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At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

DNA Reaction–Diffusion Attractor Patterns

Abstract Living systems can form and recover complex chemical patterns with precisely sized features in the ranges of tens or hundreds of microns. We show how designed reaction–diffusion processes can likewise produce precise patterns, termed attractor patterns, that reform their precise shape after being perturbed. We use oligonucleotide reaction networks, photolithography, and microfluidic delivery to form precisely controlled attractor patterns and study the responses of these patterns to different localized perturbations. Linear and “hill”‐shaped patterns formed and stabilized into shapes and at time scales consistent with reaction–diffusion models. When patterns were perturbed in particular locations with UV light, they reliably reformed their steady‐state profiles. Recovery also occurred after repeated perturbations. By designing the far‐from‐equilibrium dynamics of a chemical system, this study shows how it is possible to design spatial patterns of molecules that are sustained and regenerated by continually evolving towards a specific steady state configuration.

Dorsey, Phillip James↗
DOE OSTI2020

DNA Reaction–Diffusion Attractor Patterns

Living systems can form and recover complex chemical patterns with precisely sized features in the ranges of tens or hundreds of microns. We show how designed reaction-diffusion processes can likewise produce precise patterns, termed attractor patterns, that reform their precise shape after being perturbed. We use oligonucleotide reaction networks, photolithography and microfluidic delivery to form precisely controlled attractor patterns and study the responses of these patterns to different localized perturbations. Linear and ‘hill’-shaped patterns formed and stabilized into shapes and at time scales consistent with reaction-diffusion models. When patterns were perturbed in particular locations with UV light, they reliably reformed their steady state profiles. Recovery also occurred after repeated perturbations. As a result, by designing the far-from-equilibrium dynamics of a chemical system, this study shows how it is possible to design spatial patterns of molecules that are sustained and regenerated by continually evolving towards a specific steady state configuration

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Hysteresis Patterns of Watershed Nitrogen Retention and Loss Over the Past 50 years in United States Hydrological Basins

Patterns of watershed nitrogen (N) retention and loss are shaped by how watershed biogeochemical processes retain, biogeochemically transform, and lose incoming atmospheric deposition of N. Loss patterns represented by concentration, discharge, and their associated stream exports are important indicators of integrated watershed N retention behaviors. We examined continental United States (CONUS) scale N deposition (e.g., wet and dry atmospheric deposition), vegetation trends, and stream trends as potential indicators of watershed N-saturation and retention conditions, and how watershed N retention and losses vary over space and time. By synthesizing changes and modalities in watershed nitrogen loss patterns based on stream data from 2200 U.S. watersheds over a 50 years record, our work revealed two patterns of watershed N-retention and loss. One was a hysteresis pattern that reflects the integrated influence of hydrology, atmospheric inputs, land-use, stream temperature, elevation, and vegetation. The other pattern was a one-way transition to a new state. We found that regions with increasing atmospheric deposition and increasing vegetation health/biomass patterns have the highest N-retention capacity, become increasingly N-saturated over time, and are associated with the strongest declines in stream N exports—a pattern, that is, consistent across all land cover categories. We provide a conceptual model, validated at an unprecedented scale across the CONUS that links instream nitrogen signals to upstream mechanistic landscape processes. Our work can aid in the future interpretation of in-stream concentrations of DOC and DIN as indicators of watershed N-retention status and integrators of watershed hydrobiogeochemical processes.

58 GEOSCIENCES↗
DOE OSTI2024

Sea-surface temperature pattern effects have slowed global warming and biased warming-based constraints on climate sensitivity

The observed rate of global warming since the 1970s has been proposed as a strong constraint on equilibrium climate sensitivity (ECS) and transient climate response (TCR)—key metrics of the global climate response to greenhouse-gas forcing. Using CMIP5/6 models, we show that the inter-model relationship between warming and these climate sensitivity metrics (the basis for the constraint) arises from a similarity in transient and equilibrium warming patterns within the models, producing an effective climate sensitivity (EffCS) governing recent warming that is comparable to the value of ECS governing long-term warming under CO 2 forcing. However, CMIP5/6 historical simulations do not reproduce observed warming patterns. When driven by observed patterns, even high ECS models produce low EffCS values consistent with the observed global warming rate. The inability of CMIP5/6 models to reproduce observed warming patterns thus results in a bias in the modeled relationship between recent global warming and climate sensitivity. Correcting for this bias means that observed warming is consistent with wide ranges of ECS and TCR extending to higher values than previously recognized. These findings are corroborated by energy balance model simulations and coupled model (CESM1-CAM5) simulations that better replicate observed patterns via tropospheric wind nudging or Antarctic meltwater fluxes. Because CMIP5/6 models fail to simulate observed warming patterns, proposed warming-based constraints on ECS, TCR, and projected global warming are biased low. The results reinforce recent findings that the unique pattern of observed warming has slowed global-mean warming over recent decades and that how the pattern will evolve in the future represents a major source of uncertainty in climate projections.

58 GEOSCIENCES↗
DOE OSTI2024

Identifying Climate Patterns Using Clustering Autoencoder Techniques

Abstract The complexity of growing spatiotemporal resolution of climate simulations produces a variety of climate patterns under different projection scenarios. This paper proposes a new data-driven climate classification workflow via an unsupervised deep learning technique that can dimensionally reduce the vast volume of spatiotemporal numerical climate projection data into a compact representation. We aim to identify distinct zones that capture multiple climate variables as well as their future changes under different climate change scenarios. Our approach leverages convolutional autoencoders combined with k -means clustering (standard autoencoder) and online clustering based on the Sinkhorn–Knopp algorithm (clustering autoencoder) across the conterminous United States (CONUS) to capture unique climate patterns in a data-driven fashion from the Geophysical Fluid Dynamics Laboratory Earth System Model with GOLD component (GFDL-ESM2G). The developed approach compresses 70 years of GFDL-ESM2G simulation at 0.125° spatial resolution across the CONUS under multiple warming scenarios to a lower-dimensional space by a factor of 660 000 and then tested on 150 years of GFDL-ESM2G simulation data. The results show that five climate clusters capture physically reasonable and spatially stable climatological patterns matched to known climate classes defined by human experts. Results also show that using a clustering autoencoder can reduce the computational time for clustering by up to 9.2 times when compared to using a standard autoencoder. Our five unique climate patterns resulting from the deep learning–based clustering of the lower-dimensional space thereby enable us to provide insights on hydrometeorology and its spatial heterogeneity across the conterminous United States immediately without downloading large climate datasets. Significance Statement This paper presents a data-driven climate classification approach using unsupervised deep learning to dimensionally reduce climate model outputs and to identify distinct climate regions for their future changes. Our approach compresses climate information for 70 years of Geophysical Fluid Dynamics Laboratory Earth System Model data across the conterminous United States (CONUS) at 0.125° spatial resolution. The results reveal that five climate clusters capture reasonable and stable climatological patterns matched to known climate patterns. The embedded clustering process in deep learning provides ×9.2 times faster execution than the k -means clustering technique. These results give us insight about climate spatial patterns and heterogeneity of hydrological patterns across the conterminous United States without downloading large climate datasets.

Kurihana, Takuya↗
DOE OSTI2025

Directed self-assembly of block copolymers for high-precision patterning in the era of extreme ultraviolet lithography

Extreme ultraviolet (EUV) lithography enables unprecedented resolution in semiconductor patterning but faces critical challenges in developing resist materials that achieve high-precision at economically viable throughput. Directed self-assembly (DSA) of block copolymers (BCPs) offers a promising solution for pattern rectification by leveraging thermodynamically determined domain structures to decouple BCP pattern quality from the imperfect original lithographic pattern. This prospective presents an overview of recent progress on the EUV + DSA strategy, covering advances in BCP material design, processing, metrology, and pattern transfer. We highlight recent advances in high-χ BCPs with perpendicular orientation and domain spacings compatible with EUV dimensions, leveraging A-b-(B-r-C) architectures. We also discuss progress in chemical pre-pattern fabrication using both positive- and negative tone resists, along with processing strategies to minimize defects and roughness based on BCP thermodynamics and assembly kinetics. We further examine metrology platforms for characterizing the thermodynamics of BCP materials and quantifying the size and shape of BCP domains. Lastly, we review pattern transfer strategies for generating functional inorganic masks suitable for semiconductor manufacturing. Together, these advances highlight the potential of DSA to complement EUV lithography, offering a pathway to address critical challenges in achieving high-precision patterning for the semiconductor industry.

Lee, Kyunghyeon [Univ. of Chicago, IL (United Stat↗
DOE OSTI2025

Entropy-Assisted Quality Pattern Identification in Finance

Short-term patterns in financial time series form the cornerstone of many algorithmic trading strategies, yet extracting these patterns reliably from noisy market data remains a formidable challenge. In this paper, we propose an entropy-assisted framework for identifying high-quality, non-overlapping patterns that exhibit consistent behavior over time. We ground our approach in the premise that historical patterns, when accurately clustered and pruned, can yield substantial predictive power for short-term price movements. To achieve this, we incorporate an entropy-based measure as a proxy for information gain: patterns that lead to high one-sided movements in historical data yet retain low local entropy are more “informative” in signaling future market direction. Compared to conventional clustering techniques such as K-means and Gaussian Mixture Models (GMMs), which often yield biased or unbalanced groupings, our approach emphasizes balance over a forced visual boundary, ensuring that quality patterns are not lost due to over-segmentation. By emphasizing both predictive purity (low local entropy) and historical profitability, our method achieves a balanced representation of Buy and Sell patterns, making it better suited for short-term algorithmic trading strategies. This paper offers an in-depth illustration of our entropy-assisted framework through two case studies on Gold vs. USD and GBPUSD. While these examples demonstrate the method’s potential for extracting high-quality patterns, they do not constitute an exhaustive survey of all possible asset classes.

Physics↗
NASA NTRS1983

Average sedimentary rock rare Earth element patterns and crustal evolution: Some observations and implications from the 3800 Ma ISUA supracrustal belt, West Greenland

Rare earth element (REE) data is given on a set of clastic metasediments from the 3800 Ma Isua Supracrustal belt, West Greenland. Each of two units from the same sedimentary sequence has a distinctive REE pattern, but the average of these rocks bears a very strong resemblance to the REE pattern for the North American Shale Composite (NASC), and departs considerably from previous estimates of REE patterns in Archaean sediments. The possibility that the source area for the Isua sediments resembled that of the NASC is regarded as highly unlikely. However, REE patterns like that in the NASC may be produced by sedimentary recycling of material yielding patterns such as are found at Isua. The results lead to the following tentative conclusions: (1) The REE patterns for Isua Seq. B MBG indicate the existence of crustal materials with fractionated REE and negative Eu anomalies at 3800 Ma, (2) The average Seq. B REE pattern resembles that of the North American Shale Composite (NASC), (3) If the Seq. B average is truly representative of its crustal sources, then this early crust could have been extensively differentiated. In this regard, a proper understanding of the NASC pattern, and its relationship to post-Archaean crustal REE reservoirs, is essential, (4) The Isua results may represent a local effect.

Dymek, R. F.↗
NASA NTRS1987

An experimental and theoretical comparison of rotordynamic coefficients for sawtooth-pattern damper seals

A brief review of the annular seal theory as related to rotordynamics for liquid seals is presented. Also included is an overview of Childs and Kim's current theory for calculating empirical turbulence coefficients and predicting stiffness and damping coefficients for surface roughened damper seals. The designation sawtooth-pattern refers to a seal stator with a roughness pattern whose cross section normal to the seal axis resembles a sawtooth with the teeth directed against the flow. The net stiffness and damping coefficients for the eleven seals are compared to each other, a smooth seal, and the optimum-configuration damper seal previously tested. The experimental force coefficients, the net damping, and the net stiffness coefficients for four of the sawtooth seals are compared to the predictions of Childs and Kim's analysis. The sawtooth-pattern seal had less net damping than the hole-pattern seal but more than the smooth seal. The stiffness was comparable to the hole-pattern. Both the sawtooth and hole-pattern seals leaked less than the smooth seal, while the sawtooth-pattern seal with maximum damping leaked more than the hole-pattern seal. The theoretical predictions compared relatively poorly to the experimental results obtained for the rotordynamic coefficients of the seals investigation.

Nolan, Steven Anthony↗
NASA NTRS1986

Test results for sawtooth-pattern damper seals - Leakage and rotordynamic coefficients

Test results consisting of direct and transverse force coefficients are presented for eleven sawtooth-pattern damper-seal configurations. The designation 'damper' seal refers to a seal which uses a deliberately roughened stator and smooth rotor to increase the net seal damping force. The designation 'sawtooth-pattern' refers to a stator-roughness pattern whose cross section normal to the axis of the seal resembles saw teeth with the teeth direction opposing fluid motion in the direction of shaft rotation. The sawtooth pattern yields axial grooves in the stator which are interrupted by spacer elements which act as flow constrictions or 'dams'. Sawtooth-pattern seals had more damping than smooth seals but less than the round-hole-pattern seals tested previously. If damping is sacrificed, sawtooth-pattern seals can be designed to leak less than round-hole-pattern seals.

Childs, D.↗
NASA NTRS1989

Patterning of dispenser cathode surfaces to a controlled porosity

A process to pattern slots approximately 1.25 microns in width into 25-micron-thick W films that have been deposited onto flat or concave surfaces is discussed. A 25-micron-thick W film with a high degree of (100) orientation is chemically vapor deposited (CVD) onto a flat or concave Mo mandrel. A 5-micron-thick Al film is deposited onto the CVD W, followed by 2 microns of photoresist. On concave cathodes, XeCl2 laser ablation or X-ray lithography is used to pattern the photoresist, whereas on flat cathodes deep UV lithography can be used. The patterned photoresist serves as the mask in a Cl ion-beam-assisted etching (IBAE) process to pattern the Al. An alternative process is to deposit Al2O3 films onto the W and pattern the Al2O3 using laser ablation. The W film is then patterned to 3-6-micron slot widths using IBAE + ClF3 with the patterned Al or Al2O3 as the mask. Finally, a sputter deposition step is required to close up the slots to approximately 1 micron. The process described is capable of patterning concave dispenser cathodes to a controlled and precise porosity.

Garner, Charles E.↗
NASA NTRS1990

Analog parallel processor hardware for high speed pattern recognition

A VLSI-based analog processor for fully parallel, associative, high-speed pattern matching is reported. The processor consists of two main components: an analog memory matrix for storage of a library of patterns, and a winner-take-all (WTA) circuit for selection of the stored pattern that best matches an input pattern. An inner product is generated between the input vector and each of the stored memories. The resulting values are applied to a WTA network for determination of the closest match. Patterns with up to 22 percent overlap are successfully classified with a WTA settling time of less than 10 microsec. Applications such as star pattern recognition and mineral classification with bounded overlap patterns have been successfully demonstrated. This architecture has a potential for an overall pattern matching speed in excess of 10 exp 9 bits per second for a large memory.

Daud, T.↗
NASA NTRS1994

Time scales of pattern evolution from cross-spectrum analysis of advanced very high resolution radiometer and coastal zone color scanner imagery

We have selected square subareas (110 km on a side) from coastal zone color scanner (CZCS) and advanced very high resolution radiometer (AVHRR) images for 1981 in the California Current region off northern California for which we could identify sequences of cloud-free data over periods of days to weeks. We applied a two-dimensional fast Fourier transformation to images after median filtering, (x, y) plane removal, and cosine tapering. We formed autospectra and coherence spectra as functions of a scalar wavenumber. Coherence estimates between pairs of images were plotted against time separation between images for several wide wavenumber bands to provide a temporal lagged coherence function. The temporal rate of loss of correlation (decorrelation time scale) in surface patterns provides a measure of the rate of pattern change or evolution as a function of spatial dimension. We found that patterns evolved (or lost correlation) approximately twice as rapidly in upwelling jets as in the 'quieter' regions between jets. The rapid evolution of pigment patterns (lifetime of about 1 week or less for scales of 50-100 km) ought to hinder biomass transfer to zooplankton predators compared with phytoplankton patches that persist for longer times. We found no significant differences between the statistics of CZCS and AVHRR images (spectral shape or rate of decorrelation). In addition, in two of the three areas studied, the peak correlation between AVHRR and CZCS images from the same area occurred at zero lag, indicating that the patterns evolved simutaneously. In the third area, maximum coherence between thermal and pigment patterns occurred when pigment images lagged thermal images by 1-2 days, mirroring the expected lag of high pigment behind low temperatures (and high nutrients) in recently upwelled water. We conclude that in dynamic areas such as coastal upwelling systems, the phytoplankton cells (identified by pigment color patterns) behave largely as passive scalars at the mesoscale and that growth, death, and sinking of phytoplankton collectively play at most a mariginal role in determining the spectral statistics of the pigment patterns.

Denman, Kenneth L.↗
NASA NTRS1999

Ejecta Pattern of the Impact of Comet Shoemaker-Levy 9

The collision of Comet Shoemaker-Levy 9 (SL 9) with Jupiter created crescent-shaped ejecta patterns around impact sites. Although the observed impact plumes rose through a similar height of approx.3000 km, the radii of the created ejects patterns differ from impact to impact and generally are larger for larger impacts. The azimuthal angle of the symmetry axis of the ejects pattern is larger than that predicted by the models of oblique impacts, due to the action of the Coriolis force that rotates ejecta patterns counterclockwise from the south. We study the formation of ejects patterns using a simple model of ballistic plume above a rotating plane. The ejected particles follow ballistic trajectories and slide horizontally for about an hour after reentry into the jovian atmosphere. The lateral expansion of the plume is stopped by the friction force, which is assumed to be proportional to the square of the horizontal velocity. Two different mass-velocity distributions used in the simulations produce qualitatively similar results. The simulated ejecta patterns fit very well the "crescents" observed at the impact sites. The sizes and azimuthal angles of symmetry axis of ejects patterns depend on a parameter L, which has dimension of length and is related to the mass of the fragment. Thus more massive impacts produce larger ejects patterns that are rotated through a wider angle.

Pankine, Alexey A.↗
NASA NTRS2006

Pattern Formation in Diffusion Flames Embedded in von Karman Swirling Flows

Pattern formation is observed in nature in many so-called excitable systems that can support wave propagation. It is well-known in the field of combustion that premixed flames can exhibit patterns through differential diffusion mechanism between heat and mass. However, in the case of diffusion flames where fuel and oxidizer are separated initially there have been only a few observations of pattern formation. It is generally perceived that since diffusion flames do not possess an inherent propagation speed they are static and do not form patterns. But in diffusion flames close to their extinction local quenching can occur and produce flame edges which can propagate along stoichiometric surfaces. Recently, we reported experimental observations of rotating spiral flame edges during near-limit combustion of a downward-facing polymethylmethacrylate disk spinning in quiescent air. These spiral flames, though short-lived, exhibited many similarities to patterns commonly found in quiescent excitable media including compound tip meandering motion. Flame disks that grow or shrink with time depending on the rotational speed and in-depth heat loss history of the fuel disk have also been reported. One of the limitations of studying flame patterns with solid fuels is that steady-state conditions cannot be achieved in air at normal atmospheric pressure for experimentally reasonable fuel thickness. As a means to reproduce the flame patterns observed earlier with solid fuels, but under steady-state conditions, we have designed and built a rotating, porous-disk burner through which gaseous fuels can be injected and burned as diffusion flames. The rotating porous disk generates a flow of air toward the disk by a viscous pumping action, generating what is called the von K rm n boundary layer which is of constant thickness over the entire burner disk. In this note we present a map of the various dynamic flame patterns observed during the combustion of methane in air as a function of fuel flow rate and the burner rotational speed.

Nayagam, Vedha↗
NASA NTRS2008

System and technique for retrieving depth information about a surface by projecting a composite image of modulated light patterns

A technique, associated system and program code, for retrieving depth information about at least one surface of an object. Core features include: projecting a composite image comprising a plurality of modulated structured light patterns, at the object; capturing an image reflected from the surface; and recovering pattern information from the reflected image, for each of the modulated structured light patterns. Pattern information is preferably recovered for each modulated structured light pattern used to create the composite, by performing a demodulation of the reflected image. Reconstruction of the surface can be accomplished by using depth information from the recovered patterns to produce a depth map/mapping thereof. Each signal waveform used for the modulation of a respective structured light pattern, is distinct from each of the other signal waveforms used for the modulation of other structured light patterns of a composite image; these signal waveforms may be selected from suitable types in any combination of distinct signal waveforms, provided the waveforms used are uncorrelated with respect to each other. The depth map/mapping to be utilized in a host of applications, for example: displaying a 3-D view of the object; virtual reality user-interaction interface with a computerized device; face--or other animal feature or inanimate object--recognition and comparison techniques for security or identification purposes; and 3-D video teleconferencing/telecollaboration.

Hassebrook, Laurence G.↗
NASA NTRS2010

Automated Discovery and Modeling of Sequential Patterns Preceding Events of Interest

The integration of emerging data manipulation technologies has enabled a paradigm shift in practitioners' abilities to understand and anticipate events of interest in complex systems. Example events of interest include outbreaks of socio-political violence in nation-states. Rather than relying on human-centric modeling efforts that are limited by the availability of SMEs, automated data processing technologies has enabled the development of innovative automated complex system modeling and predictive analysis technologies. We introduce one such emerging modeling technology - the sequential pattern methodology. We have applied the sequential pattern methodology to automatically identify patterns of observed behavior that precede outbreaks of socio-political violence such as riots, rebellions and coups in nation-states. The sequential pattern methodology is a groundbreaking approach to automated complex system model discovery because it generates easily interpretable patterns based on direct observations of sampled factor data for a deeper understanding of societal behaviors that is tolerant of observation noise and missing data. The discovered patterns are simple to interpret and mimic human's identifications of observed trends in temporal data. Discovered patterns also provide an automated forecasting ability: we discuss an example of using discovered patterns coupled with a rich data environment to forecast various types of socio-political violence in nation-states.

Rohloff, Kurt↗
NASA NTRS2010

The Spatial Vision Tree: A Generic Pattern Recognition Engine- Scientific Foundations, Design Principles, and Preliminary Tree Design

New foundational ideas are used to define a novel approach to generic visual pattern recognition. These ideas proceed from the starting point of the intrinsic equivalence of noise reduction and pattern recognition when noise reduction is taken to its theoretical limit of explicit matched filtering. This led us to think of the logical extension of sparse coding using basis function transforms for both de-noising and pattern recognition to the full pattern specificity of a lexicon of matched filter pattern templates. A key hypothesis is that such a lexicon can be constructed and is, in fact, a generic visual alphabet of spatial vision. Hence it provides a tractable solution for the design of a generic pattern recognition engine. Here we present the key scientific ideas, the basic design principles which emerge from these ideas, and a preliminary design of the Spatial Vision Tree (SVT). The latter is based upon a cryptographic approach whereby we measure a large aggregate estimate of the frequency of occurrence (FOO) for each pattern. These distributions are employed together with Hamming distance criteria to design a two-tier tree. Then using information theory, these same FOO distributions are used to define a precise method for pattern representation. Finally the experimental performance of the preliminary SVT on computer generated test images and complex natural images is assessed.

Rahman, Zia-ur↗