Multivariate relationships between groundwater chemistry and toxicity in an urban aquifer

Multivariate statistical methods were used to investigate file Causes of toxicity and controls on groundwater chemistry from 274 boreholes in an Urban area (London) of the United Kingdom. The groundwater was alkaline to neutral, and chemistry was dominated by calcium, sodium, and Sulfate. Contaminants included fuels, solvents, and organic compounds derived from landfill material. The presence of organic material in the aquifer caused decreases in dissolved oxygen, sulfate and nitrate concentrations. and increases in ferrous iron and ammoniacal nitrogen concentrations. Pearson correlations between toxicity results and the concentration of individual analytes indicated that concentrations of ammoinacal nitrogen, dissolved oxygen, ferrous iron, and hydrocarbons were important where present. However, principal component and regression analysis suggested no significant correlation between toxicity and chemistry over the whole area. Multidimensional Scaling was used to investigate differences in sites caused by historical use, landfill gas status, or position within the sample area. Significant differences were observed between sites with different historical land use and those with different gas status. Examination of the principal component matrix revealed that these differences are related to changes in the importance of reduced chemical species.

Do children with Williams syndrome really have good vocabulary knowledge? Methods for comparing cognitive and linguistic abilities in developmental disorders

The comparison of cognitive and linguistic skills in individuals with developmental disorders is fraught with methodological and psychometric difficulties. In this paper, we illustrate some of these issues by comparing the receptive vocabulary knowledge and non-verbal reasoning abilities of 41 children with Williams syndrome, a genetic disorder in which language abilities are often claimed to be relatively strong. Data from this group were compared with data from typically developing children, children with Down syndrome, and children with non-specific learning difficulties using a number of approaches including comparison of age-equivalent scores, matching, analysis of covariance, and regression-based standardization. Across these analyses children with Williams syndrome consistently demonstrated relatively good receptive vocabulary knowledge, although this effect appeared strongest in the oldest children.

Multimodal semantic-associative collateral labelling and indexing of still images

A novel framework for multimodal semantic-associative collateral image labelling, aiming at associating image regions with textual keywords, is described. Both the primary image and collateral textual modalities are exploited in a cooperative and complementary fashion. The collateral content and context based knowledge is used to bias the mapping from the low-level region-based visual primitives to the high-level visual concepts defined in a visual vocabulary. We introduce the notion of collateral context, which is represented as a co-occurrence matrix, of the visual keywords, A collaborative mapping scheme is devised using statistical methods like Gaussian distribution or Euclidean distance together with collateral content and context-driven inference mechanism. Finally, we use Self Organising Maps to examine the classification and retrieval effectiveness of the proposed high-level image feature vector model which is constructed based on the image labelling results.

Sparse kernel modelling: a unified approach

A unified approach is proposed for sparse kernel data modelling that includes regression and classification as well as probability density function estimation. The orthogonal-least-squares forward selection method based on the leave-one-out test criteria is presented within this unified data-modelling framework to construct sparse kernel models that generalise well. Examples from regression, classification and density estimation applications are used to illustrate the effectiveness of this generic sparse kernel data modelling approach.

Semantic-associative visual content labelling and retrieval: A multimodal approach

A novel framework referred to as collaterally confirmed labelling (CCL) is proposed, aiming at localising the visual semantics to regions of interest in images with textual keywords. Both the primary image and collateral textual modalities are exploited in a mutually co-referencing and complementary fashion. The collateral content and context-based knowledge is used to bias the mapping from the low-level region-based visual primitives to the high-level visual concepts defined in a visual vocabulary. We introduce the notion of collateral context, which is represented as a co-occurrence matrix of the visual keywords. A collaborative mapping scheme is devised using statistical methods like Gaussian distribution or Euclidean distance together with collateral content and context-driven inference mechanism. We introduce a novel high-level visual content descriptor that is devised for performing semantic-based image classification and retrieval. The proposed image feature vector model is fundamentally underpinned by the CCL framework. Two different high-level image feature vector models are developed based on the CCL labelling of results for the purposes of image data clustering and retrieval, respectively. A subset of the Corel image collection has been used for evaluating our proposed method. The experimental results to-date already indicate that the proposed semantic-based visual content descriptors outperform both traditional visual and textual image feature models. (C) 2007 Elsevier B.V. All rights reserved.

An effective approach for land-cover classification from airborne lidar fused with co-registered data

Airborne lidar provides accurate height information of objects on the earth and has been recognized as a reliable and accurate surveying tool in many applications. In particular, lidar data offer vital and significant features for urban land-cover classification, which is an important task in urban land-use studies. In this article, we present an effective approach in which lidar data fused with its co-registered images (i.e. aerial colour images containing red, green and blue (RGB) bands and near-infrared (NIR) images) and other derived features are used effectively for accurate urban land-cover classification. The proposed approach begins with an initial classification performed by the Dempster–Shafer theory of evidence with a specifically designed basic probability assignment function. It outputs two results, i.e. the initial classification and pseudo-training samples, which are selected automatically according to the combined probability masses. Second, a support vector machine (SVM)-based probability estimator is adopted to compute the class conditional probability (CCP) for each pixel from the pseudo-training samples. Finally, a Markov random field (MRF) model is established to combine spatial contextual information into the classification. In this stage, the initial classification result and the CCP are exploited. An efficient belief propagation (EBP) algorithm is developed to search for the global minimum-energy solution for the maximum a posteriori (MAP)-MRF framework in which three techniques are developed to speed up the standard belief propagation (BP) algorithm. Lidar and its co-registered data acquired by Toposys Falcon II are used in performance tests. The experimental results prove that fusing the height data and optical images is particularly suited for urban land-cover classification. There is no training sample needed in the proposed approach, and the computational cost is relatively low. An average classification accuracy of 93.63% is achieved.

Present-day and mid-Holocene biomes reconstructed from pollen and plant macrofossil data from the former Soviet Union and Mongolia

Fossil pollen data supplemented by tree macrofossil records were used to reconstruct the vegetation of the Former Soviet Union and Mongolia at 6000 years. Pollen spectra were assigned to biomes using the plant-functional-type method developed by Prentice et al. (1996). Surface pollen data and a modern vegetation map provided a test of the method. This is the first time such a broad-scale vegetation reconstruction for the greater part of northern Eurasia has been attempted with objective techniques. The new results confirm previous regional palaeoenvironmental studies of the mid-Holocene while providing a comprehensive synopsis and firmer conclusions. West of the Ural Mountains temperate deciduous forest extended both northward and southward from its modern range. The northern limits of cool mixed and cool conifer forests were also further north than present. Taiga was reduced in European Russia, but was extended into Yakutia where now there is cold deciduous forest. The northern limit of taiga was extended (as shown by increased Picea pollen percentages, and by tree macrofossil records north of the present-day forest limit) but tundra was still present in north-eastern Siberia. The boundary between forest and steppe in the continental interior did not shift substantially, and dry conditions similar to present existed in western Mongolia and north of the Aral Sea.

Evaluation of cumulative externality: environmental economics, oligopoly and the private provision of public goods

This paper evaluates environmental externality when the structure of the externality is cumulative. The evaluation exercise is based on the assumption that the agents in question form conjectural variations. A number of environments are encompassed within this classification and have received due attention in the literature. Each of these heterogeneous environments, however, possesses considerable analytical homogeneity and permit subscription to a general model treatment. These environments include environmental externality, oligopoly and the analysis of the private provision of public goods. We highlight the general analytical approach by focusing on this latter context, in which debate centers around four issues: the existence of free-riding, the extent to which contributions are matched equally across individuals, the nature of conjectures consistent with equilibrium, and the allocative inefficiency of alternative regimes. This paper resolves each of these issues, with the following conclusions: A consistent-conjectures equilibrium exists in the private provision of public goods. It is the monopolistic-conjectures equilibrium. Agents act identically, contributing positive amounts of the public good in an efficient allocation of resources. There is complete matching of contributions among agents, no free-riding, and the allocation is independent of the number of members within the community. Thus the Olson conjecture—that inefficiency is exacerbated by community size—has no foundation in a consistent-conjectures, cumulative-externality, context (212 words).

Reproducibility of the online Food4Me food-frequency questionnaire for estimating dietary intakes across Europe

Background: Accurate dietary assessment is key to understanding nutrition-related outcomes and is essential for estimating dietary change in nutrition-based interventions. Objective: The objective of this study was to assess the pan-European reproducibility of the Food4Me food-frequency questionnaire (FFQ) in assessing the habitual diet of adults. Methods: Participantsfromthe Food4Me study, a 6-mo,Internet-based, randomizedcontrolled trial of personalized nutrition conducted in the United Kingdom, Ireland, Spain, Netherlands, Germany, Greece, and Poland were included. Screening and baseline data (both collected before commencement of the intervention) were used in the present analyses, and participants were includedonly iftheycompleted FFQs at screeningand at baselinewithin a 1-mo timeframebeforethe commencement oftheintervention. Sociodemographic (e.g., sex andcountry) andlifestyle[e.g.,bodymass index(BMI,inkg/m2)and physical activity] characteristics were collected. Linear regression, correlation coefficients, concordance (percentage) in quartile classification, and Bland-Altman plots for daily intakes were used to assess reproducibility. Results: In total, 567 participants (59% female), with a mean 6 SD age of 38.7 6 13.4 y and BMI of 25.4 6 4.8, completed bothFFQswithin 1 mo(mean 6 SD: 19.26 6.2d).Exact plus adjacent classification oftotal energy intakeinparticipants was highest in Ireland (94%) and lowest in Poland (81%). Spearman correlation coefficients (r) in total energy intake between FFQs ranged from 0.50 for obese participants to 0.68 and 0.60 in normal-weight and overweight participants, respectively. Bland-Altman plots showed a mean difference between FFQs of 210 kcal/d, with the agreement deteriorating as energy intakes increased. There was little variation in reproducibility of total energy intakes between sex and age groups. Conclusions: The online Food4Me FFQ was shown to be reproducible across 7 European countries when administered within a 1-mo period to a large number of participants. The results support the utility of the online Food4Me FFQ as a reproducible tool across multiple European populations. This trial was registered at clinicaltrials.gov as NCT01530139.

Image indexing and retrieval using an ART-2A neural network architecture

Traditional content-based image retrieval (CBIR) systems use low-level features such as colors, shapes, and textures of images. Although, users make queries based on semantics, which are not easily related to such low-level characteristics. Recent works on CBIR confirm that researchers have been trying to map visual low-level characteristics and high-level semantics. The relation between low-level characteristics and image textual information has motivated this article which proposes a model for automatic classification and categorization of words associated to images. This proposal considers a self-organizing neural network architecture, which classifies textual information without previous learning. Experimental results compare the performance results of the text-based approach to an image retrieval system based on low-level features. (c) 2008 Wiley Periodicals, Inc.

Identification of genes associated with local aggressiveness and metastatic behavior in soft tissue tumors

Soft tissue tumors represent a group of neoplasia with different histologic and biological presentations varying from benign, locally confined to very aggressive and metastatic tumors. The molecular mechanisms responsible for such differences are still unknown. The understanding of these molecular alterations mechanism will be critical to discriminate patients who need systemic treatment from those that can be treated only locally and could also guide the development of new drugs` against this tumors. Using 102 tumor samples representing a large spectrum of these tumors, we performed expression profiling and defined differentially expression genes that are likely to be involved in tumors that are locally aggressive and in tumors with metastatic potential. We described a set of 12 genes (SNRPD3, MEGF9, SPTAN-1, AFAP1L2, ENDOD1, SERPIN5, ZWINTAS, TOP2A, UBE2C, ABCF1, MCM2, and ARL6IP5) showing opposite expression when these two conditions were compared. These genes are mainly related to cell-cell and cell-extracellular matrix interactions and cell proliferation and might represent helpful tools for a more precise classification and diagnosis as well as potential drug targets.

Financial time series analysis : Chaos and neurodynamics approach

This work aims at combining the Chaos theory postulates and Artificial Neural Networks classification and predictive capability, in the field of financial time series prediction. Chaos theory, provides valuable qualitative and quantitative tools to decide on the predictability of a chaotic system. Quantitative measurements based on Chaos theory, are used, to decide a-priori whether a time series, or a portion of a time series is predictable, while Chaos theory based qualitative tools are used to provide further observations and analysis on the predictability, in cases where measurements provide negative answers. Phase space reconstruction is achieved by time delay embedding resulting in multiple embedded vectors. The cognitive approach suggested, is inspired by the capability of some chartists to predict the direction of an index by looking at the price time series. Thus, in this work, the calculation of the embedding dimension and the separation, in Takens‘ embedding theorem for phase space reconstruction, is not limited to False Nearest Neighbor, Differential Entropy or other specific method, rather, this work is interested in all embedding dimensions and separations that are regarded as different ways of looking at a time series by different chartists, based on their expectations. Prior to the prediction, the embedded vectors of the phase space are classified with Fuzzy-ART, then, for each class a back propagation Neural Network is trained to predict the last element of each vector, whereas all previous elements of a vector are used as features.

Salesperson’s Personality, Motivation and Selling Performance : The Study of New Product Selling

In the highly competitive environment businesses invest big amounts of money into the new product development. New product success potentially depends on different factors among which salespeople play an important role. The aim of this paper is to explore the potential link between salespeople’s personality, motivation to sell new products and performance in selling new products. Based on the theoretical background of the Big Five personality dimensions, motivation and selling performance hypotheses were formulated and tested using statistical methods of correlation and regression analysis. The data was collected within one technologically intensive organization – ABB AB in Sweden using online web questionnaire and self-assessment measurements. Total investigation was conducted among organization’s salesforce. The findings confirm the importance of salesperson’s personality empirically showing that the latter significantly predicts both motivation and performance in selling new products. From all the Big Five Extraversion was confirmed to be the most important predictor of both motivation and performance in selling new products. Extraversion was found positively related with both motivation and performance in selling new products. Salespeople scoring high in Extraversion and especially possessing such characteristics as confident, energetic and sociable tend to be more motivated to sell new products and show higher performance results. Other personality dimensions such as Agreeableness, Conscientiousness, Neuroticism, and Openness to experience complexly approached are not proved to be significantly related neither with motivation nor performance in selling new products. The results are explained by the extreme importance of Extraversion in new product selling situation which analyzing in combination with the other personality dimensions suppresses the others. Finding regarding controlling for certain demographical characteristics of salespeople reveal that performance in selling new products is determined by selling experience. Salespeople’s age is not proved to be significantly related neither with motivation nor performance in selling new products. Findings regarding salespeople’s gender though proposing that males are more motivated to sell new products cannot be generalized due to the study limitations.

Aplicação de redes neurais na classificação de rentabilidade futura de empresas

Este trabalho tem por motivação evidenciar a eficiência de redes neurais na classificação de rentabilidade futura de empresas, e desta forma, prover suporte para o desenvolvimento de sistemas de apoio a tomada de decisão de investimentos. Para serem comparados com o modelo de redes neurais, foram escolhidos o modelo clássico de regressão linear múltipla, como referência mínima, e o de regressão logística ordenada, como marca comparativa de desempenho (benchmark). Neste texto, extraímos dados financeiros e contábeis das 1000 melhores empresas listadas, anualmente, entre 1996 e 2006, na publicação Melhores e Maiores – Exame (Editora Abril). Os três modelos foram construídos tendo como base as informações das empresas entre 1996 e 2005. Dadas as informações de 2005 para estimar a classificação das empresas em 2006, os resultados dos três modelos foram comparados com as classificações observadas em 2006, e o modelo de redes neurais gerou o melhor resultado.

Demand and supply: price elasticities and corporate performance in the UK

This paper investigates the relationship between consumer demand and corporate performance in several consumer industries in the UK, using two independent datasets. It uses data on consumer expenditures and the retail price index to estimate Almost Ideal Demand Systems on micro-data and compute timevarying price elasticities of demand for disaggregated commodity groups. Then, it matches the product definitions to the Standard Industry Classification and uses the estimated elasticities to investigate the impact of consumer behaviour on firm-level profitability equations. The time-varying household characteristics are ideal instruments for the demand effects in the firms' supply equation. The paper concludes that demand elasticities have a significant and tangible impact on the profitability of UK firms and that this impact can shed some light on the relationship between market structure and economic performance.