Determination of the Abient Toxicity of the Tailwater of Nimrod Lake

The objective of this research was to determine if ambient toxicity exists in the receiving stream below a reservoir in which water from the hypolimnion is released. The Ceriodaphnia 7-day test was utilized to determine if toxicity existed. This test is routinely used in the monitoring of municipal and industrial effluent. It has also been utilized in determining if ambient toxicity exists within receiving streams. Nimrod Lake is a flood control impoundment on the Fourche LaFave River in west central Arkansas. The literature suggest that during stratification the hypolimnetic release contains high levels of iron, manganese, ammonia and sulfide during the period of stratification. Patterns of decreased mean productivity and percent survival of Ceriodaphnia were found in the tailwater of the lake during the time the lake was stratified

Stratified Random Sampling from Streaming and Stored Data

Stratified random sampling (SRS) is a widely used sampling technique for approximate query processing. We consider SRS on continuously arriving data streams, and make the following contributions. We present a lower bound that shows that any streaming algorithm for SRS must have (in the worst case) a variance that is Ω(r ) factor away from the optimal, where r is the number of strata. We present S-VOILA, a streaming algorithm for SRS that is locally variance-optimal. Results from experiments on real and synthetic data show that S-VOILA results in a variance that is typically close to an optimal offline algorithm, which was given the entire input beforehand. We also present a variance-optimal offline algorithm VOILA for stratified random sampling. VOILA is a strict generalization of the well-known Neyman allocation, which is optimal only under the assumption that each stratum is abundant, i.e. has a large number of data points to choose from. Experiments show that VOILA can have significantly smaller variance (1.4x to 50x) than Neyman allocation on real-world data