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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
NASA NTRS2019

Multi-Agency Annual Southeastern Beach Mouse Habitat Occupancy Monitoring

The southeastern beach mouse (Peromyscus polionotus niveiventris, hereafter SEBM), a federally threatened species was identified by Merritt Island National Wildlife Refuge (MINWR) as one of the top five priorities for management. The Kennedy Space Center (KSC) Environmental Management Branch was asked to provide support in the form of population monitoring for SEBM on KSC and the surrounding properties. When we began the project, the SEBM was known to occupy sites ranging from Volusia through Brevard and Indian River Counties, but the exact distribution within the Federal lands encompassing the Cape Canaveral Barrier Island Complex (CCBIC) was not well known. The broad range of habitat conditions encompassing the species’ historical range and the diversity of sites historically known to contain SEBM required a landscape-scale approach for both monitoring the population and for investigating species habitat relationships. We designed a monitoring plan for the SEBM with the goal of estimating the habitat occupancy of beach mice annually each winter/spring throughout the entire area of suitable coastal habitat (approximately 72 km linear distance along the beach). Methods were tested in a 2008 pilot study, and during 2010-2018 we monitored the proportion of coastal habitat occupied by the SEBM on the CCBIC. Monitoring was focused on using dynamic habitat occupancy surveys with data collected with rapid assessment techniques (i.e., track tubes or live-traps) to detect SEBM occurrence at sampling stations. An important component of the study design was the use of repeated samples that provided the information needed to estimate detectability. Measuring detectability was required to account for the fact that surveys could fail to detect animals at sites at which they were actually present (Mackenzie et al., 2002). This monitoring framework is very flexible, allowing numerous hypothesis to be tested about how various factors influence beach mouse occupancy using habitat, geographic, and environmental covariates. Our survey has been the longest survey conducted for any of the seven extant beach mouse subspecies and has encompassed nearly the entire remaining range of the SEBM. Although occupancy surveys of other beach mouse subspecies via track tubes have been conducted on the Gulf coast of Florida by Florida Fish and Wildlife Conservation Commission (FWC), United States Fish and Wildlife Service (USFWS), and various universities, we expanded previous methods to fit unique aspects of the SEBM, and thus our study was very different from any of the previous studies. To achieve this specificity, we employed an adaptive approach throughout the study, with all activities aimed at providing managers with information related to environmental compliance and natural resource management on KSC and the surrounding Federal lands.

Eric Douglas Stolen↗
DOE OSTI2024

Emulation and detection of physical faults and cyber-attacks on building energy systems through real-time hardware-in-the-loop experiments

The increasing use of remote or mobile access, integrated wearable technologies, data exchange, and cloud-based data analytics in modern smart buildings is steering the building industry towards open communication technologies. The increased connectivity and accessibility could lead to more cyber-attacks in smart buildings. On the other hand, physical faults (e.g., HVAC -heating, ventilation, and air-conditioning faults) may have similar adverse impacts as those from the cyber-attacks on building energy systems, such as occupant discomfort, energy wastage, and equipment downtime. However, current physical behavior-based anomaly detection methods fail to differentiate between cyber-attacks and physical faults in building energy systems. Moreover, the challenge in collecting real-world threat data with ground truth has led researchers to rely on numerical models with user-defined assumptions, which may not accurately reflect real-world conditions due to the lack of in-situ experimental datasets. To address these challenges and gaps, this paper presents a flexible hardware-in-the-loop (HIL) testbed for generating cyber-attack and physical fault datasets and demonstrating threat detection algorithms in a real building automation system (BAS) environment. This testbed combines hardware (i.e., real BAS with local HVAC controllers and a physical network) with software (i.e., high-fidelity models to represent behaviors of building envelope and HVAC energy systems), enabling emulations of realistic threats. Five HIL experiments, including one baseline without any threats, two with physical faults, and two with cyber-attacks, were conducted to generate datasets containing detailed network traffic and system states. A joint classification framework, incorporating a network analyzer and a physical HVAC fault detector, was proposed to automatically detect cyber-physical abnormalities on BAS at both the network and the physical HVAC levels. The network analyzer comprises a conditional random fields (CRF) based command validator and a statistics-based detection strategy. The fault detector employs a weather and schedule-based pattern matching and feature-based principal component analysis (WPM-FPCA) method. Evaluation of the classification using four metrics from the multi-class confusion matrix revealed an average accuracy of 90.2%, recall of 89.7%, precision of 88.5% and F1-score of 89.2%. Finally, these results demonstrate that the proposed joint classification framework can effectively differentiate between specific types of cyber-attacks (e.g., device reinitialization attack, network Denial-of-Service attack) and physical faults (e.g., air handling unit operational fault, cooling coil valve stuck) in real time for improved building energy management.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS2005

Geospatial Analysis and Remote Sensing from Airplanes and Satellites for Cultural Resources Management

Cultural resource management consists of research to identify, evaluate, document and assess cultural resources, planning to assist in decision-making, and stewardship to implement the preservation, protection and interpretation of these decisions and plans. One technique that may be useful in cultural resource management archaeology is remote sensing. It is the acquisition of data and derivative information about objects or materials (targets) located on the Earth's surface or in its atmosphere by using sensor mounted on platforms located at a distance from the targets to make measurements on interactions between the targets and electromagnetic radiation. Included in this definition are systems that acquire imagery by photographic methods and digital multispectral sensors. Data collected by digital multispectral sensors on aircraft and satellite platforms play a prominent role in many earth science applications, including land cover mapping, geology, soil science, agriculture, forestry, water resource management, urban and regional planning, and environmental assessments. Inherent in the analysis of remotely sensed data is the use of computer-based image processing techniques. Geographical information systems (GIS), designed for collecting, managing, and analyzing spatial information, are also useful in the analysis of remotely sensed data. A GIS can be used to integrate diverse types of spatially referenced digital data, including remotely sensed and map data. In archaeology, these tools have been used in various ways to aid in cultural resource projects. For example, they have been used to predict the presence of archaeological resources using modern environmental indicators. Remote sensing techniques have also been used to directly detect the presence of unknown sites based on the impact of past occupation on the Earth's surface. Additionally, remote sensing has been used as a mapping tool aimed at delineating the boundaries of a site or mapping previously unknown features. All of these applications are pertinent to the goals of site discovery and assessment in cultural resource management.

Giardino, Marco J.↗
DOE OSTI2022

Detecting Passing Valves at Scale Across Different Buildings and Systems: A Brick Enabled and Mortar Tested Application

Heating hot water distribution systems are typically used in commercial buildings to condition spaces to provide occupant thermal comfort. However, recent research shows significant distribution losses within these systems that drive down the overall hot water plant efficiency. This research focuses on detecting passing valves in reheat coils found in variable air volume (VAV) terminal units to reduce distribution losses. A passing valve allows hot water flow when the actuator on the valve is commanded to be closed. The fluid causes unintentional heating or cooling to occur, causing comfort and control issues, and wasting energy. We developed the passing valve detection algorithm using a framework based on the Brick schema and Mortar platform to ensure that the application is portable and can scale to many buildings. We applied the same application to analyze 1,335 VAV reheat terminal units in 20 buildings. The diversity found in these large datasets increases confidence that any building with VAV reheat terminal units with the required sensors and Brick data model can run our open-source algorithm with little or no modification. In aggregate, 5% of VAV units analyzed were categorized as having a sensor fault, 14% with potential passing valve fault, and 81% with no faults detected. However, there is a significant variation in the proportion of VAV units with a passing valve detected (1% to 83%) of each building’s analyzed units.

Roa, Carlos Duarte↗
DOE OSTI2020

Atlas of Transcription Factor Binding Sites from ENCODE DNase Hypersensitivity Data across 27 Tissue Types

Characterizing the tissue-specific binding sites of transcription factors (TFs) is essential to reconstruct gene regulatory networks and predict functions for non-coding genetic variation. DNase-seq footprinting enables the prediction of genome-wide binding sites for hundreds of TFs simultaneously. Despite the public availability of high-quality DNase-seq data from hundreds of samples, a comprehensive, up-to-date resource for the locations of genomic footprints is lacking. Here, we develop a scalable footprinting workflow using two state-of-the-art algorithms: Wellington and HINT. We apply our workflow to detect footprints in 192 ENCODE DNase-seq experiments and predict the genomic occupancy of 1,515 human TFs in 27 human tissues. We validate that these footprints overlap true-positive TF binding sites from ChIP-seq. We demonstrate that the locations, depth, and tissue specificity of footprints predict effects of genetic variants on gene expression and capture a substantial proportion of genetic risk for complex traits.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Image Processing Algorithms for Tuning Quantum Devices and Nitrogen-Vacancy Imaging

Semiconductor quantum dot devices can be challenging to configure into a regime where they are suitable for qubit operation. This challenge arises from variations in gate control of quantum dot electron occupation and tunnel coupling between quantum dots on a single device or across several devices. Furthermore, a single control gate usually has capacitive coupling to multiple quantum dots and tunnel barriers between dots. If the device operator, be it human or machine, has quantitative knowledge of how gates control the electrostatic and dynamic properties of multiqubit devices, the operator can more quickly and easily navigate the multidimensional gate space to find a qubit operating regime. We have developed and applied image analysis techniques to quantitatively detect where charge offsets from different quantum dots intersect, so called anticrossings. In this document we outline the details of our algorithm for detecting single anticrossings, which has been used to fine-tune the inter-dot tunnel rates for a three quantum dot system. Additionally, we show that our algorithm can detect multiple anticrossings in the same dataset, which can aid in the coarse tuning the electron occupation of multiple quantum dots. We also include an application of cross correlation to the imaging of magnetic fields using nitrogen vacancies.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

A traffic accident dataset for Chattanooga, Tennessee

This publication presents an annotated accident dataset which fuses traffic data from radar detection sensors, weather condition data, and light condition data with traffic accident data (as illustrated in Fig. 1) in a format that is easy to process using machine learning tools, databases, or data workflows. The purpose of this data is to analyze, predict, and detect traffic patterns when accidents occur. Each file contains a timeseries of traffic speeds, flows, and occupancies at the sensor nearest to the accident, as well as 5 neighboring sensors upstream and downstream. It also contains information about the accident type, date, and time. In addition to the accident data, we provide baseline data for typical traffic patterns during a given time of day. Overall, the dataset contains 6 months of annotated traffic data from November 2020 to April 2021. During this timeframe, and 361 accidents occurred in the monitored area around Chattanooga, Tennessee. This dataset served as the basis for a study on topology-aware automated accident detection for a companion publication [1].

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Emerging Technologies for Improved Plug Load Management Systems: Learning Behavior Algorithms and Automatic and Dynamic Load Detection

Plug loads are responsible for a significant portion of the energy consumed in commercial buildings, yet their distributed and ever-changing nature makes them one of the most challenging building end uses to manage. Plug load management systems exist today that utilize smart plugs to meter and control devices at the outlet level, however, their uptake has been relatively slow in part due to the significant labor required for installation and maintenance. Learning behavior algorithms and automatic and dynamic load detection have been identified as two technology areas that could accelerate the adoption of plug load management systems by reducing these labor demands and providing additional energy efficiency and non-energy benefits. Learning behavior algorithms learn occupant behavior and adjust plug load management systems accordingly, allowing for the automatic creation of optimized control schedules. Automatic and dynamic load detection allows a plug load management system to identify devices as they are plugged in to a building and keeps the system up to date as devices are moved throughout a building. In this paper, we present our findings with respect to the current state of these two technologies based on a review of existing research and patents, as well as a series of interviews with companies working in the plug load space. We have found that, as of now, no commercialized solutions exist for these plug load technologies and that more work is needed to bring them to market. In addition, we summarize our findings related to the technology challenges, market barriers, drivers, and opportunities for these technologies moving forward.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2026

IPC-Fusion (Infrastructure Perception and Control (IPC): Multisensor Data Fusion Software) [SWR-25-153]

As part of the National Laboratory of the Rockies' (NLR’s) Infrastructure Perception and Control Laboratory, the IPC-Fusion toolkit provides a probabilistic, scalable, multi-sensor fusion framework that integrates (late-stage fusion) heterogeneous object detection data from traffic sensors to enable robust, real-time tracking of roadway occupants. The algorithmic design of the toolkit is motivated by the need for creating a digital twin of traffic at the edge in a scalable and affordable manner. The software operates by combining object-level measurements (such as position and velocity) from a suite of sensors (such as radar, lidar, camera) using Kalman filtering and probabilistic data association techniques to overcome individual sensor limitations and achieve superior tracking performance in complex traffic zones. The framework addresses key challenges including heterogeneous measurement uncertainties, asynchronous data streams, varying spatiotemporal data resolutions, robust data association, and adaptive object lifecycle management. Validated on real-world traffic intersection data including vehicles and pedestrians, IPC-Fusion demonstrates enhanced tracking reliability across scenarios involving occlusions, sensor failures, and varying traffic densities, supporting the broader IPC initiative's goal of transforming transportation infrastructure through advanced perception capabilities for intelligent transportation systems, traffic safety applications, and autonomous vehicle support.

Sandhu, Rimple [National Laboratory of the Rockies↗
NASA NTRS1961

Classification and Evaluation of Coherent Synchronous Sampled-Data Telemetry Systems

This paper analyzes the various types of continuous wave and pulse modulation for the transmission of sampled data over channels perturbed by white gaussian noise. Optimal coherent synchronous detection schemes for all the different modulation methods are shown to belong to one of two general classes: linear synchronous detection and correlation detection. The figures of merit, mean-square signal-to-error ratio and bandwidth occupancy, are determined for each system and compared.

Viterbi, Andrew↗
DOE OSTI2021

Analysis of Automated Fault Detection and Diagnosis Records as an Indicator of HVAC Fault Prevalence: Methodology and Preliminary Results

Faults in commercial buildings can cause energy waste and other performance problems such as reduced occupant comfort, reduced equipment longevity, and increased noise. However, it is currently unknown how commonly faults occur in different equipment types. A method has been developed to estimate the prevalence of faults in air handling units, air terminal units, and rooftop units. This method includes two types of data. The first is data from several automated fault detection and diagnostics (AFDD) software technologies. This type of data provides a large sample that represents a wide range of building types, geographical locations, and equipment types. It includes fault diagnoses from thousands of buildings around the United States, as well as anonymized metadata describing the building and equipment characteristics. The number of fault records is in the order of 107. However, despite the size and richness of the data sample, this data contains some degree of inaccuracy, i.e., false positive and false negative findings. Therefore, the study includes a second type of data, coming from manual inspection of buildings that have had the same AFDD methods applied to them (from the commercial AFDD offerings). Since the field tests are conducted in buildings with AFDD-generated fault prevalence data, they can be combined with the larger sample size to provide insight into the potential biases or lower sensitivity of the AFDD data. Once a library of fault prevalence data is built, it will be studied to provide further insight into the drivers of fault prevalence, for example, whether prevalence is correlated with building type, geographical location (which is tied to climate and to utility rates), building size, etc. This paper describes the methods developed for this study and illustrates them with preliminary data. It discusses some of the challenges of harmonizing disparate outputs from multiple AFDD vendors, application of a unifying fault taxonomy, and fault prevalence metrics.

Ebrahimi Fakhar, Amir↗
DOE OSTI2020

State-of-the-art on research and applications of machine learning in the building life cycle

Fueled by big data, powerful and affordable computing resources, and advanced algorithms, machine learning has been explored and applied to buildings research for the past decades and has demonstrated its potential to enhance building performance. This study systematically surveyed how machine learning has been applied at different stages of building life cycle. By conducting a literature search on the Web of Knowledge platform, we found 9579 papers in this field and selected 153 papers for an in-depth review. The number of published papers is increasing year by year, with a focus on building design, operation, and control. However, no study was found using machine learning in building commissioning. There are successful pilot studies on fault detection and diagnosis of HVAC equipment and systems, load prediction, energy baseline estimate, load shape clustering, occupancy prediction, and learning occupant behaviors and energy use patterns. None of the existing studies were adopted broadly by the building industry, due to common challenges including (1) lack of large scale labeled data to train and validate the model, (2) lack of model transferability, which limits a model trained with one data-rich building to be used in another building with limited data, (3) lack of strong justification of costs and benefits of deploying machine learning, and (4) the performance might not be reliable and robust for the stated goals, as the method might work for some buildings but could not be generalized to others. Finally, findings from the study can inform future machine learning research to improve occupant comfort, energy efficiency, demand flexibility, and resilience of buildings, as well as to inspire young researchers in the field to explore multidisciplinary approaches that integrate building science, computing science, data science, and social science.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Hawaiian Hoary Bat Report for Sandia National Laboratories, Kauai Test Facility

The Hawaiian Hoary Bat Report for Sandia National Laboratories, Kauai Test Facility (SNL/KTF) provides data and analysis of Hawaiian hoary bat occupancy of the SNL/KTF site. The monitoring period was from 2023-2025 and used passive acoustic detection at three locations to determine whether the Hawaiian hoary bat occurs at the site and if they roost and pup in the vegetation of the site. Bats were detected at all three locations, however, given the vegetation type at SNL/KTF, it is very unlikely

Jones, Amanda Kaye [Sandia National Laboratories (↗
NASA NTRS1983

Gas Analyzer

A miniature gas chromatograph, a system which separates a gaseous mixture into its components and measures the concentration of the individual gases, was designed for the Viking Lander. The technology was further developed under National Institute for Occupational Safety and Health (NIOSH) and funded by Ames Research Center/Stanford as a toxic gas leak detection device. Three researchers on the project later formed Microsensor Technology, Inc. to commercialize the product. It is a battery-powered system consisting of a sensing wand connected to a computerized analyzer. Marketed as the Michromonitor 500, it has a wide range of applications.

Source record↗
NASA NTRS1997

Forearm muscle oxygenation decreases with low levels of voluntary contraction

The purpose of our investigation was to determine if the near infrared spectroscopy technique was sensitive to changes in tissue oxygenation at low levels of isometric contraction in the extensor carpi radialis brevis muscle. Nine subjects were seated with the right arm abducted to 45 degrees, elbow flexed to 85 degrees, forearm pronated 45 degrees, and wrist and forearm supported on an armrest throughout the protocol. Altered tissue oxygenation was measured noninvasively with near infrared spectroscopy. The near infrared spectroscopy probe was placed over the extensor carpi radialis brevis of the subject's right forearm and secured with an elastic wrap. After 1 minute of baseline measurements taken with the muscle relaxed, four different loads were applied just proximal to the metacarpophalangeal joint such that the subjects isometrically contracted the extensor carpi radialis brevis at 5, 10, 15, and 50% of the maximum voluntary contraction for 1 minute each. A 3-minute recovery period followed each level of contraction. At the end of the protocol, with the probe still in place, a value for ischemic tissue oxygenation was obtained for each subject. This value was considered the physiological zero and hence 0% tissue oxygenation. Mean tissue oxygenation (+/-SE) decreased from resting baseline (100% tissue oxygenation) to 89 +/- 4, 81 +/- 8, 78 +/- 8, and 47 +/- 8% at 5, 10, 15, and 50% of the maximum voluntary contraction, respectively. Tissue oxygenation levels at 10, 15, and 50% of the maximum voluntary contraction were significantly lower (p < 0.05) than the baseline value. Our results indicate that tissue oxygenation significantly decreases during brief, low levels of static muscle contraction and that near infrared spectroscopy is a sensitive technique for detecting deoxygenation noninvasively at low levels of forearm muscle contraction. Our findings have important implications in occupational medicine because oxygen depletion induced by low levels of muscle contraction may be directly linked to muscle fatigue.

NASA Center ARC↗
DOE OSTI2020

Redox cycling-based detection of phenazine metabolites secreted from Pseudomonas aeruginosa in nanopore electrode arrays

The opportunistic pathogen Pseudomonas aeruginosa (P. aeruginosa) produces several redox-active phenazine metabolites, including pyocyanin (PYO) and phenazine-1-carboxamide (PCN), which are electron carrier molecules that also aid in virulence. In particular, PYO is an exclusive metabolite produced by P. aeruginosa, which acts as a virulence factor in hospital-acquired infections and is therefore a good biomarker for identifying early stage colonization by this pathogen. Here, we describe the use of nanopore electrode arrays (NEAs) exhibiting metal–insulator–metal ring electrode architectures for enhanced detection of these phenazine metabolites. The size of the nanopores allows phenazine metabolites to freely diffuse into the interior and access the working electrodes, while the bacteria are excluded. Consequently, highly efficient redox cycling reactions in the NEAs can be accessed by free diffusion unhindered by the presence of bacteria. This strategy yields low limits of detection, i.e. 10.5 and 20.7 nM for PYO and PCN, respectively, values far below single molecule pore occupancy, e.g. at 10.5 nM < n pore > ~ 0.082 per nanopore – a limit which reflects the extraordinary signal amplification in the NEAs. Furthermore, experiments that compared results from minimal medium and rich medium show that P. aeruginosa produces the same types of phenazine metabolites even though growth rates and phenazine production patterns differ in these two media. Here, the NEA measurement strategy developed here should be useful as a diagnostic for pathogens generally and for understanding metabolism in clinically important microbial communities.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Neutron diffraction study of magnetism in van der Waals layered MnBi 2n Te 3n+1

Two-dimensional van der Waals MnBi 2 n Te 3 n +1 ( n = 1, 2, 3, 4) compounds have been recently found to be intrinsic magnetic topological insulators rendering quantum anomalous Hall effect and diverse topological states. Here, we summarize and compare the crystal and magnetic structures of this family, and discuss the effects of chemical composition on their magnetism. We found that a considerable fraction of Bi occupies at the Mn sites in MnBi 2 n Te 3 n +1 ( n = 1, 2, 3, 4) while there is no detectable Mn at the non-magnetic atomic sites within the resolution of neutron diffraction experiments. The occupancy of Mn monotonically decreases with the increase of n . The polarized neutron diffraction on the representative MnBi 4 Te 7 reveals that its magnetization density is exclusively accumulated at the Mn site, in good agreement with the results from the unpolarized neutron diffraction. The defects of Bi at the Mn site naturally explain the continuously reduced saturated magnetic moments from n = 1 to n = 4. The experimentally estimated critical exponents of all the compounds generally suggest a three-dimensional character of magnetism. Our work provides material-specified structural parameters that may be useful for band structure calculations to understand the observed topological surface states and for designing quantum magnetic materials through chemical doping.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2026

Mortality among workers at the Rocky Flats Plant, 1951–2017

The Rocky Flats (RFs) Plant operated from 1951–1989 as part of the U.S. Department of Energy (DOE) nuclear complex. Its primary mission was weapons component fabrication, whereby workers were potentially exposed to radioactive and non-radioactive hazards. RF worker mortality was compared to the general population, and dose-response relationships between mortality and radiation organ doses were examined. RF workers first employed between 1951 and 1979 for ⩾30 d were identified (n = 9397). Vital status was determined using national and state death records up to 2017. Organ doses from external photons and neutrons irritation and internalised plutonium (Pu), americium (Am), and uranium (U) were modelled as cumulative lagged total doses per year. Beryllium exposure was evaluated as an effect modifier using data from the DOE Nationwide Beryllium Medical Program. Statistical analyses included standardised mortality ratios (SMRs), Cox proportional hazard models, and excess relative risk (ERR) models. Approximately 53.2% of workers were deceased by the end of the study. Nearly 90% were monitored for radiation exposure, with a mean weighted absorbed dose of 59.0 mGy for the lungs. Nearly 45% of workers had intakes of alpha-particle emitting radionuclides, and 46.7% were monitored for neutrons. Leading causes of death included ischemic heart disease (n = 999) and lung cancer (n = 361). The highest SMRs were observed for berylliosis (SMR: 176.9; 95% CI: 76.2, 348.7; n < 10) and asbestosis (SMR: 4.65; 95% CI: 2.23, 8.55; n = 10). Dose-response analyses showed no statistical increase in risk from low-dose radiation including lung cancer (ERR per 100 mGy: −0.02; 95% CI: −0.11, 0.08; n = 361) and Parkinson’s disease (ERR per 100 mGy: 0.13; 95% CI: −0.26, 0.31; n = 57). Approximately 45% of workers were monitored for beryllium, with a weak non-significant indication of effect modification for lung cancer risk. The RF cohort showed no evidence of a statistically significant increase in mortality from occupational radiation exposure. However, this study was limited by low statistical power, which inhibits the ability to detect effects. Future pooling of Million Person Study (MPS) cohorts will provide further insights, particularly regarding Pu as a carcinogen.

61 RADIATION PROTECTION AND DOSIMETRY↗