A Closure for Isotropic Turbulence Based on Extended Scale Similarity Theory in Physical Space

The closure of a turbulence field is a longstanding fundamental problem, while most closure models are introduced in spectral space. Inspired by Chou＇s quasi-normal closure method in spectral space, we propose an analytical closure model for isotropic turbulence based on the extended scale similarity theory of the velocity structure function in physical space. The assumptions and certain approximations are justified with direct numerical simulation. The asymptotic scaling properties are reproduced by this new closure method, in comparison to the classical Batchelor model.

Profile and Rating Similarity Analysis for Recommendation Systems Using Deep Learning

Recommendation systems are going to be an integral part of any E-Business in near future.As in any other E-business,recommendation systems also play a key role in the travel business where the user has to be recommended with a restaurant that best suits him.In general,the recommendations to a user are made based on similarity that exists between the intended user and the other users.This similarity can be calculated either based on the similarity between the user profiles or the similarity between the ratings made by the users.First phase of this work concentrates on experimentally analyzing both these models and get a deep insight of these models.With the lessons learned from the insights,second phase of the work concentrates on developing a deep learning model.The model does not depend on the other user's profile or rating made by them.The model is tested with a small restaurant dataset and the model can predict whether a user likes the restaurant or not.The model is trained with different users and their rating.The system learns from it and in order to predict whether a new user likes or not a restaurant that he/she has not visited earlier,all the data the trained model needed is the rating made by the same user for different restaurants.The model is deployed in a cloud environment in order to extend it to be more realistic product in future.Result evaluated with dataset,it achieves 74.6%is accurate prediction of results,where as existing techniques achieves only 64%.

Designing an automated FAQ answering system for farmers based on hybrid strategies

Purpose: The purpose of this study is to develop an automated frequently asked question(FAQ) answering system for farmers. This paper presents an approach for calculating the similarity between Chinese sentences based on hybrid strategies.Design/methodology/approach: We analyzed the factors influencing the successful matching between a user's question and a question-answer(QA) pair in the FAQ database. Our approach is based on a combination of multiple factors. Experiments were conducted to test the performance of our method.Findings: Experiments show that this proposed method has higher accuracy. Compared with similarity calculation based on TF-IDF,the sentence surface forms and the semantic relations,the proposed method based on hybrid strategies has a superior performance in precision,recall and F-measure value.Research limitations: The FAQ answering system is only capable of meeting users' demand for text retrieval at present. In the future,the system needs to be improved to meet users' demand for retrieving images and videos.Practical implications: This FAQ answering system will help farmers utilize agricultural information resources more efficiently.Originality/value: We design the algorithms for calculating similarity of Chinese sentences based on hybrid strategies,which integrate the question surface similarity,the question semantic similarity and the question-answer similarity based on latent semantic analysis(LSA) to find answers to a user's question.