Washington’s 'Cutting-Edge' Technology Solution to Combating Sales Tax Fraud: Real-Time Data (Now), Real-Time Remittance in the Future

2020 ◽  
Author(s):  
Richard Thompson Ainsworth ◽  
Robert Chicoine ◽  
Andrew Leahey ◽  
Sunder Gee
Keyword(s):  
Real Time ◽  
Cutting Edge ◽  
Sales Tax ◽  
Time Data ◽  
Real Time Data ◽  
The Future ◽  
Tax Fraud ◽  
Technology Solution

scholarly journals Looking Towards the Future of Strain Scanning Using Neutron Diffraction

2014 ◽  
Vol 996 ◽  
pp. 187-191
Author(s):  
Robert Charles Wimpory ◽  
Mirko Boin
Keyword(s):  
Data Analysis ◽  
Neutron Diffraction ◽  
Data Acquisition ◽  
Real Time ◽  
Measurement Time ◽  
Time Data ◽  
Control Data ◽  
Real Time Data ◽  
The Future ◽  
Instrument Control

All aspects of instrument control, data acquisition, simulation and analysis are expected to merge in the future. For instance real time data analysis will feed back influencing the instrument control in order to optimize the measurement time and simulations themselves will control the instrument. This presentation will discuss how the all-important pre-planning of a measurement can be optimized and used to define the whole measurement, efficiently and effectively using the neutron beamtime.

scholarly journals Baby steps: the gender division of childcare during the COVID-19 pandemic

2020 ◽  
Vol 36 (Supplement_1) ◽  
pp. S169-S186 ◽  
Author(s):  
Almudena Sevilla ◽  
Sarah Smith
Keyword(s):  
Mental Health ◽  
Adverse Effects ◽  
Real Time ◽  
Time Data ◽  
Daily Lives ◽  
Real Time Data ◽  
The Future ◽  
Families With Children ◽  
The Impact

Abstract The nature and scale of the shocks to the demand for, and the supply of, home childcare during the COVID-19 pandemic provide a unique opportunity to increase our understanding of the division of home labour and the determinants of specialization within the household. We collected real-time data on daily lives to document the impact of measures to control COVID-19 on UK families with children under the age of 12. We document that these families have been doing the equivalent of a working week in childcare, with mothers bearing most of the burden. The additional hours of childcare done by women are less sensitive to their employment than they are for men, leaving many women juggling work and (a lot more) childcare, with likely adverse effects on their mental health and future careers. However, some households, those in which men have not been working, have taken greater steps towards an equal allocation, offering the prospect of sharing the burden of childcare more equally in the future.

scholarly journals On the forecastability of food insecurity

2021 ◽  
Author(s):  
Pietro Foini ◽  
Michele Tizzoni ◽  
Daniela Paolotti ◽  
Elisa Omodei
Keyword(s):  
At Risk ◽  
Food Insecurity ◽  
Real Time ◽  
Food Consumption ◽  
Boosted Regression Trees ◽  
Extreme Weather Events ◽  
Forecasting Model ◽  
Time Data ◽  
Real Time Data ◽  
The Future

Food insecurity, defined as the lack of physical or economic access to safe, nutritious and sufficient food, remains one of the main challenges included in the 2030 Agenda for Sustainable Development. Near real-time data on the food insecurity situation collected by international organizations such as the World Food Programme can be crucial to monitor and forecast time trends of insufficient food consumption levels in countries at risk. Here, using food consumption observations in combination with secondary data on conflict, extreme weather events and economic shocks, we build a forecasting model based on gradient boosted regression trees to create predictions on the evolution of insufficient food consumption trends up to 30 days in to the future in 6 countries (Burkina Faso, Cameroon, Mali, Nigeria, Syria and Yemen). Results show that the number of available historical observations is a key element for the forecasting model performance. Among the 6 countries studied in this work, for those with the longest food insecurity time series, the proposed forecasting model makes it possible to forecast the prevalence of people with insufficient food consumption up to 30 days into the future with higher accuracy than a naive approach based on the last measured prevalence only. The framework developed in this work could provide decision makers with a tool to assess how the food insecurity situation will evolve in the near future in countries at risk. Results clearly point to the added value of continuous near real-time data collection at sub-national level.

scholarly journals AI for Conservation: Aerial Monitoring to Learn and Plan against Illegal Actors

2018 ◽  
Author(s):  
Elizabeth Bondi
Keyword(s):  
Real Time ◽  
Behavior Prediction ◽  
Time Data ◽  
Multiple Strategies ◽  
Real Time Data ◽  
The Future ◽  
Aerial Vehicle ◽  
Patrol Strategies ◽  
Future Work

Conservation of our planet’s natural resources is of the utmost importance and requires constant innovation. This project focuses on innovation for one aspect of conservation: the reduction of wildlife poaching. Park rangers patrol parks to decrease poaching by searching for poachers and animal snares left by poachers. Multiple strategies exist to aid in these patrols, including adversary behavior prediction and planning optimal ranger patrol strategies. These research efforts suffer from a key shortcoming: they fail to integrate real-time data, and rely on historical data collected during ranger patrols. With the recent advances in unmanned aerial vehicle (UAV) technology, UAVs have become viable tools to aid in park ranger patrols. There is now an opportunity to augment the input for these strategies in real time. Detection is done on real-time data collected from UAVs. Detection will then be used to learn adversaries’ behaviors, or where poaching may occur in the future, in future work. This will then be used to plan where to fly in the long term, such as the next mission. Finally, planning where to fly next during the current flight will depend on the long term plan and the real-time detections in case a poacher is spotted. Through our collaboration with Air Shepherd, a program of the Charles A. and Anne Morrow Lindbergh Foundation, we have already begun deploying poacher detection prototypes in Africa and will be able to deploy further advances there in the future.

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.

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