Anxiety in Physicians and Nurses Working in Intensive Care Units in Yasuj's Hospitals/Iran

AbstractIntensive care unit is one of the stressful environments for its staff, especially physicians and nurses. This study was objective to determine anxiety in physicians and nurses working in intensive care units in Yasuj,s hospitals in Iran. This research is an intervention study conducted in Yerevan city in 2009. The number of participants in this study is 150 which are randomly selected. In this study 120 nurses and 30 specialists participated as the working in the intensive care unit of Yasuj,s hospitals in Iran. In the study, a 10-question demographic questionnaire, 20-question situational anxiety, 20-question personality anxiety Berger is used. After codification, the questionnaires Results indicate that average score for the situational anxiety of the nurses has been 46.96, average score for the situational anxiety of physicians has been 39.40 and that average score for the personality anxiety of the nurses has been 40.96, average score for the personality anxiety of physicians has been 36.73. This study provides valuable insight into the actual and perceived stressful experiences of critical care nurses, thus contributing to the ongoing effort to reduce burnout in this population

Reconstruction of gap-free time series satellite observations of land surface temperature to model spectral soil thermal admittance

The soil thermal properties (soil thermal conductivity, soil heat capacity and soil diffusivity) are the main parameters in the applications that need quantitative information on soil heat transfer. Conventionally, these properties are either measured in situ or estimated by semi-empirical models using the fractions of soil constituents. The use of such methods over large and heterogeneous areas, however, is often costly, timeconsuming and sometimes impractical. This thesis proposes and evaluates a new approach to estimate the soil thermal properties by inverse modelling of Spectral Soil Thermal Admittance (SSTA) which is determined using the time series satellite observations of Land Surface Temperature (LST) and soil heat flux (G0) over the entire Qinghai-Tibet Plateau (QTP) from 2008 to 2010. To calculate the soil thermal admittance, the amplitudes of G0 and LST at significant frequencies are required which needs consistent, continuous and long time series. The hourly FY-2C LST time series used in this study were often contaminated by missing data (gaps) and outliers. The HANTS algorithm and M-SSA were used to fill the gaps and remove the outliers in the LST time series. Then, the gap-filled hourly LST was used to identify the most significant periodic components over a three-year data. The amplitude of soil heat flux and LST were estimated at significant frequencies and then the soil thermal admittance at each frequency was determined over the study area. The SSTA, which is the variation of STA against frequency, contains information about the soil thermal properties of different soil layers. An inversion model was used to estimate soil thermal properties of different soil layers (assuming three-layer soil) over the Q-TP.Geoscience and Remote SensingCivil Engineering and Geoscience

Reconstruction of cloud-free time series satellite observations of land surface temperature

Time series satellite observations of land surface properties, like Land Surface Temperature (LST), often feature missing data or data with anomalous values due to cloud coverage, malfunction of sensor, atmospheric aerosols, defective cloud masking and retrieval algorithms. Preprocessing procedures are needed to identify anomalous observations resulting in gaps and outliers and then reconstruct the time series by filling the gaps. Hourly LST observations, estimated from radiometric data acquired by the Single channel Visible and Infrared Spin Scan Radiometer (S-VISSR) sensor onboard the Fengyun-2C (FY-2C) Chinese geostationary satellite have been used in this study which cover the whole Tibetan Plateau from 2008 through 2010 with a 5×5 km2 spatial resolution. Multi-channel Singular Spectrum Analysis (M-SSA), an advanced methodology of time series anal-ysis, has been utilized to reconstruct LST time series. The results show that this methodology has the ability to fill the gaps and also remove the outliers (both positive and negative). To validate the methodology, we employed LST ground measurements and created artificial gaps. The results indicated with 63% of hourly gaps in the time series, the Mean Absolute Error (MAE) reached 2.25 Kelvin (K) with R2 = 0.83. This study shows the ability of M-SSA that uses temporal and spatio-temporal correlation to fill the gaps to reconstruct LST time series.Geoscience & Remote SensingCivil Engineering and Geoscience