GSM Based Operating of Embedded System Cloud Computing, Mobile Application Development and Artificial Intelligence Based System

The purpose of this paper is to identify and explore the challenges for potential solutions in the field of Mobile Application Cloud Computing Artificial Intelligence Robotics and Home made Devices Television Refrigerator Air Conditioner Air Cooler Mixer Grinder in Embedded Systems This paper is an attempt to introduce the reader into the world of GSM based Operating of Embedded Systems in voice based talking GSM technology and its applications for updating the new technologies in old device in the industry of home made appliances and devices in Embedded Systems The objective of the series will be a general discussion of GSM based new operating technologies for Mobile Applications Development and Mobile Computing in terms of Artificial Intelligence Its application will working from non mobile devices in home - made appliances and robotic

Stochastic turbulence modeling in RANS simulations via Multilevel Monte Carlo

A multilevel Monte Carlo (MLMC) method for quantifying model-form uncertainties associated with the Reynolds-Averaged Navier-Stokes (RANS) simulations is presented. Two, high-dimensional, stochastic extensions of the RANS equations are considered to demonstrate the applicability of the MLMC method. The first approach is based on global perturbation of the baseline eddy viscosity field using a lognormal random field. A more general second extension is considered based on the work of [Xiao et al.(2017)], where the entire Reynolds Stress Tensor (RST) is perturbed while maintaining realizability. For two fundamental flows, we show that the MLMC method based on a hierarchy of meshes is asymptotically faster than plain Monte Carlo. Additionally, we demonstrate that for some flows an optimal multilevel estimator can be obtained for which the cost scales with the same order as a single CFD solve on the finest grid level.Comment: 40 page

Graph Data Modeling for Political Communication on Twitter

Twitter has become a political reality where political parties, presidential candidates, legislatures and journalists post tweets about the latest events sharing texts, pictures, hashtags, URLs, and mentioning other users. Gaining insight from the vast amount of political data on Twitter is only possible with proper computational tools. We propose to store and manage Twitter data in an optimized Neo4j graph database for serving queries about political communication among state legislators of 50 U.S. states, state reporters, and presidential candidates for the 2016 presidential election. Our rationale for selecting this relatively new database technology is threefold: (1) ease of use in explicitly modeling and visualizing communication relationships among entities of interest; (2) flexibility to evolve the database overtime to quickly adapt to changes in user requirements; and (3) user-friendly intuitive query interface. We developed a Python-based Google App Engine application using Twitter API to collect tweets from the Twitter’s handlers of the aforementioned political actors. We employed best practice guidelines in graph database design to develop five different database models in order to distinguish the impact of each query optimization technique. We evaluated each of the models on the same set of tweets posted during January 1, 2016 to November 11, 2016 using the same set of queries of interest to political communication scholars in terms of the average query response times. Our experimental results confirmed the benefits of the best practice design guidelines. In addition, they show that the optimized database model is able to provide significant improvement in query response times. Reducing the number of hops used in the graph queries and using database indexes on most commonly used attributes reduced the average query response time in our dataset by as much as 74.52% and by 85.27%, respectively, compared to the reference model. Nevertheless, the reduction in the average query response time comes with the cost of the increase in graph database relationship store size by 5.49% compared to the reference model. Our contributions are as follows. (1) The optimized Neo4j graph database that will be updated weekly with new tweets; the access to this database can be made available to political communication scholars. (2) The above findings added to currently limited guidelines in graph database designs. (3) The findings about political communication prior to the Iowa caucus of the 2016 primary presidential election