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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 OSTI2019

Does geography matter? A study of technological learning and innovation in the U.S. Solar photovoltaic balance-of-systems (PV BOS) industry

Different learning channels (i.e., learning from upstream actors, local network, global codified knowledge, etc.) coexist in firms’ technological innovation processes. Although firms are exposed to globalization and convenient communication and transportation tools, researchers still argue about the role of geography in technology innovation. Geographic proximity can facilitate knowledge spillover and network-building. Localized learning and the significance of geography indicate the importance of local knowledge in innovative activities. Local knowledge tends to be more closely aligned with local firms’ technological demand and easier to understand and absorb, because they are created and applied in the same context. However, following Schumpeter, innovation researchers believe that diverse knowledge, especially knowledge from external industries and geographically distant places, is a valuable source for innovation, especially disruptive innovation. Therefore, firms must balance different learning channels, especially between localized learning and global learning. However, it is still unclear about the extent to which localized learning is important for technology innovations and which factors influence the degree of localized learning. Meanwhile, most studies in the area of localized learning usually take no account of the international dimension and the interdependent of knowledge at different geographic scales. Different learning channels (i.e., learning from upstream actors, local network, global codified knowledge, etc.) coexist in firms’ technological innovation processes. Although firms are exposed to globalization and convenient communication and transportation tools, researchers still argue about the role of geography in technology innovation. Geographic proximity can facilitate knowledge spillover and network-building. Localized learning and the significance of geography indicate the importance of local knowledge in innovative activities. Local knowledge tends to be more closely aligned with local firms’ technological demand and easier to understand and absorb, because they are created and applied in the same context. However, following Schumpeter, innovation researchers believe that diverse knowledge, especially knowledge from external industries and geographically distant places, is a valuable source for innovation, especially disruptive innovation. Therefore, firms must balance different learning channels, especially between localized learning and global learning. However, it is still unclear about the extent to which localized learning is important for technology innovations and which factors influence the degree of localized learning. Meanwhile, most studies in the area of localized learning usually take no account of the international dimension and the interdependent of knowledge at different geographic scales. To address this research gap, this paper empirically investigated firms’ learning and innovation processes in relation to the geography. The aim is to answer the following three questions: 1) how does geographic distance influence knowledge spillover; 2) how technological characteristics influence knowledge acquisition choices as regards local vs. non-local knowledge? and 3) how do local and non-local knowledge affect the quality of innovation? The subject of our empirical inquiry is photovoltaic balance-of-systems (PV BOS) components. This choice is primarily motivated by two factors: (1) large-scale deployment of distributed solar PV technologies is widely considered to be an important piece in addressing the environmental impacts of the electricity sector, making PV an important technology to study; furthermore, there is a general consensus that the biggest barrier for a wider deployment of PV is the high price of non-module components, and (2) technological aspects of non-module components of PV systems make local conditions (climate, regulation, learning networks, etc.) salient, thus affording a rich empirical setting to explore the geography-innovation-economy interactions at the local level. We find that the localization effect of technological learning in the solar PV BOS industry is at state level, the number of citations-based linkages decreases with distance, and we also find that international knowledge significantly contributes to the quality of patents, which suggests that state border might be a barrier of current knowledge spillover, and only focusing on local innovation networks is not sufficient for promoting PV BOS innovation.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
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 OSTI2021

Deep active learning for classifying cancer pathology reports

Abstract Background Automated text classification has many important applications in the clinical setting; however, obtaining labelled data for training machine learning and deep learning models is often difficult and expensive. Active learning techniques may mitigate this challenge by reducing the amount of labelled data required to effectively train a model. In this study, we analyze the effectiveness of 11 active learning algorithms on classifying subsite and histology from cancer pathology reports using a Convolutional Neural Network as the text classification model. Results We compare the performance of each active learning strategy using two differently sized datasets and two different classification tasks. Our results show that on all tasks and dataset sizes, all active learning strategies except diversity-sampling strategies outperformed random sampling, i.e., no active learning. On our large dataset (15K initial labelled samples, adding 15K additional labelled samples each iteration of active learning), there was no clear winner between the different active learning strategies. On our small dataset (1K initial labelled samples, adding 1K additional labelled samples each iteration of active learning), marginal and ratio uncertainty sampling performed better than all other active learning techniques. We found that compared to random sampling, active learning strongly helps performance on rare classes by focusing on underrepresented classes. Conclusions Active learning can save annotation cost by helping human annotators efficiently and intelligently select which samples to label. Our results show that a dataset constructed using effective active learning techniques requires less than half the amount of labelled data to achieve the same performance as a dataset constructed using random sampling.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1993

Knowledge acquisition and interface design for learning on demand systems

The rapid changes in our world precipitated by technology have created new problems and new challenges for education and training. A knowledge 'explosion' is occurring as our society moves toward a service oriented economy that relies on information as the major resource. Complex computer systems are beginning to dominate the workplace, causing alarming growth and change in many fields. The rapidly changing nature of the workplace, especially in fields related to information technology, requires that our knowledge be updated constantly. This characteristic of modern society poses seemingly unsolvable instructional problems involving coverage and obsolescence. The sheer amount of information to be learned is rapidly increasing, while at the same time some information becomes obsolete in light of new information. Education, therefore, must become a lifelong process that features learning of new material and skills as needed in relation to the job to be done. Because of the problems cited above, the current model of learning in advance may no longer be feasible in our high-technology world. In many cases, learning in advance is impossible because there are simply too many things to learn. In addition, learning in advance can be time consuming, and often results in decontextualized knowledge that does not readily transfer to the work environment. The large and growing discrepancy between the amount of potentially relevant knowledge available and the amount a person can know and remember makes learning on demand an important alternative to current instructional practices. Learning on demand takes place whenever an individual must learn something new in order to perform a task or make a decision. Learning on demand is a promising approach for addressing the problems of coverage and obsolescence because learning is contextualized and integrated into the task environment rather than being relegated to a separate phase that precedes work. Learning on demand allows learners to see for themselves the usefulness of new knowledge for actual problem situations, thereby increasing the motivation for learning new information. Finally, learning on demand makes new information relevant to the task at hand, leading to more informed decision making, better quality products, and improved performance.

Nelson, Wayne A.↗
NASA NTRSDate not supplied

Exploring Informal Learning at the Airlines

Airline pilot training is extensive, highly structured, and defined by aircraft and airspace system operating requirements, yet pilots describe a tradition of between-pilot knowledge transfer and self-directed learning. This learning supplements their approved training programs. While industry and regulators focus on “formal learning” systems, pilots report relying on “informal learning” to build operational expertise. The persistence of informal learning suggests gaps in how successfully formal learning prepares pilots to handle operational complexities. The community that researches learning has extensively studied informal learning, and its characteristics seem to align with how pilots report increasing their skills and knowledge informally. However, no research into informal learning practices among airline pilots seems to exist. In this paper we provide examples of informal learning in commercial aviation, how they fit into two existing frameworks for workplace learning, and propose that researching informal learning might help identify opportunities to improve formal aviation learning systems.

pilot learning↗
DOE OSTI2011

Multi-View Budgeted Learning under Label and Feature Constraints Using Label-Guided Graph-Based Regularization

Budgeted learning under constraints on both the amount of labeled information and the availability of features at test time pertains to a large number of real world problems. Ideas from multi-view learning, semi-supervised learning, and even active learning have applicability, but a common framework whose assumptions fit these problem spaces is non-trivial to construct. We leverage ideas from these fields based on graph regularizers to construct a robust framework for learning from labeled and unlabeled samples in multiple views that are non-independent and include features that are inaccessible at the time the model would need to be applied. We describe examples of applications that fit this scenario, and we provide experimental results to demonstrate the effectiveness of knowledge carryover from training-only views. As learning algorithms are applied to more complex applications, relevant information can be found in a wider variety of forms, and the relationships between these information sources are often quite complex. The assumptions that underlie most learning algorithms do not readily or realistically permit the incorporation of many of the data sources that are available, despite an implicit understanding that useful information exists in these sources. When multiple information sources are available, they are often partially redundant, highly interdependent, and contain noise as well as other information that is irrelevant to the problem under study. In this paper, we are focused on a framework whose assumptions match this reality, as well as the reality that labeled information is usually sparse. Most significantly, we are interested in a framework that can also leverage information in scenarios where many features that would be useful for learning a model are not available when the resulting model will be applied. As with constraints on labels, there are many practical limitations on the acquisition of potentially useful features. A key difference in the case of feature acquisition is that the same constraints often don't pertain to the training samples. This difference provides an opportunity to allow features that are impractical in an applied setting to nevertheless add value during the model-building process. Unfortunately, there are few machine learning frameworks built on assumptions that allow effective utilization of features that are only available at training time. In this paper we formulate a knowledge carryover framework for the budgeted learning scenario with constraints on features and labels. The approach is based on multi-view and semi-supervised learning methods that use graph-encoded regularization. Our main contributions are the following: (1) we propose and provide justification for a methodology for ensuring that changes in the graph regularizer using alternate views are performed in a manner that is target-concept specific, allowing value to be obtained from noisy views; and (2) we demonstrate how this general set-up can be used to effectively improve models by leveraging features unavailable at test time. The rest of the paper is structured as follows. In Section 2, we outline real-world problems to motivate the approach and describe relevant prior work. Section 3 describes the graph construction process and the learning methodologies that are employed. Section 4 provides preliminary discussion regarding theoretical motivation for the method. In Section 5, effectiveness of the approach is demonstrated in a series of experiments employing modified versions of two well-known semi-supervised learning algorithms. Section 6 concludes the paper.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2012

Learning and Memory Following Conformal Radiation Therapy for Pediatric Craniopharyngioma and Low-Grade Glioma

Purpose: The primary objective of this study was to examine whether children with low-grade glioma (LGG) or craniopharyngioma had impaired learning and memory after conformal radiation therapy (CRT). A secondary objective was to determine whether children who received chemotherapy before CRT, a treatment often used to delay radiation therapy in younger children with LGG, received any protective benefit with respect to learning. Methods and Materials: Learning and memory in 57 children with LGG and 44 children with craniopharyngioma were assessed with the California Verbal Learning Test-Children's Version and the Visual-Auditory Learning tests. Learning measures were administered before CRT, 6 months later, and then yearly for a total of 5 years. Results: No decline in learning scores after CRT was observed when patients were grouped by diagnosis. For children with LGG, chemotherapy before CRT did not provide a protective effect on learning. Multiple regression analyses, which accounted for age and tumor volume and location, found that children treated with chemotherapy before CRT were at greater risk of decline on learning measures than those treated with CRT alone. Variables predictive of learning and memory decline included hydrocephalus, shunt insertion, younger age at time of treatment, female gender, and pre-CRT chemotherapy. Conclusions: This study did not reveal any impairment or decline in learning after CRT in overall aggregate learning scores. However, several important variables were found to have a significant effect on neurocognitive outcome. Specifically, chemotherapy before CRT was predictive of worse outcome on verbal learning in LGG patients. In addition, hydrocephalus and shunt insertion in craniopharyngioma were found to be predictive of worse neurocognitive outcome, suggesting a more aggressive natural history for those patients.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2014

Development of the Mathematics of Learning Curve Models for Evaluating Small Modular Reactor Economics

The cost of nuclear power is a straightforward yet complicated topic. It is straightforward in that the cost of nuclear power is a function of the cost to build the nuclear power plant, the cost to operate and maintain it, and the cost to provide fuel for it. It is complicated in that some of those costs are not necessarily known, introducing uncertainty into the analysis. For large light water reactor (LWR)-based nuclear power plants, the uncertainty is mainly contained within the cost of construction. The typical costs of operations and maintenance (O&M), as well as fuel, are well known based on the current fleet of LWRs. However, the last currently operating reactor to come online was Watts Bar 1 in May 1996; thus, the expected construction costs for gigawatt (GW)-class reactors in the United States are based on information nearly two decades old. Extrapolating construction, O&M, and fuel costs from GW-class LWRs to LWR-based small modular reactors (SMRs) introduces even more complication. The per-installed-kilowatt construction costs for SMRs are likely to be higher than those for the GW-class reactors based on the property of the economy of scale. Generally speaking, the economy of scale is the tendency for overall costs to increase slower than the overall production capacity. For power plants, this means that doubling the power production capacity would be expected to cost less than twice as much. Applying this property in the opposite direction, halving the power production capacity would be expected to cost more than half as much. This can potentially make the SMRs less competitive in the electricity market against the GW-class reactors, as well as against other power sources such as natural gas and subsidized renewables. One factor that can potentially aid the SMRs in achieving economic competitiveness is an economy of numbers, as opposed to the economy of scale, associated with learning curves. The basic concept of the learning curve is that the more a new process is repeated, the more efficient the process can be made. Assuming that efficiency directly relates to cost means that the more a new process is repeated successfully and efficiently, the less costly the process can be made. This factor ties directly into the factory fabrication and modularization aspect of the SMR paradigm—manufacturing serial, standardized, identical components for use in nuclear power plants can allow the SMR industry to use the learning curves to predict and optimize deployment costs. Thus, SMR deployment cost estimates can be modeled using learning curve theory to provide some insight into how and when SMRs would be competitive with both the GW-class reactors and other mutual competitors. However, assuming static learning curve behavior for all aspects of SMR construction is likely an invalid approach—not all components and systems in SMRs will be subject to learning, and those that are subject to learning may not learn at the same rate. A learning curvebased model must be capable of tracking these unique behaviors. At the same time, the behavior of the aggregated and integrated SMR deployment is a more useful metric for discussion and comparison—ultimately, the most useful learning curve information is that which describes power plant deployment as a whole. Therefore, the goal of SMR component and systems learning modeling includes a method to produce an overall power plant learning model. This report describes the mathematics of learning curves that can be used for estimating unit cost in future production for individual components and aggregated systems. All necessary assumptions are provided to support the derivations presented. Future work will demonstrate the implementation of this methodology and provide example calculations of SMR cost

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS2004

Perceptual learning through optimization of attentional weighting: human versus optimal Bayesian learner

Human performance in visual detection, discrimination, identification, and search tasks typically improves with practice. Psychophysical studies suggest that perceptual learning is mediated by an enhancement in the coding of the signal, and physiological studies suggest that it might be related to the plasticity in the weighting or selection of sensory units coding task relevant information (learning through attention optimization). We propose an experimental paradigm (optimal perceptual learning paradigm) to systematically study the dynamics of perceptual learning in humans by allowing comparisons to that of an optimal Bayesian algorithm and a number of suboptimal learning models. We measured improvement in human localization (eight-alternative forced-choice with feedback) performance of a target randomly sampled from four elongated Gaussian targets with different orientations and polarities and kept as a target for a block of four trials. The results suggest that the human perceptual learning can occur within a lapse of four trials (<1 min) but that human learning is slower and incomplete with respect to the optimal algorithm (23.3% reduction in human efficiency from the 1st-to-4th learning trials). The greatest improvement in human performance, occurring from the 1st-to-2nd learning trial, was also present in the optimal observer, and, thus reflects a property inherent to the visual task and not a property particular to the human perceptual learning mechanism. One notable source of human inefficiency is that, unlike the ideal observer, human learning relies more heavily on previous decisions than on the provided feedback, resulting in no human learning on trials following a previous incorrect localization decision. Finally, the proposed theory and paradigm provide a flexible framework for future studies to evaluate the optimality of human learning of other visual cues and/or sensory modalities.

Non-NASA Center↗