Search NASA⌕ Search

SEARCH · Search NASA

Results for “Embedded”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Quantitative Proteomic Analysis of Optimal Cutting Temperature (OCT) Embedded Core-Needle Biopsy of Lung Cancer

With recent advances in understanding the genomic underpinnings and oncogenic drivers of pathogenesis in different subtypes, it is increasingly clear that proper pretreatment diagnostics are essential for the choice of appropriate treatment options for non-small cell lung cancer (NSCLC). Tumor tissue preservation in optimal cutting temperature (OCT) compound is commonly used in the surgical suite. However, proteins recovered from OCT-embedded specimens pose a challenge for LC-MS/MS experiments, due to the large amounts of polymers present in OCT. Here we present a simple workflow for whole proteome analysis of OCT-embedded NSCLC tissue samples, which involves a simple trichloroacetic acid precipitation step. Comparisons of protein recovery between frozen versus OCT-embedded tissue showed excellent consistency with more than 9200 proteins identified. Using an isobaric labeling strategy, we quantified more than 5400 proteins in tumor versus normal OCT-embedded core needle biopsy samples. Gene ontology analysis indicated that a number of proliferative as well as squamous cell carcinoma (SqCC) marker proteins were overexpressed in the tumor, consistent with the patient’s pathology based diagnosis of “poorly differentiated SqCC”. Among the most downregulated proteins in the tumor sample, we noted a number of proteins with potential immunomodulatory functions. Finally, interrogation of the aberrantly expressed proteins using a candidate approach and cross-referencing with publicly available databases led to the identification of potential druggable targets in DNA replication and DNA damage repair pathways. We conclude that our approach allows LC-MS/MS proteomic analyses on OCT-embedded lung cancer specimens, opening the way to bring powerful proteomics into the clinic. .

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2018

Two diverse models of embedding class one

Highlights: •The embedding of curved spacetime in higher-dimensional flat spacetime is discussed. •The focus is on two diverse models of embedding class one. •The first model is a complete wormhole solution including the junction conditions. •The second is a new model to explain the flat galactic rotation curves without the need for dark matter. -- Abstract: Embedding theorems have continued to be a topic of interest in the general theory of relativity since these help connect the classical theory to higher-dimensional manifolds. This paper deals with spacetimes of embedding class one, i.e., spacetimes that can be embedded in a five-dimensional flat spacetime. These ideas are applied to two diverse models, a complete solution for a charged wormhole admitting a one-parameter group of conformal motions and a new model to explain the flat rotation curves in spiral galaxies without the need for dark matter.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

A new strategy on utilizing nitrogen doped TiO{sub 2} in nanostructured solar cells: Embedded multifunctional N-TiO{sub 2} scattering particles in mesoporous photoanode

Highlights: • N-doped TiO{sub 2} scattering particles were synthesized for embedding into commercial photoanode of dye sensitized solar cells. • Embedded scatterers improved optical and electrical features of the cells. • These multifunctional scatterers increased cell performance up to 17%. - Abstract: Aggregated sub-micron size nitrogen doped TiO{sub 2} (N-TiO{sub 2}) particles with superior optical and electrical features were successfully synthesized for embedding into commercial mesoporous TiO{sub 2} photoelectrode of dye sensitized solar cells (DSSCs) as the light scattering particles compared to undoped one. X-ray photoelectron spectroscopy and absorption spectra confirmed that the titanium dioxide is sufficiently doped by nitrogen in N-TiO{sub 2} sample. Employing these high-surface N-TiO{sub 2} in mesoporous photoelectrode of solar cells, the power conversion efficiency of 8% has been achieved which shows 17% improvement for the optimum embedded level of doping (30 wt%) compared to commercial photoelectrode without additive; while enhanced efficiency is only 3% embedding undoped sub-micron size TiO{sub 2} particles. These results can introduce the novel multifunctional photoelectrode for nanostructured solar cells with enhanced values of scattering efficiency and improved electrical features including trap states density reduction in comparison to commercial mesoporous photoelectrodes.

14 SOLAR ENERGY↗
DOE OSTI2019

Alloying and embedding of Cu-core/Ag-shell nanowires for ultrastable stretchable and transparent electrodes

In this paper, transparent electrodes with dense Cu@Ag alloy nanowires embedded in stretchable substrates are successfully fabricated by a high-intensity pulsed light (HIPL) technique within one step. The intense light energy not only induces rapid mutual dissolution between the Cu core and the Ag shell to form dense Cu@Ag alloy nanowires but also embeds the newly formed alloy nanowires into the stretchable substrates. The synergetic effects of alloy nanowires and embedded structures greatly improve the thermal stability of the transparent electrodes that keep high conductivity unchanged in both high temperature (140 °C) and high humidity (85 °C, 85% RH) for at least 500 h, which is much better than previous reports. The transparent electrodes also exhibit high electro-mechanical stability due to the strong adhesion between alloy nanowires and substrates, which remain stable after 1000 stretching-relaxation cycles at 30% strain. Stretchable and transparent heaters based on the alloyed and embedded electrodes have a wide outputting temperature range (up to 130 °C) and show excellent thermal stability as well as stretchability (up to 60% strain) due to the alloy nanowires and embedded structures. To sum up, this study proposes the synergetic effects of alloying and embedded structures to greatly improve the stability of Cu nanowirebased stretchable transparent electrodes, showing a huge application prospect in the field of stretchable and wearable electronics.

Zhang, Bowen↗
DOE OSTI2019

Probabilistic variable-length segmentation of protein sequences for discriminative motif discovery (DiMotif) and sequence embedding (ProtVecX)

In this paper, we present peptide-pair encoding (PPE), a general-purpose probabilistic segmentation of protein sequences into commonly occurring variable-length sub-sequences. The idea of PPE segmentation is inspired by the byte-pair encoding (BPE) text compression algorithm, which has recently gained popularity in subword neural machine translation. We modify this algorithm by adding a sampling framework allowing for multiple ways of segmenting a sequence. PPE segmentation steps can be learned over a large set of protein sequences (Swiss-Prot) or even a domain-specific dataset and then applied to a set of unseen sequences. This representation can be widely used as the input to any downstream machine learning tasks in protein bioinformatics. In particular, here, we introduce this representation through protein motif discovery and protein sequence embedding. (i) DiMotif: we present DiMotif as an alignment-free discriminative motif discovery method and evaluate the method for finding protein motifs in three different settings: (1) comparison of DiMotif with two existing approaches on 20 distinct motif discovery problems which are experimentally verified, (2) classification-based approach for the motifs extracted for integrins, integrin-binding proteins, and biofilm formation, and (3) in sequence pattern searching for nuclear localization signal. The DiMotif, in general, obtained high recall scores, while having a comparable F1 score with other methods in the discovery of experimentally verified motifs. Having high recall suggests that the DiMotif can be used for short-list creation for further experimental investigations on motifs. In the classification-based evaluation, the extracted motifs could reliably detect the integrins, integrin-binding, and biofilm formation-related proteins on a reserved set of sequences with high F1 scores. (ii) ProtVecX: we extend k-mer based protein vector (ProtVec) embedding to variablelength protein embedding using PPE sub-sequences. We show that the new method of embedding can marginally outperform ProtVec in enzyme prediction as well as toxin prediction tasks. In addition, we conclude that the embeddings are beneficial in protein classification tasks when they are combined with raw amino acids k-mer features.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2014

Accurate and systematically improvable density functional theory embedding for correlated wavefunctions

We analyze the sources of error in quantum embedding calculations in which an active subsystem is treated using wavefunction methods, and the remainder using density functional theory. We show that the embedding potential felt by the electrons in the active subsystem makes only a small contribution to the error of the method, whereas the error in the nonadditive exchange-correlation energy dominates. We test an MP2 correction for this term and demonstrate that the corrected embedding scheme accurately reproduces wavefunction calculations for a series of chemical reactions. Our projector-based embedding method uses localized occupied orbitals to partition the system; as with other local correlation methods, abrupt changes in the character of the localized orbitals along a reaction coordinate can lead to discontinuities in the embedded energy, but we show that these discontinuities are small and can be systematically reduced by increasing the size of the active region. Convergence of reaction energies with respect to the size of the active subsystem is shown to be rapid for all cases where the density functional treatment is able to capture the polarization of the environment, even in conjugated systems, and even when the partition cuts across a double bond.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2014

Time-dependent potential-functional embedding theory

We introduce a time-dependent potential-functional embedding theory (TD-PFET), in which atoms are grouped into subsystems. In TD-PFET, subsystems can be propagated by different suitable time-dependent quantum mechanical methods and their interactions can be treated in a seamless, first-principles manner. TD-PFET is formulated based on the time-dependent quantum mechanics variational principle. The action of the total quantum system is written as a functional of the time-dependent embedding potential, i.e., a potential-functional formulation. By exploiting the Runge-Gross theorem, we prove the uniqueness of the time-dependent embedding potential under the constraint that all subsystems share a common embedding potential. We derive the integral equation that such an embedding potential needs to satisfy. As proof-of-principle, we demonstrate TD-PFET for a Na{sub 4} cluster, in which each Na atom is treated as one subsystem and propagated by time-dependent Kohn-Sham density functional theory (TDDFT) using the adiabatic local density approximation (ALDA). Our results agree well with a direct TDDFT calculation on the whole Na{sub 4} cluster using ALDA. We envision that TD-PFET will ultimately be useful for studying ultrafast quantum dynamics in condensed matter, where key regions are solved by highly accurate time-dependent quantum mechanics methods, and unimportant regions are solved by faster, less accurate methods.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

Structural stability and O{sub 2} dissociation on nitrogen-doped graphene with transition metal atoms embedded: A first-principles study

By using first-principles calculations, we investigate the structural stability of nitrogen-doped (N-doped) graphene with graphitic-N, pyridinic-N and pyrrolic-N, and the transition metal (TM) atoms embedded into N-doped graphene. The structures and energetics of TM atoms from Sc to Ni embedded into N-doped graphene are studied. The TM atoms at N{sub 4}V {sub 2} forming a 4N-centered structure shows the strongest binding and the binding energies are more than 7 eV. Finally, we investigate the catalytic performance of N-doped graphene with and without TM embedding for O{sub 2} dissociation, which is a fundamental reaction in fuel cells. Compared to the pyridinic-N, the graphitic-N is more favorable to dissociate O{sub 2} molecules with a relatively low reaction barrier of 1.15 eV. However, the catalytic performance on pyridinic-N doped structure can be greatly improved by embedding TM atoms, and the energy barrier can be reduced to 0.61 eV with V atom embedded. Our results provide the stable structure of N-doped graphene and its potential applications in the oxygen reduction reactions.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Cluster embedding of ionic systems: Point charges and extended ions

The embedding of cluster models of oxides with point charges and with extensions of the embedding which take into account the spatial extent of the cations is examined with an emphasis on the consequences of this embedding for the relative ionization and excitation energies that are measured in core-level spectroscopies. It is found that the dependence of the electronic structure of the oxides and the relative energies of different levels depend only weakly on the embedding and that relatively simple embeddings may be sufficient to provide an adequate model for determining core-level spectra. This is different from absolute values of the ionizations which, as expected, depend strongly on the details of the extended crystal; however, relative values of binding energies, as measured in photoemission, are of greater interest than the absolute values.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Relativistic two-component projection-based quantum embedding for open-shell systems

In this work, we present a relativistic quantum embedding formalism capable of variationally treating relativistic effects, including scalar-relativity and spin–orbit coupling. We extend density functional theory (DFT)-in-DFT projection-based quantum embedding to a relativistic two-component formalism, where the full spin magnetization vector form is retained throughout the embedding treatment. To benchmark various relativistic embedding schemes, spin–orbit splitting of the nominally t 2g valence manifold of W(CO) 6 , exchange coupling of [(H 3 N) 4 Cr(OH) 2 Cr(NH 3 ) 4 ] 4+ , and the dissociation potential curve of WF 6 are investigated. The relativistic embedding formalism introduced in this work is well suited for efficient modeling of open-shell systems containing late transition metal, lanthanide, and actinide molecular complexes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

DFT-based QM/MM with Particle-Mesh Ewald for Direct, Long-Range Electrostatic Embedding

In this work, we present a DFT-based, QM/MM implementation with long-range electrostatic embedding achieved by direct real-space integration of the particle mesh Ewald (PME) computed electrostatic potential. The key transformation is the interpolation of the electrostatic potential from the PME grid to the DFT quadrature grid, from which integrals are easily evaluated utilizing standard DFT machinery. We provide benchmarks of the numerical accuracy with choice of grid size and real-space corrections, and demonstrate that good convergence is achieved while introducing nominal computational overhead. Furthermore, the approach requires only small modification to existing software packages, as is demonstrated with our implementation in the OpenMM and Psi 4 software. After presenting convergence benchmarks, we evaluate the importance of long-range electrostatic embedding in three solute/solvent systems modeled with QM/MM. Water and BMIM/BF 4 ionic liquid were considered as "simple" and "complex" solvents respectively, with water and p-phenylenediamine (PPD) solute molecules treated at QM level of theory. While electrostatic embedding with standard real-space truncation may introduce negligible error for simple systems such as water solute in water solvent, errors become more significant when QM/MM is applied to complex solvents such as ionic liquids. An extreme example is the electrostatic embedding energy for oxidized PPD in BMIM/BF 4 for which real-space truncation produces severe error even at 2-3 nm cutoff distances. This latter example illustrates that utilization of QM/MM to compute redox potentials within concentrated electrolytes/ionic media requires carefully chosen long-range electrostatic embedding algorithms, with our presented algorithm providing a general and robust approach.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Probabilistic variable-length segmentation of protein sequences for discriminative motif discovery (DiMotif) and sequence embedding (ProtVecX)

ABSTRACT In this paper, we present peptide-pair encoding (PPE), a general-purpose probabilistic segmentation of protein sequences into commonly occurring variable-length sub-sequences. The idea of PPE segmentation is inspired by the byte-pair encoding (BPE) text compression algorithm, which has recently gained popularity in subword neural machine translation. We modify this algorithm by adding a sampling framework allowing for multiple ways of segmenting a sequence. PPE segmentation steps can be learned over a large set of protein sequences (Swiss-Prot) or even a domain-specific dataset and then applied to a set of unseen sequences. This representation can be widely used as the input to any downstream machine learning tasks in protein bioinformatics. In particular, here, we introduce this representation through protein motif discovery and protein sequence embedding. (i) DiMotif: we present DiMotif as an alignment-free discriminative motif discovery method and evaluate the method for finding protein motifs in three different settings: (1) comparison of DiMotif with two existing approaches on 20 distinct motif discovery problems which are experimentally verified, (2) classification-based approach for the motifs extracted for integrins, integrin-binding proteins, and biofilm formation, and (3) in sequence pattern searching for nuclear localization signal. The DiMotif, in general, obtained high recall scores, while having a comparable F1 score with other methods in the discovery of experimentally verified motifs. Having high recall suggests that the DiMotif can be used for short-list creation for further experimental investigations on motifs. In the classification-based evaluation, the extracted motifs could reliably detect the integrins, integrin-binding, and biofilm formation-related proteins on a reserved set of sequences with high F1 scores. (ii) ProtVecX: we extend k-mer based protein vector (ProtVec) embedding to variable-length protein embedding using PPE sub-sequences. We show that the new method of embedding can marginally outperform ProtVec in enzyme prediction as well as toxin prediction tasks. In addition, we conclude that the embeddings are beneficial in protein classification tasks when they are combined with raw k-mer features. Availability Implementations of our method will be available under the Apache 2 licence at http://llp.berkeley.edu/dimotif and http://llp.berkeley.edu/protvecx .

Asgari, Ehsaneddin↗
DOE OSTI2015

Quantum annealing correction with minor embedding

Quantum annealing provides a promising route for the development of quantum optimization devices, but the usefulness of such devices will be limited in part by the range of implementable problems as dictated by hardware constraints. To overcome constraints imposed by restricted connectivity between qubits, a larger set of interactions can be approximated using minor embedding techniques whereby several physical qubits are used to represent a single logical qubit. However, minor embedding introduces new types of errors due to its approximate nature. Here we introduce and study quantum annealing correction schemes designed to improve the performance of quantum annealers in conjunction with minor embedding, thus leading to a hybrid scheme defined over an encoded graph. We argue that this scheme can be efficiently decoded using an energy minimization technique provided the density of errors does not exceed the per-site percolation threshold of the encoded graph. We test the hybrid scheme using a D-Wave Two processor on problems for which the encoded graph is a two-level grid and the Ising model is known to be NP-hard. The problems we consider are frustrated Ising model problem instances with “planted” (a priori known) solutions. Applied in conjunction with optimized energy penalties and decoding techniques, we find that this approach enables the quantum annealer to solve minor embedded instances with significantly higher success probability than it would without error correction. Our work demonstrates that quantum annealing correction can and should be used to improve the robustness of quantum annealing not only for natively embeddable problems but also when minor embedding is used to extend the connectivity of physical devices.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2024

eCC++ : A Compiler Construction Framework for Embedded Domain-Specific Languages

eCC++ is a new compiler construction framework for embedding domain-specific programming languages within C++. That is, the host language is C++, the guest language is the DSL to be embedded in C++, and eCC++ is the tool that enables the embedding. The eCC++ framework is composed of three main components: a front-end, an API for program verification based on a functional and declarative intermediate representation (IR), and a Multi-Level Intermediate Representation (MLIR) code generator. The eCC++ front-end consists of a library of C++ classes and operators that can be used to define the guest language. Guest sources are compiled with any standard C++ compiler, and when run, the resulting executable generates an eCC++ IR representation of the program, which can be verified within the eCC++ framework. Finally, eCC++ allows for high-level and domain-specific optimizations before generating MLIR. In summary, eCC++ aims to act as a generic front-end that enables embedding guest languages into C++, and provides necessary compiler technology for program verification, targeting the existing capabilities in the MLIR infrastructure. The paper evaluates the eCC++ expressiveness and usability describing the process of embedding GraphIt, a high-performance graph language in C++.

Gonzalez Tallada, Marc↗
DOE OSTI2022

Embedding Learning in Hybrid Quantum-Classical Neural Networks

Quantum embedding learning is an important step in the application of quantum machine learning to classical data. In this paper we propose a quantum few-shot embedding learning paradigm, which learns embeddings useful for training downstream quantum machine learning tasks. Crucially, we identify the circuit bypass problem in hybrid neural networks, where learned classical parameters are optimized to represent the dataset without the quantum kernel. We observe that the few-shot learning embeddings generalize to unseen classes, and suffer less from the circuit bypass problem in terms of better occupation of the parameter space compared with embeddings learned from regression and classification.

Liu, Minzhao↗
DOE OSTI2020

Graphite-Embedded High-Performance Insulated Metal Substrate for Wide-Bandgap Power Modules

Emerging wide-bandgap (WBG) semiconductor devices such as silicon carbide (SiC) metal–oxide semiconductor field-effect transistors (MOSFETs) and gallium nitride high-electron-mobility transistors can handle high power in reduced semiconductor areas better than conventional Si-based devices owing to superior material properties. With increased power loss density in a WBG-based converter and reduced die size in power modules, thermal management of power devices must be optimized for high performance. Here, we present a graphite-embedded insulated metal substrate (thermally-annealed-pyrolytic-graphite-embedded insulated metal substrate—IMSwTPG) designed for WBG power modules. Theoretical thermal performance analysis of graphite-embedded metal cores is presented, with design details for IMSwTPG with embedded graphite to replace a direct-bonded copper (DBC) substrate. The proposed IMSwTPG is compared with an aluminum nitride-based DBC substrate using finite-element thermal analysis for steady-state and transient thermal performance. The solutions’ thermal performances are compared under different coolant temperature and thermal loading conditions, and the proposed substrate's electrical performance is validated with static and dynamic characterization. Using graphite-embedded substrates, junction-to-case thermal resistance of SiC MOSFETs can be reduced up to 17%, and device current density can be increased by 10%, regardless of the thermal management strategy used to cool the substrate. Reduced transient thermal impedance of up to 40% of dies owing to increased heat capacity is validated in transient thermal simulations and experiments. The half-bridge power module's electrical performance is evaluated for on -state resistance, switching performance, and switching loss at three junction temperature conditions. The proposed substrate solution has minimal impact on conduction and switching performance of SiC MOSFETs.

42 ENGINEERING↗
DOE OSTI2017

Visual Exploration of Semantic Relationships in Neural Word Embeddings

Constructing distributed representations for words through neural language models and using the resulting vector spaces for analysis has become a crucial component of natural language processing (NLP). But, despite their widespread application, little is known about the structure and properties of these spaces. To gain insights into the relationship between words, the NLP community has begun to adapt high-dimensional visualization techniques. Particularly, researchers commonly use t-distributed stochastic neighbor embeddings (t-SNE) and principal component analysis (PCA) to create two-dimensional embeddings for assessing the overall structure and exploring linear relationships (e.g., word analogies), respectively. Unfortunately, these techniques often produce mediocre or even misleading results and cannot address domain-specific visualization challenges that are crucial for understanding semantic relationships in word embeddings. We introduce new embedding techniques for visualizing semantic and syntactic analogies, and the corresponding tests to determine whether the resulting views capture salient structures. Additionally, we introduce two novel views for a comprehensive study of analogy relationships. Finally, we augment t-SNE embeddings to convey uncertainty information in order to allow a reliable interpretation. Combined, the different views address a number of domain-specific tasks difficult to solve with existing tools.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Electroreduction of Zirconia Using Embedded Electrodes

Disc-shaped samples of 8 mol.% Y 2 O 3 -stabilized zirconia (YSZ) with two embedded Pt + YSZ circular electrodes and contacting Pt wires were made by sintering in air at 1500°C. In one sample, four Pt probes were embedded for local electric potential measurements. Porous surface Pt electrodes were applied on the two exposed surfaces. The samples were heated in air over a temperature range from 800°C to 900°C. A DC voltage between 1 V and 1.7 V was applied across the surface electrodes and the embedded electrodes with the positive connected to the surface electrodes. Furthermore, this resulted in the formation of black zirconia between the two embedded electrodes by electrochemical pumping out oxygen. In the sample with embedded Pt probes, the local potential was measured as a function of time.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗