Optical Diode Based on Two-Dimensional Photonic Crystal

The integrated optical diodes have been a thriving research theme due to their potential on-chip applications in photonic circuits for all-optical computing and information processing. Analogous to electronic counterparts, the unidirectional light propagation is characterized by the high contrast between forward and backward transmissions. In this chapter, we demonstrate the proposed schemes and designs for reciprocal and nonreciprocal optical diodes based on two-dimensional (2D) photonic crystal (PhC). The reciprocal devices are built by linear and passive PhC, and the spatial asymmetric mode conversion is utilized to achieve the unidirectionality. The presented nonreciprocal optical diodes rely on the optical nonlinearity of cavity. New 2D PhC optical diodes with high contrast ratio, low insertion loss, large operational bandwidth, small device footprint, and ease of fabrication are highly desirable and still pursued

Influence of shareholder equity on trade credit demand: The study of non-financial firms in Pakistan

Relevance. Most small-sized firms have little or no access to credit markets, which is why they prefer equity financing and usually pay higher dividends on this equity. When paying higher dividends, these small-sized debt-free firms continue to build a reputation in the markets.Research objective. The analysis focuses on the trade payables that impact shareholder equity. In Pakistan, most of the businesses are small and middle-sized. Most of the Pakistani SMEs have a low capital structure and these enterprises depend on their daily business needs, so equity financing is their primary source of funding.Data and Methods. The data source for our study is the financial statements of non-financial firms (in total, 156 firms) from the balance Sheet Analysis (BSA) and the Financial Statement Analysis (FSA) published by The State Bank of Pakistan (SBP). The financial statements also provide the data listed by the Pakistan Stock Exchange (PSX). The data cover the period from 2001 to 2017. This study primarily relies on the panel data model. The study applied the methods of descriptive analysis, correlation matrix, common regression model, fixed effect model, random effect model and then the Hausman test was performed to choose the best model.Results. The results of the study indicate a positive and significant relationship between shareholder equity and trade credit demand.Conclusion. Many investors require trade credit as a suitable tool for the growth of shareholders of the company. It is also used in many types of business schemes as the shareholder equity factor plays a role in profit generation through the use of trade credit transactions

Residual Control Chart for Binary Response with Multicollinearity Covariates by Neural Network Model

Quality control studies have dealt with symmetrical data having the same shape with respect to left and right. In this research, we propose the residual (r) control chart for binary asymmetrical (non-symmetric) data with multicollinearity between input variables via combining principal component analysis (PCA), functional PCA (FPCA) and the generalized linear model with probit and logit link functions, and neural network regression model. The motivation in this research is that the proposed control chart method can deal with both high-dimensional correlated multivariate data and high frequency functional multivariate data by neural network model and FPCA. We show that the neural network r control chart is relatively efficient to monitor the simulated and real binary response data with the narrow length of control limits

Explainable deep learning for insights in El Ni\~no and river flows

The El Ni\~no Southern Oscillation (ENSO) is a semi-periodic fluctuation in sea surface temperature (SST) over the tropical central and eastern Pacific Ocean that influences interannual variability in regional hydrology across the world through long-range dependence or teleconnections. Recent research has demonstrated the value of Deep Learning (DL) methods for improving ENSO prediction as well as Complex Networks (CN) for understanding teleconnections. However, gaps in predictive understanding of ENSO-driven river flows include the black box nature of DL, the use of simple ENSO indices to describe a complex phenomenon and translating DL-based ENSO predictions to river flow predictions. Here we show that eXplainable DL (XDL) methods, based on saliency maps, can extract interpretable predictive information contained in global SST and discover SST information regions and dependence structures relevant for river flows which, in tandem with climate network constructions, enable improved predictive understanding. Our results reveal additional information content in global SST beyond ENSO indices, develop understanding of how SSTs influence river flows, and generate improved river flow prediction, including uncertainty estimation. Observations, reanalysis data, and earth system model simulations are used to demonstrate the value of the XDL-CN based methods for future interannual and decadal scale climate projections

Machine Learning: The Backbone of Intelligent Trade Credit-Based Systems

Technology has turned into a significant differentiator in the money and traditional recordkeeping systems for the financial industry. To depict two customers as potential investors, it is mandatory to give the complex innovation that they anticipate and urge to purchase. In any case, it is difficult to keep on top of and be a specialist in each of the new advancements that are accessible. By reappropriating IT administrations, monetary administrations firms can acquire prompt admittance to the most recent ability and direction. Financial systems, along with machine learning (ML) algorithms, are vital for critical concerns like secure financial transactions and automated trading. These are the key to the provision of financial decisions for investors and stakeholders for the firms which are working with the trade credit (TC) approach, in Small and Medium Industries (SMEs). Huge and very sensitive data is processed in a limited time. The trade credit is a reason for more financial gains. The impact of TC with predictive machine learning algorithms is the reason why intelligent and safe revenue generation is the main target of the proposed study. That is, the combination of financial data and technology (FinTech) domains is a potential reason for sales growth and ultimately more profit.publishedVersio

Correlation between tongue manifestations and glucose, total cholesterol, and high-density lipoprotein cholesterol in patients with acute cerebral infarction

AbstractObjectiveTo analyze the association between tongue manifestations and the levels of glucose (GLU), total cholesterol (TCH), and high-density lipoprotein cholesterol (HDL-C) in subjects with acute cerebral infarction.MethodsHospitalized patients with first unilateral cerebral infarction in the Neurological Department of Xuanwu Hospital were included and the correlation between tongue fur color, fur nature, and the levels of GLU, TCH, HDL-C were analyzed.ResultsHDL level in the thin fur group was higher than that in the thick fur group (P=0.02). The difference in the levels of GLU, TCH, and HDL-C among the groups was significant (P<0.05), classified in terms of slippery, moist, and dry fur. Further comparison between the groups by Student-Newman-Keuls test showed that GLU level in the dry fur group was the highest. Moreover, the TCH level in the slippery fur group was higher than the other two groups.ConclusionA correlation between tongue manifestations and GLU, TCH, HDL-C was identified in the patients with acute cerebral infarction