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Computation, Volume 9, Issue 9 (September 2021) – 8 articles

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Article
First-Principles Study of Linear and Nonlinear Optical Properties of Multi-Layered Borophene
Computation 2021, 9(9), 101; https://0-doi-org.brum.beds.ac.uk/10.3390/computation9090101 - 18 Sep 2021
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Abstract
Anisotropic materials are of great interest due to their unique direction-dependent optical properties. Borophene, the two-dimensional analog of graphene consisting of boron atoms, has attracted immense research interest due to its exciting anisotropic electronic and mechanical properties. Its synthesis in several structural polymorphic [...] Read more.
Anisotropic materials are of great interest due to their unique direction-dependent optical properties. Borophene, the two-dimensional analog of graphene consisting of boron atoms, has attracted immense research interest due to its exciting anisotropic electronic and mechanical properties. Its synthesis in several structural polymorphic configurations has recently been reported. The present work reports the layer-dependent optical absorption and hyperpolarizabilities of the buckled borophene (δ6-borophene). The results, based on density functional theory, show that multilayer borophene is nearly transparent with only a weak absorbance in the visible region, reflecting its anisotropic structural characteristics. The static first-order hyperpolarizability significantly increases with the number of layers, due mainly to interactions among the frontier orbitals in multilayer borophene. Transparency in the visible region combined with enhanced nonlinear optical properties makes the multilayer borophene important for future photonics technologies. Full article
(This article belongs to the Section Computational Chemistry)
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Article
An Improved Robust Adaptive Controller for a Fed-Batch Bioreactor with Input Saturation and Unknown Varying Control Gain via Dead-Zone Quadratic Forms
Computation 2021, 9(9), 100; https://0-doi-org.brum.beds.ac.uk/10.3390/computation9090100 - 16 Sep 2021
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Abstract
In this work, a new adaptive controller is designed for substrate control of a fed-batch bioreactor in the presence of input saturation and unknown varying control gain with unknown upper and lower bounds. The output measurement noise and the unknown varying nature of [...] Read more.
In this work, a new adaptive controller is designed for substrate control of a fed-batch bioreactor in the presence of input saturation and unknown varying control gain with unknown upper and lower bounds. The output measurement noise and the unknown varying nature of reaction rate and biomass concentration and water volume are also handled. The design is based on dead zone quadratic forms. The designed controller ensures the convergence of the modified tracking error and the boundedness of the updated parameters. As the first distinctive feature, a new robust adaptive auxiliary system is proposed in order to tackle input saturation and control gain uncertainty. As the second distinctive feature, the modified tracking error converges to a compact region whose bound is user-defined, in contrast to related studies where the convergence region depends on upper bounds of either external disturbances, system states, model parameters or terms and model parameter values. Simulations confirm the properties of the closed loop behavior. Full article
(This article belongs to the Section Computational Engineering)
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Article
Integrating Data Mining Techniques for Naïve Bayes Classification: Applications to Medical Datasets
Computation 2021, 9(9), 99; https://0-doi-org.brum.beds.ac.uk/10.3390/computation9090099 - 13 Sep 2021
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Abstract
In this study, we designed a framework in which three techniques—classification tree, association rules analysis (ASA), and the naïve Bayes classifier—were combined to improve the performance of the latter. A classification tree was used to discretize quantitative predictors into categories and ASA was [...] Read more.
In this study, we designed a framework in which three techniques—classification tree, association rules analysis (ASA), and the naïve Bayes classifier—were combined to improve the performance of the latter. A classification tree was used to discretize quantitative predictors into categories and ASA was used to generate interactions in a fully realized way, as discretized variables and interactions are key to improving the classification accuracy of the naïve Bayes classifier. We applied our methodology to three medical datasets to demonstrate the efficacy of the proposed method. The results showed that our methodology outperformed the existing techniques for all the illustrated datasets. Although our focus here was on medical datasets, our proposed methodology is equally applicable to datasets in many other areas. Full article
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Article
Understanding the Origin of Structural Diversity of DNA Double Helix
Computation 2021, 9(9), 98; https://0-doi-org.brum.beds.ac.uk/10.3390/computation9090098 - 11 Sep 2021
Viewed by 481
Abstract
Deciphering the contribution of DNA subunits to the variability of its 3D structure represents an important step toward the elucidation of DNA functions at the atomic level. In the pursuit of that goal, our previous studies revealed that the essential conformational characteristics of [...] Read more.
Deciphering the contribution of DNA subunits to the variability of its 3D structure represents an important step toward the elucidation of DNA functions at the atomic level. In the pursuit of that goal, our previous studies revealed that the essential conformational characteristics of the most populated “canonic” BI and AI conformational families of Watson–Crick duplexes, including the sequence dependence of their 3D structure, preexist in the local energy minima of the elemental single-chain fragments, deoxydinucleoside monophosphates (dDMPs). Those computations have uncovered important sequence-dependent regularity in the superposition of neighbor bases. The present work expands our studies to new minimal fragments of DNA with Watson–Crick nucleoside pairs that differ from canonic families in the torsion angles of the sugar-phosphate backbone (SPB). To address this objective, computations have been performed on dDMPs, cdDMPs (complementary dDMPs), and minimal fragments of SPBs of respective systems by using methods of molecular and quantum mechanics. These computations reveal that the conformations of dDMPs and cdDMPs having torsion angles of SPB corresponding to the local energy minima of separate minimal units of SPB exhibit sequence-dependent characteristics representative of canonic families. In contrast, conformations of dDMP and cdDMP with SPB torsions being far from the local minima of separate SPB units exhibit more complex sequence dependence. Full article
(This article belongs to the Special Issue Computational Modeling of Structure and Function of Biomolecules)
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Article
Physics-Based Neural Network Methods for Solving Parameterized Singular Perturbation Problem
Computation 2021, 9(9), 97; https://0-doi-org.brum.beds.ac.uk/10.3390/computation9090097 - 06 Sep 2021
Viewed by 497
Abstract
This work is devoted to the description and comparative study of some methods of mathematical modeling. We consider methods that can be applied for building cyber-physical systems and digital twins. These application areas add to the usual accuracy requirements for a model the [...] Read more.
This work is devoted to the description and comparative study of some methods of mathematical modeling. We consider methods that can be applied for building cyber-physical systems and digital twins. These application areas add to the usual accuracy requirements for a model the need to be adaptable to new data and the small computational complexity allows it to be used in embedded systems. First, we regard the finite element method as one of the “pure” physics-based modeling methods and the general neural network approach as a variant of machine learning modeling with physics-based regularization (or physics-informed neural networks) and their combination. A physics-based network architecture model class has been developed by us on the basis of a modification of classical numerical methods for solving ordinary differential equations. The model problem has a parameter at some values for which the phenomenon of stiffness is observed. We consider a fixed parameter value problem statement and a case when a parameter is one of the input variables. Thus, we obtain a solution for a set of parameter values. The resulting model allows predicting the behavior of an object when its parameters change and identifying its parameters based on observational data. Full article
(This article belongs to the Section Computational Engineering)
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Article
Performance of the Boost Converter Controlled with ZAD to Regulate DC Signals
Computation 2021, 9(9), 96; https://0-doi-org.brum.beds.ac.uk/10.3390/computation9090096 - 04 Sep 2021
Viewed by 378
Abstract
This paper presents the performance of a boost converter controlled with a zero average dynamics technique to regulate direct current signals. The boost converter is modeled in a compact form, and a variable change is performed to depend only on the γ parameter. [...] Read more.
This paper presents the performance of a boost converter controlled with a zero average dynamics technique to regulate direct current signals. The boost converter is modeled in a compact form, and a variable change is performed to depend only on the γ parameter. A new sliding surface is proposed, where it is possible to regulate both the voltage and the current with low relative errors with respect to the reference signals. It is analytically demonstrated that the approximation of the switching surface by a piecewise linear technique is efficient in controlling the system. It is shown numerically that for certain operating conditions, the system is evolved into a chaotic attractor. The zero average dynamics technique implemented in the boost converter has good regulation, due to the presence of zones in the bi-parametric space. Furthermore, the zero average dynamics technique regulates the voltage well and presents a chaotic attractor with low steady-state error. Full article
(This article belongs to the Special Issue Control Systems, Mathematical Modeling and Automation)
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Communication
Is There a Quadruple Fe-C Bond in FeC(CO)3?
Computation 2021, 9(9), 95; https://0-doi-org.brum.beds.ac.uk/10.3390/computation9090095 - 30 Aug 2021
Viewed by 384
Abstract
A recent computational paper (Kalita et al., Phys. Chem. Chem. Phys. 2020, 22, 24178–24180) reports the existence of a quadruple bond between a carbon and an iron atom in the FeC(CO)3 molecule. In this communication, we perform several computations [...] Read more.
A recent computational paper (Kalita et al., Phys. Chem. Chem. Phys. 2020, 22, 24178–24180) reports the existence of a quadruple bond between a carbon and an iron atom in the FeC(CO)3 molecule. In this communication, we perform several computations on the same system, using both density functional theory and post-Hartree–Fock methods and find that the results, and in particular the Fe-C bond length and stretching frequency depend strongly on the method used. We ascribe this behavior to a strong multireference character of the FeC(CO)3 ground state, which explains the non-conclusive results obtained with single-reference methods. We therefore conclude that, while the existence of a Fe-C quadruple bond is not disproved, further investigation is required before a conclusion can be drawn. Full article
(This article belongs to the Special Issue Electronic Correlation)
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Article
EM Estimation for Zero- and k-Inflated Poisson Regression Model
Computation 2021, 9(9), 94; https://0-doi-org.brum.beds.ac.uk/10.3390/computation9090094 - 26 Aug 2021
Viewed by 379
Abstract
Count data with excessive zeros are ubiquitous in healthcare, medical, and scientific studies. There are numerous articles that show how to fit Poisson and other models which account for the excessive zeros. However, in many situations, besides zero, the frequency of another count [...] Read more.
Count data with excessive zeros are ubiquitous in healthcare, medical, and scientific studies. There are numerous articles that show how to fit Poisson and other models which account for the excessive zeros. However, in many situations, besides zero, the frequency of another count k tends to be higher in the data. The zero- and k-inflated Poisson distribution model (ZkIP) is appropriate in such situations The ZkIP distribution essentially is a mixture distribution of Poisson and degenerate distributions at points zero and k. In this article, we study the fundamental properties of this mixture distribution. Using stochastic representation, we provide details for obtaining parameter estimates of the ZkIP regression model using the Expectation–Maximization (EM) algorithm for a given data. We derive the standard errors of the EM estimates by computing the complete, missing, and observed data information matrices. We present the analysis of two real-life data using the methods outlined in the paper. Full article
(This article belongs to the Special Issue Modern Statistical Methods for Spatial and Multivariate Data)
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