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167 results for “ALARMES”
Behavioral data for Great tit responses to alarm calls under high temperatures
<p>This README accompanies the file "data_high temperatures alarm calls.txt"<br> <br> ********************************** CONTENTS ***********************************<br> The data are in table form with TABs as variables field delimiters so they can <br> be readily imported in any statistical package or spreadsheet program. Please, <br> contact us if you need the file formatted otherwise. </p> <p>This file includes a description of the variables.<br> <br> *******************************************************************************<br> Variable names and descriptions</p> <p>ID ID of the behavioral test <br> Date Date of the test experiment<br> Hour Date of the test experiment<br> Playback Playback ID of mobbing calls of great tits or Chaffinch songs used for the experiment <br> Treatment Control (Chaffinch songs) or Call (mobbing calls of great tits) treatment<br> Sex Sex of the focal individual<br> Ncalls Number of mobbing vocalizations emitted by the focal individual during 1 min of playback<br> Mindist Minimum distance to the speaker reached by the focal individual during 1 min of playback<br> T0 Air temperature during the experiment<br> T5 Mean temperature observed for the 5 hours before the test <br> T12 Mean temperature observed for the 12 hours before the test<br> T24 Mean temperature observed for the 24 hours before the test<br> T48 Mean temperature observed for the 48 hours before the test<br> Wind-km.h-1 Wind speed during the experiment<br> Humidity Air humidity during the experiment<br> P-hPa Atmospheric pressure during the experiment</p> <p>********************************* CONTACT **********************************<br> Please contact me at:</p> <p>Marion Cordonnier<br> e-mail: marion.cordonnier@hotmail.com<br> *******************************************************************************</p>
Static Code Analysis Alarms Filtering Reloaded: an ML Approach and its Empirical Evaluation on a New Real-World Dataset
<p>This is a replication data package for a paper titled "<em>Static Code Analysis Alarms Filtering Reloaded: an ML Approach and its Empirical Evaluation on a New Real-World Dataset</em>".</p> <p>Paper abstract: Even though Static Code Analysis (SCA) tools are integrated into many modern software building and testing pipelines, their practical impact is still seriously hindered by the excessive number of false positive warnings they usually produce. To cope with this problem, researchers have proposed several post-processing methods that aim to filter out false hits (or equivalently identify ``actionable'' warnings) after the SCA tool produced its results. However, we found that most of these approaches are targeted (i.e., deal with only a few SCA warning types) and evaluated on synthetic benchmarks or small-scale manually collected data sets (i.e., with typical sample sizes of several hundred).</p> <p>In this paper, we present a code embedding-based approach for filtering false positive warnings produced by 160 different SonarQube rule checks, one of the most widely adopted SCA tools today. We evaluate the method on a dataset containing 224,484 real-world warning samples fixed (true positive samples) or ignored (false positive samples) by the developers, which we collected from 9,958 different open-source Java projects from GitHub using a data mining approach. It is the most extensive real-world study and public dataset we know of in this area. Our method works with an accuracy of 91% (best F1-score of 81.3% and AUC of 95.3%) for the classification of SonarQube warnings.</p>
Suction feeding by predators limits direct release of alarm cues in fishes
<p>Chemical alarm cues alert aquatic prey to the presence of an actively foraging predator. There is a large literature based upon responses to alarm cues derived from skin extract, because it is anticipated that prey skin is damaged when prey are attacked by a predator. However, many predators feed by suction feeding whereby prey are quickly drawn into the buccal cavity and swallowed whole with little, if any, direct contact between the teeth of the predator and the prey. Here, we test if predation by suction feeding releases chemical information in sufficient quantity to elicit an antipredator response in conspecific prey. In tests of individual zebrafish <i>Danio rerio</i>, we found that odor of crushed zebrafish produced a clear antipredator behavioral response, but water collected immediately adjacent to staged predation events between a largemouth bass <i>Micropterus salmoides</i> (122-145 mm TL) and adult zebrafish (39 mm TL) did not elicit alarm behavior, and did not differ from behavioral responses to blank water or bass odor (on a diet of earthworms). In a second experiment, zebrafish swallowed by largemouth bass, then retrieved seconds later through gastric lavage, produced zebrafish that were alive and completely intact with minimal epidermal damage. Published relationships between bass length, gape size and the geometry of suction feeding suggest that in a hypothetical population of largemouth bass feeding on adult zebrafish, or fathead minnows, the majority of predation events by piscivorous fish probably would not release detectable levels of prey alarm cue. Accounting for the role of feeding mechanics by fish predators requires a recalibration of the literature on risk assessment by small prey fishes. Chemically-mediated antipredator behaviors against suction-feeding predators may occur primarily via post-ingestion dietary cues, or disturbance cues released near the moment of attack.</p>
Data from : Evaluating and Predicting the Audibility of Acoustic Alarms in the Workplace Using Experimental Methods and Deep Learning
<h2>Description</h2> <p>This repository serves as a complementary resource accompanying the academic paper titled "Evaluating and Predicting the Audibility of Acoustic Alarms in the Workplace Using Experimental Methods and Deep Learning" published in <em>Applied Acoustics </em>(available at: <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.apacoust.2024.109955" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.apacoust.2024.109955</a>). It comprises a dataset containing the acoustic data, metadata, and perceptual annotations.</p> <p>In addition, we provide the .<em>h5</em> datasets and PyTorch model weights to run the code corresponding to the neural network section discussed in the paper (publicly accessible at: <a href="https://github.com/effajr/predicting_alarm_audibility">https://github.com/effajr/predicting_alarm_audibility</a>).</p> <p>The repository is composed of five .<em>zip</em> files:</p> <ul> <li><strong>source_audio </strong>: contains the source audio files used to generate the alarms and backgrounds present in the <strong>data</strong> file.</li> <li><strong>data</strong> : contains the audio files corresponding to the alarms and backgrounds, along with the perceptual annotations.</li> <li><strong>metadata</strong> : contains the metadata related to the alarms and backgrounds present in the <strong>data</strong> file, and to the source audio files contained in <strong>source_audio</strong>.</li> <li><strong>features </strong>: contains the .<em>h5</em> files representing the development and evaluation subsets (mel-spectrograms and perceptual labels) used in the deep learning approach presented in the paper.</li> <li><strong>trained_models </strong>: contains the PyTorch model weights for the 10 runs of model training mentionned in the paper.</li> </ul> <h2>How to use the data</h2> <p>To run the code present in the GitHub repository, we recommend extracting the files <strong>data</strong>.zip, <strong>features</strong>.zip and <strong>trained_models</strong>.zip in their corresponding folders in the "<strong>application</strong>" folder (see <a href="https://github.com/effajr/predicting_alarm_audibility">https://github.com/effajr/predicting_alarm_audibility</a>).</p> <h2>Content</h2> <p>Content of <strong>data</strong>.zip </p> <pre>annotations/ ├─ dev/ │ ├─ annotation_compilation_dev.csv : Compilation of all the listening conditions and <br>│ │ individual annotator responses for the development data.<br>│ ├─ dev_conditions.csv : Unique listening conditions (extracted from annotation_compilation_dev.csv). │ ├─ dev_labels.csv : All individual annotator responses for each <br>│ │ unique listening condition (extracted from annotation_compilation_dev.csv). │ ├─ dev_train_valid_split.csv : Random 80%/20% training/validation split used for development <br>│ │ in the experiments reported in the paper. ├─ eval/<br>│ ├─ annotation_compilation_eval.csv : Compilation of all the listening conditions and individual annotator <br>│ │ responses for the evaluation data.<br>│ │ The column 'clearly_audible_mean' represents individual annotator <br>│ │ binary responses evaluated for each listening condition.<br>│ │ The column 'clearly_audible_pf' represents individual annotator <br>│ │ psychometric functions evaluated for each listening condition.<br>│ │<br>│ ├─ eval_conditions.csv : Unique listening conditions (extracted from annotation_compilation_eval.csv). │ ├─ eval_labels_apf.csv : All individual annotator psychometric function values for each <br>│ │ unique listening condition (extracted from annotation_compilation_eval.csv). │ ├─ eval_labels_mv.csv : All individual annotator binary responses for each <br>│ │ unique listening condition (extracted from annotation_compilation_eval.csv)<br>│<br>audio/ : <em>.wav</em> files corresponding to the alarms and backgrounds for Development and <br> Evaluation subsets of the dataset. ├─ dev/ │ ├─ alarms/ │ ├─ backgrounds/ ├─ eval/ │ ├─ alarms/ │ ├─ backgrounds/<br><br><br>Content of <strong>metadata</strong>.zip <br><br>├─ audio_metadata.xlsx : Table of the alarms and background files with short descriptions, <br>│ source file names, and temporal information (in seconds). ├─ source_file_metadata.xlsx : Metadata table of the original files used to generate alarms and backgrounds. </pre>
Data from: Context-dependent alarm signalling in an insect
Animals often respond to danger by raising alarm to inform others. Alarm signals come in many different forms, such as visual or mechanical display, sound or odour. Some animals produce vocal alarm signals that vary with the level of danger. For chemical alarm signals, virtually nothing is known about such context-dependent signalling due to a general notion that alarm pheromones have fixed compositions. Here, we show that larvae of the Western Flower Thrips (Frankliniella occidentalis) produce an alarm pheromone whose composition varies with the level of danger they face: the presence of a relatively harmless predator or a very dangerous predator, that is either actually attacking or not. The frequency of alarm pheromone excretion increases with the level of danger. Moreover, the composition of excreted alarm pheromone varies in the relationship between total and relative amount of the putative two components, decyl acetate (DAc) and dodecyl acetate (DDAc). When pheromone is excreted with a predator present but not attacking, the percentage DDAc increases with the total amount of pheromone. When a predator does attack, however, the relationship between percentage DDAc and total amount of pheromone is reversed. Taken together, the alarm signal of thrips larvae appears to be context dependent, which to our knowledge is the first report of context-dependent composition of an alarm pheromone.
Supplementary material 4 from: Renault D, Manfrini E, Leroy B, Diagne C, Ballesteros-Mejia L, Angulo E, Courchamp F (2021) Biological invasions in France: Alarming costs and even more alarming knowledge gaps. In: Zenni RD, McDermott S, García-Berthou E, Essl F (Eds) The economic costs of biological invasions around the world. NeoBiota 67: 191-224. https://doi.org/10.3897/neobiota.67.59134
Listing of the 2750 introduced or invasive alien species with accepted names in France
Supplementary material 2 from: Renault D, Manfrini E, Leroy B, Diagne C, Ballesteros-Mejia L, Angulo E, Courchamp F (2021) Biological invasions in France: Alarming costs and even more alarming knowledge gaps. In: Zenni RD, McDermott S, García-Berthou E, Essl F (Eds) The economic costs of biological invasions around the world. NeoBiota 67: 191-224. https://doi.org/10.3897/neobiota.67.59134
Description of the different sectors of the InvaCost database
Supplementary material 5 from: Renault D, Manfrini E, Leroy B, Diagne C, Ballesteros-Mejia L, Angulo E, Courchamp F (2021) Biological invasions in France: Alarming costs and even more alarming knowledge gaps. In: Zenni RD, McDermott S, García-Berthou E, Essl F (Eds) The economic costs of biological invasions around the world. NeoBiota 67: 191-224. https://doi.org/10.3897/neobiota.67.59134
Number of cost estimates per year for France
Supplementary material 3 from: Renault D, Manfrini E, Leroy B, Diagne C, Ballesteros-Mejia L, Angulo E, Courchamp F (2021) Biological invasions in France: Alarming costs and even more alarming knowledge gaps. In: Zenni RD, McDermott S, García-Berthou E, Essl F (Eds) The economic costs of biological invasions around the world. NeoBiota 67: 191-224. https://doi.org/10.3897/neobiota.67.59134
Listing of the environment-taxonomic groupings
The design of evacuation alarms for people who are deaf or hard of hearing
<p>This is the video in addition to the master thesis of the same topic. May, 2023</p>
Intervention Program for Alarm Fatigue in NICU Nurses Based on ABC-X Model
ClinicalTrials.gov study NCT07025863. IPD Sharing: YES. Countries: 1. Publications: 0.
Randomized Study Of Novel Enuresis Alarm vs Standard Bedwetting Alarm
ClinicalTrials.gov study NCT05214131. IPD Sharing: NO. Countries: 1. Publications: 0.
Effects of Relaxation, Mindfulness, and Breathing Therapy on Alarm Fatigue and Psychological Well-being in Intensive Care Nurses
ClinicalTrials.gov study NCT07124364. IPD Sharing: YES. Countries: 1. Publications: 0.
Evaluation of Hypo-Safe Hypoglycaemia Alarm Device - the Pilot 2 Study - Healthy Subjects
ClinicalTrials.gov study NCT02402153. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Oxygen Saturation Alarms in the Neonatal Intensive Care Unit
ClinicalTrials.gov study NCT01085539. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Data from: Personal information about danger trumps social information from avian alarm calls
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Data from: Alarm calls of a cooperative bird are referential and elicit context-specific antipredator behavior
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Data from: Wild dwarf mongooses produce general alert and predator-specific alarm calls
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Data from: Honey bees modulate their olfactory learning in the presence of hornet predators and alarm component
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Suction feeding by predators limits direct release of alarm cues in fishes
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