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Supporting Failure Analysis with Discoverable Annotated Log Datasets.
Abstract not provided.
Automated Well-Log Processing and Lithology Classification: Unsupervised or Supervised Machine Learning?
Presented by NETL at CCUS 08/19
Lithofacies analysis of two Marcellus wells using core and log data in Northern West Virginia, USA
Presented by NETL at Eastern Section AAPG Meeting, 10/19
Am-Be Versus Neutron Generator-Based Alternatives for Well Logging Measurements.
Abstract not provided.
Numerical Simulations of CO2 Storage Efficiencies for Saline Formations Using Experimental Relative Permeabilities and Petrophysical Properties Derived from Well Logs
AAPG-CCUS Conference, Virtual, March 29-31, 2022
Classification of Malicious Cyber Activity Via Machine Learning Against Event Logs.
Abstract not provided.
Dynamic NMR Logging Technologies for High-Resolution Measurement of Hydrogeologic Properties and Soil Response
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Quantifying the Limits for Acceptable Variances between Time-Lapsed Logging and Reservoir Simulations of CO2 Plume Migration
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Development of a HTHP Logging Tool for Downhole pH Measurements.
Abstract not provided.
DP-HPC: Bringing Differential Privacy to HPC Systems Log Sharing and Analysis
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Scalable GPS Data Logging To Support Advanced Fleet Analysis
This highlight details the key takeaways from a project that utilized NLR's Fleet Research, Energy Data, and Insights (FleetREDI) data analysis pipeline. National Laboratory of the Rockies researchers developed and demonstrated low-cost, open-source Arduino data loggers with 3D-printed cases that are compatible with global navigational systems and built with components available ubiquitously worldwide, enabling cost-effective collection and analysis of fleet operational data. Validated on an overseas transit bus fleet, NLR analysis showed that, with sufficient charging opportunities, 90% of observed duty cycles could be accomplished by electric buses with no modifications to operations.
Power law behavior of RR-interval variability in healthy middle-aged persons, patients with recent acute myocardial infarction, and patients with heart transplants
BACKGROUND. The purposes of the present study were (1) to establish normal values for the regression of log(power) on log(frequency) for, RR-interval fluctuations in healthy middle-aged persons, (2) to determine the effects of myocardial infarction on the regression of log(power) on log(frequency), (3) to determine the effect of cardiac denervation on the regression of log(power) on log(frequency), and (4) to assess the ability of power law regression parameters to predict death after myocardial infarction. METHODS AND RESULTS. We studied three groups: (1) 715 patients with recent myocardial infarction; (2) 274 healthy persons age and sex matched to the infarct sample; and (3) 19 patients with heart transplants. Twenty-four-hour RR-interval power spectra were computed using fast Fourier transforms and log(power) was regressed on log(frequency) between 10(-4) and 10(-2) Hz. There was a power law relation between log(power) and log(frequency). That is, the function described a descending straight line that had a slope of approximately -1 in healthy subjects. For the myocardial infarction group, the regression line for log(power) on log(frequency) was shifted downward and had a steeper negative slope (-1.15). The transplant (denervated) group showed a larger downward shift in the regression line and a much steeper negative slope (-2.08). The correlation between traditional power spectral bands and slope was weak, and that with log(power) at 10(-4) Hz was only moderate. Slope and log(power) at 10(-4) Hz were used to predict mortality and were compared with the predictive value of traditional power spectral bands. Slope and log(power) at 10(-4) Hz were excellent predictors of all-cause mortality or arrhythmic death. To optimize the prediction of death, we calculated a log(power) intercept that was uncorrelated with the slope of the power law regression line. We found that the combination of slope and zero-correlation log(power) was an outstanding predictor, with a relative risk of > 10, and was better than any combination of the traditional power spectral bands. The combination of slope and log(power) at 10(-4) Hz also was an excellent predictor of death after myocardial infarction. CONCLUSIONS. Myocardial infarction or denervation of the heart causes a steeper slope and decreased height of the power law regression relation between log(power) and log(frequency) of RR-interval fluctuations. Individually and, especially, combined, the power law regression parameters are excellent predictors of death of any cause or arrhythmic death and predict these outcomes better than the traditional power spectral bands.
Wood Decomposition: Understanding Processes Regulating Carbon Transfer to Soil Carbon Pools Using FACE Wood at Multiple Scales
Dead wood comprises approximately 20% of forest biomass. While it is widely recognized for its role in ecosystem functions relative to habitat and nutrient cycling, there are considerable uncertainties associated with its role in the forest carbon (C) cycle. The principal uncertainty is whether dead wood contributes to the soil C pool. With little supporting evidence, the presumption is that C in wood is respired back to the atmosphere through decomposition processes. The inability to attribute forest soil C to its source components has been a barrier to addressing this important forest carbon cycle question. The U.S. Dept. of Energy’s Free Air CO 2 Enrichment (FACE) experiment produced wood that has a distinct δ 13 C signature that can be used to trace its fate through organic matter decomposition processes. Using two hardwood species (aspen, birch) from the Rhinelander FACE site and pine from the Duke FACE site, the FACE Wood Decomposition Experiment (FWDE) was established in 2011 to provide the foundation for addressing wood decomposition and its associated processes at the continental scale. Plots were installed on nine experimental forests representing a range in bioclimatic forest conditions within the continental U.S. Each identical plot has logs of the three species incubating on the soil surface and pine logs suspended in the air, representing standing dead wood. Abiotic conditions are monitored continuously, and soil and forest conditions characterized. Logs on the sites have been measured every two years to assess decay. This project was initiated in 2016 with specific objectives to: (a) determine whether dead wood C contributes to the soil C pool, (b) assess the fungal community composition mediating the decay, (c) assess the interactions of termites with microbial decay, and (d) develop a mechanistic model to simulate wood decomposition. The principal field sampling was conducted in 2017; correspondingly, the results reported here represent six years of decomposition. Wood decomposition varied from 13 to 75 % loss across the bioclimatic gradient. Sites in the southeastern U.S. coastal plain and piedmont had fastest decomposition rates for each of the species. In contrast sites in the Rocky Mountains had the slowest decay rates, which were associated with cold temperatures. Standing deadwood decomposes at a much slower rate than logs on the soil surface; a response due to differences in wood moisture content. Wood carbon was measured in the mineral soil across each of the sites; accordingly, this is the first study to affirm that a portion of the wood C does become part of the soil C pool. Averaged across the FWDE sites, approximately 1.4% of the soil C in the upper 10 cm of the mineral soil directly beneath the log was derived from the deadwood; within 50 cm of the log, wood C accounted for an average of 2.1%. These results demonstrate that the contribution of deadwood to the soil C pool is a soil volume surrounding the log; and it’s likely a larger volume than what we sampled. We also hypothesized that there would be more wood C in the mineral soil when termites were present. However, that hypothesis wasn’t supported by measurements on the Santee Experimental Forest termite exclusion (SEF-TX) plots under FACE logs that incubated for 6 years. Across all the sites, the majority of the wood C was transferred to the atmosphere. Approximately 7,000 fungal species were sequenced. The fungal communities were different among each of the sites suggesting local colonization; interestingly, within sites the three species also exhibited differences in fungal community composition. White rot was the dominant fungal form in pine and hardwood, which yields less lignin in the wood residue. Brown rot occurred in approximately 20% of the pine and aspen logs, and it did not occur in birch logs. The occurrence of brown rot was only weakly correlated to site factors. We conducted two additional experiments to assess the effects of termites on wood decomposition and the associated interactions with the fungal community. Pine and birch logs were incubated on the Santee Experimental Forest termite exclusion plots in upland and riparian settings. There was no difference in decay between areas where termites were able colonize and plots where they had been excluded. Highly variable foraging by termites was a primary contributor the result. Correspondingly, fungal communities did not vary on logs incubated among the two treatments, but they did reflect site and species differences. The second experiment was conducted on each of the nine FWDE sites where pine and birch wood blocks were incubated for two years with and without a wrapping designed to exclude termites and other arthropods. The effects of termites on wood decomposition could be confirmed on two of the four sites known to have subterranean termites. On the two southeastern sites, termites contributed approximately 8% to mass loss in pine blocks over two years. A major factor clouding the role of termites in wood decomposition is the inability to assess the time, duration, and intensity of the foraging with the logs. Another uncertainty is related to whether a piece of wood will be foraged. A process-based wood decomposition model was developed representing biological processes mediated by fungi, bacteria, termites, beetles, as well as physical and chemical processes. The Coarse Wood Decomposition Model (CWDM) was parameterized based on a thorough review of the literature and it was validated using data from the FWDE. In the initial setup, the model considers softwoods and hardwoods groups, as opposed to individual species. Sensitivity analyses incorporating climate data from 89 locations in North America demonstrated that CWDM was stable from the tropics to the boreal zone. Model calibration and validation showed it to perform very well. The CWDM is poised for additional testing with subsequent FWDE data as well as other data sets, and it is available for incorporation into forest biogeochemical models or DOEs Earth System Modeling framework.
Efficient Merge and Insert Operations for Binary Heaps and Trees
Binary heaps and binary search trees merge efficiently. We introduce a new amortized analysis that allows us to prove the cost of merging either binary heaps or balanced binary trees is O(l), in the amortized sense. The standard set of other operations (create, insert, delete, extract minimum, in the case of binary heaps, and balanced binary trees, as well as a search operation for balanced binary trees) remain with a cost of O(log n). For binary heaps implemented as arrays, we show a new merge algorithm that has a single operation cost for merging two heaps, a and b, of O(absolute value of a + min(log absolute value of b log log absolute value of b. log absolute value of a log absolute value of b). This is an improvement over O(absolute value of a + log absolute value of a log absolute value of b). The cost of the new merge is so low that it can be used in a new structure which we call shadow heaps. to implement the insert operation to a tunable efficiency. Shadow heaps support the insert operation for simple priority queues in an amortized time of O(f(n)) and other operations in time O((log n log log n)/f (n)), where 1 less than or equal to f (n) less than or equal to log log n. More generally, the results here show that any data structure with operations that change its size by at most one, with the exception of a merge (aka meld) operation, can efficiently amortize the cost of the merge under conditions that are true for most implementations of binary heaps and search trees.
Gravitational radiation from the Galaxy
The spectral flux of gravitational radiation incident on earth from the Galactic W UMa binaries, unevolved binaries, cataclysmic binaries, neutron star binaries, black hole-neutron star binaries, and close white dwarf binary (CWDB) systems is calculated. The peak values for the strain amplitude hv produced by the binaries are: log hv = -17.0/sq rt Hz at log v = -4.40 Hz for W UMa's; log hv = -16.5/sq rt Hz at log v = -5.50 Hz for unevolved binaries; log hv = -18.2/sq rt Hz at log v = -5.15 Hz for neutron star binaries; log hv = -17.0/sq rt Hz at log v = -4.7 Hz for black hole-neutron star binaries; log hv = -18.0 /sq rt Hz at log v = -4.10 Hz for cataclysmic binaries, and log hv = -27.0/sq rt Hz at log v = -4.0 Hz for CWDBs. The gravitational flux at ultralow frequencies is emitted mainly by unevolved binaries. The integrated flux incident on earth is about 2.4 x 10 to the -9th ergs/sq cm/s.
Constraining the low-mass Slope of the star formation sequence at 0.5 < z < 2.5
We constrain the slope of the star formation rate (SFR; log Ψ) to stellar mass (log M {sub *}) relation down to log (M {sub *}/M {sub ☉}) = 8.4 (log (M {sub *}/M {sub ☉}) = 9.2) at z = 0.5 (z = 2.5) with a mass-complete sample of 39,106 star-forming galaxies selected from the 3D-HST photometric catalogs, using deep photometry in the CANDELS fields. For the first time, we find that the slope is dependent on stellar mass, such that it is steeper at low masses (log Ψ∝log M {sub *}) than at high masses (log Ψ∝(0.3-0.6)log M {sub *}). These steeper low-mass slopes are found for three different star formation indicators: the combination of the ultraviolet (UV) and infrared (IR), calibrated from a stacking analysis of Spitzer/MIPS 24 μm imaging; β-corrected UV SFRs; and Hα SFRs. The normalization of the sequence evolves differently in distinct mass regimes as well: for galaxies less massive than log (M {sub *}/M {sub ☉}) < 10 the specific SFR (Ψ/M {sub *}) is observed to be roughly self-similar with Ψ/M {sub *}∝(1 + z){sup 1.9}, whereas more massive galaxies show a stronger evolution with Ψ/M {sub *}∝(1 + z){sup 2.2-3.5} for log (M {sub *}/M {sub ☉}) = 10.2-11.2. The fact that we find a steep slope of the star formation sequence for the lower mass galaxies will help reconcile theoretical galaxy formation models with the observations.