Sterile neutrinos in the Milky Way: Observational constraints

We consider the possibility of constraining decaying dark matter by looking out through the Milky Way halo. Specifically we use Chandra blank sky observations to constrain the parameter space of sterile neutrinos. We find that a broad band in parameter space is still open, leaving the sterile neutrino as an excellent dark matter candidate.Comment: Submitted to ApJL, 4 pages, 4 figure

Defining the Agenda for Serious Tort Reform

In this Article, the authors support Prof. Sugarman\u27s tort reform proposals, but argue that these proposals need more development before they will produce actual change

Thissavros Hydropower Plant Managing Geotechnical Problems in the Construction

The Thissavros hydropower and pumped storage project on the Nestos river in northern Greece involved construction of a 172 m high rockfill dam and an underground power house with 300 MW installed capacity. Bedrock at the site consists of gneiss with complex geological structure and complicated hydrogeological conditions. On the right abutment, the dam partially rests on a large landslide and the toe of another large landslide extends into the plunge pool from the left bank. Initial excavations activated the dormant slides. Unloading, buttressing and drainage successfully stabilized the landslides. Core material for the dam is a silty sand and required special precautions in design and construction. Starting with an extremely rapid reservoir filling the dam has performed highly satisfactorily. The power house had to be excavated in a relatively unfavorable geological orientation but application of structural discontinuity analysis avoided wedge failures

Learned 1-D advection solver to accelerate air quality modeling

Accelerating the numerical integration of partial differential equations by learned surrogate model is a promising area of inquiry in the field of air pollution modeling. Most previous efforts in this field have been made on learned chemical operators though machine-learned fluid dynamics has been a more blooming area in machine learning community. Here we show the first trial on accelerating advection operator in the domain of air quality model using a realistic wind velocity dataset. We designed a convolutional neural network-based solver giving coefficients to integrate the advection equation. We generated a training dataset using a 2nd order Van Leer type scheme with the 10-day east-west components of wind data on 39$^{\circ}$N within North America. The trained model with coarse-graining showed good accuracy overall, but instability occurred in a few cases. Our approach achieved up to 12.5$\times$ acceleration. The learned schemes also showed fair results in generalization tests.Comment: Accepted as a workshop paper at the The Symbiosis of Deep Learning and Differential Equations (DLDE) - II in the 36th Conference on Neural Information Processing Systems (NeurIPS 2022

Learned 1-D passive scalar advection to accelerate chemical transport modeling: a case study with GEOS-FP horizontal wind fields

We developed and applied a machine-learned discretization for one-dimensional (1-D) horizontal passive scalar advection, which is an operator component common to all chemical transport models (CTMs). Our learned advection scheme resembles a second-order accuracy, three-stencil numerical solver, but differs from a traditional solver in that coefficients for each equation term are output by a neural network rather than being theoretically-derived constants. We downsampled higher-resolution simulation results -- resulting in up to 16$\times$ larger grid size and 64$\times$ larger timestep -- and trained our neural network-based scheme to match the downsampled integration data. In this way, we created an operator that is low-resolution (in time or space) but can reproduce the behavior of a high-resolution traditional solver. Our model shows high fidelity in reproducing its training dataset (a single 10-day 1-D simulation) and is similarly accurate in simulations with unseen initial conditions, wind fields, and grid spacing. In many cases, our learned solver is more accurate than a low-resolution version of the reference solver, but the low-resolution reference solver achieves greater computational speedup (500$\times$ acceleration) over the high-resolution simulation than the learned solver is able to (18$\times$ acceleration). Surprisingly, our learned 1-D scheme -- when combined with a splitting technique -- can be used to predict 2-D advection, and is in some cases more stable and accurate than the low-resolution reference solver in 2-D. Overall, our results suggest that learned advection operators may offer a higher-accuracy method for accelerating CTM simulations as compared to simply running a traditional integrator at low resolution

Dark matter line emission constraints from NuSTAR observations of the Bullet Cluster

Line emission from dark matter is well motivated for some candidates e.g. sterile neutrinos. We present the first search for dark matter line emission in the 3-80keV range in a pointed observation of the Bullet Cluster with NuSTAR. We do not detect any significant line emission and instead we derive upper limits (95% CL) on the flux, and interpret these constraints in the context of sterile neutrinos and more generic dark matter candidates. NuSTAR does not have the sensitivity to constrain the recently claimed line detection at 3.5keV, but improves on the constraints for energies of 10-25keV.Comment: 7 pages, 5 figures, submitted to Ap

Atmospheric chemistry surrogate modeling with sparse identification of nonlinear dynamics

Modeling atmospheric chemistry is computationally expensive and limits the widespread use of atmospheric chemical transport models. This computational cost arises from solving high-dimensional systems of stiff differential equations. Previous work has demonstrated the promise of machine learning (ML) to accelerate air quality model simulations but has suffered from numerical instability during long-term simulations. This may be because previous ML-based efforts have relied on explicit Euler time integration -- which is known to be unstable for stiff systems -- and have used neural networks which are prone to overfitting. We hypothesize that the creation of parsimonious models combined with modern numerical integration techniques can overcome this limitation. Using a small-scale photochemical mechanism to explore the potential of these methods, we have created a machine-learned surrogate by (1) reducing dimensionality using singular value decomposition to create an interpretably-compressed low-dimensional latent space, and (2) using Sparse Identification of Nonlinear Dynamics (SINDy) to create a differential-equation-based representation of the underlying chemical dynamics in the compressed latent space with reduced numerical stiffness. The root mean square error of the ML model prediction for ozone concentration over nine days is 37.8% of the root mean concentration across all simulations in our testing dataset. The surrogate model is 11$\times$ faster with 12$\times$ fewer integration timesteps compared to the reference model and is numerically stable in all tested simulations. Overall, we find that SINDy can be used to create fast, stable, and accurate surrogates of a simple photochemical mechanism. In future work, we will explore the application of this method to more detailed mechanisms and their use in large-scale simulations

Reengineered helmet coil for human brain studies at 3 tesla

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The Disulfide Relay of the Intermembrane Space Oxidizes the Ribosomal Subunit Mrp10 on Its Transit into the Mitochondrial Matrix

SummaryMost mitochondrial proteins are synthesized in the cytosol and directed into the organelle; matrix proteins contain presequences that guide them through translocases in contact sites of the outer and inner membrane. In contrast, the import of many intermembrane space proteins depends on cysteine residues and the oxidoreductase Mia40. Here, we show that both import machineries can cooperate in the biogenesis of matrix proteins. Mrp10, a conserved protein of the mitochondrial ribosome, interacts with Mia40 during passage into the matrix. Mrp10 contains an unconventional proline-rich matrix-targeting sequence that renders import intermediates accessible to Mia40. Although oxidation of Mrp10 is not essential for its function in mitochondrial translation, the disulfide bonds prevent proteolytic degradation of Mrp10 and thereby counteract instability of the mitochondrial genome. The unconventional import pathway of Mrp10 is presumably part of a quality-control circle that connects mitochondrial ribosome biogenesis to the functionality of the mitochondrial disulfide relay

Effects on Freshwater Organisms of Magnetic Fields Associated with Hydrokinetic Turbines

Underwater cables will be used to transmit electricity between turbines in an array (interturbine cables), between the array and a submerged step-up transformer (if part of the design), and from the transformer or array to shore. All types of electrical transmitting cables (as well as the generator itself) will emit EMF into the surrounding water. The electric current will induce magnetic fields in the immediate vicinity, which may affect the behavior or viability of animals. Because direct electrical field emissions can be prevented by shielding and armoring, we focused our studies on the magnetic fields that are unavoidably induced by electric current moving through a generator or transmission cable. These initial experiments were carried out to evaluate whether a static magnetic field, such as would be produced by a direct current (DC) transmitting cable, would affect the behavior of common freshwater fish and invertebrates