Search NASA⌕ Search

SEARCH · Search NASA

Results for “recurrence”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
NASA NTRS2014

Monitoring for Renal Stone Recurrence in Astronauts With History of Stone

After an initial stone episode persons are at increased risk for future stone formation. A systematic approach is required to monitor the efficacy of treatment and preventive measures, and to assess the risk of developing new stones. This is important for persons working in critical jobs or austere environments, such as astronauts. A literature review of the current standards of care for renal stone monitoring and imaging was done. Military and civil aviation standards were also reviewed, as well as the medical precedents from the space program. Additionally, a new, more effective, renal stone ultrasound protocol has been developed. Using this work, a monitoring algorithm was proposed that takes into consideration the unique mission and operational environment of spaceflight. The approach to imaging persons with history of renal stones varies widely in the literature. Imaging is often done yearly or biannually, which may be too long for mission critical personnel. In the proposed algorithm astronauts with a history of renal stone, who may be under consideration for assignment, are imaged by a detailed, physiciandriven, ultrasound protocol. Unassigned personnel are monitored by yearly ultrasound and urine studies. Any positive ultrasound study is then followed by low-dose renal computed tomography scan. Other criteria are also established. The proposed algorithm provides a balanced approach between efficacy and reduced radiation exposure for the monitoring of astronauts with a renal stone history. This may eventually allow a transition from a risk-averse, to a risk-modifying approach that can enable continued service of individuals with history of renal stone that have adequately controlled risk factors.

Reyes, David P.↗
NASA NTRS2024

Influence of Ductility on the Performance of Lunar Habitat Structures Under Recurrent Disturbances

This research examines how ductility affects the durability of lunar surface structures against recurring disturbances like moonquakes, micrometeorite impacts, and thermal cycles over an extended period. The structural performance at various levels of ductility was determined by adjusting material parameters and the thickness of a reference multilayered dome structure. Moonquake and micrometeorite impact-induced lateral displacements were estimated using a reduced-order model under a control-oriented dynamic computational modeling framework. The study considered the degradation of the metallic dome’s strength properties over time due to thermal cycles. Fragility curves were generated by assessing the likelihood of reaching three predefined damage levels as a result of multiple hazards. Additionally, a discounted cash flow analysis was conducted to incorporate a financial aspect into the performance comparison. The findings revealed that structures with sufficient ductility capacity have a lower probability of sustaining severe damage or collapsing within a shorter time frame. Hence, having ductile structures in lunar environments is advantageous as it allows the postponement of maintenance and repair actions, thereby conserving scarce resources for more urgent tasks. Moreover, the financial analysis demonstrated that lunar habitats with higher ductile capacities result in larger net present values, offering a higher return on the initial investment.

Arsalan Majlesi↗
NASA NTRS1978

Magnetic fields and flows between 1 AU and 0.3 AU during the primary mission of HELIOS 1

The recurrent flow and field patterns observed by HELIOS 1, and the relation between these patterns and coronal holes are discussed. Four types of recurrent patterns were observed: a large recurrent stream, a recurrent slow (quiet) flow, a rapidly evolving flow, and a recurrent compound stream. There recurrent streams were not stationary, for although the sources recurred at approximately the same longitudes on successive rotations, the shapes and latitudinal patterns changed from one rotation to the next. A type of magnetic field and plasma structure characterized by a low ion temperature and a high magnetic field intensity is described as well as the structures of stream boundaries between the sun at approximately 0.3 AU.

Burlaga, L. F.↗
NASA NTRS2014

Predicting Solar Cycle 24 Using a Geomagnetic Precursor Pair

We describe using Ap and F(10.7) as a geomagnetic-precursor pair to predict the amplitude of Solar Cycle 24. The precursor is created by using F(10.7) to remove the direct solar-activity component of Ap. Four peaks are seen in the precursor function during the decline of Solar Cycle 23. A recurrence index that is generated by a local correlation of Ap is then used to determine which peak is the correct precursor. The earliest peak is the most prominent but coincides with high levels of non-recurrent solar activity associated with the intense solar activity of October and November 2003. The second and third peaks coincide with some recurrent activity on the Sun and show that a weak cycle precursor closely following a period of strong solar activity may be difficult to resolve. A fourth peak, which appears in early 2008 and has recurrent activity similar to precursors of earlier solar cycles, appears to be the "true" precursor peak for Solar Cycle 24 and predicts the smallest amplitude for Solar Cycle 24. To determine the timing of peak activity it is noted that the average time between the precursor peak and the following maximum is approximately equal to 6.4 years. Hence, Solar Cycle 24 would peak during 2014. Several effects contribute to the smaller prediction when compared with other geomagnetic-precursor predictions. During Solar Cycle 23 the correlation between sunspot number and F(10.7) shows that F(10.7) is higher than the equivalent sunspot number over most of the cycle, implying that the sunspot number underestimates the solar-activity component described by F(10.7). During 2003 the correlation between aa and Ap shows that aa is 10 % higher than the value predicted from Ap, leading to an overestimate of the aa precursor for that year. However, the most important difference is the lack of recurrent activity in the first three peaks and the presence of significant recurrent activity in the fourth. While the prediction is for an amplitude of Solar Cycle 24 of 65 +/- 20 in smoothed sunspot number, a below-average amplitude for Solar Cycle 24, with maximum at 2014.5+/-0.5, we conclude that Solar Cycle 24 will be no stronger than average and could be much weaker than average.

predictions↗
DOE OSTI2024

Complexity-calibrated benchmarks for machine learning reveal when prediction algorithms succeed and mislead

Abstract Recurrent neural networks are used to forecast time series in finance, climate, language, and from many other domains. Reservoir computers are a particularly easily trainable form of recurrent neural network. Recently, a “next-generation” reservoir computer was introduced in which the memory trace involves only a finite number of previous symbols. We explore the inherent limitations of finite-past memory traces in this intriguing proposal. A lower bound from Fano’s inequality shows that, on highly non-Markovian processes generated by large probabilistic state machines, next-generation reservoir computers with reasonably long memory traces have an error probability that is at least $$\sim 60\%$$ ∼ 60 % higher than the minimal attainable error probability in predicting the next observation. More generally, it appears that popular recurrent neural networks fall far short of optimally predicting such complex processes. These results highlight the need for a new generation of optimized recurrent neural network architectures. Alongside this finding, we present concentration-of-measure results for randomly-generated but complex processes. One conclusion is that large probabilistic state machines—specifically, large $$\epsilon$$ ϵ -machines—are key to generating challenging and structurally-unbiased stimuli for ground-truthing recurrent neural network architectures.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1977

Some MACSYMA program for solving difference equations

A set of MACSYMA programs are described for finding closed form solutions to linear recurrence relations in equations having either constant or variable coefficients. In the homogenous case, a polymonial equation is obtained and the solution to the recurrence relation can be written as a linear combination of the roots of the polynomial. Exponential generating functions are used to solve variable coefficient relations. Taking successive derivatives and using the recurrence relation, an ordinary differential equation is obtained. Expanding the solution to the differential equation in a Taylor series, shows that the nth term of the series is the solution to the recurrence relation. For second order recurrences, a check is made for those that can be solved in terms of Bessel functions.

Ivie, J.↗