Identifying the Molecular Edge Termination of Exfoliated Hexagonal Boron Nitride Nanosheets with Solid-State NMR Spectroscopy and Plane-Wave DFT Calculations

Hexagonal boron nitride nanosheets (h-BNNS), the isoelectronic analog to graphene, have received interest over the past decade due to their high thermal oxidative resistance, high bandgap, catalytic activity, and low cost. The functional groups that terminate boron and nitrogen zigzag and/or armchair edges directly affect their chemical, physical, and electronic properties. However, an understanding of the molecular edge termination present in h-BNNS is lacking. Here, high-resolution magic-angle spinning (MAS) solid-state NMR (SSNMR) spectroscopy, and plane-wave density-functional theory (DFT) calculations are used to determine the molecular edge termination in exfoliated h-BNNS. 1H → 11B cross-polarization MAS (CPMAS) SSNMR spectra of h-BNNS revealed multiple hydroxyl/oxygen coordinated boron edge sites that were not detectable in direct excitation experiments. A dynamic nuclear polarization (DNP)-enhanced 1H → 15N CPMAS spectrum of h-BNNS displayed four distinct 15N resonances while a 2D 1H{14N} dipolar-HMQC spectrum acquired with fast MAS revealed three distinct 14N environments. Plane-wave DFT calculations were used to construct model edge structures and predict the corresponding 11B, 14N and 15N SSNMR spectra. Comparison of the experimental and predicted SSNMR spectra confirms that zigzag and armchair edges with both amine and boron hydroxide/oxide termination are present. The detailed characterization of h-BNNS molecular edge termination will prove useful for many material science applications. The techniques outlined here should also be applicable to understand the molecular edge terminations in other 2D materials

Advanced Development of Space Photovoltaic Concentrators Using Robust Lenses, Multi-Junction Cells, and Graphene Radiators

At the past three PVSCs, our team has presented recent advances in our space photovoltaic concentrator technology. In the past year, under multiple NASA-funded research and technology development programs, our team has made much additional progress in the advanced development of space photovoltaic concentrators. New robust Fresnel lenses, new high-efficiency multi-junction cells, and new graphene radiators have been developed. The paper will present the latest advances in this technology

A brief guide to multi-objective reinforcement learning and planning JAAMAS track

Real-world sequential decision-making tasks are usually complex, and require trade-offs between multiple - often conflicting - objectives. However, the majority of research in reinforcement learning (RL) and decision-theoretic planning assumes a single objective, or that multiple objectives can be handled via a predefined weighted sum over the objectives. Such approaches may oversimplify the underlying problem, and produce suboptimal results. This extended abstract outlines the limitations of using a semi-blind iterative process to solve multi-objective decision making problems. Our extended paper [4], serves as a guide for the application of explicitly multi-objective methods to difficult problems. © 2023 International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved

Using ACTH Challenges to Validate Techniques for Adrenocortical Activity Analysis in Various African Wildlife Species

Abstract: Monitoring adrenocortical activity using fecal hormonal analysis can provide information on how environmental changes are affecting non-domestic species health and success in the field; however, this noninvasive method needs proper validation to ensure that analysis reflects true physiological events. Our objectives were to use adrenocorticotropic hormone (ACTH) challenges as a physiological validation method to test the suitability of a new corticosterone enzyme immunoassay (EIA) to accurately assess the adrenocortical activity using fecal samples in four African wildlife species-the black rhinoceros (rhino; Diceros bicornis), African elephant (Loxodonta africana), chimpanzee (Pan troglodytes) and African lion (Panthera leo krugeri). In the rhino and elephant, fecal Glucocorticoid metabolites (GC) surged 75 and 51 h post-ACTH injection, respectively. In the chimpanzee, fecal GC metabolites peaked at 29 h post-injection. And the lion had a peak of fecal GC at 24 h post-ACTH. This study determined that adrenocortical activity was reflected in concentrations of fecal GC metabolites suggesting that this corticosterone EIA is an effective technique for the monitoring stress in four African species

A practical guide to multi-objective reinforcement learning and planning

Real-world sequential decision-making tasks are generally complex, requiring trade-offs between multiple, often conflicting, objectives. Despite this, the majority of research in reinforcement learning and decision-theoretic planning either assumes only a single objective, or that multiple objectives can be adequately handled via a simple linear combination. Such approaches may oversimplify the underlying problem and hence produce suboptimal results. This paper serves as a guide to the application of multi-objective methods to difficult problems, and is aimed at researchers who are already familiar with single-objective reinforcement learning and planning methods who wish to adopt a multi-objective perspective on their research, as well as practitioners who encounter multi-objective decision problems in practice. It identifies the factors that may influence the nature of the desired solution, and illustrates by example how these influence the design of multi-objective decision-making systems for complex problems. © 2022, The Author(s)

A Practical Guide to Multi-Objective Reinforcement Learning and Planning

Real-world decision-making tasks are generally complex, requiring trade-offs between multiple, often conflicting, objectives. Despite this, the majority of research in reinforcement learning and decision-theoretic planning either assumes only a single objective, or that multiple objectives can be adequately handled via a simple linear combination. Such approaches may oversimplify the underlying problem and hence produce suboptimal results. This paper serves as a guide to the application of multi-objective methods to difficult problems, and is aimed at researchers who are already familiar with single-objective reinforcement learning and planning methods who wish to adopt a multi-objective perspective on their research, as well as practitioners who encounter multi-objective decision problems in practice. It identifies the factors that may influence the nature of the desired solution, and illustrates by example how these influence the design of multi-objective decision-making systems for complex problems

PHASES High Precision Differential Astrometry of delta Equulei

delta Equulei is among the most well-studied nearby binary star systems. Results of its observation have been applied to a wide range of fundamental studies of binary systems and stellar astrophysics. It is widely used to calibrate and constrain theoretical models of the physics of stars. We report 27 high precision differential astrometry measurements of delta Equulei from the Palomar High-precision Astrometric Search for Exoplanet Systems (PHASES). The median size of the minor axes of the uncertainty ellipses for these measurements is 26 micro-arcseconds. These data are combined with previously published radial velocity data and other previously published differential astrometry measurements using other techniques to produce a combined model for the system orbit. The distance to the system is determined to within a twentieth of a parsec and the component masses are determined at the level of a percent. The constraints on masses and distance are limited by the precisions of the radial velocity data; we outline plans improve this deficiency and discuss the outlook for further study of this binary.Comment: Accepted by AJ. Complete versions of tables 2-7 now available at http://stuff.mit.edu/~matthew1/deltaEquTables/ (removed from astroph server

Virtual reality genres: Comparing preferences in immersive experiences and games

5 pagesEven though virtual reality (VR) shares features with video games, it offers a wider range of experiences. There is currently no cohesive classification for commercial VR offerings. As a first step to account for this deficiency, the work in progress considers the relationship between game genres and users’ ratings and downloads of VR experiences. We found Action, Shooter, and Simulation to be the most frequently downloaded genres; Action and Music/Rhythm the most highly rated; and Simulation and Music/Rhythm to occur at a statistically higher rate in VR compared to non-VR. Finally, we learned that VR experiences are less likely to receive positive ratings than 2D games. The findings can inform developers’ marketing decisions based on demand

Artificial intelligence and real-world data for drug and food safety - A regulatory science perspective

In 2013, the Global Coalition for Regulatory Science Research (GCRSR) was established with members from over ten countries (www.gcrsr.net). One of the main objectives of GCRSR is to facilitate communication among global regulators on the rise of new technologies with regulatory applications through the annual conference Global Summit on Regulatory Science (GSRS). The 11th annual GSRS conference (GSRS21) focused on "Regulatory Sciences for Food/Drug Safety with Real-World Data (RWD) and Artificial Intelligence (AI)." The conference discussed current advancements in both AI and RWD approaches with a specific emphasis on how they impact regulatory sciences and how regulatory agencies across the globe are pursuing the adaptation and oversight of these technologies. There were presentations from Brazil, Canada, India, Italy, Japan, Germany, Switzerland, Singapore, the United Kingdom, and the United States. These presentations highlighted how various agencies are moving forward with these technologies by either improving the agencies' operation and/or preparing regulatory mechanisms to approve the products containing these innovations. To increase the content and discussion, the GSRS21 hosted two debate sessions on the question of "Is Regulatory Science Ready for AI?" and a workshop to showcase the analytical data tools that global regulatory agencies have been using and/or plan to apply to regulatory science. Several key topics were highlighted and discussed during the conference, such as the capabilities of AI and RWD to assist regulatory science policies for drug and food safety, the readiness of AI and data science to provide solutions for regulatory science. Discussions highlighted the need for a constant effort to evaluate emerging technologies for fit-for-purpose regulatory applications. The annual GSRS conferences offer a unique platform to facilitate discussion and collaboration across regulatory agencies, modernizing regulatory approaches, and harmonizing efforts