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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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The paper describes the architectual details of a fine-grained Programmable Transistor Array (PTA) architecture and illustrates its use in evolutionary experiments on the synthesis of both analog and digital circuits.
Using the results of the most recent stellar atmosphere models applied to a sample of hot stars, we construct calibrations of effective temperature (T(sub eff)), and gravity (log(sub g)) with a spectral type and luminosity class for Galactic 0-type and early B-type stars. From the model results we also derive an empirical relation between the bolometric correction and T(sub eff) and log g. Using a sample of stars with known distances located in OB associations in the Galaxy and the Large Magellanic Cloud, we derive a new calibration of M(sub v) with spectral class. With these new calibrations and the stellar atmosphere models of Kurucz, we calculate the physical parameters and ionizing photon luminosities in the H(0) and He(0) continua for O and early B-type stars. We find substantial differences between our values of the Lyman- continuum luminosity and those reported in the literature. We also discuss the systematic discrepancy between O-type stellar masses derived from spectroscopic models and those derived from evolutionary tracks. Most likely, the cause of this 'mass discrepancy' lies primarily in the atmospheric models, which are plane parallel and hydrostatic and therefore do not account for an extended atmosphere and the velocity fields in a stellar wind. Finally, we present a new computation of the Lyman-continuum luminosity from 429 known O stars located within 2.5 kpc of the Sun. We find the total ionizing luminosity from this population ((Q(sub 0)(sup T(sub ot))) = 7.0 x 10(exp 51) photons/s) to be 47% larger than that determined using the Lyman continuum values tabulated by Panagia.
ASCA (Advanced Satellite for Cosmology and Astrophysics) and EUVE (Extreme Ultraviolet Explorer) spectra of active late-type stars imply that Fe and other medium-Z elements may be 2-10 times less abundant in the coronae of these stars than in their photo-spheres (the MAD effect). These deficiencies may be related to the solar FIP (First Ionization Potential) effect, in which Fe and other low First Ionization Potential elements appear enriched in the solar corona over their photospheric values. The FIP effect is time variable. As part of this proposal, the K0-2 III star, 29 Draconis, was observed in X rays with the ASCA spacecraft in order to measure the coronal abundances of this star at three different stellar longitudes over its 31-day rotation cycle. The goal of the observations was to learn whether coronal abundances, and hence coronal magnetic structure, vary across the surface of 29 Draconis in phase with the motion of dark star-spots across its disk. A second task included in this project was a systematic reanalysis of 18-20 deep exposures of active coronal stars, which were extracted from the ASCA public archives. New thermal models were computed for each spectrum in order to derive coronal metal abundances for each star. The goal of this survey was to search for possible trends in coronal abundance with various stellar parameters such as rotation, chromospheric activity levels at ultraviolet and optical wavelengths, or evolutionary stage.
This paper presents novel approach to applying growth control and diseases mechanisms in computational embryogeny. Our method, which mimics fundamental processes from biology, enables individuals to reach maturity in a controlled process through a stochastic environment. Three different mechanisms were implemented; disease mechanisms, gene suppression, and thermodynamic balancing. This approach was integrated as part of a structural evolutionary model. The model evolved continuum 3-D structures which support an external load. By using these mechanisms we were able to evolve individuals that reached a fixed size limit through the growth process. The growth process was an integral part of the complete development process. The size of the individuals was determined purely by the evolutionary process where different individuals matured to different sizes. Individuals which evolved with these characteristics have been found to be very robust for supporting a wide range of external loads.
We present L’-band imaging of the PDS 70 planetary system with Keck/NIRC2 using the new infrared pyramid wave front sensor. We detected both PDS 70 b and c in our images, as well as the front rim of the circumstellar disk. After subtracting off a model of the disk, we measured the astrometry and photometry of both planets. Placing priors based on the dynamics of the system, we estimated PDS 70 b to have a semimajor axis of 20{sub −4}{sup +3} au and PDS 70 c to have a semimajor axis of 34{sub −6}{sup +12} au (95% credible interval). We fit the spectral energy distribution (SED) of both planets. For PDS 70 b, we were able to place better constraints on the red half of its SED than previous studies and inferred the radius of the photosphere to be 2–3 R {sub Jup}. The SED of PDS 70 c is less well constrained, with a range of total luminosities spanning an order of magnitude. With our inferred radii and luminosities, we used evolutionary models of accreting protoplanets to derive a mass of PDS 70 b between 2 and 4 M {sub Jup} and a mean mass accretion rate between 3 × 10{sup −7} and 8 × 10{sup −7} M {sub Jup}/yr. For PDS 70 c, we computed a mass between 1 and 3 M {sub Jup} and mean mass accretion rate between 1 × 10{sup −7} and 5 × 10{sup −7} M {sub Jup}/yr. The mass accretion rates imply dust accretion timescales short enough to hide strong molecular absorption features in both planets’ SEDs.
A discussion is presented of the Centralized Storm Information System (CSIS), a joint effort by several organizations including NASA and the National Weather Service, which has been developed to aid the forecaster in evaluating weather data. The CSIS was developed to provide a handling, analyzing, intercomparison, and display system for real-time data from all available sources. The CSIS hardware and software, the impact of the CSIS, and the initial experience gained following the implementation of the CSIS are examined. Several general insights learned from this interactive processing system are examined in detail, including the benefits of an evolutionary bottom up rather than top down design approach, the crucial nature of scheduler function to any real time system, the recognition that meteorological interactive terminals requirements are different from image processing terminals requirements, and the knowledge that forecasters generate a large peak load on computer resources.
We report RNA-based macromolecular machines, such as the ribosome, have functional parts reliant on structural interactions spanning sequence-distant regions. These features limit evolutionary exploration of mutant libraries and confound three-dimensional structure-guided design. To address these challenges, we describe Evolink (evolution and linkage), a method that enables high-throughput evolution of sequence-distant regions in large macromolecular machines, and library design guided by computational RNA modeling to enable exploration of structurally stable designs. Using Evolink, we evolved a tethered ribosome with a 58% increased activity in orthogonal protein translation and a 97% improvement in doubling times in SQ171 cells compared to a previously developed tethered ribosome, and reveal new permissible sequences in a pair of ribosomal helices with previously explored biological function. The Evolink approach may enable enhanced engineering of macromolecular machines for new and improved functions for synthetic biology.
A system infrastructure must be properly designed and integrated from the conceptual development phase to accommodate evolutionary intelligent technologies. Several technology development activities were identified that may have application to rendezvous and capture systems. Optical correlators in conjunction with fuzzy logic control might be used for the identification, tracking, and capture of either cooperative or non-cooperative targets without the intensive computational requirements associated with vision processing. A hybrid digital/analog system was developed and tested with a robotic arm. An aircraft refueling application demonstration is planned within two years. Initially this demonstration will be ground based with a follow-on air based demonstration. System dependability measurement and modeling techniques are being developed for fault management applications. This involves usage of incremental solution/evaluation techniques and modularized systems to facilitate reuse and to take advantage of natural partitions in system models. Though not yet commercially available and currently subject to accuracy limitations, technology is being developed to perform optical matrix operations to enhance computational speed. Optical terrain recognition using camera image sequencing processed with optical correlators is being developed to determine position and velocity in support of lander guidance. The system is planned for testing in conjunction with Dryden Flight Research Facility. Advanced architecture technology is defining open architecture design constraints, test bed concepts (processors, multiple hardware/software and multi-dimensional user support, knowledge/tool sharing infrastructure), and software engineering interface issues.
Texture analysis would permit improved autonomous, onboard science data interpretation for adaptive navigation, sampling, and downlink decisions. These analyses would assist with terrain analysis and instrument placement in both macroscopic and microscopic image data products. Unfortunately, most state-of-the-art texture analysis demands computationally expensive convolutions of filters involving many floating-point operations. This makes them infeasible for radiation- hardened computers and spaceflight hardware. A new method approximates traditional texture classification of each image pixel with a fast decision-tree classifier. The classifier uses image features derived from simple filtering operations involving integer arithmetic. The texture analysis method is therefore amenable to implementation on FPGA (field-programmable gate array) hardware. Image features based on the "integral image" transform produce descriptive and efficient texture descriptors. Training the decision tree on a set of training data yields a classification scheme that produces reasonable approximations of optimal "texton" analysis at a fraction of the computational cost. A decision-tree learning algorithm employing the traditional k-means criterion of inter-cluster variance is used to learn tree structure from training data. The result is an efficient and accurate summary of surface morphology in images. This work is an evolutionary advance that unites several previous algorithms (k-means clustering, integral images, decision trees) and applies them to a new problem domain (morphology analysis for autonomous science during remote exploration). Advantages include order-of-magnitude improvements in runtime, feasibility for FPGA hardware, and significant improvements in texture classification accuracy.
<Type IV pili (T4P) are ubiquitous in both bacteria and archaea. They are polymers of the major pilin protein, which has an extended and protruding N-terminal helix, α1, and a globular C-terminal domain. Cryo-EM structures have revealed key differences between the bacterial and archaeal T4P in their C-terminal domain structure and in the packing and continuity of α1. This segment forms a continuous α-helix in archaeal T4P but is partially melted in all published bacterial T4P structures due to a conserved helix breaking proline at position 22. The tad (tight adhesion) T4P are found in both bacteria and archaea and are thought to have been acquired by bacteria through horizontal transfer from archaea. Tad pilins are unique among the T4 pilins, being only 40 to 60 residues in length and entirely lacking a C-terminal domain. They also lack the Pro22 found in all high-resolution bacterial T4P structures. We show using cryo-EM that the bacterial tad pilus from Caulobacter crescentus is composed of continuous helical subunits that, like the archaeal pilins, lack the melted portion seen in other bacterial T4P and share the packing arrangement of the archaeal T4P. We further show that a bacterial T4P, the Vibrio cholerae toxin coregulated pilus, which lacks Pro22 but is not in the tad family, has a continuous N-terminal α-helix, yet its α1 s are arranged similar to those in other bacterial T4P. Our results highlight the role of Pro22 in helix melting and support an evolutionary relationship between tad and archaeal T4P.
This presentation discusses the Plan for the Airport Noise Tech Challenge Area of the Supersonics Project. It is given in the context of strategic planning exercises being done in other Projects to show the strategic aspects of the Airport Noise plan rather than detailed task lists. The essence of this strategic view is the decomposition of the research plan by Concept and by Tools. Tools (computational, experimental) is the description of the plan that resources (such as researchers) most readily identify with, while Concepts (here noise reduction technologies or aircraft configurations) is the aspects that project management and outside reviewers most appreciate as deliverables and milestones. By carefully cross-linking these so that Concepts are addressed sequentially (roughly one after another) by researchers developing/applying their Tools simultaneously (in parallel with one another), the researchers can deliver milestones at a reasonable pace while doing the longer-term development that most Tools in the aeroacoustics science require. An example of this simultaneous application of tools was given for the Concept of High Aspect Ratio Nozzles. The presentation concluded with a few ideas on how this strategic view could be applied to the Subsonic Fixed Wing Project's Quiet Aircraft Tech Challenge Area as it works through its current roadmapping exercise.
Abstract There are a staggering number of publicly available bacterial genome sequences (at writing, 2.0 million assemblies in NCBI's GenBank alone), and the deposition rate continues to increase. This wealth of data begs for phylogenetic analyses to place these sequences within an evolutionary context. A phylogenetic placement not only aids in taxonomic classification but informs the evolution of novel phenotypes, targets of selection, and horizontal gene transfer. Building trees from multi-gene codon alignments is a laborious task that requires bioinformatic expertise, rigorous curation of orthologs, and heavy computation. Compounding the problem is the lack of tools that can streamline these processes for building trees from large-scale genomic data. Here we present OrthoPhyl, which takes bacterial genome assemblies and reconstructs trees from whole genome codon alignments. The analysis pipeline can analyze an arbitrarily large number of input genomes (>1200 tested here) by identifying a diversity-spanning subset of assemblies and using these genomes to build gene models to infer orthologs in the full dataset. To illustrate the versatility of OrthoPhyl, we show three use cases: E. coli/Shigella, Brucella/Ochrobactrum and the order Rickettsiales. We compare trees generated with OrthoPhyl to trees generated with kSNP3 and GToTree along with published trees using alternative methods. We show that OrthoPhyl trees are consistent with other methods while incorporating more data, allowing for greater numbers of input genomes, and more flexibility of analysis.
The demand for multifunctional materials has motivated the move from near-equilibrium materials to metastable i.e. out-of-equilibrium phases that can meet several desired target properties. The search for such metastable phases with exotic properties is non-trivial and often serendipitous. Inverse design approaches based on evolutionary search have been powerful tools, but such traditional searches have focused on identifying primarily stable and metastable materials with the lowest enthalpy. The inverse design of materials, with a focus on a desired property such as, for example, hardness is a challenging task because of the expensive computational cost involved in sampling multiple structures. The recent advances in machine learning have brought new powerful AI techniques to the forefront which can potentially revolutionize the inverse design and discovery of materials, especially metastable phases capable of meeting multifunctionality. Here, in this work, we develop and apply an automated reinforcement learning workflow for inverse design that integrates first principles physics and atomistic simulations with machine learning (ML), and high-performance computing to allow rapid exploration of the superhard and ultrahard metastable phases of Carbon. We demonstrate an automatic machine learning based inverse design workflow to map new undiscovered metastable states ranging from near equilibrium to those far-from-equilibrium that satisfy multiple property objectives, specifically bulk moduli, shear moduli and hardness. We create a comprehensive library of carbon stable and metastable phases with varying hardness and subsequently shortlist 10 top performing candidate carbon structures, including two newly reported phases, based on their hardness and characterize their temperature dependent mechanical properties. A neural network model is built using featurization of allotropes of carbon to predict the quasi-harmonic Gibbs free energies. The Gibbs free energies of the top performing phases are analyzed to get an estimate of the experimental synthesizability of these superhard and ultrahard carbon phases. In general, we show using machine learning based inverse design approaches how hitherto inaccessible metastable states can be identified and potentially synthesized to meet the demand for multifunctional materials.
Prenylated indole alkaloids featuring spirooxindole rings possess a 3 R or 3 S carbon stereocenter, which determines the bioactivities of these compounds. Despite the stereoselective advantages of spirooxindole biosynthesis compared with those of organic synthesis, the biocatalytic mechanism for controlling the 3 R or 3 S -spirooxindole formation has been elusive. Here, we report an oxygenase/semipinacolase CtdE that specifies the 3 S -spirooxindole construction in the biosynthesis of 21 R -citrinadin A. High-resolution X-ray crystal structures of CtdE with the substrate and cofactor, together with site-directed mutagenesis and computational studies, illustrate the catalytic mechanisms for the possible β-face epoxidation followed by a regioselective collapse of the epoxide intermediate, which triggers semipinacol rearrangement to form the 3 S -spirooxindole. Comparing CtdE with PhqK, which catalyzes the formation of the 3 R -spirooxindole, we reveal an evolutionary branch of CtdE in specific 3 S spirocyclization. Our study provides deeper insights into the stereoselective catalytic machinery, which is important for the biocatalysis design to synthesize spirooxindole pharmaceuticals.
We compare different neural network architectures for machine learning algorithms designed to identify the neutrino interaction vertex position in the MINERvA detector. The architectures developed and optimized by hand are compared with the architectures developed in an automated way using the package “Multi-node Evolutionary Neural Networks for Deep Learning” (MENNDL), developed at Oak Ridge National Laboratory. While the domain-expert hand-tuned network was the best performer, the differences were negligible and the auto-generated networks performed as well. There is always a trade-off between human, and computer resources for network optimization and this work suggests that automated optimization, assuming resources are available, provides a compelling way to save significant expert time.
This paper provides a broad framework for under- standing trends in Operational Data Analytics (ODA) for High- Performance Computing (HPC) facilities. The goal of ODA is to allow for the continuous monitoring, archiving, and analysis of near real-time performance data, providing immediately actionable information for multiple operational uses. In this work, we combine two models to provide a comprehensive HPC ODA framework: one is an evolutionary model of analytics capabilities that consists of four types, which are descriptive, diagnostic, predictive and prescriptive, while the other is a four- pillar model for energy-efficient HPC operations that covers facility, system hardware, system software, and applications. This new framework is then overlaid with a description of current development and production deployments of ODA within leading- edge HPC facilities. Finally, we perform a comprehensive survey of ODA works and classify them according to our framework, in order to demonstrate its effectiveness.
Densities, corrected primary colors, minimum periods, inferred masses, luminosities, and specific angular momenta are computed from data on 37 W Ursae Majoris systems. A-type systems, having lower densities and angular momenta than the W-type systems, are shown to be evolved, and a new class of contact binary is identified, the OO Aquilae systems, whose members have evolved into contact. Evolutionary grids based on the contact condition agree with observation, except in that the evolved A-type systems have lost more angular momentum than predicted by gravitational radiation alone. This is accounted for by stellar wind magnetic braking, which is shown to be effective on a shorter time scale and to be important in other kinds of binaries containing a cool, tidally coupled component.
In order to design a quantum circuit that performs a desired quantum computation, it is necessary to find a decomposition of the unitary matrix that represents that computation in terms of a sequence of quantum gate operations. To date, such designs have either been found by hand or by exhaustive enumeration of all possible circuit topologies. In this paper we propose an automated approach to quantum circuit design using search heuristics based on principles abstracted from evolutionary genetics, i.e. using a genetic programming algorithm adapted specially for this problem. We demonstrate the method on the task of discovering quantum circuit designs for quantum teleportation. We show that to find a given known circuit design (one which was hand-crafted by a human), the method considers roughly an order of magnitude fewer designs than naive enumeration. In addition, the method finds novel circuit designs superior to those previously known.