Computation

15 pages, 737 KiB

Open AccessArticle

Modelling the Impact of Cloud Storage Heterogeneity on HPC Application Performance

by Jack Marquez and Oscar H. Mondragon

Computation 2024, 12(7), 150; https://doi.org/10.3390/computation12070150 - 19 Jul 2024

Viewed by 377

Moving high-performance computing (HPC) applications from HPC clusters to cloud computing clusters, also known as the HPC cloud, has recently been proposed by the HPC research community. Migrating these applications from the former environment to the latter can have an important impact on [...] Read more.

Moving high-performance computing (HPC) applications from HPC clusters to cloud computing clusters, also known as the HPC cloud, has recently been proposed by the HPC research community. Migrating these applications from the former environment to the latter can have an important impact on their performance, due to the different technologies used and the suboptimal use and configuration of cloud resources such as heterogeneous storage. Probabilistic models can be applied to predict the performance of these applications and to optimise them for the new system. Modelling the performance in the HPC cloud of applications that use heterogeneous storage is a difficult task, due to the variations in performance. This paper presents a novel model based on Extreme Value Theory (EVT) for the analysis, characterisation and prediction of the performance of HPC applications that use heterogeneous storage technologies in the cloud and high-performance distributed parallel file systems. Unlike standard approaches, our model focuses on extreme values, capturing the true variability and potential bottlenecks in storage performance. Our model is validated using return level analysis to study the performance of representative scientific benchmarks running on heterogeneous cloud storage at a large scale and gives prediction errors of less than 7%. Full article

(This article belongs to the Section Computational Engineering)

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24 pages, 7108 KiB

Open AccessArticle

Multilevel Quasi-Interpolation on Chebyshev Sparse Grids

by Faisal Alsharif

Computation 2024, 12(7), 149; https://doi.org/10.3390/computation12070149 - 18 Jul 2024

Viewed by 303

Abstract

This paper investigates the potential of utilising multilevel quasi-interpolation techniques on Chebyshev sparse grids for complex numerical computations. The paper starts by laying down the motivations for choosing Chebyshev sparse grids and quasi-interpolation methods with Gaussian kernels. It delves into the practical aspects [...] Read more.

This paper investigates the potential of utilising multilevel quasi-interpolation techniques on Chebyshev sparse grids for complex numerical computations. The paper starts by laying down the motivations for choosing Chebyshev sparse grids and quasi-interpolation methods with Gaussian kernels. It delves into the practical aspects of implementing these techniques. Various numerical experiments are performed to evaluate the efficiency and limitations of the multilevel quasi-sparse interpolation methods with dimensions two dimension and three dimension. The work ultimately aims to provide a comprehensive understanding of the computational efficiency and accuracy achievable through this approach, comparing its performance with traditional methods. Full article

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12 pages, 3488 KiB

Open AccessArticle

Mathematical Modeling of the Heat Transfer Process in Spherical Objects with Flat, Cylindrical and Spherical Defects

by Pavel Balabanov, Andrey Egorov, Alexander Divin, Sergey Ponomarev, Victor Yudaev, Sergey Baranov and Huthefa Abu Zetoonh

Computation 2024, 12(7), 148; https://doi.org/10.3390/computation12070148 - 17 Jul 2024

Viewed by 367

Abstract

This paper proposes a method for determining the optimal parameters for the thermal testing of plant tissues of fruits and vegetables containing surface and subsurface defects in the form of areas of plant tissues with different thermophysical characteristics. Based on well-known mathematical models [...] Read more.

This paper proposes a method for determining the optimal parameters for the thermal testing of plant tissues of fruits and vegetables containing surface and subsurface defects in the form of areas of plant tissues with different thermophysical characteristics. Based on well-known mathematical models for objects of predominantly flat, cylindrical and spherical shapes containing flat, spherical and cylindrical regions of defects, numerical solutions of three-dimensional, non-stationary temperature fields were found, making it possible to measure the power and time of the thermal exposure of the sample surface to the radiation from infrared lamps using the finite element method. This made it possible to ensure the reliable detection of a temperature contrast of up to 4 °C between the defect and defect-free regions of the test object using modern thermal imaging cameras. In this case, subsurface defects can be detected at a depth of up to 3 mm from the surface. To determine the parameters of mathematical models of temperature fields, such as thermal conductivity and a coefficient of the thermal diffusivity of plant tissues, a new method of a pulsed heat flux from a flat heater is proposed; this differs in the method of processing experimental data and makes it possible to determine the required characteristics with high accuracy during the active stage of the experiment in a period not exceeding 1–3 min. Full article

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

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11 pages, 1220 KiB

Open AccessArticle

Modeling the Properties of Magnetostrictive Elements Using Quantum Emulators

by Edvard Karpukhin, Alexey Bormotov and Luiza Manukyan

Computation 2024, 12(7), 147; https://doi.org/10.3390/computation12070147 - 15 Jul 2024

Viewed by 383

Abstract

The article discusses mathematical and numerical methods for modeling magnetostrictive multielectronic systems based on a combination of quantum and classical methods. The algorithm development suitable for the investigation of magnetostrictive phenomena at the micro level using the classical-quantum method implemented on a modern [...] Read more.

The article discusses mathematical and numerical methods for modeling magnetostrictive multielectronic systems based on a combination of quantum and classical methods. The algorithm development suitable for the investigation of magnetostrictive phenomena at the micro level using the classical-quantum method implemented on a modern classical computer is justified. The algorithms and structure of the software package are given. The adequacy of the quantum-classical method is verified by comparing the calculated results of the properties of known magnetostrictive materials with the real properties of magnetostrictive alloys. Full article

(This article belongs to the Section Computational Engineering)

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25 pages, 9886 KiB

Open AccessArticle

Natural Convection Fluid Flow and Heat Transfer in a Valley-Shaped Cavity

by Sidhartha Bhowmick, Laxmi Rani Roy, Feng Xu and Suvash C. Saha

Computation 2024, 12(7), 146; https://doi.org/10.3390/computation12070146 - 14 Jul 2024

Viewed by 405

Abstract

The phenomenon of natural convection is the subject of significant research interest due to its widespread occurrence in both natural and industrial contexts. This study focuses on investigating natural convection phenomena within triangular enclosures, specifically emphasizing a valley-shaped configuration. Our research comprehensively analyses [...] Read more.

The phenomenon of natural convection is the subject of significant research interest due to its widespread occurrence in both natural and industrial contexts. This study focuses on investigating natural convection phenomena within triangular enclosures, specifically emphasizing a valley-shaped configuration. Our research comprehensively analyses unsteady, non-dimensional time-varying convection resulting from natural fluid flow within a valley-shaped cavity, where the inclined walls serve as hot surfaces and the top wall functions as a cold surface. We explore unsteady natural convection flows in this cavity, utilizing air as the operating fluid, considering a range of Rayleigh numbers from Ra = 10⁰ to 10⁸. Additionally, various non-dimensional times τ, spanning from 0 to 5000, are examined, with a fixed Prandtl number (Pr = 0.71) and aspect ratio (A = 0.5). Employing a two-dimensional framework for numerical analysis, our study focuses on identifying unstable flow mechanisms characterized by different non-dimensional times, including symmetric, asymmetric, and unsteady flow patterns. The numerical results reveal that natural convection flows remain steady in the symmetric state for Rayleigh values ranging from 10⁰ to 7 × 10³. Asymmetric flow occurs when the Ra surpasses 7 × 10³. Under the asymmetric condition, flow arrives in an unsteady stage before stabilizing at the fully formed stage for 7 × 10³ < Ra < 10⁷. This study demonstrates that periodic unsteady flows shift into chaotic situations during the transitional stage before transferring to periodic behavior in the developed stage, but the chaotic flow remains predominant in the unsteady regime with larger Rayleigh numbers. Furthermore, we present an analysis of heat transfer within the cavity, discussing and quantifying its dependence on the Rayleigh number. Full article

(This article belongs to the Special Issue 10th Anniversary of Computation—Computational Heat and Mass Transfer (ICCHMT 2023))

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11 pages, 2336 KiB

Open AccessArticle

Molecular Dynamics Simulation of Melting of the DNA Duplex with Silver-Mediated Cytosine–Cytosine Base Pair

by Elena B. Gusarova and Natalya A. Kovaleva

Computation 2024, 12(7), 145; https://doi.org/10.3390/computation12070145 - 12 Jul 2024

Viewed by 380

Abstract

Metal-mediated base pairs in DNA double helix molecules open up broad opportunities for biosensors based on DNA clusters with silver due to their low toxicity and applicability in drug design. Despite intensive experimental and computational research, molecular mechanisms of stabilization of a double [...] Read more.

Metal-mediated base pairs in DNA double helix molecules open up broad opportunities for biosensors based on DNA clusters with silver due to their low toxicity and applicability in drug design. Despite intensive experimental and computational research, molecular mechanisms of stabilization of a double helix by silver-mediated base pairs are mainly unknown. We conducted all-atom molecular dynamics simulations of a dodecameric DNA double helix (sequence 5′-TAGGTCAATACT-3′-3′ATCCACTTATGA-5′) with either cytosine–cytosine or cytosine–Ag⁺–cytosine mismatch in the center of the duplex. We extended the previously proposed set of interaction parameters for a silver ion in the silver-mediated pair in order to allow for its dissociation. With this new potential, we studied how the addition of a silver ion could stabilize a DNA double helix containing a single cytosine–cytosine mismatch. In particular, we found out that the helix with cytosine–Ag⁺–cytosine mismatch has a greater melting temperature than the helix with cytosine–cytosine one. This stabilization effect of the silver ion is in qualitative agreement with experimental data. The central region of the duplex with cytosine–Ag⁺–cytosine mismatch (unlike with cytosine–cytosine mismatch) is stable enough to prevent bubble formation at moderate temperatures during melting. The results of this simulation can be used to devise novel metal-mediated DNA structures. Full article

(This article belongs to the Section Computational Chemistry)

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16 pages, 1074 KiB

Open AccessArticle

Fractional-Order Degn–Harrison Reaction–Diffusion Model: Finite-Time Dynamics of Stability and Synchronization

by Ma’mon Abu Hammad, Issam Bendib, Waseem Ghazi Alshanti, Ahmad Alshanty, Adel Ouannas, Amel Hioual and Shaher Momani

Computation 2024, 12(7), 144; https://doi.org/10.3390/computation12070144 - 12 Jul 2024

Viewed by 417

Abstract

This study aims to address the topic of finite-time synchronization within a specific subset of fractional-order Degn–Harrison reaction–diffusion systems. To achieve this goal, we begin with the introduction of a novel lemma specific for finite-time stability analysis. Diverging from existing criteria, this lemma [...] Read more.

This study aims to address the topic of finite-time synchronization within a specific subset of fractional-order Degn–Harrison reaction–diffusion systems. To achieve this goal, we begin with the introduction of a novel lemma specific for finite-time stability analysis. Diverging from existing criteria, this lemma represents a significant extension of prior findings, laying the groundwork for subsequent investigations. Building upon this foundation, we proceed to develop efficient dependent linear controllers designed to orchestrate finite-time synchronization. Leveraging the power of a Lyapunov function, we derive new, robust conditions that ensure the attainment of synchronization within a predefined time frame. This innovative approach not only enhances our understanding of finite-time synchronization, but also offers practical solutions for its realization in complex systems. To validate the efficacy and applicability of our proposed methodology, extensive numerical simulations are conducted. Through this comprehensive analysis, we aim to contribute valuable insights to the field of fractional-order reaction–diffusion systems while paving the way for practical implementations in real-world applications. Full article

(This article belongs to the Special Issue Advanced Numerical Methods for Solving Differential Equations with Applications in Science and Engineering)

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38 pages, 6913 KiB

Open AccessReview

Computational Fluid Dynamics-Based Systems Engineering for Ground-Based Astronomy

by Konstantinos Vogiatzis, George Angeli, Gelys Trancho and Rod Conan

Computation 2024, 12(7), 143; https://doi.org/10.3390/computation12070143 - 11 Jul 2024

Viewed by 866

Abstract

This paper presents the state-of-the-art techniques employed in aerothermal modeling to respond to the current observatory design challenges, particularly those of the next generation of extremely large telescopes (ELTs), such as the European ELT, the Thirty Meter Telescope International Observatory (TIO), and the [...] Read more.

This paper presents the state-of-the-art techniques employed in aerothermal modeling to respond to the current observatory design challenges, particularly those of the next generation of extremely large telescopes (ELTs), such as the European ELT, the Thirty Meter Telescope International Observatory (TIO), and the Giant Magellan Telescope (GMT). It reviews the various aerothermal simulation techniques, the synergy between modeling outputs and observatory integrating modeling, and recent applications. The suite of aerothermal modeling presented includes thermal network models, Computational Fluid Dynamics (CFD) models, solid thermal and deformation models, and conjugate heat transfer models (concurrent fluid/solid simulations). The aerothermal suite is part of the overall observatory integrated modeling (IM) framework, which also includes optics, dynamics, and controls. The outputs of the IM framework, nominally image quality (IQ) metrics for a specific telescope state, are fed into a stochastic framework in the form of a multidimensional array that covers the range of influencing operational parameters, thus providing a statistical representation of observatory performance. The applications of the framework range from site selection, ground layer characterization, and site development to observatory performance current best estimate and optimization, active thermal control design, structural analysis, and an assortment of cost–performance trade studies. Finally, this paper addresses planned improvements, the development of new ideas, attacking new challenges, and how it all ties to the “Computational Fluid Dynamics Vision 2030” initiative. Full article

(This article belongs to the Special Issue Post-Modern Computational Fluid Dynamics)

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18 pages, 576 KiB

Open AccessArticle

The Theory and Computation of the Semi-Linear Reaction–Diffusion Equation with Dirichlet Boundaries

by Pius W. M. Chin

Computation 2024, 12(7), 142; https://doi.org/10.3390/computation12070142 - 11 Jul 2024

Viewed by 360

Abstract

In this article, we study the semi-linear two-dimensional reaction–diffusion equation with Dirichlet boundaries. A reliable numerical scheme is designed, coupling the nonstandard finite difference method in the time together with the Galerkin in combination with the compactness method in the space variables. The [...] Read more.

In this article, we study the semi-linear two-dimensional reaction–diffusion equation with Dirichlet boundaries. A reliable numerical scheme is designed, coupling the nonstandard finite difference method in the time together with the Galerkin in combination with the compactness method in the space variables. The aforementioned equation is analyzed to show that the weak or variational solution exists uniquely in specified space. The a priori estimate obtained from the existence of the weak or variational solution is used to show that the designed scheme is stable and converges optimally in specified norms. Furthermore, we show that the scheme preserves the qualitative properties of the exact solution. Numerical experiments are presented with a carefully chosen example to validate our proposed theory. Full article

(This article belongs to the Special Issue 10th Anniversary of Computation—Computational Heat and Mass Transfer (ICCHMT 2023))

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14 pages, 2795 KiB

Open AccessArticle

Hybrid Nanofluid Flow over a Shrinking Rotating Disk: Response Surface Methodology

by Rusya Iryanti Yahaya, Norihan Md Arifin, Ioan Pop, Fadzilah Md Ali and Siti Suzilliana Putri Mohamed Isa

Computation 2024, 12(7), 141; https://doi.org/10.3390/computation12070141 - 10 Jul 2024

Viewed by 367

Abstract

For efficient heating and cooling applications, minimum wall shear stress and maximum heat transfer rate are desired. The current study optimized the local skin friction coefficient and Nusselt number in Al₂O₃-Cu/water hybrid nanofluid flow over a permeable shrinking rotating [...] Read more.

For efficient heating and cooling applications, minimum wall shear stress and maximum heat transfer rate are desired. The current study optimized the local skin friction coefficient and Nusselt number in Al₂O₃-Cu/water hybrid nanofluid flow over a permeable shrinking rotating disk. First, the governing equations and boundary conditions are solved numerically using the bvp4c solver in MATLAB. Von Kármán’s transformations are used to reduce the partial differential equations into solvable non-linear ordinary differential equations. The augmentation of the mass transfer parameter is found to reduce the local skin friction coefficient and Nusselt number. Higher values of these physical quantities of interest are observed in the injection case than in the suction case. Meanwhile, the increase in the magnitude of the shrinking parameter improved and reduced the local skin friction coefficient and Nusselt number, respectively. Then, response surface methodology (RSM) is conducted to understand the interactive impacts of the controlling parameters in optimizing the physical quantities of interest. With a desirability of 66%, the local skin friction coefficient and Nusselt number are optimized at 1.528780016 and 0.888353037 when the shrinking parameter (

λ

) and mass transfer parameter (

S

) are −0.8 and −0.6, respectively. Full article

(This article belongs to the Special Issue 10th Anniversary of Computation—Computational Heat and Mass Transfer (ICCHMT 2023))

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24 pages, 16040 KiB

Open AccessArticle

Design and Evaluation of a Hypersonic Waverider Vehicle Using DSMC

by Angelos Klothakis and Ioannis K. Nikolos

Computation 2024, 12(7), 140; https://doi.org/10.3390/computation12070140 - 9 Jul 2024

Viewed by 472

Abstract

This work investigates the aerodynamic performance of a hypersonic waverider designed to operate at Mach 7, focusing on optimizing its design through advanced computational methods. Utilizing the Direct Simulation Monte Carlo (DSMC) method, the three-dimensional flow field around the specifically designed waverider was [...] Read more.

This work investigates the aerodynamic performance of a hypersonic waverider designed to operate at Mach 7, focusing on optimizing its design through advanced computational methods. Utilizing the Direct Simulation Monte Carlo (DSMC) method, the three-dimensional flow field around the specifically designed waverider was simulated to understand the shock wave interactions and thermal dynamics at an altitude of 90 km. The computational approach included detailed meshing around the vehicle’s critical leading edges and the use of three-dimensional iso-surfaces of the Q-criterion to map out the shock and vortex structures accurately. Additional simulation results demonstrate that the waverider achieved a lift–drag ratio of 2.18, confirming efficient aerodynamic performance at a zero-degree angle of attack. The study’s findings contribute to the broader understanding of hypersonic flight dynamics, highlighting the importance of precise computational modeling in developing vehicles capable of operating effectively in near-space environments. Full article

(This article belongs to the Special Issue Post-Modern Computational Fluid Dynamics)

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24 pages, 3272 KiB

Open AccessArticle

Quantifying the Health–Economy Trade-Offs: Mathematical Model of COVID-19 Pandemic Dynamics

by Dhika Surya Pangestu, Sukono, Nursanti Anggriani and Najib Majdi Yaacob

Computation 2024, 12(7), 139; https://doi.org/10.3390/computation12070139 - 8 Jul 2024

Viewed by 509

Abstract

The COVID-19 pandemic has presented a complex situation that requires a balance between control measures like lockdowns and easing restrictions. Control measures can limit the spread of the virus but can also cause economic and social issues. Easing restrictions can support economic recovery [...] Read more.

The COVID-19 pandemic has presented a complex situation that requires a balance between control measures like lockdowns and easing restrictions. Control measures can limit the spread of the virus but can also cause economic and social issues. Easing restrictions can support economic recovery but may increase the risk of virus transmission. Mathematical approaches can help address these trade-offs by modeling the interactions between factors such as virus transmission rates, public health interventions, and economic and social impacts. A study using a susceptible-infected-susceptible (SIS) model with modified discrete time was conducted to determine the cost of handling COVID-19. The results showed that, without government intervention, the number of patients rejected by health facilities and the cost of handling a pandemic increased significantly. Lockdown intervention provided the least number of rejected patients compared to social distancing, but the costs of handling the pandemic in the lockdown scenario remained higher than those of social distancing. This research demonstrates that mathematical approaches can help identify critical junctures in a pandemic, such as limited health system capacity or high transmission rates, that require rapid response and appropriate action. By using mathematical analysis, decision-makers can develop more effective and responsive strategies, considering the various factors involved in the virus’s spread and its impact on society and the economy. Full article

(This article belongs to the Topic Mathematical Modeling)

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15 pages, 10569 KiB

Open AccessArticle

Numerical Simulation and Comparison of Different Steady-State Tumble Measuring Configurations for Internal Combustion Engines

by Andreas Theodorakakos

Computation 2024, 12(7), 138; https://doi.org/10.3390/computation12070138 - 8 Jul 2024

Viewed by 354

Abstract

To enhance air–fuel mixing and turbulence during combustion, spark ignition internal combustion engines commonly employ tumble vortices of the charge inside the cylinder. The intake phase primarily dictates the generated tumble, which is influenced by the design of the intake system. Utilizing steady-state [...] Read more.

To enhance air–fuel mixing and turbulence during combustion, spark ignition internal combustion engines commonly employ tumble vortices of the charge inside the cylinder. The intake phase primarily dictates the generated tumble, which is influenced by the design of the intake system. Utilizing steady-state flow rigs provides a practical method to assess an engine’s cylinder head design’s tumble-generating characteristics. This study aims to conduct computational fluid dynamics (CFD) numerical simulations on various configurations of steady-state flow rigs and compare the resulting tumble ratios. The simulations are conducted for different inlet valve lifts of a four-valve cylinder head with a shallow pent-roof. The findings highlight variations among these widely adopted configurations. Full article

(This article belongs to the Special Issue Post-Modern Computational Fluid Dynamics)

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10 pages, 847 KiB

Open AccessBrief Report

Minimizing Cohort Discrepancies: A Comparative Analysis of Data Normalization Approaches in Biomarker Research

by Alisa Tokareva, Natalia Starodubtseva, Vladimir Frankevich and Denis Silachev

Computation 2024, 12(7), 137; https://doi.org/10.3390/computation12070137 - 5 Jul 2024

Viewed by 461

Abstract

Biological variance among samples across different cohorts can pose challenges for the long-term validation of developed models. Data-driven normalization methods offer promising tools for mitigating inter-sample biological variance. We applied seven data-driven normalization methods to quantitative metabolome data extracted from rat dried blood [...] Read more.

Biological variance among samples across different cohorts can pose challenges for the long-term validation of developed models. Data-driven normalization methods offer promising tools for mitigating inter-sample biological variance. We applied seven data-driven normalization methods to quantitative metabolome data extracted from rat dried blood spots in the context of the Rice–Vannucci model of hypoxic–ischemic encephalopathy (HIE) in rats. The quality of normalization was assessed through the performance of Orthogonal Partial Least Squares (OPLS) models built on the training datasets; the sensitivity and specificity of these models were calculated by application to validation datasets. PQN, MRN, and VSN demonstrated a higher diagnostic quality of OPLS models than the other methods studied. The OPLS model based on VSN demonstrated superior performance (86% sensitivity and 77% specificity). After VSN, the VIP-identified potential biomarkers notably diverged from those identified using other normalization methods. Glycine consistently emerged as the top marker in six out of seven models, aligning perfectly with our prior research findings. Likewise, alanine exhibited a similar pattern. Notably, VSN uniquely highlighted pathways related to the oxidation of brain fatty acids and purine metabolism. Our findings underscore the widespread utility of VSN in metabolomics, suggesting its potential for use in large-scale and cross-study investigations. Full article

(This article belongs to the Special Issue 10th Anniversary of Computation—Computational Biology)

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20 pages, 445 KiB

Open AccessArticle

Interpolation for Neural Network Operators Activated by Smooth Ramp Functions

by Fesal Baxhaku, Artan Berisha and Behar Baxhaku

Computation 2024, 12(7), 136; https://doi.org/10.3390/computation12070136 - 4 Jul 2024

Viewed by 416

Abstract

In the present article, we extend the results of the neural network interpolation operators activated by smooth ramp functions proposed by Yu (Acta Math. Sin.(Chin. Ed.) 59:623-638, 2016). We give different results from Yu (Acta Math. Sin.(Chin. Ed.) 59:623-638, 2016) we discuss the [...] Read more.

In the present article, we extend the results of the neural network interpolation operators activated by smooth ramp functions proposed by Yu (Acta Math. Sin.(Chin. Ed.) 59:623-638, 2016). We give different results from Yu (Acta Math. Sin.(Chin. Ed.) 59:623-638, 2016) we discuss the high-order approximation result using the smoothness of

φ

and a related Voronovskaya-type asymptotic expansion for the error of approximation. In addition, we showcase the related fractional estimates result and the fractional Voronovskaya type asymptotic expansion. We investigate the approximation degree for the iterated and complex extensions of the aforementioned operators. Finally, we provide numerical examples and graphs to effectively illustrate and validate our results. Full article

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21 pages, 4376 KiB

Open AccessArticle

Novel Methods for Synthesizing Self-Checking Combinational Circuits by Means of Boolean Signal Correction and Polynomial Codes

by Dmitry V. Efanov, Ruslan B. Abdullaev, Dmitry G. Plotnikov, Marina V. Bolsunovskaya, Alexey S. Odoevsky and Georgy S. Vasilyanov

Computation 2024, 12(7), 135; https://doi.org/10.3390/computation12070135 - 1 Jul 2024

Viewed by 514

Abstract

This paper proposes the use of a polynomial code for synthesizing self-checking digital devices. The code is chosen for its error detection characteristics in data symbols and is used for Boolean signals correction in embedded control circuits. In practice, it is possible to [...] Read more.

This paper proposes the use of a polynomial code for synthesizing self-checking digital devices. The code is chosen for its error detection characteristics in data symbols and is used for Boolean signals correction in embedded control circuits. In practice, it is possible to equip the device with the ability to detect faults. In contrast to the approaches found in the world literature to solve this problem, this proposal suggests identifying groups of structurally independent outputs to distinguish between convertible and non-convertible outputs of the diagnosed block in the embedded control circuit. The only outputs that can be converted are those that are used as checking symbols for the polynomial code in the embedded control circuit. The other functions remain unchanged. The polynomial codes are used to select them. The authors present algorithms for synthesizing fault detection devices using the proposed approach. Full article

(This article belongs to the Section Computational Engineering)

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19 pages, 8959 KiB

Open AccessArticle

Mathematical Modeling of the Drug Particles Deposition in the Human Respiratory System—Part 1: Development of Virtual Models of the Upper and Lower Respiratory Tract

by Natalia Menshutina, Elizaveta Mokhova and Andrey Abramov

Computation 2024, 12(7), 134; https://doi.org/10.3390/computation12070134 - 1 Jul 2024

Viewed by 515

Abstract

In order to carry out mathematical modeling of the drug particles or drop movement in the human respiratory system, an approach to reverse prototyping of the studied areas based on the medical data (computed tomography) results is presented. To adapt the computational grid, [...] Read more.

In order to carry out mathematical modeling of the drug particles or drop movement in the human respiratory system, an approach to reverse prototyping of the studied areas based on the medical data (computed tomography) results is presented. To adapt the computational grid, a mathematical model of airflow in channels of complex geometry (respiratory system) has been developed. Based on the data obtained, the results of computational experiments for a single-phase system are presented. Full article

(This article belongs to the Special Issue 10th Anniversary of Computation—Computational Biology)

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15 pages, 5474 KiB

Open AccessArticle

Comparative Analysis of Machine Learning Models for Predicting Viscosity in Tri-n-Butyl Phosphate Mixtures Using Experimental Data

by Faranak Hatami and Mousa Moradi

Computation 2024, 12(7), 133; https://doi.org/10.3390/computation12070133 - 30 Jun 2024

Viewed by 461

Abstract

Tri-n-butyl phosphate (TBP) is essential in the chemical industry for dissolving and purifying various inorganic acids and metals, especially in hydrometallurgical processes. Recent advancements suggest that machine learning can significantly improve the prediction of TBP mixture viscosities, saving time and resources while minimizing [...] Read more.

Tri-n-butyl phosphate (TBP) is essential in the chemical industry for dissolving and purifying various inorganic acids and metals, especially in hydrometallurgical processes. Recent advancements suggest that machine learning can significantly improve the prediction of TBP mixture viscosities, saving time and resources while minimizing exposure to toxic solvents. This study evaluates the effectiveness of five machine learning algorithms for automating TBP mixture viscosity prediction. Using 511 measurements collected across different compositions and temperatures, the neural network (NN) model proved to be the most accurate, achieving a Mean Squared Error (MSE) of 0.157% and an adjusted R² (a measure of how well the model predicts the variability of the outcome) of 99.72%. The NN model was particularly effective in predicting the viscosity of TBP + ethylbenzene mixtures, with a minimal deviation margin of 0.049%. These results highlight the transformative potential of machine learning to enhance the efficiency and precision of hydrometallurgical processes involving TBP mixtures, while also reducing operational risks. Full article

(This article belongs to the Section Computational Engineering)

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22 pages, 3143 KiB

Open AccessArticle

Candlestick Pattern Recognition in Cryptocurrency Price Time-Series Data Using Rule-Based Data Analysis Methods

by Illia Uzun, Mykhaylo Lobachev, Vyacheslav Kharchenko, Thorsten Schöler and Ivan Lobachev

Computation 2024, 12(7), 132; https://doi.org/10.3390/computation12070132 - 29 Jun 2024

Viewed by 437

Abstract

In the rapidly evolving domain of cryptocurrency trading, accurate market data analysis is crucial for informed decision making. Candlestick patterns, a cornerstone of technical analysis, serve as visual representations of market sentiment and potential price movements. However, the sheer volume and complexity of [...] Read more.

In the rapidly evolving domain of cryptocurrency trading, accurate market data analysis is crucial for informed decision making. Candlestick patterns, a cornerstone of technical analysis, serve as visual representations of market sentiment and potential price movements. However, the sheer volume and complexity of cryptocurrency price time-series data presents a significant challenge to traders and analysts alike. This paper introduces an innovative rule-based methodology for recognizing candlestick patterns in cryptocurrency markets using Python. By focusing on Ethereum, Bitcoin, and Litecoin, this study demonstrates the effectiveness of the proposed methodology in identifying key candlestick patterns associated with significant market movements. The structured approach simplifies the recognition process while enhancing the precision and reliability of market analysis. Through rigorous testing, this study shows that the automated recognition of these patterns provides actionable insights for traders. This paper concludes with a discussion on the implications, limitations, and potential future research directions that contribute to the field of computational finance by offering a novel tool for automated analysis in the highly volatile cryptocurrency market. Full article

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22 pages, 2165 KiB

Open AccessReview

Factors, Prediction, and Explainability of Vehicle Accident Risk Due to Driving Behavior through Machine Learning: A Systematic Literature Review, 2013–2023

by Javier Lacherre, José Luis Castillo-Sequera and David Mauricio

Computation 2024, 12(7), 131; https://doi.org/10.3390/computation12070131 - 28 Jun 2024

Viewed by 881

Abstract

Road accidents are on the rise worldwide, causing 1.35 million deaths per year, thus encouraging the search for solutions. The promising proposal of autonomous vehicles stands out in this regard, although fully automated driving is still far from being an achievable reality. Therefore, [...] Read more.

Road accidents are on the rise worldwide, causing 1.35 million deaths per year, thus encouraging the search for solutions. The promising proposal of autonomous vehicles stands out in this regard, although fully automated driving is still far from being an achievable reality. Therefore, efforts have focused on predicting and explaining the risk of accidents using real-time telematics data. This study aims to analyze the factors, machine learning algorithms, and explainability methods most used to assess the risk of vehicle accidents based on driving behavior. A systematic review of the literature produced between 2013 and July 2023 on factors, prediction algorithms, and explainability methods to predict the risk of traffic accidents was carried out. Factors were categorized into five domains, and the most commonly used predictive algorithms and explainability methods were determined. We selected 80 articles from journals indexed in the Web of Science and Scopus databases, identifying 115 factors within the domains of environment, traffic, vehicle, driver, and management, with speed and acceleration being the most extensively examined. Regarding machine learning advancements in accident risk prediction, we identified 22 base algorithms, with convolutional neural network and gradient boosting being the most commonly used. For explainability, we discovered six methods, with random forest being the predominant choice, particularly for feature importance analysis. This study categorizes the factors affecting road accident risk, presents key prediction algorithms, and outlines methods to explain the risk assessment based on driving behavior, taking vehicle weight into consideration. Full article

(This article belongs to the Section Computational Engineering)

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Computation, Volume 12, Issue 7 (July 2024) – 20 articles

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