Motorcycle riders' self-reported aggression when riding compared with car driving

Aggressive driving has been shown to be related to increased crash risk for car driving. However, less is known about aggressive behaviour and motorcycle riding and whether there are differences in on-road aggression as a function of vehicle type. If such differences exist, these could relate to differences in perceptions of relative vulnerability associated with characteristics of the type of vehicle such as level of protection and performance. Specifically, the relative lack of protection offered by motorcycles may cause riders to feel more vulnerable and therefore to be less aggressive when they are riding compared to when they are driving. This study examined differences in self-reported aggression as a function of two vehicle types: passenger cars and motorcycles. Respondents (n = 247) were all motorcyclists who also drove a car. Results were that scores for the composite driving aggression scale were significantly higher than on the composite riding aggression scale. Regression analyses identified different patterns of predictors for driving aggression from those for riding aggression. Safety attitudes followed by thrill seeking tendencies were the strongest predictors for driving aggression, with more positive safety attitudes being protective while greater thrill seeking was associated with greater self-reported aggressive driving behaviour. For riding aggression, thrill seeking was the strongest predictor (positive relationship), followed by self-rated skill, such that higher self rated skill was protective against riding aggression. Participants who scored at the 85th percentile or above for the aggressive driving and aggressive riding indices had significantly higher scores on thrill seeking, greater intentions to engage in future risk taking, and lower safety attitude scores than other participants. In addition participants with the highest aggressive driving scores also had higher levels of self-reported past traffic offences than other participants. Collectively, these findings suggest that people are less likely to act aggressively when riding a motorcycle than when driving a car, and that those who are the most aggressive drivers are different from those who are the most aggressive riders. However, aggressive riders and drivers appear to present a risk to themselves and others on road. Importantly, the underlying influences for aggressive riding or driving that were identified in this study may be amenable to education and training interventions.

Current CAPE-15: A measure of recent psychotic-like experiences and associated distress

Aim Psychotic-like experiences (PLEs) are common in young people and are associated with both distress and adverse outcomes. The Community Assessment of Psychic Experiences-Positive Scale (CAPE-P) provides a 20-item measure of lifetime PLEs. A 15-item revision of this scale was recently published (CAPE-P15). Although the CAPE-P has been used to assess PLEs in the last 12 months, there is no version of the CAPE for assessing more recent PLEs (e.g. 3 months). This study aimed to determine the reliability and validity of the current CAPE-P15 and assess its relationship with current distress. Method A cross-sectional online survey of 489 university students (17–25 years) assessed lifetime and current substance use, current distress, and lifetime and 3-month PLEs on the CAPE-P15. Results Confirmatory factor analysis indicated that the current CAPE-P15 retained the same three-factor structure as the lifetime version consisting of persecutory ideation, bizarre experiences and perceptual abnormalities. The total score of the current version was lower than the lifetime version, but the two were strongly correlated (r = .64). The current version was highly predictive of generalized distress (r = .52) and indices that combined symptom frequency with associated distress did not confer greater predictive power than frequency alone. Conclusion This study provided preliminary data that the current CAPE-P15 provides a valid and reliable measure of current PLEs. The current CAPE-P15 is likely to have substantial practical utility if it is later shown to be sensitive to change, especially in prevention and early intervention for mental disorders in young people.

Measuring the effects of mergers and acquisitions on the economic performance of real estate developers

Real estate developers in China are using mergers and acquisitions (M&As) to ensure their survival and competitiveness. However, no suitable method is yet available to assess whether such M&As provide enhanced value for those involved. Using a hybrid method of data envelopment analysis (DEA) and Malmquist total factor productivity (TFP) indices, this paper evaluates the short and medium term effects of M&As on acquirers’ economic performance with a set of 32 M&A cases occurring during 2000–2011 in China. The results of the analysis show that M&As generally have a positive effect on acquirers’ economic performance. Acquisitions on average experienced a steady growth in developer Malmquist TFP, a more progressive adoption of technology immediately after acquisition, a slight short-term decrease in technical efficiency after acquisition but followed by a marked increase in the longer term once the integration and synergy benefits were realised. However, there is no evidence to show whether developers achieved any short or long term scale efficiency improvements after M&A. The findings of this study provide useful insights on developer M&A performance from an efficiency and productivity perspective.

Eliciting and encoding expert knowledge on variable selection into classical or Bayesian species distribution models

The quality of species distribution models (SDMs) relies to a large degree on the quality of the input data, from bioclimatic indices to environmental and habitat descriptors (Austin, 2002). Recent reviews of SDM techniques, have sought to optimize predictive performance e.g. Elith et al., 2006. In general SDMs employ one of three approaches to variable selection. The simplest approach relies on the expert to select the variables, as in environmental niche models Nix, 1986 or a generalized linear model without variable selection (Miller and Franklin, 2002). A second approach explicitly incorporates variable selection into model fitting, which allows examination of particular combinations of variables. Examples include generalized linear or additive models with variable selection (Hastie et al. 2002); or classification trees with complexity or model based pruning (Breiman et al., 1984, Zeileis, 2008). A third approach uses model averaging, to summarize the overall contribution of a variable, without considering particular combinations. Examples include neural networks, boosted or bagged regression trees and Maximum Entropy as compared in Elith et al. 2006. Typically, users of SDMs will either consider a small number of variable sets, via the first approach, or else supply all of the candidate variables (often numbering more than a hundred) to the second or third approaches. Bayesian SDMs exist, with several methods for eliciting and encoding priors on model parameters (see review in Low Choy et al. 2010). However few methods have been published for informative variable selection; one example is Bayesian trees (O’Leary 2008). Here we report an elicitation protocol that helps makes explicit a priori expert judgements on the quality of candidate variables. This protocol can be flexibly applied to any of the three approaches to variable selection, described above, Bayesian or otherwise. We demonstrate how this information can be obtained then used to guide variable selection in classical or machine learning SDMs, or to define priors within Bayesian SDMs.

The navigation and visualisation of environmental audio using zooming spectrograms

Acoustic recordings play an increasingly important role in monitoring terrestrial and aquatic environments. However, rapid advances in technology make it possible to accumulate thousands of hours of recordings, more than ecologists can ever listen to. Our approach to this big-data challenge is to visualize the content of long-duration audio recordings on multiple scales, from minutes, hours, days to years. The visualization should facilitate navigation and yield ecologically meaningful information prior to listening to the audio. To construct images, we calculate acoustic indices, statistics that describe the distribution of acoustic energy and reflect content of ecological interest. We combine various indices to produce false-color spectrogram images that reveal acoustic content and facilitate navigation. The technical challenge we investigate in this work is how to navigate recordings that are days or even months in duration. We introduce a method of zooming through multiple temporal scales, analogous to Google Maps. However, the “landscape” to be navigated is not geographical and not therefore intrinsically visual, but rather a graphical representation of the underlying audio. We describe solutions to navigating spectrograms that range over three orders of magnitude of temporal scale. We make three sets of observations: 1. We determine that at least ten intermediate scale steps are required to zoom over three orders of magnitude of temporal scale; 2. We determine that three different visual representations are required to cover the range of temporal scales; 3. We present a solution to the problem of maintaining visual continuity when stepping between different visual representations. Finally, we demonstrate the utility of the approach with four case studies.