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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 829 records · Page 46
All sourcesNASA NTRSDOE OSTI
DOE OSTI2013

Epidural Analgesia Versus Patient-Controlled Analgesia for Pain Relief in Uterine Artery Embolization for Uterine Fibroids: A Decision Analysis

Purpose: This study was designed to compare the costs and effects of epidural analgesia (EDA) to those of patient-controlled intravenous analgesia (PCA) for postintervention pain relief in women having uterine artery embolization (UAE) for systematic uterine fibroids. Methods: Cost-effectiveness analysis (CEA) based on data from the literature by constructing a decision tree to model the clinical pathways for estimating the effects and costs of treatment with EDA and PCA. Literature on EDA for pain-relief after UAE was missing, and therefore, data on EDA for abdominal surgery were used. Outcome measures were compared costs to reduce one point in visual analogue score (VAS) or numeric rating scale (NRS) for pain 6 and 24 h after UAE and risk for complications. Results: Six hours after the intervention, the VAS was 3.56 when using PCA and 2.0 when using EDA. The costs for pain relief in women undergoing UAE with PCA and EDA were Euro-Sign 191 and Euro-Sign 355, respectively. The costs for EDA to reduce the VAS score 6 h after the intervention with one point compared with PCA were Euro-Sign 105 and Euro-Sign 179 after 24 h. The risk of having a complication was 2.45 times higher when using EDA. Conclusions: The results of this indirect comparison of EDA for abdominal surgery with PCA for UAE show that EDA would provide superior analgesia for post UAE pain at 6 and 24 h but with higher costs and an increased risk of complications.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2019

Evaluating the decisions of glioma patients regarding clinical trial participation: a retrospective single provider review

Clinical trial accrual is vital to advancing care. A single study elucidated demographic data correlating with glioma patients’ clinical trial enrollment. However, it did not investigate the underlying decision-making process for non-participation. In this study, we seek to understand this key aspect of patient accrual. All notes for glioma patients seen by a single neuro-oncologist from July 2010 to May 2017 were examined for mention of clinical trial offerings. When a trial was declined, the patient’s reasoning was recorded along with the following: diagnosis, KPS, extent of resection, age, gender, race, marital status, income group, religion, trial offered at initial visit versus subsequent, and distance from trial site. Of 279 consecutive glioma patients, 88 were eligible for and offered a clinical trial. Fifty-seven accepted (65%), and 31 (35%) declined participation (Fig. 1). Of those offered a clinical trial, patients with glioblastoma (GBM) were significantly more likely to accept (44 out of 57 (77%) vs. 13 out of 57 (23%), p =0.03). After we adjusted for gender and travel distance, GBM was the only significant predictor of clinical trial acceptance, with an odds ratio of 3.18 (95% CI 1.17, 8.61, p =0.02). Reasons cited for non-participation included: travel distance (39%), lack of interest (39%), visit frequency (16%), and fear of randomization (6%). This study clarified for the first time individual glioma patient rationale for non-participation and potential areas for improving enrollment. Allowing off-site treatment centers or telemedicine visits may entice rural patients to participate. Visit frequency should be carefully considered and minimized whenever possible. Further prospective study of rationale for non-participation may improve enrollment over time.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2018

An (R, S)-norm fuzzy information measure with its applications in multiple-attribute decision-making

In this paper, we introduce a quantity measure which is called (R, S)-norm entropy and discuss some of its major properties with Shannon’s and other entropies in the literature. Based on this (R, S)-norm entropy, we have proposed a new (R, S)-norm fuzzy information measure and discussed its validity and properties. Further, we have given its comparison with other fuzzy information measures to prove its effectiveness. Attribute weights play an important role in multiple-attribute decision-making problems. In the present communication, two methods of determining the attribute weights are introduced. First is the case when the information regarding attribute weights is incompletely known or completely unknown and second is when we have partial information about attribute weights. For the first case, the extension of ordinary entropy weight method is used to calculate attribute weights and minimum entropy principle method based on solving a linear programming model is used in the second case. Finally, two methods are explained through numerical examples.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2018

An (R{sup ′},S{sup ′})-norm fuzzy relative information measure and its applications in strategic decision-making

In this communication, we propose two quantities named ‘An (R{sup ′},S{sup ′})-norm directed divergence measure’ and ‘(R{sup ′},S{sup ′})-norm fuzzy directed divergence measure’, establish their validity and discuss their properties. Further, the performance of proposed divergence measure is compared with some existing measures through a numerical example. Finally, the application of proposed fuzzy directed divergence measure is given in strategic decision-making and the outcome is compared with other methods in literature.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Delamination-informed lifecycle decisions: A dielectric and machine learning framework for composite sorting and recycling

Composite materials are widely used in aerospace, marine, and automotive sectors due to their high strength-to-weight ratio and durability. However, their long-term reliability can be compromised by damage accumulation. Specifically, delamination initiation serves as a precursor to structural failure, which is often difficult to detect during damage inspection. Identifying and sorting delamination initiation in samples not only increases operational safety while providing critical information for end-of-life decisions, which influences both the service life extension value and the efficiency of fiber extraction during recycling. This research addresses two challenges: (1) developing a nondestructive, ex-situ framework to sort composite materials based on damage severity, particularly delamination, and (2) understanding how damage in composites influences resin removal during pyrolysis. Both experimental work and finite element analysis were performed to predict critical stress levels that are associated with delamination onset. Based on these results, three loading levels 50 %, 75 %, and 90 % of maximum stress, were selected for controlled experiments, generating composite samples with varying extents of damage for machine learning model training. Microscopic imaging of these samples confirmed the damage progression from matrix cracking to delamination, validating the computational predictions. We explored supervised machine learning using dielectric measurements to classify damage states. Preliminary results show an artificial neural network can identify early delamination which is a potential precursor to failure, with 94.44 % accuracy on our dataset. A parallel investigation into the effect of damage severity on pyrolysis recycling showed that heavily delaminated samples required significantly less energy for comparable matrix removal than undamaged samples.

dielectric variables↗
DOE OSTI2022

A multi-criteria land suitability assessment of field allocation decisions for switchgrass

With the goal of evaluating biomass resource availability in certain regions in the United States, the Department of Energy, Department of Agriculture, Environmental Protection Agency, national laboratories, and Forest Service research laboratories, together with academic and industry collaborators, performed a resource analysis study, 2016 Billion-Ton Report: Advancing Domestic Resources for a Thriving Bioeconomy, Volume 1: Economic Availability of Feedstocks, using POLYSYS model to estimate biomass potential. The study provided county- and state-level biomass potential projections, with biomass feedstock production estimates and suitability assessment of lands acreage likely to transition to energy crops. For this analysis, we estimate biomass supply by year 2028, more specifically, lands acreage transition to switchgrass in the Midwest of the United States, across 50 counties in 3 states (Nebraska, Kansas, and Colorado). We propose a multi-criteria site suitability framework, to determine the potential acreage and production for switchgrass, at the field level. Site selection criteria span agronomic and ecosystem services such as previous land cover, crop rotation, soil water holding capacity, crop productivity, field efficiency, topography, soil organic matter, soil leaching propensity, distance to surface water bodies, and soil erosivity. Efficient agricultural field selection for dedicated energy crops field allocation is critical to evaluate biomass resource availability, and it is our position that estimation at the field level would provide a more accurate and useful estimation to support an emerging bioeconomy. Using field suitability scores and county-level assessments, we model land allocation decision for energy crop in the future, estimating the spatial distribution of dynamics of energy crop adoption with priority to fields scoring the highest. Additionally, compared with POLYSYS results from the report mentioned above, our method shows good performance overall, with about 2% and 1.2% of difference in switchgrass acreage allocation and crop production, respectively.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Selection of suitable alternatives to reduce the environmental impact of road traffic noise using a fuzzy multi-criteria decision model

Road traffic noise is one of the most significant environmental impacts generated by transport systems. To this regard, the recent implementation of the European Environmental Noise Directive by Public Administrations of the European Union member countries has led to various noise action plans (NAPs) for reducing the noise exposure of EU inhabitants. Every country or administration is responsible for applying criteria based on their own experience or expert knowledge, but there is no regulated process for the prioritization of technical measures within these plans. This paper proposes a multi-criteria decision methodology for the selection of suitable alternatives against traffic noise in each of the road stretches included in the NAPs. The methodology first defines the main criteria and alternatives to be considered. Secondly, it determines the relative weights for the criteria and sub-criteria using the fuzzy extended analytical hierarchy process as applied to the results from an expert panel, thereby allowing expert knowledge to be captured in an automated way. A final step comprises the use of discrete multi-criteria analysis methods such as weighted sum, ELECTRE and TOPSIS, to rank the alternatives by suitability. To illustrate an application of the proposed methodology, this paper describes its implementation in a complex real case study: the selection of optimal technical solutions against traffic noise in the top priority road stretch included in the revision of the NAP of the regional road network in the province of Almeria (Spain).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Decision-Making Strategy for Rectal Cancer Management Using Radiation Therapy for Elderly or Comorbid Patients

Rectal cancer predominantly affects patients older than 70 years, with peak incidence at age 80 to 85 years. However, the standard treatment paradigm for rectal cancer oftentimes cannot be feasibly applied to these patients owing to frailty or comorbid conditions. There are currently little information and no treatment guidelines to help direct therapy for patients who are elderly and/or have significant comorbidities, because most are not included or specifically studied in clinical trials. More recently various alternative treatment options have been brought to light that may potentially be utilized in this group of patients. This critical review examines the available literature on alternative therapies for rectal cancer and proposes a treatment algorithm to help guide clinicians in treatment decision making for elderly and comorbid patients.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2025

Confidence-weighted integration of human and machine judgments for superior decision-making

Large language models (LLMs) can surpass humans in certain forecasting tasks. What role does this leave for humans in the overall decision process? One possibility is that humans, despite performing worse than LLMs, can still add value when teamed with them. A human and machine team can surpass each individual teammate when team members’ confidence is well calibrated and team members diverge in which tasks they find difficult (i.e., calibration and diversity are needed). We simplified and extended a Bayesian approach to combining judgments using a logistic regression framework that integrates confidence-weighted judgments for any number of team members. Using this straightforward method, we demonstrated its effectiveness in both image classification and neuroscience forecasting tasks. Combining human judgments with one or more machines consistently improved overall team performance. Our hope is that this simple and effective strategy for integrating the judgments of humans and machines will lead to productive collaborations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Development of heavy-duty vehicle representative driving cycles via decision tree regression

Previously, researchers who developed representative driving cycles mainly focused on light-duty vehicles and only considered vehicle speed and related derivations. In this paper, we propose a novel approach to develop representative cycles for heavy-duty vehicles. By implementing decision tree regression (DTR) to the Fleet DNA on-road vehicle data, a broader set of metrics, such as engine power and fuel consumption, can be used for more robust cycle development. Additionally, the influence of each metric on the regression target is also accounted for by a weighted number derived through the DTR to enhance the representativenss of the developed cycle. As case studies, we applied the proposed method to five heavy-duty vocations (drayage, long haul, regional haul, local delivery, and transit bus) and derived the most representative cycle, as well as four extreme cycles (maximal energy consumption, maximal power-weighted work, maximal fraction of high speed, and minimal fuel economy) to advance the related alternative powertrain design.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2015

How should greenhouse gas emissions be taken into account in the decision making of municipal solid waste management procurements? A case study of the South Karelia region, Finland

Highlights: • Environmental criteria for the MSW incineration location procurements are needed. • Focus should be placed on annual energy efficiency and on substitute fuels. • In SRF combustion it is crucial to know the share and the treatment of rejects. • The GWP of transportation is a small part of the total emissions. - Abstract: The ongoing trend in the public sector is to make more sustainable procurements by taking into account the impacts throughout the entire life cycle of the procurement. Despite the trend, the only deciding factor can still be the total costs. This article answers the question of how greenhouse gas (GHG) emissions should be taken into account in municipal solid waste (MSW) management when selecting an incineration plant for source separated mixed MSW. The aim is to guide the decision making of MSW management towards more environmentally friendly procurements. The study was carried out by calculating the global warming potentials (GWPs) and costs of mixed MSW management by using the waste composition from a case area in Finland. Scenarios of landfilling and combustion in three actual waste incineration plants were used to recognise the main processes that affect the results. GWP results show that the combustion of mixed MSW is a better alternative than landfilling the waste. The GHG results from combustion are greatly affected by emissions from the combustion and substituted energy production. The significance of collection and transportation is higher from the costs’ perspective than from the point of view of GHG emissions. The main costs, in addition to collection and transportation costs, result from the energy utilization or landfilling of mixed MSW. When tenders are invited for the incineration location of mixed MSW, the main focus should be: What are the annual electricity and heat recovery efficiencies and which are the substituted fuels in the area? In addition, in the case of a fluidized bed combustor it is crucial to know the combusted share of mixed MSW after preparing solid recovered fuel (SRF) and the treatment of rejects. The environmental criteria for the waste incineration plant procurements should be made in order to obtain clear instructions for the procurement units. The results can also be utilized more widely. The substituted fuels in the area and the effect of the plant location on the utilization of the produced energy can already be identified when planning an appropriate site for the waste incineration plant.

09 BIOMASS FUELS↗
DOE OSTI2017

An islanding detection methodology combining decision trees and Sandia frequency shift for inverter-based distributed generations

Distributed generations (DGs) for grid-connected applications require an accurate and reliable islanding detection methodology (IDM) for secure system operation. This paper presents an IDM for grid-connected inverter-based DGs. The proposed method is a combination of passive and active islanding detection techniques for aggregation of their advantages and elimination/minimisation of the drawbacks. In the proposed IDM, the passive method utilises critical system attributes extracted from local voltage measurements at target DG locations as well as employs decision tree-based classifiers for characterisation and detection of islanding events. The active method is based on Sandia frequency shift technique and is initiated only when the passive method is unable to differentiate islanding events from other system events. Thus, the power quality degradation introduced into the system by active islanding detection techniques can be minimised. Furthermore, a combination of active and passive techniques allows detection of islanding events under low power mismatch scenarios eliminating the disadvantage associated with the use of passive techniques alone. Finally, detailed case study results demonstrate the effectiveness of the proposed method in detection of islanding events under various power mismatch scenarios, load quality factors and in the presence of single or multiple grid-connected inverter-based DG units.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Visualization and Decision Making Design Under Uncertainty

Uncertainty is an important aspect to data understanding. Without awareness of the variability, error, or reliability of a dataset, the ability to make decisions on that data is limited. However, practices around uncertainty visualization remain domain-specific, rooted in convention, and in many instances, absent entirely. Part of the reason for this may be a lack of established guidelines for navigating difficult choices of when uncertainty should be added, how to visualize uncertainty, and how to evaluate its effectiveness. Unsurprisingly, the inclusion of uncertainty into visualizations is a major challenge to visualization. As work concerned with uncertainty visualization grows, it has become clear that simple visual additions of uncertainty information to traditional visualization methods do not appropriately convey the meaning of the uncertainty, pose many perceptual challenges, and, in the worst case, can lead a viewer to a completely wrong understanding of the data. These challenges are the driving motivator for this special issue.

data models↗
DOE OSTI2018

Decision Tree Based Anomaly Detection for Remedial Action Scheme in Smart Grid using PMU Data

The advanced and persistent cyber threats facing the critical infrastructure such as the smart grid are exponentially rising which require sophisticated defense strategy. Remedial Action Scheme (RAS), also known as Special Protection Scheme (SPS), relies on the interconnected cyber physical system for automated protection which is exposed to the multitude of vulnerabilities. In this paper, we propose an innovative approach to develop an Intelligent Remedial Action Scheme (IRAS) which can detect and distinguish cyber attacks from the physical disturbances in smart grid and later take smart corrective actions as required to minimize the impact on system reliability and economy. Specifically, we have proposed the decision tree based anomaly detection methodology which can distinguish between the normal tripping during power line faults and malicious tripping attack on the physical relays in the context of RAS. The classification model is developed using differential features of voltage and current phasors. Next, as a proof of concept, we have implemented and validated the proposed methodology in cyber physical environment at Iowa State's PowerCyber testbed. Finally, the proposed methodology is tested on modified IEEE 39 bus system in offline and real-time mode. Our experimental results show that the proposed method is efficient in detecting attacks and performing corrective actions within an acceptable time frame.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2014

Adaptive error detection for HDR/PDR brachytherapy: Guidance for decision making during real-time in vivo point dosimetry

Purpose: This study presents an adaptive error detection algorithm (AEDA) for real-timein vivo point dosimetry during high dose rate (HDR) or pulsed dose rate (PDR) brachytherapy (BT) where the error identification, in contrast to existing approaches, does not depend on an a priori reconstruction of the dosimeter position. Instead, the treatment is judged based on dose rate comparisons between measurements and calculations of the most viable dosimeter position provided by the AEDA in a data driven approach. As a result, the AEDA compensates for false error cases related to systematic effects of the dosimeter position reconstruction. Given its nearly exclusive dependence on stable dosimeter positioning, the AEDA allows for a substantially simplified and time efficient real-time in vivo BT dosimetry implementation. Methods: In the event of a measured potential treatment error, the AEDA proposes the most viable dosimeter position out of alternatives to the original reconstruction by means of a data driven matching procedure between dose rate distributions. If measured dose rates do not differ significantly from the most viable alternative, the initial error indication may be attributed to a mispositioned or misreconstructed dosimeter (false error). However, if the error declaration persists, no viable dosimeter position can be found to explain the error, hence the discrepancy is more likely to originate from a misplaced or misreconstructed source applicator or from erroneously connected source guide tubes (true error). Results: The AEDA applied on twoin vivo dosimetry implementations for pulsed dose rate BT demonstrated that the AEDA correctly described effects responsible for initial error indications. The AEDA was able to correctly identify the major part of all permutations of simulated guide tube swap errors and simulated shifts of individual needles from the original reconstruction. Unidentified errors corresponded to scenarios where the dosimeter position was sufficiently symmetric with respect to error and no-error source position constellations. The AEDA was able to correctly identify all false errors represented by mispositioned dosimeters contrary to an error detection algorithm relying on the original reconstruction. Conclusions: The study demonstrates that the AEDA error identification during HDR/PDR BT relies on a stable dosimeter position rather than on an accurate dosimeter reconstruction, and the AEDA’s capacity to distinguish between true and false error scenarios. The study further shows that the AEDA can offer guidance in decision making in the event of potential errors detected with real-timein vivo point dosimetry.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2015

TU-G-303-01: Radiomics: Quantitative Imaging in the Service of Improved Treatment Decision Making

‘Radiomics’ refers to studies that extract a large amount of quantitative information from medical imaging studies as a basis for characterizing a specific aspect of patient health. Radiomics models can be built to address a wide range of outcome predictions, clinical decisions, basic cancer biology, etc. For example, radiomics models can be built to predict the aggressiveness of an imaged cancer, cancer gene expression characteristics (radiogenomics), radiation therapy treatment response, etc. Technically, radiomics brings together quantitative imaging, computer vision/image processing, and machine learning. In this symposium, speakers will discuss approaches to radiomics investigations, including: longitudinal radiomics, radiomics combined with other biomarkers (‘pan-omics’), radiomics for various imaging modalities (CT, MRI, and PET), and the use of registered multi-modality imaging datasets as a basis for radiomics. There are many challenges to the eventual use of radiomics-derived methods in clinical practice, including: standardization and robustness of selected metrics, accruing the data required, building and validating the resulting models, registering longitudinal data that often involve significant patient changes, reliable automated cancer segmentation tools, etc. Despite the hurdles, results achieved so far indicate the tremendous potential of this general approach to quantifying and using data from medical images. Specific applications of radiomics to be presented in this symposium will include: the longitudinal analysis of patients with low-grade gliomas; automatic detection and assessment of patients with metastatic bone lesions; image-based monitoring of patients with growing lymph nodes; predicting radiotherapy outcomes using multi-modality radiomics; and studies relating radiomics with genomics in lung cancer and glioblastoma. Learning Objectives: Understanding the basic image features that are often used in radiomic models. Understanding requirements for reliable radiomic models, including robustness of metrics, adequate predictive accuracy, and generalizability. Understanding the methodology behind radiomic-genomic (’radiogenomics’) correlations. Research supported by NIH (US), CIHR (Canada), and NSERC (Canada)

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Metrics for Decision-Making in Energy Justice

Energy equity and justice have become priority considerations for policymakers, practitioners, and scholars alike. To ensure that energy equity is incorporated into actual decisions and analysis, it is necessary to design, use, and continually improve energy equity metrics. In this article, we review the literature and practices surrounding such metrics. We present a working definition for energy justice and equity, and connect them to both criteria for and frameworks of metrics. We then present a large sampling of energy equity metrics, including those focused on vulnerability, wealth creation, energy poverty, life cycle, and comparative country-level dynamics. We conclude with a discussion of the limitations, gaps, and trade-offs associated with these various metrics and their interactions thereof.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2019

Utility of Early Postoperative Unstimulated Thyroglobulin in Influencing Decision Making in Patients with Papillary Thyroid Carcinoma

Background: Serum thyroglobulin is used to screen for disease persistence or recurrence of papillary thyroid carcinoma (PTC). We sought to assess the utility of early postoperative unstimulated thyroglobulin levels (uTg) as a decision-making tool to guide the use of radioactive iodine (RAI) in PTC patients. Methods: We performed a retrospective analysis of a prospectively maintained database of patients surgically treated for PTC from 2015 to 2017. We analyzed uTg approximately 6 weeks postoperatively. Patients undergoing total thyroidectomy or completion thyroidectomy were included in the study, and patients were analyzed according to postoperative uTg and receipt of RAI. Results: A total of 255 patients were analyzed, with 134 patients meeting the inclusion criteria. The median postoperative uTg was 0.3 ng/mL. Overall, 49.3% (66/134) of patients achieved the target uTg of ≤ 0.2 ng/mL at a mean time of 7.9 ± 0.3 weeks postoperatively; 60% (40/66) of patients who achieved uTg ≤ 0.2 ng/mL postoperatively did not receive RAI. A uTg ≤ 0.2 ng/mL was maintained at 6 months in 98.1% of patients, including 100% of patients who received RAI and 96.7% of patients who did not receive RAI (p = 0.8). Of those who did not receive RAI, none demonstrated structural disease recurrence on 6-month ultrasound. Patients with early postoperative uTg > 0.2–2.0 ng/mL showed benefit from RAI, while patients with uTg > 2.0 ng/mL did not achieve the targeted uTg level regardless of receipt of RAI. Conclusions: Postoperative uTg may be used to guide the use of RAI. Achieving near-undetectable uTg within 6 weeks postoperatively could aid providers in assessing disease burden and minimize RAI use for patients with a low-risk of disease recurrence. Continued follow-up is necessary to accurately determine long-term outcomes.

62 RADIOLOGY AND NUCLEAR MEDICINE↗