Computation

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

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

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, 3145 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

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

22 pages, 925 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

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)

21 pages, 4645 KiB

Open AccessArticle

Human–Object Interaction: Development of a Usability Index for Product Design Using a Hierarchical Fuzzy Axiomatic Design

by Mayra Ivette Peña-Ontiveros, Cesar Omar Balderrama-Armendariz, Alberto Rossa-Sierra, Aide Aracely Maldonado-Macias, David Cortés Sáenz and Juan Luis Hernández Arellano

Computation 2024, 12(6), 130; https://doi.org/10.3390/computation12060130 - 20 Jun 2024

Abstract

Consumer product usability has been addressed using tools that evaluate objects to improve user interaction. However, such diversity in approach makes it challenging to select a method for the type of product being assessed. This article compiles the concepts used since the origin [...] Read more.

Consumer product usability has been addressed using tools that evaluate objects to improve user interaction. However, such diversity in approach makes it challenging to select a method for the type of product being assessed. This article compiles the concepts used since the origin of usability in product design. It groups them by attributes to formulate a usability index proposal. Due to the nature of the data, fuzzy, hierarchical, and axiomatic tools were applied to a trial group of experts and users. Three questionnaires were designed and administered throughout a five-stage process to collect and select attributes, rank them in importance, assign fuzzy values, obtain their numerical representation of use, and assign a qualitative category. By analyzing a case study, this research demonstrates the value of the index by comparing the use of computer mice. Unlike other approaches to evaluating usability, the proposed index incorporates the hierarchical importance of attributes. It allows for participants to express their opinions, transforming subjective responses into linguistic values represented in triangular areas, resulting in a more accurate representation of reality. Additionally, the complexity of the human–object interaction is treated by an information axiom to compute the usability index on a scale from 0 to 1, which reflects the probability of the product meeting the desired usability attributes. Full article

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27 pages, 9307 KiB

Open AccessArticle

Development and Verification of Coupled Fluid–Structure Interaction Solver

by Avery Schemmel, Seshendra Palakurthy, Anup Zope, Eric Collins and Shanti Bhushan

Computation 2024, 12(6), 129; https://doi.org/10.3390/computation12060129 - 20 Jun 2024

Abstract

Recent trends in aeroelastic analysis have shown a great interest in understanding the role of shock boundary layer interaction in predicting the dynamic instability of aircraft structural components at supersonic and hypersonic flows. The analysis of such complex dynamics requires a time-accurate fluid-structure [...] Read more.

Recent trends in aeroelastic analysis have shown a great interest in understanding the role of shock boundary layer interaction in predicting the dynamic instability of aircraft structural components at supersonic and hypersonic flows. The analysis of such complex dynamics requires a time-accurate fluid-structure interaction solver. This study focuses on the development of such a solver by coupling a finite-volume Navier-Stokes solver for fluid flow with a finite-element solver for structural dynamics. The coupled solver is then verified for the prediction of several panel instability cases in 2D and 3D uniform flows and in the presence of an impinging shock for a range of subsonic and supersonic Mach numbers, dynamic pressures, and shock strengths. The panel deflections and limit cycle oscillation amplitudes, frequencies, and bifurcation point predictions were compared within

10 %

of the benchmark results; thus, the solver was deemed verified. Future studies will focus on extending the solver to 3D turbulent flows and applying the solver to study the effect of turbulent load fluctuations and shock boundary layer interactions on the fluid-structure coupling and structural dynamics of 2D panels. Full article

(This article belongs to the Special Issue Emerging Trends and Applications in High-Fidelity Computational Fluid Dynamics Simulation)

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24 pages, 1807 KiB

Open AccessArticle

Teaching K–3 Multi-Digit Arithmetic Computation to Students with Slow Language Processing

by Richard M. Oldrieve

Computation 2024, 12(6), 128; https://doi.org/10.3390/computation12060128 - 19 Jun 2024

Abstract

The purpose of this article is to present three related studies that build on each other to demonstrate first the need and then the efficacy of the Blended Arithmetic Curriculum (BAC) to help students overcome both slow language processing and the environmental effects [...] Read more.

The purpose of this article is to present three related studies that build on each other to demonstrate first the need and then the efficacy of the Blended Arithmetic Curriculum (BAC) to help students overcome both slow language processing and the environmental effects of being a student in an urban school district. The author’s underlying theory is that K–3 students with slow language processing may be good at complex reasoning, but still struggle with retrieving basic computational facts. Nonetheless, if they did not learn their facts, these students would struggle with K–3 multi-digit arithmetic computation, and ultimately struggle with their hypothesized strength: seeing numeric patterns as would be needed in university level computation. To teach arithmetic facts conceptually, the author developed a paper and pencil curriculum that first teaches complex multi-digit addition with regrouping using a limited number of facts such as 5 + 5, 9 + 1, 1 + 9; 7 + 7, 7 + 8, 8 + 7, and 8 + 8 in problems such as 197 + 108 = 305 so that fact retrieval and computation are fast and accurate. At the end of 2nd grade, urban students with learning disabilities solved 42 two-digit by two-digit problems with 92 percent accuracy in an average of 7 min. The results matched those of suburban students and were significantly faster and more accurate than general education students in the same urban school. Full article

(This article belongs to the Special Issue Computations in Mathematics, Mathematical Education, and Science)

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23 pages, 4433 KiB

Open AccessArticle

Solution of the Optimization Problem of Magnetotelluric Sounding in Quaternions by the Differential Evolution Method

by Syrym E. Kasenov, Zhanar E. Demeubayeva, Nurlan M. Temirbekov and Laura N. Temirbekova

Computation 2024, 12(6), 127; https://doi.org/10.3390/computation12060127 - 18 Jun 2024

Abstract

The article discusses the application of quaternion Fourier transforms and quaternion algebra to transform Maxwell’s equations. This makes it possible to present the problem of magnetotelluric sensing (MTS) in a more convenient form for research. Studies of the inverse MTS problem for multi-layer [...] Read more.

The article discusses the application of quaternion Fourier transforms and quaternion algebra to transform Maxwell’s equations. This makes it possible to present the problem of magnetotelluric sensing (MTS) in a more convenient form for research. Studies of the inverse MTS problem for multi-layer regions are presented using the differential evolution method, which demonstrates high convergence. For single-layer regions, a new method for solving inverse problems based on minimizing the quadratic functional using conjugate optimization methods is considered. Numerical results obtained using special Python libraries are presented, with analysis and conclusions. Full article

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32 pages, 10061 KiB

Open AccessArticle

Residential Sizing of Solar Photovoltaic Systems and Heat Pumps for Net Zero Sustainable Thermal Building Energy

by Shafquat Rana, Uzair Jamil, Nima Asgari, Koami S. Hayibo, Julia Groza and Joshua M. Pearce

Computation 2024, 12(6), 126; https://doi.org/10.3390/computation12060126 - 17 Jun 2024

Abstract

To enable net zero sustainable thermal building energy, this study develops an open-source thermal house model to couple solar photovoltaic (PV) and heat pumps (HPs) for grid-connected residential housing. The calculation of both space heating and cooling thermal loads and the selection of [...] Read more.

To enable net zero sustainable thermal building energy, this study develops an open-source thermal house model to couple solar photovoltaic (PV) and heat pumps (HPs) for grid-connected residential housing. The calculation of both space heating and cooling thermal loads and the selection of HP is accomplished with a validated Python model for air-source heat pumps. The capacity of PV required to supply the HPs is calculated using a System Advisor Model integrated Python model. Self-sufficiency and self-consumption of PV and the energy imported/exported to the grid for a case study are provided, which shows that simulations based on the monthly load profile have a significant reduction of 43% for energy sent to/from the grid compared to the detailed hourly simulation and an increase from 30% to 60% for self-consumption and self-sufficiency. These results show the importance of more granular modeling and also indicate mismatches of PV generation and HP load based on hourly simulation datasets. The back-calculation PV sizing algorithm combined with HP and thermal loads presented in this study exhibited robust performance. The results indicate this approach can be used to accelerate the solar electrification of heating and cooling to offset the use of fossil fuels in northern climates. Full article

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

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17 pages, 1849 KiB

Open AccessArticle

Syrga2: Post-Quantum Hash-Based Signature Scheme

by Kunbolat Algazy, Kairat Sakan, Saule Nyssanbayeva and Oleg Lizunov

Computation 2024, 12(6), 125; https://doi.org/10.3390/computation12060125 - 15 Jun 2024

Abstract

This paper proposes a new post-quantum signature scheme, Syrga2, based on hash functions. As known, existing post-quantum algorithms are classified based on their structures. The proposed Syrga2 scheme belongs to the class of multi-use signatures with state retention. A distinctive feature of state-retaining [...] Read more.

This paper proposes a new post-quantum signature scheme, Syrga2, based on hash functions. As known, existing post-quantum algorithms are classified based on their structures. The proposed Syrga2 scheme belongs to the class of multi-use signatures with state retention. A distinctive feature of state-retaining signatures is achieving a compromise between performance and signature size. This scheme enables the creation of a secure signature for r messages using a single pair of secret and public keys. The strength of signature algorithms based on hash functions depends on the properties of the hash function used in their structure. Additionally, for such algorithms, it is possible to specify the security level precisely. In the proposed scheme, the HBC-256 algorithm developed at the Institute of Information and Computational Technologies (IICT) is used as the hash function. The security of the HBC-256 algorithm has been thoroughly studied in other works by the authors. In contrast to the Syrga1 scheme presented in previous works by the authors, the Syrga2 scheme provides for the definition of different security levels determined by the parameter τ. This paper experimentally demonstrates the impossibility of breaking the proposed scheme using a chosen-plaintext attack. Additionally, the scheme’s performance is evaluated for signature creation, signing, and message verification. Full article

(This article belongs to the Section Computational Engineering)

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16 pages, 8590 KiB

Open AccessArticle

Large-Eddy Simulations of a Supersonic Impinging Jet Using OpenFOAM

by Rion Guang Yi You, Tze How New and Wai Lee Chan

Computation 2024, 12(6), 124; https://doi.org/10.3390/computation12060124 - 15 Jun 2024

Abstract

Supersonic impinging jets are a versatile configuration that can model the compressible flows of cold-spray manufacturing and vertical take-off-and landing strategy. In this work, rhoCentralFoam, solver of the OpenFOAM framework, and a large-eddy simulation formulation were used to simulate an underexpanded supersonic [...] Read more.

Supersonic impinging jets are a versatile configuration that can model the compressible flows of cold-spray manufacturing and vertical take-off-and landing strategy. In this work, rhoCentralFoam, solver of the OpenFOAM framework, and a large-eddy simulation formulation were used to simulate an underexpanded supersonic jet of Mach

1.45

and nozzle pressure ratio of 4, impinging on a flat wall situated at

1.5

nozzle diameters away from the jet outlet. Care was taken in the mesh construction to properly capture the characteristic standoff shock and vortical structures. The grid convergence index was evaluated with three meshes of increasing spatial resolution. All meshes can generally be considered as sufficient in terms of results focused on time-averaged values and mean physical properties such as centerline Mach number profile. However, the highest resolution mesh was found to capture fine shear vortical structures and behaviors that are absent in the coarser cases. Therefore, the notion of adequate grid convergence may differ between analyses of time-averaged and transient information, and so should be determined by the user’s intention for conducting the simulations. To guide the selection of mesh resolution, scaling analyses were performed, for which the current rhoCentralFoam solver displays a good weak scaling performance and maintains a linear strong scaling up to 4096 cores (32 nodes) for an approximately 40 million-cell mesh. Due to the internode communication bottlenecks of OpenFOAM and improvements in central processing units, this work recommends, for future scaling analyses, adopting a “cells-per-node” basis over the conventional “cells-per-core” basis, with particular attention to the interconnect speed and architecture used. Full article

(This article belongs to the Special Issue Recent Advances in Numerical Simulation of Compressible Flows)

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18 pages, 6823 KiB

Open AccessArticle

Description of Mesoscale Static and Fatigue Analysis of 2D Woven Roving Plates with Convex Holes Subjected to Axial Tension

by Aleksander Muc

Computation 2024, 12(6), 123; https://doi.org/10.3390/computation12060123 - 13 Jun 2024

Abstract

The static and fatigue analysis of plates made of 2D woven roving composites with holes is conducted. The parametrization of convex holes is proposed. The experimental results of the specimens without holes and with different shapes of notches are discussed. The experiments and [...] Read more.

The static and fatigue analysis of plates made of 2D woven roving composites with holes is conducted. The parametrization of convex holes is proposed. The experimental results of the specimens without holes and with different shapes of notches are discussed. The experiments and the appropriate procedures are carried out with the aid of ASTM codes. The fatigue behavior is considered with the use of the low cycle fatigue method. The analysis is supplemented by numerical finite element modeling. The present work is an extension of the results discussed in the literature. The damage of plates with holes subjected to tension always occurs at the tip of the holes, i.e., (x = a, b = 0), both for static and fatigue failure. The originality and the novelty of this approach are described by the failure’s dependence on two parameters: n and the ratio of the a/b ratio characterizing the hole geometry. The fuzzy approach is employed to reduce the amount of experimental data. Full article

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

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15 pages, 7617 KiB

Open AccessArticle

Design, Fabrication and Testing of a Multifrequency Microstrip RFID Tag Antenna on Si

by Timothea Korfiati, Christos N. Vazouras, Christos Bolakis, Antonis Stavrinidis, Giorgos Stavrinidis and Aggeliki Arapogianni

Computation 2024, 12(6), 122; https://doi.org/10.3390/computation12060122 - 13 Jun 2024

Abstract

A configurable design of a microstrip square spiral RFID tag antenna, for a wide range of microwave frequencies in the S- and C-band, is presented. The design is parameterized in dimensions, and hence changing the design frequency (or frequencies) is easy, by changing [...] Read more.

A configurable design of a microstrip square spiral RFID tag antenna, for a wide range of microwave frequencies in the S- and C-band, is presented. The design is parameterized in dimensions, and hence changing the design frequency (or frequencies) is easy, by changing only an initial value for the spiral geometry. A tag specimen was fabricated using a Cu electroplating technique according to the design for frequencies of interest in the areas of 2.4 and 5.8 GHz. The substrate material is 320 μm high-resistivity Si and the bridge dielectric is 15 μm polyimide PI2525. The steps of the microfabrication process involve metallic structure pattern transfer techniques with optical UV lithography procedures. The reflection coefficient and antenna gain of the specimen were measured inside an anechoic enclosure using a vector network analyzer (VNA) and a TEM horn test antenna over a frequency range of up to 6 GHz. Simulated and measured results, exhibiting reasonable agreement, are presented and discussed. Full article

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

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14 pages, 340 KiB

Open AccessArticle

Optimizing Sensor-Controlled Systems with Minimal Intervention: A Fuzzy Relational Calculus Approach

by Zlatko Zahariev

Computation 2024, 12(6), 121; https://doi.org/10.3390/computation12060121 - 11 Jun 2024

Abstract

This article describes an approach for optimizing sensor-controlled systems through minimal intervention, utilizing fuzzy linear systems of equations (FLSEs). Starting with a generalized model of the system behavior, we incorporate an array of control units, environmental sensors, and an expert knowledge base. The [...] Read more.

This article describes an approach for optimizing sensor-controlled systems through minimal intervention, utilizing fuzzy linear systems of equations (FLSEs). Starting with a generalized model of the system behavior, we incorporate an array of control units, environmental sensors, and an expert knowledge base. The described problems of detecting the level of intervention needed to change the system state to another is handled with the help of developed methods for solving the inverse problem faced by FLSEs. By achieving minimal intervention, we ensure that the system adjustments are effective, economically optimal, and non-intrusive. A MATLAB-based implementation is presented. Full article

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

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18 pages, 1602 KiB

Open AccessArticle

Modeling of Mean-Value-at-Risk Investment Portfolio Optimization Considering Liabilities and Risk-Free Assets

by Sukono, Puspa Liza Binti Ghazali, Muhamad Deni Johansyah, Riaman, Riza Andrian Ibrahim, Mustafa Mamat and Aceng Sambas

Computation 2024, 12(6), 120; https://doi.org/10.3390/computation12060120 - 11 Jun 2024

Abstract

This paper aims to design a quadratic optimization model of an investment portfolio based on value-at-risk (VaR) by entering risk-free assets and company liabilities. The designed model develops Markowitz’s investment portfolio optimization model with risk aversion. Model development was carried out using vector [...] Read more.

This paper aims to design a quadratic optimization model of an investment portfolio based on value-at-risk (VaR) by entering risk-free assets and company liabilities. The designed model develops Markowitz’s investment portfolio optimization model with risk aversion. Model development was carried out using vector and matrix equations. The entry of risk-free assets and liabilities is essential. Risk-free assets reduce the loss risk, while liabilities accommodate a fundamental analysis of the company’s condition. The model can be applied in various sectors of capital markets worldwide. This study applied the model to Indonesia’s mining and energy sector. The application results show that risk aversion negatively correlates with the mean and VaR of the return of investment portfolios. Assuming that risk aversion is in the 5.1% to 8.2% interval, the maximum mean and VaR obtained for the next month are 0.0103316 and 0.0138270, respectively, while the minimum mean and VaR are 0.0102964 and 0.0137975, respectively. The finding of this study is that the vector equation for investment portfolio weights is obtained, which can facilitate calculating investment portfolio weight optimization. This study is expected to help investors control the quality of appropriate investment, especially in some stocks in Indonesia’s mining and energy sector. Full article

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13 pages, 1282 KiB

Open AccessArticle

Development of a Compartment Model to Study the Pharmacokinetics of Medical THC after Oral Administration

by Thanachok Mahahong and Teerapol Saleewong

Computation 2024, 12(6), 119; https://doi.org/10.3390/computation12060119 - 11 Jun 2024

Abstract

The therapeutic potential of delta9-tetrahydrocannabinol (THC), a primary cannabinoid in the cannabis plant, has led to its development into oral medical products for treating various conditions. However, THC, being a psychoactive substance, can lead to addiction if taken in inappropriate amounts. Thus, studying [...] Read more.

The therapeutic potential of delta9-tetrahydrocannabinol (THC), a primary cannabinoid in the cannabis plant, has led to its development into oral medical products for treating various conditions. However, THC, being a psychoactive substance, can lead to addiction if taken in inappropriate amounts. Thus, studying the pharmacokinetics of THC is crucial for understanding how the drug behaves in the body after administration. This study aims to develop a multi-compartmental model to investigate the pharmacokinetics of medical THC and its metabolites after oral administration. Using the law of mass action, the model was converted into ordinary differential equations (ODEs) to describe the rate of concentration changes of THC and its metabolites in each compartment. The nonstandard finite difference (NSFD) method was then applied to construct numerical solution schemes, which were implemented in MATLAB along with estimated pharmacokinetic rate constants. The results demonstrate that the simulation curves depicting the plasma concentration–time profiles of THC and 11-hydroxy-THC (THC-OH) closely resemble actual data samples, indicating the model’s accuracy. Moreover, the model predicts the pharmacokinetics of THC and its metabolites in various tissues. Consequently, this model serves as a valuable tool for enhancing our understanding of the pharmacokinetics of THC and its metabolites, guiding dosage adjustments, and determining administration durations for oral medical THC. Full article

(This article belongs to the Topic Mathematical Modeling)

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33 pages, 14134 KiB

Open AccessCommunication

Investigation of the Global Fear Associated with COVID-19 Using Subjectivity Analysis and Deep Learning

by Nirmalya Thakur, Kesha A. Patel, Audrey Poon, Rishika Shah, Nazif Azizi and Changhee Han

Computation 2024, 12(6), 118; https://doi.org/10.3390/computation12060118 - 10 Jun 2024

Abstract

The work presented in this paper makes multiple scientific contributions related to the investigation of the global fear associated with COVID-19 by performing a comprehensive analysis of a dataset comprising survey responses of participants from 40 countries. First, the results of subjectivity analysis [...] Read more.

The work presented in this paper makes multiple scientific contributions related to the investigation of the global fear associated with COVID-19 by performing a comprehensive analysis of a dataset comprising survey responses of participants from 40 countries. First, the results of subjectivity analysis performed using TextBlob, showed that in the responses where participants indicated their biggest concern related to COVID-19, the average subjectivity by the age group of 41–50 decreased from April 2020 to June 2020, the average subjectivity by the age group of 71–80 drastically increased from May 2020, and the age group of 11–20 indicated the least level of subjectivity between June 2020 to August 2020. Second, subjectivity analysis also revealed the percentage of highly opinionated, neutral opinionated, and least opinionated responses per age-group where the analyzed age groups were 11–20, 21–30, 31–40, 41–50, 51–60, 61–70, 71–80, and 81–90. For instance, the percentage of highly opinionated, neutral opinionated, and least opinionated responses by the age group of 11–20 were 17.92%, 16.24%, and 65.84%, respectively. Third, data analysis of responses from different age groups showed that the highest percentage of responses indicating that they were very worried about COVID-19 came from individuals in the age group of 21–30. Fourth, data analysis of the survey responses also revealed that in the context of taking precautions to prevent contracting COVID-19, the percentage of individuals in the age group of 31–40 taking precautions was higher as compared to the percentages of individuals from the age groups of 41–50, 51–60, 61–70, 71–80, and 81–90. Fifth, a deep learning model was developed to detect if the survey respondents were seeing or planning to see a psychologist or psychiatrist for any mental health issues related to COVID-19. The design of the deep learning model comprised 8 neurons for the input layer with the ReLU activation function, the ReLU activation function for all the hidden layers with 12 neurons each, and the sigmoid activation function for the output layer with 1 neuron. The model utilized the responses to multiple questions in the context of fear and preparedness related to COVID-19 from the dataset and achieved an accuracy of 91.62% after 500 epochs. Finally, two comparative studies with prior works in this field are presented to highlight the novelty and scientific contributions of this research work. Full article

(This article belongs to the Special Issue Computational Social Science and Complex Systems)

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18 pages, 5507 KiB

Open AccessArticle

Fractional Boundary Element Solution for Nonlinear Nonlocal Thermoelastic Problems of Anisotropic Fibrous Polymer Nanomaterials

by Mohamed Abdelsabour Fahmy and Moncef Toujani

Computation 2024, 12(6), 117; https://doi.org/10.3390/computation12060117 - 8 Jun 2024

Abstract

This paper provides a new fractional boundary element method (BEM) solution for nonlinear nonlocal thermoelastic problems with anisotropic fibrous polymer nanoparticles. This comprehensive BEM solution comprises two solutions: the anisotropic fibrous polymer nanoparticles problem solution and the nonlinear nonlocal thermoelasticity problem. The nonlinear [...] Read more.

This paper provides a new fractional boundary element method (BEM) solution for nonlinear nonlocal thermoelastic problems with anisotropic fibrous polymer nanoparticles. This comprehensive BEM solution comprises two solutions: the anisotropic fibrous polymer nanoparticles problem solution and the nonlinear nonlocal thermoelasticity problem. The nonlinear nonlocal thermoelasticity problem solution separates the displacement field into complimentary and specific components. The overall displacement is obtained using the boundary element methodology, which solves a Navier-type problem, and the specific displacement is derived using the local radial point interpolation method (LRPIM). The new modified shift-splitting (NMSS) technique, which minimizes memory and processing time requirements, was utilized to solve BEM-created linear systems. The performance of NMSS was evaluated. The numerical results show how fractional and graded parameters influence the thermal stresses of nonlinear nonlocal thermoelastic issues involving anisotropic fibrous polymer nanoparticles. The numerical findings further reveal that the BEM results correlate very well with the finite element method (FEM) and analytical results, demonstrating the validity and correctness of the proposed methodology. Full article

(This article belongs to the Special Issue Computational Approaches for Materials Engineering and Applications)

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17 pages, 7133 KiB

Open AccessArticle

Deep-Reinforcement-Learning-Based Motion Planning for a Wide Range of Robotic Structures

by Roman Parák, Jakub Kůdela, Radomil Matoušek and Martin Juříček

Computation 2024, 12(6), 116; https://doi.org/10.3390/computation12060116 - 5 Jun 2024

Abstract

The use of robot manipulators in engineering applications and scientific research has significantly increased in recent years. This can be attributed to the rise of technologies such as autonomous robotics and physics-based simulation, along with the utilization of artificial intelligence techniques. The use [...] Read more.

The use of robot manipulators in engineering applications and scientific research has significantly increased in recent years. This can be attributed to the rise of technologies such as autonomous robotics and physics-based simulation, along with the utilization of artificial intelligence techniques. The use of these technologies may be limited due to a focus on a specific type of robotic manipulator and a particular solved task, which can hinder modularity and reproducibility in future expansions. This paper presents a method for planning motion across a wide range of robotic structures using deep reinforcement learning (DRL) algorithms to solve the problem of reaching a static or random target within a pre-defined configuration space. The paper addresses the challenge of motion planning in environments under a variety of conditions, including environments with and without the presence of collision objects. It highlights the versatility and potential for future expansion through the integration of OpenAI Gym and the PyBullet physics-based simulator. Full article

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

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