scholarly journals Artificial Intelligence assisted Prediction of COVID-19 Hotspots in Third Wave using EHTERAZ

2021 ◽  
Author(s):  
Mustafa Ahmed ◽  
Mohammed Houkan ◽  
Kishor Kumar Sadasivuni
Keyword(s):  
Artificial Intelligence ◽  
Real Time ◽  
Mobile Technology ◽  
Hot Spots ◽  
Mobile Application ◽  
Local State ◽  
Third Wave ◽  
Time Data ◽  
Policy Makers ◽  
Real Time Data

The present study conceptualizes fusion of artificial intelligence and contract tracking mobile application for predicting the COVID-19 hot spots. The findings suggest that mobile technology can be used to provide real-time data on the national and local state of the pandemic, enabling policy makers to make informed decisions in a quickly moving pandemic.

A Novel Well Test Data Analyzer and Process Optimizer Using Artificial Intelligence and Machine Learning Techniques

2021 ◽  
Author(s):  
Nagaraju Reddicharla ◽  
Subba Ramarao Rachapudi ◽  
Indra Utama ◽  
Furqan Ahmed Khan ◽  
Prabhker Reddy Vanam ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Real Time ◽  
Test Data ◽  
Oil Field ◽  
Well Testing ◽  
Well Test ◽  
Time Data ◽  
Real Time Data ◽  
Well Tests

Abstract Well testing is one of the vital process as part of reservoir performance monitoring. As field matures with increase in number of well stock, testing becomes tedious job in terms of resources (MPFM and test separators) and this affect the production quota delivery. In addition, the test data validation and approval follow a business process that needs up to 10 days before to accept or reject the well tests. The volume of well tests conducted were almost 10,000 and out of them around 10 To 15 % of tests were rejected statistically per year. The objective of the paper is to develop a methodology to reduce well test rejections and timely raising the flag for operator intervention to recommence the well test. This case study was applied in a mature field, which is producing for 40 years that has good volume of historical well test data is available. This paper discusses the development of a data driven Well test data analyzer and Optimizer supported by artificial intelligence (AI) for wells being tested using MPFM in two staged approach. The motivating idea is to ingest historical, real-time data, well model performance curve and prescribe the quality of the well test data to provide flag to operator on real time. The ML prediction results helps testing operations and can reduce the test acceptance turnaround timing drastically from 10 days to hours. In Second layer, an unsupervised model with historical data is helping to identify the parameters that affecting for rejection of the well test example duration of testing, choke size, GOR etc. The outcome from the modeling will be incorporated in updating the well test procedure and testing Philosophy. This approach is being under evaluation stage in one of the asset in ADNOC Onshore. The results are expected to be reducing the well test rejection by at least 5 % that further optimize the resources required and improve the back allocation process. Furthermore, real time flagging of the test Quality will help in reduction of validation cycle from 10 days hours to improve the well testing cycle process. This methodology improves integrated reservoir management compliance of well testing requirements in asset where resources are limited. This methodology is envisioned to be integrated with full field digital oil field Implementation. This is a novel approach to apply machine learning and artificial intelligence application to well testing. It maximizes the utilization of real-time data for creating advisory system that improve test data quality monitoring and timely decision-making to reduce the well test rejection.

scholarly journals Research on Security Framework of Mobile Application in Dispatch and Control Domain of Power Grid

2018 ◽  
Vol 173 ◽  
pp. 02029
Author(s):  
XU Jiahui ◽  
YU Hongyuan ◽  
WANG Gang ◽  
WANG Zi ◽  
BAI Jingjie ◽  
...  
Keyword(s):  
Real Time ◽  
Mobile Application ◽  
Power Grid ◽  
Rapid Development ◽  
Mobile Internet ◽  
Internet Technology ◽  
Security Framework ◽  
Time Data ◽  
Real Time Data ◽  
And Control

The rapid development of mobile Internet technology and the wide spread of smart terminals have brought opportunities for the transformation of power grid business model. Compared to the non-real-time information, the real-time and running data of dispatch and control domain is easy to be intercepted and cracked. To solve this problem, this paper presents a new approach to mobile application security framework for the power grid control field. It is to realize secondary encryption by using the method of MD5+AES mixed encryption algorithm and combining the time stamp in real-time data transmission process. At the same time it is to prevent cross-border operations and brute force by using Token authentication and Session technology. China EPRI safety test results show that the application of the framework significantly improves the integrity, safety and reliability of real-time data in power grid control.

scholarly journals PubliCo. A protocol for a new risk and crisis communication platform to bridge the gap between policy makers and the public in the context of the COVID-19 crisis (Preprint)

2021 ◽  
Author(s):  
Giovanni Spitale ◽  
Sonja Merten ◽  
Kristen Jafflin ◽  
Bettina Schwind ◽  
Andrea Kaiser-Grolimund ◽  
...  
Keyword(s):  
Real Time ◽  
Crisis Communication ◽  
Public Perception ◽  
Online Survey ◽  
Control Measures ◽  
Time Data ◽  
Flexible Tool ◽  
Policy Makers ◽  
Real Time Data ◽  
Restrictive Measures

UNSTRUCTURED Background Since the end of 2019, COVID-19 has had a significant impact on citizens around the globe. As governments institute more restrictive measures, public adherence could decrease and discontent mount. Providing high-quality information and countering fake news is important. But we also need feedback loops so that government officials can refine preventive measures and communication strategies. Policy-makers need information – preferably based on real-time data – on the public’s cognitive, emotional and behavioural reaction to public health messages and restrictive measures. PubliCo aims to foster effective and tailored risk and crisis communication as well as an assessment of the risks and benefits of prevention and control measures, as their effectiveness depends on public trust and cooperation. Objective Our project aims to develop a tool that helps tackle the COVID-19 infodemic, with a focus on enabling a nuanced and in-depth understanding of public perception. The project adopts a trans-disciplinary multi-stakeholder approach, including participatory citizen science. Methods Methodologically, we combine literature and media review and analysis and empirical research using mixed methods, including an online survey and diary-based research, both of which are ongoing and continuously updated. Building on real-time data and continuous data collection, our research results will be highly adaptable to the evolving situation. Strengths and limitations of this study - PubliCo is a new modular and flexible tool to provide bi-directional interaction between citizens and policy-makers for risk and crisis communication - PubliCo relies on quantitative and qualitative data to provide a precise, timely and rich analysis of complex phenomena - PubliCo is open and transparent by design - Although important safeguards are put in place in the code, in a less democratic context it could be used for social control - Communicating complex notions with moral implications (e.g. about health risks, allocation strategies, and community benefits) is a challenge.

Spatial Temporal Tracking of Landslide Events: A Crowdsourced Mobile App

2021 ◽  
Author(s):  
Maneesha Vinodini Ramesh ◽  
Ramesh Guntha ◽  
Christian Arnhardt ◽  
Gargi Singh ◽  
Viswanathan Kr ◽  
...  
Keyword(s):  
Data Collection ◽  
Real Time ◽  
Disaster Management ◽  
Mobile Application ◽  
Mobile App ◽  
Event Data ◽  
Time Data ◽  
Landslide Event ◽  
Real Time Data ◽  
Landslide Data

<p>Monsoons are characterised by the widespread occurrence of  landslides. Tracking each landslide event, developing early warning thresholds, understanding triggers, and initiating disaster rescue and relief efforts are complex for researchers and administration. The ever increasing landslides demand real-time data collection of events to enhance disaster management. In this work we designed and developed a dedicated crowd sourced mobile application, for systematic way of collection, validation, summarization, and dissemination of landslide data in real-time. This unique design of mobile app uses a scalable real-time data collection methodology for tracking landslide events through citizen science, and is available on Google Play Store for free, and at http://landslides.amrita.edu, with software conceived and developed by Amrita University in the context of the UK NERC/FCDO funded LANDSLIP research project (http://www.landslip.org/). This work implemented a structured database that integrates heterogeneous data such as text, numerical, GPS location, landmarks, and images. This methodology enables real-time tracking of landslides utilizing the details such as GPS location, date & time of occurrence, images, type, material, size, impact, area, geology, geomorphology, and comments in real-time. The mobile application has been uniquely designed to avoid missing landslide events and to handle the tradeoff between real-time spatial data collection without compromising the reliability of the data.  To achieve this a multi level user account was created based on their expert levels such as Tracker, Investigator, Expert.  A basic tracking form is presented for the Tracker level, and an extensive form is presented to the Expert level. The reliability of landslide data enhances as the user level increases from Tracker to Expert. Unique UI designs have been utilized to capture, and track the events. The tracking interface is divided into multiple screens; the main screen captures the landslide location through GPS enabled map interface and captures the date/time of the occurrence. Three additional screens capture images, additional details and comments. The 40 questions for landslide event collection used by the Geological Survey of India has been adapted through the collaborative effort of LANDSLIP partners to collect the additional details. The submitted landslides are immediately available for all users to view. The User can view entered landslides through the landslide image listing, Google maps interface, or tabular listing. The landslides can be filtered by date/time and other parameters. The mobile app is designed to be intuitive and fast, and aims to increase awareness about landslide risk through the integrated short documents, and videos. It has guidelines for safety, capturing images, mapping, and choosing the data from the multiple options. The uniqueness of the proposed methodology is that it enhances community participation, integrates event data collection, event data organizing, spatial and temporal summarization, and validation of landslide events and the impact. It pinpoints, maps and alerts real-time landslide events to initiate right disaster management activities to reduce the risk level. The Landslide tracker app was released during the 2020 monsoon season, and more than 250 landslides were recorded through the app.</p>

Real-Time Data Analysis Using a Smartphone Mobile Application

2018 ◽  
pp. 221-240 ◽  
Author(s):  
Sumudu Hasala Marakkalage ◽  
Billy Pik Lik Lau ◽  
Sanjana Kadaba Viswanath ◽  
Chau Yuen ◽  
Belinda Yuen
Keyword(s):  
Data Analysis ◽  
Real Time ◽  
Mobile Application ◽  
Time Data ◽  
Real Time Data

scholarly journals Doctor at Your Fingertips: An Exploration of Digital Visits from Stakeholders’ Perspectives

Life ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 6
Author(s):  
Selina Demi ◽  
Samir Hilmy ◽  
Christina Keller
Keyword(s):  
Artificial Intelligence ◽  
Real Time ◽  
Empirical Data ◽  
Mobile Technologies ◽  
Structured Interviews ◽  
Time Data ◽  
Technological Advances ◽  
Real Time Data

Technological advances and the evolution of mobile technologies enable patients to meet their doctors through their smartphones. While offering the opportunity of digital visits to patients, there are also challenges for this development. The purpose of this study is to enhance the understanding of digital visits, as perceived by experts working in telemedicine companies. To serve this purpose, the authors conducted semi-structured interviews with managers and employees in eight telemedicine companies. The analysis of the empirical data confirmed the importance of digital visits and their efficiency. The potential of digital visits is expected to increase significantly, should they make use of remote devices to transfer real-time data from patients to physicians. In such a case, digital visits are expected to cover approximately 70–75% of medical cases. However, the use of remote devices must be taken with caution and specific conditions need to be taken into account. We encourage researchers to perform research on promising technologies such as artificial intelligence and remote diagnostic devices, which could make more diagnoses and conditions possible to be treated by digital visits. This is even more important in light of the ongoing Covid-19 pandemic.

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