Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Adaptive Sampling”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 919 records · Page 51
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Well Design, Operation and Maintenance and Proactive Rehabilitation to Optimize Remediation Well Performance - 18605

Optimizing performance of extraction, injection and monitoring wells, which are integral components of groundwater remediation systems, result in lower life cycle costs and a higher probability of meeting project objectives. These wells provide the means to extract contaminated groundwater, inject reagents, treated water and clean water to create groundwater reaction/flushing zones, and to provide groundwater level, contaminant concentration and geochemical data. Initial costs associated with well installation and associated pipelines and treatment systems are significant project expenditures related to site remediation. However, the life cycle costs for wells are dominated by the long-term operation and maintenance (O and M) expenditures. Remediation wells are particularly susceptible to various types of fouling that impact well performance and, therefore, drive up operating costs. Simply defined, fouling is the result of any biological, geochemical or physical process that negatively impacts the intended performance of a remediation well. Fouling decreases well performance by effectively reducing hydraulic communication between the well and the surrounding formation. Oftentimes this occurs via clogging screens and/or gravel pack/formation pore space by bacterial or chemical byproducts and gases, which can lead to corrosion of metal components and spreading fouling deposits to pipelines and surface treatment systems. Ultimately, this results in reduced hydraulic performance in extraction and injection wells and result in erroneous water level and chemical parameter concentration measurements in monitoring wells. Remediation well fouling cannot be eliminated, and natural fouling processes are exacerbated by the installation and operation of remedial systems. Steps required to extend the life of remediation wells and to lower life cycle costs are discussed in this paper in order to establish the required approach to effective system management. These optimization approaches have been developed through years of operation and maintenance of complex large-scale plume remediation systems utilizing a variety of subsurface in-situ and ex-situ technologies. Remediation well performance can be improved by: - careful site characterization, - appropriate well design, - proper well installation and development, - thoughtful and adaptable operation and maintenance, - well performance maintenance tracking and, - proactive well rehabilitation. Clear understanding of the site hydro-stratigraphy and geochemical settings places constraints on the compatibility of well materials and the configurations of well screens. Proper well design, including collection and analysis (sieve/grain-size) of screen zone formation samples, assures that wells can be optimally installed, developed and redeveloped during well rehabilitation. Design considerations should include proper sizing of filter packs and well screens, casing/screen diameter, well material selection/compatibility, expected life/use, and well head configuration to allow for future access. Applying appropriate and effective well installation and development methods are of paramount importance and rely on a good understanding of site characteristics (e.g., geologic setting) as well as local drilling company expertise and experience. Different approaches to operation and maintenance can greatly affect the degree of fouling, these include limiting reagent concentrations, maintaining submerged well screens and filtering injection water to prevent the introduction of fouling enhancing constituents. Tracking well performance by monitoring changes in system operation and metrics such as specific capacity or injectivity for extraction and injection wells, respectively, or turbidity and chemical analyses for monitoring wells is key to identifying well maintenance issues early when well rehabilitation can be most effective. Proactive well rehabilitation can include mechanical brushing, surging and jetting, chemical treatment or other specialized approaches; however, the approach needs to be based on a site-specific understanding of the predominant fouling mechanisms. Collection and analyses of fouling deposits can provide guidance for selection of rehabilitation methods and chemicals that will be most effective. Application of these approaches have led to extended operational life for reagent injection wells, which can foul quickly, from months to years. Additionally, proper maintenance will often lead to increased and extended productivity of the well. For extraction wells, this translates to a higher sustainable yield, which means shorter cleanup times. Similarly, for injection wells, proper maintenance allows for the introduction of a greater volume of reagents over a shorter period, thus, shorter cleanup times that reduce overall project costs. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2014

Locoregional Control of Non-Small Cell Lung Cancer in Relation to Automated Early Assessment of Tumor Regression on Cone Beam Computed Tomography

Purpose: Large interindividual variations in volume regression of non-small cell lung cancer (NSCLC) are observable on standard cone beam computed tomography (CBCT) during fractionated radiation therapy. Here, a method for automated assessment of tumor volume regression is presented and its potential use in response adapted personalized radiation therapy is evaluated empirically. Methods and Materials: Automated deformable registration with calculation of the Jacobian determinant was applied to serial CBCT scans in a series of 99 patients with NSCLC. Tumor volume at the end of treatment was estimated on the basis of the first one third and two thirds of the scans. The concordance between estimated and actual relative volume at the end of radiation therapy was quantified by Pearson's correlation coefficient. On the basis of the estimated relative volume, the patients were stratified into 2 groups having volume regressions below or above the population median value. Kaplan-Meier plots of locoregional disease-free rate and overall survival in the 2 groups were used to evaluate the predictive value of tumor regression during treatment. Cox proportional hazards model was used to adjust for other clinical characteristics. Results: Automatic measurement of the tumor regression from standard CBCT images was feasible. Pearson's correlation coefficient between manual and automatic measurement was 0.86 in a sample of 9 patients. Most patients experienced tumor volume regression, and this could be quantified early into the treatment course. Interestingly, patients with pronounced volume regression had worse locoregional tumor control and overall survival. This was significant on patient with non-adenocarcinoma histology. Conclusions: Evaluation of routinely acquired CBCT images during radiation therapy provides biological information on the specific tumor. This could potentially form the basis for personalized response adaptive therapy.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2017

Optimal Nested Test Plan for Combinatorial Quantitative Group Testing

We consider the quantitative group testing problem where the objective is to identify defective items in a given population based on results of tests performed on subsets of the population. Under the quantitative group testing model, the result of each test reveals the number of defective items in the tested group. The minimum number of tests achievable by nested test plans was established by Aigner and Schughart in 1985 within a minimax framework. Furthermore, the optimal nested test plan offering this performance, however, was not obtained. In this paper, we establish the optimal nested test plan in a closed form. This optimal nested test plan is also order optimal among all test plans as the population size approaches infinity. Using heavy-hitter detection as a case study, we show via simulation examples orders of magnitude improvement of the group testing approach over two prevailing sampling-based approaches in detection accuracy and counter consumption. Other applications include anomaly detection and wideband spectrum sensing in cognitive radio systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

The structure of CO{sub 2} hydrate between 0.7 and 1.0 GPa

A deuterated sample of CO{sub 2} structure I (sI) clathrate hydrate (CO{sub 2}·8.3 D{sub 2}O) has been formed and neutron diffraction experiments up to 1.0 GPa at 240 K were performed. The sI CO{sub 2} hydrate transformed at 0.7 GPa into the high pressure phase that had been observed previously by Hirai et al. [J. Phys. Chem. 133, 124511 (2010)] and Bollengier et al. [Geochim. Cosmochim. Acta 119, 322 (2013)], but which had not been structurally identified. The current neutron diffraction data were successfully fitted to a filled ice structure with CO{sub 2} molecules filling the water channels. This CO{sub 2}+water system has also been investigated using classical molecular dynamics and density functional ab initio methods to provide additional characterization of the high pressure structure. Both models indicate the water network adapts a MH-III “like” filled ice structure with considerable disorder of the orientations of the CO{sub 2} molecule. Furthermore, the disorder appears to be a direct result of the level of proton disorder in the water network. In contrast to the conclusions of Bollengier et al., our neutron diffraction data show that the filled ice phase can be recovered to ambient pressure (0.1 MPa) at 96 K, and recrystallization to sI hydrate occurs upon subsequent heating to 150 K, possibly by first forming low density amorphous ice. Unlike other clathrate hydrate systems, which transform from the sI or sII structure to the hexagonal structure (sH) then to the filled ice structure, CO{sub 2} hydrate transforms directly from the sI form to the filled ice structure.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

Consequences of sludge composition on combustion performance derived from thermogravimetry analysis

Highlights: • Volatiles, particularly proteins, play a key role in sludge combustion. • Sludge combustion performance varies with different sludge organic concentrations. • Carbohydrates significantly affect the combustion rate in the second stage. • Combustion performance of digested sludge is more negative compared with others. - Abstract: Wastewater treatment plants produce millions of tons of sewage sludge. Sewage sludge is recognized as a promising feedstock for power generation via combustion and can be used for energy crisis adaption. We aimed to investigate the quantitative effects of various sludge characteristics on the overall sludge combustion process performance. Different types of sewage sludge were derived from numerous wastewater treatment plants in Beijing for further thermogravimetric analysis. Thermogravimetric–differential thermogravimetric curves were used to compare the performance of the studied samples. Proximate analytical data, organic compositions, elementary composition, and calorific value of the samples were determined. The relationship between combustion performance and sludge composition was also investigated. Results showed that the performance of sludge combustion was significantly affected by the concentration of protein, which is the main component of volatiles. Carbohydrates and lipids were not correlated with combustion performance, unlike protein. Overall, combustion performance varied with different sludge organic composition. The combustion rate of carbohydrates was higher than those of protein and lipid, and carbohydrate weight loss mainly occurred during the second stage (175–300 °C). Carbohydrates have a substantial effect on the rate of system combustion during the second stage considering the specific combustion feature. Additionally, the combustion performance of digested sewage sludge is more negative than the others.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2014

Computing thermal Wigner densities with the phase integration method

We discuss how the Phase Integration Method (PIM), recently developed to compute symmetrized time correlation functions [M. Monteferrante, S. Bonella, and G. Ciccotti, Mol. Phys. 109, 3015 (2011)], can be adapted to sampling/generating the thermal Wigner density, a key ingredient, for example, in many approximate schemes for simulating quantum time dependent properties. PIM combines a path integral representation of the density with a cumulant expansion to represent the Wigner function in a form calculable via existing Monte Carlo algorithms for sampling noisy probability densities. The method is able to capture highly non-classical effects such as correlation among the momenta and coordinates parts of the density, or correlations among the momenta themselves. By using alternatives to cumulants, it can also indicate the presence of negative parts of the Wigner density. Both properties are demonstrated by comparing PIM results to those of reference quantum calculations on a set of model problems.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Analysis of a macrophage carbamylated proteome reveals a function in post-translational modification crosstalk

Background. Lysine carbamylation is a biomarker of rheumatoid arthritis and kidney diseases. However, its cellular function is understudied due to the lack of tools for systematic analysis of this post-translational modification (PTM). Methods. We adapted a method to analyze carbamylated peptides by co-affinity purification with acetylated peptides based on the cross-reactivity of anti-acetyllysine antibodies. We also performed immobilized-metal affinity chromatography to enrich for phosphopeptides, which allowed us to obtain multi-PTM information from the same samples. Results. By testing the pipeline with RAW 264.7 macrophages treated with bacterial lipopolysaccharide, 7,299, 8,923 and 47,637 acetylated, carbamylated, and phosphorylated peptides were identified, respectively. Our analysis showed that carbamylation occurs on proteins from a variety of functions on sites with similar as well as distinct motifs compared to acetylation. To investigate possible PTM crosstalk, we integrated the carbamylation data with acetylation and phosphorylation data, leading to the identification 1,183 proteins that were modified by all 3 PTMs. Among these proteins, 54 had all 3 PTMs regulated by lipopolysaccharide and were enriched in immune signaling pathways, and in particular, the ubiquitin-proteasome pathway. We found that carbamylation of linear diubiquitin blocks the activity of the anti-inflammatory deubiquitinase OTULIN. Conclusions. Overall, our data show that anti-acetyllysine antibodies can be used for effective enrichment of carbamylated peptides. Moreover, carbamylation may play a role in PTM crosstalk with acetylation and phosphorylation, and that it is involved in regulating ubiquitination in vitro.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

Uranium Detection - Technique Validation Report

As a LANL activity for DOE/NNSA in support of SHINE Medical Technologies™ ‘Accelerator Technology’ we have been investigating the application of UV-vis spectroscopy for uranium analysis in solution. While the technique has been developed specifically for sulfate solutions, the proposed SHINE target solutions, it can be adapted to a range of different solution matrixes. The FY15 work scope incorporated technical development that would improve accuracy, specificity, linearity & range, precision & ruggedness, and comparative analysis. Significant progress was achieved throughout FY 15 addressing these technical challenges, as is summarized in this report. In addition, comparative analysis of unknown samples using the Davies-Gray titration technique highlighted the importance of controlling temperature during analysis (impacting both technique accuracy and linearity/range). To fully understand the impact of temperature, additional experimentation and data analyses were performed during FY16. The results from this FY15/FY16 work were presented in a detailed presentation, LA-UR-16-21310, and an update of this presentation is included with this short report summarizing the key findings. The technique is based on analysis of the most intense U(VI) absorbance band in the visible region of the uranium spectra in 1 M H 2 SO 4 , at λ max = 419.5 nm.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Tetranucleotide frequencies differentiate genomic boundaries and metabolic strategies across environmental microbiomes

Microbiomes are constrained by physicochemical conditions, nutrient regimes, and community interactions across diverse environments, yet genomic signatures of this adaptation remain unclear. Metagenome sequencing is a powerful technique to analyze genomic content in the context of natural environments, establishing concepts of microbial ecological trends. Here, we developed a data discovery tool-a tetranucleotide-informed metagenome stability diagram-that is publicly available in the integrated microbial genomes and microbiomes (IMG/M) platform for metagenome ecosystem analyses. We analyzed the tetranucleotide frequencies from quality-filtered and unassembled sequence data of over 12,000 metagenomes to assess ecosystem-specific microbial community composition and function. We found that tetranucleotide frequencies can differentiate communities across various natural environments and that specific functional and metabolic trends can be observed in this structuring. Our tool places metagenomes sampled from diverse environments into clusters and along gradients of tetranucleotide frequency similarity, suggesting microbiome community compositions specific to gradient conditions. Within the resulting metagenome clusters, we identify protein-coding gene identifiers that are most differentiated between ecosystem classifications. We plan for annual updates to the metagenome stability diagram in IMG/M with new data, allowing for refinement of the ecosystem classifications delineated here. This framework has the potential to inform future studies on microbiome engineering, bioremediation, and the prediction of microbial community responses to environmental change. IMPORTANCE: Microbes adapt to diverse environments influenced by factors like temperature, acidity, and nutrient availability. We developed a new tool to analyze and visualize the genetic makeup of over 12,000 microbial communities, revealing patterns linked to specific functions and metabolic processes. This tool groups similar microbial communities and identifies characteristic genes within environments. By continually updating this tool, we aim to advance our understanding of microbial ecology, enabling applications like microbial engineering, bioremediation, and predicting responses to environmental change.

Kellom, Matthew↗
DOE OSTI2021

Adaptive hyperparameter updating for training restricted Boltzmann machines on quantum annealers

Restricted Boltzmann Machines (RBMs) have been proposed for developing neural networks for a variety of unsupervised machine learning applications such as image recognition, drug discovery, and materials design. The Boltzmann probability distribution is used as a model to identify network parameters by optimizing the likelihood of predicting an output given hidden states trained on available data. Training such networks often requires sampling over a large probability space that must be approximated during gradient based optimization. Quantum annealing has been proposed as a means to search this space more efficiently which has been experimentally investigated on D-Wave hardware. D-Wave implementation requires selection of an effective inverse temperature or hyperparameter (β) within the Boltzmann distribution which can strongly influence optimization. Here, we show how this parameter can be estimated as a hyperparameter applied to D-Wave hardware during neural network training by maximizing the likelihood or minimizing the Shannon entropy. We find both methods improve training RBMs based upon D-Wave hardware experimental validation on an image recognition problem. Neural network image reconstruction errors are evaluated using Bayesian uncertainty analysis which illustrate more than an order magnitude lower image reconstruction error using the maximum likelihood over manually optimizing the hyperparameter. The maximum likelihood method is also shown to out-perform minimizing the Shannon entropy for image reconstruction.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Performance of pumped counterflow virtual impactors to study aerosol interactions with laboratory generated warm clouds

Pumped Counterflow Virtual Impactors (PCVI) are designed to separate aerosols based on aerodynamic diameter, which is particularly useful for isolating cloud droplets and ice crystals from smaller particles. However, the PCVI transmission efficiency (TE) values reported in the literature show considerable variability, and little information is available on the TEs for cloud liquid droplets. Here, we determined the optimal flow conditions for PCVI sampling for different activation ratios from the MTU Pi-cloud chamber, highlighting the conditions that maximize droplet residual sampling while minimizing interstitial transmission. Even for a lower limit cloud activation ratio = 1:10, an add flow of 1.5 LPM achieves a residual fraction greater than 0.80, with a droplet TE of ∼15% for typical Pi-Chamber droplets with diameters between 3.5 to 10 µm. This framework can be adapted for field cloud measurements based on the cloud conditions of interest. TE for cloud droplets under flow conditions at which ∼99% of the unactivated submicron particles were removed was substantially lower between 8 and 16% than for supermicron dry particles (∼30 to 40% for 3 µm polystyrene latex spheres). In addition, we conducted a comparison of the performances of three similar PCVI units to assess repeatability for dry aerosols and cloud droplets. All three PCVIs performed similarly in rejecting submicron particles smaller than the desired cutoff diameter. One of the units showed lower TEs due to a misalignment of the internal orifice. We discuss a procedure that improved alignment and performance.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

DISCOVERY OF LOW-METALLICITY STARS IN THE CENTRAL PARSEC OF THE MILKY WAY

We present a metallicity analysis of 83 late-type giants within the central 1 pc of the Milky Way. K-band spectroscopy of these stars was obtained with the medium spectral resolution integral-field spectrograph NIFS on Gemini North using laser-guided star adaptive optics. Using spectral template fitting with the MARCS synthetic spectral grid, we find that there is a large variation in the metallicity, with stars ranging from [M/H] < −1.0 to above solar metallicity. About 6% of the stars have [M/H] < −0.5. This result is in contrast to previous observations with smaller samples that show stars at the Galactic center having approximately solar metallicity with only small variations. Our current measurement uncertainties are dominated by systematics in the model, especially at [M/H] > 0, where there are stellar lines not represented in the model. However, the conclusion that there are low-metallicity stars, as well as large variations in metallicity, is robust. The metallicity may be an indicator of the origin of these stars. The low-metallicity population is consistent with that of globular clusters in the Milky Way, but their small fraction likely means that globular cluster infall is not the dominant mechanism for forming the Milky Way nuclear star cluster. The majority of stars are at or above solar metallicity, which suggests they were formed closer to the Galactic center or from the disk. In addition, our results indicate that it will be important for star formation history analyses using red giants at the Galactic center to consider the effect of varying metallicity.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Private Tabular Survey Data Products through Synthetic Microdata Generation

We propose two synthetic microdata approaches to generate private tabular survey data products for public release. We adapt a pseudo posterior mechanism that downweights by-record likelihood contributions with weights ∈[0,1] based on their identification disclosure risks to producing tabular products for survey data. Our method applied to an observed survey database achieves an asymptotic global probabilistic differential privacy guarantee. Our two approaches synthesize the observed sample distribution of the outcome and survey weights, jointly, such that both quantities together possess a privacy guarantee. The privacy-protected outcome and survey weights are used to construct tabular cell estimates (where the cell inclusion indicators are treated as known and public) and associated standard errors to correct for survey sampling bias. Through a real data application to the Survey of Doctorate Recipients public use file and simulation studies motivated by the application, we demonstrate that our two microdata synthesis approaches to construct tabular products provide superior utility preservation as compared to the additive noise approach of the Laplace Mechanism. Moreover, our approaches allow the release of microdata to the public, enabling additional analyses at no extra privacy cost.

Mathematical Methods In Social Sciences↗
DOE OSTI2018

Quench Detection for High-Temperature Superconductor Conductors Using Acoustic Thermometry

Detecting local heat-dissipating zones in high-temperature superconductor (HTS) magnets is a challenging task due to slow propagation of such zones in HTS conductors. For long conductor lengths, voltage-based methods may not provide a sufficient sensitivity or redundancy, and therefore nonvoltage-based detection alternatives are being sought. One of those is the recently proposed method of Eigen Frequency Thermometry (EFT), which is an active acoustic technique for a fast and nonintrusive detection of 'hot spots,' utilizing temperature dependence of the conductor elastic moduli. In this work, we demonstrate the efficiency of EFT for detecting localized heating in a 1.2-m-long sample of REBCO tape immersed in liquid nitrogen, and benchmark sensitivity of the acoustic detection with respect to voltage, hot spot temperature, and power dissipation in the conductor. Modifying the original technique for differential mode of operation enables a much improved sensitivity, and adds a hot spot localization capability. Furthermore, we adapt this technique to subscale coils wound with REBCO CORC conductor built in the framework of U.S. Magnet Development Program. A successful thermal-based detection of dissipation onset at the critical current for a two-layer canted CORC dipole assembly is discussed.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

TULIP: An RNA-seq-based Primary Tumor Type Prediction Tool Using Convolutional Neural Networks

Background: With cancer as one of the leading causes of death worldwide, accurate primary tumor type prediction is critical in identifying genetic factors that can inhibit or slow tumor progression. There have been efforts to categorize primary tumor types with gene expression data using machine learning, and more recently with deep learning, in the last several years. Methods In this paper, we developed four 1-dimensional (1D) Convolutional Neural Network (CNN) models to classify RNA-seq count data as one of 17 highly represented primary tumor types or 32 primary tumor types regardless of imbalanced representation. Additionally, we adapted the models to take as input either all Ensembl genes (60,483) or protein coding genes only (19,758). Unlike previous work, we avoided selection bias by not filtering genes based on expression values. RNA-seq count data expressed as FPKM-UQ of 9,025 and 10,940 samples from The Cancer Genome Atlas (TCGA) were downloaded from the Genomic Data Commons (GDC) corresponding to 17 and 32 primary tumor types respectively for training and validating the models. Results: All 4 1D-CNN models had an overall accuracy of 94.7% to 97.6% on the test dataset. Further evaluation indicates that the models with protein coding genes only as features performed with better accuracy compared to the models with all Ensembl genes for both 17 and 32 primary tumor types. For all models, the accuracy by primary tumor type was above 80% for most primary tumor types. Conclusions: We packaged all 4 models as a Python-based deep learning classification tool called TULIP (TUmor CLassIfication Predictor) for performing quality control on primary tumor samples and characterizing cancer samples of unknown tumor type. Further optimization of the models is needed to improve the accuracy of certain primary tumor types.

Jones, Sara↗
DOE OSTI2016

Strengthening IAEA Safeguards for Research Reactors

During their December 10-11, 2013, workshop in Grenoble France, which focused on the history and future of safeguarding research reactors, the United States, France and the United Kingdom (UK) agreed to conduct a joint study exploring ways to strengthen the IAEA’s safeguards approach for declared research reactors. This decision was prompted by concerns about: 1) historical cases of non-compliance involving misuse (including the use of non-nuclear materials for production of neutron generators for weapons) and diversion that were discovered, in many cases, long after the violations took place and as part of broader pattern of undeclared activities in half a dozen countries; 2) the fact that, under the Safeguards Criteria, the IAEA inspects some reactors (e.g., those with power levels under 25 MWt) less than once per year; 3) the long-standing precedent of States using heavy water research reactors (HWRR) to produce plutonium for weapons programs; 4) the use of HEU fuel in some research reactors; and 5) various technical characteristics common to some types of research reactors that could provide an opportunity for potential proliferators to misuse the facility or divert material with low probability of detection by the IAEA. In some research reactors it is difficult to detect diversion or undeclared irradiation. In addition, infrastructure associated with research reactors could pose a safeguards challenge. To strengthen the effectiveness of safeguards at the State level, this paper advocates that the IAEA consider ways to focus additional attention and broaden its safeguards toolbox for research reactors. This increase in focus on the research reactors could begin with the recognition that the research reactor (of any size) could be a common path element on a large number of technically plausible pathways that must be considered when performing acquisition pathway analysis (APA) for developing a State Level Approach (SLA) and Annual Implementation Plan (AIP). To broaden the IAEA safeguards toolbox, the study recommends that the Agency consider closing potential gaps in safeguards coverage by, among other things: 1) adapting its safeguards measures based on a case-by-case assessment; 2) using more frequent and expanded/enhanced mailbox declarations (ideally with remote transmission of the data to IAEA Headquarters in Vienna) coupled with short-notice or unannounced inspections; 3) putting more emphasis on the collection and analysis of environmental samples at hot cells and waste storage tanks; 4) taking Safeguards by Design into account for the construction of new research reactors and best practices for existing research reactors; 5) utilizing fully all legal authorities to enhance inspection access (including a strengthened and continuing DIV process); and 6) utilizing new approaches to improve auditing activities, verify reactor operating data history, and track/monitor the movement and storage of spent fuel.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2021

Hyperdimensional Imaging Contrast Using an Optical Fiber

Fluorescence properties of a molecule can be used to study the structural and functional nature of biological processes. Physical properties, including fluorescence lifetime, emission spectrum, emission polarization, and others, help researchers probe a molecule, produce desired effects, and infer causes and consequences. Correlative imaging techniques such as hyperdimensional imaging microscopy (HDIM) combine the physical properties and biochemical states of a fluorophore. Here we present a fiber-based imaging system that can generate hyper-dimensional contrast by combining multiple fluorescence properties into a single fluorescence lifetime decay curve. Fluorescence lifetime imaging microscopy (FLIM) with controlled excitation polarization and temporally dispersed emission can generate a spectrally coded, polarization-filtered lifetime distribution for a pixel. This HDIM scheme generates a better contrast between different molecules than that from individual techniques. This setup uses only a single detector and is simpler to implement, modular, cost-efficient, and adaptable to any existing FLIM microscope. We present higher contrast data from Arabidopsis thaliana epidermal cells based on intrinsic anthocyanin emission properties under multiphoton excitation. This work lays the foundation for an alternative hyperdimensional imaging system and demonstrates that contrast-based imaging is useful to study cellular heterogeneity in biological samples.

47 OTHER INSTRUMENTATION↗
DOE OSTI2014

SU-E-J-191: Automated Detection of Anatomic Changes in H'N Patients

Purpose: To develop a novel statistics-based method for automated detection of anatomical changes using cone-beam CT data. A method was developed that can provide a reliable and automated early warning system that enables a “just-in-time” adaptation of the treatment plan. Methods: Anatomical changes were evaluated by comparing the original treatment planning CT with daily CBCT images taken prior treatment delivery. The external body contour was computed on a given CT slice and compared against the corresponding contour on the daily CBCT. In contrast to threshold-based techniques, a statistical approach was employed to evaluate the difference between the contours using a given confidence level. The detection tool used the two-sample Kolmogorov-Smirnov test, which is a non-parametric technique that compares two samples drawn from arbitrary probability distributions. 11 H'N patients were retrospectively selected from a clinical imaging database with a total of 186 CBCT images. Six patients in the database were confirmed to have anatomic changes during the course of radiotherapy. Five of the H'N patients did not have significant changes. The KS test was applied to the contour data using a sliding window analysis. The confidence level of 0.99 was used to moderate false detection. Results: The algorithm was able to correctly detect anatomical changes in 6 out of 6 patients with an excellent spatial accuracy as early as at the 14th elapsed day. The algorithm provided a consistent and accurate delineation of the detected changes. The output of the anatomical change tool is easy interpretable, and can be shown overlaid on a 3D rendering of the patient's anatomy. Conclusion: The detection method provides the basis for one of the key components of Adaptive Radiation Therapy. The method uses tools that are readily available in the clinic, including daily CBCT imaging, and image co-registration facilities.

60 APPLIED LIFE SCIENCES↗