Validating a model of cooperative procurement in the construction industry

Project management in the construction industry involves coordination of many tasks and individuals, affected by complexity and uncertainty, which increases the need for efficient cooperation. Procurement is crucial since it sets the basis for cooperation between clients and contractors. This is true whether the project is local, regional or global in scope. Traditionally, procurement procedures are competitive, resulting in conflicts, adversarial relationships and less desirable project results. The purpose of this paper is to propose and empirically test an alternative procurement model based on cooperative procurement procedures that facilitates cooperation between clients and contractors in construction projects. The model is based on four multi-item constructs – incentive-based compensation, limited bidding options, partner selection and cooperation. Based on a sample of 87 client organisations, the model was empirically tested and exhibited strong support, including content, nomological, convergent and discriminant validity, as well as reliability. Our findings indicate that partner selection based on task related attributes mediates the relationship between two important pre-selection processes (incentive-based compensation and limited bid invitation) and preferred outcome of cooperation. The contribution of the paper is identifying valid and reliable measurement constructs and confirming a unique sequential order for achieving cooperation. Moreover, the findings are applicable for many types of construction projects because of the similarities in the construction industry worldwide.

A Bayesian model predicts the response of axons to molecular gradients

Axon guidance by molecular gradients plays a crucial role in wiring up the nervous system. However, the mechanisms axons use to detect gradients are largely unknown. We first develop a Bayesian “ideal observer” analysis of gradient detection by axons, based on the hypothesis that a principal constraint on gradient detection is intrinsic receptor binding noise. Second, from this model, we derive an equation predicting how the degree of response of an axon to a gradient should vary with gradient steepness and absolute concentration. Third, we confirm this prediction quantitatively by performing the first systematic experimental analysis of how axonal response varies with both these quantities. These experiments demonstrate a degree of sensitivity much higher than previously reported for any chemotacting system. Together, these results reveal both the quantitative constraints that must be satisfied for effective axonal guidance and the computational principles that may be used by the underlying signal transduction pathways, and allow predictions for the degree of response of axons to gradients in a wide variety of in vivo and in vitro settings.

Physical activity, sitting and depression during pregnancy

Aims The aim of this cross sectional study is to explore levels of physical activity and sitting behaviour amongst a sample of pregnant Australian women (n = 81), and investigate whether reported levels of physical activity and/or time spent sitting were associated with depressive symptom scores after controlling for potential covariates. Methods Study participants were women who attended the antenatal clinic of a large Brisbane maternity hospital between October and November 2006. Data relating to participants. current levels of physical activity, sitting behaviour, depressive symptoms, demographic characteristics and exposure to known risk factors for depression during pregnancy were collected; via on-site survey, follow-up telephone interview (approximately one week later) and post delivery access to participant hospital records. Results Participants were aged 29.5 (¡¾ 5.6) years and mostly partnered (86.4%) with a gross household income above $26,000 per annum (88.9%). Levels of physical activity were generally low, with only 28.4 % of participants reporting sufficient total activity and 16% of participants reporting sufficient planned (leisure-time) activity. The sample mean for depressive symptom scores measured by the Hospital Anxiety and Depression Scale (HADS-D) was 6.38 (¡¾ 2.55). The mean depressive symptom scores for participants who reported total moderate-to-vigorous activity levels of sufficient, insufficient, and none, were 5.43 (¡¾ 1.56), 5.82 (¡¾ 1.77) and 7.63 (¡¾ 3.25), respectively. Hierarchical multivariable linear regression modelling indicated that after controlling for covariates, a statistically significant difference of 1.09 points was observed between mean depressive symptom scores of participants who reported sufficient total physical activity, compared with participants who reported they were engaging in no moderate-to-vigorous activity in a typical week (p = 0.05) but this did not reach the criteria for a clinically meaningful difference. Total physical activity was contributed 2.2% to the total 30.3% of explained variance within this model. The other main contributors to explained variance in multivariable regression models were anxiety symptom scores and the number of existing children. Further, a trend was observed between higher levels of planned sitting behaviour and higher depressive symptom scores (p = 0.06); this correlation was not clinically meaningful. Planned sitting contributed 3.2% to the total 31.3 % of explained variance. The number of regression covariates and limited sample size led to a less than ideal ratio of covariates to participants, probably attenuating this relationship. Specific information about the sitting-based activities in which participants engaged may have provided greater insight about the relationship between planned sitting and depressive symptoms, but these data were not captured by the present study. Conclusions The finding that higher levels of physical activity were associated with lower levels of depressive symptoms is consistent with the current body of existing literature in pregnant women, and with a larger body of evidence based in general population samples. Although this result was not considered clinically meaningful, the criterion for a clinically meaningful result was an a priori decision based on quality of life literature in non-pregnant populations and may not truly reflect a difference in symptoms that is meaningful to pregnant women. Further investigation to establish clinically meaningful criteria for continuous depressive symptom data in pregnant women is required. This result may have implications relating to prevention and management options for depression during pregnancy. The observed trend between planned sitting and depressive symptom scores is consistent with literature based on leisure-time sitting behaviour in general population samples, and suggests that further research in this area, with larger samples of pregnant women and more specific sitting data is required to explore potential associations between activities such as television viewing and depressive symptoms, as this may be an area of behaviour that is amenable to modification.

Margin-adaptive model selection in statistical learning

A classical condition for fast learning rates is the margin condition, first introduced by Mammen and Tsybakov. We tackle in this paper the problem of adaptivity to this condition in the context of model selection, in a general learning framework. Actually, we consider a weaker version of this condition that allows one to take into account that learning within a small model can be much easier than within a large one. Requiring this “strong margin adaptivity” makes the model selection problem more challenging. We first prove, in a general framework, that some penalization procedures (including local Rademacher complexities) exhibit this adaptivity when the models are nested. Contrary to previous results, this holds with penalties that only depend on the data. Our second main result is that strong margin adaptivity is not always possible when the models are not nested: for every model selection procedure (even a randomized one), there is a problem for which it does not demonstrate strong margin adaptivity.