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Cavitation bubbles often appear as populations in a wide range of naval and biomedical applications. While the oscillations of an isolated spherical bubble are relatively well understood, the dynamics of multiple bubbles remain less characterized due to the large number of parameters associated with bubble size and spatial configuration. In the present study, we develop an energy budget framework for quantifying energy transfer and pressure work during the oscillations of spherical bubbles while conserving the total energy of the system. We focus on a two-bubble system in the dilute limit, in which bubbles interact weakly and largely maintain spherical symmetry. The energy budget framework uses the outputs of the coupled Keller–Miksis equations to evaluate individual energy components and their temporal evolution. Three key parameters—bubble size ratio, bubble–bubble distance, and driving pressure ratio—are observed to nonlinearly alter collapse time, energy concentration, and radiated acoustic energy. We show that stronger interactions, caused by larger size ratios or smaller separation distances, lead to reduced compression and less violent and delayed collapse, thereby lowering the energy concentrated in the bubble. We further determine scaling relationships for bubble dynamics and energy components at the instant of collapse based on the initial problem parameters. We identify the initial driving pressure conditions where the dominant energy reduction mechanism shifts from bubble–bubble interactions at low driving pressures to liquid compressibility at high pressures. The proposed framework and scaling relations provide a robust basis for incorporating bubble–bubble interactions and energy redistribution mechanisms into bubble cloud models, in the dilute limit. 

Abstract We propose a coefficient that measures extremal dependence in paired samples of functions. It has properties similar to the Pearson correlation, but differs in two significant ways: (i) it is designed to measure dependence between curves; and (ii) it focuses only on extreme curves. The new coefficient is derived within the framework of regular variation in Banach spaces. A consistent estimator is proposed and justified by an asymptotic analysis and a simulation study. The usefulness of the new coefficient is illustrated using financial and climate functional data. 

Effective visual scanning and perception are essential to perform teleoperation construction tasks. In long-distance teleoperation systems, such as between Earth and the Moon, communication delays of at least 3 seconds impact operator performance and situational awareness, influencing eye movement patterns that vary with time-delay conditions and task types. This study examines features of eye movement patterns during time-delayed teleoperation tasks in lunar construction environment. We analyzed and quantified eye movement trajectories using recurrence quantification analysis to identify spatiotemporal patterns and gain insights into how operators interact with visual scenes under time-delayed and task-specific conditions. By visualizing these patterns, we interpreted their temporal and spatial characteristics and compared them in different situations. Our findings, which address a gap in understanding operator eye movement patterns during teleoperation tasks, have potential practical implications for improving visual perception by identifying quantitative patterns along with qualitative interpretations. This study will contribute to visual interface design in teleoperation systems for lunar construction, providing actionable insights for the design and operation of such systems. 

When raccoon rabies first invaded the mid-Atlantic United States, epizootics were larger, longer, and more pronounced than those in its historic, more southern, range, suggesting a North-South gradient in disease dynamics. In addition, due to higher raccoon densities and concentrated feeding sources, urban areas might sustain larger epizootics, suggesting an urban-rural gradient might likewise influence dynamics. Here we leverage long-term surveillance data on raccoon rabies, collated by the Centers for Disease Control and Prevention, United States Department of Agriculture, and state and local public health agencies to better understand the role of latitude and urbanness for raccoon rabies epizootiology. Our analysis utilizes surveillance data from the 20 states composing the raccoon rabies enzootic area across 2006–2018. We identified effects of latitude and human population density (a proxy for urbanness) on the county-level probability of detecting raccoon rabies. We find that: 1) in the northeastern US, more samples are submitted in the summer, and more positive results are obtained, albeit with a lower likelihood of a given sample being found to be rabid, while these trends are independent of season at southern latitudes; 2) the association between urbanness and risk of rabies cases varies across latitude, with greater rabies presence in rural vs. urban counties in the south and a more consistent risk across urbanness in the north; and 3) the most consistent predictors of raccoon rabies detection are spatiotemporal effects, suggesting that recent detection of cases in a county or its neighbors are more informative of raccoon rabies dynamics than are general metrics like latitude and urbanness. Statistical and spatial long-term studies like these not only can improve understanding of wildlife disease patterns but can help guide public health and wildlife management efforts in areas most at risk for raccoon rabies virus infection. 

We investigated surface nanostructures on an antiferromagnet MnBi2Te4 using a novel imaging technique, direct (real)-space and real time coherent x-ray imaging (direct-CXI). This technique has provided new insights into antiferromagnetic textures, including the formation of anti-phase antiferromagnetic (AFM) domains and thermal dynamics of AFM domains and domain walls. While this method produces real-space images of AFM textures without requiring a complex imaging retrieval process, its underlying imaging mechanism has not been fully understood, limiting a deep understanding of AFM textures and the information they contain. By investigating the well-defined structural characteristics of the nanostructures fabricated on MnBi2Te4, we elucidate the imaging principle of this novel technique. We find that the observed images can be well explained by the Fresnel diffraction integral. Using a simple model from classical optics, our calculations successfully reproduce the experimentally observed images of the nanostructures. This demonstrates that direct-CXI not only provides straightforward real-space imaging but also contains phase information through its Fresnel diffraction integral. 

The stable isotope values of seawater (δ18O and δ2H) provide valuable information on the exchange of water between the ocean, atmosphere, and cryosphere and on ocean mixing processes. As such, observational seawater δ18O and δ2H data place powerful constraints on hydrologic changes in the modern ocean. Seawater δ18O data are also essential for calibrating paleoclimate proxies based on the δ18O of marine carbonates and are an increasingly critical diagnostic tool for assessing model performance and skill in isotope-enabled climate models. Despite their broad value, no centralized and actively-curated database for this type of data exists, even though a growing number of new seawater δ18O datasets have been generated over the last decade. As such, many seawater δ18O datasets remain “hidden”. To improve the accessibility of seawater δ18O data for the Earth Science research community, the Past Global Changes (PAGES) CoralHydro2k project has created a new, machine-readable, and metadata-rich database of observational seawater δ18O data, paired with seawater δ2H and salinity data, that is compliant with findability, accessibility, interoperability, and reusability (FAIR) standards for digital assets. The data has been collected from public databases and repositories, direct researcher data submissions, scientific papers, and student theses. In total, the PAGES CoralHydro2k Seawater δ18O Database contains over 18 600 data points with extensive metadata that makes the database suitable for a myriad of research applications. For hidden data, we searched for and included all datasets within the global ocean. For public data, our data collation efforts were focused on the upper 50 m from 35° N to 35° S (to aid in CoralHydro2k's seawater δ18O reconstruction studies using δ18O and Sr/Ca in tropical-subtropical coral skeletons). We also provide a set of best practices to the community for reporting seawater isotope data in the future. The database is available on the NOAA NCEI World Data Service for Paleoclimatology landing page: https://www.ncei.noaa.gov/access/paleo-search/study/34575 (last access: 11 February 2026; https://doi.org/10.25921/ap7d-2k16, Atwood et al., 2026). A Seawater Oxygen Isotopes Community was also developed within the EarthChem Library (https://www.earthchem.org/communities/seawater-oxygen-isotopes/, last access: 20 February 2026) to help researchers submit new datasets and obtain a dataset DOI. This template is aligned with the CoralHydro2k Seawater δ18O Database to facilitate future updates to the database. 

Reef‐building corals provide seasonally resolved records of past climate variability based on the oxygen isotope composition (δ18O) of their skeletons. However, many non‐climatic factors can alter coral δ18O values. These include coral biomineralization and post‐depositional alteration of the coral skeleton, which can add uncertainty to coral based paleoclimate reconstructions. These uncertainties are apparent in mean climate reconstructions developed from coral δ18O values. We present a framework to minimize this uncertainty in mean coral δ18O records using a multi‐variate regression model that incorporates four commonly measured properties in coral δ18O records. We test the ability of the model to reduce variability in a Holocene climate reconstruction comprised of 37 coral δ18O records from Kiritimati. 38% of the variance in the mean coral δ18O values across the Holocene is accounted for by a combination of four predictors: (a) mm‐scale variability in coral δ18O, (b) the mean coral δ13C value, (c) the mean coral extension rate, and (d) the extent of diagenetic alteration identified in Scanning Electron Microscope images. Once these non‐climatic artifacts are minimized in the reconstruction, the weighted variance of the Holocene data set is reduced by 43% and the uncertainty in the trend of mean coral δ18O is reduced by 18%. The model is validated using three well‐characterized modern coral records with pristine and altered sections. These results have important implications for the climate interpretation of this Holocene data set. This framework also has the potential to improve other paleoclimate reconstructions based on ensembles of mean coral δ18O records.