Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “log”

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 1,009 records · Page 56
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

A Communication-Avoiding 3D LU Factorization Algorithm for Sparse Matrices

We propose a new algorithm to improve the strong scalability of right-looking sparse LU factorization on distributed memory systems. Our 3D sparse LU algorithm uses a three-dimensional MPI process grid, aggressively exploits elimination tree parallelism and trades off increased memory for reduced per-process communication. We also analyze the asymptotic improvements for planar graphs (e.g., from 2D grid or mesh domains) and certain non-planar graphs (specifically for 3D grids and meshes). For planar graphs with n vertices, our algorithm reduces communication volume asymptotically in n by a factor of O{log n} and latency by a factor of O{log n}. For non-planar cases, our algorithm can reduce the per-process communication volume by 3× and latency by O{n^1/3} times. In all cases, the memory needed to achieve these gains is a constant factor. We implemented our algorithm by extending the 2D data structure used in superLU. Our new 3D code achieves speedups up to 27× for planar graphs and up to 3.3× for non-planar graphs over the baseline 2D superLU when run on 24,000 cores of a Cray XC30.

Sao, Piyush↗
DOE OSTI2022

Robust Event Classification Using Imperfect Real-world PMU Data

Here, this paper studies robust event classification using imperfect real-world phasor measurement unit (PMU) data. By analyzing the real-world PMU data, we find it is challenging to directly use this dataset for event classifiers due to the low data quality observed in PMU measurements and event logs. To address these challenges, we develop a novel machine learning framework for training robust event classifiers, which consists of three main steps: data preprocessing, fine-grained event data extraction, and feature engineering. Specifically, the data preprocessing step addresses the data quality issues of PMU measurements (e.g., bad data and missing data); in the fine-grained event data extraction step, a model-free event detection method is developed to accurately localize the events from the inaccurate event timestamps in the event logs; and the feature engineering step constructs the event features based on the patterns of different event types, in order to improve the performance and the interpretability of the event classifiers. Based on the proposed framework, we develop a workflow for event classification using the real-world PMU data streaming into the system in real time. Using the proposed framework, robust event classifiers can be efficiently trained based on many off-the-shelf lightweight machine learning models. Numerical experiments using the real-world dataset from the Western Interconnection of the U.S power transmission grid show that the event classifiers trained under the proposed framework can achieve high classification accuracy while being robust against low-quality data.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2019

On the Design of Sinuous Antennas for UWB Radar Applications

We present that sinuous antennas are capable of producing ultra-wideband radiation with polarization diversity. This capability makes the sinuous antenna an attractive candidate for UWB polarimetric radar applications. Additionally, the ability of the sinuous antenna to be implemented as a planar structure makes it a good fit for close in sensing applications such as ground penetrating radar. However, recent literature has shown the sinuous antenna to suffer from resonances which degrade performance. Such resonances produce late time ringing which is particularly troubling for pulsed close in sensing applications. The resonances occur in two forms: log-periodic resonances on the arms, and a resonance due to the sharp ends left by the outer truncation. A detailed investigation as to the correlation between the log-periodic resonances and the sinuous antenna design parameters indicates the resonances may be mitigated by selecting appropriate design parameters. Additionally, a novel truncation method is proposed to remove the sharp end resonance. Both simulation and measured results are provided to support the developed sinuous antenna design guidance.

42 ENGINEERING↗
DOE OSTI2019

Profiling the Usage of an Extreme-Scale Archival Storage System

Profiling the archival storage system in scientific computing environments has received much less attention compared to the parallel file system, but is equally important since it stores the final data products safely, for a long duration. In this paper, we analyze eight years worth of data transfer logs for accessing the archival file system (HPSS) in the Oak Ridge Leadership Computing Facility (OLCF), which has been hosting the world's largest supercomputers and file systems. Our analysis encompasses about 135 million data transfer activities to the 80 PB High Performance Storage System (HPSS), between 2010 and 2017. We analyze the logs from several dimensions, including studying the workload characteristics (e.g., access patterns, frequency of accesses and temporal behavior), file system characteristics (e.g., directory depth, file system scaling trends, file types), and scientific user behavior (e.g., domain-specific usage and organization). Based on the analysis, we derive insights into the future evolution of the archive in terms of provisioning, desired features and functionality from the archive software, role and right sizing of the archive tiers, quota management, and the importance of smart and efficient metadata and storage management. We believe our study will prove useful for both operating current archival storage and better provisioning future systems.

Sim, Hyogi↗
DOE OSTI2019

An Automated Vehicle Fuel Economy Benefits Evaluation Framework Using Real-World Travel and Traffic Data

Increasing automation is a consistent development trend in the automobile industry. However, real-world evaluation of the operational and energy consumption differences between automated vehicles and comparable manually driven vehicles has been limited. This study helps fill the information gap by comparing the operation and fuel economy of vehicles in adaptive cruise control (ACC) and non-ACC modes based on large-scale field test data collected by Volvo Car Corporation (Volvo Cars) from vehicles traveling on the designated 'Drive Me' project road network in Gothenburg, Sweden. The test vehicles' travel data are classified by driving mode (ACC vs. non-ACC) and driving conditions, which refer to traffic speed and road grade in this study. The results from the data logging fleet are used to estimate the aggregate fuel consumption differences at the Drive Me road-network level for vehicles traveling in ACC vs. non-ACC mode based on appropriately weighting the total amount of travel that took place on the network under different driving conditions. At the ACC penetration levels observed in the field test data, vehicles tended to drive more smoothly in ACC mode than in non-ACC mode. The corresponding travel-weighted fuel consumption rate for vehicles in ACC mode was about 5%-7% lower than for vehicles in non-ACC mode when traveling at similar conditions. Sensitivity analyses impart confidence in this result, and in the future, the established evaluation framework could be used to objectively quantify potential on-road fuel consumption impacts from vehicles with even higher levels of automated driving capability.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2017

A Scientific Data Provenance Harvester for Distributed Applications

Data provenance provides a way for scientists to observe how experimental data originates, conveys process history, and explains influential factors such as experimental rationale and associated environmental factors from system metrics measured at runtime. The US Department of Energy Office of Science Integrated end-to-end Performance Prediction and Diagnosis for Extreme Scientific Workflows (IPPD) project has developed a provenance harvester that is capable of collecting observations from file based evidence typically produced by distributed applications. To achieve this, file based evidence is extracted and transformed into an intermediate data format inspired in part by W3C CSV on the Web recommendations, called the Harvester Provenance Application Interface (HAPI) syntax. This syntax provides a general means to pre-stage provenance into messages that are both human readable and capable of being written to a provenance store, Provenance Environment (ProvEn). HAPI is being applied to harvest provenance from climate ensemble runs for Accelerated Climate Modeling for Energy (ACME) project funded under the U.S. Department of Energy’s Office of Biological and Environmental Research (BER) Earth System Modeling (ESM) program. ACME informally provides provenance in a native form through configuration files, directory structures, and log files that contain success/failure indicators, code traces, and performance measurements. Because of its generic format, HAPI is also being applied to harvest tabular job management provenance from Belle II DIRAC scheduler relational database tables as well as other scientific applications that log provenance related information.

Stephan, Eric G.↗
DOE OSTI2021

A Butterfly-Accelerated Volume Integral Equation Solver for Broad Permittivity and Large-Scale Electromagnetic Analysis

In this work, a butterfly-accelerated volume integral equation (VIE) solver is proposed for fast and accurate electromagnetic (EM) analysis of scattering from heterogeneous objects. The proposed solver leverages the hierarchical off-diagonal butterfly (HOD-BF) scheme to construct the system matrix and obtain its approximate inverse, used as a preconditioner. Complexity analysis and numerical experiments validate the O(N log 2 N) construction cost of the HOD-BF-compressed system matrix and O(N log 1.5 N) inversion cost for the preconditioner, where N is the number of unknowns in the high-frequency EM scattering problem. For many practical scenarios, the proposed VIE solver requires less memory and computational time to construct the system matrix and obtain its approximate inverse compared to a H matrix-accelerated VIE solver. The accuracy and efficiency of the proposed solver have been demonstrated via its application to the EM analysis of large-scale canonical and real-world structures comprising of broad permittivity values and involving millions of unknowns.

42 ENGINEERING↗
DOE OSTI2024

GraphCH: A Deep Framework for Assessing Cyber-Human Aspects in Insider Threat Detection

Insider threat is one of the most damaging cyber attacks that could cause the loss of intellectual property and enterprise data security breaches. Action sequence data such as host logs are used to investigate such threats and develop anomaly-based AI detectors. However, insider threat actions are similar to legitimate user activities, causing AI detectors to fail and suffer from high false alarm rates. Therefore, user cyber activity logs are inadequate to fully unfold insider threats. In this study, we adopt human psychological principles of risk-taking and impulsiveness along with host data to assess the influence and usefulness of human behavioral aspects in insider threat detection. Here, we hypothesize that individuals' impulsive and risk-taking behavior correlates with cyberspace activities. To validate our hypothesis, we conducted an IRB-approved study recruiting 35 participants who work in a large U.S. university and collected their cyber and psychological data for 90 days. Host and human-behavioral data analysis and mapping indicate that impulsive and risk-taking users trigger more system errors causing (un)intentional insider threats and are susceptible to attackers' social engineering and cognitive hacking. Utilizing cyber-human aspects, we introduce a Cyber-Human Graph Neural Network (GNN) based framework GraphCH to identify abnormal user behaviors and detect insider threats.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Investigating the Effects of Individual Neutron-Induced Defects in Bipolar Junction Transistors

Here, this study investigates neutron-induced displacement damage in Bipolar Junction Transistors (BJTs) using TCAD models informed by Deep-Level-Transient-Spectroscopy (DLTS) data. These models are calibrated and validated against experimental measurements performed at various neutron fluences. Both npn and pnp transistor configurations are studied to analyze the effects of individual traps on carrier recombination and base leakage currents. In npn transistors, deep traps (0.42 eV from the conduction band) dominate at low voltages, while shallow traps (0.17 eV from the conduction band) become prominent at higher voltages. Conversely, pnp transistors have base leakage current predominantly due to deep-level traps. The study observes a notable trend in trap density versus fluence, characterized by a linear relationship on a log-log scale. These insights into defect evolution under radiation conditions are crucial for optimizing semiconductor device reliability and performance in radiation-prone environments.

42 ENGINEERING↗
DOE OSTI2020

HIDES: Hybrid Intrusion Detector for Energy Systems

The establishment of a resilient electric grid accompanied by a secure communications network is an ongoing battle as advanced persistent threats continue to exploit existing vulnerabilities in legacy supervisory control and data acquisition system (SCADA) infrastructure. Traditional intrusion detection systems (IDSs) lack consistent performance because of the continuously evolving attack surface of SCADA systems. These shortcomings can be overcome by integrating logical system behavior, protocol-specific knowledge, and data-based learning to develop a comprehensive IDS solution. In this paper, we present a Hybrid Intrusion Detector for Energy Systems by integrating a network-based IDS, state-of-the-art machine learning-based IDS, and model-based IDS to detect unknown and stealthy cyberattacks targeting the SCADA networks. The proposed IDS uses synchrophasor measurements and cyber logs to learn patterns of different scenarios based on spatiotemporal behaviors of power systems. As a proof of concept, we implement and validate the proposed IDS by leveraging resources available at the National Renewable Energy Laboratory's Energy Systems Integration Facility test bed. Experimental results show promising performance in detecting cyberattacks while providing realtime visualization of power system measurements and cyber logs.

machine-learning intrusion detection system↗
DOE OSTI2020

Melting temperature measurement of refractory oxide ceramics as a function of oxygen fugacity using containerless methods

An aerodynamic conical nozzle levitator (CNL) has been used to measure the melting temperatures of refractory ceramics. The well-established method of cooling traces has been adapted to allow measurements for air-sensitive samples using a controlled atmosphere, where the oxygen partial pressure can be adjusted and monitored in real time. In this study, we explain the details of a system that we developed for use at the Spallation Neutron Source (Oak Ridge National Laboratory). Measurements of the melting behavior of (Fe,Cr,Al) 3 O 4 spinels were made between log (PO 2 (atm)) of -0.7 and -5. The melting temperature of hercynite is found to be oxygen potential dependent, ranging between 1692 and 1767°C at log (PO 2 ) values from -4.42 to -0.70.

36 MATERIALS SCIENCE↗
DOE OSTI2020

HIV influences clustering and intracellular replication of hepatitis C virus

We report HCV and HIV coinfection is common and HIV leads to increased HCV viraemia and accelerated disease progression. However, the biological basis of this interaction remains poorly understood and little is known about the impact of HIV on HCV replication at the cellular level. We analysed HCV RNA, based on single-cell laser-capture microdissection, in liver biopsies from monoinfected (n = 4) and HCV/HIV-coinfected (n = 5) participants. HCV RNA was assayed in 3200 hepatocytes with information of spatial position. We compared HCV RNA levels and clustering properties of infection between mono- and coinfected participants, and developed a mathematical model of infection. Although the median plasma HCV RNA level and the fraction of infected cells were comparable in monoinfected (7.0 log 10 IU/mL and ~ 30%) and coinfected (7.3 log 10 IU/mL and ~ 40%) participants, the median HCV RNA per infected hepatocyte in monoinfected (2.8IU) was significantly lower than in coinfected (8.2IU) participants (p = .03). Clustering of infected cells was more prominent in monoinfected participants (91% of samples) than in coinfected participants (~48%), p = .0045, suggesting that spatial spread may be influenced by HIV coinfection. Interestingly, when clustering does occur, the size of clusters is similar in both types of infection. A mathematical model of infection suggested that HIV allows higher intracellular accumulation of HCV RNA by impeding the export of HCV RNA. Our observations show that HIV coinfection impacts intracellular accumulation of HCV RNA and the clustering of HCV-infected cells, but to a less extent the fraction of HCV-infected cells.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

SU-D-BRC-03: Development and Validation of an Online 2D Dose Verification System for Daily Patient Plan Delivery Accuracy Check

Purpose: All plan verification systems for particle therapy are designed to do plan verification before treatment. However, the actual dose distributions during patient treatment are not known. This study develops an online 2D dose verification tool to check the daily dose delivery accuracy. Methods: A Siemens particle treatment system with a modulated scanning spot beam is used in our center. In order to do online dose verification, we made a program to reconstruct the delivered 2D dose distributions based on the daily treatment log files and depth dose distributions. In the log files we can get the focus size, position and particle number for each spot. A gamma analysis is used to compare the reconstructed dose distributions with the dose distributions from the TPS to assess the daily dose delivery accuracy. To verify the dose reconstruction algorithm, we compared the reconstructed dose distributions to dose distributions measured using PTW 729XDR ion chamber matrix for 13 real patient plans. Then we analyzed 100 treatment beams (58 carbon and 42 proton) for prostate, lung, ACC, NPC and chordoma patients. Results: For algorithm verification, the gamma passing rate was 97.95% for the 3%/3mm and 92.36% for the 2%/2mm criteria. For patient treatment analysis,the results were 97.7%±1.1% and 91.7%±2.5% for carbon and 89.9%±4.8% and 79.7%±7.7% for proton using 3%/3mm and 2%/2mm criteria, respectively. The reason for the lower passing rate for the proton beam is that the focus size deviations were larger than for the carbon beam. The average focus size deviations were −14.27% and −6.73% for proton and −5.26% and −0.93% for carbon in the x and y direction respectively. Conclusion: The verification software meets our requirements to check for daily dose delivery discrepancies. Such tools can enhance the current treatment plan and delivery verification processes and improve safety of clinical treatments.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-R-24: Identifying Prognostic Imaging Biomarkers in Early Stage Lung Cancer Using Radiomics

Purpose: Patients diagnosed with early stage lung cancer have favorable outcomes when treated with surgery or stereotactic radiotherapy. However, a significant proportion (∼20%) of patients will develop metastatic disease and eventually die of the disease. The purpose of this work is to identify quantitative imaging biomarkers from CT for predicting overall survival in early stage lung cancer. Methods: In this institutional review board-approved HIPPA-compliant retrospective study, we retrospectively analyzed the diagnostic CT scans of 110 patients with early stage lung cancer. Data from 70 patients were used for training/discovery purposes, while those of remaining 40 patients were used for independent validation. We extracted 191 radiomic features, including statistical, histogram, morphological, and texture features. Cox proportional hazard regression model, coupled with the least absolute shrinkage and selection operator (LASSO), was used to predict overall survival based on the radiomic features. Results: The optimal prognostic model included three image features from the Law’s feature and wavelet texture. In the discovery cohort, this model achieved a concordance index or CI=0.67, and it separated the low-risk from high-risk groups in predicting overall survival (hazard ratio=2.72, log-rank p=0.007). In the independent validation cohort, this radiomic signature achieved a CI=0.62, and significantly stratified the low-risk and high-risk groups in terms of overall survival (hazard ratio=2.20, log-rank p=0.042). Conclusion: We identified CT imaging characteristics associated with overall survival in early stage lung cancer. If prospectively validated, this could potentially help identify high-risk patients who might benefit from adjuvant systemic therapy.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-I-11: Software Development for 4D-CBCT Research of Real-Time-Image Gated Spot Scanning Proton Therapy

Purpose: To acquire correct information for inside the body in patient positioning of Real-time-image Gated spot scanning Proton Therapy (RGPT), utilization of tomographic image at exhale phase of patient respiration obtained from 4-dimensional Cone beam CT (4D-CBCT) has been desired. We developed software named “Image Analysis Platform” for 4D-CBCT researches which has technique to segment projection-images based on 3D marker position in the body. The 3D marker position can be obtained by using two axes CBCT system at Hokkaido University Hospital Proton Therapy Center. Performance verification of the software was implemented. Methods: The software calculates 3D marker position retrospectively by using matching positions on pair projection-images obtained by two axes fluoroscopy mode of CBCT system. Log data of 3D marker tracking are outputted after the tracking. By linking the Log data and gantry-angle file of projection-image, all projection-images are equally segmented to spatial five-phases according to marker 3D position of SI direction and saved to specified phase folder. Segmented projection-images are used for CBCT reconstruction of each phase. As performance verification of the software, test of segmented projection-images was implemented for sample CT phantom (Catphan) image acquired by two axes fluoroscopy mode of CBCT. Dummy marker was added on the images. Motion of the marker was modeled to move in 3D space. Motion type of marker is sin4 wave function has amplitude 10.0 mm/5.0 mm/0 mm, cycle 4 s/4 s/0 s for SI/AP/RL direction. Results: The marker was tracked within 0.58 mm accuracy in 3D for all images, and it was confirmed that all projection-images were segmented and saved to each phase folder correctly. Conclusion: We developed software for 4D-CBCT research which can segment projection-image based on 3D marker position. It will be helpful to create high quality of 4D-CBCT reconstruction image for RGPT.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-J-80: Deformable Image Registration for Residual Organ Motion Estimation in Respiratory Gated Treatments with Scanned Carbon Ion Beams

Purpose: At the Centro Nazionale di Adroterapia Oncologica (CNAO, Pavia, Italy) C-ions respiratory gated treatments of patients with abdominal tumours started in 2014. In these cases, the therapeutic dose is delivered around end-exhale. We propose the use of a respiratory motion model to evaluate residual tumour motion. Such a model requires motion fields obtained from deformable image registration (DIR) between 4DCT phases, estimating anatomical motion through interpolation. The aim of this work is to identify the optimal DIR technique to be integrated in the modeling pipeline. Methods: We used 4DCT datasets from 4 patients to test 4 DIR algorithms: Bspline, demons, log-domain and symmetric log domain diffeomorphic demons. We evaluate DIR performance in terms of registration accuracy (RMSE between registered images) and anatomical consistency of the motion field (Jacobian) when registering end-inhale to end-exhale. We subsequently employed the model to estimate the tumour trajectory within the ideal gating window. Results: Within the liver contour, the RMSE is in the range 31–46 HU for the best performing algorithm (Bspline) and 43–145 HU for the worst one (demons). The Jacobians featured zero negative voxels (which indicate singularities in the motion field) for the Bspline fields in 3 of 4 patients, whereas diffeomorphic demons fields showed a non-null number of negative voxels in every case. GTV motion in the gating window measured less than 7 mm for every patient, displaying a predominant superior-inferior (SI) component. Conclusion: The Bspline algorithm allows for acceptable DIR results in the abdominal region, exhibiting the property of anatomical consistency of the computed field. Computed trajectories are in agreement with clinical expectations (small and prevalent SI displacements), since patients lie wearing semi-rigid immobilizing masks. In future, the model could be used for retrospective estimation of organ motion during treatment, as guided by the breathing surrogate signal.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-T-101: Insight into Dosimetry Workload and Planning Timelines: A 6 Year Review at One Institution

Purpose: To elucidate realistic clinical treatment planning workload and timelines to improve understanding for patients, payers, and other institutions involved in radiotherapy processes. Methods: A web based tool was developed using Oracle Express (Oracle Corp, Redwood City, CA) which allowed communication between the physicians and staff about the current state of the patient plan. For 6 years, all patient courses were logged and time-stamped in 22 discreet steps which detailed start and stop times for simulation, contouring, and treatment planning tasks. This data was combined with the treatment planning database (TPDB) using the Eclipse Scripting API (Varian Medical Systems, Palo Alto, CA) to cross-identify plans between the two systems. This time data was analyzed across our dosimetry staff and treatment modality. Results: In 6 years, 110,477 patient statuses were time-logged for 9683 courses of treatment using our internal software. The courses contained 8305 unique patients who were binned into one of 11 diagnosis site categories. 8253 courses could be reconciled against the TPDB using timestamp data from patient statuses. The average planning volume per dosimetrist was 375.8 ± 142.4 plans per year with the average number of planning revisions per dosimetrist of 71.0 ± 27.1 plans per year. The median treatment planning times by modality ranged from to 48.3 hours for IMRT plans 5 fields or less to 119.6 hours for IMRT with 8 or more fields. Two arc VMAT, three arc VMAT, and 3D plans median times were 89.1 hours, 113.8 hours, and 50.9 hours respectively. Conclusion: Using our web based tool, we have demonstrated the ability to quantify treatment planning timelines and workloads which could help in setting appropriate expectations for patients, payers, and hospital administration. COI: Author received monies from Varian Medical Systems for research and teaching honorarium.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-T-303: Quantification of MLC Positioning Accuracy in VMAT Delivery of Head and Neck Cancer Treatment

Purpose: Knowing MLC leaf positioning error over the course of treatment would be valuable for treatment planning, QA design, and patient safety. The objective of the current study was to quantify the MLC positioning accuracy for VMAT delivery of head and neck treatment plans. Methods: A total of 837 MLC log files were collected from 14 head and neck cancer patients undergoing full arc VMAT treatment on one Varian Trilogy machine. The actual and planned leaf gaps were extracted from the retrieved MLC log files. For a given patient, the leaf gap error percentage (LGEP), defined as the ratio of the actual leaf gap over the planned, was evaluated for each leaf pair at all the gantry angles recorded over the course of the treatment. Statistics describing the distribution of the largest LGEP (LLGEP) of the 60 leaf pairs including the maximum, minimum, mean, Kurtosis, and skewness were evaluated. Results: For the 14 studied patients, their PTV located at tonsil, base of tongue, larynx, supraglottis, nasal cavity, and thyroid gland with volume ranging from 72.0 cm{sup 3} to 602.0 cm{sup 3}. The identified LLGEP differed between patients. It ranged from 183.9% to 457.7% with a mean of 368.6%. For the majority of the patients, the LLGEP distributions peaked at non-zero positions and showed no obvious dependence on gantry rotations. Kurtosis and skewness, with minimum/maximum of 66.6/217.9 and 6.5/12.6, respectively, suggested relatively more peaked while right-skewed leaf error distribution pattern. Conclusion: The results indicate pattern of MLC leaf gap error differs between patients of lesion located at similar anatomic site. Understanding the systemic mechanisms underlying these observed error patterns necessitates examining more patient-specific plan parameters in a large patient cohort setting.

60 APPLIED LIFE SCIENCES↗