Potential Use of Circulation/Pollutant Transport Models for Impact Assessment on the U.S. EEZ

1990 ◽  
pp. 309-319
Author(s):  
Robert P. LaBelle
Keyword(s):  
Impact Assessment ◽  
Pollutant Transport ◽  
Transport Models ◽  
Potential Use ◽  
The U.S

Use of Health Impact Assessment in the U.S

2008 ◽  
Vol 34 (3) ◽  
pp. 241-256 ◽  
Author(s):  
Andrew L. Dannenberg ◽  
Rajiv Bhatia ◽  
Brian L. Cole ◽  
Sarah K. Heaton ◽  
Jason D. Feldman ◽  
...  
Keyword(s):  
Impact Assessment ◽  
Health Impact Assessment ◽  
Health Impact ◽  
The U.S

scholarly journals A Two-Regime Markov-Switching GARCH Active Trading Algorithm for Coffee, Cocoa, and Sugar Futures

Mathematics ◽  
2020 ◽  
Vol 8 (6) ◽  
pp. 1001 ◽  
Author(s):  
Oscar V. De la Torre-Torres ◽  
Dora Aguilasocho-Montoya ◽  
María de la Cruz del Río-Rama
Keyword(s):  
Markov Switching ◽  
Garch Models ◽  
Arch Model ◽  
Conditional Heteroscedasticity ◽  
Arch Models ◽  
Trading Decisions ◽  
Portfolio Managers ◽  
Autoregressive Conditional Heteroscedasticity ◽  
Potential Use ◽  
The U.S

In the present paper we tested the use of Markov-switching Generalized AutoRegressive Conditional Heteroscedasticity (MS-GARCH) models and their not generalized (MS-ARCH) version. This, for active trading decisions in the coffee, cocoa, and sugar future markets. With weekly data from 7 January 2000 to 3 April 2020, we simulated the performance that a futures’ trader would have had, had she used the next trading algorithm: To invest in the security if the probability of being in a distress regime is less or equal to 50% or to invest in the U.S. three-month Treasury bill otherwise. Our results suggest that the use of t-student Markov Switching Component ARCH Model (MS-ARCH) models is appropriate for active trading in the cocoa futures and the Gaussian MS-GARCH is appropriate for sugar. For the specific case of the coffee market, we did not find evidence in favor of the use of MS-GARCH models. This is so by the fact that the trading algorithm led to inaccurate trading signs. Our results are of potential use for futures’ position traders or portfolio managers who want a quantitative trading algorithm for active trading in these commodity futures.

scholarly journals A comprehensive one-dimensional numerical model for solute transport in rivers

2017 ◽  
Vol 21 (1) ◽  
pp. 99-116 ◽  
Author(s):  
Maryam Barati Moghaddam ◽  
Mehdi Mazaheri ◽  
Jamal MohammadVali Samani
Keyword(s):  
Cross Sections ◽  
Pollutant Transport ◽  
Study Data ◽  
Transient Storage ◽  
Third Order ◽  
Transport Models ◽  
Storage Model ◽  
Proposed Model ◽  
Main Effect ◽  
Transient Storage Zones

Abstract. One of the mechanisms that greatly affect the pollutant transport in rivers, especially in mountain streams, is the effect of transient storage zones. The main effect of these zones is to retain pollutants temporarily and then release them gradually. Transient storage zones indirectly influence all phenomena related to mass transport in rivers. This paper presents the TOASTS (third-order accuracy simulation of transient storage) model to simulate 1-D pollutant transport in rivers with irregular cross-sections under unsteady flow and transient storage zones. The proposed model was verified versus some analytical solutions and a 2-D hydrodynamic model. In addition, in order to demonstrate the model applicability, two hypothetical examples were designed and four sets of well-established frequently cited tracer study data were used. These cases cover different processes governing transport, cross-section types and flow regimes. The results of the TOASTS model, in comparison with two common contaminant transport models, shows better accuracy and numerical stability.

Potential Use of DNA Barcodes in Regulatory Science: Identification of the U.S. Food and Drug Administration's “Dirty 22,” Contributors to the Spread of Foodborne Pathogens

2013 ◽  
Vol 76 (1) ◽  
pp. 144-149 ◽  
Author(s):  
YOLANDA L. JONES ◽  
SHARLA M. PETERS ◽  
CHRIS WELAND ◽  
NATALIA V. IVANOVA ◽  
HAILE F. YANCY
Keyword(s):  
Foodborne Pathogens ◽  
Visual Inspection ◽  
Current Method ◽  
Foodborne Disease ◽  
Dna Barcodes ◽  
Cytochrome Oxidase I Gene ◽  
Extraneous Material ◽  
Potential Use ◽  
The U.S ◽  
I Gene

The U.S. Food, Drug, and Cosmetic Act prohibits the distribution of food that is adulterated, and the regulatory mission of the U.S. Food and Drug Administration (FDA) is to enforce this Act. FDA field laboratories have identified the 22 most common pests that contribute to the spread of foodborne disease (the “Dirty 22”). The current method of detecting filth and extraneous material (tails, legs, carcasses, etc.) is visual inspection using microscopy. Because microscopy can be time-consuming and may yield inaccurate and/or nonspecific results due to lack of expertise, an alternative method of detecting these adulterants is needed. In this study, we sequenced DNA from the 5′ region of the cytochrome oxidase I gene of these 22 common pests that contribute to the spread of foodborne pathogens. Here, we describe the generation of DNA barcodes for all 22 species. To date, this is the first attempt to develop a sequence-based regulatory database and systematic primer strategy to identify these FDA-targeted species. DNA barcoding can be a powerful tool that can aid the FDA in promoting the protection and safety of the U.S. food supply.

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