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DOE OSTI2023

Agilent AgileBioFoundry CRADA (Final Report)

The mission of this CRADA with Agilent was to couple powerful MS platforms (QQQ, IM-QTOF-MS) with Agilent’s novel Ultra-High-Performance Liquid Chromatography (UHPLC) fast metabolomic workflows and perform ABF Machine Learning (ML) to generated datasets. Agilent transferred UHPLC methods to PNNL and LBNL and methods were implemented and demonstrated in both labs, achieving total acquisition times of < 10 min. Metabolites analyzed using Agilent’s shared methods included metabolites from central carbon metabolism, common across hosts, and metabolites unique to engineered strains. Standards were acquired in an UHPLC-Drift Tube Ion Mobility Mass Spectrometer (DTIMS) system for the first time within the context of ABF and methods were optimized based on Agilent’s protocols. Samples from ABF hosts Pseudomonas putida, Aspergillus pseudoterreus, Aspergillus niger and Rhodosporidium toruloides were analyzed using the UHPLC-DTIMS platform for a total of 276 runs. A data analysis workflow compatible with the Experimental Data Depot (EDD) and completely shareable was developed for the acquired UHPLC-DTIMS data. Samples were analyzed using a Data Independent Acquisition Approach (DIA), which for most of the standards provided more transitions therefore increasing detection confidence. Using the data acquired by PNNL, LBNL, and Agilent’s specifications from previous ML projects, SNL applied an ensemble ML strategy to pick the best performing model for automated LC-method selection. Finally, with the contribution of the participant labs and Agilent, SNL developed an Automated Method Selection (AMS) software tool to predict the best liquid chromatography method for analysis of any new molecules of interest. Samples with novel pathways and new metabolite targets of interest are generated at a high pace in the ABF. Therefore, our accomplishment in this CRADA improved the efficiency and accuracy of strain testing by developing and implementing fast analytical methods, robust processing tools, and software for predicting the best methods for UHPLC analysis.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Studying information recurrence, gatekeeping, and the role of communities during internet outages in Venezuela

Many authoritarian regimes have taken to censoring internet access in order to stop the spread of misinformation, restrict citizens from discussing certain topics, and prevent mobilization, among other reasons. There are several theories about the effectiveness of censorship. Some suggest that censorship will effectively limit the flow of information, whereas others predict that a backlash will form, resulting in ultimately more discussion about the topic. In this work, we analyze the role of communities and gatekeepers during multiple internet outages in Venezuela in January 2019. First, we measure how critical information (e.g., entities and hashtags) spreads during outages focusing on information recurrence and burstiness within and across language and location communities. We discover that information bursts tend to cross both language and location community boundaries rather than being limited to a single community during several outages. Then we identify users who play central roles and propose a novel method to detect gatekeepers—users who prevent critical information from spreading across communities during outages. We show that bilingual and English-speaking users play more central roles compared to Spanish-speaking users, but users inside and outside Venezuela have similar distribution of centrality. Finally, we measure the differences in social network structure before and after each outage event and discuss its effect on how information spreads. We find that with each outage event social connections tend to get less connected with higher mean shortest path, indicating that the effect of censorship makes it harder for information to spread.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Design and simulation of a muon detector to characterize geological overburden

This study presents the design, construction, and simulation of a mobile muon detector tailored for geological overburden characterization. The detector employs plastic scintillator paddles with silicon photomultipliers (SiPMs) and a QuarkNet data acquisition system, offering a portable solution suitable for remote field deployment. The simulator’s modular aluminum frame allows for adjustable geometry and directional sensitivity, while its battery system supports over a week of autonomous operation. Preliminary experimental tests confirmed that its muon flux measurements were consistent with theoretical expectations. A comprehensive simulation framework using Geant4 and CORSIKA was developed to model detector response and overburden effects. Analytical and Monte Carlo methods were used to assess quadrant resolution and infer muon directionality. This work lays the foundation for future overburden mapping and supports the development of reconstruction algorithms for geological applications.

72 - PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2016

SU-F-T-80: A Mobile Application for Intra-Operative Electron Radiotherapy Treatment Planning

Purpose: Intraoperative electron radiotherapy (IORT) poses a unique set of challenges for treatment planning. Planning must be performed in a busy operating room environment over a short timeframe often with little advance knowledge of the treatment depth or applicator size. Furthermore, IORT accelerators can have a large number of possible applicators, requiring extensive databooks that must be searched for the appropriate dosimetric parameters. The goal of this work is to develop a software tool to assist in the planning process that is suited to the challenges faced in the IORT environment. Methods: We developed a mobile application using HTML5 and Javascript that can be deployed to tablet devices suitable for use in the operating room. The user selects the desired treatment parameters cone diameter, bevel angle, and energy (a total of 141 datasets) and desired bolus. The application generates an interactive display that allows the user to dynamically select points on the depth-dose curve and to visualize the shape of the corresponding isodose contours. The user can indicate a prescription isodose line or depth. The software performs a monitor unit calculation and generates a PDF report. Results: We present our application, which is now used routinely in our IORT practice. It has been employed successfully in over 23 cases. The interactivity of the isodose distributions was found to be of particular use to physicians who are less-frequent IORT users, as well as for the education of residents and trainees. Conclusion: This software has served as a useful tool in IORT planning, and demonstrates the need for treatment planning tools that are designed for the specialized challenges encountered in IORT. This software is the subject of a license agreement with the IntraOp Medical Corporation. This software is the subject of a license agreement between Massachusetts General Hospital / Partners Healthcare and the IntraOp Medical Corporation. CLW is consulting on software development with the IntraOp Medical Corporation.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Seasonal controls on dynamic hyporheic zone redox biogeochemistry

Hyporheic zones (where surface water and groundwater mix in streambeds) play a critical role in the movement of chemicals within watersheds, particularly in upland headwater streams. Seasonal hydrology controls the expansion and contraction of hyporheic zones, with greater downwelling oxic river water influence during periods of peak river discharge (linked to snowmelt), and greater influence of upwelling anoxic groundwater during periods of low- and base-flow. Through this work, we aimed to quantify upwelling and downwelling fluxes across seasons and assess the impact of these dynamics on microbial community assembly and geochemical gradients at East River, Colorado. Further, we performed high spatial resolution sampling to capture heterogeneous mixing patterns around a characteristic meander on the river and developed reactive transport models to explain seasonal patterns of manganese (Mn) cycling in riverbed sediments. We were able to measure a dominant period of downwelling river water associated with high river discharge in Spring 2017 that led to the mixing of microbial communities across a 60-cm depth profile through the riverbed at three locations around the meander, and higher rates of aerobic respiration via delivery of dissolved oxygen (O2) and dissolved organic carbon (DOC). Conversely, we demonstrated that depth-resolved microbial communities became more distinct and stratified across a depth gradient during periods of low- and base-flow when upwelling groundwater exerted more influence in the riverbed. We also observed an increase in Mn concentrations within deeper streambed sediments during the baseflow season. Reactive transport models were developed to understand seasonal changes in Mn cycling in the streambed because Mn oxides influence the mobility of other metals in streams. In field observations and models, dissolved Mn is flushed from the streambed during spring snowmelt. A shift to upwelling conditions over the subsequent baseflow period allows for groundwater rich in dissolved Mn to mix with oxygenated river water in the shallow subsurface, resulting in net accumulation of Mn-oxides until the bed freezes in winter. We also developed models for hydrograph scenarios with snowmelt events of various size and timing. Our scenarios suggest that in years with less snowpack and a longer baseflow season, Mn oxidation will be favored in the upper riverbed sediments over more of the year, which may increase the sorption capacity of the streambed for other metals. Finally, we assessed fine-scale geochemical and hydrologic heterogeneity in the meandering riverbed by sampling pore water at 20 cm depth across more than 100 locations in August 2018. Sample locations span distinct zones of up-welling groundwater that contrast against dominant down-welling conditions. Clear differences in carbon quality and concentrations of redox sensitive solutes were detected between these zones, highlighting the importance of understanding heterogeneity at small scales across streambeds. Geochemical differences were associated with differences in microbial assemblage, but microbial diversity was generally uniform and high across all locations at the same depth, regardless of the extent of groundwater influence.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Zero Trust Strategies for Chemical, Biological, Radiological, and Nuclear Detection Systems: D.1 Cyber Scenarios

The evolving landscape of cybersecurity necessitates a paradigm shift to a Zero Trust (ZT) model, which assumes breaches and continuously verifies trust. This approach reshapes how trust boundaries are established, focusing on identities, devices, networks, applications, and data, rather than solely relying on perimeter defenses such as firewalls. Central to this transformation is the National Institute of Standards and Technology's (NIST) Special Publication 800-207, outlining the Zero Trust Architecture (ZTA), along with Executive Order 14028, which mandates federal agencies to adopt ZT principles. Complementary to these efforts, the Cybersecurity and Infrastructure Security Agency (CISA) developed the Zero Trust Maturity Model (ZTMM), providing a framework with five pillars and three cross-cutting capabilities to guide agencies toward enhanced cybersecurity maturity. In support of these initiatives, the DHS Countering Weapons of Mass Destruction Office (CWMD) is applying ZT principles to secure Chemical, Biological, Radiological, and Nuclear (CBRN) detection systems. Recognizing the diverse deployment models and network connectivity of these systems—from stationary, non-networked units to mobile, cloud-connected devices—the Pacific Northwest National Laboratory (PNNL) is developing cybersecurity scenarios specifically for CBRN environments. These scenarios examine various configurations and technological capabilities, offering insights into the application of ZTMM pillars in enhancing the security postures of CBRN devices. The cybersecurity scenarios presented by PNNL are hypothetical, crafted to explore theoretical situations and stimulate discussion on the potential use or compromise of CBRN detection systems in varied contexts. These narratives are illustrative and do not reference any real events or actual networks. Instead, they employ generalized reference models to highlight concepts and potential issues within CBRN security, focusing on how Zero Trust strategies can be adapted to address these challenges effectively.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2019

Port Hope Area Initiative: Information Management for A Very Large Radiation Survey - 19567

The Port Hope Area Initiative (PHAI) represents the Government of Canada's commitment to the cleanup and safe, local, long-term management of historic low-level radioactive waste (LLRW) in two Southern Ontario municipalities - Port Hope. The PHAI is based on community-recommended solutions for the cleanup and safe long-term management of approximately 1.7 million cubic meters of LLRW. It is currently one of Canada's largest environmental remediation projects at a cost of over one billion dollars. The PHAI is being implemented by Canadian Nuclear Laboratories (CNL) on behalf of Atomic Energy of Canada Limited, a federal Crown corporation. The waste is the result of radium and uranium processing in Port Hope by the former federal crown corporation Eldorado Nuclear Limited between 1933 and 1988. The waste has been distributed in the Municipality of Port Hope through placement of contaminated materials (e.g. fill), reuse of contaminated objects (e.g. tailings use in concrete, steel posts, structural wood) and air-borne deposition. Signature contaminants of potential concern (COPCs) are Ra-226, Th-230, uranium and arsenic; however, there are an additional 17 COPCs that also need to be considered. A very large amount of information has been collected during the survey and effectively managing and analyzing this information is critical to the success for the property owners, CNL's implementation of the PHAI, and Arcadis. Data collected during the survey includes information pertaining to long-term interior radon gas measurements, interior gamma radiation surveys, contaminated objects and surfaces, exterior gamma radiation and subsurface investigations. exterior gamma radiation surveys. The information management (IM) is structured with SharePoint as a focal point supported by an EQuIS database, mobile data collection and GIS support. The IM process for this project is being advanced using recent software developments to assist with internal activities (e.g. QA) and interactions with CNL including notifications and scheduling and status of property completion. Different applications have been developed to help automate, visualize, actively track, and QA different processes using recent software developments by Microsoft. We have used SharePoint to organize and track our data. Power BI has been used to actively track and visualize our data from Interior gamma surveying to exterior drilling. The creation of dynamic/advanced visualizations in Power BI gathers data daily from our multiple sources and analyzes it for anomalous data and trends. Microsoft Flow syncs with Power BI and has been implemented to automate many processes including the QA of surveys and soil results for identification of LLRW. To tie everything together, our GIS and EQuIS systems are our primary data compilation source that makes our advanced analytics/visualization and automation/tracking our data possible. Specific examples will be discussed, as will challenges encountered during the continuing development of the IM system. The incorporation of software advancements to the existing IM system for Port Hope characterization has improved the quality of the surveys, the tracking of completion, meeting schedule requirements and providing the required data for tracking, report preparation and supporting remediation of contamination on the properties. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

Development of Gamma Background Radiation Digital Twin with Machine Learning Algorithms: Application of Unsupervised Machine Learning to Detection of Anomalies and Nuisances in Gamma Background Radiation Environmental Screening Data

Environmental screening of gamma radiation consists of detecting weak nuisance and anomaly signal in the presence of strong and highly varying background. In a typical scenario, a mobile detector-spectrometer continuously measures gamma radiation spectra in short, e.g., one-second, signal acquisition intervals. The measurement data is a 2D matrix, where one dimension is gamma ray energy, and the other dimension is the number of measurements or total time. In principle, gamma radiation sources can be detected and identified from the measured data by their unique spectral lines. Detecting sources from data measured in a search scenario is difficult due to the highly varying background because of naturally occurring radioactive material (NORM), and low signal-to-noise ratio (S/N) of spectral signal measured during one-second acquisition intervals. The objective of this work is to explore unsupervised machine learning (ML) algorithms for development of a digital twin of gamma radiation background, and for detection and identification of weak nuisances and anomalies events in the presence of highly fluctuating background. In one segment of work, we developed a gamma background estimation model using a Longshort term memory (LSTM) network for one-step CPS time series prediction. The LSTM model was validated with two data sets of measurements from two independent NaI detectors positioned on a mobile platform. The data sets contained background radiation only and no orphan isotope sources. The LSTM model was constructed and tested using data from one of the detectors. Performance of the LSTM model was validate through one-step prediction of CPS time series of another NaI detector without re-training. This approach allows to create a digital twin for nuclear background estimation. Using LSTM, it could be possible to detect a source through subtraction of the estimated counts from the measured background. In another segment of work, we investigated detection of gamma emitting sources in the presence of complex background using unsupervised machine learning. Spectral lines of isotopes are difficult to observe in one-second measurements. Averaging over the entire measurement campaign data set reveals spectral lines of most common background isotopes. Spectral lines of orphan sources, which might appear only in a few measurements during the campaign, will be washed out if averaging is performed over the entire measurement data set. The approach we have explored consists of extracting one-second measurements containing weak spectral features through data clustering. Averaging one-second spectra in a cluster should reveal the presence of anomaly sources. We created two ML models using K-means clustering and Neural Network Self-organizing Map (SOM). Performance of these ML models was benchmarked using search data. One data set contained 137 Cs source, and another dataset contained 131 I source.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Demand-Side Grid (dsgrid) TEMPO Light-Duty Vehicle Charging Profiles v2022

Simulated hourly electric vehicle charging profiles for light-duty household passenger vehicles in the contiguous United States, 2018-2050. Profiles are differentiated by scenario, county, household and vehicle types, and charging type. Data was produced in 2022 using the Transportation Energy & Mobility Pathway Options (TEMPO) model and published in demand-side grid (dsgrid) toolkit format. Data are available for three adoption scenarios: "AEO Reference Case", which is aligned with the U.S. EIA Annual Energy Outlook 2018 (linked below), "EFS High Electrification", which is aligned with the High Electrification scenario of the Electrification Futures Study (linked below), and "All EV Sales by 2035", which assumes that average passenger light-duty EV sales reach 50% in 2030 and 100% in 2035. The charging shapes are derived from two key assumptions of which data users should be aware: "ubiquitous charger access", meaning that drivers of vehicles are assumed to have access to a charger whenever a trip is not in progress, and "immediate charging", meaning that immediately after trip completion, vehicles are plugged in and charge until they are either fully recharged or taken on another trip. These assumptions result in a bounding case in which vehicles' state of charge is maximized at all times. This bounding case would minimize range anxiety, but is unrealistic from the point of view of both electric vehicle service equipment (EVSE) (i.e., charger) access, and plug-in behavior as it can result in dozens of charging sessions per week for battery electric vehicles (BEVs) that in reality are often only plugged in a few times per week.

Array↗
DOE OSTI2016

Recombination in liquid-filled ionization chambers beyond the Boag limit

Purpose: The high mass density and low mobilities of charge carriers can cause important recombination in liquid-filled ionization chambers (LICs). Saturation correction methods have been proposed for LICs. Correction methods for pulsed irradiation are based on Boag equation. However, Boag equation assumes that the charge ionized by one pulse is fully collected before the arrival of the next pulse. This condition does not hold in many clinical beams where the pulse repetition period may be shorter than the charge collection time, causing overlapping between charge carriers ionized by different pulses, and Boag equation is not applicable there. In this work, the authors present an experimental and numerical characterization of collection efficiencies in LICs beyond the Boag limit, with overlapping between charge carriers ionized by different pulses. Methods: The authors have studied recombination in a LIC array for different dose-per-pulse, pulse repetition frequency, and polarization voltage values. Measurements were performed in a Truebeam Linac using FF and FFF modalities. Dose-per-pulse and pulse repetition frequency have been obtained by monitoring the target current with an oscilloscope. Experimental collection efficiencies have been obtained by using a combination of the two-dose-rate method and ratios to the readout of a reference chamber (CC13, IBA). The authors have also used numerical simulation to complement the experimental data. Results: The authors have found that overlap significantly increases recombination in LICs, as expected. However, the functional dependence of collection efficiencies on the dose-per-pulse does not change (a linear dependence has been observed in the near-saturation region for different degrees of overlapping, the same dependence observed in the nonoverlapping scenario). On the other hand, the dependence of collection efficiencies on the polarization voltage changes in the overlapping scenario and does not follow that of Boag equation, the reason being that changing the polarization voltage also affects the charge collection time, thus changing the amount of overlapping. Conclusions: These results have important consequences for saturation correction methods for LICs. On one hand, the two-dose-rate method, which relies on the functional dependence of the collection efficiencies on dose-per-pulse, can also be used in the overlapping situation, provided that the two measurements needed to feed the method are performed at the same pulse repetition frequency (monitor unit rate). This result opens the door to computing collection efficiencies in LICs in many clinical setups where charge overlap in the LIC exists. On the other hand, correction methods based on the voltage-dependence of Boag equation like the three-voltage method or the modified two-voltage method will not work in the overlapping scenario due to the different functional dependence of collection efficiencies on the polarization voltage.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Review of Authentication Strategies and Trends for Distributed Energy Resources (DERs)

In this study we review literature on machine to machine (M2M) authentication and encryption pertaining to communication with grid-attached power inverters. We regard security recommendations from NIST, constrained device recommendations from CoAP, as well as influences from the existing markets. We will not focus on passwordless or multifactor schemes of user authentication, the handover/roaming authentication of mobile systems, or the group authentication of WiMAX/LTE communications. The de-facto standards for authentication and encryption are certificate-based public key cryptography and AES, respectively. While certificate-based public key cryptography is widely adopted, certificate management is seen as an Achilles heel of public key infrastructure (PKI). State of the art authentication system research includes work on certificateless authentication; however, much work in the areas of privacy preservation, efficient or lightweight systems continue to be based in public key methods. We will see efforts such as bilinear pairing, aggregate message authentication codes, one-time signatures, and Merkle trees surface and resurface with improved authentication approaches. Though research continues to produce new encryption schemes, AES prevails as a viable choice, as it can be implemented across a variety of resource constrained devices. Other lightweight encryption algorithms often employ the same fundamental addition-rotation-xor operations as AES while achieving higher efficiency, but at steep tradeoffs to security. Despite mathematical proofs of the security of cryptographic algorithms, in practice the greatest weaknesses continue to be incurred during implementation. Security researchers will find edge cases and bugs that allow unintentional behavior. In the following sections, accepted methodologies of authentication and encryption are discussed. Due diligence for securing M2M communications requires consideration during planning, design, implementation and product lifetime, as opposed to a set-it and forget-it policy. Best practices can be gleaned from published successes and failures, with no single end-all, be-all detailed solution.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

SU-D-204-07: Comparison of AAPM TG150 Draft Image Receptor Tests with Vendor Automated QC Tests for Five Mobile DR Units

Purpose: To determine the consistency of digital detector (DR) tests recommended by AAPM TG150 and tests provided by commercially available DirectView Total Quality Tool (TQT). Methods: The DR tests recommended by the TG150 Detector Subgroup[1] were performed on 4 new Carestream DRX-Revolution and one Carestream DRX1C retrofit of a GE AMX-4 that had been in service for three years. After detector calibration, flat-field images plus images of two bar patterns oriented parallel and perpendicular to the A-C axis, were acquired at conditions recommended by TG150. Raw images were harvested and then analyzed using a MATLAB software previously validated[2,3,4]. Data were analyzed using ROIs of two different dimensions: 1) 128 x 128 ROIs matching the detector electronics; and 2) 256 x 256 ROIs, each including 4 adjacent smaller ROIs. TG150 metrics from 128 x 128 ROIs were compared to TQT metrics, which are also obtained from 128 x 128 ROIs[5]. Results: The results show that both TG150 and TQT measurements were consistent among these detectors. Differences between TG150 and TQT values appear systematic. Compared with 128 x 128 ROIs, noise and SNR non-uniformity were lower with 256 x 256 ROIs, although signal non-uniformity was similar, indicating detectors were appropriately calibrated for gain and offset. MTF of the retrofit unit remained essentially the same between 2012 and 2015, but was inferior to the new units. The older generator focal spot is smaller (0.75mm vs. 1.2mm), and the SID for acquisition is 182cm as well, so focal spot dimensions cannot explain the difference. The difference in MTF may be secondary to differences in generator X-ray spectrum or by unannounced changes in detector architecture. Further investigation is needed. Conclusion: The study shows that both TG150 and TQT tests are consistent. The numerical value of some metrics are dependent on ROI size.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

Non-Invasive Electrode Impedance Estimation for Optimized Charge Profile Parameterization of Lithium-Ion Batteries

This work presents a non-invasive method for parameterizing a physically motivated equivalent circuit model of lithium-ion batteries using operando electrochemical impedance spectroscopy and time-domain data. The proposed model consists exclusively of linear circuit elements, enabling computationally efficient simulation and real-time implementation on battery management system chips. By integrating frequency- and time-domain measurements, the model accurately estimates internal states such as the negative electrode potential, achieving a root mean square error of 12.3 mV during fast charging validation. Parameterization requires only rate tests with sinusoidal perturbations at three different ambient temperatures, making the approach experimentally accessible. The model reveals key insights into battery behavior, including rate-dependent overpotentials primarily governed by charge transfer kinetics at the positive electrode, and temperature-dependent impedance contributions from both charge transfer and solid-state diffusion processes. Validation using reference electrodes confirms the model’s ability to detect lithium plating onset and reproduce impedance behavior across a wide range of operating conditions. The approach enables in situ optimization of fast charging profiles and lays the foundation for future extensions incorporating aging effects and plating dynamics.

25 ENERGY STORAGE↗
DOE OSTI2021

Detection of Anomalies in Gamma Background Radiation Data with K-Means and Self-Organizing Map Clustering Algorithms (Consortium on Nuclear Security Technologies (CONNECT) Q1 Report)

Environmental screening of gamma radiation consists of detecting weak nuisance and anomaly signal in the presence of strong and highly varying background. In a typical scenario, a mobile detector-spectrometer continuously measures gamma radiation spectra in short, e.g., one-second, signal acquisition intervals. The measurement data is a 2D matrix, where one dimension is gamma ray energy, and the other dimension is the number of measurements or total time. In principle, gamma radiation sources can be detected and identified from the measured data by their unique spectral lines. Detecting sources from data measured in a search scenario is difficult due to the highly varying background because of naturally occurring radioactive material (NORM), and low signal-to-noise ratio (S/N) of spectral signal measured during one-second acquisition intervals. The objective of this work is to explore unsupervised machine learning (ML) algorithms for detection and identification of weak nuisances and anomalies events in the presence of highly fluctuating background. The challenge is that spectral lines of isotopes are difficult to observe in one-second measurements. Averaging over the entire measurement campaign data set reveals spectral lines of most common background isotopes. Spectral lines of orphan sources, which might appear only in a few measurements during the campaign, will be washed out if averaging is performed over the entire measurement data set. The approach we have explored consists of extracting one-second measurements containing weak spectral features through data clustering. Averaging one-second spectra in a cluster should reveal the presence of anomaly sources. We created two ML models using K-means clustering and Neural Network Self-organizing Map (SOM). Performance of these ML models was benchmarked using search data. One data set contained 137 Cs source, and another dataset contained 131 I source.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2024

Learning From User Behavior: A Survey-Assist Algorithm for Longitudinal Mobility Data Collection

GPS-based travel surveys are widely used in mobility studies to gather crucial qualitative data, like purpose, transportation mode and replaced mode. However, survey response still poses a burden to users, especially in long-term mobility studies, leading to response fatigue. We explore a survey-assist strategy to ease this burden by a novel, user-level modeling approach that leverages past responses from each user to predict responses for new trips, without relying on external data sources like GIS data. We investigate three main algorithms for predicting responses: (i) clustering trips and extrapolating responses for similar trips, (ii) using random forest classification, and (iii) clustering that uses a hybrid algorithm to determine spatial structure, which is then fed as input to a classic random forest classifier. The clustering approach can flexibly predict responses for even complex qualitative survey questions; it achieved F-scores of 65%. The random forest pipeline uses architecture that restricts it to predicting three predetermined survey questions: trip purpose, mode, and replaced mode. However, it achieved F-scores of 78%. While the survey-assist approach has been implemented by several proprietary systems, to our knowledge, this is the first exploration in the academic literature. It follows that this is also the first rigorous evaluation of multiple algorithms that can implement the approach. The evaluation uses a large scale, publicly available, longitudinal dataset consisting of ~ 92k trips from 235 users over a period of roughly one and a half years. With this approach, travel surveys can be pre-filled with the predicted responses for each trip, thus streamlining the survey process for users. Combined with an active learning system that requests user input on low-confidence predictions, models can be updated and improved over time to better support the long-term collection of longitudinal qualitative data.

clustering↗
DOE OSTI2015

SU-E-T-651: Quantification of Dosimetric Accuracy of Respiratory Gated Stereotactic Body Radiation Therapy

Purpose: To quantify the dosimetric accuracy of respiratory gated stereotactic body radiation therapy delivery using dynamic thorax phantom. Methods: Three patients with mobile target (2 lung, 1liver) were chosen. Retrospective 4DCT image sets were acquired for using Varian RPM system. An in-house MATLAB program was designed for MIP, MinIP and AvgIP generation. ITV was contoured on MIP image set for lung patients and on MinIP for liver patient. Dynamic IMRT plans were generated on selected phase bin image set in Eclipse (v10.0) planning system. CIRS dynamic thorax phantom was used to perform the dosimetric quality assurance. Patient breathing pattern file from RPM system was converted to phantom compatible file by an in-house MATLAB program. This respiratory pattern fed to the CIRS dynamic thorax phantom. 4DCT image set was acquired for this phantom using patient breathing pattern. Verification plans were generated using patient gating window and delivered on the phantom. Measurements were carried out using with ion chamber and EBT2 film. Exposed films were analyzed and evaluated in FilmQA software. Results: The stability of gated output in comparison with un-gated output was within 0.5%. The Ion chamber measured and TPS calculated dose compared for all the patients. The difference observed was 0.45%, −0.52% and −0.54 for Patient 1, Patient2 and Patient 3 respectively.Gamma value evaluated from EBT film shows pass rates from 92.41% to 99.93% for 3% dose difference and 3mm distance to agreement criteria. Conclusion: Dosimetric accuracy of respiratory gated SBRT delivery for lung and liver was dosimetrically acceptable. The Ion chamber measured dose was within 0.203±0.5659% of the expected dose. Gamma pass rates were within 96.63±3.84% of the expected dose.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2024

FIU Project 2: Environmental Remediation Science & Technology [Slides]

FIU’s research under this project involves conducting basic and applied science to fill knowledge gaps and validate potential remediation technologies for contaminated soil and groundwater and the assessment of the fate and transport of contaminants in the environment. The aim of FIU’s research is to reduce the potential for contaminant mobility or toxicity in the surface and subsurface through the development and application of state-of-the-art scientific and environmental remediation technologies at the Hanford Site, Savannah River Site (SRS), and the Waste Isolation Pilot Plant (WIPP), which is the Nation’s only mined geologic repository for permanent disposal of transuranic waste. FIU collaborates with scientists from Pacific Northwest National Laboratory (PNNL), Savannah River National Laboratory (SRNL), Savannah River Ecology Laboratory (SREL), Los Alamos National Laboratory (LANL) and the DOE Carlsbad Field Office (CBFO) in order to plan and execute research that is synergistic with the work being conducted at the sites, and that supports the resolution of critical science and engineering needs which leads to a better understanding of the long-term behavior of subsurface contaminants. The knowledge gained through this research will be used to transform experimental and modeling innovations into practical applications deployed at the sites to support EM’s primary goal of expediting the closure of major contaminated soil and groundwater sites and waste units. Collaborative relationships between FIU and the national laboratories have provided large benefits over the years to FIU, the national laboratories, the DOE complex, and the DOE EM mission. By working closely with the national laboratories, FIU’s research is not only closely aligned with the cleanup mission priorities at the DOE sites, but complements and supports ongoing work at the national laboratories for screening of new remedial technologies. This coordination and leveraging of research efforts results in time- and cost-savings, and will accelerate progress of the DOE EM environmental restoration mission.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗