Assessment of changes in potential nutrient limitation in an impounded river after application of lanthanum-modified bentonite

With the advent of phosphorus (P)-adsorbent materials and techniques to address eutrophication in aquatic systems, there is a need to develop interpretive techniques to rapidly assess changes in potential nutrient limitation. In a trial application of the P-adsorbent, lanthanum-modified bentonite (LMB) to an impounded section of the Canning River, Western Australia, a combination of potential P, nitrogen (N) and silicon (Si) nutrient limitation diagrams based on dissolved molar nutrient ratios and actual dissolved nutrient concentrations have been used to interpret trial outcomes. Application of LMB resulted in rapid and effective removal of filterable reactive P (FRP) from the water column and also effectively intercepted FRP released from bottom sediments until the advent of a major unseasonal flood event. A shift from potential N-limitation to potential P-limitation also occurred in surface waters. In the absence of other factors, the reduction in FRP was likely to be sufficient to induce actual nutrient limitation of phytoplankton growth. The outcomes of this experiment underpins the concept that, where possible in the short-term, in managing eutrophication the focus should not be on the limiting nutrient under eutrophic conditions (here N), but the one that can be made limiting most rapidly and cost-effectively (P)

The impact of phosphorus inputs from small discharges on designated freshwater sites

Natural England, with a contribution from the Broads Authority, commissioned the Centre for Ecology & Hydrology (CEH) in 2009 to conduct a review of the potential risk posed by small domestic discharges, such as from septic tanks, to freshwater SSSIs. The particular focus of this work was the risk of phosphorus (P) pollution to sites that are vulnerable to hyper-eutrophication

Justice Franklin S. Spears: A Son\u27s Perspective on His Father In Memoriam.

Abstract Forthcoming

Scale-invariant temporal history (SITH): optimal slicing of the past in an uncertain world

In both the human brain and any general artificial intelligence (AI), a representation of the past is necessary to predict the future. However, perfect storage of all experiences is not possible. One possibility, utilized in many applications, is to retain information about the past in a buffer. A limitation of this approach is that although events in the buffer are represented with perfect accuracy, the resources necessary to represent information at a particular time scale go up rapidly. Here we present a neurally-plausible, compressed, scale-free memory representation we call Scale-Invariant Temporal History (SITH). This representation covers an exponentially large period of time in the past at the cost of sacrificing temporal accuracy for events further in the past. The form of this decay is scale-invariant and can be shown to be optimal in that it is able to respond to worlds with a wide range of time scales. We demonstrate the utility of this representation in learning to play a simple video game. In this environment, SITH exhibits better learning performance than a fixed-size buffer history representation. Whereas the buffer performs well as long as the temporal dependencies can be represented within the buffer, SITH performs well over a much larger range of time scales for the same amount of resources. Finally, we discuss how the application of SITH, along with other human-inspired models of cognition, could improve reinforcement and machine learning algorithms in general.First author draf

Reduction of the size of datasets by using evolutionary feature selection: the case of noise in a modern city

Smart city initiatives have emerged to mitigate the negative effects of a very fast growth of urban areas. Most of the population in our cities are exposed to high levels of noise that generate discomfort and different health problems. These issues may be mitigated by applying different smart cities solutions, some of them require high accurate noise information to provide the best quality of serve possible. In this study, we have designed a machine learning approach based on genetic algorithms to analyze noise data captured in the university campus. This method reduces the amount of data required to classify the noise by addressing a feature selection optimization problem. The experimental results have shown that our approach improved the accuracy in 20% (achieving an accuracy of 87% with a reduction of up to 85% on the original dataset).Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech. This research has been partially funded by the Spanish MINECO and FEDER projects TIN2016-81766-REDT (http://cirti.es), and TIN2017-88213-R (http://6city.lcc.uma.es)