Is a single item stress measure independently associated with subsequent severe injury: a prospective cohort study of 16,385 forest industry employees

Background: A previous review showed that high stress increases the risk of occupational injury by three- to five-fold. However, most of the prior studies have relied on short follow-ups. In this prospective cohort study we examined the effect of stress on recorded hospitalised injuries in an 8-year follow-up.
Methods: A total of 16,385 employees of a Finnish forest company responded to the questionnaire. Perceived stress was measured with a validated single-item measure, and analysed in relation recorded hospitalised injuries from 1986 to 2008. We used Cox proportional hazard regression models to examine the prospective associations between work stress, injuries and confounding factors.
Results: Highly stressed participants were approximately 40% more likely to be hospitalised due to injury over the follow-up period than participants with low stress. This association remained significant after adjustment for age, gender, marital status, occupational status, educational level, and physical work environment.
Conclusions: High stress is associated with an increased risk of severe injury.

New age estimates and climatostratigraphic correlations for the Borrobol and Penifiler Tephras: evidence from Abernethy Forest, Scotland

The emerging tephrostratigraphy of NW Europe spanning the last termination (ca. 15–9 ka) provides the potential for synchronizing marine, ice-core and terrestrial records, but is currently compromised by stratigraphic complications, geochemical ambiguity and imprecise age estimates for some layers. Here we present new tephrostratigraphic, radiocarbon and chironomid-based
palaeotemperature data from Abernethy Forest, Scotland, that refine the ages and stratigraphic positions of the Borrobol and Penifiler tephras. The Borrobol Tephra (14.14–13.95 cal ka BP) was deposited in a relatively warm period equated with Greenland Interstadial sub-stage GI-1e. The younger Penifiler Tephra (14.09–13.65 cal ka BP) is closely associated with a cold oscillation equated with GI-
1d. We also present evidence for a previously undescribed tephra layer that has a major-element chemical signature identical to the Vedde Ash. It is associated with the warming trend at the end of the Younger Dryas, and dates between 11.79 and 11.20 cal ka BP.

Predicting forest attributes In southeast Alaska using artificial neural networks

Artificial neural network (ANN) methods are used to predict forest characteristics. The data source is the Southeast Alaska (SEAK) Grid Inventory, a ground survey compiled by the USDA Forest Service at several thousand sites. The main objective of this article is to predict characteristics at unsurveyed locations between grid sites. A secondary objective is to evaluate the relative performance of different ANNs. Data from the grid sites are used to train six ANNs: multilayer perceptron, fuzzy ARTMAP, probabilistic, generalized regression, radial basis function, and learning vector quantization. A classification and regression tree method is used for comparison. Topographic variables are used to construct models: latitude and longitude coordinates, elevation, slope, and aspect. The models classify three forest characteristics: crown closure, species land cover, and tree size/structure. Models are constructed using n-fold cross-validation. Predictive accuracy is calculated using a method that accounts for the influence of misclassification as well as measuring correct classifications. The probabilistic and generalized regression networks are found to be the most accurate. The predictions of the ANN models are compared with a classification of the Tongass national forest in southeast Alaska based on the interpretation of satellite imagery and are found to be of similar accuracy.

Enchytraeid worms retard polycyclic aromatic hydrocarbon degradation in a coniferous forest soil

In this study the fate of naphthalene, fluorene and pyrene were investigated in the presence and absence of enchytraeid worms. Microcosms were used, which enabled the full fate of ¹⁴C-labelled PAHs to be followed. Between 60 and 70% of naphthalene was either mineralised or volatilised, whereas over 90% of the fluorene and pyrene was retained within the soil. Mineralisation and volatilisation of naphthalene was lower in the presence of enchytraeid worms. The hypothesis that microbial mineralisation of naphthalene was limited by enchytraeids because they reduce nutrient availability, and hence limit microbial carbon turnover in these nutrient poor soils, was tested. Ammonia concentrations increased and phosphorus concentrations decreased in all microcosms over the 56 d experimental period. The soil nutrient chemistry was only altered slightly by enchytraeid worms, and did not appear to be the cause of retardation of naphthalene mineralisation. The results suggest that microbial availability and volatilisation of naphthalene is altered as it passes through enchytraeid worms due to organic material encapsulation. © 2004 Elsevier Ltd. All rights reserved.

Privacy-Protected Facial Biometric Verification via Fuzzy Forest Learning

Although visual surveillance has emerged as an effective technolody for public security, privacy has become an issue of great concern in the transmission and distribution of surveillance videos. For example, personal facial images should not be browsed without permission. To cope with this issue, face image scrambling has emerged as a simple solution for privacyrelated applications. Consequently, online facial biometric verification needs to be carried out in the scrambled domain thus bringing a new challenge to face classification. In this paper, we investigate face verification issues in the scrambled domain and propose a novel scheme to handle this challenge. In our proposed method, to make feature extraction from scrambled face images robust, a biased random subspace sampling scheme is applied to construct fuzzy decision trees from randomly selected features, and fuzzy forest decision using fuzzy memberships is then obtained from combining all fuzzy tree decisions. In our experiment, we first estimated the optimal parameters for the construction of the random forest, and then applied the optimized model to the benchmark tests using three publically available face datasets. The experimental results validated that our proposed scheme can robustly cope with the challenging tests in the scrambled domain, and achieved an improved accuracy over all tests, making our method a promising candidate for the emerging privacy-related facial biometric applications.