Associations of office workers’ objectively assessed occupational sitting,standing and stepping time with musculoskeletal symptoms

We examined the association of musculoskeletal symptoms (MSS) with workplace sitting, standing and stepping time, as well as sitting and standing time accumulation (i.e. usual bout duration of these activities), measured objectively with the activPAL3 monitor. Using baseline data from the Stand Up Victoria trial (216 office workers, 14 workplaces), cross-sectional associations of occupational activities with self-reported MSS (low-back, upper and lower extremity symptoms in the last three months) were examined using probit regression, correcting for clustering and adjusting for confounders. Sitting bout duration was significantly (p < 0.05) associated, non-linearly, with MSS, such that those in the middle tertile displayed the highest prevalence of upper extremity symptoms. Other associations were non-significant but sometimes involved large differences in symptom prevalence (e.g. 38%) by activity. Though causation is unclear, these non-linear associations suggest that sitting and its alternatives (i.e. standing and stepping) interact with MSS and this should be considered when designing safe work systems.

Practitioner summary: We studied associations of objectively assessed occupational activities with musculoskeletal symptoms in office workers. Workers who accumulated longer sitting bouts reported fewer upper extremity symptoms. Total activity duration was not significantly associated with musculoskeletal symptoms. We underline the importance of considering total volumes and patterns of activity time in musculoskeletal research.     

Cluster recognition in spatial-temporal sequences: the case of forest fires

Forest fire sequences can be modelled as a stochastic point process where events are characterized by their spatial locations and occurrence in time. Cluster analysis permits the detection of the space/time pattern distribution of forest fires. These analyses are useful to assist fire-managers in identifying risk areas, implementing preventive measures and conducting strategies for an efficient distribution of the firefighting resources. This paper aims to identify hot spots in forest fire sequences by means of the space-time scan statistics permutation model (STSSP) and a geographical information system (GIS) for data and results visualization. The scan statistical methodology uses a scanning window, which moves across space and time, detecting local excesses of events in specific areas over a certain period of time. Finally, the statistical significance of each cluster is evaluated through Monte Carlo hypothesis testing. The case study is the forest fires registered by the Forest Service in Canton Ticino (Switzerland) from 1969 to 2008. This dataset consists of geo-referenced single events including the location of the ignition points and additional information. The data were aggregated into three sub-periods (considering important preventive legal dispositions) and two main ignition-causes (lightning and anthropogenic causes). Results revealed that forest fire events in Ticino are mainly clustered in the southern region where most of the population is settled. Our analysis uncovered local hot spots arising from extemporaneous arson activities. Results regarding the naturally-caused fires (lightning fires) disclosed two clusters detected in the northern mountainous area.     

Agricultural drought forecasting using satellite images,climate indices and artificial neural network

A new model is proposed for agricultural drought forecasting based on normalized difference vegetation index (NDVI), which is based on satellite data, using effective climatic signals and artificial neural network (ANN). The applied ANN is a feedforward multiple neural network. The inputs of the model are the climatic signals Southern Oscillation Index (SOI) and North Atlantic Oscillation (NAO). In order to forecast NDVI with ANN, the normal method (NM) was used for the recent period, and for evaluation, the moving window method (MWM) was used for a longer (18 years) period. This model was applied to Ahar-chay Basin in Azerbaijan Province, which is located in the northwest of Iran. The results show that in spring (May, June and July (MJJ)) synthetic NDVI can be predicted using ANN, with the input of SOI and NAO indices of the preceding (1 year) spring period. The determinant coefficient (R ²) between observed and predicted NDVI is 0.79, the root mean square error (RMSE) is 0.011 and the discrepancies are less than 1 SD.