Festival of Science 2014 Abstracts
Festival of Science 2014 was held May 2nd, Friday, 1:00 pm to 5:00pm in Eben Holden
Roselyne Laboso and Evan Walsh
Weekly Monitoring of River Chemistry in St. Lawrence County, New York
Abstracat: Four rivers in the northern Adirondack region flowing into the St. Lawrence River were sampled weekly for one year (June 2011 – May 2012) for 72 elements and 8 anions. From West to East, the rivers include the Oswegatchie, Grasse, Raquette, and St. Regis. The headwaters of all the rivers begin in the acidified Adirondack Highlands (crystalline acidic bedrock) and flow across the Adirondack Lowlands (marble-rich metasedimentary sequence) and early Paleozoic sedimentary rocks (sandstone and dolostone) of the St. Lawrence River Valley. Sampling was conducted close to US Geological Survey gauging stations in the St. Lawrence River Valley so that loading of the analytes could be determined. In general, the waters were dilute: OSW (128.0±37.4), GRA (125.3±40.6), RAQ (61.8±21.7), and STR (96.1±36.2) μS. Their mean pH was 7.11±0.52, 7.62±0.66, 7.22±0.50, and 7.33±0.50. The most abundant cations were Ca, Na, Si, Mg, and K. The chief anions were CO32-, Cl-, and SO42-. Mean calculated ANC values in the four rivers are 43.1±6.4, 43.6±9.0, 17.2±5.4, and 31.5±7.2 (mg/L). The chemistry of the rivers varies from west to east and is influenced by the nature of the bedrock. The chemistry of the Raquette River is clearly anomalous and has the least amount of total dissolved solids and the least seasonal variability in nearly every analyte. This homogeneity is likely related to a number (n = 17) of hydropower reservoirs along its length. Runoff from a dolostone quarry upriver is also investigated to determine effects on chemistry of Raquette River.
Kevin Angstadt
Accelerating Database Joins Using a General Purpose GPU
Abstract: We demonstrate a significant speedup in database operations by repurposing hardware normally dedicated for computer graphics. In recent years, the computer manufacturing industry has achieved significant advances in the design of graphics cards (formally known as graphics processing units or GPUs). These add-on cards are now more computationally powerful than modern CPUs and often less expensive. Graphics processing units are highly parallel devices, meaning that the hardware can run thousands of instructions simultaneously, whereas the average CPU can only execute four to eight operations at the same time. Manufacturers have also developed software interfaces for graphics cards that allow developers to harness this power in their programs, as well as special hardware to run such computations. Previous research has shown the use of these general purpose graphics processing units (GPGPUs) have many applications in the research area of database systems. We extend previous work demonstrating efficient data storage and processing techniques on GPGPUs for single-table data queries to multi-table queries. Queries of this nature (known in database theory as joins) require far more processing and storage space, and our research presents novel techniques for efficiently harnessing the power of a graphics card to compute such data requests. Our project demonstrates a low-cost method for accelerating data processing, with initial results indicating speedups of two- to four-times on consumer-level GPUs over CPUs
POSTER ABSTRACTS:
Katherine Abramski
Improving the Statistical Method for Classifying Geomagnetic Storms
Abstract: Solar storms create disturbances in the Earth’s magnetic field that can damage satellites and cause power grids to fail. In an effort to better understand solar storms, we explore ways to improve the current method for classifying events as storms based on the Disturbance Storm Time (Dst) index, which measures disturbances in the Earth’s magnetic field. Several methods currently exist for classifying events as storms based on their Dst observation, including the threshold method, which classifies an event as a storm if Dst below a specified value is observed. This method is rather simple and results in significant error (high false positive and false negative rates). We investigate how these classification methods were derived and search for a more statistically justified way to define storms in an attempt to reduce error.
We explore three different logistic regression methods of classifying events as storms based on Dst. Our logistic regression methods use predictors to create a model that can determine the probability that a given event is a storm. We varied our predictors in order to find a method will low error rates. First, we used Dst lows as our predictors. Then we detrended the Dst data and used Dst lows as our predictors. Finally we used the percent decrease in Dst from the day before as a predictor. We found that all of these methods were more effective than the threshold method.
Alex Gladwin
Who Writes the Unwritable?: The Issue of Authorship in H. P. Lovecraft and C. M. Eddy’s “The Loved Dead.”
Abstract: In 1924, Weird Tales published a controversial story called “The Loved Dead,” which is told from the perspective of a necrophile. The issue was banned in at least Indiana. Today, the controversy surrounds the story’s authorship; while it was originally published under the name C. M. Eddy, Jr., a contemporary weird fiction writer and friend of Eddy’s, H. P. Lovecraft, is known to have done some sort of revision work on the story. The extent, however, is uncertain, and has caused debate. Thus, we will look at “The Loved Dead” using Stylometry, a study that attempts to quantify underlying aspects of style, in order to provide evidence toward a more informed claim of the controversial tale’s authorship.
John Balderston
Foxes, Hipsters, and The Internet Meme: A First-World Social Epidemic
Abstract: The Internet Meme: a fast spreading, sometimes “viral,” internet fad that is quite possibly the fastest mutating disease known to mankind. The Meme virus threatens the health and abdominal circumference of individuals everywhere. The multiple strains of the virus and its speedy mutation rate have left the grand majority of the human race perpetually infected. For this reason, we create a mathematical SIR model to demonstrate the spread of memes, where individuals can either be Susceptible (S), Addicted (A), or Rehabilitated (R). Our SAR model incorporates social impacts on the spread of this dreaded plague, including personal preference, hipster effects, boredom, and meme mutation. Observing the internet meme in this manner allows for the relevant understanding of social diseases, in which interactions within the population can result in a form of vaccination or devaccination, unseen in typical SIR modeling. Our hope is that our SARs will lend insight into combating the spread of this debilitating disease.
Juan Chang
Predicting Owner Tendencies in Fantasy Football Drafts
Abstract: As the world of professional sports grows more popular every year, so do interactive competitions such as fantasy sports. Fantasy Football, in particular, is among the most widely enjoyed competitions in the United States, today. Fantasy Football has become more popular, and league play has become more competitive as owners decide to gamble their money for potential profit. As the competitiveness increases, owners strive to compile the best possible team. In particular, owners will have an advantage if they can pick the player they want each and every round of the Fantasy Football Draft. This research will create a model that will be beneficial for Fantasy Football owners who want to succeed in their leagues, specifically the draft. By gathering information to develop a data set, multinomial logistic regression analysis will be used in order to develop this model. This model will act as a forecast, predicting the upcoming picks in the draft, so that owners can successfully anticipate which specific NFL players will be available at any point in time. This will allow the owner to produce a team of players he or she wants and, ultimately, have the most successful team by the end of the season.