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At least 217 records · Page 12

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DOE OSTI2024

Verbal Learning and Memory Deficits across Neurological and Neuropsychiatric Disorders: Insights from an ENIGMA Mega Analysis

Deficits in memory performance have been linked to a wide range of neurological and neuropsychiatric conditions. While many studies have assessed the memory impacts of individual conditions, this study considers a broader perspective by evaluating how memory recall is differentially associated with nine common neuropsychiatric conditions using data drawn from 55 international studies, aggregating 15,883 unique participants aged 15–90. The effects of dementia, mild cognitive impairment, Parkinson’s disease, traumatic brain injury, stroke, depression, attention-deficit/hyperactivity disorder (ADHD), schizophrenia, and bipolar disorder on immediate, short-, and long-delay verbal learning and memory (VLM) scores were estimated relative to matched healthy individuals. Random forest models identified age, years of education, and site as important VLM covariates. A Bayesian harmonization approach was used to isolate and remove site effects. Regression estimated the adjusted association of each clinical group with VLM scores. Memory deficits were strongly associated with dementia and schizophrenia (p < 0.001), while neither depression nor ADHD showed consistent associations with VLM scores (p > 0.05). Differences associated with clinical conditions were larger for longer delayed recall duration items. By comparing VLM across clinical conditions, this study provides a foundation for enhanced diagnostic precision and offers new insights into disease management of comorbid disorders.

Neurosciences & Neurology↗

NASA NTRS1998

Two memories for geographical slant: separation and interdependence of action and awareness

The present study extended previous findings of geographical slant perception, in which verbal judgments of the incline of hills were greatly overestimated but motoric (haptic) adjustments were much more accurate. In judging slant from memory following a brief or extended time delay, subjects' verbal judgments were greater than those given when viewing hills. Motoric estimates differed depending on the length of the delay and place of response. With a short delay, motoric adjustments made in the proximity of the hill did not differ from those evoked during perception. When given a longer delay or when taken away from the hill, subjects' motoric responses increased along with the increase in verbal reports. These results suggest two different memorial influences on action. With a short delay at the hill, memory for visual guidance is separate from the explicit memory informing the conscious response. With short or long delays away from the hill, short-term visual guidance memory no longer persists, and both motor and verbal responses are driven by an explicit representation. These results support recent research involving visual guidance from memory, where actions become influenced by conscious awareness, and provide evidence for communication between the "what" and "how" visual processing systems.

NASA Discipline Space Human Factors↗

NASA NTRS2010

Radiation-Hardened Solid-State Drive

A method is provided for a radiationhardened (rad-hard) solid-state drive for space mission memory applications by combining rad-hard and commercial off-the-shelf (COTS) non-volatile memories (NVMs) into a hybrid architecture. The architecture is controlled by a rad-hard ASIC (application specific integrated circuit) or a FPGA (field programmable gate array). Specific error handling and data management protocols are developed for use in a rad-hard environment. The rad-hard memories are smaller in overall memory density, but are used to control and manage radiation-induced errors in the main, and much larger density, non-rad-hard COTS memory devices. Small amounts of rad-hard memory are used as error buffers and temporary caches for radiation-induced errors in the large COTS memories. The rad-hard ASIC/FPGA implements a variety of error-handling protocols to manage these radiation-induced errors. The large COTS memory is triplicated for protection, and CRC-based counters are calculated for sub-areas in each COTS NVM array. These counters are stored in the rad-hard non-volatile memory. Through monitoring, rewriting, regeneration, triplication, and long-term storage, radiation-induced errors in the large NV memory are managed. The rad-hard ASIC/FPGA also interfaces with the external computer buses.

Sheldon, Douglas J.↗

NASA NTRS2011

AC Electric Field Activated Shape Memory Polymer Composite

Shape memory materials have drawn interest for applications like intelligent medical devices, deployable space structures and morphing structures. Compared to other shape memory materials like shape memory alloys (SMAs) or shape memory ceramics (SMCs), shape memory polymers (SMPs) have high elastic deformation that is amenable to tailored of mechanical properties, have lower density, and are easily processed. However, SMPs have low recovery stress and long response times. A new shape memory thermosetting polymer nanocomposite (LaRC-SMPC) was synthesized with conductive fillers to enhance its thermo-mechanical characteristics. A new composition of shape memory thermosetting polymer nanocomposite (LaRC-SMPC) was synthesized with conductive functionalized graphene sheets (FGS) to enhance its thermo-mechanical characteristics. The elastic modulus of LaRC-SMPC is approximately 2.7 GPa at room temperature and 4.3 MPa above its glass transition temperature. Conductive FGSs-doped LaRC-SMPC exhibited higher conductivity compared to pristine LaRC SMP. Applying an electric field at between 0.1 Hz and 1 kHz induced faster heating to activate the LaRC-SMPC s shape memory effect relative to applying DC electric field or AC electric field at frequencies exceeding1 kHz.

Kang, Jin Ho↗

DOE OSTI2024

Accelerating cavity fault prediction using deep learning at Jefferson Laboratory

Abstract Accelerating cavities are an integral part of the continuous electron beam accelerator facility (CEBAF) at Jefferson Laboratory. When any of the over 400 cavities in CEBAF experiences a fault, it disrupts beam delivery to experimental user halls. In this study, we propose the use of a deep learning model to predict slowly developing cavity faults. By utilizing pre-fault signals, we train a long short-term memory-convolutional neural network binary classifier to distinguish between radio-frequency (RF) signals during normal operation and RF signals indicative of impending faults. We optimize the model by adjusting the fault confidence threshold and implementing a multiple consecutive window criterion to identify fault events, ensuring a low false positive rate. Results obtained from analysis of a real dataset collected from the accelerating cavities simulating a deployed scenario demonstrate the model’s ability to identify normal signals with 99.99% accuracy and correctly predict 80% of slowly developing faults. Notably, these achievements were achieved in the context of a highly imbalanced dataset, and fault predictions were made several hundred milliseconds before the onset of the fault. Anticipating faults enables preemptive measures to improve operational efficiency by preventing or mitigating their occurrence.

43 PARTICLE ACCELERATORS↗

DOE OSTI2025

LLM-Based Adaptive Distribution Voltage Regulation Under Frequent Topology Changes: An In-Context MPC Framework

This paper proposes a large language model (LLM) based adaptive inverter control for distribution voltage regulation under frequent topology changes. We leverage the ability of the LLM to perform in-context learning and create a topology-adaptive surrogate model for power flow calculation. The surrogate model is then integrated with a long short-term memory-based load forecaster and a model predictive control (MPC) scheme to achieve the optimal inverter control that adapts to frequent topology changes. Unlike many existing works that assume fixed-topology grids or require the knowledge of all possible topologies when training a model, the proposed in-context MPC method tackles the distribution voltage control problem under various topologies and adapts to unknown topologies with limited data requirement for fine-tuning. The effectiveness of our method is demonstrated on a modified IEEE 123-bus test system.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2026

Deep learning-assisted modeling for χ (2) nonlinear optics

Modeling second-order (χ(2)) nonlinear optical processes remains computationally expensive due to the need to resolve fast field oscillations and simulate wave propagation using methods such as the split-step Fourier method (SSFM). This can become a bottleneck in real-time applications, such as high-repetition-rate laser systems requiring rapid feedback and control. We present a long short-term memory-based surrogate model trained on SSFM simulations generated from a start-to-end model of the photocathode drive laser at SLAC National Accelerator Laboratory’s Linac Coherent Light Source II. The model achieves over 250× speedup while maintaining high fidelity, enabling future real-time optimization and laying the foundation for data-integrated modeling frameworks and digital twins of laser systems.

Accelerator Physics (physics.acc-ph)↗

NASA NTRS1982

Detection and analysis of radio frequency lightning emissions

The feasibility study of detection of lightning discharges from a geosynchronous satellite requires adequate ground-based information regarding emission characteristics. In this investigation, a measurement system for collection of S-band emission data is set up and calibrated, and the operations procedures for rapid data collection during a storm activity developed. The system collects emission data in two modes; a digitized, high-resolution, short duration record stored in solid-state memory, and a continuous long-duration record on magnetic tape. Representative lightning flash data are shown. Preliminary results indicate appreciable RF emissions at 2 gHz from both the leader and return strokes portions of the cloud-to-ground discharge with strong peaks associated with the return strokes.

Jalali, F.↗

NASA NTRS1998

Array Transposition in SSD

One obstacle to running very large two- and three-dimensional codes on the Cray X-MP and Y-MP systems is to efficiently perform array transpositions using SSD storage. This article discusses how such transpositions can be performed by means of algorithms that feature exclusively unit stride, long vector transfers between main memory and SSD, and which only require a single pass through the data (provided sufficient main memory buffers are available).

Bailey, David H.↗

NASA NTRS1989

Array Transposition in SSD

Bailey, David H.↗

NASA NTRS2002

Assessment of Various Flow Solvers Used to Predict the Thermal Environment inside Space Shuttle Solid Rocket Motor Joints

It is very important to accurately predict the gas pressure, gas and solid temperature, as well as the amount of O-ring erosion inside the space shuttle Reusable Solid Rocket Motor (RSRM) joints in the event of a leak path. The scenarios considered are typically hot combustion gas rapid pressurization events of small volumes through narrow and restricted flow paths. The ideal method for this prediction is a transient three-dimensional computational fluid dynamics (CFD) simulation with a computational domain including both combustion gas and surrounding solid regions. However, this has not yet been demonstrated to be economical for this application due to the enormous amount of CPU time and memory resulting from the relatively long fill time as well as the large pressure and temperature rising rate. Consequently, all CFD applications in RSRM joints so far are steady-state simulations with solid regions being excluded from the computational domain by assuming either a constant wall temperature or no heat transfer between the hot combustion gas and cool solid walls.

Wang, Qun-Zhen↗

NASA NTRS2011

A Tribute to Professor Rene H. Miller - A Pioneer in Aeromechanics and Rotary Wing Flight Transportation

Rene H. Miller (May 19, 1916 January 28, 2003), Emeritus H. N. Slater Professor of Flight Transportation, was one of the most influential pioneers in rotary wing aeromechanics as well as a visionary whose dream was the development of a tilt-rotor based short haul air transportation system. This paper pays a long overdue tribute to his memory and to his extraordinary contributions.

Friedmann, Peretz P.↗

NASA NTRS2017

Precipitation Strengthenable NiTiPd High Temperature Shape Memory Alloys

In binary NiTi alloys, it has long been known that Ni-rich alloys can be heat treated to produce precipitates which both strengthen the matrix against dislocations and improve the behavior of the material under thermal and mechanical cycling. Within recent years, the same effect has been observed in Ni-rich NiTiHf high temperature shape memory alloys and heat treatment regimens have been defined which will reliably produce improved properties. In NiTiPd alloys, precipitation has also been observed, but studies are still underway to define reliable heat treatments and compositions which will provide a balance of strengthening and good thermomechanical properties. For this study, a series of NiTi-32 at.Pd alloys was produced to determine the effect of changing nickeltitanium content on the transformation behavior and heat treatability of the material. Samples were aged at temperatures between 350C and 450C for times up to 100 hours. Actuation type behavior was evaluated using uniaxial constant force thermal cycling (UCFTC) to determine the effect of composition and aging on the material behavior. TEMSEM was used to evaluate the microstructure and determine the types of precipitates formed. The correlation between composition, heat treat, microstructure, and thermomechanical behavior will be addressed and discussed.

Transformation strain↗

NASA NTRS1998

Working and strategic memory deficits in schizophrenia

Working memory and its contribution to performance on strategic memory tests in schizophrenia were studied. Patients (n = 18) and control participants (n = 15), all men, received tests of immediate memory (forward digit span), working memory (listening, computation, and backward digit span), and long-term strategic (free recall, temporal order, and self-ordered pointing) and nonstrategic (recognition) memory. Schizophrenia patients performed worse on all tests. Education, verbal intelligence, and immediate memory capacity did not account for deficits in working memory in schizophrenia patients. Reduced working memory capacity accounted for group differences in strategic memory but not in recognition memory. Working memory impairment may be central to the profile of impaired cognitive performance in schizophrenia and is consistent with hypothesized frontal lobe dysfunction associated with this disease. Additional medial-temporal dysfunction may account for the recognition memory deficit.

Memory Disorders/etiology/physiopathology↗

NASA NTRS1974

Augmented burst-error correction for UNICON laser memory

A single-burst-error correction system is described for data stored in the UNICON laser memory. In the proposed system, a long fire code with code length n greater than 16,768 bits was used as an outer code to augment an existing inner shorter fire code for burst error corrections. The inner fire code is a (80,64) code shortened from the (630,614) code, and it is used to correct a single-burst-error on a per-word basis with burst length b less than or equal to 6. The outer code, with b less than or equal to 12, would be used to correct a single-burst-error on a per-page basis, where a page consists of 512 32-bit words. In the proposed system, the encoding and error detection processes are implemented by hardware. A minicomputer, currently used as a UNICON memory management processor, is used on a time-demanding basis for error correction. Based upon existing error statistics, this combination of an inner code and an outer code would enable the UNICON system to obtain a very low error rate in spite of flaws affecting the recorded data.

Lim, R. S.↗

DOE OSTI2025

Detection of False Data Injection Attacks (FDIA) on Power Dynamical Systems With a State Prediction Method

Not Available

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Memcomputing the Spectrum of Correlated Quantum Systems

The goals and objectives of the grant DE‐SC0020892 were to apply a new computing paradigm, MemComputing, to efficiently simulate properties of correlated quantum systems. The method has been applied to a wide set of problems ranging from quantum state tomography to finding the ground state of correlated systems. In all cases, substantial advantages compared to state-of-the-art approaches have been obtained. The project has also led to the suggestion of the transformer architecture (used nowadays in large-language models) as an efficient quantum state representation, and a better understanding of the role of memory in the generation of long-range order in neural networks. This grant has supported the work of a PhD student, inspired a new class on unconventional computing taught at the University of California, San Diego and has generated several peer-reviewed papers.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

Efficient derivative computation for unsteady fatigue-constrained nonlinear aero-structural wind turbine blade optimization

Gradient-based optimization offers significant efficiency advantages for wind turbine blade design, but its application has often been limited by the cost and accuracy of finite-difference derivative calculations, especially when fatigue constraints are considered. In this work, we systematically compare and evaluate four differentiation techniques, namely algorithmic differentiation, implicit differentiation, sparsity exploitation, and parallelization, to determine their effectiveness in computing accurate gradients through time-domain aero-structural simulations. By integrating these techniques with unsteady nonlinear aerodynamic and structural models, we develop software designed for accurate gradient computation. We show that combining these techniques addresses memory and runtime challenges associated with long simulations required by design load cases. Specifically, the most effective combination reduces derivative computation wall time by over an order of magnitude compared to finite differencing while maintaining superior accuracy. We demonstrate this approach in a proof-of-concept aero-structural optimization of a wind turbine blade that improves the cost of energy by 12.78 %. This comparative study establishes a viable approach for fatigue-aware blade design that balances computational efficiency with modeling accuracy.

17 WIND ENERGY↗