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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Con Connections: Detecting Fraud from Abstracts using Topological Data Analysis

In this paper we present a novel approach for identifying fraudulent papers from their titles and abstracts. The premise of the approach is that there are holes in the presentation of the approach and findings of fraudulent research papers. As an abstract is intended to highlight key features of the approach as well as important conclusions the authors seek to determine if the assumed existence of holes can be identified from analysis of abstracts alone. The data set considered is derived from papers sharing a single author with labels determined based on a formal linguistic analysis of the complete documents. To detect these logical and literary holes we utilize techniques from topological data analysis which summarizes data based on the presence of multi-dimensional, topological holes. We find that, in fact, topological features derived through a combination of techniques in natural language processing and time-series analysis allow for superior detection of the fraudulent papers than the natural language processing tools alone. Thus we conclude that the connections and holes present in the abstracts of research cons contributes to an ability to infer the scientific validity of the corresponding work.

Tymochko, Sarah J.↗
DOE OSTI2026

Generative AI in Supply Chain Management: Applications, Challenges, and Future Directions

Supply chain management (SCM) is undergoing rapid transformation due to increasing global complexity, demand volatility, and operational disruptions. Generative Artificial Intelligence (GenAI) has emerged as a powerful paradigm capable of synthesizing data, simulating operational scenarios, and enabling adaptive decision-making across supply chain networks. This paper presents a survey of GenAI’s role in SCM, focusing on its applications in predictive analytics, autonomous logistics, and fraud detection. Unlike traditional AI systems that rely primarily on predictive analytics, GenAI models, including large language models, generative adversarial networks, and diffusion-based architectures, enable the creation of synthetic supply chain scenarios and autonomous optimization strategies. This survey provides (1) a taxonomy of GenAI techniques for supply chain applications, (2) a comparative analysis of generative AI approaches with traditional machine learning, reinforcement learning, and blockchain-based methods, and (3) a discussion of key challenges such as data privacy, interpretability, and integration with legacy enterprise systems. Furthermore, we outline open research problems and propose directions for future research toward autonomous, resilient, and sustainable AI-driven supply chains.

15 - GEOTHERMAL ENERGY↗
DOE OSTI2023

Fast Bayesian optimization of Needle-in-a-Haystack problems using zooming memory-based initialization (ZoMBI)

Abstract Needle-in-a-Haystack problems exist across a wide range of applications including rare disease prediction, ecological resource management, fraud detection, and material property optimization. A Needle-in-a-Haystack problem arises when there is an extreme imbalance of optimum conditions relative to the size of the dataset. However, current state-of-the-art optimization algorithms are not designed with the capabilities to find solutions to these challenging multidimensional Needle-in-a-Haystack problems, resulting in slow convergence or pigeonholing into a local minimum. In this paper, we present a Zooming Memory-Based Initialization algorithm, entitled ZoMBI, that builds on conventional Bayesian optimization principles to quickly and efficiently optimize Needle-in-a-Haystack problems in both less time and fewer experiments. The ZoMBI algorithm demonstrates compute time speed-ups of 400× compared to traditional Bayesian optimization as well as efficiently discovering optima in under 100 experiments that are up to 3× more highly optimized than those discovered by similar methods.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Accelerating Random Forest Classification on GPU and FPGA

Random Forests (RFs) are a commonly used machine learning method for classification and regression tasks spanning a variety of application domains, including bioinformatics, business analytics, and software optimization. While prior work has focused primarily on improving performance of the training of RFs, many applications, such as malware identification, cancer prediction, and banking fraud detection, require fast RF classification. In this work, we accelerate RF classification on GPU and FPGA. In order to provide efficient support for large datasets, we propose a hierarchical memory layout suitable to the GPU/FPGA memory hierarchy. We design three RF classification code variants based on that layout, and we investigate GPU- and FPGA-specific considerations for these kernels. Our experimental evaluation, performed on an Nvidia Xp GPU and on a Xilinx Alveo U250 FPGA accelerator card using publicly available datasets on the scale of millions of samples and tens of features, covers various aspects. First, we evaluate the performance benefits of our hierarchical data structure over the standard compressed sparse row (CSR) format. Second, we compare our GPU implementation with cuML, a machine learning library targeting Nvidia GPUs. Third, we explore the performance/accuracy tradeoff resulting from the use of different tree depths in the RF. Finally, we perform a comparative performance analysis of our GPU and FPGA implementations. Our evaluation shows that for high accuracy targets, our GPU implementation yields 5-9x speedup over CSR, and up to a 2x speedup over cuML.

FPGA, Xilinx FPGA, GPU, Random Forest classificati↗
DOE OSTI2023

“Shoulda, Coulda, Woulda”: Conceptualizing the Differences in Trust Between Human-Human Teaming and Human-Machine Teaming

Intelligent decision support systems (IDSSs) are machine teammates designed to facilitate better human decision-making in high-consequence domains such as health care, power grid operations, and fraud detection. IDSSs identify patterns in datasets and provide intelligent decision-making recommendations to human teammates. However, previous research indicates that humans often trust IDSS recommendations less than the recommendations from their human teammates, even when the machine teammate is more accurate. To conceptualize why trust differs, we review the literature surrounding trust, error, and predictability. Then, we compile and compare participant trust ratings and decision-making in an abridged systematic review of previous studies manipulating teammate type, error rate, and error type. Finally, we conduct a content analysis of participants’ qualitative responses to trust queries from a survey on generative language models. Results suggest that humans may trust IDSS teammates less than other human teammates because of differences in (1) interaction complexity, (2) blame attribution, and (3) swift trust. We conclude that human factors practitioners should collaborate with data scientists and domain experts to build and maintain trust in IDSSs by anthropomorphizing algorithms, matching mental models, and considering individual differences.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1994

Neural network applications in telecommunications

Neural network capabilities include automatic and organized handling of complex information, quick adaptation to continuously changing environments, nonlinear modeling, and parallel implementation. This viewgraph presentation presents Bellcore work on applications, learning chip computational function, learning system block diagram, neural network equalization, broadband access control, calling-card fraud detection, software reliability prediction, and conclusions.

Alspector, Joshua↗
DOE OSTI2021

Topological Machine Learning Methods for Power System Responses to Contingencies: Preprint

While deep learning tools, coupled with the emerging machinery of topological data analysis, are proven to deliver various performance gains in a broad range of applications, from image classification to biosurveillance to blockchain fraud detection, their utility in areas of high societal importance such as power system modeling and, particularly, resilience quantification in the energy sector yet remain untapped. To provide fast acting synthetic regulation and contingency reserve services to the grid while having minimal disruptions on customer quality of service, we propose a new topologybased system that depends on neural network architecture for impact metrics classification and prediction in power systems. This novel topology-based system allows one to evaluate the impact of three power system contingency types, namely, in conjunction with transmission lines, transformers, and transmission lines combined with transformers. We show that the proposed new neural network architecture equipped with local topological measures facilitates both more accurate classification of unserved load as well as the amount of unserved load. In addition, we are able to learn complex relationships between electrical properties and local topological measurements on the simulated response to contingencies for NREL-SIIP power system.

graph theory↗
DOE OSTI2021

Topological Machine Learning Methods for Power System Responses to Contingencies

While deep learning tools, coupled with the emerging machinery of topological data analysis, are proven to deliver various performance gains in a broad range of applications, from image classification to biosurveillance to blockchain fraud detection, their utility in areas of high societal importance such as power system modeling and, particularly, resilience quantification in the energy sector yet remains untapped. To provide fast acting synthetic regulation and contingency reserve services to the grid while having minimal disruptions on customer quality of service, we propose a new topology-based system that depends on a neural network architecture for impact metric classification and prediction in power systems. This novel topology-based system allows one to evaluate the impact of three power system contingency types, in conjunction with transmission lines, transformers, and transmission lines combined with transformers. We show that the proposed new neural network architecture equipped with local topological measures facilitates more accurate classification of unserved load as well as the amount of unserved load. In addition, we are able to learn more about the complex relationships between electrical properties and local topological measurements on their simulated response to contingencies for the NREL-SIIP power system.

contingency analysis↗
NASA NTRS2004

The Office of Inspector General (OIG)

The NASA Office of Inspector General is the Federal Law Enforcement Agency at NASA which conducts criminal and regulatory investigations in which NASA is a victim. The OIG prevents and detects crime, fiaud, waste and abuse and assists NASA management in promoting economy, efficiency, and effectiveness in its programs and operations. Investigations (OI) and the Office of Audits (OA). The investigations side deals with criminal Investigations, administrative investigations, and civil investigations. The Audits side deals with inspections and assessments as well as the Auditing of NASA Programs and Activities. Our mission at the OIG is to conduct and supervise independent and objective audits and investigations relating to agency programs and operations; to promote economy, effectiveness and efficiency within the agency; to prevent and detect crime, fraud, waste and abuse in agency programs and operations; to review and make recommendations regarding existing and proposed legislation and regulations relating to agency programs and operations. We are also responsible for keeping the agency head and the Congress fully and currently informed of problems in agency programs and operations. deal with False Claims, False Statements, Conspiracy, Theft, Computer Crime, Mail Fraud, the Procurement Integrity Act, the Anti-Kickback Act, as well as noncompliance with NASA Management Instructions, the Federal Acquisition Regulations (FAR), and the Code of Federal Regulations (CFR). Most of the casework that is dealt with in our office is generated through gum shoe work or cases that we generate on our own. These cases can come from Law Enforcement Referrals, GIDEP Reports, EPlMS (NASA Quality System), Defense Contract Audit Agency, Newspaper Articles, and Confidential Information. In many cases, confidentiality is the biggest factor to informants coming forward. We are able to maintain confidentiality because the 01 is independent of NASA Management and doesn t report to the Center Directors, therefore the informant s mangers and supervisors are unaware of the informants actions. The only time when an informant s confidentiality may be compromised is when it is needed in a Court of Law and is released through a Judicial Court Order. During my tenure here at the NASA OIG/OI at Glenn Research Center, I have been involved in many different tasks. They have ranged from updating Suspected Unapproved Parts case files to independently interviewing NASA employees to turn up general background information. The 01 has the duty of informing NASA aeronautical safety managers of potential Nonconforming products. My mission is to compile a database of Nonconformance reports for distribution. The background information that I turn up from my interviews is then used to determine NASA s susceptibility to acceptance of unapproved parts. The IG organization is divided up into two separate disciplines, the Office of 01 investigations primarily focus on violations of Federal laws. Some of these violations

Macisco, Christopher A.↗
DOE OSTI2022

Synthetic data generation for machine learning model training for energy theft scenarios using cosimulation

Abstract Technical and non‐technical losses in distribution circuits result in significant economic costs to power utilities. One type of non‐technical loss is energy theft by various means including illegal tapping of feeders, bypassing the meter, and billing fraud. These losses are usually hard to detect, and can remain undetected for long periods of time. Machine learning models have been proven effective in detecting these conditions, but rely on the availability of large, good‐quality training data sets. The problem is exacerbated by the imbalanced nature of data related to these conditions—energy theft, though costly, is very rare. The available data sets generally have very few samples of theft with most of the data pertaining to normal operation. Such data sets are generally not suitable to train machine learning models. In this paper, an overview of energy theft detection techniques, the challenges with their data needs, and the limitations of current techniques to bridge such data limitations is presented. A co‐simulation framework is proposed to generate reliable training data for machine learning algorithms for theft detection. An example scenario is presented and a machine learning model is built to detect certain kinds of energy theft.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Emerging anomaly detection techniques for electronic health records: A survey

Background Anomaly detection in electronic health records (EHRs) is a cornerstone of biomedical informatics, with direct implications for patient safety, clinical decision-making, and the prevention of healthcare fraud. Once guided primarily by simple rule-based methods, the field has advanced rapidly, driven by increased computing power, richer and more detailed health data, and the rise of machine learning and deep learning techniques. The objective of this paper is to provide a comprehensive overview of modern approaches to detecting anomalies in EHRs, outlining their strengths, limitations, and relevance to key healthcare challenges. We review traditional statistical methods alongside newer ML- and DL-based strategies and hybrid models, with particular attention to how these techniques support transparency and build clinical trust. Methods This paper presents a thorough and critical survey through systematic review (PRISMA-based) of the latest anomaly detection strategies in time-sequence data domains within electronic health record systems. Results We explore a broad spectrum of methodologies, including statistical models, supervised and unsupervised learning approaches, hybrid frameworks, and state-of-the-art ML-based techniques that collectively advance the precision and scalability of detecting anomalies in complex clinical datasets. In addition to mapping current capabilities, we address the enduring challenges that hinder widespread implementation and provide a forward-looking perspective on the future of anomaly detection in the data-rich landscape of modern healthcare. Summary The advancement in AI-based approaches is reported along with the basic principles of the individual approaches and their applicability. The increased availability of high-quality data, advancements in DL approaches, and enhanced computation power are leading to more frequent adaptation of DL-based approaches. Emerging DL-based approaches that have been adapted in other domains or recently applied in the EHR domain are also discussed in detail. Although DL-based approaches can improve model predictions by incorporating comorbidities, their application is limited in low-frequency data domains (e.g., when the total available data remains in the single digits). Therefore, the user must carefully consider the application based on data availability.

Anomaly detection↗
DOE OSTI2023

Preventative studies should begin now for detecting AI-generated microscopy images

With the rapid progress made in artificial intelligence (AI), text, images, and even videos that are often indistinguishable from human-created content can now be easily generated using new AI models. In this essay, we call attention to the potential misuse of advanced AI models to generate microscopy images, as it could challenge reviewers and fraud hunters in an unprecedented way. This concern is more than hypothetical because relevant open-sourced implementations and datasets are widely available. As a result, we urge that preventative studies on methods to detect microscopy image fabrication should begin now before potential misconduct materializes and becomes widespread.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Reading Between the Lines: Measuring the Effects of Linguistic-Based Indicators of Deception on Experts’ Identification and Categorization of Disinformation

There is currently very limited research into how experts analyze and assess potentially fraudulent content in their expertise areas, and most research within the disinformation space involves very limited text samples (e.g., news headlines). The overarching goal of the present study was to explore how an individual’s psychological profile and the linguistic features in text might influence an expert’s ability to discern disinformation/fraudulent content in academic journal articles. At a high level, the current design tasked experts with reading journal articles from their area of expertise and indicating if they thought an article was deceptive or not. Half the articles they read were journal papers that had been retracted due to academic fraud. Demographic and psychological inventory data collected on the participants was combined with performance data to generate insights about individual expert susceptibility to deception. Our data show that our population of experts were unable to reliably detect deception in formal technical writing. Several psychological dimensions such as comfort with uncertainty and intellectual humility may provide some protection against deception. This work informs our understanding of expert susceptibility to potentially fraudulent content within official, technical information and can be used to inform future mitigative efforts and provide a building block for future disinformation work.

99 GENERAL AND MISCELLANEOUS↗
NASA NTRS2005

Identifying Objects via Encased X-Ray-Fluorescent Materials - the Bar Code Inside

Systems for identifying objects by means of x-ray fluorescence (XRF) of encased labeling elements have been developed. The XRF spectra of objects so labeled would be analogous to the external bar code labels now used to track objects in everyday commerce. In conjunction with computer-based tracking systems, databases, and labeling conventions, the XRF labels could be used in essentially the same manner as that of bar codes to track inventories and to record and process commercial transactions. In addition, as summarized briefly below, embedded XRF labels could be used to verify the authenticity of products, thereby helping to deter counterfeiting and fraud. A system, as described above, is called an encased core product identification and authentication system (ECPIAS). The ECPIAS concept is a modified version of that of a related recently initiated commercial development of handheld XRF spectral scanners that would identify alloys or detect labeling elements deposited on the surfaces of objects. In contrast, an ECPIAS would utilize labeling elements encased within the objects of interest. The basic ECPIAS concept is best illustrated by means of an example of one of several potential applications: labeling of cultured pearls by labeling the seed particles implanted in oysters to grow the pearls. Each pearl farmer would be assigned a unique mixture of labeling elements that could be distinguished from the corresponding mixtures of other farmers. The mixture would be either incorporated into or applied to the surfaces of the seed prior to implantation in the oyster. If necessary, the labeled seed would be further coated to make it nontoxic to the oyster. After implantation, the growth of layers of mother of pearl on the seed would encase the XRF labels, making these labels integral, permanent parts of the pearls that could not be removed without destroying the pearls themselves. The XRF labels would be read by use of XRF scanners, the spectral data outputs of which would be converted to alphanumeric data in a digital equivalent data system (DEDS), which is the subject of the previous article. These alphanumeric data would be used to track the pearls through all stages of commerce, from the farmer to the retail customer.

Schramm, Harry F.↗
NASA NTRS1995

Using X-rays to determine which compact groups are illusory

If the large-scale galaxy distribution is filamentary, as suggested by some observations and recent hydrodynamical simulations, then lengthwise views of filaments will apparently produce compact groups (CGs) that are in reality stretched out along the line of sight. This possibility has been advocated recently by Hernquist, Katz, & Weinberg (1995). Here we propose a test for this hypothesis using X-ray emission from CGs. The observable quantity Q identical with L(sub x)a(sup 3)(sup p)/(L(sup 2)(sup g))T(sup 1/2)(sub x) should be proportional to the axis ratio of the group, a/c, where a and c are the short and long axes of a prolate distribution, a(sub p) is the radius of the group projected onto the sky, L(sub x) is the bolometric X-ray luminosity, L(sub g) is the group blue luminosity, and T(sub x) is the gas temperature. We find that the distribution of Q is consistent with the notion that many CGs with unusually small values of a/c are frauds, i.e., that the values of Q are anomalously small. Absent other information, it is equally possible that CGs are very gas-poor relative to rich clusters; however, this can be tested using the Sunyaev-Zeldovich effect. If the groups have a close to normal ratio of gas to total mass, but are simply stretched out along the line of sight, a Sunyaev-Zeldovich signal should be detectable.

Ostriker, Jeremiah P.↗