Verifying Qthreads: Is Model Checking viable for User-Level Tasking runtimes?.
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This dataset contains de-identified data from human subjects experiments, along with the images and code that were used to run the experiments (as a crowdsourced online study). In this study, participants were shown the probability of a house being in the burn zone of a wildfire. They were asked if they would stay in the house or evacuate in that scenario. The probability information was presented in different ways, including text and maps. The studies tested the impact of different visual cues on the participants' patterns of decisions.
With recent computed tomography (CT) efforts using Artificial Intelligence (AI) and Deep Learning (DL)techniques, there is a strong need for differentiable forward projection models that can be integrated into existing DL frameworks. We developed a pytorch-based package library providing differentiable forward and back projection functions and classes to facilitate forward and back propagation of CT operations in the training procedure. This forward projectors support three CT projection geometries: cone, parallel and modular beams. This package can be used with both CPU and GPU with CUDA.
Demonstrate a novel computer-based procedure solution for a multi-unit power plant. TIPS is developed to demonstrate a way to operate a highly automated plant. Currently, there are no plants (nuclear or non-nuclear) that can support this high level of automation. The purpose of TIPS is to help the industries think beyond the current conduct of operations. TIPS is designed to work with the GSE Systems' Combine-cycle gas turbine power plant simulator. TIPS uses the plant data from the simulator to make the user experience of the computer-based procedure more realistic.
MOOSEenger is an open-source, terminal-first chat application for the MOOSE ecosystem that couples specialized parsing of MOOSE documentation and “.i” input files with retrieval-augmented generation to deliver grounded answers about multiphysics modeling and workflows. It includes dedicated readers for MOOSE-style HTML and a pyhit-based parser that uses the MOOSE syntax tree to preserve block structure and attach retrieval metadata. A data-ingestion pipeline performs semantic chunking into atomic facts and stores them hierarchically in a local Chroma vector database that maintains parent–child relationships across documents; the system can ingest directories, individual files, and single-page web content, and it provides CRUD operations (insert, update, delete) to manage the corpus. At query time, relevant chunks are embedded, retrieved, and fused into the model context, with interactive features such as token streaming, persistent chat history, and dynamic RAG (retrieval triggered by user input or intermediate model output). Deployment is flexible: MOOSEenger runs with local Ollama models or remote Hugging Face/OpenAI backends—typically coordinating generation, lightweight tagging/summarization, and embeddings across three models—and it also supports a server mode and integration with the VS Code Continue interface.
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The Cellular-Potts model is a powerful and ubiquitous framework for developing computational models for simulating complex multicellular biological systems. Cellular-Potts models (CPMs) are often computationally expensive due to the explicit modeling of interactions among large numbers of individual model agents and diffusive fields described by partial differential equations (PDEs). In this work, we develop a convolutional neural network (CNN) surrogate model using a U-Net architecture that accounts for periodic boundary conditions. We use this model to accelerate the evaluation of a mechanistic CPM previously used to investigate in vitro vasculogenesis. The surrogate model was trained to predict 100 computational steps ahead (Monte-Carlo steps, MCS), accelerating simulation evaluations by a factor of 562 times compared to single-core CPM code execution on CPU. Over short timescales of up to 3 recursive evaluations, or 300 MCS, our model captures the emergent behaviors demonstrated by the original Cellular-Potts model such as vessel sprouting, extension and anastomosis, and contraction of vascular lacunae. This approach demonstrates the potential for deep learning to serve as a step toward efficient surrogate models for CPM simulations, enabling faster evaluation of computationally expensive CPM simulations of biological processes.
Directional Cooling-Induced Fracturing (DCIF) experiments were conducted on rectangular Westerly granite blocks (width=depth=4.0", height=2.0"). Liquid nitrogen was poured in a small, 1"-diameter copper cup attached to the top of the sample, and the resulting acoustic emissions (AEs) and temperature changes on the surface of the sample were monitored. Several confining stresses were applied bi-axially to the sides of the samples so that the onset of AE activity and the stress applied to the sample were correlated. The obtained AEs were used to determine the microcracking source locations and amplitude, and the associated moment tensors. Included in this submission are the animations of the AE locations and graphics displaying the measured temperature-AE activity changes for different stresses.
Directional Cooling-Induced Fracturing (DCIF) experiments were conducted on three rectangular Westerly granite blocks (width=depth=4.0", height=2.0") which were preheated to 200, 400, and 600 degree C to induce damage (microcracks) with varying degrees. Liquid nitrogen was poured in a small, 1"-diameter copper cup attached to the top of the sample, and the resulting acoustic emissions (AEs) and temperature changes on the surface of the sample were monitored. The experiments were conducted under one selected biaxial stress (5.8MPa). The obtained AEs were used to determine the microcracking source locations and amplitude, and the associated moment tensors. The onset time of the AEs was correlated with the cooling temperature, which was used to show that the temperature at the onset of microcracking is not affected significantly by the preexisting damage, compared to the impact of the stress in the sample. Included in this submission are the animations of the AE locations and graphics displaying the measured temperature-AE activity changes for samples with different degrees of microcrack damage.
In the United States, a wide variety of studies show that the geoscience community does not reflect the broader societal makeup (e.g., Velasco and Jaurrieta de Velasco, 2010; Dutt, 2020; Howley, 2020). In fact, only about 10% of all Science, Technology, Engineering, Mathematics (STEM) Ph.D. degrees are awarded to people of color, although they represent more than a third of the population (Dutt, 2020). These numbers have changed little over the past 40 years (e.g., Bernard and Cooperdock, 2018; Dutt, 2020). Recent events in the United States have again raised awareness of this discrepancy in many fields; similar divergences may be present throughout the world. How this discrepancy represents a tremendous loss of talent and contributes to ongoing bias and racism is discussed.
In preparation for non-light water reactor (non-LWR) activities, US Nuclear Regulatory Commission (NRC) staff are advancing risk-informed and performance-based licensing approaches and addressing key policy issues. One non-LWR reactor concept is a pebble bed reactor (PBR). This reactor design uses spherical fuel elements (pebbles) that are continually added to and removed from the reactor core. The free movement of the fuel in this design presents new challenges for material control and accounting (MC&A) programs. Therefore, an assessment of MC&A program features and measures for a PBR was performed to help NRC staff develop associated MC&A regulations or regulatory guides. The current regulatory framework for non-LWR fuel cycles excludes support for licensing reviews for MC&A programs for PBRs. Licensing reviews of an MC&A program for PBRs can be facilitated by (1) a model MC&A program for a PBR based on identification and assessment of MC&A program features and recommended measures for a reference PBR and (2) a methodology for assessing MC&A performance that can help assess different MC&A program features and measures. This report supports the NRC’s non-LWR Vision and Strategy Near-Term Implementation Action Plans.
A new language is beneficial to take advantage of emerging architectures, and existing and new software technologies. Contra is a new language to provide performance portable code and consists of two innovations, which are described. A current status and illustrations of the code are shown.
Milestone report on sampling of long-term dry-stored ATR spent fuel elements. It describes tooling, sample acquisition methods and analysis of the aluminum oxide samples by scanning electron microscopy, thermogravimetric analysis, X-ray diffraction and transmission electron microscopy. The observations provide input to projections on controls necessary for maintaining the integrity of the fuel during extended interim storage.
Air conditioning (AC) and refrigeration systems account for a large share of today’s global energy consumption, and this demand is expected to increase sharply over the next 50 years unless actions are taken to ameliorate the increase. The adoption of AC in developed countries increased rapidly in the twentieth century, and the twenty-first century is expected to see increased adoption in developing countries—especially those with hotter climates and large, growing populations, such as India, China, Brazil, and Middle Eastern and African nations. The International Energy Agency (IEA) projects that by 2050, AC energy consumption levels will increase by 4.3 times over the 2010 levels for non-Organization of Economic Coordination and Development (OECD) countries vs. only 1.5 times for OECD countries (Figure 1).