Different Data Sources (different + data_source)

Distribution by Scientific Domains

Selected Abstracts

Empowering Automated Trading in Multi-Agent Environments

David W. Ash
Trading in the financial markets often requires that information be available in real time to be effectively processed. Furthermore, complete information is not always available about the reliability of data, or its timeliness,nevertheless, a decision must still be made about whether to trade or not. We propose a mechanism whereby different data sources are monitored, using Semantic Web facilities, by different agents, which communicate among each other to determine the presence of good trading opportunities. When a trading opportunity presents itself, the human traders are notified to determine whether or not to execute the trade. The Semantic Web, Web Services, and URML technologies are used to enable this mechanism. The human traders are notified of the trade at the optimal time so as not to either waste their resources or lose a good trading opportunity. We also have designed a rudimentary prototype system for simulating the interaction between the intelligent agents and the human beings, and show some results through experiments on this simulation for trading of the Chicago Board Options Exchange (CBOE) options. [source]

The European carbon balance.

Part 2: croplands
Abstract We estimated the long-term carbon balance [net biome production (NBP)] of European (EU-25) croplands and its component fluxes, over the last two decades. Net primary production (NPP) estimates, from different data sources ranged between 490 and 846 gC m,2 yr,1, and mostly reflect uncertainties in allocation, and in cropland area when using yield statistics. Inventories of soil C change over arable lands may be the most reliable source of information on NBP, but inventories lack full and harmonized coverage of EU-25. From a compilation of inventories we infer a mean loss of soil C amounting to 17 g m,2 yr,1. In addition, three process-based models, driven by historical climate and evolving agricultural technology, estimate a small sink of 15 g C m,2 yr,1 or a small source of 7.6 g C m,2 yr,1. Neither the soil C inventory data, nor the process model results support the previous European-scale NBP estimate by Janssens and colleagues of a large soil C loss of 90 ± 50 gC m,2 yr,1. Discrepancy between measured and modeled NBP is caused by erosion which is not inventoried, and the burning of harvest residues which is not modeled. When correcting the inventory NBP for the erosion flux, and the modeled NBP for agricultural fire losses, the discrepancy is reduced, and cropland NBP ranges between ,8.3 ± 13 and ,13 ± 33 g C m,2 yr,1 from the mean of the models and inventories, respectively. The mean nitrous oxide (N2O) flux estimates ranges between 32 and 37 g C Eq m,2 yr,1, which nearly doubles the CO2 losses. European croplands act as small CH4 sink of 3.3 g C Eq m,2 yr,1. Considering ecosystem CO2, N2O and CH4 fluxes provides for the net greenhouse gas balance a net source of 42,47 g C Eq m,2 yr,1. Intensifying agriculture in Eastern Europe to the same level Western Europe amounts is expected to result in a near doubling of the N2O emissions in Eastern Europe. N2O emissions will then become the main source of concern for the impact of European agriculture on climate. [source]

The effects of different input data and their spatial resolution on the results obtained from a conceptual nutrient emissions model: the River Stör case study

Markus Venohr
Abstract This paper focuses on the influences of different data sources, and the variation in spatial resolution of input data and analysis, on the calculated nutrient emissions using the conceptual model MONERIS. MONERIS calculates both nitrogen and phosphorus emissions from point and diffuse sources and the riverine nutrient retention. By subtracting the retention from the emissions, a riverine nutrient load was estimated and compared with the observed nutrient river load. All calculations were conducted for the period from 1991 to 1993. The River Stör, with a catchment area of 1135 km2, located in a postglacial lowland landscape in northern Germany, was chosen as a case study area. Two different data sets (e.g. land use, soil type or wastewater treatment plant inventory) were used: a commonly available standard data set (German or European maps) and a more detailed set with a higher spatial resolution derived from several studies at the Ecosystem Research Centre in Kiel. Initially, both data sets were used to apply MONERIS to the total catchment. The results were compared to adapt some of the free model-parameters to the conditions in the relatively small lowland river catchment. Using the standard data set, total nutrient emissions of 2320 tons year,1 of nitrogen and 96 tons year,1 phosphorus were calculated. The detailed data set yielded slightly higher emissions for nitrogen (2420 tons year,1) and for phosphorus (102 tons year,1). According to the spatial resolution, the proportion of the area of tile drainages and sandy soils derived from the different data sets varies considerably. This causes great differences in the total nutrient emissions estimated by the two approaches. Comparing the observed and the calculated nutrient loads, reliable results for catchments larger than 50 km2, or third-order streams, could be shown. Copyright © 2005 John Wiley & Sons, Ltd. [source]

Development of a hydrometeorological forcing data set for global soil moisture estimation

A. A Berg
Abstract Off-line land surface modeling simulations require accurate meteorological forcing with consistent spatial and temporal resolutions. Although reanalysis products present an attractive data source for these types of applications, bias to many of the reanalysis fields limits their use for hydrological modeling. In this study, we develop a global 0.5° forcing data sets for the time period 1979,1993 on a 6-hourly time step through application of a bias correction scheme to reanalysis products. We then use this forcing data to drive a land surface model for global estimation of soil moisture and other hydrological states and fluxes. The simulated soil moisture estimates are compared to in situ measurements, satellite observations and to a modeled data set of root zone soil moisture produced within a separate land surface model, using a different data set of hydrometeorological forcing. In general, there is good agreement between anomalies in modeled and observed (in situ) root zone soil moisture. Similarly, for the surface soil wetness state, modeled estimates and satellite observations are in general statistical agreement; however, correlations decline with increasing vegetation amount. Comparisons to a modeled data set of soil moisture also demonstrates that both simulations present estimates that are well correlated for the soil moisture in the anomaly time series, despite being derived from different land surface models, using different data sources for meteorological forcing, and with different specifications of the land surfaces properties. Copyright © 2005 Royal Meteorological Society [source]

Comparing bibliometric statistics obtained from the Web of Science and Scopus

Éric Archambault
For more than 40 years, the Institute for Scientific Information (ISI, now part of Thomson Reuters) produced the only available bibliographic databases from which bibliometricians could compile large-scale bibliometric indicators. ISI's citation indexes, now regrouped under the Web of Science (WoS), were the major sources of bibliometric data until 2004, when Scopus was launched by the publisher Reed Elsevier. For those who perform bibliometric analyses and comparisons of countries or institutions, the existence of these two major databases raises the important question of the comparability and stability of statistics obtained from different data sources. This paper uses macrolevel bibliometric indicators to compare results obtained from the WoS and Scopus. It shows that the correlations between the measures obtained with both databases for the number of papers and the number of citations received by countries, as well as for their ranks, are extremely high (R2 , .99). There is also a very high correlation when countries' papers are broken down by field. The paper thus provides evidence that indicators of scientific production and citations at the country level are stable and largely independent of the database. [source]

Labour Market in Motion: Analysing Regional Flows in a Multi-accounting System

LABOUR, Issue 4-5 2007
Anette Haas
We develop a flexible flow approach system , a multi-accounting system (MAS) , dealing with flows and stocks on regional labour markets. Combining administrative data at the micro level with various macro data, the MAS describes the dynamic transition process of the 180 local labour market areas in Germany. We use a new algorithm, related to entropy optimization, to estimate unknown transitions. Compared with conventional methods, the main advantage of our proceeding is that additional information from different data sources can be included that is of an inherently fuzzy character. [source]

Precipitation analysis using the Advanced Microwave Sounding Unit in support of nowcasting applications

Ralf Bennartz
We describe a method to remotely sense precipitation and classify its intensity over water, coasts and land surfaces. This method is intended to be used in an operational nowcasting environment. It is based on data obtained from the Advanced Microwave Sounding Unit (AMSU) onboard NOAA-15. Each observation is assigned a probability of belonging to four classes: precipitation-free, risk of precipitation, precipitation between 0.5 and 5 mm/h, and precipitation higher than 5 mm/h. Since the method is designed to work over different surface types, it relies mainly on the scattering signal of precipitation-sized ice particles received at high frequencies. For the calibration and validation of the method we use an eight-month dataset of combined weather radar and AMSU data obtained over the Baltic area. We compare results for the AMSU-B channels at 89 GHz and 150 GHz and find that the high frequency channel at 150 GHz allows for a much better discrimination of different types of precipitation than the 89 GHz channel. While precipitation-free areas, as well as heavily precipitating areas (>5 mm/h), can be identified to high accuracy, the intermediate classes are more ambiguous. This stems from the ambiguity of the passive microwave observations as well as from the non-perfect matching of the different data sources and sub-optimal radar adjustment. In addition to a statistical assessment of the method's accuracy, we present case studies to demonstrate its capabilities to classify different types of precipitation and to work over highly structured, inhomogeneous surfaces. Copyright © 2002 Royal Meteorological Society [source]