Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Risk Analyses”

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 703 records · Page 39
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Long-Term Effects of Very Low Dose Particle Radiation on Gene Expression in the Heart: Degenerative Disease Risks

Compared to low doses of gamma irradiation (γ-IR), high-charge-and-energy (HZE) particle IR may have different biological response thresholds in cardiac tissue at lower doses, and these effects may be IR type and dose dependent. Three- to four-month-old female CB6F1/Hsd mice were exposed once to one of four different doses of the following types of radiation: γ-IR 137Cs (40-160 cGy, 0.662 MeV), 14Si-IR (4-32 cGy, 260 MeV/n), or 22Ti-IR (3-26 cGy, 1 GeV/n). At 16 months post-exposure, animals were sacrificed and hearts were harvested and archived as part of the NASA Space Radiation Tissue Sharing Forum. These heart tissue samples were used in our study for RNA isolation and microarray hybridization. Functional annotation of twofold up/down differentially expressed genes (DEGs) and bioinformatics analyses revealed the following: (i) there were no clear lower IR thresholds for HZE- or γ-IR; (ii) there were 12 common DEGs across all 3 IR types; (iii) these 12 overlapping genes predicted various degrees of cardiovascular, pulmonary, and metabolic diseases, cancer, and aging; and (iv) these 12 genes revealed an exclusive non-linear DEG pattern in 14Si- and 22Ti-IR-exposed hearts, whereas two-thirds of γ-IR-exposed hearts revealed a linear pattern of DEGs. Thus, our study may provide experimental evidence of excess relative risk (ERR) quantification of low/very low doses of full-body space-type IR-associated degenerative disease development.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗
DOE OSTI2025

De-Risking High-Recovery Brackish Water Desalination via Flow Reversal and Feed Flushing Using Techno-Economic Assessment

Novel desalination technologies have demonstrated enhanced performance and improved financial metrics over existing processes adopted by industry. Establishing quantitative performance targets is essential for achieving financial benefits over the current state of the art. Herein, we demonstrate how WaterTAP, a techno-economic assessment (TEA) tool, can be used to identify minimum performance metrics necessary to achieve financial benefit over using existing processes. This study evaluates the feasibility of increasing water recovery at the Chino Desalter I above 90 % through the addition of a third variable configuration reverse osmosis (VCRO) stage. Sensitivity analyses revealed flow reversal frequency, feed flushing volume (used as a cleaning step), and membrane lifespan are key factors influencing the financial viability of the VCRO process. The TEA analysis demonstrated that the system must achieve a recovery of 84 % and a 1-year membrane lifespan to have a breakeven LCOW, while achieving 90 % recovery can ultimately reduce the LCOW by 16 %. Notably, a trade-off between decreasing frictional losses and increased osmotic pressure across the recovery range, resulted in a stable specific energy consumption across the recovery range, enabling meaningful LCOW reductions without an energy penalty, a key finding that contrasts with conventional RO. This work demonstrates how TEA can guide system design by identifying key performance targets and exploring trade-offs, enabling data-driven decisions to de-risk innovative processes. These findings underscore the importance of leveraging TEA to evaluate scaling mitigation strategies and optimize inland desalination systems for sustainable and cost-effective operation.

14 SOLAR ENERGY↗
DOE OSTI2021

Association Between Dispatch Complaint and Critical Prehospital Time Intervals in Suspected Stroke 911 Activations in the National Emergency Medical Services Information System, 2012–2016

Emergency Medical Services can help improve stroke outcomes by recognizing stroke symptoms, establishing response priority for 911 calls, and minimizing prehospital delays. This study examines 911 stroke events and evaluates associations between events dispatched as stroke and critical EMS time intervals. Data from the National Emergency Medical Services Information System, 2012 to 2016, were analyzed. Activations from 911 calls with a primary or secondary provider impression of stroke were included for adult patients transported to a hospital destination. Three prehospital time intervals were evaluated: (1) response time (RT) ≤8 min, (2) on-scene time (OST) ≤15 min, and (3) transport time (TT) ≤12 min. Associations between stroke dispatch complaint and prehospital time intervals were assessed using multivariate regression to estimate adjusted risk ratios (ARR) and 95% confidence intervals (CIs). Approximately 37% of stroke dispatch complaints were identified by EMS as a suspected stroke. Compared to stroke events without a stroke dispatch complaint, median OST was shorter for events with a stroke dispatch (16 min vs. 14 min, respectively). In adjusted analyses, events dispatched as stroke were more likely to meet the EMS time benchmark for OST ≤15 min (OST, 1.20 [1.20–1.21]), but not RT or TT (RT, [1.00–1.01]; TT, 0.95 [0.94–0.95]). Furthermore, our results indicate that dispatcher recognition of stroke symptoms reduces the time spent on-scene by EMS personnel. These findings can inform future EMS stroke education and quality improvement efforts to emphasize dispatcher recognition of stroke signs and symptoms, as EMS dispatchers play a crucial role in optimizing the prehospital response.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Geographic, sectoral, and constituent characteristics of US off‐site manufacturing wastewater disposal

This study employs recent US Environmental Protection Agency off-site manufacturing wastewater disposal data and a socioenvironmental assessment tool for the United States to understand the characteristics of such disposal in terms of geography, major contributing sectors and pollutants, and environmental impacts. Environmental impact analyses of manufacturing are insufficient without encompassing the extended physical boundary of waste management. Our analysis reveals that off-site manufacturing wastewater disposal occurs disproportionately in populations residing in census tracts already negatively impacted by environmental hazards (impacted populations, or IPs) with 44% of transfers and 55% of Risk Screening Environmental Indicators (RSEI) Hazard going to these areas, compared to their national share of 37%. Disposal hazard is concentrated in a small number of populations, with a Gini coefficient of 0.99 for RSEI Hazard. Four manufacturing sub-sectors are significant generators: Chemicals, Fabricated Metals, Primary Metals, and Transportation Equipment, with Chemicals off-site wastewater disposal largely occurring in IPs. For individual contaminants, chromium compounds and chromium represent more than 85% of the hazard but less than 10% of transfers. We explore transfer distances and waste generation and disposal hotspots, finding that the Midwest hosts a disproportionate share of off-site wastewater disposal. Further, RSEI Hazard steeply rises at shorter distances and plateaus over distances >500 miles, revealing opportunities to reduce hazard by reducing 20–500-mile transfers. Our findings strongly support targeted mitigation strategies like process substitutions, control technologies, on-site recycling and treatment, and minimizing transfer distances. This article met the requirements for a gold-gold JIE data openness badge described at http://jie.click/badges.

Fuchs, Heidi [Lawrence Berkeley National Laborator↗
DOE OSTI2024

MSD CoP Webinar: Accounting for distributive justice in model-based decision support

Context: This webinar was hosted by the MultiSector Dynamics Community of Practice (MSD CoP; https://multisectordynamics.org). Abstract: Model-based analyses play an ever-increasing role in informing policy and decision-making. However, many large-scale societal challenges unavoidably involve questions about the fair distribution of positive and negative consequences. How are the costs of the energy transition distributed over households and businesses? How are flood risks redistributed under different flood risk management plans? How are the impacts of climate change and climate mitigation distributed over different parts of the world?These kinds of questions play an important role when deliberating public policy, and the lack, in many cases, of clear answers becomes an obstacle to decision-making. At recent COPs, for example, wicked questions about loss and damages and the phase-out versus phase-down of coal became obstacles to global climate action, illustrating the misalignment of the direction of scientific research and decision-makers' information needs. In this talk, I'll explore advances that are being made that enable analysts to start providing grounded model-based answers to questions of distributive justice, as well as argue that analysts should embrace and explicate the normative nature of their work instead of hiding behind the purported neutrality of science. Presenters: Dr. Jan Kwakkel (Delft University of Technology) Moderator(s): Rebecca Saari (MSD CoP Working Group Co-Chair; Univ. of Waterloo), Matt Sparks (Univ. of Waterloo); Sarah Fletcher (MSD CoP Working Group Co-Chair; Stanford University), Juan Moreno-Cruz (Univ. of Waterloo), Patrick Reed (MSD CoP Facilitation Team Member, Moderator and Organizer) This webinar was held on: November 13, 2024 from 1 PM - 2:15 PM ET

Kwakkel, Jan H.↗
NASA NTRS1980

Orbit transfer vehicle engine study, phase A extension. Volume 2A: Study results

Engine trade studies and systems analyses leading to a baseline engine selection for advanced expander cycle engine are discussed with emphasis on: (1) performance optimization of advanced expander cycle engines in the 10 to 20K pound thrust range; (2) selection of a recommended advanced expander engine configuration based on maximized performance and minimized mission risk, and definition of the components for this configuration; (3) characterization of the low thrust adaptation requirements and performance for the staged combustion engine; (4) generation of a suggested safety and reliability approach for OTV engines independent of engine cycle; (5) definition of program risk relationships between expander and staged combustion cycle engines; and (6) development of schedules and costs for the DDT&E, production, and operation phases of the 10K pound thrust expander engine program.

Source record↗
NASA NTRS1988

Environmental control medical support team

The activities conducted in support of the Environmental Control and Life Support Team during December 7, 1987 through September 30, 1988 are summarized. The majority of the ongoing support has focused on the ECLSS area. Through a series of initial meetings with the ECLSS team and technical literature review, an initial list of critical topics was developed. Subtasks were then identified or additional related tasks received as action items from the ECLSS group meetings. Although most of the efforts focused on providing MSFC personnel with information regarding specific questions and problems related to ECLSS issues, other efforts regarding identifying an ECLSS Medical Support Team and constructing data bases of technical information were also initiated and completed. The specific tasks are as follows: (1) Provide support to the mechanical design and integration of test systems as related to microbiological concerns; (2) Assist with design of Human Subjects Test Protocols; (3) Interpretation and recommendations pertaining to air/water quality requirements; (4) Assist in determining the design specifications required as related to the Technical Demonstration Program; (5) Develop a data base of all microorganisms recovered from previous subsystem testing; (6) Estimates of health risk of individual microbes to test subjects; (7) Assist with setting limits for safety of test subjects; (8) Health monitoring of test subjects; (9) Assist in the preparation of test plans; (10) Assist in the development of a QA/QC program to assure the validity, accuracy and precision of the analyses; and (11) Assist in developing test plans required for future man in the loop testing.

Crump, William J.↗
NASA NTRS2012

Using NASA Remotely Sensed Data to Help Characterize Environmental Risk Factors for National Public Health Applications

The overall goal of this study is to address issues of environmental health and enhance public health decision making by using NASA remotely sensed data and products. This study is a collaboration between NASA Marshall Space Flight Center, Universities Space Research Association (USRA), the University of Alabama at Birmingham (UAB) School of Public Health and the Centers for Disease Control and Prevention (CDC) Office of Surveillance, Epidemiology and Laboratory Services. The objectives of this study are to develop high-quality spatial data sets of environmental variables, link these with public health data from a national cohort study, and deliver the environmental data sets and associated public health analyses to local, state and federal end ]user groups. Three daily environmental data sets were developed for the conterminous U.S. on different spatial resolutions for the period 2003-2008: (1) spatial surfaces of estimated fine particulate matter (PM2.5) on a 10-km grid using US Environmental Protection Agency (EPA) ground observations and NASA's MODerate-resolution Imaging Spectroradiometer (MODIS) data; (2) a 1-km grid of MODIS Land Surface Temperature (LST); and (3) a 12-km grid of daily incoming solar radiation and maximum and minimum air temperature using the North American Land Data Assimilation System (NLDAS) data. These environmental datasets were linked with public health data from the UAB REasons for Geographic and Racial Differences in Stroke (REGARDS) national cohort study to determine whether exposures to these environmental risk factors are related to cognitive decline, stroke and other health outcomes. These environmental national datasets will also be made available to public health professionals, researchers and the general public via the CDC Wide-ranging Online Data for Epidemiologic Research (WONDER) system, where they can be aggregated to the county-level, state-level, or regional-level as per users f need and downloaded in tabular, graphical, and map formats. This provides a significant addition to the CDC WONDER online system, allowing public health researchers and policy makers to better include environmental exposure data in the context of other health data available in CDC WONDER. It also substantially expands public access to NASA data, making their use by a wide range of decisionmakers feasible.

Al-Hamdan, Mohammad↗
DOE OSTI2024

Evidential Deep Learning: Enhancing Predictive Uncertainty Estimation for Earth System Science Applications

Abstract Robust quantification of predictive uncertainty is a critical addition needed for machine learning applied to weather and climate problems to improve the understanding of what is driving prediction sensitivity. Ensembles of machine learning models provide predictive uncertainty estimates in a conceptually simple way but require multiple models for training and prediction, increasing computational cost and latency. Parametric deep learning can estimate uncertainty with one model by predicting the parameters of a probability distribution but does not account for epistemic uncertainty. Evidential deep learning, a technique that extends parametric deep learning to higher-order distributions, can account for both aleatoric and epistemic uncertainties with one model. This study compares the uncertainty derived from evidential neural networks to that obtained from ensembles. Through applications of the classification of winter precipitation type and regression of surface-layer fluxes, we show evidential deep learning models attaining predictive accuracy rivaling standard methods while robustly quantifying both sources of uncertainty. We evaluate the uncertainty in terms of how well the predictions are calibrated and how well the uncertainty correlates with prediction error. Analyses of uncertainty in the context of the inputs reveal sensitivities to underlying meteorological processes, facilitating interpretation of the models. The conceptual simplicity, interpretability, and computational efficiency of evidential neural networks make them highly extensible, offering a promising approach for reliable and practical uncertainty quantification in Earth system science modeling. To encourage broader adoption of evidential deep learning, we have developed a new Python package, Machine Integration and Learning for Earth Systems (MILES) group Generalized Uncertainty for Earth System Science (GUESS) (MILES-GUESS) ( https://github.com/ai2es/miles-guess ), that enables users to train and evaluate both evidential and ensemble deep learning. Significance Statement This study demonstrates a new technique, evidential deep learning, for robust and computationally efficient uncertainty quantification in modeling the Earth system. The method integrates probabilistic principles into deep neural networks, enabling the estimation of both aleatoric uncertainty from noisy data and epistemic uncertainty from model limitations using a single model. Our analyses reveal how decomposing these uncertainties provides valuable insights into reliability, accuracy, and model shortcomings. We show that the approach can rival standard methods in classification and regression tasks within atmospheric science while offering practical advantages such as computational efficiency. With further advances, evidential networks have the potential to enhance risk assessment and decision-making across meteorology by improving uncertainty quantification, a longstanding challenge. This work establishes a strong foundation and motivation for the broader adoption of evidential learning, where properly quantifying uncertainties is critical yet lacking.

Schreck, John S.↗
NASA NTRS2012

Use of Commercial Electrical, Electronic and Electromechanical (EEE) Parts in NASA's Commercial Crew Program (CCP)

NASA's Commercial Crew and Cargo Program (CCP) is stimulating efforts within the private sector to develop and demonstrate safe, reliable, and cost-effective space transportation capabilities. One initiative involves investigating the use of commercial electronic parts. NASA's CCP asked the NASA Engineering and Safety Center (NESC) to collect data to help frame the technical, cost, and schedule risk trades associated with electrical, electronic and electromechanical (EEE) parts selection and specifically expressed desire of some of the CCP partners to employ EEE parts of a lower grade than traditionally used in most NASA safety-critical applications. This document contains the outcome from the NESC's review and analyses.

Gonzalex, Oscar↗
NASA NTRS2017

A Network-Based Algorithm for Clustering Multivariate Repeated Measures Data

The National Aeronautics and Space Administration (NASA) Astronaut Corps is a unique occupational cohort for which vast amounts of measures data have been collected repeatedly in research or operational studies pre-, in-, and post-flight, as well as during multiple clinical care visits. In exploratory analyses aimed at generating hypotheses regarding physiological changes associated with spaceflight exposure, such as impaired vision, it is of interest to identify anomalies and trends across these expansive datasets. Multivariate clustering algorithms for repeated measures data may help parse the data to identify homogeneous groups of astronauts that have higher risks for a particular physiological change. However, available clustering methods may not be able to accommodate the complex data structures found in NASA data, since the methods often rely on strict model assumptions, require equally-spaced and balanced assessment times, cannot accommodate missing data or differing time scales across variables, and cannot process continuous and discrete data simultaneously. To fill this gap, we propose a network-based, multivariate clustering algorithm for repeated measures data that can be tailored to fit various research settings. Using simulated data, we demonstrate how our method can be used to identify patterns in complex data structures found in practice.

Koslovsky, Matthew↗
NASA NTRSDate not supplied

Neurobiological Outcomes of Mice Exposed to Combined Spaceflight Stressors

As NASA plans for exploration beyond Low-Earth Orbit, it is imperative to understand how deep spaceflight stressors impact human physiology. Deep space exposes crew members to multiple NASA-defined risks, including altered gravity, ionizing radiation, and social isolation. While the effects of individual stressors have been studied, there are still gaps in our understanding of their combined effects. In this study, we utilized the simulated five-ion galactic cosmic radiation (GCR) and isolation to assess the combinatorial effects of space stressors on the central nervous system (CNS) in mice. Our study captures sexually dimorphic responses by quantifying CNS outcomes in crewage-matched male and female mice. We assessed CNS responses in mice exposed to four days of isolation, followed by three different dosage of acute radiation exposure (5 cGy, 15 cGy, and 50 cGy 5-ion GCRsim). Comprehensive hippocampal cytokine biomarker analyses were conducted in both males and females at two weeks post-irradiation (intermediate cohort) and 124 days post-irradiation (delayed cohort). Proteomics on hippocampal lysates were conducted in the delayed cohort. Our results provide evidence for sex-specific differentially regulated hippocampal cytokines and proteomic profiles. Immunohistochemical analysis of the mice brain from intermediate and delayed cohorts is currently underway for multiple protein markers, including microglial activation, astrocytes, and dopaminergic neuronal population, among others. This project closely aligns with NASA’s efforts to characterize sex-specific risks associated with deep space exploration. We anticipate the results from this project will aid in our understanding of spaceflight stressors to ensure crew health and performance.

spaceflight↗
NASA NTRS1994

Advanced automated glass cockpit certification: Being wary of human factors

This paper presents some facets of the French experience with human factors in the process of certification of advanced automated cockpits. Three types of difficulties are described: first, the difficulties concerning the hotly debated concept of human error and its non-linear relationship to risk of accident; a typology of errors to be taken into account in the certification process is put forward to respond to this issue. Next, the difficulties connected to the basically gradual and evolving nature of pilot expertise on a given type of aircraft, which contrasts with the immediate and definitive style of certifying systems. The last difficulties to be considered are those related to the goals of certification itself on these new aircraft and the status of findings from human factor analyses (in particular, what should be done with disappointing results, how much can the changes induced by human factors investigation economically affect aircraft design, how many errors do we need to accumulate before we revise the system, what should be remedied when human factor problems are discovered at the certification stage: the machine? pilot training? the rules? or everything?). The growth of advanced-automated glass cockpits has forced the international aeronautical community to pay more attention to human factors during the design phase, the certification phase and pilot training. The recent creation of a human factor desk at the DGAC-SFACT (Official French services) is a direct consequence of this. The paper is divided into three parts. Part one debates human error and its relationship with system design and accident risk. Part two describes difficulties connected to the basically gradual and evolving nature of pilot expertise on a given type of aircraft, which contrasts with the immediate and definitive style of certifying systems. Part three focuses on concrete outcomes of human factors for certification purposes.

Amalberti, Rene↗
NASA NTRS2002

Preliminary Integrated Safety Analysis of Synthetic Vision Conducted

The goal of the NASA Aviation Safety Program is to develop and demonstrate technologies that could help reduce the aviation fatal accident rate by a factor of 5 by the year 2007 and by a factor of 10 by the year 2022. Integrated safety analysis of day-to-day operations and risks within those operations will provide an understanding of the Aviation Safety Program portfolio beyond what is now available. Synthetic vision is the first of the Aviation Safety Program technologies that has been analyzed by the Logistics Management Institute under a contract with the NASA Glenn Research Center. These synthetic vision analyses include both a reliability analysis and a computer simulation model.

Reveley, Mary S.↗
NASA NTRS2019

The NASA Orbital Debris Engineering Model 3.1: Development, Verification, and Validation

The NASA Orbital Debris Program Office has developed the Orbital Debris Engineering Model (ORDEM) primarily as a tool for spacecraft designers and other users to understand the long-term risk of collisions with orbital debris. The newest version, ORDEM 3.1, incorporates the latest and highest fidelity datasets available to build and validate representative orbital debris populations encompassing low Earth orbit (LEO) to geosynchronous orbit (GEO) altitudes for the years 2016-2050. ORDEM 3.1 models fluxes for object sizes > 10 μm within or transiting LEO and > 10 cm in GEO. The deterministic portion of the populations in ORDEM 3.1 is based on the U.S. Space Surveillance Network (SSN) catalog, which provides coverage down to approximately 10 cm in LEO and 1 m in GEO. Observational datasets from radar, in situ, and optical sources provide a foundation from which the model populations are statistically extrapolated to smaller sizes and orbit regions that are not well-covered by the SSN catalog, yet may pose the greatest threat to operational spacecraft. Objects in LEO ranging from approximately 5 mm to 10 cm are modeled using observational data from ground-based radar, namely the Haystack Ultrawideband Satellite Imaging Radar (HUSIR – formerly known as Haystack). The LEO population smaller than approximately 3 mm in size is characterized based on a reanalysis of in situ data from impacts to the windows and radiators of the U.S. Space Transportation System orbiter vehicle, i.e., the Space Shuttle. Data from impacts on the Hubble Space Telescope are also used to validate the sub-millimeter model populations in LEO. Debris in GEO with sizes ranging from 10 cm to 1 m is modeled using optical measurement data from the Michigan Orbital DEbris Survey Telescope (MODEST). Specific, major debris-producing events, including the Fengyun-1C, Iridium 33, and Cosmos 2251 debris clouds, and unique populations, such as sodium-potassium droplets, have been re-examined and are modeled and added to the ORDEM environment separately. The debris environment greater than 1 mm is forecast using NASA’s LEO-to- GEO ENvironment Debris model (LEGEND). Future explosions of intact objects and collisions involving objects greater than 10 cm are assessed statistically, and the NASA Standard Satellite Breakup Model is used to generate fragments from these events. Fragments smaller than 10 cm are further differentiated based on material density categories, i.e., high-, medium-, and low-density, to better characterize the potential debris risk posed to spacecraft. The future projection of the sub-millimeter environment is computed using a special small-particle degradation model where small particles are created from intact spacecraft and rocket bodies. This work discusses the development, features, and capabilities of the ORDEM 3.1 model; the ne new data analyses used to build the model populations; and sample verification and validation results.

Matney, M.↗
DOE OSTI2022

Improving Traditional Registrational Trial End Points: Development and Application of a Desirability of Outcome Ranking End Point for Complicated Urinary Tract Infection Clinical Trials

Abstract Background Traditional end points used in registrational randomized, controlled trials (RCTs) often do not allow for complete interpretation of the full range of potential clinical outcomes. Desirability of outcome ranking (DOOR) is an approach to the design and analysis of clinical trials that incorporates benefits and risks of novel treatment strategies and provides a global assessment of patient experience. Methods Through a multidisciplinary committee of experts in infectious diseases, clinical trial design, drug regulation, and patient experience, we developed a DOOR end point for infectious disease syndromes and demonstrated how this could be applied to 3 registrational drug trials (ZEUS, APEKS-cUTI, and DORI-05) for complicated urinary tract infections (cUTIs). ZEUS compared fosfomycin to piperacillin/tazobactam, APEKS-cUTI compared cefiderocol to imipenem, and DORI-05 compared doripenem to levofloxacin. Using DOOR, we estimated the probability of a more desirable outcome with each investigational antibacterial drug. Results In each RCT, the DOOR distribution was similar and the probability that a patient in the investigational arm would have a more desirable outcome than a patient in the control arm had a 95% confidence interval containing 50%, indicating no significant difference between treatment arms. DOOR facilitated improved understanding of potential trade-offs between clinical efficacy and safety. Partial credit and subgroup analyses also highlight unique attributes of DOOR. Conclusions DOOR can effectively be used in registrational cUTI trials. The DOOR end point presented here can be adapted for other infectious disease syndromes and prospectively incorporated into future clinical trials.

Immunology↗
DOE OSTI2026

Reassessing Hybrid Vigor or Hybrid Dysfunction Using Physiological Trade-Offs in an Endangered Salamander System

Fitness of interspecific hybrids can be higher or lower than that of their parental genotypes, with major ramifications for evolution and conservation. A recent study found that hybrid tiger salamanders had higher metabolic rates than their parental forms. Specifically, the ratio of resting metabolic rate to water loss, which was used as a measure of performance, was greater in hybrids. Recent genetic research showed that over half of the experimental subjects in that study were genetically pure California tiger salamanders (Ambystoma californiense) misclassified as hybrids. We reanalyzed the data using the corrected identification of hybrids and parentals. As expected, updating the classification revealed even stronger evidence for differences between hybrids and parentals. Hybrids had higher resting metabolic rates and greater water loss than parentals. The ratio of resting metabolic rate to water loss was also higher in hybrids. However, we suggest that this ratio might not be a positive measure of salamander performance. Higher metabolic rate means higher energy expenditure, and higher water loss suggests increased risk of desiccation. Both are major challenges for terrestrial amphibians, especially salamanders, which are hypothesized to be low-energy, low-water-loss specialists. Under this hypothesis, the ratio of energy expenditure to water loss is a ratio of two costs rather than a ratio of benefit to cost. While our analyses corroborate and strengthen the previous statistical results, we suggest that high resting metabolic rate is better interpreted as hybrid dysfunction, not vigor. Here, both hypotheses are speculative, and specific research on the fitness effects of these physiological differences is needed to resolve this important ecological question.

Ambystoma californiense↗
DOE OSTI2020

CO2 injection with fracturing in geomechanically protected caprock: Task 6 of LLNL's Research Activities to Support DOE's Carbon Storage Program (FWP-FEW0191) (Final Technical Report)

Conventional principles of the design and operation of geologic carbon storage (GCS) require injecting CO 2 below the caprock fracturing pressure to ensure the integrity of the storage complex. In non-ideal storage reservoirs with relatively low permeability, modest injection rates can lead to pressure buildup and hydraulic fracturing of the reservoir and caprock. While the GCS community has generally viewed hydraulic fractures as a key risk to storage integrity, the actual behavior of a caprock hydraulic fracture, particularly from a geomechanical perspective, has not been thoroughly studied. The prevailing method of treating a hydraulic fracture as a high-permeability wing of the storage reservoir might have resulted in erroneous understanding of caprock hydraulic fracture behavior. Comprehensive analyses of monitoring data from the In Salah project suggested that one or more hydraulic fractures may have been created in the reservoir and lower caprock system during injection operations, but did not cause detectable leakage of CO 2 out of the storage complex. This observation coincides with a well-known phenomenon in the oil and gas industry: sedimentary rock formations have many inherent features that naturally protect the formations from unbounded vertical growth of hydraulic fractures.

58 GEOSCIENCES↗