Abstract

Abstract

Abstract
The recent emergence of e-commerce has brought a shifting paradigm into global markets. This revolutionary framework relying upon technological progress has conveyed a new era of commerce. More than ever, businesses are using digital marketplaces to stay relevant and competitive. Suddenly, buying online has become part of their daily routines. Accessibility, flexibility, and convenience make the internet the ideal platform for modern age consumers. Small and medium enterprises predominate in almost every industry generating employment, income, and sustainability. Nonetheless, e-commerce adoption among these organizations is yet to be widely undertaken. This article has a twofold objective: first, it gathers data regarding the emergence of e-commerce adoption by SMEs through a systematic literature review encompassing 32 indexed articles (published between 2003 and 2021). Secondly, it provides a quantitative and qualitative analysis identifying strategic options and guidelines for a smooth digital transition among these players. Lastly, some recommendations to policy makers were clipped to work as facilitators, given SMEs specificities. The future is digital and the struggle for e-commerce adoption and exploitation among these organizations is at the top of the agenda. It is central in maintaining the vibrancy of the business ecosystem, and is therefore a turnkey for economic recovery.

Abstract

Abstract
In this paper the comparison between three convolutional neural networks, used for the control of bio-inspired multi-robots in a simulated environment, is performed through manual gestures captured in real time by a webcam. The neural networks are: VGG19, GoogLeNet and Alexnet. For the training of networks and control of robots, six gestures were used, each gesture corresponding to one action, collective and individual actions were defined, the simulation contains four bio-inspired robots. In this work the performance of the networks in the classification of gestures to control robots is compared. They proved to be efficient in the classification and control of agents, with Alexnet achieving an accuracy of 98.33%, VGG19 98.06% e Googlelenet 96.94%.

Abstract
Index structures were often used to optimise fetch operations to external storage devices (secondary memory). Nowadays, this also holds for increasingly large amounts of data residing in main-memory (primary memory). Within this scope, this work focuses on index structures that efficiently insert, query and delete valid-time data from very large datasets. This work performs a comparative study on the performance of the Interval B+ tree (IB+ tree) and the Improved Interval B+ tree (I2B+ tree): a variant that improves the time-efficiency of the deletion operation by reducing the number of traversed nodes to access siblings. We performed an extensive analysis of the performance of two operations: insertions and deletions, on both index structures, using multiple datasets with growing volumes of data, distinct temporal distributions and tree parameters (time-split alpha and node order). Results confirm that the I2B+ tree globally outperforms the IB+ tree, since, on average, deletion operations are 7% faster, despite insertions requiring 2% more time. Furthermore, results also allowed to determine the key factors that augment the performance difference on deletions between both trees. Copyright © 2021 by Author/s and Licensed by Veritas Publications Ltd., UK.