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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
NASA NTRS1995

The composite classification problem in optical information processing

Optical pattern recognition allows objects to be recognized from their images and permits their positional parameters to be estimated accurately in real time. The guiding principle behind optical pattern recognition is that a lens focusing a beam of coherent light modulated with an image produces the two-dimensinal Fourier transform of that image. When the resulting output is further transformed by the matched filter corresponding to the original image, one obtains the autocorrelation function of the original image, which has a peak at the origin. Such a device is called an optical correlator and may be used to recognize the locate the image for which it is designed. (From a practical perspective, an approximation to the matched filter must be used since the spatial light modulator (SLM) on which the filter is implemented usually does not allow one to independently control both the magnitude and phase of the filter.) Generally, one is not just concerned with recognizing a single image but is interested in recognizing a variety of rotated and scaled views of a particular image. In order to recognize these different views using an optical correlator, one may select a subset of these views (whose elements are called training images) and then use a composite filter that is designed to produce a correlation peak for each training image. Presumably, these peaks should be sharp and easily distinguishable from the surrounding correlation plane values. In this report we consider two areas of research regarding composite optical correlators. First, we consider the question of how best to choose the training images that are used to design the composite filter. With regard to quantity, the number of training images should be large enough to adequately represent all possible views of the targeted object yet small enough to ensure that the resolution of the filter is not exhausted. As for the images themselves, they should be distinct enough to avoid numerical difficulties yet similar enough to avoid gaps in which certain views of the target will be unrecognized. One method that we introduce to study this problem is called probing and involves the creation of the artificial imagery. The second problem we consider involves the clasification of the composite filter's correlation plane data. In particular, we would like to determine not only whether or not we are viewing a training image, but, in the former case, we would like to determine which training image is being viewed. This second problem is investigated using traditional M-ary hypothesis testing techniques.

Hall, Eric B.↗
DOE OSTI2026

Anisotropic multi- Q order in Co x TaS 2

The cobalt-intercalated transition metal dichalcogenide Co x TaS 2 hosts a rich landscape of magnetic phases that depend sensitively on x. While the stoichiometric compound with x = 1/3 exhibits a single magnetic transition, samples with x≤0.325 display two transitions with an anomalous Hall effect (AHE) emerging in the lower temperature phase. Here, we resolve the spin structure in each phase by employing a suite of magneto-optical probes that include the discovery of anomalous magneto-birefringence: a spontaneous time-reversal sensitive rotation of the principal optic axes. A symmetry-based analysis identifies the AHE-active phase as an anisotropic (2+1)Q state, in which magnetic modulation at one wavevector (Q) differs in symmetry from that at the remaining two. The (2+1)Q state naturally exhibits scalar spin chirality as a mechanism for the AHE and expands the classification of multi-Q magnetic phases.

Kruppe, Jonathon [University of California, Berkel↗
NASA NTRS1988

Learning from examples - Generation and evaluation of decision trees for software resource analysis

A general solution method for the automatic generation of decision (or classification) trees is investigated. The approach is to provide insights through in-depth empirical characterization and evaluation of decision trees for software resource data analysis. The trees identify classes of objects (software modules) that had high development effort. Sixteen software systems ranging from 3,000 to 112,000 source lines were selected for analysis from a NASA production environment. The collection and analysis of 74 attributes (or metrics), for over 4,700 objects, captured information about the development effort, faults, changes, design style, and implementation style. A total of 9,600 decision trees were automatically generated and evaluated. The trees correctly identified 79.3 percent of the software modules that had high development effort or faults, and the trees generated from the best parameter combinations correctly identified 88.4 percent of the modules on the average.

Selby, Richard W.↗
DOE OSTI2023

Structure of LARP7 Protein p65–telomerase RNA Complex in Telomerase Revealed by Cryo-EM and NMR

La-related protein 7 (LARP7) are a family of RNA chaperones that protect the 3'-end of RNA and are components of specific ribonucleoprotein complexes (RNP). Here, in Tetrahymena thermophila telomerase, LARP7 protein p65 together with telomerase reverse transcriptase (TERT) and telomerase RNA (TER) form the core RNP. p65 has four known domains—N-terminal domain (NTD), La motif (LaM), RNA recognition motif 1 (RRM1), and C-terminal xRRM2. To date, only the xRRM2 and LaM and their interactions with TER have been structurally characterized. Conformational dynamics leading to low resolution in cryo-EM density maps have limited our understanding of how full-length p65 specifically recognizes and remodels TER for telomerase assembly. Here, we combined focused classification of Tetrahymena telomerase cryo-EM maps with NMR spectroscopy to determine the structure of p65–TER. Three previously unknown helices are identified, one in the otherwise intrinsically disordered NTD that binds the La module, one that extends RRM1, and another preceding xRRM2, that stabilize p65–TER interactions. The extended La module (αN, LaM and RRM1) interacts with the four 3' terminal U nucleotides, while LaM and αN additionally interact with TER pseudoknot, and LaM with stem 1 and 5' end. Our results reveal the extensive p65–TER interactions that promote TER 3'-end protection, TER folding, and core RNP assembly and stabilization. The structure of full-length p65 with TER also sheds light on the biological roles of genuine La and LARP7 proteins as RNA chaperones and core RNP components.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1984

Studies of optical and biological properties of terrestrial land cover using multispectral linear array technology

A series of experiments to study the optical and biological properties of terrestrial land cover are planned for late 1987 using a six-channel imaging spectroradiometer based on newly developed multispectral linear array (MLA) detector technology. Data from selected portions of the Sahel and rain forests of Africa and South America will be used to delineate biomass classes and estimate spherical albedos. A spatial resolution of 15 meters in the four visible-near IR channels and 30 meters in two shortwave IR channels, including a 'new' channel centered at 1.24 micrometers when combined with a spectral width of 20 nm for all channels, will be used to investigate possible improvements in land cover classification. Technology demonstrations include a test of data compression on data quality, the first spaceborne utilization of short wave infrared Schottky barrier Pd2Si detector arrays, and the use of close-butted, multi-array modules with attached spectral filters.

Barnes, W. L.↗
DOE OSTI2020

Unified architecture for data-driven metadata tagging of building automation systems

This article presents a Unified Architecture (UA) for automated point tagging of Building Automation System (BAS) data, based on a combination of data-driven approaches. Advanced energy analytics applications—including fault detection and diagnostics and supervisory control—have emerged as a significant opportunity for improving the performance of our built environment. Effective application of these analytics depends on harnessing structured data from the various building control and monitoring systems, but typical BAS implementations do not employ any standardized metadata schema. While standards such as Project Haystack and Brick Schema have been developed to address this issue, the process of structuring the data, i.e., tagging the points to apply a standard metadata schema, has, to date, been a manual process. This process is typically costly, labor-intensive, and error-prone. In this work we address this gap by proposing a UA that automates the process of point tagging by leveraging the data accessible through connection to the BAS, including time-series data and the raw point names. The UA intertwines supervised classification and unsupervised clustering techniques from machine learning and leverages both their deterministic and probabilistic outputs to inform the point tagging process. Furthermore, we extend the UA to embed additional input and output data-processing modules that are designed to address the challenges associated with the real-time deployment of this automation solution. We test the UA on two datasets for real-life buildings: (i) commercial retail buildings and (ii) office buildings from the National Renewable Energy Laboratory (NREL) campus. We report the proposed methodology correctly applied 85–90% and 70–75% of the tags in each of these test scenarios, respectively for two significantly different building types used for testing UA's fully-functional prototype. The proposed UA, therefore, offers promising approach for automatically tagging BAS data as it reaches close to 90% accuracy. Further building upon this framework to algorithmically identify the equipment type and their relationships is an apt future research direction to pursue.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Jet classification using high-level features from anatomy of top jets

Recent advancements in deep learning models have significantly enhanced jet classification performance by analyzing low-level features (LLFs). However, this approach often leads to less interpretable models, emphasizing the need to understand the decision-making process and to identify the high-level features (HLFs) crucial for explaining jet classification. To address this, we consider the top jet tagging problems and introduce an analysis model (AM) that analyzes selected HLFs designed to capture important features of top jets. Our AM mainly consists of the following three modules: a relation network analyzing two-point energy correlations, mathematical morphology and Minkowski functionals for generalizing jet constituent multiplicities, and a recursive neural network analyzing subjet constituent multiplicity to enhance sensitivity to subjet color charges. We demonstrate that our AM achieves performance comparable to the Particle Transformer (ParT) while requiring fewer computational resources in a comparison of top jet tagging using jets simulated at the hadronic calorimeter angular resolution scale. Furthermore, as a more constrained architecture than ParT, the AM exhibits smaller training uncertainties because of the bias-variance tradeoff. We also compare the information content of AM and ParT by decorrelating the features already learned by AM. Lastly, we briefly comment on the results of AM with finer angular resolution inputs.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2018

Characteristic Atmospheric Radiative Heating Rate Profiles in Arctic Clouds as Observed at Barrow, Alaska

Abstract A 2-yr cloud microphysical property dataset derived from ground-based remote sensors at the Atmospheric Radiation Measurement site near Barrow, Alaska, was used as input into a radiative transfer model to compute radiative heating rate (RHR) profiles in the atmosphere. Both the longwave (LW; 5–100 μm) and shortwave (SW; 0.2–5 μm) RHR profiles show significant month-to-month variability because of seasonal dependence in the vertical profiles of cloud liquid and ice water contents, with additional contributions from the seasonal dependencies of solar zenith angle, water vapor amount, and temperature. The LW and SW RHR profiles were binned to provide characteristic profiles as a function of cloud type and liquid water path (LWP). Single-layer liquid-only clouds are shown to have larger (10–30 K day−1) LW radiative cooling rates at the top of the cloud layer than single-layer mixed-phase clouds; this is due primarily to differences in the vertical distribution of liquid water between the two classes. However, differences in SW RHR profiles at the top of these two classes of clouds are less than 3 K day−1. The absolute value of the RHR in single-layer ice-only clouds is an order of magnitude smaller than in liquid-bearing clouds. Furthermore, for double-layer cloud systems, the phase and condensed water path of the upper cloud strongly modulate the radiative cooling both at the top and within the lower-level cloud. While sensitivity to cloud overlap and phase has been shown previously, the characteristic RHR profiles are markedly different between the different cloud classifications.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Michel Electron Selection with SPINE for DUNE Far Detector Simulation

Michel electrons are a valuable input for particle detector calibration due to their consistent kinetic energy distribution. This report details the evaluation of a Michel electron identification method's application to simulated data from the DUNE (Deep Underground Neutrino Experiment) far detector. This method, which relies on the neural network-based particle classification software SPINE (Scalable Particle Imaging with Neural Embeddings), was developed and calibrated using simulated data for the SBND (Short-Baseline Neutrino Detector) experiment before being applied to simulated DUNE data from a 1x2x6 subset of far detector modules.

Wilson, Dante [Colorado State U.]↗
NASA NTRS1992

Real-valued composite filters for correlation-based optical pattern recognition

Advances in the technology of optical devices such as spatial light modulators (SLMs) have influenced the research and growth of optical pattern recognition. In the research leading to this report, the design of real-valued composite filters that can be implemented using currently available SLMs for optical pattern recognition and classification was investigated. The design of real-valued minimum average correlation energy (RMACE) filter was investigated. Proper selection of the phase of the output response was shown to reduce the correlation energy. The performance of the filter was evaluated using computer simulations and compared with the complex filters. It was found that the performance degraded only slightly. Continuing the above investigation, the design of a real filter that minimizes the output correlation energy and the output variance due to noise was developed. Simulation studies showed that this filter had better tolerance to distortion and noise compared to that of the RMACE filter. Finally, the space domain design of RMACE filter was developed and implemented on the computer. It was found that the sharpness of the correlation peak was slightly reduced but the filter design was more computationally efficient than the complex filter.

Rajan, P. K.↗
DOE OSTI2017

Investigation of instrumentation containing an embedded digital device - 257

The U.S. nuclear power industry primarily relies on mature, well-proven analog technology as the basis for most instrumentation and control (I and C) equipment employed in its nuclear power plants (NPPs). However, the market for this legacy instrumentation is very small and availability of such equipment for spare parts or replacement is increasingly limited. Frequently, modern I and C equipment contains embedded digital devices (EDDs), such as microprocessors and programmable logic devices. While the enhanced functional capabilities provided by the digital components offer improved performance, reliability, and maintainability, the corresponding complexity of sophisticated digital implementations also exacerbate concerns about an increased potential for undetected systematic faults, which can lead to common-cause failure (CCF). As part of a research project sponsored by the U.S. Department of Energy, an investigation has been conducted of commercially available instrumentation marketed for nuclear or industrial application. The objective was to identify the usage of EDDs and to determine the types of functional roles allocated to the devices. Examples of the equipment surveyed include smart sensors, sequencers for emergency diesel generators, pumps, valve actuators, motor control centers, breakers, priority logic modules, and uninterruptible power sources. Where sufficient detail was found, an evaluation was performed to ascertain the nature and role of the digital technology implemented in the instrumentation. The information gained through this investigation serves as technical input for defining a classification approach for EDDs based on the functional impact of the device. For example, the role of an EDD can involve any of the following functions: performance monitoring and diagnostics, information extraction and communication, instrument condition monitoring and protection, or execution of core instrument functions. Classifying digital devices according to their role in the operation of the instrumentation in which they are embedded can enable determination of the potential functional impact of failure of that device and contribute to determination of the potential safety significance of a CCF event disabling all instances of that device. This paper will report on the findings of the investigation of instrumentation with EDDs and indicate considerations for the emerging classification approach. The higher-level goal of this effort is to determine whether a graded approach to qualification testing may be possible based on classification. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS2026

Long-Term Evaluation of Landsat 9 Oli Data Product Uniformity at Focal Plane Module-Boundaries Using Earth Scenes

The uniformity performance characteristic in remote sensing data products is a metric that helps evaluate how an image accurately represents radiometric responses to geophysical variability at the Earth’s surface or within the atmosphere. These uniformity attributes are used in Earth remote sensing science applications such as investigating water quality, land-cover/land-use classifications, and crop health assessments. To ensure these applications perform effectively, it is crucial to maintain and regularly assess the uniformity of calibrated remote sensing data products. In the Landsat missions, data from the onboard radiometric reference Solar diffuser device are used as the standard approach to derive the calibration parameters, ensuring the maintenance of both absolute radiance and radiometric uniformity through quarterly updates. This publication presents an alternative approach that leverages Earth scene data to assess the on-orbit uniformity performance of the Landsat 9 (L9) mission. The analysis characterizes uniformity at a specific set of spatial positions (the focal-plane boundary zones) that are intrinsic to the Operational Land Imager (OLI) focal plane design. By employing these Earth data statistics, the article further explores the temporal trends in the radiometric uniformity results. The results shown in this article demonstrate that for pushbroom imaging design systems with staggered sensor chip focal plane architectures, the co-registered pixels, i.e., overlapping measurements of the same ground target viewed by different detectors, offer a self-contained mechanism for assessing product uniformity. Remarkably, this alternative approach achieves a level of radiometric quality comparable to that delivered by a stable, well-characterized onboard Solar diffuser calibration device. The findings demonstrate the potential and capability of such Earth scene statistics in maintaining radiometric uniformity in calibrated data products to better than 1% (1-sigma) throughout the mission’s operational lifetime.

trend↗
DOE OSTI2025

Using Eye Tracking to Elucidate the Mechanisms Underlying Stimulation-Enhanced Visual Target Detection

Transcranial direct current stimulation (tDCS) is a noninvasive form of brain stimulation that involves passing a weak electrical current between electrodes on the scalp to modulate underlying neural tissue. TDCS has been shown to modulate cognition in a variety of domains, including memory, attention, and visual processing. Prior work from our laboratory has shown positive effects of tDCS on learning to detect target objects hidden in complex naturalistic visual scenes and learn rules for categorizing images, though the mechanism for these benefits remains unknown. One possibility is that tDCS optimizes visual search by modulating visual attention or via the reduction in search errors. One method of quantifying visual attention is to use eye tracking to record search patterns to determine if and how visual search is adjusted under verum stimulation conditions. Eye tracking data allows classification of errors into error types, including sampling errors (failing to look in the relevant region), recognition errors (looking at the critical portion of a scene, but failing to recognize it as such as evidenced by visual fixation), and decision-making errors (fixating on the relevant portion of a scene, but making the wrong determination). Our results indicate that the benefit tDCS confers on visual search for targets stems from the reduction in decision-making errors when targets are present (Cohen’s d = 0.86). Also reported is a replication of previous findings showing a tDCS-dependent improvement in learning this task, learning score (Cohen’s d = 0.88); d’ (Cohen’s d = 1.00). This provides support for moving tDCS into the application space by pairing it with analysts who are concerned with the type of search error that is corrected via stimulation.

attention↗
DOE OSTI2016

Understanding the optical properties of ambient sub- and supermicron particulate matter: results from the CARES 2010 field study in northern California

Abstract. Measurements of the optical properties (absorption, scattering and extinction) of PM1, PM2.5 and PM10 made at two sites around Sacramento, CA, during the June 2010 Carbonaceous Aerosols and Radiative Effects Study (CARES) are reported. These observations are used to establish relationships between various intensive optical properties and to derive information about the dependence of the optical properties on photochemical aging and sources. Supermicron particles contributed substantially to the total light scattering at both sites, about 50 % on average. A strong, linear relationship is observed between the scattering Ångström exponent for PM10 and the fraction of the scattering that is contributed by submicron particles (fsca, PM1) at both sites and with similar slopes and intercepts (for a given pair of wavelengths), suggesting that the derived relationship may be generally applicable for understanding variations in particle size distributions from remote sensing measurements. At the more urban T0 site, the fsca, PM1 increased with photochemical age, whereas at the downwind, more rural T1 site the fsca, PM1 decreased slightly with photochemical age. This difference in behavior reflects differences in transport, local production and local emission of supermicron particles between the sites. Light absorption is dominated by submicron particles, but there is some absorption by supermicron particles ( ∼ 15 % of the total). The supermicron absorption derives from a combination of black carbon that has penetrated into the supermicron mode and from dust, and there is a clear increase in the mass absorption coefficient of just the supermicron particles with increasing average particle size. The mass scattering coefficient (MSC) for the supermicron particles was directly observed to vary inversely with the average particle size, demonstrating that MSC cannot always be treated as a constant in estimating mass concentrations from scattering measurements, or vice versa. The total particle backscatter fraction exhibited some dependence upon the relative abundance of sub- versus supermicron particles; however this was modulated by variations in the median size of particles within a given size range; variations in the submicron size distribution had a particularly large influence on the observed backscatter efficiency and an approximate method to account for this variability is introduced. The relationship between the absorption and scattering Ångström exponents is examined and used to update a previously suggested particle classification scheme. Differences in composition led to differences in the sensitivity of PM2.5 to heating in a thermodenuder to the average particle size, with more extensive evaporation (observed as a larger decrease in the PM2.5 extinction coefficient) corresponding to smaller particles; i.e., submicron particles were generally more susceptible to heating than the supermicron particles. The influence of heating on the particle hygroscopicity varied with the effective particle size, with larger changes observed when the PM2.5 distribution was dominated by smaller particles.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

GEDet: Detecting Erroneous Nodes with A Few Examples

Detecting nodes with erroneous values in real-world graphs re- mains challenging due to the lack of examples and various error scenarios. We demonstrate GEDet, an error detection engine that can detect erroneous nodes in graphs with a few examples. The GEDet framework tackles error detection as a few-shot node classification problem. We invite the attendees to experience the following unique features. (1) Few-shot detection. Users only need to provide a few examples of erroneous nodes to perform error detection with GEDet. GEDet achieves desirable accuracy with (a) a graph augmentation module, which automatically generates synthetic examples to learn the classifier, and (b) an adversarial detection module, which improves classifiers to better distinguish erroneous nodes from both cleaned nodes and synthetic examples. We show that GEDet significantly improves the state-of-the-art error detection methods. (2) Diverse error scenarios. GEDet profiles data errors with a built-in library of transformation functions from correct values to errors. Users can also easily “plug in” new error types or examples. (3) User-centric detection. GEDet supports (a) an active learning mode to engage users to verify detected results, and adapts the error detection process accordingly; and (b) visual interfaces to interpret and track detected errors.

Guan, Sheng↗
DOE OSTI2025

Uncovering Sequence and Structural Characteristics of Fungal Expansin‐Related Proteins With Potential to Drive Substrate Targeting

Expansins loosen plant cell wall networks through disrupting non-covalent bonds between cellulose microfibrils and matrix polysaccharides. Whereas expansins were first discovered in plants, expansin-related proteins have since been identified in bacteria and fungi. The biological function of microbial expansins remains unclear; however, several studies have shown distinct binding preferences toward different structural polysaccharides. Earlier studies of bacterial expansin-related proteins uncovered sequence and structural features that correlate to substrate binding. Herein, 20 fungal expansin-related sequences were recombinantly produced in Komagataella phaffii, and the purified proteins were compared in terms of substrate binding to cellulosic and chitinous substrates. The impact of pH on the zeta potential of prioritized substrates was also measured, and Principal Component Analysis was performed to uncover correlations between protein characteristics (e.g., pI, hydrophobicity, surface charge distribution) and measured substrate binding preferences. Whereas acidic proteins with a predicted pI less than 5.0 preferentially bound to chitin, basic proteins with pI greater than 8.0 preferentially bound to xylan and xylan-containing fiber. Similar to many cellulases, binding to cellulose was correlated to relatively high aromatic amino acid content in the protein sequence and presence of a carbohydrate binding module (CBM), which in the case of expansins is a C-terminal CBM63. Whereas overall sequence characteristics could be correlated to substrate binding preference, the identity of amino acids occupying conserved positions that impact protein activity was better correlated with loosenin versus expansin classifications.

chitin↗
NASA NTRS2017

Critical Software for Human Spaceflight

The NASA Orion vehicle that will fly to the moon in the next years is propelled along its mission by the European Service Module (ESM), developed by ESA and its prime contractor Airbus Defense and Space. This paper describes the development of the Propulsion Drive Electronics (PDE) Software that provides the interface between the propulsion hardware of the European Service Module with the Orion flight computers, and highlights the challenges that have been faced during the development. Particularly, the specific aspects relevant to Human Spaceflight in an international cooperation are presented, as the compliance to both European and US standards and the software criticality classification to the highest category A. An innovative aspect of the PDE SW is its Time- Triggered Ethernet interface with the Orion Flight Computers, which has never been flown so far on any European spacecraft. Finally the verification aspects are presented, applying the most exigent quality requirements defined in the European Cooperation for Space Standardization (ECSS) standards such as the structural coverage analysis of the object code and the recourse to an independent software verification and validation activity carried on in parallel by a different team.

Preden, Antonio↗
NASA NTRS1995

Product Assurance for Spaceflight Hardware

This report contains information about the tasks I have completed and the valuable experience I have gained at NASA. The report is divided into two different sections followed by a program summary sheet. The first section describes the two reports I have completed for the Office of Mission Assurance (OMA). I describe the approach and the resources and facilities used to complete each report. The second section describes my experience working in the Receipt Inspection/Quality Assurance Lab (RI/QA). The first report described is a Product Assurance Plan for the Gas Permeable Polymer Materials (GPPM) mission. The purpose of the Product Assurance Plan is to define the various requirements which are to be met through completion of the GPPM mission. The GPPM experiment is a space payload which will be flown in the shuttle's SPACEHAB module. The experiment will use microgravity to enable production of complex polymeric gas permeable materials. The second report described in the first section is a Fracture Analysis for the Mir Environmental Effects Payload (MEEP). The Fracture Analysis report is a summary of the fracture control classifications for all structural elements of the MEEP. The MEEP hardware consists of four experiment carriers, each of which contains an experiment container holding a passive experiment. The MEEP hardware will be attached to the cargo bay of the space shuttle. It will be transferred by Extravehicular Activity and mounted on the Mir space station. The second section of this report describes my experiences in the RVQA lab. I listed the different equipment I used at the lab and their functions. I described the extensive inspection process that must be completed for spaceflight hardware. Included, at the end of this section, are pictures of most of the equipment used in the lab. There is a summary sheet located at the end of this report. It briefly describes the valuable experience I have gained at NASA this summer and what I will be able to take with me as I return to college. I briefly discuss the experiences I mentioned in the previous three paragraphs along with training classes I attended and courses I completed at the Learning Lab.

Monroe, Mike↗