scholarly journals Characteristics of the 2018 Bago River Flood of Myanmar

2020 ◽  
Vol 15 (3) ◽  
pp. 256-266 ◽  
Author(s):  
Daisuke Komori ◽  
Akiyuki Kawasaki ◽  
Nanami Sakai ◽  
Natsumi Shimomura ◽  
Akira Harada ◽  
...  
Keyword(s):  
Real Time ◽  
Flood Disaster ◽  
Data Availability ◽  
Total Precipitation ◽  
Time Data ◽  
River Watershed ◽  
The Past ◽  
Real Time Data ◽  
River Flood ◽  
The Right

A massive flood in Myanmar struck the Bago river in July, 2018. In this study, because of the limitation of real-time data availability, the satellite-based precipitation was used for clarifying the characteristics of the flood. The total precipitation during 10 days from July 22, when the flood first began at the western Bago city, was estimated approximately 753 mm and 527 mm at the Bago and Zaungts stations in the Bago river watershed. These values were corresponding to 355% and 294% of average of the 10-day total precipitation at the Bago (1967–2015) and Zaungts (1987–2014) stations. Furthermore, not only the 3-day and weekly peak precipitations but also the annual accumulative precipitations during July 22 and August 16 were estimated larger than the largest recorded precipitations at both stations. Although the Zaungts dam stored approximately 140 million m3 during this period, which was an amount equivalent to 40% of inflow volume during July 22 and 28, the resulting flood widely propagated in the Bago city. Based on the flood survey, the 2018 Bago river flood was classified into 4 areas; the right bank of the Bago river, the eastern town, the northern town, and the downstream from the Zaungts Weir and Bago city. These areas were marked as vulnerable areas in the Bago city. The Bago river watershed has experienced many floods in the past, and floods on the same scale as this flood are expected to occur in the future. Therefore, it is essential to understand the characteristics of the 2018 Bago river flood and develop near real-time monitoring of hydrometeorological situation to be prepared for the next flood disaster.

scholarly journals Frontiers of Real-Time Data Analysis

2011 ◽  
Vol 49 (1) ◽  
pp. 72-100 ◽  
Author(s):  
Dean Croushore
Keyword(s):  
Data Analysis ◽  
Real Time ◽  
Data Sets ◽  
Current Analysis ◽  
Time Data ◽  
The Past ◽  
Data Revisions ◽  
The World ◽  
Real Time Data ◽  
The Impact

In the past ten years, researchers have explored the impact of data revisions in many different contexts. Researchers have examined the properties of data revisions, how structural modeling is affected by data revisions, how data revisions affect forecasting, the impact of data revisions on monetary policy analysis, and the use of real-time data in current analysis. This paper summarizes many of the questions for which real-time data analysis has provided answers. In addition, researchers and institutions have developed better real-time data sets around the world. Still, additional research is needed in key areas and research to date has uncovered even more fruitful areas worth exploring. (JEL C52, C53, C80, E01)

Shifting the Paradigm: The Effective Way to Use Real-Time Data for Improved and Safer Oilwell Operations

2012 ◽  
Author(s):  
David M. Pritchard ◽  
Jesse Roye ◽  
J. C. Cunha
Keyword(s):  
Real Time ◽  
Control Analysis ◽  
Time Data ◽  
Well Control ◽  
Real Time Data ◽  
Drilling Operations ◽  
Recorded Data ◽  
Well Data ◽  
The Right ◽  
Real Time Information

When analyzing root causes for minor or major problems occurring in oilwell drilling operations, investigators almost always can track past events, step by step, using recorded data that was produced when the operation occurred. In recent catastrophic blow-outs, investigators were able not only to determine the causes of the accidents but also to indicate mitigating actions, which could have prevented the accident if they were taken when the operation actually took place. This is a strong indicator that, even though the industry has valuable real-time information available, it is not using it as a tool to avoid harmful events and improve performance. Real-time data is not about well control, it is about well control avoidance. Recent catastrophic events have underscored the value of having the right kind of experience to understand and interpret well data in real time, taking the necessary corrective actions before it escalates to more serious problems. What is the well telling us? How do we use real time data to ensure a stable wellbore? Real-time monitoring, integrated with rigorous total well control analysis, is required to embrace and achieve continuous improvements — and ensure the safest possible environment. Next generation monitoring requires a step change that includes hazards avoidance as a precursor to drilling optimization. Real-time data can be used effectively in operations to avoid, minimize, and better manage operational events associated with drilling and completion. Real-time data can also provide the foundational support to improve training in the industry as well as develop hands-on simulators for hazards avoidance.

Real-time probability forecasts of UK macroeconomic events

2008 ◽  
Vol 203 ◽  
pp. 78-90
Author(s):  
Anthony Garratt ◽  
Kevin Lee ◽  
Shaun Vahey
Keyword(s):  
Real Time ◽  
Aggregate Demand ◽  
Time Data ◽  
Probability Forecast ◽  
The Past ◽  
Data Revisions ◽  
Real Time Data ◽  
Probability Forecasts ◽  
Density Forecasts ◽  
Release Data

An overview is provided of the issues raised in the recent literature on the use of real-time data in the context of nowcasting and forecasting UK macroeconomic events. The ideas are illustrated through two specific applications using UK real-time data available over 1961-2006 and providing probability forecasts that could have been produced in real time over the past twenty years. In the first, we consider the reliability of first-release data on the components of UK aggregate demand by looking at forecasts of the probability of substantial data revisions. In the second, we consider the estimation of the output gap, illustrating the uncertainty surrounding its measurement through density forecasts and focusing on its interpretation in terms of inflationary pressure through an event probability forecast.

RFID-Enabled Real-Time Production Tracking System for PCB Assembly Industry

2011 ◽  
Vol 80-81 ◽  
pp. 1330-1334 ◽  
Author(s):  
Gong Zhang ◽  
Jie Zhang ◽  
Shi Yong Tian
Keyword(s):  
Real Time ◽  
Production Management ◽  
Tracking System ◽  
Production Data ◽  
Time Data ◽  
Time Production ◽  
Pcb Assembly ◽  
Real Time Data ◽  
Information Tracking ◽  
The Right

There are many varieties of materials and suppliers for the PCB assembly process; meanwhile, process modifications as well as order changings happen frequently during production. The PCB assembly industry is suffering uncertainty and unknowingness due to the lack of timely, accurate, and consistent production data. Therefore, real-time production information tracking plays an important role for the PCB assembly industry, which provides the right information to the right person at right time to support the decision making and optimize the production management. This paper applies RFID technology to capture the production data and process production information for PCB assembly enterprises. In a PCB assembly line, machines and materials are equipped with RFID device such as RFID readers and tags to build the real-time data collecting environment. A number of production information processing methods are proposed to extract the production tracking information such as progress, WIP (Work-in-progress) inventory from the mass real-time data through data filtering and selection. Finally, a case study is given to demonstrate the developed methodologies.

scholarly journals Re-ADP: Real-Time Data Aggregation with Adaptive ω-Event Differential Privacy for Fog Computing

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Yan Huo ◽  
Chengtao Yong ◽  
Yanfei Lu
Keyword(s):  
Real Time ◽  
Data Aggregation ◽  
Differential Privacy ◽  
Fog Computing ◽  
Data Encryption ◽  
Privacy Preserving ◽  
Data Availability ◽  
Time Data ◽  
Real Time Data ◽  
Cloud Servers

In the Internet of Things (IoT), aggregation and release of real-time data can often be used for mining more useful information so as to make humans lives more convenient and efficient. However, privacy disclosure is one of the most concerning issues because sensitive information usually comes with users in aggregated data. Thus, various data encryption technologies have emerged to achieve privacy preserving. These technologies may not only introduce complicated computing and high communication overhead but also do not work on the protection of endless data streams. Considering these challenges, we propose a real-time stream data aggregation framework with adaptive ω-event differential privacy (Re-ADP). Based on adaptive ω-event differential privacy, the framework can protect any data collected by sensors over any dynamic ω time stamp successively over infinite stream. It is designed for the fog computing architecture that dramatically extends the cloud computing to the edge of networks. In our proposed framework, fog servers will only send aggregated secure data to cloud servers, which can relieve the computing overhead of cloud servers, improve communication efficiency, and protect data privacy. Finally, experimental results demonstrate that our framework outperforms the existing methods and improves data availability with stronger privacy preserving.

scholarly journals An online COVID-19 self-assessment framework supported by IoMT technology

2021 ◽  
Vol 30 (1) ◽  
pp. 966-975
Author(s):  
Mohammed Kamal Nsaif ◽  
Bilal Adil Mahdi ◽  
Yusor Rafid Bahar Al-Mayouf ◽  
Omar Adil Mahdi ◽  
Ahmed J. Aljaaf ◽  
...  
Keyword(s):  
Real Time ◽  
Healthcare System ◽  
Assessment Tool ◽  
Signs And Symptoms ◽  
The Internet ◽  
Time Data ◽  
Self Assessment ◽  
Course Of Action ◽  
Real Time Data ◽  
The Right

Abstract As COVID-19 pandemic continued to propagate, millions of lives are currently at risk especially elderly, people with chronic conditions and pregnant women. Iraq is one of the countries affected by the COVID-19 pandemic. Currently, in Iraq, there is a need for a self-assessment tool to be available in hand for people with COVID-19 concerns. Such a tool would guide people, after an automated assessment, to the right decision such as seeking medical advice, self-isolate, or testing for COVID-19. This study proposes an online COVID-19 self-assessment tool supported by the internet of medical things (IoMT) technology as a means to fight this pandemic and mitigate the burden on our nation’s healthcare system. Advances in IoMT technology allow us to connect all medical tools, medical databases, and devices via the internet in one collaborative network, which conveys real-time data integration and analysis. Our IoMT framework-driven COVID-19 self-assessment tool will capture signs and symptoms through multiple probing questions, storing the data to our COVID-19 patient database, then analyze the data to determine whether a person needs to be tested for COVID-19 or other actions may require to be taken. Further to this, collected data can be integrated and analyzed collaboratively for developing a national health policy and help to manage healthcare resources more efficiently. The IoMT framework-driven online COVID-19 self-assessment tool has a big potential to prevent our healthcare system from being overwhelmed using real-time data collection, COVID-19 databases, analysis, and management of people with COVID-19 concerns, plus providing proper guidance and course of action.

399-P: Nurse-Driven Intervention Using Real-Time Data to Improve Hypoglycemia Rates in a Respiratory Care Unit

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 399-P
Author(s):  
ANN MARIE HASSE ◽  
RIFKA SCHULMAN ◽  
TORI CALDER
Keyword(s):  
Real Time ◽  
Respiratory Care ◽  
Time Data ◽  
Real Time Data
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