Occupational light vehicle-use: a research driven strategy to improve surveillance and inform policy

Background: Occupational light vehicles (OLV) are light passenger and loadshaped vehicles used for work. The OLV-associated injury burden is as great as that of heavy vehicle users, but has been largely ignored by occupational health and safety (OHS) regulators. Contingent employment growth has accentuated existing gaps in the policy framework between OHS and road-safety. Frequent burden shifting from OHS to road-related health systems undermines the evidence base necessary to inform policy development. Aims: To provide evidence-based recommendations for the collection of OLVuser surveillance data and to underpin OHS procedures and policies for OLVusers. Method: The literature was systematically analyzed to identify OLV-user OHS policy and practice gaps. Strategies to improve and co-ordinate surveillance systems were developed to address the identified limitations. Results: Gaps were identified in OLV-user legislation, data collection, and riskmanagement. These require strategies to improve identification of all OLV-users and to co-ordinate surveillance and OHS practice. Discussion: Contemporary reform of road and OHS, policy, provides a timely opportunity for the implementation of strategic responses to this serious road safety and occupational, public health problem.

Climate-driven impacts of prey abundance on the population structure of a tropical aquatic predator

In the present study we explore how annual variation in climate (late wet-season rainfall) affects population demography in a gape-limited obligate piscivorous predator, the Arafura filesnake Acrochordus arafurae in the Australian tropics. These aquatic snakes display extreme sexual dimorphism, with body sizes and relative head sizes of females much larger than those of males. Two consecutive years with low rainfall during the late wet season reduced the abundance of small but not large sized fish. Although snake residual body mass (RBM, calculated from a general linear regression of ln-transformed mass to ln-SVL) decreased after the first year with low prey availability, it was not until the second year that reduced prey abundance caused a dramatic decline in filesnake survival, and hence in population numbers. Thus, our results suggest that most snakes survived the first year of reduced prey abundance, but a successive year with low prey availability proved fatal for many animals. However, the effects of prey scarcity on RBM and survival fell disproportionately on some size classes of snakes. Medium-sized animals (large males and intermediate-sized females) were affected more dramatically than were small or large snakes. We attribute the higher survival of small snakes to their lower energy needs compared to medium-sized individuals, and the higher survival of large snakes to the continued abundance of large prey (mainly large catfish). Two successive years with low abundance of smaller sized prey thus massively modified the size-structure of the filesnake population, virtually eliminating large males and intermediate-sized females. Our field data provide a clear demonstration of the ways in which stochastic variation in climatic conditions can have dramatic effects on predator population demography, mediated via effects on prey availability.

Flood forecasting in large rivers with data-driven models

Results from the application of adaptive neuro-fuzzy inference system (ANFIS) to forecast water levels at 3 stations along the mainstream of the Lower Mekong River are reported in this paper. The study investigated the effects of including water levels from upstream stations and tributaries, and rainfall as inputs to ANFIS models developed for the 3 stations. When upstream water levels in the mainstream were used as input, improvements to forecasts were realized only when the water levels from 1 or at most 2 upstream stations were included. This is because when there are significant contributions of flow from the tributaries, the correlation between the water levels in the upstream stations and stations of interest decreases, limiting the effectiveness of including water levels from upstream stations as inputs. In addition, only improvements at short lead times were achieved. Including the water level from the tributaries did not significantly improve forecast results. This is attributed mainly to the fact that the flow contributions represented by the tributaries may not be significant enough, given that there could be large volume of flow discharging directly from the catchments which are ungauged, into the mainstream. The largest improvement for 1-day forecasts was obtained for Kratie station where lateral flow contribution was 17 %, the highest for the 3 stations considered. The inclusion of rainfall as input resulted in significant improvements to long-term forecasts. For Thakhek, where rainfall is most significant, the persistence index and coefficient of efficiency for 5-lead-day forecasts improved from 0.17 to 0.44 and 0.89 to 0.93, respectively, whereas the root mean square error decreased from 0.83 to 0.69 m.

An ontology-driven topic mapping approach to multi-level management of e-learning resources

An appropriate use of various pedagogical strategies is fundamental for the effective transfer of knowledge in a flourishing e-learning environment. The resultant information superfluity, however, needs to be tackled for developing sustainable e-learning. This necessitates an effective representation and intelligent access to learning resources. Topic maps address these problems of representation and retrieval of information in a distributed environment. The former aspect is particularly relevant where the subject domain is complex and the later aspect is important where the amount of resources is abundant but not easily accessible. Conversely, effective presentation of learning resources based on various pedagogical strategies along with global capturing and authentication of learning resources are an intrinsic part of effective management of learning resources. Towards fulfilling this objective, this paper proposes a multi-level ontology-driven topic mapping approach to facilitate an effective visualization, classification and global authoring of learning resources in e-learning.