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59 results for “air pressure”
Marcell Experimental Forest 30-minute air temperature, relative humidity, and barometric pressure, 2015 - ongoing
This data publication contains air temperature, relative humidity, and atmospheric pressure data collected at 30-minute resolution from 2015-ongoing at three long term meteorological monitoring stations at the Marcell Experimental Forest (MEF). The MEF is located in Itasca County, Minnesota and is operated and maintained by the USDA Forest Service, Northern Research Station.
Remote Sensing based Sea Surface partial pressure of CO2 (pCO2) and air-sea CO2 flux (FCO2) in the East China Sea (2003-2019)
<p>Based on <em>in situ</em> seawater <em>p</em>CO<sub>2</sub> data collected on 51 cruises/legs over the past two decades, a satellite retrieval algorithm for seawater <em>p</em>CO<sub>2</sub> was developed by combining the semi-mechanistic algorithm and machine learning method (MeSAA-ML). MeSAA-ML introduces semi-analytical parameters, including the temperature-dependent seawater <em>p</em>CO<sub>2</sub> (<em>p</em>CO<sub>2,therm</sub> ) and upwelling index (<em>UI<sub>SST</sub></em>), to characterise the combined effect of atmospheric CO<sub>2</sub> forcing, thermodynamic effects, and multiple mixing processes on seawater <em>p</em>CO<sub>2</sub>. Additionally, considering the biological effects and various sub-regional features, multiple ocean colour parameters were also used as inputs in XGBoost, the best-selected machine learning algorithm. Independent cruise-based data were used to validate the satellite-derived <em>p</em>CO<sub>2</sub>, which achieved excellent performance in this complicated marginal sea, with low root mean square error (RMSE=19.6 μatm) and mean absolute percentage deviation (APD=4.12%). Air-sea CO2 fluxes are calculated based on retrieved seawater <em>p</em>CO<sub>2</sub>. </p>
Remote Sensing based Sea Surface partial pressure of CO2 (pCO2) and air-sea CO2 flux (FCO2) in the South China Sea (2003-2019)
<p>The South China Sea (SCS) is one of the largest marginal seas worldwide. It includes a river-dominated, highly productive marginal sea on the north shelf and a wide, oligotrophic ocean-dominated basin with various dynamic sub-regions. Based on an <em>in situ</em> seawater partial pressure of CO<sub>2</sub> (<em>p</em>CO<sub>2</sub>) datasets of 44 cruises/legs collected for the last two decades in the SCS, we proposed a seawater <em>p</em>CO<sub>2</sub> retrieval algorithm by combining the semi-mechanistic and machine learning (ML) methods (MeSAA-ML). The parameter selection strategy was based on the mechanistic analysis of <em>p</em>CO<sub>2</sub> variation, separating impacts of thermodynamics, biological activities, water mixing, and the atmospheric CO<sub>2</sub> forcing. We set a few semi-analytical parameters: <em>p</em>CO<sub>2</sub><sub>_<em>therm</em></sub>, which was a proxy for the combined effect of thermodynamics and the atmospheric CO<sub>2</sub> forcing on seawater <em>p</em>CO<sub>2</sub>; an upwelling index (UI<em><sub>SST</sub></em>) and mixing layer depth (MLD) to characterize the multiple mixing processes; chlorophyll-a concentration (Chl-a) with remote sensing reflectance at 443 and 555 nm (Rrs(443) and Rrs(555)), which were the inputs to proxy the biological effect and other characteristics for distinguishing shelf, basin, and sub-regions. As the seawater <em>p</em>CO<sub>2 </sub>and atmospheric <em>p</em>CO<sub>2</sub> ( <em>p</em>CO<sub>2</sub><sup>air</sup>) have similar data values and characteristics in the vast SCS oligotrophic basin, it will cause instability of the model if one is input and the other is output; thus the difference between them (<em>Δp</em>CO<sub>2</sub><sup>sea-air</sup>) was set as the output, and the seawater <em>p</em>CO<sub>2</sub> was obtained finally by summing <em>p</em>CO<sub>2</sub><sup>air </sup>and <em>Δp</em>CO<sub>2</sub><sup>sea-air</sup>. We compared several ML models, and the XGBoost model was confirmed as the best model. Completely independent cruise-based and observed datasets from Southeastern Asia Time-series Study (SEATS) were used to validate the satellite products, with low root mean square error (RMSE = 11.69 μatm) and mean absolute percentage deviation (APD = 1.59%). The increasing trend of satellite-derived <em>p</em>CO<sub>2</sub> (2.44 ± 0.24 μatm/yr) at the location of SEATS was found to be consistent with observed data. We presented that the SCS as a whole is a source of atmospheric CO<sub>2</sub>, releasing an average of 11.00 ± 2.45 Tg C/yr from a total area of 3.32 × 10<sup>6</sup> km<sup>2,</sup> and the northern shelf is a sink (1.69 ± 0.53 Tg C/yr). The area-integrated CO<sub>2</sub> efflux over the entire SCS may decrease with a rate of 0.34 Tg C/yr during 2003–2019. This high-accuracy dataset with 1 km resolution provides a refined understanding of the air-sea CO<sub>2</sub> exchange dynamics in the SCS during 2003–2019.</p>
Remote Sensing based Sea Surface partial pressure of CO2 (pCO2) and air-sea CO2 flux (FCO2) in the East China Sea (2003-2019)
<p>Based on <em>in situ</em> seawater <em>p</em>CO<sub>2</sub> data collected on 51 cruises/legs over the past two decades, a satellite retrieval algorithm for seawater <em>p</em>CO<sub>2</sub> was developed by combining the semi-mechanistic algorithm and machine learning method (MeSAA-ML). MeSAA-ML introduces semi-analytical parameters, including the temperature-dependent seawater <em>p</em>CO<sub>2</sub> (<em>p</em>CO<sub>2,therm</sub> ) and upwelling index (<em>UI<sub>SST</sub></em>), to characterise the combined effect of atmospheric CO<sub>2</sub> forcing, thermodynamic effects, and multiple mixing processes on seawater <em>p</em>CO<sub>2</sub>. Additionally, considering the biological effects and various sub-regional features, multiple ocean colour parameters were also used as inputs in XGBoost, the best-selected machine learning algorithm. Independent cruise-based data were used to validate the satellite-derived <em>p</em>CO<sub>2</sub>, which achieved excellent performance in this complicated marginal sea, with low root mean square error (RMSE=19.6 μatm) and mean absolute percentage deviation (APD=4.12%). Air-sea CO2 fluxes are calculated based on retrieved seawater <em>p</em>CO<sub>2</sub>. </p>
Adiabatic flame temperatures of common fuels in air at constant pressure
<p><strong>Adiabatic flame temperatures of common fuels in air</strong> <strong>at constant pressure</strong></p> <p>Junjie Chen</p> <p>Department of Energy and Power Engineering, School of Mechanical and Power Engineering, Henan Polytechnic University, 2000 Century Avenue, Jiaozuo, Henan, 454000, P.R. China</p> <p>Contributor: Junjie Chen, ORCID: 0000-0002-5022-6863, E-mail address: koncjj@gmail.com</p> <p> </p> <p>In the study of combustion, the adiabatic flame temperature is the temperature reached by a flame under ideal conditions. It is an upper bound of the temperature that is reached in actual processes. There are two types adiabatic flame temperature: constant volume and constant pressure, depending on how the process is completed. The constant volume adiabatic flame temperature is the temperature that results from a complete combustion process that occurs without any work, heat transfer or changes in kinetic or potential energy. Its temperature is higher than in the constant pressure process because no energy is utilized to change the volume of the system.</p> <p>Fuel, Oxidizer, Adiabatic flame temperature (degrees Celsius)</p> <p>Acetylene Air 2500</p> <p>Butane Air 4074</p> <p>Ethane Air 1955</p> <p>Ethanol Air 2082</p> <p>Gasoline Air 2138</p> <p>Hydrogen Air 2254</p> <p>Magnesium Air 1982</p> <p>Methane Air 1963</p> <p>Methanol Air 1949</p> <p>Naphtha Air 4591</p> <p>Natural gas Air 1960</p> <p>Pentane Air 1977</p> <p>Propane Air 1980</p> <p>Methylacetylene Air 2010</p> <p>Toluene Air 2071</p> <p>Kerosene Air 2093</p> <p>Bituminous Coal Air 2172</p> <p>Anthracite Air 2180</p>
Data for "Hunting for gravity waves in non-orographic winter storms using 3+ years of regional surface air pressure networks and radar observations"
<p>These data are shown in the figures included with the article "Hunting for gravity waves in non-orographic winter storms using 3+ years of regional surface air pressure networks and radar observations," submitted to Atmospheric Chemistry and Physics.</p>
X-ray micro-computed tomography of mushrooms during the instant controlled pressure drop (DIC) combined hot air drying
<p>These videos present the microstructure evolution of the <em>shiitake </em>mushrooms during the instant controlled pressure drop (DIC) combined hot air drying as well as the comparison of the microstructure of dried mushrooms treated by different drying methods.</p> <p>Fresh-skin and fresh-lamella indicate fresh mushroom cubes including skin and lamella parts respectively.</p> <p>DIC-skin and DIC-lamella indicate DIC treated mushroom cubes including skin and lamella parts respectively.</p> <p>DIC-HA35 dried-skin, DIC-HA65 dried-skin, and HA35 dried-skin indicate mushroom cubes including skin parts that were dried by DIC combined hot air drying at 35 ℃, DIC combined hot air drying at 65 ℃ and hot air drying at 35 ℃, respectively.</p>
Continuous data of air temperature, relative humidity, and air pressure collected at Kyoto University in January 2022
<p>I present the continuous data of air temperature, relative humidity, and air pressure collected at the Kyoto-A gravity measurement room (latitude: 35.02938 N, longitude: 135.78347 E, elevation: 60.82 m), Graduate School of Science, Kyoto University in January 2022. The values of air temperature, relative humidity, and air pressure were obtained every 1 second by a BME280 sensor (Bosch Sensortec GmbH) on a RT-USB-THP module (RT Corporation). The data were time-stamped and recorded using the Tera Term software on a Windows PC, which was synchronized in time with the NTP server of NICT (ntp.nict.jp).</p> <p>photo.zip contains the photographs of the Kyoto-A gravity measurement room taken in December 2022. The RT-USB-THP module and the Windows PC were located on the basement rock of the Kyoto-A absolute gravity point.</p> <p>data.zip contains the continuous data of air temperature, relative humidity, and air pressure collected at the Kyoto-A gravity measurement room. Each file of 2201??.txt stores the data obtained from 11:55 JST of the corresponding day to 11:54 JST of the next day, according to the operating time of the Tera Term software. The columns in 2201??.txt are as follows from left to right: year, month, day, hour, minute, second, air temperature [degC], air pressure [hPa], and relative humidity [%]. Note that 220101.txt was not recorded due to the failure of the Windows PC on January 1, 2022.</p> <p>figure.zip contains the graphs of air temperature, relative humidity, and air pressure drawn using the GMT4 software. Each graph of 2201??.png shows the time variation in air temperature, relative humidity, and air pressure from 0:00 JST to 24:00 JST of the corresponding day. tonga.png shows the air pressure variation from 12:00 JST on January 15 to 11:54 JST on January 16, and its original data is available as 220115.txt in data.zip. The pressure change of about 2 hPa was observed around 20:40 JST on January 15, associated with the propagation of the Lamb wave generated by the massive eruption of the Hunga Tonga-Hunga Ha’apai volcano.</p>
Pressure Ulcer Incidence Cat. II+ on a Static Air Mattress: a Multicenter Cohort Study in Nursing Home Residents at Risk
ClinicalTrials.gov study NCT03725787. IPD Sharing: UNDECIDED. Countries: 1. Publications: 2.
RESTART the Prevention of Pressure Ulcers: Comparing the Effectiveness and Cost of a Reactive Static Air Mattress and Alternating Air Pressure Mattress
ClinicalTrials.gov study NCT06294327. IPD Sharing: UNDECIDED. Countries: 1. Publications: 4.
Laparoscope Combined Ureteroscopic Air-pressure Ballistic Lithotripsy to Treat Patients With Hepatolithiasis
ClinicalTrials.gov study NCT02127242. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Air Leakage Under Continuous Positive Airway Pressure
ClinicalTrials.gov study NCT03381508. IPD Sharing: Not stated. Countries: 1. Publications: 2.
LMA Unique TM & the Self Pressurized Air-Q TM Intubating Laryngeal Airway in Children
ClinicalTrials.gov study NCT01385761. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Accuracy of Using the CPAP Technique to Eliminate Air Leak and Prevent Excessive Endotracheal Tube Cuff Pressures
ClinicalTrials.gov study NCT01384318. IPD Sharing: Not stated. Countries: 1. Publications: 3.
The Clinical Efficacy of Supraglottic Airway Device, Self-pressurized Air-Q Intubating Laryngeal Airway (ILA -SP) in Adult Patients: Comparison With Classic-laryngeal Airway (c-LMA)
ClinicalTrials.gov study NCT02206438. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Comparing Endotracheal Tube Cuff Pressure in Laparoscopic Abdominal Surgery: Saline vs. Air Inflation
ClinicalTrials.gov study NCT06089187. IPD Sharing: NO. Countries: 1. Publications: 1.
Comparison of Static Air Support Devices (Repose®) and Alternating-Pressure Devices in the Prevention of Pressure Ulcers
ClinicalTrials.gov study NCT03597750. IPD Sharing: UNDECIDED. Countries: 1. Publications: 2.
Comparison of Spontaneous Ventilation Time in Air Without Desaturation After Positive Pressure Extubation Versus Aspiration Extubation When Awakening From General Anesthesia
ClinicalTrials.gov study NCT01323049. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Air Cold Atmospheric Pressure Plasma Treatment for Acceleration of Venous Ulcer Healing
ClinicalTrials.gov study NCT05894096. IPD Sharing: YES. Countries: 1. Publications: 12.
Pressure Support Ventilation Versus Continuous Positive Air Way Pressure (CPAP) Using I Gelin Adult Patients,
ClinicalTrials.gov study NCT05116839. IPD Sharing: NO. Countries: 1. Publications: 11.
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