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Wind

Wind refers to the movement of air, driven by differences in atmospheric pressure.
It plays a crucial role in various scientific and industrial applications, including renewable energy, meteorology, and transportation.
The study of wind encompasses topics such as wind patterns, wind turbine design, and the impact of wind on the environment.
Researchers can optimize their wind-related studies by leveraging AI-driven comparisons using platforms like PubCompare.ai, which helps locate the best protocols and products from literature, preprints, and patents.
This enhances reproducibility, accuracy, and the ability to advance wind-related research.
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Most cited protocols related to «Wind»

We aimed to have a motor with the same aspirating power of the CDC-BP because blowers that have more suction generally injure or kill mosquitoes (Clark et al. 1994 (link)). We measured the aspiration power of a brand-new CDC-BP (John W.Hook, Gainesville, FL) and of the Prokopack at 0, 5, and 10 cm from end of collection cup by using a hand-held digital wind gauge (Kestrel 4000; Kestrel Meters, Sylvan Lake, MI). For each aspiration device and distance, we recorded the average wind speed over a 1-min interval for a total of 10 repetitions.
From 24 November 2008 to 11 March 2009, two combined sewer overflow (CSO) tunnels (Greensferry and Tanyard Creek) in Atlanta, GA, were visited to collect overwintering mosquitoes by using one Prokopack in the upper walls (above 1.5 m) and ceiling and one CDC-BP in the lower walls (<1.5 m). Seven 10-m sections of the tunnels (three in Greensferry and four in Tanyard) were carefully aspirated by three field technicians with the aid of flashlights to spot overwintering mosquitoes. Collection effort was fixed (≈20 min per tunnel section) for each aspirator. We aimed to assess how our collections could be improved by aspirating on the upper wall and ceiling. The tunnel concrete surface walls were uneven and required maneuvering around pipes and drains, the ceilings were high (up to 5 m), and some surfaces were partially wet. Collected mosquitoes were kept alive in glass breeding chambers (30×30×30 cm) containing a 10% sucrose solution and then identified by species and individually stored at −80°C for further virus testing.
During 7–22 May 2009, a paired trial between the Prokopack and the CDC-BP was performed in 71 houses in Iquitos, Peru. Randomly selected houses were visited by two field technicians who tested the performance of each mosquito aspirator in indoor collections. At each house, a collection sequence alternating the use of the CDC-BP and the Prokopack in the lower (<1.5 m) walls and furniture was followed. After using one of the aspirators (e.g., Prokopack), the same technician was in charge of repeating the collection with the alternative aspirator (e.g., CDC-BP), making sure to cover a similar area as in the initial collection. Concurrently with the lower wall collections, a Prokopack with an extension pole was used to collect the mosquitoes resting on the higher (>1.5 m) walls and the ceiling. Collection effort in each house was fixed (≈10 min) for each aspirator. Aspiration was performed in all rooms and hallways of each house as described by Scott et al. (2000) (link) and collected mosquitoes were processed as described above. In a first assessment, we found several damaged mosquitoes, because the collection cups were too close to the aspirator fan. We fixed this problem by adding a rigid wire transversally at 2.5 cm from the end of the rubber coupler (see 2b in Fig. 1A).
Publication 2009
ARID1A protein, human Culicidae Fingers Medical Devices Muscle Rigidity piperazine-N,N'-bis(2-ethanesulfonic acid) Rubber Silvan Sucrose Suction Drainage Virus Wind
To compare UTCI to selected bioclimatic indices, different datasets of meteorological variables were used. The data were based on various sources: the control run (1971–1980) of the General Circulation Model ECHAM 4 has a resolution of about 1.1° (Stendel and Roeckner 1998 ). The data consisted of about 65,500 random samples that represent wide range and combinations of meteorological variables. Air temperature (T) varied from −74.6°C to 47.4°C, air vapor pressure (vp) from 0 hPa to 40.2 hPa, wind speed (v10) from 0.5 m s−1 to 30 m s−1. Mean radiant temperature (Tmrt) changed from −92.3°C to 78.7°C. The difference between Tmrt and T was within the range of −18.0°C to 54.0°C.
The second data set used in these studies are synoptic data from Freiburg from the period September 1966–August 1985. The data provided all meteorological parameters used to calculate UTCI and bioclimatic indices. Freiburg is located in the upper Rhine valley in Southwest-Germany. It shows a moderate transient climate dominated by maritime rather than continental air masses.
The third temporal level of comparisons refers to microclimatic data. For the present paper, measurement campaigns were carried out within the frame of COST Action 730 at different locations:

Svalbard archipelago (in March 2008)—arctic climate,

Negev Desert (in September 2008)—dry subtropical climate,

Madagascar Island (in August 2007)—wet subtropical climate,

Warsaw, Poland (in October 2007)—downtown city in a moderate, transient climate.

The following simple bioclimatic indices were compared with UTCI: Heat index (HI), Humidex, Wet Bulb Globe Temperature (WBGT), Wind Chill Temperature (WCT), Effective Temperature (ET, NET). We also analyzed relationships between UTCI and indices derived from heat budget models: Standard Effective Temperature (SET*), Physiological Equivalent Temperature (PET), Perceived Temperature (PT), Physiological Subjective Temperature (PST). Two non-thermal indices were also used for comparative analysis: Predicted Mean Vote (PMV) and Physiological Strain (PhS).
UTCI and some indices (HI, AT, Humidex, WBGT, WCT, ET, PST and PhS) were calculated using the BioKlima 2.6 software package. PET, PMV and SET* were calculated by Rayman software and PT by the special PT module. The STATGRAPHICS 2.1 software package was used for statistical analysis of compared indices.
Publication 2011
Air Pressure Chills Climate Eye Medulla Oblongata Microclimate physiology Reading Frames Strains Transients Wind

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Publication 2020
Air Pressure COVID 19 gamma-glutamylaminomethylsulfonic acid Humidity Hypersensitivity Wind
Backbone parameters comprise the single bond torsions along the phosphodiester chain and the conformation of the sugar ring. In a conventional DNA strand, the backbone segment associated with each nucleotide (in the 5′→3′ direction) is described by the torsions α (03′-P-O5′-C5′), β (P-O5′-C5′-C4′), γ (O5′-C5′-C4′-C3′), δ (C5′-C4′-C3′-O3′), ϵ (C4′-C3′-O3′-P) and ζ (C3′-O3′-P-O5′), to which we must add the glycosidic angle χ (O4′-C1′-N1-C2 for pyrimidines and O4′-C1′-N9-C4 for purines) joining the sugar to the base and the ribose OH torsion (C1′-C2′-O2′-H2′) in the case of RNA.
We remark that calculating averages and standard deviations of angular variables is not trivial, unless they cover restricted angular ranges. There is also no simple definition of maximal and minimal values. This problem occurs in many branches of science with broadly distributed angular variables, for example, in analysing wind directions (30 ). While angular helical variables generally lie within limited ranges, backbone dihedrals can easily span the full range of 360°. In this case, maximal and minimal values in the Curves+ analysis are replaced with the parameter ‘range’ and angular averages and standard deviations are calculated using a vectorial approach. Range is defined as the number of 1° bins visited by a given variable in the interval 0–360°. This gives a good idea of the angular spread of variables. Note that when analysing molecular dynamics trajectories, this value may increase with sampling, giving an indication that more sampling probably needs to be done. However, the details of the angular distribution can be checked using the histogram output option of the supplementary program Canal (see below). For averages, angles are added as vectors in 2D space (with an angle θ having components x = Cos θ and y = Sin θ). The result is converted to a unit vector, whose X and Y components yield the average. Other approaches require assuming that the angles obey a presupposed type of distribution. We have checked our values against one such model (31 ), and found negligible differences for standard deviations up to roughly 20°. Larger values differ more significantly (5–10°), but in these cases it is the qualitative result that the variables in question fluctuate very strongly that is the most important.
The sugar ring is usefully described using pseudorotation parameters. Although strictly speaking there are four pseudorotation parameters for a five-membered ring (32 (link)), only two of these, the so-called phase (Pha) and amplitude (Amp), are generally useful. While the amplitude describes the degree of ring puckering, the phase describes which atoms are most displaced from the mean ring plane. We calculate these parameters using the formulae given below (33 ), which have the advantage of treating the ring dihedrals ν1 (C1′-C2′-C3′-C4′) to ν5 (O4′-C1′-C2′-C3′) in an equivalent manner. In this approach:

where and b =−0.4 note, if then .
Conventionally, sugar ring puckers are divided into 10 families described by the atom which is most displaced from the mean ring plane (C1′, C2′, C3′, C4′ or O4′) and the direction of this displacement (endo for displacements on the side of the C5′ atom and exo for displacements on the other side). These pucker families can be easily calculated from the phase angle and are also output by the Curves+ program.
In order to deal with non-standard nucleic acids the backbone parameters are not hard-wired into the program, but are contained in a data file (standard_s.lib) which can be modified or extended by this user. This makes it easy to analyse chemically modified backbones such as those, for example, in PNA (34 (link)).
Publication 2009
Carbohydrates Cloning Vectors Displacement, Psychology Endometriosis Glycosides Helix (Snails) Molecular Dynamics Nucleic Acids Nucleotides Pulp Canals purine Pyrimidines Ribose single bond Vertebral Column Wind

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Publication 2020
Climate COVID 19 Humidity Wind

Most recents protocols related to «Wind»

Example 2

In some applications, an infrasonic sensor is desired, with a frequency response fl that extends to an arbitrarily low frequency, such as a tenth of hundredth of a Hertz. Such a sensor might be useful for detecting fluid flows associated with movement of objects, acoustic impulses, and the like. Such an application works according to the same principles as the sonic sensor applications, though the length of individual runs of fibers might have to be greater.

In addition, the voltage response of the electrode output to movements is proportional to the velocity of the fiber, and therefore one would typically expect that the velocity of movement of fluid particles at infrasonic frequencies would low, leading to low output voltages. However, in some applications, the fluid movement is macroscopic, and therefore velocities may be appreciable. For example, in wake detection applications, the amplitude may be quite robust.

Generally, low frequency sound is detected by sensors which are sensitive to pressure such as infrasound microphones and microbarometers. As pressure is a scaler, multiple sensors should be used to identify the source location. Meanwhile, due to the long wave length of low frequency sound, multiple sensors have to be aligned far away to distinguish the pressure difference so as to identify the source location. As velocity is a vector, sensing sound flow can be beneficial to source localization. There is no available flow sensor that can detect infrasound flow in a broad frequency range with a flat frequency response currently. However, as discussed above, thin fibers can follow the medium (air, water) movement with high velocity transfer ratio (approximate to 1 when the fiber diameter is in the range of nanoscale), from zero Hertz to tens of thousands Hertz. If a fiber is thin enough, it can follow the medium (air, water) movement nearly exactly. This provides an approach to detect low frequency sound flow directly and effectively, with flat frequency response in a broad frequency range. This provides an approach to detect low frequency sound flow directly. The fiber motion due to the medium flow can be transduced by various principles such as electrodynamic sensing of the movement of a conductive fiber within a magnetic field, capacitive sensing, optical sensing and so on. Application example based on electromagnetic transduction is given. It can detect sound flow with flat frequency response in a broad frequency range.

For the infrasound detection, this can be used to detect manmade and natural events such as nuclear explosion, volcanic explosion, severe storm, chemical explosion. For the source localization and identification, the fiber flow sensor can be applied to form a ranging system and noise control to find and identify the low frequency source. For the low frequency flow sensing, this can also be used to detect air flow distribution in buildings and transportations such as airplanes, land vehicles, and seafaring vessels.

The infrasound pressure sensors are sensitive to various environmental parameters such as pressure, temperature, moisture. Limited by the diaphragm of the pressure sensor, there is resonance. The fiber flow sensor avoids the key mentioned disadvantages above. The advantages include, for example: Sensing sound flow has inherent benefit to applications which require direction information, such as source localization. The fiber flow sensor is much cheaper to manufacture than the sound pressure sensor. Mechanically, the fiber can follow the medium movement exactly in a broad frequency range, from infrasound to ultrasound. If the fiber movement is transduced to the electric signal proportionally, for example using electromagnetic transduction, the flow sensor will have a flat frequency response in a broad frequency range. As the flow sensor is not sensitive to the pressure, it has a large dynamic range. As the fiber motion is not sensitive to temperature, the sensor is robust to temperature variation. The fiber flow sensor is not sensitive to moisture. The size of the flow sensor is small (though parallel arrays of fibers may consume volume). The fiber flow sensor can respond to the infrasound instantly.

Note that a flow sensor is, or would be, sensitive to wind. The sensor may also respond to inertial perturbances. For example, the pressure in the space will be responsive to acceleration of the frame. This will cause bulk fluid flows of a compressible fluid (e.g., a gas), resulting in signal output due to motion of the sensor, even without external waves. This can be advantages and disadvantages depends on the detailed applications. For example, it can be used to detect flow distribution in the buildings. If used to detect infrasound, the wind influence be overcome by using an effective wind noise reduction approach.

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Patent 2024
Acceleration Acoustics A Fibers Blast Injuries Blood Vessel Cloning Vectors Dietary Fiber Electric Conductivity Electricity Electromagnetics Fibrosis Magnetic Fields Movement Pressure Reading Frames Sound Sound Waves Toxic Epidermal Necrolysis Ultrasonics Vaginal Diaphragm Vibration Water Movements Wind
Meteorological data such as net short wave radiation flux, temperature, relative humidity, wind speed, and pressure were gathered from the Global Land Data Assimilation System (GLDAS). The GLDAS meteorological data is hosted in Google Earth Engine (GEE) platform (Rodell et al., 2004 (link)). The climatological data were downloaded using Javascript language from the gridded images for the study of the Western Anatolia and Western Black Sea regions.
The data on air quality parameters were acquired from the Ministry of Environment and Urbanization (Ministry of Environment and Urbanization, 2021 ). Air quality parameters such as particulate matter (PM10) having an aerodynamic diameter of less than or equal to 10 m (PM10), nitrogen dioxide (NO2), sulfur dioxide (SO2), carbon monoxide (CO), nitrogen oxides (NOx), and ground-level ozone pollution (O3). The data on confirmed cases and new hospitalizations on COVID-19 were obtained from the COVID-19 information page of the Republic of Turkey Ministry of Health on December 12, 2020 (https://covid19.saglik.gov.tr/). The daily data were collected from June 29, 2020, to November 23, 2020, for the meteorological data and air quality parameters which are equal to the available data on the number of COVID-19 cases
Publication 2023
COVID 19 Ground Level Ozone Hospitalization Humidity Monoxide, Carbon Nitrogen Dioxide Nitrogen Oxides Pressure Radiation Short Waves Urbanization Wind
We computed de-seasoned anomalies of monthly Chl a, east-west winds and sea ice concentration by first removing the monthly climatology from monthly means, producing de-seasoned anomalies from September 1997 to December 2019. Thereafter, we produced composite maps by averaging monthly de-seasoned anomalies during and outside of the bloom period. Composites maps of the de-seasoned anomalies are presented in Fig. 2.
Furthermore, correlation maps between the satellite-derived Chl a and the east-west wind de-seasoned anomalies and between the satellite-derived Chl a and the sea ice concentration de-seasoned anomalies were produced by using the Pearson Correlation test. Correlation maps were assessed for multiple lag periods with the highest correlation coefficients found for winds leading bloom by 1-month.
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Publication 2023
Microtubule-Associated Proteins Sea Ice Cover Wind
The Chl a concentration maps are extracted from the Ocean and Land Colour Instrument (OLCI) Level-2 full resolution Near Real Time (NRT) product, obtained by the EUMETCast broadcast system. The OLCI sensors on board ESA Sentinel-3A and B satellites launched in February 2016 respectively April 2018, and have large swath widths (~1270 km) covering large regions with high temporal resolution (approximately 1.5-day global coverage with both sensors). The OC4ME algorithm which uses a polynomial approach of a maximum band ratio algorithm of 4 reflectances at 443, 490 and 510 nm over the 560 nm was extracted directly from OLCI Level 2 products and gives the Chl a pigment concentration in [mg/m3]. The cloud free chlorophyll orbit tiles are binned and averaged on a daily base to obtain one image per day. This leads to a very good resolution, coverage in reasonable timeframe and meets the requirements of monitoring water dynamics also on smaller spatial scales.
The analysis of satellite-derived ocean color was complemented using SeaWiFs (1997–2002, https://oceandata.sci.gsfc.nasa.gov/SeaWiFS/) and MODIS (2003–2020, https://oceandata.sci.gsfc.nasa.gov/MODIS-Aqua/) level 3 Chl a data processed with the default chlorophyll algorithm (chlor_a) which employs the standard OC3/OC4 (OCx) band ratio algorithm merged with the color index (CI) in 8-day and 9 km resolution.
First, the long-term analysis of the bloom presence (Fig. 2a) was performed from January 1997 to December 2020, covering the ocean-color satellite era. We defined the bloom presence when the 8-day mean satellite-derived Chl a, averaged over 4°–8° E and 67.8°–68.4° S, was larger than the 23-year long mean Chl a + 1 standard deviation over that area (i.e., 1.14 mg m−3, Fig. 2a).
In addition, the bloom duration in 2019 was calculated as the period between the first occurrence of the bloom during the Austral summer 2019 and its end, before the following Austral winter. The long-term median of the average Chl a concentration over the area 4°–8° E and 67.8°–68.4° S was used as a threshold to detect the bloom onset and end above which we considered a phytoplankton bloom present. Because of the presence of clouds and sea ice, satellite-derived ocean color did not detect any pixel with Chl-a data in the bloom area before January 9, 2019, and after March 14, 2019, which we, thus, defined as the bloom duration. It is possible, however, that the bloom started earlier and terminated later than our conservative estimate.
Daily sea ice concentration (1997–2020) were obtained from the NASA’s Nimbus‐7 Scanning Multichannel Microwave Radiometer (SMMR) and Defense Meteorological Satellite Program (DMSP)‐F13, ‐F17, and ‐F18 Special Sensor Microwave/Imager (SSM/I). Data with a spatial resolution of 25 km were provided by the National Snow and Ice Data Centre, University of Colorado in Boulder, CO (http://nsidc.org), with prior processing using the NASA team algorithms62 .
Daily east-west and north-south wind components at the sea surface were obtained for the study region from the ERA-Interim global atmospheric reanalysis63 .
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Publication 2023
Chlorophyll Microtubule-Associated Proteins Microwaves Orbit Phytoplankton Pigmentation Satellite Viruses Sea Ice Cover Snow Temporal Lobe Wind
The 37th PKO Wrocław Marathon (Wrocław, Poland, 19 September 2019) was organized by the City of Wrocław, Poland. Since the beginning of the run, The PKO Wrocław Marathon has been organized by the city of Wroclaw and is considered one of Poland’s most significant running events. The PKO Wroclaw Marathon takes place annually at the beginning of September. It was sunny day of the marathon, the air temperature during the start was 21 degrees Celsius, humidity 72%, with a wind of 3.3 m/s. At the end of the run, the temperature rose to 24 degrees Celsius, humidity 46%, with a wind of 5 m/s. The data on the running time was obtained from the electronic database of the marathon’s organizers. It included the number of runners who started and finished the run, the personal identification number of the run, and the place and time of the run for each participant of the marathon. The individual runtime registered in the event was automatically measured using a radio frequency identification chip system. Intermediate times every 5 km were measured for the experimental group to analyze their running pace variability accurately. In addition, the heart rate (HR) was recorded using a monitor (Polar RS300X GPS; Finland) to examine each participant during the marathon run.
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Publication 2023
DNA Chips Humidity Marathon composite resin Rate, Heart Wind

Top products related to «Wind»

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The LI-7500 is an open-path CO2/H2O gas analyzer. It uses a non-dispersive infrared (NDIR) sensor to measure the concentrations of carbon dioxide and water vapor in the atmosphere.
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The CR1000 is a high-performance, versatile datalogger designed for a wide range of measurement and control applications. It features a 16-bit analog-to-digital converter, multiple communication options, and programmable control capabilities.
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The CSAT3 is a 3-dimensional sonic anemometer manufactured by Campbell Scientific. It is designed to measure wind speed and direction with high accuracy and a fast response time. The CSAT3 uses ultrasonic transducers to determine the speed of sound, which is then used to calculate the wind vector.
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The CS215 is a temperature and relative humidity sensor. It measures air temperature and relative humidity and outputs the data as an analog voltage signal.
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SPSS Statistics 25 is a software package used for statistical analysis. It provides a wide range of data management and analysis capabilities, including advanced statistical techniques, data visualization, and reporting tools. The software is designed to help users analyze and interpret data from various sources, supporting decision-making processes across different industries and research fields.
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More about "Wind"

Airflow, Atmospherics, Breeze, Gales, Gusts, Meteorology, Renewable Energy, Ventilation, Zephyrs.
The movement of air, known as wind, is a crucial force in various scientific and industrial applications.
It plays a pivotal role in renewable energy production, weather forecasting, and transportation.
The study of wind encompasses diverse topics, including wind patterns, wind turbine design, and the environmental impact of wind.
Researchers can optimize their wind-related studies by leveraging AI-driven comparisons using platforms like PubCompare.ai, which helps locate the best protocols and products from literature, preprints, and patents.
This enhances reproducibility, accuracy, and the ability to advance wind-related research.
Expereince the power of PubCompare.ai today to take your wind research to new heights.
Leverage instruments like the LI-7500 gas analyzer, CR1000 data logger, CSAT3 sonic anemometer, CS215 temperature and humidity sensor, SPSS Statistics 25, SAS 9.4, MATLAB, CS616 water content reflectometer, and WatchDog 2900ET weather station to collect and analyze wind-related data, enhancing the depth and quality of your investigations.