Search results for "Neural"

The Effect of Cycle Time and Offset on Roadside Pollutant Concentrations

2009

Hourly Forecasting of SO2 Pollutant Concentration Using an Elman Neural Network

2006

In this paper the first results produced by an Elman neural network for hourly SO2 ground concentration forecasting are presented. Time series has been recorded between 1998 and 2001 and are referred to a monitoring station of SO2 in the industrial site of Priolo, Syracuse, Italy. Data has been kindly provided by CIPA (Consorzio Industriale per la Protezione dell'Ambiente, Siracusa, Italia). Time series parameters are the horizontal and vertical wind velocity, the wind direction, the stability classes of Thomas, the base level of the layer of the atmospheric stability, the gradient of the potential temperature and the difference of the potential temperature of reference.

Influence of coordinated traffic lights parameters on roadside pollutant concentrations

2009

Abstract The paper examines the effects of coordinated traffic lights on CO and C 6 H 6 roadside concentrations in an urban area of Palermo in Southern Italy. Traffic loop detectors and one pollution-monitoring are used to collect data for use in DRACULA traffic microsimulator software. CO and C 6 H 6 roadside concentrations associated with varying cycle and offset times of the coordinated traffic lights are estimated using a neural network. Two functions were set up describing the relations of pollutant concentrations in term of cycle and offset time.

Traffic Parameters Estimation to Predict Road Side Pollutant Concentrations using Neural Networks

2007

The analysis aims to evaluate which is the most important among traffic parameters (flows, queues length, occupancy degree, and travel time) to forecast CO and C6H6 concentrations. The study area was identified by Notarbartolo Road and bounded by Liberta Street and Sciuti Street in the urban area of Palermo in Southern Italy. In this area, various loop detectors and one pollution-monitoring site were located. Traffic data related to the pollution-monitoring site immediately near the road link were estimated by Simulation of Urban MObility (SUMO) traffic microsimulator software using as input the flows measured by loop detectors on other links of road network. Traffic and weather data were u…

Improving the prediction of air pollution peak episodes generated by urban transport networks

2016

Abstract This paper illustrates the early results of ongoing research developing novel methods to analyse and simulate the relationship between trasport-related air pollutant concentrations and easily accessible explanatory variables. The final scope is to integrate the new models in traditional traffic management support systems for a sustainable mobility of road vehicles in urban areas. This first stage concerns the relationship between the hourly mean concentration of nitrogen dioxide (NO2) and explanatory factors reflecting the NO2 mean level one hour back, along with traffic and weather conditions. Particular attention is given to the prediction of pollution peaks, defined as exceedanc…

Three Hours Ahead Prevision of SO2 Pollutant Concentration Using an Elman Neural based Forecaster

2008

Abstract Indoor air quality near the industrial site is tightly joined to pollutant concentration level, since outdoor pollution heavily influences air quality and, consequently, inhabitants health. A pollution management system is essential for health protection. Automatic air quality management systems have became an important research issue with strong implications for inhabitants’ health. In this paper an automatic forecaster based on neural networks for SO 2 concentration prevision is proposed. The analyzed area covers different small towns near the industrial site of Priolo, in the south of the world. Among these towns, Melilli was the first town in Italy that was evacuated for high l…

Synapses between NG2 glia and neurons

2011

NG2-expressing glia are precursors to oligodendrocytes and subpopulations of astrocytes. They are unique among glial cells in that they enter into synaptic specialisations with neurons throughout all areas of grey and white matter and at all ages. To date, the NG2 cells appear to represent a postsynaptic compartment, and synapses are formed with axons. With differentiation to oligodendrocytes, NG2 is downregulated and myelin antigens upregulated: this coincides with a loss of the synaptic contacts between neurons and NG2 glial cells. The functional roles of this glial–neuron synapse in regulation of differentiation into myelinating oligodendrocytes or additionally responding to and modulati…

Differentiation-regulated loss of the polysialylated embryonic form and expression of the different polypeptides of the neural cell adhesion molecule…

1989

The expression of the neural cell adhesion molecule (N-CAM) on cultured murine oligodendrocytes, their precursors, and myelin was examined by indirect immunofluorescence, biosynthetic radiolabeling followed by immunoprecipitation and Western blot analysis, using antibodies specific for various forms of the molecule. In all culture systems studied, whether the oligodendrocytes were cultured as an enriched fraction containing precursor cells or in the presence of astrocytes and neurons, a similar differentiation-stage-related expression of N-CAM was seen. At early developmental stages many tetanus toxin receptor- and A2B5 antigen-positive putative oligodendrocyte precursors with bipolar morph…

Spatial heterogeneity in glassy polystyrene detected by deuteron NMR relaxation

1999

Using deuteron NMR, the dynamics of supercooled polystyrene-d 3 was investigated near the calorimetric glass transition. At these temperatures non-exponential spin lattice relaxation is found, indicating the presence of spatial heterogeneity. With increasing temperature, structural relaxation becomes fast enough to average efficiently over different spatial environments, leading to exponential magnetization decays. A qualitative comparison with toluene as a representative of a low molecular weight glass former is carried out. Indications are found that in polystyrene the observed averaging process is more effective at T g than it is in toluene.

Machine Learning Regression Approaches for Colored Dissolved Organic Matter (CDOM) Retrieval with S2-MSI and S3-OLCI Simulated Data

2018

The colored dissolved organic matter (CDOM) variable is the standard measure of humic substance in waters optics. CDOM is optically characterized by its spectral absorption coefficient, a C D O M at at reference wavelength (e.g., ≈ 440 nm). Retrieval of CDOM is traditionally done using bio-optical models. As an alternative, this paper presents a comparison of five machine learning methods applied to Sentinel-2 and Sentinel-3 simulated reflectance ( R r s ) data for the retrieval of CDOM: regularized linear regression (RLR), random forest regression (RFR), kernel ridge regression (KRR), Gaussian process regression (GPR) and support vector machines (SVR). Two different datasets of radiative t…