Research on the Application of E-commerce to Small and Medium Enterprises (SMEs): the Case of India

SMEs account for a large proportion and play an important role in the development of each country in the world, including India. The globalization will bring many advantages for enterprises however SMEs will face fierce competition at the local, national and International level. In order to maintain and promote the important role of SMEs in the context of increased competition, SMEs have to change and adopt new technologies. E-commerce and digital technologies are bringing opportunities to help SMEs improve their competitiveness, narrow the gap with big enterprises thanks to their fairness and flexibility of the digital business environment.       According to UNIDO (2017), India is one of the countries successfully applying e-commerce to SMEs. Contributing to this success is the important role of the Indian government. Therefore, this paper focuses on researching the application of e-commerce to SMEs in terms of the role of government in promoting and creating an ecosystem for SMEs and e-commerce development

Nonlinear Dynamic Analysis of Cracked Beam on Elastic Foundation Subjected to Moving Mass

This paper presents a finite element algorithm for nonlinear dynamic analysis of cracked beams on an elastic foundation subjected to moving mass. Quantity surveying with parameters of varied cracks, foundation and loads shows their influence levels on the nonlinear dynamic response of the beams. The findings of the paper are the basis for the analysis, evaluation, and diagnosis of damages of beam structures on the elastic foundation subjected to moving loads, in which the common defects of the beams such as cracks are considered in order to improve the system's operational efficiency in a wide range of engineering applications

A Generalization Bound of Deep Neural Networks for Dependent Data

Existing generalization bounds for deep neural networks require data to be independent and identically distributed (iid). This assumption may not hold in real-life applications such as evolutionary biology, infectious disease epidemiology, and stock price prediction. This work establishes a generalization bound of feed-forward neural networks for non-stationary $\phi$-mixing data