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Undirected, binary network data consist of indicators of symmetric relations between pairs of actors. Regression models of such data allow for the estimation of effects of exogenous covariates on the network and for prediction of unobserved data. Ideally, estimators of the regression parameters should account for the inherent dependencies among relations in the network that involve the same actor. To account for such dependencies, researchers have developed a host of latent variable network models; however, estimation of many latent variable network models is computationally onerous and which model is best to base inference upon may not be clear. We propose the probit exchangeable (PX) model for undirected binary network data that is based on an assumption of exchangeability, which is common to many of the latent variable network models in the literature. The PX model can represent the first two moments of any exchangeable network model. We leverage the EM algorithm to obtain an approximate maximum likelihood estimator of the PX model that is extremely computationally efficient. Using simulation studies, we demonstrate the improvement in estimation of regression coefficients of the proposed model over existing latent variable network models. In an analysis of purchases of politically aligned books, we demonstrate political polarization in purchase behavior and show that the proposed estimator significantly reduces runtime relative to estimators of latent variable network models, while maintaining predictive performance. 

Abstract We present the design and performance of a four-phased radiofrequency (RF) carpet system for ion transport between 200–600 mbar, significantly higher than previously demonstrated RF carpet applications. The RF carpet, designed with a 160 $$\upmu $$ $\mu$m pitch, is applied to the lateral collection of ions in xenon at pressures up to 600 mbar. We demonstrate transport efficiency of caesium ions across varying pressures, and compare with microscopic simulations made in the SIMION package. The novel use of an N-phased RF carpet can achieve ion levitation and controlled lateral motion in a denser environment than is typical for RF ion transport in gases. This feature makes such carpets strong candidates for ion transport to single ion sensors envisaged for future neutrinoless double-beta decay experiments in xenon gas. 

Abstract We investigate the performance of , a 7.5 GPU-accelerated photon propagation tool compared with a single-threaded simulation. We compare the simulations using an improved model of the gaseous time projection chamber. Performance results suggest that improves simulation speeds by between$$58.47\pm {0.02}$$ $58.47\pm 0.02$and$$181.39\pm {0.28}$$ $181.39\pm 0.28$times relative to a CPU-only simulation and these results vary between different types of GPU and CPU. A detailed comparison shows that the number of detected photons, along with their times and wavelengths, are in good agreement between and . 

Low-cost and scalable superhydrophobic coating methods provide viable approaches for energy-efficient separation of immiscible liquid/liquid mixtures. A scalable photopolymerization method is developed to functionalize porous substrates with a hybrid coating of tetrapodal ZnO (T-ZnO) and polymethacrylate, which exhibits simultaneous superhydrophobicity and superoleophilicity. Here, T-ZnO serves dual purposes by (i) initiating radical photopolymerization during the fabrication process through a hole-mediated pathway and (ii) providing a hierarchical surface roughness to amplify wettability characteristics and suspend liquid droplets in the metastable Cassie—Baxter regime. Photopolymerization provides a means to finely control the conversion and spatial distribution of the formed polymer, whilst allowing for facile large-area fabrication and potential coating on heat-sensitive substrates. Coated stainless-steel meshes and filter papers with desired superhydrophobic/superoleophilic properties exhibit excellent performance in separating stratified oil/water, oil/ionic-liquid, and water/ionic-liquid mixtures as well as water-in-oil emulsions. The hybrid coating demonstrates desired mechanical robustness and chemical resistance for their long-term application in large-scale energy-efficient separation of immiscible liquid/liquid mixtures. 

A<sc>bstract</sc> If neutrinoless double beta decay is discovered, the next natural step would be understanding the lepton number violating physics responsible for it. Several alternatives exist beyond the exchange of light neutrinos. Some of these mechanisms can be distinguished by measuring phase-space observables, namely the opening angle cosθamong the two decay electrons, and the electron energy spectra,T₁andT₂. In this work, we study the statistical accuracy and precision in measuring these kinematic observables in a future xenon gas detector with the added capability to precisely locate the decay vertex. For realistic detector conditions (a gas pressure of 10 bar and spatial resolution of 4 mm), we find that the average$$ \overline{\cos\ \theta } $$ $\overline{cos\theta }$and$$ \overline{T_1} $$ $\overline{T_1}$values can be reconstructed with a precision of 0.19 and 110 keV, respectively, assuming that only 10 neutrinoless double beta decay events are detected. 

Abstract The imaging of individual Ba²⁺ions in high pressure xenon gas is one possible way to attain background-free sensitivity to neutrinoless double beta decay and hence establish the Majorana nature of the neutrino. In this paper we demonstrate selective single Ba²⁺ion imaging inside a high-pressure xenon gas environment. Ba²⁺ions chelated with molecular chemosensors are resolved at the gas-solid interface using a diffraction-limited imaging system with scan area of 1 × 1 cm²located inside 10 bar of xenon gas. This form of microscopy represents key ingredient in the development of barium tagging for neutrinoless double beta decay searches in¹³⁶Xe. This also provides a new tool for studying the photophysics of fluorescent molecules and chemosensors at the solid-gas interface to enable bottom-up design of catalysts and sensors. 

Abstract A two decade‐long megadrought, with likely anthropogenic causes, has impacted forest growth and mortality across the southwestern U.S. Given this event, and the future likelihood of similar climate challenges, it is important to understand how different water resources are used by semi‐arid forests in this region. Within the geographic domain of the North American Monsoon climate system, we studied seasonal water‐use in eight differentPinus ponderosamontane forests distributed across a climate gradient with varying contributions from winter and summer precipitation. We collected oxygen isotopes from precipitation, soil, and xylem water during two contrasting hydrologic years to determine how trees differentially use winter versus summer precipitation sources. Most trees switched from using snowmelt water as the primary source during the early‐summer hyper‐arid period, to monsoon rainwater during the late‐summer. However, during the low snowpack year, which represents the most common climate phenomenon during the megadrought, trees at all sites used less summer rain when compared to the higher snowpack year, demonstrating a drought‐induced antecedent influence of winter precipitation on the uptake of summer rain. A possible mechanism to explain the antecedent effect is an earlier snow disappearance during the low snowpack year weakening hydrologic connectivity within the soil profile, decreasing the soil infiltration of summer rains. However, in years with higher snowpack, the snow lasts longer, and this can improve the hydrologic connectivity within the soil profile. As a result, there is more infiltration of summer rains into the soils. This can enhance the maintenance of active shallow fine‐root biomass during the period when snowpack disappears, and monsoon rains have yet to arrive. These findings provide insight into how the seasonal interactions between major seasonal climate systems influence forest tree water use in the face of an extreme megadrought.