Browsing by Author "Marcia L. Gumpertz, Committee Member"

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3D Structures formed by a Robotic and Meltblowing Integrated System
(2003-04-09) Velu, Yogeshwar Karunakaran; Abdelfattah M. Seyam, Committee Co-Chair; Tushar K. Ghosh, Committee Co-Chair; Behnam Pourdeyhimi, Committee Member; Marcia L. Gumpertz, Committee Member; Martin W. King, Committee Member; Donald A. Shiffler, Committee Member
Meltblown nonwovens have been produced as 2D web structures for a variety of end uses. Investigation into the development of 3D structures, has led to the integration of meltblown and robotic technology to form the Robotic Fiber Assembly and Control System. The effects of various process parameters including the fiber stream approach angle and the curvature of the collecting surface on the structural properties of the webs such as the diameter and orientation distribution of the fibers and the pore size distribution on the webs has been investigated. The interrelationships between these structural parameters have been explored and a statistical model developed. Orientation distribution, and the fiber diameter distribution of the webs were measured on image analysis software, while the pore size distribution was measured using equipment developed on the basis of capillary flow technique. SAS was used to develop the correlations between the structural parameters of the web. In general, all the webs show a larger percentage of fibers orienting in the machine direction (MD). The webs with finer fiber diameter produced webs with smaller pore diameter. The take-up speed of the collector had a significant influence on the orientation and diameters of the fibers in the web. Finer fibers were formed which are more oriented in the machine direction as the take-up speed of the collecting surface increased resulting in the formation of a web which has pores with finer diameter. A decrease in the polymer throughput demonstrated a decrease in the fiber diameter, the pore diameter and the basis weights of the webs. The resulting webs also produced pores that are of finer diameter. Lower attenuating air pressures produced larger diameter fibers. The average pore diameter of the analyzed meltblown fabrics decreased significantly when the attenuating air pressure was increased. Increasing the die to collector distance (DCD) shows a decrease in the percentage of fibers that are oriented in the machine direction. An increase in the DCD also exhibits an initial decrease followed by an increase in the average pore size confirming the existence of different 'zones' in the space between the die and collector. The increase in fiber stream approach angle shows an initial decrease followed by significant increase in the pore size of the web. Compared to the webs formed at low approach angles, analyses of the webs formed at higher approach angles shows that the fibers are more randomly arranged at higher approach angles. The relative frequency of fiber oriented in the machine direction increased significantly when the curvature of the collecting surface increases while the average pore size of the web decreases, due to the increased orientation of fibers in the direction of collection. The pore diameter is found to be directly proportional to the fiber diameter and inversely related to the web anisotropy parameter. The relationship that was established for the 2D webs correlates to the relationship developed for the 3D web structures.
Deterministic and Semi-Mechanistic Approaches in Predictive Fermentation Microbiology
(2002-10-10) Dougherty, Daniel Patrick; Marcia L. Gumpertz, Committee Member; Zhilin Li, Committee Member; Frederick Breidt Jr., Committee Co-Chair; Sharon R. Lubkin, Committee Chair
Predictive fermentation microbiology utilizes deterministic and stochastic mathematical models to study the growth dynamics of microorganisms. If the components of such models represent known or hypothesized biological growth processes then these models can be used to refine existing hypotheses or generate new hypotheses about the factors controlling growth. Special techniques must be used when fitting such models to experimental data. Methods are suggested for model re-parameterization and model fitting which improve the estimation of model parameters. Once estimates of model parameters have been made, temporal and multivariate sensitivity analyses can assess important relationships among the model parameters. A deterministic dynamic model of batch growth by a homofermentative lactic acid bacterium growing in a variable temperature environment was derived. This model predicts cell growth as well as changes in the chemical composition of the medium. This model was fit to experimental data. Analysis of the model revealed a quantitative reversal in parameter sensitivities across temperatures. Although mechanistic, this model neglected the effects of pH, organic acid dissociation and ionic strength of the medium. It is shown that these chemical dynamics are important and can be modeled through a convenient semi-mechanistic approach. The ability to model these chemical dynamics appropriately allows for a modeling framework in which the acid tolerance strategies commonly exhibited by bacteria can be studied.
Factors Influencing Responses of Loblolly Pine Stands to Fertilization
(2005-08-08) Rojas, Julio Cesar; Jeffery A. Wright, Committee Member; Bronson P. Bullock, Committee Member; Marcia L. Gumpertz, Committee Member; H. Lee Allen, Committee Chair
Fertilization of pine plantations has increased dramatically in the last decade. Over 600,000 hectares are being fertilized annually to overcome chronic widespread nitrogen (N) and phosphorus (P) limitations. However, responses to fertilization vary widely since specific responses after fertilization for any particular stand will be the result of complex interactions of nutrients and rates applied, stand and site conditions at time of application, years since application, and climatic conditions after application. Stand, soil and forest floor (FF) responses to fertilizations were assessed at three different sites located in the 'flatwoods' area of southeast Georgia and northeast Florida after five years of repeated fertilizer additions. Significant leaf area index (LAI), stemwood growth and FF responses were found at all three sites. Leaf area index was double for some treated plots as compared to control plots (from 1.4 to 3.0), five year cumulative growth on treated plots almost tripled that of the control plots (32 to 89 cubic meter per hectare). Soil N availability increased dramatically soon after fertilization however, it decreased few months after application. Several nutrients affected growth at these three sites, N and P at all three sites and potassium (K) and manganese (Mn) at the Georgia study sites. Factors affecting growth efficiency (GE) of loblolly pine plantations across the southeast were examined using 86 studies sites with different climatic, edaphic and stand conditions. Two modeling approaches were developed, one where GE would change with levels of LAI (non-linear using Gompertz model) and a second where GE was independent of the level of LAI (linear model), in both cases significant reduction in RMSE (>200%) was achieved when parameters in the models were allowed to be functions of edaphic, climatic and stand characteristics. In conclusion, GE is a dynamic rather than static stand parameter; it changes with stand age, drainage, soil texture and climate. Current year climatic variables were better predictors of GE than long term climatic averages, indicating that the inter-annual variation on temperature and rainfall exerts great influence on GE. Further modeling efforts were undertaken to determine factors affecting the variation in growth responses to N+P fertilization in loblolly pine stands. By utilizing a standardization procedure on the original data, 66% of the variation in the standard response was explained by LAI, Foliar N, and growth efficiency. These variables are ecophysiological variables proposed as drivers of stand response to fertilizer application(s). Two other variables, quadratic mean diameter (Dq), and stand age were also significant predictors in the model.
Frequentist and Bayesian Analysis of Random Coefficient Autoregressive models
(2004-01-08) Wang, Dazhe; Sastry G. Pantula, Committee Co-Chair; David A. Dickey, Committee Member; Marcia L. Gumpertz, Committee Member; Sujit K. Ghosh, Committee Co-Chair
Random Coefficient Autoregressive (RCA) models are obtained by introducing random coefficients to an AR or more generally ARMA model. These models have second order properties similar to that of ARCH and GARCH models. Historically an RCA model has been used to model the conditional mean of a time series, but it can also be viewed as a volatility model. In this thesis, we consider both Frequentist and Bayesian approaches to analyze the first order RCA models. For a weakly stationary RCA(1), it has been shown that the Maximum Likelihood Estimates (MLEs) are strongly consistent and satisfy a classical Central Limit Theorem. We consider a broader class of RCA(1) models whose parameters lie in the region of strict stationarity and ergodicity. We show that similar asymptotic properties can be extended to this class of models which includes the unit-root RCA(1) as a special case. The existence of a unit root in an RCA(1) has significant impact on the inference of data especially in the aspect of model forecasting. We develop the Wald criterion based on MLEs for testing unit root and evaluate its power via simulation studies. In addition to the Frequentist approach to RCA(1) models, Bayesian methods can also be used. We propose non-informative priors for the model parameters and apply them in Bayesian estimation procedure. Two model selection criteria are investigated to see their performance in choosing between RCA(1) and AR(1) models. We use two Bayesian methods to test for the unit-root hypothesis: one is based on the Posterior Interval (PI), and the other one is by means of Bayes Factor (BF). We apply both flat and mixed priors for the stationarity parameter in RCA(1) and compare the performance of different Bayesian unit-root testing criteria using these two types of prior densities through simulation. At the end of the thesis, two real life examples involving the daily stock volume transaction data are presented to show the applicability of the proposed methods.
Leaf Area Assessments of Overstory and Understory Vegetation in Pine Plantations Located in South Georgia and North Florida, US
(2007-04-11) Peduzzi, Alicia; H. Lee Allen, Committee Chair; Marcia L. Gumpertz, Committee Member; Stacy A. C. Nelson, Committee Member; Randolph H. Wynne, Committee Member
Leaf Area Index (LAI) was estimated in summer 2005 and winter 2006 for overstory and understory in loblolly pine and slash pine plantations at ages 7 and 10 year-old and on poorly, somewhat poorly and moderately-well drained soils located in the flatwoods region. Additionally, stand and site factors such as basal area, pine dominant height, understory height and understory coverage were estimated for each of the 40 plots established, and leaf area index and vegetation indices (SR, NDVI, VI and EVI) were calculated using remote sensing imagery. The objectives of this study were to determine the understory (competing vegetation) and overstory (crop-trees) leaf area index, to relate the variation in understory and overstory LAI to stand and site factors and to examine the relationships among understory and overstory leaf area index and spectral reflectance data captured by satellite imagery. Leaf area index values observed for the overstory were low in most of the plots (around 2 m2m-2 in slash pine and around 3 m2m-2 in loblolly pine), while the understory LAI was very high (around 2 m2m-2), which can be attributed to the lack of canopy closure observed in all plots. A negative relationship was observed between the overstory and the understory, where the higher the understory LAI the lower the overstory LAI. No significant differences were found in the understory LAI values across soil drainage classes. Low heights and short crown lengths were generally observed and could be explained by nutrient deficiency in most of the sites; which could be attributable to the belowground competition for water and nutrients. LAI and basal area were not correlated. Total LAI (overstory LAI plus understory LAI) estimated values on the ground were high and weakly correlated with the Landsat-derived vegetation indices, and the LAI values estimated with a LAI model were typically half of the values estimated on the ground. These results could be influenced by the contribution of different backgrounds, such as soil moisture and understory vegetation, plus the saturated response of the vegetation indices at high LAI values. Significant correlations were observed between the vegetation indices (SR and NDVI) and stand and site factors, suggesting that the satellite derived indices were more related to the stand biophysical parameters than in situ LAI estimates.
Leaf Extracellular Ascorbate Metabolism in Relation to Ozone Tolerance in Two Soybean Cultivars
(2004-11-11) Cheng, Fang-yi; Fitzgerald L. Booker, Committee Member; Kent O. Burkey, Committee Chair; Marcia L. Gumpertz, Committee Member
Antioxidants in the leaf apoplast have the potential to detoxify ozone (O3) and active oxygen species (AOS), and thus may play a role in preventing plant injury. To investigate this possibility, two experiments were conducted to compare antioxidant metabolism in the leaf extracellular spaces of O3-tolerant (Essex) and O3-sensitive (Forrest) cultivars of soybean [Glycine max (L.) Merr.]. In the first experiment, a canopy profile consisting of the 2nd, 4th and 6th main stem trifoliates was sampled from plants grown for four weeks in a greenhouse supplied with charcoal-filtered air (CF) to assess genetic and leaf age effects under low stress conditions. In general, apoplastic ascorbate (AA) levels were low (< 30 nmol AA g-1 FW) in both cultivars. Apoplastic dehydroascorbate (DHA) levels were higher (> 50 nmol DHA g-1 FW) relative to AA, particularly in younger leaves (250-450 nmol DHA g-1 FW), resulting in a low ascorbate redox state. Ascorbate oxidase (AO) and ascorbate peroxidase (APX) activity were found in leaf apoplast samples, suggesting that the enzymes played a role in maintaining ascorbate primarily in the oxidized state. In the second experiment, the second main stem trifoliates of four week-old plants were compared in the two genotypes following exposure to CF or elevated O3 conditions. Following a six-day treatment period with 77 ppb O3 for 7 h d-1, foliar injury was greater in O3-treated Forrest than in Essex, and total leaf guaiacol peroxidase activity was correlated with the greater O3 sensitivity of Forrest. Under both CF and elevated O3 conditions, there was a significantly higher apoplastic total antioxidant capacity in Essex than in Forrest correlating with greater O3 tolerance. Although reduced AA concentration was greater in apoplasts sample from O3-treated Essex than Forrest, the difference (6.5 nmol g-1 FW) was unlikely to play a major role in the differential O3 sensitivity of these two cultivars. Apoplast AA was generally less than 30% of the total antioxidant capacity found in apoplast samples, suggesting that other antioxidants might be involved in the detoxification of O3 in the extracellular space in these soybean cultivars.
Modeling and Prediction of Nonstationary Spatial Environmental Processes
(2002-08-19) Barber, Jarrett Jay; Montserrat Fuentes, Committee Chair; Peter Bloomfield, Committee Member; Jerry M. Davis, Committee Member; Marc G. Genton, Committee Member; Marcia L. Gumpertz, Committee Member
Spatial data are often collected for the purpose of producing spatial predictions (i.e., maps), the accuracy of which relies on a good estimate of the spatial covariance. Traditional geostatistical methods for spatial interpolation assume covariance stationarity. However, spatial data often exhibit nonstationary covariance, and traditional methods can produce maps that are misleading. Some existing approaches to nonstationarity feature process models which lead naturally to a globally defined covariance but do not retain a familiar interpretation in terms of local stationarity, while other approaches focus on local stationarity but rely on ad hoc methods for calculating covariance. We present a different approach with a relatively simple but useful model for space-time data. The model is simultaneously defined everywhere (globally) and leads immediately to a globally defined covariance, and, locally, the model behaves like a stationary process. A nonparametric approach to estimating the nonstationary spatial covariance is presented along with some asymptotic properties. The approach is particularly suited to time-rich, spatially-sparse networks. We illustrate this nonparametric approach for spatial prediction of atmospheric pollution data collected periodically from an EPA environmental monitoring network. We also propose an alternative, parametric approach to estimation and prediction using a Bayesian formulation of a nonstationary spatial model.
Pair-edge Approximation for Heterogeneous Lattice Population Models
(2002-10-08) Thomson, Nikkala; Stephen P. Ellner, Committee Chair; Marcia L. Gumpertz, Committee Member; John E. Franke, Committee Member
To increase the analytical tractability of lattice stochastic spatial population models, several approximations have been developed. The pair-edge approximation is a moment-closure method that is effective in predicting persistence criteria and invasion speeds on a homogeneous lattice. Here the effectiveness of the pair-edge approximation is evaluated on a spatially heterogeneous lattice in which some sites are unoccupiable, or 'dead'. This model has several possible interpretations, including a spatial SIS epidemic model, in which immobile host-species individuals occupy some sites while others are empty. As in the homogeneous model, the pair-edge approximation is found to be significantly more accurate than the ordinary pair approximation in determining conditions for persistence. However, habitat heterogeneity decreases invasion speed more than is predicted by the pair-edge approximation, and the discrepancy increases with greater clustering of dead sites. The accuracy of the approximation validates the underlying heuristic picture of population spread and therefore provides qualitative insight into the dynamics of lattice models. Conversely, the situations where the approximation is less accurate reveal limitations of pair approximation in the presence of spatial heterogeneity.
Spatial Analysis of In-Season Site-Specific Nitrogen Management Effects on Groundwater Nitrate and Agronomic Performance
(2004-10-21) Hong, Nan; Marcia L. Gumpertz, Committee Member; Hugh Devine, Committee Member; Jeffrey G. White, Committee Co-Chair; D. Keith Cassel, Committee Chair
In-season, site-specific (SS) N management based on remote sensing (RS) has been suggested as a way of reducing groundwater NO3-N contamination. In-season N management seeks to match the temporal variability of crop N needs by applying appropriate amounts of N at critical crop growth stages. Site-specific N management attempts to match the spatial variability of crop N requirements by applying appropriate, spatially variable N rates within fields. We evaluated the environmental and agronomic benefits of two in-season, RS-informed N management strategies applied on a uniform field-average (FA) or SS basis. We compared these to current uniform N recommendations based on "Realistic Yield Expectations" (RYE) in a typical coastal plain cropping system. We also sought to understand the spatial and temporal dynamics of shallow groundwater NO3-N. An additional objective was to develop a statistical procedure for the analysis of spatially dense, georeferenced subsample data in randomized complete block designs, a common characteristic of precision agriculture research. The experiment was established in a 12-ha North Carolina field with a 2-yr winter wheat double-crop soybean-corn rotation. The three N management treatments were applied to 0.37 ha plots in a randomized complete block design with 10 replications. Groundwater NO3-N and water table depth were measured every two weeks at 60 well nests (two per plot) sampling 0.9- to 1.8-, 1.8- to 2.7-, and 2.7- to 3.7-m depths from 2001 to 2003. We developed a statistical procedure for selecting an appropriate covariance model in randomized complete block analyses in the presence of spatial correlation. When warranted, incorporating spatial covariance in the statistical analysis provides greater efficiency in estimating treatment effects. Elevations, soil organic matter (SOM), and water table elevations (WTE) were spatial covariates used for explaining NO3-N spatial correlation. Compared to RYE, SS achieved: (i) less groundwater NO3-N by reducing fertilizer N and increasing the harvest N ratio (the ratio of N harvested in grain or forage to the total fertilizer N applied) for wheat in 2001; (ii) increased yield associated with higher N applied and decreased harvest N ratio for corn in 2002; and (iii) increased yield associated with similar fertilizer N and increased harvest N ratio for wheat in 2003. Overall, FA performed similarly to SS for wheat, but differed greatly for corn due to an overapplication of N at tasselling. These results indicate that RS-informed SS and FA might improve groundwater quality with no sacrifice in yield, or increase grain yield with similar fertilizer N compared to RYE-based N recommendations in the Coastal Plain. Mean NO3-N concentrations averaged over sampling depth at each well nest showed clear temporal fluctuations and were positively correlated with WTE. Groundwater NO3-N was frequently spatially correlated and spatial covariance structure changed periodically. The spatial correlation range varied over time from 46 to 551 m, and appeared to follow the trend of the mean water table depth. Blocking alone or together with elevation, SOM, and WTE frequently explained NO3-N spatial correlation. Our data suggest that to assess the environmental efficacy of N management, frequent and periodic monitoring of groundwater NO3-N, especially after significant rainfall, is essential to capture in-season treatment effects. Simultaneous measurement of precipitation and water table depth facilitate understanding of these effects. The traditional sampling of NO3-N only at or after harvest is likely to be insufficient to capture the entirety of treatment effects throughout the growing season. This is especially true in coastal plain and other coarse-textured soils where in-season NO3-N leaching may be pronounced. Our data also suggest that residual effects of differential N management may appear long after N application, even on these coarse-textured soils, indicating a need for longitudinal sampling.