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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 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Incorporating space and time into random forest models for analyzing geospatial patterns of drug-related crime incidents in a major U.S. metropolitan area

The opioid crisis has hit American cities hard, and research on spatial and temporal patterns of drug-related activities including detecting and predicting clusters of crime incidents involving particular types of drugs is useful for distinguishing hot zones where drugs are present that in turn can further provide a basis for assessing and providing related treatment services. In this study, we investigated spatiotemporal patterns of more than 52,000 reported incidents of drug-related crime at block group granularity in Chicago, IL between 2016 and 2019. We applied a space-time analysis framework and machine learning approaches to build a model using training data that identified whether certain locations and built environment and sociodemographic factors were correlated with drug-related crime incident patterns, and establish the top contributing factors that underlaid the trends. Space and time, together with multiple driving factors, were incorporated into a random forest model to analyze these changing patterns. We accommodated both spatial and temporal autocorrelation in the model learning process to assist with capturing the changes over time and tested the capabilities of the space-time random forest model by predicting drug-related activity hot zones. Overall, we focused particularly on crime incidents that involved heroin and synthetic drugs as these have been key drug types that have highly impacted cities during the opioid crisis in the U.S.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Introducing a Markov chain-based time calibration procedure for multi-channel particle detectors: application to the SuperFGD and ToF detectors of the T2K experiment

Inter-channel mis-synchronisation can be a limiting factor to the time resolution of high performance timing detectors with multiple readout channels and independent electronics units. In these systems, time calibration methods employed must be able to efficiently correct for minimal mis-synchronisation between channels and achieve the best detector performance. We present an iterative time calibration method based on Markov Chains, suitable for detector systems with multiple readout channels. Starting from correlated hit pairs alone, and without requiring an external reference time measurement, the method solves for fixed per-channel offsets, with precision limited only by the intrinsic single-channel resolution. A mathematical proof that the method is able to find the correct time offsets to be assigned to each detector channel in order to achieve inter-channel synchronisation is given, and it is shown that the number of iterations to reach convergence within the desired precision is controllable with a single parameter. Numerical studies are used to confirm unbiased recovery of true offsets. Finally, the application of the calibration method to the Super Fine-Grained Detector (SuperFGD) and the Time of Flight (TOF) detector at the upgraded T2K near detector (ND280) shows good improvement in overall timing resolution, demonstrating the effectiveness in a real-world scenario and scalability.

calibration and fitting methods↗
DOE OSTI2024

Frosted Tracks

SAND2025-01893O Frosted Tracks is a software tool to group trajectories according to sequences of their behavior. The goal is to start with a very large number of trajectories and identify groups that exhibit similar behavior patterns. The application combines TICC and Metric DBSCAN clustering algorithms for behavioral segmentation and labeling of air/sea trajectory data. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Dalbey, Keith↗
DOE OSTI2017

Whole-Genome and Epigenomic Landscapes of Etiologically Distinct Subtypes of Cholangiocarcinoma

Cholangiocarcinoma (CCA) is a hepatobiliary malignancy exhibiting high incidence in countries with endemic liver-fluke infection. We analysed 489 CCAs from 10 countries, combining whole-genome (71 cases), targeted/exome, copy-number, gene expression, and DNA methylation information. Integrative clustering defined four CCA clusters - Fluke- Positive CCAs (Clusters 1/2) are enriched in ERBB2 amplifications and TP53 mutations, conversely Fluke-Negative CCAs (Clusters 3/4) exhibit high copy-number alterations and PD-1/PD-L2 expression, or epigenetic mutations (IDH1/2, BAP1) and FGFR/PRKA-related gene rearrangements. Whole-genome analysis highlighted FGFR2 3’UTR deletion as a mechanism of FGFR2 upregulation. Integration of non-coding promoter mutations with protein-DNA binding profiles demonstrates pervasive modulation of H3K27me3-associated sites in CCA. Clusters 1 and 4 exhibit distinct DNA hypermethylation patterns targeting either CpG islands or shores - mutation signature and subclonality analysis suggests that these reflect different mutational pathways. Lastly, our results exemplify how genetics, epigenetics and environmental carcinogens can interplay across different geographies to generate distinct molecular subtypes of cancer.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1993

Constraints on chemical evolution models from QSOALS abundances

Models of the formation and early chemical evolution of our Galaxy are guided and constrained by our knowledge of abundances in globular cluster stars and halo field stars. The abundance patterns identified in halo and disk stars should be discernible in absorption lines of gas clouds in forming galaxies which are accidentally lying in front of background QSO's. Conversely, the ensemble of QSO absorption line systems (QSOALS) at each redshift may suggest a detailed model for the formation of our Galaxy that is testable using abundance patterns in halo stars.

Lauroesch, J. T.↗
NASA NTRS2003

Control of T lymphocyte morphology by the GTPase Rho

BACKGROUND: Rho family GTPase regulation of the actin cytoskeleton governs a variety of cell responses. In this report, we have analyzed the role of the GTPase Rho in maintenance of the T lymphocyte actin cytoskeleton. RESULTS: Inactivation of the GTPase Rho in the human T lymphocytic cell line HPB-ALL does not inhibit constitutively high adhesion to the integrin beta1 substrate fibronectin. It did however result in the aberrant extension of finger-like dendritic processes on the substrates VCAM-1, Fn, and mAb specific to beta1 integrins. Time-lapse video microscopy demonstrated that C3 induced extensions were primarily the result of an altered pseudopod elongation rather than retraction. Once the stellate pseudopodia extended, none retracted, and cells became completely immobile. Filipodial structures were absent and the dendritic-like processes in C3 treated cells were rich in filamentous actin. Immunolocalization of RhoA in untreated HPB-ALL cells spreading on fibronectin demonstrated a diffuse staining pattern within the pseudopodia. In C3 treated cells, clusters of RhoA were pronounced and localized within the altered extensions. CONCLUSIONS: GTPase Rho is actively involved in the regulation of T lymphocyte morphology and motility.

Non-NASA Center↗
NASA NTRS2020

Evaluating China's fossil-fuel CO2 emissions from a comprehensive dataset of nine inventories

China's fossil-fuel CO2 (FFCO2) emissions accounted for approximately 28 % of the global total FFCO2 in 2016. An accurate estimate of China's FFCO2 emissions is a prerequisite for global and regional carbon budget analyses and the monitoring of carbon emission reduction efforts. However, significant uncertainties and discrepancies exist in estimations of China's FFCO2 emissions due to a lack of detailed traceable emission factors (EFs) and multiple statistical data sources. Here, we evaluated China's FFCO2 emissions from nine published global and regional emission datasets. These datasets show that the total emissions increased from 3.4 (3.0–3.7) in 2000 to 9.8 (9.2–10.4) Gt CO2 per yr in 2016. The variations in these estimates were largely due to the different EF (0.491–0.746 t C per t of coal) and activity data. The large-scale patterns of gridded emissions showed a reasonable agreement, with high emissions being concentrated in major city clusters, and the standard deviation mostly ranged from 10 % to 40 % at the provincial level. However, patterns beyond the provincial scale varied significantly, with the top 5 % of the grid level accounting for 50 %–90 % of total emissions in these datasets. Our findings highlight the significance of using locally measured EF for Chinese coal. To reduce uncertainty, we recommend using physical CO2 measurements and use these values for dataset validation, key input data sharing (e.g., point sources), and finer-resolution validations at various levels.

Pengfei Han↗
DOE OSTI2017

Robust X-ray angular correlations for the study of meso-structures

As self-assembling nanomaterials become more sophisticated, it is becoming increasingly important to measure the structural order of finite-sized assemblies of nano-objects. These mesoscale clusters represent an acute challenge to conventional structural probes, owing to the range of implicated size scales (10 nm to several micrometres), the weak scattering signal and the dynamic nature of meso-clusters in native solution environments. The high X-ray flux and coherence of modern synchrotrons present an opportunity to extract structural information from these challenging systems, but conventional ensemble X-ray scattering averages out crucial information about local particle configurations. Conversely, a single meso-cluster scatters too weakly to recover the full diffraction pattern. Using X-ray angular cross-correlation analysis, it is possible to combine multiple noisy measurements to obtain robust structural information. This paper explores the key theoretical limits and experimental challenges that constrain the application of these methods to probing structural order in real nanomaterials. A metric is presented to quantify the signal-to-noise ratio of angular correlations, and it is used to identify several experimental artifacts that arise. In particular, it is found that background scattering, data masking and inter-cluster interference profoundly affect the quality of correlation analyses. A robust workflow is demonstrated for mitigating these effects and extracting reliable angular correlations from realistic experimental data.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2015

Ultraviolet and infrared laser-induced fragmentation of free (CF{sub 3}I){sub n} clusters in a molecular beam and (CF{sub 3}I){sub n} clusters inside or on the surface of large (Xe){sub m} clusters

The fragmentation of free homogeneous (CF{sub 3}I){sub n} clusters in a molecular beam (n ≤ 45 is the average number of molecules in the cluster) and (CF{sub 3}I){sub n} clusters inside or on the surface of large (Xe){sub m} clusters (m ≥ 100 is the average number of atoms in the cluster) by ultraviolet and infrared laser radiations has been studied. These three types of (CF{sub 3}I){sub n} clusters are shown to have different stabilities with respect to fragmentation by both ultraviolet and infrared radiations and completely different dependences of the fragmentation probability on the energy of ultraviolet and infrared radiations. When exposed to ultraviolet radiation, the free (CF{sub 3}I){sub n} clusters fragment at comparatively low fluences (Φ{sub UV} ≤ 0.15 J cm{sup −2}) and the weakest energy dependence of the fragmentation probability is observed for them. A stronger energy dependence of the fragmentation probability is observed for the (CF{sub 3}I){sub n} clusters localized inside (Xe){sub m} clusters, and the strongest dependence is observed for the (CF{sub 3}I){sub n} clusters located on the surface of (Xe){sub m} clusters. When the clusters are exposed to infrared radiation, the homogeneous (CF{sub 3}I){sub n} clusters efficiently fragment at low fluences (Φ{sub IR} ≤ 25 mJ cm{sup −2}), higher fluences (Φ{sub IR} ≈ 75 mJ cm{sup −2}) are needed for the fragmentation of the (CF{sub 3}I){sub n} localized inside (Xe){sub m} clusters, and even higher fluences (Φ{sub IR} ≈ 150 mJ cm{sup −2}) are needed for the fragmentation of the (CF{sub 3}I){sub n} clusters located on the surface of (Xe){sub m} clusters. It has been established that small (CF{sub 3}I){sub n} clusters located on the surface of (Xe){sub m} clusters do not fragment up to fluences Φ{sub IR} ≈ 250 mJ cm{sup −2}. The fragmentation efficiency of (CF{sub 3}I){sub n} clusters is shown to be the same (at the same fluence) when they are excited by both pulsed (τ{sub p} ≈ 150 ns) and continuous-wave infrared laser radiations. Possible causes of such a pattern of ultraviolet and infrared laser-induced fragmentation of these clusters are discussed.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

Effects of bursting dynamic features on the generation of multi-clustered structure of neural network with symmetric spike-timing-dependent plasticity learning rule

In this paper, the generation of multi-clustered structure of self-organized neural network with different neuronal firing patterns, i.e., bursting or spiking, has been investigated. The initially all-to-all-connected spiking neural network or bursting neural network can be self-organized into clustered structure through the symmetric spike-timing-dependent plasticity learning for both bursting and spiking neurons. However, the time consumption of this clustering procedure of the burst-based self-organized neural network (BSON) is much shorter than the spike-based self-organized neural network (SSON). Our results show that the BSON network has more obvious small-world properties, i.e., higher clustering coefficient and smaller shortest path length than the SSON network. Also, the results of larger structure entropy and activity entropy of the BSON network demonstrate that this network has higher topological complexity and dynamical diversity, which benefits for enhancing information transmission of neural circuits. Hence, we conclude that the burst firing can significantly enhance the efficiency of clustering procedure and the emergent clustered structure renders the whole network more synchronous and therefore more sensitive to weak input. This result is further confirmed from its improved performance on stochastic resonance. Therefore, we believe that the multi-clustered neural network which self-organized from the bursting dynamics has high efficiency in information processing.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2017

Microbial Community Structure and the Persistence of Cyanobacterial Populations in Salt Crusts of the Hyperarid Atacama Desert from Genome-Resolved Metagenomics

Although once thought to be devoid of biology, recent studies have identified salt deposits as oases for life in the hyperarid Atacama Desert. To examine spatial patterns of microbial species and key nutrient sources, we genomically characterized 26 salt crusts from three sites along a fog gradient. The communities are dominated by a large variety of Halobacteriales and Bacteroidetes, plus a few algal and Cyanobacterial species. CRISPR locus analysis suggests the distribution of a single Cyanobacterial population among all sites. This is in stark contrast to the extremely high sample specificity of most other community members. Only present at the highest moisture site is a genomically characterized Thermoplasmatales archaeon (Marine Group II) and six Nanohaloarchaea, one of which is represented by a complete genome. Parcubacteria (OD1) and Saccharibacteria (TM7), not previously reported from hypersaline environments, were found at low abundances. We found no indication of a N 2 fixation pathway in the communities, suggesting acquisition of bioavailable nitrogen from atmospherically derived nitrate. Samples cluster by site based on bacterial and archaeal abundance patterns and photosynthetic capacity decreases with increasing distance from the ocean. We conclude that moisture level, controlled by coastal fog intensity, is the strongest driver of community membership.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Machine learning reveals cyclic changes in seismic source spectra in Geysers geothermal field

The earthquake rupture process comprises complex interactions of stress, fracture, and frictional properties. New machine learning methods demonstrate great potential to reveal patterns in time-dependent spectral properties of seismic signals and enable identification of changes in faulting processes. Clustering of 46,000 earthquakes of 0.3 < ML < 1.5 from the Geysers geothermal field (CA) yields groupings that have no reservoir-scale spatial patterns but clear temporal patterns. Events with similar spectral properties repeat on annual cycles within each cluster and track changes in the water injection rates into the Geysers reservoir, indicating that changes in acoustic properties and faulting processes accompany changes in thermomechanical state. The methods open new means to identify and characterize subtle changes in seismic source properties, with applications to tectonic and geothermal seismicity.

15 GEOTHERMAL ENERGY↗
DOE OSTI2017

Comparison of Clustering Techniques for Residential Energy Behavior using Smart Meter Data

Current practice in whole time series clustering of residential meter data focuses on aggregated or subsampled load data at the customer level, which ignores day-to-day differences within customers. This information is critical to determine each customer’s suitability to various demand side management strategies that support intelligent power grids and smart energy management. Clustering daily load shapes provides fine-grained information on customer attributes and sources of variation for subsequent models and customer segmentation. In this paper, we apply 11 clustering methods to daily residential meter data. We evaluate their parameter settings and suitability based on 6 generic performance metrics and post-checking of resulting clusters. Finally, we recommend suitable techniques and parameters based on the goal of discovering diverse daily load patterns among residential customers. To the authors’ knowledge, this paper is the first robust comparative review of clustering techniques applied to daily residential load shape time series in the power systems’ literature.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Characterization of Surface Heterogeneity-Induced Convection Using Cluster Analysis

Land-atmosphere interactions and boundary layer processes often control the formation of shallow clouds and subsequently deep convective precipitation over the Southern Great Plains. In this study, we examine the impacts of large-scale advection on the cloud populations and land-atmospheric coupling observed during the Holistic Interactions of Shallow Clouds, Aerosols, and Land-Ecosystems (HI-SCALE) field campaign in 2016. In this work, we performed two Large Eddy Simulations (LES) using the Weather Research and Forecasting Model for a day in which the transition from clear-sky to shallow precipitating clouds and isolated deep convection was observed. The control simulation reproduced the overall distribution of cloud populations by using realistic soil conditions with an interactive land model. In the sensitivity simulation where large-scale advection is removed, a strong relationship between the land and boundary layer is found. To study the timing, location, and intensity of convective initiation and the relationship of clouds with land surface properties, a cluster analysis of equivalent potential temperature is performed for the simulation without large-scale advection. That analysis shows that convective clouds first form over regions with higher surface sensible heat flux. Precipitation from those convective clouds likely triggers new updrafts nearby about two hours later through the lifting associated with cold pools. Furthermore, the cluster analysis also shows that in addition to the spatial pattern of soil moisture, land use and soil texture in western Oklahoma also influence the location of convective initiation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Evidence of Specific MJO Phase Occurrence with Summertime California Central Valley Extreme Hot Weather

This study examines associations between California Central Valley (CCV) heat waves and the Madden Julian Oscillation (MJO). These heat waves have major economic impact. Our prior work showed that CCV heat waves are frequently preceded by convection over the tropical Indian and eastern Pacific oceans, in patterns identifiable with MJO phases. The main analysis method is lagged composites (formed after each MJO phase pair) of CCV synoptic station temperature, outgoing longwave radiation (OLR), and velocity potential (VP). Over the CCV, positive temperature anomalies occur only after the Indian Ocean (phases 2–3) or eastern Pacific Ocean (phases 8–1) convection (implied by OLR and VP fields). The largest fractions of CCV hot days occur in the two weeks after onset of those two phase pairs. OLR and VP composites have significant subsidence and convergence above divergence over the CCV during heat waves, and these structures are each part of larger patterns having significant areas over the Indian and Pacific Oceans. Prior studies showed that CCV heat waves can be roughly grouped into two clusters: Cluster 2 is preceded by a heat wave over northwestern North America, while Cluster 1 is not. OLR and VP composite analyses are applied separately to these two clusters. However, for Cluster 2, the subsidence and VP over the CCV are not significant, and the large-scale VP pattern has low correlation with the MJO lagged composite field. Therefore, the association between the MJO convection and subsequent CCV heat wave is more evident in Cluster 1 than Cluster 2.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Construction of Reconfigurable and Polymorphic DNA Origami Assemblies with Coiled‐Coil Patches and Patterns

Abstract DNA origami nanodevices achieve programmable structure and tunable mechanical and dynamic properties by leveraging the sequence‐specific interactions of nucleic acids. Previous advances have also established DNA origami as a useful building block to make well‐defined micron‐scale structures through hierarchical self‐assembly, but these efforts have largely leveraged the structural features of DNA origami. The tunable dynamic and mechanical properties also provide an opportunity to make assemblies with adaptive structures and properties. Here the integration of DNA origami hinge nanodevices and coiled‐coil peptides are reported into hybrid reconfigurable assemblies. With the same dynamic device and peptide interaction, it is made multiple higher‐order assemblies (i.e., polymorphic assembly) by organizing clusters of peptides into patches or arranging single peptides into patterns on the surfaces of DNA origami to control the relative orientation of devices. The coiled‐coil interactions are used to construct circular and linear assemblies whose structure and mechanical properties can be modulated with DNA‐based reconfiguration. Reconfiguration of linear assemblies leads to micron scale motions and ≈2.5‐10‐fold increase in bending stiffness. The results provide a foundation for stimulus‐responsive hybrid assemblies that can adapt their structure and properties in response to nucleic acid, peptide, protein, or other triggers.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Subseasonal Representation and Predictability of North American Weather Regimes Using Cluster Analysis

Abstract This study focuses on assessing the representation and predictability of North American weather regimes, which are persistent large-scale atmospheric patterns, in a set of initialized subseasonal reforecasts created using the Community Earth System Model, version 2 (CESM2). The k -means clustering was used to extract four key North American (10°–70°N, 150°–40°W) weather regimes within ERA5 reanalysis, which were used to interpret CESM2 subseasonal forecast performance. Results show that CESM2 can recreate the climatology of the four main North American weather regimes with skill but exhibits biases during later lead times with overoccurrence of the West Coast high regime and underoccurrence of the Greenland high and Alaskan ridge regimes. Overall, the West Coast high and Pacific trough regimes exhibited higher predictability within CESM2, partly related to El Niño. Despite biases, several reforecasts were skillful and exhibited high predictability during later lead times, which could be partly attributed to skillful representation of the atmosphere from the tropics to extratropics upstream of North America. The high predictability at the subseasonal time scale of these case-study examples was manifested as an “ensemble realignment,” in which most ensemble members agreed on a prediction despite ensemble trajectory dispersion during earlier lead times. Weather regimes were also shown to project distinct temperature and precipitation anomalies across North America that largely agree with observational products. This study further demonstrates that unsupervised learning methods can be used to uncover sources and limits of subseasonal predictability, along with systematic biases present in numerical prediction systems. Significance Statement North American weather regimes are large-scale atmospheric patterns that can persist for several days. Their skillful subseasonal (2 weeks or greater) prediction can provide valuable lead time to prepare for temperature and precipitation anomalies that can stress energy and water resources. The purpose of this study was to assess the climatological representation and subseasonal predictability of four key North American weather regimes using a research subseasonal prediction system and clustering analysis. We found that the Pacific trough and West Coast high regimes exhibited higher predictability than other regimes and that skillful representation of conditions across the tropics and extratropics can increase predictability during later lead times. Future work will quantify causal pathways associated with high predictability.

58 GEOSCIENCES↗
DOE OSTI2025

An atomic cluster expansion potential for twisted multilayer graphene

Twisted multilayer graphene, characterized by its moiré patterns arising from inter-layer rotational misalignment, serves as a rich platform for exploring quantum phenomena. Machine learning interatomic potentials (MLIPs) are a promising approach to model such systems. Our work develops a method to generate training and test datasets for fitting MLIPs that capture all possible misalignments but remain small-scale to facilitate efficient data generation and parameter estimation. To achieve this, we generate configurations with periodic boundary conditions suitable for density functional theory calculations, and then introduce an internal twist and shift within those supercell structures. Using this technique, supplemented with an active learning workflow, we fit an Atomic Cluster Expansion potential for simulating twisted multilayer graphene and test it for accuracy and robustness on a range of simulation tasks.

2D materials↗