Major Baltic inflows are an important process to sustain the sensitive steady state of the Baltic Sea. We introduce an algorithm to identify atmospheric variability favourable for major Baltic inflows. The algorithm i...
详细信息
Major Baltic inflows are an important process to sustain the sensitive steady state of the Baltic Sea. We introduce an algorithm to identify atmospheric variability favourable for major Baltic inflows. The algorithm is based on sea-level pressure (SLP) fields as the only parameter. Characteristic SLP pattern fluctuations include a precursory phase of 30 days and 10 days of inflow period. The algorithm identifies successfully the majority of observed major Baltic inflows between 1961 and 2010. In addition, the algorithm finds some occurrences which cannot be related to observed inflows. In these cases with favourable atmospheric conditions, inflows were precluded by contemporaneously existing saline water masses or strong freshwater supply. Moreover, the algorithm clearly identifies the stagnation periods as a lack of SLP variability favourable for MBIs. This indicates that the lack of inflows is mainly a consequence of missing atmospheric forcing during this period. The only striking inflow which is not identified by the algorithm is the event in January 2003. We demonstrate that this is due to the special evolution of SLP fields which are not comparable with any other event. Finally, the algorithm is applied to an ensemble of scenario simulations. The result indicates that the number of atmospheric events favourable for major Baltic inflows increases slightly in all scenarios.
Today's economic environment has highlighted the importance of properly optimizing every organization's asset including space and facilities portfolio. Reducing space cost by using space more efficiently can r...
详细信息
Background: The detection of early changes in vital signs (VSs) enables timely intervention;however, the measurement of VSs requires hands-on technical expertise and is often time-consuming. The contactless measuremen...
详细信息
Background: The detection of early changes in vital signs (VSs) enables timely intervention;however, the measurement of VSs requires hands-on technical expertise and is often time-consuming. The contactless measurement of VSs is beneficial to prevent infection, such as during the COVID-19 pandemic. Lifelight is a novel software being developed to measure VSs by remote photoplethysmography based on video captures of the face via the integral camera on mobile phones and tablets. We report two early studies in the development of ***: The objective of the Vital Sign Comparison Between Lifelight and Standard of Care: development (VISION-D) study (NCT04763746) was to measure respiratory rate (RR), pulse rate (PR), and blood pressure (BP) simultaneously by using the current standard of care manual methods and the Lifelight software to iteratively refine the software algorithms. The objective of the Vital Sign Comparison Between Lifelight and Standard of Care: Validation (VISION-V) study (NCT03998098) was to validate the use of Lifelight software to accurately measure ***: BP, PR, and RR were measured simultaneously using Lifelight, a sphygmomanometer (BP and PR), and the manual counting of RR. Accuracy performance targets for each VS were defined from a systematic literature review of the performance of state-of-the-art VSs ***: The VISION-D data set (17,233 measurements from 8585 participants) met the accuracy targets for RR (mean error 0.3, SD 3.6 vs target mean error 2.3, SD 5.0;n=7462), PR (mean error 0.3, SD 4.0 vs mean error 2.2, SD 9.2;n=10,214), and diastolic BP (mean error -0.4, SD 8.5 vs mean error 5.5, SD 8.9;n=8951);for systolic BP, the mean error target was met but not the SD (mean error 3.5, SD 16.8 vs mean error 6.7, SD 15.3;n=9233). Fitzpatrick skin type did not affect accuracy. The VISION-V data set (679 measurements from 127 participants) met all the standards: mean error -0.1, SD 3.4 for RR;mean error 1.4, SD 3.8
An adaptive approach for modelling individual‐level choice among multiattribute alternatives using the binary logit model is presented. The algorithm involves the collection of paired comparison data. In an effort to...
详细信息
Major Baltic inflows are an important process to sustain the sensitive steady state of the Baltic Sea. We introduce an algorithm to identify atmospheric variability favourable for major Baltic inflows. The algorithm i...
详细信息
Major Baltic inflows are an important process to sustain the sensitive steady state of the Baltic Sea. We introduce an algorithm to identify atmospheric variability favourable for major Baltic inflows. The algorithm is based on sea-level pressure (SLP) fields as the only parameter. Characteristic SLP pattern fluctuations include a precursory phase of 30 days and 10 days of inflow period. The algorithm identifies successfully the majority of observed major Baltic inflows between 1961 and 2010. In addition, the algorithm finds some occurrences which cannot be related to observed inflows. In these cases with favourable atmospheric conditions, inflows were precluded by contemporaneously existing saline water masses or strong freshwater supply. Moreover, the algorithm clearly identifies the stagnation periods as a lack of SLP variability favourable for MBIs. This indicates that the lack of inflows is mainly a consequence of missing atmospheric forcing during this period. The only striking inflow which is not identified by the algorithm is the event in January 2003. We demonstrate that this is due to the special evolution of SLP fields which are not comparable with any other event. Finally, the algorithm is applied to an ensemble of scenario simulations. The result indicates that the number of atmospheric events favourable for major Baltic inflows increases slightly in all scenarios.
作者：
Jeremy AustinCranfield University
Department of Analytical Science and Informatics (DASI) Cranfield University Barton Rd Silsoe Bedfordshire MK45 4DT UK
暂无评论