83 resultados para Artificial wetland abatement


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Artificial Intelligence: The Basics is a concise and cutting-edge introduction to the fast moving world of AI. The author Kevin Warwick, a pioneer in the field, examines issues of what it means to be man or machine and looks at advances in robotics which have blurred the boundaries. Topics covered include: how intelligence can be defined, whether machines can 'think', sensory input in machine systems, the nature of consciousness, the controversial culturing of human neurons. Exploring issues at the heart of the subject, this book is suitable for anyone interested in AI, and provides an illuminating and accessible introduction to this fascinating subject.

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The accumulation of phosphorus (P) in the bottom sediment of field drainage ditches poses a threat to the ecology both of the ditch water and downstream water courses. We investigated the amounts, forms and internal loading of sediment-bound P along two drainage ditches that regulate water levels in a basin fen (~ 200 ha) supporting a mixture of restored wetland and drained agricultural fields. Water levels in the Lady's Drove Rhyne are currently managed to enhance the biodiversity of the wetland (Catcott Lows Reserve — an area formerly cultivated for arable crop production); whereas, the East Ditch is managed to drain adjoining land that remains under arable and livestock production. Laboratory-based chemical fractionation schemes were used to characterise the forms and potential mobility of the sediment-bound P, whilst pore-water equilibrators were employed in situ to evaluate the diffusive flux of P through the sediment–water column, and to characterise the corresponding redox conditions. Along both ditches, sediment pore-water profiles indicated conditions ranging from weakly to very reducing conditions with increasing depth, and net fluxes of P from the sediment to overlying water. P flux values ranged from 0.33 to 1.30 mg m− 2 day− 1. Both the degree of P saturation (DPS) of the sediment and NaOH extractable (Fe/Al-bound) P correlated significantly (P < 0.05) with P flux. Both in the wetland and agricultural ditches, by far the highest values for P flux were recorded at sites closest to points of drainage water entry from the corresponding, adjoining land. Although the P flux data were obtained from only a single sampling event, this study highlights the contribution of historical as well as ongoing agricultural land use on the sustained elevated P status of ditch sediments in lowland catchments.

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The development of an Artificial Neural Network model of UK domestic appliance energy consumption is presented. The model uses diary-style appliance use data and a survey questionnaire collected from 51 households during the summer of 2010. It also incorporates measured energy data and is sensitive to socioeconomic, physical dwelling and temperature variables. A prototype model is constructed in MATLAB using a two layer feed forward network with backpropagation training and has a12:10:24architecture.Model outputs include appliance load profiles which can be applied to the fields of energy planning (micro renewables and smart grids), building simulation tools and energy policy.

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To investigate the relative importance of instream nutrient spiralling and wetland transformation processes on surface water quality, total nitrogen (TN) and total phosphorus (TP) concentrations in a 200 m reach of the River Lambourn in the south-east of England were monitored over a 2-year period. In addition, the soil pore water nutrient dynamics in a riparian ecosystem adjacent to the river were investigated. Analysis of variance indicated that TN, TP and suspended sediment concentrations recorded upstream of the wetland were statistically significantly higher (P<0.05) than those downstream of the site. Such results suggest that the wetland was performing a nutrient retention function. Indeed, analysis of soil pore waters within the site show that up to 85% of TN and 70% of TP was removed from water flowing through the wetland during baseflow conditions, thus supporting the theory that the wetland played an important role in the regulation of surface water quality at the site. However, the small variations observed (0.034 mg TN l-1 and 0.031 mg P l-1) are consistent with the theory of nutrient spiralling suggesting that both instream and wetland retention processes have a causal effect on surface water quality.

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In this paper, we will address the endeavors of three disciplines, Psychology, Neuroscience, and Artificial Neural Network (ANN) modeling, in explaining how the mind perceives and attends information. More precisely, we will shed some light on the efforts to understand the allocation of attentional resources to the processing of emotional stimuli. This review aims at informing the three disciplines about converging points of their research and to provide a starting point for discussion.

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This paper compares the performance of artificial neural networks (ANNs) with that of the modified Black model in both pricing and hedging Short Sterling options. Using high frequency data, standard and hybrid ANNs are trained to generate option prices. The hybrid ANN is significantly superior to both the modified Black model and the standard ANN in pricing call and put options. Hedge ratios for hedging Short Sterling options positions using Short Sterling futures are produced using the standard and hybrid ANN pricing models, the modified Black model, and also standard and hybrid ANNs trained directly on the hedge ratios. The performance of hedge ratios from ANNs directly trained on actual hedge ratios is significantly superior to those based on a pricing model, and to the modified Black model.