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Computation, Volume 12, Issue 6 (June 2024) – 19 articles

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23 pages, 863 KiB  
Article
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 (registering DOI) - 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
32 pages, 2566 KiB  
Article
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
Viewed by 174
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
17 pages, 1195 KiB  
Article
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
Viewed by 242
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)
16 pages, 8583 KiB  
Article
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
Viewed by 146
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 jet [...] 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)
18 pages, 6823 KiB  
Article
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
Viewed by 243
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  
Article
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
Viewed by 261
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
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14 pages, 340 KiB  
Article
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
Viewed by 249
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, 644 KiB  
Article
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
Viewed by 268
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
13 pages, 1282 KiB  
Article
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
Viewed by 279
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, 4335 KiB  
Communication
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
Viewed by 270
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)
18 pages, 2368 KiB  
Article
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
Viewed by 203
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)
17 pages, 7133 KiB  
Article
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
Viewed by 373
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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22 pages, 4282 KiB  
Article
High-Performance Krawtchouk Polynomials of High Order Based on Multithreading
by Wameedh Nazar Flayyih, Ahlam Hanoon Al-sudani, Basheera M. Mahmmod, Sadiq H. Abdulhussain and Muntadher Alsabah
Computation 2024, 12(6), 115; https://doi.org/10.3390/computation12060115 - 4 Jun 2024
Viewed by 312
Abstract
Orthogonal polynomials and their moments serve as pivotal elements across various fields. Discrete Krawtchouk polynomials (DKraPs) are considered a versatile family of orthogonal polynomials and are widely used in different fields such as probability theory, signal processing, digital communications, and image processing. Various [...] Read more.
Orthogonal polynomials and their moments serve as pivotal elements across various fields. Discrete Krawtchouk polynomials (DKraPs) are considered a versatile family of orthogonal polynomials and are widely used in different fields such as probability theory, signal processing, digital communications, and image processing. Various recurrence algorithms have been proposed so far to address the challenge of numerical instability for large values of orders and signal sizes. The computation of DKraP coefficients was typically computed using sequential algorithms, which are computationally extensive for large order values and polynomial sizes. To this end, this paper introduces a computationally efficient solution that utilizes the parallel processing capabilities of modern central processing units (CPUs), namely the availability of multiple cores and multithreading. The proposed multi-threaded implementations for computing DKraP coefficients divide the computations into multiple independent tasks, which are executed concurrently by different threads distributed among the independent cores. This multi-threaded approach has been evaluated across a range of DKraP sizes and various values of polynomial parameters. The results show that the proposed method achieves a significant reduction in computation time. In addition, the proposed method has the added benefit of applying to larger polynomial sizes and a wider range of Krawtchouk polynomial parameters. Furthermore, an accurate and appropriate selection scheme of the recurrence algorithm is introduced. The proposed approach introduced in this paper makes the DKraP coefficient computation an attractive solution for a variety of applications. Full article
(This article belongs to the Section Computational Engineering)
17 pages, 3016 KiB  
Article
Implications of Using Scalar Forcing to Sustain Reactant Mixture Stratification in Direct Numerical Simulations of Turbulent Combustion
by Peter Brearley, Umair Ahmed and Nilanjan Chakraborty
Computation 2024, 12(6), 114; https://doi.org/10.3390/computation12060114 - 3 Jun 2024
Viewed by 234
Abstract
A recently proposed scalar forcing scheme that maintains the mixture fraction mean, root-mean-square and probability density function in the unburned gas can lead to a statistically quasi-stationary state in direct numerical simulations of turbulent stratified combustion when combined with velocity forcing. Scalar forcing [...] Read more.
A recently proposed scalar forcing scheme that maintains the mixture fraction mean, root-mean-square and probability density function in the unburned gas can lead to a statistically quasi-stationary state in direct numerical simulations of turbulent stratified combustion when combined with velocity forcing. Scalar forcing alongside turbulence forcing leads to greater values of turbulent burning velocity and flame surface area in comparison to unforced simulations for globally fuel-lean mixtures. The sustained unburned gas mixture inhomogeneity changes the percentage shares of back- and front-supported flame elements in comparison to unforced simulations, and this effect is particularly apparent for high turbulence intensities. Scalar forcing does not significantly affect the heat release rates due to different modes of combustion and the micro-mixing rate within the flame characterised by scalar dissipation rate of the reaction progress variable. Thus, scalar forcing has a significant potential for enabling detailed parametric studies as well as providing well-converged time-averaged statistics for stratified-mixture combustion using Direct Numerical Simulations in canonical configurations. Full article
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17 pages, 4810 KiB  
Article
Analysing the Performance and Interpretability of CNN-Based Architectures for Plant Nutrient Deficiency Identification
by Junior Mkhatshwa, Tatenda Kavu and Olawande Daramola
Computation 2024, 12(6), 113; https://doi.org/10.3390/computation12060113 - 3 Jun 2024
Viewed by 259
Abstract
Early detection of plant nutrient deficiency is crucial for agricultural productivity. This study investigated the performance and interpretability of Convolutional Neural Networks (CNNs) for this task. Using the rice and banana datasets, we compared three CNN architectures (CNN, VGG-16, Inception-V3). Inception-V3 achieved the [...] Read more.
Early detection of plant nutrient deficiency is crucial for agricultural productivity. This study investigated the performance and interpretability of Convolutional Neural Networks (CNNs) for this task. Using the rice and banana datasets, we compared three CNN architectures (CNN, VGG-16, Inception-V3). Inception-V3 achieved the highest accuracy (93% for rice and banana), but simpler models such as VGG-16 might be easier to understand. To address this trade-off, we employed Explainable AI (XAI) techniques (SHAP and Grad-CAM) to gain insights into model decision-making. This study emphasises the importance of both accuracy and interpretability in agricultural AI and demonstrates the value of XAI for building trust in these models. Full article
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41 pages, 3950 KiB  
Article
Structure-Based Discovery of Potential HPV E6 and EBNA1 Inhibitors: Implications for Cervical Cancer Treatment
by Emmanuel Broni, Carolyn N. Ashley, Miriam Velazquez, Patrick O. Sakyi, Samuel K. Kwofie and Whelton A. Miller III
Computation 2024, 12(6), 112; https://doi.org/10.3390/computation12060112 - 31 May 2024
Viewed by 341
Abstract
Cervical cancer is the fourth most diagnosed cancer and the fourth leading cause of cancer death in women globally. Its onset and progression have been attributed to high-risk human papillomavirus (HPV) types, especially 16 and 18, while the Epstein–Barr virus (EBV) is believed [...] Read more.
Cervical cancer is the fourth most diagnosed cancer and the fourth leading cause of cancer death in women globally. Its onset and progression have been attributed to high-risk human papillomavirus (HPV) types, especially 16 and 18, while the Epstein–Barr virus (EBV) is believed to also significantly contribute to cervical cancer growth. The E6 protein associated with high-risk HPV strains, such as HPV16 and HPV18, is known for its role in promoting cervical cancer and other anogenital cancers. E6 proteins contribute to the malignant transformation of infected cells by targeting and degrading tumor suppressor proteins, especially p53. On the other hand, EBV nuclear antigen 1 (EBNA1) plays a crucial role in the maintenance and replication of the EBV genome in infected cells. EBNA1 is believed to increase HPV E6 and E7 levels, as well as c-MYC, and BIRC5 cellular genes in the HeLa cell line, implying that HPV/EBV co-infection accelerates cervical cancer onset and growth. Thus, the E6 and EBNA1 antigens of HPV and EBV, respectively, are attractive targets for cervical cancer immunotherapy. This study, therefore, virtually screened for potential drug candidates with good binding affinity to all three oncoviral proteins, HPV16 E6, HPV18 E6, and EBNA1. The compounds were further subjected to ADMET profiling, biological activity predictions, molecular dynamics (MD) simulations, and molecular mechanics Poisson–Boltzmann surface area (MM/PBSA) calculations. A total of six compounds comprising ZINC000013380012, ZINC000070454124, ZINC000014588133, ZINC000085568136, ZINC000095909247, and ZINC000085597263 demonstrated very strong affinity (≤−60 kJ/mol) to the three oncoviral proteins (EBNA1, HPV16 E6, and HPV18 E6) after being subjected to docking, MD, and MM/PBSA. These compounds demonstrated relatively stronger binding than the controls used, inhibitors of EBNA1 (VK-1727) and HPV E6 (baicalein and gossypetin). Biological activity predictions also corroborated their antineoplastic, p53-enhancing, Pin1 inhibitory, and JAK2 inhibitory activities. Further experimental testing is required to validate the ability of the shortlisted compounds to silence the insidious effects of HPV E6 and EBNA1 proteins in cervical cancers. Full article
(This article belongs to the Special Issue 10th Anniversary of Computation—Computational Biology)
12 pages, 932 KiB  
Article
The Effect of Critical Distance in Digital Levelling
by Jana Izvoltova, Jakub Chromcak and Dasa Bacova
Computation 2024, 12(6), 111; https://doi.org/10.3390/computation12060111 - 31 May 2024
Viewed by 206
Abstract
Critical distance concerns precise digital levelling, which has inaccurate results at a certain sighting distance. The influence of critical distance on a measured height difference has been confirmed by calibrating certain digital levels and their appropriate code devices on a vertical comparator under [...] Read more.
Critical distance concerns precise digital levelling, which has inaccurate results at a certain sighting distance. The influence of critical distance on a measured height difference has been confirmed by calibrating certain digital levels and their appropriate code devices on a vertical comparator under laboratory conditions. The paper aims to explore the influence of critical distance on height differences obtained by precise digital levels of Leica NA3003 and DNA03 by experimental measurements realised in situ. The processing of the measurement results consisted of defining a random error on a station by using parameter estimation of an error model to specify a partial error on a station dependent on sighting distance. Then the processing phase continues with the finding of the relation between the sighting distance and the dispersion of height differences acquired by digital levelling under terrain conditions. The theoretical part involves the development of levelling accuracy theories that vary over time by view on random and systematic error propagation. The numerical and graphical solution of the experimental measurements involves ordering the height differences into sighting groups according to the sighting distance. The standard deviation computed in each sighting group represents a measure of the dispersion of height differences. Suppose the standard deviation in the sighting group in both independent experimental locations K1 and K2 exceeds twice the total standard deviation. In that case, it is most likely considered to be the influence of the critical distance, which is then compared with values obtained by laboratory calibration of the same digital levels. Full article
(This article belongs to the Special Issue Causal Inference, Probability Theory and Graphical Concepts)
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20 pages, 1714 KiB  
Article
Computational Analysis of Hemodynamic Indices in Multivessel Coronary Artery Disease in the Presence of Myocardial Perfusion Dysfunction
by Timur Gamilov, Alexander Danilov, Peter Chomakhidze, Philipp Kopylov and Sergey Simakov
Computation 2024, 12(6), 110; https://doi.org/10.3390/computation12060110 - 30 May 2024
Viewed by 270
Abstract
Coronary artery disease (CAD) is one of the main causes of death in the world. Functional indices such as fractional flow reserve (FFR), coronary flow reserve (CFR) and instantaneous wave-free ratio (iFR) are used to estimate the severity of CAD. Approximately 30–50% of [...] Read more.
Coronary artery disease (CAD) is one of the main causes of death in the world. Functional indices such as fractional flow reserve (FFR), coronary flow reserve (CFR) and instantaneous wave-free ratio (iFR) are used to estimate the severity of CAD. Approximately 30–50% of patients have residual myocardial ischaemia even after formally successful percutaneous coronary intervention (PCI). Myocardial perfusion impairment is one of the main factors responsible for recurrence. We propose a novel 1D model of coronary hemodynamics that takes into account myocardial contraction, stenoses and impaired microcirculation. It uses non-invasively acquired data. The model is able to simulate FFR and iFR with a mean relative error of 3% and a standard mean deviation of 0.04. We find that healthy FFR and iFR values in the short and long term do not always correspond to healthy CFR values and recovery of coronary blood flow. We also show that PCI of stenosis also improves hemodynamic indices in adjacent stenosed vessels, with a more pronounced effect in the long term. Full article
(This article belongs to the Special Issue Recent Advances in Numerical Simulation of Compressible Flows)
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Editorial
Computational Medical Image Analysis: A Preface
by Anando Sen
Computation 2024, 12(6), 109; https://doi.org/10.3390/computation12060109 - 24 May 2024
Viewed by 405
Abstract
There has been immense progress in medical image analysis over the past decade [...] Full article
(This article belongs to the Special Issue Computational Medical Image Analysis)
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