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1,265 results for “discharges”
Potential for shoreline recession to accelerate discharge of groundwater pollutants to coastal waters
<p>Included in this repository are supporting field data used to produce the results of the manuscript submitted to Water Resources Research:</p> <p>Sabina Rakhimbekova, Christopher Power, Denis M. O’Carroll, Clare E. Robinson, (2023) "<strong>Potential for shoreline recession to accelerate discharge of groundwater pollutants to coastal waters"</strong></p>
Time-to-Event analysis of factors influencing delay in discharge from a subacute Complex Discharge Unit during the first year of the pandemic (2020) in an Irish tertiary centre hospital
<p><strong>Figure S1:</strong> Forest plots 1 and 2 depicting Age and Gender strata associated Hazard ratio (Markers) estimates (95% Confidence Interval demonstrated by horizontal line) exhibited statistically significant results for individuals <65 years of age who had a delay in discharge due to complications from comorbidities; those in 65-75 years of age category, had prolonged LOS due to admission with frailty, falls and/or integrated rehabilitation needs; and 75-85 years of age category showed an association of at least 4 out of the 5 common delaying factors. Strata Gender exhibited a significant delay in discharge due to complications from comorbidities and patient-centred needs; in comparison to the female gender who also experienced a delay in discharge as a result of both factors alongside frailty, falls and/or integrated rehabilitation needs.<strong>[A. </strong>Complications/comorbidities prolonging discharge, <strong>B.</strong> Healthcare-associated infection, <strong>C</strong>. Frailty, falls and/or integrated rehabilitation needs, <strong>D</strong>. Patient-centred needs, <strong>E</strong>. Community services]. </p> <p><strong>Figure S2:</strong> Forest plot 3 depicting Multimorbidity (MM) strata-associated Hazard ratio (Markers) estimates (95% Confidence Interval demonstrated by horizontal line) exhibited a significant delay in discharge due to complications from comorbidities, frailty, falls, and/or integrated rehabilitation and patient-centred needs in patients with ≤4 MM. In contrast patients with >4 MM experienced significant delays in discharge due to complications from comorbidities and patient-centred needs. <strong>[A. </strong>Complications/comorbidities prolonging discharge, <strong>B.</strong> Healthcare-associated infection, <strong>C</strong>. Frailty, falls and/or integrated rehabilitation needs, <strong>D</strong>. Patient-centred needs, <strong>E</strong>. Community Services].</p>
The influence of electric circuit parameters on NOx generation by transient spark discharge _ data
<p>dataset for</p> <p>The influence of electric circuit parameters on NOx generationby transient spark discharge</p> <p> </p> <p>Abstract</p> <p>Nitrogen fixation, production of NO and NO<sub>2</sub> from N<sub>2</sub> and O<sub>2</sub> in air, has been investigated with<br> transient spark self-pulsing DC discharges. NO production is boosted by the addition of capacitors<br> and an inductor to the electrical circuit which drives the discharge. The quantity of NO produced<br> per joule of electrical input energy is doubled, though the quantity of NO<sub>2</sub> produced drops. The<br> yield of NO is also increased because the modified circuit enables higher discharge currents to be<br> used. NO concentrations as high as 2000 ppm were obtained with input energy densities of around<br> 300 J per litre of input gas, whilst NO<sub>2</sub> concentrations were around 150 ppm. This simple<br> modification of the driving circuit may have potential for optimizing the plasma chemistry with<br> other input gas mixtures and for scaling up nitrogen fixation from air.</p>
Acoustic activity of bats at power lines correlates with relative humidity: A potential role for corona discharges
<p><span>With the ever-increasing dependency on electric power, electrical grid networks are expanding worldwide. Bats exhibit a wide diversity of foraging and flight behaviours, and their sensitivity to anthropogenic stressors suggests this group is very likely to be affected by power lines in a myriad of ways. Yet the effects of power lines on bats remain unknown. Here we assessed the responses of insectivorous bats to very-high-voltage power lines (>220 kV, VHVPL). We implemented a paired sampling design and monitored bats acoustically at 25 pairs, one pair consisting of one forest edge near to VHVPL matched with one control forest edge. Relative humidity mediates the effects of power lines on bats: we detected bat attraction to VHVPL at high relative humidity levels and avoidance of VHVPL by bats at low relative humidity levels. We argue that the former could be explained by insect attraction to the light emitted by VHVPL due to corona discharges while the latter may be due to the physical presence of pylons/cables at foraging height and/or because of electromagnetic fields. Our work highlights the response of bats to power lines at foraging habitats, providing new insight into the interactions between power lines and biodiversity.</span></p>
Hourly discharge database HydroCH
<p>Hourly discharge data covering the whole hydrological Switzerland (i.e. 291 catchments in Switzerland plus those in the neighbouring countries: Austria, France, Germany and Italy).</p> <p>Note: all coordinates in <a href="https://zenodo.org/api/files/4dc63f3a-fbba-4acf-9012-24e1a71911c7/Inventory_discharge_hydroCH.xlsx?versionId=119521a1-889b-4ae8-b1a7-cfaad414c38c">Inventory_discharge_hydroCH.xlsx</a> are provided in the Swiss coordinate system CH1903/LV03 (EPSG: 21781)</p> <p>**## FORMAT ##**</p> <p>All data are in winter time = UTC+1=MEZ=CET, <br> and the notation for the hourly data is HH=(HH):00-(HH+1):00 </p> <p>=> this corresponds to the format used by the FOEN</p> <p><br> All files are saved with the ending **_Q_hourly.asc (see the overview file for all details about the stations and the corresponding file names)</p> <p><br> All data are are save in 5 columns separated by tabs (year, month, day, hour, 'Q.' followed by the ID6 name given to the station):</p> <p>YYYY MM DD HH Q.**stationID6** </p> <p>---------------------------------------------------------------------------------------------------------------------------------------------------</p> <p>**## OVERVIEW ##**</p> <p>Please find an overview of all available data in Inventory_discharge_hydroCH.xlsx</p> <p>---------------------------------------------------------------------------------------------------------------------------------------------------</p> <p>**## DATA SOURCES ##**</p> <p>Data were kindly provided for Switzerland and for the four neighbouring countries by</p> <p>Switzerland (CH): Swiss Federal Office of the Environment (FOEN), Ittigen, Switzerland;<br> data provided 17th April 2020<br> <br> Austria (A): Office of the Federal State of Vorarlberg, Division of Water Management, Bregenz, Austria (VRB);<br> data provided 20th August 2019<br> Data from the LAMAH-CE data set by Klingler et al.2021 (LAMAH)<br> <br> Germany (DE): Bavarian State Office for the Environment – Hydrographic Service, Munich, Germany (GKDB)<br> data downloaded from https://www.gkd.bayern.de/en/rivers/discharge/tables on 14th May 2019 and 5th October 2020;<br> State Agency for the Environment Baden-Württemberg – Hydrographic Service, Karlsruhe, Germany (LUBW)<br> data downloaded from http://udo.lubw.baden-wuerttemberg.de/public/p/pegel_messwerte_leer on 26th October 2020<br> <br> France (FR): french database of discharge Banque Hydro (https://www.hydro.eaufrance.fr/; <br> see also https://zenodo.org/record/3744183#.ZABfMnbMJGF<br> for scripted help in downloading (webscrapping) flow rates data from the french database);<br> data downloaded between December 2019 and January 2020<br> <br> Italy (I): Italy (I): ARPA Lombardia (the regional agency for environmental protection for the region Lombardia), Milano, Italy;<br> data provided in November 2020<br> ARPA Piemonte (the regional agency for environmental protection for the region Piemonte), Torino, Italy;<br> data provided in October 2020 </p> <p>---------------------------------------------------------------------------------------------------------------------------------------------------</p> <p>**## ADDITIONAL INFORMATION ##**</p> <p>Please find additional information in the document Zusatzinfos.docx (for now only in German)</p> <p>---------------------------------------------------------------------------------------------------------------------------------------------------</p> <p>**## REFERENCES ##**</p> <p>Klingler, C., Schulz, K., and Herrnegger, M.: LamaH-CE: LArge-SaMple DAta for Hydrology and Environmental Sciences for Central Europe, <br> Earth Syst. Sci. Data, 13, 4529–4565, https://doi.org/10.5194/essd-13-4529-2021, 2021</p>
Locating Manning-based Satellite Gauging Reach for River Discharge Estimation from Remotely Sensed Imagery
<p>River discharge is critical for understanding river hydrological condition and water resource management. With the advance of earth observation technologies, estimating river discharge through remote sensing using Manning's Equation has become more and more popular for filling the gaps in gauging observations. However, finding a Manning-based Satellite Gauging Reach (MSGR) that can successfully transfer satellite signals into river discharge based on the Equation is not easy and lacks proper guidance. Theredore, we provide a practical approach for locating MSGR.<br> The manuscript is currently under review by Water Resources Research.<br> This repositry is the data for reproducing the figures in our manuscript.</p> <p>This repositry consists of four part. They are the corresponding data and results at MSGR, CR_FSE, CR_SCT locations, and elevation values along river centerline for calculating riverbed slope.</p> <p>MSGR: Manning-based Satellite Gauging Reach<br> CR_FSE: comparison reach with unsatisfactory FSE<br> CR_SCT: comparison reach with unsatisfactory SCT<br> FSE: Fluctuation degree of surface water extent<br> SCT: Stability of Channel Terrain</p>
Most global gauging stations present biased estimations of total catchment discharge
<p>This dataset includes the data generated for the main figures, the codes of the Mabcd model used to simulate catchment hydrological processes in this study, and the neural network model (NN2) trained to evaluate the Budyko parameter ω.</p>
Assessment of the Quality of Life and Resilience in Ventilated Patients with COVID-19: Findings One year After Hospital Discharge
<p>Assessment of the Quality of Life and Resilience in Ventilated Patients with COVID-19: Findings One year After Hospital Discharge</p> <p>This dataset was collected in a prospective cohort study to evaluate the quality of life and resilience of patients managed with invasive mechanical ventilation due to severe COVID-19, one year after hospital discharge.</p> <p><strong>Methodology</strong></p> <p><strong>Design </strong></p> <p>A prospective cohort study was conducted, and the present study received approval from the Ethics Committee of Universidad del Rosario (DVO005-1957- CV1534). Informed consent was obtained from each patient or their legal caregiver, which was given via telephone.</p> <p><strong>Patients/Population</strong></p> <p>Data were collected from patients who received MV support for respiratory failure secondary to critical illness due to COVID-19. The study included consecutively patients discharged alive from the Hospital Universitario Mayor Méderi located in the city of Bogota, Colombia from March 19, 2020, to April 30, 2021. Patients who passed away after discharge, those with whom telephone communication was not feasible, individuals with cognitive impairment preventing them from responding to the survey without obtaining information from a caregiver, and those who declined to participate in the study (as indicated by the patient and/or caregiver) were excluded from the final analysis.</p> <p><strong>Intervention/Measurement</strong></p> <p>Participants were administered a structured questionnaire that included three scales: PCFS (Post-COVID-19 functional Status Scale), EQ-5D-3L (EuroQol 5 dimensions 3 levels) (administered via telephone application), and CD-RISC (Connor-Davidson Resilience Scale). Permission for the non-commercial use of the EQ-5D-3L scale was obtained from the EuroQol Customer Portal (Registration No. 55265).</p> <p><strong>Statistical Analysis</strong></p> <p>The general characteristics of the population and the results of the PCFS, EQ-5D-3L, and CD-RISC were described. Continuous variables were presented as mean and standard deviation (SD) or median and interquartile range (IQR) depending on the data distribution. Categorical variables were described using absolute and relative frequencies.</p> <p>The PCFS was reported using a grading system ranging from Grade 0, indicating patients who were able to resume their daily activities without limitations, to Grade 4, representing severe functional limitations.</p> <p>The five dimensions of the EQ-5D-3L were considered as numerical variables and scored as follows: 1 for no alteration, 2 for moderate alteration, and 3 for severe alteration. The total score was computed by summing the scores from each dimension, ranging from 5 (indicating optimal quality of life) to 15 (indicating severely compromised quality of life). To assess the usefulness of the PCFS in measuring QoL, Pearson correlations were performed between the total score of the EQ-5D-3L and each individual dimension with the PCFS score.</p> <p>The CD-RISC and its 25 dimensions were evaluated as well. Each dimension was treated as a numerical variable and scored as follows: 0 for no alteration, 1 for mild alteration, 2 for moderate alteration, 3 for severe alteration, and 4 for critical alteration. The total CD-RISC score ranged from 0 (indicating no resilience) to 100 (indicating the best possible resilience). Furthermore, it was hypothesized that patients with higher resilience, as measured by the CD-RISC scale, would have better quality of life (QoL). Pearson correlations were examined between the PCFS score and the total CD-RISC score.</p>
Validation of the EQ-5D-3L in Patients with Severe COVID-19 One Year After Hospital Discharge
<p>This is a prospective cohort study database aimed at validating the EQ-5D-3L in patients with COVID-19 who underwent invasive mechanical ventilation, assessing their quality of life one year after hospital discharge.</p>
Dielectric Barrier and gliding arc discharge plasma
<p>Picture of Dielectric Barrier and gliding arc discharge plasma test rig</p>
Data from: The combined effects of treated sewage discharge and land use on rivers
<p>Freshwater ecosystems are increasingly threatened by multiple anthropogenic stressors. Release of treated sewage effluent and pollution from agricultural or urban sources can independently reduce water quality with implications for ecological communities. However, our knowledge of the combined effects of these stressors is limited. We performed a field study to quantify the combined effect of treated sewage discharge and land use on nutrient concentrations, sewage fungus presence, and communities of macroinvertebrates and benthic algae. Over three seasons in four rivers we found that a model which included an interaction between sewage pollution and time of the year (i.e., months) was the best predictor of nutrient concentrations and the abundance of algae and sewage fungus. Both macroinvertebrate and algae communities shifted downstream of sewage input. Specifically, more tolerant groups, such as cyanobacteria and Oligochaetes, were more abundant. The EPT (Ephemeroptera, Plecoptera, and Tricoptera) water quality score was best explained by an interaction between month and agriculture in the surrounding landscape. Overall, our results show that sewage discharge has a significant impact on water quality and benthic riverine communities, regardless of the surrounding land uses. Agricultural inputs, however, could be more important than treated sewage discharge in reducing the abundance of sensitive invertebrate taxa. We need both improvements to wastewater treatment processes and reductions in agricultural pollution to reduce threats to vulnerable freshwater communities.</p>
Hydrological Turnover, discharge, net change of discharge, near stream gradients and variation of silicate concentrations - Hydrograph - two reaches of the Olewiger Bach, Germany.
<p>Measurements took place at the 'Olewiger Bach', a small low-mountain creek in Rhineland-Palatinate (Germany) at two distinct reaches with contrasting morphology from 2017 until 2022. The collected data consists of hydrological turnover data acquired by dilution gauging, discharge, net discharge changes, near stream groundwater gradients and the variation coefficient of silicate concentrations between the stream and groundwater. Hydrological turnover data regarding differences of reach sections and the resulting ratio of spatial turnover contribution of that reach is provided as well. In addition to that, hydrograph data of both reaches is provided in 6h interval, which was utilized to characterize reach drainage behavior via the calculation of the delayed flow index (DFI).</p>
DataSet: Partial Discharge Power Flow in Gas-Insulated Substations Using Magnetic and Electric Antennas
<p>Dataset of the measurements presented in the paper "Partial Discharge Power Flow in Gas-Insulated Substations Using Magnetic and Electric Antennas" in the conference ISH 2023</p>
Dataset: Magnetic and electric antennas synergy for partial discharge measurements in gas-insulated substations: Power flow and reflection suppression
<p>Data set for the publication named: Magnetic and electric antennas synergy for partial discharge measurements in gas-insulated substations: Power flow and reflection suppression. Each header corresponds to the figure and legend.</p>
Dataset: Magnetic and electric antennas calibration for partial discharge charge estimation in gas-insulated substations
<p>Data set for the publication named: Magnetic and electric antennas calibration for partial discharge charge estimation in gas-insulated substations</p>
Early Occupational Therapy Intervention in the Hospital Discharge After Stroke
ClinicalTrials.gov study NCT04835363. IPD Sharing: NO. Countries: 1. Publications: 1.
Value of Technology to Transfer Discharge Information
ClinicalTrials.gov study NCT00101868. IPD Sharing: Not stated. Countries: 1. Publications: 54.
Post-Discharge Smoking Cessation Strategies: Helping HAND 4
ClinicalTrials.gov study NCT03603496. IPD Sharing: NO. Countries: 1. Publications: 3.
Verification of the Safety of Early Discharge in Patients After Acute ST-segment Myocardial Infarction
ClinicalTrials.gov study NCT02023983. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Comparison of Pre- and Post-discharge Initiation of LCZ696 Therapy in HFrEF Patients After an Acute Decompensation Event
ClinicalTrials.gov study NCT02661217. IPD Sharing: NO. Countries: 19. Publications: 5.
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OpenNeuro
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