UC Berkeley

Abstract: The ATLAS Google Project was established as part of an ongoing evaluation of the use of commercial clouds by the ATLAS Collaboration, in anticipation of the potential future adoption of such resources by WLCG grid sites to fulfil or complement their computing pledges. Seamless integration of Google cloud resources into the worldwide ATLAS distributed computing infrastructure was achieved at large scale and for an extended period of time, and hence cloud resources are shown to be an effective mechanism to provide additional, flexible computing capacity to ATLAS. For the first time a total cost of ownership analysis has been performed, to identify the dominant cost drivers and explore effective mechanisms for cost control. Network usage significantly impacts the costs of certain ATLAS workflows, underscoring the importance of implementing such mechanisms. Resource bursting has been successfully demonstrated, whilst exposing the true cost of this type of activity. A follow-up to the project is underway to investigate methods for improving the integration of cloud resources in data-intensive distributed computing environments and reducing costs related to network connectivity, which represents the primary expense when extensively utilising cloud resources.

BACKGROUND: Depression and anxiety disorders are highly prevalent among young adults, with evidence suggesting sleep problems as key risk factors. OBJECTIVE: This study aimed to examine the association between insomnia and sleep characteristics with major depressive episode (MDE) and anxiety disorders, and the association after accounting for baseline mental health symptoms. METHODS: We conducted a prospective cohort study using data from the Students Health and Wellbeing Study (SHoT), surveying Norwegian higher education students aged 18 to 35 (N = 53,362). A diagnostic assessment of 10,460 participants was conducted in 2023. Self-reported insomnia, sleep duration, sleep onset latency, and wake after sleep onset were recorded in 2022. MDE and five types of anxiety disorders were assessed after one year using a self-administered CIDI 5.0. Analyses adjusted for age, sex, baseline mental health symptoms, and somatic conditions. RESULTS: Insomnia in young adults was associated with a significantly increased risk of MDE (adjusted RR = 3.50, 95 % CI = 3.18-3.84) and generalized anxiety disorder (GAD) (adjusted RR = 2.82, 95 % CI = 2.55-3.12) one year later. Sleep duration showed a reversed J-shaped association with mental disorders, with both short and, to a lesser extent, long sleep durations linked to elevated risks, even after adjusting for baseline mental health symptoms and somatic conditions. Although the associations were attenuated after adjustment, they remained statistically significant. CONCLUSION: Sleep disturbances, including insomnia and abnormal sleep durations, predict mental health issues in young adults, even after accounting for baseline mental health and somatic health. Addressing sleep problems early may help prevent subsequent mental health conditions in this population.

Increasing wind energy generation is central to grid decarbonization, yet methods to estimate wind energy potential are not standardized, leading to inconsistencies and even skewed results. This study aims to improve the fidelity of wind energy potential estimates through an approach that integrates geospatial analysis and machine learning (i.e., Gaussian process regression). We demonstrate this approach to assess the spatial distribution of wind energy capacity potential in the Contiguous United States (CONUS). We find that the capacity-based power density ranges from 1.70 MW/km2 (25th percentile) to 3.88 MW/km2 (75th percentile) for existing wind farms in the CONUS. The value is lower in agricultural areas (2.73 ± 0.02 MW/km2, mean ± 95 % confidence interval) and higher in other land cover types (3.30± 0.03 MW/km2). Notably, advancements in turbine manufacturing could reduce power density in areas with lower wind speeds by adopting low specific-power turbines, but improve power density in areas with higher wind speeds (>8.35 m/s at 120m above the ground), highlighting opportunities for repowering existing wind farms. Wind energy potential is shaped by wind resource quality and is regionally characterized by land cover and physical conditions, revealing significant capacity potential in the Great Plains and Upper Texas. The results indicate that areas previously identified as hot spots using existing approaches (e.g., the west of the Rocky Mountains) may have a limited capacity potential due to low wind resource quality. Improvements in methodology and capacity potential estimates in this study could serve as a new basis for future energy systems analysis and planning.

Growing the bioeconomy requires products and pathways that are cost-competitive. Technoeconomic analyses (TEAs) aim to predict the long-term economic viability and often use what are known as n^th plant cost and performance parameters. However, as TEA is more widely adopted to inform everything from early-stage research to company and investor decision-making, the n^th plant approach is inadequate and risks being misused to inform the early stages of scale-up. Some methods exist for conducting first-of-a-kind/pioneer plant cost analyses, but these receive less attention and have not been critically evaluated. This article explores TEA methods for early-stage scale-up, critically evaluates their applicability to biofuels and bioproducts, and recommends strategies for producing TEA results better suited to guiding prioritization and successful scale-up of bioprocesses.

This study focuses on solving the numerical challenges of imposing absorbing boundary conditions for dynamic simulations in the material point method (MPM). To attenuate elastic waves leaving the computational domain, the current work integrates the Perfectly Matched Layer (PML) theory into the implicit MPM framework. The proposed approach introduces absorbing particles surrounding the computational domain that efficiently absorb outgoing waves and reduce reflections, allowing for accurate modeling of wave propagation and its further impact on geotechnical slope stability analysis. The study also includes several benchmark tests to validate the effectiveness of the proposed method, such as several types of impulse loading and symmetric and asymmetric base shaking. The conducted numerical tests also demonstrate the ability to handle large deformation problems, including the failure of elasto-plastic soils under gravity and dynamic excitations. The findings extend the capability of MPM in simulating continuous analysis of earthquake-induced landslides, from shaking to failure.

The frequent and intensifying droughts caused by climate warming are leading to widespread declines in tree growth and increased mortality, posing a significant threat to the health and vitality of forest ecosystems. While competition among trees is recognized as a critical factor influencing their growth, the precise mechanism underlying its impact remains unclear. Here, we investigate the role of crown size in the process of competition affecting tree growth across varying gradients of age and moisture conditions. Our analysis is based on comprehensive data collected from natural forests of Xing'an larch (Larix gmelinii) located in the northeastern region of China. We observed that competition indirectly impacts tree growth by diminishing crown size, and this influence is modulated by both tree age and environmental moisture conditions. Specifically, mature trees are better able to withstand competition pressure than young trees. The stimulatory effect of crown size on tree growth enhances in young tress, but diminishes in mature trees. Additionally, the negative impact of competition on tree crown size is more pronounced in high moisture regions, and larches experiencing crown reduction under intense competition exhibit a heightened sensitivity to water availability. Our findings provide robust evidence that competition indirectly influences tree growth by modifying their phenotypic traits. Notably, the crown, serving as a crucial organ for nutrient acquisition in trees, was a mediating factor between competition and growth. This result holds significant implications for the sustainable management of forest ecosystems in the face of a warming climate in the future.

Real-time reactor power monitoring is critical for a variety of nuclear applications, spanning safety, security, operations, and maintenance. While machine learning methods have shown promise in monitoring reactor power levels, there is limited research on their efficacy in label-starved environments. The goal of this work is to assess the feasibility of classifying nuclear reactor power level using multisource data in scenarios with limited labels. Data were collected using low-resolution multisensors at four nuclear reactor facilities: two large research reactors and two TRIGA reactors. Within each pair, one reactor dataset served as the source and the other as the target in a transfer learning paradigm. Twenty-three supervised models were trained on labeled sequences of magnetic field and acceleration data from each of the target sites. Self-learning and transfer learning methods were applied to the top performing models to assess their classification performance with increasing amounts of labeled data. While reactor power level classification was achieved with a Matthews Correlation Coefficient of up to 0.739 ± 0.003 and 0.622 ± 0.009 with only 400 sequences per power state for the large research reactor and TRIGA target sites, respectively, self-learning and transfer learning leveraging source site data did not improve target classification performance. These findings suggest that alternative methods, such as higher sensitivity sensors, digital twins, or the use of physics-informed models, are required to enable high-performance classification in machine learning approaches to reactor monitoring with a dearth of target ground truth.