Multi-year droughts in Europe: analysis of development and causes

Whilst hydrological systems can show resilience to short-term streamflow deficiencies during within-year droughts, prolonged deficits during multi-year droughts are a significant threat to water resources security in Europe. This study uses a threshold-based objective classification of regional hydrological drought to qualitatively examine the characteristics, spatio-temporal evolution and synoptic climatic drivers of multi-year drought events in 1962–64, 1975–76 and 1995–97, on a European scale but with particular focus on the UK. Whilst all three events are multi-year, pan-European phenomena, their development and causes can be contrasted. The critical factor in explaining the unprecedented severity of the 1975–76 event is the consecutive occurrence of winter and summer drought. In contrast, 1962–64 was a succession of dry winters, mitigated by quiescent summers, whilst 1995–97 lacked spatial coherence and was interrupted by wet interludes. Synoptic climatic conditions vary within and between multi-year droughts, suggesting that regional factors modulate the climate signal in streamflow drought occurrence. Despite being underpinned by qualitatively similar climatic conditions and commonalities in evolution and characteristics, each of the three droughts has a unique spatio-temporal signature. An improved understanding of the spatio-temporal evolution and characteristics of multi-year droughts has much to contribute to monitoring and forecasting capability, and to improved mitigation strategies.

Haplotype analysis of vernalization loci in European barley germplasm reveals novel VRN-H1 alleles and a predominant winter VRN-H1/VRN-H2 multi-locus haplotype

In barley, variation in the requirement for vernalization (an extended period of low temperature before flowering can occur) is determined by the VRN-H1, -H2 and -H3 loci. In European cultivated germplasm, most variation in vernalization requirement is accounted for by alleles at VRN-H1 and VRN-H2 only, but the range of allelic variation is largely unexplored. Here we characterise VRN-H1 and VRN-H2 haplotypes in 429 varieties representing a large portion of the acreage sown to barley in Western Europe over the last 60 years. Analysis of genotype, intron I sequencing data and growth habit tests identified three novel VRN-H1 alleles and determined the most frequent VRN-H1 intron I rearrangements. Combined analysis of VRN-H1 and VRN-H2 alleles resulted in the classification of seventeen VRN-H1/VRN-H2 multi-locus haplotypes, three of which account for 79% of varieties. The molecular markers employed here represent powerful diagnostic tools for prediction of growth habit and assessment of varietal purity. These markers will also allow development of germplasm to test the behaviour of individual alleles with the aim of understanding the relationship between allelic variation and adaptation to specific agri-environments.

Analysis of frequency and intensity of European winter storm events from a multi-model perspective, at synoptic and regional scales

This study focuses on the analysis of winter (October-November-December-January-February-March; ONDJFM) storm events and their changes due to increased anthropogenic greenhouse gas concentrations over Europe. In order to assess uncertainties that are due to model formulation, 4 regional climate models (RCMs) with 5 high resolution experiments, and 4 global general circulation models (GCMs) are considered. Firstly, cyclone systems as synoptic scale processes in winter are investigated, as they are a principal cause of the occurrence of extreme, damage-causing wind speeds. This is achieved by use of an objective cyclone identification and tracking algorithm applied to GCMs. Secondly, changes in extreme near-surface wind speeds are analysed. Based on percentile thresholds, the studied extreme wind speed indices allow a consistent analysis over Europe that takes systematic deviations of the models into account. Relative changes in both intensity and frequency of extreme winds and their related uncertainties are assessed and related to changing patterns of extreme cyclones. A common feature of all investigated GCMs is a reduced track density over central Europe under climate change conditions, if all systems are considered. If only extreme (i.e. the strongest 5%) cyclones are taken into account, an increasing cyclone activity for western parts of central Europe is apparent; however, the climate change signal reveals a reduced spatial coherency when compared to all systems, which exposes partially contrary results. With respect to extreme wind speeds, significant positive changes in intensity and frequency are obtained over at least 3 and 20% of the European domain under study (35–72°N and 15°W–43°E), respectively. Location and extension of the affected areas (up to 60 and 50% of the domain for intensity and frequency, respectively), as well as levels of changes (up to +15 and +200% for intensity and frequency, respectively) are shown to be highly dependent on the driving GCM, whereas differences between RCMs when driven by the same GCM are relatively small.

Multi‐method global sensitivity analysis (MMGSA) for modelling floodplain hydrological processes

When studying hydrological processes with a numerical model, global sensitivity analysis (GSA) is essential if one is to understand the impact of model parameters and model formulation on results. However, different definitions of sensitivity can lead to a difference in the ranking of importance of the different model factors. Here we combine a fuzzy performance function with different methods of calculating global sensitivity to perform a multi-method global sensitivity analysis (MMGSA). We use an application of a finite element subsurface flow model (ESTEL-2D) on a flood inundation event on a floodplain of the River Severn to illustrate this new methodology. We demonstrate the utility of the method for model understanding and show how the prediction of state variables, such as Darcian velocity vectors, can be affected by such a MMGSA. This paper is a first attempt to use GSA with a numerically intensive hydrological model.

Multi-method global sensitivity analysis (MMGSA) for modelling floodplain hydrological processes

When studying hydrological processes with a numerical model, global sensitivity analysis (GSA) is essential if one is to understand the impact of model parameters and model formulation on results. However, different definitions of sensitivity can lead to a difference in the ranking of importance of the different model factors. Here we combine a fuzzy performance function with different methods of calculating global sensitivity to perform a multi-method global sensitivity analysis (MMGSA). We use an application of a finite element subsurface flow model (ESTEL-2D) on a flood inundation event on a floodplain of the River Severn to illustrate this new methodology. We demonstrate the utility of the method for model understanding and show how the prediction of state variables, such as Darcian velocity vectors, can be affected by such a MMGSA. This paper is a first attempt to use GSA with a numerically intensive hydrological model

Cloud information content analysis of multi-angular measurements in the oxygen A-band: application to 3MI and MSPI

The vertical distribution of cloud cover has a significant impact on a large number of meteorological and climatic processes. Cloud top altitude and cloud geometrical thickness are then essential. Previous studies established the possibility of retrieving those parameters from multi-angular oxygen A-band measurements. Here we perform a study and comparison of the performances of future instruments. The 3MI (Multi-angle, Multi-channel and Multi-polarization Imager) instrument developed by EUMETSAT, which is an extension of the POLDER/PARASOL instrument, and MSPI (Multi-angles Spectro-Polarimetric Imager) develoloped by NASA's Jet Propulsion Laboratory will measure total and polarized light reflected by the Earth's atmosphere–surface system in several spectral bands (from UV to SWIR) and several viewing geometries. Those instruments should provide opportunities to observe the links between the cloud structures and the anisotropy of the reflected solar radiation into space. Specific algorithms will need be developed in order to take advantage of the new capabilities of this instrument. However, prior to this effort, we need to understand, through a theoretical Shannon information content analysis, the limits and advantages of these new instruments for retrieving liquid and ice cloud properties, and especially, in this study, the amount of information coming from the A-Band channel on the cloud top altitude (CTOP) and geometrical thickness (CGT). We compare the information content of 3MI A-Band in two configurations and that of MSPI. Quantitative information content estimates show that the retrieval of CTOP with a high accuracy is possible in almost all cases investigated. The retrieval of CGT seems less easy but possible for optically thick clouds above a black surface, at least when CGT > 1–2 km.

Multi-objective clustering ensemble for gene expression data analysis

In this paper, we present an algorithm for cluster analysis that integrates aspects from cluster ensemble and multi-objective clustering. The algorithm is based on a Pareto-based multi-objective genetic algorithm, with a special crossover operator, which uses clustering validation measures as objective functions. The algorithm proposed can deal with data sets presenting different types of clusters, without the need of expertise in cluster analysis. its result is a concise set of partitions representing alternative trade-offs among the objective functions. We compare the results obtained with our algorithm, in the context of gene expression data sets, to those achieved with multi-objective Clustering with automatic K-determination (MOCK). the algorithm most closely related to ours. (C) 2009 Elsevier B.V. All rights reserved.

Topological multi-contour decomposition for image analysis and image retrieval

Successful classification, information retrieval and image analysis tools are intimately related with the quality of the features employed in the process. Pixel intensities, color, texture and shape are, generally, the basis from which most of the features are Computed and used in such fields. This papers presents a novel shape-based feature extraction approach where an image is decomposed into multiple contours, and further characterized by Fourier descriptors. Unlike traditional approaches we make use of topological knowledge to generate well-defined closed contours, which are efficient signatures for image retrieval. The method has been evaluated in the CBIR context and image analysis. The results have shown that the multi-contour decomposition, as opposed to a single shape information, introduced a significant improvement in the discrimination power. (c) 2008 Elsevier B.V. All rights reserved,

The influence of the supply chain agents on the new product development's performance: an analysis based on the multi-group moderation

Este estudo buscou verificar a influencia dos agentes da cadeia de suprimentos no desempenho do desenvolvimento de novos produtos quando os agentes são analisados em conjunto. A motivação desta pesquisa veio de estudos que alertaram para a consideração da integração da cadeia de suprimentos como um constructo multidimensional, englobando o envolvimento da manufatura, fornecedores e clientes no desenvolvimento de novos produtos; e devido à falta de informação sobre as influencias individuais destes agentes no desenvolvimento de novos produtos. Sob essas considerações, buscou-se construir um modelo analítico baseado na Teoria do Capital Social e Capacidade Absortiva, construir hipóteses a partir da revisão da literatura e conectar constructos como cooperação, envolvimento do fornecedor no desenvolvimento de novos produtos (DNP), envolvimento do cliente no DNP, envolvimento da manufatura no DNP, antecipação de novas tecnologias, melhoria contínua, desempenho operacional do DNP, desempenho de mercado do NPD e desempenho de negócio do DNP. Para testar as hipóteses foram consideradas três variáveis moderadoras, tais como turbulência ambiental (baixa, média e alta), indústria (eletrônicos, maquinários e equipamentos de transporte) e localização (América, Europa e Ásia). Para testar o modelo foram usados dados do projeto High Performance Manufacturing que contém 339 empresas das indústrias de eletrônicos, maquinários e equipamentos de transporte, localizadas em onze países. As hipóteses foram testadas por meio da Análise Fatorial Confirmatória (AFC) incluindo a moderação muti-grupo para as três variáveis moderadoras mencionadas anteriormente. Os principais resultados apontaram que as hipóteses relacionadas com cooperação foram confirmadas em ambientes de média turbulência, enquanto as hipóteses relacionadas ao desempenho no DNP foram confirmadas em ambientes de baixa turbulência ambiental e em países asiáticos. Adicionalmente, sob as mesmas condições, fornecedores, clientes e manufatura influenciam diferentemente no desempenho de novos produtos. Assim, o envolvimento de fornecedores influencia diretamente no desempenho operacional e indiretamente no desempenho de mercado e de negócio em baixos níveis de turbulência ambiental, na indústria de equipamentos de transporte em países da Americanos e Europeus. De igual forma, o envolvimento do cliente influenciou diretamente no desempenho operacional e indiretamente no desempenho de mercado e do negócio em médio nível de turbulência ambiental, na indústria de maquinários e em países Asiáticos. Fornecedores e clientes não influenciam diretamente no desempenho de mercado e do negócio e não influenciam indiretamente no desempenho operacional. O envolvimento da manufatura não influenciou nenhum tipo de desempenho do desenvolvimento de novos produtos em todos os cenários testados.

Neural network based on adaptive resonance theory with continuous training for multi-configuration transient stability analysis of electric power systems

This work presents a methodology to analyze electric power systems transient stability for first swing using a neural network based on adaptive resonance theory (ART) architecture, called Euclidean ARTMAP neural network. The ART architectures present plasticity and stability characteristics, which are very important for the training and to execute the analysis in a fast way. The Euclidean ARTMAP version provides more accurate and faster solutions, when compared to the fuzzy ARTMAP configuration. Three steps are necessary for the network working, training, analysis and continuous training. The training step requires much effort (processing) while the analysis is effectuated almost without computational effort. The proposed network allows approaching several topologies of the electric system at the same time; therefore it is an alternative for real time transient stability of electric power systems. To illustrate the proposed neural network an application is presented for a multi-machine electric power systems composed of 10 synchronous machines, 45 buses and 73 transmission lines. (C) 2010 Elsevier B.V. All rights reserved.

A multi-agent architecture for intelligent data analysis

The present study introduces a multi-agent architecture designed for doing automation process of data integration and intelligent data analysis. Different from other approaches the multi-agent architecture was designed using a multi-agent based methodology. Tropos, an agent based methodology was used for design. Based on the proposed architecture, we describe a Web based application where the agents are responsible to analyse petroleum well drilling data to identify possible abnormalities occurrence. The intelligent data analysis methods used was the Neural Network.

Multi-scale structural and chemical analysis of sugarcane bagasse in the process of sequential acid-base pretreatment and ethanol production by Scheffersomyces shehatae and Saccharomyces cerevisiae

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Nonlinear dynamical analysis of an aeroelastic system with multi-segmented moment in the pitch degree-of-freedom

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Multi-q pattern analysis: A case study in image classification

This paper compares the effectiveness of the Tsallis entropy over the classic Boltzmann-Gibbs-Shannon entropy for general pattern recognition, and proposes a multi-q approach to improve pattern analysis using entropy. A series of experiments were carried out for the problem of classifying image patterns. Given a dataset of 40 pattern classes, the goal of our image case study is to assess how well the different entropies can be used to determine the class of a newly given image sample. Our experiments show that the Tsallis entropy using the proposed multi-q approach has great advantages over the Boltzmann-Gibbs-Shannon entropy for pattern classification, boosting image recognition rates by a factor of 3. We discuss the reasons behind this success, shedding light on the usefulness of the Tsallis entropy and the multi-q approach. (C) 2012 Elsevier B.V. All rights reserved.