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"Maximum entropy method."
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Ni-doped SnS2: an investigation into its optical, magnetic, and electronic structures
The electronic and local structure of dilute magnetic materials with 2.5%, 5%, and 7.5% Ni-doped SnS
2
was characterized using X-ray diffraction (XRD) data. These magnetic semiconductors can be used in spintronics, half-metals, and valleytronics. This research utilizes XRD data to elucidate the electron density mapping (electronic structure) of 3D and 2D MEM (maximum entropy method), focusing on bonding behavior and the accumulation of interstitial charges in regions outside the regular lattice. Pure tin disulfide (SnS
2
) is diamagnetic, but nickel (Ni) doping converts it to mild ferromagnetism, with a maximum magnetization of 0.4726 emu/g and 0.4659 emu/g and a coercivity of 78 Oe and 93 Oe at 2.5% and 7.5% Ni concentrations, respectively. Using MEM electron density analysis, magnetic saturation and coercivity are also highly connected. The 5% Ni-doped SnS
2
composition has the highest interstitial charge, resulting in a more covalent character responsible for excellent electrical conduction and reduced magnetism. Optical absorption and energy gap engineering are discussed based on cation deficiency analysis employing XRD data. Photoluminescence (PL) emission reveals that Ni doping has no direct influence on SnS
2
systems. However, Ni doping in SnS
2
increases the vacancy/interstitial charge, which indirectly corresponds with PL emission. Electron spin resonance (ESR) analysis reveals the presence of interstitial Ni
2+
and substitutional Ni
3+
ions. This study found a correlation between charge buildup at substitutional and interstitial sites, type, and strength of bonding, and physical properties like magnetism and optical properties.
Journal Article
Entropy theory and its application in environmental and water engineering
Entropy Theory and its Application in Environmental and Water Engineering responds to the need for a book that deals with basic concepts of entropy theory from a hydrologic and water engineering perspective and then for a book that deals with applications of these concepts to a range of water engineering problems. The range of applications of entropy is constantly expanding and new areas finding a use for the theory are continually emerging. The applications of concepts and techniques vary across different subject areas and this book aims to relate them directly to practical problems of environmental and water engineering.
The book presents and explains the Principle of Maximum Entropy (POME) and the Principle of Minimum Cross Entropy (POMCE) and their applications to different types of probability distributions. Spatial and inverse spatial entropy are important for urban planning and are presented with clarity. Maximum entropy spectral analysis and minimum cross entropy spectral analysis are powerful techniques for addressing a variety of problems faced by environmental and water scientists and engineers and are described here with illustrative examples.
Giving a thorough introduction to the use of entropy to measure the unpredictability in environmental and water systems this book will add an essential statistical method to the toolkit of postgraduates, researchers and academic hydrologists, water resource managers, environmental scientists and engineers. It will also offer a valuable resource for professionals in the same areas, governmental organizations, private companies as well as students in earth sciences, civil and agricultural engineering, and agricultural and rangeland sciences.
This book:
* Provides a thorough introduction to entropy for beginners and more experienced users
* Uses numerous examples to illustrate the applications of the theoretical principles
* Allows the reader to apply entropy theory to the solution of practical problems
* Assumes minimal existing mathematical knowledge
* Discusses the theory and its various aspects in both univariate and bivariate cases
* Covers newly expanding areas including neural networks from an entropy perspective and future developments.
eBook
Structural global reliability assessment considering nonlinear correlation effects by enhanced high-order moment method
The global reliability analysis of complex engineering structures considering the correlations between basic random variables remains a challenge, especially for nonlinear correlation problems. In this work, to take into account the influence of nonlinear correlation between random variables, an enhanced high-order moment method is proposed for structural global reliability analysis. Firstly, the traditional Nataf transformation is reviewed, and the generalized Nataf transformation is presented by introducing the Copula theory. Secondly, the corresponding performance functions of global reliability problems are described uniformly by the state variable description method, and the GL
2
-discrepancy point set is developed for the high-order moments estimation and sensitivity analysis of the state variable. Thirdly, the global reliability of the structures is accurately determined by using the improved maximum entropy method (IMEM). Finally, two examples, including one static and one dynamic, are investigated to demonstrate the accuracy and efficiency of the proposed method and the influence of nonlinear correlation between random variables on the global reliability of the structures, in which the results obtained from the proposed method are compared with Monte Carlo simulation (MCS) method. The results of the examples show the nonlinear correlation between random variables has a significant impact on the global reliability of structures, and the proposed method has fairly high accuracy and efficiency for structural high-order moments estimation and global reliability analysis.
Journal Article
Location of Cu2+ in CHA zeolite investigated by X-ray diffraction using the Rietveld/maximum entropy method
Accurate structural models of reaction centres in zeolite catalysts are a prerequisite for mechanistic studies and further improvements to the catalytic performance. The Rietveld/maximum entropy method is applied to synchrotron powder X-ray diffraction data on fully dehydrated CHA-type zeolites with and without loading of catalytically active Cu2+ for the selective catalytic reduction of NOx with NH3 . The method identifies the known Cu2+ sites in the six-membered ring and a not previously observed site in the eight-membered ring. The sum of the refined Cu occupancies for these two sites matches the chemical analysis and thus all the Cu is accounted for. It is furthermore shown that approximately 80% of the Cu2+ is located in the new 8-ring site for an industrially relevant CHA zeolite with Si/Al = 15.5 and Cu/Al = 0.45. Density functional theory calculations are used to corroborate the positions and identity of the two Cu sites, leading to the most complete structural description of dehydrated silicoaluminate CHA loaded with catalytically active Cu2+ cations.
Journal Article
Entropic Density Functional Theory
A formulation of density functional theory (DFT) is constructed as an application of the method of maximum entropy for an inhomogeneous fluid in thermal equilibrium. The use of entropy as a systematic method to generate optimal approximations is extended from the classical to the quantum domain. This process introduces a family of trial density operators that are parameterized by the particle density. The optimal density operator is that which maximizes the quantum entropy relative to the exact canonical density operator. This approach reproduces the variational principle of DFT and allows a simple proof of the Hohenberg–Kohn theorem at finite temperature. Finally, as an illustration, we discuss the Kohn–Sham approximation scheme at finite temperature.
Journal Article
A combined reliability analysis approach with dimension reduction method and maximum entropy method
This paper presents a combined reliability analysis approach which is composed of Dimension Reduction Method (DRM) and Maximum Entropy Method (MEM). DRM has emerged as a new approach in this field with the advantages of its sensitivity-free nature and efficiency instead of searching for the most probable point (MPP). However, in some recent implementations, the Moment Based Quadrature Rule (MBQR) in the DRM was found to be numerically instable when solving a system of linear equations for the integration points. In this study, a normalized Moment Based Quadrature Rule (NMBQR) is proposed to solve this problem, which can reduce the condition number of the coefficient matrix of the linear equations considerably and improve the robustness and stableness. Based on the statistical moments obtained by DRM+NMBQR, the MEM is applied to construct the probability density function (PDF) of the response. A number of numerical examples are calculated and compared to the Monte Carlo simulation (MCS), the First Order Reliability Method (FORM), the Extended Generalized Lambda Distribution (EGLD) and Saddlepoint Approximation (SA). The results show the accuracy and efficiency of the proposed method, especially for the multimodal PDF problem and multiple design point problem.
Journal Article
Dynamics of the US Housing Market: A Quantal Response Statistical Equilibrium Approach
In this article, we demonstrate that a quantal response statistical equilibrium approach to the US housing market with the help of the maximum entropy method of modeling is a powerful way of revealing different characteristics of the housing market behavior before, during and after the recent housing market crash in the US. In this line, a maximum entropy approach to quantal response statistical equilibrium model (QRSE) is employed in order to model housing market dynamics in different phases of the most recent housing market cycle using the S&P Case Shiller housing price index for 20 largest- Metropolitan Regions, and Freddie Mac housing price index (FMHPI) for 367 Metropolitan Cities for the US between 2000 and 2015. Estimated model parameters provide an alternative way to understand and explain the behaviors of economic agents, and market dynamics by questioning the traditional economic theory, which takes assumption for the behavior of rational utility maximizing representative agent with self-fulfilled expectations as given.
Journal Article
Modified Maximum Entropy Method and Estimating the AIF via DCE-MRI Data Analysis
Background: For the kinetic models used in contrast-based medical imaging, the assignment of the arterial input function named AIF is essential for the estimation of the physiological parameters of the tissue via solving an optimization problem. Objective: In the current study, we estimate the AIF relayed on the modified maximum entropy method. The effectiveness of several numerical methods to determine kinetic parameters and the AIF is evaluated—in situations where enough information about the AIF is not available. The purpose of this study is to identify an appropriate method for estimating this function. Materials and Methods: The modified algorithm is a mixture of the maximum entropy approach with an optimization method, named the teaching-learning method. In here, we applied this algorithm in a Bayesian framework to estimate the kinetic parameters when specifying the unique form of the AIF by the maximum entropy method. We assessed the proficiency of the proposed method for assigning the kinetic parameters in the dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), when determining AIF with some other parameter-estimation methods and a standard fixed AIF method. A previously analyzed dataset consisting of contrast agent concentrations in tissue and plasma was used. Results and Conclusions: We compared the accuracy of the results for the estimated parameters obtained from the MMEM with those of the empirical method, maximum likelihood method, moment matching (“method of moments”), the least-square method, the modified maximum likelihood approach, and our previous work. Since the current algorithm does not have the problem of starting point in the parameter estimation phase, it could find the best and nearest model to the empirical model of data, and therefore, the results indicated the Weibull distribution as an appropriate and robust AIF and also illustrated the power and effectiveness of the proposed method to estimate the kinetic parameters.
Journal Article
Improved Oxide Ion Conductivity of Hexagonal Perovskite-Related Oxides Ba3W1+xV1−xO8.5+x/2
Hexagonal perovskite-related oxides such as Ba3WVO8.5 have attracted much attention due to their unique crystal structures and significant oxide ion conduction. However, the oxide ion conductivity of Ba3WVO8.5 is not very high. Herein, we report new hexagonal perovskite-related oxides Ba3W1+xV1−xO8.5+x/2 (x = −0.1, −0.05, 0.05, 0.1, 0.25, 0.4, 0.5, 0.6, and 0.75). The bulk conductivity of Ba3W1.6V0.4O8.8 was found to be 21 times higher than that of the mother material Ba3WVO8.5 at 500 °C. Maximum entropy method (MEM) neutron scattering length density (NSLD) analyses of neutron diffraction data at 800 °C experimentally visualized the oxide ion diffusion pathways through the octahedral O2 and tetrahedral O3 sites in intrinsically oxygen-deficient layers. By increasing the excess W content x in Ba3W1+xV1−xO8.5+x/2, the excess oxygen content x/2 increases, which leads to more oxygen atoms at the O2 and O3 oxygen sites, a higher minimum NSLD on the O2–O3 path, and a higher level of conductivity. Another reason for the increased conductivity of Ba3W1.6V0.4O8.8 is the lower activation energy for oxide ion conduction, which can be ascribed to the longer (W/V)–O2 and (W/V)–O3 distances due to the substitution of V atoms with large-sized W species. The present findings open new avenues in the science and technology of oxide ion conductors.
Journal Article