Large Scale Geospatial Analysis on Mobile Application Usage

2014 ◽  
Author(s):  
Maria Gerontini ◽  
Simon Moritz
Keyword(s):  
Mobile Application ◽  
Large Scale ◽  
Geospatial Analysis

scholarly journals Glyphosate residue concentrations in honey attributed through geospatial analysis to proximity of large-scale agriculture and transfer off-site by bees

PLoS ONE ◽  
2018 ◽  
Vol 13 (7) ◽  
pp. e0198876 ◽  
Author(s):  
Carl J. Berg ◽  
H. Peter King ◽  
Glenda Delenstarr ◽  
Ritikaa Kumar ◽  
Fernando Rubio ◽  
...  
Keyword(s):  
Large Scale ◽  
Geospatial Analysis

scholarly journals A Digital Health Weight Loss Program in 250,000 Individuals

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Conor Senecal ◽  
Robert Jay Widmer ◽  
Beth R. Larrabee ◽  
Mariza de Andrade ◽  
Lilach O. Lerman ◽  
...  
Keyword(s):  
Weight Loss ◽  
Mobile Application ◽  
Weight Control ◽  
Large Scale ◽  
Digital Health ◽  
Large Population ◽  
Weight Loss Program ◽  
Significant Weight Loss ◽  
Face To Face ◽  
Clinically Significant

Importance. Obesity is a worsening epidemic worldwide. Effective and accessible weight loss programs to combat obesity on a large scale are warranted, but a need for frequent face-to-face care might impose a limitation. Objective. To evaluate whether individuals following a weight loss program based on a mobile application, wireless scale, and nutritional program but no face-to-face care can achieve clinically significant weight loss in a large cohort. Design. Retrospective observational analysis. Setting. China from October 2016 to December 2017. Participants. Mobile application users with a minimum of 2 weights (baseline and ≥35 days). Intervention. A commercial (Weijian Technologies) weight loss program consisting of a dietary replacement, self-monitoring using a wireless home scale, and frequent guidance via mobile application. Main Outcome. Mean weight change around 42, 60, 90, and 120 days after program initiation with subgroup analysis by gender, age, and frequency of use. Results. 251,718 individuals, with a mean age of 37.3 years (SD: 9.86) (79% female), were included with a mean weight loss of 4.3 kg (CI: ±0.02) and a mean follow-up of 120 days (SD: 76.8 days). Mean weight loss at 42, 60, 90, and 120 d was 4.1 kg (CI: ±0.02), 4.9 kg (CI: ±0.02), 5.6 kg (CI: ±0.03), and 5.4 kg (CI: ±0.04), respectively. At 120 d, 62.7% of participants had lost at least 5% of their initial weight. Both genders and all usage frequency tertiles showed statistically significant weight loss from baseline at each interval (P<0.001), and this loss was greater in men than in women (120 d: 6.5 vs. 5.2 kg; P<0.001). The frequency of recording (categorized as high-, medium-, or low-frequency users) was associated with greater weight loss when comparing high, medium, and low tertile use groups at all time intervals investigated (e.g., 120 d: −8.6, −5.6, and −2.2 kg, respectively; P<0.001). Conclusions. People following a commercially available hybrid weight loss program using a mobile application, wireless scale, and nutritional program without face-to-face interaction on average achieved clinically significant short- and midterm weight loss. These results support the implementation of comparable technologies for weight control in a large population.

scholarly journals Quantifying Drosophila adults with the use of a smartphone

Biology Open ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. bio054452 ◽  
Author(s):  
Evgenia K. Karpova ◽  
Evgenii G. Komyshev ◽  
Mikhail A. Genaev ◽  
Natalya V. Adonyeva ◽  
Dmitry A. Afonnikov ◽  
...  
Keyword(s):  
Mobile Application ◽  
Quantitative Estimate ◽  
Large Scale ◽  
High Accuracy ◽  
Mobile App ◽  
Average Error ◽  
Lighting Conditions ◽  
Manual Counting ◽  
Long Time ◽  
Imaging Conditions

ABSTRACTA method for automation of imago quantifying and fecundity assessment in Drosophila with the use of mobile devices running Android operating system is proposed. The traditional manual method of counting the progeny takes a long time and limits the opportunity of making large-scale experiments. Thus, the development of computerized methods that would allow us to automatically make a quantitative estimate of Drosophilamelanogaster fecundity is an urgent requirement. We offer a modification of the mobile application SeedCounter that analyzes images of objects placed on a standard sheet of paper for an automatic calculation of D. melanogaster offspring or quantification of adult flies in any other kind of experiment. The relative average error in estimates of the number of flies by mobile app is about 2% in comparison with the manual counting and the processing time is six times shorter. Study of the effects of imaging conditions on accuracy of flies counting showed that lighting conditions do not significantly affect this parameter, and higher accuracy can be achieved using high-resolution smartphone cameras (8 Mpx and more). These results indicate the high accuracy and efficiency of the method suggested.This article has an associated First Person interview with the first author of the paper.

Discovering Mobile Application Usage Patterns from a Large-Scale Dataset

2018 ◽  
Vol 12 (5) ◽  
pp. 1-36 ◽  
Author(s):  
Fabrício A. Silva ◽  
Augusto C. S. A. Domingues ◽  
Thais R. M. Braga Silva
Keyword(s):  
Mobile Application ◽  
Large Scale ◽  
Large Scale Dataset ◽  
Usage Patterns

scholarly journals UAV DATA PROCESSING FOR RAPID MAPPING ACTIVITIES

2015 ◽  
Vol XL-3/W3 ◽  
pp. 371-377 ◽  
Author(s):  
W. Tampubolon ◽  
W. Reinhardt
Keyword(s):  
Disaster Preparedness ◽  
Large Scale ◽  
Low Cost ◽  
Geospatial Analysis ◽  
Geospatial Data ◽  
Feature Extraction Method ◽  
Emergency Situations ◽  
Rapid Mapping ◽  
Related Object ◽  
Aerial Vehicle

During disaster and emergency situations, geospatial data plays an important role to serve as a framework for decision support system. As one component of basic geospatial data, large scale topographical maps are mandatory in order to enable geospatial analysis within quite a number of societal challenges. <br><br> The increasing role of geo-information in disaster management nowadays consequently needs to include geospatial aspects on its analysis. Therefore different geospatial datasets can be combined in order to produce reliable geospatial analysis especially in the context of disaster preparedness and emergency response. A very well-known issue in this context is the fast delivery of geospatial relevant data which is expressed by the term “Rapid Mapping”. <br><br> Unmanned Aerial Vehicle (UAV) is the rising geospatial data platform nowadays that can be attractive for modelling and monitoring the disaster area with a low cost and timely acquisition in such critical period of time. Disaster-related object extraction is of special interest for many applications. <br><br> In this paper, UAV-borne data has been used for supporting rapid mapping activities in combination with high resolution airborne Interferometric Synthetic Aperture Radar (IFSAR) data. A real disaster instance from 2013 in conjunction with Mount Sinabung eruption, Northern Sumatra, Indonesia, is used as the benchmark test for the rapid mapping activities presented in this paper. On this context, the reliable IFSAR dataset from airborne data acquisition in 2011 has been used as a comparable dataset for accuracy investigation and assessment purpose in 3 D reconstructions. After all, this paper presents a proper geo-referencing and feature extraction method of UAV data to support rapid mapping activities.

scholarly journals easyFulcrum: An R package to process and analyze ecological sampling data generated using the Fulcrum mobile application

PLoS ONE ◽  
2021 ◽  
Vol 16 (10) ◽  
pp. e0254293 ◽  
Author(s):  
Matteo Di Bernardo ◽  
Timothy A. Crombie ◽  
Daniel E. Cook ◽  
Erik C. Andersen
Keyword(s):  
Mobile Application ◽  
Large Scale ◽  
Data Entry ◽  
R Package ◽  
Environmental Data ◽  
Software Environment ◽  
R Software ◽  
Natural Sampling ◽  
Disparate Data ◽  
Ecological Sampling

Large-scale ecological sampling can be difficult and costly, especially for organisms that are too small to be easily identified in a natural environment by eye. Typically, these microscopic floral and fauna are sampled by collecting substrates from nature and then separating organisms from substrates in the laboratory. In many cases, diverse organisms can be identified to the species-level using molecular barcodes. To facilitate large-scale ecological sampling of microscopic organisms, we used a geographic data-collection platform for mobile devices called Fulcrum that streamlines the organization of geospatial sampling data, substrate photographs, and environmental data at natural sampling sites. These sampling data are then linked to organism isolation data from the laboratory. Here, we describe the easyFulcrum R package, which can be used to clean, process, and visualize ecological field sampling and isolation data exported from the Fulcrum mobile application. We developed this package for wild nematode sampling, but it can be used with other organisms. The advantages of using Fulcrum combined with easyFulcrum are (1) the elimination of transcription errors by replacing manual data entry and/or spreadsheets with a mobile application, (2) the ability to clean, process, and visualize sampling data using a standardized set of functions in the R software environment, and (3) the ability to join disparate data to each other, including environmental data from the field and the molecularly defined identities of individual specimens isolated from samples.

Building a Cloud-Based Mobile Application Testbed

2013 ◽  
pp. 879-899 ◽  
Author(s):  
Hamilton Turner ◽  
Jules White ◽  
Jeff Reed ◽  
José Galindo ◽  
Adam Porter ◽  
...  
Keyword(s):  
Social Networking ◽  
Mobile Application ◽  
Large Scale ◽  
Smartphone Application ◽  
Application Research ◽  
Application Development ◽  
Production Scale ◽  
Cloud Computing Environment ◽  
Mobile Application Development ◽  
Mobile Smartphone

A proliferation of mobile smartphone platforms, including Android devices, has triggered a rise in mobile application development for a diverse set of situations. Testing of these smartphone applications can be exceptionally difficult, due to the challenges of orchestrating production-scale quantities of smartphones such as difficulty in managing thousands of sensory inputs to each individual smartphone device. This work presents the Android Tactical Application Assessment and Knowledge (ATAACK) Cloud, which utilizes a cloud computing environment to allow smartphone-based security, sensing, and social networking researchers to rapidly use model-based tools to provision experiments with a combination of 1,000+ emulated smartphone instances and tens of actual devices. The ATAACK Cloud provides a large-scale smartphone application research testbed.

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