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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.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Psychometric property study of the Affective Lability Scale-short form in Chinese patients with mood disorders

Introduction This study aimed to investigate the psychometric properties of the Affective Lability Scale-short form (ALS-SF) among Chinese patients with mood disorders, and to compare ALS-SF subscale scores between patients with major depressive disorder (MDD) and patients with bipolar disorder (BD) depression. Methods A total of 344 patients with mood disorders were included in our study. Participants were measured through a set of questionnaires including the Chinese version of ALS-SF, Patient Health Questionnaire-9 (PHQ-9), Generalized Anxiety Disorder 7-item (GAD-7), and NEO-Five Factor Inventory (NEO-FFI). Exploratory factor analysis and confirmatory factor analysis were applied to examine the psychometric properties of ALS-SF. Besides, correlation and regression analyses were performed to explore the relationship between affective lability and depression, anxiety, and neuroticism. Independent samples t -tests were used to compare the subscale scores of ALS-SF between the MDD and BD depression groups. Results Results of factor analysis indicated that the model of ALS-SF was consistent with ALS-SF. The ALS-SF showed a solid validity and high internal consistency (Cronbach’s alpha = 0.861). In addition, each subscale of ALS-SF was significantly correlated with PHQ-9, GAD-7, and NEO-FFI neuroticism subscale, except for the anger subscale showed no significant correlation with PHQ-9. Besides, the depression/elation and anger factor scores in patients with BD depression were higher than in patients with MDD. Conclusion Our study suggests that the Chinese version of ALS-SF has good reliability and validity for measuring affective lability in Chinese patients with mood disorders. Assessing affective lability would assist clinicians to distinguish between MDD and BP depression and may decrease the risks of misdiagnosis.

Ma, Mohan↗
DOE OSTI2025

Barriers and Benefits: Understanding Riders’ Views on Pooled Rideshare in the U.S.

This manuscript provides actionable recommendations to enhance user satisfaction and address existing barriers regarding pooled rideshare (PR) in the United States. Despite PR’s intended benefits, such as reduced traffic congestion and cost savings, its adoption remains limited. To identify these actionable items, a U.S. nationwide survey with 5385 participants explored transportation preferences, barriers, and motivators for PR use in the summer of 2021. First, two factor analyses were conducted. The first factor analysis identified the five factors associated with one’s willingness to consider PR (time/cost, traffic/environment, safety, privacy, and service experience). The second factor analysis revealed the four factors related to ways to optimize one’s PR experience (comfort/ease of use, convenience, vehicle technology/accessibility, and passenger safety). Privacy concerns, for instance, were found to reduce the likelihood of PR adoption by 77%, and convenience had the potential to increase it by 156%. A structural equation model evaluated the relationships among these nine key factors influencing PR usage to develop the Pooled Rideshare Acceptance Model (PRAM). The privacy, safety, trust service, and convenience factors each had a significant large effect (Cohen’s f 2 > 0.35) on the model. PRAM was extended using multigroup analyses to reveal the nuanced impact of 16 demographics, including gender, generation, rideshare experience, etc., highlighting the need for tailored strategies to improve PR acceptance through the Pooled Rideshare Acceptance Model Multigroup Analyses (PRAMMAs). Multiple workshops were held with diverse audiences to translate the team’s findings to date into 84 actionable recommendations, categorized across topical areas like safety, routing, driver and passenger selection, user education, etc. These findings are a foundation for a future study to determine which items resonate with different user groups. In the meantime, the actional items serve as a user-driven resource for policymakers, transportation network companies, and researchers, offering a roadmap to potential improvements to PR services to address existing concerns with the goal of increasing the usage of PR.

actionable recommendations↗
DOE OSTI2017

Human Factors in Streaming Data Analysis: Challenges and Opportunities for Information Visualization: Human Factors in Streaming Data Analysis

State-of-the-art visual analytics models and frameworks mostly assume a static snapshot of the data, while in many cases it is a stream with constant updates and changes. Exploration of streaming data poses unique challenges as machine-level computations and abstractions need to be synchronized with the visual representation of the data and the temporally evolving human insights. In the visual analytics literature, we lack a thorough characterization of streaming data and analysis of the challenges associated with task abstraction, visualization design, and adaptation of the role of human-in-the-loop for exploration of data streams. We aim to fill this gap by conducting a survey of the state-of-the-art in visual analytics of streaming data for systematically describing the contributions and shortcomings of current techniques and analyzing the research gaps that need to be addressed in the future. Our contributions are: i) problem characterization for identifying challenges that are unique to streaming data analysis tasks, ii) a survey and analysis of the state-of-the-art in streaming data visualization research with a focus on the visualization design space for dynamic data and the role of the human-in-the-loop, and iii) reflections on the design-trade-offs for streaming visual analytics techniques and their practical applicability in real-world application scenarios.

streaming data↗
DOE OSTI2021

Modeling and Control of Cascaded Bridgeless Multilevel Rectifier Under Unbalanced Load Conditions

The goal of this project is to model and control a novel unidirectional cascaded multilevel bridgeless rectifier as an active front end in medium and high voltage applications. This topology has many advantages over a conventional cascaded H-bridge rectifier, such as lower implementation cost, higher reliability, and greater flexibility with similar power quality.The steady-state mathematical model is used to develop a method for the voltage balancing of dc cells. Power factor analysis is discussed to achieve unity power factor using fully controlled hbridge cells. Power loss, efficiency, and cost comparison studies between the traditional cascaded H-Bridge converter and the proposed bridgeless converter demonstrate the advantages. A novel control strategy is proposed to achieve dc voltage balancing, fast and robust grid synchronization and power factor correction under unbalanced load conditions. Simulation and experimental results validate the models and control method.

Cascaded Bridgeless Rectifier, Power factor analys↗
DOE OSTI2019

Effect of Off-Target Binding on 18 F-Flortaucipir Variability in Healthy Controls Across the Life Span

Measuring early tau accumulation is important in studying aging and Alzheimer disease and is only as accurate as the signal-to-noise ratio of the tracer. Along with aggregated tau in the form of neurofibrillary tangles, 18 F-flortaucipir has been reported to bind to neuromelanin, monoamine oxidase, calcifications, iron, leptomeningeal melanocytes, and microhemorrages. Although 18 F-flortaucipir successfully differentiates healthy controls (HCs) from subjects with Alzheimer disease, variability exists in the cortical signal in amyloid-negative HCs. We aimed to explore the relationship between off-target binding signal and variability in the cortical signal in HCs. Methods: Subjects ($n$ = 139) received 11 C-Pittsburgh compound B (PIB) and 18 F-flortaucipir PET scans and a magnetization-prepared rapid gradient echo MRI scan. PET frames were realigned and coregistered to the MR images, which were segmented using FreeSurfer. In amyloid-negative HCs ($n$ = 90; age range, 21-94 y), 7 nonspecific or off-target binding regions were considered: caudate, pallidum, putamen, thalamus, cerebellar white matter, hemispheric white matter, and choroid plexus. These regions of interest were assigned to 3 similarly behaving groups using principle components analysis, exploratory factor analysis, and Pearson correlations for caudate, putamen, and pallidum (also correlated with age); thalamus and white matter; and choroid plexus. In amyloid-negative HCs with 11 C-PIB and 18 F-flortaucipir scans, correlations were calculated between white and gray matter before and after partial-volume correction. Results: The correlation between white and gray matter disappeared after partial-volume correction in 11 C-PIB (r 2 = 0) but persisted for 18 F-flortaucipir (r 2 = 0.27), demonstrating that the correlation between white and gray matter signal in 18 F-flortaucipir is not solely due to partial-volume effects. A linear regression showed that off-target signal from putamen and thalamus together explained 64% of the variability in the cortical signal in amyloid-negative HCs (not seen in amyloid-positive HCs). Variability in amyloid-negative HCs but not amyloid-positive HCs correlated with white matter signal (unrelated to partial-volume effects) and age-related off-target signal (possibly related to iron load). Conclusion: The noise in the 18 F-flortaucipir measurement could pose challenges when studying early tau accumulation.

18F-flortaucipir PET↗
DOE OSTI2018

Comparison of sparse domain approaches for 4D SPECT dynamic image reconstruction

Purpose: Dynamic imaging (DI) provides additional diagnostic information in emission tomography in comparison to conventional static imaging at the cost of being computationally more challenging. Dynamic single photon emission computed tomography (SPECT) reconstruction is particularly difficult because of the limitations in the sampling geometry present in most existing scanners. We have developed an algorithm Spline Initialized Factor Analysis of Dynamic Structures (SIFADS) that is a matrix factorization method for reconstructing the dynamics of tracers in tissues and blood directly from the projections in dynamic cardiac SPECT, without first resorting to any 3D reconstruction. Methods: SIFADS is different from "pure" factor analysis in dynamic structures (FADS) in that it employs a dedicated spline-based pre-initialization. In this paper, we analyze the convergence properties of SIFADS and FADS using multiple metrics. The performances of the two approaches are evaluated for numerically simulated data and for real dynamic SPECT data from canine and human subjects. Results: For SIFADS, metrics analyzed for reconstruction algorithm convergence show better features of the metric curves vs iterations. In addition, SIAFDS provides better tissue segmentations than that from pure FADS. Measured computational times are also typically better for SIFADS implementations than those with pure FADS. Conclusion: The analysis supports the utility of the pre-initialization of a factorization algorithm for better dynamic SPECT image reconstruction.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2018

Analysis of Factors Limiting Bacterial Growth in PDMS Mother Machine Devices

The microfluidic mother machine platform has attracted much interest for its potential in studies of bacterial physiology, cellular organization, and cell mechanics. Despite numerous experiments and development of dedicated analysis software, differences in bacterial growth and morphology in narrow mother machine channels compared to typical liquid media conditions have not been systematically characterized. Here we determine changes in E. coli growth rates and cell dimensions in different sized dead-end microfluidic channels using high resolution optical microscopy. We find that E. coli adapt to the confined channel environment by becoming narrower and longer compared to the same strain grown in liquid culture. Cell dimensions decrease as the channel length increases and width decreases. These changes are accompanied by increases in doubling times in agreement with the universal growth law. In channels 100 μm and longer, cell doublings can completely stop as a result of frictional forces that oppose cell elongation. Before complete cessation of elongation, mechanical stresses lead to substantial deformation of cells and changes in their morphology. Lastly, our work shows that mechanical forces rather than nutrient limitation are the main growth limiting factor for bacterial growth in long and narrow channels.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

Analysis of Factors that Influence Infiltration Rates using the HELP Model

The Hydrologic Evaluation of Landfill Performance (HELP) model is used by Savannah River National Laboratory (SRNL) in conjunction with PORFLOW groundwater flow simulation software to make longterm predictions of the fate and transport of radionuclides in the environment at radiological waste sites. The work summarized in this report supports preparation of the planned 2018 Performance Assessment for the E-Area Low-Level Waste Facility (LLWF) at the Savannah River Site (SRS). More specifically, this project focused on conducting a sensitivity analysis of infiltration (i.e., the rate at which water travels vertically in soil) through the proposed E-Area LLWF closure cap. A sensitivity analysis was completed using HELP v3.95D to identify the cap design and material property parameters that most impact infiltration rates through the proposed closure cap for a 10,000-year simulation period. The results of the sensitivity analysis indicate that saturated hydraulic conductivity (Ksat) for select cap layers, precipitation rate, surface vegetation type, and geomembrane layer defect density are dominant factors limiting infiltration rate. Interestingly, calculated infiltration rates were substantially influenced by changes in the saturated hydraulic conductivity of the Upper Foundation and Lateral Drainage layers. For example, an order-of-magnitude decrease in Ksat for the Upper Foundation layer lowered the maximum infiltration rate from a base-case 11 inches per year to only two inches per year. Conversely, an order-of-magnitude increase in Ksat led to an increase in infiltration rate from 11 to 15 inches per year. This work and its results provide a framework for quantifying uncertainty in the radionuclide transport and dose models for the planned 2018 E-Area Performance Assessment. Future work will focus on the development of a nonlinear regression model for infiltration rate using Minitab 17® to facilitate execution of probabilistic simulations in the GoldSim® overall system model for the E-Area LLWF.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2018

Fast Decomposition of Three-Component Spectra of Fluorescence Quenching by White and Grey Methods of Data Modeling

‘White’ and ‘grey’ methods of data modeling have been employed to resolve the heterogeneous fluorescence from a fluorophore mixture of 9-cyanoanthracene (CNA), 10-chloro-9-cyanoanthracene (ClCNA) and 9,10-dicyanoanthracene (DCNA) into component individual fluorescence spectra. The three-component spectra of fluorescence quenching in methanol were recorded for increasing amounts of lithium bromide used as a quencher. The associated intensity decay profiles of differentially quenched fluorescence of single components were modeled on the basis of a linear Stern-Volmer plot. These profiles are necessary to initiate the fitting procedure in both ‘white’ and ‘grey’ modeling of the original data matrices. ‘White’ methods of data modeling, called also ‘hard’ methods, are based on chemical/physical laws expressed in terms of some well-known or generally accepted mathematical equations. The parameters of these models are not known and they are estimated by least squares curve fitting. ‘Grey’ approaches to data modeling, also known as hard-soft modeling techniques, make use of both hard-model and soft-model parts. In practice, the difference between ‘white’ and ‘grey’ methods lies in the way in which the ‘crude’ fluorescence intensity decays of the mixture components are estimated. In the former case they are given in a functional form while in the latter as digitized curves which, in general, can only be obtained by using dedicated techniques of factor analysis. In the paper, the initial values of the Stern-Volmer constants of pure components were evaluated by both ‘point-by-point’ and ‘matrix’ versions of the method making use of the concept of wavelength dependent intensity fractions as well as by the rank annihilation factor analysis applied to the data matrices of the difference fluorescence spectra constructed in two ways: from the spectra recorded for a few excitation lines at the same concentration of a fluorescence quencher or classically from a series of the spectra measured for one selected excitation line but for increasing concentration of the quencher. The results of multiple curve resolution obtained by all types of the applied methods have been scrutinized and compared. In addition, the effect of inadequacy of sample preparation and increasing instrumental noise on the shape of the resolved spectral profiles has been studied on several datasets mimicking the measured data matrices. .

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2019

Dual gene expression analysis identifies factors associated with Staphylococcus aureus virulence in diabetic mice

Staphylococcus aureus is a major human pathogen of the skin. The global burden of diabetes is high, with S. aureus a major complication of diabetic wound infections. We investigated how the diabetic environment influences S. aureus skin infection and observed an increased susceptibility to infection in mouse models of both type I and type II diabetes. A dual gene expression approach was taken to investigate transcriptional alterations in both the host and bacterium after infection. While analysis of the host response revealed only minor changes between infected control and diabetic mice, we observed that S. aureus isolated from diabetic mice had significant increases in genes associated with translation, posttranslational modification and chaperones, and reductions in genes associated with amino acid transport and metabolism. One family of genes upregulated in S. aureus isolated from diabetic lesions encoded the Clp proteases associated with the misfolded protein response. The Clp proteases were found to be partially glucose regulated as well as influencing the hemolytic activity of S. aureus . Strains lacking the Clp proteases, ClpX, ClpC and ClpP were significantly attenuated in our animal model of skin infection, with significant reductions observed in dermonecrosis and bacterial burden. In particular, mutations in clpP and clpX were significantly attenuated and remained attenuation in both normal and diabetic mice. As a result, our data suggests that the diabetic environment also causes changes to occur in invading pathogens and one of these virulence determinants is the Clp protease system.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

Analysis of Factors Affecting Successful Clinical Trial Enrollment in the Context of Three Prospective, Randomized, Controlled Trials

Purpose: Challenges can arise when attempting to maximize patient enrollment in clinical trials. There have been limited studies focusing on the barriers to enrollment and the efficacy of alternative study design to improve accrual. We analyzed barriers to clinical trial enrollment, particularly the influence of timing, in context of three prospective, randomized oncology trials where one arm was considered more aggressive than the other. Methods and Materials: From June 2011 to March 2015, patients who were enrolled on 3 prospective institutional protocols (an oligometastatic non-small cell lung cancer [NSCLC] trial and 2 proton vs intensity modulated radiation therapy trials in NSCLC and esophageal cancer) were screened for protocol eligibility. Eligible candidates were approached about trial participation, and patient characteristics (age, sex, T/N categorization) were recorded along with details surrounding trial presentation (appointment number). Fisher's exact test, Student's t tests, and multivariate analysis were performed to assess differences between enrolled and refusal patients. Results: A total of 309 eligible patients were approached about trial enrollment. The enrollment success rate during this time span was 52% (n=160 patients). Enrolled patients were more likely to be presented trial information at an earlier appointment (oligometastatic protocol: 5 vs 3 appointments [P<.001]; NSCLC protocol: 4 vs 3 appointments [P=.0018]; esophageal protocol: 3 vs 2 appointments [P=.0086]). No other factors or patient characteristics significantly affected enrollment success rate. Conclusion: Improvement in enrollment rates for randomized control trials is possible, even in difficult accrual settings. Earlier presentation of trial information to patients is the most influential factor for success and may help overcome accrual barriers without compromising trial design.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2019

Effect of Stress States on Twinning Behavior in Twinning-Induced Plasticity Steel

The effects of stress state on the twin structures and kinetics in twinning-induced plasticity (TWIP) steel have been investigated to understand the twinning behaviors in more detail. Twinning behaviors of tension, compression, wire drawing, and caliber rolling were evaluated and compared using the EBSD technique, TEM measurement, and Schmid factor analysis. Twin structures, such as twin thickness, twin variant, twin shape, and twin kinetics were different with stress states. The specimen under compressive stress had wavy-shaped twins, less twin variant, lower twin thickness, and more twin volume fraction in comparison with the specimen under tensile stress. The different structures and kinetics of deformation twin with stress state were highly related to the grain rotation during plastic deformation. Under tensile stress, the grain rotation to <111> orientation, twinning-favored regions by Schmid factor analysis, encouraged the lateral growth of twins, more twin variants, and straight-type twins, while the grain rotation to <110> orientation under compressive stress suppressed the twin growth and twin variant and developed wavy-shaped twins. Drawn wire and caliber-rolled wire had both twin features under tensile and compressive stress states and higher twin volume fraction since complex stress state that combined tension and compression was applied in these processes. The hardness values were strongly related to the twin volume fraction with strain and stress state.

36 MATERIALS SCIENCE↗
DOE OSTI2016

A Parametric Analysis of Factors Affecting Calculations of Estimated Dose Rates from Spent Nuclear Fuel Shipments - 16458

Planning for transportation of SNF requires estimation of the radiation dose to the members of the public associated with the transportation. This dose is expected to be very low compared to natural background radiation because dose rates and exposure durations are minimal. A preliminary investigation was performed to estimate the dose rate from a SNF transportation package as a function of distance and evaluate factors affecting the estimated dose rate, such as local environment modeling. Effects on the dose rate are determined for changes in temperature and relative humidity of the air surrounding a transportation package and for different compositions of the ground underneath it. The impact of different vapor content in air was evaluated to be small, within the statistical uncertainty of the dose rate estimates. The neutron dose rate contributions from scattering interactions within different soil/pavement materials were from approximately 20% to 70% at 25 cm from the ground and from approximately 10% to 40% at 175 cm from the ground. The effect of photon scattering interactions within different soil/pavement materials was an increase in photon dose rate of less than 20% at 25 cm from the ground and less than 10% at 175 cm from the ground. The impact of different ground materials on the total dose rate would depend on the relative contributions of photon and neutron dose rates to the total dose rate. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

Report on PTT Imaging of Defects in AM Metallic Materials-Part 2

Metal Additive Manufacturing (AM) is a promising method for cost-efficient fabrication of complex shape structures for applications in harsh environment, such as in a nuclear reactor. However, internal defects (pores) occur in high-strength AM alloys, which are manufactured with Laser Powder Bed Fusion (LPBF) AM method. Pulsed Infrared Thermography (PIT) is an efficient nondestructive evaluation (NDE) method to examine actual structures, because this method offers one-sided non-contact measurements, and fast processing of large sample areas. However, imaging of material defects, particularly defects with sizes at microscopic level, is challenging. In this report, we benchmark the performance of several Unsupervised Learning (UL) algorithms designed to enhance imaging of microscopic defects in metals with PIT. UL aims to learn the latent principal patterns (dictionaries) in PIT data to detect defects with minimal human supervision. Performance of Independent Component Analysis (ICA), Sparse Coding (SC), Principal Component Analysis (PCA) and Exploratory Factor Analysis (EFA) was compared using F-score, UL model training time and defects reconstruction time. We obtained the average F-score of 0.75, and a highest F-score of 0.89 for the EFA algorithm. Overall, EFA outperforms other UL algorithms considered in this study.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Pulsed Thermal Tomography Nondestructive Examination of Additively Manufactured Reactor Materials and Components (Final Technical Report)

Metal Additive Manufacturing (AM) is a promising method for cost-efficient fabrication of complex shape structures for applications in harsh environment, such as in a nuclear reactor. However, internal defects (pores) occur in high-strength AM alloys, which are manufactured with Laser Powder Bed Fusion (LPBF) AM method. Pulsed Infrared Thermography (PIT) is an efficient nondestructive evaluation (NDE) method to examine actual structures, because this method offers one-sided non-contact measurements, and fast processing of large sample areas. However, imaging of material defects, particularly defects with sizes at microscopic level, is challenging. In this report, we benchmark the performance of several Unsupervised Learning (UL) algorithms designed to enhance imaging of microscopic defects in metals with PIT. UL aims to learn the latent principal patterns (dictionaries) in PIT data to detect defects with minimal human supervision. Performance of Independent Component Analysis (ICA), Sparse Coding (SC), Principal Component Analysis (PCA) and Exploratory Factor Analysis (EFA) was compared using F-score, UL model training time and defects reconstruction time. We obtained the average F-score of 0.75, and a highest F-score of 0.89 for the EFA algorithm. Overall, EFA outperforms other UL algorithms considered in this study. In another approach, we investigate Thermal Tomography (TT), which is a computational method for reconstruction of depth profile of internal material defects from PIT nondestructive evaluation (NDE). TT algorithm obtains depth reconstructions of thermal effusivity, which has been shown to provide visualization of subsurface internals defects in metals. In many applications, one needs to determine the defect shape and orientation from reconstructed effusivity images. Interpretation of TT images is non-trivial because of blurring, which increases with depth due to heat diffusion-based nature of image formation. We have developed a deep learning convolutional neural network (CNN) to classify size and orientation of subsurface material defects in TT images. CNN was trained with TT images produced with computer simulations of 2D metallic structures (thin plates) containing elliptical subsurface voids. Performance of CNN was investigated using test TT images developed with computer simulations of plates containing elliptical defects, and defects with shape imported from scanning electron microscopy (SEM) images. CNN demonstrated the ability to classify radii and angular orientation of elliptical defects in previously unseen test TT images. We have also demonstrated that CNN trained on TT images of elliptical defects is capable of classifying shape and orientation of irregular defects. Training the CNN on irregular defect shapes instead of on elliptical shapes would make the resulting classifications more descriptive of actual defect shapes. However, this requires a much higher volume of SEM images of material defects, which are difficult to obtain because of random occurrence of defects in LPBF. To address this challenge, we developed a generative adversarial network (GAN) to augment the existing dataset of SEM defect images. The GAN model is demonstrated to create novel yet realistic defect shapes that can be used as input for simulated PTT images to train CNN. We also investigate several approaches based on Gaussian Random Circle and Bezier Curves for constructing parametric models of irregular-shape defects.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Effects of Demographic Variables on Subjective Neurocognitive Complaints Using the Neurocognitive Questionnaire (NCQ) in an Aged Japanese Population

Objectives: In an aged Japanese population, we investigated associations of demographic variables with subjective neurocognitive complaints using the Neurocognitive Questionnaire (NCQ). Methods: Participants (N = 649) provided answers to the NCQ in both 2011 and 2013. Using fully-completed NCQs from 503 participants in 2011, we identified latent factors of subjective neurocognitive complaints using exploratory factor analysis; then examined associations of demographic variables with the identified factors for all 649 participants over the two years. We also examined changes in factor scores over the 2-year period. Results: We identified four factors representing 20 of the 25 NCQ items and labelled them metacognition, emotional regulation, motivation/organization, and processing speed. In a regression model using all participants, we observed linear deterioration with age on emotional regulation and linear-quadratic deterioration with age on the other factors. Less education was associated with more problems for all factors, but we detected no evidence of interaction between age and education. In 314 participants completing both assessments, paired t-tests comparing the 2013 to 2011 responses corroborated the regression results, except for emotional regulation. Conclusions: On the NCQ, older age and less education were associated with more subjective neurocognitive complaints. This is compatible with the association of the same factors with objective cognition and suggests that subjective cognitive complaints complement objective cognition as a prodrome of non-normative cognitive decline.

questionnaire↗
DOE OSTI2019

The Shifting Role of mRUE for Regulating Ecosystem Production

To design a comprehensive view of ecosystem resource use, in this work we integrated parallel resource use efficiency observations into a multiple-resource use efficiency (mRUE) framework using a dynamic factor analysis model. Results from 56 site-years of eddy covariance data and mRUE factors for a site in the US Midwest show temporal dynamics and coherence (using Pearson’s R) among resources are associated with interannual variation in precipitation. Loading factors are derived from mRUE observations and quantify how strongly data are connected to the underlying ecosystem state. Water and light resource use loading factors are coherent at annual timescales (Pearson’s R of 0.86), whereas declining patterns of carbon use efficiency loading factors highlight the ecosystem’s trade-off between carbon uptake and respiration during the growing season. At annual and monthly timescales, influence decreases from ~85 to ~65% for loading factors for carbon use, while influence of light use loading factors peaks to ~60% at growing season timescales. Quantifying variation in ecosystem function provides novel insights into the temporal dynamics of changing significance of multiple resources to ecosystem function.

54 ENVIRONMENTAL SCIENCES↗