Looking Glass

2012 ◽  
Vol 4 (1) ◽  
pp. 17-28
Author(s):  
Anni R. Coden ◽  
John R. Harrald ◽  
Michael Tanenblatt ◽  
Theresa Jefferson ◽  
Pamela Murray-Tuite
Keyword(s):  
Ad Hoc ◽  
Data Driven ◽  
Performance Parameters ◽  
Human In The Loop ◽  
Preparedness Planning ◽  
Financial Costs ◽  
Decision Support Framework ◽  
Driven System ◽  
Looking Glass ◽  
Over Time

Looking Glass enables the discovery of a city’s vulnerabilities in a scenario along with the exploration of alternative resolutions and their accompanying side effects. It is a tool for enabling city officials to bridge the silos defined by people, processes, and organizations; the decision support framework can be used to discover interdependencies between a city’s infrastructure elements, its protocols (procedures) and its people’s actions over time. It is a tool for preparedness planning for natural and man-made threats, providing visualization of scenarios as they unfold, allowing observation and measurement of the effects of ad-hoc decisions. Looking Glass is a dynamic data driven system where the data can be interactively manipulated with the human-in-the-loop module during simulation. In general, the key performance parameters are the time, resources, and cost of resolving an incident, both financial costs and the costs associated with the health, safety, and happiness of the population. A prototype was demonstrated to city and county officials who were excited about the benefits of Looking Glass for their organizations.

Data Driven Gas Well Performance Model Hands Back Control to Engineers

2021 ◽  
Author(s):  
Cornelis Veeken
Keyword(s):  
Real Time ◽  
Performance Model ◽  
Data Driven ◽  
Production Data ◽  
Performance Parameters ◽  
Well Performance ◽  
Gas Well ◽  
Reservoir Performance ◽  
Fit For Purpose ◽  
Over Time

Abstract This paper presents a fit-for-purpose gas well performance model that utilizes a minimum set of inflow and outflow performance parameters, and demonstrates the use of this model to describe real-time well performance, to compare well performance over time and between wells, and to generate production forecasts in support of well interventions. The inflow and outflow parameters are directly related to well-known reservoir and well properties, and can be calibrated against common well surveillance and production data. By adopting this approach, engineers develop a better appreciation of the magnitude and uncertainty of gas well and reservoir performance parameters.

scholarly journals An Exploratory Study of COVID-19 Information on Twitter in the Greater Region

2021 ◽  
Vol 5 (1) ◽  
pp. 5
Author(s):  
Ninghan Chen ◽  
Zhiqiang Zhong ◽  
Jun Pang
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Online Social Networks ◽  
Exploratory Study ◽  
Representation Learning ◽  
Data Driven ◽  
Learning Methods ◽  
Twitter Users ◽  
Over Time

The outbreak of the COVID-19 led to a burst of information in major online social networks (OSNs). Facing this constantly changing situation, OSNs have become an essential platform for people expressing opinions and seeking up-to-the-minute information. Thus, discussions on OSNs may become a reflection of reality. This paper aims to figure out how Twitter users in the Greater Region (GR) and related countries react differently over time through conducting a data-driven exploratory study of COVID-19 information using machine learning and representation learning methods. We find that tweet volume and COVID-19 cases in GR and related countries are correlated, but this correlation only exists in a particular period of the pandemic. Moreover, we plot the changing of topics in each country and region from 22 January 2020 to 5 June 2020, figuring out the main differences between GR and related countries.

Self-adjusted Data-Driven System for Prediction of Human Performance

2020 ◽  
pp. 282-287
Author(s):  
Oleksandr Burov ◽  
Evgeniy Lavrov ◽  
Nadiia Pasko ◽  
Olena Hlazunova ◽  
Olga Lavrova ◽  
...  
Keyword(s):  
Human Performance ◽  
Data Driven ◽  
Driven System

scholarly journals Machine Learning the Phenomenology of COVID-19 From Early Infection Dynamics

2020 ◽  
Author(s):  
Malik Magdon-Ismail
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Early Infection ◽  
Data Driven ◽  
Infection Dynamics ◽  
Serious Infection ◽  
Specific Virus ◽  
Different Populations ◽  
Learning Analysis ◽  
Over Time

AbstractWe present a robust data-driven machine learning analysis of the COVID-19 pandemic from its early infection dynamics, specifically infection counts over time. The goal is to extract actionable public health insights. These insights include the infectious force, the rate of a mild infection becoming serious, estimates for asymtomatic infections and predictions of new infections over time. We focus on USA data starting from the first confirmed infection on January 20 2020. Our methods reveal significant asymptomatic (hidden) infection, a lag of about 10 days, and we quantitatively confirm that the infectious force is strong with about a 0.14% transition from mild to serious infection. Our methods are efficient, robust and general, being agnostic to the specific virus and applicable to different populations or cohorts.

scholarly journals More alike than different: Novel methods for measuring and modeling executive function development

2021 ◽  
Author(s):  
Jessica Younger ◽  
Kristine O'Laughlin ◽  
Joaquin Anguera ◽  
Silvia Bunge ◽  
Emilio Ferrer ◽  
...  
Keyword(s):  
Executive Function ◽  
Executive Functions ◽  
Middle Childhood ◽  
Analytic Approach ◽  
Data Driven ◽  
Positive Outcomes ◽  
Novel Methods ◽  
Students Success ◽  
Over Time ◽  
Longitudinal Dataset

Abstract Executive functions (EFs) are linked to positive outcomes across the lifespan. Yet, methodological challenges have prevented rigorous understanding of the precise ways EFs are organized in childhood and how they develop over time. We introduce novel methods to address these challenges for both measuring and modeling EFs using a large, accelerated longitudinal dataset from a diverse sample of students in middle childhood (approximately ages 8 to 14; N = 1,286). Adaptive assessments allowed us to equate EF challenge across ages and a data-driven, network analytic approach revealed the evolving diversity of EFs while accounting for their unity. Our results suggest EF organization stabilizes around age 10, but continues refining through at least age 14. This approach brings new precision to EFs’ development by removing interpretative ambiguities associated with previous methodologies. By improving EF measurement, the field can move towards improving EF training, to provide a strong foundation for students’ success.

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