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NASA NTRS1999

On the Bimodality of ENSO Cycle Extremes

On the basis of sea surface temperature in the Nino 3.4 region (5 deg N-5 deg S, 120 deg- 170 deg W) during the interval of 1950-1997, Kevin Trenberth previously has identified some 16 El Nino and 10 La Nina, these 26 events representing the extremes of the quasi-periodic El Nino-Southern Oscillation (ENSO) cycle. Runs testing shows that the duration and recurrence period associated with these extremes vary randomly, as does the sequencing of the extremes. Hence, the frequency of occurrence of these events during the 1990s, especially, for El Nino should not be construed as being significantly different from that of previous epochs. Additionally, the distribution of duration for both El Nino and La Nina looks bimodal, consisting of two preferred modes - about 8 and 16 months in length for El Nino and about 9 and 18 months in length for La Nina. Likewise, the distribution of recurrence period, especially, for El Nino looks bimodal, consisting of two preferred modes - about 21 and 50 months in length. Scatter plots of the recurrence period versus duration for El Nino strongly suggest preferential associations between them, linking shorter (longer) duration with shorter (longer) recurrence period. Because the last known onset of El Nino occurred in April 1997 and the event was of longer than average duration, one infers that the onset of the next expected El Nino will not occur until February 2000 or later.

Wilson, Robert M.↗

NASA NTRS2000

On the Bimodality of ENSO Cycle Extremes

On the basis of sea surface temperature in the El Nino 3.4 region (5 deg. N.,-5 deg. S., 120-170 deg. W.) during the interval of 1950-1997, Kevin Trenberth previously has identified some 16 El Nino and 10 La Nina, these 26 events representing the extremes of the quasi-periodic El Nino-Southern Oscillation (ENSO) cycle. Runs testing shows that the duration, recurrence period, and sequencing of these extremes vary randomly. Hence, the decade of the 1990's, especially for El Nino, is not significantly different from that of previous decadal epochs, at least, on the basis of the frequency of onsets of ENSO extremes. Additionally, the distribution of duration for both El Nino and La Nina looks strikingly bimodal, each consisting of two preferred modes, about 8- and 16-mo long for El Nino and about 9- and 18-mo long for La Nina, as does the distribution of the recurrence period for El Nino, consisting of two preferred modes about 21- and 50-mo long. Scatterplots of the recurrence period versus duration for El Nino are found to be statistically important, displaying preferential associations that link shorter (longer) duration with shorter (longer) recurrence periods. Because the last onset of El Nino occurred in April 1997 and the event was of longer than average duration, onset of the next anticipated El Nino is not expected until February 2000 or later.

Wilson, Robert M.↗

NASA NTRS2000

On The Bimodality of ENSO Cycle Extremes

On the basis of sea surface temperature in the El Nino 3.4 region (5N.-5S., 120-170W.) during the interval of 1950-1997, Kevin Trenberth previously has identified some 16 El Nino and 10 La Nina, these 26 events representing the extremes of the quasi-periodic El Nino-Southern Oscillation (ENSO) cycle. Runs testing shows that the duration, recurrence period, and sequencing of these extremes vary randomly. Hence, the decade of the 1990's, especially for El Nino, is not significantly different from that of previous decadal epochs, at least, on the basis of the frequency of onsets of ENSO extremes. Additionally, the distribution of duration for both El Nino and La Nina looks strikingly bimodal, each consisting of two preferred modes, about 8- and 16-months long for El Nino and about 9- and 18-months long for La Nina, as does the distribution of the recurrence period for El Nino, consisting of two preferred modes about 21- and 50- mo long. Scatterplots of the recurrence period versus duration for El Nino are found to be statistically important, displaying preferential associations that link shorter (longer) duration with shorter (longer) recurrence periods. Because the last onset of El Nino occurred in April 1997 and the event was of longer than average duration, onset of the next anticipated El Nino is not expected until February 2000 or later.

Wilson, Robert M.↗

NASA NTRS2014

An Accreting White Dwarf near the Chandrasekhar Limit in the Andromeda Galaxy

The iPTF (Intermediate Palomar Transient Factory) detection of the most recent outburst of the recurrent nova system RX J0045.4+4154 in the Andromeda Galaxy has enabled the unprecedented study of a massive (mass is greater than 1.3 solar masses) accreting white dwarf (WD). We detected this nova as part of the near daily iPTF monitoring of M31 to a depth of R (red band-pass filter) approximately equal to magnitude 21 and triggered optical photometry, spectroscopy and soft X-ray monitoring of the outburst. Peaking at an absolute magnitude of MR (red, mid-infrared band-pass filter) equals magnitude −6.6, and with a decay time of 1 magnitude per day, it is a faint and very fast nova. It shows optical emission lines of He/N and expansion velocities of 1900 to 2600 kilometers per second 1-4 days after the optical peak. The Swift monitoring of the X-ray evolution revealed a supersoft source (SSS) with kT (energy: Boltzmann constant times temperature) (sub eff (effective)) approximately equal to 90-110 electronvolts that appeared within 5 days after the optical peak, and lasted only 12 days. Most remarkably, this is not the first event from this system, rather it is a recurrent nova with a time between outbursts of approximately 1 year, the shortest known. Recurrent X-ray emission from this binary was detected by ROSAT in 1992 and 1993, and the source was well characterized as a mass greater than 1.3 solar masses WD SSS. Based on the observed recurrence time between different outbursts, the duration and effective temperature of the SS phase, MESA models of accreting WDs allow us to constrain the accretion rate to mass greater than 1.7x10 (sup −7) solar masses per year and WD mass greater than 1.30 solar masses. If the WD keeps 30 percent of the accreted material, it will take less than a million years to reach core densities high enough for carbon ignition (if made of C/O) or electron capture (if made of O/Ne) to end the binary evolution.

dwarf↗

DOE OSTI2024

Multi-scale signaling and tumor evolution in high-grade gliomas

Although genomic anomalies in glioblastoma (GBM) have been well studied for over a decade, its 5-year survival rate remains lower than 5%. We seek to expand the molecular landscape of high-grade glioma, composed of IDH-wildtype GBM and IDH-mutant grade 4 astrocytoma, by integrating proteomic, metabolomic, lipidomic, and post-translational modifications (PTMs) with genomic and transcriptomic measurements to uncover multi-scale regulatory interactions governing tumor development and evolution. Applying 14 proteogenomic and metabolomic platforms to 228 tumors (212 GBM and 16 grade 4 IDH-mutant astrocytoma), including 28 at recurrence, plus 18 normal brain samples and 14 brain metastases as comparators, reveals heterogeneous upstream alterations converging on common downstream events at the proteomic and metabolomic levels and changes in protein-protein interactions and glycosylation site occupancy at recurrence. Recurrent genetic alterations and phosphorylation events on PTPN11 map to important regulatory domains in three dimensions, suggesting a central role for PTPN11 signaling across high-grade gliomas.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2021

Understanding Competing Risks

Highlights: • Clinicians and researchers need to be cognizant of competing risks (ie, the occurrence of an event that precludes future development of the outcome of interest). • Censoring is appropriate when a patient being censored at a particular time (eg, owing to loss to follow-up) is deemed to have approximately the same future risk of developing the outcome of interest as patients still being followed, and when the reason for censoring is not related to events of interest. • Censoring is not appropriate in many scenarios; for example, a dead patient can never develop a future cancer recurrence or late effect. • Kaplan-Meier (KM) analysis is commonly used for a time-to-event analysis when the primary outcome (eg, overall survival) has no meaningful competing risk and censoring is appropriate. Cox proportional hazards modeling can be used for multivariable analysis to examine the association between covariates and the primary outcome. • In the presence of competing events, incorrect use of the KM analysis will likely lead to an overestimation of the risk of the outcome of interest and biased estimates from both univariable and multivariable analyses. Therefore, KM usually should not be used to determine local recurrence risk because humanity is always at risk for dying, which is a competing risk. • A competing risk analysis is therefore most appropriate when there are competing events to the primary outcome of interest. The most commonly used multivariable model in these situations is the Fine-Gray model. • Many fundamental questions in radiation oncology—local and regional recurrence, treatment-related toxicity—are best addressed using competing risk methodologies.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2021

Long noncoding RNA AK023096 interacts with hnRNP-K and contributes to the maintenance of self-renewal in bladder cancer stem-like cells

Highlights: • BCSLCs play a critical role for the high risk of recurrence and we identified that lncRNA-AK023096 was required for controls stemness of BCSLCs. • LncRNA-AK023096 recruits hnRNP-K and epigenetically upregulated SOX2 expression by modulating H3K4 trimethylation of SOX2 promoter. • Overexpression of lncRNA-AK023096 expression in the tumor tissue was significantly associated with high risk of recurrence following TUR-Bt. LncRNA contribution to self-renewal of bladder cancer stem-like cells (CSLCs) remains largely unknown. We investigated the expression profile and biological function of lncRNAs in urothelial CSLCs by microarray analysis. Among these, lncRNA-AK023096 was identified as potentially playing a role in maintaining self-renewal of CSLCs. Knockdown of this transcript inhibited spheroid formation and tumor formation. We found that AK023096 mediates recruitment of hnRNP-K to SOX2 promoter and increases H3K4 trimethylation status on SOX2 promoter, leading to a robust change in SOX2 mRNA and protein levels. Moreover, AK023096 expression in primary tumors was found to be a powerful predictor of recurrence following transurethral resection in patients with nonmuscle-invasive bladder cancer, highlighting the critical role of lncRNA in the bladder cancer regulatory network.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2020

Dynamic Amplification of Subtropical Extreme Precipitation in a Warming Climate

Projected precipitation changes in a warming climate vary considerably, spatially, and between intensities. The changes can be greater or less than the ~7% K -1 Clausius-Clapeyron (CC) prediction, owing to dynamic effects. Using two global-climate-model large ensembles, we quantify the dynamically induced changes to precipitation extremes from the present (1996–2005) to late-21st-century (2071–2080) climates, as a function of recurrence interval, focusing on the subtropics. We separate non-CC changes into a term proportional to the present-day vertical-velocity spatial pattern (i.e., an amplification or damping thereof by a constant factor) and a residual. The amplitude term varies with recurrence interval, approximately canceling (doubling) CC for moderate (large) extremes, increasing precipitation variability. Contrastingly, the residual is quasi-uniform across recurrence intervals but spatially heterogeneous, weakening extremes over dry zones. This residual may be related to Hadley cell expansion, although this explanation is insufficient to explain many features, and other possible mechanisms are discussed.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1973

Artifacts produced during electrical stimulation of the vestibular nerve in cats

Evidence is presented to indicate that evoked potentials in the recurrent laryngeal, the cervical sympathetic, and the phrenic nerve, commonly reported as being elicited by vestibular nerve stimulation, may be due to stimulation of structures other than the vestibular nerve. Experiments carried out in decerebrated cats indicated that stimulation of the petrous bone and not that of the vestibular nerve is responsible for the genesis of evoked potentials in the recurrent laryngeal and the cervical sympathetic nerves. The phrenic response to electrical stimulation applied through bipolar straight electrodes appears to be the result of stimulation of the facial nerve in the facial canal by current spread along the petrous bone, since stimulation of the suspended facial nerve evoked potentials only in the phrenic nerve and not in the recurrent laryngeal nerve. These findings indicate that autonomic components of motion sickness represent the secondary reactions and not the primary responses to vestibular stimulation.

Tang, P. C.↗

NASA NTRS1993

Implementation of the block-Krylov boundary flexibility method of component synthesis

Carney, Kelly S.↗

NASA NTRS1995

Pseudogradient Training For A Class Of Neural Networks

Developmental second-order recurrent neural networks of special type modified to enhance stability in face of inputs beyond range of inputs on which trained. Second-order recurrent neural networks contain product feedback units and can be trained, by use of example inputs and outputs, to act as finite-state automatons. Particular second-order recurrent neural networks in question learn grammars in sense they are trained to generate binary responses to input training sequences of ones and zeros, each sequence being marked "legal" or "illegal" according to grammar to be learned.

Zeng, Zheng↗

NASA NTRS1997

Improvements in Block-Krylov Ritz Vectors and the Boundary Flexibility Method of Component Synthesis

A method of dynamic substructuring is presented which utilizes a set of static Ritz vectors as a replacement for normal eigenvectors in component mode synthesis. This set of Ritz vectors is generated in a recurrence relationship, proposed by Wilson, which has the form of a block-Krylov subspace. The initial seed to the recurrence algorithm is based upon the boundary flexibility vectors of the component. Improvements have been made in the formulation of the initial seed to the Krylov sequence, through the use of block-filtering. A method to shift the Krylov sequence to create Ritz vectors that will represent the dynamic behavior of the component at target frequencies, the target frequency being determined by the applied forcing functions, has been developed. A method to terminate the Krylov sequence has also been developed. Various orthonormalization schemes have been developed and evaluated, including the Cholesky/QR method. Several auxiliary theorems and proofs which illustrate issues in component mode synthesis and loss of orthogonality in the Krylov sequence have also been presented. The resulting methodology is applicable to both fixed and free- interface boundary components, and results in a general component model appropriate for any type of dynamic analysis. The accuracy is found to be comparable to that of component synthesis based upon normal modes, using fewer generalized coordinates. In addition, the block-Krylov recurrence algorithm is a series of static solutions and so requires significantly less computation than solving the normal eigenspace problem. The requirement for less vectors to form the component, coupled with the lower computational expense of calculating these Ritz vectors, combine to create a method more efficient than traditional component mode synthesis.

Carney, Kelly Scott↗

NASA NTRS2003

Risk-based Classification of Incidents

As the penetration of software into safety-critical systems progresses, accidents and incidents involving software will inevitably become more frequent. Identifying lessons from these occurrences and applying them to existing and future systems is essential if recurrences are to be prevented. Unfortunately, investigative agencies do not have the resources to fully investigate every incident under their jurisdictions and domains of expertise and thus must prioritize certain occurrences when allocating investigative resources. In the aviation community, most investigative agencies prioritize occurrences based on the severity of their associated losses, allocating more resources to accidents resulting in injury to passengers or extensive aircraft damage. We argue that this scheme is inappropriate because it undervalues incidents whose recurrence could have a high potential for loss while overvaluing fairly straightforward accidents involving accepted risks. We then suggest a new strategy for prioritizing occurrences based on the risk arising from incident recurrence.

Greenwell, William S.↗

NASA NTRS2006

Education and Training Module in Alertness Management

The education and training module (ETM) in alertness management has now been integrated as part of the training regimen of the Pilot Proficiency Awards Program ("WINGS") of the Federal Aviation Administration. Originated and now maintained current by the Fatigue Countermeasures Group at NASA Ames Research Center, the ETM in Alertness Management is designed to give pilots the benefit of the best and most recent research on the basics of sleep physiology, the causes of fatigue, and strategies for managing alertness during flight operations. The WINGS program is an incentive program that encourages pilots at all licensing levels to participate in recurrent training, upon completion of which distinctive lapel or tie pins (wings) and certificates of completion are awarded. In addition to flight training, all WINGS applicants must attend at least one FAA-sponsored safety seminar, FAA-sanctioned safety seminar, or industry recurrent training program. The Fatigue Countermeasures Group provides an FAA-approved industry recurrent training program through an on-line General Aviation (GA) WINGS ETM in alertness management to satisfy this requirement. Since 1993, the Fatigue Countermeasures Group has translated fatigue and alertness information to operational environments by conducting two-day ETM workshops oriented primarily toward air-carrier operations subject to Part 121 of the Federal Aviation Regulations pertaining to such operations. On the basis of the information presented in the two-day ETM workshops, an ETM was created for GA pilots and was transferred to a Web-based version. To comply with the requirements of the WINGS Program, the original Web-based version has been modified to include hypertext markup language (HTML) content that makes information easily accessible, in-depth testing of alertness-management knowledge, new interactive features, and increased informational resources for GA pilots. Upon successful completion of this training module, a participant receives a computer- screen display of a certificate of completion. The certificate, which includes the pilot s name and an identifying number, can be printed out and submitted, for ground training credit, with the pilot s WINGS application.

Mallis, M. M.↗

DOE OSTI2023

Online evolutionary neural architecture search for multivariate non-stationary time series forecasting

Time series forecasting (TSF) is one of the most important tasks in data science. TSF models are usually pre-trained with historical data and then applied on future unseen datapoints. However, real-world time series data is usually non-stationary and models trained offline usually face problems from data drift. Models trained and designed in an offline fashion can not quickly adapt to changes quickly or be deployed in real-time. To address these issues, this work presents the Online NeuroEvolution-based Neural Architecture Search (ONE-NAS) algorithm, which is a novel neural architecture search method capable of automatically designing and dynamically training recurrent neural networks (RNNs) for online forecasting tasks. Without any pre-training, ONE-NAS utilizes populations of RNNs that are continuously updated with new network structures and weights in response to new multivariate input data. ONE-NAS is tested on real-world, large-scale multivariate wind turbine data as well as the univariate Dow Jones Industrial Average (DJIA) dataset. These results demonstrate that ONE-NAS outperforms traditional statistical time series forecasting methods, including online linear regression, fixed long short-term memory (LSTM) and gated recurrent unit (GRU) models trained online, as well as state-of-the-art, online ARIMA strategies. Additionally, results show that utilizing multiple populations of RNNs which are periodically repopulated provide significant performance improvements, allowing this online neural network architecture design and training to be successful.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Performance analysis and comparison of data-driven models for predicting indoor temperature in multi-zone commercial buildings

Building thermal models, which characterize the properties of a building’s envelope and thermal mass, are essential for accurate indoor temperature and cooling/heating demand prediction. Because of their flexibility and ease of use, data-driven models are increasingly used. Here, this study compared and analyzed the performance of gray-box (resistance-capacitance) and black-box (recurrent neural network) models for predicting indoor air temperature in a real multi-zone commercial building. The developed resistance-capacitance model served as a benchmark model for which full sets of temporal data and building information were used as inputs. The recurrent neural network models were trained and tested assuming various available types and amounts of temporal data and known building physical information to investigate the effects of data and information availability. Feature importance analysis was conducted to select the key variables for different prediction targets under different scenarios. This research provides guidance in selecting an appropriate building thermal response modeling method based on the measured data availability, building physical information, and application.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2022

Dynamic machine learning-based optimization algorithm to improve boiler efficiency

With decreasing computational costs, improvement in algorithms, and the aggregation of large industrial and commercial datasets, machine learning is becoming a ubiquitous tool for process and business innovations. Machine learning is still lacking applications in the field of dynamic optimization for real-time control. This work presents a novel framework for performing constrained dynamic optimization using a recurrent neural network model combined with a metaheuristic optimizer. The framework is designed to augment an existing control system and is purely data-driven, like most industrial Model Predictive Control applications. Several recurrent neural network models are compared as well as several metaheuristic optimizers. Hyperparameters and optimizer parameters are tuned with parameter sweeps, and the resulting values are reported. Further, the best parameters for each optimizer and model combination are demonstrated in closed-loop control of a dynamic simulation, and several recommendations are made for generalizing this framework to other systems. Up to 0.953% improvement is realized over the non-optimized case for a simulated coal-fired boiler. While this is not a large improvement in percentage, the total economic impact is $991,000 per year, and this study builds a foundation for future machine learning with dynamic optimization.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2022

Machine learning for continuous quantum error correction on superconducting qubits

Abstract Continuous quantum error correction has been found to have certain advantages over discrete quantum error correction, such as a reduction in hardware resources and the elimination of error mechanisms introduced by having entangling gates and ancilla qubits. We propose a machine learning algorithm for continuous quantum error correction that is based on the use of a recurrent neural network to identify bit-flip errors from continuous noisy syndrome measurements. The algorithm is designed to operate on measurement signals deviating from the ideal behavior in which the mean value corresponds to a code syndrome value and the measurement has white noise. We analyze continuous measurements taken from a superconducting architecture using three transmon qubits to identify three significant practical examples of non-ideal behavior, namely auto-correlation at temporal short lags, transient syndrome dynamics after each bit-flip, and drift in the steady-state syndrome values over the course of many experiments. Based on these real-world imperfections, we generate synthetic measurement signals from which to train the recurrent neural network, and then test its proficiency when implementing active error correction, comparing this with a traditional double threshold scheme and a discrete Bayesian classifier. The results show that our machine learning protocol is able to outperform the double threshold protocol across all tests, achieving a final state fidelity comparable to the discrete Bayesian classifier.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗