Past Data (past + data)

Distribution by Scientific Domains

Selected Abstracts

A Nationwide Assessment of the Biodiversity Value of Uganda's Important Bird Areas Network

AIAs; complementariedad; congruencia trans-taxón; selección de sitios de conservación Abstract:,BirdLife International's Important Bird Areas (IBA) program is the most developed global system for identifying sites of conservation priority. There have been few assessments, however, of the conservation value of IBAs for nonavian taxa. We combined past data with extensive new survey results for Uganda's IBAs in the most comprehensive assessment to date of the wider biodiversity value of a tropical country's IBA network. The combined data set included more than 35,000 site × species records for birds, butterflies, and woody plants at 86 Ugandan sites (23,400 km2), including 29 of the country's 30 IBAs, with data on additional taxa for many sites. Uganda's IBAs contained at least 70% of the country's butterfly and woody plant species, 86% of its dragonflies and 97% of its birds. They also included 21 of Uganda's 22 major vegetation types. For butterflies, dragonflies, and some families of plants assessed, species of high conservation concern were well represented (less so for the latter). The IBAs successfully represented wider biodiversity largely because many have distinctive avifaunas and, as shown by high cross-taxon congruence in complementarity, such sites tended to be distinctive for other groups too. Cross-taxon congruence in overall species richness was weaker and mainly associated with differences in site size. When compared with alternative sets of sites selected using complementarity-based, area-based, or random site-selection algorithms, the IBA network was efficient in terms of the number of sites required to represent species but inefficient in terms of total area. This was mainly because IBA selection considers factors other than area, however, which probably improves both the cost-effectiveness of the network and the persistence of represented species. Resumen:,El programa de Áreas de Importancia para las Aves (AIAs) de Birdlife International es el sistema global más desarrollado para la identificación de sitios de prioridad para la conservación. Sin embargo, ha habido pocas evaluaciones del valor de conservación de las AIAs para taxa no aviares. En la evaluación más integral, hasta la fecha, del valor de la biodiversidad en general de la red de AIAs de un país tropical, combinamos datos antiguos con los resultados de muestreos extensivos recientes de las AIAs de Uganda. El conjunto de datos combinados incluyó más de 35000 registros de sitios x especies de aves, mariposas y plantas leñosas en 86 sitios en Uganda (23400 km2), incluyendo 29 de las 30 AIAs del país, con datos sobre taxa adicionales en muchos sitios. Las AIAs de Uganda contenían por lo menos un 70% de las especies de mariposas y plantas leñosas del país, 86% de sus libélulas y 97% de sus aves. También incluyeron 21 de los 22 principales tipos de vegetación. En las mariposas, libélulas y algunas de las familias de plantas evaluadas, la representación de especies de alto interés para la conservación fue buena (menor en las plantas). Las Áreas de Importancia para las Aves representaron exitosamente a la biodiversidad en general principalmente porque muchas tienen avifaunas distintivas y, como muestra la alta congruencia trans-taxón en complementariedad, tales sitios tendieron a ser distintivos para otros grupos también. La congruencia trans-taxón en la riqueza de especies total fue más débil y se asoció principalmente con diferencias en el tamaño del sitio. Cuando se compara con conjuntos alternativos de sitios seleccionados mediante algoritmos basados en complementariedad, área o selección aleatoria de sitios, la red de AIAs fue eficiente en términos del número de sitios requeridos para representar especies, pero ineficiente en términos del área total. Sin embargo, esto se debió principalmente a que la selección de AIA considera factores distintos al área que probablemente mejoran tanto la efectividad de la red como la persistencia de las especies representadas. [source]

An operational algorithm for residential cogeneration systems based on the monitored daily-basis energy demand

Yuka Yamagishi
Abstract Residential cogeneration systems with PEFC are promising as distributed power system resources with the ability to improve energy system efficiency. However, it is important to develop an efficient algorithm for operation because the energy demand at each house differs greatly from day to day. In this paper, we propose an operational algorithm and evaluate it from the viewpoint of energy conservation and economic effectiveness based on the energy demand characteristics. In the algorithm, the hot water and electricity demand on the next day are estimated based on the average of past data. The results of simulations using actually monitored energy demand data indicate that (1) the greater the electrical demand of a household, the more effective this algorithm becomes with respect to energy conservation; (2) the greater the hot water demand of a household, the more effective this algorithm becomes with respect to economic effectiveness. © 2009 Wiley Periodicals, Inc. Electr Eng Jpn, 170(2): 37,45, 2010; Published online in Wiley InterScience (www.interscience.wiley. com). DOI 10.1002/eej.20892 [source]

An engineering approach to dynamic prediction of network performance from application logs

Zalal Uddin Mohammad Abusina
Network measurement traces contain information regarding network behavior over the period of observation. Research carried out from different contexts shows predictions of network behavior can be made depending on network past history. Existing works on network performance prediction use a complicated stochastic modeling approach that extrapolates past data to yield a rough estimate of long-term future network performance. However, prediction of network performance in the immediate future is still an unresolved problem. In this paper, we address network performance prediction as an engineering problem. The main contribution of this paper is to predict network performance dynamically for the immediate future. Our proposal also considers the practical implication of prediction. Therefore, instead of following the conventional approach to predict one single value, we predict a range within which network performance may lie. This range is bounded by our two newly proposed indices, namely, Optimistic Network Performance Index (ONPI) and Robust Network Performance Index (RNPI). Experiments carried out using one-year-long traffic traces between several pairs of real-life networks validate the usefulness of our model.,Copyright © 2005 John Wiley & Sons, Ltd. [source]


Abstract This paper describes an adaptive learning framework for forecasting end-season water allocations using climate forecasts, historic allocation data, and results of other detailed hydrological models. The adaptive learning framework is based on artificial neural network (ANN) method, which can be trained using past data to predict future water allocations. Using this technique, it was possible to develop forecast models for end-irrigation-season water allocations from allocation data available from 1891 to 2005 based on the allocation level at the start of the irrigation season. The model forecasting skill was further improved by the incorporation of a set of correlating clusters of sea surface temperature (SST) and the Southern oscillation index (SOI) data. A key feature of the model is to include a risk factor for the end-season water allocations based on the start of the season water allocation. The interactive ANN model works in a risk-management context by providing probability of availability of water for allocation for the prediction month using historic data and/or with the incorporation of SST/SOI information from the previous months. All four developed ANN models (historic data only, SST incorporated, SOI incorporated, SST-SOI incorporated) demonstrated ANN capability of forecasting end-of-season water allocation provided sufficient data on historic allocation are available. SOI incorporated ANN model was the most promising forecasting tool that showed good performance during the field testing of the model. [source]

Adaptive Non-Interventional Heuristics for Covariation Detection in Causal Induction: Model Comparison and Rational Analysis

Masasi Hattori
Abstract In this article, 41 models of covariation detection from 2 × 2 contingency tables were evaluated against past data in the literature and against data from new experiments. A new model was also included based on a limiting case of the normative phi-coefficient under an extreme rarity assumption, which has been shown to be an important factor in covariation detection (McKenzie & Mikkelsen, 2007) and data selection (Hattori, 2002; Oaksford & Chater, 1994, 2003). The results were supportive of the new model. To investigate its explanatory adequacy, a rational analysis using two computer simulations was conducted. These simulations revealed the environmental conditions and the memory restrictions under which the new model best approximates the normative model of covariation detection in these tasks. They thus demonstrated the adaptive rationality of the new model. [source]