4 research outputs found
A Framework to Evaluate Aircraft Trajectory Generation Methods
No full textAircraft trajectory generation is a widely addressed problem with applications including emergency trajectory generation, collision risk models, air traffic flow and capacity management or airspace design. State of the art methods to generate individual trajectories and optimise some performance or emergency criterion may lack of realism with respect to common situations implemented by air traffic controllers. On the other hand, statistical data-driven methods to generate aircraft trajectories excel at imitating operational practice but may be difficult to implement even in simulations due to aircraft performance limitations. This contribution proposes a common baseline to compare literature and bleeding-edge methods to generate air traffic trajectories. Keeping in mind that the most appropriate criterion should always depend on the targeted application, we present here an extensive set of metrics to evaluate the quality of generated trajectories, before assessing two generation methods in light of these indicators.Control & Simulatio
Provision of instructions to drink ad libitum or according to thirst sensation: impact during 120 km of cycling in the heat in men
No full textThe terms drinking to thirst and ad libitum drinking are used interchangeably, but should they? We investigated the differences in how athletes consume fluids during exercise when instructed to drink according to thirst or ad libitum. Using a randomized, crossover and counterbalanced design, 10 males (27 4 y) cycled 120 km (48 4% of peak power, 33C, 40% relative humidity) on two occasions, while drinking water according to thirst or ad libitum. Participants covered the cycling trials in 222 11 min (p = 0.29). Although the body mass loss at the end of exercise and total volume of water consumed were similar between trials, thirst perception before each sip and the volume consumed per sip were significantly higher with thirst than ad libitum drinking, whereas the total number of sips was significantly lower with thirst than ad libitum drinking. Perceived exertion, rectal temperature and heart rate were all significantly higher with thirst than ad libitum drinking, but the difference was trivial. In conclusion, thirst and ad libitum drinking are associated with different drinking patterns, but equally maintain fluid balance during prolonged exercise. The terms drinking to thirst and ad libitum drinking can be used interchangeably for guiding fluid intake during prolonged exercise.
NOVELTY
• Both strategies are associated with different patterns of fluid ingestion during prolonged exercise, but are equally effective in maintaining fluid balance;
• Perceived exertion, rectal temperature and heart rate are regulated dissimilarly by thirst and ad libitum drinking, but the difference is trivial.The presentation of the authors' names and (or) special characters in the title of the pdf file of the accepted manuscript may differ slightly from what is displayed on the item page. The information in the pdf file of the accepted manuscript reflects the original submission by the author
The Adoption and Use of Artificial Intelligence and Machine Learning in Clinical Development.
No full textThe use of artificial intelligence (AI) and machine learning (ML) in drug discovery has been well documented, but measures of levels of adoption, investments, and efficiencies gained from its use in clinical development have not yet been developed, captured or published. AI/ML use in clinical development is expected to increase, but its impact has not yet been systematically measured until now.The Tufts Center for the Study of Drug Development conducted a global online survey among pharmaceutical and biotechnology companies, contract research organizations (CROs), and data and technology vendors servicing drug developers. The survey gathered 302 responses assessing levels of AI/ML implementation across 36 distinct clinical trial planning and design, trial execution, and regulatory submission activities. The survey collected data on US dollar investment, time savings, and challenges and opportunities of AI/ML use in clinical development.Approximately one-third of the sample (36.9%) was not yet using or implementing AI/ML across 36 design and planning, execution, and regulatory submission activities; another 30.3% was beginning their AI/ML implementation (or piloting), 22.1% was partially implementing (or moving beyond pilots), and on average only 10.7% had fully implemented AI/ML (i.e., uses AI in most trials employing a repeatable process)
