Deciding the price of fame

Previous studies of ignorance-driven decision-making have either analyzed when ignorance should prove advantageous on theoretical grounds, or else they have examined whether human behavior is consistent with an ignorance driven inference strategy (e.g., the recognition heuristic). The current study merges these research goals by examining whether – under conditions where ignorance driven inference might be expected – the type of advantages theoretical analyses predict are evident in human performance data. A single experiment shows that, when asked to make relative wealth judgments, participants reliably use recognition as a basis for their judgments. Their wealth judgments under these conditions are reliably more accurate when some of the target names are unknown than when participants recognize all the names (the “less-is-more effect”). these data are robust against a number of variations on the size of the pool from which participants have to choose and the nature of the wealth judgment.

Semantic-driven context-aware visual information indexing and retrieval: applied in the film post-production domain

A large volume of visual content is inaccessible until effective and efficient indexing and retrieval of such data is achieved. In this paper, we introduce the DREAM system, which is a knowledge-assisted semantic-driven context-aware visual information retrieval system applied in the film post production domain. We mainly focus on the automatic labelling and topic map related aspects of the framework. The use of the context- related collateral knowledge, represented by a novel probabilistic based visual keyword co-occurrence matrix, had been proven effective via the experiments conducted during system evaluation. The automatically generated semantic labels were fed into the Topic Map Engine which can automatically construct ontological networks using Topic Maps technology, which dramatically enhances the indexing and retrieval performance of the system towards an even higher semantic level.

A design for business intelligence service in demand driven supply chain management

This paper discusses the problems inherent within traditional supply chain management's forecast and inventory management processes arising when tackling demand driven supply chain. A demand driven supply chain management architecture developed by Orchestr8 Ltd., U.K. is described to demonstrate its advantages over traditional supply chain management. Within this architecture, a metrics reporting system is designed by adopting business intelligence technology that supports users for decision making and planning supply activities over supply chain health.

Have increased dissolved organic carbon (DOC) losses from UK peat and organo-mineral soils been driven by the decline in acid rain?

Dissolved organic carbon (DOC) concentrations have been rising in streams and lakes draining catchments with organic soils across Northern Europe. These increases have shown a correlation with decreased sulphate and chloride concentrations. One hypothesis to explain this phenomenon is that these relationships are due an increased in DOC release from soils to freshwaters, caused by a decline in pollutant sulphur and sea-salt deposition. We carried out controlled deposition experiments in the laboratory on intact peat and organomineral O-horizon cores to test this hypothesis. Preliminary data showed a clear correlation between the change in soil water pH and change in DOC concentrations, however uncertainty still remains about whether this is due to changes in biological activity or chemical solubility.

An RCT study to evaluate a targeted, theory driven healthy eating leaflet

A theory based healthy eating leaflet was evaluated against an existing publicly available standard leaflet. The intervention leaflet was designed to encourage healthy eating in 18-30 year olds and was developed by modifying an existing British Nutrition Foundation leaflet. The intervention leaflet targeted attitudes and self-efficacy. Participants (n=104) were randomly assigned either to the intervention, Foundation or a local food leaflet control condition. Cognitions were measured pre-intervention, immediately after reading the corresponding leaflet, and once again at two weeks follow-up. Critically, intentions to eat healthily were significantly greater at follow-up in the Intervention group compared to the other two groups, with the former leaflet also being perceived as more persuasive. The Intervention group also showed evidence of healthier eating at two weeks compared to the other two groups. Collectively the results illustrate the utility of a targeted theory-based approach.

Atmospheric blocking and mean biases in climate models

Models often underestimate blocking in the Atlantic and Pacific basins and this can lead to errors in both weather and climate predictions. Horizontal resolution is often cited as the main culprit for blocking errors due to poorly resolved small-scale variability, the upscale effects of which help to maintain blocks. Although these processes are important for blocking, the authors show that much of the blocking error diagnosed using common methods of analysis and current climate models is directly attributable to the climatological bias of the model. This explains a large proportion of diagnosed blocking error in models used in the recent Intergovernmental Panel for Climate Change report. Furthermore, greatly improved statistics are obtained by diagnosing blocking using climate model data corrected to account for mean model biases. To the extent that mean biases may be corrected in low-resolution models, this suggests that such models may be able to generate greatly improved levels of atmospheric blocking.

Análise de dados coletados por respondent-driven sampling(RDS): um estudo da prevalência de HIV e fatores associados entre mulheres trabalhadoras do sexo em 10 cidades brasileiras

O grupo das mulheres trabalhadoras do sexo (MTS) é reconhecido como uma populaçãode maior risco à infecção pelo HIV, tanto pela prevalência elevada, como por suavulnerabilidade social como pelos fatores relacionados à própria atividade profissional. Porém, arealização de estudos nos subgrupos de maior risco ao HIV mediante estratégias convencionaisde amostragem é, em geral, problemática por essas populações possuírem pequena magnitudeem termos populacionais e por estarem vinculados a comportamentos estigmatizados ouatividades ilegais. Em 1997, foi proposto um método de amostragem probabilística parapopulações de difícil acesso denominado Respondent-Driven Sampling (RDS). O método éconsiderado como uma variante da amostragem em cadeia e possibilita a estimação estatísticados parâmetros de interesse. Na literatura internacional, para análise de dados coletados porRDS, muitos autores têm utilizado técnicas estatísticas multivariadas tradicionais, sem levar emconta a estrutura de dependência das observações, presente nos dados coletados por RDS.A presente tese tem por objetivo contribuir para suprir informações sobre as práticas derisco relacionadas ao HIV entre as mulheres trabalhadoras do sexo (MTS) com odesenvolvimento de método estatístico para análise de dados coletados com o método deamostragem RDS. Com tal finalidade, foram utilizadas as informações coletadas na PesquisaCorrente da Saúde realizada em dez cidades brasileiras, com 2.523 MTS recrutadas por RDS,entre os anos de 2008 e 2009. O questionário foi autopreenchido e incluiu módulos sobrecaracterísticas da atividade profissional, práticas sexuais, uso de drogas, testes periódicos deHIV, e acesso aos serviços de saúde.Primeiramente, foram descritos alguns pressupostos do RDS e todas as etapas deimplantação da pesquisa. Em seguida, foram propostos métodos de análise multivariada, considerando o RDS como um desenho complexo de amostragem.