Sweden's drinking water quality is considered to be high partly due to a high quality of the raw water and a well developed sewage infrastructure. Despite this, there is water contamination that could be prevented by installation of a sophisticated early warning system. Some of the major players in the production of drinking water have already invested in different types of early warning systems to ensure drinking water of high quality. There are various forms of early warning systems where automatic monitoring of E. Coli is an interesting alternative. Today's technology allows for this type of measurement, but it is often expensive and sometimes slow. This work aims to investigate whether the phenomenon of Extraordinary Optical Transmission (EOT) can be used as the technology of sensors for real-time measurement of indicators of fecal contaminated water. EOT is a transmissions peak which arises due to plasmon resonance. The study was conducted by performing verification measurements by spectrophotometry to detect the EOT. After this biomeasurements where done to show that the surface of the sample can be functionalized to provide the opportunity to choose which analytes should be detected. The verifying measurements showed that EOT was detected for one of the five samples where the hole size was 400 nm and the hole spacing was 600 nm. This is due to the high signal strength and to the fact that the phenomenon occurred within the measurement range of the instruments. Samples where designated with the name sPa where s indicates hole size in nm and a hole spacing in nm. The signal strength of the sample 140P600 and 200P600 was too low to determine whether EOT occurred. For samples 400P1000 and 600P1000 calculations showed that EOT occurred outside the measurement range, which meant that EOT could not be measured. Sample 400P600 was chosen to implement bio-measurements wherein the surface was functionalized with biotin Bovine serum albumin (B-BSA) to later bind with fluidMAG Streptavidin. Both B-BSA and streptavidin was bound as shown by the kinetic measurements.

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Green roofs can be an effective and appealing way to increase the energy efficiency of buildings by providing active insulation. As plants in the green roof transpire, there is a reduction in heat flux that is conducted through the green roof. The R-value, or thermal resistance, of a green roof is an effective measurement of thermal performance because it can be easily included in building energy calculations applicable to many different buildings and situations. The purpose of this study was to determine if an increase in ambient temperature would cause an increase in the R-value of green roofs. Test trays containing green roof materials were tested in a low speed wind tunnel equipped to determine the R-value of the trays. Three different plant species were tested in this study, ryegrass (Lolium perenne), sedum (Sedum hispanicum), and vinca (Vinca minor). For each test in this study the relative humidity was maintained at 45% and the soil was saturated with water. The trays were tested at four different ambient temperatures, ranging from room temperature to 120ºF. The resulting R-values for sedum ranged from 1.37 to 3.28 ft²h°F/BTU, for ryegrass the R-values ranged from 2.15 to 3.62 ft²h°F/BTU, and for vinca the R-values ranged from 3.15 to 5.19 ft²h°F/BTU. The average R-value for all the tests in this study was 3.20 ft²h°F/BTU. The results showed an increase in R-value with increasing temperature. Applying an ANOVA analysis to the data, the relationship between temperature and R-value for all three plant species was found to be statistically significant.

This study presents our attempt to examine the linkages and to investigate the linkage of stock price indexes among Taiwan, China and its major trading countries. Our empirical analysis employs daily data on stock price indexes over the period of January 2, 2000 to May 10, 2010. The total number of observations is about 2500. This study employ a sequence of time-series methodologies, including unit root test, cointegration test, vector error correction model, Granger causality test, Criterion, autocorrelation test, heteroscedasticity test, GARCH and Bi-GARCH. The findings of this study as follows. First, after first difference, every stock price indexes series all became stationary. Second, we found there has no long-run interrelationship among these stock markets. Third, we found that Taiwan¡¦s stock market exits leading role to China¡¦s stock market, but other countries¡¦ stock market lead Taiwan¡¦s stock market. For China, the stock market of United States, Japan, Taiwan and Hong Kong has a leading role to China¡¦s stock market. Only the rela-tionship between South Korea and China¡¦s stock market is independent. Forth, the result of autocorrelation test and ARCH test indicates that the influence of stock price indexes of major trading countries to Taiwan and China¡¦s stock price index has changed over time. Finally, the result of study indicates that every stock market can forecast its future trend by using its past stock data and investor can use the past stock data of stock market of major trading countries to forecast Taiwan and China¡¦s stock market.