Dynamic Strike Force Asset Reallocation for Time Critical Targeting

2002 ◽  
Author(s):  
John R. McDonnell ◽  
Nicholas Gizzi
Keyword(s):  
Time Critical

A Retrospective Single-center Study on Time Critical Neurological Disorders in the Emergency Room of a Tertiary Pediatric Center

2019 ◽  
Author(s):  
Michaela Bonfert ◽  
Claire Andonian ◽  
Christoph Bidlingmaier ◽  
Claudia Berlin ◽  
Ingo Borggraefe ◽  
...  
Keyword(s):  
Emergency Room ◽  
Neurological Disorders ◽  
Single Center ◽  
Single Center Study ◽  
Time Critical ◽  
Pediatric Center ◽  
Center Study

Creating adaptive predictions for packaging-critical quality parameters using advanced analytics and machine learning

TAPPI Journal ◽  
2019 ◽  
Vol 18 (11) ◽  
pp. 679-689
Author(s):  
CYDNEY RECHTIN ◽  
CHITTA RANJAN ◽  
ANTHONY LEWIS ◽  
BETH ANN ZARKO
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Predictive Analytics ◽  
Paper Industry ◽  
Quality Parameters ◽  
Fast Change ◽  
The Face ◽  
Model Tuning ◽  
Reducing Costs ◽  
Time Critical

Packaging manufacturers are challenged to achieve consistent strength targets and maximize production while reducing costs through smarter fiber utilization, chemical optimization, energy reduction, and more. With innovative instrumentation readily accessible, mills are collecting vast amounts of data that provide them with ever increasing visibility into their processes. Turning this visibility into actionable insight is key to successfully exceeding customer expectations and reducing costs. Predictive analytics supported by machine learning can provide real-time quality measures that remain robust and accurate in the face of changing machine conditions. These adaptive quality “soft sensors” allow for more informed, on-the-fly process changes; fast change detection; and process control optimization without requiring periodic model tuning. The use of predictive modeling in the paper industry has increased in recent years; however, little attention has been given to packaging finished quality. The use of machine learning to maintain prediction relevancy under everchanging machine conditions is novel. In this paper, we demonstrate the process of establishing real-time, adaptive quality predictions in an industry focused on reel-to-reel quality control, and we discuss the value created through the availability and use of real-time critical quality.

Casing Drilling Package Reduces Overall Drilling Time: Critical Analysis of an Operation in Papua New Guinea

2008 ◽  
Author(s):  
Christopher J. Westren ◽  
Lester Ian Clark ◽  
Azam Zreik ◽  
Ben Ersan ◽  
Chad Jurica
Keyword(s):  
Papua New Guinea ◽  
Critical Analysis ◽  
New Guinea ◽  
Time Critical ◽  
Drilling Time ◽  
Casing Drilling

scholarly journals Structured and Systematic Team and Procedure Training in Severe Trauma: Going from ‘Zero to Hero’ for a Time-Critical, Low-Volume Emergency Procedure Over Three Time Periods

2021 ◽  
Vol 45 (5) ◽  
pp. 1340-1348
Author(s):  
Maryam Meshkinfamfard ◽  
Jon Kristian Narvestad ◽  
Johannes Wiik Larsen ◽  
Arezo Kanani ◽  
Jørgen Vennesland ◽  
...  
Keyword(s):  
Injury Severity ◽  
Trauma System ◽  
Severe Trauma ◽  
Emergency Procedure ◽  
Emergency Thoracotomy ◽  
Severity Scores ◽  
Time Periods ◽  
Low Volume ◽  
Time Critical ◽  
Over Time

Abstract Background Resuscitative emergency thoracotomy is a potential life-saving procedure but is rarely performed outside of busy trauma centers. Yet the intervention cannot be deferred nor centralized for critically injured patients presenting in extremis. Low-volume experience may be mitigated by structured training. The aim of this study was to describe concurrent development of training and simulation in a trauma system and associated effect on one time-critical emergency procedure on patient outcome. Methods An observational cohort study split into 3 arbitrary time-phases of trauma system development referred to as ‘early’, ‘developing’ and ‘mature’ time-periods. Core characteristics of the system is described for each phase and concurrent outcomes for all consecutive emergency thoracotomies described with focus on patient characteristics and outcome analyzed for trends in time. Results Over the study period, a total of 36 emergency thoracotomies were performed, of which 5 survived (13.9%). The “early” phase had no survivors (0/10), with 2 of 13 (15%) and 3 of 13 (23%) surviving in the development and mature phase, respectively. A decline in ‘elderly’ (>55 years) patients who had emergency thoracotomy occurred with each time period (from 50%, 31% to 7.7%, respectively). The gender distribution and the injury severity scores on admission remained unchanged, while the rate of patients with signs on life (SOL) increased over time. Conclusion The improvement over time in survival for one time-critical emergency procedure may be attributed to structured implementation of team and procedure training. The findings may be transferred to other low-volume regions for improved trauma care.

scholarly journals The Relevance of Bioinformatics Applications in the Discovery of Vaccine Candidates and Potential Drugs for COVID-19 Treatment

2021 ◽  
Vol 15 ◽  
pp. 117793222110021
Author(s):  
Onyeka S Chukwudozie ◽  
Vincent C Duru ◽  
Charlotte C Ndiribe ◽  
Abdullahi T Aborode ◽  
Victor O Oyebanji ◽  
...  
Keyword(s):  
Viral Genome ◽  
Drug Repurposing ◽  
Protein Docking ◽  
Vaccine Research ◽  
Vaccine Candidates ◽  
Genome Data ◽  
Disease Agent ◽  
Time Critical ◽  
Antigen Antibody ◽  
Antigenic Epitopes

The application of bioinformatics to vaccine research and drug discovery has never been so essential in the fight against infectious diseases. The greatest combat of the 21st century against a debilitating disease agent SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) virus discovered in Wuhan, China, December 2019, has piqued an unprecedented usage of bioinformatics tools in deciphering the molecular characterizations of infectious pathogens. With the viral genome data of SARS-COV-2 been made available barely weeks after the reported outbreak, bioinformatics platforms have become an all-time critical tool to gain time in the fight against the disease pandemic. Before the outbreak, different platforms have been developed to explore antigenic epitopes, predict peptide-protein docking and antibody structures, and simulate antigen-antibody reactions and lots more. However, the advent of the pandemic witnessed an upsurge in the application of these pipelines with the development of newer ones such as the Coronavirus Explorer in the development of efficacious vaccines, drug repurposing, and/or discovery. In this review, we have explored the various pipelines available for use, their relevance, and limitations in the timely development of useful therapeutic candidates from genomic data knowledge to clinical therapy.

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