An integrated mobility system using real-time data for traffic simulation

2016 ◽  
Author(s):  
Mirco Sturari ◽  
Ludovico Catani ◽  
Adriano Mancini ◽  
Emanuele Frontoni
Keyword(s):  
Real Time ◽  
Traffic Simulation ◽  
Time Data ◽  
Real Time Data ◽  
Mobility System

Real-time data-driven traffic simulation for performance measure estimation

2016 ◽  
Vol 10 (8) ◽  
pp. 562-571 ◽  
Author(s):  
Dwayne Henclewood ◽  
Randall Guensler ◽  
Richard M. Fujimoto ◽  
Michael P. Hunter ◽  
Wonho Suh ◽  
...  
Keyword(s):  
Real Time ◽  
Traffic Simulation ◽  
Performance Measure ◽  
Data Driven ◽  
Time Data ◽  
Real Time Data

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

MHA releases largest real‐time data from MH help‐seekers

2021 ◽  
Vol 31 (6) ◽  
pp. 7-7
Author(s):  
Valerie A. Canady
Keyword(s):  
Real Time ◽  
Time Data ◽  
Real Time Data

Why Ecological Momentary Assessment Surveys Go Incomplete?: When It Happens and How It Impacts Data

2020 ◽  
Author(s):  
Yu-Hsiang Wu ◽  
Jingjing Xu ◽  
Elizabeth Stangl ◽  
Shareka Pentony ◽  
Dhruv Vyas ◽  
...  
Keyword(s):  
Real Time ◽  
Self Report ◽  
Data Logging ◽  
Time Data ◽  
Real Time Data ◽  
Report Data ◽  
Self Report Data ◽  
Ecological Momentary ◽  
The Impact ◽  
Momentary Assessment

Abstract Background Ecological momentary assessment (EMA) often requires respondents to complete surveys in the moment to report real-time experiences. Because EMA may seem disruptive or intrusive, respondents may not complete surveys as directed in certain circumstances. Purpose This article aims to determine the effect of environmental characteristics on the likelihood of instances where respondents do not complete EMA surveys (referred to as survey incompletion), and to estimate the impact of survey incompletion on EMA self-report data. Research Design An observational study. Study Sample Ten adults hearing aid (HA) users. Data Collection and Analysis Experienced, bilateral HA users were recruited and fit with study HAs. The study HAs were equipped with real-time data loggers, an algorithm that logged the data generated by HAs (e.g., overall sound level, environment classification, and feature status including microphone mode and amount of gain reduction). The study HAs were also connected via Bluetooth to a smartphone app, which collected the real-time data logging data as well as presented the participants with EMA surveys about their listening environments and experiences. The participants were sent out to wear the HAs and complete surveys for 1 week. Real-time data logging was triggered when participants completed surveys and when participants ignored or snoozed surveys. Data logging data were used to estimate the effect of environmental characteristics on the likelihood of survey incompletion, and to predict participants' responses to survey questions in the instances of survey incompletion. Results Across the 10 participants, 715 surveys were completed and survey incompletion occurred 228 times. Mixed effects logistic regression models indicated that survey incompletion was more likely to happen in the environments that were less quiet and contained more speech, noise, and machine sounds, and in the environments wherein directional microphones and noise reduction algorithms were enabled. The results of survey response prediction further indicated that the participants could have reported more challenging environments and more listening difficulty in the instances of survey incompletion. However, the difference in the distribution of survey responses between the observed responses and the combined observed and predicted responses was small. Conclusion The present study indicates that EMA survey incompletion occurs systematically. Although survey incompletion could bias EMA self-report data, the impact is likely to be small.

scholarly journals The Importance of Implementing Cyber Physical Systems to Acquire Real-Time Data and Indicators

J ◽  
2021 ◽  
Vol 4 (2) ◽  
pp. 147-153
Author(s):  
Paula Morella ◽  
María Pilar Lambán ◽  
Jesús Antonio Royo ◽  
Juan Carlos Sánchez
Keyword(s):  
Decision Making ◽  
Data Acquisition ◽  
Real Time ◽  
Failure Detection ◽  
Cyber Physical Systems ◽  
Future Research ◽  
Time Data ◽  
Physical Systems ◽  
The Real ◽  
Real Time Data

Among the new trends in technology that have emerged through the Industry 4.0, Cyber Physical Systems (CPS) and Internet of Things (IoT) are crucial for the real-time data acquisition. This data acquisition, together with its transformation in valuable information, are indispensable for the development of real-time indicators. Moreover, real-time indicators provide companies with a competitive advantage over the competition since they enhance the calculus and speed up the decision-making and failure detection. Our research highlights the advantages of real-time data acquisition for supply chains, developing indicators that would be impossible to achieve with traditional systems, improving the accuracy of the existing ones and enhancing the real-time decision-making. Moreover, it brings out the importance of integrating technologies 4.0 in industry, in this case, CPS and IoT, and establishes the main points for a future research agenda of this topic.

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