A Behavioral Confirmation and Reduction of the Natural versus Synthetic Drug Bias

Research reveals a biased preference for natural versus synthetic drugs; however, this research is based upon self-report and has not examined ways to reduce the bias. We examined these issues in five studies involving 1,125 participants. In a Pilot Study (N = 110), participants rated the term natural to be more positive than the term synthetic, which reveals a default natural-is-better belief. In Studies 1 (N = 109) and 2 (N = 100), after a supposed personality study, participants were offered a thank you “gift” of a natural or synthetic pain reliever. Approximately 86% (Study 1) and 93% (Study 2) of participants chose the natural versus synthetic pain reliever, which provide a behavioral choice confirmation of the natural drug bias. In Studies 3 (N = 350) and 4 (N = 356), participants were randomly assigned to a control or experimental condition and were asked to consider a scenario in which they had a medical issue requiring a natural versus synthetic drug. The experimental condition included a stronger (Study 3) or weaker (Study 4) rational appeal about the natural drug bias and a statement suggesting that natural and synthetic drugs can be good or bad depending upon the context. In both studies, the natural bias was reduced in the experimental condition, and perceived safety and effectiveness mediated this effect. Overall, these data indicate a bias for natural over synthetic drugs in preferences and behavioral choices, which might be reduced with a rational appeal

Herpetofaunal Inventories of the National Parks of South Florida and the Caribbean: Volume III. Big Cypress National Preserve

Amphibian declines and extinctions have been documented around the world, often in protected natural areas. Concern for this trend has prompted the U.S. Geological Survey and the National Park Service to document all species of amphibians that occur within U.S. National Parks and to search for any signs that amphibians may be declining. This study, an inventory of amphibian species in Big Cypress National Preserve, was conducted from 2002 to 2003. The goals of the project were to create a georeferenced inventory of amphibian species, use new analytical techniques to estimate proportion of sites occupied by each species, look for any signs of amphibian decline (missing species, disease, die-offs, and so forth.), and to establish a protocol that could be used for future monitoring efforts. Several sampling methods were used to accomplish these goals. Visual encounter surveys and anuran vocalization surveys were conducted in all habitats throughout the park to estimate the proportion of sites or proportion of area occupied (PAO) by each amphibian species in each habitat. Opportunistic collections, as well as limited drift fence data, were used to augment the visual encounter methods for highly aquatic or cryptic species. A total of 545 visits to 104 sites were conducted for standard sampling alone, and 2,358 individual amphibians and 374 reptiles were encountered. Data analysis was conducted in program PRESENCE to provide PAO estimates for each of the anuran species. All of the amphibian species historically found in Big Cypress National Preserve were detected during this project. At least one individual of each of the four salamander species was captured during sampling. Each of the anuran species in the preserve was adequately sampled using standard herpetological sampling methods, and PAO estimates were produced for each species of anuran by habitat. This information serves as an indicator of habitat associations of the species and relative abundance of sites occupied, but it will also be useful as a comparative baseline for future monitoring efforts. In addition to sampling for amphibians, all encounters with reptiles were documented. The sampling methods used for detecting amphibians are also appropriate for many reptile species. These reptile locations are included in this report, but the number of reptile observations was not sufficient to estimate PAO for reptile species. We encountered 35 of the 46 species of reptiles believed to be present in Big Cypress National Preserve during this study, and evidence exists of the presence of four other reptile species in the Preserve. This study found no evidence of amphibian decline in Big Cypress National Preserve. Although no evidence of decline was observed, several threats to amphibians were identified. Introduced species, especially the Cuban treefrog (Osteopilus septentrionalis), are predators and competitors with several native frog species. The recreational use of off-road vehicles has the potential to affect some amphibian populations, and a study on those potential impacts is currently underway. Also, interference by humans with the natural hydrologic cycle of south Florida has the potential to alter the amphibian community. Continued monitoring of the amphibian species in Big Cypress National Preserve is recommended. The methods used in this study were adequate to produce reliable estimates of the proportion of sites occupied by most anuran species, and are a cost-effective means of determining the status of their populations

Self-Driving Telescopes: Autonomous Scheduling of Astronomical Observation Campaigns with Offline Reinforcement Learning

Modern astronomical experiments are designed to achieve multiple scientific goals, from studies of galaxy evolution to cosmic acceleration. These goals require data of many different classes of night-sky objects, each of which has a particular set of observational needs. These observational needs are typically in strong competition with one another. This poses a challenging multi-objective optimization problem that remains unsolved. The effectiveness of Reinforcement Learning (RL) as a valuable paradigm for training autonomous systems has been well-demonstrated, and it may provide the basis for self-driving telescopes capable of optimizing the scheduling for astronomy campaigns. Simulated datasets containing examples of interactions between a telescope and a discrete set of sky locations on the celestial sphere can be used to train an RL model to sequentially gather data from these several locations to maximize a cumulative reward as a measure of the quality of the data gathered. We use simulated data to test and compare multiple implementations of a Deep Q-Network (DQN) for the task of optimizing the schedule of observations from the Stone Edge Observatory (SEO). We combine multiple improvements on the DQN and adjustments to the dataset, showing that DQNs can achieve an average reward of 87%+-6% of the maximum achievable reward in each state on the test set. This is the first comparison of offline RL algorithms for a particular astronomical challenge and the first open-source framework for performing such a comparison and assessment task.Comment: Accepted in Machine Learning and the Physical Sciences Workshop at NeurIPS 2023; 6 pages, 5 figure

Data related to cyclic deformation and fatigue behavior of direct laser deposited Ti–6Al–4V with and without heat treatment

AbstractData is presented describing the strain-controlled, fully-reversed uniaxial cyclic deformation and fatigue behavior of Ti–6Al–4V specimens additively manufactured via Laser Engineered Net Shaping (LENS) – a Direct Laser Deposition (DLD) process. The data was collected by performing multiple fatigue tests on specimens with various microstructural states/conditions, i.e. in their ‘as-built’, annealed (below the beta transus temperature), or heat treated (above the beta transus temperature) condition. Such data aids in characterizing the mechanical integrity and fatigue resistance of DLD parts. Data presented herein also allows for elucidating the strong microstructure coupling of the fatigue behavior of DLD Ti–6Al–4V, as the data trends were found to vary with material condition (i.e. as-built, annealed or heat treated) [1]. This data is of interest to the additive manufacturing and fatigue scientific communities, as well as the aerospace and biomedical industries, since additively-manufactured parts cannot be reliably deployed for public use, until their mechanical properties are understood with high certainty