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Turbulence and sound are important cues for oyster reef larval recruitment. Numerous studies have found a relationship between turbulence intensity and swimming behaviors of marine larvae, while others have documented the importance of sounds in enhancing larval recruitment to oyster reefs. However, the relationship between turbulence and the reef soundscape is not well understood. In this study we made side-by-side acoustic Doppler velocimeter turbulence measurements and hydrophone soundscape recordings over 2 intertidal oyster reefs (1 natural and 1 restored) and 1 adjacent bare mudflat as a reference. Sound pressure levels (SPL) were similar across all three sites, although SPL >  2000 Hz was highest at the restored reef, likely due to its larger area that contained a greater number of sound-producing organisms. Flow noise (FN), defined as the mean of pressure fluctuations recorded by the hydrophone atf<  100 Hz, was significantly related to mean flow speed, turbulent kinetic energy, and turbulence dissipation rate (ε), agreeing with theoretical calculations for turbulence. Our results also show a similar relationship between ε andFNto what has been previously reported for ε vs. downward larval swimming velocity (w_b), with bothFNandw_bdemonstrating rapid growth at ε >  0.1 cm²s⁻³. These results suggest that reef turbulence and sounds may attract oyster larvae in complementary and synergistic ways. 

In data lakes, one of the core challenges remains finding rele- vant tables. We introduce the notion of semantic data lakes, i.e., repositories where datasets are linked to concepts and entities described in a knowledge graph (KG). We formalize the problem of semantic table search, i.e., retrieving tables containing informa- tion semantically related to a given set of entities, and provide the first formal definition of semantic relatedness of a dataset to tuples of entities. Our solution offers the first general framework to compute the semantic relevance of the contents of a table w.r.t. entity tuples, as well as efficient algorithms (exploiting seman- tic signals, such as entity types and embeddings) to scale the semantic search to repositories with hundreds of thousands of distinct tables. Our extensive experiments on both real-world and synthetic benchmarks show that our approach is able to retrieve more relevant tables (up to 5.4 times higher recall) in comparison to existing methods while ensuring fast response times (up to 17 times faster with LSH). 

We find all solutions to the constant Yang–Baxter equation $R_{12}{R}_{13}{R}_{23}=R_{23}{R}_{13}{R}_{12}$in three dimensions, subject to an additive charge-conservation (ACC) ansatz. This ansatz is a generalization of (strict) charge-conservation, for which a complete classification in all dimensions was recently obtained. ACC introduces additional sector-coupling parameters—in three dimensions, there are four such parameters. In the generic dimension 3 case, in which all of the four parameters are non-zero, we find there is a single three parameter family of solutions. We give a complete analysis of this solution, giving the structure of the centralizer (symmetry) algebra in all orders. We also solve the remaining cases with three, two or one non-zero sector-coupling parameter(s). 

The pressing issue of pesticide exposure disproportionately affecting marginalized communities underscores the immediate necessity to tackle pesticide drift from nearby agricultural areas, especially aggravated by the impacts of climate change. Effective measures including stricter regulations, enhanced monitoring, alternative agricultural practices, and community engagement are essential to mitigate environmental injustices and safeguard community health. This article delves into the intricate relationship between pesticide transport, groundwater vulnerability, and environmental justice within the context of climate change. Employing a geospatial analytical hierarchy overlay model, we comprehensively assess the impact of pesticide transport on groundwater vulnerability while scrutinizing climate change and associated environmental justice concerns. Groundwater vulnerability across the Kentucky River Basin varies, with 18% classified as very low, 23% as low, 27% as prone, and 20% and 12% as high and very high, respectively, concentrated mainly in the mid-eastern and southern regions due to population density and biodiversity. The research integrates a robust analytical detection technique, with a focus on glyphosate and its metabolites concentrations, to validate and refine spatial models. By engaging with communities, this study enhances understanding of environmental complexities, offering insights for sustainable environmental management. 

Abstract Recently the field of cavity magnonics, a field focused on controlling the interaction between magnons and photons confined within microwave resonators, has drawn significant attention as it offers a platform for enabling advancements in quantum- and spin-based technologies. Here, we introduce excitation vector fields, whose polarisation and profile can be easily tuned in a two-port cavity setup, thus acting as an effective experimental dial to explore the coupled dynamics of cavity magnon-polaritons. Moreover, we develop theoretical models that accurately predict and reproduce the experimental results for any polarisation state and field profile within the cavity resonator. This versatile experimental platform offers a new avenue for controlling spin-photon interactions by manipulating the polarisation of excitation fields. By introducing real-time tunable parameters that control the polarisation state, our experiment delivers a mechanism to readily control the exchange of information between hybrid systems. 

Developing accurate process–structure–property models for metal additive manufacturing is crucial due to the numerous process parameters, extended build times, and high material costs which make it impractical to rely solely on an experimental trial and error approach when optimizing the process. In this work, a multiscale digital approach to estimate tensile anisotropy along selective laser melted titanium meta-stable alloys is presented. The approach uses a component scale thermal FEA model of the process to calculate temperature, a meso-scale phase field model to calculate microstructure evolution, and a microscale crystal plasticity model to calculate the effect of texture on the tensile properties in different directions. The model has predicted isotropic yield strength for this material, which could guide designers to choose orientations freely. However, anisotropy in hardening behavior could be expected but is caused by porosity and cracking, which are not considered in the presented models. We believe the presented approach, which relies solely on easy to use commercial simulation tools, lays a good foundation for the development of process–structure–property models to optimize process parameters. The modeling approach should be applicable to other mechanical properties and materials with appropriate considerations. 

The GO DNA repair system protects against GC → TA mutations by finding and removing oxidized guanine. The system is mechanistically well understood but its origins are unknown. We searched metagenomes and abundantly found the genes encoding GO DNA repair at the Lost City Hydrothermal Field (LCHF). We recombinantly expressed the final enzyme in the system to show MutY homologs function to suppress mutations. Microbes at the LCHF thrive without sunlight, fueled by the products of geochemical transformations of seafloor rocks, under conditions believed to resemble a young Earth. High levels of the reductant H₂and low levels of O₂in this environment raise the question, why are resident microbes equipped to repair damage caused by oxidative stress? MutY genes could be assigned to metagenome-assembled genomes (MAGs), and thereby associate GO DNA repair with metabolic pathways that generate reactive oxygen, nitrogen and sulfur species. Our results indicate that cell-based life was under evolutionary pressure to cope with oxidized guanine well before O₂levels rose following the great oxidation event. 

Algorithms with predictions is a new research direction that leverages machine learned predictions for algorithm design. So far a plethora of recent works have incorporated predictions to improve on worst-case bounds for online problems. In this paper, we initiate the study of complexity of dynamic data structures with predictions, including dynamic graph algorithms. Unlike online algorithms, the goal in dynamic data structures is to maintain the solution efficiently with every update. We investigate three natural models of prediction: (1) δ-accurate predictions where each predicted request matches the true request with probability δ, (2) list-accurate predictions where a true request comes from a list of possible requests, and (3) bounded delay predictions where the true requests are a permutation of the predicted requests. We give general reductions among the prediction models, showing that bounded delay is the strongest prediction model, followed by list-accurate, and δ-accurate. Further, we identify two broad problem classes based on lower bounds due to the Online Matrix Vector (OMv) conjecture. Specifically, we show that locally correctable dynamic problems have strong conditional lower bounds for list-accurate predictions that are equivalent to the non-prediction setting, unless list-accurate predictions are perfect. Moreover, we show that locally reducible dynamic problems have time complexity that degrades gracefully with the quality of bounded delay predictions. We categorize problems with known OMv lower bounds accordingly and give several upper bounds in the delay model that show that our lower bounds are almost tight. We note that concurrent work by v.d.Brand et al. [SODA '24] and Liu and Srinivas [arXiv:2307.08890] independently study dynamic graph algorithms with predictions, but their work is mostly focused on showing upper bounds. 

Abstract The GO DNA repair system protects against GC → TA mutations by finding and removing oxidized guanine. The system is mechanistically well understood but its origins are unknown. We searched metagenomes and abundantly found the genes encoding GO DNA repair at the Lost City Hydrothermal Field (LCHF). We recombinantly expressed the final enzyme in the system to show MutY homologs function to suppress mutations. Microbes at the LCHF thrive without sunlight, fueled by the products of geochemical transformations of seafloor rocks, under conditions believed to resemble a young Earth. High levels of the reductant H₂and low levels of O₂in this environment raise the question, why are resident microbes equipped to repair damage caused by oxidative stress? MutY genes could be assigned to metagenome assembled genomes (MAGs), and thereby associate GO DNA repair with metabolic pathways that generate reactive oxygen, nitrogen and sulfur species. Our results indicate that cell-based life was under evolutionary pressure to cope with oxidized guanine well before O₂levels rose following the great oxidation event.