Modelling attitudes to climate change : an order effect and a test between alternatives

Quantum-like models can be fruitfully used to model attitude change in a social context. Next steps require data, and higher dimensional models. Here, we discuss an exploratory study that demonstrates an order effect when three question sets about Climate Beliefs, Political Affiliation and Attitudes Towards Science are presented in different orders within a larger study of n=533 subjects. A quantum-like model seems possible, and we propose a new experiment which could be used to test between three possible models for this scenario.

Incidence of paediatric fatal and non-fatal low speed vehicle run over events in Queensland, Australia : eleven year analysis

Background The purpose of this study was to estimate the incidence of fatal and non-fatal Low Speed Vehicle Run Over (LSVRO) events among children aged 0–15 years in Queensland, Australia, at a population level. Methods Fatal and non-fatal LSVRO events that occurred in children resident in Queensland over eleven calendar years (1999-2009) were identified using ICD codes, text description, word searches and medical notes clarification, obtained from five health related data bases across the continuum of care (pre-hospital to fatality). Data were manually linked. Population data provided by the Australian Bureau of Statistics were used to calculate crude incidence rates for fatal and non-fatal LSVRO events. Results There were 1611 LSVROs between 1999–2009 (IR = 16.87/100,000/annum). Incidence of non-fatal events (IR = 16.60/100,000/annum) was 61.5 times higher than fatal events (IR = 0.27/100,000/annum). LSVRO events were more common in boys (IR = 20.97/100,000/annum) than girls (IR = 12.55/100,000/annum), and among younger children aged 0–4 years (IR = 21.45/100000/annum; 39% or all events) than older children (5–9 years: IR = 16.47/100,000/annum; 10–15 years IR = 13.59/100,000/annum). A total of 896 (56.8%) children were admitted to hospital for 24 hours of more following an LSVRO event (IR = 9.38/100,000/annum). Total LSVROs increased from 1999 (IR = 14.79/100,000) to 2009 (IR = 18.56/100,000), but not significantly. Over the 11 year period, there was a slight (non –significant) increase in fatalities (IR = 0.37-0.42/100,000/annum); a significant decrease in admissions (IR = 12.39–5.36/100,000/annum), and significant increase in non-admissions (IR = 2.02-12.77/100,000/annum). Trends over time differed by age, gender and severity. Conclusion This is the most comprehensive, population-based epidemiological study on fatal and non-fatal LSVRO events to date. Results from this study indicate that LSVROs incur a substantial burden. Further research is required on the characteristics and risk factors associated with these events, in order to adequately inform injury prevention. Strategies are urgently required in order to prevent these events, especially among young children aged 0-4 years.

Cidade é uma palavra plural [City is a manifold word]

This text elaborates on the city as cultural construct and representation and Lisbocópio, the installation by Pancho Guedes and Ricardo Jacinto in the context of the Official Representation of Portugal at the 10. Mostra Internazionale di Architettura-La Biennale di Venezia.

Higher order spectra based support vector machine for arrhythmia classification

Heart rate variability (HRV) refers to the regulation of the sinoatrial node, the natural pacemaker of the heart by the sympathetic and parasympathetic branches of the autonomic nervous system. HRV analysis is an important tool to observe the heart’s ability to respond to normal regulatory impulses that affect its rhythm. Like many bio-signals, HRV signals are non-linear in nature. Higher order spectral analysis (HOS) is known to be a good tool for the analysis of non-linear systems and provides good noise immunity. A computer-based arrhythmia detection system of cardiac states is very useful in diagnostics and disease management. In this work, we studied the identification of the HRV signals using features derived from HOS. These features were fed to the support vector machine (SVM) for classification. Our proposed system can classify the normal and other four classes of arrhythmia with an average accuracy of more than 85%.

A new approach to spatial data interpolation using higher-order statistics

Interpolation techniques for spatial data have been applied frequently in various fields of geosciences. Although most conventional interpolation methods assume that it is sufficient to use first- and second-order statistics to characterize random fields, researchers have now realized that these methods cannot always provide reliable interpolation results, since geological and environmental phenomena tend to be very complex, presenting non-Gaussian distribution and/or non-linear inter-variable relationship. This paper proposes a new approach to the interpolation of spatial data, which can be applied with great flexibility. Suitable cross-variable higher-order spatial statistics are developed to measure the spatial relationship between the random variable at an unsampled location and those in its neighbourhood. Given the computed cross-variable higher-order spatial statistics, the conditional probability density function (CPDF) is approximated via polynomial expansions, which is then utilized to determine the interpolated value at the unsampled location as an expectation. In addition, the uncertainty associated with the interpolation is quantified by constructing prediction intervals of interpolated values. The proposed method is applied to a mineral deposit dataset, and the results demonstrate that it outperforms kriging methods in uncertainty quantification. The introduction of the cross-variable higher-order spatial statistics noticeably improves the quality of the interpolation since it enriches the information that can be extracted from the observed data, and this benefit is substantial when working with data that are sparse or have non-trivial dependence structures.