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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Anomalous Resistivity and Electron Heating by Lower Hybrid Drift Waves during Magnetic Reconnection with a Guide Field

The lower hybrid drift wave (LHDW) has been a candidate for anomalous resistivity and electron heating inside the electron diffusion region of magnetic reconnection. In a laboratory reconnection layer with a finite guide field, quasielectrostatic LHDW (ES-LHDW) propagating along the direction nearly perpendicular to the local magnetic field is excited in the electron diffusion region. ES-LHDW generates large density fluctuations ($δn_{\text e}$, about 25% of the mean density) that are correlated with fluctuations in the out-of-plane electric field ($δE_Y$, about twice larger than the mean reconnection electric field). With a small phase difference (~ 30°) between two fluctuating quantities, the anomalous resistivity associated with the observed ES-LHDW is twice larger than the classical resistivity and accounts for 20% of the mean reconnection electric field. After we verify the linear relationship between $δn_{\text e}$ and $δE_Y$, anomalous electron heating by LHDW is estimated by a quasilinear analysis. The estimated electron heating is about 2.6 ± 0.3 MW / m 3 , which exceeds the classical Ohmic heating of about 2.0 ± 0.2 MW / m 3 . This LHDW-driven heating is consistent with the observed trend of higher electron temperatures when the wave amplitude is larger. Finally, presented results provide the first direct estimate of anomalous resistivity and electron heating power by LHDW, which demonstrates the importance of wave-particle interactions in magnetic reconnection.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

A Solid State Zwitterionic Plastic Crystal with High Static Dielectric Constant

The dielectric data in Figure 3, Figure 4, Figure S6 of the published paper was extracted from 2EOIMTSA-BDS-DATA .txt file. This file can be directly opened using a text file editor. It can also be imported to Excel/ Origin for further plotting and analysis. The G' and G'' in Figure 3 of the publihsed paper was plotted from data in file 2EOImTSA-temperature-sweep.xlsx. This file can be directly opend using Excel. The details of DFT simulations mentioned in Figure 2, Figure 7, and Figure S9 of the published paper are included in the DFT.zip file.

Huang, Zitan [Pennsylvania State University]↗
NASA NTRS1975

An optical/digital processor - Hardware and applications

A real-time two-dimensional hybrid processor consisting of a coherent optical system, an optical/digital interface, and a PDP-11/15 control minicomputer is described. The input electrical-to-optical transducer is an electron-beam addressed potassium dideuterium phosphate (KD2PO4) light valve. The requirements and hardware for the output optical-to-digital interface, which is constructed from modular computer building blocks, are presented. Initial experimental results demonstrating the operation of this hybrid processor in phased-array radar data processing, synthetic-aperture image correlation, and text correlation are included. The applications chosen emphasize the role of the interface in the analysis of data from an optical processor and possible extensions to the digital feedback control of an optical processor.

Casasent, D.↗
DOE OSTI2017

Fishing for Clickbaits in Social Images and Texts with Linguistically-Infused Neural Network Models

This paper presents the results and conclusions of our participation in the Clickbait Challenge 2017 on automatic clickbait detection in social media. We first describe linguistically-infused neural network models and identify informative representations to predict the level of clickbaiting present in Twitter posts. Our models allow to answer the question not only whether a post is a clickbait or not, but to what extent it is a clickbait post e.g., not at all, slightly, considerably, or heavily clickbaity using a score ranging from 0 to 1. We evaluate the predictive power of models trained on varied text and image representations extracted from tweets. Our best performing model that relies on the tweet text and linguistic markers of biased language extracted from the tweet and the corresponding page yields mean squared error (MSE) of 0.04, mean absolute error (MAE) of 0.16 and R2 of 0.43 on the held-out test data. For the binary classification setup (clickbait vs. non-clickbait), our model achieved F1 score of 0.69. We have not found that image representations combined with text yield significant performance improvement yet. Nevertheless, this work is the first to present preliminary analysis of objects extracted using Google Tensorflow object detection API from images in clickbait vs. non-clickbait Twitter posts. Finally, we outline several steps to improve model performance as a part of the future work.

dlickbait challenge, Twitter, neural networks, mac↗
DOE OSTI2020

Combined search for neutrinos from dark matter self-annihilation in the Galactic Center with ANTARES and IceCube

In this work, we present the results of the first combined dark matter search targeting the Galactic Centre using the ANTARES and IceCube neutrino telescopes. For dark matter particles with masses from 50 to 1000 GeV, the sensitivities on the self-annihilation cross section set by ANTARES and IceCube are comparable, making this mass range particularly interesting for a joint analysis. Dark matter self-annihilation through the $\tau^+\tau^-$, $\mu^+\mu^-$, $b\bar{b}$ and $W^+W^-$ channels is considered for both the Navarro-Frenk-White and Burkert halo profiles. In the combination of 2,101.6 days of ANTARES data and 1,007 days of IceCube data, no excess over the expected background is observed. Limits on the thermally-averaged dark matter annihilation cross section $\langle\sigma_A\upsilon\rangle$ are set. These limits present an improvement of up to a factor of two in the studied dark matter mass range with respect to the individual limits published by both collaborations. When considering dark matter particles with a mass of 200 GeV annihilating through the $\tau^+\tau^-$ channel, the value obtained for the limit is $7.44 \times 10^{-24} \text{cm}^{3}\text{s}^{-1}$ for the Navarro-Frenk-White halo profile. For the purpose of this joint analysis, the model parameters and the likelihood are unified, providing a benchmark for forthcoming dark matter searches performed by neutrino telescopes.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2008

Visualization Component of Vehicle Health Decision Support System

The visualization front-end of a Decision Support System (DSS) also includes an analysis engine linked to vehicle telemetry, and a database of learned models for known behaviors. Because the display is graphical rather than text-based, the summarization it provides has a greater information density on one screen for evaluation by a flight controller.This tool provides a system-level visualization of the state of a vehicle, and drill-down capability for more details and interfaces to separate analysis algorithms and sensor data streams. The system-level view is a 3D rendering of the vehicle, with sensors represented as icons, tied to appropriate positions within the vehicle body and colored to indicate sensor state (e.g., normal, warning, anomalous state, etc.). The sensor data is received via an Information Sharing Protocol (ISP) client that connects to an external server for real-time telemetry. Users can interactively pan, zoom, and rotate this 3D view, as well as select sensors for a detail plot of the associated time series data. Subsets of the plotted data can be selected and sent to an external analysis engine to either search for a similar time series in an historical database, or to detect anomalous events. The system overview and plotting capabilities are completely general in that they can be applied to any vehicle instrumented with a collection of sensors. This visualization component can interface with the ISP for data streams used by NASA s Mission Control Center at Johnson Space Center. In addition, it can connect to, and display results from, separate analysis engine components that identify anomalies or that search for past instances of similar behavior. This software supports NASA's Software, Intelligent Systems, and Modeling element in the Exploration Systems Research and Technology Program by augmenting the capability of human flight controllers to make correct decisions, thus increasing safety and reliability. It was designed specifically as a tool for NASA's flight controllers to monitor the International Space Station and a future Crew Exploration Vehicle.

Jacob, Joseph↗
DOE OSTI2021

Model estimates of dissolved organic carbon, runoff, snowmelt, and snow water equivalent from 1981-2010 across the western Arctic

This dataset contains estimates of dissolved organic carbon (DOC) yield (mg C m-2), runoff (mm day-1), snow water equivalent (SWE, mm day-1), and snowmelt (mm day-1) from a simulation of the Permafrost Water Balance Model (PWBM v4). The simulation effectively quantifies snowpack accumulation and melt, runoff, and DOC leaching, and broadly capture the seasonal cycle in DOC concentration and mass loadings. Daily air temperature, precipitation, and wind speed data from the Modern-Era Retrospective Analysis for Research and Applications (MERRA) were used for meteorological forcings. The model outputs are organized by grid and day of the month in gzipped ASCII text files contained within each archived ZIP file. No special software is required to work with these data. The basin and domain information files are also ASCII text. The gridded estimates are most useful for analyses of the spatial and temporal variations in terrestrial hydrology and leachate DOC concentrations and loadings across parts of the western Arctic. A manuscript describing the data and associated analysis has been accepted for publication in Journal of Geophysical Research - Biogeosciences (Rawlins et al., 2021).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

AGR 5/6/7 Data Qualification Report for ATR Cycles 162B through 168A

This report provides the qualification status of experimental data for the Advanced Gas Reactor (AGR) 5/6/7 fuel irradiation. AGR-5/6/7 was conducted in the Advanced Test Reactor (ATR) at Idaho National Laboratory (INL) in support of development and qualification of tri-structural isotropic (TRISO) low-enriched fuel for use in high temperature gas-cooled reactors. The objectives of the AGR-5/6/7 experiments are to: (i) irradiate reference-design fuel particles to support fuel qualification, (ii) establish operating margins for the fuel beyond normal operating conditions, and (iii) provide irradiated-fuel performance data and irradiated-fuel samples for post-irradiation examination (PIE) and safety testing. The test train contains five separate capsules that were independently controlled and monitored. Each capsule contains multiple 12.51-mm-long compacts filled with low enriched uranium carbide/oxide (UCO) TRISO fuel particles. The primary objective of the AGR-5/6 test (Capsules 1, 2, 4, and 5) is to verify successful performance of the reference-design fuel under normal operating conditions. The AGR-7 test (Capsule 3) was designed to explore fuel performance at higher temperatures to demonstrate the capability of the fuel to withstand conditions beyond normal operating conditions in support of plant design and licensing. AGR 5/6/7 will also provide irradiated-fuel performance data on fission-gas release from failed particles during irradiation. The AGR-5/6/7 capsules were irradiated in the ATR northeast flux trap location. The experiment began on February 16, 2018 and ended on July 22, 2020, spanning nine ATR cycles over two and a half years. Thus, the AGR-5/6/7 fuel compacts were irradiated for a total of 360.9 effective full power days. The AGR 5/6/7 experiment was able to remain in the reactor core during all three Powered Axial Locator Mechanism (PALM) cycles (163A, 165A, and 167A) without overheating its fuel compacts. This report includes irradiation monitoring data from nine ATR Cycles: 162B, 163A, 164A, 164B, 165A, 166A, 166B, 167A, and 168A, as stored in the Nuclear Data Management and Analysis System (NDMAS). During irradiation, data records consisted of instantaneous measurements recorded every minute and provided by text files automatically every 2 hours. The AGR 5/6/7 data streams addressed in this report include thermocouple (TC) temperatures, sweep gas data (flow rates [capsule inlet, outlet, and downstream at detector], pressure, and moisture content), and Fission Product Monitoring System (FPMS) data (release rates and release to birth rate ratios [R/Bs]) for each of the five capsules. A total of 94,989,908 TC temperature and sweep gas data records were received and processed by NDMAS for AGR 5/6/7 irradiation. Of these records, 41,593,387 (or 43.7% of the total) met data collection and accuracy requirements and are labeled as Qualified. A total of 57,746,693 TC temperature readings were captured from 54 installed TCs. Among them, 10,034,676 TC temperature records (only 17.4%) were Qualified and 47,701,371 TC temperatures (or 82.6%) are Failed due to 48 TC failures (63.5%) and due to missing values (19.1%). To assess performance of the operational TCs, analysis of daily correlations between TCs found no evidence of virtual junction failure for any TCs. Analyses on control charts of TC temperature differences revealed trending in TC readings for TC2, 4, 5, and 13 in Capsule 3, but there is no conclusive indication of TC drift failure that caused those trends. Therefore, TC control charts are not used to disqualify TC data, but only for users’ consideration. For sweep gas flow rates, a total of 31,519,747 gas flow records (84.4%) are Qualified for use for AGR-5/6/7 experiment; 5,723,468 gas flow records (15.4%) are Failed due mostly to missing values; and 74,641 high sweep gas flow rates (0.2 %) are Trend. A large number of Failed missing TC temperature and gas flow values were caused by an error in the data output script that outputted a ‘NULL’ value when values were unchanged. This problem was fixed during the outage of Cycle 166B, which led to a substantially decreased number of missing values during the last three cycles. Nonetheless, a large amount of non-missing data remained because of the high data acquisition frequency (1-minute) and still provided sufficient data to effectively monitor the experiment as designed. For FPMS data, NDMAS received and processed fission product release and R/B data for nine ATR cycles, when ATR core reached full power during AGR 5/6/7 irradiation. These data consist of 110,388 release rate records and 110,388 R/B records for the twelve radionuclides (Kr 85m, Kr 87, Kr 88, Kr 89, Kr 90, Xe 131m, Xe 133, Xe 135, Xe 135m, Xe 137, Xe 138, and Xe 139) for each of the five capsules. Equivalent numbers of uncertainty records associated the release rates and R/B values were provided. To date, qualification status of the FPMS data stored in the NDMAS dat

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2010

IBRD Operational Decision Framework

The IBRD Operational Decision Framework in this document is an expansion of an emerging general risk management framework under development by an interagency working group. It provides the level of detail necessary to develop a general Consequence Management Guidance Document for biological contamination remediation and restoration. It is the intent of this document to support both wide area and individual site remediation and restoration activities. This product was initiated as a portion of the IBRD Task 1 Systems Analysis to aid in identification of wide area remediation and restoration shortcomings and gaps. The draft interagency general risk management framework was used as the basis for the analysis. The initial Task 1 analysis document expanded the draft interagency framework to a higher level of resolution, building on both the logic structure and the accompanying text explanations. It was then employed in a qualitative manner to identify responsible agencies, data requirements, tool requirements, and current capabilities for each decision and task. This resulted in identifying shortcomings and gaps needing resolution. Several meetings of a joint LLNL/SNL working group reviewed and approved the initial content of this analysis. At the conclusion of Task 1, work continued on the expanded framework to generate this Operational Decision Framework which is consistent with the existing interagency general risk management framework. A large LLNL task group met repeatedly over a three-month period to develop the expanded framework, coordinate the framework with the biological remediation checklist, and synchronize the logic with the Consequence Management Plan table of contents. The expanded framework was briefed at a large table top exercise reviewing the interagency risk management framework. This exercise had representation from major US metropolitan areas as well as national agencies. This product received positive comments from the participants. Upon completion of the Operational Decision Framework, another joint LLNL/SNL working group conducted a day-long review. Identified modifications were made to the document, resulting in the included product.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2023

Dynamic Network Analysis of Nuclear Science Literature for Research Influence Assessment

Analyzing nuclear science literature via data-driven methods is a critical step for assessing research influence and technology advancements. Indicators of scholarly activities may be buried in large volumes of nuclear research publications and collaboration networks over time. Mining for relevant scholarly influence trends in large volumes of text can be computationally challenging; however, open-source information on research collaborations over time can offer opportunities to extract meaningful insights. While network centrality analysis of scholarly research provides topology-based insights, additional emphasis on dynamics associated with the diffusion of information through these networks is important. Here this paper represents a step in that direction through the development of a novel dynamic network analysis framework and computational engine to identify key entities and capabilities over time within global scholarly nuclear science collaboration networks. Network theoretic, stochastic simulation, and optimization methods are leveraged to address variability in scholarly interactions, influence propagation, and collaboration patterns via network connections. A topic-aware influence maximization algorithm is developed to address the goal of identifying key influential authors in diverse research topics over time. Efficient parallelized implementation of the algorithm is applied to reduce computational costs. A proof-of-concept case study using open-source Scopus data with 33,517 published nuclear research papers from 2000-2019 is presented and representative analytic insights are generated. Broad implications of these insights are discussed and future research directions are also identified.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2017

A novel tool for improving the data collection process during control room modernization human-system interface testing and evaluation activities - 271

The U.S. Department of Energy (DOE) Light Water Reactor Sustainability (LWRS) Program is developing a scientific basis to extend the existing U.S. nuclear power plant (NPP) operating life beyond the current 60-year licensing period and to ensure NPP long-term reliability, productivity, safety, and security. Under the Advanced Instrumentation, Information, and Control (II and C) Systems Technologies pathway, the LWRS Program conducts targeted research and development to address long-term aging and obsolescence of existing instrumentation and control technologies, as well as to implement and test new digital II and C technology that enables broad innovation and business improvement in the NPP operating model. To this end, the role of human factors engineering (HFE) is important as part of ensuring that the control room modernization (CRM) activities account for the operator's cognitive and physiological characteristics. Part of the HFE process for CRM entails conducting tests and evaluations (T and Es) that will inform the design of new human-system interface (HSI) technologies. Qualitative data that captures operator feedback and important human-system interactions are an important part of T and E. However, the analyzing this qualitative data in order to provide actionable design recommendations can be time-consuming and error prone. This paper presents an early version of a tool, which uses a combination of natural language processing and supervised machine learning to support the analysis of qualitative data. The objectives of this tool is to improve the process of (1) sifting through freeform text for improved design issue identification, (2) maintaining consistency in theme coding conventions, and (3) readily providing actionable design guidance that can be traceable to state-of-the-art HFE guidelines such as NUREG-0700. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2026

Augmented Reality Technologies for Radiation Safety Training: A Systematic Review of Sensor Integration and Visualization Approaches

This paper presents a comprehensive systematic review examining the application of augmented reality (AR) and sensor technologies for visualizing ionizing radiation in virtual training environments. The review methodology involved systematic identification and analysis of the relevant literature based on predetermined criteria including publication type, year of publication, application domain, and technological approach. The literature search encompassed publications from 2011 to 2021 across four major academic databases: Web of Science, Google Scholar, IEEE Xplore, and Scopus. Through rigorous screening following PRISMA 2020 guidelines, 23 research articles met the inclusion criteria for detailed analysis. From 404 initial database records, 360 were excluded during title/abstract screening (primarily for lacking AR components, radiation focus, or training applications) and 4 during full-text assessment (all for lacking sensor integration). The findings reveal that AR-based ionizing radiation visualization has been successfully implemented across diverse domains, including nuclear facility operations, medical procedures, CERN research activities, and educational and monitoring applications. The analysis identified multiple dimensions of impact, encompassing distinct benefits, emerging opportunities, and implementation challenges associated with AR deployment for ionizing radiation training. Each of these dimensions is comprehensively examined and documented within this review. Additionally, this study identifies critical research gaps that currently limit the full potential of AR technology in supporting ionizing radiation training programs. These gaps are systematically analyzed and discussed to establish clear directions for future research endeavors in this emerging field.

61 - RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2013

Massively Scalable Near Duplicate Detection in Streams of Documents using MDSH

In a world where large-scale text collections are not only becoming ubiquitous but also are growing at increasing rates, near duplicate documents are becoming a growing concern that has the potential to hinder many different information filtering tasks. While others have tried to address this problem, prior techniques have only been used on limited collection sizes and static cases. We will briefly describe the problem in the context of Open Source Intelligence (OSINT) along with our additional constraints for performance. In this work we propose two variations on Multi-dimensional Spectral Hash (MDSH) tailored for working on extremely large, growing sets of text documents. We analyze the memory and runtime characteristics of our techniques and provide an informal analysis of the quality of the near-duplicate clusters produced by our techniques.

Bogen, Paul Logasa↗
DOE OSTI2025

Debiasing Watermarks for Large Language Models via Maximal Coupling

Watermarking language models is essential for distinguishing between human and machine-generated text and thus maintaining the integrity and trustworthiness of digital communication. Here, we present a novel green/red list watermarking approach that partitions the token set into “green” and “red” lists, subtly increasing the generation probability for green tokens. To correct token distribution bias, our method employs maximal coupling, using a uniform coin flip to decide whether to apply bias correction, with the result embedded as a pseudorandom watermark signal. Theoretical analysis confirms this approach’s unbiased nature and robust detection capabilities. Experimental results show that it outperforms prior techniques by preserving text quality while maintaining high detectability, and it demonstrates resilience to targeted modifications aimed at improving text quality. This research provides a promising watermarking solution for language models, balancing effective detection with minimal impact on text quality.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Two-particle correlations in azimuthal angle and pseudorapidity in central $^7\hbox {Be}+{}^9\hbox {Be}$ collisions at the CERN Super Proton Synchrotron

A measurement of charged hadron pair correlations in two-dimensional $\Delta \eta \Delta \phi $ space is presented. The analysis is based on total 30 million central Be + Be collisions observed in the NA61/SHINE detector at the CERN SPS for incident beam momenta of 19A, 30A, 40A, 75A, and 150A $\text {Ge} \text {V}/c$. Measurements were carried out for unlike-sign and like-sign charge hadron pairs independently. The $C(\Delta \eta ,\Delta \phi )$ correlation functions were compared with results from a similar analysis on p + p interactions at similar beam momenta per nucleon. General trends of the back-to-back correlations are similar in central Be + Be collisions and p + p interactions, but are suppressed in magnitude due to the increased combinatorial background. Predictions from the Epos and UrQMD models are compared to the measurements. Evolution of an enhancement around $(\Delta \eta ,\Delta \phi ) = (0,0)$ with incident energy is observed in central Be + Be collisions. It is not predicted by both models and almost non-existing in proton–proton collisions at the same momentum per nucleon.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2012

Effectiveness of Reirradiation for Painful Bone Metastases: A Systematic Review and Meta-Analysis

Purpose: Reirradiation of painful bone metastases in nonresponders or patients with recurrent pain after initial response is performed in up to 42% of patients initially treated with radiotherapy. Literature on the effect of reirradiation for pain control in those patients is scarce. In this systematic review and meta-analysis, we quantify the effectiveness of reirradiation for achieving pain control in patients with painful bone metastases. Methods and Materials: A free text search was performed to identify eligible studies using the MEDLINE, EMBASE, and the Cochrane Collaboration library electronic databases. After study selection and quality assessment, a pooled estimate was calculated for overall pain response for reirradiation of metastatic bone pain. Results: Our literature search identified 707 titles, of which 10 articles were selected for systematic review and seven entered the meta-analysis. Overall study quality was mediocre. Of the 2,694 patients initially treated for metastatic bone pain, 527 (20%) patients underwent reirradiation. Overall, a pain response after reirradiation was achieved in 58% of patients (pooled overall response rate 0.58, 95% confidence interval = 0.49-0.67). There was a substantial between-study heterogeneity (I{sup 2} = 63.3%, p = 0.01) because of clinical and methodological differences between studies. Conclusions: Reirradiation of painful bone metastases is effective in terms of pain relief for a small majority of patients; approximately 40% of patients do not benefit from reirradiation. Although the validity of results is limited, this meta-analysis provides a comprehensive overview and the most quantitative estimate of reirradiation effectiveness to date.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2026

Precision measurement of positron decay modes of Xe-125 in the LUX-ZEPLIN experiment

The radioisotope $^{125}\text{Xe}$ is a short-lived ($T_{1/2}\sim16.9 h$) activation product of the neutron calibrations performed in the LUX-ZEPLIN experiment. Subsequently, $^{125}$Xe decays primarily ($>99\%$) via electron capture, but positron emission has been confirmed by direct measurement to at least the 243 keV level of $^{125}\text{I}$. An additional decay to the 188keV level is expected from triple-coincident measurements of the annihilation and relaxation $γ$ rays, but has not been directly confirmed. By utilizing multiple-scatter event analysis and the pre-activation data to constrain backgrounds, this work reports positron emission with a statistical significance of 5.5$σ$. This corresponds to a total branching ratio of $0.29\pm0.08_{\text{stat.}}\pm0.04_{\text{sys.}}$%, and is the first constraint to the individual branching levels of $^{125}\text{I}$.

Akerib, D. S. [SLAC; KIPAC, Menlo Park]↗
DOE OSTI2015

MO-DE-BRA-05: Developing Effective Medical Physics Knowledge Structures: Models and Methods

Purpose: Develop a method and supporting online resources to be used by medical physics educators for teaching medical imaging professionals and trainees so they develop highly-effective physics knowledge structures that can contribute to improved diagnostic image quality on a global basis. Methods: The different types of mental knowledge structures were analyzed and modeled with respect to both the learning and teaching process for their development and the functions or tasks that can be performed with the knowledge. While symbolic verbal and mathematical knowledge structures are very important in medical physics for many purposes, the tasks of applying physics in clinical imaging--especially to optimize image quality and diagnostic accuracy--requires a sensory conceptual knowledge structure, specifically, an interconnected network of visually based concepts. This type of knowledge supports tasks such as analysis, evaluation, problem solving, interacting, and creating solutions. Traditional educational methods including lectures, online modules, and many texts are serial procedures and limited with respect to developing interconnected conceptual networks. A method consisting of the synergistic combination of on-site medical physics teachers and the online resource, CONET (Concept network developer), has been developed and made available for the topic Radiographic Image Quality. This was selected as the inaugural topic, others to follow, because it can be used by medical physicists teaching the large population of medical imaging professionals, such as radiology residents, who can apply the knowledge. Results: Tutorials for medical physics educators on developing effective knowledge structures are being presented and published and CONET is available with open access for all to use. Conclusion: An adjunct to traditional medical physics educational methods with the added focus on sensory concept development provides opportunities for medical physics teachers to share their knowledge and experience at a higher cognitive level and produce medical professionals with the enhanced ability to apply physics to clinical procedures.

60 APPLIED LIFE SCIENCES↗