A symbiotic relationship between human factors and safety scientists is needed to ensure the provision of holistic solutions for problems emerging in modern socio-technical systems. System Theoretic Accident Model and Processes (STAMP) tackles both interactions and individual failures of human and technological elements of systems. Human factors topics and indicative models, tools and methods were reviewed against the approach of STAMP. The results showed that STAMP engulfs many human factors subjects, is more descriptive than human factors models and tools, provides analytical power, and might be further improved by including more aspects of human factors. STAMP can serve in minimizing the gap between human factors and safety engineering sciences, which can collectively offer inclusive solutions to the industry.
Objectives To report (1) the injury incidence in recreational runners in preparation for a 8-km or 16-km running event and (2) which factors were associated withan increased injury risk. Methods Prospective cohort study in Amsterdam, the Netherlands. Participants (n=5327) received a baseline survey to determine event distance (8 km or 16 km), main sport, running experience, previous injuries, recent overuse injuries and personal characteristics. Three days after the race, they received a follow-up survey to determine duration of training period, running distance per week, training hours, injuries during preparation and use oftechnology. Univariate and multivariate regression models were applied to examine potential risk factors for injuries. Results 1304 (24.5%) participants completed both surveys. After excluding participants with current health problems, no signed informed consent, missing or incorrect data, we included 706 (13.3%) participants. In total, 142 participants (20.1%) reported an injury during preparation for the event. Univariate analyses (OR: 1.7, 95% CI 1.1 to 2.4) and multivariate analyses (OR: 1.7, 95% CI 1.1 to 2.5) showed that injury history was a significant risk factor for running injuries (Nagelkerke R-square=0.06). Conclusion An injury incidence for recreational runners in preparation for a running event was 20%. A previous injury was the only significant risk factor for runningrelated injuries.
Local Gabor features (jets) have been widely used in face recognition systems. Once the sets of jets have been extracted from the two faces to be compared, a proper measure of similarity (or distance) between corresponding features should be chosen. For instance, in the well known Elastic Bunch Graph Matching (EBGM) approach and other Gabor-based face recognition systems, the cosine distance was used as a measure. In this paper, we provide an empirical evaluation of seven distance measures for comparison, using a recently introduced face recognition system, based on Shape Driven Gabor Jets (SDGJ). Moreover we evaluate different normalization factors that are used to pre-process the jets. Experimental results on the BANCA database suggest that the concrete type of normalization applied to jets is a critical factor, and that some combinations of normalization + distance achieve better performance than the classical cosine measure for jet comparison.
