Oceans will play a crucial role in our efforts to combat the growing climate emergency. Researchers have proposed several strategies to harness greener energy through oceans and use oceans as carbon sinks. However, the risks these strategies might pose to the ocean and marine ecosystem are not well understood. It is imperative that we quickly develop a range of tools to monitor ocean processes and marine ecosystems alongside the technology to deploy these solutions on a large scale into the oceans. Large arrays of inexpensive cameras placed deep underwater coupled with machine learning pipelines to automatically detect, classify, count, and estimate fish populations have the potential to continuously monitor marine ecosystems and help study the impacts of these solutions on the ocean. In this paper, we successfully demonstrate the application of YOLOv4 and YOLOv7 deep learning models to classify and detect six species of fish in a dark artificially lit underwater video dataset captured 500 m below the surface, with a mAP of 76.01% and 85.0%, respectively. We show that 2,000 images per species, for each of the six species of fish is sufficient to train a machine-learning species classification model for this low-light environment. This research is a first step toward systems to autonomously monitor fish deep underwater while causing as little disruption as possible. As such, we discuss the advances that will be needed to apply such systems on a large scale and propose several avenues of research toward this goal.

Presidential elections can be forecast using information from political and economic conditions, polls, and a statistical model of changes in public opinion over time. However, these “knowns” about how to make a good presidential election forecast come with many unknowns due to the challenges of evaluating forecast calibration and communication. We highlight how incentives may shape forecasts, and particularly forecast uncertainty, in light of calibration challenges. We illustrate these challenges in creating, communicating, and evaluating election predictions, using the Economist and Fivethirtyeight forecasts of the 2020 election as examples, and offer recommendations for forecasters and scholars.

Antibiotics are among the most common medications prescribed in nursing homes. The annual prevalence of antibiotic use in residents of nursing homes ranges from 47% to 79%, and more than half of antibiotic courses initiated in nursing-home settings are unnecessary or prescribed inappropriately (wrong drug, dose, or duration). Inappropriate antibiotic use is associated with a variety of negative consequences including Clostridioides difficile infection (CDI), adverse drug effects, drug–drug interactions, and antimicrobial resistance. In response to this problem, public health authorities have called for efforts to improve the quality of antibiotic prescribing in nursing homes.

Through diversity of composition, sequence, and interfacial structure, hybrid materials greatly expand the palette of materials available to access novel functionality. The NSF Division of Materials Research recently supported a workshop (October 17–18, 2019) aiming to (1) identify fundamental questions and potential solutions common to multiple disciplines within the hybrid materials community; (2) initiate interfield collaborations between hybrid materials researchers; and (3) raise awareness in the wider community about experimental toolsets, simulation capabilities, and shared facilities that can accelerate this research. This article reports on the outcomes of the workshop as a basis for cross-community discussion. The interdisciplinary challenges and opportunities are presented, and followed with a discussion of current areas of progress in subdisciplines including hybrid synthesis, functional surfaces, and functional interfaces.

Neptunium-237 will be present in radioactive wastes over extended time periods due to its long half-life (2.13 × 106 years). Understanding its behaviour under conditions relevant to radioactive waste disposal is therefore of particular importance. Here, microcosm experiments were established using sediments from a legacy lime workings with high-pH conditions as an analogue of cementitious intermediate-level radioactive waste disposal. To probe the influence of Fe biogeochemistry on Np(V) in these systems, additional Fe(III) (as ferrihydrite) was added to select experiments. Biogeochemical changes were tracked in experiments with low levels of Np(V) (20 Bq ml–1; 3.3 μM), whilst parallel higher concentration systems (2.5 KBq ml–1; 414 μM) allowed X-ray absorption spectroscopy. As expected, microbial reduction processes developed in microbially-active systems with an initial pH of 10; however, during microbial incubations the pH dropped from 10 to ∼7, reflecting the high levels of microbial metabolism occurring in these systems. In microbially-active systems without added Fe(III), 90% sorption of Np(V) occurred within one hour with essentially complete removal by one day. In the ferrihydrite-amended systems, complete sorption of Np(V) to ferrihydrite occurred within one hour. For higher-activity sediments, X-ray absorption spectroscopy (XAS) at end points where Fe(II) ingrowth was observed confirmed that complete reductive precipitation of Np(V) to Np(IV) had occurred under similar conditions to low-level Np experiments. Finally, pre-reduced, Fe(III)-reducing sediments, with and without added Fe(III) and held at pH 10, were spiked with Np(V). These alkaline pre-reduced sediments showed significant removal of Np to sediments, and XAS confirmed partial reduction to Np(IV) with the no Fe system, and essentially complete reduction to Np(IV) in the Fe(III)-enriched systems. This suggested an indirect, Fe(II)-mediated pathway for Np(V) reduction under alkaline conditions. Microbial analyses using 16S rRNA gene pyrosequencing suggested a role for alkali-tolerant, Gram-positive Firmicutes in coupled Fe(III) reduction and Np immobilization in these experiments.

Rapid or explosive heating of electrically conductive films has several applications, and the use of reactive laminates to increase output energy is an intriguing concept. Past studies have shown electrically heated aluminum/nickel (Al/Ni) nano-laminate films to augment this energy by an amount approximately equivalent to the expected heat of mixing between the two elements, which for most intermetallics is a significant fraction of the total heat of reaction (86% for Al/Ni). In this study, we investigate the use of sputtered aluminum/boron (Al/B) laminates to determine whether a similar increase, as measured by the velocity of an ejected flyer layer, occurs. However, observed velocities in any samples containing boron were 38% to 45% lower than samples without boron, despite much higher heats of reaction reported in the literature for Al/B. We attributed this reduction to the vaporization temperature of boron being much higher than that of Al, and because Al electrical resistivity at elevated temperatures was still much lower than boron, boron heating was less efficient as vaporized Al expanded and drove the ejected flyer. These results and analysis give insight into other reactive material combinations in which one of the constituents is an electrical insulator.