What interventions increase commuter cycling? A systematic review.

OBJECTIVE: To identify interventions that will increase commuter cycling. SETTING: All settings where commuter cycling might take place. PARTICIPANTS: Adults (aged 18+) in any country. INTERVENTIONS: Individual, group or environmental interventions including policies and infrastructure. PRIMARY AND SECONDARY OUTCOME MEASURES: A wide range of 'changes in commuter cycling' indicators, including frequency of cycling, change in workforce commuting mode, change in commuting population transport mode, use of infrastructure by defined populations and population modal shift. RESULTS: 12 studies from 6 countries (6 from the UK, 2 from Australia, 1 each from Sweden, Ireland, New Zealand and the USA) met the inclusion criteria. Of those, 2 studies were randomised control trials and the remainder preintervention and postintervention studies. The majority of studies (n=7) evaluated individual-based or group-based interventions and the rest environmental interventions. Individual-based or group-based interventions in 6/7 studies were found to increase commuter cycling of which the effect was significant in only 3/6 studies. Environmental interventions, however, had small but positive effects in much larger but more difficult to define populations. Almost all studies had substantial loss to follow-up. CONCLUSIONS: Despite commuter cycling prevalence varying widely between countries, robust evidence of what interventions will increase commuter cycling in low cycling prevalence nations is sparse. Wider environmental interventions that make cycling conducive appear to reach out to hard to define but larger populations. This could mean that environmental interventions, despite their small positive effects, have greater public health significance than individual-based or group-based measures because those interventions encourage a larger number of people to integrate physical activity into their everyday lives

Determination of machinable volume for finish cuts in CAPP

Identification of machinable volume for finish cut is a complex task as it involves the details not only of the final product but also the intermediate part obtained from rough machining of the blank. A feature recognition technique that adopts a rule-based methodology is required for calculating this small, complex shaped finish cut volume. This paper presents the feature recognition module in a CAPP system that calculates the intermediate finish cut volume by adopting a rule based syntactic pattern recognition approach. In this module, the interfacer uses STEP AP203/214, a CAD neutral format, to trace the coordinate point information and to calculate the machinable volume. Two illustrative examples are given to explain the proposed syntactic pattern approach for prismatic parts

A comparison of two methods for determining titanium dioxide marker content in broiler digestibility studies

The use of inert markers in broiler diets eliminates the need to quantitatively evaluate feed intake and excreta output to determine diet digestibility, and enables nutrient uptake at specific points along the gastrointestinal tract to be examined. Titanium dioxide (TiO2) is commonly used for this purpose and measured using a UV spectrophotometric assay. Two experiments were conducted to observe whether an inductively coupled plasma optical emission spectrophotometer (ICP-OES) assay is able to replace the UV-spectroscopy assay for rapid analysis of TiO2 in broiler feed and ileal digesta samples. In the first experiment, TiO2 was added at 5g/kg to 19 broiler diets. Ross 308, male broilers (n=452) fed these diets were involved in a series of digestion studies to determine ileal digesta recovery of TiO2. In the second experiment, defined amounts of TiO2 were added to ileal digesta samples from Ross 308, male broilers (n=176) and TiO2 recoveries were determined

Measuring the effect of opportunity cost of time on participation in sports and exercise

This article has been made available through the Brunel Open Access Publishing Fund.Background: There is limited research on the association between opportunity cost of time and sports and exercise due to lack of data on opportunity cost of time. Using a sample of 14142 adults from Health Survey for England (2006), we develop and test a composite index of op-portunity cost of time (to address the current issues with data constraint on opportunity cost of time) in order to explore the relationship between opportunity cost of time and sports participation. Methods: Probit regression models are fitted adjusting for a range of covariates. Opportunity cost of time is measured with two proxy measures: a) composite index (consisting of various indicators of wage earnings) con-structed using principal component analysis; and b) education and employment, approach in the literature. We estimate the relative impact of the composite index compared with current proxy measures, on prediction of sports participation. Findings: Findings suggest that higher opportunity cost of time is associated with increased likelihood of sports participation, regardless of the time intensity of activity or the measure of opportunity cost of time used. The relative impacts of the two proxy measures are comparable. Sports and exercise was found to be positively correlated with income. Another important positive correlate of sports and exercise is participation in voluntary activity. The research and policy implications of our findings are discussed

Behaviour Change in Public Health: Evidence and Implications

Article ID 598672The evidence on the role of particular lifestyles, smoking, binge drinking, lack of physical activity, and poor health care seeking, in increased risks for mortality and morbidity is compelling [1]. Understanding the pathways through which these various “unhealthy” behaviours affect health is complicated by the broader ecological context in which they occur. The complexity is further enhanced because behaviours do not occur in isolation and there is often a convergence of associations. Interventions to achieve changes in either single or multiple behaviours have therefore often been limited in their effectiveness and longer term sustainability. In order to develop and implement a meaningful behaviour change agenda we need to establish innovative ways of operationalizing and understanding the complexity of behavioural factors and their dynamic interrelationships and how these collectively affect health. The Behaviour Change Research Cycle (BCRC) (Figure 1) provides a simple illustration of the life cycle of evidence required

Multimodel combination techniques for analysis of hydrological simulations: Application to distributed model intercomparison project results

This paper examines several multimodel combination techniques that are used for streamflow forecasting: the simple model average (SMA), the multimodel superensemble (MMSE), modified multimodel superensemble (M3SE), and the weighted average method (WAM). These model combination techniques were evaluated using the results from the Distributed Model Intercomparison Project (DMIP), an international project sponsored by the National Weather Service (NWS) Office of Hydrologic Development (OHD). All of the multimodel combination results were obtained using uncalibrated DMIP model simulations and were compared against the best-uncalibrated as well as the best-calibrated individual model results. The purpose of this study is to understand how different combination techniques affect the accuracy levels of the multimodel simulations. This study revealed that the multimodel simulations obtained from uncalibrated single-model simulations are generally better than any single-member model simulations, even the best-calibrated single-model simulations. Furthermore, more sophisticated multimodel combination techniques that incorporated bias correction step work better than simple multimodel average simulations or multimodel simulations without bias correction. © 2006 American Meteorological Society

Multi-model ensemble hydrologic prediction using Bayesian model averaging

Multi-model ensemble strategy is a means to exploit the diversity of skillful predictions from different models. This paper studies the use of Bayesian model averaging (BMA) scheme to develop more skillful and reliable probabilistic hydrologic predictions from multiple competing predictions made by several hydrologic models. BMA is a statistical procedure that infers consensus predictions by weighing individual predictions based on their probabilistic likelihood measures, with the better performing predictions receiving higher weights than the worse performing ones. Furthermore, BMA provides a more reliable description of the total predictive uncertainty than the original ensemble, leading to a sharper and better calibrated probability density function (PDF) for the probabilistic predictions. In this study, a nine-member ensemble of hydrologic predictions was used to test and evaluate the BMA scheme. This ensemble was generated by calibrating three different hydrologic models using three distinct objective functions. These objective functions were chosen in a way that forces the models to capture certain aspects of the hydrograph well (e.g., peaks, mid-flows and low flows). Two sets of numerical experiments were carried out on three test basins in the US to explore the best way of using the BMA scheme. In the first set, a single set of BMA weights was computed to obtain BMA predictions, while the second set employed multiple sets of weights, with distinct sets corresponding to different flow intervals. In both sets, the streamflow values were transformed using Box-Cox transformation to ensure that the probability distribution of the prediction errors is approximately Gaussian. A split sample approach was used to obtain and validate the BMA predictions. The test results showed that BMA scheme has the advantage of generating more skillful and equally reliable probabilistic predictions than original ensemble. The performance of the expected BMA predictions in terms of daily root mean square error (DRMS) and daily absolute mean error (DABS) is generally superior to that of the best individual predictions. Furthermore, the BMA predictions employing multiple sets of weights are generally better than those using single set of weights. © 2006 Elsevier Ltd. All rights reserved