A collection of open datasets created by different groups across Elsevier in collaboration with our research collaboration partners. In line with FAIR data practices, these data are openly shared to foster research and promote reproducibility.
Contributors:Gupta (PT) Dr. Muskan, Srivastav (PT) Dr. Adarsh, Vishal Gupta Vishal
Background: Physiotherapy is a kind of science which helps and supports the patient to live a healthy lifestyle. It plays a crucial role in rehabilitating a patient. Physiotherapy is defined as a health care profession dealing with human mobility and function maximizing quality on one’s life and movement strength within the loop of prevention, promotion, treatment/intervention, habilitation and rehabilitation. Still, there are people who aren’t aware about the kind of treatment it can provide. In order to make attempts to make people aware, it is necessary to evaluate the level of awareness among medical practitioners. Hereby, objective of this study is to find out how much aware the medical practitioners are in terms of importance and need of physiotherapy for the treatment of the patients.
Materials and Methods: Apparently, a cross sectional pilot study was carried out based purely on questionnaire method, prior to the data collection the content validation of the questionnaire was executed by online Delphi method. Questions wording and the order of the questions underwent review by experts in the field of physiotherapy. Valid and approved questionnaire was sent through Google form link for the data collection. All willing medical practitioners from different streams along with graduates and super specialists were included, whereas students and non internet users were excluded. Responses of each and every participant were analyzed and were represented in percentage, graph or pie charts.
Result: There is awareness regarding the term physiotherapy (98.4%), but specialization in physiotherapy is less known, maximum of the subjects were aware about specialization in orthopedics and specialization in women’s health, community based rehabilitation and dermatology is least known. It was found out that 79% of the medical practitioners have objection in physiotherapist having first contact with the patient. Moreover, only 67.7% of the participants agreed to let a physiotherapist decide the physiotherapy treatment protocol.
Conclusion: The study revealed that there is lack of awareness regarding assessment and treatment protocol provided by physiotherapy. However, doctors believe physiotherapist has big role in treating ICU and immobilized patients. There is less information regarding radiation modalities as well as recent advances in rehabilitation.
Keywords: medical practitioners, awareness, physiotherapist, rehabilitation.
Explosive volcanic eruptions are severe natural phenomena that produce pyroclastic materials, eruption columns, and volcanic ash clouds. During moist weather conditions, volcanic eruption products can be coated with water, resulting in wet ash and/or mixtures of ash and rain. Wet ash, which is heavier than dry ash increases the risk of towers and poles collapsing, and rain mixed with volcanic ash is a harmful natural phenomenon that threatens human life, infrastructures, economies, agriculture, etc. Optical measurements, which are made with ground-based instruments (ex, two-dimensional video disdrometer (2DVD), parsivel, etc.), cameras, and satellites, are limited in their ability to detect volcanic ash clouds and eruption columns in cloudy or precipitation conditions. Weather radar is one of the key instruments for studying and monitoring both precipitation and volcanic ash clouds, since it can observe both types of system and can provide valuable information that can discriminate between the two systems through the use of polarimetric parameters. In this paper, our goal is to find characteristic of polarimetric parameters for volcanic ash clouds and precipitation using observed radar data, and to make a classification algorithm for discriminating two systems.
Assessment of seed germination is an essential task for seed researchers to measure the quality and performance of seeds. Usually, seed assessments are done mainly manual, which is a cumbersome, time consuming and error-prone process. Classical image analyses methods are not well suited for large-scale germination experiments, because they often rely on manual adjustments of color-based thresholds. We here propose a machine learning approach using modern artificial neural networks for accurate seed germination detection for high-throughput seed germination experiments.
This dataset contains captures of Germination Experiments of 824 Zea mays seeds, 811 Secale cereale seeds and 814 Pennisetum glaucum seeds. Approximately 10 seeds were placed in one petri dish and captured by a low-cost Raspberry Pi Camera Module (v2.1) in intervals of 30 minutes for ~ 2 days. All Images were annotated by Bounding Boxes containing their germination state (germinated/non-germinated). The code for running the models that are built with this data can be found on GitHub (https://github.com/grimmlab/GerminationPrediction).
This database contains facial images of volunteers in frontal and random poses. Each facial image collection has a visible light image, an infrared image and a depth image.
The images in this database version were collected by a single person during the period of December 17 2018 to 01 July of the year 2019 at Univali University.
This dataset was created with the approval of CEP (Research Ethics Committee) from University of Itajaí Valley - Brazil with CAAE (Certificate of Presentation of Ethical Appreciation) number: 97615018.9.0000.012.
The V2 database contains:
1600 facial image samples (approximately)
a frontal image (VIS, IR, DEPTH) by volunteer
an image (VIS, IR, DEPTH) with a face turned to the right by a volunteer
an image (VIS, IR, DEPTH) with a face turned to the left by a volunteer
an image (VIS, IR, DEPTH) with a face turned up by a volunteer
an image (VIS, IR, DEPTH) with a face turned down by a volunteer
an image (VIS, IR, DEPTH) with random pose by volunteer
The images in this database version were collected by a single person during the period of October 10 to 27 of the year 2017. 8 classes in the computer science course at UNIVALI University were invited to participate in this paper, where 64 accepted to participate in this work.
The V1 database contains:
267 facial image samples
267 facial images of the visible light spectrum
267 facial images of the infrared spectrum (corrupted)
267 depth images (3D)
Linear regression analysis was used to investigate the relationship among the variables. The results showed that academic stress was positively related to psychological distress, which may further lead to severe smartphone dependence. Psychological distress partially mediated the relationship between academic stress and smartphone dependence. The mediating effect of psychological distress between academic stress and smartphone dependence was moderated by academic resilience. Specifically, academic resilience weakened the indirect relationship between academic stress and smartphone dependence that was mediated by psychological distress.