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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Determine an ergonomic glovebox glove port center line location which will be used for standardization in new designs, thus allowing for predictable human work performance, reduced worker exposure to radiation and musculoskeletal injury risks, and improved worker comfort, efficiency, health, and safety.
The purpose of a Learning Team is to transfer and communicate the information into operational feedback and improvement. We want to pay attention to the small things that go wrong because they are often early warning signals and may provide insight into the health of the whole system. The incident involved the collision of a van with a forklift having raised tines in rainy, overcast weather.
In this project, our goal was to develop methods that would allow us to make accurate predictions about individual differences in human cognition. Understanding such differences is important for maximizing human and human-system performance. There is a large body of research on individual differences in the academic literature. Unfortunately, it is often difficult to connect this literature to applied problems, where we must predict how specific people will perform or process information. In an effort to bridge this gap, we set out to answer the question: can we train a model to make predictions about which people understand which languages? We chose language processing as our domain of interest because of the well- characterized differences in neural processing that occur when people are presented with linguistic stimuli that they do or do not understand. Although our original plan to conduct several electroencephalography (EEG) studies was disrupted by the COVID-19 pandemic, we were able to collect data from one EEG study and a series of behavioral experiments in which data were collected online. The results of this project indicate that machine learning tools can make reasonably accurate predictions about an individual?s proficiency in different languages, using EEG data or behavioral data alone.
Image-based geological interpretation has been a labor-intensive and time-consuming process because it requires well-trained geoscientists to identify geological structures, features, and textures from various types of images. These images include scanning electron microscopic images, optical microscopic images, optical photos, resistivity images, seismic volumes, remote-sensing images, etc. With fast-evolving machine learning (ML) technology and computing power in recent decades, computers can achieve nearhuman-level to super-human-level performance with scalable high efficiency in the computer vision field. These technological revolutions facilitated image-based geological interpretation in petroleum exploration and production. For example, a fault picking method applied to 3-D seismic volume data using deep learning can achieve superior performance in comparison to conventional auto-picking methods. In addition, under the new normal of low oil prices, the petroleum industry seeks cost-effective strategies such as automating traditionally labor-intensive processes. Nevertheless, the potential of applying ML to geological image interpretation is still facing a few key challenges including data scarcity, data distribution, poor data and/or label quality, data leakage, learning algorithms, model architecture, training methodologies, testing and evaluation metrics, hyper-parameters optimization, model drift, production deployment, and the like.
Naturalistic Driving Studies (NDS) collect and utilize data on drivers in real-world environments in instrumented vehicles. A common problem with such studies is driver privacy. In this work we collected a dataset of 77 human subjects performing scripted driving-related activities. We used three camera systems for the collection, including two high-resolution webcam devices as well as a third system from an actual NDS (the Second Strategic Highway Research Project, or SHRP2). This report covers the data collection process and summarizes the dataset, which will be made publicly available to researchers under a data usage license.
In the last few years, the development and use of generative artificial intelligence (AI) and large-language models (LLMs) have changed the landscape of how AI and machine learning (ML) are being used in power systems. LLMs are built on foundational models based on large data sets that can be trained to provide information rapidly and through simple natural language prompts. Generative AI can then perform human-like tasks using ML models to identify and mimic pattens in the data sets. This presentation explores how generative AI can enhance grid operations by improving forecasts, enabling rapid contingency analyses, and offering real-time operational suggestions. By providing grid operators with valuable insights, generative AI will empower them to manage power systems more effectively.
Introduction: Neurotensin receptor 1 (NTSR1) is an emerging target for imaging and therapy of many types of cancer. Nuclear imaging of NTSR1 allows for noninvasive assessment of the receptor levels of NTSR1 on the primary tumor, as well as potential metastases. This work focuses on a the neurotensin peptide analogue NT-20.3 conjugated to the chelator NOTA for radiolabeling for use in noninvasive positron emission tomography (PET). NOTA-NT-20.3 was radiolabeled with gallium-68, copper-64, and cobalt-55 to determine the effect that modification of the radiometal has on imaging and potential therapeutic properties of NOTA-NT-20.3. Methods: In vitro assays investigating cell uptake and subcellular localization of the radiolabeled peptides were performed using human colorectal adenocarcinoma HT29 cells. In vivo PET/CT imaging was used to determine the distribution and clearance of the peptide in mice bearing NTSR1 expressing HT29 tumors. Results: Cell uptake studies showed that the highest uptake was obtained with [ 55 Co] Co-NOTA-NT-20.3 (18.70 ± 1.30%ID/mg), followed by [ 64 Cu] Cu-NOTA-NT-20.3 (15.46 ± 0.91%ID/mg), and lastly [ 68 Ga] Ga-NOTA-NT-20.3 (10.94 ± 0.46%ID/mg) (p < 0.001). Subcellular distribution was similar across the three constructs, with the membranous fraction containing the highest amount of radioactivity. In vivo PET/CT imaging of the three constructs revealed similar distribution and tumor uptake at the 1 h imaging timepoint. Tumor uptake was receptor-specific and blockable by co-injection of non-radiolabeled NOTA-NT-20.3. SUV ratios of tumor to heart at the 24 h imaging timepoint show that [ 55 Co] Co-NOTA-NT-20.3 (20.28 ± 3.04) outperformed [ 64 Cu] Cu-NOTA-NT-20.3 (6.52 ± 1.97). In conclusion, our studies show that enhanced cell uptake and increasing tumor to blood ratios over time displayed the superiority of [ 55 Co] Co-NOTA-NT-20.3 over [ 68 Ga] Ga-NOTA-NT-20.3 and [ 64 Cu] Cu-NOTA-NT-20.3 for the targeting of NTSR1.
Human tolerance to abrupt accelerations
Transistorized real-time computer for transformation of vectorcardiographic data
Physical, psychological, and aptitude requirements for astronaut selection
Visual boundary for human perception of vertical rate of descent
Discussion of mans capability to successfully accomplish a visual spacecraft rendezvous
Thresholds of spatial discrimination by human fingers
Interaction of spatial, dynamic, and temporal components of working movements in learning to file metal
Role of spatial perception in working at conveyer
Role of spatial concepts in map reading and interpretation of aerial photographs
Process of spatial conceptualization in students of drawing and designing