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Abstract Oil and gas production regions are significant sources of greenhouse gases and reactive pollutants such as nitrogen oxides (NO_x) and volatile organic compounds. Research has also shown that methane (CH₄) emissions reported to the Environmental Protection Agency's (EPA) Greenhouse Gas Reporting Program (GHGRP) are generally underestimated. The Arctic accounted for 5.5% of global oil and gas production in 2022 but is estimated to contain significant undiscovered resources. The emitted NO_xand volatile organic compounds can impact the composition and chemistry of the Arctic atmosphere. The Prudhoe Bay Oil Field in Alaska is one of the 10 largest oil fields in the US and has been approved for significant development expansion. However, only one recent study has reported measurements of its greenhouse gas emissions. We estimate the emission rates for carbon dioxide (CO₂), CH₄, and NO_xfrom the Prudhoe Bay Oil Field during the spring of 2022 using airborne mass balance methods and emission ratios. We also discuss emissions per energy produced and show an increase over time, with values higher than the national average for oil and gas producing regions, though within uncertainties. Our estimates are lower than the NO_xemission estimate reported in the National Emissions Inventory (NEI), as seen in other oil and gas studies, but fall within the uncertainty range of the greenhouse gases reported in the GHGRP. This work provides a valuable snapshot of emissions before further expansion of extraction activities. 

Content caching is vital for enhancing web server efficiency and reducing network congestion, particularly in platforms predicting user actions. Despite many studies conducted toimprove cache replacement strategies, there remains space for improvement. This paper introduces STRCacheML, a Machine Learning (ML) assisted Content Caching Policy. STRCacheML leverages available attributes within a platform to make intelligent cache replacement decisions offline. We have tested various Machine Learning and Deep Learning algorithms to adapt the one with the highest accuracy; we have integrated that algorithm into our cache replacement policy. This selected ML algorithm was employed to estimate the likelihood of cache objects being requested again, an essential factor in cache eviction scenarios. The IMDb dataset, constituting numerous videos with corresponding attributes, was utilized to conduct our experiment. The experimental section highlights our model’s efficacy, presenting comparative results compared to the established approaches based on raw cache hits and cache hit rates. 

Content caching is vital for enhancing web server efficiency and reducing network congestion, particularly in platforms predicting user actions. Despite many studies conducted toimprove cache replacement strategies, there remains space for improvement. This paper introduces STRCacheML, a Machine Learning (ML) assisted Content Caching Policy. STRCacheML leverages available attributes within a platform to make intelligent cache replacement decisions offline. We have tested various Machine Learning and Deep Learning algorithms to adapt the one with the highest accuracy; we have integrated that algorithm into our cache replacement policy. This selected ML algorithm was employed to estimate the likelihood of cache objects being requested again, an essential factor in cache eviction scenarios. The IMDb dataset, constituting numerous videos with corresponding attributes, was utilized to conduct our experiment. The experimental section highlights our model’s efficacy, presenting comparative results compared to the established approaches based on raw cache hits and cache hit rates. 

Content caching is vital for enhancing web server efficiency and reducing network congestion, particularly in platforms predicting user actions Despite many studies conducted to improve cache replacement strategies , there remains space for improvement. This paper introduces STRCacheML, a Machine Learning (ML) assisted Content Caching Policy. STRCacheML leverages available attributes within a platform to make intelligent cache replacement decisions offline. We have t ested various Machine Learning and Deep Learning algorithms to adapt the one with the highest accuracy; we have integrated that algorithm into our cache replacement policy. This selected ML algorithm was employed to estimate the likelihood of cache objects being requested again, an essential factor in cache eviction scenarios. The IMDb dataset, constituting numerous videos with corresponding attributes, was utilized to conduct our experiment. The experimental section highlights our model’s efficacy, present ing comparative results compared to the established approaches based on raw cache hits and cache hit rates. 

Abstract Meridional circulation regulates the Sun’s interior dynamics and magnetism. While it is well accepted that meridional flows are poleward at the Sun’s surface, helioseismic observations have yet to provide a definitive answer for the depth at which those flows return to the equator, or the number of circulation cells in depth. Here, we explore the observability of multiple circulation cells stacked in radius. Specifically, we examine the seismic signature of several meridional flow profiles by convolving time–distance averaging kernels with mean flows obtained from a suite of 3D hydrodynamic simulations. At mid and high latitudes, we find that weak flow structures in the deep convection zone can be obscured by signals from the much stronger surface flows. This contamination of 1–2 m s⁻¹is caused by extended side lobes in the averaging kernels, which produce a spurious equatorward signal with flow speeds that are 1 order of magnitude stronger than the original flow speeds in the simulations. At low latitudes, the flows in the deep layers of the simulations are stronger (>2 m s⁻¹) and multiple cells across the convection zone can produce a sufficiently strong signal to survive the convolution process. Now that meridional flows can be measured over two decades of data, the uncertainties arising from convective noise have fallen to a level where they are comparable in magnitude to the systematic biases caused by nonlocal features in the averaging kernels. Hence, these systematic errors are beginning to influence current helioseismic deductions and need broader consideration. 

Abstract Radio images of protoplanetary disks demonstrate that dust grains tend to organize themselves into rings. These rings may be a consequence of dust trapping within gas pressure maxima, wherein the local high dust-to-gas ratio is expected to trigger the formation of planetesimals and eventually planets. We revisit the behavior of dust near gas pressure perturbations enforced by a planet in two-dimensional, shearing-box simulations. While dust grains collect into generally long-lived rings, particles with a small Stokes parameter τ s < 0.1 tend to advect out of the ring within a few drift timescales. Scaled to the properties of ALMA disks, we find that rings composed of larger particles ( τ s ≥ 0.1) can nucleate a dust clump massive enough to trigger pebble accretion, which proceeds to ingest the entire dust ring well within ∼1 Myr. To ensure the survival of the dust rings, we favor a nonplanetary origin and typical grain size τ s ≲ 0.05–0.1. Planet-driven rings may still be possible but if so we would expect the orbital distance of the dust rings to be larger for older systems. 

Abstract Stellar evolution models calculate convective boundaries using either the Schwarzschild or Ledoux criterion, but confusion remains regarding which criterion to use. Here we present a 3D hydrodynamical simulation of a convection zone and adjacent radiative zone, including both thermal and compositional buoyancy forces. As expected, regions that are unstable according to the Ledoux criterion are convective. Initially, the radiative zone adjacent to the convection zone is Schwarzschild unstable but Ledoux stable due to a composition gradient. Over many convective overturn timescales, the convection zone grows via entrainment. The convection zone saturates at the size originally predicted by the Schwarzschild criterion, although in this final state the Schwarzschild and Ledoux criteria agree. Therefore, the Schwarzschild criterion should be used to determine the size of stellar convection zones, except possibly during short-lived evolutionary stages in which entrainment persists. 

Abstract Proton radioactivity was discovered exactly 50 years ago. First, this nuclear decay mode sets the limit of existence on the nuclear landscape on the neutron-deficient side. Second, it comprises fundamental aspects of both quantum tunnelling as well as the coupling of (quasi)bound quantum states with the continuum in mesoscopic systems such as the atomic nucleus. Theoretical approaches can start either from bound-state nuclear shell-model theory or from resonance scattering. Thus, proton-radioactivity guides merging these types of theoretical approaches, which is of broader relevance for any few-body quantum system. Here, we report experimental measurements of proton-emission branches from an isomeric state in 54m Ni, which were visualized in four dimensions in a newly developed detector. We show that these decays, which carry an unusually high angular momentum, ℓ = 5 and ℓ = 7, respectively, can be approximated theoretically with a potential model for the proton barrier penetration and a shell-model calculation for the overlap of the initial and final wave functions. 

Abstract This article presents the results of a week of observations around the 2 July 2019, total Chilean eclipse. The eclipse occurred between 19:22 and 21:46 UTC, with complete sun disc obscuration at 20:38–20:40 UTC (16:38–16:40 LT) over the Andes Lidar Observatory (ALO) at (30.3°S, 70.7°W). Observations were carried out using ALO instrumentation with the goal to observe possible eclipse‐induced effects on the mesosphere and lower thermosphere region (MLT; 75–105 km altitude). To complement our data set, we have also utilized TIMED/SABER temperatures and ionosonde electron density measurements taken at the University of La Serena's Juan Soldado Observatory. Observed events include an unusual fast, bow‐shaped gravity wave structure in airglow images, mesosphere temperature mapper brightness as well as in lidar temperature with 150 km horizontal wavelength 24 min observed period, and vertical wavelength of 25 km. Also, a strong zonal wind shear above 100 km in meteor radar scans as well as the occurrence of a sporadic E layer around 100 km from ionosonde measurements. Finally, variations in temperature and density and the presence of a descending sporadic sodium layer near 98 km were seen in lidar data. We discuss the effects of the eclipse in the MLT, which can shed light on a sparse set of measurements during this type of event. Our results point out several effects of eclipse‐associated changes in the atmosphere below and above but not directly within the MLT.