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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 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

How R Developers explain their Package Choice: A Survey

Background: Contemporary software development relies heavily on reusing already implemented functionality, usually in the form of packages. Aims: We aim to shed light on developers' preferences when selecting packages in R language. Method: To do that, we create and administer a survey to over 1000 developers who have added one of two common dataframe enhancement libraries in R to their projects: data.table or tidyr. We design a questionnaire using the Social Contagion Theory (SCT) following prior work on technology adoption and ensure that key dimensions affecting developer choice are considered. Results: Of the 1085 developers we contacted, 803 completed the survey asking them to prioritize various factors known to affect developer perceptions of package quality and to provide their background. Most developers self-identified as data scientists with two to five years of work experience. We found significant differences between the preferences of developers who chose data.table and tidyr. Surprisingly, package reputation based on easy-to-see measures, such as the number of stars on GitHub, was not an important factor for either group. Conclusions: Our findings demonstrate the inherently social nature of package adoption. They can help design future studies on how different populations of developers make decisions on which software packages to use in their projects. Finally, package developers and maintainers can benefit by better understanding the prime concerns of the users of their packages.

Malviya Thakur, Addi↗
DOE OSTI2021

Assessing Machine Learning as a Tool to Explain Variance in Deployed Photovoltaic (PV) System Degradation

Degradation remains a large uncertainty in forecasting production for PV plants, creating significant risk for developers and financiers. This study aims to quantify the distribution and drivers of degradation across 10,000 PV systems deployed for distributed or utility generation by training a machine learning model to predict year-over-year degradation rates from metadata characteristics. A combination of K-Means clustering and random forest regressor were found to associate multiple metadata features as potential drivers of degradation, including module characteristics, system design, and climate features. From this, it is inferred that if machine learning is able to find complex patterns between metadata features and system performance loss, such methods can be employed to help developers and financiers make data-informed decisions when estimating long-term energy production forecasts in financial models.

Dunn, Jimmy C.↗
DOE OSTI2025

Behavior of Single-Line-Ground Faults in Inverter-Based Resource Dominated Grids Explained

It has been observed by protection engineers that it is difficult for a protective relay to identify the faulted phase during a single-line-ground (SLG) fault in a power system with a high ingression of inverter-based resources (IBR) using currents (phase or sequence). Further studies using electromagnetic transient (EMT) simulation show that the initial operating conditions of the IBRs influence the response of phase currents during an SLG fault. In this letter, we conduct a quantitative analysis using sequence components. We find that the pre-fault condition determines the relative position of the current contributed by the grid versus that from the IBR, and further dictates which phase has the largest magnitude during an SLG condition. Finally, this finding is further verified by the EMT simulation results.

electromagnetic transient simulation↗
DOE OSTI2018

Situ: Identifying and Explaining Suspicious Behavior in Networks

Despite the best efforts of cyber security analysts, networked computing assets are routinely compromised, resulting in the loss of intellectual property, the disclosure of state secrets, and major financial damages. Anomaly detection methods are beneficial for detecting new types of attacks and abnormal network activity, but such algorithms can be difficult to understand and trust. Network operators and cyber analysts need fast and scalable tools to help identify suspicious behavior that bypasses automated security systems, but operators do not want another automated tool with algorithms they do not trust. Experts need tools to augment their own domain expertise and to provide a contextual understanding of suspicious behavior to help them make decisions. In this paper we present Situ, a visual analytics system for discovering suspicious behavior in streaming network data. Situ provides a scalable solution that combines anomaly detection with information visualization. The system's visualizations enable operators to identify and investigate the most anomalous events and IP addresses, and the tool provides context to help operators understand why they are anomalous. Finally, operators need tools that can be integrated into their workflow and with their existing tools. This paper describes the Situ platform and its deployment in an operational network setting. Furthermore, we discuss how operators are currently using the tool in a large organization's security operations center and present the results of expert reviews with professionals.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

SigTime: Learning and Visually Explaining Time Series Signatures

Understanding and distinguishing temporal patterns in time series data is essential for scientific discovery and decision-making. For example, in biomedical research, uncovering meaningful patterns in physiological signals can improve diagnosis, risk assessment, and patient outcomes. However, existing methods for time series pattern discovery face major challenges, including high computational complexity, limited interpretability, and difficulty in capturing meaningful temporal structures. Here, to address these gaps, we introduce a novel learning framework that jointly trains two Transformer models using complementary time series representations: shapelet-based representations to capture localized temporal structures and traditional feature engineering to encode statistical properties. The learned shapelets serve as interpretable signatures that differentiate time series across classification labels. Additionally, we develop a visual analytics system—SigTime—with coordinated views to facilitate exploration of time series signatures from multiple perspectives, aiding in useful insights generation. We quantitatively evaluate our learning framework on eight publicly available datasets and one proprietary clinical dataset. Additionally, we demonstrate the effectiveness of our system through two usage scenarios along with the domain experts: one involving public ECG data and the other focused on preterm labor analysis.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Bacterial community dynamics explain carbon mineralization and assimilation in soils of different land-use history

Soil dwelling microorganisms are key players in the terrestrial carbon cycle, driving both the degradation and stabilization of soil organic matter. Bacterial community structure and function vary with respect to land use; yet the ecological drivers of this variation remain poorly described and difficult to predict. For this work, we conducted a multi-substrate DNA-stable isotope probing experiment across cropland, old-field, and forest habitats to link carbon mineralization dynamics with the dynamics of bacterial growth and carbon assimilation. We tracked the movement of 13 C derived from five distinct carbon sources as it was assimilated into bacterial DNA over time. We show that carbon mineralization, community composition, and carbon assimilation dynamics all differed with respect to land use. We also show that microbial community dynamics affect carbon assimilation dynamics and are associated with soil DNA content. Soil DNA yield is easy to measure and may be useful in predicting microbial community dynamics linked to soil carbon cycling. Soil dwelling microorganisms are key players in the terrestrial carbon cycle, driving both the degradation and stabilization of soil organic matter. Microbial communities vary with respect to land use, but we still have an incomplete understanding of how variation in community structure links to variation in community function. DNA stable isotope probing (DNA-SIP) is a high-resolution method that can identify specific microbial taxa that assimilate carbon in situ. We conducted a large-scale multi-substrate DNA-SIP experiment to explore differences in bacterial activity across land-use regimes. We show that microbial community dynamics vary with land use, that these dynamics are linked to soil carbon cycling, and that they are associated with easily measured soil properties.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Forest Age Rivals Climate to Explain Reproductive Allocation Patterns in Forest Ecosystems Globally

ABSTRACT Forest allocation of net primary productivity (NPP) to reproduction (carbon required for flowers, fruits, and seeds) is poorly quantified globally, despite its critical role in forest regeneration and a well‐supported trade‐off with allocation to growth. Here, we present the first global synthesis of a biometric proxy for forest reproductive allocation (RA) across environmental and stand age gradients from a compiled dataset of 824 observations across 393 sites. We find that ecosystem‐scale RA increases ~60% from boreal to tropical forests. Climate shows important non‐linear relationships with RA, but is not the sole predictor. Forest age effects are comparable to climate in magnitude (MAT: ß = 0.24,p = 0.021; old growth forest: ß = 0.22,p < 0.001), while metrics of soil fertility show small but significant relationships with RA (soil pH: ß = 0.07,p = 0.001; soil N: ß = −0.07,p = 0.001). These results provide strong evidence that ecosystem‐scale RA is mediated by climate, forest age, and soil conditions, and is not a globally fixed fraction of positive NPP as assumed by most vegetation and ecosystem models. Our dataset and findings can be used by modellers to improve predictions of forest regeneration and carbon cycling.

Environmental Sciences & Ecology↗
DOE OSTI2017

Differences in xylem and leaf hydraulic traits explain differences in drought tolerance among mature Amazon rainforest trees

Considerable uncertainty surrounds the impacts of anthropogenic climate change on the composition and structure of Amazon forests. In this study, building upon results from two large-scale ecosystem drought experiments in the eastern Brazilian Amazon that observed increases in mortality rates among some tree species but not others, we investigate the physiological traits underpinning these differential demographic responses. Xylem pressure at 50% conductivity (xylem-P 50 ), leaf turgor loss point (TLP), cellular osmotic potential (π o ), and cellular bulk modulus of elasticity (ε), all traits mechanistically linked to drought tolerance, were measured on upper canopy branches and leaves of mature trees from selected species growing at the two drought experiment sites. Each species was placed a priori into one of four plant functional type (PFT) categories: drought-tolerant versus drought-intolerant based on observed mortality rates, and subdivided into early- versus late-successional based on wood density. We tested the hypotheses that the measured traits would be significantly different between the four PFTs and that they would be spatially conserved across the two experimental sites. Xylem-P 50 , TLP, and π o , but not ε, occurred at significantly higher water potentials for the drought-intolerant PFT compared to the drought-tolerant PFT; however, there were no significant differences between the early- and late-successional PFTs. These results suggest that these three traits are important for determining drought tolerance, and are largely independent of wood density—a trait commonly associated with successional status. Differences in these physiological traits that occurred between the drought-tolerant and drought-intolerant PFTs were conserved between the two research sites, even though they had different soil types and dry-season lengths. Lastly, this more detailed understanding of how xylem and leaf hydraulic traits vary between co-occuring drought-tolerant and drought-intolerant tropical tree species promises to facilitate a much-needed improvement in the representation of plant hydraulics within terrestrial ecosystem and biosphere models, which will enhance our ability to make robust predictions of how future changes in climate will affect tropical forests.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Coupling plant litter quantity to a novel metric for litter quality explains C storage changes in a thawing permafrost peatland

Abstract Permafrost thaw is a major potential feedback source to climate change as it can drive the increased release of greenhouse gases carbon dioxide (CO 2 ) and methane (CH 4 ). This carbon release from the decomposition of thawing soil organic material can be mitigated by increased net primary productivity (NPP) caused by warming, increasing atmospheric CO 2 , and plant community transition. However, the net effect on C storage also depends on how these plant community changes alter plant litter quantity, quality, and decomposition rates. Predicting decomposition rates based on litter quality remains challenging, but a promising new way forward is to incorporate measures of the energetic favorability to soil microbes of plant biomass decomposition. We asked how the variation in one such measure, the nominal oxidation state of carbon (NOSC), interacts with changing quantities of plant material inputs to influence the net C balance of a thawing permafrost peatland. We found: (1) Plant productivity (NPP) increased post‐thaw, but instead of contributing to increased standing biomass, it increased plant biomass turnover via increased litter inputs to soil; (2) Plant litter thermodynamic favorability (NOSC) and decomposition rate both increased post‐thaw, despite limited changes in bulk C:N ratios; (3) these increases caused the higher NPP to cycle more rapidly through both plants and soil, contributing to higher CO 2 and CH 4 fluxes from decomposition. Thus, the increased C‐storage expected from higher productivity was limited and the high global warming potential of CH 4 contributed a net positive warming effect. Although post‐thaw peatlands are currently C sinks due to high NPP offsetting high CO 2 release, this status is very sensitive to the plant community's litter input rate and quality. Integration of novel bioavailability metrics based on litter chemistry, including NOSC, into studies of ecosystem dynamics, is needed to improve the understanding of controls on arctic C stocks under continued ecosystem transition.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Modified niche optima and breadths explain the historical contingency of bacterial community responses to eutrophication in coastal sediments

Abstract Previous studies have shown that the response of bacterial communities to disturbances depends on their environmental history. Historically fluctuating habitats host communities that respond better to disturbance than communities of historically stable habitats. However, the exact ecological mechanism that drives this dependency remains unknown. Here, we experimentally demonstrate that modifications of niche optima and niche breadths of the community members are driving this dependency of bacterial responses to past environmental conditions. First, we develop a novel, simple method to calculate the niche optima and breadths of bacterial taxa regarding single environmental gradients. Then, we test this method on sediment bacterial communities of three habitats, one historically stable and less loaded and two historically more variable and more loaded habitats in terms of historical chlorophyll‐α water concentration, that we subject to hypoxia via organic matter addition ex situ . We find that communities containing bacterial taxa differently adapted to hypoxia show different structural and functional responses, depending on the sediment's environmental history. Specifically, in the historically less fluctuating and loaded sediments where we find more taxa poorly adapted to hypoxic conditions, communities change a lot over time and organic matter is not degraded efficiently. The opposite is true for the historically more fluctuating and loaded sediments where we find more taxa well adapted to hypoxia. Based on the community responses observed here, we also propose an alternative calculation of community resistance that takes into account how rapidly the communities respond to disturbances and not just the initial and final states of the community.

Fodelianakis, S.↗
DOE OSTI2023

Evolutionary lineage explains trait variation among 75 coexisting grass species

Summary Evolutionary history plays a key role driving patterns of trait variation across plant species. For scaling and modeling purposes, grass species are typically organized into C 3 vs C 4 plant functional types (PFTs). Plant functional type groupings may obscure important functional differences among species. Rather, grouping grasses by evolutionary lineage may better represent grass functional diversity. We measured 11 structural and physiological traits in situ from 75 grass species within the North American tallgrass prairie. We tested whether traits differed significantly among photosynthetic pathways or lineages (tribe) in annual and perennial grass species. Critically, we found evidence that grass traits varied among lineages, including independent origins of C 4 photosynthesis. Using a rigorous model selection approach, tribe was included in the top models for five of nine traits for perennial species. Tribes were separable in a multivariate and phylogenetically controlled analysis of traits, owing to coordination of important structural and ecophysiological characteristics. Our findings suggest grouping grass species by photosynthetic pathway overlooks variation in several functional traits, particularly for C 4 species. These results indicate that further assessment of lineage‐based differences at other sites and across other grass species distributions may improve representation of C 4 species in trait comparison analyses and modeling investigations.

Donnelly, Ryan C.↗
DOE OSTI2023

Multiple mechanisms explain loss of anthocyanins from betalain‐pigmented Caryophyllales, including repeated wholesale loss of a key anthocyanidin synthesis enzyme

Summary In this study, we investigate the genetic mechanisms responsible for the loss of anthocyanins in betalain‐pigmented Caryophyllales, considering our hypothesis of multiple transitions to betalain pigmentation. Utilizing transcriptomic and genomic datasets across 357 species and 31 families, we scrutinize 18 flavonoid pathway genes and six regulatory genes spanning four transitions to betalain pigmentation. We examined evidence for hypotheses of wholesale gene loss, modified gene function, altered gene expression, and degeneration of the MBW (MYB‐bHLH‐WD40) trasnscription factor complex, within betalain‐pigmented lineages. Our analyses reveal that most flavonoid synthesis genes remain conserved in betalain‐pigmented lineages, with the notable exception of TT19 orthologs, essential for the final step in anthocyanidin synthesis, which appear to have been repeatedly and entirely lost. Additional late‐stage flavonoid pathway genes upstream of TT19 also manifest strikingly reduced expression in betalain‐pigmented species. Additionally, we find repeated loss and alteration in the MBW transcription complex essential for canonical anthocyanin synthesis. Consequently, the loss and exclusion of anthocyanins in betalain‐pigmented species appear to be orchestrated through several mechanisms: loss of a key enzyme, downregulation of synthesis genes, and degeneration of regulatory complexes. These changes have occurred iteratively in Caryophyllales, often coinciding with evolutionary transitions to betalain pigmentation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Rapid in situ diversification rates in Rhamnaceae explain the parallel evolution of high diversity in temperate biomes from global to local scales

Summary The macroevolutionary processes that have shaped biodiversity across the temperate realm remain poorly understood and may have resulted from evolutionary dynamics related to diversification rates, dispersal rates, and colonization times, closely coupled with Cenozoic climate change. We integrated phylogenomic, environmental ordination, and macroevolutionary analyses for the cosmopolitan angiosperm family Rhamnaceae to disentangle the evolutionary processes that have contributed to high species diversity within and across temperate biomes. Our results show independent colonization of environmentally similar but geographically separated temperate regions mainly during the Oligocene, consistent with the global expansion of temperate biomes. High global, regional, and local temperate diversity was the result of high in situ diversification rates, rather than high immigration rates or accumulation time, except for Southern China, which was colonized much earlier than the other regions. The relatively common lineage dispersals out of temperate hotspots highlight strong source‐sink dynamics across the cosmopolitan distribution of Rhamnaceae. The proliferation of temperate environments since the Oligocene may have provided the ecological opportunity for rapid in situ diversification of Rhamnaceae across the temperate realm. Our study illustrates the importance of high in situ diversification rates for the establishment of modern temperate biomes and biodiversity hotspots across spatial scales.

Plant Sciences↗