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Computation, Volume 11, Issue 6 (June 2023) – 18 articles

Cover Story (view full-size image): One of the most important applications of graph theory is the study of metabolic networks, i.e., those networks in which occurs the transformation of one or several molecules into one or several final products. In living organisms, metabolic network evolution is explained by assuming the independent evolution of the enzymes involved in the isolated biochemical reactions. In this paper, we present a simulation model in which enzymes, the real drivers of metabolism, are modeled as automata simulating their evolution with a genetic algorithm. The results obtained not only give clues about the evolution of metabolic pathways in organisms, but also introduce a new approach to simulating the evolution of other complex networks. View this paper
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15 pages, 5427 KiB  
Article
Simulation of Multi-Phase Flow to Test the Effectiveness of the Casting Yard Aspiration System
by Serghii Lobov, Yevhen Pylypko, Viktoriya Kruchyna and Ihor Bereshko
Computation 2023, 11(6), 121; https://doi.org/10.3390/computation11060121 - 20 Jun 2023
Cited by 1 | Viewed by 1207
Abstract
The metallurgical industry is in second place among all other industries in terms of emissions into the atmosphere, and air pollution is the main cause of environmental problems arising from the activities of metallurgical enterprises. In some existing systems for localization, in the [...] Read more.
The metallurgical industry is in second place among all other industries in terms of emissions into the atmosphere, and air pollution is the main cause of environmental problems arising from the activities of metallurgical enterprises. In some existing systems for localization, in the trapping and removal of dust emissions from tapholes and cast-iron gutters of foundries, air flow parameters may differ from the optimal ones for solving aspiration problems. The largest emissions are observed in the area of the taphole (40–60%) and from the ladles during their filling (35–50%). In this paper, it is proposed to consider a variant of the blast furnace aspiration system with the simultaneous supply of a dust–gas–air mixture from two-side smoke exhausters and two upper hoods with two simultaneously operating tapholes, that is, when the blast furnace operates in the maximum emissions mode. This article proposes an assessment of the effectiveness of the modernized blast furnace aspiration system using computer CFD modeling, where its main parameters are given. It is shown that the efficiency of dust collection in the proposed system is more than 90%, and the speed of the gas–dust mixture is no lower than 20 m/s, which prevents gravitational settling on the walls. The distribution fields of temperatures and velocities are obtained for further engineering analysis and the possible improvement of aspiration systems. Full article
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21 pages, 1204 KiB  
Article
Research on the Development of a Proctoring System for Conducting Online Exams in Kazakhstan
by Ardak Nurpeisova, Anargul Shaushenova, Zhazira Mutalova, Maral Ongarbayeva, Shakizada Niyazbekova, Anargul Bekenova, Lyazzat Zhumaliyeva and Samal Zhumasseitova
Computation 2023, 11(6), 120; https://doi.org/10.3390/computation11060120 - 19 Jun 2023
Cited by 2 | Viewed by 2694
Abstract
The demand for online education is gradually growing. Most universities and other institutions are faced with the fact that it is almost impossible to track how honestly test takers take exams remotely. In online formats, there are many simple opportunities that allow for [...] Read more.
The demand for online education is gradually growing. Most universities and other institutions are faced with the fact that it is almost impossible to track how honestly test takers take exams remotely. In online formats, there are many simple opportunities that allow for cheating and using the use of outside help. Online proctoring based on artificial intelligence technologies in distance education is an effective technological solution to prevent academic dishonesty. This article explores the development and implementation of an online control proctoring system using artificial intelligence technology for conducting online exams. The article discusses the proctoring systems used in Kazakhstan, compares the functional features of the selected proctoring systems, and describes the architecture of Proctor SU. A prototype of the Proctor SU proctoring system has been developed. As a pilot program, the authors used this system during an online university exam and examined the results of the test. According to the author’s examination, students have a positive attitude towards the use of Proctor SU online proctoring. The proposed proctor system includes features of face detection, face tracking, audio capture, and the active capture of system windows. Models CNN, R-CNN, and YOLOv3 were used in the development process. The YOLOv3 model processed images in real time at 45 frames per second, and CNN and R-CNN processed images in real time at 30 and 38 frames per second. The YOLOv3 model showed better results in terms of real-time face recognition. Therefore, the YOLOv3 model was implemented into the Proctor SU proctoring system. Full article
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15 pages, 321 KiB  
Article
Spherical Subspace Potential Functional Theory
by Ágnes Nagy
Computation 2023, 11(6), 119; https://doi.org/10.3390/computation11060119 - 15 Jun 2023
Cited by 1 | Viewed by 962
Abstract
The recently introduced version of the density functional theory that employs a set of spherically symmetric densities instead of the density has a ‘set-representability problem’. It is not known if a density exists for a given set of the spherically symmetric densities. This [...] Read more.
The recently introduced version of the density functional theory that employs a set of spherically symmetric densities instead of the density has a ‘set-representability problem’. It is not known if a density exists for a given set of the spherically symmetric densities. This problem can be eliminated if potentials are applied instead of densities as basic variables. Now, the spherical subspace potential functional theory is established. Full article
(This article belongs to the Special Issue 10th Anniversary of Computation—Computational Chemistry)
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17 pages, 1411 KiB  
Article
A Parallel Computing Approach to Gene Expression and Phenotype Correlation for Identifying Retinitis Pigmentosa Modifiers in Drosophila
by Chawin Metah, Amal Khalifa and Rebecca Palu
Computation 2023, 11(6), 118; https://doi.org/10.3390/computation11060118 - 14 Jun 2023
Viewed by 1175
Abstract
As a genetic eye disorder, retinitis pigmentosa (RP) has been a focus of researchers to find a diagnosis through either genome-wide association (GWA) or RNAseq analysis. In fact, GWA and RNAseq are considered two complementary approaches to gaining a more comprehensive understanding of [...] Read more.
As a genetic eye disorder, retinitis pigmentosa (RP) has been a focus of researchers to find a diagnosis through either genome-wide association (GWA) or RNAseq analysis. In fact, GWA and RNAseq are considered two complementary approaches to gaining a more comprehensive understanding of the genetics of different diseases. However, RNAseq analysis can provide information about the specific mechanisms underlying the disease and the potential targets for therapy. This research proposes a new approach to differential gene expression (DGE) analysis, which is the heart of the core-analysis phase in any RNAseq study. Based on the Drosophila Genetic Reference Panel (DGRP), the gene expression dataset is computationally analyzed in light of eye-size phenotypes. We utilized the foreach and the doParallel R packages to run the code on a multicore machine to reduce the running time of the original algorithm, which exhibited an exponential time complexity. Experimental results showed an outstanding performance, reducing the running time by 95% while using 32 processes. In addition, more candidate modifier genes for RP were identified by increasing the scope of the analysis and considering more datasets that represent different phenotype models. Full article
(This article belongs to the Special Issue Computational Biology and High-Performance Computing)
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35 pages, 5956 KiB  
Article
Social Networks in Military Powers: Network and Sentiment Analysis during the COVID-19 Pandemic
by Alberto Quilez-Robres, Marian Acero-Ferrero, Diego Delgado-Bujedo, Raquel Lozano-Blasco and Montserrat Aiger-Valles
Computation 2023, 11(6), 117; https://doi.org/10.3390/computation11060117 - 13 Jun 2023
Viewed by 1494
Abstract
The outbreak of the COVID-19 pandemic shifted socialization and information seeking to social media platforms. The armed forces of the major military powers initiated civil support operations to combat the invisible and common enemy. The aim of this study is to analyze the [...] Read more.
The outbreak of the COVID-19 pandemic shifted socialization and information seeking to social media platforms. The armed forces of the major military powers initiated civil support operations to combat the invisible and common enemy. The aim of this study is to analyze the existence of differential behavior in the corporate profiles of the major military powers on Twitter, Instagram, and Facebook during the COVID-19 pandemic. The principles of social network analysis were followed, along with sentiment analysis, to study web positioning and the emotional content of the posts (N = 25,328). The principles of data mining were applied to process the KPIs (Fanpage Karma), and an artificial intelligence (meaning cloud) sentiment analysis was applied to study the emotionality of the publications. The analysis was carried out using the IBM SPSS Statistics 25 statistical software. Subsequently, a qualitative content analysis was carried out using frequency graphs or word clouds (the application “nubedepalabras” used in English). Significant differences were found between the behavior on social media and the organizational and communicative culture of the nations. It is highlighted that some nations present different preferences from the main communicative strategy developed by their armed forces. Corporate communication of the major military powers should consider the emotional nature of their posts to align with the preferences of their population. Full article
(This article belongs to the Special Issue Computational Social Science and Complex Systems)
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17 pages, 5574 KiB  
Communication
On the Time Frequency Compactness of the Slepian Basis of Order Zero for Engineering Applications
by Zuwen Sun and Natalie Baddour
Computation 2023, 11(6), 116; https://doi.org/10.3390/computation11060116 - 13 Jun 2023
Cited by 2 | Viewed by 889
Abstract
Time and frequency concentrations of waveforms are often of interest in engineering applications. The Slepian basis of order zero is an index-limited (finite) vector that is known to be optimally concentrated in the frequency domain. This paper proposes a method of mapping the [...] Read more.
Time and frequency concentrations of waveforms are often of interest in engineering applications. The Slepian basis of order zero is an index-limited (finite) vector that is known to be optimally concentrated in the frequency domain. This paper proposes a method of mapping the index-limited Slepian basis to a discrete-time vector, hence obtaining a time-limited, discrete-time Slepian basis that is optimally concentrated in frequency. The main result of this note is to demonstrate that the (discrete-time) Slepian basis achieves minimum time-bandwidth compactness under certain conditions. We distinguish between the characteristic (effective) time/bandwidth of the Slepians and their defining time/bandwidth (the time and bandwidth parameters used to generate the Slepian basis). Using two different definitions of effective time and bandwidth of a signal, we show that when the defining time-bandwidth product of the Slepian basis increases, its effective time-bandwidth product tends to a minimum value. This implies that not only are the zeroth order Slepian bases known to be optimally time-limited and band-concentrated basis vectors, but also as their defining time-bandwidth products increase, their effective time-bandwidth properties approach the known minimum compactness allowed by the uncertainty principle. Conclusions are also drawn about the smallest defining time-bandwidth parameters to reach the minimum possible compactness. These conclusions give guidance for applications where the time-bandwidth product is free to be selected and hence may be selected to achieve minimum compactness. Full article
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18 pages, 1903 KiB  
Review
Machine Learning in X-ray Diagnosis for Oral Health: A Review of Recent Progress
by Mónica Vieira Martins, Luís Baptista, Henrique Luís, Victor Assunção, Mário-Rui Araújo and Valentim Realinho
Computation 2023, 11(6), 115; https://doi.org/10.3390/computation11060115 - 10 Jun 2023
Cited by 5 | Viewed by 2357
Abstract
The past few decades have witnessed remarkable progress in the application of artificial intelligence (AI) and machine learning (ML) in medicine, notably in medical imaging. The application of ML to dental and oral imaging has also been developed, powered by the availability of [...] Read more.
The past few decades have witnessed remarkable progress in the application of artificial intelligence (AI) and machine learning (ML) in medicine, notably in medical imaging. The application of ML to dental and oral imaging has also been developed, powered by the availability of clinical dental images. The present work aims to investigate recent progress concerning the application of ML in the diagnosis of oral diseases using oral X-ray imaging, namely the quality and outcome of such methods. The specific research question was developed using the PICOT methodology. The review was conducted in the Web of Science, Science Direct, and IEEE Xplore databases, for articles reporting the use of ML and AI for diagnostic purposes in X-ray-based oral imaging. Imaging types included panoramic, periapical, bitewing X-ray images, and oral cone beam computed tomography (CBCT). The search was limited to papers published in the English language from 2018 to 2022. The initial search included 104 papers that were assessed for eligibility. Of these, 22 were included for a final appraisal. The full text of the articles was carefully analyzed and the relevant data such as the clinical application, the ML models, the metrics used to assess their performance, and the characteristics of the datasets, were registered for further analysis. The paper discusses the opportunities, challenges, and limitations found. Full article
(This article belongs to the Special Issue Computational Medical Image Analysis)
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18 pages, 3337 KiB  
Article
Extended Online DMD and Weighted Modifications for Streaming Data Analysis
by Gyurhan Nedzhibov
Computation 2023, 11(6), 114; https://doi.org/10.3390/computation11060114 - 9 Jun 2023
Cited by 1 | Viewed by 1318
Abstract
We present novel methods for computing the online dynamic mode decomposition (online DMD) for streaming datasets. We propose a framework that allows incremental updates to the DMD operator as data become available. Due to its ability to work on datasets with lower ranks, [...] Read more.
We present novel methods for computing the online dynamic mode decomposition (online DMD) for streaming datasets. We propose a framework that allows incremental updates to the DMD operator as data become available. Due to its ability to work on datasets with lower ranks, the proposed method is more advantageous than existing ones. A noteworthy feature of the method is that it is entirely data-driven and does not require knowledge of any underlying governing equations. Additionally, we present a modified version of our proposed approach that utilizes a weighted alternative to online DMD. The suggested techniques are demonstrated using several numerical examples. Full article
(This article belongs to the Special Issue Mathematical Modeling and Study of Nonlinear Dynamic Processes)
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23 pages, 5297 KiB  
Article
Feet Segmentation for Regional Analgesia Monitoring Using Convolutional RFF and Layer-Wise Weighted CAM Interpretability
by Juan Carlos Aguirre-Arango, Andrés Marino Álvarez-Meza and German Castellanos-Dominguez
Computation 2023, 11(6), 113; https://doi.org/10.3390/computation11060113 - 8 Jun 2023
Viewed by 1345
Abstract
Regional neuraxial analgesia for pain relief during labor is a universally accepted, safe, and effective procedure involving administering medication into the epidural. Still, an adequate assessment requires continuous patient monitoring after catheter placement. This research introduces a cutting-edge semantic thermal image segmentation method [...] Read more.
Regional neuraxial analgesia for pain relief during labor is a universally accepted, safe, and effective procedure involving administering medication into the epidural. Still, an adequate assessment requires continuous patient monitoring after catheter placement. This research introduces a cutting-edge semantic thermal image segmentation method emphasizing superior interpretability for regional neuraxial analgesia monitoring. Namely, we propose a novel Convolutional Random Fourier Features-based approach, termed CRFFg, and custom-designed layer-wise weighted class-activation maps created explicitly for foot segmentation. Our method aims to enhance three well-known semantic segmentation (FCN, UNet, and ResUNet). We have rigorously evaluated our methodology on a challenging dataset of foot thermal images from pregnant women who underwent epidural anesthesia. Its limited size and significant variability distinguish this dataset. Furthermore, our validation results indicate that our proposed methodology not only delivers competitive results in foot segmentation but also significantly improves the explainability of the process. Full article
(This article belongs to the Special Issue Computational Medical Image Analysis)
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15 pages, 4280 KiB  
Article
Mathematical Modeling of Multi-Phase Filtration in a Deformable Porous Medium
by V. F. Burnashev, K. K. Viswanathan and Z. D. Kaytarov
Computation 2023, 11(6), 112; https://doi.org/10.3390/computation11060112 - 8 Jun 2023
Cited by 3 | Viewed by 1276
Abstract
In this paper, a mathematical model of multiphase filtration in a deformable porous medium is presented. Based on the proposed model, the influence of the deformation of a porous medium on the filtration processes is studied. Numerical calculations are performed and the characteristics [...] Read more.
In this paper, a mathematical model of multiphase filtration in a deformable porous medium is presented. Based on the proposed model, the influence of the deformation of a porous medium on the filtration processes is studied. Numerical calculations are performed and the characteristics of the process are determined. This paper shows that an increase in the compressibility coefficient leads to a sharp decrease in porosity, absolute permeability and internal pressure of the medium near the well, and a decrease in the distance between wells leads to a sharp decrease in hydrodynamic parameters in the inter-well zone. Full article
(This article belongs to the Special Issue Computational Techniques for Fluid Dynamics Problems)
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15 pages, 1271 KiB  
Article
Determination of Characteristics of Associative Storage Devices in Radio Telemetry Systems with Data Compression
by Bulat-Batyr Yesmagambetov, Akhmetbek Mussabekov, Nurlybek Alymov, Abdulkhak Apsemetov, Madina Balabekova, Kamil Kayumov, Kuttybek Arystanbayev and Aigul Imanbayeva
Computation 2023, 11(6), 111; https://doi.org/10.3390/computation11060111 - 6 Jun 2023
Cited by 1 | Viewed by 886
Abstract
In the radio telemetry systems of spacecraft, various data compression methods are used for data processing. When using any compression methods, the data obtained as a result of compression is formed randomly, and transmission over radio communication channels should be carried out evenly [...] Read more.
In the radio telemetry systems of spacecraft, various data compression methods are used for data processing. When using any compression methods, the data obtained as a result of compression is formed randomly, and transmission over radio communication channels should be carried out evenly over time. This leads to the need to use special buffer storage devices. In addition, existing spacecraft radio telemetry systems require grouping of compressed data streams by certain characteristics. This leads to the need to sort compressed data by streams. Therefore, it is advisable to use associative buffer storage devices in such systems. This article is devoted to the analysis of the processes of formation of output streams of compressed data generated at the output of an associative storage device (ASD). Since the output stream of compressed data is random, queue theory and probability theory are used for analysis. At the same time, associative memory is represented as a queue system. Writing and reading in an ASD can be interpreted as servicing orders in a queue system. The purpose of the analysis is to determine the characteristics of an associative storage device (ASD). Such characteristics are the queue length M{N} in the ASD, the deviation of the queue length D{N} in the ASD and the probability pn of a given volume n of compressed data in the ASD (including the probability of emptying and the probability of memory overflow). The results obtained are of great practical importance, since they can be used to select the amount of memory of an associative storage device (ASD) when designing compression devices for telemetry systems of spacecraft. Full article
(This article belongs to the Section Computational Engineering)
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16 pages, 3410 KiB  
Article
Buckling Analysis of Laminated Stiffened Plates with Material Anisotropy Using the Rayleigh–Ritz Approach
by Dimitrios G. Stamatelos and George N. Labeas
Computation 2023, 11(6), 110; https://doi.org/10.3390/computation11060110 - 30 May 2023
Viewed by 1504
Abstract
An energy-based solution for calculating the buckling loads of partially anisotropic stiffened plates is presented, such as antisymmetric cross-ply and angle-ply laminations. A discrete approach, for the mathematical modelling and formulations of the stiffened plates, is followed. The developed formulations extend the Rayleigh–Ritz [...] Read more.
An energy-based solution for calculating the buckling loads of partially anisotropic stiffened plates is presented, such as antisymmetric cross-ply and angle-ply laminations. A discrete approach, for the mathematical modelling and formulations of the stiffened plates, is followed. The developed formulations extend the Rayleigh–Ritz method and explore the available anisotropic unstiffened plate buckling solutions to the interesting cases of stiffened plates with some degree of material anisotropy. The examined cases consider simply supported unstiffened and stiffened plates under uniform and linearly varying compressive loading. Additionally, a reference finite element (FE) model is developed to compare the calculated buckling loads and validate the modelling approach for its accuracy. The results of the developed method are also compared with the respective experimental results for the cases where they were available in the literature. Finally, an extended discussion regarding the assumptions and restrictions of the applied Rayleigh–Ritz method is made, so that the limitations of the developed method are identified and documented. Full article
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17 pages, 3802 KiB  
Article
Calculation of Linear Buckling Load for Frames Modeled with One-Finite-Element Beams and Columns
by Javier Urruzola and Iñaki Garmendia
Computation 2023, 11(6), 109; https://doi.org/10.3390/computation11060109 - 30 May 2023
Cited by 1 | Viewed by 2090
Abstract
Critical linear buckling load calculation is one of the possible ways to check structural stability. Structural analysis programs usually model beams and columns with just one element, but this is not enough to obtain an accurate value of the critical buckling load when [...] Read more.
Critical linear buckling load calculation is one of the possible ways to check structural stability. Structural analysis programs usually model beams and columns with just one element, but this is not enough to obtain an accurate value of the critical buckling load when the buckling mode is associated with an effective length that is less than twice the element length. This paper presents a method for the accurate calculation of the buckling load of frames modeled with only one finite element per structural element. For this purpose, a local correction is applied to some elements a few times until convergence is achieved. The validity of the presented method is confirmed by several examples ranging from simple canonical cases to large structures. Full article
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18 pages, 5437 KiB  
Article
Application of DFT and TD-DFT on Langmuir Adsorption of Nitrogen and Sulfur Heterocycle Dopants on an Aluminum Surface Decorated with Magnesium and Silicon
by Fatemeh Mollaamin and Majid Monajjemi
Computation 2023, 11(6), 108; https://doi.org/10.3390/computation11060108 - 29 May 2023
Cited by 11 | Viewed by 1793
Abstract
In this study, we investigated the abilities of nitrogen and sulfur heterocyclic carbenes of benzotriazole, 2-mercaptobenzothiazole, 8-hydroxyquinoline, and 3-amino-1,2,4-triazole-5-thiol regarding adsorption on an Al-Mg-Si alloy toward corrosion inhibition of the surface. Al-Si(14), Al-Si(19), and Al-Si(21) in the Al-Mg-Si alloy surface with the highest [...] Read more.
In this study, we investigated the abilities of nitrogen and sulfur heterocyclic carbenes of benzotriazole, 2-mercaptobenzothiazole, 8-hydroxyquinoline, and 3-amino-1,2,4-triazole-5-thiol regarding adsorption on an Al-Mg-Si alloy toward corrosion inhibition of the surface. Al-Si(14), Al-Si(19), and Al-Si(21) in the Al-Mg-Si alloy surface with the highest fluctuation in the shielding tensors of the “NMR” spectrum generated by intra-atomic interaction directed us to the most influence in the neighbor atoms generated by interatomic reactions of N → Al, O → Al, and S → Al through the coating and adsorbing process of Langmuir adsorption. The values of various thermodynamic properties and dipole moments of benzotriazole, 2-mercaptobenzothiazole, 8-hydroxyquinoline, and 3-amino-1,2,4-triazole-5-thiol adsorbed on the Al-Mg-Si increased by enhancing the molecular weight of these compounds as well as the charge distribution between organic compounds (electron donor) and the alloy surface (electron acceptor). Finally, this research can build up our knowledge of the electronic structure, relative stability, and surface bonding of various metal alloy surfaces, metal-doped alloy nanosheets, and other dependent mechanisms such as heterogeneous catalysis, friction lubrication, and biological systems. Full article
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21 pages, 4559 KiB  
Article
Application of Graph Theory and Automata Modeling for the Study of the Evolution of Metabolic Pathways with Glycolysis and Krebs Cycle as Case Studies
by Carlos De Las Morenas Mateos and Rafael Lahoz-Beltra
Computation 2023, 11(6), 107; https://doi.org/10.3390/computation11060107 - 28 May 2023
Cited by 1 | Viewed by 2198
Abstract
Today, graph theory represents one of the most important modeling techniques in biology. One of the most important applications is in the study of metabolic networks. During metabolism, a set of sequential biochemical reactions takes place, which convert one or more molecules into [...] Read more.
Today, graph theory represents one of the most important modeling techniques in biology. One of the most important applications is in the study of metabolic networks. During metabolism, a set of sequential biochemical reactions takes place, which convert one or more molecules into one or more final products. In a biochemical reaction, the transformation of one metabolite into the next requires a class of proteins called enzymes that are responsible for catalyzing the reaction. Whether by applying differential equations or automata theory, it is not easy to explain how the evolution of metabolic networks could have taken place within living organisms. Obviously, in the past, the assembly of biochemical reactions into a metabolic network depended on the independent evolution of the enzymes involved in the isolated biochemical reactions. In this work, a simulation model is presented where enzymes are modeled as automata, and their evolution is simulated with a genetic algorithm. This protocol is applied to the evolution of glycolysis and the Krebs cycle, two of the most important metabolic networks for the survival of organisms. The results obtained show how Darwinian evolution is able to optimize a biological network, such as in the case of glycolysis and Krebs metabolic networks. Full article
(This article belongs to the Special Issue 10th Anniversary of Computation—Computational Biology)
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12 pages, 3338 KiB  
Article
Addressing the Folding of Intermolecular Springs in Particle Simulations: Fixed Image Convention
by Aristotelis P. Sgouros and Doros N. Theodorou
Computation 2023, 11(6), 106; https://doi.org/10.3390/computation11060106 - 26 May 2023
Viewed by 973
Abstract
Mesoscopic simulations of long polymer chains and soft matter systems are conducted routinely in the literature in order to assess the long-lived relaxation processes manifested in these systems. Coarse-grained chains are, however, prone to unphysical intercrossing due to their inherent softness. This issue [...] Read more.
Mesoscopic simulations of long polymer chains and soft matter systems are conducted routinely in the literature in order to assess the long-lived relaxation processes manifested in these systems. Coarse-grained chains are, however, prone to unphysical intercrossing due to their inherent softness. This issue can be resolved by introducing long intermolecular bonds (the so-called slip-springs) which restore these topological constraints. The separation vector of intermolecular bonds can be determined by enforcing the commonly adopted minimum image convention (MIC). Because these bonds are soft and long (ca 3–20 nm), subjecting the samples to extreme deformations can lead to topology violations when enforcing the MIC. We propose the fixed image convention (FIC) for determining the separation vectors of overextended bonds, which is more stable than the MIC and applicable to extreme deformations. The FIC is simple to implement and, in general, more efficient than the MIC. Side-by-side comparisons between the MIC and FIC demonstrate that, when using the FIC, the topology remains intact even in situations with extreme particle displacement and nonaffine deformation. The accuracy of these conventions is the same when applying affine deformation. The article is accompanied by the corresponding code for implementing the FIC. Full article
(This article belongs to the Special Issue 10th Anniversary of Computation—Computational Chemistry)
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12 pages, 3819 KiB  
Article
CrohnDB: A Web Database for Expression Profiling of Protein-Coding and Long Non-Coding RNA Genes in Crohn Disease
by Rebecca Distefano, Mirolyuba Ilieva, Jens Hedelund Madsen and Shizuka Uchida
Computation 2023, 11(6), 105; https://doi.org/10.3390/computation11060105 - 24 May 2023
Cited by 1 | Viewed by 1714
Abstract
Crohn disease (CD) is a type of inflammatory bowel disease that causes inflammation in the digestive tract. Cases of CD are increasing worldwide, calling for more research to elucidate the pathogenesis of CD. For this purpose, the usage of the RNA-sequencing (RNA-seq) technique [...] Read more.
Crohn disease (CD) is a type of inflammatory bowel disease that causes inflammation in the digestive tract. Cases of CD are increasing worldwide, calling for more research to elucidate the pathogenesis of CD. For this purpose, the usage of the RNA-sequencing (RNA-seq) technique is increasingly appreciated, as it captures RNA expression patterns at a particular time point in a high-throughput manner. Although many RNA-seq datasets are generated from CD patients and compared to those of healthy donors, most of these datasets are analyzed only for protein-coding genes, leaving non-coding RNAs (ncRNAs) undiscovered. Long non-coding RNAs (lncRNAs) are any ncRNAs that are longer than 200 nucleotides. Interest in studying lncRNAs is increasing rapidly, as lncRNAs bind other macromolecules (DNA, RNA, and/or proteins) to finetune signaling pathways. To fill the gap in knowledge about lncRNAs in CD, we performed secondary analysis of published RNA-seq data of CD patients compared to healthy donors to identify lncRNA genes and their expression changes. To further facilitate lncRNA research in CD, we built a web database, CrohnDB, to provide a one-stop-shop for expression profiling of protein-coding and lncRNA genes in CD patients compared to healthy donors. Full article
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17 pages, 3313 KiB  
Article
Explainable Ensemble-Based Machine Learning Models for Detecting the Presence of Cirrhosis in Hepatitis C Patients
by Abrar Alotaibi, Lujain Alnajrani, Nawal Alsheikh, Alhatoon Alanazy, Salam Alshammasi, Meshael Almusairii, Shoog Alrassan and Aisha Alansari
Computation 2023, 11(6), 104; https://doi.org/10.3390/computation11060104 - 23 May 2023
Cited by 4 | Viewed by 2064
Abstract
Hepatitis C is a liver infection caused by a virus, which results in mild to severe inflammation of the liver. Over many years, hepatitis C gradually damages the liver, often leading to permanent scarring, known as cirrhosis. Patients sometimes have moderate or no [...] Read more.
Hepatitis C is a liver infection caused by a virus, which results in mild to severe inflammation of the liver. Over many years, hepatitis C gradually damages the liver, often leading to permanent scarring, known as cirrhosis. Patients sometimes have moderate or no symptoms of liver illness for decades before developing cirrhosis. Cirrhosis typically worsens to the point of liver failure. Patients with cirrhosis may also experience brain and nerve system damage, as well as gastrointestinal hemorrhage. Treatment for cirrhosis focuses on preventing further progression of the disease. Detecting cirrhosis earlier is therefore crucial for avoiding complications. Machine learning (ML) has been shown to be effective at providing precise and accurate information for use in diagnosing several diseases. Despite this, no studies have so far used ML to detect cirrhosis in patients with hepatitis C. This study obtained a dataset consisting of 28 attributes of 2038 Egyptian patients from the ML Repository of the University of California at Irvine. Four ML algorithms were trained on the dataset to diagnose cirrhosis in hepatitis C patients: a Random Forest, a Gradient Boosting Machine, an Extreme Gradient Boosting, and an Extra Trees model. The Extra Trees model outperformed the other models achieving an accuracy of 96.92%, a recall of 94.00%, a precision of 99.81%, and an area under the receiver operating characteristic curve of 96% using only 16 of the 28 features. Full article
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