Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Social sciences”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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

At least 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Unraveling the Threads of Environmental Justice in Critical Mineral Extraction: A Framework for Regionalized Life Cycle Data

As we transition to a more sustainable energy system, the extraction and processing of critical minerals becomes increasingly important. However, these industries often raise concerns about environmental and social impacts, particularly in disadvantaged communities. To address these concerns, our research focuses on regionalizing environmental life cycle data to connect it with communities affected by mineral extraction. A framework was developed for collecting life cycle background data that supports the Justice40 Toolset, a market-based approach to evaluating net benefits and costs of critical mineral material recovery pathways. A goal was to alleviate public skepticism around mineral extraction processes, including secondary and unconventional feedstocks, by highlighting both environmental and social impacts. To achieve this, computational analysis and geospatial data science techniques were employed, such as within-scale and across-scale methods and proxy dataset usage. By doing so, we were able to develop a framework for identifying and disaggregating data down to regions small enough to support Justice40 goals. Our approach not only provides valuable tools fo insight into the environmental implications of mineral extraction but also helps policymakers evaluate the social impacts on local communities. This research contributes to a more just and equitable transition to a sustainable energy system, ensuring that marginalized voices are heard in decision-making process.

Davis, Tyler [NETL Site Support Contractor, Nation↗
DOE OSTI2023

Data from "The Influence of Climate Change on Flooding and Social Inequalities from Remnants of Hurricane Ida"

Previous research has demonstrated that tropical cyclone-related precipitation and flooding have been increased by anthropogenic global warming. Our work aims to quantify the contribution of climate change to the deadly flooding from the remnants of Hurricane Ida (2021) and its impact on human society. We developed an analysis framework that combines lessons from climate change attribution science, two-dimensional hydrodynamic modeling, and flood impact evaluation, including a social inequality index, to estimate remnants of Ida flooding and consequences responding to current (locally 1°C warmer) and future (another 1°C warmer) climate change. We find that an additional quarter to a half million people were exposed to flooding due to current and future climate change. The human influence on flood impacts was larger with deeper flood water depth (≥ 1 m) than with shallow depths. Socially vulnerable populations were found to be disproportionately more affected, and climate change exacerbates this inequality

Climate Change↗
DOE OSTI2021

A bootstrapping approach to social media quantification

Abstract This work considers the use of classifiers in a downstream aggregation task estimating class proportions, such as estimating the percentage of reviews for a movie with positive sentiment. We derive the bias and variance of the class proportion estimator when taking classification error into account to determine how to best trade off different error types when tuning a classifier for these tasks. Additionally, we propose a method for constructing confidence intervals that correctly adjusts for classification error when estimating these statistics. We conduct experiments on four document classification tasks comparing our methods to prior approaches across classifier thresholds, sample sizes, and label distributions. Prior approaches have focused on providing the most accurate point estimate while this work focuses on the creation of correct confidence intervals that appropriately account for classifier error. Compared to the prior approaches, our methods provide lower error and more accurate confidence intervals.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2023

People are like plutonium

An analogy is drawn between the study of human behavior and the study of plutonium to demonstrate that soft and hard sciences are more similar than different, making the distinction moot and unproductive. The studies of human behavior and plutonium follow a common scientific research cycle that aligns with Thomas Kuhn’s views of scientific change. This common research cycle provides evidence that the thought processes and methodologies required for success are congruent in the soft and hard sciences. The primary implication from this analogy is that scientists in all disciplines should eradicate the distinction between soft and hard sciences. Focusing on similarities rather than differences among researchers from different disciplines is necessary to enhance collective intelligence and the type of transdisciplinary collaboration required to tackle difficult sociotechnical problems. CCS Concepts: • Social and professional topics • User characteristics • Cultural characteristics.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2024

Evaluating disease surveillance strategies for early outbreak detection in contact networks with varying community structure

Disease surveillance systems allow public health agencies to respond to emerging diseases before they become widespread. Developing such systems requires identifying optimal ways to monitor in the context of an epidemic outbreak; this problem is known as sensor selection. Contact networks represent the dynamics of interaction in a population and are used to model how a disease spreads in a population and to explore strategies of sensor selection. We evaluated five sensor selection strategies on their ability to provide an early warning of a COVID-like outbreak in synthetic contact networks encapsulated in four network scenarios. Three of these scenarios assessed different aspects of community structure. The fourth scenario employed a contact network representing the population and interactions of 6.8 million people in New York City, constructed from an agent-based simulation using census and transportation data. This scenario exemplifies how sensor selection strategies may perform in a real-world, urban context. Our findings suggest that the choice of the optimal strategy depends heavily on the community structure of the network. Strategies that select highly connected nodes or maximize network coverage are the optimal surveillance strategy for outbreak detection in many network community structures. However, a naive implementation of these strategies may fail to provide an early warning at all—including in the New York City scenario. Moreover, these methods are impractical for real-world use as they require knowledge of the underlying contact network. Instead, a selection strategy that starts with a set of random nodes and then performs a random walk through a chain of neighbors reliably provides early warnings without requiring prior knowledge of the network. We find this method, called “random chain”, to be the most pragmatic for implementation in a real-world disease surveillance context.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Reimagine fire science for the anthropocene

Fire is an integral component of ecosystems globally and a tool that humans have harnessed for millennia. Altered fire regimes are a fundamental cause and consequence of global change, impacting people and the biophysical systems on which they depend. As part of the newly emerging Anthropocene, marked by human-caused climate change and radical changes to ecosystems, fire danger is increasing, and fires are having increasingly devastating impacts on human health, infrastructure, and ecosystem services. Increasing fire danger is a vexing problem that requires deep transdisciplinary, trans-sector, and inclusive partnerships to address. Here, we outline barriers and opportunities in the next generation of fire science and provide guidance for investment in future research. We synthesize insights needed to better address the long-standing challenges of innovation across disciplines to (i) promote coordinated research efforts; (ii) embrace different ways of knowing and knowledge generation; (iii) promote exploration of fundamental science; (iv) capitalize on the “firehose” of data for societal benefit; and (v) integrate human and natural systems into models across multiple scales. Fire science is thus at a critical transitional moment. We need to shift from observation and modeled representations of varying components of climate, people, vegetation, and fire to more integrative and predictive approaches that support pathways toward mitigating and adapting to our increasingly flammable world, including the utilization of fire for human safety and benefit. Only through overcoming institutional silos and accessing knowledge across diverse communities can we effectively undertake research that improves outcomes in our more fiery future.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

An Integrated Feasibility Study of Reservoir Thermal Energy Storage (RTES) in Portland, OR, USA

In regions with long cold overcast winters and sunny summers, Deep Direct-Use (DDU) can be coupled with Reservoir Thermal Energy Storage (RTES) technology to take advantage of pre-existing subsurface permeability to save summer heat for later use during cold seasons. Many aquifers worldwide are underlain by permeable regions (reservoirs) containing brackish or saline groundwater that has limited beneficial use due to poor water quality. We investigate the utility of these relatively deep, slow flowing reservoirs for RTES by conducting an integrated feasibility study in the Portland Basin, Oregon, USA, developing methods and obtaining results that can be widely applied to RTES systems elsewhere. As a case study, we have conducted an economic and social cost-benefit analysis for the Oregon Health and Science University (OHSU), a teaching hospital that is recognized as critical infrastructure in the Portland Metropolitan Area. Our investigation covers key factors that influence feasibility including 1) the geologic framework, 2) heat and fluid flow modeling, 3) capital and maintenance costs, 4) the regulatory framework, and 5) operational risks. By pairing a model of building seasonal heat demand with an integrated model of RTES resource supply, we determine that the most important factors that influence RTES efficacy in the study area are operational schedule, well spacing, the amount of summer heat stored (in our model, a function of solar array size), and longevity of the system. Generally, heat recovery efficiency increases as the reservoir and surrounding rocks warm, making RTES more economical with time. Selecting a base-case scenario, we estimate a levelized cost of heat (LCOH) to compare with other sources of heating available to OHSU and find that it is comparable to unsubsidized solar and nuclear, but more expensive than natural gas. Additional benefits of RTES include energy resiliency in the event that conventional energy supplies are disrupted (e.g., natural disaster) and a reduction in fossil fuel consumption resulting in a smaller carbon footprint. Key risks include reservoir heterogeneity and a possible reduction in permeability through time due to scaling (mineral precipitation). Lastly, a map of thermal energy storage capacity for the Portland Basin yields a total of 43,400 GWh, suggesting tremendous potential for RTES in the Portland Metropolitan Area.

14 SOLAR ENERGY↗
DOE OSTI2023

Minding the Gaps

Increasingly, researchers share climate information as narratives to support decision-making and public action. In these contexts, however, scientists remain the focal storytellers. This article offers our methodology for researchers and communities to share narratives with each other and then to engage in collaborative storytelling. At the center of this work is how the humanities embrace the importance of narratives having gaps—narrative lacunae into which individuals can insert their experiences, needs, and values. Our storytelling- and gaps-based methodology allows communities and researchers to enter and transform each other’s stories. We offer a simulation model that fosters collaborative storytelling and give examples from the storytelling and social-environmental action projects that have emerged over three years of partnership with communities and university students.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Scalable edge clustering of dynamic graphs via weighted line graphs

Timestamped relational datasets consisting of records (or connections) between pairs of entities are ubiquitous in network science. For applications like peer-to-peer communication, email, various social network interactions, and computer network security, it is useful to organize these records into groups based on how and when they are occurring. Weighted line graphs offer a natural way to model how records are related in such datasets but for large real-world graph topologies, building and utilizing the line graph is prohibitively expensive. Here, we present the framework to cluster the edges of a dynamic graph via the associated line graph that contains two major contributions. The first is a method to work with the line graph implicitly and the second is a distributed scale implementation of an agglomerative hierarchical graph clustering algorithm. We outline a novel hierarchical dynamic graph edge clustering approach that efficiently breaks massive relational datasets into small sets of edges containing events at various timescales. This is in stark contrast to traditional graph clustering algorithms that prioritize highly connected (clique-like) community structures. Our approach relies on constructing a sufficient subgraph of a weighted line graph and applying a hierarchical agglomerative clustering. This approach is related to scalable techniques from spatial clustering, nonlinear-dimension reduction, topological data analysis, and draws particular inspiration from HDBSCAN. As an edge clustering, this method yields an overlapping node clustering. Our algorithm is parallelizable and we demonstrate efficient clustering of a billion-scale, real-world dynamic graph into small edge sets that correlate in topology and time. The entire clustering process for a graph with tens of billions of edges takes just a few minutes of run time on 256 nodes of a distributed compute environment. We argue how the output of the edge clustering is useful for a multitude of data visualization and powerful machine learning tasks, both involving the original massive dynamic graph data and metadata associated with the nodes and edges. Finally, we describe how this approach can be extended to dynamic hypergraphs and dynamic graphs/hypergraphs with unstructured data living on vertices and edges.

Data Analysis↗
DOE OSTI2021

Human Performance Improvement Task Group Task 21-1 Best Practice: Using virtual capabilities or options for HPI application (to reduce errors, strengthening defenses, strengthening the organization, and/or increasing capacity)

In 2020 the world was impacted by COVID-19. How work is done was dramatically changed for a large portion of the workforce. Many have made the abrupt shift to working from their home while others have adapted to COVID protocols (such as social distancing and wearing facemasks). Fundamentally, Human and Organizational Performance Improvement concepts and science did not change – only the delivery method changed (in some cases). The primary effect has been to accelerate dynamics already present in society – from e-commerce to online education to remote healthcare. Change is never easy for humans. However, remote work should be viewed as an opportunity; not a fix to a problem. In this Best Practice paper, you’ll get practical suggestions to help you reduce errors and improve Human Performance in remote, socially-distanced “work-from-home” environments.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Inequality in the availability of residential air conditioning across 115 US metropolitan areas

Continued climate change is increasing the frequency, severity, and duration of populations’ high temperature exposures. Indoor cooling is a key adaptation, especially in urban areas, where heat extremes are intensified—the urban heat island effect (UHI)—making residential air conditioning (AC) availability critical to protecting human health. In the United States, the differences in residential AC prevalence from one metropolitan area to another is well understood, but its intra-urban variation is poorly characterized, obscuring neighborhood-scale variability in populations’ heat vulnerability and adaptive capacity. We address this gap by constructing empirically derived probabilities of residential AC for 45,995 census tracts across 115 metropolitan areas. Within cities, AC is unequally distributed, with census tracts in the urban “core” exhibiting systematically lower prevalence than their suburban counterparts. Moreover, this disparity correlates strongly with multiple indicators of social vulnerability and summer daytime surface UHI intensity, highlighting the challenges that vulnerable urban populations face in adapting to climate-change driven heat stress amplification.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Sentiment analysis of the United States public support of nuclear power on social media using large language models

This study utilized large language models (LLMs) to analyze public sentiment in the United States (US) regarding nuclear power on social media, focusing on X/Twitter, considering climate change challenges and advancements in nuclear power technology. Approximately, 1.26 million nuclear tweets from 2008–2023 were examined to fine-tune LLMs for sentiment classification. We found the crucial role of accurate data labeling for model performance, with potential implications for a 15% improvement, achieved through high-confidence labels. LLMs demonstrated better performance compared to traditional machine learning classifiers, with reduced susceptibility to overfitting and up to 96% classification accuracy. LLMs are used to segment the US public tweets into policy and energy-related categories, revealing that 68% are politically themed. Policy tweets tended to convey negative sentiment, often reflecting opposing political perspectives and focusing on nuclear deals and international relations. Energy-related tweets covered diverse topics with predominantly neutral to positive sentiment, indicating broad support for nuclear power in 48 out of 50 US states. The US public positive sentiments toward nuclear power stemmed from its high power density, reliability regardless of weather conditions, environmental benefits, application versatility, and recent innovations and advancements in both fission and fusion technologies. Negative sentiments primarily focused on waste management, high capital costs, and safety concerns. The neutral campaign highlighted global nuclear facts and advancements, with varying tones leaning towards positivity or negativity. An interesting neutral theme was the advocacy for the combined use of renewable and nuclear energy to attain net-zero goals.

Energy & Fuels↗
DOE OSTI2022

Social Disparities of Pain and Pain Intensity Among Women Diagnosed With Early Stage Breast Cancer

Background: Breast cancer is one of the most commonly diagnosed cancers among women in the United States and pain is the most common side effect of breast cancer and its treatment. Yet, the relationships between social determinants of pain and pain experience/intensity remain under-investigated. We examined the associations between social determinants of pain both at the individual level and the neighborhood level to understand how social conditions are associated with pain perception among early stage breast cancer patients. Methods: We conducted integrated statistical analysis of 1,191 women with early stage breast cancer treated at a large cancer center in Memphis, Tennessee. Combining electronic health records, patient-reported data and census data regarding residential address at the time of first diagnosis, we evaluated the relationships between social determinants and pain perception. Pain responses were self-reported by a patient as a numerical rating scale score at the patient’s initial diagnosis and follow-up clinical visits. We implemented two sets of statistical analyses of the zero-inflated Poisson model and estimated the associations between neighborhood poverty prevalence and breast cancer pain intensity. After adjustment for demographic characteristics, cancer stage, and chemotherapy, pain perception was significantly associated with poverty and blight level of the neighborhood. Results: Among women living in the highest-poverty areas, the odds of reporting pain were 2.48 times higher than those in the lowest-poverty area. Women living in the highest-blight area had 5.43 times higher odds of reporting pain than those in the lowest-blight area. Neighborhood-level social determinants were significantly associated with pain intensity among women diagnosed with early-stage breast cancer. Conclusions: Distressed neighborhood conditions are significantly associated with higher pain perception. Breast cancer patients living in socio-economically disadvantaged neighborhoods and in poor environmental conditions reported higher pain severity compared to patients from less distressed neighborhoods. Therefore, post-diagnosis pain treatment design needs to be tailored to the social determinants of the breast cancer patients.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Threat Reduction Research Networks: Fostering Sustainable Collaborations Through Trainings for Genomics for Biosurveillance

Scientific research communities can be represented as heterogeneous or multidimensional networks encompassing multiple types of entities and relationships. These networks might include researchers, institutions, meetings, and publications, connected by relationships like authorship, employment, and attendance. We describe a method for efficiently and flexibly capturing, storing, and extracting information from multidimensional scientific networks using a graph database. The database structure is based on an ontology that captures allowable types of entities and relationships. This allows us to construct a variety of projections of the underlying multidimensional graph through database queries to answer specific research questions. We demonstrate this process through a study of the U.S. Biological Threat Reduction Program (BTRP), which seeks to develop Threat Reduction Networks to build and strengthen a sustainable international community of biosecurity, biosafety, and biosurveillance experts to address shared biological threat reduction challenges. Networks like these create connectional intelligence among researchers and institutions around the world, and are central to the concept of cooperative threat reduction. Our analysis focuses on a series of seven BTRP genome sequencing training workshops, showing how they created a growing network of participants and countries over time, which is also reflected in coauthorship relationships among attendees. By capturing concept and relationship hierarchies, our ontology-based approach allows us to pose general or specific questions about networks within the same framework. This approach can be applied to other research communities or multidimensional social networks to capture, analyze, and visualize different types of interactions and how they change over time.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

How Artificial Intelligence and Machine Learning Transform the Human Condition

Our July 2021 symposium, “How Artificial Intelligence and Machine Learning Transform the Human Condition,” was hosted through a partnership between Los Alamos National Laboratory and the National Academies of Sciences, Engineering, and Medicine’s Committee on Science, Technology, and Law. The symposium is part of a broader initiative focusing on harnessing transformative technologies, and builds on our September 2020 symposium titled, “COVID-19: Harnessing a Transformational Pandemic.” Topics such as systems biology and artificial intelligence not only represent compelling research frontiers but also highlight national security challenges with social, ethical, and legal implications.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Mining and Validating Social Media Data for COVID-19–Related Human Behaviors Between January and July 2020: Infodemiology Study

Background: Health authorities can minimize the impact of an emergent infectious disease outbreak through effective and timely risk communication, which can build trust and adherence to subsequent behavioral messaging. Monitoring the psychological impacts of an outbreak, as well as public adherence to such messaging, is also important for minimizing long-term effects of an outbreak. Objective: We used social media data from Twitter to identify human behaviors relevant to COVID-19 transmission, as well as the perceived impacts of COVID-19 on individuals, as a first step toward real-time monitoring of public perceptions to inform public health communications. Methods: We developed a coding schema for 6 categories and 11 subcategories, which included both a wide number of behaviors as well codes focused on the impacts of the pandemic (eg, economic and mental health impacts). We used this to develop training data and develop supervised learning classifiers for classes with sufficient labels. Classifiers that performed adequately were applied to our remaining corpus, and temporal and geospatial trends were assessed. We compared the classified patterns to ground truth mobility data and actual COVID-19 confirmed cases to assess the signal achieved here. Results: We applied our labeling schema to approximately 7200 tweets. The worst-performing classifiers had F1 scores of only 0.18 to 0.28 when trying to identify tweets about monitoring symptoms and testing. Classifiers about social distancing, however, were much stronger, with F1 scores of 0.64 to 0.66. We applied the social distancing classifiers to over 228 million tweets. We showed temporal patterns consistent with real-world events, and we showed correlations of up to –0.5 between social distancing signals on Twitter and ground truth mobility throughout the United States. Conclusions: Behaviors discussed on Twitter are exceptionally varied. Twitter can provide useful information for parameterizing models that incorporate human behavior, as well as for informing public health communication strategies by describing awareness of and compliance with suggested behaviors.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Defining and Measuring Forest Dependence in the United States: Operationalization and Sensitivity Analysis

This manuscript helps bridge a gap between theoretical work that advocates for a broad view of forest dependence, and empirical work that has focused narrowly on economic measures. Background: Forest dependence has been widely recognized as a valuable concept for understanding human communities’ well-being and vulnerability to shocks and changes. Past theoretical literature has highlighted the importance of recognizing various types of dependence—environmental, economic, and social—yet past empirical literature on the topic in the United States has almost exclusively relied on measures of economic dependence such as employment and earnings from the traditional forest products sector. Objective and Methods: As a first step to bridge the gap between the theoretical and empirical, we reviewed the existing, publicly available, reliable, wall-to-wall data sources to identify alternate proxy measures for forest dependence. Data availability made the analysis feasible only at the county level—the administrative subdivisions of the state—or higher. Results and Conclusions: We created environmental, economic, and social criteria based on threshold levels of the following proxy variables: forest area, earnings, employment, and indigenous population. Using these criteria, we identified 524 counties to be potentially forest-dependent of 3140 total counties in the United States. The largest concentration was in the Pacific Northwest and Southeast regions, and a higher proportion were non-metro counties than metro. Varying the threshold levels significantly changes the number of counties identified but does not alter the overall geographic trends.

54 ENVIRONMENTAL SCIENCES↗