Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Deep”

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 1,351 records · Page 75
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Deep Learning in Label-free Cell Classification

Label-free cell analysis is essential to personalized genomics, cancer diagnostics, and drug development as it avoids adverse effects of staining reagents on cellular viability and cell signaling. However, currently available label-free cell assays mostly rely only on a single feature and lack sufficient differentiation. Also, the sample size analyzed by these assays is limited due to their low throughput. Here, we integrate feature extraction and deep learning with high-throughput quantitative imaging enabled by photonic time stretch, achieving record high accuracy in label-free cell classification. Our system captures quantitative optical phase and intensity images and extracts multiple biophysical features of individual cells. These biophysical measurements form a hyperdimensional feature space in which supervised learning is performed for cell classification. We compare various learning algorithms including artificial neural network, support vector machine, logistic regression, and a novel deep learning pipeline, which adopts global optimization of receiver operating characteristics. As a validation of the enhanced sensitivity and specificity of our system, we show classification of white blood T-cells against colon cancer cells, as well as lipid accumulating algal strains for biofuel production. In conclusion, this system opens up a new path to data-driven phenotypic diagnosis and better understanding of the heterogeneous gene expressions in cells.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Quantitative analysis of hydrogen sites and occupancy in deep mantle hydrous wadsleyite using single crystal neutron diffraction

Evidence from seismological and mineralogical studies increasingly indicates that water from the oceans has been transported to the deep earth to form water-bearing dense mantle minerals. Wadsleyite [(Mg, Fe2+)2SiO4] has been identified as one of the most important host minerals incorporating this type of water, which is capable of storing the entire mass of the oceans as a hidden reservoir. To understand the effects of such water on the physical properties and chemical evolution of Earth’s interior, it is essential to determine where in the crystal structure the hydration occurs and which chemical bonds are altered and weakened after hydration. Here, we conduct a neutron time-of-flight single-crystal Laue diffraction study on hydrous wadsleyite. Single crystals were grown under pressure to a size suitable for the experiment and with physical qualities representative of wet, deep mantle conditions. The results of this neutron single crystal diffraction study unambiguously demonstrate the method of hydrogen incorporation into the wadsleyite, which is qualitatively different from that of its denser polymorph, ringwoodite, in the wet mantle. The difference is a vital clue towards understanding why these dense mantle minerals show distinctly different softening behaviours after hydration.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Benzobisoxazole cruciforms: A tunable, cross-conjugated platform for the generation of deep blue OLED materials

Four new cross-conjugated small molecules based on a central benzo[1,2-d:4,5-d']bisoxazole moiety possessing semi-independently tunable HOMO and LUMO levels were synthesized and the properties of these materials were evaluated experimentally and theoretically. The molecules were thermally stable with 5% weight loss occurring well above 350 °C. The cruciforms all exhibited blue emission in solution ranging from 433–450 nm. Host–guest OLEDs fabricated from various concentrations of these materials using the small molecule host 4,4'-bis(9-carbazolyl)-biphenyl (CBP) exhibited deep blue-emission with Commission Internationale de L'Eclairage (CIE) coordinates of (0.15 ≤ x ≤ 0.17, 0.05 ≤ y ≤ 0.11), and maximum luminance efficiencies as high as ~2 cd A–1. Lastly, these results demonstrate the potential of benzobisoxazole cruciforms as emitters for developing high-performance deep blue OLEDs.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Long term stable deep red light-emitting electrochemical cells based on an emissive, rigid cationic Ir( iii ) complex

Here, the new cationic iridium complex [Ir(bzq) 2 (biq)][PF 6 ] (bzq = benzo[ h ]quinolinato and biq = 2,2'-biquinoline) has been synthesized for application as an emitter in light emitting electrochemical cells (LECs). The molecular structure and crystal packing of this complex were established by single X-ray diffraction (SXRD). The electrochemical and photophysical properties of the complex were examined to determine the frontier orbital energies as well as the optical transitions that led to photoemission. The complex was found to emit at 644 nm and 662 nm for powder and thin films, respectively. A high powder photoluminescence quantum yield of 25% was determined, which is attributed to a reduction in vibrational modes of the complex due to the use of the rigid cyclometalated (C^N) bzq ligand. A LEC with [Ir(bzq) 2 (biq)][PF 6 ] as the emitter was fabricated which showed a deep red emission (662 nm) with a luminance of 33.65 cd m –2 , yielding a current efficiency of 0.33 cd A –1 and a power efficiency of 0.2 lm W –1 . Most importantly, the LEC based on [Ir(bzq) 2 (biq)][PF 6 ] demonstrated a lifetime of 280 hours which is among the longest device lifetimes reported for any deep red light emitting LEC.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Application and assessment of deep learning for the generation of potential NMDA receptor antagonists

Uncompetitive antagonists of the N-methyl D-aspartate receptor (NMDAR) have demonstrated therapeutic benefit in the treatment of neurological diseases such as Parkinson's and Alzheimer's, but some also cause dissociative effects that have led to the synthesis of illicit drugs. The ability to generate NMDAR antagonists in silico is therefore desirable for both new medication development and preempting and identifying new designer drugs. Recently, generative deep learning models have been applied to de novo drug design as a means to expand the amount of chemical space that can be explored for potential drug-like compounds. In this study, we assess the application of a generative model to the NMDAR to achieve two primary objectives: (i) the creation and release of a comprehensive library of experimentally validated NMDAR phencyclidine (PCP) site antagonists to assist the drug discovery community and (ii) an analysis of both the advantages conferred by applying such generative artificial intelligence models to drug design and the current limitations of the approach. We apply, and provide source code for, a variety of ligand- and structure-based assessment techniques used in standard drug discovery analyses to the deep learning-generated compounds. Overall, we present twelve candidate antagonists that are not available in existing chemical databases to provide an example of what this type of workflow can achieve, though synthesis and experimental validation of these compounds are still required.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Accurate prediction of carbon dioxide capture by deep eutectic solvents using quantum chemistry and a neural network

Carbon dioxide (CO 2 ) emissions from fossil fuel combustion are a significant source of greenhouse gas, contributing in a major way to global warming and climate change. Carbon dioxide capture and sequestration is gaining much attention as a potential method for controlling these greenhouse gas emissions. Among the environmentally friendly solvents, deep eutectic solvents (DESs) have demonstrated the potential capability for carbon capture. To establish a theoretical framework for DES activity, thermodynamics modeling and solubility predictions are significant factors to anticipate and understand the system behavior. Here, in this study, we combine the COSMO-RS model with machine learning techniques to predict the solubility of CO 2 in various deep eutectic solvents. A comprehensive data set was established comprising 1973 CO 2 solubility data points in 132 different DESs at a variety of temperatures, pressures, and DES molar ratios. This data set was then utilized for the further verification and development of the COSMO-RS model. The CO 2 solubility (ln(x CO 2 )) in DESs calculated with the COSMO-RS model differs significantly from the experiment with an average absolute relative deviation (AARD) of 23.4%. A multilinear regression model was developed using the COSMO-RS predicted solubility and a temperature-pressure dependent parameter, which improved the AARD to 12%. Finally, a machine learning model using COSMO-RS-derived features was developed based on an artificial neural network algorithm. The results are in excellent agreement with the experimental CO 2 solubilities, with an AARD of only 2.72%. The ML model will be a potentially useful tool for the design and selection of DESs for CO 2 capture and utilization.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Geolocation tracking for human identification and activity recognition using radar deep transfer learning

Abstract Human identification and activity recognition (HIAR) is crucial for many applications, such as surveillance, smart homes, and assisted living. As a sensing modality, radar has many unique characteristics including privacy protection, and contactless sensing. Single classification systems have shown to be accurate, but for long‐term solutions both human identification (ID) and human activity recognition (HAR) will need to be integrated in one system where it can be utilised simultaneously. In this article, a novel radar‐based human tracking system is presented where three classifiers are utilised to identify the subject and his/her behaviour. For any kind of motion, the system tracks the subject and detect the type of his/her motion. Based on the detected type of motion, the three classifiers are utilised for identification and activity recognition. The classifiers are built utilising deep transfer learning where three radar datasets are established to train and validate each of the deep networks. To recognise six activities and 10 human subjects, the three classifiers, namely, HAR, Gait ID, and Heart sound ID, achieve superior performance compared to the best reported results in literature with classification accuracies of 97.6%, 100%, and 41.8% respectively. Three successful examples are presented to demonstrate the introduced concept.

Alkasimi, Ahmad↗
DOE OSTI2024

Ultra-deep imaging of NGC 1052-DF2 and NGC 1052-DF4 to unravel their origins

A number of scenarios have been proposed to explain the low velocity dispersion (and hence possible absence of dark matter) of the low surface brightness galaxies NGC 1052-DF2 and NGC 1052-DF4. Most of the proposed mechanisms are based on the removal of dark matter via the interaction of these galaxies with other objects. A common feature of these processes is the prediction of very faint tidal tails, which should be revealed by deep imaging (μ g > 30 mag arcsec −2 ). Using ultra-deep images obtained with the Gemini telescopes, about 1 mag deeper than previously published data, we analyzed the possible presence of tidal tails in both galaxies. We confirm the presence of tidal tails in NGC 1052-DF4, but see no evidence for tidal effects in NGC 1052-DF2, down to surface brightnesses ofμ g = 30.9 mag arcsec −2 . We therefore conclude that while the absence of dark matter in NGC 1052-DF4 could be attributed to the removal of dark matter by gravitational interactions, in the case of NGC 1052-DF2 this explanation seems less plausible, and therefore other possibilities such as an incorrect distance measurement or that the system may be rotating could alleviate the dark matter problem.

Astronomy & Astrophysics↗
DOE OSTI2019

Exploring End-to-end Deep Learning Applications for Event Classification at CMS

An essential part of new physics searches at the Large Hadron Collider (LHC) at CERN involves event classification, or distinguishing potential signal events from those coming from background processes. Current machine learning techniques accomplish this using traditional hand-engineered features like particle 4-momenta, motivated by our understanding of particle decay phenomenology. While such techniques have proven useful for simple decays, they are highly dependent on our ability to model all aspects of the phenomenology and detector response. Meanwhile, powerful deep learning algorithms are capable of not only training on high-level features, but of performing feature extraction. In computer vision, convolutional neural networks have become the state-of-the-art for many applications. Motivated by their success, we apply deep learning algorithms to low-level detector data from the 2012 CMS Simulated Open Data to directly learn useful features, in what we call, end-to-end event classification. We demonstrate the power of this approach in the context of a physics search and offer solutions to some of the inherent challenges, such as image construction, image sparsity, combining multiple sub-detectors, and de-correlating the classifier from the search observable, among others.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Kubernetes for the Deep Underground Neutrino Experiment Data Acquisition

The Deep Underground Neutrino Experiment (DUNE) is a next-generation long-baseline neutrino experiment based in the USA which is expected to start taking data in 2029. DUNE aims to precisely measure neutrino oscillation parameters by detecting neutrinos from the LBNF beamline (Fermilab) at the Far Detector, 1,300 kilometres away, in South Dakota at the Sanford Underground Research Facility. The Far Detector will consist of four cryogenic Liquid Argon Time Projection Chamber detectors of 17 kT, each producing more than 1 TB/sec of data. The main requirements for the data acquisition system are the ability to run continuously for extended periods of time, with a 99% up-time requirement, and the functionality to record both beam neutrinos and low energy neutrinos from the explosion of a neighbouring supernova, should one occur during the lifetime of the experiment. The key challenges are the high data rates that the detectors generate and the deep underground environment, which places constraints on power and space. To overcome these challenges, DUNE plans to use a highly optimised C++ software suite and a server farm of about 110 nodes continuously running about two hundred multicore processes located close to the detector, 1.5 kilometres underground. Thirty nodes will be at the surface and will run around two hundred processes simultaneously. DUNE is studying the use of the Kubernetes framework to manage containerised workloads and take advantage of its resource definitions and high up-time services to run the DAQ system. Progress in deploying these systems at the CERN neutrino platform on the prototype DUNE experiments is reported.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2014

Rapid production of large-area deep sub-wavelength hybrid structures by femtosecond laser light-field tailoring

The goal of creation of large-area deep sub-wavelength nanostructures by femtosecond laser irradiation onto various materials is being hindered by the limited coherence length. Here, we report solution of the problem by light field tailoring of the incident beam with a phase mask, which serves generation of wavelets. Direct interference between the wavelets, here the first-order diffracted beams, and interference between a wavelet and its induced waves such as surface plasmon polariton are responsible for creation of microgratings and superimposed nanogratings, respectively. The principle of wavelets interference enables extension of uniformly induced hybrid structures containing deep sub-wavelength nanofeatures to macro-dimension.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2013

Persistent photoconductivity in AlGaN/GaN heterojunction channels caused by the ionization of deep levels in the AlGaN barrier layer

Time-dependent responses of drain current (I{sub d}) in an AlGaN/GaN HEMT under UV (3.3 eV) and red (2.0 eV) light illumination have been studied at 300 K and 250 K. UV illumination enhances I{sub d} by about 10 %, indicating that the density of two-dimensional electrons is raised by about 10{sup 12} cm{sup −2}. When UV light is turned off at 300 K, a part of increased I{sub d} decays quickly but the other part of increment is persistent, showing a slow decay. At 250 K, the majority of increment remains persistent. It is found that such a persistent increase of I{sub d} at 250 K can be partially erased by the illumination of red light. These photo-responses are explained by a simple band-bending model in which deep levels in the AlGaN barrier get positively charged by the UV light, resulting in a parabolic band bending in the AlGaN layer, while some potion of those deep levels are neutralized by the red light.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2013

Validation of formability of laminated sheet metal for deep drawing process using GTN damage model

In this study, we studied formability of PET/PVC laminated sheet metal which named VCM (Vinyl Coated Metal). VCM offers various patterns and good-looking metal steel used for appliances such as refrigerator and washing machine. But, this sheet has problems which are crack and peeling of film when the material is formed by deep drawing process. To predict the problems, we used finite element method and GTN (Gurson-Tvergaard-Needleman) damage model to represent damage of material. We divided the VCM into 3 layers (PET film, adhesive and steel added PVC) in finite element analysis model to express the crack and peeling phenomenon. The material properties of each layer are determined by reverse engineering based on tensile test result. Furthermore, we performed the simple rectangular deep drawing and simulated it. The simulation result shows good agreement with drawing experiment result in position, punch stroke of crack occurrence. Also, we studied the fracture mechanism of PET film on VCM by comparing the width direction strain of metal and PET film.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Acceptor levels in ZnMgO:N probed by deep level optical spectroscopy

A combination of deep level optical spectroscopy and lighted capacitance voltage profiling has been used to analyze the effect of N into the energy levels close to the valence band of Zn{sub 0.9}Mg{sub 0.1}O. Three energy levels at E{sub V} + 0.47 eV, E{sub V} + 0.35 eV, and E{sub V} + 0.16 eV are observed in all films with concentrations in the range of 10{sup 15}–10{sup 18} cm{sup −3}. The two shallowest traps at E{sub V} + 0.35 eV and E{sub V} + 0.16 eV have very large concentrations that scale with the N exposure and are thus potential acceptor levels. In order to correctly quantify the deep level concentrations, a metal-insulator-semiconductor model has been invoked, explaining well the resulting capacitance-voltage curves.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2014

Three-dimensional flow contrast imaging of deep tissue using noncontact diffuse correlation tomography

This study extended our recently developed noncontact diffuse correlation spectroscopy flowmetry system into noncontact diffuse correlation tomography (ncDCT) for three-dimensional (3-D) flow imaging of deep tissue. A linear array of 15 photodetectors and two laser sources connected to a mobile lens-focusing system enabled automatic and noncontact scanning of flow in a region of interest. These boundary measurements were combined with a finite element framework for DCT image reconstruction implemented into an existing software package. This technique was tested in computer simulations and using a tissue-like phantom with anomaly flow contrast design. The cylindrical tube-shaped anomaly was clearly reconstructed in both simulation and phantom. Recovered and assigned flow contrast changes in anomaly were found to be highly correlated: regression slope = 1.00, R{sup 2} = 1.00, and p < 10{sup −5} in simulation and regression slope ≥ 0.97, R{sup 2} ≥ 0.96, and p < 10{sup −3} in phantom. These results exhibit promise of our ncDCT technique for 3-D imaging of deep tissue blood flow heterogeneities.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2014

Mixed quantum classical calculation of proton transfer reaction rates: From deep tunneling to over the barrier regimes

We present mixed quantum classical calculations of the proton transfer (PT) reaction rates represented by a double well system coupled to a dissipative bath. The rate constants are calculated within the so called nontraditional view of the PT reaction, where the proton motion is quantized and the solvent polarization is used as the reaction coordinate. Quantization of the proton degree of freedom results in a problem of non-adiabatic dynamics. By employing the reactive flux formulation of the rate constant, the initial sampling starts from the transition state defined using the collective reaction coordinate. Dynamics of the collective reaction coordinate is treated classically as over damped diffusive motion, for which the equation of motion can be derived using the path integral, or the mixed quantum classical Liouville equation methods. The calculated mixed quantum classical rate constants agree well with the results from the numerically exact hierarchical equation of motion approach for a broad range of model parameters. Moreover, we are able to obtain contributions from each vibrational state to the total reaction rate, which helps to understand the reaction mechanism from the deep tunneling to over the barrier regimes. The numerical results are also compared with those from existing approximate theories based on calculations of the non-adiabatic transmission coefficients. It is found that the two-surface Landau-Zener formula works well in calculating the transmission coefficients in the deep tunneling regime, where the crossing point between the two lowest vibrational states dominates the total reaction rate. When multiple vibrational levels are involved, including additional crossing points on the free energy surfaces is important to obtain the correct reaction rate using the Landau-Zener formula.

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

The role of the carbon-silicon complex in eliminating deep ultraviolet absorption in AlN

Co-doping AlN crystals with Si is found to suppress the unwanted 4.7 eV (265 nm) deep ultraviolet absorption associated with isolated carbon acceptors common in materials grown by physical vapor transport. Density functional theory calculations with hybrid functionals demonstrate that silicon forms a stable nearest-neighbor defect complex with carbon. This complex is predicted to absorb at 5.5 eV and emit at or above 4.3 eV. Absorption and photoluminescence measurements of co-doped samples confirm the presence of the predicted C-N-Si-Al complex absorption and emission peaks and significant reduction of the 4.7 eV absorption. Other sources of deep ultraviolet absorption in AlN are also discussed. (C) 2014 AIP Publishing LLC.

Gaddy, BE↗