RETRA

2013 ◽  
Vol 4 (3) ◽  
pp. 80-93
Author(s):  
Venkata S. Inampudi ◽  
Russell Kondaveti ◽  
Aura Ganz
Keyword(s):  
Real Time ◽  
Optimal Solution ◽  
Initial Response ◽  
Geographical Proximity ◽  
Complex Task ◽  
Web Based ◽  
Resource Managers ◽  
Optimal Deployment ◽  
Resource Deployment ◽  
Emergency Resource

In this paper, the authors introduce a real time web based tool for resource allocation (RETRA) that can assist the incident commanders and resource managers in the complex task of emergency resource deployment for multiple simultaneous incidents that occur in close geographical proximity. RETRA real time inputs include the location of the emergency sites and the required resources with associated priorities. It generates an optimal deployment plan so that emergency sites with highest priorities for a resource are assigned that resource in the least amount of time. The optimal solution is presented graphically using Google Maps. RETRA can be used for emergency resource deployment at the initial response stage of a disaster. 1

RETRA

2015 ◽  
pp. 836-848
Author(s):  
Venkata S. Inampudi ◽  
Russell Kondaveti ◽  
Aura Ganz
Keyword(s):  
Real Time ◽  
Optimal Solution ◽  
Initial Response ◽  
Geographical Proximity ◽  
Complex Task ◽  
Web Based ◽  
Resource Managers ◽  
Optimal Deployment ◽  
Resource Deployment ◽  
Emergency Resource

In this paper, the authors introduce a real time web based tool for resource allocation (RETRA) that can assist the incident commanders and resource managers in the complex task of emergency resource deployment for multiple simultaneous incidents that occur in close geographical proximity. RETRA real time inputs include the location of the emergency sites and the required resources with associated priorities. It generates an optimal deployment plan so that emergency sites with highest priorities for a resource are assigned that resource in the least amount of time. The optimal solution is presented graphically using Google Maps. RETRA can be used for emergency resource deployment at the initial response stage of a disaster. 1

SPLASSH: A Collaborative Web-Based Application That Crowdsources Environmental Data in Real Time

2018 ◽  
Vol 52 (1) ◽  
pp. 18-27 ◽  
Author(s):  
Lisa G. Adams ◽  
John N. Mwaniki ◽  
Salim J. Dabdoub ◽  
Michael G. Adams
Keyword(s):  
Real Time ◽  
Environmental Data ◽  
Web Based ◽  
The Public ◽  
Resource Managers ◽  
Aquatic Science ◽  
Public Citizen ◽  
Definition Of ◽  
Management Capabilities ◽  
Beta Version

AbstractSPLASSH (Student Programs Like Aquatic Science Sampling Headquarters, <ext-link ext-link-type="uri" href="https://splassh.org">https://splassh.org</ext-link>) is a collaborative web-based application that crowdsources environmental data in real time. Originally launched in 2014, SPLASSH beta version 1.0 was designed to showcase water projects conducted by students. Through its development, it has broadened its reach from students to educators (formal and informal), researchers, resource managers, science professionals, and the public (citizen scientists). SPLASSH's beta version 2.0 (Patent Pending) has an innovative, customizable environmental tracker and project management capabilities that foster community building through collaboration. SPLASSH offers the public an opportunity to contribute more than just data to an existing project. It encourages citizens to play a lead role by initiating their own projects, truly validating and broadening the definition of citizen science. Learning and project outcomes will be measured for their impact and effectiveness.

scholarly journals Real-time SARS-CoV-2 diagnostic and variants tracking over multiple candidates using nanopore DNA sequencing

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
François Stüder ◽  
Jean-Louis Petit ◽  
Stefan Engelen ◽  
Marco Antonio Mendoza-Parra
Keyword(s):  
Real Time ◽  
Optimal Solution ◽  
Proof Of Concept ◽  
Gene Encoding ◽  
Spike Gene ◽  
Long Period ◽  
The World ◽  
Receptor Recognition ◽  
Nanopore Dna Sequencing ◽  
Novel Coronavirus

AbstractSince December 2019, a novel coronavirus responsible for a severe acute respiratory syndrome (SARS-CoV-2) is accountable for a major pandemic situation. The emergence of the B.1.1.7 strain, as a highly transmissible variant has accelerated the world-wide interest in tracking SARS-CoV-2 variants’ occurrence. Similarly, other extremely infectious variants, were described and further others are expected to be discovered due to the long period of time on which the pandemic situation is lasting. All described SARS-CoV-2 variants present several mutations within the gene encoding the Spike protein, involved in host receptor recognition and entry into the cell. Hence, instead of sequencing the whole viral genome for variants’ tracking, herein we propose to focus on the SPIKE region to increase the number of candidate samples to screen at once; an essential aspect to accelerate diagnostics, but also variants’ emergence/progression surveillance. This proof of concept study accomplishes both at once, population-scale diagnostics and variants' tracking. This strategy relies on (1) the use of the portable MinION DNA sequencer; (2) a DNA barcoding and a SPIKE gene-centered variant’s tracking, increasing the number of candidates per assay; and (3) a real-time diagnostics and variant’s tracking monitoring thanks to our software RETIVAD. This strategy represents an optimal solution for addressing the current needs on SARS-CoV-2 progression surveillance, notably due to its affordable implementation, allowing its implantation even in remote places over the world.

scholarly journals An Autonomous Platform for Near Real-Time Surveillance of Harmful Algae and Their Toxins in Dynamic Coastal Shelf Environments

2021 ◽  
Vol 9 (3) ◽  
pp. 336
Author(s):  
Stephanie K. Moore ◽  
John B. Mickett ◽  
Gregory J. Doucette ◽  
Nicolaus G. Adams ◽  
Christina M. Mikulski ◽  
...  
Keyword(s):  
Real Time ◽  
Domoic Acid ◽  
Harmful Algae ◽  
Coastal Trapped Waves ◽  
Resource Managers ◽  
Bloom Formation ◽  
High Concentrations ◽  
Data Gap ◽  
Autonomous Technology

Efforts to identify in situ the mechanisms underpinning the response of harmful algae to climate change demand frequent observations in dynamic and often difficult to access marine and freshwater environments. Increasingly, resource managers and researchers are looking to fill this data gap using unmanned systems. In this study we integrated the Environmental Sample Processor (ESP) into an autonomous platform to provide near real-time surveillance of harmful algae and the toxin domoic acid on the Washington State continental shelf over a three-year period (2016–2018). The ESP mooring design accommodated the necessary subsystems to sustain ESP operations, supporting deployment durations of up to 7.5 weeks. The combination of ESP observations and a suite of contextual measurements from the ESP mooring and a nearby surface buoy permitted an investigation into toxic Pseudo-nitzschia spp. bloom dynamics. Preliminary findings suggest a connection between bloom formation and nutrient availability that is modulated by wind-forced coastal-trapped waves. In addition, high concentrations of Pseudo-nitzschia spp. and elevated levels of domoic acid observed at the ESP mooring location were not necessarily associated with the advection of water from known bloom initiation sites. Such insights, made possible by this autonomous technology, enable the formulation of testable hypotheses on climate-driven changes in HAB dynamics that can be investigated during future deployments.

scholarly journals Deep-Framework: A Distributed, Scalable, and Edge-Oriented Framework for Real-Time Analysis of Video Streams

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4045
Author(s):  
Alessandro Sassu ◽  
Jose Francisco Saenz-Cogollo ◽  
Maurizio Agelli
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Video Data ◽  
Video Analytics ◽  
Web Based ◽  
Real Time Analysis ◽  
Open Source Framework ◽  
Cluster Configuration ◽  
Time Requirements ◽  
High Level

Edge computing is the best approach for meeting the exponential demand and the real-time requirements of many video analytics applications. Since most of the recent advances regarding the extraction of information from images and video rely on computation heavy deep learning algorithms, there is a growing need for solutions that allow the deployment and use of new models on scalable and flexible edge architectures. In this work, we present Deep-Framework, a novel open source framework for developing edge-oriented real-time video analytics applications based on deep learning. Deep-Framework has a scalable multi-stream architecture based on Docker and abstracts away from the user the complexity of cluster configuration, orchestration of services, and GPU resources allocation. It provides Python interfaces for integrating deep learning models developed with the most popular frameworks and also provides high-level APIs based on standard HTTP and WebRTC interfaces for consuming the extracted video data on clients running on browsers or any other web-based platform.

scholarly journals Webinar : An Initial Experience with Web-based Real Time Interactive Clinical Seminars for Japanese Medical Students

2010 ◽  
Vol 11 (2) ◽  
pp. 87-90 ◽  
Author(s):  
Gerald H. Stein ◽  
Ayako Shibata ◽  
Miho Kojima Bautista ◽  
Yasuharu Tokuda
Keyword(s):  
Medical Students ◽  
Real Time ◽  
Initial Experience ◽  
Web Based

scholarly journals Multi-Parameter Vital Sign Telemedicine System Using Web Socket for COVID-19 Pandemics

Healthcare ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 285
Author(s):  
Chuchart Pintavirooj ◽  
Tanapon Keatsamarn ◽  
Treesukon Treebupachatsakul
Keyword(s):  
Real Time ◽  
Vital Sign ◽  
Vital Signs ◽  
Mobile Network ◽  
Cloud Platform ◽  
Telemedicine System ◽  
Web Based ◽  
The Real ◽  
Real Time Application ◽  
Commercial Platform

Telemedicine has become an increasingly important part of the modern healthcare infrastructure, especially in the present situation with the COVID-19 pandemics. Many cloud platforms have been used intensively for Telemedicine. The most popular ones include PubNub, Amazon Web Service, Google Cloud Platform and Microsoft Azure. One of the crucial challenges of telemedicine is the real-time application monitoring for the vital sign. The commercial platform is, by far, not suitable for real-time applications. The alternative is to design a web-based application exploiting Web Socket. This research paper concerns the real-time six-parameter vital-sign monitoring using a web-based application. The six vital-sign parameters are electrocardiogram, temperature, plethysmogram, percent saturation oxygen, blood pressure and heart rate. The six vital-sign parameters were encoded in a web server site and sent to a client site upon logging on. The encoded parameters were then decoded into six vital sign signals. Our proposed multi-parameter vital-sign telemedicine system using Web Socket has successfully remotely monitored the six-parameter vital signs on 4G mobile network with a latency of less than 5 milliseconds.

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