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At least 289 records · Page 16

Integration of Risk Survival Measures Estimated From Pre- and Posttreatment Computed Tomography Scans Improves Stratification of Patients With Early-Stage Non-small Cell Lung Cancer Treated With Stereotactic Body Radiation Therapy

To predict overall survival of patients receiving stereotactic body radiation therapy (SBRT) for early-stage non-small cell lung cancer (ES-NSCLC), we developed a radiomic model that integrates risk of death estimates and changes based on pre- and posttreatment computed tomography (CT) scans. We hypothesize this innovation will improve our ability to stratify patients into various oncologic outcomes with greater accuracy.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

Stochastic Models, Indices & Optimization Algorithms for Pricing & Hedging Reliability Risks in Modern Power Grids: Data Plan - Princeton

We collected and cleaned the synthetic grid data produced by NREL for the Texas and New York synthetic grids. We developed a high dimensional joint stochastic model for load at the zone level, and solar and wind power productions at the asset level, capturing the spatial and temporal dependencies between all the variables, and demonstrated how such a model could be fitted to historical data. We designed and implemented a simulation engine which can produce Monte Carlo scenarios for the hourly day-ahead values of load, and solar and wind power productions at the spatial and temporal resolutions of the historical data used to fit the model. Finally we developed an open-source Python package which can, from an input grid model, efficiently use forecasts and large numbers of Monte Carlo scenarios to provide unit commitment and economic dispatch for each of these scenarios. The high dimensional stochastic model and the subsequent Monte Carlo simulation engine were implemented in the package PGscen and the corresponding UC and ED optimization programs in the package Vatic.

14 SOLAR ENERGY↗

How much have adverse occupational health outcomes among construction workers improved over time? Evidence from 25 years of medical screening

Background Construction workers have always had a high risk of occupational illnesses. We used 25 years of data from a medical screening program serving older construction workers to determine how much health outcomes have improved over the past 60 years. Methods We investigated changes in relative risk for chest radiographs consistent with pneumoconiosis, COPD by spirometry, lung cancer mortality, and audiometry-assessed hearing impairment among workers participating in a medical screening program. Results were stratified by decade of first construction employment: before 1960, 1960–1969, 1970–1979, 1980–1989, and after 1990. Poisson and Cox regression analyses assessed relative risk by decade adjusted for age, sex, smoking, and years of construction trade work. Results Subjects were 94% male and, on average, 60 years old with 25 years of construction work. When compared to workers employed before 1960, those first employed after 1990 experienced the following reductions in model-adjusted relative risks: chronic obstructive pulmonary disease, 32%; all pneumoconiosis, 68%; parenchymal abnormalities, 35%; pleural abnormalities, 71%; hearing impairment, 20%; and lung cancer mortality, 48%. Risks started to decline in the 1960s with greatest reductions among workers first employed after 1970. Conclusions This study demonstrates the positive impact that adoption of occupational health protections have had over the past 60 years. The greatest risk reductions were observed for outcomes with strong regulatory and legal incentives to reduce exposures and associated risks, such as those associated with inhalation hazards (asbestos and silica), while lowest improvement was for hearing impairment, for which little regulatory enforcement and few prevention incentives have been adopted.

60 APPLIED LIFE SCIENCES↗

Understanding local plant extinctions before it is too late: bridging evolutionary genomics with global ecology

Understanding evolutionary genomic and population processes within a species range is key to anticipating the extinction of plant species before it is too late. However, most models of biodiversity risk under global change do not account for the genetic variation and local adaptation of different populations. Population diversity is critical to understanding extinction because different populations may be more or less susceptible to global change and, if lost, would reduce the total diversity within a species. Here, two new modeling frameworks advance our understanding of extinction from a population and evolutionary angle: Rapid climate change-driven disruptions in population adaptation are predicted from associations between genomes and local climates. Furthermore, losses of population diversity from global land-use transformations are estimated by scaling relationships of species' genomic diversity with habitat area. Overall, these global eco-evolutionary methods advance the predictability – and possibly the preventability – of the ongoing extinction of plant species.

60 APPLIED LIFE SCIENCES↗

Predictive Risk Mapping of Schistosomiasis in Madagascar Using Ecological Niche Modeling and Precision Mapping

Schistosomiasis is a neglected tropical disease (NTD) found throughout tropical and subtropical Africa. In Madagascar, the condition is widespread and endemic in 74% of all administrative districts in the country. Despite the significant burden of the disease, high-resolution risk maps have yet to be produced to guide national control programs. This study used an ecological niche modeling (ENM) and precision mapping approach to estimate environmental suitability and disease transmission risk. The results show that suitability for schistosomiasis is widespread and covers 264,781 km 2 (102,232 sq miles). Covariates of significance to the model were the accessibility to cities, distance to water, enhanced vegetation index (EVI), annual mean temperature, land surface temperature (LST), clay content, and annual precipitation. Disease transmission risk is greatest in the central highlands, tropical east coast, arid-southwest, and northwest. An estimated 14.9 million people could be at risk of schistosomiasis; 11.4 million reside in rural areas, while 3.5 million are in urban areas. This study provides valuable insight into the geography of schistosomiasis in Madagascar and its potential risk to human populations. Because of the focal nature of the disease, these maps can inform national surveillance programs while improving understanding of areas in need of medical interventions.

60 APPLIED LIFE SCIENCES↗

Radium Dial Workers: Back to the Future

This report reviews the history of the radium dial workers in the United States, summarizes the scientific progress made since the last evaluation in the early 1990s, and discusses current progress in updating the epidemiologic cohort and applying new dosimetric models for radiation risk assessment. The discoveries of radiation and radioactivity led quickly to medical and commercial applications at the turn of the 20th century, including the development of radioluminescent paint, made by combining radium with phosphorescent material and adhesive. Workers involved with the painting of dials and instruments included painters, handlers, ancillary workers, and chemists who fabricated the paint. Dial painters were primarily women and, prior to the mid to late 1920s, would use their lips to give the brush a fine point, resulting in high intakes of radium. The tragic experience of the dial painters had a significant impact on industrial safety standards, including protection measures taken during the Manhattan Project. The dial workers study has formed the basis for radiation protection standards for intakes of radionuclides by workers and the public. The mortality experience of 3,276 radium dial painters and handlers employed between 1913-1949 is being determined through 2019. The last epidemiologic follow-up was 30 years ago when most of these workers were still alive. Nearly 65% were born before 1920, 37.5% were teenagers when first hired, and nearly 50% were hired before 1930 when the habit of placing brushes in mouths essentially stopped. Comprehensive dose reconstruction techniques are being applied to estimate organ doses for each worker related to the intake of 226Ra, 228Ra, and associated photon exposures. Time dependent dose-response analyses will estimate lifetime risks for specific causes of death. The study of radium dial workers is part of the Million Person Study of low-dose health effects that is designed to evaluate radiation risks among healthy American workers and veterans. Despite being one of the most important and influential radiation effects studies ever conducted, shifting programmatic responsibilities and declining funding led to the termination of the radium program of studies in the early 1990s. Renewed interest and opportunity have arisen. With scientific progress made in dosimetric methodology and models, the ability to perform a study over the entire life span, and the potential applicability to other scenarios such as medicine, environmental contamination and space exploration, the radium dial workers have once again come to the forefront.

61 RADIATION PROTECTION AND DOSIMETRY↗

Evaluating efficacy of indoor non-pharmaceutical interventions against COVID-19 outbreaks with a coupled spatial-SIR agent-based simulation framework

Contagious respiratory diseases, such as COVID-19, depend on sufficiently prolonged exposures for the successful transmission of the underlying pathogen. It is important that organizations evaluate the efficacy of non-pharmaceutical interventions aimed at mitigating viral transmission among their personnel. We have developed a operational risk assessment simulation framework that couples a spatial agent-based model of movement with an agent-based SIR model to assess the relative risks of different intervention strategies. By applying our model on MIT’s Stata center, we assess the impacts of three possible dimensions of intervention: one-way vs unrestricted movement, population size allowed onsite, and frequency of leaving designated work location for breaks. We find that there is no significant impact made by one-way movement restrictions over unrestricted movement. Instead, we find that reducing the frequency at which individuals leave their workstations combined with lowering the number of individuals admitted below the current recommendations lowers the likelihood of highly connected individuals within the contact networks that emerge, which in turn lowers the overall risk of infection. We discover three classes of possible interventions based on their epidemiological effects. By assuming a direct relationship between data on secondary attack rates and transmissibility in the agent-based SIR model, we compare relative infection risk of four respiratory illnesses, MERS, SARS, COVID-19, and Measles, within the simulated area, and recommend appropriate intervention guidelines.

60 APPLIED LIFE SCIENCES↗

A framework to implement human reliability analysis during early design stages of advanced reactors

Nuclear power plants require human actions throughout their lifecycle from design, construction, operation, and decommissioning. However, for advanced reactors (e.g., Generation IV), the reliance on human intervention in safety-related actions is expected to be reduced or completely replaced by automated actions. The Probabilistic Risk Assessment (PRA) Standard for Advanced Non-LWR Nuclear Power Plants requires that the impacts of all operator actions are captured and incorporated in the risk of the modeled plant. Moreover, the Modernization of Technical Requirements for Licensing Advanced Reactors requires human reliability analysis (HRA) to be included throughout all design and PRA development stages. However, due to the lack of details during the early design stages, HRA is often postponed until the design is mature enough. Conducting HRA in later design stages, though it may be adequate in capturing pre-, at-, and post-initiators comes short of informing the design itself in the iterative design lifecycle. Hence, this paper presents a framework to include HRA during the design's early stages, pre-conceptual or conceptual. The proposed framework provides a process for the removal of operator actions that do not contribute to the risk and the identification of all key operator actions that are critical to the safety of the design. The results of this framework are then used to inform the design of those safety-related operator actions to update the design further. Then, using information from the updated design, this framework can be reapplied to investigate the impact of the design update on human reliability. The PRA model of the X-energy's pre-conceptual Xe-100 high-temperature gas-cooled pebble-bed reactor (HTGR-PB) design is used to demonstrate the approach. In the pre-conceptual Xe-100 PRA model, also called Phase 0 PRA model, human actions were considered an integral part of analyzing the plant response to different initiating events. Hence, in this paper, all possible human actions in the Xe-100 PRA model are identified, analyzed, and removed to emulate a design relying only on the available automated control systems. The preliminary results of this assessment show how safe the Xe-100 design is even without crediting any human actions. The results also list necessary sequences in which operator actions are critical to the risk profile of the design.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Digital Twin for Optimizing Real-time Economy of the Integrated Energy Systems

Economic and safe operation of integrated energy systems (IES) requires real-time optimization (RTO) of the control and actions conducted on each system component. In this regard, digital twins (DTs), which consist of a physical system, a virtual system, and the data communication that occurs between the two, are essential for effective RTO. Through the data warehouse, the virtual system is constantly updated with real-time data from the physical system, and functions as the model in the optimization framework. The reduced-order model of the dynamic process model in the virtual system is used in the optimization framework. The optimization results are then returned, via the data warehouse, as control actions to the physical system. This work demonstrates the software capabilities of DT assets for an IES in the context of preparing a DT for an experimental system comprised of Idaho National Laboratory (INL)’s Thermal Energy Delivery System and battery system. For the virtual demonstration, the DTs encompass (1) a physical system, including the Modelica models of the Thermal Energy Delivery System and the battery system; (2) virtual optimization via the Optimization of Real-Time Capacity Allocation (ORCA) platform; and (3) the open-source data warehouse software DeepLynx. This work assesses the performance of ORCA, which utilizes a reduced-order model built using the Risk Analysis Virtual Environment (RAVEN) and trained on the Modelica models and real-time data pipeline through the graph database hosted in DeepLynx. The proposed optimization workflow will be an RTO model based on DTs and the data they generate.

25 ENERGY STORAGE↗

Regulatory Decision-Making Process Using 'Weight of Evidence: An Evidence Integration Approach' by the State of Washington at the Hanford Site - 20163

Performance Assessment and Environmental Impact Statement Modeling, Human and Ecological Risk Assessments are the tools used by the Washington State Department of Ecology (Ecology) to make regulatory decisions for permitting and cleanup. These efforts all inherently have large amounts of uncertainty no matter how many alternative approaches are attempted. Professional feedback from tribal governments, stakeholders, federal and state regulators, the public and independent peer review groups are taken into account in this process of informed decision making. The process also includes full evaluation of the comments and the associated reviews made by the various peer review groups such as the Low -Level Waste Disposal Facility Federal Review Group (DOE order 435.1) and the Nuclear Regulatory Commission (NRC). The evolution of technology has produced quicker and more efficient fate and transport modeling tools with related quantification of uncertainties and their significance. Groundwater and vadose zone models have been expanded across the entire Hanford site. Many of the technical approaches identified in the Washington State Regulations are updated using current knowledge to better understand the complexities of the Hanford Site using the latest state of the art tools for risk assessment and modeling. More emphasis and time are directed on defining uncertainty and then managing the uncertainty. One added tool is to run sensitivity cases based on informed decision making. The weight of evidence approach considers all relevant information in an integrative assessment that takes into account the kinds of evidence available, the quality and quantity, the uncertainty and error associated with each type, and how they fit together. Evidence of integration involves looking at systematic reviews and evaluating their strengths and weaknesses. In conclusion, Ecology, Washington State's lead regulatory agency at the Hanford site, has had to modify its site closure approach compared to the rest of the state. A more holistic approach dealing with the Hanford site as an integrated system has evolved leading to a defensible informed approach for regulatory closure decisions. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Explaining Health Risk Behaviors in the U.S. with Social Deprivation at Local and Regional Levels

Health risk behaviors are precursors to many chronic health outcomes, and hence, they pose a challenge to public health. Social deprivation undoubtedly creates circumstances that limit access to healthy habits. Moreover, broad regional effects (weather patterns, political ideology, social norms), and local characteristics (cultural notions and barriers, urban places) also influence lifestyle choices and must be accounted for to truly understand the impact of social deprivation on risky behaviors. This research fills the knowledge gap in epidemiological modeling of health risk behaviors by leveraging machine learning to find associations between social deprivation and health risk behaviors, when adjusted by regional and local effects. Four health risk behaviors, namely, binge drinking, smoking, lack of sleep, and lack of physical activity from the CDC PLACES project are considered in a single framework to understand and compare the interplay between local/regional characteristics and seven measures of social deprivation. Our results indicate that local and/or regional factors rise to the top for three out of four risk behaviors (binge drinking, smoking and lack of sleep) out-competing social deprivation measures. Un-entangling the geographical effects reveals that poverty, educational attainment and non-employment are the three deprivation measures most significantly associated with all four health risk factors. The research thus indicates that public health policies to promote healthy lifestyle behaviors must seek to remedy social deprivation, but using socially and culturally sensitive interventions.

Gokhale, Swapna↗

Risk-Informed Condition Evaluation of Solar-centered Energy Generation and Distribution Networks through Bayesian Learning and Inference

We develop a methodology based on Bayesian inference over Probabilistic Graphical Models (PGMs) to understand and quantify risk in solar-centered grids using targeted measurements and learned system behavior. Being non-prescriptive but, rather, able to infer system behavior and, ultimately, address risk queries from data, our machine learning-type paradigm is tailored for diverse topologies and threat scenarios often associated with distributed energy generation and photovoltaic distributed energy resources (PV-DERs) in particular. We describe algorithmic processes for: (i) learning the structure of PGMs that result from attack-prone PV-DER-proliferated distribution systems, (ii) quantifying cause-effect relationships, and (iii) evaluating risk queries based on diverse evidence. The contributions are illustrated on a residential grid subject to output impairment attacks on its PV-DER infrastructure.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Plastics Environmental Risk Calculator

SF-24-074 The Plastics Environmental Risk Calculator (PERC) was developed in Microsoft Excel and estimates the environmental distribution and lifetime of new biobased and conventional plastics from commonly measured properties of the plastics. A Random Forest regression model embedded in the calculator calculates plastic degradation rates and lifetimes as a proxy for environmental risk. Model default assumptions may be overwritten by the user.

Beckman, Kevin [Argonne National Laboratory (ANL),↗

Evaluating the Nation's Pipeline Infrastructure with NETL's Advanced Infrastructure Integrity Model (AIIM)

This poster is a part of BIL-EDX4CCS Task 36: Advanced Infrastructure Integrity Modeling to Evaluate Existing Energy Infrastructure Reusability and Risk, the goal of which is to produce a smart tool that will assess existing energy infrastructure reusability and risk using the Advanced Infrastructure Integrity Model (AIIM). This model forecasts lifespan and potential risk using a multitude of factors such as incidents reports, structural characteristics, and the surrounding environment. The project aims to provide scientific insights for a better understanding of carbon storage (CS), potential to support CS stakeholder needs, national decarbonization, and mitigating climate change. AIIM will utilize an energy infrastructure database as its input, developed by acquiring publicly available data as well as NETL derived products. These resources include incidents, geohazards, and infrastructure variables. Soil data in the form of rasters and pipeline incident reports were processed and a script was developed to count the number of times features such as roads, railroads, and rivers intersected with pipeline segments which were then converted to points. Distance to oil and natural gas wells, petroleum ports, intermodal freight facilities, and geologic structures were also calculated. After data preparation and quality control was completed, the data was integrated into the pipeline points. Once models are complete, a smart tool will be created in the form of an online dashboard.

Malay, Caleb↗

Airborne LiDAR to Improve Canopy Fuels Mapping for Wildfire Modeling

Increasing conflict between wildfire and the built environment has increased the need for more up-to-date and finer resolution canopy fuels data to improve wildfire modeling and associated risk forecasts. The US Forest Service and US Department of the Interior’s LANDFIRE product, which provides 30-m resolution canopy fuels data for the entire US, is one of the most widely used sources of fuels data. However, the last complete mapping effort for LANDFIRE is based on 2016 conditions, and subsequent updates reflect disturbances 1-2 years behind the release year. Airborne systems equipped with Light Detection and Ranging (LiDAR) sensors can be deployed to actively sense canopy structure and estimate canopy fuels data (cover, height, base height, bulk density) at finer resolutions. Canopy base height (CBH) and canopy bulk density (CBD) are difficult to measure both in the field and in LiDAR point clouds. Still, they are important for accurately modeling crown fires, which are often intense and difficult to contain. Additionally, point cloud datasets are large, and calculations require efficient utilization of computational resources. To address these challenges, we are working on an approach that uses openly available National Ecological Observatory Network (NEON) airborne LiDAR data, with calculations processed in the R programming language and parallelized through the lidR package. CBH and CBD are often derived from tree height, diameter at breast height, and species-specific allometries using the Fire and Fuels Extension of the Forest Vegetation Simulator (FFE-FVS). We aim to test if airborne LiDAR can estimate CBH and CBD without the use of empirical equations. Reliable estimates of canopy fuels data directly from airborne LiDAR could streamline quick, fine-resolution updates for use in wildfire behavior models.

54 ENVIRONMENTAL SCIENCES↗

The Changing Influence of Precipitation on Soil Moisture Drought With Warming in the Mediterranean and Western North America

Abstract Anthropogenic climate change has already affected drought severity and risk across many regions, and climate models project additional increases in drought risk with future warming. Historically, droughts are typically caused by periods of below‐normal precipitation and terminated by average or above‐normal precipitation. In many regions, however, soil moisture is projected to decrease primarily through warming‐driven increases in evaporative demand, potentially affecting the ability of negative precipitation anomalies to cause drought and positive precipitation anomalies to terminate drought. Here, we use climate model simulations from Phase Six of the Coupled Model Intercomparison Project (CMIP6) to investigate how different levels of warming (1, 2, and 3°C) affect the influence of precipitation on soil moisture drought in the Mediterranean and Western North America regions. We demonstrate that the same monthly precipitation deficits (25th percentile relative to a preindustrial baseline) at a global warming level of 2°C increase the probability of both surface and rootzone soil moisture drought by 29% in the Mediterranean and 32% and 6% in Western North America compared to the preindustrial baseline. Furthermore, the probability of a dry (25th percentile relative to a preindustrial baseline) surface soil moisture month given a high (75th percentile relative to a preindustrial baseline) precipitation month is 6 (Mediterranean) and 3 (Western North America) times more likely in a 2°C world compared to the preindustrial baseline. For these regions, warming will likely increase the risk of soil moisture drought during low precipitation periods while simultaneously reducing the efficacy of high precipitation periods to terminate droughts.

Environmental Sciences & Ecology↗

Bench Testing Data and Report for an Early Prototype Pitch Resonator WEC

This dataset encompasses data and documentation from bench tests conducted on an early prototype of a "pitch resonator" wave energy converter (WEC). The testing aimed to validate numerical models and reduce risks associated with the pitch resonator concept, which is designed to convert the pitching and rolling motions of a buoy into electrical power. The project's goal is to provide supplementary power, in the range of 10-100 watts, to the National Science Foundation's Ocean Observatories Initiative Pioneer Array. Two distinct testing phases are documented: one using a single degree of freedom (1DOF) test rig, and another employing a six degree of freedom (6DOF) Stewart platform, known as the Large Amplitude Motion Platform (LAMP). These tests assessed various factors, such as system performance in different motion scenarios, the torque exerted by wave forces, and the impact of mounting configurations. The dataset includes raw test data in MATLAB (.mat) format, detailed metadata, and a report describing the experimental procedures and preliminary findings.

16 TIDAL AND WAVE POWER↗