The reaction NH2 + PH3 yields NH3 + PH2: Absolute rate constant measurement and implication for NH3 and PH3 photochemistry in the atmosphere of Jupiter

The rate constant is measured over the temperature interval 218-456 K using the technique of flash photolysis-laser-induced fluorescence. NH2 radicals are produced by the flash photolysis of ammonia highly diluted in argon, and the decay of fluorescent NH2 photons is measured by multiscaling techniques. For each of the five temperatures employed in the study, the results are shown to be independent of variations in PH3 concentration, total pressure (argon), and flash intensity. It is found that the rate constant results are best represented for T between 218 and 456 K by the expression k = (1.52 + or - 0.16) x 10 to the -12th exp(-928 + or - 56/T) cu cm per molecule per sec; the error quoted is 1 standard deviation. This is the first determination of the rate constant for the reaction NH2 + PH3. The data are compared with an estimate made in order to explain results of the radiolysis of NH3-PH3 mixtures. The Arrhenius parameters determined here for NH2 + PH3 are then constrasted with those for the corresponding reactions of H and OH with PH3

A recurrent neural network approach to quantitatively studying solar wind effects on TEC derived from GPS; preliminary results

This paper attempts to describe the search for the parameter(s) to represent solar wind effects in Global Positioning System total electron content (GPS TEC) modelling using the technique of neural networks (NNs). A study is carried out by including solar wind velocity (Vsw), proton number density (Np) and the Bz component of the interplanetary magnetic field (IMF Bz) obtained from the Advanced Composition Explorer (ACE) satellite as separate inputs to the NN each along with day number of the year (DN), hour (HR), a 4-month running mean of the daily sunspot number (R4) and the running mean of the previous eight 3-hourly magnetic A index values (A8). Hourly GPS TEC values derived from a dual frequency receiver located at Sutherland (32.38° S, 20.81° E), South Africa for 8 years (2000–2007) have been used to train the Elman neural network (ENN) and the result has been used to predict TEC variations for a GPS station located at Cape Town (33.95° S, 18.47° E). Quantitative results indicate that each of the parameters considered may have some degree of influence on GPS TEC at certain periods although a decrease in prediction accuracy is also observed for some parameters for different days and seasons. It is also evident that there is still a difficulty in predicting TEC values during disturbed conditions. The improvements and degradation in prediction accuracies are both close to the benchmark values which lends weight to the belief that diurnal, seasonal, solar and magnetic variabilities may be the major determinants of TEC variability

Towards a GPS-based TEC prediction model for Southern Africa with feed forward networks

In this paper, first results from a national Global Positioning System (GPS) based total electron content (TEC) prediction model over South Africa are presented. Data for 10 GPS receiver stations distributed through out the country were used to train a feed forward neural network (NN) over an interval of at most five years. In the NN training, validating and testing processes, five factors which are well known to influence TEC variability namely diurnal variation, seasonal variation, magnetic activity, solar activity and the geographic position of the GPS receivers were included in the NN model. The database consisted of 1-min data and therefore the NN model developed can be used to forecast TEC values 1 min in advance. Results from the NN national model (NM) were compared with hourly TEC values generated by the earlier developed NN single station models (SSMs) at Sutherland (32.38°S, 20.81°E) and Springbok (29.67°S, 17.88°E), to predict TEC variations over the Cape Town (33.95°S, 18.47°E) and Upington (28.41°S, 21.26°E) stations, respectively, during equinoxes and solstices. This revealed that, on average, the NM led to an improvement in TEC prediction accuracy compared to the SSMs for the considered testing periods

Towards a Reproducible Pan-European Soil Erosion Risk Assessment - RUSLE

Soil is a valuable, non-renewable natural resource that offers a multitude of ecosystems goods and services. Given the increasing threat of soil erosion in Europe and the implications this has on future food security and water quality, it is important that land managers and decision makers are provided with accurate and appropriate information on the areas more prone to erosion phenomena. The present study shows an attempt to locate, at regional scale, the most sensitive areas and to highlight any changes of soil erosion trends with climate change. The choice of the input datasets is crucial as they have to offer the most homogeneous and complete covering at the pan-European level and to allow the produced information to be harmonized and easily validated. The model is based on available datasets (HWSD, SGDBE, SRTM, CLC and E-OBS) and The Revised Universal Soil Loss Equation (RUSLE) is used because of its flexibility and least data demanding. A significant effort has been made to select the better simplified equations to be used when a strict application of the RUSLE model was not possible. In particular for the computation of the Rainfall Erosivity factor a validation based on measured precipitation time series (having a temporal resolution of 10-15 minutes) has been implemented to be easily reproducible. The validation computational framework is available as free software. Designing the computational modeling architecture with the aim to ease as much as possible the future reuse of the model in analyzing climate change scenarios has also been a challenging goal of the research

Prevalence and pharmacologic management of familial hypercholesterolemia in an unselected contemporary cohort of patients with stable coronary artery disease

INTRODUCTION: Familial hypercholesterolemia (FH) is an inherited disorder characterized by elevated plasma levels of low-density lipoprotein cholesterol (LDL-C) associated with premature cardiovascular disease. METHODS: Using the data from the START (STable Coronary Artery Diseases RegisTry) study, a nationwide, prospective survey on patients with stable coronary artery disease (CAD), we described prevalence and lipid lowering strategies commonly employed in these patients. The study population was divided into "definite/probable FH," defined as a Dutch Lipid Clinic Network (DLCN) score ≥6, "possible FH" with DLCN 3-5, and "unlikely FH" in presence of a DLCN <3. RESULTS: Among the 4030 patients with the DLCN score available, 132 (3.3%) were classified as FH (2.3% with definite/probable and 1.0% with possible FH) and 3898 (96.7%) had unlikely FH. Patients with both definite/probable and possible FH were younger compared to patients not presenting FH. Mean on-treatment LDL-C levels were 107.8 ± 41.5, 84.4 ± 40.9, and 85.8 ± 32.3 (P < 0.0001) and a target of ≤70 mg/dL was reached in 10.9%, 30.0%, and 22.0% (P < 0.0001) of patents with definite/probable, possible FH, and unlikely FH, respectively. Statin therapy was prescribed in 85 (92.4%) patients with definite/probable FH, in 38 (95.0%) with possible FH, and in 3621 (92.9%) with unlikely FH (P = 0.86). The association of statin and ezetimibe, in absence of other lipid-lowering therapy, was more frequently used in patients with definite/probable FH compared to patients without FH (31.5% vs 17.5% vs 9.5%; P < 0.0001). CONCLUSIONS: In this large cohort of consecutive patients with stable CAD, FH was highly prevalent and generally undertreated with lipid lowering therapies

Application of neural networks to South African GPS TEC modelling

The propagation of radio signals in the Earth’s atmosphere is dominantly affected by the ionosphere due to its dispersive nature. Global Positioning System (GPS) data provides relevant information that leads to the derivation of total electron content (TEC) which can be considered as the ionosphere’s measure of ionisation. This paper presents part of a feasibility study for the development of a Neural Network (NN) based model for the prediction of South African GPS derived TEC. The South African GPS receiver network is operated and maintained by the Chief Directorate Surveys and Mapping (CDSM) in Cape Town, South Africa. Vertical total electron content (VTEC) was calculated for four GPS receiver stations using the Adjusted Spherical Harmonic (ASHA) model. Factors that influence TEC were then identified and used to derive input parameters for the NN. The well established factors used are seasonal variation, diurnal variation, solar activity and magnetic activity. Comparison of diurnal predicted TEC values from both the NN model and the International Reference Ionosphere (IRI-2001) with GPS TEC revealed that the IRI provides more accurate predictions than the NN model during the spring equinoxes. However, on average the NN model predicts GPS TEC more accurately than the IRI model over the GPS locations considered within South Africa

A New Dissimilarity Measure for Clustering Seismic Signals

Hypocenter and focal mechanism of an earthquake can be determined by the analysis of signals, named waveforms, related to the wave field produced and recorded by a seismic network. Assuming that waveform similarity implies the similarity of focal parameters, the analysis of those signals characterized by very similar shapes can be used to give important details about the physical phenomena which have generated an earthquake. Recent works have shown the effectiveness of cross-correlation and/or cross-spectral dissimilarities to identify clusters of seismic events. In this work we propose a new dissimilarity measure between seismic signals whose reliability has been tested on real seismic data by computing external and internal validation indices on the obtained clustering. Results show its superior quality in terms of cluster homogeneity and computational time with respect to the largely adopted cross correlation dissimilarit