Results for “health” — Page 14

DOE OSTI2020

Health co-benefits of achieving sustainable net-zero greenhouse gas emissions in California

The achievement of net-zero greenhouse gas (GHG) emissions by 2100 is required to limit global temperature rise below 2 °C above preindustrial levels. Earlier accomplishments of net-zero GHG emissions in developed regions support this global target. In this study, we have developed a road map for California to achieve net-zero GHG emissions sustainably in 2050 by using detailed modelling of energy system transformation, cross-sectoral connectivity and technology penetration, as well as quantify the associated health co-benefits from reduced co-emitted air pollutants. We find that approximately 14,000 premature deaths can be avoided in California in 2050 and that these health co-benefits are disproportionately higher in disadvantaged communities (that is, 35% of avoided deaths will come from 25% of the state’s population). The annualized monetary benefits (US$215 billion) exceed the GHG abatement cost (US$106 billion) by US$109 billion. This road map requires the use of bioenergy with carbon capture and sequestration technology to offset some GHG emissions. However, this technology comes at a price as it would emit a considerable amount of air pollutants and reduce health co-benefits by US$4 billion. Nevertheless, our analysis shows that ambitious GHG reduction efforts can provide substantial health co-benefits, especially for residents of disadvantaged communities.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Centralized Interactive Phenomics Resource: an integrated online phenomics knowledgebase for health data users

Development of clinical phenotypes from electronic health records (EHRs) can be resource intensive. Several phenotype libraries have been created to facilitate reuse of definitions. However, these platforms vary in target audience and utility. Here, we describe the development of the Centralized Interactive Phenomics Resource (CIPHER) knowledgebase, a comprehensive public-facing phenotype library, which aims to facilitate clinical and health services research. The platform was designed to collect and catalog EHR-based computable phenotype algorithms from any healthcare system, scale metadata management, facilitate phenotype discovery, and allow for integration of tools and user workflows. Phenomics experts were engaged in the development and testing of the site. The knowledgebase stores phenotype metadata using the CIPHER standard, and definitions are accessible through complex searching. Phenotypes are contributed to the knowledgebase via webform, allowing metadata validation. Data visualization tools linking to the knowledgebase enhance user interaction with content and accelerate phenotype development. The CIPHER knowledgebase was developed in the largest healthcare system in the United States and piloted with external partners. The design of the CIPHER website supports a variety of front-end tools and features to facilitate phenotype development and reuse. Health data users are encouraged to contribute their algorithms to the knowledgebase for wider dissemination to the research community, and to use the platform as a springboard for phenotyping. CIPHER is a public resource for all health data users available at https://phenomics.va.ornl.gov/ which facilitates phenotype reuse, development, and dissemination of phenotyping knowledge.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2020

On the vulnerability of data-driven structural health monitoring models to adversarial attack

Many approaches at the forefront of structural health monitoring rely on cutting-edge techniques from the field of machine learning. Recently, much interest has been directed towards the study of so-called adversarial examples; deliberate input perturbations that deceive machine learning models while remaining semantically identical. This article demonstrates that data-driven approaches to structural health monitoring are vulnerable to attacks of this kind. In the perfect information or ‘white-box’ scenario, a transformation is found that maps every example in the Los Alamos National Laboratory three-storey structure dataset to an adversarial example. Also presented is an adversarial threat model specific to structural health monitoring. The threat model is proposed with a view to motivate discussion into ways in which structural health monitoring approaches might be made more robust to the threat of adversarial attack.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2020

Smart Composite Pressure Vessels (SCPV) with Integrated Health Monitoring

The U.S. Department of Energy (DOE) is promoting and developing more energy efficient and environmentally friendly technologies that will enable America to use less petroleum. Hydrogen fuel cells – which directly convert the chemical energy in hydrogen to electricity with only water and heat as byproducts – are a very attractive solution that can enable this to happen. To date, DOE's efforts have culminated in commercial demonstration of on-board vehicular hydrogen storage systems that can allow for a driving range of greater than 300 miles. This requires storing 5 Kgs of hydrogen onboard a light vehicle. In order to store this quantity of hydrogen, the hydrogen gas needs to be stored in a composite overwrapped pressure vessel (COPV) at a very high internal pressure. The objective of the program was to demonstrate continuous and predictable health-monitoring of composite pressure vessels. A higher confidence in the operational safety of the vessels will lead to reduced burst factor of safety imposed by regulatory standards and hence will help reduce the structural wall thickness that drives the cost of the vessel. Highly optimized Type III (metal lined) and Type IV (polymer lined) COPVs that are manufactured using filament winding process have been designed and qualified for the above mentioned application. However, the main structural component of the COPVs is carbon fiber and the high cost of carbon fiber composite in a pressure vessel is a primary challenge in reducing the cost of gaseous hydrogen storage. Continuous and remote monitoring of structural health of the COPVs as well as optimization of the strength translation of carbon fibers have the potential to allow for reduced factor of safety, thereby reduced amount of carbon and associated cost of the vessel. In the current program, the project team including Steelhead Composites (SHC), University of Tennessee, Knoxville (UTK), Teijin Carbon America, Oak Ridge National Laboratory (ORNL) and LUNA designed, fabricated, and tested smart composite pressure vessels with integrated sensors. Iterative loops of design and testing using coupons, subscale STEB vessels and full size vessels proved that a new generation of high performance carbon fiber can be used for efficient design of Type 3 vessels for H2 storage. Novel analysis techniques were developed to predict the initiation and propagation of interlaminar damage inside the composite shell due to impact damage, a real threat in practical operation. Such an analysis scheme is typically not used in tank design but is an essential tool for health monitoring of composite vessels. Remote sensing of the key signatures of the tank’s operating parameters such as pressure, acceleration and humidity was demonstrated using a unique device that can wirelessly transmit and stream the data to a remote server. Fiber optic sensors were successfully integrated during fabrication of the tanks. These sensors provide a wealth of information regarding the structural health of the vessel when it is subsequently pressurized or subjected to impact damage. Excellent correlation was demonstrated between the fiber optic sensor and mechanical strain gage data, and between the measurements and analytical predictions.

08 HYDROGEN↗

DOE OSTI2020

Task 12 PV Sustainability - Human Health Risk Assessment Methods for PV, Part 3: Module Disposal Risks

End-of-life management is important for addressing large future photovoltaic (PV) waste volumes and conserving raw materials for use in new PV modules. In regions without regulatory mandates for PV recycling, end-of-life PV modules can be disposed in accordance with general waste laws. Given the use of various metals as raw materials in semiconductor compounds and electrical contacts in commercial PV modules, some stakeholders have raised concerns regarding potential environmental impacts if PV modules are subject to improper disposal instead of being recycled or disposed in sanitary landfills, as is required in most world regions. To evaluate these concerns, screening-level risk assessment methods are developed herein that evaluate potential human health risks from groundwater and surface (air, soil, surface water) exposure pathways. The methods estimate potential impacts from disposal of end-of-life (EOL) PV modules in non-sanitary landfills under the following worst-case conditions: no leachate collection or groundwater monitoring, no liner for preventing leachate migration, uncovered waste, and lack of stormwater management. Examining worst-case conditions allows the exploration of maximum potential risk to attempt to ensure disposal does not increase health risk above regulatory thresholds. Specifically, this report presents an analysis of potential human health risks associated with non-sanitary landfill disposal for three PV technologies, focusing on release of the highest-prioritized chemical element for each: lead (Pb) in crystalline-silicon (c-Si) PV modules, cadmium (Cd) in thin film cadmium telluride (CdTe) PV modules, and selenium (Se) in thin film copper indium selenide (CIS) PV modules. The prioritization of these chemical elements for analysis is based on stakeholder interest. Because the methodology is chemical-specific, the risk assessment results for these chemicals cannot be directly generalized to other chemicals, although the risk assessment methodology can be applied to other chemicals. If the chemicals chosen are indeed the ones presenting greatest risk, then the results herein should represent the upper bound of health risk from exposure to a single constituent.

14 SOLAR ENERGY↗

NASA NTRS2005

Behavioral health in Antarctica: implications for long-duration space missions

Ideally, evidence from long-duration spaceflight should be used to predict likely occurrences of behavioral health events and for planning management strategies for such events. With small numbers of space travelers, and limited long-duration missions of a year or more, Earth analogues and simulations must be used as the evidence base, despite such analogues lacking microgravity, radiation, rapidly altering photoperiodicity, and fidelity to space. Antarctic health data are reviewed and an assessment made of the likely frequency of behavioral health events. Based on the Antarctic evidence, the likelihood of behavioral health problems in space is low. However, such cases may be serious and of high consequence, placing considerable demands on the mission crew and ground support to achieve a successful outcome, given the availability of pharmaceuticals and resources.

Review↗

NASA NTRS2014

Using Satellite Remote Sensing and Household Survey Data to Assess Human Health and Nutrition Response to Environmental Change

Climate change and degradation of ecosystem services functioning may threaten the ability of current agricultural systems to keep up with demand for adequate and inexpensive food and for clean water, waste disposal and other broader ecosystem services. Human health is likely to be affected by changes occurring across multiple geographic and time scales. Impacts range from increasing transmissibility and the range of vector-borne diseases, such as malaria and yellow fever, to undermining nutrition through deleterious impacts on food production and concomitant increases in food prices. This paper uses case studies to describe methods that make use of satellite remote sensing and Demographic and Health Survey data to better understand individual-level human health and nutrition outcomes. By bringing these diverse datasets together, the connection between environmental change and human health outcomes can be described through new research and analysis.

Health↗

NASA NTRS2020

Philadelphia Health & Air Quality: Assessing Land Surface Temperature, Vegetation Cover, and Compounding Vulnerability Factors to Identify High Priority Areas for Cooling Initiatives in Philadelphia, Pennsylvania

Heat is the leading cause of weather-related deaths in the US, with heat-related hospitalizations increasing by 2-5% between 2001-2010. In Philadelphia alone, 137 heat-related deaths were recorded between 2010-2018, while a total of 18 daily temperature records have been set since 2010. Temperature is relatively higher in cities compared to rural areas, a phenomenon known as the urban heat island effect. This effect exaggerates daytime maximum temperatures and nighttime heat retention in urban areas, which increases heat exposure in urban environments and especially impacts vulnerable populations. Vulnerability to heat-related illnesses is determined by a combination of risk factors, such as demographics, socioeconomic status, and preexisting health conditions. This project supported the Philadelphia Department of Public Health and Office of Sustainability by identifying priority areas for cooling interventions, such as heat danger educational outreach and urban tree planting. The team developed heat vulnerability scores for each census tract within Philadelphia. Remotely sensed land surface temperature, normalized difference vegetation index, normalized difference built-up index, normalized difference water index, and albedo data were calculated from Aqua Moderate Resolution Imaging Spectroradiometer and Landsat 8 Operational Land Imager/Thermal Infrared Sensor instruments. These variables were weighted against socioeconomic variables and preexisting health conditions using a principal component analysis. A total of 74 census tracts clustered were identified as high-risk areas for heat-related illnesses. 15 of these census tracts also had very low tree density (lower 20th percentile) and should be targeted for tree planting initiatives. The findings of this project will help target interventions to mitigate heat-related health issues and improve the overall wellness of Philadelphia residents.

Health & Air Quality↗

NASA NTRS2020

Philadelphia Health & Air Quality: Assessing Land Surface Temperature, Vegetation Cover, and Compounding Vulnerability Factors to Identify High Priority Areas for Cooling Initiatives in Philadelphia, Pennsylvania

Heat is the leading cause of weather-related deaths in the US, with heat-related hospitalizations increasing by 2-5% between 2001-2010. In Philadelphia alone, 137 heat-related deaths were recorded between 2010-2018, while a total of 18 daily temperature records have been set since 2010. Temperature is relatively higher in cities compared to rural areas, a phenomenon known as the urban heat island effect.This effect exaggerates daytime maximum temperatures and nighttime heat retention in urban areas, which increases heat exposure inurban environments and especially impacts vulnerable populations. Vulnerability to heat-related illnesses is determined by a combination of risk factors, such as demographics, socioeconomic status, and preexisting health conditions. This project supported the Philadelphia Department of Public Health and Office of Sustainability by identifying priority areas for cooling interventions, such as heat danger educational outreach and urban tree planting. The team developed heat vulnerability scores for each census tract within Philadelphia. Remotely sensed land surface temperature, normalized difference vegetation index, normalized difference built-up index, normalized difference water index, and albedo data were calculated from Aqua Moderate Resolution Imaging Spectroradiometer and Landsat 8 Operational Land Imager/Thermal Infrared Sensor instruments. These variables were weighted against socioeconomic variables and preexisting health conditions using a principal component analysis. A total of 74 census tracts clustered were identified as high-risk areas for heat-related illnesses. 15 of these census tracts also had very low tree density (lower 20th percentile) and should be targeted for tree planting initiatives. The findings of this project will help target interventions to mitigate heat-related health issues and improve the overall wellness of Philadelphia residents.

Health & Air Quality↗

NASA NTRS2021

An Uncertainty Quantification Framework for Autonomous Flight System Tracking and Health Monitoring

This work proposes a perspective towards establishing a framework for uncertainty quantification of autonomous system tracking and health monitoring. The approach leverages the use of a predictive process structure, which maps uncertainty sources and their interaction according to the quantity of interest and the goal of the predictive estimation. It is systematic and uses basic elements that are system agnostic, and therefore needs to be tailored according to the specificity of the application. This work is motivated by the interest in low-altitude unmanned aerial vehicle operations, where awareness of vehicle and airspace state becomes more relevant as the density of autonomous operations grows rapidly. Predicted scenarios in the area of small vehicle operations and urban air mobility have no precedent, and holistic frameworks to perform prognostics and health management (PHM) at the system- and airspace-level are missing formal approaches to account for uncertainty. At the end of the paper, two case studies demonstrate implementation framework of trajectory tracking and health diagnosis for a small unmanned aerial vehicle. This work has been accepted for publication at the International Journal of Prognostics and Health Management Jan 2021. Minor edits have been incorporated to this original submission to incorporate complete overview and software integration.

Uncertainty Quantification↗

NASA NTRSDate not supplied

Biological Research and Space Health Enabled by Machine Learning to Support Deep Space Missions

A key science goal of the NASA “Moon to Mars” campaign is to understand how biology responds to the Lunar, Martian, and deep space environments in order to advance fundamental knowledge, reduce risk, and support safe, productive human space missions. Through the powerful emerging computer science approaches of artificial intelligence (AI) and machine learning (ML), a paradigm shift has begun in biomedical science and engineered astronaut health systems, to enable Earth-independence and autonomy of mission operations. We present a decadal view of AI/ML architecture to support deep space mission goals, developed in concert with leaders in the field. We describe current AI/ML methods to support 1) fundamental biology, 2) in situ analytics, 3) high performance computing hardware, 4) automated science, 5) self-driving labs, 6) remote data management, 7) integrated real-time mission biomonitoring, and 8) a Precision Space Health system. Cutting-edge AI/ML approaches that can be integrated to support these domains include active learning, explainable AI, adaptive learning, causal inference, knowledge graphs, federated learning, transfer learning, and large language models. Finally, we present results from several current ML projects that are underway in the field to address key challenges of small sample n, high feature count, heterogeneity, and sparse data. These include 1) connecting omics data to phenotypic data using an ensemble model to infer causality of spaceflight rodent liver health disruption, 2) usage of explainable ML to interrogate the muscular underpinnings of spaceflight muscle atrophy, 3) ML models analyzing and determining directed acyclic graphs of human space health risk leveraging rodent bone datasets, 4) usage of large pre-trained models connecting biomedical knowledgebases with small spaceflight datasets to understand gene-to-gene interaction networks, and 5) a suite of benchmarked open science datasets (spaceflight mouse liver; radiation DNA damage) enabling programmers to identify the best ML algorithms to answer space biological science questions.

space biology↗

NASA NTRSDate not supplied

Biological Research and Space Health Enabled by Machine Learning to Support Deep Space Missions

A key science goal of the NASA “Moon to Mars” campaign is to understand how biology responds to the Lunar, Martian, and deep space environments in order to advance fundamental knowledge and support human space missions. Through artificial intelligence (AI) and machine learning (ML), a paradigm shift has begun in space biosciences and engineered astronaut health systems, to enable Earth-independence and mission operations autonomy. We describe current AI/ML methods to support 1) fundamental biology, 2) in situ analytics, 3) high performance computing, 4) automated science, 5) self-driving labs, 6) remote data management, 7) integrated mission biomonitoring, and 8) a Precision Space Health system. AI/ML approaches that can be integrated to support these domains include active learning, explainable AI, adaptive learning, causal inference, knowledge graphs, federated learning, transfer learning, and large language models. Finally, we present results from several current ML projects that are underway in the space biology field to address key challenges of small sample n, high feature count, heterogeneity, and sparse data. These include 1) connecting omics to phenotypic data using an ensemble model to infer causality of rodent liver health disruption, 2) usage of explainable ML to interrogate muscular underpinnings of muscle atrophy, 3) ML models analyzing and determining directed acyclic graphs of human health risk leveraging rodent bone datasets, 4) usage of large pre-trained models connecting biomedical knowledgebases with small spaceflight datasets to understand gene-to-gene interactions, and 5) a suite of benchmarked open science datasets enabling programmers to identify best algorithms to answer space biology questions.

space biology↗

NASA NTRS2023

Health and Safety Effects of Airborne Soil Dust in the Americas and Beyond

Risks associated with dust hazards are often underappreciated, a gap between the knowledge pool and public awareness that can be costly for impacted communities. This study reviews the emission sources and chemical, physical, and biological characteristics of airborne soil particles (dust) and their effects on human and environmental health and safety in the Pan-American region. American dust originates from both local sources (western United States, northern Mexico, Peru, Bolivia, Chile, and Argentina) and long-range transport from Africa and Asia. Dust properties, as well as the trends and interactions with criteria air pollutants, are summarized. Human exposure to dust is associated with adverse health effects, including asthma, allergies, fungal infections, and premature death. In the Americas, a well-documented and striking effect of soil dust is its association with Coccidioidomycosis, commonly known as Valley fever, an infection caused by inhalation of soil-dwelling fungi unique to this region. Besides human health, dust affects environmental health through nutrients that increase phytoplankton biomass, contaminants that diminish water supply and affect food (crops/fruits/vegetables and ready-to-eat meat), spread crop and marine pathogens, cause Valley fever among domestic and wild animals, transport heavy metals, radionuclides and microplastics, and reduce solar and wind power generation. Dust is also a safety hazard to road transportation and aviation, in the southwestern US where blowing dust is one of the deadliest weather hazards. To mitigate the harmful effects, coordinated regional and international efforts are needed to enhance dust observations and prediction capabilities, soil conservation measures, and Valley fever and other disease surveillance.

health↗

NASA NTRSDate not supplied

Biological Research and Space Health Enabled by Machine Learning to Support Deep Space Missions

A key science goal of the NASA “Moon to Mars” campaign is to understand how biology responds to the Lunar, Martian, and deep space environments in order to advance fundamental knowledge, reduce risk, and support safe, productive human space missions. Through the powerful emerging computer science approaches of artificial intelligence (AI) and machine learning (ML), a paradigm shift has begun in biomedical science and engineered astronaut health systems, to enable Earth-independence and autonomy of mission operations. We present a decadal view of AI/ML architecture to support deep space mission goals, developed in concert with leaders in the field. We describe current AI/ML methods to support 1) fundamental biology, 2) in situ analytics, 3) high performance computing hardware, 4) automated science, 5) self-driving labs, 6) remote data management, 7) integrated real-time mission biomonitoring, and 8) a Precision Space Health system. Cutting-edge AI/ML approaches that can be integrated to support these domains include active learning, explainable AI, adaptive learning, causal inference, knowledge graphs, federated learning, transfer learning, and large language models. Finally, we present results from several current ML projects that are underway in the field to address key challenges of small sample n, high feature count, heterogeneity, and sparse data. These include 1) connecting omics data to phenotypic data using an ensemble model to infer causality of spaceflight rodent liver health disruption, 2) usage of explainable ML to interrogate the muscular underpinnings of spaceflight muscle atrophy, 3) ML models analyzing and determining directed acyclic graphs of human space health risk leveraging rodent bone datasets, 4) usage of large pre-trained models connecting biomedical knowledgebases with small spaceflight datasets to understand gene-to-gene interaction networks, and 5) a suite of benchmarked open science datasets (spaceflight mouse liver; radiation DNA damage) enabling programmers to identify the best ML algorithms to answer space biological science questions.

space biology↗

NASA NTRSDate not supplied

Biological Research and Space Health Enabled by Machine Learning to Support Deep Space Missions

A key science goal of the NASA “Moon to Mars” campaign is to understand how biology responds to the Lunar, Martian, and deep space environments in order to advance fundamental knowledge, reduce risk, and support safe, productive human space missions. Through the powerful emerging approaches of artificial intelligence (AI) and machine learning (ML), a paradigm shift has begun in biomedical science and engineered astronaut health systems, to enable Earth independence and autonomy of mission operations. Here we present an overview of AI/ML architecture to support deep space mission goals, developed with leaders in the field. First, we focus on the fundamental biological research that supports our understanding of physiological responses to spaceflight, and we describe current efforts to support AI/ML research including data standardization and data engineering through maximally open and FAIR (findable, accessible, interoperable, reusable) databases and the generation of AI-ready datasets for reuse and analysis. We also discuss remote data management frameworks for research data as well as environmental and health data that are generated during deep space missions. We highlight several research projects that leverage data standardization and management for fundamental biological discovery to uncover the complex effects of space travel on living systems. Next, we provide an overview of cutting-edge AI/ML approaches that can be integrated to support remote monitoring and analysis during deep space missions, including generative models and large language models to learn the underlying biomedical patterns and predict outcomes or answer questions during off world medical scenarios. We also describe current AI/ML methods to support this research and monitoring through automated cloud-based labs which enable limited human intervention and closed-loop experimentation in remote settings. These labs could support mission autonomy by analyzing environmental data streams, and would be facilitated through in situ analytics capabilities to avoid sending large raw data files through low bandwidth communications. Finally, in the context of deep space missions with limited communications or access to medical advice from Earth, we describe a solution for integrated, real-time mission biomonitoring across hierarchical levels from continuous environmental monitoring, to wearables and point-of-care devices, to molecular and physiological monitoring. We introduce a precision space health system that will ensure that the future of space health is predictive, preventative, participatory and personalized.

artificial intelligence↗

NASA NTRSDate not supplied

Behavioral Health Benefits of the ISS Pick-and-Eat Crop Growth System

Summary: We summarize findings related to assessing the pick-and-eat salad-crop productivity and acceptability of the ISS food system as a countermeasure to maintain behavioral health in long-duration spaceflight. BACKGROUND Plants are a potential countermeasure for the stresses of living in space. Caring for plants and eating fresh food have been shown to serve as a psychological benefit for previous astronauts and for others in analogous environments such as Antarctica [1,2]. Gardening can be therapeutic in terrestrial settings [3]. These effects may carry over or be more pronounced in austere environments such as spaceflight. It can reduce stress and increase sensory stimulation and enjoyment [2]. Growing crops may offer astronauts meaningful and engaging work as they care for living things in an austere environment away from Earth’s nature and as they provide sustenance for their crew. We examined the extent to which interacting with plants (e.g., tending to, consuming) was related to behavioral health outcomes in long-duration space missions. METHOD Participants were 27 astronauts that interacted with the plant system VEG-04, VEG-05, and HRF-VEG (VEG-03 I-L, PH-04). In total, there were 106 in-flight observations. Participants completed monthly measures of mood and well-being, enjoyment and time spent performing specific crop growth and consuming the plants, meaningfulness, performance, connections with others and the Earth, desire to work with and eat plants, and experiences with struggling or dying plants. Higher ends of the 7-point Likert scale indicated more positive outcomes, lower ends of the scale were anchored with more negative outcomes, and the scale midpoints indicated neutral positions. Participants selected a specific survey version based on whether they interacted with the plant system(s) or not, and we included only the scores from the crewmembers that participated in any given activity. RESULTS & DISCUSSION Results indicated that participants generally rated tasks related to the plants and crop growth as enjoyable. Consuming the harvested plants and voluntary viewing were the most enjoyable activities. Tending to the plants was reported as moderately enjoyable, indicating BHP benefits from this work task. The BHP impacts of whether the task was engaging, meaningful, supported well-being, or was demanding for a crewmember were generally positive and increased over time. As shown in the Figure, working with plants was reported as more engaging, meaningful, and beneficial to well-being over time, while the demand from the task was moderate to low and consistent over time. Engaging, meaningful, and supporting well-being were strongly, positively correlated with each other, which is consistent with research that suggests engaging and meaningful work can support well-being. Plant activities such as voluntary viewing, watering, harvesting, and consuming, were positively related to task engagement and meaning, and enjoyment and well-being. Findings suggest that working with plants and consuming them can be a behavioral health countermeasure for long-duration spaceflight. REFERENCES [1] Schlacht I, et al. (2020) Impact of plants in isolation: The EDEN-ISS…in Antarctica. In Advances in Human Factors of Transportation, Washington DC. [2] Vessel E, Russo S (2015) Effects of reduced sensory stimulation and…countermeasures…. NASA/TM-2015-218576. [3] Odeh R, Guy C (2017) Gardening for therapeutic people-plant interactions during LDSM. Open Agriculture, 2(1), 1-13. ACKNOWLEDGEMENTS This research was supported by the Human Health and Performance Contract NNJ15HK11B, and NASA grant MTL #1075 (PI G.D. Massa).

behavioral health↗

NASA NTRSDate not supplied

Behavioral Health Benefits of the ISS Pick-and-Eat Crop Growth System

Summary: We summarize findings related to assessing the pick-and-eat salad-crop productivity and acceptability of the ISS food system as a countermeasure to maintain behavioral health in long-duration spaceflight. BACKGROUND Plants are a potential countermeasure for the stresses of living in space. Caring for plants and eating fresh food have been shown to serve as a psychological benefit for previous astronauts and for others in analogous environments such as Antarctica [1,2]. Gardening can be therapeutic in terrestrial settings [3]. These effects may carry over or be more pronounced in austere environments such as spaceflight. It can reduce stress and increase sensory stimulation and enjoyment [2]. Growing crops may offer astronauts meaningful and engaging work as they care for living things in an austere environment away from Earth’s nature and as they provide sustenance for their crew. We examined the extent to which interacting with plants (e.g., tending to, consuming) was related to behavioral health outcomes in long-duration space missions. METHOD Participants were 27 astronauts that interacted with the plant system VEG-04, VEG-05, and HRF-VEG (VEG-03 I-L, PH-04). In total, there were 106 in-flight observations. Participants completed monthly measures of mood and well-being, enjoyment and time spent performing specific crop growth and consuming the plants, meaningfulness, performance, connections with others and the Earth, desire to work with and eat plants, and experiences with struggling or dying plants. Higher ends of the 7-point Likert scale indicated more positive outcomes, lower ends of the scale were anchored with more negative outcomes, and the scale midpoints indicated neutral positions. Participants selected a specific survey version based on whether they interacted with the plant system(s) or not, and we included only the scores from the crewmembers that participated in any given activity. RESULTS & DISCUSSION Results indicated that participants generally rated tasks related to the plants and crop growth as enjoyable. Consuming the harvested plants and voluntary viewing were the most enjoyable activities. Tending to the plants was reported as moderately enjoyable, indicating BHP benefits from this work task. The BHP impacts of whether the task was engaging, meaningful, supported well-being, or was demanding for a crewmember were generally positive and increased over time. As shown in the Figure, working with plants was reported as more engaging, meaningful, and beneficial to well-being over time, while the demand from the task was moderate to low and consistent over time. Engaging, meaningful, and supporting well-being were strongly, positively correlated with each other, which is consistent with research that suggests engaging and meaningful work can support well-being. Plant activities such as voluntary viewing, watering, harvesting, and consuming, were positively related to task engagement and meaning, and enjoyment and well-being. Findings suggest that working with plants and consuming them can be a behavioral health countermeasure for long-duration spaceflight. REFERENCES [1] Schlacht I, et al. (2020) Impact of plants in isolation: The EDEN-ISS…in Antarctica. In Advances in Human Factors of Transportation, Washington DC. [2] Vessel E, Russo S (2015) Effects of reduced sensory stimulation and…countermeasures…. NASA/TM-2015-218576. [3] Odeh R, Guy C (2017) Gardening for therapeutic people-plant interactions during LDSM. Open Agriculture, 2(1), 1-13. ACKNOWLEDGEMENTS This research was supported by the Human Health and Performance Contract NNJ15HK11B, and NASA grant MTL #1075 (PI G.D. Massa).

countermeasure↗

DOE OSTI2022

Optimizing DER Deployment for Climate, Health, Resilience, and Energy Bill Benefits Using the REopt Model

Climate change, public health, and resilience to power outages are of critical concern to local governments, federal agencies, and the private sector, and are increasingly motivating investments in distributed energy resources (DERs). However, designing a solar-plus-storage system to co-optimize for climate, health, resilience, and energy bill benefits requires complex trade-offs. To address this need, the National Renewable Energy Laboratory has integrated climate and health impacts of grid-purchased electricity and on-site fuel consumption into the publicly-available REopt web tool and API - a techno-economic model that determines the cost-optimal DER system sizes and dispatch strategy. This presentation will provide an overview of the tool's methodology, datasets, and capabilities, including costing emissions into system sizing and dispatch and setting emissions reductions targets. Results of previous work will demonstrate the use of new emissions accounting capabilities by quantifying the impact of including climate and health costs on the optimal resilient microgrid configurations for a hospital, school, and warehouse across 14 U.S. cities.

climate↗