Refine
Has Fulltext
- no (2)
Year of publication
- 2016 (2) (remove)
Document Type
- Article (2)
Language
- English (2)
Is part of the Bibliography
- yes (2)
Keywords
Institute
Nowadays, working in an office environment is ubiquitous. At the same time, progressively more people suffer from occupational musculoskeletal disorders. Therefore, the aim of this pilot study was to analyse the influence of back pain on sitting behaviour in the office environment. A textile pressure mat (64-sensor-matrix) placed on the seat pan was used to identify the adopted sitting positions of 20 office workers by means of random forest classification. Additionally, two standardised questionnaires (Korff, BPI) were used to assess short and long-term back pain in order to divide the subjects into two groups (with and without back pain). Independent t-test indicated that subjects who registered back pain within the last 24 h showed a clear trend towards a more static sitting behaviour. Therefore, the developed sensor system has successfully been introduced to characterise and compare sitting behaviour of subjects with and without back pain. (C) 2016 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licensesiby-nc-nd/4.0/).
The use of high-frequency sensors on profiling buoys to investigate physical, chemical, and biological processes in lakes is
increasing rapidly. Profiling buoys with automated winches and sensors that collect high-frequency chlorophyll fluorescence
(ChlF) profiles in 11 lakes in the Global Lake Ecological Observatory Network (GLEON) allowed the study of the vertical
and temporal distribution of ChlF, including the formation of subsurface chlorophyll maxima (SSCM). The effectiveness of 3
methods for sampling phytoplankton distributions in lakes, including (1) manual profiles, (2) single-depth buoys, and (3)
profiling buoys were assessed. High-frequency ChlF surface data and profiles were compared to predictions from the
Plankton Ecology Group (PEG) model. The depth-integrated ChlF dynamics measured by the profiling buoy data revealed a
greater complexity that neither conventional sampling nor the generalized PEG model captured. Conventional sampling
techniques would have missed SSCM in 7 of 11 study lakes. Although surface-only ChlF data underestimated average water
column ChlF, at times by nearly 2-fold in 4 of the lakes, overall there was a remarkable similarity between surface and mean
water column data. Contrary to the PEG model’s proposed negligible role for physical control of phytoplankton during the
growing season, thermal structure and light availability were closely associated with ChlF seasonal depth distribution. Thus,
an extension of the PEG model is proposed, with a new conceptual framework that explicitly includes physical metrics to
better predict SSCM formation in lakes and highlight when profiling buoys are especially informative.