of the abovementioned. Being in the preliminary stage of hydrogen use, the utilization of the already existing natural gas pipeline networks for hydrogen mixtures transportation has been suggested as an efficient means of expanding hydrogen infrastructure. Yet, exploring this alternative, major challenges such as the pre-existence of cracks in the pipelines and the effect of hydrogen embrittlement on the material of the pipelines exist. In this paper, the macroscopic numerical modeling of pipeline segments with the use of the finite element method is performed. In more details, the structural integrity of intact and damaged pipeline segments, of different geometry and mechanical properties, was estimated. The effect of the pipeline geometry and material has been investigated in terms of stress contours with and without the influence of hydrogen. The results suggest that the structural integrity of the pipeline segments is more compromised by pre-existing longitudinal cracks, which might lead to an increase in the maximum value of equivalent Von Mises stress by up to four times, depending on their length-to-thickness ratio. This effect becomes more pronounced with the existence of hydrogen in the pipeline network. Full article

(This article belongs to the Special Issue Experiments/Process/System Modeling/Simulation/Optimization (IC-EPSMSO 2023))

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15 pages, 2574 KiB

Open AccessArticle

An Actual Case Study of a Deterministic Multi-Objective Optimization Model in a Defined Contribution Faculty Pension System

by Marco Antonio Montufar-Benítez, Jaime Mora-Vargas, José Ramón Corona-Armenta, Gustavo Erick Anaya-Fuentes, Héctor Rivera-Gómez and Mayra Rivera-Anaya

Computation 2025, 13(2), 25; https://doi.org/10.3390/computation13020025 - 24 Jan 2025

Abstract

The optimal management of pension funds has become increasingly critical. As the population ages, the effective management of pension funds is essential for the social security system. The primary goal of this paper is to develop a deterministic nonlinear multi-objective optimization model to [...] Read more.

The optimal management of pension funds has become increasingly critical. As the population ages, the effective management of pension funds is essential for the social security system. The primary goal of this paper is to develop a deterministic nonlinear multi-objective optimization model to determine the contribution rates in a defined contribution pension system. The computational optimization model was implemented using the LINGO language. In the first part of this study, three main scenarios were analyzed considering different inflation rates, focusing on the objective function that minimizes the salary percentages workers pay when saving for a specified period while aiming to achieve a certain number of coverage years. The first scenario assumes that the worker desires an economic quality equivalent to their working life, showing that contribution rates range from 10% to 30% (with a 3% inflation rate). The second scenario posits that the worker only requires 80% of their equivalent salary during retirement, resulting in contribution rates directly proportional to those in scenario 1 (using the same parameters). The third scenario speculates that inflation may reach 7% per year, causing contribution rates to rise significantly from 40% to 80%. Finally, the Pareto front illustrates the trade-off between the contribution rate and the coverage years based on scenario 1 parameters. Full article

(This article belongs to the Section Computational Social Science)

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27 pages, 1915 KiB

Open AccessArticle

A Computational Study on Two-Parameter Singularly Perturbed Third-Order Delay Differential Equations

by Mahendran Rajendran, Senthilkumar Sethurathinam, Subburayan Veerasamy and Ravi P. Agarwal

Computation 2025, 13(2), 24; https://doi.org/10.3390/computation13020024 - 23 Jan 2025

Abstract

A class of third-order singularly perturbed two-parameter delay differential equations of boundary value problems is studied in this paper. Regular and singular components are used to estimate the solution’s a priori bounds and derivatives. A fitted finite-difference method is constructed to solve the [...] Read more.

A class of third-order singularly perturbed two-parameter delay differential equations of boundary value problems is studied in this paper. Regular and singular components are used to estimate the solution’s a priori bounds and derivatives. A fitted finite-difference method is constructed to solve the problem on a Shishkin mesh. The numerical solution converges uniformly to the exact solution; it is validated via numerical test problems. The order of convergence of the numerical method is almost first-order, which is independent of the parameters

ε

and

μ

. Full article

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16 pages, 3325 KiB

Open AccessArticle

Solving and Optimization of Cobb–Douglas Function by Genetic Algorithm: A Step-by-Step Implementation

by Ali Dinc, Faruk Yildiz, Kaushik Nag, Murat Otkur and Ali Mamedov

Computation 2025, 13(2), 23; https://doi.org/10.3390/computation13020023 - 23 Jan 2025

Abstract

This study presents an innovative application of genetic algorithms (GAs) for optimizing the Cobb–Douglas production function, a cornerstone of economic modeling that examines the relationship between production output and the inputs of labor and capital. This research integrates traditional optimization methods, such as [...] Read more.

This study presents an innovative application of genetic algorithms (GAs) for optimizing the Cobb–Douglas production function, a cornerstone of economic modeling that examines the relationship between production output and the inputs of labor and capital. This research integrates traditional optimization methods, such as partial derivatives, with evolutionary computation techniques to address complex economic constraints. The methodology demonstrates how GAs outperform classical techniques in solving constrained optimization problems, offering superior robustness, adaptability, and efficiency. Key results highlight the alignment between GA solutions and traditional Lagrangian methods while underscoring the computational advantages of GAs in navigating non-linear and multi-modal landscapes. This work serves as a valuable resource for both educators and practitioners, offering insights into the potential of GAs to enhance optimization processes in engineering, economics, and interdisciplinary applications. Visual aids and pedagogical recommendations further illustrate the algorithm’s utility, making this study a significant contribution to the computational optimization literature. Additionally, the optimization process using genetic algorithms is presented in a step-by-step manner, with accompanying visual graphs that enhance comprehension and demonstrate the method’s effectiveness in solving mathematical problems, as validated by the study’s results. Full article

(This article belongs to the Section Computational Engineering)

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17 pages, 4218 KiB

Open AccessArticle

Operational Robustness of Amino Acid Recognition via Transverse Tunnelling Current Across Metallic Graphene Nano-Ribbon Electrodes: The Pro-Ser Case

by Giuseppe Zollo

Computation 2025, 13(2), 22; https://doi.org/10.3390/computation13020022 - 21 Jan 2025

Abstract

Asymmetric cove-edged graphene nano-ribbons were employed as metallic electrodes in a hybrid gap device structure with zig-zag graphene nano-ribbons terminations for amino acid recognition and peptide sequencing. On a theoretical basis, amino acid recognition is attained by calculating, using the non equilibrium Green [...] Read more.

Asymmetric cove-edged graphene nano-ribbons were employed as metallic electrodes in a hybrid gap device structure with zig-zag graphene nano-ribbons terminations for amino acid recognition and peptide sequencing. On a theoretical basis, amino acid recognition is attained by calculating, using the non equilibrium Green function scheme based on density functional theory, the transversal tunnelling current flowing across the gap device during the peptide translocation through the device. The reliability and robustness of this sequencing method versus relevant operations parameters, such as the bias, the gap size, and small perturbations of the atomistic structures, are studied for the paradigmatic case of Pro-Ser model peptide. I evidence that the main features of the tunnelling signal, that allow the recognition, survive for all of the operational conditions explored. I also evidence a sort of geometrical selective sensitivity of the hybrid cove-edged graphene nano-ribbons versus the bias that should be carefully considered for recognition. Full article

(This article belongs to the Section Computational Chemistry)

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19 pages, 881 KiB

Open AccessArticle

Exploring Flexible Penalization of Bayesian Survival Analysis Using Beta Process Prior for Baseline Hazard

by Kazeem A. Dauda, Ebenezer J. Adeniyi, Rasheed K. Lamidi and Olalekan T. Wahab

Computation 2025, 13(2), 21; https://doi.org/10.3390/computation13020021 - 21 Jan 2025

Abstract

High-dimensional data have attracted considerable interest from researchers, especially in the area of variable selection. However, when dealing with time-to-event data in survival analysis, where censoring is a key consideration, progress in addressing this complex problem has remained somewhat limited. Moreover, in microarray [...] Read more.

High-dimensional data have attracted considerable interest from researchers, especially in the area of variable selection. However, when dealing with time-to-event data in survival analysis, where censoring is a key consideration, progress in addressing this complex problem has remained somewhat limited. Moreover, in microarray research, it is common to identify groupings of genes involved in the same biological pathways. These gene groupings frequently collaborate and operate as a unified entity. Therefore, this study is motivated to adopt the idea of a penalized semi-parametric Bayesian Cox (PSBC) model through elastic-net and group lasso penalty functions (PSBC-EN and PSBC-GL) to incorporate the grouping structure of the covariates (genes) and optimally perform variable selection. The proposed methods assign a beta process prior to the cumulative baseline hazard function (PSBC-EN-B and PSBC-GL-B), instead of the gamma process prior used in existing methods (PSBC-EN-G and PSBC-GL-G). Three real-life datasets and simulation scenarios were considered to compare and validate the efficiency of the modified methods with existing techniques, using Bayesian information criteria (BIC). The results of the simulated studies provided empirical evidence that the proposed methods performed better than the existing methods across a wide range of data scenarios. Similarly, the results of the real-life study showed that the proposed methods revealed a substantial improvement over the existing techniques in terms of feature selection and grouping behavior. Full article

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16 pages, 1606 KiB

Open AccessArticle

Estimation of Wind Farm Losses Using a Jensen Model Based on Actual Wind Turbine Characteristics for an Offshore Wind Farm in the Baltic Sea

by Ziemowit Malecha and Maciej Chorowski

Computation 2025, 13(1), 20; https://doi.org/10.3390/computation13010020 - 20 Jan 2025

Abstract

This study investigates the effects of velocity deficits on the performance of wind turbines in multi-row wind farms, focusing on two types of turbines: Gamesa G132 and Gamesa SG8. The analysis examines the impact of turbine spacing on key performance metrics, including Annual [...] Read more.

This study investigates the effects of velocity deficits on the performance of wind turbines in multi-row wind farms, focusing on two types of turbines: Gamesa G132 and Gamesa SG8. The analysis examines the impact of turbine spacing on key performance metrics, including Annual Energy Production, energy production losses, and the capacity factor. Two models are used: the classical Jensen model, assuming a constant thrust coefficient (

C_{T}

), and an updated model that incorporates the actual turbine-specific

C_{T} (U)

characteristics. The results demonstrate that as turbine spacing decreases, the velocity deficit behind the turbines increases, leading to significant reductions in AEP and higher energy losses. These effects are particularly pronounced for spacings of 5D and 3D, raising concerns about the economic feasibility of such wind farms. This study also highlights that the proposed updated Jensen model, which accounts for the specific turbine characteristics, provides results that are closer to real-world observations. This study showed that for a Baltic Sea wind farm location, the capacity factor for the wind farm is in the range of 0.366 to 0.476, depending on the turbine spacing. Full article

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

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28 pages, 418 KiB

Open AccessArticle

Spectral Representation and Simulation of Fractional Brownian Motion

by Konstantin Rybakov

Computation 2025, 13(1), 19; https://doi.org/10.3390/computation13010019 - 20 Jan 2025

Abstract

This paper gives a new representation for the fractional Brownian motion that can be applied to simulate this self-similar random process in continuous time. Such a representation is based on the spectral form of mathematical description and the spectral method. The Legendre polynomials [...] Read more.

This paper gives a new representation for the fractional Brownian motion that can be applied to simulate this self-similar random process in continuous time. Such a representation is based on the spectral form of mathematical description and the spectral method. The Legendre polynomials are used as the orthonormal basis. The paper contains all the necessary algorithms and their theoretical foundation, as well as the results of numerical experiments. Full article

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30 pages, 8724 KiB

Open AccessArticle

Analysis of Empirical Models for Predicting the Rupture Force in Four-Pile Caps

by Raphael Saverio Spozito, André Luis Christoforo, Fernando Menezes de Almeida Filho, Rodrigo Gustavo Delalibera, Elvys Dias Reis and André Luís Lima Velame Branco

Computation 2025, 13(1), 18; https://doi.org/10.3390/computation13010018 - 17 Jan 2025

Abstract

Technical literature provides experimental campaign results for four-pile caps with significant value in structural engineering. However, failure modes and measurement of these specimens are particularly challenging due to their volumetric nature. These experimental records are frequently utilized to evaluate analytical models focusing on [...] Read more.

Technical literature provides experimental campaign results for four-pile caps with significant value in structural engineering. However, failure modes and measurement of these specimens are particularly challenging due to their volumetric nature. These experimental records are frequently utilized to evaluate analytical models focusing on less conservative than normative models to predict rupture force. Moreover, recent studies emphasize the necessity of developing simplified predictive models. In this context, the objective of this study was to evaluate the feasibility of regression models for estimating the rupture force associated with strut failure. The evaluation was based on a commonly used database and employed analysis of variance (ANOVA) at a 5% significance level to identify critical variables. The regression models were developed with variable interactions incorporated into the equations in three forms: (i) without interaction, (ii) with linear interaction, and (iii) with quadratic interaction. An analysis of the developed regression models identified a model with satisfactory accuracy. This model achieved an average predicted force ratio of 1.00 (COV = 14%) for the database and 1.03 (COV = 16.68%) for extrapolated numeric models in finite elements with the concrete damaged and plasticity (CDP) constitutive model, initially calibrated with experimental tests. A methodology was proposed to assist in the initial design of four-pile caps. Full article

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39 pages, 8637 KiB

Open AccessArticle

Comparative Analysis of Machine Learning Models for Predicting Interfacial Bond Strength of Fiber-Reinforced Polymer-Concrete

by Miljan Kovačević, Marijana Hadzima-Nyarko, Predrag Petronijević, Tatijana Vasiljević and Miroslav Radomirović

Computation 2025, 13(1), 17; https://doi.org/10.3390/computation13010017 - 17 Jan 2025

Abstract

This study presents a detailed analysis of various machine learning models for predicting the interfacial bond strength of fiber-reinforced polymer (FRP) concrete, including multiple linear regression, Multigene Genetic Programming (MGGP), an ensemble of regression trees, Gaussian Process Regression (GPR), Support Vector Regression (SVR), [...] Read more.

This study presents a detailed analysis of various machine learning models for predicting the interfacial bond strength of fiber-reinforced polymer (FRP) concrete, including multiple linear regression, Multigene Genetic Programming (MGGP), an ensemble of regression trees, Gaussian Process Regression (GPR), Support Vector Regression (SVR), and neural networks. The evaluation was based on their predictive accuracy. The optimal model identified was the GPR ARD Exponential model, which achieved a mean absolute error (MAE) of 1.8953 MPa and a correlation coefficient (R) of 0.9658. An analysis of this optimal model highlighted the most influential variables affecting the bond strength. Additionally, the research identified several models with lower expression complexity and reduced accuracy, which may still be applicable in practical scenarios. Full article

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

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