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Abstract The architecture of magma plumbing systems plays a fundamental role in volcano eruption and evolution. However, the precise configuration of crustal magma reservoirs and conduits responsible for supplying eruptions are difficult to explore across most active volcanic systems. Consequently, our understanding of their correlation with eruption dynamics is limited. Axial Seamount is an active submarine volcano located along the Juan de Fuca Ridge, with known eruptions in 1998, 2011, and 2015. Here we present high-resolution images of P-wave velocity, attenuation, and estimates of temperature and partial melt beneath the summit of Axial Seamount, derived from multi-parameter full waveform inversion of a 2D multi-channel seismic line. Multiple magma reservoirs, including a newly discovered western magma reservoir, are identified in the upper crust, with the maximum melt fraction of ~15–32% in the upper main magma reservoir (MMR) and lower fractions of 10% to 26% in other satellite reservoirs. In addition, a feeding conduit below the MMR with a melt fraction of ~4–11% and a low-velocity throat beneath the eastern caldera wall connecting the MMR roof with eruptive fissures are imaged. These findings delineate an asymmetric shallow plumbing system beneath Axial Seamount, providing insights into the magma pathways that fed recent eruptions. 

Abstract Fractionally doped perovskites oxides (FDPOs) have demonstrated ubiquitous applications such as energy conversion, storage and harvesting, catalysis, sensor, superconductor, ferroelectric, piezoelectric, magnetic, and luminescence. Hence, an accurate, cost-effective, and easy-to-use methodology to discover new compositions is much needed. Here, we developed a function-confined machine learning methodology to discover new FDPOs with high prediction accuracy from limited experimental data. By focusing on a specific application, namely solar thermochemical hydrogen production, we collected 632 training data and defined 21 desirable features. Our gradient boosting classifier model achieved a high prediction accuracy of 95.4% and a high F1 score of 0.921. Furthermore, when verified on additional 36 experimental data from existing literature, the model showed a prediction accuracy of 94.4%. With the help of this machine learning approach, we identified and synthesized 11 new FDPO compositions, 7 of which are relevant for solar thermochemical hydrogen production. We believe this confined machine learning methodology can be used to discover, from limited data, FDPOs with other specific application purposes. 

Efficient contact tracing and isolation is an effective strategy to control epidemics. It was used effectively during the Ebola epidemic and successfully implemented in several parts of the world during the ongoing COVID-19 pandemic. An important consideration in contact tracing is the budget on the number of individuals asked to quarantine -- the budget is limited for socioeconomic reasons. In this paper, we present a Markov Decision Process (MDP) framework to formulate the problem of using contact tracing to reduce the size of an outbreak while asking a limited number of people to quarantine. We formulate each step of the MDP as a combinatorial problem, MinExposed, which we demonstrate is NP-Hard; as a result, we develop an LP-based approximation algorithm. Though this algorithm directly solves MinExposed, it is often impractical in the real world due to information constraints. To this end, we develop a greedy approach based on insights from the analysis of the previous algorithm, which we show is more interpretable. A key feature of the greedy algorithm is that it does not need complete information of the underlying social contact network. This makes the heuristic implementable in practice and is an important consideration. Finally, we carry out experiments on simulations of the MDP run on real-world networks, and show how the algorithms can help in bending the epidemic curve while limiting the number of isolated individuals. Our experimental results demonstrate that the greedy algorithm and its variants are especially effective, robust, and practical in a variety of realistic scenarios, such as when the contact graph and specific transmission probabilities are not known. All code can be found in our GitHub repository: this https URL. 

Abstract Electrically accelerated self‐healable poly(ionic liquids) copolymers that exhibit resistor‐capacitor (RC) circuit properties are developed. At low alternating current (AC) frequencies these materials behave as a resistor (R), whereas at higher frequencies as a capacitor (C). These properties are attributed to a combination of dipolar and electrostatic interactions in (1‐[(2‐methacryloyloxy)ethyl]‐3‐butylimidazolium bis(trifluoromethyl‐sulfonyl)imide) copolymerized with methyl methacrylate (MMA) monomers to form p(MEBIm‐TSFI/MMA)] copolymers. When the monomer molar ratio (MEBIm‐TSFI:MMA) is 40/60, these copolymers are capable of undergoing multiple damage‐repair cycles and self‐healing is accelerated by the application of alternating 1.0–4.0 V electric field (EF). Self‐healing in the absence of EFs is facilitated by van der Waals (vdW) interactions, but the application of AC EF induces back and forth movement of charges against the opposing force that result in dithering of electrostatic dipoles giving rise to interchain physical crosslinks. Electrostatic inter‐ and intrachain interactions facilitated by copolymerization of ionic liquid monomers with typically dielectric acrylic‐based monomers result in enhanced cohesive energy densities that accelerate the recovery of vdW forces facilitating self‐healing. Incorporating ionic liquids into commodity polymers offers promising uses as green conducting solid polyelectrolytes in self‐healable energy storage, energy‐harvesting devices, and many other applications.