Next Issue
Volume 7, March
Previous Issue
Volume 7, September

Table of Contents

Computation, Volume 7, Issue 4 (December 2019) – 16 articles

  • Issues are regarded as officially published after their release is announced to the table of contents alert mailing list.
  • You may sign up for e-mail alerts to receive table of contents of newly released issues.
  • PDF is the official format for papers published in both, html and pdf forms. To view the papers in pdf format, click on the "PDF Full-text" link, and use the free Adobe Readerexternal link to open them.
Order results
Result details
Select all
Export citation of selected articles as:
Open AccessArticle
Three-Stage Sequential Estimation of the Inverse Coefficient of Variation of the Normal Distribution
Computation 2019, 7(4), 69; https://doi.org/10.3390/computation7040069 - 04 Dec 2019
Viewed by 250
Abstract
This paper sequentially estimates the inverse coefficient of variation of the normal distribution using Hall’s three-stage procedure. We find theorems that facilitate finding a confidence interval for the inverse coefficient of variation that has pre-determined width and coverage probability. We also discuss the [...] Read more.
This paper sequentially estimates the inverse coefficient of variation of the normal distribution using Hall’s three-stage procedure. We find theorems that facilitate finding a confidence interval for the inverse coefficient of variation that has pre-determined width and coverage probability. We also discuss the sensitivity of the constructed confidence interval to detect a possible shift in the inverse coefficient of variation. Finally, we find the asymptotic regret encountered in point estimation of the inverse coefficient of variation under the squared-error loss function with linear sampling cost. The asymptotic regret provides negative values, which indicate that the three-stage sampling does better than the optimal fixed sample size had the population inverse coefficient of variation been known. Full article
Show Figures

Figure 1

Open AccessArticle
The Low Lying Double-Exciton State of Conjugated Diradicals: Assessment of TDUDFT and Spin-Flip TDDFT Predictions
Computation 2019, 7(4), 68; https://doi.org/10.3390/computation7040068 - 26 Nov 2019
Viewed by 295
Abstract
Conjugated singlet ground state diradicals have received remarkable attention owing to their potential applications in optoelectronic devices. A distinctive character of these systems is the location of the double-exciton state, a low lying excited state dominated by the doubly excited HOMO,HOMOLUMO,LUMO configuration, (where [...] Read more.
Conjugated singlet ground state diradicals have received remarkable attention owing to their potential applications in optoelectronic devices. A distinctive character of these systems is the location of the double-exciton state, a low lying excited state dominated by the doubly excited HOMO,HOMOLUMO,LUMO configuration, (where HOMO=highest occupied molecular orbital, LUMO=lowest unoccupied molecular orbital) which may influence optical and other photophysical properties. In this contribution we investigate this specific excited state, for a series of recently synthesized conjugated diradicals, employing time dependent density functional theory (TDDFT) based on the unrestricted parallel spin reference configuration in the spin-flip formulation (SF-TDDFT) and standard TD calculations based on the unrestricted antiparallel spin reference configuration (TDUDFT). The quality of computed results is assessed considering diradical and multiradical descriptors, and the excited state wavefunction composition. Full article
(This article belongs to the Special Issue New Advances in Density Functional Theory and Its Application)
Show Figures

Figure 1

Open AccessArticle
A Holistic Auto-Configurable Ensemble Machine Learning Strategy for Financial Trading
Computation 2019, 7(4), 67; https://doi.org/10.3390/computation7040067 - 20 Nov 2019
Viewed by 308
Abstract
Financial markets forecasting represents a challenging task for a series of reasons, such as the irregularity, high fluctuation, noise of the involved data, and the peculiar high unpredictability of the financial domain. Moreover, literature does not offer a proper methodology to systematically identify [...] Read more.
Financial markets forecasting represents a challenging task for a series of reasons, such as the irregularity, high fluctuation, noise of the involved data, and the peculiar high unpredictability of the financial domain. Moreover, literature does not offer a proper methodology to systematically identify intrinsic and hyper-parameters, input features, and base algorithms of a forecasting strategy in order to automatically adapt itself to the chosen market. To tackle these issues, this paper introduces a fully automated optimized ensemble approach, where an optimized feature selection process has been combined with an automatic ensemble machine learning strategy, created by a set of classifiers with intrinsic and hyper-parameters learned in each marked under consideration. A series of experiments performed on different real-world futures markets demonstrate the effectiveness of such an approach with regard to both to the Buy and Hold baseline strategy and to several canonical state-of-the-art solutions. Full article
(This article belongs to the Special Issue Machine Learning for Computational Science and Engineering)
Show Figures

Figure 1

Open AccessReview
Recent Progress in Lattice Density Functional Theory
Computation 2019, 7(4), 66; https://doi.org/10.3390/computation7040066 - 20 Nov 2019
Viewed by 294
Abstract
Recent developments in the density-functional theory of electron correlations in many-body lattice models are reviewed. The theoretical framework of lattice density-functional theory (LDFT) is briefly recalled, giving emphasis to its universality and to the central role played by the single-particle density-matrix γ . [...] Read more.
Recent developments in the density-functional theory of electron correlations in many-body lattice models are reviewed. The theoretical framework of lattice density-functional theory (LDFT) is briefly recalled, giving emphasis to its universality and to the central role played by the single-particle density-matrix γ . The Hubbard model and the Anderson single-impurity model are considered as relevant explicit problems for the applications. Real-space and reciprocal-space approximations to the fundamental interaction-energy functional W [ γ ] are introduced, in the framework of which the most important ground-state properties are derived. The predictions of LDFT are contrasted with available exact analytical results and state-of-the-art numerical calculations. Thus, the goals and limitations of the method are discussed. Full article
Show Figures

Figure 1

Open AccessArticle
The Role of the Reduced Laplacian Renormalization in the Kinetic Energy Functional Development
Computation 2019, 7(4), 65; https://doi.org/10.3390/computation7040065 - 12 Nov 2019
Viewed by 317
Abstract
The Laplacian of the electronic density diverges at the nuclear cusp, which complicates the development of Laplacian-level meta-GGA (LLMGGA) kinetic energy functionals for all-electron calculations. Here, we investigate some Laplacian renormalization methods, which avoid this divergence. We developed two different LLMGGA functionals, which [...] Read more.
The Laplacian of the electronic density diverges at the nuclear cusp, which complicates the development of Laplacian-level meta-GGA (LLMGGA) kinetic energy functionals for all-electron calculations. Here, we investigate some Laplacian renormalization methods, which avoid this divergence. We developed two different LLMGGA functionals, which improve the kinetic energy or the kinetic potential. We test these KE functionals in the context of Frozen-Density-Embedding (FDE), for a large palette of non-covalently interacting molecular systems. These functionals improve over the present state-of-the-art LLMGGA functionals for the FDE calculations. Full article
(This article belongs to the Special Issue New Advances in Density Functional Theory and Its Application)
Show Figures

Figure 1

Open AccessArticle
Efficient Evaluation of Molecular Electrostatic Potential in Large Systems
Computation 2019, 7(4), 64; https://doi.org/10.3390/computation7040064 - 12 Nov 2019
Viewed by 323
Abstract
An algorithm for the efficient computation of molecular electrostatic potential is reported. It is based on the partition/expansion of density into (pseudo) atomic fragments with the method of Deformed Atoms in Molecules, which allows to compute the potential as a sum of atomic [...] Read more.
An algorithm for the efficient computation of molecular electrostatic potential is reported. It is based on the partition/expansion of density into (pseudo) atomic fragments with the method of Deformed Atoms in Molecules, which allows to compute the potential as a sum of atomic contributions. These contributions are expressed as a series of irregular spherical harmonics times effective multipole moments and inverse multipole moments, including short-range terms. The problem is split into two steps. The first one consists of the partition/expansion of density accompanied by the computation of multipole moments, and its cost depends on the size of the basis set used in the computation of electron density within the Linear Combination of Atomic Orbitals framework. The second one is the actual computation of the electrostatic potential from the quantities calculated in the first step, and its cost depends on the number of computation points. For a precision in the electrostatic potential of six decimal figures, the algorithm leads to a dramatic reduction of the computation time with respect to the calculation from electron density matrix and integrals involving basis set functions. Full article
(This article belongs to the Special Issue New Advances in Density Functional Theory and Its Application)
Show Figures

Figure 1

Open AccessArticle
Field Programmable Gate Array Applications—A Scientometric Review
Computation 2019, 7(4), 63; https://doi.org/10.3390/computation7040063 - 11 Nov 2019
Cited by 1 | Viewed by 591
Abstract
Field Programmable Gate Array (FPGA) is a general purpose programmable logic device that can be configured by a customer after manufacturing to perform from a simple logic gate operations to complex systems on chip or even artificial intelligence systems. Scientific publications related to [...] Read more.
Field Programmable Gate Array (FPGA) is a general purpose programmable logic device that can be configured by a customer after manufacturing to perform from a simple logic gate operations to complex systems on chip or even artificial intelligence systems. Scientific publications related to FPGA started in 1992 and, up to now, we found more than 70,000 documents in the two leading scientific databases (Scopus and Clarivative Web of Science). These publications show the vast range of applications based on FPGAs, from the new mechanism that enables the magnetic suspension system for the kilogram redefinition, to the Mars rovers’ navigation systems. This paper reviews the top FPGAs’ applications by a scientometric analysis in ScientoPy, covering publications related to FPGAs from 1992 to 2018. Here we found the top 150 applications that we divided into the following categories: digital control, communication interfaces, networking, computer security, cryptography techniques, machine learning, digital signal processing, image and video processing, big data, computer algorithms and other applications. Also, we present an evolution and trend analysis of the related applications. Full article
(This article belongs to the Special Issue Bibliometrics)
Show Figures

Graphical abstract

Open AccessReview
Emerging DFT Methods and Their Importance for Challenging Molecular Systems with Orbital Degeneracy
Computation 2019, 7(4), 62; https://doi.org/10.3390/computation7040062 - 03 Nov 2019
Viewed by 384
Abstract
We briefly present some of the most modern and outstanding non-conventional density-functional theory (DFT) methods, which have largely broadened the field of applications with respect to more traditional calculations. The results of these ongoing efforts reveal that a DFT-inspired solution always exists even [...] Read more.
We briefly present some of the most modern and outstanding non-conventional density-functional theory (DFT) methods, which have largely broadened the field of applications with respect to more traditional calculations. The results of these ongoing efforts reveal that a DFT-inspired solution always exists even for pathological cases. Among the set of emerging methods, we specifically mention FT-DFT, OO-DFT, RSX-DFT, MC-PDFT, and FLOSIC-DFT, complementing the last generation of existing density functionals, such as local hybrid and double-hybrid expressions. Full article
(This article belongs to the Special Issue New Advances in Density Functional Theory and Its Application)
Show Figures

Figure 1

Open AccessArticle
A DFT Study on Structure and Electronic Properties of BN Nanostructures Adsorbed with Dopamine
Computation 2019, 7(4), 61; https://doi.org/10.3390/computation7040061 - 01 Nov 2019
Viewed by 371
Abstract
Density functional theory calculations were carried out to investigate the adsorption behaviors of dopamine (DPM) on the BN nanostructures in gas and solvent phases. Our results revealed that the adsorption of DPM on BN nano-cages was stronger than other BN nanotubes. It was [...] Read more.
Density functional theory calculations were carried out to investigate the adsorption behaviors of dopamine (DPM) on the BN nanostructures in gas and solvent phases. Our results revealed that the adsorption of DPM on BN nano-cages was stronger than other BN nanotubes. It was found that the adsorption of two DPM (−1.30 eV) upon B12N12 was weaker than those of a single DPM (−1.41 eV). The Ga-doped B12N12 had better conditions for the detection of DPM than that of the Al-doped B12N12 nano-cage. The solvation effects for the most stable systems were calculated which showed that it had positive impacts upon the adsorption behavior of the applied systems than those studied in gas phase. The available results are expected to provide a useful guidance for the adsorption of DPM and generation of the new hybrid compounds. Full article
(This article belongs to the Special Issue Computational Studies of Adsorption on Nanoparticles and 2D-Materials)
Show Figures

Figure 1

Open AccessArticle
Computational Study of Lithium Intercalation in Silicene Channels on a Carbon Substrate after Nuclear Transmutation Doping
Computation 2019, 7(4), 60; https://doi.org/10.3390/computation7040060 - 24 Oct 2019
Viewed by 311
Abstract
Silicene is considered to be the most promising anode material for lithium-ion batteries. In this work, we show that transmutation doping makes silicene substantially more suitable for use as an anode material. Pristine and modified bilayer silicene was simulated on a graphite substrate [...] Read more.
Silicene is considered to be the most promising anode material for lithium-ion batteries. In this work, we show that transmutation doping makes silicene substantially more suitable for use as an anode material. Pristine and modified bilayer silicene was simulated on a graphite substrate using the classical molecular dynamics method. The parameters of Morse potentials for alloying elements were determined using quantum mechanical calculations. The main advantage of modified silicene is its low deformability during lithium intercalation and its possibility of obtaining a significantly higher battery charge capacity. Horizontal and vertical profiles of the density of lithium as well as distributions of the most significant stresses in the walls of the channels were calculated both in undoped and doped systems with different gaps in silicene channels. The energies of lithium adsorption on silicene, including phosphorus-doped silicene, were determined. High values of the self-diffusion coefficient of lithium atoms in the silicene channels were obtained, which ensured a high cycling rate. The calculations showed that such doping increased the normal stress on the walls of the channel filled with lithium to 67% but did not provoke a loss of mechanical strength. In addition, doping achieved a greater battery capacity and higher charging/discharging rates. Full article
(This article belongs to the Special Issue Computational Studies of Adsorption on Nanoparticles and 2D-Materials)
Show Figures

Graphical abstract

Open AccessArticle
Coordinate Scaling in Time-Independent Excited-State Density Functional Theory for Coulomb Systems
Computation 2019, 7(4), 59; https://doi.org/10.3390/computation7040059 - 13 Oct 2019
Viewed by 335
Abstract
A time-independent density functional theory for excited states of Coulomb systems has recently been proposed in a series of papers. It has been revealed that the Coulomb density determines not only its Hamiltonian, but the degree of excitation as well. A universal functional [...] Read more.
A time-independent density functional theory for excited states of Coulomb systems has recently been proposed in a series of papers. It has been revealed that the Coulomb density determines not only its Hamiltonian, but the degree of excitation as well. A universal functional valid for any excited state has been constructed. The excited-state Kohn–Sham equations bear resemblance to those of the ground-state theory. In this paper, it is studied how the excited-state functionals behave under coordinate scaling. A few relations for the scaled exchange, correlation, exchange-correlation, and kinetic functionals are presented. These relations are expected to be advantageous for designing approximate functionals. Full article
(This article belongs to the Special Issue New Advances in Density Functional Theory and Its Application)
Open AccessArticle
Machine-Learning Prediction of Underwater Shock Loading on Structures
Computation 2019, 7(4), 58; https://doi.org/10.3390/computation7040058 - 08 Oct 2019
Viewed by 345
Abstract
Due to the complex physics of underwater explosion problems, it is difficult to derive analytical solutions with accurate results. In this study, a machine-learning method to train a back-propagation neural network for parameter prediction is presented for the first time in literature. The [...] Read more.
Due to the complex physics of underwater explosion problems, it is difficult to derive analytical solutions with accurate results. In this study, a machine-learning method to train a back-propagation neural network for parameter prediction is presented for the first time in literature. The specific problem is the response of a structure submerged in water subjected to shock loads produced by an underwater explosion, with the detonation point being far away from the structure so that the loading wave can be regarded as a planar shock wave. Two rigid parallel plates connected by a linear spring and a linear dashpot that simulate structural stiffness and damping respectively, represent the structure. Taking the Laplace transform of the governing equations, solving the resulting equations, and then taking the inverse Laplace transform, the simplified problem is analyzed theoretically. The coupled ordinary differential equations governing the motion of the system are also solved numerically by the fourth order Runge–Kutta method and then verified by a finite element method using Ansys/LSDYNA. The parametric training with the back-propagation neural network algorithm was conducted to delineate the effects of structural stiffness and damping on the attenuation of shock waves, the cavitation time, and the time of maximum momentum transfer. The prediction results agree well with the validation and test sample results. Full article
(This article belongs to the Special Issue Machine Learning for Computational Science and Engineering)
Show Figures

Figure 1

Open AccessArticle
First-Principles Calculations of Structural, Mechanical, and Electronic Properties of the B2-Phase NiTi Shape-Memory Alloy Under High Pressure
by Fang Yu and Yu Liu
Computation 2019, 7(4), 57; https://doi.org/10.3390/computation7040057 - 30 Sep 2019
Viewed by 437
Abstract
A first-principles calculation program is used for investigating the structural, mechanical, and electronic properties of the cubic NiTi shape-memory alloy (SMA) with the B2 phase under high pressure. Physical parameters including dimensionless ratio, elastic constants, Young’s modulus, bulk modulus, shear modulus, ductile-brittle transition, [...] Read more.
A first-principles calculation program is used for investigating the structural, mechanical, and electronic properties of the cubic NiTi shape-memory alloy (SMA) with the B2 phase under high pressure. Physical parameters including dimensionless ratio, elastic constants, Young’s modulus, bulk modulus, shear modulus, ductile-brittle transition, elastic anisotropy, and Poisson’s ratio are computed under different pressures. Results indicate that high pressure enhances the ability to resist volume deformation along with the ductility and metallic bonds, but the biggest resistances to elastic and shear deformation occur at P = 35   GPa for the B2-phase NiTi SMA. Meanwhile, the strong anisotropy produced by the high pressure will motivate the cross-slip process of screw dislocations, thereby improving the plasticity of the B2-phase NiTi SMA. Additionally, the results of the density of states (DOS) reveal that the B2-phase NiTi SMA is essentially characterized by the metallicity, and it is hard to induce the structural phase transition for the B2-phase NiTi SMA under high pressure, which provides valuable guidance for designing and applying the NiTi SMA under high pressure. Full article
(This article belongs to the Special Issue New Advances in Density Functional Theory and Its Application)
Show Figures

Figure 1

Open AccessFeature PaperArticle
Enzyme Immobilization on Polymer Membranes: A Quantum and Molecular Mechanics Study
Computation 2019, 7(4), 56; https://doi.org/10.3390/computation7040056 - 28 Sep 2019
Viewed by 401
Abstract
Adsorption of the phosphotriesterase on a polysulfone membrane surface was investigated in this paper through a double-scale computational approach. Surface charges of the enzyme, as well as membrane, were calculated at sub and nanoscale while protein adsorption was simulated at larger scale. Adsorption [...] Read more.
Adsorption of the phosphotriesterase on a polysulfone membrane surface was investigated in this paper through a double-scale computational approach. Surface charges of the enzyme, as well as membrane, were calculated at sub and nanoscale while protein adsorption was simulated at larger scale. Adsorption energies were calculated as a function of the enzyme–surface distance, and for each distance, several protein rotations were tested to find the most stable orientations of the macromolecule. The results of this model were useful in obtaining information about the adhesion of the enzyme and to give indications on the orientations of its binding site. Adsorption energies agreed with the literature data. Furthermore, the binding site of the immobilized phosphotriesterase was less accessible with respect to native enzymes due to the steric hindrance of the polymer surface; thus, a reduction of its efficiency is expected. The proposed methodology made use of fundamental quantities, calculated without resorting to adjustable or empirical parameters, providing basic outputs useful for ascertaining enzymatic catalysis rate. Full article
Show Figures

Figure 1

Open AccessArticle
Computation and Experiment on Linearly and Circularly Polarized Electromagnetic Wave Backscattering by Corner Reflectors in an Anechoic Chamber
Computation 2019, 7(4), 55; https://doi.org/10.3390/computation7040055 - 24 Sep 2019
Viewed by 543
Abstract
Electromagnetic wave backscattering by corner reflectors in an anechoic chamber is studied using our developed computational tool. The tool applies the Finite-Difference Time-Domain (FDTD) method to simulate the propagation of the wave’s electric and magnetic fields. Experimental measurement in an anechoic chamber is [...] Read more.
Electromagnetic wave backscattering by corner reflectors in an anechoic chamber is studied using our developed computational tool. The tool applies the Finite-Difference Time-Domain (FDTD) method to simulate the propagation of the wave’s electric and magnetic fields. Experimental measurement in an anechoic chamber is also carried out as a comparison. The two results show agreement, including the finding that the backscatter intensity variation amongst the four circularly polarized modes is significantly smaller than the variation amongst the four linearly polarization modes. Full article
(This article belongs to the Section Computational Engineering)
Show Figures

Figure 1

Open AccessArticle
Fuzzy C-Means Based Clustering and Rule Formation Approach for Classification of Bearing Faults Using Discrete Wavelet Transform
Computation 2019, 7(4), 54; https://doi.org/10.3390/computation7040054 - 23 Sep 2019
Cited by 1 | Viewed by 470
Abstract
The rolling bearings are considered as the heart of rotating machinery and early fault diagnosis is one of the biggest challenges during operation. Due to complicated mechanical assemblies, detection of the advancing fault and faults at the incipient stage is very difficult and [...] Read more.
The rolling bearings are considered as the heart of rotating machinery and early fault diagnosis is one of the biggest challenges during operation. Due to complicated mechanical assemblies, detection of the advancing fault and faults at the incipient stage is very difficult and tedious. This work presents a fuzzy rule based classification of bearing faults using Fuzzy C-means clustering method using vibration measurements. Experiments were conducted to collect the vibration signals of a normal bearing and bearings with faults in the inner race, outer race and ball fault. Discrete Wavelet Transform (DWT) technique is used to decompose the vibration signals into different frequency bands. In order to detect the early faults in the bearings, various statistical features were extracted from this decomposed signal of each frequency band. Based on the extracted features, Fuzzy C-means clustering method (FCM) is developed to classify the faults using suitable membership functions and fuzzy rule base is developed for each class of the bearing fault using labeled data. The experimental results show that the proposed method is able to classify the condition of the bearing using the extracted features. The proposed FCM based clustering and classification model provides easier interpretation and implementation for monitoring the condition of the rolling bearings at an early stage and it will be helpful to take the preventive action before a large-scale failure. Full article
(This article belongs to the Special Issue Machine Learning for Computational Science and Engineering)
Show Figures

Figure 1

Previous Issue
Next Issue
Back to TopTop