Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “model weights”

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 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Morphological traits for allometric scaling of the European Sea Bass Dicentrarchus labrax (Linnaeus, 1758) from Southern Portugal population

Abstract The present study aimed to determine the allometric scaling among a selection of morphological traits in European sea bass ( Dicentrarchus labrax ) to estimate fish body weight. A set of morphological traits (fish body weight, length, height, and width) were directly measured in 146 fish of a recirculating aquaculture system, with body weights ranging from 17.11 to 652.21 g. In addition, a collection of digital imagery of each anesthetized fish from the side and top views were used to estimate other traits (indirect measures). Multiple regression analysis and regression coefficients were calculated using all possible combinations of biometric data (predictors) to estimate fish body weight, applying different numerical fitting models (linear, log‐linear, quadratic, exponential). The results showed that the best combination of traits for estimating fish body weight were fish body width, length and height, collected from direct measure ( R 2 = 0.995), for a log‐linear model fitting, which revealed more accurate determinations than the most commonly used length–weight relationship. Nevertheless, other combinations of morphological traits and fitting models were also found to be suitable in successfully predict fish body weight, with variability ranging between 92.5% and 98.5%. For indirect measures, the best predictor was a combination of traits from top view (width, eye distance and area without fins) fitted with a log‐linear function. These results comprise a relevant baseline in supporting the high potential of noninvasive methods to accurately follow the growth of European sea bass juveniles, recurring to imagery analysis of anesthetized fish. It has major potential applications in feeding consumption trials and fish growth models, as it allows for continuously following up fish growth under different experimental conditions without therein distress derived from manipulation.

Azevedo, Ana↗
DOE OSTI2018

Determination of Strain Rate Sensitivity of Micro-struts Manufactured Using the Selective Laser Melting Method

Micro-lattice structures manufactured using the selective laser melting (SLM) process provides the opportunity to realize optimal cellular materials for impact energy absorption. In this paper, strain rate-dependent material properties are measured for stainless steel 316L SLM micro-lattice struts in the strain rate range of 10{sup −3} to 6000 s{sup −1}. At high strain rates, a novel version of the split Hopkinson Bar has been developed. Strain rate-dependent materials data have been used in Cowper–Symonds material model, and the scope and limit of this model in the context of SLM struts have been discussed. Strain rate material data and the Cowper–Symonds model have been applied to the finite element analysis of a micro-lattice block subjected to drop weight impact loading. The model output has been compared to experimental results, and it has been shown that the increase in crush stress due to impact loading is mainly the result of strain rate material behavior. Hence, a systematic methodology has been developed to investigate the impact energy absorption of a micro-lattice structure manufactured using additive layer manufacture (SLM). This methodology can be extended to other micro-lattice materials and configurations, and to other impact conditions.

36 MATERIALS SCIENCE↗
NASA NTRS2017

Modeling and Simulation Plans in Support of Low Cost, Size, Weight, and Power Surveillance Systems for Detecting and Tracking Non-Cooperative Aircraft

RTCA Special Committee (SC) 228 has initiated a second phase for the development of minimum operational performance standards (MOPS) for UAS detect and avoid (DAA) systems. Technologies to enable UAS with less available Size, Weight, and Power (SWaP) will be considered. RTCA SC-228 has established sub-working groups and one of the sub-working groups is focused on aligning modeling and simulations activities across all participating committee members. This briefing will describe NASAs modeling and simulation plans for the development of performance standards for low cost, size, weight, and power (C-SWaP) surveillance systems that detect and track non-cooperative aircraft. The briefing will also describe the simulation platform NASA intends to use to support end-to-end verification and validation for these DAA systems. Lastly, the briefing will highlight the experiment plan for our first simulation study, and provide a high-level description of our future flight test plans. This briefing does not contain any results or data.

surveillance↗
DOE OSTI2025

Dataset for "Machine Learning Ensembles Can Enhance Hydrologic Predictions and Uncertainty Quantification" Willard et al. (2025).

This data release provides all data and code used in the paper " "Machine Learning Ensembles Can Enhance Hydrologic Predictions and Uncertainty Quantifications" Willard et al. (2025)" to model stream temperature, evaluate, and assess results. The associated manuscript explores the effect of different ensemble construction techniques across different common machine learning (ML) architectures for predictions in unmonitored basins. Modeling was done using long short-term memory (LSTM), gated recurrent unit (GRU), temporal convolution network (TCN), and extreme gradient boosting (XGBoost) models, and stream site coverage spans 1362 locations across the conterminous United States. The ensemble construction techniques investigated include ensemble by random weight initialization, differing hyperparameters, different random subsets of training data, different subselections of input features, different architectures, and Monte Carlo Dropout. The data is organized into these items items:Code repository and data for the paper " "Machine Learning Ensembles Can Enhance Hydrologic Predictions and Uncertainty Quantifications" Willard et al. (2025).Code: stream_temp_ml_regionalization.zip contains the code repositoryData to run the code:- data_dir.zip -- contains all files that should be moved to the "DATA_DIR" variable defined in the "set_env_vars.sh" script in the code repository- metadata_dir.zip -- contains all files that should be moved to the "METADATA_DIR" variable defined in the "set_env_vars.sh" script in the code repositoryData produced by the code and used in the paper:- outputs_dir.zip - contains model output and results (outputs_dir/results), model weights (outputs_dir/models), and all other outputs used for the paper including feature importances.To cite this code, please use the following BibTeX or MLA entries:bibtex:@misc{willard2025streamensembles,author = {Jared Willard and Charuleka Varadharajan},title = {Dataset for "Machine Learning Ensembles Can Enhance Hydrologic Predictions and Uncertainty Quantification"},year = {2024},doi = {10.15485/2527393},publisher = {ESS-DIVE Repository},url = {https://data.ess-dive.lbl.gov/datasets/doi:10.15485/2527393}}MLA: Willard, Jared, et al. Dataset for "Machine Learning Ensembles Can Enhance Hydrologic Predictions and Uncertainty Quantification". 2025. ESS-DIVE Repository, doi:10.15485/2448016.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1992

Relating multifrequency radar backscattering to forest biomass: Modeling and AIRSAR measurement

During the last several years, significant efforts in microwave remote sensing were devoted to relating forest parameters to radar backscattering coefficients. These and other studies showed that in most cases, the longer wavelength (i.e. P band) and cross-polarization (HV) backscattering had higher sensitivity and better correlation to forest biomass. This research examines this relationship in a northern forest area through both backscatter modeling and synthetic aperture radar (SAR) data analysis. The field measurements were used to estimate stand biomass from forest weight tables. The backscatter model described by Sun et al. was modified to simulate the backscattering coefficients with respect to stand biomass. The average number of trees per square meter or radar resolution cell, and the average tree height or diameter breast height (dbh) in the forest stand are the driving parameters of the model. The rest of the soil surface, orientation, and size distributions of leaves and branches, remain unchanged in the simulations.

Sun, Guo-Qing↗
NASA NTRS1991

COSP - A computer model of cyclic oxidation

A computer model useful in predicting the cyclic oxidation behavior of alloys is presented. The model considers the oxygen uptake due to scale formation during the heating cycle and the loss of oxide due to spalling during the cooling cycle. The balance between scale formation and scale loss is modeled and used to predict weight change and metal loss kinetics. A simple uniform spalling model is compared to a more complex random spall site model. In nearly all cases, the simpler uniform spall model gave predictions as accurate as the more complex model. The model has been applied to several nickel-base alloys which, depending upon composition, form Al2O3 or Cr2O3 during oxidation. The model has been validated by several experimental approaches. Versions of the model that run on a personal computer are available.

Lowell, Carl E.↗
DOE OSTI2013

Distribution function approach to redshift space distortions. Part V: perturbation theory applied to dark matter halos

Numerical simulations show that redshift space distortions (RSD) introduce strong scale dependence in the power spectra of halos, with ten percent deviations relative to linear theory predictions even on relatively large scales (k < 0.1h/Mpc) and even in the absence of satellites (which induce Fingers-of-God, FoG, effects). If unmodeled these effects prevent one from extracting cosmological information from RSD surveys. In this paper we use Eulerian perturbation theory (PT) and Eulerian halo biasing model and apply it to the distribution function approach to RSD, in which RSD is decomposed into several correlators of density weighted velocity moments. We model each of these correlators using PT and compare the results to simulations over a wide range of halo masses and redshifts. We find that with an introduction of a physically motivated halo biasing, and using dark matter power spectra from simulations, we can reproduce the simulation results at a percent level on scales up to k ∼ 0.15h/Mpc at z = 0, without the need to have free FoG parameters in the model.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Foundational Dataset for Developing Large-Sample Stream Temperature Models in the Conterminous United States

This dataset provides inputs, evaluation results, and trained weights from a large-sample Long Short-Term Memory (LSTM) model designed to predict daily stream temperatures across unregulated river reaches in the conterminous United States (CONUS). It includes dynamic meteorological and hydrologic forcings, static physiographic attributes, and model outputs from cross-validation experiments spanning 300 basins. It supports reproducible modeling, direct application for new basins, and provides data suitable for integration with reservoir and river simulations under current and future climates. It contains two .zip files described below · RQ-AI_runs.zip: Model outputs from 10-fold cross-validation experiments, including observed and predicted daily stream temperatures, along with test performance metrics for water years 2017–2019. Two versions are included: 1. Model trained and validated using subbasin-area weighted dynamic features. 2. Model trained and validated using whole-basin area weighted dynamic features. · RQ-AI_inputs.zip: Collection of all formatted dynamic and static predictor datasets (meteorological, hydrologic, and physiographic features) used in model training and analysis. Detailed instructions and data structure is held at the following GitLab repository: https://code.ornl.gov/tempwise/training.

Gomez-Velez, Jesus [Oak Ridge National Laboratory ↗
NASA NTRS1973

A search for global and seasonal variation of methane from Nimbus 4 IRIS measurements

The Nimbus 4 infrared interferometer spectrometer (IRIS) measurements in the region around wave number 1304 show absorption due to methane in the earth's atmosphere. From the laboratory measurements of the absorption coefficient and a selected vertical distribution corresponding to 1.13 atm cm of methane, a theoretical model for the transmittance at wave number 1304 is developed. The weighting function deduced from this model shows a maximum around 300 mb. Some weak absorption due to nitrous oxide in the atmosphere has been taken into account. The vertical temperature profile, derived from the 15 micron CO2 band in the IRIS spectrum, together with the methane weighting function have been used in a consistent way to compute the upwelling intensity at wave number 1304. The brightness temperature corresponding to the IRIS observed radiance at wave number 1304 has been compared with the brightness temperature deduced from the calculated upwelling intensity from 80 deg North to 80 deg South and for different periods of the year. This comparison shows that the two brightness temperatures agree with one another to within the accuracy of measurements about 2 K. From this result it was found that global or seasonal variability of methane is less than + or - 0.25 atm cm.

Prabhakara, C.↗
DOE OSTI2016

TH-CD-202-07: A Methodology for Generating Numerical Phantoms for Radiation Therapy Using Geometric Attribute Distribution Models

Purpose: A persistent challenge for the quality assessment of radiation therapy treatments (e.g. contouring accuracy) is the absence of the known, ground truth for patient data. Moreover, assessment results are often patient-dependent. Computer simulation studies utilizing numerical phantoms can be performed for quality assessment with a known ground truth. However, previously reported numerical phantoms do not include the statistical properties of inter-patient variations, as their models are based on only one patient. In addition, these models do not incorporate tumor data. In this study, a methodology was developed for generating numerical phantoms which encapsulate the statistical variations of patients within radiation therapy, including tumors. Methods: Based on previous work in contouring assessment, geometric attribute distribution (GAD) models were employed to model both the deterministic and stochastic properties of individual organs via principle component analysis. Using pre-existing radiation therapy contour data, the GAD models are trained to model the shape and centroid distributions of each organ. Then, organs with different shapes and positions can be generated by assigning statistically sound weights to the GAD model parameters. Organ contour data from 20 retrospective prostate patient cases were manually extracted and utilized to train the GAD models. As a demonstration, computer-simulated CT images of generated numerical phantoms were calculated and assessed subjectively and objectively for realism. Results: A cohort of numerical phantoms of the male human pelvis was generated. CT images were deemed realistic both subjectively and objectively in terms of image noise power spectrum. Conclusion: A methodology has been developed to generate realistic numerical anthropomorphic phantoms using pre-existing radiation therapy data. The GAD models guarantee that generated organs span the statistical distribution of observed radiation therapy patients, according to the training dataset. The methodology enables radiation therapy treatment assessment with multi-modality imaging and a known ground truth, and without patient-dependent bias.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

FPGA-accelerated SpeckleNN with SNL for real-time X-ray single-particle imaging

We present the implementation of a specialized version of our previously published unified embedding model, SpeckleNN, for real-time speckle pattern classification in X-ray Single-Particle Imaging (SPI), using the SLAC Neural Network Library (SNL) on an FPGA platform. This hardware realization transitions SpeckleNN from a prototypic model into a practical edge solution, optimized for running inference near the detector in high-throughput X-ray free-electron laser (XFEL) facilities, such as those found at the Linac Coherent Light Source (LCLS). To address the resource constraints inherent in FPGAs, we developed a more specialized version of SpeckleNN. The original model, which was designed for broader classification across multiple biological samples, comprised ~5.6 million parameters. The new implementation, while reducing the parameter count to 64.6K (a 98.8% reduction), focuses on maintaining the model's essential functionality for real-time operation, achieving an accuracy of 90%. Furthermore, we compressed the latent space from 128 to 50 dimensions. This implementation was demonstrated on the KCU1500 FPGA board, utilizing 71% of available DSPs, 75% of LUTs, and 48% of FFs, with an average power consumption of 9.4W according to the Vivado post-implementation report. The FPGA performed inference on a single image with a latency of 45.015 microseconds at a 200 MHz clock rate. In comparison, running the same inference on an NVIDIA A100 GPU resulted in an average power consumption of ~73W and an image processing latency of around 400 microseconds. Our FPGA-accelerated version of SpeckleNN demonstrated significant improvements, achieving an 8.9 × speedup and a 7.8 × reduction in power consumption compared to the GPU implementation. Key advancements include model specialization and dynamic weight loading through SNL, which eliminates the need for time-consuming FPGA design re-synthesis, allowing fast and continuous deployment of models (re)trained online. These innovations enable real-time adaptive classification and efficient vetoing of speckle patterns, making SpeckleNN more suited for deployment in XFEL facilities. This implementation has the potential to significantly accelerate SPI experiments and enhance adaptability to evolving experimental conditions.

47 OTHER INSTRUMENTATION↗
NASA NTRS1985

Application of lagged averaged forecasting to medium range forecasting

The lagged average forecast (LAF) method for predicting and compensating for forecast error growth is applied to two 100 day samples of 10 day forecasts at the 500 mb altitude for winter-summer 1980-81. The LAF parameterizes the forecast error growth in order to weight the forecast with regression calculations. A 5 day LAF forecast was generated for a 10 day forecast made with a 1681 d.o.f. model which produced predictions using a truncated spherical harmonic expansion. A 100 day data set was employed, with the first 90 days serving for LAF forecasts for the model forecast. Significant improvements were obtained in the model forecast when the LAF weightings were introduced into the model variables.

Dalcher, A.↗
DOE OSTI2021

PRISM3D: a 3-D reference seismic model for Iberia and adjacent areas

SUMMARY We present PRISM3D, a 3-D reference seismic model of P- and S-wave velocities for Iberia and adjacent areas. PRISM3D results from the combination of the most up-to-date earth models available for the region. It extends horizontally from 15°W to 5°E in longitude, 34°N to 46°N in latitude and vertically from 3.5 km above to 200 km below sea level, and is modelled on a regular grid with 10 and 0.5 km of grid node spacing in the horizontal and vertical directions, respectively. It was designed using models inferred from local and teleseismic body-wave tomography, earthquake and ambient noise surface wave tomography, receiver function analysis and active source experiments. It includes two interfaces, namely the topography/bathymetry and the Mohorovičić (Moho) discontinuity. The Moho was modelled from previously published receiver function analysis and deep seismic sounding results. To that end we used a probabilistic surface reconstruction algorithm that allowed to extract the mean of the Moho depth surface along with its associated standard deviation, which provides a depth uncertainty estimate. The Moho depth model is in good agreement with previously published models, although it presents slightly sharper gardients in orogenic areas such as the Pyrenees or the Betic-Rif system. Crustal and mantle P- and S-wave wave speed grids were built separately on each side of the Moho depth surface by weighted average of existing models, thus allowing to realistically render the speed gradients across that interface. The associated weighted standard deviation was also calculated, which provides an uncertainty estimation on the average wave speed values at any point of the grid. At shallow depths (<10 km), low P and S wave speeds and high VP/VS are observed in offshore basins, while the Iberian Massif, which covers a large part of western Iberia, appears characterized by a rather flat Moho, higher than average VP and VS and low VP/VS. Conversely, the Betic-Rif system seems to be associated with low VP and VS, combined with high VP/VS in comparison to the rest of the study area. The most prominent feature of the mantle is the well known high wave speed anomaly related to the Alboran slab imaged in various mantle tomography studies. The consistency of PRISM3D with previous work is verified by comparing it with two recent studies, with which it shows a good general agreement.The impact of the new 3-D model is illustrated through a simple synthetic experiment, which shows that the lateral variations of the wave speed can produce traveltime differences ranging from –1.5 and 1.5 s for P waves and from –2.5 and 2.5 s for S waves at local to regional distances. Such values are far larger than phase picking uncertainties and would likely affect earthquake hypocentral parameter estimations. The new 3-D model thus provides a basis for regional studies including earthquake source studies, Earth structure investigations and geodynamic modelling of Iberia and its surroundings.

Arroucau, P.↗
NASA NTRS1984

Tissue fluid shift, forelimb loading, and tail tension in tail-suspended rats

The tail suspension model (head-down tilt) simulates hypogravity in terms of musculoskeletal loss in the rat. However, little is known of tissue fluid shifts and body weight distribution in this model. Tissue fluid pressures were measured by wick catheters in 12 Munich-Wistar rats before, during, and after 48 hrs of tail suspension (about 30 deg head-down tilt). Subcutaneous tissue fluid pressure in the neck increased from -2.2 + or - 0.4 (normal horizontal position) to +4.0 + or - 1.5 cm H2O during tail suspension, indicating a cephalic fluid shift and significant edema during head-down tilt. In a separate study, six rats were suspended at 30-70 deg, and forelimb load and tail tension were measured by a balance and force transducer, respectively. Approximately 50 percent of body weight (BW) was loaded on forelimbs at a head-down tilt angle of 30 deg and forelimb load declined linearly to 10 percent BW at 70 deg. Furthermore, tail tension increased from 50 percent BW at 30 deg to 85 percent BW at 70 deg. These results indicate that less than normal loads are applied to forelimbs of rats suspended at angles of less than 30 deg and that the tail bears an increasing proportion of the rat's body weight at head-down tilt angles of less than 30 deg.

Hargens, A. R.↗
DOE OSTI2022

Determination of Infinite Dilution Activity Coefficients of Molecular Solutes in Ionic Liquids and Deep Eutectic Solvents by Factorization-Machine-Based Neural Networks

Widely known as “green solvents,” ionic liquids (ILs) and deep eutectic solvents (DESs) have been used as substitutes for traditional organic solvents in separation science. To achieve better separations using ILs and DESs, this work aimed to predict the infinite dilution activity coefficients (IDACs) of molecular solutes in these solvents with the state-of-the-art factorization-machine-based neural network (DeepFM). DeepFM combines the benefits of factorization machines and deep neural networks to learn both low-order and high-order interactions among features. The IDAC prediction model was established with 52,372 experimental IDAC datapoints including 260 solvents (252 ILs and 8 DESs) and 112 molecular solutes collected at various temperatures from 288.15 to 428.15 K. Chemical information describing the ILs, DESs, and molecular solutes was included in the IDAC prediction model, including chemical functional groups, molecular weights, and Abraham solvation parameters. The IDAC prediction model showed an improved accuracy compared with alternative models; additionally, DESs were included in the IDAC prediction model for the first time. Here, the model will reduce the energy and resources needed to optimize the selection of ILs and DESs for specific separations, which will promote the development of these green solvents for sustainable chemical processes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Integrating multi-modal remote sensing, deep learning, and attention mechanisms for yield prediction in plant breeding experiments

In both plant breeding and crop management, interpretability plays a crucial role in instilling trust in AI-driven approaches and enabling the provision of actionable insights. The primary objective of this research is to explore and evaluate the potential contributions of deep learning network architectures that employ stacked LSTM for end-of-season maize grain yield prediction. A secondary aim is to expand the capabilities of these networks by adapting them to better accommodate and leverage the multi-modality properties of remote sensing data. In this study, a multi-modal deep learning architecture that assimilates inputs from heterogeneous data streams, including high-resolution hyperspectral imagery, LiDAR point clouds, and environmental data, is proposed to forecast maize crop yields. The architecture includes attention mechanisms that assign varying levels of importance to different modalities and temporal features that, reflect the dynamics of plant growth and environmental interactions. The interpretability of the attention weights is investigated in multi-modal networks that seek to both improve predictions and attribute crop yield outcomes to genetic and environmental variables. This approach also contributes to increased interpretability of the model's predictions. The temporal attention weight distributions highlighted relevant factors and critical growth stages that contribute to the predictions. The results of this study affirm that the attention weights are consistent with recognized biological growth stages, thereby substantiating the network's capability to learn biologically interpretable features. Accuracies of the model's predictions of yield ranged from 0.82-0.93 R 2 ref in this genetics-focused study, further highlighting the potential of attention-based models. Further, this research facilitates understanding of how multi-modality remote sensing aligns with the physiological stages of maize. The proposed architecture shows promise in improving predictions and offering interpretable insights into the factors affecting maize crop yields, while demonstrating the impact of data collection by different modalities through the growing season. By identifying relevant factors and critical growth stages, the model's attention weights provide valuable information that can be used in both plant breeding and crop management. The consistency of attention weights with biological growth stages reinforces the potential of deep learning networks in agricultural applications, particularly in leveraging remote sensing data for yield prediction. To the best of our knowledge, this is the first study that investigates the use of hyperspectral and LiDAR UAV time series data for explaining/interpreting plant growth stages within deep learning networks and forecasting plot-level maize grain yield using late fusion modalities with attention mechanisms.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Effects of macroparticle weighting in axisymmetric particle-in-cell Monte Carlo collision simulations

In this paper, the effects of macroparticle (MP) weighting on the plasma discharge, particularly near the centerline, are investigated using a two-dimensional axisymmetric particle-in-cell Monte Carlo collision (PIC/MCC) model. A variable MP weight according to the radial position of the MPs is employed to maintain sufficient number of MPs near the centerline of the plasma source. Finally, the plasma density obtained from the PIC/MCC simulations for low-pressure (25–100 mTorr) capacitively coupled plasmas is found to be artificially large when the MP weight near the centerline is not well resolved, demonstrating the need for particle convergence studies for axisymmetric PIC/MCC simulations.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS2002

Preliminary Thermal-Mechanical Sizing of Metallic TPS: Process Development and Sensitivity Studies

The purpose of this research was to perform sensitivity studies and develop a process to perform thermal and structural analysis and sizing of the latest Metallic Thermal Protection System (TPS) developed at NASA LaRC (Langley Research Center). Metallic TPS is a key technology for reducing the cost of reusable launch vehicles (RLV), offering the combination of increased durability and competitive weights when compared to other systems. Accurate sizing of metallic TPS requires combined thermal and structural analysis. Initial sensitivity studies were conducted using transient one-dimensional finite element thermal analysis to determine the influence of various TPS and analysis parameters on TPS weight. The thermal analysis model was then used in combination with static deflection and failure mode analysis of the sandwich panel outer surface of the TPS to obtain minimum weight TPS configurations at three vehicle stations on the windward centerline of a representative RLV. The coupled nature of the analysis requires an iterative analysis process, which will be described herein. Findings from the sensitivity analysis are reported, along with TPS designs at the three RLV vehicle stations considered.

Poteet, Carl C.↗