RESUMO
High-density low-cost air quality sensor networks are a promising technology to monitor air quality at high temporal and spatial resolution. However the collected data is high-dimensional and it is not always clear how to best leverage this information, particularly given the lower data quality coming from the sensors. Here we report on the use of robust Principal Component Analysis (RPCA) using nitrogen dioxide data obtained from a recently deployed dense network of 225 air pollution monitoring nodes based on low-cost sensors in the Borough of Camden in London. RPCA addresses the brittleness of singular value decomposition towards outliers by using a decomposition of the data into low-rank and sparse contributions, with the latter containing outliers. The modal decomposition enabled by RPCA identifies major periodic patterns including spatial and temporal bias, dominant spatial variance, and north-south bias. The five most descriptive components capture 98 % of the data's variance, achieving a compression by a factor of 1500. We present a new technique that uses the sparse part of the data to identify hotspots. The data indicates that at the locations of the top 15 % most susceptible nodes in the network, the model identifies 23 % more hotspots than in all other locations combined. Moreover, the median hotspot event at these at-risk locations exceeds the mean NO2concentration by 33µg/m3. We show the potential of RPCA for signal correction; it corrects random errors yielding a reference signal with R2>0.8. Moreover, RPCA successfully reconstructs missing data from a sensor with R2=0.72 from the rest of the sensor network, an improvement upon PCA of around 50 %, allowing air quality estimations even if a sensor is out of use temporarily.
RESUMO
AIMS: The aim of this study was to assess associations between a general nursing funding scale and an intensive care unit specific nursing workload scale and the cost of nursing staff. BACKGROUND: Nurse staffing represents the most important cost in the intensive care unit, so it is essential to evaluate it accurately. In addition, the assessment of nursing workload is important for the daily management of the intensive care unit and to ensure quality of care. METHODS: This was a retrospective and quantitative study carried out in the intensive care unit of a Belgian hospital. The extraction of data from the Nursing Activities Score and the Minimum Hospital Summary Nursing Dataset were carried out during 2 periods of 15 days, from 1 June 2018 to 15 June 2018 and from 1 September 2018 to 15 September 2018. RESULTS: A total of 234 patients were included in the study. A total of 773 Nursing Activities Score and Minimum Hospital Summary Nursing Dataset recordings were analyzed in the study per intensive care unit day. A strong correlation was observed between Nursing Activities Score and Minimum Hospital Summary Nursing Dataset for the entire intensive care unit stay with a rho (95% CI) of .88 (0.83-.93); however, the correlation was moderate per intensive care unit day with a rho of .51 (0.45-0.57). A strong association was observed between the Minimum Hospital Summary Nursing Dataset and the Nursing Activities Score with the costs of intensive care unit nurses with a rho (95% CI) of .78 (0.72-0.86) and .74 (0.65-0.84), respectively. CONCLUSIONS: A general nursing funding scale in Belgium was strongly correlated with the nursing workload for the whole intensive care unit stay, but this correlation was moderate per intensive care unit day. In contrast, both scales showed a good correlation with intensive care unit nursing costs. IMPLICATIONS FOR NURSING MANAGEMENT: In Belgium, a general funding scale for nurses does not allow for an assessment of the nursing workload in the intensive care unit. The Nursing Activities Score is strongly correlated with the cost of nursing staff in the intensive care unit. The authors recommend that the Belgian authorities carry out this type of study in several intensive care units in the country and eventually replace the general funding scale for nurses with the Nursing Activities Score.
