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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 OSTI2022

Uncertainty in land carbon budget simulated by terrestrial biosphere models: the role of atmospheric forcing

Global estimates of the land carbon sink are often based on simulations by terrestrial biosphere models (TBMs). The use of a large number of models that differ in their underlying hypotheses, structure and parameters is one way to assess the uncertainty in the historical land carbon sink. Here we show that the atmospheric forcing datasets used to drive these TBMs represent a significant source of uncertainty that is currently not systematically accounted for in land carbon cycle evaluations. We present results from three TBMs each forced with three different historical atmospheric forcing reconstructions over the period 1850–2015. We perform an analysis of variance to quantify the relative uncertainty in carbon fluxes arising from the models themselves, atmospheric forcing, and model-forcing interactions. We find that atmospheric forcing in this set of simulations plays a dominant role on uncertainties in global gross primary productivity (GPP) (75% of variability) and autotrophic respiration (90%), and a significant but reduced role on net primary productivity and heterotrophic respiration (30%). Atmospheric forcing is the dominant driver (52%) of variability for the net ecosystem exchange flux, defined as the difference between GPP and respiration (both autotrophic and heterotrophic respiration). In contrast, for wildfire-driven carbon emissions model uncertainties dominate and, as a result, model uncertainties dominate for net ecosystem productivity. At regional scales, the contribution of atmospheric forcing to uncertainty shows a very heterogeneous pattern and is smaller on average than at the global scale. We find that this difference in the relative importance of forcing uncertainty between global and regional scales is related to large differences in regional model flux estimates, which partially offset each other when integrated globally, while the flux differences driven by forcing are mainly consistent across the world and therefore add up to a larger fractional contribution to global uncertainty.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Characterizing Canopy Openness in Open Forests: Spherical Densiometer and Canopy Photography Are Equivalent but Less Sensitive than Direct Measurements of Solar Radiation

In forest ecosystems, canopy openness affects understory light availability, plant growth, and tree species recruitment, thus shaping future forest composition, structure, and functional diversity. Foresters must correctly and quickly measure canopy openness to meet their management objectives. To help guide the selection of an appropriate method for measuring canopy openness, we compared three common techniques that vary in cost, complexity, and time required for measurements and data processing: smartphone-based hemispherical photography, spherical densiometer measurements, and direct measurements of solar radiation (using AccuPAR ceptometer). We measured canopy openness using these three methods on 28 permanent forest health monitoring plots in pine-oak forests of the Central Pine Barrens of Long Island in New York State. By analysis of variance and regression analyses, we found the three methods (particularly densiometer and hemispherical photographs) yielded broadly equivalent and strongly positively correlated descriptions of canopy openness. The direct measurements of solar radiation seemed to have a greater potential to detect subtle variation in forest understory light. Forest managers may sufficiently characterize canopy openness using quick and cheap methods (e.g., spherical densiometers) and avoid larger costs of devices for direct light measurements (e.g., ceptometers) and the larger data processing times of hemispherical photography.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Machine Learning Based Multi-Physical-Model Blending for Enhancing Renewable Energy Forecast -- Improvement via Situation Dependent Error Correction

With increasing penetration of solar and wind energy to the total energy supply mix, the pressing need for accurate energy forecasting has become well-recognized. Here we report the development of a machine-learning based model blending approach for statistically combining multiple meteorological models for improving the accuracy of solar/wind power forecast. Importantly, we demonstrate that in addition to parameters to be predicted (such as solar irradiance and power), including additional atmospheric state parameters which collectively define weather situations as machine learning input provides further enhanced accuracy for the blended result. Functional analysis of variance shows that the error of individual model has substantial dependence on the weather situation. The machine-learning approach effectively reduces such situation dependent error thus produces more accurate results compared to conventional multi-model ensemble approaches based on simplistic equally or unequally weighted model averaging. Validation over an extended period of time results show over 30% improvement in solar irradiance/power forecast accuracy compared to forecasts based on the best individual model.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2018

Machine Learning of Factors Influencing Damping and Frequency of Dominant Inter-area Modes in the WECC Interconnect

The stability of inter-area electromechanical oscillations are critical to power system reliability. Due to the complexities of power systems, relationships between system conditions and oscillation characteristics, such as damping and frequency, tend to be expressed only in generalities. In this study, a list of influential factors on Western Electricity Coordinating Council (WECC) interconnect modal characteristics are identified and evaluated with advanced machine learning techniques including principal component analysis, analysis of variance, random forest feature selection, support vector machine, and artificial neural network approaches.

Hou, Zhangshuan↗
DOE OSTI2014

Selective Herbivory by an Invasive Cyprinid, the Rudd Scardinius erythrophthalmus

1. Herbivory by non-native animals is a problem of growing concern globally, especially for ecosystems where significant native herbivores did not previously exist or have been replaced by non-natives. The rudd (Scardinius erythrophthalmus) is an omnivorous cyprinid that has a nearly global longitudinal distribution due to human translocations, yet it is unknown whether the rudd feeds selectively among aquatic macrophyte species common to North American waters. 2. We tested a null hypothesis of non-selective feeding by rudds using five species of aquatic macrophytes: Ceratophyllum demersum, Elodea canadensis, Najas flexilis, Stuckenia pectinata, and Vallisneria americana. Four rudds were placed in 15 different 890-L tanks and presented with known quantities of each macrophyte species (each tank serving as a block in a randomized complete block design). Each macrophyte bundle was weighed on six dates during a 13 d experiment. Differences in mean percent weight remaining among macrophyte species were tested using repeated measures analysis of variance. We also quantified differences among chemical attributes of the five macrophyte species and qualitatively determined if selective feeding by rudds was related to dry matter content (DMC), percent C by dry weight (%C), percent N by dry weight (%N), and the concentrations of total soluble proteins, two organic acids (aconitic and oxalic acid), total soluble phenolic compounds (<1,000 Da), nine soluble phenolic metabolites, and total phenolic compounds. 3. Selective feeding by rudds was evident, with the order of macrophyte removal (from highest to lowest) being: N. flexilis > E. canadensis > S. pectinata > V. americana > C. demersum. Selection was positively related to %C and atomic C:N, but not DMC, %N, or concentration of total soluble proteins, contrary to the expectation that rudds would select the most nutritious plants available. The concentration of aconitic acid was greatest in N. flexilis, a preferred macrophyte, contrary to the expectation that this compound provides resistance to herbivory. The concentration of oxalic acid, which negatively affects palatability, was highest in C. demersum, the least preferred macrophyte. Selection was also positively related to the concentration of total soluble phenolic compounds; however, examination of the influence of specific phenolic metabolites provided further insights. Concentrations of caffeic acid, trans-caftaric acid, and quercetin were positively related to macrophyte preference by rudds, whereas concentrations of cis-4-O- and trans-4-O-ferulic acid glucoside were negatively related. Patterns between the concentrations of p-coumaric acid, rosmarinic acid, and macrophyte preference by rudds were less obvious. 4. Our results suggest that selective feeding by rudds has the potential to alter macrophyte assemblages and jeopardize habitat restoration projects seeking to establish a diverse plant assemblage. Studies of selective herbivory by various aquatic taxa have provided evidence that selection is simultaneously influenced by multiple plant characteristics, including nutritional quality, morphology, rigidity, and chemical defenses. Future research designed to elucidate the mechanisms by which specific chemical attributes of macrophytes influence selective herbivory by rudds and other taxa will help provide an understanding of how herbivores have changed macrophyte assemblages and make predictions about how macrophyte assemblages will be altered following biological invasions.

Kapuscinski, Kevin L↗
DOE OSTI2016

Hotspots of uncertainty in land-use and land-cover change projections: A global-scale model comparison

Model-based global projections of future land use and land cover (LULC) change are frequently used in environmental assessments to study the impact of LULC change on environmental services and to provide decision support for policy. These projections are characterized by a high uncertainty in terms of quantity and allocation of projected changes, which can severely impact the results of environmental assessments. In this study, we identify hotspots of uncertainty, based on 43 simulations from 11 global-scale LULC change models representing a wide range of assumptions of future biophysical and socio-economic conditions. We attribute components of uncertainty to input data, model structure, scenario storyline and a residual term, based on a regression analysis and analysis of variance. From this diverse set of models and scenarios we find that the uncertainty varies, depending on the region and the LULC type under consideration. Hotspots of uncertainty appear mainly at the edges of globally important biomes (e.g. boreal and tropical forests). Our results indicate that an important source of uncertainty in forest and pasture areas originates from different input data applied in the models. Cropland, in contrast, is more consistent among the starting conditions, while variation in the projections gradually increases over time due to diverse scenario assumptions and different modeling approaches. Comparisons at the grid cell level indicate that disagreement is mainly related to LULC type definitions and the individual model allocation schemes. We conclude that improving the quality and consistency of observational data utilized in the modeling process as well as improving the allocation mechanisms of LULC change models remain important challenges. Furthermore, current LULC representation in environmental assessments might miss the uncertainty arising from the diversity of LULC change modeling approaches and many studies ignore the uncertainty in LULC projections in assessments of LULC change impacts on climate, water resources or biodiversity.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Evaluation of U-net-based Image Segmentation Model to Digital Mammography

Detecting suspicious lesions in medical imaging is the important first step in computer-aided detection (CAD) systems. However, detecting abnormalities in breast tissue is difficult due to the lesion's varying size, shape, margin, and contrast with the background tissue. We focused on mass segmentation, a method that provides notable morphological features by outlining contours of masses. Accurate segmentation is crucial for correct diagnosis. Recent advancements in deep learning have improved object detection and segmentation, and these techniques are also being applied to medical imaging studies. We focused on U-net, which is a recently developed mass segmentation algorithm based on a fully convolutional network. The U-net architecture consists of (1) a contracting path to increase the resolution of the output and (2) a symmetric expanding path to better locate the region of interest. The performance of a U-net model was tested with 63 digital mammograms from INbreast, a publicly available database. We trained the model with images resized to 40x40 pixels and conducted 10-fold cross-validation to prevent overfitting. The model's performance with respect to breast density and the lesion's BI-RADS rating was also investigated. Dice coefficients (DC) were used as a performance measure to compare the predicted segmentation of the model with the ground truth. Logistic regression and an analysis of variance were performed to determine the significance of the DCs with regards to breast density and lesion behavior and to calculate the 95% confidence interval. The average DC was 0.80. The difference between DCs for BI-RADS 2 and 4c and for BI-RADS 2 and 5 were significant, suggesting that the model has more difficulty in segmenting benign lesions.

Cho, Priscilla↗
DOE OSTI2014

Analysis of perceived similarity between pairs of microcalcification clusters in mammograms

Purpose: Content-based image retrieval aims to assist radiologists by presenting example images with known pathology that are visually similar to the case being evaluated. In this work, the authors investigate several fundamental issues underlying the similarity ratings between pairs of microcalcification (MC) lesions on mammograms as judged by radiologists: the degree of variability in the similarity ratings, the impact of this variability on agreement between readers in retrieval of similar lesions, and the factors contributing to the readers’ similarity ratings. Methods: The authors conduct a reader study on a set of 1000 image pairs of MC lesions, in which a group of experienced breast radiologists rated the degree of similarity between each image pair. The image pairs are selected, from among possible pairings of 222 cases (110 malignant, 112 benign), based on quantitative image attributes (features) and the results of a preliminary reader study. Next, the authors apply analysis of variance (ANOVA) to quantify the level of variability in the readers’ similarity ratings, and study how the variability in individual reader ratings affects consistency between readers. The authors also measure the extent to which readers agree on images which are most similar to a given query, for which the Dice coefficient is used. To investigate how the similarity ratings potentially relate to the attributes underlying the cases, the authors study the fraction of perceptually similar images that also share the same benign or malignant pathology as the query image; moreover, the authors apply multidimensional scaling (MDS) to embed the cases according to their mutual perceptual similarity in a two-dimensional plot, which allows the authors to examine the manner in which similar lesions relate to one another in terms of benign or malignant pathology and clustered MCs. Results: The ANOVA results show that the coefficient of determination in the reader similarity ratings is 0.59. The variability level in the similarity ratings is proved to be a limiting factor, leading to only moderate correlation between the readers in their readings. The Dice coefficient, measuring agreement between readers in retrieval of similar images, can vary from 0.45 to 0.64 with different levels of similarity for individual readers, but is higher for average ratings from a group of readers (from 0.59 to 0.78). More importantly, the fraction of retrieved cases that match the benign or malignant pathology of the query image was found to increase with the degree of similarity among the retrieved images, reaching average value as high as 0.69 for the radiologists (p-value <10{sup −4} compared to random guessing). Moreover, MDS embedding of all the cases shows that cases having the same pathology tend to cluster together, and that neighboring cases in the plot tend to be similar in their clustered MCs. Conclusions: While individual readers exhibit substantial variability in their similarity ratings, similarity ratings averaged from a group of readers can achieve a high level of intergroup consistency and agreement in retrieval of similar images. More importantly, perceptually similar cases are also likely to be similar in their underlying benign or malignant pathology and image features of clustered MCs, which could be of diagnostic value in computer-aided diagnosis for lesions with clustered MCs.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2014

MO-E-17A-06: Organ Dose in Abdomen-Pelvis CT: Does TG 111 Equilibrium Dose Concept Better Accounts for KVp Dependence Than Conventional CTDI?

Purpose: In CT imaging, a desirable quality assurance (QA) dose quantity should account for the dose variability across scan parameters and scanner models. Recently, AAPM TG 111 proposed to use equilibrium dose-pitch product, in place of CT dose index (CTDI100), for scan modes involving table translation. The purpose of this work is to investigate whether this new concept better accounts for the kVp dependence of organ dose than the conventional CTDI concept. Methods: The adult reference female extended cardiac-torso (XCAT) phantom was used for this study. A Monte Carlo program developed and validated for a 128-slice CT system (Definition Flash, Siemens Healthcare) was used to simulate organ dose for abdomenpelvis scans at five tube voltages (70, 80, 100, 120, 140 kVp) with a pitch of 0.8 and a detector configuration of 2x64x0.6 mm. The same Monte Carlo program was used to simulate CTDI100 and equilibrium dose-pitch product. For both metrics, the central and peripheral values were used together with helical pitch to calculate a volume-weighted average, i.e., CTDIvol and (Deq)vol, respectively. Results: While other scan parameters were kept constant, organ dose depended strongly on kVp; the coefficient of variation (COV) across the five kVp values ranged between 70–75% for liver, spleen, stomach, pancreas, kidneys, colon, small intestine, bladder, and ovaries, all of which were inside the primary radiation beam. One-way analysis of variance (ANOVA) for the effect of kVp was highly significant (p=3e−30). When organ dose was normalized by CTDIvol, the COV across the five kVp values reduced to 7–16%. The effect of kVp was still highly significant (p=4e−4). When organ dose was normalized by (Deq)vol, the COV further reduced to 4−12%. The effect of kVp was borderline significant (p=0.04). Conclusion: In abdomen-pelvis CT, TG 111 equilibrium dose concept better accounts for kVp dependence than the conventional CTDI. This work is supported by a faculty startup fund from the Cleveland State University.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

TU-C-12A-05: Repeatability Study of Reduced Field-Of-View Diffusion-Weighted MRI On Human Thyroid Gland

Purpose: To investigate the repeatability of reduced field-of-view diffusion-weighted imaging (rFOV DWI) in quantifying apparent diffusion coefficients (ADCs) for human thyroid glands in a clinical setting. Methods: Nine healthy human volunteers were enrolled and underwent 3T MRI exams. For each volunteer, 3 longitudinal exams (2 weeks apart) with 2 repetitive sessions within each exam, including rFOV and conventional full field-of-view (fFOV) DWI scans, were performed. In the acquired DWI images, a fixed-size region of interest (ROI; diameter=8mm) was placed on thyroid glands to calculate ADC. ADC was calculated using a monoexponential function with a noise correction scheme. The repeatability of ADC was assessed by using coefficient variation (CV) across sessions or exams, which was defined to be: r = 1-CV, 0 < r < 1, where CV=STD/m, STD is the standard deviation of ADC, and m is the average of ADC across sessions or exams. An experienced radiologist assessed and scored rFOV and fFOV DW images based on image characteristics (1, nondiagnostic; 2, poor; 3, satisfactory; 4, good; and 5, excellent).Analysis of variance (ANOVA) was performed to compare ADC values, CV of ADC, repeatability of ADC across sessions and exams, and radiologic scores between rFOV and fFOV DWI techniques. Results: There was no significant difference in ADC values across sessions and exams either in rFOV or fFOV DWI. The average CVs of both rFOV and fFOV DWI were less than 13%. The repeatability of ADC measurement between rFOV and fFOV DWI was not significantly different. The overall image quality was significantly higher with rFOV DWI than with fFOV DWI. Conclusion: This study suggested that ADCs from both rFOV and fFOV DWI were repeatable, but rFOV DWI had superior imaging quality for human thyroid glands in a clinical setting.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

4D-CT scans reveal reduced magnitude of respiratory liver motion achieved by different abdominal compression plate positions in patients with intrahepatic tumors undergoing helical tomotherapy

Purpose: While abdominal compression (AC) can be used to reduce respiratory liver motion in patients receiving helical tomotherapy for hepatocellular carcinoma, the nature and extent of this effect is not well described. The purpose of this study was to evaluate the changes in magnitude of three-dimensional liver motion with abdominal compression using four-dimensional (4D) computed tomography (CT) images of several plate positions. Methods: From January 2012 to October 2015, 72 patients with intrahepatic carcinoma and divided into four groups underwent 4D-CT scans to assess respiratory liver motion. Of the 72 patients, 19 underwent abdominal compression of the cephalic area between the subxiphoid and umbilicus (group A), 16 underwent abdominal compression of the caudal region between the subxiphoid area and the umbilicus (group B), 11 patients underwent abdominal compression of the caudal umbilicus (group C), and 26 patients remained free breathing (group D). 4D-CT images were sorted into ten-image series, according to the respiratory phase from the end inspiration to the end expiration, and then transferred to treatment planning software. All liver contours were drawn by a single physician and confirmed by a second physician. Liver relative coordinates were automatically generated to calculate the liver respiratory motion in different axial directions to compile the 10 ten contours into a single composite image. Differences in respiratory liver motion were assessed with a one-way analysis of variance test of significance. Results: The average respiratory liver motion in the Y axial direction was 4.53 ± 1.16, 7.56 ± 1.30, 9.95 ± 2.32, and 9.53 ± 2.62 mm in groups A, B, C, and D, respectively, with a significant change among the four groups (p < 0.001). Abdominal compression was most effective in group A (compression plate on the subxiphoid area), with liver displacement being 2.53 ± 0.93, 4.53 ± 1.16, and 2.14 ± 0.92 mm on the X-, Y-, and Z-axes, respectively. There was no significant difference in respiratory liver motion between group C (displacement: 3.23 ± 1.47, 9.95 ± 2.32, and 2.92 ± 1.10 mm on the X-, Y-, and Z-axes, respectively) and group D (displacement: 3.35 ± 1.55, 9.53 ± 2.62, and 3.35 ± 1.73 mm on the X-, Y-, and Z-axes, respectively). Abdominal compression was least effective in group C (compression on caudal umbilicus), with liver motion in this group similar to that of free-breathing patients (group D). Conclusions: 4D-CT scans revealed significant liver motion control via abdominal compression of the subxiphoid area; however, this control of liver motion was not observed with compression of the caudal umbilicus. The authors, therefore, recommend compression of the subxiphoid area in patients undergoing external radiotherapy for intrahepatic carcinoma.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

TU-FG-201-09: Predicting Accelerator Dysfunction

Purpose: To develop an integrated statistical process control (SPC) framework using digital performance and component data accumulated within the accelerator system that can detect dysfunction prior to unscheduled downtime. Methods: Seven digital accelerators were monitored for twelve to 18 months. The accelerators were operated in a ‘run to failure mode’ with the individual institutions determining when service would be initiated. Institutions were required to submit detailed service reports. Trajectory and text log files resulting from a robust daily VMAT QA delivery were decoded and evaluated using Individual and Moving Range (I/MR) control charts. The SPC evaluation was presented in a customized dashboard interface that allows the user to review 525 monitored parameters (480 MLC parameters). Chart limits were calculated using a hybrid technique that includes the standard SPC 3σ limits and an empirical factor based on the parameter/system specification. The individual (I) grand mean values and control limit ranges of the I/MR charts of all accelerators were compared using statistical (ranked analysis of variance (ANOVA)) and graphical analyses to determine consistency of operating parameters. Results: When an alarm or warning was directly connected to field service, process control charts predicted dysfunction consistently on beam generation related parameters (BGP)– RF Driver Voltage, Gun Grid Voltage, and Forward Power (W); beam uniformity parameters – angle and position steering coil currents; and Gantry position accuracy parameter: cross correlation max-value. Control charts for individual MLC – cross correlation max-value/position detected 50% to 60% of MLCs serviced prior to dysfunction or failure. In general, non-random changes were detected 5 to 80 days prior to a service intervention. The ANOVA comparison of BGP determined that each accelerator parameter operated at a distinct value. Conclusion: The SPC framework shows promise. Long term monitoring coordinated with service will be required to definitively determine the effectiveness of the model. Varian Medical System, Inc. provided funding in support of the research presented.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2013

Survival of Seaward-Migrating PIT and Acoustic-Tagged Juvenile Chinook Salmon in the Snake and Columbia Rivers: An Evaluation of Length-Specific Tagging Effects

Studies examining the survival of juvenile salmon as they emigrate to the ocean provide important information regarding the management of regulated river systems. Acoustic telemetry is a widely used tool for evaluating the behavior and survival of juvenile salmonids in the Columbia River basin. Thus, it is important to understand how the surgical tagging process and the presence of a transmitter affect survival so any biases can be accounted for or eliminated. This study evaluated the effects of fish length and tag type on the survival of yearling and subyearling Chinook salmon during their seaward migrations through the Snake and Columbia rivers during 2006, 2007, and 2008. Fish were collected at Lower Granite Dam on the Snake River (river kilometer 695) and implanted with either only a passive integrated transponder (PIT) tag (PIT fish) or both a PIT tag and an acoustic transmitter (AT fish). Survival was estimated from release at Lower Granite Dam to multiple downstream locations (dams) using the Cormack–Jolly–Seber single release model, and analysis of variance was used to test for differences among length-classes and between tag types. No length-specific tag effect was detected between PIT and AT fish (i.e., length affected the survival of PIT fish in a manner similar to which it affected the survival of AT fish). Survival among the smallest length class (i.e., 80–89 mm) of both PIT and AT subyearling Chinook salmon was markedly low (i.e., 4%). Fish length was positively correlated with the survival of both PIT and AT fish. Significant differences in survival were detected between tag types; the survival of PIT fish was generally greater than that of AT fish. However, confounding variables warrant caution in making strong inferences regarding this factor. Further, results suggest that tag effects may be due to the process of surgically implanting the transmitter rather than the presence of the transmitter.

juvenile Chinook salmon↗
DOE OSTI2022

OperonSEQer: A set of machine-learning algorithms with threshold voting for detection of operon pairs using short-read RNA-sequencing data

Operon prediction in prokaryotes is critical not only for understanding the regulation of endogenous gene expression, but also for exogenous targeting of genes using newly developed tools such as CRISPR-based gene modulation. A number of methods have used transcriptomics data to predict operons, based on the premise that contiguous genes in an operon will be expressed at similar levels. While promising results have been observed using these methods, most of them do not address uncertainty caused by technical variability between experiments, which is especially relevant when the amount of data available is small. In addition, many existing methods do not provide the flexibility to determine the stringency with which genes should be evaluated for being in an operon pair. We present OperonSEQer, a set of machine learning algorithms that uses the statistic and p-value from a non-parametric analysis of variance test (Kruskal-Wallis) to determine the likelihood that two adjacent genes are expressed from the same RNA molecule. We implement a voting system to allow users to choose the stringency of operon calls depending on whether your priority is high recall or high specificity. In addition, we provide the code so that users can retrain the algorithm and re-establish hyperparameters based on any data they choose, allowing for this method to be expanded as additional data is generated. We show that our approach detects operon pairs that are missed by current methods by comparing our predictions to publicly available long-read sequencing data. OperonSEQer therefore improves on existing methods in terms of accuracy, flexibility, and adaptability.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Load Deflection Data (LDDATA)

The current data set is load deflection data (Stress vs strain for compression stress and stress vs elongation for tension stress) from a material test. The goal of the test was to characterize this rubber-like material by looking at different variables: sample creation process, anisotropy, temperature, etc. The data will be used with the Texas A&M Online Research Experience for Undergraduates (O-REU) program. This program is joint with LANL. The student is not an official LANL student so they will not be able to access data and documents on the yellow. The load deflection data set is unclassified and will be sent to the student for analysis. Analysis will include curve fitting, scalar value analysis (stiffness, toughness, etc.) and analysis of variance to determine what variables statistically affect the response while correcting for covariates (temperature soak time and humidity). Data will be stored as Matlab structs and as HDF5 files. Same data, different formats.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Near-Efficient and Non-Asymptotic Multiway Inference

We establish non-asymptotic efficiency guarantees for tensor decomposition–based inference in count data models. Under a Poisson framework, we consider two related goals: (i) parametric inference , the estimation of the full distributional parameter tensor, and (ii) multiway analysis , the recovery of its canonical polyadic (CP) decomposition factors. Our main result shows that in the rank-one setting, a rank-constrained maximum-likelihood estimator achieves multiway analysis with variance matching the Cramér–Rao Lower Bound (CRLB) up to absolute constants and logarithmic factors. This provides a general framework for studying “near-efficient” multiway estimators in finite-sample settings. For higher ranks, we illustrate that our multiway estimator may not attain the CRLB; nevertheless, CP-based parametric inference remains nearly minimax optimal, with error bounds that improve on prior work by offering more favorable dependence on the CP rank. Numerical experiments corroborate near-efficiency in the rank-one case and highlight the efficiency gap in higher-rank scenarios.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Experimental Studies on Wood Pellets Combustion in a Fixed Bed Combustor Using Taguchi Method

The combustion of wood pellets in a fixed bed combustor of a 20 kW capacity domestic pellet boiler was tested according to several factors including Power, Excess Air (EA), Primary/Secondary air Split Ratio (SR) and Grate Area (GA). The Taguchi method was applied to program the experimental design. Several parameters were measured, including gas emissions (CO), fuel bed temperature (measured at 4 different heights), and efficiency. The experimental results show that the lower CO emission and the higher efficiency were obtained at medium thermal loads and the highest temperature on the fuel bed was obtained at about ¼ of its height (15 mm). The results obtained from the analysis of variance (ANOVA) show that the SR and the Power are the most important factors contributing to the CO reduction and also increase the fuel bed temperature.

Castro, Carlos↗
DOE OSTI2020

Multi-Temporal Predictive Modelling of Sorghum Biomass Using UAV-Based Hyperspectral and LiDAR Data

High-throughput phenotyping using high spatial, spectral, and temporal resolution remote sensing (RS) data has become a critical part of the plant breeding chain focused on reducing the time and cost of the selection process for the “best” genotypes with respect to the trait(s) of interest. In this paper, the potential of accurate and reliable sorghum biomass prediction using visible and near infrared (VNIR) and short-wave infrared (SWIR) hyperspectral data as well as light detection and ranging (LiDAR) data acquired by sensors mounted on UAV platforms is investigated. Predictive models are developed using classical regression-based machine learning methods for nine experiments conducted during the 2017 and 2018 growing seasons at the Agronomy Center for Research and Education (ACRE) at Purdue University, Indiana, USA. The impact of the regression method, data source, timing of RS and field-based biomass reference data acquisition, and the number of samples on the prediction results are investigated. R2 values for end-of-season biomass ranged from 0.64 to 0.89 for different experiments when features from all the data sources were included. Geometry-based features derived from the LiDAR point cloud to characterize plant structure and chemistry-based features extracted from hyperspectral data provided the most accurate predictions. Evaluation of the impact of the time of data acquisition during the growing season on the prediction results indicated that although the most accurate and reliable predictions of final biomass were achieved using remotely sensed data from mid-season to end-of-season, predictions in mid-season provided adequate results to differentiate between promising varieties for selection. The analysis of variance (ANOVA) of the accuracies of the predictive models showed that both the data source and regression method are important factors for a reliable prediction; however, the data source was more important with 69% significance, versus 28% significance for the regression method.

09 BIOMASS FUELS↗