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Saltstone First Quarter Calendar Year 2021 (1QCY21) Toxicity Characteristic Leaching Procedure (TCLP) Results

The aqueous waste from Tank 50 (salt solution) is sampled quarterly for transfers to the Saltstone Production Facility (SPF). Salt solution is treated at SPF and disposed of in the Saltstone Disposal Facility (SDF). Per request of customer, X-TTR-Z00023, Revision 0, two SDF waste form (saltstone) samples were prepared in the Savannah River National Laboratory (SRNL) from the Tank 50 Waste Acceptance Criteria (WAC) sample and Z-Area premix material for the first quarter of calendar year 2021(1QCY21). One sample contained a Full Premix which included 10:45:45 (by weight) of cement, slag and fly ash. The second sample contained 60:40 (by weight) of slag and fly ash only referred to as the “Cement-Free grout sample”. Results from this technical report support Task 2: ‘Grout Leaching Analyses’ of the Task Technical Request (TTR) prepared by Savannah River Remediation (SRR). After at least 28 days cured, a sample of each of the SDF waste forms was collected and shipped to a certified laboratory for analysis using the Toxicity Characteristic Leaching Procedure (TCLP). The 1QCY21 saltstone (Full Premix) and the Cement-Free grout samples met the South Carolina (SC) Code of Regulations for Hazardous Waste Management Regulations (HWMR) 61-79.261.24 and 61-79-268.48 requirements for a non-hazardous waste form with respect to Resource Conservation and Recovery Act (RCRA) metals and Underlying Hazardous Constituents (UHCs), and also met the SPF WAC that was in effect at the time of the tank sampling.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Saltstone Third Quarter Calendar Year 2021 (3QCY21) Toxicity Characteristic Leading Procedure (TCLP) Results

The aqueous waste from Tank 50 (salt solution) is sampled quarterly for transfers to the Saltstone Production Facility (SPF). Salt solution is treated at SPF and disposed of in the Saltstone Disposal Facility (SDF). Per request of customer, X-TTR-Z-00023, Revision 0, two SDF waste form (saltstone) samples were prepared in the Savannah River National Laboratory (SRNL) from the Tank 50 Waste Acceptance Criteria (WAC) sample and Z-Area premix material for the third quarter of calendar year 2021(3QCY21). One sample contained a Full Premix which included 10:45:45 (by weight) of cement, slag and fly ash. The second sample contained 60:40 (by weight) of slag and fly ash only referred to as the “Cement-Free grout sample”. Results from this technical report support Task 2: ‘Grout Leaching Analyses’ of the Task Technical Request (TTR) prepared by Savannah River Remediation (SRR). After at least 28 days cured, a sample of each of the SDF waste forms was collected and shipped to a certified laboratory for analysis using the Toxicity Characteristic Leaching Procedure (TCLP). The 3QCY21 saltstone (Full Premix) and the Cement-Free grout samples met the South Carolina (SC) Code of Regulations for Hazardous Waste Management Regulations (HWMR) 61-79.261.24 and 61-79-268.48 requirements for a non-hazardous waste form with respect to Resource Conservation and Recovery Act (RCRA) metals and Underlying Hazardous Constituents (UHCs), and also met the SPF WAC that was in effect at the time of the tank sampling.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Artificial Intelligence for Accelerating Nuclear Applications, Science, and Technology

Artificial intelligence (AI) and machine learning (ML) methods have had significant impacts in science and technology in recent years. These methods for generating models from datasets or logic-based algorithms that emulate aspects of human performance can similarly accelerate the fields of nuclear applications, science, and technology toward the IAEA goals of contributing to peace, health, and prosperity. In order to accomplish advances with AI in general and ML in particular across these fields, IAEA can play a significant role by establishing, hosting and curating centralised resources, including databases, adhering to FAIR (findable, accessible, interoperable and reusable) principles and Open Science best practices, providing stewardship of data sharing, supporting training efforts and development of relevant workforces, as well as enabling connections among the scientific, technology, mathematics, AI and ethics communities. Many areas can benefit from the use of AI in the realm of nuclear applications. In human health, these areas include clinical research, epidemiology, nutrition, medical imaging, radiotherapy and education of health professionals. AI-based tools are also being used to facilitate different clinical tasks in imaging, computer-assisted diagnosis in mammography and lung cancer screening programmes, and dose prediction in nuclear medicine procedures. ML methods in particular may also increase the efficiency and accuracy of the analysis of computerised tomography and dual-energy absorptiometry scans for body composition and bone analysis. The application of AI methods to nuclear and related technologies in food and agriculture can lead to significant advances and improved efficiency in the optimisation of agricultural production, food product development, management of supply chains, food safety and food authenticity control. In the water and environmental sector, AI can help inform policies to mitigate the world’s water problems. The application of AI techniques to hydrology and environmental sciences is expected to improve patterns identification and enable model predictions under a changing climate.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Taming the Wild West: Assessing Impacts-Relevant Climate Data Products (Abbreviated Report)

Impacts-relevant Earth system data refers to observational and ESM data that are downscaled, debiased, validated, and provisioned for use by decision-makers. Impacts-relevant Earth system data is essential for mitigation and adaptation planning across a variety of regions and sectors. A vast number of these data products have emerged in recent years, which has led to confusion among stakeholders and scientists as to the best product to use. With no standard evaluation protocol available for these products, the decision on which product to use was sometimes made because it was pragmatic rather than the best product to use. This project sought to develop foundational capabilities around impacts-relevant data products that would support more informed selection and application of these products. This work has been immensely successful, driving several academic publications and supported the development of a community of practice around impacts-relevant data products. Over the project’s three years we have addressed six tasks: First, the development of standard evaluation metrics for impacts-relevant climate data; second, the development of a novel suite of atmospheric river metrics; third, the development of novel metrics for precipitation feature analysis; fourth, the development of novel metrics for assessing co-variances between temperature and precipitation; fifth, the development of a dashboard for interactive examination of impacts-relevant climate data; and sixth, the establishment of a community of practice around impacts-relevant climate data that will continue beyond the conclusion of this project.

54 ENVIRONMENTAL SCIENCES↗

Explaining Multimodal Deceptive News Prediction Models

Social media plays a valuable role in rapid news dissemination, but it also serves as a vehicle to propagate unverified information. For example, news shared on Facebook or Twitter may actually contain disinformation, propaganda, hoaxes, conspiracies, clickbait or satire. This paper presents an in-depth analysis of the behavior of suspicious news classification models including error analysis and prediction confidence. We consider five deep learning architectures that leverage combinations of text, linguistic and image input signals from tweets. The behavior of these models is analyzed across four suspicious news prediction tasks. Our findings include that models leveraging only the text of tweets outperform those leveraging only the image (by 3-13% absolute in F-measure), and that models that combine image and text signals with linguistic cues e.g., biased and subjective language markers can, but do not always, perform even better. Finally, our main contribution is a series of analyses, in which we characterize text and image traits of our classes of suspicious news and analyze patterns of errors made by the various models to inform the design of future deceptive news prediction models.

deep learning, social media, deception↗

BETO 2021 Peer Review - Analytical Development & Support WBS 2.5.1.101

The objective of the Analytical Development and Support (ADS) Project is to produce and maintain the critical analytical methods and tools that enable evaluation of emerging biofuels R&D at NREL and in the broader biofuels research community. Our project is divided into two tasks: one task to develop novel analytical techniques and improve existing methods and one task to maintain existing analytical capabilities at NREL and provide outreach to the wider community. The ADS Project is world-recognized for our Laboratory Analytical Procedures (LAPS) which provide detailed procedures for compositional analysis of biomass and have been adopted as the de facto standards within the biofuels community largely due to the transparency of the methods and the high reputation of NREL's research. Our dialog with stakeholders allows us to provide robust, precise, accurate, and publicly available analytical procedures for better valuation of scientific tools such as our recent accomplishment in developing a cellulose assay to support the EPA and industry in calculation of converted cellulose during starch ethanol production. We continue to develop analytical capabilities to support BETO's directives to research cost advantaged fuels such as animal wastes and novel bioproducts like 2,3-Butane-diol.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Screening and Brief Interventions for Alcohol Use During Pregnancy: Practices Among US Primary Care Clinicians, DocStyles 2019

Alcohol use during pregnancy can cause birth defects and developmental disabilities. From 2018 through 2020, 13.5% of pregnant women reported current drinking. The US Preventive Services Task Force recommends evidence-based tools (eg, AUDIT-C and SASQ) for implementing screening and brief interventions to reduce excessive alcohol use among adults, including pregnant people, for whom any alcohol use is considered excessive. We used DocStyles 2019 data to conduct a cross-sectional analysis to examine current screening and brief intervention practices that primary care clinicians conduct among pregnant patients; clinicians’ confidence levels in conducting screening, brief interventions, and referral to treatment; and the documentation of brief interventions in the medical record. A total of 1,500 US adult medicine clinicians completed the entire survey. Among the respondents who conduct screening (N = 1,373) and brief interventions (N = 1,357) in their practice, nearly all reported implementing screening (94.6%) and brief interventions (94.9%) with their pregnant patients for alcohol use, but fewer than half felt confident about conducting their screening practices (46.5%). Two-thirds (64%) reported using a tool that met the criteria recommended by the US Preventive Services Task Force (USPSTF). Over half documented brief interventions in electronic health record notes (51.7%) or designated space (50.7%). Pregnancy presents a unique opportunity for clinicians to incorporate screening into routine obstetric care and encourage behavior change among patients. Most providers reported always screening their pregnant patients for alcohol use, but fewer used evidence-based USPSTF-recommended screening tools. Increased clinician confidence in screening and brief intervention, the use of standardized screening tools tailored to pregnant people, and maximal use of electronic health record technology may enhance the benefits of their application to alcohol use, which ultimately can reduce adverse outcomes associated with alcohol use during pregnancy.

60 APPLIED LIFE SCIENCES↗

New NDA Methods for Thorium Fuel Cycle Safeguards (Mid-Project Report)

This project is developing new Non-Destructive Assay (NDA) safeguards techniques for emergent thorium fuel cycles based on Neutron Resonance Transmission Analysis (NRTA), which can assay 233 U and 235 U when they are present together in a sample with potentially high gamma-ray backgrounds from fission products and 232 U. Existing passive techniques face large challenges for this task so new active interrogation methods are needed. This effort is also exploring how gamma-ray signatures can complement NRTA for enhanced assayed performance. This project leverages an NRTA system being developed at PNNL in collaboration with MIT, which uses a commercially available deuterium-tritium neutron generator at short standoff (~2 m). We aim to assess the feasibility and performance of these new NDA techniques for the range of relevant samples in thorium fuel cycles. Key advancements described in this mid-project report include NRTA system design for thorium safeguards measurements, characterization of 233 U oxide powder at PNNL, a survey of detector technologies suitable for NRTA in high gamma background environments, successful preliminary demonstration of a quantitative isotopic estimation algorithm, and first NRTA measurements of a thorium sample.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

RU-net for automatic characterization of TRISO fuel cross sections

During irradiation, phenomena such as kernel swelling and buffer densification may impact the performance of tristructural isotropic (TRISO) particle fuel. Post-irradiation microscopy is often used to identify these irradiation-induced morphologic changes. However, each fuel compact generally contains thousands of TRISO particles. Manually performing the work to get statistical information on these phenomena is cumbersome and subjective. Here, to reduce the subjectivity inherent in that process and to accelerate data analysis, we used convolutional neural networks (CNNs) to automatically segment cross-sectional images of microscopic TRISO layers. CNNs are a class of machine-learning algorithms specifically designed for processing structured grid data. They have gained popularity in recent years due to their remarkable performance in various computer vision tasks, including image classification, object detection, and image segmentation. In this research, we generated a large irradiated TRISO layer dataset with more than 2,000 microscopic images of cross-sectional TRISO particles and the corresponding annotated images. Based on these annotated images, we used different CNNs to automatically segment different TRISO layers. These CNNs include RU-Net (developed in this study), as well as three existing architectures: U-Net, Residual Network (ResNet), and Attention U-Net. The preliminary results show that the model based on RU-Net performs best in terms of Intersection over Union (IoU). Using CNN models, we can expedite the analysis of TRISO particle cross sections, significantly reducing the manual labor involved and improving the objectivity of the segmentation results.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Condition monitoring and anomaly detection in cyber-physical systems

The modern industrial environment is equipping myriads of smart manufacturing machines where the state of each device can be monitored continuously. Such monitoring can help identify possible future failures and develop a cost-effective maintenance plan. However, it is a daunting task to perform early detection with low false positives and negatives from the huge volume of collected data. This requires developing a holistic machine learning framework to address the issues in condition monitoring of high priority components and develop efficient techniques to detect anomalies that can detect and possibly localize the faulty components. This paper presents a comparative analysis of recent machine learning approaches for robust, cost-effective anomaly detection in cyber-physical systems. While detection has been extensively studied, very few researchers have analyzed the localization of the anomalies. We show that supervised learning outperforms unsupervised algorithms. For supervised cases, we achieve near-perfect accuracy of 98% (specifically for tree-based algorithms). In contrast, the best-case accuracy in the unsupervised cases was 63%—the area under the receiver operating characteristic curve (AUC) exhibits similar outcomes as an additional metric.

Marfo, William↗

Materials for Advanced Ultrasupercritical Steam Turbines Task 4: Cast Superalloy Development

The Steam Turbine critical stationary structural components are high integrity Large Shell and Valve Casing heavy section Castings, containing high temperature steam under high pressures. Hence to support the development of advanced materials technology for use in an AUSC steam turbine capable of operating with steam conditions of 760°C (1400°F) and 35 Mpa (5000 psia), Casting alloy selection and evaluation of mechanical, metallurgical properties and castability with robust manufacturing methods are mandated. Alloy down select from Phase 1 based on producability criteria and creep rupture properties tested by NETL-Albany and ORNL directed the consortium to investigate cast properties of Haynes 282 and Haynes 263. The goals of Task 4 in Phase 2 are to understand a broader range of mechanical properties, the impact of manufacturing variables on those properties. Scale up the size of heats to production levels to facilitate the understanding of the impact of heat and component weight, on metallurgical and mechanical behavior. GE Power & Water Materials and Processes Engineering for the Phase 2, Task 4.0 Castings work, systematically designed and executed casting material property evaluation, multiple test programs. Starting from 15 lbs. cylinder castings to world’s first 17,000 lbs. poured weight, heavy section large steam turbine partial valve Haynes 282 super alloy casting. This has demonstrated scalability of the material for steam Turbine applications. Activities under Task 4.0, Investigated and characterized various mechanical properties of Cast Haynes 282 and Cast Nimonic 263. The development stages involved were: 1) Small Cast Evaluation: 4 inch diam. Haynes 282 and Nimonic 263 Cylinders. This provided effects of liquidus super heat range and first baseline mechanical data on cast versions of conventional vacuum re-melted and forged Ni based super alloys. 2) Step block castings of 300 lbs. and 600 lbs. Haynes 282 from 2 foundry heats were evaluated which demonstrated the importance of proper heat treat cycles for Homogenization, and Solutionizing parameters selection and implementation. 3) Step blocks casting of Nimonic 263: Carried out casting solidification simulation analysis, NDT inspection methods evaluation, detailed test matrix for Chemical, Tensile, LCF, stress rupture, CVN impact, hardness and J1C Fracture toughness section sensitivity data and were reported. 4) Centrifugal Casting of Haynes 282, weighing 1400 lbs. with hybrid mold (half Graphite and half Chromite sand) mold assembly was cast using compressor casing production tooling. This test provided Mold cooling rates influence on centrifugally cast microstructure and mechanical properties. Graphite mold section out performs sand mold across all temperatures for 0.2% YS; %Elongation, %RA, UTS at 1400°F. Both Stress-LMP and conditional Fracture toughness plots data were in the scatter band of the wrought alloy. 5) Fundamental Studies on Cooling rates and SDAS test program. Evaluated the influence of 6 mold materials Silica, Chromite, Alumina, Silica with Indirect Chills, Zircon and Graphite on casting solidification cooling rates. Actual Casting cooling rates through Liquidus to Solidus phase transition were measured with 3 different locations based thermocouples placed in each mold. Compared with solidification simulation cooling rates and measurement of SDAS, microstructure features were reported. The test results provided engineered casting potential methods, applicable for heavy section Haynes 282 castings for optimal properties, with foundry process methods and tools. 6) Large casting of Haynes 282 Drawings and Engineering FEM models and supplemental requirements with applicable specifications were provided to suppliers for the steam turbine proto type feature valve casing casting. Molding, melting and casting pouring completed per approved Manufacturing Process Plan during 2014 Q4. The partial valve casing was successfully cast after casting methods were validated with solidification simulation analysis and the casting met NDT inspection and acceptance criteria. Heat treated and sectioned to extract trepan samples at different locations comparing with cast on coupons test data. Material properties requisite for design, such as tensile, creep/rupture, LCF, Fracture Toughness, Charpy V-notch chemical analysis testing were carried out. The test results will be presented in the final report. The typical Haynes 282 large size Steam Turbine production casting from Order to Delivery foundry schedule with the activity break up is shown in Figures 107 and 108. • From Purchase Order placement to Casting pouring ~ 26 weeks. 1. Sales and commercial review 3 2. Engineering Drawings/models review 4 3. Pattern and core box manufacturing 6 4. Casting process engineering review 4 5. FEM and solidification simulation analysis 4 6. Gating & Feeder Attachments, Ceramic tiling 2 7. Molding and coremaking production scheduling 6 8. Melting planning and schedule 3 9. Pouring, cooling and shake out 2 • From Pouring to casting Delivery ~ 29 weeks 10. Shot blast and riser cutting, gates removal 3 11. Homogenizing , solutionizing HT furnace prep 4 12. Grinding, Fettling 2 13. Aging HT Cycle, cooling 2 14. VT and LPT NDT inspections 2 15. Radiographic inspection 4 16. Mechanical testing, Chemical analysis test certs 4 17. Casting weld repair upgrades and Aging PWHT 4 18. NDT after weld repairs and casting upgrades 3 19. Casting Final Inspection and test certifications 3 20. Package and delivery 2 Hence the Total Lead time from P.O to Casting delivery is approximately 55 weeks. The Task 4.2 and Task 4.3 activities and reporting completed.

Thangirala, Mani↗

Phenomena Identification and Ranking Tables (PIRT) analysis of wind turbine blade leading edge erosion

Leading edge erosion (LEE) of wind turbine blades has been identified as a major factor in decreased wind turbine blade lifetimes and energy output over time. Accordingly, the International Energy Agency Wind Technology Collaboration Programme (IEA Wind TCP) has created the Task 46 Phase 2 to undertake cooperative research in the key topic of blade erosion. The purpose of Task 46 Erosion Phase 2 is to further improve understanding of erosion driving factors, develop datasets and model tools to enhance prediction of leading-edge erosion likelihood, identify damage at the earliest possible stage and advance potential solutions. The scope of work covers several technical areas, reflecting the multidisciplinary nature of the challenge. Participants in the task are given in Table 1.

17 WIND ENERGY↗

Analysis of Hybrid Gas–Dust Outbursts Observed at 67P/Churyumov–Gerasimenko

Cometary outbursts offer a valuable window into the composition of comet nuclei with their forceful ejection of dust and volatiles in explosive events, revealing the interior components of the comet. Understanding how different types of outbursts influence the dust properties and volatile abundances, to better interpret what signatures can be attributed to primordial composition and what features are the result of processing, is an important task best undertaken with a multi-instrument approach. The European Space Agency Rosetta mission to 67P/Churyumov–Gerasimenko carried a suite of instruments capable of carrying out this task in the near-nucleus coma with unprecedented spatial and spectral resolution. In this work, we discuss two outbursts that occurred 2015 November 7 and were observed by three instruments on board: the Alice ultraviolet spectrograph, the Visual Infrared and Thermal Imaging Spectrometer, and the Optical, Spectroscopic, and Infrared Remote Imaging System. Together, the observations show that mixed gas and dust outbursts can have different spectral signatures representative of their initiating mechanisms, with the first outburst showing indicators of a cliff collapse origin and the second more representative of fresh volatiles being exposed via a deepening fracture. This analysis opens up the possibility of remote spectral classification of cometary outbursts with future work.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Savannah River Site H-Canyon Collaborative Advanced Technology Demonstration (ATD) Program Review - 19499

The purpose of the Department of Energy-Environmental Management Office of Technology Development's (DOE-EM TD) Science of Safety (SOS) Robotics Initiative is to pull innovative commercial off the shelf (COTS) and near ready technologies to the field to expedite remaining EM mission clean up and enhance worker safety. A goal of this program is to demonstrate to DOE facilities the value of these available new technologies and their potential uses to solve difficult problems. In support of this goal, the Savannah River Site (SRS) H-Canyon Air Exhaust (HCAEX) tunnel biannual inspection task has been selected to demonstrate and evaluate the benefit of pulling advanced technology solutions to EM facilities. This is a highly collaborative task facilitated by SRNL and engaging SRS H-Canyon facility stakeholders, National Laboratory robotic and concrete inspection SMEs and university robotic and sensor researchers. Presently, the SRS HCAEX tunnel inspection consists of a visual examination to evaluate the structural integrity of the tunnel. Personal access is prohibited in the tunnel due to extreme conditions, thus the examination is accomplished remotely by deploying cameras mounted on a robotic crawler deployed into the tunnel. While the ability to see inside the tunnel in recent years has been highly valuable, it is desirable to have a better understanding of the environment to support a more thorough structural analysis and for long term planning purposes. This higher order of understanding can be accomplished through advanced COTS sensors and tools and those currently in development and near ready. The advanced technologies selected for demonstration will be evaluated for hosting on the FY19 HCAEX tunnel inspection crawler platform. The HCAEX ATD task is expected to be performed over a two-year period with year one now concluding. At WM2018, we presented the H-Canyon stakeholder's high priority inspection and remote handling needs, the collaboration team and the planned process of developing an integrated solution. This presentation will review the initial scope and inspection needs, then discuss the past year's progress to address these needs. The discussion includes the results of the Concrete Integrated Product Team (CIPT) formed to evaluate promising concrete NDE inspection technologies and a Lidar based tunnel mapping sensor suite solution. Highlighted in the discussion will be the engagement and pulling of university developed research and development to include student engagement through the formation and hosting of the SRNL Robotics and Sensors Intern team and collaboration with MSIPP funded Robotic Arm Concrete NDT Inspection University researchers. Lastly, the path forward to complete the demonstration will be presented. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Novel Analysis and Visualization of Chemical Events for Public Health Surveillance

Pacific Northwest National Laboratory hosted an intern-based web application development contest in the summer of 2016 centered around developing novel chemical surveillance applications to aid in health situational awareness. Making up the three teams were three graduate students (n=9) from various US schools majoring in non-public health domains, such as computer science and user design. The interns successfully developed three applications that demonstrated a value-add to chemical surveillance—ChemAnalyzer (text analytics), RetroSpect (retrospective analysis of chemical events),and ToxicBusters (geo-based trend analytics). These applications will be the basis for the first chemical surveillance application to be incorporated into the DTRA Biosurveillance Ecosystem (BSVE).Pacific Northwest National Laboratory (PNNL), on behalf the Defense Threat Reduction Agency (DTRA; project number CB10190), hosts an annual intern- based web app development contest. Previous competitions have focused on mobile biosurveillance. The 2016 competition pivoted away from biosurveillance to focus on addressing challenges within the field of chemical surveillance and increasing public health chemical situational awareness. The result of the app will be integrated within the DTRA BSVE. PNNL hosted nine graduate interns for a 10-week period in the summer of 2016 as participants in a summer web application development contest. Students were drawn from such fields as software engineering and user experience and design and placed into three teams of three students. The challenge presented to the interns was to design and develop a fully-functional web application that would address a critical need within the chemical surveillance community. The interns developed their own ideas (vetted by PNNL and DTRA), discovered and integrated their own data sources, and produced their own visualizations and analytics, independent of any assistance outside of that provided in an advisory capacity. The competition end with a judging event with a panel of subject matter experts and cash awards were distributed to the teams. Each team produced a unique application. Although there was mild overlap between some of the ideas, the applications were developed independently and each reflected the unique contributions of the teams. ChemAnalyzer is a text-analytics platform designed to facilitate more data-driven decision, given a corpus of text data about a chemical event. Their platform provided the ability to automatically identify and highlight key words in documents related to chemical events. The keywords are drawn from an ontology installed with the system, as well as any user-identified keywords. The ChemAnalyzer team finished in third place. The RetroSpect team developed a visual analytic tool for performing retrospective analysis and monitoring of chemical events. Their app provided the ability to search and analyze past events, as well as visualization of state and county information for the recorded chemical events. The RetroSpect team finished in second place. The Toxic busters team - the winners of the competition - created a geo-based situational awareness tool for tracking chemical events. Their app featured an updateable map overlay, search functionality for finding specific or related events, incident and city/state/national-level statistics and trends, as well as news and social media integration based on keywords related to chemical surveillance. Each of the apps developed by the teams provides value to an analyst tasked with monitoring chemical events. The apps integrated unique data sources to provides a full picture of a chemical event, and its effects upon the surrounding population. This integrated analytics provides a valuable benefit over existing workflows, where analysts must monitor news, social, and other information sources manually for real-time information. The apps developed by these interns are designed to enable identification and analysis of the incident as quickly as possible, allowing for more timely assessments of the incident and its impacts. The web app development contest provided a unique opportunity for students to learn about the emerging needs in chemical surveillance as it relates to health situational awareness. Students were drawn from a variety of fields and were tasked with developing novel web apps addressing some of the most pressing challenges in the field of chemical surveillance. The ideas generated by the students will help form the basis for future chemical surveillance application development to be integrated with the DTRABSVE.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Guided Text Analysis Using Adaptive Visual Analytics

This paper demonstrates the promise of augmenting interactive visualizations with semi-supervised machine learning techniques to improve the discovery of significant associations and insight in the search and analysis of textual information. More specifically, we have developed a system called Gryffin that hosts a unique collection of techniques that facilitate individualized investigative search pertaining to an ever-changing set of analytical questions over an indexed collection of open-source publications related to national infrastructure. The Gryffin client hosts dynamic displays of the search results via focus+context record listings, temporal timelines, term- frequency views, and multiple coordinated views. Furthermore, as the analyst interacts with the display, the interactions are recorded and used to label the search records. These labeled records are then used to drive semi-supervised machine learning algorithms that re-rank the unlabeled search records such that potentially relevant records are moved to the top of the record listing. Gryffin is described in the context of the daily tasks encountered at the Department of Homeland Securitys Fusion Centers, with whom we are collaborating in its development. The resulting system is capable of addressing the analysts information overload that can be directly attributed to the deluge of information that must be addressed in search and investigative analysis of textual information.

machine learning↗

Semi-supervised permutation invariant particle-level anomaly detection

The development of analysis methods to distinguish potential beyond the Standard Model phenomena in a model-agnostic way can significantly enhance the discovery reach in collider experiments. However, the typical machine learning (ML) algorithms employed for this task require fixed length and ordered inputs that break the natural permutation invariance in collision events. To address this, a semi-supervised anomaly detection tool is presented that takes a variable number of particle-level inputs and leverages a signal model to encode this information into a permutation invariant, event-level representation via supervised training with a Particle Flow Network (PFN). Data events are then encoded into this representation and given as input to an autoencoder for unsupervised ANomaly deTEction on particLe flOw latent sPacE (ANTELOPE), classifying anomalous events based on a low-level and permutation invariant input modeling. Performance of the ANTELOPE architecture is evaluated on simulated samples of hadronic processes in a high energy collider experiment, showing good capability to distinguish disparate models of new physics.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

A Framework to Analyze the Performance of Load Balancing Schemes for Ensembles of Stochastic Simulations

Ensembles of simulations are employed to estimate the statistics of possible future states of a system, and are widely used in important applications such as climate change and biological modeling. Ensembles of runs can naturally be executed in parallel. However, when the CPU times of individual simulations vary considerably, a simple strategy of assigning an equal number of tasks per processor can lead to serious work imbalances and low parallel efficiency. This paper presents a new probabilistic framework to analyze the performance of dynamic load balancing algorithms for ensembles of simulations where many tasks are mapped onto each processor, and where the individual compute times vary considerably among tasks. Four load balancing strategies are discussed: most-dividing, all-redistribution, random-polling, and neighbor-redistribution. Simulation results with a stochastic budding yeast cell cycle model are consistent with the theoretical analysis. It is especially significant that there is a provable global decrease in load imbalance for the local rebalancing algorithms due to scalability concerns for the global rebalancing algorithms. The overall simulation time is reduced by up to 25 %, and the total processor idle time by 85 %.

ALGORITHMS↗