AUSTIN davis - Academia.edu (original) (raw)
Papers by AUSTIN davis
Geoderma, 2019
The multiple schema for the classification of soils rely on differing criteria but the major soil... more The multiple schema for the classification of soils rely on differing criteria but the major soil science systems, including the United States Department of Agriculture (USDA) and the international harmonized World Reference Base for Soil Resources soil classification systems, are primarily based on inferred pedogenesis. Largely these classifications are compiled from individual observations of soil characteristics within soil profiles, and the vast majority of this pedologic information is contained in non-quantitative text descriptions. We present initial text mining analyses of parsed text in the digitally available USDA soil taxonomy documentation and the Soil Survey Geographic database. Previous research has shown that latent information structure can be extracted from scientific literature using Natural Language Processing techniques, and we show that this latent information can be used to expedite query performance by using syntactic elements and part-of-speech tags as indices. Technical vocabulary often poses a text mining challenge due to the rarity of its diction in the broader context. We introduce an extension to the common English vocabulary that allows for nearly-complete indexing of USDA Soil Series Descriptions.
This technical note describes initial investigative efforts to use soil classification data in a ... more This technical note describes initial investigative efforts to use soil classification data in a manner suitable for producing more accurate soil analogues for specific purposes. Those purposes include improving environmental modeling efforts and predicting complex biogeochemical processes affecting the fate and transport of contaminants and affecting spectral responses. This technical note also documents initial data analysis methods and the data structure and query system; further, this publication discusses the team's next steps. Geomatics is the study of spatial properties, processes, and patterns inherent in existing spatial data. Soils data is therefore a suitable topic for geomatic research-and in fact, pedo-informatics is the hybrid discipline synthesizing information science, soils science, and geography. It is well known that soils mapping is an evolving science, constrained by the complex nature of soils, including geological heterogeneity, climatic and landscape variation, and anthropomorphic effects. These challenges create a ubiquitous and inescapable heterogeneity that confounds precise environmental modeling and prediction systems. Current modeling and geospatial tools cannot predict complex biogeochemical processes, because statistically accurate multivariate soil characteristics datasets do not exist. The lack of such datasets has been a limiting factor in the production of accurate soil analogues for predicting soil properties in austere and expeditionary environments. Here, the authors discuss the foundation upon which an evolutionary data system could yield better soil analogue suggestions over existing methodologies.
This technical note describes initial investigative efforts to use soil classification data in a ... more This technical note describes initial investigative efforts to use soil classification data in a manner suitable for producing more accurate soil analogues for specific purposes. Those purposes include improving environmental modeling efforts and predicting complex biogeochemical processes affecting the fate and transport of contaminants and affecting spectral responses. This technical note also documents initial data analysis methods and the data structure and query system; further, this publication discusses the team's next steps. Geomatics is the study of spatial properties, processes, and patterns inherent in existing spatial data. Soils data is therefore a suitable topic for geomatic research-and in fact, pedo-informatics is the hybrid discipline synthesizing information science, soils science, and geography. It is well known that soils mapping is an evolving science, constrained by the complex nature of soils, including geological heterogeneity, climatic and landscape variation, and anthropomorphic effects. These challenges create a ubiquitous and inescapable heterogeneity that confounds precise environmental modeling and prediction systems. Current modeling and geospatial tools cannot predict complex biogeochemical processes, because statistically accurate multivariate soil characteristics datasets do not exist. The lack of such datasets has been a limiting factor in the production of accurate soil analogues for predicting soil properties in austere and expeditionary environments. Here, the authors discuss the foundation upon which an evolutionary data system could yield better soil analogue suggestions over existing methodologies.
Monthly Notices of the Royal Astronomical Society, 2018
Convective boundary mixing (CBM) in the advanced evolutionary stages of massive stars is not well... more Convective boundary mixing (CBM) in the advanced evolutionary stages of massive stars is not well understood. Structural changes caused by convection have an impact on the evolution as well as the subsequent supernova, or lack thereof. The effects of convectively driven mixing across convective boundaries during the post-He core burning evolution of 25 M , solarmetallicity, non-rotating stellar models is studied using the MESA stellar evolution code. CBM is modelled using the exponentially decaying diffusion coefficient equation, the free parameter of which, f CBM , is varied systematically throughout the course of the stellar model's evolution with values of (0.002, 0.012, 0.022, 0.032). The effect of varying this parameter produces mass ranges at collapse in the ONe, Si, Fe cores of (1.82 M , 4.36 M), (1.67 M , 1.99 M) and (1.46 M , 1.70 M) respectively, with per cent differences from the model with minimal CBM as large as 86.3 per cent. At the pre-supernova stage, the compactness of the stellar cores, ξ M , exhibit a range of (0.120, 0.354), suggesting that the extent of CBM in the advanced burning stages of massive stars is an important consideration for the explodability and type of compact remnant. The nucleosynthetic yields from the models, most notably C, O, Ne, Mg, and Si are also significantly affected by the CBM assumptions, showing non-linear trends with increased mixing. The simulations show that interactions between convective C, Ne, and O shells produce significant non-linear changes in the evolution, whereas from the end of Si burning, the structural changes attributed to the CBM are dominated by the growth of the convective C shell. Structure evolution data sets for all the models are available online.
Monthly Notices of the Royal Astronomical Society, 2016
This work investigates the properties of convection in stars with particular emphasis on entrainm... more This work investigates the properties of convection in stars with particular emphasis on entrainment across the upper convective boundary (CB). Idealized simulations of turbulent convection in the O-burning shell of a massive star are performed in 4π geometry on 768 3 and 1536 3 grids, driven by a representative heating rate. A heating series is also performed on the 768 3 grid. The 1536 3 simulation exhibits an entrainment rate at the upper CB of 1.33 × 10 −6 M s −1. The 768 3 simulation with the same heating rate agrees within 17 per cent. The entrainment rate at the upper CB is found to scale linearly with the driving luminosity and with the cube of the shear velocity at the upper boundary, while the radial rms fluid velocity scales with the cube root of the driving luminosity, as expected. The mixing is analysed in a 1D diffusion framework, resulting in a simple model for CB mixing. The analysis confirms that limiting the MLT mixing length to the distance to the CB in 1D simulations better represents the spherically averaged radial velocity profiles from the 3D simulations and provide an improved determination of the reference diffusion coefficient D 0 for the exponential diffusion CB mixing model in 1D. From the 3D simulation data, we adopt as the CB the location of the maximum gradient in the horizontal velocity component which has 2σ spatial fluctuations of ≈0.17H P. The exponentially decaying diffusion CB mixing model with f = 0.03 reproduces the spherically averaged 3D abundance profiles.
The ability of engineering students to identify and mitigate risks in a design project is an esse... more The ability of engineering students to identify and mitigate risks in a design project is an essential skill desired for professional engineering practice. However, this skill is not adequately addressed in undergraduate curriculum. This paper discusses the utilization of the CDIO (Conceiving, Designing, Implementing, Operating) framework to adapt and integrate NASA’s N x M Risk Matrix into a first year introduction to mechanical engineering course, and to evaluate student achievement. The paper first presents an overview of risk management and its application in aerospace. The paper next describes how the NASA N x M Risk Matrix is adapted and incorporated into an introduction to mechanical engineering course at California State University, Northridge, which was developed based on CDIO Standard 4. The Risk Matrix was integrated into the course’s design build project, which requires students to work in teams to: 1) identify the risks in the project; 2) write simple risk statements de...
Geoderma, 2019
The multiple schema for the classification of soils rely on differing criteria but the major soil... more The multiple schema for the classification of soils rely on differing criteria but the major soil science systems, including the United States Department of Agriculture (USDA) and the international harmonized World Reference Base for Soil Resources soil classification systems, are primarily based on inferred pedogenesis. Largely these classifications are compiled from individual observations of soil characteristics within soil profiles, and the vast majority of this pedologic information is contained in non-quantitative text descriptions. We present initial text mining analyses of parsed text in the digitally available USDA soil taxonomy documentation and the Soil Survey Geographic database. Previous research has shown that latent information structure can be extracted from scientific literature using Natural Language Processing techniques, and we show that this latent information can be used to expedite query performance by using syntactic elements and part-of-speech tags as indices. Technical vocabulary often poses a text mining challenge due to the rarity of its diction in the broader context. We introduce an extension to the common English vocabulary that allows for nearly-complete indexing of USDA Soil Series Descriptions.
This technical note describes initial investigative efforts to use soil classification data in a ... more This technical note describes initial investigative efforts to use soil classification data in a manner suitable for producing more accurate soil analogues for specific purposes. Those purposes include improving environmental modeling efforts and predicting complex biogeochemical processes affecting the fate and transport of contaminants and affecting spectral responses. This technical note also documents initial data analysis methods and the data structure and query system; further, this publication discusses the team's next steps. Geomatics is the study of spatial properties, processes, and patterns inherent in existing spatial data. Soils data is therefore a suitable topic for geomatic research-and in fact, pedo-informatics is the hybrid discipline synthesizing information science, soils science, and geography. It is well known that soils mapping is an evolving science, constrained by the complex nature of soils, including geological heterogeneity, climatic and landscape variation, and anthropomorphic effects. These challenges create a ubiquitous and inescapable heterogeneity that confounds precise environmental modeling and prediction systems. Current modeling and geospatial tools cannot predict complex biogeochemical processes, because statistically accurate multivariate soil characteristics datasets do not exist. The lack of such datasets has been a limiting factor in the production of accurate soil analogues for predicting soil properties in austere and expeditionary environments. Here, the authors discuss the foundation upon which an evolutionary data system could yield better soil analogue suggestions over existing methodologies.
This technical note describes initial investigative efforts to use soil classification data in a ... more This technical note describes initial investigative efforts to use soil classification data in a manner suitable for producing more accurate soil analogues for specific purposes. Those purposes include improving environmental modeling efforts and predicting complex biogeochemical processes affecting the fate and transport of contaminants and affecting spectral responses. This technical note also documents initial data analysis methods and the data structure and query system; further, this publication discusses the team's next steps. Geomatics is the study of spatial properties, processes, and patterns inherent in existing spatial data. Soils data is therefore a suitable topic for geomatic research-and in fact, pedo-informatics is the hybrid discipline synthesizing information science, soils science, and geography. It is well known that soils mapping is an evolving science, constrained by the complex nature of soils, including geological heterogeneity, climatic and landscape variation, and anthropomorphic effects. These challenges create a ubiquitous and inescapable heterogeneity that confounds precise environmental modeling and prediction systems. Current modeling and geospatial tools cannot predict complex biogeochemical processes, because statistically accurate multivariate soil characteristics datasets do not exist. The lack of such datasets has been a limiting factor in the production of accurate soil analogues for predicting soil properties in austere and expeditionary environments. Here, the authors discuss the foundation upon which an evolutionary data system could yield better soil analogue suggestions over existing methodologies.
Monthly Notices of the Royal Astronomical Society, 2018
Convective boundary mixing (CBM) in the advanced evolutionary stages of massive stars is not well... more Convective boundary mixing (CBM) in the advanced evolutionary stages of massive stars is not well understood. Structural changes caused by convection have an impact on the evolution as well as the subsequent supernova, or lack thereof. The effects of convectively driven mixing across convective boundaries during the post-He core burning evolution of 25 M , solarmetallicity, non-rotating stellar models is studied using the MESA stellar evolution code. CBM is modelled using the exponentially decaying diffusion coefficient equation, the free parameter of which, f CBM , is varied systematically throughout the course of the stellar model's evolution with values of (0.002, 0.012, 0.022, 0.032). The effect of varying this parameter produces mass ranges at collapse in the ONe, Si, Fe cores of (1.82 M , 4.36 M), (1.67 M , 1.99 M) and (1.46 M , 1.70 M) respectively, with per cent differences from the model with minimal CBM as large as 86.3 per cent. At the pre-supernova stage, the compactness of the stellar cores, ξ M , exhibit a range of (0.120, 0.354), suggesting that the extent of CBM in the advanced burning stages of massive stars is an important consideration for the explodability and type of compact remnant. The nucleosynthetic yields from the models, most notably C, O, Ne, Mg, and Si are also significantly affected by the CBM assumptions, showing non-linear trends with increased mixing. The simulations show that interactions between convective C, Ne, and O shells produce significant non-linear changes in the evolution, whereas from the end of Si burning, the structural changes attributed to the CBM are dominated by the growth of the convective C shell. Structure evolution data sets for all the models are available online.
Monthly Notices of the Royal Astronomical Society, 2016
This work investigates the properties of convection in stars with particular emphasis on entrainm... more This work investigates the properties of convection in stars with particular emphasis on entrainment across the upper convective boundary (CB). Idealized simulations of turbulent convection in the O-burning shell of a massive star are performed in 4π geometry on 768 3 and 1536 3 grids, driven by a representative heating rate. A heating series is also performed on the 768 3 grid. The 1536 3 simulation exhibits an entrainment rate at the upper CB of 1.33 × 10 −6 M s −1. The 768 3 simulation with the same heating rate agrees within 17 per cent. The entrainment rate at the upper CB is found to scale linearly with the driving luminosity and with the cube of the shear velocity at the upper boundary, while the radial rms fluid velocity scales with the cube root of the driving luminosity, as expected. The mixing is analysed in a 1D diffusion framework, resulting in a simple model for CB mixing. The analysis confirms that limiting the MLT mixing length to the distance to the CB in 1D simulations better represents the spherically averaged radial velocity profiles from the 3D simulations and provide an improved determination of the reference diffusion coefficient D 0 for the exponential diffusion CB mixing model in 1D. From the 3D simulation data, we adopt as the CB the location of the maximum gradient in the horizontal velocity component which has 2σ spatial fluctuations of ≈0.17H P. The exponentially decaying diffusion CB mixing model with f = 0.03 reproduces the spherically averaged 3D abundance profiles.
The ability of engineering students to identify and mitigate risks in a design project is an esse... more The ability of engineering students to identify and mitigate risks in a design project is an essential skill desired for professional engineering practice. However, this skill is not adequately addressed in undergraduate curriculum. This paper discusses the utilization of the CDIO (Conceiving, Designing, Implementing, Operating) framework to adapt and integrate NASA’s N x M Risk Matrix into a first year introduction to mechanical engineering course, and to evaluate student achievement. The paper first presents an overview of risk management and its application in aerospace. The paper next describes how the NASA N x M Risk Matrix is adapted and incorporated into an introduction to mechanical engineering course at California State University, Northridge, which was developed based on CDIO Standard 4. The Risk Matrix was integrated into the course’s design build project, which requires students to work in teams to: 1) identify the risks in the project; 2) write simple risk statements de...