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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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At least 973 records · Page 54
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Persistent organic pollutants, skull size and bone density of polar bears (Ursus maritimus) from East Greenland 1892–2015 and Svalbard 1964–2004

Highlights: • Density in East Greenland males decreased over time. • Size in East Greenland females tented to decrease over time. • Size was highest in Svalbard polar bears. • Density was positive correlated to POPs. • Environmental changes and POPs may affect bone density and size. We investigated skull size (condylobasal length; CBL) and bone mineral density (BMD) in polar bears (Ursus maritimus) from East Greenland (n = 307) and Svalbard (n = 173) sampled during the period 1892–2015 in East Greenland and 1964–2004 at Svalbard. Adult males from East Greenland showed a continuous decrease in BMD from 1892 to 2015 (linear regression: p < 0.01) indicating that adult male skulls collected in the early pre-pollution period had the highest BMD. A similar decrease in BMD over time was not found for the East Greenland adult females. However, there was a non-significant trend that the skull size of adult East Greenland females was negatively correlated with collection year 1892–2015 (linear regression: p = 0.06). No temporal change was found for BMD or skull size in Svalbard polar bears (ANOVA: all p > 0.05) nor was there any significant difference in BMD between Svalbard and East Greenland subpopulations. Skull size was larger in polar bears from Svalbard than from East Greenland (two-way ANOVA: p = 0.003). T-scores reflecting risk of osteoporosis showed that adult males from both East Greenland and Svalbard are at risk of developing osteopenia. Finally, when correcting for age and sex, BMD in East Greenland polar bears increased with increasing concentrations of persistent organic pollutants (POPs) i.e. ΣPCB (polychlorinated biphenyls), ΣHCH (hexachlorohexane), HCB (hexachlorobenzene) and ΣPBDE (polybrominated diphenyl ethers) while skull size increased with ΣHCH concentrations all in the period 1999–2014 (multiple linear regression: all p < 0.05, n = 175). The results suggest that environmental changes over time, including exposure to POPs, may affect bone density and size of polar bears.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Does the air pollution model influence the evidence of socio-economic disparities in exposure and susceptibility?

Highlights: • Daily exposure to ozone may vary across air pollution models, considerably. • The exposure model influenced disparity of exposure across socio-economic strata. • The air pollution model does not affect disparity of susceptibility. • Multiple air pollution models should be used to assess socio-economic disparities. Studies assessing socio-economic disparities in air pollution exposure and susceptibility are usually based on a single air pollution model. A time stratified case-crossover study was designed to assess the impact of the type of model on differential exposure and on the differential susceptibility in the relationship between ozone exposure and daily mortality by socio-economic strata (SES) in Montreal. Non-accidental deaths along with deaths from cardiovascular and respiratory causes on the island of Montreal for the period 1991–2002 were included as cases. Daily ozone concentration estimates at partictaipants’ residence were obtained from the five following air pollution models: Average value (AV), Nearest station model (NS), Inverse-distance weighting interpolation (IDW), Land-use regression model with back-extrapolation (LUR-BE) and Bayesian maximum entropy model combined with a land-use regression (BME-LUR). The prevalence of a low household income (< 20,000/year) was used as socio-economic variable, divided into two categories as a proxy for deprivation. Multivariable conditional logistic regressions were used considering 3-day average concentrations. Multiplicative and additive interactions (using Relative Excess Risk due to Interaction) as well as Cochran's tests were calculated and results were compared across the different air pollution models. Heterogeneity of susceptibility and exposure according to socio-economic status (SES) were found. Ratio of exposure across SES groups means ranged from 0.75 [0.74–0.76] to 1.01 [1.00–1.02], respectively for the LUR-BE and the BME-LUR models. Ratio of mortality odds ratios ranged from 1.01 [0.96–1.05] to 1.02 [0.97–1.08], respectively for the IDW and LUR-BE models. Cochran's test of heterogeneity between the air pollution models showed important heterogeneity regarding the differential exposure by SES, but the air pollution model was not found to influence heterogeneity regarding the differential susceptibility. The study showed air pollution models can influence the assessment of disparities in exposure according to SES in Montreal but not that of disparities in susceptibility.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Machine learning and deep learning for mineralogy interpretation and CO 2 saturation estimation in geological carbon Storage: A case study in the Illinois Basin

Carbon capture and storage (CCS) is a promising approach to simultaneously maintaining energy security and reducing carbon dioxide (CO 2 ) emissions under the current energy portfolio that is dominated by fossil fuel energy. Pre-injection formation characterization and post-injection CO 2 monitoring are two critical tasks to guarantee storage efficiency in CCS. The CCS projects in the Illinois Basin, the first large-scale CO 2 injection into saline aquifers in the United States, employed conventional and the latest pulsed neutron logging (PNL) tools for mineralogy interpretation and CO 2 saturation estimation, which provide valuable references for future CCS projects. Because of the inherent fuzziness of petrophysical measurements and complex subsurface heterogeneity, interpreting well-logging data is time-consuming, and its accuracy can be user-biased. In recent years, data-driven methods have been widely used to capture the non-linear patterns between input features and interpretation results. This work applied and evaluated four commonly used machine learning (ML) models, including ridge regression (RR), random forest (RF), gradient boosting regression (GBR), support vector regression (SVR), and one deep learning (DL) model, the artificial neural network (ANN). We optimized the hyperparameters of the four ML models and the DL model using the simulated annealing algorithm and the grid search strategy, respectively. The input features of the mineralogy interpretation models were eleven conventional well-logging parameters, and the label data (i.e., ground truth) were the porosity and volumetric fractions of six minerals, including quartz, feldspar, dolomite, calcite, clay, and iron minerals. The results demonstrated that the GBR and RF models were superior in predicting volumetric fractions of minerals and porosity; label data with low coefficient of variation (CV) values tended to yield better performance. For CO 2 saturation estimation, the RF was the best-performing model, followed by SVR, ANN, GBR, and RR. Furthermore, we conducted feature importance ranking using the permutation importance algorithm and found that the formation sigma and well pressure were the most important features in this study. In conclusion, the study of CCS projects in the Illinois Basin bridges the gap between the limited knowledge and understanding of geological carbon storage and the increasing demand for reliable, cost-effective, and sustainable energy solutions.

58 GEOSCIENCES↗
DOE OSTI2024

Predicting nutrition and environmental factors associated with female reproductive disorders using a knowledge graph and random forests

Female reproductive disorders (FRDs) are common health conditions that may present with significant symptoms. Diet and environment are potential areas for FRD interventions. We utilized a knowledge graph (KG) method to predict factors associated with common FRDs (for example, endometriosis, ovarian cyst, and uterine fibroids). We harmonized survey data from the Personalized Environment and Genes Study (PEGS) on internal and external environmental exposures and health conditions with biomedical ontology content. We merged the harmonized data and ontologies with supplemental nutrient and agricultural chemical data to create a KG. We analyzed the KG by embedding edges and applying a random forest for edge prediction to identify variables potentially associated with FRDs. We also conducted logistic regression analysis for comparison. Across 9765 PEGS respondents, the KG analysis resulted in 8535 significant or suggestive predicted links between FRDs and chemicals, phenotypes, and diseases. Amongst these links, 32 were exact matches when compared with the logistic regression results, including comorbidities, medications, foods, and occupational exposures. Mechanistic underpinnings of predicted links documented in the literature may support some of our findings. Our KG methods are useful for predicting possible associations in large, survey-based datasets with added information on directionality and magnitude of effect from logistic regression. These results should not be construed as causal but can support hypothesis generation. This investigation enabled the generation of hypotheses on a variety of potential links between FRDs and exposures. Future investigations should prospectively evaluate the variables hypothesized to impact FRDs.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

Long-Term Results of a Randomized Trial in Locally Advanced Rectal Cancer: No Benefit From Adding a Brachytherapy Boost

Purpose/Objective(s): Mature data on tumor control and survival are presented from a randomized trial of the addition of a brachytherapy boost to long-course neoadjuvant chemoradiation therapy (CRT) for locally advanced rectal cancer. Methods and Materials: Between March 2005 and November 2008, 248 patients with T3-4N0-2M0 rectal cancer were prospectively randomized to either long-course preoperative CRT (50.4 Gy in 28 fractions, per oral tegafur-uracil and L-leucovorin) alone or the same CRT schedule plus a brachytherapy boost (10 Gy in 2 fractions). The primary trial endpoint was pathologic complete response (pCR) at the time of surgery; secondary endpoints included overall survival (OS), progression-free survival (PFS), and freedom from locoregional failure. Results: Results for the primary endpoint have previously been reported. This analysis presents survival data for the 224 patients in the Danish part of the trial. In all, 221 patients (111 control arm, 110 brachytherapy boost arm) had data available for analysis, with a median follow-up time of 5.4 years. Despite a significant increase in tumor response at the time of surgery, no differences in 5-year OS (70.6% vs 63.6%, hazard ratio [HR] = 1.24, P=.34) and PFS (63.9% vs 52.0%, HR=1.22, P=.32) were observed. Freedom from locoregional failure at 5 years were 93.9% and 85.7% (HR=2.60, P=.06) in the standard and in the brachytherapy arms, respectively. There was no difference in the prevalence of stoma. Explorative analysis based on stratification for tumor regression grade and resection margin status indicated the presence of response migration. Conclusions: Despite increased pathologic tumor regression at the time of surgery, we observed no benefit on late outcome. Improved tumor regression does not necessarily lead to a relevant clinical benefit when the neoadjuvant treatment is followed by high-quality surgery.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2014

Neoadjuvant Sandwich Treatment With Oxaliplatin and Capecitabine Administered Prior to, Concurrently With, and Following Radiation Therapy in Locally Advanced Rectal Cancer: A Prospective Phase 2 Trial

Purpose: Systemic failure remains the major challenge in management of locally advanced rectal cancer (LARC). To optimize the timing of neoadjuvant treatment and enhance systemic control, we initiated a phase 2 trial to evaluate a new strategy of neoadjuvant sandwich treatment, integrating induction chemotherapy, concurrent chemoradiation therapy, and consolidation chemotherapy. Here, we present preliminary results of this trial, reporting the tumor response, toxicities, and surgical complications. Methods and Materials: Fifty-one patients with LARC were enrolled, among which were two patients who were ineligible because of distant metastases before treatment. Patients were treated first with one cycle of induction chemotherapy consisting of oxaliplatin, 130 mg/m² on day 1, with capecitabine, 1000 mg/m² twice daily for 14 days every 3 weeks (the XELOX regimen), followed by chemoradiation therapy, 50 Gy over 5 weeks, with the modified XELOX regimen (oxaliplatin 100 mg/m²), and then with another cycle of consolidation chemotherapy with the XELOX regimen. Surgery was performed 6 to 8 weeks after completion of radiation therapy. Tumor responses, toxicities, and surgical complications were recorded. Results: All but one patent completed the planned schedule of neoadjuvant sandwich treatment. Neither life-threatening blood count decrease nor febrile neutropenia were observed. Forty-five patents underwent optimal surgery with total mesorectal excision (TME). Four patients refused surgery because of clinically complete response. There was no perioperative mortality in this cohort. Five patients (11.1%) developed postoperative complications. Among the 45 patients who underwent TME, pathologic complete response (pCR), pCR or major regression, and at least moderate regression were achieved in 19 (42.2%), 37 (82.2%), and 44 patients (97.8%), respectively. Conclusions: Preliminary results suggest that the strategy of neoadjuvant sandwich treatment using XELOX regimen as induction, concomitant, and consolidation chemotherapy to the conventional radiation is well tolerated. The strategy is highly effective in terms of pCR and major regression, which warrants further investigation.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2016

Outcomes of Sinonasal Cancer Treated With Proton Therapy

Purpose: To report disease outcomes after proton therapy (PT) for sinonasal cancer. Methods and Materials: Eighty-four adult patients without metastases received primary (13%) or adjuvant (87%) PT for sinonasal cancers (excluding melanoma, sarcoma, and lymphoma). Common histologies were olfactory neuroblastoma (23%), squamous cell carcinoma (22%), and adenoid cystic carcinoma (17%). Advanced stage (T3 in 25% and T4 in 69%) and high-grade histology (51%) were common. Surgical procedures included endoscopic resection alone (45%), endoscopic resection with craniotomy (12%), or open resection (30%). Gross residual disease was present in 26% of patients. Most patients received hyperfractionated PT (1.2 Gy [relative biological effectiveness (RBE)] twice daily, 99%) and chemotherapy (75%). The median PT dose was 73.8 Gy (RBE), with 85% of patients receiving more than 70 Gy (RBE). Prognostic factors were analyzed using Kaplan-Meier analysis and proportional hazards regression for multiple regression. Dosimetric parameters were evaluated using logistic regression. Serious, late grade 3 or higher toxicity was reported using the National Cancer Institute Common Terminology Criteria for Adverse Events, version 4. The median follow-up was 2.4 years for all patients and 2.7 years among living patients. Results: The local control (LC), neck control, freedom from distant metastasis, disease-free survival, cause-specific survival, and overall survival rates were 83%, 94%, 73%, 63%, 70%, and 68%, respectively, at 3 years. Gross total resection and PT resulted in a 90% 3-year LC rate. The 3-year LC rate was 61% for primary radiation therapy and 59% for patients with gross disease. Gross disease was the only significant factor for LC on multivariate analysis, whereas grade and continuous LC were prognostic for overall survival. Six of 12 local recurrences were marginal. Dural dissemination represented 26% of distant recurrences. Late toxicity occurred in 24% of patients (with grade 3 or higher unilateral vision loss in 2%). Conclusions: Dose-intensified, hyperfractionated PT with or without concurrent chemotherapy results in excellent LC after gross total resection, and results in patients with gross disease are encouraging. Patients with high-grade histology are at greater risk of death from distant dissemination. Continuous LC is a major determinant of survival justifying aggressive local therapy in nearly all cases.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2017

Dosimetric Predictors of Patient-Reported Xerostomia and Dysphagia With Deintensified Chemoradiation Therapy for HPV-Associated Oropharyngeal Squamous Cell Carcinoma

Purpose/Objective(s): To estimate the association between different dose-volume metrics of the salivary glands and pharyngeal constrictors with patient reported severity of xerostomia/dysphagia in the setting of deintensified chemoradiation therapy (CRT). Methods and Materials: Forty-five patients were treated on a phase 2 study assessing the efficacy of deintensified CRT for favorable-risk, HPV-associated oropharyngeal squamous cell carcinoma. Patients received 60 Gy intensity modulated radiation therapy with concurrent weekly cisplatin (30 mg/m{sup 2}), and reported the severity of their xerostomia/dysphagia (before and after treatment) using the patient-reported outcome version of the Common Terminology Criteria for Adverse Events (CTCAE) (PRO-CTCAE). Individual patient dosimetric data of the contralateral parotid and submandibular glands and pharyngeal constrictors were correlated with changes in PRO-CTCAE severity. A change in severity (from baseline) of ≥2 was considered clinically meaningful. Associations between dose-volume metrics and patient outcomes were assessed with receiver operating characteristic (ROC) curve and logistic regression model. Results: Six months after CRT, patients reporting <2 change in xerostomia severity (n=14) had an average D{sub mean} = 22 ± 9 Gy to the sum of the contralateral glands (parotid + submandibular) compared with the patients reporting ≥2 change (n=21), who had an average D{sub mean} = 34 ± 8 Gy. V15 to V55 for the combined contralateral glands showed the strongest association with xerostomia (area under the curve [AUC] = 0.83-0.86). Based on the regression analysis, a 20% risk of toxicity was associated with V15 = 48%, V25 = 30%, and D{sub mean}=21 Gy. Six months after CRT, patients reporting <2 change in dysphagia severity (n=26) had an average V55 = 76 ± 13 (%) to the superior pharyngeal constrictor compared with the patients reporting ≥2 change in severity (n=9), who had average V55 = 89 ± 13 (%). V55to V60 had the strongest association with dysphagia (AUC = 0.70-0.75). Based on the regression analysis, a 20% risk of toxicity was associated with V55 = 78%, V60 = 40%. The findings at 12 months were similar. Conclusions: After deintensified CRT, the rate of patient-reported xerostomia/dysphagia appears to be associated with the V15 of the combined contralateral salivary glands and V55 to V60 of the superior pharyngeal constrictors.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2015

An algebraic method for constructing stable and consistent autoregressive filters

In this paper, we introduce an algebraic method to construct stable and consistent univariate autoregressive (AR) models of low order for filtering and predicting nonlinear turbulent signals with memory depth. By stable, we refer to the classical stability condition for the AR model. By consistent, we refer to the classical consistency constraints of Adams–Bashforth methods of order-two. One attractive feature of this algebraic method is that the model parameters can be obtained without directly knowing any training data set as opposed to many standard, regression-based parameterization methods. It takes only long-time average statistics as inputs. The proposed method provides a discretization time step interval which guarantees the existence of stable and consistent AR model and simultaneously produces the parameters for the AR models. In our numerical examples with two chaotic time series with different characteristics of decaying time scales, we find that the proposed AR models produce significantly more accurate short-term predictive skill and comparable filtering skill relative to the linear regression-based AR models. These encouraging results are robust across wide ranges of discretization times, observation times, and observation noise variances. Finally, we also find that the proposed model produces an improved short-time prediction relative to the linear regression-based AR-models in forecasting a data set that characterizes the variability of the Madden–Julian Oscillation, a dominant tropical atmospheric wave pattern.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2017

On state-of-charge determination for lithium-ion batteries

Accurate estimation of state-of-charge (SOC) of a battery through its life remains challenging in battery research. Although improved precisions continue to be reported at times, almost all are based on regression methods empirically, while accuracy is often not properly addressed. Here, a comprehensive review is set to address such issues, from fundamental principles that are supposed to define SOC to methodologies to estimate SOC for practical use. It covers topics from calibration, regression (including modeling methods) to validation in terms of precision and accuracy. At the end, we intend to answer the following questions: 1) can SOC estimation be self-adaptive without bias? 2) Why Ah-counting is a necessity in almost all battery-model-assisted regression methods? 3) How to establish a consistent framework of coupling in multi-physics battery models? 4) To assess the accuracy in the SOC estimation, statistical methods should be employed to analyze factors that contribute to the uncertainty. Finally, we hope, through this proper discussion of the principles, accurate SOC estimation can be widely achieved.

25 ENERGY STORAGE↗
DOE OSTI2025

Jensen–Shannon divergence based novel loss functions for Bayesian neural networks

Bayesian neural networks (BNNs) are state-of-the-art machine learning methods that can naturally regularize and systematically quantify uncertainties using their stochastic parameters. Kullback–Leibler (KL) divergence-based variational inference used in BNNs suffer from unstable optimization and challenges in approximating light-tailed posteriors due to the unbounded nature of the KL divergence. To resolve these issues, we formulate a novel loss function for BNNs based on a new modification to the generalized Jensen–Shannon (JS) divergence, which is bounded. In addition, we propose a Geometric JS divergence-based loss, which is computationally efficient since it can be evaluated analytically. We found that the JS divergence-based variational inference is intractable, and hence employed a constrained optimization framework to formulate these losses. Our theoretical analysis and empirical experiments on multiple regression and classification data sets suggest that the proposed losses perform better than the KL divergence-based loss, especially when the data sets are noisy or biased. Specifically, there are approximately 5% and 8% improvements in accuracy for a noise-added CIFAR-10 dataset and a regression dataset, respectively. There is about 13% reduction in false negative predictions of a biased histopathology dataset. Additionally, we quantify and compare the uncertainty metrics for the regression and classification tasks.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Characterizing forecastability of wind sites in the United States

With the rapid growth of wind power, managing its uncertainty and variability becomes critical in power system operations. Wind forecasting is one of the enablers to partially tackle challenges associated with wind power uncertainty. To improve the 'forecasting ability', defined as forecastability, different forecasting methods have been developed to assist grid integration of wind energy. However, forecasting performance not only relies on the power of forecasting models, but is also related to local weather conditions and (known as wind characteristics) wind farm properties. In this study, geospatial and instance spatial distributions of six wind characteristics and two forecasting error metrics are first analyzed based on 126,000 + wind sites in the United States. Forecasts in different look-ahead times are generated by using a machine learning based multi-model forecasting framework model and the Weather Research and Forecasting model. A forecastability quantification method is developed by characterizing the relationship between forecastability and wind series entropy using three regression methods, i.e., linear approximation, locally weighted scatterplot smoother nonlinear nonparametric regression, and quantile regression. It is found that the forecastability of a wind site can be successfully characterized by wind series characteristics, thereby providing valuable information at different stages of wind energy projects.

17 WIND ENERGY↗
DOE OSTI2022

A physical method for downscaling land surface temperatures using surface energy balance theory

Fine-resolution land surface temperature (LST) derived from thermal infrared remote sensing images is a good indicator of surface water status and plays an essential role in the exchange of energy and water between land and atmosphere. A physical surface energy balance (SEB)-based LST downscaling method (DTsEB) is developed to downscale coarse remotely sensed thermal infrared LST products with fine-resolution visible and near-infrared data. Here, the DTsEB method is advantageous for its ability to mechanically interrelate surface variables contributing to the spatial variation of LST, to quantitatively weigh the contributions of each related variable within a physical framework, and to efficaciously avoid the subjective selection of scaling factors and the establishment of statistical regression relationships. The applicability of the DTsEB method was tested by downscaling 12 scenes of 990 m Moderate Resolution Imaging Spectroradiometer (MODIS) and aggregated Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) LST products to 90 m resolution at six overpass times between 2005 and 2015 over three 9.9 km by 9.9 km cropland (mixed by grass, tree, and built-up land) study areas. Three typical LST downscaling methods, namely the widely applied TsHARP, the later developed least median square regression downscaling (LMS) and the geographically weighted regression (GWR), were introduced for intercomparison. The results showed that the DTsEB method could more effectively reconstruct the subpixel spatial variations in LST within the coarse-resolution pixels and achieve a better downscaling accuracy than the TsHARP, LMS and GWR methods. The DTsEB method yielded, on average, root mean square errors (RMSEs) of 2.01 K and 1.42 K when applied to the MODIS datasets and aggregated ASTER datasets, respectively, which were lower than those obtained with the TsHARP method, with average RMSEs of 2.41 K and 1.71 K, the LMS method, with average RMSEs of 2.35 K and 1.63 K, and the GWR method, with average RMSEs of 2.38 K and 1.64 K, respectively. The contributions of the related surface variables to the subpixel spatial variation in the LST varied both spatially and temporally and were different from each other. In summary, the DTsEB method was demonstrated to outperform the TsHARP, LMS, and GWR methods and could be used as a good alternative for downscaling LST products from coarse to fine resolution with high robustness and accuracy.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Inorganic characterization of switchgrass biomass using laser-induced breakdown spectroscopy

The inorganic characterization of 74 samples of switchgrass using laser-induced breakdown spectroscopy (LIBS) was undertaken. Determination of ash and inorganic elements content in biomass materials is vital for feedstock screening for bioconversion processes. Hierarchical models using principal component analysis (PCA) and partial least square analysis (PLS) were used to determine the presence of specific elemental micronutrients that are important in determining plant health for robust biomass production. LIBS uses a 532 nm laser with 45 mJ of laser power to excite the samples of switchgrass plant material and the emission of all the elements present in the plant samples were recorded in single spectra with a wide wavelength range of 200–800 nm. The results were compared to the laboratory standard technique, e.g., ICP-OES technique, to determine the true values for major micronutrients such as, silicon (Si), potassium (K), calcium (Ca), magnesium (Mg), phosphorus (P), and sulfur (S). Overall, our objectives were: 1) To determine the spectral features of switchgrass containing different amounts of these elements and 2) To examine the viability of this technique for determining the quality of the feedstock in terms of its inorganic composition. Cross-validation results showed that the broad-based model developed is promising for inorganics prediction in switchgrass. The LIBS validation prediction for the micronutrient elements mentioned here have been obtained. The regression coefficients for Si, were obtained to be 0.995, 0.994 for calibration and validation respectively, in case of Ca the regression coefficients were, 0.994 and 0.992 for calibration and validation. Similarly, in the case of Mg and K these were calculated to be 0.992 and 0.985, and 0.994 and 0.993 respectively. The regression coefficients are not as good as those for the elements mentioned, in case of the two elements S and P. They are 0.957, and 0.878, and 0.952 and 0.894 respectively for calibration, validation for the two elements. This demonstrates that LIBS-based techniques are inherently well suited for diverse environmental applications. Furthermore, LIBS along with PLS model can show capability in determining the viability of switchgrass as a biomass in the production of biofuels and survivability of switchgrass in processes associated with climate change. LIBS can help determining which switchgrass would be appropriate for a specific conversion process that favors low ash content overall or low value of specific inorganics.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Predictive modeling of Néel temperature in austenitic alloys using CALPHAD and data analytics

The Néel temperature is a crucial yet often overlooked parameter in calculating the stacking fault energy (SFE) of austenitic alloys. Several empirical equations have been proposed to estimate the Néel temperature of austenitic alloys, which are then used to calculate the SFE and explain deformation mechanisms. However, these empirical equations, typically derived using linear regression algorithms, are often simplistic and may fail to capture the complex interactions among multiple alloying elements that influence the Néel temperature. Moreover, their applicability is usually limited to specific compositional ranges. In this study, we propose a CALPHAD based approach and develop a surrogate decision tree based regression model capable of capturing the interactions among multiple alloying elements to predict the Néel temperature. Predictions from both the CALPHAD approach and the regression model show close agreement with experimental measurements reported in the literature. In conclusion, the implications of accurate Néel temperature predictions on the calculated SFE and deformation mechanisms are also discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Attitude towards the incorporation of the selective collection of biowaste in a municipal solid waste management system. A case study

Highlights: • Attitude towards incorporating biowaste selective collection is analysed. • Willingness to participate and to pay in biowaste selective collection is obtained. • Socioeconomic aspects affecting WtParticipate and WtPay are identified. - Abstract: European waste legislation has been encouraging for years the incorporation of selective collection systems for the biowaste fraction. European countries are therefore incorporating it into their current municipal solid waste management (MSWM) systems. However, this incorporation involves changes in the current waste management habits of households. In this paper, the attitude of the public towards the incorporation of selective collection of biowaste into an existing MSWM system in a Spanish municipality is analysed. A semi-structured telephone interview was used to obtain information regarding aspects such as: level of participation in current waste collection systems, willingness to participate in selective collection of biowaste, reasons and barriers that affect participation, willingness to pay for the incorporation of the selective collection of biowaste and the socioeconomic characteristics of citizens who are willing to participate and pay for selective collection of biowaste. The results showed that approximately 81% of the respondents were willing to participate in selective collection of biowaste. This percentage would increase until 89% if the Town Council provided specific waste bins and bags, since the main barrier to participate in the new selective collection system is the need to use specific waste bin and bags for the separation of biowaste. A logit response model was applied to estimate the average willingness to pay, obtaining an estimated mean of 7.5% on top of the current waste management annual tax. The relationship of willingness to participate and willingness to pay for the implementation of this new selective collection with the socioeconomic variables (age, gender, size of the household, work, education and income) was analysed. Chi-square independence tests and binary logistic regression was used for willingness to participate, not being obtained any significant relationship. Chi-square independence tests, ordinal logistic regression and ordinary linear regression was applied for willingness to pay, obtaining statistically significant relationship for most of the socioeconomic variables.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

Uncertainty quantification in machine learning for engineering design and health prognostics: A tutorial

On top of machine learning (ML) models, uncertainty quantification (UQ) functions as an essential layer of safety assurance that could lead to more principled decision making by enabling sound risk assessment and management. The safety and reliability improvement of ML models empowered by UQ has the potential to significantly facilitate the broad adoption of ML solutions in high-stakes decision settings, such as healthcare, manufacturing, and aviation, to name a few. In this tutorial, we aim to provide a holistic lens on emerging UQ methods for ML models with a particular focus on neural networks and the applications of these UQ methods in tackling engineering design as well as prognostics and health management problems. Towards this goal, we start with a comprehensive classification of uncertainty types, sources, and causes pertaining to UQ of ML models. Next, we provide a tutorial-style description of several state-of-the-art UQ methods: Gaussian process regression, Bayesian neural network, neural network ensemble, and deterministic UQ methods focusing on spectral-normalized neural Gaussian process. Established upon the mathematical formulations, we subsequently examine the soundness of these UQ methods quantitatively and qualitatively (by a toy regression example) to examine their strengths and shortcomings from different dimensions. Then, we review quantitative metrics commonly used to assess the quality of predictive uncertainty in classification and regression problems. Afterward, we discuss the increasingly important role of UQ of ML models in solving challenging problems in engineering design and health prognostics. In conclusion, two case studies with source codes available on GitHub are used to demonstrate these UQ methods and compare their performance in the life prediction of lithium-ion batteries at the early stage (case study 1) and the remaining useful life prediction of turbofan engines (case study 2).

97 MATHEMATICS AND COMPUTING↗