Computation

13 pages, 7597 KiB

Open AccessArticle

Density Functional Theory Study of Metal and Metal-Oxide Nucleation and Growth on the Anatase TiO₂(101) Surface

by Leila Kalantari, Fabien Tran and Peter Blaha

Computation 2021, 9(11), 125; https://doi.org/10.3390/computation9110125 - 19 Nov 2021

Cited by 5 | Viewed by 3006

Experimental studies have shown the possible production of hydrogen through photocatalytic water splitting using metal oxide (MO

_{y}

) nanoparticles attached to an anatase TiO

_{2}

surface. In this work, we performed density functional theory (DFT) calculations to provide a detailed description of [...] Read more.

Experimental studies have shown the possible production of hydrogen through photocatalytic water splitting using metal oxide (MO

_{y}

) nanoparticles attached to an anatase TiO

_{2}

surface. In this work, we performed density functional theory (DFT) calculations to provide a detailed description of the stability and geometry of M

_{x}

O

_{y}

clusters M = Cu, Ni, Co, Fe and Mn, x = 1–5, and y = 0–5 on the anatase TiO

_{2}

(101) surface. It is found that unsaturated 2-fold-coordinated O-sites may serve as nucleation centers for the growth of metal clusters. The formation energy of Ni-containing clusters on the anatase surface is larger than for other M clusters. In addition, the Ni

_{n}

adsorption energy increases with cluster size n, which makes the formation of bigger Ni clusters plausible as confirmed by transition electron microscopy images. Another particularity for Ni-containing clusters is that the adsorption energy per atom gets larger when the O-content is reduced, while for other M atoms it remains almost constant or, as for Mn, even decreases. This trend is in line with experimental results. Also provided is a discussion of the oxidation states of M

_{5}

O

_{y}

clusters based on their magnetic moments and Bader charges and their possible reduction with oxygen depletion. Full article

(This article belongs to the Special Issue Commemorative Issue in Honor of Professor Karlheinz Schwarz on the Occasion of his 80th Birthday)

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15 pages, 678 KiB

Open AccessArticle

Self-Adaptive Acceptance Rate-Driven Markov Chain Monte Carlo Method Applied to the Study of Magnetic Nanoparticles

by Juan Camilo Zapata and Johans Restrepo

Computation 2021, 9(11), 124; https://doi.org/10.3390/computation9110124 - 19 Nov 2021

Cited by 2 | Viewed by 2713

Abstract

A standard canonical Markov Chain Monte Carlo method implemented with a single-macrospin movement Metropolis dynamics was conducted to study the hysteretic properties of an ensemble of independent and non-interacting magnetic nanoparticles with uniaxial magneto-crystalline anisotropy randomly distributed. In our model, the acceptance-rate algorithm [...] Read more.

A standard canonical Markov Chain Monte Carlo method implemented with a single-macrospin movement Metropolis dynamics was conducted to study the hysteretic properties of an ensemble of independent and non-interacting magnetic nanoparticles with uniaxial magneto-crystalline anisotropy randomly distributed. In our model, the acceptance-rate algorithm allows accepting new updates at a constant rate by means of a self-adaptive mechanism of the amplitude of Néel rotation of magnetic moments. The influence of this proposal upon the magnetic properties of our system is explored by analyzing the behavior of the magnetization versus field isotherms for a wide range of acceptance rates. Our results allows reproduction of the occurrence of both blocked and superparamagnetic states for high and low acceptance-rate values respectively, from which a link with the measurement time is inferred. Finally, the interplay between acceptance rate with temperature in hysteresis curves and the time evolution of the saturation processes is also presented and discussed. Full article

(This article belongs to the Section Computational Engineering)

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27 pages, 3732 KiB

Open AccessArticle

Some Finite Difference Methods to Model Biofilm Growth and Decay: Classical and Non-Standard

by Yusuf Olatunji Tijani, Appanah Rao Appadu and Adebayo Abiodun Aderogba

Computation 2021, 9(11), 123; https://doi.org/10.3390/computation9110123 - 17 Nov 2021

Cited by 8 | Viewed by 3062

Abstract

The study of biofilm formation is undoubtedly important due to micro-organisms forming a protected mode from the host defense mechanism, which may result in alteration in the host gene transcription and growth rate. A mathematical model of the nonlinear advection–diffusion–reaction equation has been [...] Read more.

The study of biofilm formation is undoubtedly important due to micro-organisms forming a protected mode from the host defense mechanism, which may result in alteration in the host gene transcription and growth rate. A mathematical model of the nonlinear advection–diffusion–reaction equation has been studied for biofilm formation. In this paper, we present two novel non-standard finite difference schemes to obtain an approximate solution to the mathematical model of biofilm formation. One explicit non-standard finite difference scheme is proposed for biomass density equation and one property-conserving scheme for a coupled substrate–biomass system of equations. The nonlinear term in the mathematical model has been handled efficiently. The proposed schemes maintain dynamical consistency (positivity, boundedness, merging of colonies, biofilm annihilation), which is revealed through experimental observation. In order to verify the accuracy and effectiveness of our proposed schemes, we compare our results with those obtained from standard finite difference schemes and earlier known results in the literature. The proposed schemes (NSFD1 and NSFD2) show good performance. The NSFD2 scheme reveals that the processes of biofilm formation and nutritive substrate growth are intricately linked. Full article

(This article belongs to the Section Computational Biology)

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13 pages, 3563 KiB

Open AccessArticle

Analytical and Numerical Solutions for the Thermal Problem in a Friction Clutch System

by Laith A. Sabri, Katarzyna Topczewska, Muhsin Jaber Jweeg, Oday I. Abdullah and Azher M. Abed

Computation 2021, 9(11), 122; https://doi.org/10.3390/computation9110122 - 15 Nov 2021

Cited by 5 | Viewed by 3105

Abstract

The dry friction clutch is an important part in vehicles, which has more than one function, but the most important function is to connect and disconnect the engine (driving part) with driven parts. This work presents a developed numerical solution applying a finite [...] Read more.

The dry friction clutch is an important part in vehicles, which has more than one function, but the most important function is to connect and disconnect the engine (driving part) with driven parts. This work presents a developed numerical solution applying a finite element technique in order to obtain results with high precision. A new three-dimensional model of a single-disc clutch operating in dry conditions was built from scratch. As the new model represents the real friction clutch including all details, the complexity in the geometry of the clutch is considered one of the difficulties that the researchers faced using the numerical solution. The thermal behaviour of the friction clutch during the slip phase was studied. Meanwhile, in the second part of this work, the transient thermal equations were derived from scratch to find the analytical solution for the thermal problem of a clutch disc in order to verify the numerical results. It was found, after comparison of the numerical results with analytical results, that the results of the numerical model are very accurate and the difference between them does not exceed 1%. Full article

(This article belongs to the Section Computational Engineering)

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22 pages, 2298 KiB

Open AccessArticle

Correlation Effects in Trimeric Acylphloroglucinols

by Liliana Mammino

Computation 2021, 9(11), 121; https://doi.org/10.3390/computation9110121 - 15 Nov 2021

Cited by 10 | Viewed by 2359

Abstract

Trimeric acylphloroglucinols (T-ACPLs) are a subclass of the large class of acylphloroglucinols—derivatives of 1,3,5-trihydroxybenzene containing an R–C=O group. T-ACPL molecules contain three acylphloroglucinol moieties linked by methylene bridges. Many of them are present in natural sources and exhibit biological activities, often better than [...] Read more.

Trimeric acylphloroglucinols (T-ACPLs) are a subclass of the large class of acylphloroglucinols—derivatives of 1,3,5-trihydroxybenzene containing an R–C=O group. T-ACPL molecules contain three acylphloroglucinol moieties linked by methylene bridges. Many of them are present in natural sources and exhibit biological activities, often better than the corresponding activities of monomeric acylphloroglucinols. All the stable conformers of T-ACPLs contain seven intramolecular hydrogen bonds, which constitute the dominant stabilising factors. A total of 38 different T-ACPLs, including both naturally occurring and model molecules, have been calculated at the HF and DFT/B3LYP levels. The DFT/B3LYP calculations were carried out both without and with Grimme’s dispersion correction, to highlight the dispersion (and, therefore, also electron correlation) effects for these molecules. The roles of dispersion are evaluated considering the effects of Grimme’s correction on the estimation of the conformers’ energies, the description of the characteristics of the individual hydrogen bonds, the conformers’ geometries and other molecular properties. Overall, the results offer a comprehensive overview of the conformational preferences of T-ACPL molecules, their intramolecular hydrogen bond patterns, and the correlation effects on their properties. Full article

(This article belongs to the Special Issue Electronic Correlation)

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20 pages, 1509 KiB

Open AccessArticle

Direct Sampling for Recovering Sound Soft Scatterers from Point Source Measurements

by Isaac Harris

Computation 2021, 9(11), 120; https://doi.org/10.3390/computation9110120 - 14 Nov 2021

Cited by 5 | Viewed by 2748

Abstract

In this paper, we consider the inverse problem of recovering a sound soft scatterer from the measured scattered field. The scattered field is assumed to be induced by a point source on a curve/surface that is known. Here, we propose and analyze new [...] Read more.

In this paper, we consider the inverse problem of recovering a sound soft scatterer from the measured scattered field. The scattered field is assumed to be induced by a point source on a curve/surface that is known. Here, we propose and analyze new direct sampling methods for this problem. The first method we consider uses a far-field transformation of the near-field data, which allows us to derive explicit bounds in the resolution analysis for the direct sampling method’s imaging functional. Two direct sampling methods are studied, using the far-field transformation. For these imaging functionals, we use the Funk–Hecke identities to study the resolution analysis. We also study a direct sampling method for the case of the given Cauchy data. Numerical examples are given to show the applicability of the new imaging functionals for recovering a sound soft scatterer with full and partial aperture data. Full article

(This article belongs to the Special Issue Inverse Problems with Partial Data)

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15 pages, 341 KiB

Open AccessArticle

Multiscale Convergence of the Inverse Problem for Chemotaxis in the Bayesian Setting

by Kathrin Hellmuth, Christian Klingenberg, Qin Li and Min Tang

Computation 2021, 9(11), 119; https://doi.org/10.3390/computation9110119 - 11 Nov 2021

Cited by 3 | Viewed by 2655

Abstract

Chemotaxis describes the movement of an organism, such as single or multi-cellular organisms and bacteria, in response to a chemical stimulus. Two widely used models to describe the phenomenon are the celebrated Keller–Segel equation and a chemotaxis kinetic equation. These two equations describe [...] Read more.

Chemotaxis describes the movement of an organism, such as single or multi-cellular organisms and bacteria, in response to a chemical stimulus. Two widely used models to describe the phenomenon are the celebrated Keller–Segel equation and a chemotaxis kinetic equation. These two equations describe the organism’s movement at the macro- and mesoscopic level, respectively, and are asymptotically equivalent in the parabolic regime. The way in which the organism responds to a chemical stimulus is embedded in the diffusion/advection coefficients of the Keller–Segel equation or the turning kernel of the chemotaxis kinetic equation. Experiments are conducted to measure the time dynamics of the organisms’ population level movement when reacting to certain stimulation. From this, one infers the chemotaxis response, which constitutes an inverse problem. In this paper, we discuss the relation between both the macro- and mesoscopic inverse problems, each of which is associated with two different forward models. The discussion is presented in the Bayesian framework, where the posterior distribution of the turning kernel of the organism population is sought. We prove the asymptotic equivalence of the two posterior distributions. Full article

(This article belongs to the Special Issue Inverse Problems with Partial Data)

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14 pages, 3151 KiB

Open AccessArticle

Darcy Brinkman Equations for Hybrid Dusty Nanofluid Flow with Heat Transfer and Mass Transpiration

by K. N. Sneha, U. S. Mahabaleshwar, Rachid Bennacer and Mohammed EL. Ganaoui

Computation 2021, 9(11), 118; https://doi.org/10.3390/computation9110118 - 9 Nov 2021

Cited by 28 | Viewed by 3108

Abstract

In the current work, we have investigated the flow past a semi-infinite porous solid media, after presenting a similarity transformation, governing equations mapped to a system of non-linear PDE. The flow of a dusty fluid and heat transfer through a porous medium have [...] Read more.

In the current work, we have investigated the flow past a semi-infinite porous solid media, after presenting a similarity transformation, governing equations mapped to a system of non-linear PDE. The flow of a dusty fluid and heat transfer through a porous medium have few applications, viz., the polymer processing unit of a geophysical, allied area, and chemical engineering plant. Further, we had the option to get an exact analytical solution for the velocity to the equation that is non-linear. The highlight of the current work is the flow of hybrid dusty nanofluid due to Darcy porous media through linear thermal radiation with the assistance of an analytical process. The hybrid dusty nanofluid has significant features improving the heat transfer process and is extensively developed in manufacturing industrial uses. It was found that the basic similarity equations admit two phases for both stretching/shrinking surfaces. The existence of computation on velocity and temperature profile is presented graphically for different estimations of various physical parameters. Full article

(This article belongs to the Special Issue Computational Heat, Mass, and Momentum Transfer—III)

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17 pages, 3997 KiB

Open AccessArticle

Intelligent Real-Time Deep System for Robust Objects Tracking in Low-Light Driving Scenario

by Francesco Rundo

Computation 2021, 9(11), 117; https://doi.org/10.3390/computation9110117 - 8 Nov 2021

Cited by 4 | Viewed by 2774

Abstract

The detection of moving objects, animals, or pedestrians, as well as static objects such as road signs, is one of the fundamental tasks for assisted or self-driving vehicles. This accomplishment becomes even more difficult in low light conditions such as driving at night [...] Read more.

The detection of moving objects, animals, or pedestrians, as well as static objects such as road signs, is one of the fundamental tasks for assisted or self-driving vehicles. This accomplishment becomes even more difficult in low light conditions such as driving at night or inside road tunnels. Since the objects found in the driving scene represent a significant collision risk, the aim of this scientific contribution is to propose an innovative pipeline that allows real time low-light driving salient objects tracking. Using a combination of the time-transient non-linear cellular networks and deep architectures with self-attention, the proposed solution will be able to perform a real-time enhancement of the low-light driving scenario frames. The downstream deep network will learn from the frames thus improved in terms of brightness in order to identify and segment salient objects by bounding-box based approach. The proposed algorithm is ongoing to be ported over a hybrid architecture consisting of a an embedded system with SPC5x Chorus MCU integrated with an automotive-grade system based on STA1295 MCU core. The performances (accuracy of about 90% and correlation coefficient of about 0.49) obtained in the experimental validation phase confirmed the effectiveness of the proposed method. Full article

(This article belongs to the Section Computational Engineering)

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8 pages, 614 KiB

Open AccessArticle

Characteristics and Research Techniques Associated with the Journal Impact Factor and Other Key Metrics in Pharmacology Journals

by Mingkwan Na Takuathung, Wannachai Sakuludomkan, Supanimit Teekachunhatean and Nut Koonrungsesomboon

Computation 2021, 9(11), 116; https://doi.org/10.3390/computation9110116 - 5 Nov 2021

Viewed by 2573

Abstract

In the present age, there is intense pressure on researchers to publish their research in ‘high-impact factor’ journals. It would be interesting to understand the trend of research publications in the field of pharmacology by exploring the characteristics of research articles, including research [...] Read more.

In the present age, there is intense pressure on researchers to publish their research in ‘high-impact factor’ journals. It would be interesting to understand the trend of research publications in the field of pharmacology by exploring the characteristics of research articles, including research techniques, in relation to the journal’s key bibliometrics, particularly journal impact factor (JIF), the seemingly most mentioned metric. This study aimed to determine the characteristics and research techniques in relation to research articles in pharmacology journals with higher or lower JIF values. A cross-sectional study was conducted on primary research journals under the ‘Pharmacology and Pharmacy’ category. Analysis of 768 original research articles across 32 journals (with an average JIF of 2.565 ± 0.887) demonstrated that research studies involving molecular techniques, in vivo experiments on animals, and bioinformatics and computational modeling were significantly associated with a higher JIF value of the journal in which such contributions were published. Our analysis suggests that research studies involving such techniques/approaches are more likely to be published in higher-ranked pharmacology journals. Full article

(This article belongs to the Special Issue Bibliometrics)

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21 pages, 530 KiB

Open AccessArticle

An Invariant-Preserving Scheme for the Viscous Burgers-Poisson System

by Chayapa Darayon, Morrakot Khebchareon and Nattapol Ploymaklam

Computation 2021, 9(11), 115; https://doi.org/10.3390/computation9110115 - 30 Oct 2021

Cited by 2 | Viewed by 2084

Abstract

We formulate and analyze a new finite difference scheme for a shallow water model in the form of viscous Burgers-Poisson system with periodic boundary conditions. The proposed scheme belongs to a family of three-level linearized finite difference methods. It is proved to preserve [...] Read more.

We formulate and analyze a new finite difference scheme for a shallow water model in the form of viscous Burgers-Poisson system with periodic boundary conditions. The proposed scheme belongs to a family of three-level linearized finite difference methods. It is proved to preserve both momentum and energy in the discrete sense. In addition, we proved that the method converges uniformly and has second order of accuracy in space. The analysis given in this work is the first time a pointwise error estimation is done on a second-order finite difference operator applied to the Burgers-Poisson system. We validate our findings by performing various numerical simulations on both viscous and inviscous problems. These numerical examples show the efficacy of the proposed method and confirm the proven theoretical results. Full article

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23 pages, 1894 KiB

Open AccessFeature PaperArticle

Conditional Variational Autoencoder for Learned Image Reconstruction

by Chen Zhang, Riccardo Barbano and Bangti Jin

Computation 2021, 9(11), 114; https://doi.org/10.3390/computation9110114 - 28 Oct 2021

Cited by 9 | Viewed by 5273

Abstract

Learned image reconstruction techniques using deep neural networks have recently gained popularity and have delivered promising empirical results. However, most approaches focus on one single recovery for each observation, and thus neglect information uncertainty. In this work, we develop a novel computational framework [...] Read more.

Learned image reconstruction techniques using deep neural networks have recently gained popularity and have delivered promising empirical results. However, most approaches focus on one single recovery for each observation, and thus neglect information uncertainty. In this work, we develop a novel computational framework that approximates the posterior distribution of the unknown image at each query observation. The proposed framework is very flexible: it handles implicit noise models and priors, it incorporates the data formation process (i.e., the forward operator), and the learned reconstructive properties are transferable between different datasets. Once the network is trained using the conditional variational autoencoder loss, it provides a computationally efficient sampler for the approximate posterior distribution via feed-forward propagation, and the summarizing statistics of the generated samples are used for both point-estimation and uncertainty quantification. We illustrate the proposed framework with extensive numerical experiments on positron emission tomography (with both moderate and low-count levels) showing that the framework generates high-quality samples when compared with state-of-the-art methods. Full article

(This article belongs to the Special Issue Inverse Problems with Partial Data)

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32 pages, 750 KiB

Open AccessArticle

Exact Boolean Abstraction of Linear Equation Systems

by Emilie Allart, Joachim Niehren and Cristian Versari

Computation 2021, 9(11), 113; https://doi.org/10.3390/computation9110113 - 21 Oct 2021

Cited by 1 | Viewed by 2419

Abstract

We study the problem of how to compute the boolean abstraction of the solution set of a linear equation system over the positive reals. We call a linear equation system ϕ exact for the boolean abstraction if the abstract interpretation of ϕ over [...] Read more.

We study the problem of how to compute the boolean abstraction of the solution set of a linear equation system over the positive reals. We call a linear equation system ϕ exact for the boolean abstraction if the abstract interpretation of ϕ over the structure of booleans is equal to the boolean abstraction of the solution set of ϕ over the positive reals. Abstract interpretation over the booleans is thus complete for the boolean abstraction when restricted to exact linear equation systems, while it is not complete more generally. We present a new rewriting algorithm that makes linear equation systems exact for the boolean abstraction while preserving the solutions over the positive reals. The rewriting algorithm is based on the elementary modes of the linear equation system. The computation of the elementary modes may require exponential time in the worst case, but is often feasible in practice with freely available tools. For exact linear equation systems, we can compute the boolean abstraction by finite domain constraint programming. This yields a solution of the initial problem that is often feasible in practice. Our exact rewriting algorithm has two further applications. Firstly, it can be used to compute the sign abstraction of linear equation systems over the reals, as needed for analyzing function programs with linear arithmetics. Secondly, it can be applied to compute the difference abstraction of a linear equation system as used in change prediction algorithms for flux networks in systems biology. Full article

(This article belongs to the Special Issue Formal Method for Biological Systems Modelling)

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Computation, Volume 9, Issue 11 (November 2021) – 13 articles

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