ORC: Optimized Routing Capability

No abstract availabl

ATD-3 Overview Brief

No abstract availabl

Air Traffic Management Technology Demonstration - 3 (ATD-3): Operational Concept for the Integration of ATD-3 Capabilities Version 1.0

ATD-3 has developed four capabilities to address its goal and objectives. The four ATD-3 capabilities include: Dynamic Weather Routes (DWR), Multi-Flight Common Routes (MFCR), Traffic Aware Strategic Aircrew Requests (TASAR), and Dynamic Routes for Arrivals in Weather (DRAW). This document describes the long-term, mature vision for the use and incorporation of the ATD-3 capabilities into the National Airspace System (NAS). This vision describes their complementary interaction and the benefit capture that accrues from use. Recognizing that all capabilities are unlikely to be implemented in unison, each of the capabilities is designed and able to be implemented independently. As discrete portions of the integrated capabilities are planned, additional integration efforts should be undertaken to validate the complementary interactions and benefit pool are realized from the selected subset

Sonic boom prediction and minimization using computational fluid dynamics

This paper describes the NASA ARC program in sonic boom prediction methodologies. This activity supports NASA's High Speed Research Program (HSRP). An overview of the program, recent results, conclusions, and current effort will be given. This effort complements research in sonic boom acceptability and validation being conducted at LaRC and ARC. The goals of the sonic boom element are as follows: to establish a predictive capability for sonic booms generated by High-Speed Civil Transport (HSCT) concepts; to establish guidelines of acceptability for supersonic overland flight; and to validate these findings with wind tunnel and flight tests. The cumulative result of these efforts will be an assessment of economic viability for supersonic transportation. This determination will ultimately be made by the aerospace industry

FY-2007 PNNL Voluntary Protection Program (VPP) Program Evaluation

This document reports the results of the FY-2007 PNNL VPP Program Evaluation, which is a self-assessment of the operational and programmatic performance of the Laboratory related to worker safety and health. The report was compiled by a team of worker representatives and safety professionals who evaluated the Laboratory's worker safety and health programs on the basis of DOE-VPP criteria. The principle elements of DOE's VPP program are: Management Leadership, Employee Involvement, Worksite Analysis, Hazard Prevention and Control, and Safety and Health Training

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost