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Abstract Herein high-strength composites are prepared from elemental sulfur, sunflower oil, and wastewater sludge. Fats extracted from dissolved air flotation (DAF) solids were reacted with elemental sulfur to yield compositeDAFS(10 wt% DAF fats and 90 wt% sulfur). Additional composites were prepared from DAF fat, sunflower oil and sulfur to giveSunDAF_x(x = wt% sulfur, varied from 85–90%). The composites were characterized by spectroscopic, thermal, and mechanical methods. FT-IR spectra revealed a notable peak at 798 cm^–1indicating a C–S stretch inDAFS,SunDAF₉₀, andSunDAF₈₅indicating successful crosslinking of polymeric sulfur with olefin units. SEM/EDX analysis revealed homogenous distribution of carbon, oxygen, and sulfur inSunDAF₉₀andSunDAF₈₅. The percent crystallinity exhibited byDAFS(37%),SunDAF₉₀(39%), andSunDAF₈₅(45%) was observed to be slightly lower than that of previous composites prepared from elemental sulfur and fats and oils.DAFSandSunDAF_xdisplayed compressive strengths (26.4–38.7 MPa) of up to 227% above that required (17 MPa) of ordinary Portland cement for residential building foundations. The composite decomposition temperatures ranged from 211 to 219 °C, with glass transition temperatures of − 37 °C to − 39 °C. These composites thus provide a potential route to reclaim wastewater organics for use in value-added structural materials having mechanical properties competitive with those of commercial products. 

Abstract Quasars are bright active galactic nuclei powered by the accretion of matter around supermassive black holes at the center of galaxies. Their stochastic brightness variability depends on the physical properties of the accretion disk and black hole. The upcoming Rubin Observatory Legacy Survey of Space and Time (LSST) is expected to observe tens of millions of quasars, so there is a need for efficient techniques like machine learning that can handle the large volume of data. Quasar variability is believed to be driven by an X-ray corona, which is reprocessed by the accretion disk and emitted as UV/optical variability. We are the first to introduce an auto-differentiable simulation of the accretion disk and reprocessing. We use the simulation as a direct component of our neural network to jointly model the driving variability and reprocessing, trained with supervised learning on simulated LSST-like 10 yr quasar light curves. We encode the light curves using a transformer encoder, and the driving variability is reconstructed using latent stochastic differential equations, a physically motivated generative deep learning method that can model continuous-time stochastic dynamics. By embedding the physical processes of the driving signal and reprocessing into our network, we achieve a model that is more robust and interpretable. We demonstrate that our model outperforms a Gaussian process regression baseline and can infer accretion disk parameters and time delays between wave bands, even for out-of-distribution driving signals. Our approach provides a powerful framework that can be adapted to solve other inverse problems in multivariate time series. 

The construction of bounded-degree plane geometric spanners has been a focus of interest since 2002 when Bose, Gudmundsson, and Smid proposed the first algorithm to construct such spanners. To date, 11 algorithms have been designed with various tradeoffs in degree and stretch-factor. We have implemented these sophisticated spanner algorithms in C ++ using the CGAL library and experimented with them using large synthetic and real-world pointsets. Our experiments have revealed their practical behavior and real-world efficacy. We share the implementations via GitHub for broader uses and future research. We design and engineer EstimateStretchFactor , a simple practical algorithm, which can estimate stretch-factors (obtains lower bounds on the exact stretch-factors) of geometric spanners—a challenging problem for which no practical algorithm is known yet. In our experiments with bounded-degree plane geometric spanners, we found that EstimateStretchFactor estimated stretch-factors almost precisely. Further, it gave linear runtime performance in practice for the pointset distributions considered in this work, making it much faster than the naive Dijkstra-based algorithm for calculating stretch-factors. 

Scorpions are ancient and historically renowned for their potent venom. Traditionally, the systematics of this group of arthropods was supported by morphological characters, until recent phylogenomic analyses (using RNAseq data) revealed most of the higher‐level taxa to be non‐monophyletic. While these phylogenomic hypotheses are stable for almost all lineages, some nodes have been hard to resolve due to minimal taxonomic sampling (e.g. family Chactidae). In the same line, it has been shown that some nodes in the Arachnid Tree of Life show disagreement between hypotheses generated using transcritptomes and other genomic sources such as the ultraconserved elements (UCEs). Here, we compared the phylogenetic signal of transcriptomes vs. UCEs by retrieving UCEs from new and previously published scorpion transcriptomes and genomes, and reconstructed phylogenies using both datasets independently. We reexamined the monophyly and phylogenetic placement of Chactidae, sampling an additional chactid species using both datasets. Our results showed that both sets of genome‐scale datasets recovered highly similar topologies, with Chactidae rendered paraphyletic owing to the placement of Nullibrotheas allenii. As a first step toward redressing the systematics of Chactidae, we establish the family Anuroctonidae (new family) to accommodate the genus Anuroctonus 

Abstract Quasars are bright and unobscured active galactic nuclei (AGN) thought to be powered by the accretion of matter around supermassive black holes at the centers of galaxies. The temporal variability of a quasar’s brightness contains valuable information about its physical properties. The UV/optical variability is thought to be a stochastic process, often represented as a damped random walk described by a stochastic differential equation (SDE). Upcoming wide-field telescopes such as the Rubin Observatory Legacy Survey of Space and Time (LSST) are expected to observe tens of millions of AGN in multiple filters over a ten year period, so there is a need for efficient and automated modeling techniques that can handle the large volume of data. Latent SDEs are machine learning models well suited for modeling quasar variability, as they can explicitly capture the underlying stochastic dynamics. In this work, we adapt latent SDEs to jointly reconstruct multivariate quasar light curves and infer their physical properties such as the black hole mass, inclination angle, and temperature slope. Our model is trained on realistic simulations of LSST ten year quasar light curves, and we demonstrate its ability to reconstruct quasar light curves even in the presence of long seasonal gaps and irregular sampling across different bands, outperforming a multioutput Gaussian process regression baseline. Our method has the potential to provide a deeper understanding of the physical properties of quasars and is applicable to a wide range of other multivariate time series with missing data and irregular sampling. 

ABSTRACT Stars embedded in active galactic nucleus (AGN) discs or captured by them may scatter onto the supermassive black hole (SMBH), leading to a tidal disruption event (TDE). Using the moving-mesh hydrodynamics simulations with arepo, we investigate the dependence of debris properties in in-plane TDEs in AGN discs on the disc density and the orientation of stellar orbits relative to the disc gas (pro- and retro-grade). Key findings are: (1) Debris experiences continuous perturbations from the disc gas, which can result in significant and continuous changes in debris energy and angular momentum compared to ‘naked’ TDEs. (2) Above a critical density of a disc around an SMBH with mass M• [ρcrit ∼ 10−8 g cm−3 (M•/106 M⊙)−2.5] for retrograde stars, both bound and unbound debris is fully mixed into the disc. The density threshold for no bound debris return, inhibiting the accretion component of TDEs, is $$\rho _{\rm crit,bound} \sim 10^{-9}{\rm g~cm^{-3}}(M_{\bullet }/10^{6}\, {\rm M}_{\odot })^{-2.5}$$. (3) Observationally, AGN-TDEs transition from resembling naked TDEs in the limit of ρdisc ≲ 10−2ρcrit,bound to fully muffled TDEs with associated inner disc state changes at ρdisc ≳ ρcrit,bound, with a superposition of AGN + TDE in between. Stellar or remnant passages themselves can significantly perturb the inner disc. This can lead to an immediate X-ray signature and optically detectable inner disc state changes, potentially contributing to the changing-look AGN phenomenon. (4) Debris mixing can enrich the average disc metallicity over time if the star’s metallicity exceeds that of the disc gas. We point out that signatures of AGN-TDEs may be found in large AGN surveys.