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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 OSTI2012

Low p53 Binding Protein 1 (53BP1) Expression Is Associated With Increased Local Recurrence in Breast Cancer Patients Treated With Breast-Conserving Surgery and Radiotherapy

Purpose: To investigate whether the expression of p53 binding protein 1 (53BP1) has prognostic significance in a cohort of early-stage breast cancer patients treated with breast-conserving surgery and radiotherapy (BCS+RT). Methods and Materials: A tissue microarray of early-stage breast cancer treated with BCS+RT from a cohort of 514 women was assayed for 53BP1, estrogen receptor, progesterone receptor, and HER2 expression by immunohistochemistry. Through log-rank tests and univariate and multivariate models, the staining profile of each tumor was correlated with clinical endpoints, including ipsilateral breast recurrence-free survival (IBRFS), distant metastasis-free survival (DMFS), cause-specific survival (CSS), recurrence-free survival (RFS), and overall survival (OS). Results: Of the 477 (93%) evaluable tumors, 63 (13%) were scored as low. Low expression of 53BP1 was associated with worse outcomes for all endpoints studied, including 10-year IBRFS (76.8% vs. 90.5%; P=.01), OS (66.4% vs. 81.7%; P=.02), CSS (66.0% vs. 87.4%; P<.01), DMFS (55.9% vs. 87.0%; P<.01), and RFS (45.2% vs. 80.6%; P<.01). Multivariate analysis incorporating various clinico-pathologic markers and 53BP1 expression found that 53BP1 expression was again an independent predictor of all endpoints (IBRFS: P=.0254; OS: P=.0094; CSS: P=.0033; DMFS: P=.0006; RFS: P=.0002). Low 53BP1 expression was also found to correlate with triple-negative (TN) phenotype (P<.01). Furthermore, in subset analysis of all TN breast cancer, negative 53BP1 expression trended for lower IBRFS (72.3% vs. 93.9%; P=.0361) and was significant for worse DMFS (48.2% vs. 86.8%; P=.0035) and RFS (37.8% vs. 83.7%; P=.0014). Conclusion: Our data indicate that low 53BP1 expression is an independent prognostic indicator for local relapse among other endpoints in early-stage breast cancer and TN breast cancer patients treated with BCS+RT. These results should be verified in larger cohorts of patients to validate their clinical significance.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2015

Total Skin Electron Therapy for Cutaneous T-Cell Lymphoma Using a Modern Dual-Field Rotational Technique

Purpose: To report our experience with rotational total skin electron irradiation (RTSEI) in cutaneous T-cell lymphoma (CTCL), and to examine response by disease stage and race. Methods and Materials: We reviewed our outcomes for 68 CTCL patients who received RTSEI (≥30 Gy) from 2000 to 2013. Primary outcomes were complete clinical response (CCR), recurrence-free survival (RFS), and overall survival (OS). Using log–rank tests and Cox proportional hazards, OS and RFS were compared across tumor stages at time of RTSEI with further racial subgroup analysis. Results: Median age at diagnosis and at time of radiation was 52 and 56 years, respectively. Median follow-up was 5.1 years, 49% were African American, and 49% were female. At time of treatment, 18, 37, and 13 patients were T stage 2, 3, and 4, respectively. At 6 weeks after RTSEI, overall CCR was 82% (88%, 83%, and 69% for T2, T3, and T4, respectively). Median RFS was 11 months for all patients and 14, 10, and 12 months for stage T2, T3, and T4, respectively. Tumor stage was not associated with RFS or CCR. Maintenance therapy after RTSEI was associated with improved RFS in both crude and multivariable analysis, controlling for T stage. Median OS was 76 months (91 and 59 months for T3 and T4, respectively). With the exception of improved OS in African Americans compared with whites at stage T2, race was not associated with CCR, RFS, or OS. Conclusions: These results represent the largest RTSEI clinical outcomes study in the modern era using a dual-field rotational technique. Our observed response rates match or improve upon the standard set by previous outcome studies using conventional TSEI techniques, despite a large percentage of advanced CTCL lesions in our cohort. We found that clinical response after RTSEI did not seem to be affected by T stage or race.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2017

Adjuvant Radiation Therapy for Margin-Positive Vulvar Squamous Cell Carcinoma: Defining the Ideal Dose-Response Using the National Cancer Data Base

Purpose: Positive surgical margins after radical vulvectomy for vulvar cancer portend a high risk for local relapse, which may be challenging to salvage. We assessed the impact of adjuvant radiation therapy (aRT) on overall survival (OS) and the dose-response relationship using the National Cancer Data Base. Methods and Materials: Patients with vulvar squamous cell carcinoma who underwent initial extirpative surgery with positive margins from 1998 to 2012 were included. Factors associated with aRT and specific dose levels were analyzed using logistic regression. Log-rank and multivariable Cox proportional hazards modeling were used for OS analysis. Results: We identified 3075 patients with a median age of 66 years (range, 22-90 years); the median follow-up time was 36.4 months (interquartile range [IQR] 15.4-71.0 months). Stage IA/B disease represented 41.2% of the cohort. Sixty-three percent underwent lymph node assessment, with a 45% positivity rate. In total, 1035 patients (35.3%) received aRT, with a median dose of 54.0 Gy (IQR 48.6-60.0 Gy). The 3-year OS improved from 58.5% to 67.4% with aRT (P<.001). On multivariable analysis, age, Charlson-Deyo score ≥1, stage ≥II, tumors ≥4 cm, no aRT, and adverse nodal characteristics led to inferior survival. Dose of aRT was positively associated with OS as a continuous variable on univariate analysis (P<.001). The unadjusted 3-year OS for dose subsets 30.0 to 45.0 Gy, 45.1 to 53.9 Gy, 54.0 to 59.9 Gy, and ≥60 Gy was 54.3%, 55.7%, 70.1%, and 65.3%, respectively (P<.001). Multivariable analysis using a 4-month conditional landmark revealed that the greatest mortality reduction occurred in cumulative doses ≥54 Gy: 45.1 to 53.9 Gy (hazard ratio [HR] 0.94, P=.373), 54.0 to 59.9 Gy (HR 0.75, P=.024), ≥60 Gy (HR 0.71, P=.015). No survival benefit was seen with ≥60 Gy compared with 54.0 to 59.9 Gy (HR 0.95, P=.779). Conclusions: Patients with vulvar squamous cell carcinoma and positive surgical margins derive an OS benefit from aRT with a seemingly optimal dose in the range of 54.0 to 59.9 Gy.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2021

MULTICOM2 open-source protein structure prediction system powered by deep learning and distance prediction

Protein structure prediction is an important problem in bioinformatics and has been studied for decades. However, there are still few open-source comprehensive protein structure prediction packages publicly available in the field. In this paper, we present our latest open-source protein tertiary structure prediction system—MULTICOM2, an integration of template-based modeling (TBM) and template-free modeling (FM) methods. The template-based modeling uses sequence alignment tools with deep multiple sequence alignments to search for structural templates, which are much faster and more accurate than MULTICOM1. The template-free (ab initio or de novo) modeling uses the inter-residue distances predicted by DeepDist to reconstruct tertiary structure models without using any known structure as template. In the blind CASP14 experiment, the average TM-score of the models predicted by our server predictor based on the MULTICOM2 system is 0.720 for 58 TBM (regular) domains and 0.514 for 38 FM and FM/TBM (hard) domains, indicating that MULTICOM2 is capable of predicting good tertiary structures across the board. It can predict the correct fold for 76 CASP14 domains (95% regular domains and 55% hard domains) if only one prediction is made for a domain. The success rate is increased to 3% for both regular and hard domains if five predictions are made per domain. Moreover, the prediction accuracy of the pure template-free structure modeling method on both TBM and FM targets is very close to the combination of template-based and template-free modeling methods. This demonstrates that the distance-based template-free modeling method powered by deep learning can largely replace the traditional template-based modeling method even on TBM targets that TBM methods used to dominate and therefore provides a uniform structure modeling approach to any protein. Finally, on the 38 CASP14 FM and FM/TBM hard domains, MULTICOM2 server predictors (MULTICOM-HYBRID, MULTICOM-DEEP, MULTICOM-DIST) were ranked among the top 20 automated server predictors in the CASP14 experiment. After combining multiple predictors from the same research group as one entry, MULTICOM-HYBRID was ranked no. 5. The source code of MULTICOM2 is freely available at https://github.com/multicom-toolbox/multicom/tree/multicom_v2.0 .

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1972

Failure modes, effects and criticality analyses.

Failure mode, effects and criticality analyses were developed by NASA as a means of assuring that hardware built for space applications has the desired reliability characteristics. The failure mode and effects analysis is a qualitative reliability technique for systematically analyzing each possible failure mode within a hardware system, and identifying the resulting effect on that system, the mission and personnel. The criticality analysis is a quantitative procedure which ranks the critical failure modes according to their probability of occurrence. This paper describes the failure modes, effects analysis and the criticality analysis. It employs a simple hardware system, not related to the aerospace field, to illustrate the method. It encourages application of this type of analysis to industrial development programs outside the aerospace and defense complex.

Jordan, W. E.↗
DOE OSTI2017

Assessing the costs of municipal solid waste treatment technologies in developing Asian countries

Highlights: • Municipal waste treatment costs are assessed for the first time in developing Asia. • A database with primary and secondary data from existing plants was prepared. • Capital and operational expenditures patterns were analyzed and discussed. • Incineration identified by large margin as the costliest approach for treating waste. • Context specific circumstances are key to explain deviations from the norm. - Abstract: The management of municipal solid waste (MSW) is one of the main costs incurred by local authorities in developing countries. According to some estimates, these costs can account for up to 50% of city government budgets. It is therefore of importance that policymakers, urban planners and practitioners have an adequate understanding of what these costs consist of, from collection to final waste disposal. This article focuses on a specific stage of the MSW value chain, the treatment of waste, and it aims to identify cost patterns associated with the implementation and operation of waste treatment approaches in developing Asian countries. An analysis of the capital (CAPEX) and operational expenditures (OPEX) of a number of facilities located in countries of the region was conducted based on a database gathering nearly 100 projects and which served as basis for assessing four technology categories: composting, anaerobic digestion (AD), thermal treatment, and the production of refuse-derived fuel (RDF). Among these, it was found that the least costly to invest, as a function of the capacity to process waste, are composting facilities, with an average CAPEX per ton of 21,493 USD{sub 2015}/ton. Conversely, at the upper end featured incineration plants, with an average CAPEX of 81,880 USD{sub 2015}/ton, with this treatment approach ranking by and large as the most capital intensive of the four categories assessed. OPEX figures of the plants, normalized and analyzed in the form of OPEX/ton, were also found to be higher for incineration than for biological treatment methods, although on this component differences amongst the technology groups were less pronounced than those observed for CAPEX. While the results indicated the existence of distinct cost implications for available treatment approaches in the developing Asian context, the analysis also underscored the importance of understanding the local context as a means to properly identify the cost structure of each specific plant. Moreover, even though CAPEX and OPEX figures are important elements to assess the costs of a waste treatment system, these should not be considered on a standalone basis for decision making purposes. In complement to this internal cost dimension, the broader impacts – to the economy, society and the environment – resulting from the adoption of a certain treatment approach should be properly understood and, ideally, measured and expressed in monetary terms.

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

Exploiting regulatory heterogeneity to systematically identify enhancers with high accuracy

Identifying functional enhancer elements in metazoan systems is a major challenge. Large-scale validation of enhancers predicted by ENCODE reveal false-positive rates of at least 70%. We used the pregrastrula-patterning network of Drosophila melanogaster to demonstrate that loss in accuracy in held-out data results from heterogeneity of functional signatures in enhancer elements. We show that at least two classes of enhancers are active during early Drosophila embryogenesis and that by focusing on a single, relatively homogeneous class of elements, greater than 98% prediction accuracy can be achieved in a balanced, completely held-out test set. The class of well-predicted elements is composed predominantly of enhancers driving multistage segmentation patterns, which we designate segmentation driving enhancers (SDE). Prediction is driven by the DNA occupancy of early developmental transcription factors, with almost no additional power derived from histone modifications. We further show that improved accuracy is not a property of a particular prediction method: after conditioning on the SDE set, naïve Bayes and logistic regression perform as well as more sophisticated tools. Applying this method to a genome-wide scan, we predict 1,640 SDEs that cover 1.6% of the genome. An analysis of 32 SDEs using whole-mount embryonic imaging of stably integrated reporter constructs chosen throughout our prediction rank-list showed >90% drove expression patterns. We achieved 86.7% precision on a genome-wide scan, with an estimated recall of at least 98%, indicating high accuracy and completeness in annotating this class of functional elements.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

FROSch Preconditioners for Land Ice Simulations of Greenland and Antarctica

Numerical simulations of Greenland and Antarctic ice sheets involve the solution of large-scale highly nonlinear systems of equations on complex shallow geometries. This work is concerned with the construction of Schwarz preconditioners for the solution of the associated tangent problems, which are challenging for solvers mainly because of the strong anisotropy of the meshes and wildly changing boundary conditions that can lead to poorly constrained problems on large portions of the domain. In this study, two-level generalized Dryja--Smith--Widlund (GDSW)--type Schwarz preconditioners are applied to different land ice problems, i.e., a velocity problem, a temperature problem, as well as the coupling of the former two problems. We employ the message passing interface (MPI)--parallel implementation of multilevel Schwarz preconditioners provided by the package FROSch (fast and robust Schwarz) from the Trilinos library. The strength of the proposed preconditioner is that it yields out-of-the-box scalable and robust preconditioners for the single physics problems. To the best of our knowledge, this is the first time two-level Schwarz preconditioners have been applied to the ice sheet problem and a scalable preconditioner has been used for the coupled problem. The preconditioner for the coupled problem differs from previous monolithic GDSW preconditioners in the sense that decoupled extension operators are used to compute the values in the interior of the subdomains. Additionally, several approaches for improving the performance, such as reuse strategies and shared memory OpenMP parallelization, are explored. In our numerical study we target both uniform meshes of varying resolution for the Antarctic ice sheet as well as nonuniform meshes for the Greenland ice sheet. We present several weak and strong scaling studies confirming the robustness of the approach and the parallel scalability of the FROSch implementation. Among the highlights of the numerical results are a weak scaling study for up to 32,K processor cores (8,K MPI ranks and 4 OpenMP threads) and 566,M degrees of freedom for the velocity problem as well as a strong scaling study for up to 4,K processor cores (and MPI ranks) and 68,M degrees of freedom for the coupled problem.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Global Market Analysis of Microreactors

Microreactors are smaller and simplified versions of nuclear power plants using current generation and advanced nuclear technology designs. The purpose of this report is to assesses the unique capabilities of microreactors and assess potential deployment in specific global markets in the 2030-2050 timeframe, with consideration for regulatory limits. The methods include developing unique microreactor deployment indicators and matching use cases to define future profile markets. Top-down and bottom-up analysis techniques were used to evaluate emerging market trends, derive a range of possible demands, and rank potential markets in 63 countries including current nuclear users and newcomer countries. Deployment is considered from regulatory and planning perspectives. Results indicate significant potential for global deployment of microreactors, but also significant challenges in achieving the technical capacities, meeting regulatory requirements and international accords, achieving competitive costs, and for gaining public acceptance. Profile microreactor markets were identified that include isolated operations, distributed energy, resilient urban applications, marine propulsion, and disaster relief. Market demands could be strong in Western Europe and possibly even the U.S., but particularly strong across Asia and Eastern Europe in isolated operations and distributed energy applications. Build rates in the hundreds of units by 2040 and thousands by 2050 would be needed to attain market penetration at scale.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2023

A Sparse Distributed Gigascale Resolution Material Point Method

In this paper, we present a four-layer distributed simulation system and its adaptation to the Material Point Method (MPM). The system is built upon a performance portable C++ programming model targeting major High-Performance-Computing (HPC) platforms. A key ingredient of our system is a hierarchical block-tile-cell sparse grid data structure that is distributable to an arbitrary number of Message Passing Interface (MPI) ranks. We additionally propose strategies for efficient dynamic load balance optimization to maximize the efficiency of MPI tasks. Our simulation pipeline can easily switch among backend programming models, including OpenMP and CUDA, and can be effortlessly dispatched onto supercomputers and the cloud. Finally, we construct benchmark experiments and ablation studies on supercomputers and consumer workstations in a local network to evaluate the scalability and load balancing criteria. We demonstrate massively parallel, highly scalable, and gigascale resolution MPM simulations of up to 1.01 billion particles for less than 323.25 seconds per frame with 8 OpenSSH-connected workstations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

hIPPYlib-MUQ: A Bayesian Inference Software Framework for Integration of Data with Complex Predictive Models under Uncertainty

Bayesian inference provides a systematic framework for integration of data with mathematical models to quantify the uncertainty in the solution of the inverse problem. However, the solution of Bayesian inverse problems governed by complex forward models described by partial differential equations (PDEs) remains prohibitive with black-box Markov chain Monte Carlo (MCMC) methods. We present hIPPYlib-MUQ, an extensible and scalable software framework that contains implementations of state-of-the art algorithms aimed to overcome the challenges of high-dimensional, PDE-constrained Bayesian inverse problems. These algorithms accelerate MCMC sampling by exploiting the geometry and intrinsic low-dimensionality of parameter space via derivative information and low rank approximation. The software integrates two complementary open-source software packages, hIPPYlib and MUQ. hIPPYlib solves PDE-constrained inverse problems using automatically-generated adjoint-based derivatives, but it lacks full Bayesian capabilities. MUQ provides a spectrum of powerful Bayesian inversion models and algorithms, but expects forward models to come equipped with gradients and Hessians to permit large-scale solution. By combining these two complementary libraries, we created a robust, scalable, and efficient software framework that realizes the benefits of each and allows us to tackle complex large-scale Bayesian inverse problems across a broad spectrum of scientific and engineering disciplines. To illustrate the capabilities of hIPPYlib-MUQ, we present a comparison of a number of MCMC methods available in the integrated software on several high-dimensional Bayesian inverse problems. These include problems characterized by both linear and nonlinear PDEs, various noise models, and different parameter dimensions. The results demonstrate that large (~ 50×) speedups over conventional black box and gradient-based MCMC algorithms can be obtained by exploiting Hessian information (from the log-posterior), underscoring the power of the integrated hIPPYlib-MUQ framework.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

A matrix completion algorithm for efficient calculation of quantum and variational effects in chemical reactions

This work examines the viability of matrix completion methods as cost-effective alternatives to full nuclear Hessians for calculating quantum and variational effects in chemical reactions. The harmonic variety-based matrix completion (HVMC) algorithm, developed in a previous study (https://doi.org/10.1063/5.0018326), exploits the low-rank character of the polynomial expansion of potential energy to recover, using a small sample, vibrational frequencies (square roots of nuclear Hessian eigenvalues) constituting the reaction path. Furthermore, these frequencies are essential for calculating rate coefficients using variational transition state theory with multidimensional tunneling (VTST-MT). HVMC performance is examined for four SN2 reactions and five hydrogen transfer reactions, with each H-transfer reaction consisting of at least one vibrational mode strongly coupled to the reaction coordinate. HVMC is robust and captures zero-point energies, vibrational free energies, zero-curvature tunneling, and adiabatic ground state and free energy barriers as well as their positions on the reaction coordinate. For medium to large reactions involving H-transfer, with the exception of the most complex Ir catalysis system, less than 35% of total eigenvalue information is necessary for accurate recovery of key VTST-MT observables.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Technical Maturity Assessment of Risk-Informed Safety Analysis Tools

Ensuring maximum safety while enhancing economic benefit is one of most important goal of In the of US Light Water Reactor Sustainability (LWRS) program. Optimization of the safety margins will provide best practice to achieve this goal which can also lead to cost reduction. Under the LWRS framework, the Risk-Informed Systems Analysis (RISA) Pathway has been focusing on the optimization of safety margin and minimization of uncertainties to ensure both safety and economics at the highest level. One of the important activities of the pathway is to deploy risk-informed analysis tools to related nuclear industry to support precise representation of safety margins and factors that contribute to cost and safety. The tools therefore need highest technical maturity so that industry can use immediately with strong credibility. The tools should have a capability to support risk-informed decision making for both probabilistic and deterministic elements of safety. The RISA Pathway, therefore, have been performing a comprehensive assessment of technical maturity and verification and validation (V&V) status of selected tools to improve adaptability to the industry. The technical maturity assessment includes three work scope: (a) define requirements based on risk-informed concept; (b) investigate and review development and V&V status for technical maturity assessment; and (c) identify technical gap and propose improvement to meet RISA toolkit requirements. The Requirement Traceability Matrix (RTM) concept was used to capture the requirements from user and developer of the project and/or software. The importance of each requirements was evaluated by Phenomena Identification and Ranking Technology (PIRT) which systematically gathers information and ranks the importance of the information. Finally, degree of the maturity was measured by Technology Readiness Level (TRL) for estimating the maturity of the technologies during the development and acquisition phase of certain technology. This paper summarizes development of assessment method and technical maturity evaluation of multi-purpose probabilistic risk analysis tool RAVEN.

, PIRT↗
DOE OSTI2024

Artificial Intelligence and Machine Learning Support for Probabilistic Fracture Mechanics

In this research, artificial intelligence and machine learning (ML) methods are used to search an uncertain parameter space more efficiently for the most important inputs with respect to response sensitivities. These methods are applied to the Extremely Low Probability of Rupture (xLPR) probabilistic fracture mechanics code used at the U.S. Nuclear Regulatory Commission (NRC) in support of nuclear regulatory research. This report documents two separate but related sub-tasks: (1) ranking important uncertain input features with respect to target outputs, determined by convergence in confidence intervals for increasing sample sizes using simple random sampling; and (2) implementation of a reduced-order surrogate model for fast, approximate sample generation. Unoptimized readily available off-the-shelf ML models were used in both sub-tasks.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2020

Object and Gas Source Detection with Robotic Platforms in Perceptually-Degraded Environments

In exploration-oriented robotic missions for disaster relief in unknown subterranean environments, it is of prime importance for a human supervisor to rapidly gain situational awareness of salient objects within the environment. In this paper we present an automated object detection pipeline that is adaptable to heterogeneous robots with arbitrary sensor configurations. It has been deployed in time-critical scenarios with multiple collaborative robots in a variety of demanding underground environments. For visually observable objects, detections are made in both the visible and thermal spectra using a state-of-the-art machine learning framework for object detection and classification. Our pipeline can be rapidly adapted to a specific task by using a small, structured dataset to fine-tune a pre-trained convolutional neural network (CNN). Relative localization is separated from the CNN for speed of operation. A robust architecture for localization is used with outlier rejection and a hierarchy of fall-back distance measurement methods. Point-source objects such as gas and WiFi hotspots can also be detected, by tracking signal strength over time and presenting an intuitive visualization on a map. Observations of each object types are presented to the operator in ranked confidence order for final evaluation.

Agha-mohammadi, Ali-akbar↗
DOE OSTI2014

WE-D-BRE-07: Variance-Based Sensitivity Analysis to Quantify the Impact of Biological Uncertainties in Particle Therapy

Purpose: In particle therapy, treatment planning and evaluation are frequently based on biological models to estimate the relative biological effectiveness (RBE) or the equivalent dose in 2 Gy fractions (EQD2). In the context of the linear-quadratic model, these quantities depend on biological parameters (α, β) for ions as well as for the reference radiation and on the dose per fraction. The needed biological parameters as well as their dependency on ion species and ion energy typically are subject to large (relative) uncertainties of up to 20–40% or even more. Therefore it is necessary to estimate the resulting uncertainties in e.g. RBE or EQD2 caused by the uncertainties of the relevant input parameters. Methods: We use a variance-based sensitivity analysis (SA) approach, in which uncertainties in input parameters are modeled by random number distributions. The evaluated function is executed 10{sup 4} to 10{sup 6} times, each run with a different set of input parameters, randomly varied according to their assigned distribution. The sensitivity S is a variance-based ranking (from S = 0, no impact, to S = 1, only influential part) of the impact of input uncertainties. The SA approach is implemented for carbon ion treatment plans on 3D patient data, providing information about variations (and their origin) in RBE and EQD2. Results: The quantification enables 3D sensitivity maps, showing dependencies of RBE and EQD2 on different input uncertainties. The high number of runs allows displaying the interplay between different input uncertainties. The SA identifies input parameter combinations which result in extreme deviations of the result and the input parameter for which an uncertainty reduction is the most rewarding. Conclusion: The presented variance-based SA provides advantageous properties in terms of visualization and quantification of (biological) uncertainties and their impact. The method is very flexible, model independent, and enables a broad assessment of uncertainties. Supported by DFG grant WI 3745/1-1 and DFG cluster of excellence: Munich-Centre for Advanced Photonics.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

An integrated approach for water scarcity evaluation—a case study of Yunnan, China

In this paper, an integrated approach based on multiple indicators and individual and combinational evaluation methods is proposed to evaluate water scarcity. Four evaluation methods are employed, and their results are joined through three combination methods in order to obtain a consistent evaluation. This iterative-correction approach has been applied to Yunnan Province in China for water scarcity evaluation. The results demonstrate the capability of the iterative-correction framework to provide insightful analyses of water scarcity. Diqing Tibetan Autonomous Prefecture (Diqing) and Nujiang Li Autonomous Prefecture (Nujiang) ranked 1st and 2nd, indicating the best situation for water scarcity evaluation, while Yuxi and Zhaotong ranked 15th and 16th, indicating the worst situation. Two types of major water scarcities were observed in Yunnan Province. One was a typical representative of lack of water quality in the central part of Yunnan, and the other was a typical engineering type in the western part of Yunnan. The water scarcity has been found to arise from non-judicious water use, rapid urbanization and lack of protective measures for water resources. Finally and therefore, it is necessary to conduct more water conservancy projects in future to alleviate the water scarcity issue.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Dynamics retrieval from stochastically weighted incomplete data by low-pass spectral analysis

Time-resolved serial femtosecond crystallography (TR-SFX) provides access to protein dynamics on sub-picosecond timescales, and with atomic resolution. Due to the nature of the experiment, these datasets are often highly incomplete and the measured diffracted intensities are affected by partiality. To tackle these issues, one established procedure is that of splitting the data into time bins, and averaging the multiple measurements of equivalent reflections within each bin. This binning and averaging often involve a loss of information. Here, we propose an alternative approach, which we call low-pass spectral analysis (LPSA). In this method, the data are projected onto the subspace defined by a set of trigonometric functions, with frequencies up to a certain cutoff. This approach attenuates undesirable high-frequency features and facilitates retrieving the underlying dynamics. A time-lagged embedding step can be included prior to subspace projection to improve the stability of the results with respect to the parameters involved. Subsequent modal decomposition allows to produce a low-rank description of the system's evolution. Using a synthetic time-evolving model with incomplete and partial observations, we analyze the LPSA results in terms of quality of the retrieved signal, as a function of the parameters involved. We compare the performance of LPSA to that of a range of other sophisticated data analysis techniques. We show that LPSA allows to achieve excellent dynamics reconstruction at modest computational cost. Finally, we demonstrate the superiority of dynamics retrieval by LPSA compared to time binning and merging, which is, to date, the most commonly used method to extract dynamical information from TR-SFX data.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗