Do storm event samples bias the comparison between sewer deposits contribution?

Previous researches demonstrated the occurrence of unique in-sewer sediment, the organic layer, on the Marais site in Paris, capable of explaining the entire wet weather sewer production of suspended particles. Other studies on sites having no similar organic sediment, Clichy in Paris and Ecully in Lyon, demonstrated a wet weather sewer deposits contribution (SDC) to effluent pollution comparable to that of the Marais site, casting therefore doubts on the implication of the organic layer to the outlet discharge pollution. So, an in-depth comparative investigation of the different sites' mean SDC was carried out to confirm or refute the major role of this layer vis-à-vis sewer production. The size and characteristics of the events' sample used to calculate the SDC were analyzed to find whether a statistical bias may have masked a difference that would be more coherent with field observations. After homogenizing these elements, the organic layer regained some of its previously alleged participation in sewer contribution (a maximum of 36% of the total SDC) but another unknown source was still dominant. This suggests that sewer sediment production during wet weather is a result of multiple sediment erosion: the organic layer and another major source not yet identified.

Mathematical constraints for building learning rules in the Purkinje cell system.

We first present a method to mathematically build a learning rule for closed-loop neural networks. This rule is then applied to climbing fibers in the cerebellar cortex. Our analytical study is based on previous experimental non-analytical studies, which suggests that climbing fibers carry out an error signal to the brain. Thus, our goal is to find the class of functions for the activity propagated by climbing fibers, allowing the output of the Purkinje cell to converge towards a desired output. These functions must tend towards zero when the objective is reached. Our techniques are generalized to other network models.