Extracting Additional Information From Biotic Index Samples

Macroinvertebrates were collected from a small midwestern stream over a 3-year period as part of a non-point source pollution study. Temporal and spatial variability in standard biotic index values (BIs) were computed and compared with variability expressed by a series of additional community measurements, including the mean tolerance value of all taxa present in a sample, irrespective of the numerical abundance of individual taxa. The mean tolerance value exhibited lower spatial and temporal variability than the standard BI; therefore, mean tolerance values may be useful in estimating a stream\u27s long-term ambient water quality and its recovery potential. Computations of additional BI metrics are easily accomplished with no additional lab work required, and comparisons of mean tolerance values with standard BIs should aid investigators in interpreting changes in water quality

Non-Arbitrage Under Additional Information for Thin Semimartingale Models

This paper completes the two studies undertaken in \cite{aksamit/choulli/deng/jeanblanc2} and \cite{aksamit/choulli/deng/jeanblanc3}, where the authors quantify the impact of a random time on the No-Unbounded-Risk-with-Bounded-Profit concept (called NUPBR hereafter) when the stock price processes are quasi-left-continuous (do not jump on predictable stopping times). Herein, we focus on the NUPBR for semimartingales models that live on thin predictable sets only and the progressive enlargement with a random time. For this flow of information, we explain how far the NUPBR property is affected when one stops the model by an arbitrary random time or when one incorporates fully an honest time into the model. This also generalizes \cite{choulli/deng} to the case when the jump times are not ordered in anyway. Furthermore, for the current context, we show how to construct explicitly local martingale deflator under the bigger filtration from those of the smaller filtration.Comment: This paper develops the part of thin and single jump processes mentioned in our earlier version: "Non-arbitrage up to random horizon and after honest times for semimartingale models", Available at: arXiv:1310.1142v1. arXiv admin note: text overlap with arXiv:1404.041

The use of additional information in problem-oriented learning environments

Self-directed learning with authentic and complex problems (problem-oriented learning) requires that learners observe their own learning and use additional information when it is appropriate – e.g. hypertextual information in computer-supported learning environments. Research results indicate that learners in problem-oriented learning environments often have difficulties using additional information adequately, and that they should be supported. Two studies with a computer-supported problem-oriented learning environment in the domain of medicine analyzed the effects of strategy instruction on the use of additional information and the quality of the problem representation. In study 1, an expert model was used for strategy instruction. Two groups were compared: one group with strategy modeling and one group without. Strategy modeling influenced the frequency of looked-up hypertextual information, but did not influence the quality of learners' problem representations. This could be explained by difficulties in applying the general hypertext information to the problem. In study 2, the additional information was presented in a more contextualized way as graphical representation of the case and its relevant concepts. Again, two groups were compared: one with a strategy instruction text and one without. Strategy instruction texts supported an adequate use of this graphical information by learners and had an effect on the quality of their problem representations. These findings are discussed with respect to the design of additional help systems in problem-oriented learning environments