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166 results for “Victimization”
Nurse-Led Social Emotional Learning Program for Reducing Bullying, Victimization and Bystander Behaviors in Adolescents
ClinicalTrials.gov study NCT07372456. IPD Sharing: NO. Countries: 1. Publications: 3.
Re-Step: Dynamic Balance Treatment of Gait for Acquired Brain Injury (ABI) Victims
ClinicalTrials.gov study NCT02215590. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Our Anesthesia Experiment Applied to Child Earthquake Victims in the February 6, 2023 Earthquake
ClinicalTrials.gov study NCT06310265. IPD Sharing: NO. Countries: 1. Publications: 1.
Therapeutic Exercise on the Psychological Well-being of Women Victims of Gender Violence.
ClinicalTrials.gov study NCT05981417. IPD Sharing: NO. Countries: 1. Publications: 12.
Relief of Symptoms of Post-traumatic Stress for Victims of Gun Violence
ClinicalTrials.gov study NCT03916614. IPD Sharing: Not stated. Countries: 1. Publications: 1.
SSRI Administration to Reduce Acute Stress Disorder Symptoms and Prevent Depression and PTSD in Physical Trauma Victims
ClinicalTrials.gov study NCT00114374. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Effects of Repetitive Transcranial Magnetic Stimulation in the Treatment of Phantom Limb Pain in Landmine Victims: ANTARES
ClinicalTrials.gov study NCT01872481. IPD Sharing: Not stated. Countries: 1. Publications: 54.
IRT and CBT in Sexual Assault Victims With PTSD
ClinicalTrials.gov study NCT03169712. IPD Sharing: NO. Countries: 1. Publications: 2.
Attachment Style and Mentalization Impact Among Women Victims of Domestic Violence
ClinicalTrials.gov study NCT05331352. IPD Sharing: NO. Countries: 1. Publications: 1.
Family-Based Drug Services for Young Disaster Victims
ClinicalTrials.gov study NCT01859000. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Effectiveness of Interpersonal Relationship Psychotherapy Intervention for Female Victims of Intimate Partner Violence
ClinicalTrials.gov study NCT06687278. IPD Sharing: NO. Countries: 1. Publications: 5.
Data from: Distinguishing the victim from the threat: SNP‐based methods reveal the extent of introgressive hybridization between wildcats and domestic cats in Scotland and inform future in situ and ex situ management options for species restoration
Open the record for dataset details and reuse information.
Data from: The journey from traffic offender to severe road trauma victim: destiny or preventive opportunity?
Open the record for dataset details and reuse information.
Data from: baobabs: symbol of resilience or victim of climate change?
Open the record for dataset details and reuse information.
Vehemence and Victims
<p>In this video we present our working paper 'Vehemence and Victims: Using generic lexicons to investigate emotions in historical Dutch parliamentary debates on war victim legislation (1945-1990)'. In this presentation we briefly introduce the topic of investigation, relevant historiography, questions asked, methods used, and some preliminary findings.</p> <p> </p>
FIGURE 22 in The Architectonicidae and Mathildidae (Gastropoda, Heterobranchia) of the Miocene Paratethys Sea-victims of the Miocene Climatic Transition
FIGURE 22. Whorl profiles of Mathilda (A–E), Fimbriatella (F–H) and Pseudotuba (I).
FIGURE 1 in The Architectonicidae and Mathildidae (Gastropoda, Heterobranchia) of the Miocene Paratethys Sea-victims of the Miocene Climatic Transition
FIGURE 1. Descriptive terms used in descriptions of Architectonicidae (modified from Bieler 1993).
FIGURE 3 in The Architectonicidae and Mathildidae (Gastropoda, Heterobranchia) of the Miocene Paratethys Sea-victims of the Miocene Climatic Transition
FIGURE 3. Descriptive terms used in descriptions of Mathildidae.
A UWB Radar and Machine Learning-Based Tool for Detecting Victims Through Foliage in Search and Rescue Operations
<h1>Project Description</h1> <p>During our research in University of West Attica (UniWA) we addressed the problem of victim detection through foliage in Search and Rescue operations. For this purpose, a dataset of respiration signal sessions in the field was collected using a proposed tool consiting of a UWB pulsed radar system, and then these data fed a machine learning tool to enhance FR's operations by providing predictions about human presence behind foliage. In addition, two anemometer sensors were used to record wind data, and a respiration belt was employed to obtain the ground truth measurements about the subject's respiration rate.</p> <p>The setup for each session was the same. The UWB radar [<a href="https://sensorlogic.ai/sensor-products">1</a>] was mounted on tripod facing the foliage, the subject was located behind the foliage wearing a respiration belt [<a href="https://www.zephyranywhere.com/">4</a>] for breath recording. On the same tripod two anemometers [<a href="https://gr.mouser.com/new/dfrobot/dfrobot-rs485-wind-speed-transmitter/">2</a>],[<a href="https://gr.mouser.com/new/dfrobot/dfrobot-rs485-wind-direction-transmitter/">3</a>] were placed so a comprehesive image of the wind condiditon during the session could be obtained. These sensors were connected to a laptop via USB, about 3 meters away. The distance between the tripod and the foliage was fixed at 1 meter. Foliage (mostly bushes and small olive trees) had length varying from 1 to 3 meters and the subject (in case of presence session) was from 0.5 to 3 meters away from the foliage. In total we never exceeded the 9.2 meters range (unambiguous range) limit of the radar.</p> <h1>Dataset Description</h1> <p>The dataset consists of 268 sessions of radar, wind and respiration belt data, of which 141 sessions correspond to human presence and 127 to human absence. Each session has a duration of 150 seconds, thus amounting to approximately 6 hours of data for human presence and approximately 5.5 hours of data for human absence.</p> <h2>Dataset Contents</h2> <p>Each session folder is given an individual name X = posixtime; this name designates the exact time (in posixtime format) when the session was started. For example, in the dataset preview below there can be seen one folder named "1688457913"; this folder corresponds to the measurement session that was initiated exactly on 1688457913 in posixtime format (in this example, X = 1688457913). Furthermore, for the "X" posixtime-named folder, there are the following subfolders and files:</p> <p>1. One subfolder named Workspaces_X, containing:</p> <ul> <li>Files named "<em>Workspace_k.mat</em>", where k the number of the created workspaces containing radar signal recording at 16 FPS.</li> <li>A file named "<em>settings.mat</em>", containing the device settings and the session's distances regarding topology.</li> <li>A file named "<em>windData_original.mat</em>", containing the original data from anemometer sensors saved from the data stream at 4 FPS, provided from a microcontroller followed RS485 protocol.</li> </ul> <p>2. Two files containing the raw data recorded from the respiration belt (only for folders corresponding to human presence and for which a respiration belt was used for obtaining the ground truth measurements of the subject's respiration data.)</p> <ul> <li>The "<em>1_YY_MM_DD_HH_MM_general.csv</em>", contains the timestamp in datetime of the sensor and the Android device, the heart rate estimation, the mean breaths per minute and the included IMU belt sensor measurement.</li> <li>The "<em>1_YY_MM_DD_HH_MM_wave.csv</em>", contains the timestamp in datetime of the sensor and the Android device, and 18 values (FPS) of the strain gauge sensor changes from the respiration belt.</li> </ul> <p>3. A file named "<em>X.xlsx</em>", containing the concatenation of the workspaces of the radar signal.</p> <p>4. A file named "<em>windData_X.csv</em>", containing the synchronized data of anemometer sensors with radar data.</p> <p>5. A file named "<em>BeltWfm_X.xlsx</em>", containing the synchronized data of respiration belt with radar data (only for folders corresponding to human presence and for which a respiration belt was used for obtaining the ground truth measurements of the subject's respiration data).</p> <h1>Proposed Tool COTS components</h1> <ol> <li>SLMX4 UWB pulse radar [<a href="https://sensorlogic.ai/sensor-products">1</a>]</li> <li>Wind Speed [<a href="https://gr.mouser.com/new/dfrobot/dfrobot-rs485-wind-speed-transmitter/">2</a>] and Direction [<a href="https://gr.mouser.com/new/dfrobot/dfrobot-rs485-wind-direction-transmitter/">3</a>] sensors</li> <li>Wind data recording equipment (UART TTL to RS485 Converter, MT3608 DC/DC converter, Arduino)</li> <li>Respiration belt [<a href="https://www.zephyranywhere.com/">4</a>]</li> </ol>
Lethal interactions among forest-grouse predators are numerous, motivated by hunger and carcasses, and their impacts determined by the demographic value of the victims
<p>New vertebrate communities are emerging in Europe following the recovery of multiple native predators to highly anthropized landscapes where predator control is still prevalent. While the lack of reference points for these communities creates novel challenges for conservationists and wildlife managers, they also provide opportunities to further our understanding of species interactions. Despite a growing body of evidence, many aspects of interactions among predators remain poorly understood, impairing our ability to anticipate the effects of such changes in predator communities. Through a systematic literature review, we gathered all the available evidence concerning the existence, strength, and demographic impacts of lethal predator interactions among forest grouse predators in Europe. We found a highly interconnected predator community, with 44 pairwise lethal interactions among 12 taxa. Three of these resulted in some degree of population suppression of the victim, while another three did not. However, most interactions (38) have not been evaluated for population suppression. Additionally, we highlight how predators interact simultaneously with a large range of other predators and identified at least two further species likely suppressed through the combined impacts of multiple predators. We propose that interactions causing demographic suppression are characterized by impacts on individuals with high survival elasticity, and that they are motivated by food limitation and additionally, in mammals, by competition for carcasses. Predator interactions, and our still poor understanding of them, introduce large uncertainties to conservation actions based on the management of predator abundances, which should be carefully evaluated.</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.