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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Discrete generative diffusion models without stochastic differential equations: A tensor network approach

Diffusion models (DMs) are a class of generative machine learning methods that sample a target distribution by transforming samples of a trivial (often Gaussian) distribution using a learned stochastic differential equation. In standard DMs, this is done by learning a “score function” that reverses the effect of adding diffusive noise to the distribution of interest. Here we consider the generalisation of DMs to lattice systems with discrete degrees of freedom, and where noise is added via Markov chain jump dynamics. We show how to use tensor networks (TNs) to efficiently define and sample such “discrete diffusion models” (DDMs) without explicitly having to solve a stochastic differential equation. We show the following: (i) by parametrising the data and evolution operators as TNs, the denoising dynamics can be represented exactly; (ii) the auto-regressive nature of TNs allows to generate samples efficiently and without bias; (iii) for sampling Boltzmann-like distributions, TNs allow to construct an efficient learning scheme that integrates well with Monte Carlo. We illustrate this approach to study the equilibrium of two models with non-trivial thermodynamics, the d = 1 constrained Fredkin chain and the d = 2 Ising model. Published by the American Physical Society 2025

Causer, Luke (ORCID:0000000194243473)↗
DOE OSTI2016

Investigating ion recombination effects in a liquid-filled ionization chamber array used for IMRT QA measurements

Purpose: PTW’s Octavius 1000 SRS array performs IMRT quality assurance (QA) measurements with liquid-filled ionization chambers (LICs) to allow closer detector spacing and higher resolution, compared to air-filled QA devices. However, reduced ion mobility in LICs relative to air leads to increased ion recombination effects and reduced collection efficiencies that are dependent on Linac pulse frequency and pulse dose. These pulse parameters are variable during an IMRT delivery, which affects QA results. In this study, (1) 1000 SRS collection efficiencies were measured as a function of pulse frequency and pulse dose, (2) two methods were developed to correct changes in collection efficiencies during IMRT QA measurements, and the effects of these corrections on QA pass rates were compared. Methods: To obtain collection efficiencies, the OCTAVIUS 1000 SRS was used to measure open fields of varying pulse frequency, pulse dose, and beam energy with results normalized to air-filled chamber measurements. Changes in ratios of 1000 SRS to chamber measured dose were attributed to changing collection efficiencies, which were then correlated to pulse parameters using regression analysis. The usefulness of the derived corrections was then evaluated using 6 MV and 10FFF SBRT RapidArc plans delivered to the OCTAVIUS 4D system using a TrueBeam (Varian Medical Systems) linear accelerator equipped with a high definition multileaf collimator. For the first correction, MATLAB software was developed that calculates pulse frequency and pulse dose for each detector, using measurement and DICOM RT Plan files. Pulse information is converted to collection efficiency, and measurements are corrected by multiplying detector dose by ratios of calibration to measured collection efficiencies. For the second correction the MU/min in the daily 1000 SRS calibration was chosen to match the average MU/min of the volumetric modulated arc therapy plan. Effects of the two corrections on QA results were examined by performing 3D gamma analysis comparing predicted to measured dose, with and without corrections. Results: Collection efficiencies correlated linearly to pulse dose, while correlations with pulse frequency were less defined, generally increasing as pulse frequency decreased. After complex MATLAB corrections, average 3D gamma pass rates improved by [0.07%,0.40%,1.17%] for 6 MV and [0.29%,1.40%,4.57%] for 10FFF using [3%/3 mm,2%/2 mm,1%/1 mm] criteria. Maximum changes in gamma pass rates were [0.43%,1.63%,3.05%] for 6 MV and [1.00%,4.80%,11.2%] for 10FFF using [3%/3 mm,2%/2 mm,1%/1 mm] criteria. On average, pass rates of simple daily calibration corrections were within 1% of complex MATLAB corrections. Conclusions: OCTAVIUS 1000 SRS ion recombination effects have little effect on 6 MV measurements. However, the effect could potentially be clinically significant for higher pulse dose unflattened beams when using tighter gamma tolerances, especially when small aperture sizes are used, as is common for SRS/SBRT. In addition, ion recombination effects are strongly correlated to changing MU/min, therefore MU/min used in daily 1000 SRS calibrations should be matched to the expected average MU/min of the IMRT plan.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Characterization of Arsenic and Selenium in Coal Fly Ash to Improve Evaluations for Disposal and Reuse Potential (Final Technical Report)

Coal fly ash is a high volume waste material that is discarded in landfills and surface water impoundments across the U.S. and is also widely recycled for a variety of applications. The leaching of potential of contaminants of concern, such as arsenic (As) and selenium (Se), is often the driver of risk assessments for coal ash disposal and reuse. The extent of leachable As and Se depends on several factors related to environmental conditions and fly ash characteristics. Previous studies employed various methods to delineate the concentration, chemical form, and distribution of As and Se in fly ash materials. However, few studies have attempted to directly correlate these properties to mobilization parameters relevant to disposal and reuse. Instead, the coal residuals industries often rely upon standardized leaching protocols that can be laborious or involve hazardous chemicals. The goals of the project were to: 1) Develop and evaluate a characterization protocol that can be used to screen fly ash samples for leachability of As and Se; 2) Characterize As, Se, and associated constituents of fly ash particles at multiple length scales (nanometer to micrometer) to determine if elemental associations differ as a function of the resolution of characterization; and 3) Establish a predictive model for the chemical composition of coal ash produced annually at major U.S. coal fired power facilities on 50-year national coal supply records. For the first objective, we performed leaching experiments with 52 fly ash samples collected from 15 different U.S. power plants and representing coal feedstocks from the three major domestic coal regions. For this work, we assessed the mobilization potential of As and Se in fly ash based on standardized leaching protocols and performed multivariate and lasso regression analyses to explore correlations of leachable As and Se contents with characteristics such as major element contents, loss on ignition (LOI) and pH. The results of regression models indicated that major elements (Fe, Ca, Al) for a wide range of fly ashes can serve as predictor variables for the leaching potential of As, but not for Se. LOI and pH were not important predictive variables in the models. Both regression approaches resulted in relatively strong fits for leachable As (correlation coefficient R 2 = 0.78 for both models) compared to models for leachable Se (R 2 = 0.49). Overall, these results suggest that correlation models combined with on-site elemental analysis with portable analyzers may enable a screening method for leachable As in coal ash. For the second objective, we utilized nanoscale 2-D imaging (30-50 nm spot size) with the Hard X-ray Nanoprobe (HXN) in combination with microprobe X-ray capabilities (~5 µm resolution) to determine As and Se elemental associations in fly ash particles. Speciation of As and Se was also measured at the nano- to microscale with X-ray absorption spectroscopy. The enhanced resolution of HXN showed As and Se that were diffusely located around or comingled with Ca- and Fe-rich particles. The results also showed nanoparticles of Se attached to the surface of fly ash grains. Overall, a comparison of As and Se species across scales highlights the heterogeneity and complexity of chemical associations for these trace elements of concern in coal fly ash. For the final objective, we developed a predictive model for major element composition of coal ash in reserve at disposal sites of major U.S. coal fired power plants. This model was constructed from coal purchase records of 705 power stations from 1973-2022 and was trained on coal ash composition data showing that coal ash elemental composition is strongly associated with the source of feedstock coal. The model showed regional shifts in the major element contents of ash produced by power plants in the last 50 years, particularly for calcium and iron (expressed as %CaO and %Fe 2 O 3 ), as coal-fired power stations changed their source of coal over this time frame. Our approach enables an estimation of coal ash chemical composition that is stored in waste impoundments at individual power stations. Such information can help delineate the regional market potential for material applications that would utilize coal ash harvested from disposal sites across the U.S.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2026

Transient Catalytic Reaction Analysis Through Signal Defragmentation

The Temporal Analysis of Products (TAP) pulse response technique provides valuable insights into catalytic function and reaction kinetics. However, complex fragmentation patterns in the TAP mass spectrometry signals can complicate precise quantification, particularly when analyzing transient gas flux data typical of TAP experiments. This work demonstrates a standard defragmentation method that deconvolves transient TAP signals while maintaining the temporal resolution of the experiment. First, the integrals of calibration gas fluxes are used to determine the fingerprint fragmentation pattern and construct a fragmentation matrix. This matrix is then used to defragment experimental flux data at each recorded time point via a non-negative least squares regression. The effectiveness of this method is demonstrated using virtual data and control experiments with a TAP reactor system. The defragmentation is then applied to the more complex propane dehydrogenation reaction on a chromia/alumina catalyst, which can contain up to ten significant gas species in the reactor outlet. Initial propane pulsing reveals an induction period during which propane is fully oxidized to CO2, followed by partial reduction to CO. Afterwards, there is a transition in chemistries towards coking and propylene production. Our example illustrates a practical method for the accurate determination of the time-dependent reactant/product concentrations and rates for a thorough analysis of the propane dehydrogenation kinetics. This approach can be broadly applied to any transient mass spectrometry experiment for a better understanding of catalyst-reaction dynamics.

36 - MATERIALS SCIENCE↗
DOE OSTI2014

Predicting Student Success using Analytics in Course Learning Management Systems

Educational data analytics is an emerging discipline, concerned with developing methods for exploring the unique types of data that come from the educational context. For example, predicting college student performance is crucial for both the student and educational institutions. It can support timely intervention to prevent students from failing a course, increasing efficacy of advising functions, and improving course completion rate. In this paper, we present the efforts carried out at Oak Ridge National Laboratory (ORNL) toward conducting predictive analytics to academic data collected from 2009 through 2013 and available in one of the most commonly used learning management systems, called Moodle. First, we have identified the data features useful for predicting student outcomes such as students scores in homework assignments, quizzes, exams, in addition to their activities in discussion forums and their total GPA at the same term they enrolled in the course. Then, Logistic Regression and Neural Network predictive models are used to identify students as early as possible that are in danger of failing the course they are currently enrolled in. These models compute the likelihood of any given student failing (or passing) the current course. Numerical results are presented to evaluate and compare the performance of the developed models and their predictive accuracy.

Educational data mining↗
DOE OSTI2025

Challenges in integrating dissolved organic matter chemodiversity into kinetic models of soil respiration

The chemodiversity of dissolved organic matter (DOM) in soil has been proposed to influence the microbial metabolism and fate of belowground organic carbon (C). However, integrating DOM chemistry into soil C cycle models to improve predictions of C stocks and fluxes—beyond simply considering DOM pool size—remains a challenge. While recent research suggests that incorporating DOM chemodiversity into models can improve predictions of microbial respiration, there is still a lack of mechanistic understanding describing how DOM chemodiversity affects microbial metabolism and soil respiration. Here, we evaluated whether DOM chemodiversity was a determinant of soil respiration using paired measurements of high-resolution DOM chemistry, obtained from Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS), and potential soil respiration rates from across the United States (U.S.), all data provided by the Molecular Observation Network. Our objectives were to (1) assess statistical relationships between DOM chemodiversity and microbial respiration, and (2) evaluate the ability of kinetic models to leverage DOM chemistry to explain empirical relationships found in statistical models. Statistical regressions revealed that DOM chemodiversity (alpha diversity) was nonlinearly related to potential soil respiration rates, both independently and through its interactions with DOM and total C concentrations. In soils with relatively high DOM but low total C concentrations, potential soil respiration rates were negatively correlated with DOM alpha diversity, whereas in soils with relatively low DOM and high total C concentrations showed the opposite trend. However, when metabolic transition theory kinetic models were modified to include chemodiversity, their performance was comparable to traditional Monod kinetics approaches, which simulate respiration rates as a function of DOM concentration. The inability to account for nonlinearities in DOM chemodiversity–respiration relationships highlight an opportunity to advance substrate uptake kinetics by establishing causal links between DOM chemodiversity, microbial metabolism trade-offs, and potential interactions under varied environmental conditions.

Bioenergetic model↗
DOE OSTI2024

Deep kernel methods learn better: from cards to process optimization

Abstract The ability of deep learning methods to perform classification and regression tasks relies heavily on their capacity to uncover manifolds in high-dimensional data spaces and project them into low-dimensional representation spaces. In this study, we investigate the structure and character of the manifolds generated by classical variational autoencoder (VAE) approaches and deep kernel learning (DKL). In the former case, the structure of the latent space is determined by the properties of the input data alone, while in the latter, the latent manifold forms as a result of an active learning process that balances the data distribution and target functionalities. We show that DKL with active learning can produce a more compact and smooth latent space which is more conducive to optimization compared to previously reported methods, such as the VAE. We demonstrate this behavior using a simple cards dataset and extend it to the optimization of domain-generated trajectories in physical systems. Our findings suggest that latent manifolds constructed through active learning have a more beneficial structure for optimization problems, especially in feature-rich target-poor scenarios that are common in domain sciences, such as materials synthesis, energy storage, and molecular discovery. The Jupyter Notebooks that encapsulate the complete analysis accompany the article.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Information-incorporated gene network construction with FDR control

Abstract Motivation Large-scale gene expression studies allow gene network construction to uncover associations among genes. To study direct associations among genes, partial correlation-based networks are preferred over marginal correlations. However, FDR control for partial correlation-based network construction is not well-studied. In addition, currently available partial correlation-based methods cannot take existing biological knowledge to help network construction while controlling FDR. Results In this paper, we propose a method called Partial Correlation Graph with Information Incorporation (PCGII). PCGII estimates partial correlations between each pair of genes by regularized node-wise regression that can incorporate prior knowledge while controlling the effects of all other genes. It handles high-dimensional data where the number of genes can be much larger than the sample size and controls FDR at the same time. We compare PCGII with several existing approaches through extensive simulation studies and demonstrate that PCGII has better FDR control and higher power. We apply PCGII to a plant gene expression dataset where it recovers confirmed regulatory relationships and a hub node, as well as several direct associations that shed light on potential functional relationships in the system. We also introduce a method to supplement observed data with a pseudogene to apply PCGII when no prior information is available, which also allows checking FDR control and power for real data analysis. Availability and implementation R package is freely available for download at https://cran.r-project.org/package=PCGII.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Automated Credibility Assessments of User Features in Scientific Software

Scientific software (SciSoft) is complex, often containing a mixture of production capabilities co-mingled with features under active research and development. Furthermore, SciSoft is often developed over decades by non-computer scientists who may not have a strong background in or prioritize software architecture design, testing, and quality (e.g., test coverage). These conditions lead to difficulty in understanding which software components or functions implement what user-facing features and therefore those features’ software quality pedigree. This lack of understanding poses challenges in assessing readiness and credibility of user features, and often relies on a SciSoft subject matter expert’s (SME) laborious investigation and assertion. This final report of a one-year Computing and Information Sciences Lab Directed Research and Development project presents a general framework for modeling SciSoft architecture as a direct relationship between user features and the software components/functions that implement them. Our approach leverages automated labeling of the SciSoft’s regression test suite and employs machine learning algorithms to construct the architecture model. We demonstrate this framework on the Solid Mechanics component of the SIERRA multi-physics engineering analysis suite developed at Sandia National Laboratories.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Discerning Deception: An Empirically-Driven Agent-Based Model of Expert Evaluation of Scientific Content

Both human subject experiments and computational, modeling and simulations have been used to study detection of deception. This work aims to combine these two methods by integrating empirically-derived information (from human subject experiments) into agent-based models to generate novel insights into the complex problems of detection of disinformation content. Computational experiments are used to simulate across multiple scenarios for evaluation and decision-making regarding the validity of potentially deceptive scientific documents. Factors influencing the human agent behaviors in the model were identified through a human subject experiment that was conducted to evaluate and characterize decision making related to disinformation discernment. Correlation and regression analyses were used to translate insights from the human subjects experiment to inform the parameterization of agent features and scenario development. Three scenarios were evaluated with the agent-based models to help evaluate the replicability of the simulations (validation analysis) and assess the influence of human agent and document features (sensitivity analyses). A replication of the human participant experiment demonstrated that the agent-based simulations compare favorably to empirical findings. The agent-based modeling was then used to conduct sensitivity analysis on the accuracy of deception detection as a function of document proportions and human agent features. Results indicate that precision values are adversely impacted when the proportion of deceptive documents is lower in the overall sample, whereas recall values are more sensitive to changes in human agent features. These findings indicate important nuances in accuracy evaluations that should be further considered (including consideration of potential alternate metrics) in future agent-based models of disinformation. Additional areas for future exploration include extension of simulations to consider other ways to align the agent-based model design with psychological theory and inclusion of agent-agent interactions, especially as it pertains to sharing of scientific information within an organizational context.

99 GENERAL AND MISCELLANEOUS↗
NASA NTRS1999

Solar Cycle Predictions

We currently lack a comprehensive model for the solar dynamo which might be used for predicting solar cycle activity levels. We can, nonetheless, predict these activity levels with some skill using a variety of techniques based on correlations between activity indicators at different times. A number of the techniques currently in use for predicting solar activity on a solar cycle time-scale have been tested with historical data. Some techniques, e.g., regression and curve fitting, work well as solar activity approaches maximum and provide a month by month description of future activity, while others, e.g., geomagnetic precursors, work well near solar minimum but only provide an estimate of the amplitude of the next cycle. A synthesis of different techniques is shown to provide a more accurate and useful forecast of solar cycle activity levels. The combination of two uncorrelated geomagnetic precursor techniques provides a more accurate prediction for the amplitude of a solar activity cycle of a time well before activity minimum. A mathematical function dependent on the time of cycle initiation and the cycle amplitude can then be used to describe the level of solar activity month by month for the next cycle. As the time of cycle maximum approaches a better estimate of the cycle activity is obtained by including the fit between previous activity levels and this function. The success of the geomagnetic precursors in predicting the amplitude of the next solar cycle supports the idea of extended solar cycle in which the activity cycles overlap. While the current cycle is producing sunspots and active regions in the Sun's equatorial regions the next cycle is already actively influencing the geomagnetic field, The precise nature and source of this influence remains unclear.

Hathaway, D. H.↗
DOE OSTI2020

Machine Learning-Enabled Correlation and Modeling of Multimodal Response of Thin Film to Environment on Macro and Nanoscale Using “Lab-on-a-Crystal”

To close the feedback loop between artificial intellegence-controlled materials synthesis and characterization, material functionality must be rapidly tested. A platform for high-throughput multifunctional materials characterization is developed using a quartz crystal microbalance with auxiliary in-plane electrodes and a custom gas/vapor flow cell, enabling simultaneous scanning probe microscopy and electrical, optical, gravimetric, and viscoelastic characterization on the same film under controlled environment. The lab-on-a-crystal in situ multifunctional output allows direct correlations between the gravimetric/viscoelastic, electrical, and optical responses of polymer film in response to environment. Finally, when multiple film properties are used to augment the training set for machine learning regression, prediction of material response to the environment improves by a factor of 13 when <5% of the total dataset is used for model training.

36 MATERIALS SCIENCE↗
DOE OSTI2018

UBE2S enhances the ubiquitination of p53 and exerts oncogenic activities in hepatocellular carcinoma

Highlights: • High expression of UBE2S is significantly correlated with poor outcomes in two independent cohorts containing 845 patients. • UBE2S promotes cell proliferation and migration in HCC. • UBE2S exerts oncogenic activities via enhancing the ubiquitination of p53. Ubiquitin-conjugating enzyme E2S (UBE2S) plays pivotal roles in the progression of human cancers. However, its clinical significance and role in hepatocellular carcinoma (HCC) remain unknown. Here, we show that UBE2S is upregulated in HCC and exhibits oncogenic activities via enhancing the ubiquitination of p53. Increased expression of UBE2S was significantly correlated with higher serum AFP level, higher pathological grade, advanced TNM stage, larger tumor size, vascular invasion and unfavorable patient survivals in two independent cohorts containing a total of 845 patients with HCC. Multivariate analyses by cox regression model suggested UBE2S as an independent factor for overall survival. In vitro experiments demonstrated that UBE2S overexpression promoted, whereas UBE2S knockdown suppressed cell proliferation and migration via modulation of p53 signaling pathway. Ectopic expression of UBE2S upregulated the expression of p53 and its downstream effectors, such as p21 and Cyclin D1. Mechanistically, UBE2S enhanced the ubiquitination of p53 protein to facilitate its degradation in HCC cells. Re-expression of p53 partially attenuated the UBE2S-promoted malignant phenotypes. Collectively, our study provides compelling evidence that UBE2S is a potential prognostic factor and functions as an oncogene in HCC.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

Ripening of Rh Nanoparticle Catalysts in Reverse Water–Gas Shift via a Data-Driven Model Combining Physics, Theory, and Experiment

Degradation via sintering is an ongoing challenge that impedes the broad commercial success of supported metallic nanoparticle catalysts. To mitigate degradation via informed catalyst design and process operations, here we aim to disambiguate the underlying mechanisms of sintering by combining theory and experiment in a quantitative framework. While mechanistic sintering models exist, they only model a single sintering pathway, even though multiple sintering mechanisms can occur simultaneously or dominate at different stages of the process. Data-driven machine learning models have emerged as a means to represent complex processes through data regression. However, machine learning models have very large data needs and lack mechanistic insights due to their black-box encoding. To develop an interpretive model of catalyst degradation via sintering, we constructed a hybrid model combining mechanistic “physics-based” models and data-driven methods to obtain both reliable predictions and mechanistic insights regarding experimentally observed sintering phenomena. Focusing on nanoparticle sintering in the Rh–TiO 2 catalyst for the reverse water–gas shift (RWGS) reaction, the hybrid model couples a mechanistic term for Ostwald ripening with energy values calculated via density functional theory (DFT) with a parametric, data-driven discrepancy function term for unmodeled mechanisms. The hybrid model is trained using Bayesian inference with data collected from small-angle X-ray scattering (SAXS) in situ experiments wherein average nanoparticle diameter versus time was measured at three relevant operating temperatures. The calibrated hybrid model results show that an Ostwald ripening-only model parameterized with fixed DFT energies does not fully capture the time and temperature dependence of the SAXS-observed sintering kinetics, and that an additional functional contribution, or DFT energy calibration, is required to reconcile simulation and experiment. Analysis of the hybrid-model error confirms that the hybrid model outperforms both the purely mechanistic and purely data-driven alternatives in terms of expected predictive accuracy for time-evolving average particle sizes. Furthermore, the results support the hypothesis that the Ostwald ripening mechanism is less important for explaining the sintering phenomena as operating temperature increases under an assumed fixed DFT parameterization. This could be explained in one of two ways: either latent, unmodeled sintering mechanisms dominate at higher temperatures, or the DFT uncertainty increases with temperature. The proposed modeling approach directly links theory to experiments and simulations via a statistical hybrid modeling framework and can be extended to other catalytic systems to improve predictive models and mechanistic understanding.

Bayesian hybrid modeling↗
DOE OSTI2023

Predictive machine learning approaches for the microstructural behavior of multiphase zirconium alloys

Abstract Zirconium alloys are widely used in harsh environments characterized by high temperatures, corrosivity, and radiation exposure. These alloys, which have a hexagonal closed packed (h.c.p.) structure thermo-mechanically degrade, when exposed to severe operating environments due to hydride formation. These hydrides have a different crystalline structure, than the matrix, which results in a multiphase alloy. To accurately model these materials at the relevant physical scale, it is necessary to fully characterize them based on a microstructural fingerprint, which is defined here as a combination of features that include hydride geometry, parent and hydride texture and crystalline structure of these multiphase alloys. Hence, this investigation will develop a reduced order modeling approach, where this microstructural fingerprint is used to predict critical fracture stress levels that are physically consistent with microstructural deformation and fracture modes. Machine Learning (ML) methodologies based on Gaussian Process Regression, random forests, and multilayer perceptrons (MLP) were used to predict material fracture critical stress states. MLPs, or neural networks, had the highest accuracy on held-out test sets across three predetermined strain levels of interest. Hydride orientation, grain orientation or texture, and hydride volume fraction had the greatest effect on critical fracture stress levels and had partial dependencies that were highly significant, and in comparison hydride length and hydride spacing have less effects on fracture stresses. Furthermore, these models were also used accurately predicted material response to nominal applied strains as a function of the microstructural fingerprint.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Younger facial looks are associate with a lower likelihood of several age-related morbidities in the middle-aged to elderly

Abstract Background Looking older for one’s chronological age is associated with a higher mortality rate. Yet it remains unclear how perceived facial age relates to morbidity and the degree to which facial ageing reflects systemic ageing of the human body. Objectives To investigate the association between ΔPA and age-related morbidities of different organ systems, where ΔPA represents the difference between perceived age (PA) and chronological age. Methods We performed a cross-sectional analysis on data from the Rotterdam Study, a population-based cohort study in the Netherlands. High-resolution facial photographs of 2679 men and women aged 51.5–87.8 years of European descent were used to assess PA. PA was estimated and scored in 5-year categories using these photographs by a panel of men and women who were blinded for chronological age and medical history. A linear mixed model was used to generate the mean PAs. The difference between the mean PA and chronological age was calculated (ΔPA), where a higher (positive) ΔPA means that the person looks younger for their age and a lower (negative) ΔPA that the person looks older. ΔPA was tested as a continuous variable for association with ageing-related morbidities including cardiovascular, pulmonary, ophthalmological, neurocognitive, renal, skeletal and auditory morbidities in separate regression analyses, adjusted for age and sex (model 1) and additionally for body mass index, smoking and sun exposure (model 2). Results We observed 5-year higher ΔPA (i.e. looking younger by 5 years for one’s age) to be associated with less osteoporosis [odds ratio (OR) 0.76, 95% confidence interval (CI) 0.62–0.93], less chronic obstructive pulmonary disease (OR 0.85, 95% CI 0.77–0.95), less age-related hearing loss (model 2; B = −0.76, 95% CI −1.35 to −0.17) and fewer cataracts (OR 0.84, 95% CI 0.73–0.97), but with better global cognitive functioning (g-factor; model 2; B = 0.07, 95% CI 0.04–0.10). Conclusions PA is associated with multiple morbidities and better cognitive function, suggesting that systemic ageing and cognitive ageing are, to an extent, externally visible in the human face.

Mekić, Selma↗
DOE OSTI2017

Indirect Validation of Probe Speed Data on Arterial Corridors

This study aimed to estimate the accuracy of probe speed data on arterial corridors on the basis of roadway geometric attributes and functional classification. It was assumed that functional class (medium and low) along with other road characteristics (such as weighted average of the annual average daily traffic, average signal density, average access point density, and average speed) were available as correlation factors to estimate the accuracy of probe traffic data. This study tested these factors as predictors of the fidelity of probe traffic data by using the results of an extensive validation exercise. This study showed strong correlations between these geometric attributes and the accuracy of probe data when they were assessed by using average absolute speed error. Linear models were regressed to existing data to estimate appropriate models for medium- and low-type arterial corridors. The proposed models for medium- and low-type arterials were validated further on the basis of the results of a slowdown analysis. These models can be used to predict the accuracy of probe data indirectly in medium and low types of arterial corridors.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
NASA NTRS1995

Supporting technology for the development of Controlled Ecological Life Support Systems (CELSS)

To support the development of Controlled Ecological Life Support Systems (CELSS) in the space program, a metabolic simulator has been selected for use in a closed chamber to test functions of the CELSS. This metabolic simulator is a catalytic reactor which oxidizes the methyl acetate to produce carbon dioxide and water vapor. In this project, kinetic studies of catalytic oxidation of methyl acetate were conducted using monolithic and pellet catalysts with 0.5% (by weight) platinum (Pt) on aluminum oxide (Al2O3). The reaction was studied at a pressure of one atmosphere and at temperatures varying from 160 C to 420 C. By-products were identified at the exit of the preheater and reactor. For the kinetic study with the monolithic catalyst, a linear regression method was used to correlate the kinetic data with zero-order, first-order and Langmuir-Hinshelwood models. Results indicate that the first-order model represents the data adequately at low concentrations of methyl acetate. For higher concentrations of methyl acetate, the Langmuir-Hinshelwood model best represents the kinetic data. Both rate constant and adsorption equilibrium constants were estimated from the regression. A Taguchi orthogonal array (L(sub 9)) was used to investigate the effects of temperature, flow rate, and concentration on the catalytic oxidation of methyl acetate. For the monolithic catalyst, temperature exerts the most significant effect, followed by concentration of methyl acetate. For the pellet catalyst, reaction temperature is the most significant factor, followed by gas flow rate and methyl acetate concentration. Concentrations of either carbon dioxide or oxygen were seen to have insignificant effect on the methyl acetate conversion process. Experimental results indicate that the preheater with glass beads can accomplish thermal cracking and catalytic reaction of methyl acetate to produce acetic acid, methanol, methyl formate, and 1-propanol. The concentration of all by-products was measured in ppmv (parts per million by volume). At higher temperatures, greater amounts of these products are produced, as expected. In all cases, methanol was the predominant concentration detected, followed by methyl formate. At temperatures lower than 320 C for the P-type monolithic catalyst, methanol, acetic acid, and acetone were detected, whereas, for the E-type monolithic catalyst, only methanol was detected at 160 C. Both P and E types of the monolithic catalyst were specified with the same substrates (ceramic), washcoat (Al2O3), and promoter (Pt). However, the manufacturing and treatment procedures were quite different. It was therefore concluded that the performance of the E-type monolithic catalyst is superior to that of the P-type for oxidation of methyl acetate. At higher reaction temperatures, e.g., above 420 C, all reactants and byproducts were completely oxidized using these two types of monolithic catalyst to produce carbon dioxide and water vapor. A complex heterogenous catalytic reaction mechanism was proposed to explain the formation of the byproducts (methanol, acetic acid, and methyl formate) as the methyl acetate traveled through the preheater packed with glass beads. The by-product, 1-propanol, may be formed only through a homogeneous reaction, since it is difficult to develop a reasonable sequence of heterogeneous reaction steps to explain its formation. The homogeneous thermal decomposition of methyl acetate to form free radicals was proposed to explain the formation of 1-propanol, and also methanol, in the preheater. A dual-site catalytic reaction mechanism was proposed for the oxidation of methyl acetate over Pt/Al2O3 monolithic catalyst. The dual-site mechanism describes the chemisorption of oxygen molecules as well as a physical adsorption of methyl acetate on the active sites. On the active sites, methyl acetate is oxidized rapidly to form carbon dioxide and water vapor. A rate equation derived from this mechanism gives the Langmuir-Hinshelwood rate formula which has been observed from the experimental data obtained in this project for high methyl acetate concentration (greater than 1000 ppmv) over a monolithic catalyst. If the oxygen concentration is very high and methyl acetate concentration is very low, the reaction rate equation is then reduced to a first-order with respect to methyl acetate concentration. The first-order model has also been observed from the experimental data obtained in this project for low methyl acetate concentration (less than 1000 ppmv).

Li, Ku-Yen↗