The Chesapeake Bay is the largest estuary in the United States, where its watershed is home to more than 3,600 species of plants and animals and more than 16.6 million people. However one of its major issues is water pollution. Good water quality is vital for the health of all these plants, animals and people. In order to act upon this problem and restore the water, there is the need to monitor the water quality. There are currently several organizations and agencies monitoring different parts of the bay and working to restore the bay.[1]This project analyzes innovative ways to improve water quality monitoring in the West and Rhode rivers. The water in these rivers is currently monitored by a River Keeper. There is the need for an improved system with higher frequency of data input, higher accuracy of higher quality sensors, and a wider range of parameters being monitored.The motivation behind this project is to develop a transfer function between water quality and source of pollution. An improved model will allow the river keeper to have a better understanding of the conditions of the water and track the sources of pollution. With this new system, he will be able to act upon this acquired data and help to restore these rivers and subsequently the Chesapeake Bay.This design evaluated various sensor alternatives, transmission technologies, and used GIS mapping software in order to implement an automated water monitoring system for the West and Rhode rivers. A notional utility curve between available sensors and transmission techniques was developed where preliminary results indicate a system will fit the river keepers' needs and desired goals
Player selection is one of the great challenges of professional soccer clubs. Despite extensive use of performance data, a large number of player transfers at the highest level of club soccer have less than satisfactory outcome. This study uses player performance and decision making data to estimate team performance in terms of goal differential and model the effects of team compatibility on player and team performance. In this methodology, players' attributes are assessed with respect to the potential contribution to team performance, given the attributes of surrounding players. The study is using a semi-Markov decision process to model game flow. Performance data from the English Premier League between seasons 2008/2009 and 2011/2012 is used to predict the outcome of 69 transfers. The model provides an average error of 7.86 in predicting teams' goal differential in a season with current squad and 18.91 in estimating the effect of a future transfer on team performance.
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