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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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At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Smart connected worker edge platform for smart manufacturing: Part 2—Implementation and on‐site deployment case study

Abstract In this paper, we describe specific deployments of the Smart Connected Worker (SCW) Edge Platform for Smart Manufacturing through implementation of four instructive real‐world use cases that illustrate the role of people in a Smart Manufacturing paradigm through which affordable, scalable, accessible, and portable (ASAP) information technology (IT) acquires and contextualizes data into information for transmission to operation technologies (OT). For case one, the platform captures the relationships between energy consumption and human workflows for improved energy productivity while workers interact with machines during semiconductor manufacturing. The platform utilizes human cognition to identify anomalous machine behavior for root cause analysis of system faults via neural network (NN) that recognize alarm postures of workers with cameras. For case two, a smart assembly line is demonstrated for state monitoring and fault detection. Machine learning (ML) models are used to recognize system states and identify fault scenarios with human intervention. For case three, the platform monitors human–machine interactions to classify manufacturing machine states for proper operations and energy productivity. Internal energy states of individual or collections of manufacturing equipment are determined via NN based algorithms that disaggregate signals associated with smart metering typically deployed at manufacturing facilities. These methods predict the real time energy profile of each machine from the total energy profile of a manufacturing site. For case four, a software defined sensor system built with scientific workflow engines is demonstrated for contextualizing data from laser surface refraction for characterization, and diagnostics in the processing of additively manufactured titanium alloy.

Donovan, Richard P.↗
DOE OSTI2022

Inferring protein expression changes from mRNA in Alzheimer’s dementia using deep neural networks

Identifying the molecular systems and proteins that modify the progression of Alzheimer’s disease and related dementias (ADRD) is central to drug target selection. However, discordance between mRNA and protein abundance, and the scarcity of proteomic data, has limited our ability to advance candidate targets that are mainly based on gene expression. Therefore, by using a deep neural network that predicts protein abundance from mRNA expression, here we attempt to track the early protein drivers of ADRD. Specifically, by applying the clei2block deep learning model to 1192 brain RNA-seq samples, we identify protein modules and disease-associated expression changes that were not directly observed at the mRNA level. Moreover, pseudo-temporal trajectory inference based on the predicted proteome became more closely correlated with cognitive decline and hippocampal atrophy compared to RNA-based trajectories. This suggests that the predicted changes in protein expression could provide a better molecular representation of ADRD progression. Furthermore, overlaying clinical traits on protein pseudotime trajectory identifies protein modules altered before cognitive impairment. These results demonstrate how our method can be used to identify potential early protein drivers and possible drug targets for treating and/or preventing ADRD.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

YOLO for Radio Frequency Signal Classification

Radio frequency signal classification plays a pivotal role in various applications, including spectrum management, wireless security, and cognitive radio. Extant signal classification methods require significant data throughput and are not multilabel. We propose a novel approach to radio frequency signal classification by leveraging the You Only Look Once (YOLO) object detection method. YOLO is a state-of-the-art deep learning model renowned for its real-time object detection capabilities in computer vision applications. We adapt YOLO for signal classification to enable the automatic and efficient identification of various signal types within a power spectral density image. Index Terms—radio-frequency analysis, object detection, neural networks, machine learning, deep learning.

42 ENGINEERING↗
DOE OSTI2023

Effects of Substance Use and Antisocial Personality on Neuroimaging-Based Machine Learning Prediction of Schizophrenia

Abstract Background and hypothesis Neuroimaging-based machine learning (ML) algorithms have the potential to aid the clinical diagnosis of schizophrenia. However, literature on the effect of prevalent comorbidities such as substance use disorder (SUD) and antisocial personality (ASPD) on these models’ performance has remained unexplored. We investigated whether the presence of SUD or ASPD affects the performance of neuroimaging-based ML models trained to discern patients with schizophrenia (SCH) from controls. Study design We trained an ML model on structural MRI data from public datasets to distinguish between SCH and controls (SCH = 347, controls = 341). We then investigated the model’s performance in two independent samples of individuals undergoing forensic psychiatric examination: sample 1 was used for sensitivity analysis to discern ASPD (N = 52) from SCH (N = 66), and sample 2 was used for specificity analysis to discern ASPD (N = 26) from controls (N = 25). Both samples included individuals with SUD. Study results In sample 1, 94.4% of SCH with comorbid ASPD and SUD were classified as SCH, followed by patients with SCH + SUD (78.8% classified as SCH) and patients with SCH (60.0% classified as SCH). The model failed to discern SCH without comorbidities from ASPD + SUD (AUC = 0.562, 95%CI = 0.400–0.723). In sample 2, the model’s specificity to predict controls was 84.0%. In both samples, about half of the ASPD + SUD were misclassified as SCH. Data-driven functional characterization revealed associations between the classification as SCH and cognition-related brain regions. Conclusion Altogether, ASPD and SUD appear to have effects on ML prediction performance, which potentially results from converging cognition-related brain abnormalities between SCH, ASPD, and SUD.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

Heuristic Evaluation Methods Applied to a Predictive Maintenance Chatbot

The need for an accessible iterative approach for evaluating prospective artificial intelligence (AI)/ML based technologies in the nuclear industry is needed, given the nature of algorithms and rapid advancements. This paper explores existing heuristic design principles for user-centered design and evaluates them based on their relevancy and usefulness for evaluating AI/ ML based technologies. Researchers at the Idaho National Laboratory (INL) have developed a machine learning software application called VIsualization for PrEdictive maintenance Recommendation (VIPER), which is used to help users understand and engage with the tool to learn more about work orders, data used, predictive maintenance, and machine learning (ML) algorithms. Early user research studies used to access VIPER’s technology readiness level have occurred; however, there is room for further improvement of the software through heuristic evaluations along with other methods and user testing. This work describes the applicability of heuristic evaluation methods and cognitive walkthroughs to help ensure human readiness for prospective AI/ ML based applications, using VIPER as a candidate use case. This work supports industry in ensuring that prospective AI/ML based technologies are usable and useful for plant personnel at nuclear power plants, ultimately leading to their safe, reliable, and efficient use.

99 - GENERAL AND MISCELLANEOUS↗
DOE OSTI2020

Cognition at the Point of Sensing

Over the last 15 years, compressive sensing techniques have been developed which have the potential to greatly reduce the amount of data collected by systems while preserving the amount of information obtained. A cost of this efficiency is that a computationally-intensive optimization routine must be used to put the sensed data into a form that a person can interpret. At the same time, machine learning techniques have experienced tremendous growth as well. Machines have demonstrated the ability learn how to effectively perform tasks such as detection and classification at speeds much faster than humanly possible. Our goal in this project was to study the feasibility of using compressive sensing systems "at the edge." That is, how can compressive sensing sensors be deployed such that information is created at the remote sensor rather than sending raw data to a central processing location? Studies were performed to analyze whether machine learning could be done on the compressively sensed data in its raw form. If a machine is performing the task, is it possible to do so without putting the data into a human interpretable form? We show that this is possible for some systems, in particular a compressive sensing snapshot imaging spectrometer. Machine learning tasks were demonstrated to be more effective and more robust to noise when the machine learning algorithm worked on data in its raw form. This system is shown to outperform a traditional spectrometer. Techniques for reducing the complexity of the reconstruction routine were also analyzed. Techniques for such as data regularization, deep neural networks, and matrix completion were studied and shown to have benefits over traditional reconstruction techniques. In this project we showed that compressive sensing sensors are indeed feasible at the edge. As always, sensors and algorithms must be carefully tuned to work in the constrained environment. In this project we developed tools and techniques to enable those analyses.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Data-driven causal model discovery and personalized prediction in Alzheimer's disease

Abstract With the explosive growth of biomarker data in Alzheimer’s disease (AD) clinical trials, numerous mathematical models have been developed to characterize disease-relevant biomarker trajectories over time. While some of these models are purely empiric, others are causal, built upon various hypotheses of AD pathophysiology, a complex and incompletely understood area of research. One of the most challenging problems in computational causal modeling is using a purely data-driven approach to derive the model’s parameters and the mathematical model itself, without any prior hypothesis bias. In this paper, we develop an innovative data-driven modeling approach to build and parameterize a causal model to characterize the trajectories of AD biomarkers. This approach integrates causal model learning, population parameterization, parameter sensitivity analysis, and personalized prediction. By applying this integrated approach to a large multicenter database of AD biomarkers, the Alzheimer’s Disease Neuroimaging Initiative, several causal models for different AD stages are revealed. In addition, personalized models for each subject are calibrated and provide accurate predictions of future cognitive status.

Zheng, Haoyang (ORCID:0000000168358242)↗
DOE OSTI2023

Method to Investigate Cognitive Aging Effects in Nuclear Operations Using the Rancor Microworld Simulator

Many nuclear power plant (NPP) operators are close to, or at retirement age. Since NPP operators are responsible for tasks that engage different and complex cognitive abilities, human factors researchers recently called for the need to examine cognitive aging effects and potential interactions with modernized control rooms. One way control rooms are being modernized is by replacing paper-based procedures with computer-based procedures (CBPs). This paper outlines a method for testing preference and usability of three types of CBPs as a function of age, in simulated NPP operations. The Rancor Microworld Simulator will be used, which mimics an NPP control room, allowing novices to learn to perform operator tasks quickly. The experiment will have two independent variables: age (young and old) and CBP-type (Type 1, Type 2, and Type 3). The three types of CBPs vary in levels of embedded instrumentation and controls. Adults aged 18–25 years (young group) and =60 years (older group) will be recruited from the general population. The dependent variables will be preference and usability metrics via survey and simulator logs. All participants will perform two scenario types (startup and loss of feedwater operations) and will be randomly assigned to one type of CBP to guide them through the scenario. Analyses of variance (ANOVAs) will be used to determine whether there is a main effect of age and any age x CBP-type interactions on the preference and usability dependent variables.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

New Era Towards Autonomous Additive Manufacturing: A Review of Recent Trends and Future Perspectives

Abstract The Additive Manufacturing (AM) landscape has significantly transformed in alignment with Industry 4.0 principles, primarily driven by the integration of Artificial Intelligence (AI) and Digital Twin (DT). However, current Intelligent Additive Manufacturing (IAM) systems face limitations such as fragmented AI tool usage and suboptimal human-machine interaction (HMI). This paper reviews existing IAM solutions, emphasizing control, monitoring, process autonomy, and end-to-end integration, and identifies key limitations, such as the absence of a high-level controller for global decision-making. To address these gaps, we propose a transition from IAM to Autonomous Additive Manufacturing (AAM), featuring a hierarchical framework with four integrated layers: knowledge, generative solution, operational, and cognitive. In the cognitive layer, AI agents notably enable machines to independently observe, analyze, plan, and execute operations that traditionally require human intervention. These capabilities streamline production processes and expand the possibilities for innovation, particularly in sectors like in-space manufacturing (ISM). Additionally, this paper discusses the role of AI in self-optimization and lifelong learning, positing that the future of AM will be characterized by a symbiotic relationship between human expertise and advanced autonomy, fostering a more adaptive, resilient manufacturing ecosystem.

Fan, Haolin↗
DOE OSTI2025

The relationship between below average cognitive ability at age 5 years and the child’s experience of school at age 9

Background At age 5, while only embarking on their educational journey, substantial differences in children’s cognitive ability will already exist. The aim of this study was to examine the causal association between below average cognitive ability at age 5 years and child-reported experience of school and self-concept, and teacher-reported class engagement and emotional-behavioural function at age 9 years. Methods This longitudinal cohort study used data from 7,392 children in the Growing Up in Ireland Infant Cohort, who had completed the Picture Similarities and Naming Vocabulary subtests of the British Abilities Scales at age 5. Principal components analysis was used to produce a composite general cognitive ability score for each child. Children with a general cognitive ability score more than 1 standard deviation (SD) below the mean at age 5 were categorised as ‘Below Average Cognitive Ability’ (BACA), and those scoring above this as ‘Typical Cognitive Development’ (TCD). The outcomes of interest, measured at age 9, were child-reported experience of school, child’s self-concept, teacher-reported class engagement, and teacher-reported emotional behavioural function. Binary and multinomial logistic regression models were used to examine the association between BACA and these outcomes. Results Compared to those with TCD, those with BACA had significantly higher odds of never liking school [Adjusted odds ratio (AOR) 1.82, 95% CI 1.37–2.43, p < 0.001], of being picked on (AOR 1.27, 95% CI 1.09–1.48) and of picking on others (AOR 1.53, 95% CI 1.27–1.84). They had significantly higher odds of experiencing low self-concept (AOR 1.20, 95% CI 1.02–1.42) and emotional-behavioural difficulties (AOR 1.34, 95% CI 1.10–1.63, p = 0.003). Compared to those with TCD, children with BACA had significantly higher odds of hardly ever or never being interested, motivated and excited to learn (AOR 2.29, 95% CI 1.70–3.10). Conclusion Children with BACA at school-entry had significantly higher odds of reporting a negative school experience and low self-concept at age 9. They had significantly higher odds of having teacher-reported poor class engagement and problematic emotional-behavioural function at age 9. The findings of this study suggest BACA has a causal role in these adverse outcomes. Early childhood policy and intervention design should be cognisant of the important role of cognitive ability in school and childhood outcomes.

Bowe, Andrea K.↗
DOE OSTI2021

Machine Intelligence to Detect, Characterise, and Defend against Influence Operations in the Information Environment

Social media has enabled a new era of manipulation in the information and cognitive domains. Deceptive content—misleading, falsified, and fabricated—is routinely created and spread in the modern social media environment with the intent to create confusion and widen political and social divides, and exploit the societal conflict exacerbated by these divides in the real-world (aka physical domain). Such disinformation campaigns demonstrate a threat to the integrity of economic, political, cultural, public health, and national security institutions around the world. In this work we overview our artificial intelligence (AI) capabilities to detect, describe, and defend against information operations on Twitter as an example social platform to understand the influence of misleading and falsified content diffusion and better enable those charged with defending against such manipulation to enable responsive parties to counter it. We first present novel linguistically-informed deep learning (DL) models for misinformation and disinformation detection, and present an in-depth linguistic analysis of psycho-linguistic markers across broad deception categories. We then demonstrate how our models perform in the multilingual and multimodal setting and categorize falsified and misleading content based on the intent to deceive. We also provide a large-scale analysis to describe user behavior and spread patterns while engaging with deceptive content and report novel findings about the immediate diffusion of deceptive content by characterizing the vulnerable sub-populations and their demographics, and explicitly measuring speed and scale of deception spread to uncover who shares deceptive content, how quickly, how much, and how evenly. In addition, we measure audience reactions to misinformation and disinformation at scale, distinguishing the reactions of users identified as bots versus humans. Finally, we take advantage of deep translation and generation models to create unique solutions for real-time defense against digital deception and discuss how to apply causal inference to prescribe and intervene into strategic communications jointly across information, cognitive, and physical domains.

artificial intelligence, deep learning, neural lan↗
DOE OSTI2022

The Impact of Specificity on Human Interpretations of State Uncertainty

The goal of this project was test how different representations of state uncertainty impact human decision making. Across a series of experiments, we sought to answer fundamental questions about human cognitive biases and how they are impacted by visual and numerical information. The results of these experiments identify problems and pitfalls to avoid when for presenting algorithmic outputs that include state uncertainty to human decision makers. Our findings also point to important areas for future research that will enable system designers to minimize biases in human interpretation for the outputs of artificial intelligence, machine learning, and other advanced analytic systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Developing a Disaster-Ready Power Grid Agent Through Geophysically-Informed Fault Event Scenarios

Management of the nation’s power grid over the coming decades will need to factor multiple climate-driven threats to the power system that can be stressful or even detrimental to its operations. During disasters, grid operators suffer from cognitive overload where the electric grid is severely impacted by the weather, yet grid operators have limited awareness of these factors. Meanwhile, the electric grid is facing an explosion of data coming from a variety of sources which can enable the operator to evaluate the risks and develop mitigation strategies against hazardous events. In this work, we processed heterogeneous environmental and power grid data to learn and model grid behavior caused by extreme weather events. In this study, we focused on two weather-driven hazards, hurricanes and wildfires, which were analysed for the electric grid of Texas. We the used this data to train a Reinforcement Learning (RL) agent by analysing and predicting future behaviour of the grid during those hazard events. All these parts are incorporated in one framework to have a geophysically informed power simulators that can be used to train RL agents.

electic grid, reinforcement learning, climate adap↗
DOE OSTI2020

Research Needs for Trusted Analytics in National Security Settings

As artificial intelligence, machine learning, and statistical modeling methods become commonplace in national security applications, the drive to create trusted analytics becomes increasingly important. The goal of this report is to identify areas of research that can provide the foundational understanding and technical prerequisites for the development and deployment of trusted analytics in national security settings. Our review of the literature covered several disjoint research communities, including computer science, statistics, human factors, and several branches of psychology and cognitive science, which tend not to interact with one another or cite each other's literatures. As a result, there exists no agreed-upon theoretical framework for understanding how various factors influence trust and no well-established empirical paradigm for studying these effects. This report therefore takes three steps. First, we define several key terms in an effort to provide a unifying language for trusted analytics and to manage the scope of the problem. Second, we outline an empirical perspective that identifies key independent, moderating, and dependent variables in assessing trusted analytics. Though not a substitute for a theoretical framework, the empirical perspective does support research and development of trusted analytics in the national security domain. Finally, we discuss several research gaps relevant to developing trusted analytics for the national security mission space.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Leveraging dendritic complexity for neuromorphic computing

Abstract Beyond-von Neumann computing approaches are necessary to sustain the growth of microelectronics and the increasing appetite for artificial intelligence/machine learning algorithms. Neuromorphic computing is an emerging paradigm that takes inspiration from the brain to provide a path forward to improve the computational efficiency and computational density of next-generation computing architectures. In nature, we observe brains performing complex computations with a much smaller energy footprint than conventional computing approaches. Current neuromorphic systems are focused primarily on scalability, namely, increasing the number of computational units (neurons) and connections between units (synapses). However, for brain-like cognition and efficiency in next-generation computing hardware, we need increased complexity in function, as well as improved connection density for scalability. Here, we present our work that aims to incorporate dendrites for ‘compute-on-wire’ in neuromorphic architectures to increase the computational complexity (e.g. number of programmable parameters, nonlinear dynamics) as well as computational efficiency (energy/compute) of artificial neural networks (ANNs). We do this by showcasing neuromorphic dendrite elements that can be leveraged for various applications. We will present examples of neuroscience-inspired direction-selective circuits and an ANN with active dendrites leveraging shunting inhibition. We also demonstrate the benefits of using dendrites in deep neural networks. To conclude, we discuss how we can utilize emerging hardware devices in these systems and design next-generation neuromorphic architectures with dendrites.

Cardwell, Suma G. (ORCID:0000000226575545)↗
DOE OSTI2022

A Methodology for Evaluating Operator Usage of Machine Learning Recommendations for Power Grid Contingency Analysis

This work presents the application of a methodology to measure domain expert trust and workload, elicit feedback, and understand the technological usability and impact when a machine learning assistant is introduced into contingency analysis for real-time power grid simulation. The goal of this framework is to rapidly collect and analyze a broad variety of human factors data in order to accelerate the development and evaluation loop for deploying machine learning applications. We describe our methodology and analysis, and we discuss insights gained from a pilot participant about the current usability state of an early technology readiness level (TRL) artificial neural network (ANN) recommender.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Accelerating the rate of discovery: toward high-repetition-rate HED science

As high-intensity short-pulse lasers that can operate at high-repetition-rate (HRR) (>10 H z ) come online around the world, the high energy density (HED) science they enable will experience a radical paradigm shift. The >10 3 increase in shot rate over today's shot-per-hour drivers translates into dramatically faster data acquisition, more experiments, and the ability to exploit machine learning, and thus the potential to significantly accelerate the advancement of HED science. A wide range of HED experiments, from opacity investigations to secondary source generation to plasma nuclear physics, will benefit from the increased statistics, precision, and exploration of phase space. Besides increasing the rate at which scientific experiments can be performed, HRR also allows for the rapid delivery of optimal experiments supported by simulations and modeling augmented by close coupling to empirical data. To fully realize such an HRR framework, numerous subsystems must be developed and brought together, including feedback laser control loops, high-throughput targetry and diagnostics, cognitive simulation, enhanced HED codes, and advanced data analytics. This paper describes the vision for an integrated HRR laser experimental HED system and outlines some of the major considerations and challenges for realizing it.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

CACTUS: Chemistry Agent Connecting Tool Usage to Science

Large language models (LLMs) have shown remarkable potential in various domains but often lack the ability to access and reason over domain-specific knowledge and tools. In this article, we introduce Chemistry Agent Connecting Tool-Usage to Science (CACTUS), an LLM-based agent that integrates existing cheminformatics tools to enable accurate and advanced reasoning and problem-solving in chemistry and molecular discovery. We evaluate the performance of CACTUS using a diverse set of open-source LLMs, including Gemma-7b, Falcon-7b, MPT-7b, Llama3-8b, and Mistral-7b, on a benchmark of thousands of chemistry questions. Our results demonstrate that CACTUS significantly outperforms baseline LLMs, with the Gemma-7b, Mistral-7b, and Llama3-8b models achieving the highest accuracy regardless of the prompting strategy used. Moreover, we explore the impact of domain-specific prompting and hardware configurations on model performance, highlighting the importance of prompt engineering and the potential for deploying smaller models on consumer-grade hardware without a significant loss in accuracy. By combining the cognitive capabilities of open-source LLMs with widely used domain-specific tools provided by RDKit, CACTUS can assist researchers in tasks such as molecular property prediction, similarity searching, and drug-likeness assessment.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗