This study aimed to determine the effects of climate change on forest fire trends in Canada by measuring correlations between weather conditions and the frequency and size of forest fires. Upon identifying the correlations, a model was created to understand future forest fire trends. The purpose of this study was to prevent the increasing trend of forest fires and devise solutions to reduce their damages. The data obtained from the Canadian National Fire Database underwent a linear regression and a machine learning algorithm to respectively predict and correlate weather conditions with future forest fire trends. It was concluded that temperature and wind speed experienced a positive correlation with forest fire frequency and size and precipitation experienced a negative correlation. To reduce the harmful effects of forest fires, cloud seeding can be used to create more precipitation and wind farms can be built to lower wind speed and attract lightning. However, more research and stricter policies directly targeting climate change are necessary for long term stability or decrease in forest fire trends.
In this paper, we measure the memory performance throughout the Phoronix test "RAMspeed SMP". We decide to test this specific benchmark because we know how important is the memory for the system performance. This document shows how much the memory performance could change if we modify some variables in the linux kernel.
The Internet has become the broadest area in which to exchange information and communicate.Some use this function in a positive way, whilst others do so negatively. With the growth of the Internet, social networks have also grown. Social networks are used in different fields and for different proposes. They are used in higher education to enhance training and collaborative learning and exchange knowledge in an interaction environment.
This paper aims at finding the 10 best universities by measuring the use of social networks in education.Universities are selected for this experiment from the Academic Influence Ranking website for the domain of computer science overall (type A) (for more information about the selected universities please visit this link: http://pubstat.org/).
This project focuses on a modification of a greedy transition based dependency parser. Typically a Part-Of-Speech (POS) tagger models a probability distribution over all the possible tags for each word in the given sentence and chooses one as its best guess. This is then pass on to the parser which uses this information to build a parse tree. The current state of the art for POS tagging is about 97% word accuracy, which seems high but results in a around 56% sentence accuracy. Small errors at the POS tagging phase can lead to large errors down the NLP pipeline and transition based parsers are particularity sensitive to these types of mistakes. A maximum entropy Markov model was trained as a POS multi-tagger passing more than its 1-best guess to the parser which was thought could make a better decision when committing to a parse for the sentence. This has been shown to give improved accuracy in other parsing approaches. We shown there is a correlation between tagging ambiguity and parsers accuracy and in fact the higher the average tags per word the higher the accuracy.
The future of alternative energy storage for vehicles is
bright because the use of hydrogen in the economy provides
various solutions to environmental situations. Hydrogen is
as flexible as electricity in that it can be produced in both
renewable and non-renewable sources of energy. However,
the literature written focuses on the conditions that are
needed to be accounted for when it comes to switching to
hydrogen as an alternative energy storage using statistical
analysis. By taking an economic and environmental investigation, hydrogen has the best air pollution emissions in
comparison to conventional, hybrid and electric vehicles. It
is also found that hydrogen fuel cell vehicles are simpler in
design which accounts for its weight that is much lighter
than most vehicles. Ergo, hydrogen is the preferred fuel for
fuel cell vehicles because of efficiency that can increase the
potential for a sustainable climate.