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At least 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

A risk index for multicriterial selection of a logging system with low environmental impact

Setting up the working stages in forest operations is conditioned by environmental protection and forest health requirements. This paper exposes a method for improving the decision-making process by choosing the most environmentally effective logging systems according to terrain configuration and stand characteristics. Such a methodology for selecting machines or logging systems accounting for environment, safety as well as economics, becomes mandatory in the context of sustainable management of forest with multiple functions. Based on analytic hierarchy process analysis the following classification of the environmental performance for four considered alternatives was obtained: skyline system (42.43%), forwarder system (20.22%), skidder system (19.92%) and horse logging system (17.43%). Further, an environmental risk matrix for the most important 28 risk factors specific to any work equipment used in forest operations was produced. In the end, a multicriterial analysis generated a risk index RI ranging between 1.0 and 3.5, which could help choosing the optimal combination of logging system and logging equipment with low environmental impact. In order to demonstrate the usefulness of the proposed approach, a simple application in specific conditions of a harvesting site is presented. - Highlights: • We propose a decision-making algorithm to select eco-friendly logging systems. • Analytic hierarchy process was applied for ranking 4 types of logging systems. • An environmental risk matrix with 28 risk factors in forest operations was made up.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Automatic log analysis with NLP for the CMS workflow handling

The central Monte-Carlo production of the CMS experiment utilizes the WLCG infrastructure and manages daily thousands of tasks, each up to thousands of jobs. The distributed computing system is bound to sustain a certain rate of failures of various types, which are currently handled by computing operators a posteriori. Within the context of computing operations, and operation intelligence, we propose a Machine Learning technique to learn from the operators with a view to reduce the operational workload and delays. This work is in continuation of CMS work on operation intelligence to try and reach accurate predictions with Machine Learning. We present an approach to consider the log files of the workflows as regular text to leverage modern techniques from Natural Language Processing (NLP). In general, log files contain a substantial amount of text that is not human language. Therefore, different log parsing approaches are studied in order to map the log files’ words to high dimensional vectors. These vectors are then exploited as feature space to train a model that predicts the action that the operator has to take. This approach has the advantage that the information of the log files is extracted automatically and the format of the logs can be arbitrary. In this work the performance of the log file analysis with NLP is presented and compared to previous approaches.

Layer, Lukas↗
DOE OSTI2015

SU-E-T-142: Automatic Linac Log File: Analysis and Reporting

Purpose: End to end QA for IMRT/VMAT is time consuming. Automated linac log file analysis and recalculation of daily recorded fluence, and hence dose, distribution bring this closer. Methods: Matlab (R2014b, Mathworks) software was written to read in and analyse IMRT/VMAT trajectory log files (TrueBeam 1.5, Varian Medical Systems) overnight, and are archived on a backed-up network drive (figure). A summary report (PDF) is sent by email to the duty linac physicist. A structured summary report (PDF) for each patient is automatically updated for embedding into the R&V system (Mosaiq 2.5, Elekta AG). The report contains cross-referenced hyperlinks to ease navigation between treatment fractions. Gamma analysis can be performed on planned (DICOM RTPlan) and treated (trajectory log) fluence distributions. Trajectory log files can be converted into RTPlan files for dose distribution calculation (Eclipse, AAA10.0.28, VMS). Results: All leaf positions are within +/−0.10mm: 57% within +/−0.01mm; 89% within 0.05mm. Mean leaf position deviation is 0.02mm. Gantry angle variations lie in the range −0.1 to 0.3 degrees, mean 0.04 degrees. Fluence verification shows excellent agreement between planned and treated fluence. Agreement between planned and treated dose distribution, the derived from log files, is very good. Conclusion: Automated log file analysis is a valuable tool for the busy physicist, enabling potential treated fluence distribution errors to be quickly identified. In the near future we will correlate trajectory log analysis with routine IMRT/VMAT QA analysis. This has the potential to reduce, but not eliminate, the QA workload.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

SU-E-T-184: Clinical VMAT QA Practice Using LINAC Delivery Log Files

Purpose: To evaluate the accuracy of volumetric modulated arc therapy (VMAT) treatment delivery dose clouds by comparing linac log data to doses measured using an ionization chamber and film. Methods: A commercial IMRT quality assurance (QA) process utilizing a DICOM-RT framework was tested for clinical practice using 30 prostate and 30 head and neck VMAT plans. Delivered 3D VMAT dose distributions were independently checked using a PinPoint ionization chamber and radiographic film in a solid water phantom. DICOM RT coordinates were used to extract the corresponding point and planar doses from 3D log file dose distributions. Point doses were evaluated by computing the percent error between log file and chamber measured values. A planar dose evaluation was performed for each plan using a 2D gamma analysis with 3% global dose difference and 3 mm isodose point distance criteria. The same analysis was performed to compare treatment planning system (TPS) doses to measured values to establish a baseline assessment of agreement. Results: The mean percent error between log file and ionization chamber dose was 1.0%±2.1% for prostate VMAT plans and −0.2%±1.4% for head and neck plans. The corresponding TPS calculated and measured ionization chamber values agree within 1.7%±1.6%. The average 2D gamma passing rates for the log file comparison to film are 98.8%±1.0% and 96.2%±4.2% for the prostate and head and neck plans, respectively. The corresponding passing rates for the TPS comparison to film are 99.4%±0.5% and 93.9%±5.1%. Overall, the point dose and film data indicate that log file determined doses are in excellent agreement with measured values. Conclusion: Clinical VMAT QA practice using LINAC treatment log files is a fast and reliable method for patient-specific plan evaluation.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

A clinically observed discrepancy between image-based and log-based MLC positions

Purpose: To present a clinical case in which real-time intratreatment imaging identified an multileaf collimator (MLC) leaf to be consistently deviating from its programmed and logged position by >1 mm. Methods: An EPID-based exit-fluence dosimetry system designed to prevent gross delivery errors was used to capture cine during treatment images. The author serendipitously visually identified a suspected MLC leaf displacement that was not otherwise detected. The leaf position as recorded on the EPID images was measured and log-files were analyzed for the treatment in question, the prior day’s treatment, and for daily MLC test patterns acquired on those treatment days. Additional standard test patterns were used to quantify the leaf position. Results: Whereas the log-file reported no difference between planned and recorded positions, image-based measurements showed the leaf to be 1.3 ± 0.1 mm medial from the planned position. This offset was confirmed with the test pattern irradiations. Conclusions: It has been clinically observed that log-file derived leaf positions can differ from their actual position by >1 mm, and therefore cannot be considered to be the actual leaf positions. This cautions the use of log-based methods for MLC or patient quality assurance without independent confirmation of log integrity. Frequent verification of MLC positions through independent means is a necessary precondition to trust log-file records. Intratreatment EPID imaging provides a method to capture departures from MLC planned positions.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-T-172: A Method for Log File QA On An IBA Proteus System for Patient Specific Spot Scanning Quality Assurance

Purpose: The use of log files to perform patient specific quality assurance for both protons and IMRT has been established. Here, we extend that approach to a proprietary log file format and compare our results to measurements in phantom. Our goal was to generate a system that would permit gross errors to be found within 3 fractions until direct measurements. This approach could eventually replace direct measurements. Methods: Spot scanning protons pass through multi-wire ionization chambers which provide information about the charge, location, and size of each delivered spot. We have generated a program that calculates the dose in phantom from these log files and compares the measurements with the plan. The program has 3 different spot shape models: single Gaussian, double Gaussian and the ASTROID model. The program was benchmarked across different treatment sites for 23 patients and 74 fields. Results: The dose calculated from the log files were compared to those generate by the treatment planning system (Raystation). While the dual Gaussian model often gave better agreement, overall, the ASTROID model gave the most consistent results. Using a 5%–3 mm gamma with a 90% passing criteria and excluding doses below 20% of prescription all patient samples passed. However, the degree of agreement of the log file approach was slightly worse than that of the chamber array measurement approach. Operationally, this implies that if the beam passes the log file model, it should pass direct measurement. Conclusion: We have established and benchmarked a model for log file QA in an IBA proteus plus system. The choice of optimal spot model for a given class of patients may be affected by factors such as site, field size, and range shifter and will be investigated further.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Finite element simulations of viscoelastic flow of blade coating using the log-conformation tensor

Previous studies have demonstrated the benefits of the log-conformation formulation to model viscoelastic fluids; it increases stability at high Weissenberg numbers and ensures that the conformation tensor remains positive-definite. Many studies have applied the log-conformation tensor formulation to benchmark cases; however, relatively few studies investigate using the formulation on more complex flows. In this paper, we extend the log-conformation formulation to the manufacturing-relevant flow of blade coating. We first verify the log-conformation formulation on the benchmark problem of flow past a cylinder using the finite element method, and then apply it to the blade-coating process, in which a viscoelastic fluid entrained by a moving substrate passes under a blade at a constant web speed. We investigate various rheological effects and the resulting film thickness for the blade-coating problem, and compare the results from the log-conformation formulation to those of the original stress formulation. To this end, we show that the log-conformation formulation agrees well with other established methods, and also increases the maximum achievable web speed in the blade-coating problem.

36 MATERIALS SCIENCE↗
DOE OSTI2010

Long-term Carbon Loss and Recovery Following Selective Logging in Amazon Forests

Amazon deforestation contributes significantly to global carbon (C) emissions. In comparison, the contribution from selective logging to atmospheric CO2 emissions, and its impact on regional C dynamics, is highly uncertain. Using a new geographically-based modeling approach in combination with high resolution remote sensing data from 1999-2002, we estimate that C emissions were 0.04 – 0.05 Pg C yr-1 due to selective logging from a ~2,664,960 km2 region of the Brazilian Amazon. Selective logging was responsible for 15-19% higher carbon emissions than reported from deforestation (clear-cutting) alone. Our simulations indicated that forest carbon lost via selective logging lasts two to three decades following harvest, and that the original live biomass takes up to a century to recover, if the forests are not subsequently cleared. The two- to three-decade loss of carbon results from the biomass damaged by logging activities, including leaves, wood, and roots, estimated to be 89.1 Tg C yr-1 from 1999-2002 over the study region, leaving 70.0 Tg C yr-1 and 7.9 Tg C yr-1 to accumulate as coarse woody debris and soil C, respectively. While avoided deforestation is central to crediting rainforest nations for reduced carbon emissions, the extent and intensity of selective logging are also critical to determining carbon emissions in the context of Reduced Emissions from Deforestation and Forest Degradation (REDD). We show that a combination of automated high-resolution satellite monitoring and detailed forest C modeling can yield spatially explicit estimates of harvest related C losses and subsequent recovery in support of REDD and other international carbon market mechanisms.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Expandable Log Analyzing Framework

Prior to my internship, I was informed that a previous intern had built a tool to analyse MongoDB logs and look for invalid access attempts, which served as a great reference point for my project. I was initially tasked with expanding on her prototype and filling in the gaps such as integrating it with the main monitoring tool the lab uses. Eventually, the scope grew, expanding to support other databases and a growing collection of tools. I organized the framework around an observer pattern, meaning one point in the program sending updates to the rest of the framework. Every time a log was read and parsed, it was sent to be processed by the tools, using the type of event as a means to determine which tools should get a chance to act on the log. This decouples the tools from the log reader, making future updates and additions much easier. The framework processes MongoDB logs at ~135,000 entries per second and PostgreSQL logs at ~170,500 entries per second, accurately detecting anomalies such as slow queries and connections from unknown addresses. This framework serves to fill gaps in database monitoring tools currently implemented at the lab, such as tracking failed authentication for PostgreSQL and MongoDB which had very minimal or none before this framework. National labs such as Fermilab hold sensitive data and valuable computing resources, making them attractive targets. Monitoring intrusion attempts on databases is made much easier by this comprehensive monitoring suite.

Clark, Dylan [Unlisted, IL]↗
DOE OSTI2026

Expandable Log Analyzing Framework

Prior to my internship, I was informed that a previous intern had built a tool to analyse MongoDB logs and look for invalid access attempts, which served as a great reference point for my project. I was initially tasked with expanding on her prototype and filling in the gaps such as integrating it with the main monitoring tool the lab uses. Eventually, the scope grew, expanding to support other databases and a growing collection of tools. I organized the framework around an observer pattern, meaning one point in the program sending updates to the rest of the framework. Every time a log was read and parsed, it was sent to be processed by the tools, using the type of event as a means to determine which tools should get a chance to act on the log. This decouples the tools from the log reader, making future updates and additions much easier. The framework processes MongoDB logs at ~135,000 entries per second and PostgreSQL logs at ~170,500 entries per second, accurately detecting anomalies such as slow queries and connections from unknown addresses. This framework serves to fill gaps in database monitoring tools currently implemented at the lab, such as tracking failed authentication for PostgreSQL and MongoDB which had very minimal or none before this framework. National labs such as Fermilab hold sensitive data and valuable computing resources, making them attractive targets. Monitoring intrusion attempts on databases is made much easier by this comprehensive monitoring suite.

Clark, Dylan [Unlisted, IL]↗
DOE OSTI2026

Database-Agnostic Log Analysis and Monitoring Framework

Prior to my internship, I was informed that a previous intern had built a tool to analyse MongoDB logs and look for invalid access attempts, which served as a great reference point for my project. I was initially tasked with expanding on her prototype and filling in the gaps such as integrating it with the main monitoring tool the lab uses. Eventually, the scope grew, expanding to support other databases and a growing collection of tools. I organized the framework around an observer pattern, meaning one point in the program sending updates to the rest of the framework. Every time a log was read and parsed, it was sent to be processed by the tools, using the type of event as a means to determine which tools should get a chance to act on the log. This decouples the tools from the log reader, making future updates and additions much easier. The framework processes MongoDB logs at ~135,000 entries per second and PostgreSQL logs at ~170,500 entries per second, accurately detecting anomalies such as slow queries and connections from unknown addresses. This framework serves to fill gaps in database monitoring tools currently implemented at the lab, such as tracking failed authentication for PostgreSQL and MongoDB which had very minimal or none before this framework. National labs such as Fermilab hold sensitive data and valuable computing resources, making them attractive targets. Monitoring intrusion attempts on databases is made much easier by this comprehensive monitoring suite.

Clark, Dylan [Unlisted, US, IL; Fermilab]↗
DOE OSTI2019

EGS Collab Experiment 2: Core Logs

Core logs and photos from the EGS Collab project Experiment 2 for the Top Vertical well (TV4100) and the Top Horizontal well (TV 4100) on the 4100 Level of SURF (the Sanford Underground Research Facility). The core logs are stored in a single PDF file with 5-ft run intervals. In the monitoring well IDs, "O" indicates that the well is orthogonal to the anticipated fracture plane, "T" refers to top, and "H" refers to horizontal. A core log CT scan for TV4100 and a layout image of the 4100 wells are included as well. Logs include: experiment number; borehole ID; depth interval; run number; final packed core box number; scribe line (yes/no; red-on-right convention); logging dates; logger initials; as well as sketches of core foliation, folding, and fracturing with additional details and notes on other features of interest. Shift reports include: date, location, personnel, summary of site activity, and field notes.

15 GEOTHERMAL ENERGY↗
DOE OSTI2015

Correction of steel casing effect for density log using numerical and experimental methods in the slim borehole

Density log is widely applied for a variety of fields such as the petroleum exploration, mineral exploration, and geotechnical survey. The logging condition of density log is normally open holes but there are frequently cased boreholes. The primary calibration curve by slim hole logging manufacturer is normally the calibration curves for the variation of borehole diameter. In this study, we have performed the correction of steel casing effects using numerical and experimental methods. We have performed numerical modeling using the Monte Carlo N-Particle (MCNP) code based on Monte Carlo method, and field experimental method from open and cased hole log. In this study, we used the FDGS (Formation Density Gamma Sonde) for slim borehole with a 100 mCi 137Cs source, three inch borehole and steel casing. The casing effect between numerical and experimental method is well matched.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

Log-periodic quantum magneto-oscillations and discrete-scale invariance in topological material HfTe 5

Discrete-scale invariance (DSI) is a phenomenon featuring intriguing log-periodicity that can be rarely observed in quantum systems. Here, we report the log-periodic quantum oscillations in the longitudinal magnetoresistivity ( ρ xx ) and the Hall traces ( ρ yx ) of HfTe 5 crystals, which reveal the DSI in the transport-coefficients matrix. The oscillations in ρ xx and ρ yx show the consistent log B -periodicity with a phase shift. The finding of the log B oscillations in the Hall resistance supports the physical mechanism as a general quantum effect originating from the resonant scattering. Combined with theoretical simulations, we further clarify the origin of the log-periodic oscillations and the DSI in the topological materials. This work evidences the universality of the DSI in the Dirac materials and provides indispensable information for a full understanding of this novel phenomenon.

magnetoresistance↗
DOE OSTI2013

Borehole Calibration Facilities to Support Gamma Logging for Hanford Subsurface Investigation and Contaminant Monitoring - 13516

Repeated gamma logging in cased holes represents a cost-effective means to monitor gamma-emitting contamination in the deep vadose zone over time. Careful calibration and standardization of gamma log results are required to track changes and to compare results over time from different detectors and logging systems. This paper provides a summary description of Hanford facilities currently available for calibration of logging equipment. Ideally, all logging organizations conducting borehole gamma measurements at the Hanford Site will take advantage of these facilities to produce standardized and comparable results. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Machine Learning for Well Log Analysis in Uranium Mining

This project explores the use of Artificial Intelligence (AI) and Machine Learning (ML) techniques to automate well log analysis for uranium mining. Geophysical log data—spontaneous potential, resistivity, and gamma ray—were used to classify lithology, correlate well logs and identify roll front zonation patterns, which are critical for locating uranium ore bodies. Supervised ML algorithms such as eXtreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), and Random Forest were trained to classify lithology with high accuracy. Gradient Boosting Machines (GBM), XGBoost, Random Forest, and Neural Networks were also used for role front zone identification. Moreover, a Fast Dynamic Time Warping (FastDTW) algorithm was employed for well log correlation. Additionally, sample lag was addressed using dynamic programming. Results demonstrate the potential of AI and ML to streamline well log analysis and enhance uranium exploration workflows.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2018

Coulomb log for conductivity of dense plasmas

The Coulomb log (log Λ) approximation is widely used to approximate electron transport coefficients in dense plasmas. It is a classical approximation to the momentum transport cross section. Here, the accuracy of this approximation for electrical conductivity in dense plasmas is assessed by comparing to fully quantum mechanical calculations for realistic scattering potentials. It is found that the classical approximation is accurate to ±10% when log Λ > 3, irrespective of the plasma species. The thermodynamic regime (density and temperature) for which log Λ > 3 corresponds to does, however, strongly depend on the material. For increasing Z, log Λ is greater than 3 for increasingly high temperatures and lower densities.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2013

THE IMACS CLUSTER BUILDING SURVEY. IV. THE LOG-NORMAL STAR FORMATION HISTORY OF GALAXIES

We present here a simple model for the star formation history (SFH) of galaxies that is successful in describing both the star formation rate density (SFRD) over cosmic time, as well as the distribution of specific star formation rates (sSFRs) of galaxies at the current epoch, and the evolution of this quantity in galaxy populations to a redshift of z = 1. We show first that the cosmic SFRD is remarkably well described by a simple log-normal in time. We next postulate that this functional form for the ensemble is also a reasonable description for the SFHs of individual galaxies. Using the measured sSFRs for galaxies at z {approx} 0 from Paper III in this series, we then construct a realization of a universe populated by such galaxies in which the parameters of the log-normal SFH of each galaxy are adjusted to match the sSFRs at z {approx} 0 as well as fitting, in ensemble, the cosmic SFRD from z = 0 to z = 8. This model predicts, with striking fidelity, the distribution of sSFRs in mass-limited galaxy samples to z = 1; this match is not achieved by other models with a different functional form for the SFHs of individual galaxies, but with the same number of degrees of freedom, suggesting that the log-normal form is well matched to the likely actual histories of individual galaxies. We also impose the sSFR versus mass distributions at higher redshifts from Paper III as constraints on the model, and show that, as previously suggested, some galaxies in the field, particularly low mass galaxies, are quite young at intermediate redshifts. As emphasized in Paper III, starbursts are insufficient to explain the enhanced sSFRs in intermediate redshift galaxies; we show here that a model using only smoothly varying log-normal SFHs for galaxies, which allows for some fraction of the population to have peak star formation at late times, does however fully explain the observations. Finally, we show that this model, constrained in detail only at redshifts z < 1, also produces the main sequence of star-formation observed at 1.5 < z < 2.5, again suggesting that the log-normal SFHs are a close approximation to the actual histories of typical galaxies.

79 ASTRONOMY AND ASTROPHYSICS↗