Communicating about Contaminated Site Cleanup using Coordinated and Consistent Metrics: Opportunity and Challenge for the U.S. Department of Energy
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Mathematical Methods In Social Sciences↗
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Update on NREL Outdoor Exposure Campaign of Solar Mirrors
Beginning in 2018, the National Renewable Energy Laboratory restarted exposure campaigns on new and archived samples as part of a multiyear project, some with outdoor exposure for more than a decade. By resuming exposure and collecting and analyzing data on thousands of samples going back decades, several goals can be advanced that can be difficult to determine within the timeline of most projects: 1) correlating an accelerated exposure campaign to outdoor aging, specifically with xenon arc lamp exposure chambers; 2) drawing conclusions between specific corrosion mechanisms and weather patterns; and 3) finding novel relationships between mirror composition and performance. In addition to building and mining a database, we will experiment with new characterization techniques, primarily focused on macroscopic and microscopic imaging. In introducing these techniques more broadly, it may be possible to reveal a more direct line between optical performance and exposure campaigns by better understanding the degradation mechanisms occurring.
characterization techniques↗
SU-E-E-02: Dashboard for Tracking Physics Resident Progress
Purpose: Design a system to easily and securely track the progress of medical physics residents through their residency. Paper sign-offs while offering a real signature are not easily updated or summarized. A resident or mentor needs to be able to quickly assess what the current assignments are, what are overdue, and whether the resident is on track to complete all the tasks in a timely fashion. An electronic version can accomplish all these goals. Methods: An electronic dashboard was created in excel to not only house the tasks and sign-off but to succinctly summarize the residents progress. The first tab contains the dashboard which displays tables of the progress of the residents in each rotation, their current task, and overdue tasks. It also displays the last meetings with the residents, and timeline of important items, and a burn-down chart of the remaining tasks. This are all tied to the data and current date which auto fills the tables. The second tab contains the data. This is comprised of lists of rotations and their associated tasks along with their due dates. A signature column was also created which is password protected but allows special subset users i.e. mentors to alter without using a password. Results: The dashboard has allowed residents to better track their progress and tells them what they should be working on. It has also allowed the mentors and the program director to rapid assess their progress. Conclusion: The dashboard is successful and has been created to allow easy addition and subtraction of required tasks as the residency evolves. The next step is to create a web app version of the excel sheet with logins.
61 RADIATION PROTECTION AND DOSIMETRY↗
WE-E-BRD-01: HDR Brachytherapy I: Overview of Clinical Application and QA
With the increased usage of high dose rate (HDR) brachytherapy and the introduction of dedicated image guided brachytherapy suites, it is necessary to review the processes and procedures associated with safely delivering these treatments in the expedited time scales that dedicated treatment suites afford. The speakers will present the clinical aspects of switching from LDR to HDR treatments, including guidelines for patient selection, and the clinical outcomes comparing LDR to HDR. The speakers will also discuss the HDR treatment process itself, because the shortened clinical timeline involved with a streamlined scan/plan/treat workflow can introduce other issues. Safety and QA aspects involved with the streamlined process, including increased personnel required for parallel tasks, and possible interfering tasks causing delays in patient treatments will also be discussed. Learning Objectives: To understand the clinical aspects of HDR Brachytherapy, including common clinical indications, patient selection, and the evolving evidence in support of this therapeutic modality To review the current prominent clinical trials for HDR brachytherapy To interpret the established guidelines for HDR brachytherapy quality assurance for implementation into practical clinical settings. To introduce the basic requirements for image guided brachytherapy.
60 APPLIED LIFE SCIENCES↗
SU-E-T-575: Novel Quality Assurance Procedure for the Small Animal Radiation Research Platform Using Commercial OSLDs
Purpose: To support radiobiological research with the Xstrahl small animal radiation research platform (SARRP) by developing a simple and effective method using commercially available optically stimulated luminescent dosimeters (OSLDs) that ensures dose output consistency. Methods: The SARRP output is calibrated according to the vendor standards and TG-61 protocol utilizing an ADCL calibrated ion chamber and electrometer at 2 cm depth of solid water. A cross calibration is performed by replacing the ion chamber with five OSLDs at the 2 cm depth. The OSLDs are irradiated to 500 cGy with 220 keV at 13 mA (78s delivery time) with a copper filter for an uncollimated 17×17 cm{sup 2} aperture. Instead of the absolute dose, the total amount of raw counts are collected from the OSLD reader and used for analysis. This constancy procedure was performed two more times over the course of three weeks with two OLSDs for validity. Results: The average reading for all OSLDs is 494939 with a 1-sigma standard deviation of the 5.8%. With an acceptable dose output range of ±10%, the OSLD readings have a counts range of [445445, 544433]. Conclusion: This method of using nanoDot™ OSLDs to perform output constancy checks for the SARRP ensures the output of the device is within 10% from the time of calibration and is convenient as well as time efficient. Because this, the frequency of output checks can be increased, which can improve the output stability for research with this device. The output trend of the SARRP will continue to be monitored in the future to establish a timeline for constancy checks and recalibration.
60 APPLIED LIFE SCIENCES↗
MO-FG-213-04: Managing the Relationship
Academic-industrial collaborations are playing an increasingly prominent role in medical physics research. Successful partnerships have enabled truly translational, benchtop-to-bedside research that has significantly and positively impacted our field. Given the current flat or depleting trends in federal funding, and the increasing emphasis in the medical physics community on demonstrating translatability, efficacy, and safety prior to widespread clinical use, it is anticipated that such partnerships will become ever more important in advancing our field. In the future, it is likely that neither academia alone, nor industry alone, will be able to bring significant innovation to successfully impact patient care in radiotherapy. Such innovation will require partnership and symbiosis through the lifetime of the project. This symposium is aimed at professionals from academia and the industry who are interested in either initiating new collaborations or maintaining and improving existing partnerships. We will hear from four thought leaders from industry and academia on critical aspects of building and maintaining successful academic-industrial research partnerships. The following topics will be addressed: 1. Motivations: The industry perspective [Kevin Brown, Elekta] What goes into the development of a company’s R&D roadmap What are the motivations and expectations of the industry from academic partners How can academics make a successful pitch by identifying projects aligned with a company’s roadmap Why are some relationships successful and some not Milestones and progress reports - Why do we need them 2. Motivations: The academic perspective [T. Rock Mackie, Univ. of Madison, WI; Tomotherapy] What are the motivations and expectations of academic partners from the industry What are the cultural differences in academia vs industry on sharing information, exchanging ideas Partnership or entrepreneurship? When does each option make sense 3. Industrial Grants and Research Contracts – a Primer [Norbert Pelc; Stanford University] What topics are suited to industrial grants vs. academic grants How do resources (personnel effort, tuition) get allocated and charged in academia What are reasonable resource and timeline requests for small and big projects; from small and big companies “Fair market value” considerations Sections of a research agreement (IP including software confidential information, publications, data, exclusivity, support) 4. Managing the relationship [John Wong, Johns Hopkins University] How to effectively collaborate with each other How to adapt to changes in the relationship How to renew funding with the industry Moderated Panel Discussion [All speakers + moderators] Learning Objectives: 1. Understand the motivations of each partner 2. Elements that contribute to a successful partnership 3. Avoiding pitfalls - Challenges on either side that may derail a project 4. How to frame a research grant and agreement 5. How to sustain the collaborative relationship Amit Sawant: Funding from: NIH. Varian, Elekta and VisionRT; Michelle Svatos: Employee and shareholder: Varian, Board Member, TopasMC; Kevin Brown: Employee, Elekta; T. Rock Mackie: Consultant, Accuray; Norbert Pelc: Research support: Philips Healthcare, GE Healthcare; John Wong: Funding from NIH, Xstrahl and Elekta. Co-founder and advisor for JPLC Associates, LLC.
07 ISOTOPE AND RADIATION SOURCES↗
Designing Size Consistent Statistics for Accurate Anomaly Detection in Dynamic Networks
An important task in network analysis is the detection of anomalous events in a network time series. These events could merely be times of interest in the network timeline or they could be examples of malicious activity or network malfunction. Hypothesis testing using network statistics to summarize the behavior of the network provides a robust framework for the anomaly detection decision process. Unfortunately, choosing network statistics that are dependent on confounding factors like the total number of nodes or edges can lead to incorrect conclusions (e.g., false positives and false negatives). In this article, we describe the challenges that face anomaly detection in dynamic network streams regarding confounding factors. We also provide two solutions to avoiding error due to confounding factors: the first is a randomization testing method that controls for confounding factors, and the second is a set of size-consistent network statistics that avoid confounding due to the most common factors, edge count and node count.
97 MATHEMATICS AND COMPUTING↗
RAVIS: Resource Forecast and Ramp Visualization for Situational Awareness of Variable Renewable Generation
The Resource Forecast and Ramp Visualization for Situational Awareness (RAVIS) is an innovative, open-source tool for visualizing variable renewable resource forecasts and alerts for significant up- and down-ramps in renewable generation and the consequent system net load. The modular dashboard of RAVIS contains configurable panes for viewing probabilistic time-series forecasts, ramp event alerts on the look-ahead timeline, and spatially resolved resource sites, and forecasts. For comprehensive situational awareness, the tool can add additional data layers from simulation and independent system operator (ISO) market clearing data---including electric line utilization, nodal price, and available generation flexibility---in response to continuously updated renewable forecasts. This paper introduces the RAVIS technology suite employed to provide optimum visualization and flexible design characteristics. The paper also illustrates some use cases of the tool using site-specific solar power forecast data obtained from the IBM Watt-Sun forecasting platform for the California ISO and Midcontinent ISO footprints. The source code for RAVIS is public (https://github.com/ravis-nrel/ravis), and the intended users are forecasters, utility planners, ISO operators, and researchers.
41 EE - Solar Energy Technologies Office (EE-4S)↗
Resource Forecast and Ramp Visualization for Situational Awareness (RAVIS)
The Resource Forecast and Ramp Visualization for Situational Awareness (RAVIS) is an open-source tool for visualizing variable renewable resource forecasts and ramp alerts for significant up/down ramps in renewable resource and the consequent net-load. The modular dashboard of RAVIS contains configurable panes for viewing- probabilistic time series forecasts, ramp event alerts on the look-ahead timeline, spatially resolved resource sites and forecasts, and system simulation and market clearing data such as transmission lines utilization, nodal prices and available generation flexibility.
Krishnan, Venkat↗
ManiPIO - Manipulate Process I/O
SAND2021-15053 O ManiPIO is a Python-based tool used to test Programmable Logic Controllers (PLCs), Industrial Control Systems (ICS), and ICS networks. It reads an input script to construct complex Events on ICS networks and uses the ModBus communication standard to communicate. Users can use ManiPIO to construct complex timelines of ICS communication events to simulate any number of network-based scenarios. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.
Hahn, Andrew↗
FLOW BATTERY COST AND COMMERCIALIZATION TOOL (FlowBaCC) v.1.0
The Flow Battery Cost and Commercialization Tool (FlowBaCC) is a python-based framework used to project the costs and commercialization timelines for redox flow batteries. FlowBaCC combines learning curves for component costs and performance with adoption curves for market diffusion to project future system costs. The tool uses the bottom-up, spreadsheet-based Battery Performance and Cost Model for Flow Batteries (BatPaC-Flow) as the engine to translate learning curve inputs into full system costs. FlowBaCC provides capital costs and levelized costs of energy storage. The tool enables scenario analysis, sensitivity analysis, and uncertainty quantification (via grid and Monte Carlo methods).
Fu, Xiaoxu [Argonne National Laboratory (ANL), Arg↗
Evaluating the Interplay between Trajectory Segmentation and Mode Inference Error
Travel behavior changes are essential to transportation decarbonization. Travel diaries, consisting of sequences of trips between places, are typically used to instrument human travel behavior. However, these diaries are only as accurate as the underlying methods used to construct them. Travel diary algorithms have been a popular research topic since the advent of Global Positioning System tracking surveys. These algorithms have typically been validated using prompted recall of presegmented trips, thus disregarding the continuity of mode inference. Phone operating systems have adopted battery-conserving techniques, but the resulting data collection errors have not been studied extensively. We introduce a framework to evaluate the accuracy of trip length computations and mode inference by analyzing continuous mode-segmented trajectories for groups of trips. We then use the framework to identify the input data quality and the impact of postprocessing. Our primary inputs to this evaluation are MobilityNet, a public dataset containing information from three artificial timelines covering 15 different travel modes, and sample open-source travel diary creation algorithms from the OpenPATH project. Our framework concretely shows that the variance of the distance error drops from (0.217, 0.0848) to (o.011, 0.0407) (Android, iOS) after postprocessing. Similarly, the weighted F-scores for mode inference increase from (0.25, 0.29) to (0.60, 0.74) (iOS, Android) between random forest and geographic information system-based models. We hope that this standardized method will be adapted to evaluate other, potentially proprietary, travel diary algorithms. Finally, the results can be used to understand and improve the state of the art in the travel diary creation field.
33 ADVANCED PROPULSION SYSTEMS↗
Zinc against COVID-19? Symptom surveillance and deficiency risk groups
A wide variety of symptoms is associated with Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection, and these symptoms can overlap with other conditions and diseases. Knowing the distribution of symptoms across diseases and individuals can support clinical actions on timelines shorter than those for drug and vaccine development. Here, we focus on zinc deficiency symptoms, symptom overlap with other conditions, as well as zinc effects on immune health and mechanistic zinc deficiency risk groups. There are well-studied beneficial effects of zinc on the immune system including a decreased susceptibility to and improved clinical outcomes for infectious pathogens including multiple viruses. Zinc is also an anti-inflammatory and anti-oxidative stress agent, relevant to some severe Coronavirus Disease 2019 (COVID-19) symptoms. Unfortunately, zinc deficiency is common worldwide and not exclusive to the developing world. Lifestyle choices and preexisting conditions alone can result in zinc deficiency, and we compile zinc risk groups based on a review of the literature. It is also important to distinguish chronic zinc deficiency from deficiency acquired upon viral infection and immune response and their different supplementation strategies. Zinc is being considered as prophylactic or adjunct therapy for COVID-19, with 12 clinical trials underway, highlighting the relevance of this trace element for global pandemics. Using the example of zinc, we show that there is a critical need for a deeper understanding of essential trace elements in human health, and the resulting deficiency symptoms and their overlap with other conditions. This knowledge will directly support human immune health for decreasing susceptibility, shortening illness duration, and preventing progression to severe cases in the current and future pandemics.
59 BASIC BIOLOGICAL SCIENCES↗
Longitudinal Assessment of Lung Cancer Progression in Mice Using the Sodium Iodide Symporter Reporter Gene and SPECT/CT Imaging
Lung cancer has the highest mortality rate of any tissue-specific cancer in both men and women. Research continues to investigate novel drugs and therapies to mitigate poor treatment efficacy, but the lack of a good descriptive lung cancer animal model for preclinical drug evaluation remains an obstacle. Here we describe the development of an orthotopic lung cancer animal model which utilizes the human sodium iodide symporter gene (hNIS; SLC5A5) as an imaging reporter gene for the purpose of non-invasive, longitudinal tumor quantification. hNIS is a glycoprotein that naturally transports iodide (I- ) into thyroid cells and has the ability to symport the radiotracer 99mTc-pertechnetate ( 99mTcO4 - ). A549 lung adenocarcinoma cells were genetically modified with plasmid or lentiviral vectors to express hNIS. Modified cells were implanted into athymic nude mice to develop two tumor models: a subcutaneous and an orthotopic xenograft tumor model. Tumor progression was longitudinally imaged using SPECT/CT and quantified by SPECT voxel analysis. hNIS expression in lung tumors was analyzed by quantitative real-time PCR. Additionally, hematoxylin and eosin staining and visual inspection of pulmonary tumors was performed. We observed that lentiviral transduction provided enhanced and stable hNIS expression in A549 cells. Furthermore, 99mTcO4 - uptake and accumulation was observed within lung tumors allowing for imaging and quantification of tumor mass at twotime points. This study illustrates the development of an orthotopic lung cancer model that can be longitudinally imaged throughout the experimental timeline thus avoiding inter-animal variability and leading to a reduction in total animal numbers. Furthermore, our orthotopic lung cancer animal model is clinically relevant and the genetic modification of cells for SPECT/CT imaging can be translated to other tissue-specific tumor animal models.
60 APPLIED LIFE SCIENCES↗
COVID-19 through Adverse Outcome Pathways: Building networks to better understand the disease – 3rd CIAO AOP Design Workshop
On April 28-29, 2021, 50 scientists from different fields of expertise met for the 3rd online CIAO workshop. The CIAO project “Modelling the Pathogenesis of COVID-19 using the Adverse Outcome Pathway (AOP) framework” aims at building a holistic assembly of the available scientific knowledge on COVID-19 using the AOP framework. An individual AOP depicts the disease progression from the initial contact with the SARS-CoV-2 virus through biological key events (KE) toward an adverse outcome such as respiratory distress, anosmia or multiorgan failure. Assembling the individual AOPs into a network highlights shared KEs as central biological nodes involved in multiple outcomes observed in COVID-19 patients. During the workshop, the KEs and AOPs established so far by the CIAO members were presented and positioned on a timeline of the disease course. Modulating factors influencing the progression and severity of the disease were also addressed as well as factors beyond purely biological phenomena. CIAO relies on an interdisciplinary crowdsourcing effort, therefore, approaches to expand the CIAO network by widening the crowd and reaching stakeholders were also discussed. To conclude the workshop, it was decided that the AOPs/KEs will be further consolidated, integrating virus variants and long COVID when relevant, while an outreach campaign will be launched to broaden the CIAO scientific crowd.
60 APPLIED LIFE SCIENCES↗
Data associated with Quaternary Research manuscript Latest Pleistocene glacial chronology and paleoclimate reconstruction for the East River watershed, Colorado, USA
The data here are associated with Quaternary Research manuscript Latest Pleistocene glacial chronology and paleoclimate reconstruction for the East River watershed, Colorado, USA and include data associated with cosmogenic exposure and depth profile dating as well as glacier-climate numerical modeling. Reconstructing Pleistocene glaciation timing and extent is vital for understanding paleoclimate. While late Pleistocene glaciation has been studied extensively in western North American mountain ranges, the glacial history of the western Elk Range in Colorado remains understudied, particularly in the East River watershed, a site of intense scientific focus. Here we use cosmogenic nuclide exposure and depth–profile dating methods to determine the timing of glaciation in the East River watershed. We use glacier modeling to reconstruct paleoglacier extents and quantify past climate conditions. Our findings indicate that the East River glacier retreated from its maximum position approximately 17–18 ka, moving to recessional positions between 13–15 ka, before experiencing more substantial retreat to high-elevation cirques around 12.9 ka. Glacier modeling suggests that the maximum ice extents at 17–18 ka could have been sustained by temperature depressions of approximately −6.5°C compared to modern conditions, assuming consistent precipitation. Additionally, the ice position at 13–15 ka could have been supported by temperature depressions of around −4.0°C. These results offer insights into the deglaciation timeline in the East River watershed and broader western Elk Range as well as paleoclimate conditions during the late Pleistocene, which may aid future research on critical zone evolution in the East River watershed.The data files include: 1. Table 1 In situ-produced 10Be sample data (.pdf, .csv, .xlsx)2. Table 2 In situ-produced 10Be exposure age results (.pdf, .csv, .xlsx)3. S1_A CRONUS calculator input assuming no erosion (.csv)4. S1_B CRONUS calculator input assuming erosion (.csv)5. S1_C CRONUS calculator results and comparison (.csv)6. S2_A In situ-produced 10Be sample data for depth profile (.csv)7. S2_B In situ-produced 10Be depth profile model input (.csv)8. S2_C In situ-produced 10Be depth profile model results (.csv)9. S3 Monthly cloudiness, rH, and windspeed data used in glacier climate model (.csv)10. S4 Glacier climate model results (.csv)
54 ENVIRONMENTAL SCIENCES↗
High-energy x-ray scattering studies of battery materials
High-energy x-ray (HEX) scattering is a sensitive and powerful tool to nondestructively probe the atomic and mesoscale structures of battery materials under synthesis and operational conditions. The penetration power of HEXs enables the use of large, practical samples and realistic environments, allowing researchers to explore the inner workings of batteries in both laboratory and commercial formats. This article highlights the capability and versatility of HEX techniques, particularly from synchrotron sources, to elucidate materials synthesis processes and thermal instability mechanisms in situ, to understand (dis)charging mechanisms in operando under a variety of cycling conditions, and to spatially resolve electrode/electrolyte responses to highlight connections between inhomogeneity and performance. Such studies have increased our understanding of the fundamental mechanisms underlying battery performance. Here, by deepening our understanding of the linkages between microstructure and overall performance, HEXs represent a powerful tool for validating existing batteries and shortening battery-development timelines.
25 ENERGY STORAGE↗
GeoThermalCloud: Machine Learning for Geothermal Resource Exploration
Geothermal is a renewable energy source that can provide reliable and flexible electricity generation for the world. In the past decade, the U.S. Geological Survey's resource assessments, Play Fairway Analyses (PFA), and GeoVision report by the U.S. Department of Energy's Geothermal Technologies Office provided insights on enormous untapped potential for geothermal energy to contribute to the U.S. domestic energy needs. The past studies identified that geothermal resources without surface expression (e.g., blind/hidden hydrothermal systems) comprise a huge potential. These blind systems can significantly increase power generation. But a primary challenge is locating and quantifying these hidden resources, which do not have any thermal manifestations on the surface. PFA has successfully identified some blind systems in the western USA (e.g., specific locations in the Great Basin region within Nevada). However, a comprehensive search for these blind systems can be time-consuming, expensive, and resource-intensive with a low probability of success. Accelerated discovery of these blind resources is needed with growing energy needs and higher chances of exploration success. Recent advances in machine learning (ML) have shown promise in shortening the timeline for this discovery. This paper presents a novel ML-based methodology for geothermal exploration towards PFA applications. Our methodology is provided through our open-source ML framework called GeoThermalCloud \url{https://github.com/SmartTensors/GeoThermalCloud.jl}. GeoThermalCloud uses a series of unsupervised, supervised, and physics-informed ML methods available in SmartTensors AI platform \url{https://github.com/SmartTensors}. Here, the presented analyses are performed using our unsupervised ML algorithm called NMF$k$, which is available in the SmartTensors AI platform. Our ML algorithm facilitates the discovery of new phenomena, hidden patterns, and mechanisms that helps us to make informed decisions. Moreover, the GeoThermalCloud enhances the collected PFA data and discovers signatures representative of geothermal resources. Through GeoThermalCloud, we were able to identify hidden patterns in the geothermal field data needed for the efficient discovery of blind systems. Crucial geothermal signatures often overlooked in traditional PFA are extracted using GeoThermalCloud and analyzed by the subject matter experts to provide ML-enhanced PFA, which is informative for efficient exploration. We applied our ML methodology on various open-source geothermal datasets within the U.S. (some of these are collected by past PFA work), and the results provide valuable insights on resource types within those explored regions. This ML-enhanced workflow makes GeoThermalCloud attractive for the geothermal community to improve existing datasets and extract valuable information often unnoticed during geothermal exploration.
machine learning (ML), geothermal energy↗