Daily staffing trends and variation in Swiss long-term care from 2018–2023: A retrospective longitudinal analysis

Temporal patterns of ungulate-vehicle collisions in Lithuania

In order to investigate the sources and effects of atmospheric volatile organic compounds (VOCs) in the southeast coastal region and to formulate effective ozone control policies, 96 VOCs were measured in the southeast coastal region from September to October 2016 using an online gas chromatograph-mass spectrometer/flame ionization detector (Online-GC-MS/FID). The composition, daily trends, sources, and contribution to ozone production of 96 VOCs in the southeast coastal region were analyzed. The results of the study showed that the volume mixing ratio of alkanes was the highest, followed by aromatic hydrocarbons; the daily variation trend of alkanes and aromatic hydrocarbons was obvious, with a double-peaked feature; propane, isoprene, and aromatic hydrocarbons in the atmosphere of the southeast coastal region came from liquefied petroleum gas (LPG), natural sources, and industrial emissions, respectively. The analysis of the activity of VOCs shows that aromatic hydrocarbons and alkanes are the most important contributors to OFP in the southeast coastal region, with toluene, m-/paraxylene, and ethylene being the species that contribute the most to OFP. Therefore, the control of VOC emissions from anthropogenic sources is a priority for ozone pollution control in the southeast coastal region in the future.

High blood levels of endotoxin on admission to the intensive care unit are predictive of adverse outcomes, including organ failure and death. However, the significance of changes in endotoxin levels over time has not been evaluated. We examined whether dynamic daily changes in endotoxin levels resulted in the development of greater organ dysfunction over time in critically ill patients. The study was a retrospective analysis of data from the longitudinal phase of a prospective observational multicenter cohort study of endotoxin levels in patients admitted to the intensive care unit. We analyzed 345 patients. Daily variation in endotoxin levels was assessed by calculating the number of inflections in the curve generated by plotting endotoxin levels against time. The degree of organ dysfunction over time was analyzed using a calculation of the total area under the curve generated by plotting the Multi Organ Dysfunction Score against time. From 1,301 endotoxin activity assay results, patients with dynamic daily variation in endotoxin levels as measured by a greater number of inflections had a greater degree of total organ dysfunction as measured by Multi Organ Dysfunction Score against time (P < 0.05). The arithmetic mean standard deviation of endotoxin activity assay results increased stepwise in the zero, one, and two inflection groups supporting the association between inflections and variability. Endotoxin activity assay variability was found to be independent of infection status (P = 0.52). Daily dynamic variation in endotoxin levels is a marker of increased severity of illness as measured by burden of total organ dysfunction over time. Further studies are warranted to assess the role of daily variation in endotoxin levels in the pathogenesis and potential therapy of organ failure in the critically ill.

Humpback whales ( Megaptera novaeangliae ) are well known to be particularly active acoustically. In 2007 the research team started to investigate humpback whales in the breeding area in the Sainte Marie channel (Madagascar). An array of 2 synchronous hydrophones was deployed in 2012 as part of a feasibility study for the deployment of a permanent acoustic array in the channel for the research programme, BAOBAB. Eight continuous sets (mean duration: 27 h 15 min) were recorded between 3 August and 11 September. Stereophonic recording allows the discrimination of acoustic sources that are not spatially overlapping, as the bearing to the emitting whale can be estimated from the Time Difference Of Arrival between the sensors. Based on cross-correlation functions analyses, this paper proposes an assessment of the number of emitting whales in a given underwater soundscape, and changes in their numbers over the covered time period. The first results showed that this value changed between 0 and 10 singers over the survey period of more than a month, and a peak in density was observed in the third week of August. Daily trends indicated highest density at night and lowest density just before nightfall. The study attempts to link acoustic activity and spatial occupation to reveal interactions between the detected emitting whales.

Meteorological influences on daily variation and trend of summertime surface ozone over years of 2015–2020: Quantification for cities in the Yangtze River Delta

Average staffing measures are a focus of nursing homes' quality assessments and reporting. They may, however, mask daily variation in staffing, additional information that could be important for understanding nursing home quality and relative ranking. To examine daily variation in staffing, its association with quality, and whether daily variation provides information regarding quality ranking of nursing homes over and above the information provided by average staffing levels. This quality improvement study included registered nurses (RNs) and certified nurse aide (CNAs) at 13 339 certified nursing homes throughout the United States during 2017 to 2018. Retrospective analyses of the Payroll-Based Journal, Medicare Cost Reports, and Nursing Home Care Compare were conducted. Data were analyzed from January 2017 to December 2018. Three measures of daily variation, ie, coefficient of variation (COV), total outlier days (TOD), and low outlier days (LOD), were calculated for RNs and CNAs. The association between these measures and quality rankings and other facility characteristics were evaluated. A total of 13 339 nursing homes were included in this study, with 9476 (71%) for-profit facilities. The mean (SD) hours-per-resident-day were 0.41 (0.29) for RNs and 2.16 (0.49) for CNAs, and a mean (SD) 55% (26%) of residents were Medicaid beneficiaries. Outcome measures were as follows: mean (SD) COV, 0.5 (0.6) for RNs and 0.1 (0.1) for CNAs; mean (SD) TOD, 220 (69) for RNs and 44 (45) for CNAs; and mean (SD) LOD, 116 (45) for RNs and 22 (24) for CNAs. All 3 variation measures, for both RNs and CNAs, were significantly associated with both the 5-Star Quality Measures (COV among RNs, -0.014 [95% CI, -0.021 to -0.007]; P < .001; COV among CNAs: -0.004 [95% CI, -0.006 to -0.003]; P < .001; TOD among RNs, -3.79 [95% CI, -4.59 to -2.99]; P < .001; TOD among CNAs, -2.52 [95% CI, -3.08 to -1.96]; P < .001; LOD among RNs, -2.46 [95% CI, -3.03 to -1.88]; P < .001; LOD among CNAs, -1.29 [95% CI, -1.58 to -0.99]; P < .001) and the 5-Star Survey rankings (COV among RNs,-0.026 [95% CI, -0.033 to -0.019]; P < .001; COV among CNAs: -0.006 [95% CI, -0.007 to -0.004]; P < .001; TOD among RNs, -5.10 [95% CI, -5.97 to -4.23]; P < .001; TOD among CNAs, -4.16 [95% CI, -4.77 to -3.55]; P < .001; LOD among RNs, -3.04 [95% CI, -3.65 to -2.44]; P < .001; LOD among CNAs, -1.97 [95% CI, -2.29 to -1.65]; P < .001) published in Nursing Home Care Compare. Low κ values, ranging from 0.23 to 0.63, indicated that the variation measures add information about ranking to the information provided by average staffing measure. These findings highlight the importance of reporting daily variation in staffing to improve understanding of the relationship between staffing and quality. They suggest that 2 facilities with the same average staffing achieve different quality of resident care and survey ratings in association with on the day-to-day variation in staffing. Measures of daily staffing may enhance the value of Nursing Home Care Compare for nursing homes and others engaged in quality improvement and consumers searching for high quality nursing homes.

Daily and seasonal variation of CO in the city of Rome in relationship with the traffic volume

Meteorological pressure at Gale crater from a comparison of REMS/MSL data and MCD modelling: Effect of dust storms

The urban thermal environment has become a challenging issue. Streets, as an essential public space in cities, can be laid out in such a way to effectively alleviate a problematic urban thermal environment. In this study, we calculated the layout characteristics of various types of streets in the study area based on network big data combined with field research. We used ENVI-met thermal environment numerical simulation technology to simulate the thermal environment, with a multiple linear regression model to explore the internal relationship between the street-level thermal environment of each land use type and its layout factors. The results indicate that streets in the study area are primarily oriented north–south and east–west, with distinct layout differences among various street types. Additionally, the daily variation trend of thermal comfort PMV of all street types is similar, though north–south streets show a larger daily variation than east–west streets. Lastly, the optimal thermal environment layout differs for each type of street; the relationship between street form and thermal comfort PMV also varies across different street types. This study provides scientific evidence to better understand the relationship between a street’s layout and its thermal environment, offering a theoretical basis for street layout planning aimed at mitigating the urban thermal environment.

The atmospheric electric field (AEF) is an important electrical phenomenon that occurs between the Earth’s surface and the ionosphere. It is primarily formed by negative charges on the surface and positive charges in the ionosphere, and is regulated by multiple factors, such as solar activity, meteorological conditions, and air pollution. On the basis of AEF’s observational data from fair weather days in the Chenggong district of Yunnan, this study analyzes its daily variation trends, focusing on its correlations with the air quality indicator concentrations of PM2.5 and PM10, solar irradiance and meteorological parameters. The results indicate that the daily average variation in the AEF presents a typical “W” shape, During the period from 00:00 to 08:00, AEF exhibits a strong positive correlation with PM2.5 and maximum wind speed. From 08:00 to 17:00, AEF shows a strong positive correlation with solar irradiance. Between 17:00 and 23:00, AEF demonstrates a strong positive correlation with PM2.5, PM10, and relative humidity (RH), and a strong negative correlation with maximum wind speed. At night, when there is no irradiance, the increase in PM2.5 and PM10 concentrations leads to a significant increase in the AEF. We believe this is due to aerosol particles adsorbing charged ions from the air, which decreases the local conductivity and causes an increase in the AEF. During the day, from 08:00 to 17:00, the changes in the AEF are synchronous with solar irradiance, both of which exhibit a trend of first increasing and then decreasing. This is attributed primarily to the enhanced atmospheric thermal convection and turbulence caused by irradiance, which drive fluctuations in near-surface water vapor and aerosol concentrations, leading to a gradual increase in the AEF from 08:00 to 12:00. Subsequently, from 12:00 to 17:00, as the irradiance and PM2.5 concentrations decline, the decrease in aerosol concentrations results in a corresponding reduction in the atmospheric electric field. This study provides strong evidence for more deeply understanding the relationship between aerosol concentrations and the near-surface AEF.

Remote monitoring of soil evaporation and soil water status is necessary for water resource and environment management. Ground based remote sensing can be the bridge between satellite remote sensing and ground-based point measurement. The primary object of this study is to provide an algorithm to estimate evaporation and soil water status by remote sensing and to verify its accuracy. Observations were carried out in a flat field with varied soil water content. High-resolution thermal images were taken with a thermal camera; soil evaporation was measured with a weighing lysimeter; weather data were recorded at a nearby meteorological station. Based on the thermal imaging and the three-temperatures model (3T model), we developed an algorithm to estimate soil evaporation and soil water status. The required parameters of the proposed method were soil surface temperature, air temperature, and solar radiation. By using the proposed method, daily variation in soil evaporation was estimated. Meanwhile, soil water status was remotely monitored by using the soil evaporation transfer coefficient. Results showed that the daily variation trends of measured and estimated evaporation agreed with each other, with a regression line of y = 0.92x and coefficient of determination R(2) = 0.69. The simplicity of the proposed method makes the 3T model a potentially valuable tool for remote sensing.

BackgroundIgE sensitisation to Aspergillus fumigatus is seen in a significant proportion of patients with refractory asthma. The EVITA3 study recently showed that three months’ treatment with voriconazole did not improve...

PURPOSE: To study the daily variations in bladder and rectum during a course of definitive prostate radiotherapy and evaluate the necessity of adaptive planning for patients. METHODS and MATERIALS: Two patients who finished the hypofactionated radiotherapy with the prescribed dose 60 Gy in 20 fractions were selected to have the retrospective analysis. Daily MV‐CT images were obtained and registered. Contours of bladder, rectum and prostate were redrawn. The delivered doses of bladder and rectum were recalculated. In order to sum the cumulative doses of these two organs, the demons algorithm was used to register the daily image sets to reconstruct the total delivered doses. RESULTS: The rectum volumes in simulation were 85.85 cc and 84.67 cc for patient 1 and 2 respectively. During treatment, their average volumes were 136.8 cc (±38.6) and 127.9 cc (±30.1). The mean rectum volume encompassed by the prescription dose was 14.3% less in patient 1 and 46.6% more in patient 2. The mean bladder volumes in treatment were 6.8% less (166.2 ± 18.7 cc) and 4.5% more (124.3 ±39.6 cc) for patient 1 and 2 compared to their simulation volumes. The bladder volumes encompassed by the prescription dose were 14.3% and 12.5% higher in patient 1 and 2 respectively. The demons algorithm was used to register the daily MV‐CT image set to the first fraction MV‐CT image set. CONCLUSIONS: Significant variations of bladder and rectum doses were found during the course of prostate treatment. These variations can be ‘blurred’ out during a protracted, fractionated radiotherapy because of the randomness of these variations. They can be significant in hypofractionation radiotherapy. Cumulative doses can be obtained through demons algorithm and adaptive plans can be generated by using the adaptive tool.

In recent years, the Beijing–Tianjin–Hebei region has become one of the worst areas for haze pollution in China. Sun photometers are widely used for aerosol optical property monitoring due to the advantages of fully automatic acquisition, simple maintenance, standardization of data processing, and low uncertainty. Research sites are mostly concentrated in cities, while the long-term analysis of aerosol optical depth (AOD) for the pollution transmission channel in rural Beijing is still lacking. Here, we obtained an AOD monitoring dataset from August 2017 to March 2019 using the ground-based CE-318 sun photometer at the Gucheng meteorological observation site in southwest Beijing. These sun photometer AOD data were used for the ground-based validation of MODIS (Moderate Resolution Imaging Spectroradiometer) and AHI (Advanced Himawari Imager) AOD data. It was found that MODIS and AHI can reflect AOD variation trends by sun photometer on daily, monthly, and seasonal scales. The original AOD measurements of the sun photometer show good correlations with satellite observations by MODIS (R = 0.97), and AHI (R = 0.89), respectively, corresponding to their different optimal spatial and temporal windows for matching with collocated satellite ground pixels. However, MODIS is less stable for aerosols of different concentrations and particle sizes. Most of the linear regression intercepts between the satellite and the photometer are less than 0.1, indicating that the errors due to surface reflectance in the inversion are small, and the slope is least biased (AHI: slope = 0.91, MODIS: slope = 0.18) in the noon period (11 a.m.–2 p.m.) and most biased in summer (AHI: slope = 0.77, MODIS: slope = 1.31), probably due to errors in the aerosol model. The daily and seasonal variation trends between CE-318 AOD measurements in the Gucheng site and fine particulate observations from the national air quality site nearby were also compared and investigated. In addition, a typical haze–dust complex pollution event in North China was analyzed and the changes in AOD during the pollution event were quantified. In processing, we use sun photometer and satellite AOD data in combination with meteorological and PM data. Overall, this paper has implications for the study of AOD evolution patterns at different time scales, the association between PM2.5 concentrations and AOD changes, and pollution monitoring.

This paper introduces a new approach to forecast daily stock trends by merging structured and unstructured datasets. This study intends to reveal the effectiveness of using supplemental datasets for accurate prediction of stock prices. A set of features, which is seemingly highly correlated with daily stock price variations, are selected using random forest optimization technique. Stock-relevant keywords that are extracted from news articles are converted into a time-series dataset in terms of temporal frequency. Convolution neural network (CNN) based deep learning models are generated separately for stock trading data and keyword frequencies from news articles, and two CNN models are merged together for training input datasets. The analysis results show that merging two different datasets may generate the better forecasting results than using stock trading datasets only. Additional issues for future analysis and implementations are discussed.