Mean values, standard deviations (SD), minimum and maximum values, median and quartiles (Q1–Q3) of the total bacterial load (CFU/m3) and counts of particles (Sampler A) of diameter ≥0.5 µm and diameter ≥5 µm (no./m3) in all the procedures monitored and in each of the two types of procedure.

The time course of changes of all the residual deformation parameters. The opening angle, curvature and length for intact artery (I), media-intima (MI) and adventitia (A) are given. For datasets where normal distribution was confirmed (significance level 0.05), mean ± SD are given, otherwise median and first and third quartile (Q1, Q3) are used.

Edmonton, Canada, 1992–2002.

1Median (Q1–Q3) provided for individual likert scale items (range 1–5), mean (SD) provided for subscales (range 1–5) and binary type items (range 0 or 1).2For likert scale items, percent of respondents who “somewhat agree” or “strongly agree”, for binary type items, percent of respondents answering “yes”.3Professional attitudes score = sum (improving quality of care, maintaining professional competence, fulfilling professional responsibility, Interprofessional collaboration) – 4 (ranges from 0–16).4Interprofessional collaboration = mean of shared education and collaboration and physician authority.5All professional behaviour items are binary (Yes/No) type items.6Professional reactions to colleagues’ performance not aggregated as a subscale.7Sample size restricted to those (physicians/nurses) who observed the specific type of underperformance in the past 3 years.

Seaport was the Boston district with the highest net absorption in the office real estate sector in the first three quarters of 2024. Conversely, Downtown recorded the lowest net absorption, at negative ******* square feet. A negative net absorption means that more space was vacated or supplied than absorbed. That is a sign of declining demand for offices. Across the U.S. many of the major markets were impacted by this trend.

AdriaClim Indicators | RD and MH | RD-2 Mean River Discharge (WRFHydro, Q2, 1992-2020) adriaclim_dataset=indicator adriaclim_model=WRFHydro adriaclim_scale=adriatic adriaclim_timeperiod=monthly adriaclim_type=climatology auxiliary_file1=adriaclim_WRFHydro_indicator_RD2-P05_climatology_hist_monthly_adriatic_1992-2020.csv auxiliary_file2=adriaclim_WRFHydro_indicator_RD2-Q1_climatology_hist_monthly_adriatic_1992-2020.csv auxiliary_file3=adriaclim_WRFHydro_indicator_RD2-Q2_climatology_hist_monthly_adriatic_1992-2020.csv auxiliary_file4=adriaclim_WRFHydro_indicator_RD2-Q3_climatology_hist_monthly_adriatic_1992-2020.csv auxiliary_file5=adriaclim_WRFHydro_indicator_RD2-P95_climatology_hist_monthly_adriatic_1992-2020.csv auxiliary_info1=5th percentile (P05) of RD2 auxiliary_info2=25th percentile (Q1) of RD2 auxiliary_info3=50th percentile (Q2) of RD2 auxiliary_info4=75th percentile (Q3) of RD2 auxiliary_info5=95th percentile (P95) of RD2 basin1_centroid=43.379112,14.840040 basin1_distribution=Avg 3728, P05 1477, Q1 2282, Q2 3144, Q3 4716, P95 7389, basin1_name=Adriatic Sea basin1_polygon=adriaclim_indicator_RD_MH_polygon1.csv basin1_rivers=1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145, basin2_centroid=44.912257,12.987451 basin2_distribution=Avg 2311, P05 788, Q1 1337, Q2 1900, Q3 2794, P95 5200, basin2_name=Shallow Northern Adriatic Sea basin2_polygon=adriaclim_indicator_RD_MH_polygon2.csv basin2_rivers=99,100,101,102,103,104,106,107,108,109,110,111,112,114,115,117,118,119,121,122,124,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145, basin3_centroid=44.060441,14.244517 basin3_distribution=Avg 145, P05 25.7, Q1 66, Q2 105, Q3 195, P95 368, basin3_name=Northern Adriatic Sea basin3_polygon=adriaclim_indicator_RD_MH_polygon3.csv basin3_rivers=72,73,74,75,76,77,79,80,81,83,84,85,86,88,89,91,93,94,96,97,98,105,113,116,120,123,125, basin4_centroid=42.782794,15.802863 basin4_distribution=Avg 735, P05 114, Q1 374, Q2 601, Q3 938, P95 1871, basin4_name=Central Adriatic Sea basin4_polygon=adriaclim_indicator_RD_MH_polygon4.csv basin4_rivers=53,54,55,56,57,58,60,61,62,63,64,65,66,67,68,70,71,78,82,87,90,92,95, basin5_centroid=41.332801,18.137200 basin5_distribution=Avg 535, P05 77, Q1 204, Q2 441, Q3 697, P95 1446, basin5_name=Southern Adriatic Sea basin5_polygon=adriaclim_indicator_RD_MH_polygon5.csv basin5_rivers=8,10,14,15,21,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,59,69, basin6_centroid=44.549283,12.689859 basin6_distribution=Avg 1485, P05 555, Q1 884, Q2 1247, Q3 1765, P95 3140, basin6_name=PA3 Emilia-Romagna basin6_polygon=adriaclim_indicator_RD_MH_polygon6.csv basin6_rivers=99,100,101,102,103,104,106,107,108,109,110,111,112,115, cdm_data_type=Point contact=leonardo.aragao@unibo.it Conventions=COARDS, CF-1.6, ACDD-1.3, NCCSV-1.2 description=Climatology of monthly mean river discharge based on daily river discharge estimated at the river-mouth position. Adriatic Sea sections and Pilot Areas aggregate discharges of all rivers within the respective catchment area. Easternmost_Easting=20.2298 featureType=Point geospatial_lat_max=45.8061 geospatial_lat_min=39.1965 geospatial_lat_units=degrees_north geospatial_lon_max=20.2298 geospatial_lon_min=11.6044 geospatial_lon_units=degrees_east infoUrl=https://www.unibo.it institution=UNIBO Northernmost_Northing=45.8061 river001_basin=Out river001_country=Italy river001_distribution=Avg 14.4, P05 0.4, Q1 3.5, Q2 7.6, Q3 18.5, P95 49.3, river001_name=Neto river001_position=39.196537,17.113887 river002_basin=Out river002_country=Italy river002_distribution=Avg 0.1, P05 0.0, Q1 0.0, Q2 0.1, Q3 0.1, P95 0.3, river002_name=Lipuda river002_position=39.350710,17.118340 river003_basin=Out river003_country=Italy river003_distribution=Avg 0.3, P05 0.0, Q1 0.1, Q2 0.2, Q3 0.3, P95 1.3, river003_name=Nica river003_position=39.475000,17.016980 river004_basin=Out river004_country=Greece river004_distribution=Avg 34.8, P05 0.6, Q1 3.6, Q2 18.1, Q3 45.7, P95 125, river004_name=Thyramis river004_position=39.585057,20.229830 river005_basin=Out river005_country=Italy river005_distribution=Avg 1.6, P05 0.0, Q1 0.1, Q2 0.1, Q3 0.9, P95 7.2, river005_name=Trionto river005_position=39.610570,16.761533 river006_basin=Out river006_country=Italy river006_distribution=Avg 21.1, P05 1.4, Q1 8.2, Q2 14.6, Q3 27.4, P95 64, river006_name=Crati river006_position=39.733800,16.491701 river007_basin=Out river007_country=Italy river007_distribution=Avg 0.3, P05 0.1, Q1 0.1, Q2 0.2, Q3 0.4, P95 0.9, river007_name=Raganello river007_position=39.773051,16.475633 river008_basin=SAd river008_country=Albania river008_distribution=Avg 12.2, P05 0.2, Q1 1.0, Q2 5.7, Q3 17.0, P95 44.0, river008_name=Bistrica-Pavla-Kalimiti river008_position=39.825897,20.034141 river009_basin=Out river009_country=Italy river009_distribution=Avg 0.0, P05 0.0, Q1 0.0, Q2 0.0, Q3 0.0, P95 0.0, river009_name=Saraceno river009_position=39.849506,16.513430 river010_basin=SAd river010_country=Italy river010_distribution=Avg 0.1, P05 0.0, Q1 0.0, Q2 0.0, Q3 0.0, P95 0.3, river010_name=Mini18 river010_position=39.926038,18.388768 river011_basin=Out river011_country=Italy river011_distribution=Avg 0.0, P05 0.0, Q1 0.0, Q2 0.0, Q3 0.0, P95 0.0, river011_name=Ferro river011_position=39.964045,16.613279 river012_basin=Out river012_country=Italy river012_distribution=Avg 0.1, P05 0.0, Q1 0.0, Q2 0.1, Q3 0.1, P95 0.6, river012_name=Mini19 river012_position=40.016594,18.033938 river013_basin=Out river013_country=Italy river013_distribution=Avg 4.3, P05 0.2, Q1 1.0, Q2 2.7, Q3 5.7, P95 15.4, river013_name=Sinni river013_position=40.164129,16.689050 river014_basin=SAd river014_country=Italy river014_distribution=Avg 0.5, P05 0.1, Q1 0.1, Q2 0.1, Q3 0.3, P95 2.7, river014_name=Mini20 river014_position=40.207323,17.946361 river015_basin=SAd river015_country=Italy river015_distribution=Avg 0.1, P05 0.0, Q1 0.0, Q2 0.0, Q3 0.1, P95 0.3, river015_name=Mini17 river015_position=40.207551,18.445060 river016_basin=Out river016_country=Italy river016_distribution=Avg 4.6, P05 0.3, Q1 1.1, Q2 2.3, Q3 6.1, P95 15.1, river016_name=Agri river016_position=40.221716,16.718912 river017_basin=Out river017_country=Italy river017_distribution=Avg 0.7, P05 0.1, Q1 0.2, Q2 0.3, Q3 0.7, P95 3.0, river017_name=Cavone river017_position=40.292690,16.752159 river018_basin=Out river018_country=Italy river018_distribution=Avg 0.0, P05 0.0, Q1 0.0, Q2 0.0, Q3 0.0, P95 0.0, river018_name=Mini21 river018_position=40.307675,17.753212 river019_basin=Out river019_country=Italy river019_distribution=Avg 4.5, P05 0.3, Q1 0.9, Q2 2.5, Q3 5.0, P95 18.4, river019_name=Basento river019_position=40.374490,16.785240 river020_basin=Out river020_country=Italy river020_distribution=Avg 4.9, P05 0.5, Q1 1.1, Q2 2.4, Q3 5.2, P95 16.9, river020_name=Bradano river020_position=40.420475,16.836800 river021_basin=SAd river021_country=Italy river021_distribution=Avg 0.7, P05 0.3, Q1 0.4, Q2 0.6, Q3 0.8, P95 1.4, river021_name=Mini16 river021_position=40.445815,18.220611 river022_basin=Out river022_country=Italy river022_distribution=Avg 0.5, P05 0.2, Q1 0.3, Q2 0.4, Q3 0.6, P95 0.8, river022_name=Daledda river022_position=40.478058,17.340645 river023_basin=Out river023_country=Italy river023_distribution=Avg 0.6, P05 0.1, Q1 0.1, Q2 0.3, Q3 0.7, P95 2.2, river023_name=Lato river023_position=40.497392,16.955212 river024_basin=Out river024_country=Italy river024_distribution=Avg 0.0, P05 0.0, Q1 0.0, Q2 0.0, Q3 0.0, P95 0.0, river024_name=Mini23 river024_position=40.511168,17.011222 river025_basin=Out river025_country=Italy river025_distribution=Avg 2.5, P05 0.3, Q1 0.5, Q2 0.7, Q3 6.7, P95 6.7, river025_name=Mini22 river025_position=40.525232,17.149314 river026_basin=Out river026_country=Italy river026_distribution=Avg 0.0, P05 0.0, Q1 0.0, Q2 0.0, Q3 0.0, P95 0.0, river026_name=LamaLenne river026_position=40.524660,17.067843 river027_basin=SAd river027_country=Italy river027_distribution=Avg 0.6, P05 0.2, Q1 0.4, Q2 0.5, Q3 0.7, P95 1.0, river027_name=Brindisi river027_position=40.638876,17.915655 river028_basin=SAd river028_country=Italy river028_distribution=Avg 0.0, P05 0.0, Q1 0.0, Q2 0.0, Q3 0.0, P95 0.0, river028_name=Giancala river028_position=40.672284,17.871380 river029_basin=SAd river029_country=Italy river029_distribution=Avg 0.0, P05 0.0, Q1 0.0, Q2 0.0, Q3 0.0, P95 0.1, river029_name=Mini15 river029_position=40.681902,17.827953 river030_basin=SAd river030_country=Italy river030_distribution=Avg 0.0, P05 0.0, Q1 0.0, Q2 0.0, Q3 0.0, P95 0.2, river030_name=Mini14 river030_position=40.694277,17.808598 river031_basin=SAd river031_country=Albania river031_distribution=Avg 49.1, P05 4.1, Q1 11.1, Q2 32.7, Q3 64, P95 146, river031_name=Vjose river031_position=40.714724,19.381013 river032_basin=SAd river032_country=Italy river032_distribution=Avg 0.0, P05 0.0, Q1 0.0, Q2 0.0, Q3 0.0, P95 0.0, river032_name=Mini13 river032_position=40.861281,17.416519 river033_basin=SAd river033_country=Italy river033_distribution=Avg 0.0, P05 0.0, Q1 0.0, Q2 0.0, Q3 0.0, P95 0.0, river033_name=Mini12 river033_position=40.997879,17.192519 river034_basin=SAd river034_country=Albania river034_distribution=Avg 54, P05 2.2, Q1 6.5, Q2 41.8, Q3 81, P95 157, river034_name=Seman+Shkumbini river034_position=41.050677,19.526251 river035_basin=SAd river035_country=Italy river035_distribution=Avg 0.0, P05 0.0, Q1 0.0, Q2 0.0, Q3 0.0, P95

The number of HD (high definition) TV accesses in households in the United Kingdom (UK) increased over the period from the first quarter of 2010 to the third quarter of 2020. The increase was about ** million accesses over the given period. The number of accesses does not represent the number of HD TV sets in the household, but the number of members of the household that have access. With the exception of the third quarter of 2016, the number of individuals that had access to HD TV's increased quarterly between 2010 and 2020.

Mean ± sd or median with the 25% quartile (Q1) and 75% quartile (Q3) for each of the behavioural variables.

Every investment carries a risk, and in an uncertain economic environment, this risk can cost investors. In almost all major markets surveyed, the risk premium for office investments decreased between the first and the third quarter of 2022. One of the reasons for the decrease is the rising borrowing costs due to soaring inflation. Dubai had the highest risk premium amounting to **** percent, whereas Singapore, Seoul and Hong Kong had negative risk premiums. What this means is that investors in these markets are hypothetically likely to make higher returns from investment in government bonds than offices.

This statistic shows the percentage of survey respondents that have digital television via aerial or Freeview in their household in Great Britain from the third quarter of 2010 to the third quarter of 2019. ** percent of respondents had digital TV in the third quarter of 2010. The first quarter of 2019 showed that ** percent of respondents reported to have digital TV.Digital television is the transmission of audio and video by means of a digitally processed and multiplexed signal. When it comes to its use, the number of households with digital television sets was increasing throughout the years in the United Kingdom (UK).

Mental Health Minimum Data Set (MHMDS) is a regular return of data from providers of NHS funded adult secondary mental health services, produced during in the course of delivering services to patients. Providers currently submit data every quarter. From Quarter 1 2011/12, a new version of MHMDS (version 4) includes new data items and is processed using a new system. Some of the changes have been introduced to support the implementation of of Payment by Results for mental health. Note: Incomplete reference data was used in the production of the data quality measure (DQM9) for NHS Occupation Code, which means that the published measure for Q1, Q2 and Q3 final quarterly data, for this measure, are unreliable. The reference data has been updated for the Q4 reports. Southwark PCT (5LE) was not able to submit a quarter 1 provisional Community Mental Health Activity return. This is likely to have had a negligible effect on England totals for each indicator, but a more pronounced effect on London Strategic Health Authority totals. Please see the background data quality report for further details.

The Gull Alpha Power Lomax distribution is a new extension of the Lomax distribution that we developed in this paper (GAPL). The proposed distribution’s appropriateness stems from its usefulness to model both monotonic and non-monotonic hazard rate functions, which are widely used in reliability engineering and survival analysis. In addition to their special cases, many statistical features were determined. The maximum likelihood method is used to estimate the model’s unknown parameters. Furthermore, the proposed distribution’s usefulness is demonstrated using two medical data sets dealing with COVID-19 patients’ mortality rates, as well as extensive simulated data applied to assess the performance of the estimators of the proposed distribution.

According to the source, in the third quarter of 2023 the unemployment rate of Extremadura in Spain was 16.5 percent, thus recording almost one percent point more than during the same period of 2022.

Stiffness values (N/mm) in each sample of the four groups (T+T, L, L+T, O), and the values behind the mean, standard deviation (SD), 1st quartile (Q1), maximum, median, minimum, and 3rd quartile (Q3).

Last line shows the p−value for the influence of the condition on the results, and F and the effect size η2 if data are parametric.

Note: estimates do not include Hawaii, Alaska, or trees on non-forest land (e.g., agricultural trees and urban parks).*AG = aboveground, BG = belowground, dead = standing and downed dead wood, SOC = soil organic carbon.

Healthcare Waste (HCW) is a special waste produced in healthcare institutions, including hospitals. It has a high potential for infection and injuries. The issue of waste disposal is growing as the number of hospitals, clinics, and diagnostic laboratories in Nepal continues to increase. The study aimed to assess the adherence to healthcare waste management (HCWM) practices and knowledge among waste handlers at the government district hospitals of Madhesh Province of Nepal which lies in the southern part of the country. A cross-sectional mixed-method study design was employed to assess the adherence of healthcare waste management practices and knowledge of healthcare waste management guideline among HCW handlers from 10 district-level hospitals in Madhesh Province. We developed a semi-structured questionnaire from Nepal’s National HCWM Guideline 2014 and the World Health Organization HCWM Rapid Assessment Tool 2011, to interview 60 HCW handlers for quantitative information. Then 10 key informant interviews were conducted using KII guidelines with related stakeholders of district hospitals of Madhesh Province. A four-point Likert scale was used to assess the practices and knowledge of HCW handlers and health facility-related factors. Descriptive data analysis was presented in tables for frequency, percentage, mean, and standard deviation, and correlation was presented in Graphs. A thematic analysis was performed for qualitative data by using RQDA and discussing the findings before concluding the study. Among the sixty participants, the median age was thirty-five years while thirty percent were less than the median age. Among total participants, the majority of female were 65% and almost all of them (96.67%) were married. The majority (65%) were females and almost all (96.67%) were married. About one-third (36.67%) of participants were illiterate. Most of the participants had experience of 5 to 10 years. The mean adherence of HCWM was 74.88±9.66 SD. Among the participants, half of them had adequate knowledge while the median knowledge of HCWM was 39 and the inter-quartile range was 5 (q3 = 41, q1 = 36). The mean of the HCWM practice was 24.18±5.96. The median of health facilities-related factors was 13 and the interquartile range was 3 (q3 = 15 and q1 = 12). The full adherence to HCWM guideline 2014 was extremely low among healthcare waste handlers. The HCWs had less adequate knowledge of HCWM and they did not practice to manage HCW adequately in district hospitals. However, the hospitals had adequately provided amenities to manage healthcare waste.

*RMSEP – root mean squared error of prediction; Q1 – first quartile; Q3 – third quartile.

All sounds analysed are pooled together. Q3 Freq = Q3 frequency; Peak Freq = peak frequency; Q1 Freq = Q1 frequency.