Search

The power available in flowing water can be used for street-light, battery charging devices, etc., applications near the flowing water region. Many sites are available with a free flow of fluids. The high-density fluid from industries to the river has kinetic energy. The Savonius rotor was initially used for wind power, but recently it was employed for free water stream; however, any flowing fluid can have the ability to generate power. To improve the Savonius hydrokinetic turbine's power extraction capacity, several efforts are made by many researchers. From published literature, a reference design of a modified Savonius hydrokinetic turbine was taken. For further improvement in the performance, the different end arc radius provided at the modified Savonius hydrokinetic turbine's inner side was simulated for five values. In the present simulation, the turbine blade was taken as a rigid solid wall. The obtained results indicate that the initial selected design shows optimum performance compared to any arc radius provided at the end edge.

In the derived approach, an analysis is performed on Twitter data for World Cup soccer 2014 held in Brazil to detect the sentiment of the people throughout the world using machine learning techniques. By filtering and analyzing the data using natural language processing techniques, sentiment polarity was calculated based on the emotion words detected in the user tweets. The dataset is normalized to be used by machine learning algorithms and prepared using natural language processing techniques like word tokenization, stemming and lemmatization, part-of-speech (POS) tagger, name entity recognition (NER), and parser to extract emotions for the textual data from each tweet. This approach is implemented using Python programming language and Natural Language Toolkit (NLTK). A derived algorithm extracts emotional words using WordNet with its POS (part-of-speech) for the word in a sentence that has a meaning in the current context, and is assigned sentiment polarity using the SentiWordNet dictionary or using a lexicon-based method. The resultant polarity assigned is further analyzed using na&iuml, ve Bayes, support vector machine (SVM), K-nearest neighbor (KNN), and random forest machine learning algorithms and visualized on the Weka platform. Na&iuml, ve Bayes gives the best accuracy of 88.17% whereas random forest gives the best area under the receiver operating characteristics curve (AUC) of 0.97.

Today SoC designs are complex and dense with millions of transistors. These designs have complex DFT architecture for testing. Thousands of DFT patterns are used during silicon bring up. All these patterns are going through multiple stages of pre silicon verification to ensure bug free design and meet higher pass rate on silicon. In this paper, we will talk about DFT pre silicon validation flow enhancements at different stages to verify design quality and ensure patterns quality delivered for post silicon validation with maximum patterns passing without re-generation in short duration of post silicon testing. This paper talks about enhancement done across different stages of verification cycle to meet rapid silicon bring-up with zero post silicon DFT bugs.

Xanthomonas vasicola pv. vasculorum (Xvv) is a bacterial pathogen that causes both bacterial leaf streak of maize and sugarcane gumming disease. After decades limited to South Africa, bacterial leaf streak of maize spread rapidly through maize-growing areas of Argentina, Brazil and the USA since 2014. The origin, method and biological underpinnings of this sudden spread are not well understood but are the subject of active research. Effective control methods remain elusive, but sanitation and crop debris management may limit the disease. Yield impact data are not yet available, but lesions may become severe enough to limit plant productivity in some varieties. The pathogen is not currently considered a quarantine threat by the USDA, EPPO or IPPC. Taxonomic Tree Top of page Domain: Bacteria Phylum: Proteobacteria Class: Gammaproteobacteria Order: Xanthomonadales Family: Xanthomonadaceae Genus: Xanthomonas Species: Xanthomonas vasicola pv. vasculorum Notes on Taxonomy and Nomenclature Top of page The taxonomic nomenclature of the pathogen has undergone several changes and is still being resolved at the time of this report. The Xanthomonas clade causing.