Toward Coarse-Grained Elasticity of Single-Layer Covalent Organic Frameworks

Two-dimensional covalent organic frameworks (2D COFs) are an interesting class of 2D materials since their reticular synthesis allows the tailored design of structures and functionalities. For many of their applications, the mechanical stability and performance is an important aspect. Here, we use a computational approach involving a density-functional based tight-binding method to calculate the in-plane elastic properties of 45 COFs with a honeycomb lattice. Based on those calculations, we develop two coarse-grained descriptions: one based on a spring network and the second using a network of elastic beams. The models allow us to connect the COF force constants to the molecular force constants of the linker molecules and thus enable an efficient description of elastic deformations. To illustrate this aspect, we calculate the deformation energy of different COFs containing the equivalent of a Stone-Wales defect and find very good agreement with the coarse-grained description

The Application of the Conjugate Gradient Method to the Solution of Transient Electromagnetic Scattering from Thin Wires

Previous approaches to the problem of computing scattering by conducting bodies have utilized the well-known marching-on-in-time solution procedures. However, these procedures are very dependent on discretization techniques and sometimes lead to instabilities as the time progresses. Moreover, the accuracy of the solution cannot be verified easily, and usually there is no error estimation. In this paper we describe the conjugate gradient method for solving transient problems. For this method, the time and space discretizations are independent of one another. The method has the advantage of a direct method as the solution is obtained in a finite number of steps and also of an iterative method since the roundoff and truncation errors are limited only to the last stage of iteration. The conjugate gradient method converges for any initial guess; however, a good initial guess may significantly reduce the computation time. Also, explicit error formulas are given for the rate of convergence of this method. Hence any problem may be solved to a prespecified degree of accuracy. The procedure is stable with respect to roundoff and truncation errors and simple to apply. As an example, we apply the method of conjugate gradient to the problem of scattering from a thin conducting wire illuminated by a Gaussian pulse. The results compare well with the marching-on-in-time procedure

Automatic recognition of retinal diseases using mathematical models of image processing, based on multilayer-dictionary learning

Background and Objective:Image processing is one of the most important issues in the field of artificial intelligence, which is used in various industrial, medical, military, and security systems. One of the most important applications of image processing is the extraction of different types of classification in the field of medical sciences. By using powerful algorithms in this field, intelligent systems can be invented that automatically understand and interpret the medical characteristics of individuals without the need to the physician supervision can discover useful information to help experts make good judgments. When the necessary parameters for the diagnosis of the disease increase, the diagnosis and prognosis of the disease becomes very difficult even for an expert, which is why computer diagnostic tools have been used in recent decades to help the physicians. This has led to a reduction in possible errors due to fatigue or inexperience of the specialist, and to provide the required medical data to the physician in less time and with more detail and accuracy. The purpose of this study is to improve the classification of new methods using a multi-layered model to address retinal diseases diagnosis. Methods: This paper presents a multi-layer dictionary learning method for classification tasks.  Our multi-layer framework uses a label consistent in K-SVD algorithm to learn a discriminative dictionary for sparse coding in order to learn better features in retinal optical coherence tomography images. In addition to using class labels of training data, we also associate label information with each dictionary item (columns of the dictionary matrix) to enforce discrimination in sparse codes during dictionary learning process. In fact, it relies on a succession of sparse coding and pooling steps in order to find an effective representation of data for classification. Moreover, we apply Duke dataset for validating our algorithm: Duke spectral domain OCT (SD-OCT) dataset, consisting of volumetric scans acquired from 45 subjects 15 normal subjects, 15 AMD patients, and 15 DME patients. Findings: Our classifier leads to a correct classification rate of 95.85% and 100.00% for normal and abnormal (DME and AMD). Experimental results demonstrate that our algorithm outperforms compared to many recent proposed supervised dictionary learning and sparse representation techniques. Conlusion: The results of this study were to provide an automatic system for the diagnosis of some retinal abnormalities in a way that it could do data analysis with high accuracy in comparison to other modern methods to diagnosis delicate patterns of OCT, separate images of normal and patient the normal and in two age-related macular degeneration diseases (AMD), and diabetic macular degeneration (DME), and help the physician to diagnose retinal pathology with great care. As a suggestion for professionals and future research, by generalizing this method to the more classes, we can cover the entire retinal myopia and use it as a potentially effective tool in computerized diagnosis and screening for retinal disease or in the wider eye area.   ===================================================================================== COPYRIGHTS  ©2019 The author(s). This is an open access article distributed under the terms of the Creative Commons Attribution (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, as long as the original authors and source are cited. No permission is required from the authors or the publishers.  ====================================================================================

The contribution of the Chirality-Induced Spin Selectivity (CISS) effect to the dispersion interaction between chiral molecules

Dispersion interactions are one of the components of van der Waals forces, which play a key role in the understanding of intermolecular interactions in many physical, chemical and biological processes. The theory of dispersion forces was developed by London in the early years of quantum mechanics. However, it was only in the 1960s that it was recognized that for molecules lacking an inversion center such as chiral and helical molecules, there are chirality-sensitive corrections to the dispersion forces proportional to the rotatory power known from the theory of circular dichroism and with the same distance scaling law R-6 as the London energy. The discovery of the Chirality-Induced Spin Selectivity (CISS) effect in recent years has led to an additional twist in the study of chiral molecular systems, showing a close relation between spin and molecular geometry. Motivated by it, we propose in this investigation that there may exist additional contributions to the dispersion energy related to intermolecular, induced spin-orbit (ISOC) interactions. Within a second-order perturbative approach, these forces manifest as an effective intermolecular spin-spin exchange interaction. Although they are weaker than the standard London forces, the ISOC interactions turn out to be nevertheless not negligible and display the same R$^{-6}$ distance scaling. Our results suggest that classical force field descriptions of van-der Waals interactions may require additional modifications to include the effects discussed here.Comment: 21 pages, 2 figure