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Manure recycling can ameliorate pollution and fertilizer demand, but varying assumptions about recoverable manure nutrients and crop requirements complicate understanding of manure recycling potential. Using nitrogen (N) in the contiguous United States as a case study, we applied methods from six studies to compare manure N balance estimates (recoverable manure minus crop demand). We then developed a framework to assess both current and future potentials of manure recycling. The current balance in the U.S. is -13.3 ± 1.1 Tg N yr-1, reflecting large crop demand currently met with synthetic fertilizer. Improved adoption of current manure management technologies could decrease this deficit by 5%, while future technologies could enable another 21% reduction. However, new manure N application should be reduced by 33-36% to avoid phosphorous (P) overapplication. Improved crop N efficiency could decrease the deficit by 27%. Applying this framework at county level demonstrates variable regional opportunities to improve manure recycling. 

Superconductivity in strongly correlated electron systems frequently exhibits broken rotational symmetry, raising fundamental questions about the underlying order parameter symmetry. In this work, we demonstrate that electronic nematicity--driven by Coulomb-mediated rotational symmetry breaking--serves as a crucial link to understanding the nature of superconductivity. Utilizing a novel framework of angle-resolved measurement, we reveal an interring angular interplay among nematicity, superconductivity, and strange metallicity in magic-angle twisted trilayer graphene. By establishing a direct correlation between the preferred superconducting transport direction and the principal axis of the metallic phase, our findings place strong constrains on the symmetry of the superconducting order parameter. This work introduces a new paradigm for probing the microscopic mechanisms governing superconductivity in strongly interacting two-dimensional systems. 

Ice rises are slow moving areas of grounded ice adjacent to ice shelves, where ice flows outwards from a flow center that is independent of the rest of the ice sheet. Not only do these grounded regions provide buttressing forces that enhance ice-shelf stability, but their relative flow stability and isolation also make them ideal locations for extracting ice-core climate records and for reconstructing regional millenial-scale ice flow changes from internal radiostratigraphy. However, dating ice cores and flow changes recorded in radiostratigraphy can require knowledge of ice-rise flow and rheology, both of which are poorly constrained; models employing a standard rheological parameterization often fail to reproduce observed vertical velocities derived from ice-penetrating radar. These discrepancies highlight the need for improved constraints on the rheological properties of ice within ice rises. We present an inversion technique built around physics-informed neural networks (PINNs) using the DIFFICE.jax package to infer ice viscosity directly using observations from a phase-sensitive ice-penetrating radar observations and the mass and momentum balance equations. First, we use a mass balance equation to obtain velocity and ice density profiles from the radar observations. These velocities and densities are subsequently used with a full-Stokes momentum balance equation to obtain estimates for ice pressure and viscosity. We demonstrate the functionality of our inversion approach using synthetic data, and apply it to ice-penetrating radar data from the Weddell Sea Sector of Antarctica. This work will reveal the spatial variability in ice viscosity within these ice rises, for the first time, improving ice rheology parameterizations and ultimately improving how we reconstruct past climate change with ice cores and glaciological change with ice-rise internal stratigraphy. 

Low-income households (LIH), exposed to the uncertain modern grid, bear greater energy burdens and face inequitable access to reliable power compared to high-income households (HIH). This paper proposes a two-stage stochastic community-based microgrid planning (CMP) framework to boost energy justice within the system. To reduce the negative impact of income levels, a weighted energy cost model for households within the microgrid (MG) is designed. To address the multisource uncertainty during the operation period, a two-stage stochastic framework is developed. Moreover, to assess the proposed method, the unbalanced IEEE 123 node system is employed and modified as an isolated MG. The analysis reveals the proposed model can achieve a risk-averse solution while economic optimality is guaranteed. Additionally, the designed weighted method improves the LIH’s impact rate to 67.95% and decreases the total planning cost by 22.43%. 

Low-income households (LIH), exposed to the uncertain modern grid, bear greater energy burdens and face inequitable access to reliable power compared to high-income households (HIH). This paper proposes a two-stage stochastic community-based microgrid planning (CMP) framework to boost energy justice within the system. To reduce the negative impact of income levels, a weighted energy cost model for households within the microgrid (MG) is designed. To address the multisource uncertainty during the operation period, a two-stage stochastic framework is developed. Moreover, to assess the proposed method, the unbalanced IEEE 123 node system is employed and modified as an isolated MG. The analysis reveals the proposed model can achieve a risk-averse solution while economic optimality is guaranteed. Additionally, the designed weighted method improves the LIH’s impact rate to 67.95% and decreases the total planning cost by 22.43%.