Secular effects in the orbits of the Galilean satellites

This paper presents the results of an investigation into the secular behavior of the orbits of the Galilean satellites of Jupiter. Kamel's perturbation method is used to remove all the explicitly periodic variables from the differential equations that describe the long period behavior of the orbits to third order in the masses, and the resulting differential equations for the secular behavior are then solved. Several numerical examples are given to illustrate the sensitivity of the solution to variations in the masses of the satellites.     

A statistical study of environmental factors controlling concentrations of heavy metals in the burrowing bivalve Scrobicularia plana and the polychaete Nereis diversicolor

Multiple regression analyses indicate that the most important factors controlling Ag, Cd, Co, Cu, Pb and Zn concentrations in the estuarine bivalve Scrobicularia plana and the polychaete Nereis diversicolor are the concentrations of these metals in surface sediments and the partitioning of sediment-bound metals between different sediment constituents. Extraction of the sediments with 1-n HCl provides more information about the bioavailability of the metals than do extractions with five other techniques. Biologically available Cd and Co in solution contribute significantly to Cd and Co concentrations in both Scrobicularia and Nereis. Concentrations of Ag in Scrobicularia are reduced where Cu concentrations in sediments are high. An unexplained increase in concentrations of Cu in Scrobicularia at specific stations in six estuaries suggests that under certain very anoxic conditions the availability of copper may be exceptionally high.     

Nitrogen isotope ratios in estuarine biota collected along a nutrient gradient in Narragansett Bay, Rhode Island, USA

Stable nitrogen isotope ratios were used to study the incorporation of anthropogenically-derived nitrogen into the food webs of salt marsh systems along a contamination gradient in Narragansett Bay. Nitrogen isotope ratios (delta(15)N) were measured in six estuarine species collected from three marshes along this gradient, monthly from June to October between 1997 and 1999. A significant decrease in delta(15)N was found with distance along the estuary for four of the six species. Significant differences were found among monthly isotope ratios for some species. Nitrogen isotope ratios in sea lettuce (Ulva lactuca) increased during the summer season with highest delta(15)N values measured during September and October. This trend was most pronounced at the station receiving the highest nutrient inputs. Elevated delta(15)N values at this station appeared to correlate with seawater ammonia/nitrate concentration ratios. The temporal variations in delta(15)N suggest that care should be taken in species selection and the design of sampling schemes of studies using delta(15)N for monitoring anthropogenic nutrients in aquatic systems. Sampling programs designed to determine long-term trends should consider species that do not show rapid fluctuations in isotope ratios. The mud snail, Nassarius obsoletus, responded this way in the present study. Studies designed to measure short-term changes should include species such as U. lactuca, which rapidly respond to isotope changes. The results from this study also help to establish a baseline for nitrogen isotope values in Narragansett Bay. This information can be used to monitor future trends in nitrogen inputs to this estuary.     

Mental maps and travel behaviour: meanings and models

In this paper, the “mental map” concept is positioned with regard to individual travel behaviour to start with. Based on Ogden and Richards’ triangle of meaning (The meaning of meaning: a study of the influence of language upon thought and of the science of symbolism. International library of psychology, philosophy and scientific method. Routledge and Kegan Paul, London, 1966) distinct thoughts, referents and symbols originating from different scientific disciplines are identified and explained in order to clear up the notion’s fuzziness. Next, the use of this concept in two major areas of research relevant to travel demand modelling is indicated and discussed in detail: spatial cognition and decision-making. The relevance of these constructs to understand and model individual travel behaviour is explained and current research efforts to implement these concepts in travel demand models are addressed. Furthermore, these mental map notions are specified in two types of computational models, i.e. a Bayesian Inference Network (BIN) and a Fuzzy Cognitive Map (FCM). Both models are explained, and a numerical and a real-life example are provided. Both approaches yield a detailed quantitative representation of the mental map of decision-making problems in travel behaviour.     

Snow water changes with elevation over the Western United States in IPCC AR4 models

This work analyzes variations of snow water with elevation for a group of AR4 models over the Western United States. The results are expressed in terms of both snow water equivalent (SWE), which is the depth of snow if melted, and snow water volume (SWV), which is the total volume of SWE for a specified region. The decrease in total SWV over the study region between 1905 and 25 and 1980–99 is about 22%, which is in the range of the observed values. The results for both the A1b and B1 scenarios for the middle twenty-first century both show a near total loss of SWE at lower elevations. However, the largest losses for SWV are near 1800 m. Furthermore, the total SWV loss for the A1b scenario is about 63%, whereas that for the more moderate B1 scenario is about 49%. Thus, a reduction of greenhouse gas emissions is likely to reduce the loss of snow, which is vital to society in the dry Western United States.     

Statistical Downscaling of Wind Variability from Meteorological Fields

Measurements show that on numerous occasions the low-level wind is highly variable across a large portion of south-eastern Australia. Under such conditions the risk of a large rapid change in total wind power is increased. While variability tends to increase with mean wind speed, a large component of wind variability is not explained by wind speed alone. In this work, reanalysis fields from the US National Centers for Environmental Prediction (NCEP) are statistically downscaled to model wind variability at a coastal location in Victoria, Australia. In order to reduce the dimensionality of the problem, the NCEP fields are each decomposed using empirical orthogonal function (EOF) techniques. The downscaling technique is applied to two periods in the seasonal cycle, namely (i) winter to early spring, and (ii) summer. In each case, data representing 2 years are used to form a model that is then validated using independent data from another year. The EOFs that best predict wind variability are examined. To allow for non-linearity and complex interaction between variables, all empirical models are built using random forests. Quantitatively, the model compares favourably with a simple regression of wind variability against wind speed, as well as multiple linear regression models.     

Prediction of pile settlement using artificial neural networks based on standard penetration test data

In recent years artificial neural networks (ANNs) have been applied to many geotechnical engineering problems with some degree of success. With respect to the design of pile foundations, accurate prediction of pile settlement is necessary to ensure appropriate structural and serviceability performance. In this paper, an ANN model is developed for predicting pile settlement based on standard penetration test (SPT) data. Approximately 1000 data sets, obtained from the published literature, are used to develop the ANN model. In addition, the paper discusses the choice of input and internal network parameters which were examined to obtain the optimum model. Finally, the paper compares the predictions obtained by the ANN with those given by a number of traditional methods. It is demonstrated that the ANN model outperforms the traditional methods and provides accurate pile settlement predictions.