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5,023 results for “landing”
FIGURES 8–13 in Anatomical deviation of male organs of land planarians from Rio de Janeiro, Brazil, with description of two new species of Cratera (Platyhelminthes, Tricladida)
FIGURES 8–13. Cratera cuarassu sp. nov. Holotype. Anterior to the right. (8–9) micrographs of a sagittal section of the copulatory apparatus of the holotype. (10) micrograph of a detail of the large cavity within the penis papilla of the holotype. (11) micrograph of a parasagittal section of the copulatory apparatus of the holotype. (12) micrograph of the genital female canal and posterior aspect of the female atrium of the holotype in a sagittal section. (13) diagrammatic reconstruction of copulatory apparatus of the holotype from sagittal sections.
FIGURES 4–7 in Anatomical deviation of male organs of land planarians from Rio de Janeiro, Brazil, with description of two new species of Cratera (Platyhelminthes, Tricladida)
FIGURES 4–7. Cratera cuarassu sp. nov. Micrographs of the holotype. (4) micrograph of the glandular margin of holotype in a transverse section of the pre-pharyngeal region. (5) micrograph of the ventral cutaneous musculature tangentially cut in a horizontal section. Red line indicates longitudinal fibers; the pinkish line indicates decussate fibers. Anterior to the bottom. (6) micrograph of a detail of ventral cutaneous musculature tangentially cut in a horizontal section. Red line indicates longitudinal fibers; the pinkish line delimits the decussate fibers. Anterior to the bottom. (7) micrograph of a sagittal section of the pharynx of the holotype. Anterior to the right.
FIGURES 1–3 in Anatomical deviation of male organs of land planarians from Rio de Janeiro, Brazil, with description of two new species of Cratera (Platyhelminthes, Tricladida)
FIGURES 1–3. Cratera cuarassu sp. nov. (1) living paratype PL 805 creeping in dorsal view. About 60 mm in length. (2) living paratype PL 805 in ventral view. (3) fixed holotype on a millimetered paper after cutting off a piece of the central body.
Social-ecological cascade effects of land-use on vertebrate pest dynamics in arid agricultural communities
<p>Extensive land conversion to agriculture in drylands and associated resource use have wide-ranging impacts on desert ecosystems globally. Incorporating the impacts of human-social aspects is thus imperative in examining ecological interactions. The provision of agricultural inputs in these resource-scarce regions supports invasive and pest species, negatively impacting both agricultural productivity and native desert ecosystems. Understanding the spatial dynamics of invasive and pest species requires analyzing both bottom-up resource availability factors underlying animal distributions and top-down biological controls. Here, we evaluate the social-ecological cascading effects of dryland agriculture on vertebrate pest communities in dryland agricultural communities of Israel. Our study region is characterized by 18 agricultural cooperatives with distinct crop regimes due to contrasting social decision-making and resource allocation schemes (i.e., communal Kibbutzim vs. privatized Moshavim). Crop choices further affect land management (e.g., enclosed vs. open farm systems) and resource intensity. This system is ideal to study trophic mechanisms underlying animal assemblages between agricultural regimes. We examine the role of agricultural land-use practices on pest spatial distributions based on multi-year vertebrate pest observations with agricultural datasets. We use structural equation modelling (SEM) to quantify the relative importance of added agricultural resources underlying bottom-up and top-down trophic processes regulating vertebrate pest assemblages. Results reveal that crop choices determine pest distributions through bottom-up processes directly, while simultaneously driving pest competitive interactions through indirect top-down cascades impacting pest communities. For example, due to the indirect negative effect of wolves on meso-predators (foxes and jackals) mediated by livestock, the total positive effect of livestock on the abundance of meso-predators is reduced. Our study illustrates the social-ecological cascading effects of agricultural regimes on pest community assemblages mediated by contrasting agricultural land-use practices. Considering the expansion of dryland agro-ecological systems globally, understanding the intricate cascading pathways of predator- and prey-pest communities has important implications for agricultural management, biological invasions in drylands and fragile desert environments.</p>
Large Landing Trajectory Data Set for Go-Around Analysis
<p>Large go-around, also referred to as missed approach, data set. The data set is in support of <a href="https://www.mdpi.com/2673-4591/28/1/2">the paper</a> presented at the OpenSky Symposium on November the 10th.</p> <p>If you use this data for a scientific publication, please consider citing <a href="https://doi.org/10.3390/engproc2022028002">our paper</a>.</p> <p>The data set contains landings from 176 (mostly) large airports from 44 different countries. The landings are labelled as performing a go-around (GA) or not. In total, the data set contains almost 9 million landings with more than 33000 GAs. The data was collected from <a href="https://opensky-network.org/data/impala">OpenSky Network's historical data base</a> for the year 2019. The published data set contains multiple files:</p> <p><strong>go_arounds_minimal.csv.gz</strong></p> <p>Compressed CSV containing the minimal data set. It contains a row for each landing and a minimal amount of information about the landing, and if it was a GA. The data is structured in the following way:</p> <table> <caption> </caption> <thead> <tr> <th scope="col">Column name</th> <th scope="col">Type</th> <th scope="col">Description</th> </tr> </thead> <tbody> <tr> <td>time</td> <td>date time</td> <td>UTC time of landing or first GA attempt</td> </tr> <tr> <td>icao24</td> <td>string</td> <td>Unique 24-bit (hexadecimal number) ICAO identifier of the aircraft concerned</td> </tr> <tr> <td>callsign</td> <td>string</td> <td>Aircraft identifier in air-ground communications</td> </tr> <tr> <td>airport</td> <td>string</td> <td>ICAO airport code where the aircraft is landing</td> </tr> <tr> <td>runway</td> <td>string</td> <td>Runway designator on which the aircraft landed</td> </tr> <tr> <td>has_ga</td> <td>string</td> <td>"True" if at least one GA was performed, otherwise "False"</td> </tr> <tr> <td>n_approaches</td> <td>integer</td> <td>Number of approaches identified for this flight</td> </tr> <tr> <td>n_rwy_approached</td> <td>integer</td> <td>Number of unique runways approached by this flight</td> </tr> </tbody> </table> <p>The last two columns, <em>n_approaches</em> and <em>n_rwy_approached</em>, are useful to filter out training and calibration flight. These have usually a large number of <em>n_approaches</em>, so an easy way to exclude them is to filter by <em>n_approaches > 2.</em></p> <p><strong>go_arounds_augmented.csv.gz</strong></p> <p>Compressed CSV containing the augmented data set. It contains a row for each landing and additional information about the landing, and if it was a GA. The data is structured in the following way:</p> <table> <thead> <tr> <th scope="col">Column name</th> <th scope="col">Type</th> <th scope="col">Description</th> </tr> </thead> <tbody> <tr> <td>time</td> <td>date time</td> <td>UTC time of landing or first GA attempt</td> </tr> <tr> <td>icao24</td> <td>string</td> <td>Unique 24-bit (hexadecimal number) ICAO identifier of the aircraft concerned</td> </tr> <tr> <td>callsign</td> <td>string</td> <td>Aircraft identifier in air-ground communications</td> </tr> <tr> <td>airport</td> <td>string</td> <td>ICAO airport code where the aircraft is landing</td> </tr> <tr> <td>runway</td> <td>string</td> <td>Runway designator on which the aircraft landed</td> </tr> <tr> <td>has_ga</td> <td>string</td> <td>"True" if at least one GA was performed, otherwise "False"</td> </tr> <tr> <td>n_approaches</td> <td>integer</td> <td>Number of approaches identified for this flight</td> </tr> <tr> <td>n_rwy_approached</td> <td>integer</td> <td>Number of unique runways approached by this flight</td> </tr> <tr> <td>registration</td> <td>string</td> <td>Aircraft registration</td> </tr> <tr> <td>typecode</td> <td>string</td> <td>Aircraft ICAO typecode</td> </tr> <tr> <td>icaoaircrafttype</td> <td>string</td> <td>ICAO aircraft type</td> </tr> <tr> <td>wtc</td> <td>string</td> <td>ICAO wake turbulence category</td> </tr> <tr> <td>glide_slope_angle</td> <td>float</td> <td>Angle of the ILS glide slope in degrees</td> </tr> <tr> <td>has_intersection</td> <td> <p>string</p> </td> <td>Boolean that is true if the runway has an other runway intersecting it, otherwise false</td> </tr> <tr> <td>rwy_length</td> <td>float</td> <td>Length of the runway in kilometre</td> </tr> <tr> <td>airport_country</td> <td>string</td> <td>ISO Alpha-3 country code of the airport</td> </tr> <tr> <td>airport_region</td> <td>string</td> <td>Geographical region of the airport (either Europe, North America, South America, Asia, Africa, or Oceania)</td> </tr> <tr> <td>operator_country</td> <td>string</td> <td>ISO Alpha-3 country code of the operator</td> </tr> <tr> <td>operator_region</td> <td>string</td> <td>Geographical region of the operator of the aircraft (either Europe, North America, South America, Asia, Africa, or Oceania)</td> </tr> <tr> <td>wind_speed_knts</td> <td>integer</td> <td>METAR, surface wind speed in knots</td> </tr> <tr> <td>wind_dir_deg</td> <td>integer</td> <td>METAR, surface wind direction in degrees</td> </tr> <tr> <td>wind_gust_knts</td> <td>integer</td> <td>METAR, surface wind gust speed in knots</td> </tr> <tr> <td>visibility_m</td> <td>float</td> <td>METAR, visibility in m</td> </tr> <tr> <td>temperature_deg</td> <td>integer</td> <td>METAR, temperature in degrees Celsius</td> </tr> <tr> <td>press_sea_level_p</td> <td>float</td> <td>METAR, sea level pressure in hPa</td> </tr> <tr> <td>press_p</td> <td>float</td> <td>METAR, QNH in hPA</td> </tr> <tr> <td>weather_intensity</td> <td>list</td> <td>METAR, list of present weather codes: qualifier - intensity</td> </tr> <tr> <td>weather_precipitation</td> <td>list</td> <td>METAR, list of present weather codes: weather phenomena - precipitation</td> </tr> <tr> <td>weather_desc</td> <td>list</td> <td>METAR, list of present weather codes: qualifier - descriptor</td> </tr> <tr> <td>weather_obscuration</td> <td>list</td> <td>METAR, list of present weather codes: weather phenomena - obscuration</td> </tr> <tr> <td>weather_other</td> <td>list</td> <td>METAR, list of present weather codes: weather phenomena - other</td> </tr> </tbody> </table> <p>This data set is augmented with data from various public data sources. Aircraft related data is mostly from the <a href="https://opensky-network.org/aircraft-database">OpenSky Network's aircraft data base</a>, the METAR information is from the <a href="https://mesonet.agron.iastate.edu/info/datasets/metar.html">Iowa State University</a>, and the rest is mostly scraped from different web sites. If you need help with the METAR information, you can consult the <a href="https://library.wmo.int/doc_num.php?explnum_id=11220">WMO's Aerodrom Reports and Forecasts handbook</a>.</p> <p><strong>go_arounds_agg.csv.gz</strong></p> <p>Compressed CSV containing the aggregated data set. It contains a row for each airport-runway, i.e. every runway at every airport for which data is available. The data is structured in the following way:</p> <table> <thead> <tr> <th scope="col">Column name</th> <th scope="col">Type</th> <th scope="col">Description</th> </tr> </thead> <tbody> <tr> <td>airport</td> <td>string</td> <td>ICAO airport code where the aircraft is landing</td> </tr> <tr> <td>runway</td> <td>string</td> <td>Runway designator on which the aircraft landed</td> </tr> <tr> <td>n_landings</td> <td>integer</td> <td>Total number of landings observed on this runway in 2019</td> </tr> <tr> <td>ga_rate</td> <td>float</td> <td>Go-around rate, per 1000 landings</td> </tr> <tr> <td>glide_slope_angle</td> <td>float</td> <td>Angle of the ILS glide slope in degrees</td> </tr> <tr> <td>has_intersection</td> <td>string</td> <td>Boolean that is true if the runway has an other runway intersecting it, otherwise false</td> </tr> <tr> <td>rwy_length</td> <td>float</td> <td>Length of the runway in kilometres</td> </tr> <tr> <td>airport_country</td> <td>string</td> <td>ISO Alpha-3 country code of the airport</td> </tr> <tr> <td>airport_region</td> <td>string</td> <td>Geographical region of the airport (either Europe, North America, South America, Asia, Africa, or Oceania)</td> </tr> </tbody> </table> <p>This aggregated data set is used in the paper for the generalized linear regression model.</p> <p><strong>Downloading the trajectories</strong></p> <p>Users of this data set with access to OpenSky Network's Impala shell can download the historical trajectories from the historical data base with a few lines of Python code. For example, you want to get all the go-arounds of the 4th of January 2019 at London City Airport (EGLC). You can use the <a href="https://github.com/xoolive/traffic">Traffic</a> library for easy access to the database:</p> <pre><code class="language-python">import datetime from tqdm.auto import tqdm import pandas as pd from traffic.data import opensky from traffic.core import Traffic # load minimum data set df = pd.read_csv("go_arounds_minimal.csv.gz", low_memory=False) df["time"] = pd.to_datetime(df["time"]) # select London City Airport, go-arounds, and 2019-01-04 airport = "EGLC" start = datetime.datetime(year=2019, month=1, day=4).replace( tzinfo=datetime.timezone.utc ) stop = datetime.datetime(year=2019, month=1, day=5).replace( tzinfo=datetime.timezone.utc ) df_selection = df.query("airport==@airport & has_ga & (@start <= time <= @stop)") # iterate over flights and pull the data from OpenSky Network flights = [] delta_time = pd.Timedelta(minutes=10) for _, row in tqdm(df_selection.iterrows(), total=df_selection.shape[0]): # take at most 10 minutes before and 10 minutes after the landing or go-around start_time = row["time"] - delta_time stop_time = row["time"] + delta_time # fetch the data from OpenSky Network flights.append( opensky.history( start=start_time.strftime("%Y-%m-%d %H:%M:%S"), stop=stop_time.strftime("%Y-%m-%d %H:%M:%S"), callsign=row["callsign"], return_flight=True, ) ) # The flights can be converted into a Traffic object Traffic.from_flights(flights)</code></pre> <p><strong>Additional files</strong></p> <p>Additional files are available to check the quality of the classification into GA/not GA and the selection of the landing runway. These are:</p> <ul> <li><strong>validation_table.xlsx</strong>: This Excel sheet was manually completed during the review of the samples for each runway in the data set. It provides an estimate of the false positive and false negative rate of the go-around classification. It also provides an estimate of the runway misclassification rate when the airport has two or more parallel runways. The columns with the headers highlighted in red were filled in manually, the rest is generated automatically.</li> <li><strong>validation_sample.zip</strong>: For each runway, 8 batches of 500 randomly selected trajectories (or as many as available, if fewer than 4000) classified as not having a GA and up to 8 batches of 10 random landings, classified as GA, are plotted. This allows the interested user to visually inspect a random sample of the landings and go-arounds easily.</li> </ul>
FIGURES 24–27 in Two new land planarian species (Platyhelminthes: Tricladida) from the Cerrado biome in southwestern Brazil
FIGURES 24–27. Paraba aurantia Marques & Leal-Zanchet, sp. nov., microphotographs of the copulatory apparatus in sagittal sections: (24–25) paratype MZU PL. 00320, details of the prostatic vesicle and female canal; (26–27) holotype, details of the female organs. Anterior tip to the left. The arrows indicate the epithelium with a multilayered aspect of the female atrium in (27).
FIGURE 19 in Two new land planarian species (Platyhelminthes: Tricladida) from the Cerrado biome in southwestern Brazil
FIGURE 19. Paraba aurantia Marques & Leal-Zanchet, sp. nov., paratype MZU PL. 00320: microphotograph of a sagittal section of the pharynx. Anterior tip to the left.
FIGURES 6–9 in Two new land planarian species (Platyhelminthes: Tricladida) from the Cerrado biome in southwestern Brazil
FIGURES 6–9. Paraba aurantia Marques & Leal-Zanchet, sp. nov.: (6–7) photographs of the paratype MZU PL. 00320, in dorsal view, before fixation; (8) photograph of the anterior body region of the preserved paratype MZU PL. 00319 in ventral view; (9) photograph of the preserved holotype in dorsal view. Anterior tip to the left. Scale bars: (6–7) 5 mm; (8–9) 1 mm. The arrow indicates the ventral surface, which is partially visible in (7). The arrowheads indicate the brownish spots in the ventral surface in (8).
FIGURE 45 in Two new land planarian species (Platyhelminthes: Tricladida) from the Cerrado biome in southwestern Brazil
FIGURE 45. Phylogenetic relationships of cytochrome c oxidase subunit I gene inferred from (a) maximum likelihood (GTR+G+I model) and (b) species delimitation analyses of species of Pasipha. Values of weakly supported nodes (bootstrap support <70%) are not reported in the tree. Scale bar represents substitutions per nucleotide site. In parentheses, GenBank accession numbers (see Appendix S1).
FIGURES 39–40 in Two new land planarian species (Platyhelminthes: Tricladida) from the Cerrado biome in southwestern Brazil
FIGURES 39–40. Pasipha liviae Marques & Leal-Zanchet, sp. nov., holotype, microphotographs of sagittal sections: (39) pharynx; (40) ovary. Anterior tip to the left.
FIGURES 2–5 in Two new land planarian species (Platyhelminthes: Tricladida) from the Cerrado biome in southwestern Brazil
FIGURES 2–5. Photographs of type-localities and other sampling sites of Paraba aurantia Marques & Leal-Zanchet, sp. nov. and Pasipha liviae Marques & Leal-Zanchet, sp. nov. in the municipalities of Bonito and Bodoquena, Mato Grosso do Sul, Brazil: (2) municipal leisure area of Bodoquena; (3) National Park of Serra da Bodoquena (4) cave entrance of "Gruta das Pitangueiras"; (5) cave entrance of "Gruta do Mimoso".
FIGURES 13–18 in Two new land planarian species (Platyhelminthes: Tricladida) from the Cerrado biome in southwestern Brazil
FIGURES 13–18. Paraba aurantia Marques & Leal-Zanchet, sp. nov., paratype MZU PL. 00320, microphotographs of transverse sections: (13–14) cephalic region of body; (15–18) anterior region of body at the level of ovaries.
FIGURES 22–23 in Two new land planarian species (Platyhelminthes: Tricladida) from the Cerrado biome in southwestern Brazil
FIGURES 22–23. Paraba aurantia Marques & Leal-Zanchet, sp. nov., microphotographs of the copulatory apparatus in sagittal sections: (22) holotype; (23) paratype MZU PL. 00320. Anterior tip to the left.
FIGURES 20–21 in Two new land planarian species (Platyhelminthes: Tricladida) from the Cerrado biome in southwestern Brazil
FIGURES 20–21. Paraba aurantia Marques & Leal-Zanchet, sp. nov., sagittal composite reconstructions of the copulatory apparatus of the holotype (20) and paratype MZU PL. 00320 (21). Anterior tip to the left.
FIGURES 10–12 in Two new land planarian species (Platyhelminthes: Tricladida) from the Cerrado biome in southwestern Brazil
FIGURES 10–12. Paraba aurantia Marques & Leal-Zanchet, sp. nov.: (10–11) detail of the eye pattern in the cephalic region in paratypes MZU PL. 00320 (10) and MZU PL. 00319 (11); (12) eye pattern of the holotype. Anterior tip to the left.
FIGURE 1 in Two new land planarian species (Platyhelminthes: Tricladida) from the Cerrado biome in southwestern Brazil
FIGURE 1. Location of type-localities and other sampling sites of Paraba aurantia Marques & Leal-Zanchet, sp. nov. and Pasipha liviae Marques & Leal-Zanchet, sp. nov. in the municipalities of Bonito and Bodoquena, Mato Grosso do Sul, Brazil.
FIGURES 41 in Two new land planarian species (Platyhelminthes: Tricladida) from the Cerrado biome in southwestern Brazil
FIGURES 41. Pasipha liviae Marques & Leal-Zanchet, sp. nov., holotype, sagittal composite reconstruction of the copulatory apparatus. Anterior tip to the left.
FIGURES 42–44 in Two new land planarian species (Platyhelminthes: Tricladida) from the Cerrado biome in southwestern Brazil
FIGURES 42–44. Pasipha liviae Marques & Leal-Zanchet, sp. nov., holotype, microphotographs of the copulatory apparatus in sagittal sections: (42) general view; (43) prostatic vesicle; (44) female organs. Anterior tip to the left.
FIGURES 34–38 in Two new land planarian species (Platyhelminthes: Tricladida) from the Cerrado biome in southwestern Brazil
FIGURES 34–38. Pasipha liviae Marques & Leal-Zanchet, sp. nov., holotype, microphotographs of transverse sections: (34– 35) cephalic region of body; (36–38) pre-pharyngeal region.
FIGURES 31–33 in Two new land planarian species (Platyhelminthes: Tricladida) from the Cerrado biome in southwestern Brazil
FIGURES 31–33. Pasipha liviae Marques & Leal-Zanchet, sp. nov., preserved type-specimens: (31–33) photographs in dorsal (31) and ventral (32) views of paratype MZU PL. 00324; (33) schematic drawing of the eye pattern of the holotype, all dots representing eyes. Anterior tip to the left.
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