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52 results for “ignitions”
Discrete fire events, their severity, and their ignitions, as derived from MODIS MCD 14ML active-fire detection data for Indonesia, 2002-2019
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Soil loss on ignition for Saddle Stream Network, 2017
Soil loss on ignition (LOI), as a proxy for total soil organic matter, is an important control on surface hydrology. To measure soil LOI across the Niwot Ridge LTER Saddle Stream Catchment, surface (0 – 10 cm) soil samples (n = 84) were collected on roughly a 60 x 60 m grid, including sensor network sites. After drying, sieving to < 2mm, and grinding to a fine powder, a ~20 g subsample of soil was combusted at 550°C for 4 hours. LOI is reported as a percent difference between dry weights at 550°C and 105°C. The average LOI was 19.23%, but soils had a wide range from 4.80% to 46.77%. This dataset is useful for studies of spatial patterns in organic matter accumulation, biogeochemical process rates, and surface hydrology.
Investigation of lightning ignition characteristics based on an impulse current generator.
<p>Lightning strike is an important ignition source of forest fires. Artificial lightning discharge is a method for studying lightning fires. However, there is not enough data on the ignition of combustible materials caused by artificial lightning discharge. Previous studies on lightning ignition have focused on the heating and ignition effects of long continuing current (LCC), but the function of the impulse current that occurs before the LCC has not been taken into account. In this paper, an impulse current generator of 8/20 μs was used to simulate the ignition effect of impulse current on conifer needle beds. Different current waveforms have different ignition characteristics. We compared five kinds of conifer needle beds. The average of the current needed to ignite the needle bed of <i>Larix gmelinii (Ruprecht) Kuzeneva</i><i> </i>was the smallest, and the average of the breakdown voltage was the smallest for the needle bed of <i>Pinus massoniana Lamb</i>. The total energy input to the conifer needle beds was fitted as a multiple log-linear regression model. The heating energy proportion value varies with different bulk densities, current amplitudes, and moisture contents. Based on this data, the heating energy of the impulse current transferred to the needles can be predicted. This information in conjunction with previous research on LCC was used to derive a lightning ignition prediction model of the full waveform for conifer needle beds.</p>
Compiled bulk rock uranium, thorium and loss on ignition (LOI) concentrations in serpentinites and altered oceanic crust
<p>The dataset includes a compilation of published uranium, thorium and loss on ignition (LOI) concentrations for bulk rock serpentinite samples organized by tectonic setting. All references are included in the file.</p>
Ignition delay data of straight-chain alkanes
<p>The dataset has ignition delay values of butane, pentane, hexane, heptane, decane, nonane, decane, dodecane and hexadecane. The dataset contains below columns:</p> <p> Data_Source : The code associated with a source of the data (Data is obtained from multiple sources. This code is useful to identify the source)<br> Diluant Type : Diluant used Ar/N2<br> Diluant(%) : Percentage of diluant used<br> Equv(phi) : Equivalence ratio<br> Fuel : SMILE of fuel (useful to extract bonds)<br> Fuel(%) : Percentage of fuel used<br> Measured_wavelength(nm) : Species are measured at the wavelength<br> Mode_of_measurement : Type of technique used to measure Ignition delay time. (Species/Pressure/Temperature profile)<br> Oxidizer(%) : Percentage of oxygen supplied<br> P(atm) : Shock-tube Pressure<br> P_Error(%) : Error in measurement of pressure<br> Research_group : Indicates data associated with combustion group<br> Shocktube_dia(cm) : Diameter of shock-tube used to measure the ignition delay<br> Species_measurement_Error: Error in the measurement of species profile<br> Species_name : Species name by which ignition delay is measured<br> T(K) : Reported temperature at which ignition delay is measured<br> T_Error(%) : Error in measurement of temperature<br> Time(μs) : Ignition delay time (target variable)</p> <p> </p>
Data for '3D Radio Frequency Mapping and Polarization Observations Show Lightning is Ignited by Cosmic-ray Shower' by Shao et al., 2024
<p>Data for manuscript "<a name="_Hlk177653706"></a><strong><span>3D Radio Frequency Mapping and Polarization Observations Show Lightning is Ignited by Cosmic-ray Shower" <br></span></strong></p>
Controlling fire color, ignition and a shape with magnetic field
<p>Few interesting experiments about the effect of magnetic field on fire ignition color and size when using a mixture of ferroelectric material (Iron oxide) and combustible material (Parafin Oil, paraffin). The effect of flame size and ignition can probably be explained by spikes increasing the material surface area, and the iron oxide act as a wick (like in a candle). The color switch when adding Boric Acid (B(OH)3 and sea salt (NaCl) when applying a magnetic field is harder to explain.</p> <p><strong>Procedures:</strong></p> <p><br> <strong>a) Flame color changes when applying a magnetic field (no idea why this happens)</strong><br> 1) To a small glass, add 16 ml of alcoholic gel (Dr. Fischer Alco-Gel 70%) or paraffin oil.<br> 2) Add one spoon of table salt (NaCl). Add one spoon of boric acid (B(OH)3). Add one spoon of Magnetite (Fe3O4) powder and stir.<br> 3) Pour a drop of the mixture on a glass plate and ignite. Use the magnet below the plate to control the flame color. <br> 4) To change the flame color from blue to red, put the magnet directly below the flame. <br> 5) To change the flame color from red to blue, move the magnet sideways along the plate.<br> To extinguish the fire, cover the flame with a small bowl.</p> <p><strong>b) Using a magnetic field to control fire ignition extinguishing and flame size.</strong><br> Ferromagnetic wax preparation:<br> 1. Add two spoons of paraffin wax (or oil) to glass (4-5 grams) and heat until the wax melt (60-100C).<br> 2. Add two spoons of magnetite (Fe₃O₄) powder (6-7 grams) to the melted wax. <br> 3. Mix until getting a uniform black solution and cool until the wax solidifies.<br> Usage: 1. Put a small amount of the ferromagnetic wax on a glass plate. 2. When touched by flame, the material will not ignite unless exposed to a magnetic field. 3. To enable ignition put a magnet directly below the wax. 4. To increase flame size, put the magnet directly below the fire. 5. To reduce the flame size or to extinguish/suppress flame put a magnet below the fire and move it sideways along the plate (fast movements will extinguish the fire).</p> <p><strong>c) Controlling flame movement with magnetic field:</strong></p> <p> First, create a flammable magnetic material (most standard ferrofluid will do well for this) <br> The ferrofluid can be prepared by the following steps<br> 1) Add about 6 ml of flammable oil (Parafin Oil or WD-40 oil works great). <br> 2) Add one spoon (about 8 grams) of Magnetite (Fe3O4) powder or any other ferromagnetic powder. <br> 3) stir until you get a uniform black mixture<br> Experiment:<br> 1) Pour a drop of the mixture on a glass plate and ignite. <br> 2) Use a magnet from below the plate to control the flame. <br> 3) To extinguish the fire, cover the flame with a small bowl. </p>
Database on holdover time of lightning-ignited wildfires
<p>This database contains open, harmonized, and ready-to-use global data on holdover time. Holdover time is defined as the time between lightning-induced fire ignition and fire detection. The first version of the database is composed of three data files (censored data, non-censored data, ancillary data) and three metadata files (description of database variables, list of references, reproducible examples). These data were collected through a literature review of LIW studies and some datasets were assembled by authors of the original studies, covering more than 150,000 LIW from 13 countries in five continents and a time span of a century from 1921 to 2020. Censored data are the core of the database and consist of frequency data reporting the number or relative frequency of LIW per interval of holdover time. Ancillary data provide additional information on the methods and contexts in which the data were generated in the original studies. Potential contributors to the database are encouraged to contact the corresponding author in the readme file.</p>
A Study Evaluating Rocatinlimab in Moderate-to-severe Atopic Dermatitis (ROCKET-IGNITE)
ClinicalTrials.gov study NCT05398445. IPD Sharing: YES. Countries: 21. Publications: 2.
Data from: A novel approach for predicting the probability of ignition of palaeofires using fossil leaf assemblages
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Data from: The early spread and epidemic ignition of HIV-1 in human populations
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Investigation of lightning ignition characteristics based on an impulse current generator.
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Dataset of "Characterisation of thunderstorms that caused lightning-ignited wildfires" by Soler et al 2021 IJWF Ref WF21076
<p>Remote Sensing data from Servei Meteorològic de Catalunya in relation to the study </p> <p>"Characterisation of thunderstorms that caused lightning-ignited wildfires" by Soler et al 2021</p> <p>published in the InternationaL Journal of Wildland Fire Ref WF21076</p> <p>Dataset:</p> <p>Radar: reflectivity, VIL and Echotops 12 and 35, daily QPE (quantitative Precipitation Estimates)</p> <p>Lightning: IC and CG flashes</p> <p>Tracking vectors</p> <p>Daily summary maps</p>
IGNITE (Impact of Glucose moNitoring and nutrItion on Time in rangE)
ClinicalTrials.gov study NCT05516797. IPD Sharing: NO. Countries: 1. Publications: 0.
Ignite Pilot: Goal Setting in a Digital Weight Loss Intervention
ClinicalTrials.gov study NCT05715242. IPD Sharing: NO. Countries: 1. Publications: 0.
A Phase 2 IV Gallium Study for Patients With Cystic Fibrosis (IGNITE Study)
ClinicalTrials.gov study NCT02354859. IPD Sharing: YES. Countries: 1. Publications: 0.
Data from: Gap initiation with 20.35 mm: an initiator integrating the Al/CuOx multilayer film and traditional electronic plug to enhance the ignition ability
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ABoVE: Ignitions of ABoVE-FED Fires in Alaska and Canada
This dataset provides daily fire ignition locations and timing for boreal fires in Alaska, U.S., and Canada between 2001 and 2019. The fire ignition locations and timing are extracted from the ABoVE Fire Emission Database; however, the temperate prairies of Canada, the Atlantic Highlands, and Mixed Wood Plains were not included. Fires were detected from Landsat differenced normalized burn ratio (dNBR) and the daily MODIS burned area and active fire products. Detections by dNBR were limited to fire perimeters from national fire databases. Fire ignition locations were retrieved using a local minimum within the fire perimeters. However, when fire locations were confounded due to simultaneous active fire detections, the fire ignition location was set as the centroid of these pixels. A spatial uncertainty equaling the standard deviation of the pixels' coordinates and the nominal nadir of 1000 m was applied to the fire ignition location. The temporal resolution of the ignition timing is within one day. Data is provided in comma separated values (CSV) and shapefile formats.
ABoVE: Ignitions, Burned Area, and Emissions of Fires in AK, YT, and NWT, 2001-2018
This dataset provides estimates of daily burned area, carbon emissions, and uncertainty, and daily fire ignition locations for boreal fires in Alaska, U.S., and in the Yukon and Northwest Territories, Canada. The data are at 500 m resolution for the 18-year period from 2001-2018. Burned area was retrieved from combining fire perimeter data from the Alaskan and Canadian Large Fire Databases with surface reflectance and active fire data from the Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6. Per-pixel carbon consumption was estimated based on a statistical relationship between field estimates of pyrogenic consumption and several environmental variables. To derive the carbon consumption estimates, the approach from Alaskan Fire Emissions Database (AKFED) was updated and extended for the period 2001-2018. Fire weather variables, temperature, and the drought code complemented remotely sensed tree cover and burn severity as model predictors. Fire ignition location and timing were extracted from the daily burned area maps.
Piezo1-dependent activation of stromal cells ignites muscle inflammation in exercise and injury and is associated with inflammaging
GEO Series GSE297515. Mus musculus. 90 samples. Type: Expression profiling by high throughput sequencing.
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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