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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.
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Prognostic nomogram in patients with metastatic adenoid cystic carcinoma of the salivary glands
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The prognostic effect of recurrent laryngeal nerve palsy after esophagectomy in locally advanced esophageal squamous cell carcinoma: A multi-center retrospective study
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Prognostic Implications of MRI-Detected EMVI and Tumor Deposits and Their Response to Neoadjuvant Therapy in cT3 and cT4 Rectal Cancer
Magnetic resonance imaging–detected extramural venous invasion (mrEMVI) and tumor deposits (TDs) are risk factors for the development of local recurrence and distant metastases (DMs) in rectal cancer. However, little is known about their response to neoadjuvant treatment and its relation to oncologic outcomes. This study evaluated the incidence and features of mrEMVI and TDs before and after neoadjuvant treatment in relation to the development of local recurrence and DMs.
Assessment of Prognostic Value of High-Sensitivity Cardiac Troponin T for Early Prediction of Chemoradiation Therapy-Induced Cardiotoxicity in Patients with Non-Small Cell Lung Cancer: A Secondary Analysis of a Prospective Randomized Trial
Cardiotoxicities induced by cancer therapy can negatively affect quality of life and survival. We investigated whether high-sensitivity cardiac troponin T (hs-cTnT) levels could serve as biomarker for early detection of cardiac adverse events (CAEs) after chemoradiation therapy (CRT) for non-small cell lung cancer (NSCLC).
A system diagnostic and prognostic framework based on deep learning for advanced reactors
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Prognostics and Health Management for Maintenance-Dependent Processes
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Robust errant beam prognostics with conditional modeling for particle accelerators
Abstract Particle accelerators are complex and comprise thousands of components, with many pieces of equipment running at their peak power. Consequently, they can fault and abort operations for numerous reasons, lowering efficiency and science output. To avoid these faults, we apply anomaly detection techniques to predict unusual behavior and perform preemptive actions to improve the total availability. Supervised machine learning (ML) techniques such as siamese neural network models can outperform the often-used unsupervised or semi-supervised approaches for anomaly detection by leveraging the label information. One of the challenges specific to anomaly detection for particle accelerators is the data’s variability due to accelerator configuration changes within a production run of several months. ML models fail at providing accurate predictions when data changes due to changes in the configuration. To address this challenge, we include the configuration settings into our models and training to improve the results. Beam configurations are used as a conditional input for the model to learn any cross-correlation between the data from different conditions and retain its performance. We employ conditional siamese neural network (CSNN) models and conditional variational auto encoder (CVAE) models to predict errant beam pulses at the spallation neutron source under different system configurations and compare their performance. We demonstrate that CSNNs outperform CVAEs in our application.
Building Local Models for Flexible Degradation Modeling and Prognostics
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Prognostics With Variational Autoencoder by Generative Adversarial Learning
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Deep Learning-Based Failure Prognostic Model for PV Inverter Using Field Measurements
Here, this study presents a novel approach for the precise monitoring and prognosis of photovoltaic (PV) inverter status, which is crucial for the proactive maintenance of PV systems. It addresses the gaps in traditional model-based methods, which tend to neglect the overall reliability of inverters, and the limitations of data-driven approaches that largely depend on simulated data. This research presents a robust solution applicable to real-world scenarios. The proposed data-driven model for PV inverter failure prognosis employs actual inverter measurements, integrating various operational and weather-related factors based on domain knowledge. This approach effectively represents inverter stressors and operational status. Utilizing an Enhanced Siamese Convolutional Neural Network (ESCNN), the model merges operational data with domain knowledge features, redefining the prognosis challenge as a classification task. Furthermore, the paper discusses an ESCNN-based real-time inverter failure monitoring method developed on the well-trained model. The proposed models are rigorously trained and tested with real inverter data and a novel filtering method is included to address accidental failures in practical scenarios. The results validate the model's efficacy, and the directions for future research are also outlined.
Nuclear Power Prognostic Model Assessment For Component Health Monitoring
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A Simple Chemistry Approach to Prognostic Volcanic Aerosol in E3SM ? Modeling the Mt. Pinatubo Eruption for CLDERA.
Abstract not provided.
Pre-industrial Control and Historical Simulations for CLDERA-E3SMv2: an Earth System Model with Prognostic Volcanic Aerosol
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Machine Learning for Prognostics and Control of Particle Accelerators
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Embedded Sensors for Diagnostics and Prognostics
This is a powerpoint report that accompanies a poster presentation at the DOE Vehicle Technologies Office Annual Merit Review (AMR)