A review of deep learning applications for the next generation of cognitive networks

Intelligence capabilities will be the cornerstone in the development of next-generation cognitive networks. These capabilities allow them to observe network conditions, learn from them, and then, using prior knowledge gained, respond to its operating environment to optimize network performance. This study aims to offer an overview of the current state of the art related to the use of deep learning in applications for intelligent cognitive networks that can serve as a reference for future initiatives in this field. For this, a systematic literature review was carried out in three databases, and eligible articles were selected that focused on using deep learning to solve challenges presented by current cognitive networks. As a result, 14 articles were analyzed. The results showed that applying algorithms based on deep learning to optimize cognitive data networks has been approached from different perspectives in recent years and in an experimental way to test its technological feasibility. In addition, its implications for solving fundamental challenges in current wireless networks are discussed

Method and Experimental Study of Zeolite Crystal Manipulation Based in Hydrodynamic Forces for Single Crystal Assessment

In this work, we report an optofluidic system for manipulation of orientation of zeolite crystals near the bottom of a rectangular cross-sectional, straight, quartz microfluidic channel. Manipulation is accomplished by using two computer-controlled syringe pumps that generate adequate hydrodynamic forces for translation and rotation of crystals. Rotation of a crystal around its longitudinal axis allows us to inspect its four major faces for defects. Coffin-shaped zeolite crystals have been studied by several authors by fixing them to a substrate, using two different crystals to assess the roof and gable orientations. The proposed system permits complete assessment of a single crystal by shifting it between roof and gable orientations; moreover the medium can be controlled. Computational fluid dynamics simulations show that crystals in free motion near the bottom of the channel should move faster than the velocity estimated from video. An opposing force, which prevents the crystals from moving freely, has been calculated in order to match translation velocities from simulations and experiments for three given flow rates. The reported optofluidic system is proposed as a novel tool that we believe will open new possibilities for individual zeolite crystal assessment by manipulation of its orientation and medium control

Characteristics of a Persuasive Educational System: A Systematic Literature Review

The need to compete for users’ attention and provide them with the best user experience has increased the use of persuasion strategies in modern systems. Currently, Persuasive Systems (PSs) promote healthy behavior for well-being, energy consumption, and learning. Although the educational domain has limited investigation compared to other areas, PSs in education have been shown to be effective in motivating students. This paper summarizes the existing evidence on Persuasive Educational Systems (PESs), emphasizing research on the current design methods, evaluation methods, and characteristics. We follow Kitchenham’s method to perform a systematic literature review about PESs published between 2014 and 2020, with 19 relevant studies selected. We highlight some results from the analysis of selected papers such as persuasion strategies, use of a personalized persuasion technique, study of students’ susceptibility to strategies, integration of gamification mechanisms, and proposed tools to design PES. Moreover, we discuss interesting facts such as the common practice of using more than one tool to design PES, aspects of interaction, persuasion, learning, and the challenges in evaluating persuasive impact. Finally, as the main contribution of the paper we identify the seven necessary characteristics to build a persuasive educational system