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At least 217 records · Page 12

Disorder effects in nitride semiconductors: impact on fundamental and device properties

Abstract Semiconductor structures used for fundamental or device applications most often incorporate alloy materials. In “usual” or “common” III–V alloys, based on the InGaAsP or InGaAlAs material systems, the effects of compositional disorder on the electronic properties can be treated in a perturbative approach. This is not the case in the more recent nitride-based GaInAlN alloys, where the potential changes associated with the various atoms induce strong localization effects, which cannot be described perturbatively. Since the early studies of these materials and devices, disorder effects have indeed been identified to play a major role in their properties. Although many studies have been performed on the structural characterization of materials, on intrinsic electronic localization properties, and on the impact of disorder on device operation, there are still many open questions on all these topics. Taking disorder into account also leads to unmanageable problems in simulations. As a prerequisite to address material and device simulations, a critical examination of experiments must be considered to ensure that one measures intrinsic parameters as these materials are difficult to grow with low defect densities. A specific property of nitride semiconductors that can obscure intrinsic properties is the strong spontaneous and piezoelectric fields. We outline in this review the remaining challenges faced when attempting to fully describe nitride-based material systems, taking the examples of LEDs. The objectives of a better understanding of disorder phenomena are to explain the hidden phenomena often forcing one to use ad hoc parameters, or additional poorly defined concepts, to make simulations agree with experiments. Finally, we describe a novel simulation tool based on a mathematical breakthrough to solve the Schrödinger equation in disordered potentials that facilitates 3D simulations that include alloy disorder.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Terahertz Science, Technology, and Communication

The term "terahertz" has been ubiquitous in the arena of technology over the past couple of years. New applications are emerging every day which are exploiting the promises of terahertz - its small wavelength; capability of penetrating dust, clouds, and fog; and possibility of having large instantaneous bandwidth for high-speed communication channels. Until very recently, space-based instruments for astrophysics, planetary science, and Earth science missions have been the primary motivator for the development of terahertz sensors, sources, and systems. However, in recent years the emerging areas such as imaging from space platforms, surveillance of person-borne hidden weapons or contraband from a safe stand-off distance and reconnaissance, medical imaging and DNA sequencing, and in the world high speed communications have been the driving force for this area of research.

Chattopadhyay, Goutam↗

Multi-Area Distribution System State Estimation Using Decentralized Physics-Aware Neural Networks

The development of active distribution grids requires more accurate and lower computational cost state estimation. In this paper, the authors investigate a decentralized learning-based distribution system state estimation (DSSE) approach for large distribution grids. The proposed approach decomposes the feeder-level DSSE into subarea-level estimation problems that can be solved independently. The proposed method is decentralized pruned physics-aware neural network (D-P2N2). The physical grid topology is used to parsimoniously design the connections between different hidden layers of the D-P2N2. Monte Carlo simulations based on one-year of load consumption data collected from smart meters for a three-phase distribution system power flow are developed to generate the measurement and voltage state data. The IEEE 123-node system is selected as the test network to benchmark the proposed algorithm against the classic weighted least squares and state-of-the-art learning-based DSSE approaches. Numerical results show that the D-P2N2 outperforms the state-of-the-art methods in terms of estimation accuracy and computational efficiency.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Hidden-line removal at 20 pictures/second through hybrid techniques.

A real-time computer image generation method is presented that is designed to operate in a parallel-computing environment. It can provide good quality pictures of limited complexity if implemented on a general purpose hybrid computing system. Such a hybrid approach can generate a new image every 50 milliseconds. These results have been demonstrated by a feasibility test program which impelements the hybrid concepts described. This hybrid-picture generation capability is shown to represent a significant contribution to the area of real-time hidden-line removal.

Brown, R. M.↗

Proving Correctness for Pointer Programs in a Verifying Compiler

This research describes a component-based approach to proving the correctness of programs involving pointer behavior. The approach supports modular reasoning and is designed to be used within the larger context of a verifying compiler. The approach consists of two parts. When a system component requires the direct manipulation of pointer operations in its implementation, we implement it using a built-in component specifically designed to capture the functional and performance behavior of pointers. When a system component requires pointer behavior via a linked data structure, we ensure that the complexities of the pointer operations are encapsulated within the data structure and are hidden to the client component. In this way, programs that rely on pointers can be verified modularly, without requiring special rules for pointers. The ultimate objective of a verifying compiler is to prove-with as little human intervention as possible-that proposed program code is correct with respect to a full behavioral specification. Full verification for software is especially important for an agency like NASA that is routinely involved in the development of mission critical systems.

Kulczycki, Gregory↗

Time-of-flight detection of terahertz phonon-polariton

A polariton is a fundamental quasiparticle that arises from strong light-matter interaction and as such has attracted wide scientific and practical interest. When light is strongly coupled to the crystal lattice, it gives rise to phonon-polaritons (PPs), which have been proven useful in the dynamical manipulation of quantum materials and the advancement of terahertz technologies. Yet, current detection and characterization methods of polaritons are still limited. Traditional techniques such as Raman or transient grating either rely on fine-tuning of external parameters or complex phase extraction techniques. To overcome these inherent limitations, we propose and demonstrate a technique based on a time-of-flight measurement of PPs. We resonantly launch broadband PPs with intense terahertz fields and measure the time-of-flight of each spectral component with time-resolved second harmonic generation. The time-of-flight information, combined with the PP attenuation, enables us to resolve the real and imaginary parts of the PP dispersion relation. We demonstrate this technique in the van der Waals magnets NiI 2 and MnPS 3 and reveal a hidden magnon-phonon interaction. We believe that this approach will unlock new opportunities for studying polaritons across diverse material systems and enhance our understanding of strong light-matter interaction.

condensed-matter physics↗

Cost-Effective Icy Bodies Exploration using Small Satellite Missions

It has long been known that Saturn's moon Enceladus is expelling water-rich plumes into space, providing passing spacecraft with a window into what is hidden underneath its frozen crust. Recent discoveries indicate that similar events could also occur on other bodies in the solar system, such as Jupiter's moon Europa and the dwarf planet Ceres in the asteroid belt. These plumes provide a possible giant leap forward in the search for organics and assessing habitability beyond Earth, stepping stones toward the long-term goal of finding extraterrestrial life. The United States Congress recently requested mission designs to Europa, to fit within a cost cap of $1B, much less than previous mission designs' estimates. Here, innovative cost-effective small spacecraft designs for the deep-space exploration of these icy worlds, using new and emerging enabling technologies, and how to explore the outer solar system on a budget below the cost horizon of a flagship mission, are investigated. Science requirements, instruments selection, rendezvous trajectories, and spacecraft designs are some topics detailed. The mission concepts revolve around a comparably small-sized and low-cost Plume Chaser spacecraft, instrumented to characterize the vapor constituents encountered on its trajectory. In the event that a plume is not encountered, an ejecta plume can be artificially created by a companion spacecraft, the Plume Maker, on the target body at a location timed with the passage of the Plume Chaser spacecraft. Especially in the case of Ceres, such a mission could be a great complimentary mission to Dawn, as well as a possible future Europa Clipper mission. The comparably small volume of the spacecraft enables a launch to GTO as a secondary payload, providing multiple launch opportunities per year. Plume Maker's design is nearly identical to the Plume Chaser, and fits within the constraints for a secondary payload launch. The cost-effectiveness of small spacecraft missions enables the exploration of multiple solar system bodies in reasonable timeframes despite budgetary constraints, with only minor adaptations. The work presented here is a summary of concepts targeting icy bodies, such as Europa and Ceres, which have been developed over the last year at NASA Ames Research Center's Mission Design Division. The platforms detailed in this work are also applicable to the cost-effective exploration of many other small icy bodies in the solar system.

Mission Design↗

Hidden Allies: Decoding the Core Endohyphal Bacteriome of Aspergillus fumigatus

ABSTRACT Bacterial–fungal interactions that influence the behaviour of one or both organisms are common in nature. Well‐studied systems include endosymbiotic relationships that range from transient to long‐term associations. Diverse endohyphal bacteria associate with fungal hosts, emphasising the need to better comprehend the fungal bacteriome. We evaluated the hypothesis thatAspergillus fumigatusharbours an endohyphal community of bacteria that influence the host phenotype. We analysed whether 38A. fumigatusstrains show stable association with diverse endohyphal bacteria; all derived from single‐conidium cultures that were subjected to antibiotic and heat treatments. The fungal bacteriome, inferred through analysis of bacterial diversity within the fungal strains (short‐ and long‐ read sequencing methods), revealed the presence of core endohyphal bacterial genera. Microscopic analysis further confirmed the presence of endohyphal bacteria. The fungal strains exhibited high genetic diversity and phenotypic heterogeneity in drug susceptibility and in vivo virulence. No correlations were observed between genomic or functional traits and bacteriome diversity, but the abundance of some bacterial genera correlated with fungal virulence or posaconazole susceptibility. The observed endobacteriome may play functional roles, for example, nitrogen fixation. Our study emphasises the existence of complex interactions between fungi and endohyphal bacteria, possibly impacting the phenotype of the fungal host, including virulence.

Environmental Sciences & Ecology↗

Self-Supervised T-GCN for Detection of Disturbance and Propagation in Power Grid

Urban power systems increasingly rely on dense sensing to monitor grid reliability, yet disturbance labels are scarce and events are rare. We present a self-supervised spatio-temporal method that detects, localizes, and characterizes grid frequency disturbances across urban areas using only unlabeled data. Our approach trains a tiny Temporal Graph Convolutional Network (T-GCN) to forecast per-site frequency residuals (deviation from 60 Hz). The sensor graph is constructed directly from signals using pre-event Pearson correlation with a cross-correlation lag penalty without geocoding. At inference, node-level anomalies are the model's forecast errors; region-level alarms arise from connected components of high-score nodes. We estimate disturbance propagation by computing per-node arrival times (first persistent exceedance), then fit a planar or time-of-arrival model to obtain direction, speed, and an epicenter proxy. With only three real events collected at decisecond resolution across U.S. cities, we evaluate the T-GCN and report time-to-detect, footprint size, and propagation consistency. We further show that short-window embeddings from the T-GCN's hidden states enable few-shot event-vs-background recognition via a simple prototypical classifier. Despite minimal data and no labels, our system yields fast, spatially coherent detection and interpretable propagation maps, offering a practical, lightweight pathway to city-scale grid resilience analytics.

Niu, Haoran [ORNL] (ORCID:0000000155228297)↗

Results of a Formal Methods Demonstration Project

This paper describes the results of a cooperative study conducted by a team of researchers in formal methods at three NASA Centers to demonstrate FM techniques and to tailor them to critical NASA software systems. This pilot project applied FM to an existing critical software subsystem, the Shuttle's Jet Select subsystem (Phase I of an ongoing study). The present study shows that FM can be used successfully to uncover hidden issues in a highly critical and mature Functional Subsystem Software Requirements (FSSR) specification which are very difficult to discover by traditional means.

formal methods computer system specification compu↗

Data source authentication of synchrophasor measurement devices based on 1D-CNN and GRU

Synchrophasor measurement devices (SMDs) have been widely deployed to support real-time monitoring and control of power systems. In the meantime, data spoofing has emerged in recent years. Therefore, it is of great importance to study data authentication algorithms for detecting and defending the data spoofing effectively. Here, a one-dimensional convolutional neural network (1D-CNN) is utilized to extract temporal signatures hidden in frequency, voltage angle and amplitude data; then the gated recurrent unit (GRU) employs these temporal signatures for data source authentication. In case studies, the performances of different algorithms are tested in large-scale power systems with numerous SMDs for the first time, and comparisons among different algorithms show that the proposed algorithm can achieve a higher accuracy of data source authentication with a shorter time window.

47 OTHER INSTRUMENTATION↗

Application of Machine Learning to Rotorcraft Health Monitoring

Machine learning is a powerful tool for data exploration and model building with large data sets. This project aimed to use machine learning techniques to explore the inherent structure of data from rotorcraft gear tests, relationships between features and damage states, and to build a system for predicting gear health for future rotorcraft transmission applications. Classical machine learning techniques are difficult, if not irresponsible to apply to time series data because many make the assumption of independence between samples. To overcome this, Hidden Markov Models were used to create a binary classifier for identifying scuffing transitions and Recurrent Neural Networks were used to leverage long distance relationships in predicting discrete damage states. When combined in a workflow, where the binary classifier acted as a filter for the fatigue monitor, the system was able to demonstrate accuracy in damage state prediction and scuffing identification. The time dependent nature of the data restricted data exploration to collecting and analyzing data from the model selection process. The limited amount of available data was unable to give useful information, and the division of training and testing sets tended to heavily influence the scores of the models across combinations of features and hyper-parameters. This work built a framework for tracking scuffing and fatigue on streaming data and demonstrates that machine learning has much to offer rotorcraft health monitoring by using Bayesian learning and deep learning methods to capture the time dependent nature of the data. Suggested future work is to implement the framework developed in this project using a larger variety of data sets to test the generalization capabilities of the models and allow for data exploration.

machine learning↗

Interaction of feel system and flight control system dynamics on lateral flying qualities

An experimental investigation of the influence of lateral feel system characteristics on fighter aircraft roll flying qualities was conducted using the variable stability USAF NT-33. Forty-two evaluation flights were flown by three engineering test pilots. The investigation utilized the power approach, visual landing task and up-and-away tasks including formation, gun tracking, and computer-generated compensatory attitude tracking tasks displayed on the Head-Up Display. Experimental variations included the feel system frequency, force-deflection gradient, control system command type (force or position input command), aircraft roll mode time constant, control system prefilter frequency, and control system time delay. The primary data were task performance records and evaluation pilot comments and ratings using the Cooper-Harper scale. The data highlight the unique and powerful effect of the feel system of flying qualities. The data show that the feel system is not 'equivalent' in flying qualities influence to analogous control system elements. A lower limit of allowable feel system frequency appears warranted to ensure good lateral flying qualities. Flying qualities criteria should most properly treat the feel system dynamic influence separately from the control system, since the input and output of this dynamic element is apparent to the pilot and thus, does not produce a 'hidden' effect.

Bailey, R. E.↗

Monitoring Integrity Of Composite Aircraft Components

System developed that monitors integrity of composite-material structural components of aircraft in service. Includes strain gauges and accelerometers installed permanently in components to monitor vibrations, microprocessor-based data-acquisition system to process outputs of these vibration sensors, and desktop computer to analyze acquired data. By automating significant part of inspection process, system reduces amount of time needed for inspection and cost of inspection equipment. Contributes to safety by giving timely warning of hidden flaws that necessitate early, detailed inspection of critical components to determine whether components should be replaced immediately.

Tang, Shu Shing↗

On-Wafer Measurement of a Silicon-Based CMOS VCO at 324 GHz

The world s first silicon-based complementary metal oxide/semiconductor (CMOS) integrated-circuit voltage-controlled oscillator (VCO) operating in a frequency range around 324 GHz has been built and tested. Concomitantly, equipment for measuring the performance of this oscillator has been built and tested. These accomplishments are intermediate steps in a continuing effort to develop low-power-consumption, low-phase-noise, electronically tunable signal generators as local oscillators for heterodyne receivers in submillimeter-wavelength (frequency > 300 GHz) scientific instruments and imaging systems. Submillimeter-wavelength imaging systems are of special interest for military and law-enforcement use because they could, potentially, be used to detect weapons hidden behind clothing and other opaque dielectric materials. In comparison with prior submillimeter- wavelength signal generators, CMOS VCOs offer significant potential advantages, including great reductions in power consumption, mass, size, and complexity. In addition, there is potential for on-chip integration of CMOS VCOs with other CMOS integrated circuitry, including phase-lock loops, analog- to-digital converters, and advanced microprocessors.

Samoska, Lorene↗

NASA Tech Briefs, September 2010

Topics covered include: Instrument for Measuring Thermal Conductivity of Materials at Low Temperatures; Multi-Axis Accelerometer Calibration System; Pupil Alignment Measuring Technique and Alignment Reference for Instruments or Optical Systems; Autonomous System for Monitoring the Integrity of Composite Fan Housings; A Safe, Self-Calibrating, Wireless System for Measuring Volume of Any Fuel at Non-Horizontal Orientation; Adaptation of the Camera Link Interface for Flight-Instrument Applications; High-Performance CCSDS Encapsulation Service Implementation in FPGA; High-Performance CCSDS AOS Protocol Implementation in FPGA; Advanced Flip Chips in Extreme Temperature Environments; Diffuse-Illumination Systems for Growing Plants; Microwave Plasma Hydrogen Recovery System; Producing Hydrogen by Plasma Pyrolysis of Methane; Self-Deployable Membrane Structures; Reactivation of a Tin-Oxide-Containing Catalys; Functionalization of Single-Wall Carbon Nanotubes by Photo-Oxidation; Miniature Piezoelectric Macro-Mass Balance; Acoustic Liner for Turbomachinery Applications; Metering Gas Strut for Separating Rocket Stages; Large-Flow-Area Flow-Selective Liquid/Gas Separator; Counterflowing Jet Subsystem Design; Water Tank with Capillary Air/Liquid Separation; True Shear Parallel Plate Viscometer; Focusing Diffraction Grating Element with Aberration Control; Universal Millimeter-Wave Radar Front End; Mode Selection for a Single-Frequency Fiber Laser; Qualification and Selection of Flight Diode Lasers for Space Applications; Plenoptic Imager for Automated Surface Navigation; Maglev Facility for Simulating Variable Gravity; Hybrid AlGaN-SiC Avalanche Photodiode for Deep-UV Photon Detection; High-Speed Operation of Interband Cascade Lasers; 3D GeoWall Analysis System for Shuttle External Tank Foreign Object Debris Events; Charge-Spot Model for Electrostatic Forces in Simulation of Fine Particulates; Hidden Statistics Approach to Quantum Simulations; Reconstituted Three-Dimensional Interactive Imaging; Determining Atmospheric-Density Profile of Titan; Digital Microfluidics Sample Analyzer; Radiation Protection Using Carbon Nanotube Derivatives; Process to Selectively Distinguish Viable from Non-Viable Bacterial Cells; and TEAMS Model Analyzer.

Source record↗

Scalable algorithms for physics-informed neural and graph networks

Physics-informed machine learning (PIML) has emerged as a promising new approach for simulating complex physical and biological systems that are governed by complex multiscale processes for which some data are also available. In some instances, the objective is to discover part of the hidden physics from the available data, and PIML has been shown to be particularly effective for such problems for which conventional methods may fail. Unlike commercial machine learning where training of deep neural networks requires big data, in PIML big data are not available. Instead, we can train such networks from additional information obtained by employing the physical laws and evaluating them at random points in the space–time domain. Such PIML integrates multimodality and multifidelity data with mathematical models, and implements them using neural networks or graph networks. Here, we review some of the prevailing trends in embedding physics into machine learning, using physics-informed neural networks (PINNs) based primarily on feed-forward neural networks and automatic differentiation. For more complex systems or systems of systems and unstructured data, graph neural networks (GNNs) present some distinct advantages, and here we review how physics-informed learning can be accomplished with GNNs based on graph exterior calculus to construct differential operators; we refer to these architectures as physics-informed graph networks (PIGNs). We present representative examples for both forward and inverse problems and discuss what advances are needed to scale up PINNs, PIGNs and more broadly GNNs for large-scale engineering problems.

42 ENGINEERING↗

Analyzing the Quantum Approximate Optimization Algorithm: Ansätze, Symmetries, and Lie Algebras

The quantum approximate optimization algorithm (QAOA) has been proposed as a method to obtain approximate solutions for combinatorial optimization tasks. In this work, we study the underlying algebraic properties of three QAOA ansätze for the maximum-cut problem on connected graphs, while focusing on the generated Lie algebras as well as their invariant subspaces. Specifically, we analyze the standard QAOA ansatz as well as the orbit and multiangle ansätze. We are able to fully characterize the Lie algebras of the multiangle ansatz across arbitrary connected graphs, finding that they only fall into one of just six families. Aside from the cycle and path graphs, the Lie dimensions for every graph are exponentially large in the system size, meaning that multiangle ansätze are extremely prone to exhibiting barren plateaus. Then, a similar quasi-graph-independent Lie-algebraic characterization beyond the multiangle ansatz is impeded as the circuit exhibits additional “hidden” symmetries besides those naturally arising from a certain parity-superselection operator and all automorphisms of the considered graph. Disregarding the “hidden” symmetries, we can upper bound the dimensions of the orbit and the standard Lie algebras, and the dimensions of the associated invariant subspaces are determined via explicit character formulas. To finish, we conjecture that (for most graphs) the standard Lie algebras have only components that are either exponential or that grow, at most, polynomially with the system size. This would imply that the QAOA is either prone to barren plateaus or classically simulable. More generally, our work provides a symmetry framework and tools to analyze any desired variational quantum algorithm.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗