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All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Industrial CO 2 and Carbon Capture: Near-term Benefit, Long-term Necessity

Industry emits approximately 1 billion tonnes of CO 2 each year (1 GTCO 2 /yr) in the United States–one-third of US stationary emissions–and an even greater amount in industry-heavy China. We hypothesize that capturing industrial CO 2 offers a near-term pathway to jumpstart CO 2 capture and storage (CCS) and is a long-term necessity to stabilize atmospheric CO 2 in order to avoid catastrophic climate impacts. We then explore this proposition using several new CO 2 emissions and storage databases for the United States, as well as emerging research including the economics of industrial CO 2 capture. We refer to our new “sources of CO 2 that are not electricity” database as SCO 2 NE.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2012

Knowledge, data and interests: Challenges in participation of diverse stakeholders in HIA

Stakeholder participation is considered an integral part of HIA. However, the challenges that participation implies in a multi-disciplinary and multi-ethnic society are less studied. This paper presents the manifestations of the multiplicity of sectors and population groups in HIA and discusses the challenges that such diversity imposes. Specifically, there is no common ground between participants, as their positions entail contradictory knowledge regarding the current situation, reliance on distinct data and conflicting interests. This entails usage of multiple professional and ethnic languages, disagreements regarding the definition of health and prioritizing health issues in HIA, and divergent perceptions of risk. These differences between participants are embedded culturally, socially, individually and, maybe most importantly, professionally. This complex picture of diverse stakeholder attributes is grounded in a case study of stakeholder participation in HIA, regarding zoning of a hazardous industry site in Israel. The implication is that participatory HIAs should address the multiplicity of stakeholders and types of knowledge, data and interests in a more comprehensive way. - Highlights: Black-Right-Pointing-Pointer This paper analyses challenges in participation of diverse stakeholders in HIA. Black-Right-Pointing-Pointer The multiplicity of disciplines and population groups raises fundamental challenges. Black-Right-Pointing-Pointer Stakeholders possess distinct and often contradictory knowledge, data and interests. Black-Right-Pointing-Pointer They speak different languages, and differ on approaches to health and risk perceptions. Black-Right-Pointing-Pointer Substantial amendments to diverse participation are needed, in HIA and generally.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2013

Knowledge brokerage - potential for increased capacities and shared power in impact assessment

Constructive and collaborative planning theory has exposed the perceived limitations of public participation in impact assessment. At strategic levels of assessment the established norm can be misleading and practice is illusive. For example, debates on SEA effectiveness recognize insufficiencies, but are often based on questionable premises. The authors of this paper argue that public participation in strategic assessment requires new forms of information and engagement, consistent with the complexity of the issues at these levels and that strategic assessments can act as knowledge brokerage instruments with the potential to generate more participative environments and attitudes. The paper explores barriers and limitations, as well as the role of knowledge brokerage in stimulating the engagement of the public, through learning-oriented processes and responsibility sharing in more participative models of governance. The paper concludes with a discussion on building and inter-change of knowledge, towards creative solutions to identified problems, stimulating learning processes, largely beyond simple information transfer mechanisms through consultative processes. The paper argues fundamentally for the need to conceive strategic assessments as learning platforms and design knowledge brokerage opportunities explicitly as a means to enhance learning processes and power sharing in IA. - Highlights: Black-Right-Pointing-Pointer Debates on SEA recognize insufficiencies on public participation Black-Right-Pointing-Pointer We propose new forms of engagement consistent with complex situations at strategic levels of decision-making Black-Right-Pointing-Pointer Constructive and collaborative planning theories help explain how different actors acquire knowledge and the value of knowledge exchange Black-Right-Pointing-Pointer Strategic assessments can act as knowledge brokerage instruments Black-Right-Pointing-Pointer The paper argues for strategic assessments as learning platforms as a means to enhance learning processes and power sharing in IA.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Conceptualizing impact assessment as a learning process

This paper explores how project developers and their consultants, government regulators and stakeholders can learn from the impact assessment (IA) process, thus potentially improving its effectiveness and enhancing project sustainability. Despite the benefits that learning can bring to an organization, failure to learn appears commonplace both within the IA process and, once approved, subsequent industrial development. To nurture organizational learning through IA, enabling structures that foster information sharing and interpretation and enhance organizational memory are needed. In this paper learning outcomes are grouped into three categories: acquisition of knowledge and skills, developing new behaviors and developing sustainability-oriented norms and values. Means to achieve such outcomes include education and training, experiential learning, learning through public participation (social learning) and a ‘learning organization approach’. Societal expectations increasingly demand not only projects that ‘pass’ the review criteria of regulators, financiers and the community, but IA processes capable of delivering sustainable outcomes that include learning and sharing of knowledge. It is proposed that learning be treated as a purposeful – not as an accidental – outcome of IA, and facilitated by adopting a ‘learning organization approach’ coupled with best practice such as early stakeholder engagement. - Highlights: • Proponents are challenged to develop projects that deliver sustainable outcomes. • Passing the test of government approval may be insufficient to obtain a social license. • Learning by all stakeholders is vital to meet these challenges. • Learning outcomes have to go beyond instrumental learning to reach new behaviors, norms and values. • A “learning organization approach” can promote mutual learning and improve project design.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

On choosing the resolution of normative models

Long time horizon normative models are frequently used for policy analysis, strategic planning, and system analysis. Choosing the granularity of the temporal or spatial resolution of such models is an important modeling decision, often having a first order impact on model results. This type of decision is frequently made by modeler judgment, specifically when the predictive power of alternative choices cannot be tested. In this paper, we show how the implicit tradeoffs modelers make in these formulation decisions, in particular in the tradeoff between the accuracy of representation enabled by the available data and model parsimony, may be addressed with established information theoretic ideas. The paper provides guidance for modelers making these tradeoffs or, in certain cases, enables explicit tests for assessing appropriate levels of resolution. Here, we focus on optimization based normative models in the discussion, and draw our examples from the energy and climate domain.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2011

The American Society for Radiation Oncology's 2010 Core Physics Curriculum for Radiation Oncology Residents

Purpose: In 2004, the American Society for Radiation Oncology (ASTRO) published its first physics education curriculum for residents, which was updated in 2007. A committee composed of physicists and physicians from various residency program teaching institutions was reconvened again to update the curriculum in 2009. Methods and Materials: Members of this committee have associations with ASTRO, the American Association of Physicists in Medicine, the Association of Residents in Radiation Oncology, the American Board of Radiology (ABR), and the American College of Radiology. Members reviewed and updated assigned subjects from the last curriculum. The updated curriculum was carefully reviewed by a representative from the ABR and other physics and clinical experts. Results: The new curriculum resulted in a recommended 56-h course, excluding initial orientation. Learning objectives are provided for each subject area, and a detailed outline of material to be covered is given for each lecture hour. Some recent changes in the curriculum include the addition of Radiation Incidents and Bioterrorism Response Training as a subject and updates that reflect new treatment techniques and modalities in a number of core subjects. The new curriculum was approved by the ASTRO board in April 2010. We anticipate that physicists will use this curriculum for structuring their teaching programs, and subsequently the ABR will adopt this educational program for its written examination. Currently, the American College of Radiology uses the ASTRO curriculum for their training examination topics. In addition to the curriculum, the committee updated suggested references and the glossary. Conclusions: The ASTRO physics education curriculum for radiation oncology residents has been updated. To ensure continued commitment to a current and relevant curriculum, the subject matter will be updated again in 2 years.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2012

Excellence in Radiation Research for the 21st Century (EIRR21): Description of an Innovative Research Training Program

Purpose: To describe and assess an interdisciplinary research training program for graduate students, postdoctoral fellows, and clinical fellows focused on radiation medicine; funded by the Canadian Institutes for Health Research since 2003, the program entitled 'Excellence in Radiation Research for the 21st Century' (EIRR21) aims to train the next generation of interdisciplinary radiation medicine researchers. Methods and Materials: Online surveys evaluating EIRR21 were sent to trainees (n=56), mentors (n=36), and seminar speakers (n=72). Face-to-face interviews were also conducted for trainee liaisons (n=4) and participants in the international exchange program (n=2). Results: Overall response rates ranged from 53% (mentors) to 91% (trainees). EIRR21 was well received by trainees, with the acquisition of several important skills related to their research endeavors. An innovative seminar series, entitled Brainstorm sessions, imparting 'extracurricular' knowledge in intellectual property protection, commercialization strategies, and effective communication, was considered to be the most valuable component of the program. Networking with researchers in other disciplines was also facilitated owing to program participation. Conclusions: EIRR21 is an innovative training program that positively impacts the biomedical community and imparts valuable skill sets to foster success for the future generation of radiation medicine researchers.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2019

Scalable and efficient learning from crowds with Gaussian processes

Over the last few years, multiply-annotated data has become a very popular source of information. Online platforms such as Amazon Mechanical Turk have revolutionized the labelling process needed for any classification task, sharing the effort between a number of annotators (instead of the classical single expert). This crowdsourcing approach has introduced new challenging problems, such as handling disagreements on the annotated samples or combining the unknown expertise of the annotators. Probabilistic methods, such as Gaussian Processes (GP), have proven successful to model this new crowdsourcing scenario. However, GPs do not scale up well with the training set size, which makes them prohibitive for medium-to-large datasets (beyond 10K training instances). This constitutes a serious limitation for current real-world applications. In this work, we introduce two scalable and efficient GP-based crowdsourcing methods that allow for processing previously-prohibitive datasets. The first one is an efficient and fast approximation to GP with squared exponential (SE) kernel. The second allows for learning a more flexible kernel at the expense of a heavier training (but still scalable to large datasets). Since the latter is not a GP-SE approximation, it can be also considered as a whole new scalable and efficient crowdsourcing method, useful for any dataset size. Both methods use Fourier features and variational inference, can predict the class of new samples, and estimate the expertise of the involved annotators. A complete experimentation compares them with state-of-the-art probabilistic approaches in synthetic and real crowdsourcing datasets of different sizes. Finally, they stand out as the best performing approach for large scale problems. Moreover, the second method is competitive with the current state-of-the-art for small datasets.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2017

An enhanced artificial neural network with a shuffled complex evolutionary global optimization with principal component analysis

The classical Back-Propagation (BP) scheme with gradient-based optimization in training Artificial Neural Networks (ANNs) suffers from many drawbacks, such as the premature convergence, and the tendency of being trapped in local optimums. Therefore, as an alternative for the BP and gradient-based optimization schemes, various Evolutionary Algorithms (EAs), i.e., Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Simulated Annealing (SA), and Differential Evolution (DE), have gained popularity in the field of ANN weight training. This study applied a new efficient and effective Shuffled Complex Evolutionary Global Optimization Algorithm with Principal Component Analysis – University of California Irvine (SP-UCI) to the weight training process of a three-layer feed-forward ANN. A large-scale numerical comparison is conducted among the SP-UCI-, PSO-, GA-, SA-, and DE-based ANNs on 17 benchmark, complex, and real-world datasets. Here the results show that SP-UCI-based ANN outperforms other EA-based ANNs in the context of convergence and generalization. Results suggest that the SP-UCI algorithm possesses good potential in support of the weight training of ANN in real-word problems. In addition, the suitability of different kinds of EAs on training ANN is discussed. The large-scale comparison experiments conducted in this paper are fundamental references for selecting proper ANN weight training algorithms in practice.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2022

Process mining for healthcare: Characteristics and challenges

Process mining techniques can be used to analyse business processes using the data logged during their execution. These techniques are leveraged in a wide range of domains, including healthcare, where it focuses mainly on the analysis of diagnostic, treatment, and organisational processes. Despite the huge amount of data generated in hospitals by staff and machinery involved in healthcare processes, there is no evidence of a systematic uptake of process mining beyond targeted case studies in a research context. When developing and using process mining in healthcare, distinguishing characteristics of healthcare processes such as their variability and patient-centred focus require targeted attention. Against this background, the Process-Oriented Data Science in Healthcare Alliance has been established to propagate the research and application of techniques targeting the data-driven improvement of healthcare processes. This paper, an initiative of the alliance, presents the distinguishing characteristics of the healthcare domain that need to be considered to successfully use process mining, as well as open challenges that need to be addressed by the community in the future.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Detecting anomalous sequences in electronic health records using higher-order tensor networks

Detecting anomalous sequences is an integral part of building and protecting modern large-scale health information technology (HIT) systems. These HIT systems generate a large volume of records of patients’ state and significant events, which provide a valuable resource to help improve clinical decisions, patient care processes, and other issues. However, detecting anomalous sequences in electronic health records (EHR) remains a challenge in healthcare applications for several reasons, including imbalances in the data, complexity of relationships between events in the sequence, and the curse of dimensionality. Conventional anomaly detection methods use the finite sequence of events to discriminate sequences. They fail to incorporate salient event details under variable higher-order dependencies (e.g., duration between events) that can provide better discrimination of sequences in their models. To address this problem, we propose event sequence and subsequence anomaly detection algorithms that (1) use network-based representations of interactions in the data, (2) account for variable higher-order dependencies in the data, and (3) incorporate events duration for adequate discrimination of the data. The proposed approach identifies anomalies by monitoring the change in the graph after the test sequence is removed from the network. The change is quantified using graph distance metrics so that dramatic changes in the network can be attributed to the removed sequence. Furthermore, the proposed subsequence algorithm recommends plausible paths and salient information for the detected anomalous subsequences. Our results show that the proposed event sequence anomaly detection algorithm outperforms the baseline methods for both synthetic data and real-world EHR data.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2024

EHR-BERT: A BERT-based model for effective anomaly detection in electronic health records

Objective: Physicians and clinicians rely on data contained in electronic health records (EHRs), as recorded by health information technology (HIT), to make informed decisions about their patients. The reliability of HIT systems in this regard is critical to patient safety. Consequently, better tools are needed to monitor the performance of HIT systems for potential hazards that could compromise the collected EHRs, which in turn could affect patient safety. In this paper, we propose a new framework for detecting anomalies in EHRs using sequence of clinical events. This new framework, EHR-Bidirectional Encoder Representations from Transformers (BERT), is motivated by the gaps in the existing deep-learning related methods, including high false negatives, sub-optimal accuracy, higher computational cost, and the risk of information loss. EHR-BERT is an innovative framework rooted in the BERT architecture, meticulously tailored to navigate the hurdles in the contemporary BERT method; thus, enhancing anomaly detection in EHRs for healthcare applications.Methods: The EHR-BERT framework was designed using the Sequential Masked Token Prediction (SMTP) method. This approach treats EHRs as natural language sentences and iteratively masks input tokens during both training and prediction stages. This method facilitates the learning of EHR sequence patterns in both directions for each event and identifies anomalies based on deviations from the normal execution models trained on EHR sequences.Results: Extensive experiments on large EHR datasets across various medical domains demonstrate that EHR-BERT markedly improves upon existing models. It significantly reduces the number of false positives and enhances the detection rate, thus bolstering the reliability of anomaly detection in electronic health records. This improvement is attributed to the model’s ability to minimize information loss and maximize data utilization effectively.Conclusion: EHR-BERT showcases immense potential in decreasing medical errors related to anomalous clinical events, positioning itself as an indispensable asset for enhancing patient safety and the overall standard of healthcare services. The framework effectively overcomes the drawbacks of earlier models, making it a promising solution for healthcare professionals to ensure the reliability and quality of health data.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2025

DOME: Directional medical embedding vectors from Electronic Health Records

Motivation: The increasing availability of Electronic Health Record (EHR) systems has created enormous potential for translational research. Recent developments in representation learning techniques have led to effective large-scale representations of EHR concepts along with knowledge graphs that empower downstream EHR studies. However, most existing methods require training with patient-level data, limiting their abilities to expand the training with multi-institutional EHR data. On the other hand, scalable approaches that only require summary-level data do not incorporate temporal dependencies between concepts. Methods: We introduce a DirectiOnal Medical Embedding (DOME) algorithm to encode temporally directional relationships between medical concepts, using summary-level EHR data. Specifically, DOME first aggregates patient-level EHR data into an asymmetric co-occurrence matrix. Then it computes two Positive Pointwise Mutual Information (PPMI) matrices to correspondingly encode the pairwise prior and posterior dependencies between medical concepts. Following that, a joint matrix factorization is performed on the two PPMI matrices, which results in three vectors for each concept: a semantic embedding and two directional context embeddings. They collectively provide a comprehensive depiction of the temporal relationship between EHR concepts. Results: We highlight the advantages and translational potential of DOME through three sets of validation studies. First, DOME consistently improves existing direction-agnostic embedding vectors for disease risk prediction in several diseases, for example achieving a relative gain of 5.5% in the area under the receiver operating characteristic (AUROC) for lung cancer. Second, DOME excels in directional drug-disease relationship inference by successfully differentiating between drug side effects and indications, correspondingly achieving relative AUROC gain over the state-of-the-art methods by 10.8% and 6.6%. Finally, DOME effectively constructs directional knowledge graphs, which distinguish disease risk factors from comorbidities, thereby revealing disease progression trajectories. The source codes are provided at https://github.com/celehs/Directional-EHRembedding.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

A high-order Lagrangian discontinuous Galerkin hydrodynamic method for quadratic cells using a subcell mesh stabilization scheme

We present here a Lagrangian discontinuous Galerkin (DG) hydrodynamic method that is up to third-order accurate using subcell mesh stabilization (SMS) for compressible flows on quadratic meshes in two-dimensional (2D) Cartesian coordinates. Similar to the second-order accurate Lagrangian DG method with linear meshes, the physical evolution equations for the specific volume, velocity, and specific total energy are discretized using a modal DG method with Taylor series polynomials. The Riemann velocity at the vertices of a curvilinear cell, and the corresponding surface forces, are calculated by solving a multidirectional approximate Riemann problem. Curvilinear cells (e.g., quadratic quadrilateral meshes in this work) have many deformational degrees of freedom, and with these cells, they can deform in unphysical ways. Likewise, the Riemann solution at an edge vertex differs from the one at the corner of a cell. With SMS, each quadratic quadrilateral cell is decomposed into four quadrilateral subcells, that move in a Lagrangian manner. The edge vertex is surrounded by four subcells so that it is similar to the vertex at the cell corner. SMS can detect inconsistent density fields between the cell and subcells. The difference between these two density fields is used to correct the stress (pressure) input to the Riemann solver. This SMS scheme enables stable mesh motion and accurate solutions in the context of a Lagrangian high-order DG method that is up to third-order with quadratic cells. We also present effective limiting strategies that ensure monotonicity of the primitive variables with the high-order DG method. This Lagrangian DG hydrodynamic method with SMS conserves mass, momentum, and total energy. A suite of test problems are calculated to demonstrate the designed order of accuracy (up to third-order accurate) of this method, and that the Lagrangian DG method using SMS preserves cylindrical symmetry on 1D radial flow problems with an equal-angle polar quadratic mesh.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2014

Educational Outreach Efforts at the NNDC

Isotopes and nuclides are important in our everyday life. The general public and most students are never exposed to the concepts of stable and radioactive isotopes/nuclides. The National Nuclear Data Center (NNDC) is involved in an international project to develop a Periodic Table of the Isotopes for the educational community to illustrate the importance of isotopes and nuclides in understanding the world around us. This effort should aid teachers in introducing these concepts to students from the high school to the graduate school level.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2020

Enhancing nuclear data validation analysis by using machine learning

In this work, we demonstrate how machine learning (ML) techniques can be used as an auxiliary tool for nuclear data validation analysis. The output of the ML analysis can inform evaluators and validators on the quality, or lack thereof, of specific nuclear data and benchmark experiments with respect to simulating these experimental benchmark values. To this end, measured and simulated effective neutron multiplication factors, k eff , of 875 selected ICSBEP critical assemblies and the latter's sensitivities with respect to nuclear data as well as benchmarks' features (e.g., material nuclide, core geometry) are used as input for a random forest (RF) regression model. The RF is used to encode the complex inter-dependencies between thousands of nuclear data features (i.e., sensitivity profiles and aspects describing the measurements) and 875 simulated and experimental k eff values in order to understand what nuclear data features are most informative for predicting bias. The complexity of relationships and high-dimensional space is difficult-to-impossible to search fully using simply expert judgment. As a first proof-of-concept—step, it is demonstrated that this technique is able to correctly trace large discrepancies between simulated and experimental k eff back to fabricated shortcomings in nuclear data that were perturbed to simulated k eff values. In a second, real-case scenario, step, the RF algorithm is used to validate the ENDF/B-VIII.0 library in comparison to ENDF/B-VII.1 nuclear data. One case is showcased where the chosen ML algorithms highlighted nuclear data (the 19 F(n,inl) cross section from 0.4–0.9 MeV) that are shown to be problematic by comparing them to associated differential experimental data and nuclear data from other libraries. In addition to that, it is shown that the RF results point towards poor benchmark experiments and associated underestimated uncertainties (e.g., the PU-SOL-THERM-028 series). However, using the RF algorithm for validating nuclear data with respect to k eff is currently limited to pinpointing groups of questionable nuclear data due to the inherent correlations between features introduced by the nuclear data themselves and how k eff is simulated. Due to this, we recommend that the ML methods presented be used to augment—rather than replace—the expert knowledge of evaluators and validators.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗