scholarly journals A Quality Assessment of the Real-Time Mesoscale Analysis (RTMA) for Aviation

2020 ◽  
Vol 35 (3) ◽  
pp. 977-996 ◽  
Author(s):  
Matthew T. Morris ◽  
Jacob R. Carley ◽  
Edward Colón ◽  
Annette Gibbs ◽  
Manuel S. F. V. De Pondeca ◽  
...  
Keyword(s):  
United States ◽  
Wind Speed ◽  
Quality Assessment ◽  
Real Time ◽  
Federal Aviation Administration ◽  
The United States ◽  
Environmental Modeling ◽  
Missing Observations ◽  
Mesoscale Analysis ◽  
Temperature Observations

Abstract Missing observations at airports can cause delays in commercial and general aviation in the United States owing to Federal Aviation Administration (FAA) safety regulations. The Environmental Modeling Center (EMC) has provided interpolated temperature data from the National Oceanic and Atmospheric Administration’s Real-Time Mesoscale Analysis (RTMA) at airport locations throughout the United States since 2015, with these data substituting for missing temperature observations and mitigating impacts on air travel. A quality assessment of the RTMA is performed to determine if the RTMA could be used in a similar fashion for other weather observations, such as 10-m wind, ceiling, and visibility. Retrospective, data-denial experiments are used to perform the quality assessment by withholding observations from FAA-specified airports. Outliers seen in the RTMA ceiling and visibility analyses during events meeting or exceeding instrument flight rules suggest the RTMA should not be substituted for missing ceiling and visibility observations at this time. The RTMA is a suitable replacement for missing temperature observations for a subset of airports throughout most of the CONUS and Alaska, but not at all stations. Likewise, the RTMA is a suitable substitute for missing surface pressure observations at a subset of airports, with notable exceptions in regions of complex terrain. The RTMA may also be a suitable substitute for missing wind speed observations, provided the wind speed is ≤15 kt (1 kt ≈ 0.51 m s−1). Overall, these results suggest the potential for RTMA to substitute for additional missing observations while highlighting priority areas of future work for improving the RTMA.

Quality assessment of fluconazole capsules and oral suspensions compounded by pharmacies located in the United States

2017 ◽  
Vol 78 (4) ◽  
pp. 421-432 ◽  
Author(s):  
Carine M. Laporte ◽  
Crisanta Cruz-Espindola ◽  
Kamoltip Thungrat ◽  
Anthea E. Schick ◽  
Thomas P. Lewis ◽  
...  
Keyword(s):  
United States ◽  
Quality Assessment ◽  
The United States

scholarly journals A collaborative multiyear, multimodel assessment of seasonal influenza forecasting in the United States

2019 ◽  
Vol 116 (8) ◽  
pp. 3146-3154 ◽  
Author(s):  
Nicholas G. Reich ◽  
Logan C. Brooks ◽  
Spencer J. Fox ◽  
Sasikiran Kandula ◽  
Craig J. McGowan ◽  
...  
Keyword(s):  
Public Health ◽  
United States ◽  
Real Time ◽  
Forecast Accuracy ◽  
The United States ◽  
Public Health Response ◽  
Public Health Decision ◽  
Health Decision Making ◽  
Health Response ◽  
Health Decision

Influenza infects an estimated 9–35 million individuals each year in the United States and is a contributing cause for between 12,000 and 56,000 deaths annually. Seasonal outbreaks of influenza are common in temperate regions of the world, with highest incidence typically occurring in colder and drier months of the year. Real-time forecasts of influenza transmission can inform public health response to outbreaks. We present the results of a multiinstitution collaborative effort to standardize the collection and evaluation of forecasting models for influenza in the United States for the 2010/2011 through 2016/2017 influenza seasons. For these seven seasons, we assembled weekly real-time forecasts of seven targets of public health interest from 22 different models. We compared forecast accuracy of each model relative to a historical baseline seasonal average. Across all regions of the United States, over half of the models showed consistently better performance than the historical baseline when forecasting incidence of influenza-like illness 1 wk, 2 wk, and 3 wk ahead of available data and when forecasting the timing and magnitude of the seasonal peak. In some regions, delays in data reporting were strongly and negatively associated with forecast accuracy. More timely reporting and an improved overall accessibility to novel and traditional data sources are needed to improve forecasting accuracy and its integration with real-time public health decision making.

Emerging Synthetic Cannabinoids: Development and Validation of a Novel Liquid Chromatography Quadrupole Time-of-Flight Mass Spectrometry Assay for Real-Time Detection

2019 ◽  
Vol 44 (3) ◽  
pp. 207-217 ◽  
Author(s):  
Alex J Krotulski ◽  
Amanda L A Mohr ◽  
Barry K Logan
Keyword(s):  
United States ◽  
Mass Spectrometry ◽  
Liquid Chromatography ◽  
Real Time ◽  
Synthetic Cannabinoids ◽  
Time Of Flight ◽  
Forensic Toxicology ◽  
The United States ◽  
Synthetic Cannabinoid ◽  
Flight Mass Spectrometry

Abstract Synthetic cannabinoids pose significant threats to public health and safety, as their implications in overdose and adverse events continue to arise in United States and around the world. Synthetic cannabinoids have seen several generations of chemically diverse structural elements, impacting potency and effects. These factors create new analytical challenges for forensic laboratories. This report describes an efficient liquid chromatography/quadrupole time-of-flight mass spectrometry (LC-QTOF-MS) assay for the identification of synthetic cannabinoid parent compounds and metabolites, including real-time identification of emergent compounds, using a SCIEX TripleTOF® 5600+ with non-targeted SWATH® acquisition. Method validation evaluated precision/accuracy, limits of detection, interferences, processed sample stability and carryover, for which 19 parent compounds and 19 metabolites were tested. To demonstrate feasibility, de-identified blood sample extracts were acquired from a large forensic toxicology laboratory and analyzed using the validated LC-QTOF-MS assay. In mid-2018, 200 blood extracts were analyzed, demonstrating a 19% positivity rate with > 94% agreement rate with original testing. In addition, three newly discovered synthetic cannabinoids were identified, including 5F-MDMB-PICA, 4-cyano CUMYL-BUTINACA and 5F-EDMB-PINACA. These synthetic cannabinoids were previously unreported in forensic toxicology casework in the United States. 5F-MDMB-PICA has become the most prevalent synthetic cannabinoid in United States, as of early 2019. These results demonstrate the effectiveness of this assay and workflow in the identification and characterization of synthetic cannabinoids, as well as the usefulness of sample-mining using non-targeted mass acquisition by LC-QTOF-MS for the discovery of NPS. High resolution mass spectrometry should be considered when developing new or novel assays for synthetic cannabinoids.

An Evaluation of NDFD Weather Forecasts for Wildland Fire Behavior Prediction

2018 ◽  
Vol 33 (1) ◽  
pp. 301-315 ◽  
Author(s):  
Wesley G. Page ◽  
Natalie S. Wagenbrenner ◽  
Bret W. Butler ◽  
Jason M. Forthofer ◽  
Chris Gibson
Keyword(s):  
United States ◽  
Wind Speed ◽  
Relative Humidity ◽  
Air Temperature ◽  
Wildland Fire ◽  
Fire Behavior ◽  
The United States ◽  
Mean Bias Error ◽  
Bias Error ◽  
Behavior Prediction

Abstract Wildland fire managers in the United States currently utilize the gridded forecasts from the National Digital Forecast Database (NDFD) to make fire behavior predictions across complex landscapes during large wildfires. However, little is known about the NDFDs performance in remote locations with complex topography for weather variables important for fire behavior prediction, including air temperature, relative humidity, and wind speed. In this study NDFD forecasts for calendar year 2015 were evaluated in fire-prone locations across the conterminous United States during periods with the potential for active fire spread using the model performance statistics of root-mean-square error (RMSE), mean fractional bias (MFB), and mean bias error (MBE). Results indicated that NDFD forecasts of air temperature and relative humidity performed well with RMSEs of about 2°C and 10%–11%, respectively. However, wind speed was increasingly underpredicted when observed wind speeds exceeded about 4 m s−1, with MFB and MBE values of approximately −15% and −0.5 m s−1, respectively. The importance of accurate wind speed forecasts in terms of fire behavior prediction was confirmed, and the forecast accuracies needed to achieve “good” surface head fire rate-of-spread predictions were estimated as ±20%–30% of the observed wind speed. Weather station location, the specific forecast office, and terrain complexity had the largest impacts on wind speed forecast error, although the relatively low variance explained by the model (~37%) suggests that other variables are likely to be important. Based on these results it is suggested that wildland fire managers should use caution when utilizing the NDFD wind speed forecasts if high wind speed events are anticipated.

scholarly journals The Last Mile: DSCSA Solution Through Blockchain Technology: Drug Tracking, Tracing, and Verification at the Last Mile of the Pharmaceutical Supply Chain with BRUINchain

2020 ◽  
Author(s):  
William Chien ◽  
Josenor De Jesus ◽  
Ben Taylor ◽  
Victor Dods ◽  
Leo Alekseyev ◽  
...  
Keyword(s):  
United States ◽  
Supply Chain ◽  
Real Time ◽  
Real Data ◽  
The United States ◽  
Successful Implementation ◽  
Drug Supply ◽  
Compliance Cost ◽  
Last Mile ◽  
Drug Supply Chain

Purpose: As part of the FDA’s DSCSA Pilot Project Program, UCLA and its solution partner, LedgerDomain (collectively referred to as the team hereafter), focused on building a complete, working blockchain-based system, BRUINchain, which would meet all the key objectives of the Drug Supply Chain Security Act (DSCSA) for a dispenser operating solely on commercial off-the-shelf (COTS) technology. Methods: The BRUINchain system requirements include scanning the drug package for a correctly formatted 2D barcode, flagging expired product, verifying the product with the manufacturer, and quarantining suspect and illegitimate products at the last mile: pharmacist to patient, the most complex area of the drug supply chain. The authors demonstrate a successful implementation where product-tracing notifications are sent automatically to key stakeholders, resulting in enhanced timeliness and reduction in paperwork burden. At the core of this effort was a blockchain-based solution to track and trace changes in custody of drug. As an immutable, time-stamped, near-real-time (50-millisecond latency), auditable record of transactions, BRUINchain makes it possible for supply chain communities to arrive at a single version of the truth. BRUINchain was tested with real data on real caregivers administering life-saving medications to real patients at one of the busiest pharmacies in the United States. Results: In addition to communicating with the manufacturer directly for verification, BRUINchain also initiated suspect product notifications. During the study, a 100% success rate was observed across scanning, expiration detection, and counterfeit detection; and paperwork reduction from approximately 1 hour to less than a minute. The authors demonstrate a successful implementation where product-tracing notifications are sent automatically to key stakeholders, resulting in enhanced timeliness and reduction in paperwork burden. At the core of this effort was a blockchain-based solution to track and trace changes in custody of drug. As an immutable, time-stamped, near-real-time (50-millisecond latency), auditable record of transactions, BRUINchain makes it possible for supply chain communities to arrive at a single version of the truth. BRUINchain was tested with real data on real caregivers administering life-saving medications to real patients at one of the busiest pharmacies in the United States.   Conclusions: By automatically interrogating the manufacturer’s relational database with our blockchain-based system, our results indicate a projected DSCSA compliance cost of 17 cents per unit, and potentially much more depending on regulatory interpretation and speed of verification. We project that this cost could be reduced with manufacturers’ adoption of a highly performant, fully automated end-to-end system based on digital ledger technology (DLT). In an examination of the interoperability of such a system, we elaborate on its capacity to enable verification in real time without a human in the loop, the key feature driving lower compliance cost. With 4.2 billion prescriptions being dispensed each year in the United States, DLT would not only reduce the projected per-unit cost to 13 cents per unit (saving $183 million in annual labor costs), but also serve as a major bulwark against bad or fraudulent transactions, reduce the need for safety stock, and enhance the detection and removal of potentially dangerous drugs from the drug supply chain to protect U.S. consumers.

scholarly journals Mapping Air Population

2018 ◽  
Vol 1 ◽  
pp. 1-3
Author(s):  
Michael P. Peterson ◽  
Paul Hunt ◽  
Konrad Weiß
Keyword(s):  
United States ◽  
Real Time ◽  
The United States ◽  
Occupancy Rate ◽  
The Earth ◽  
Mapping System ◽  
System Maps

“Air population” refers to the total number of people flying above the earth at any point in time. The total number of passengers can then be estimated by multiplying the number of seats for each aircraft by the current seat occupancy rate. Using this method, the estimated air population is determined by state for the airspace over the United States. In the interactive, real-time mapping system, maps are provided to show total air population, the density of air population (air population / area of state), and the ratio of air population to ground population.

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