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The objectives of this work were to evaluate the vegetative parameters used to estimate crop N demand and to estimate the accuracy and precision of the conceptual model of fertilization using an error propagation method. Corn plants were collected throughout the entire crop life cycle to determine the fresh and dry weight of the aboveground biomass and roots, root index, plant height, and corn grain yield. Three experiments were conducted, two under field conditions and one under greenhouse conditions. In the first field experiment in 2002, three sites were selected. The first site was the Texas A&M University (TAMU) Agricultural Experiment Station Research Farm in which a Ships clay soil was used. The second site was a cooperative farmer's land on a Weswood silt loam soil in Burleson County. These first two sites used Pioneer 32R25 as the corn hybrid. The third site was also a Ships soil in the TAMU Farm, but Dekalb 687 was the corn variety. In 2003, the second experiment was on a Ships soil in the field of TAMU Farm, and the third experiment was conducted in a greenhouse using Ships and Weswood soil. No differences in the root index and harvest index were observed, even when the Dekalb 687 hybrid was included. Variations in plant N concentration, moisture content, and yield were noted, but followed predictable patterns with time over the season. These parameters were consistent throughout the entire life cycle of the crop. The linear relationship between the fresh weight of aboveground biomass and fresh weight of roots was R2 = 0.92, the moisture content of corn plants over time was fit to a second grade polynomial with R2 = 0.98, and plant N content had a close linear relationship (R2=0.90) with the total plant dry weight, including roots, at harvest. The accuracy of the conceptual model was low under field conditions (55%), but high under greenhouse conditions (90%). Precision of the conceptual model was low both in the field (194%) and the greenhouse (115%) conditions.

With increasing demand and pressures on groundwater resources, accurate and reliable groundwater prediction models are essential for sustainable groundwater management. Groundwater models are merely approximations of reality, and we are unable to either fully characterize or mathematically describe the true complexity of the hydrologic system; therefore, inherent in all models are varying degree of uncertainty. A robust management policy should consider uncertainties in both the imprecise nature of conceptual/numerical models and their parameters. This study addresses the critical question of whether the use of multiple conceptual models to explicitly account for conceptual model uncertainty improves the ability of the models to assist in management decisions. Twelve unique conceptual models, characterized by three alternative geological interpretations, two recharge estimations, and two boundary condition implementations, were formulated to estimate sustainable extraction rates from Thailand’s Thaphra Area, where increasing groundwater withdrawals may result in water level declination and saline water upconing. The models were developed with MODFLOW and calibrated using PEST with the same set of observed hydraulic head data. All of the models were found to reasonably produce predictions of the available heads data. To select the best among the alternative models, multiple criteria have been defined and applied to evaluate the quality of individual models. It was found that models perform differently with respect to different evaluation criteria, and that it is unlikely that a single inter-model comparison criterion will ever be sufficient for general use. The chosen alternative models were applied both individually and jointly to quantify uncertainty in the groundwater management context. Different model-averaging methods were assessed in terms of their ability to assist in quantifying uncertainty in sustainable yield estimation. The twelve groundwater simulation models were additionally linked with optimization techniques to determine appropriate groundwater abstraction rates in the TPA Phu Thok aquifer. The management models aim to obtain maximal yields while protecting water level decline. Despite similar performances among the calibrated models, total sustainable yield estimates vary substantially depending on the conceptual model used and range widely, by a factor of 0.6 in total, and by as much as a factor of 4 in each management area. The comparison results demonstrate that simple averaging achieves a better performance than formal and sophisticated averaging methods such as Maximum Likelihood Bayesian Model Averaging, and produce a similar performance to GLUE and combined-multiple criteria averaging methods for both validation testing and management applications, but is much simpler to implement and use, and computationally much less demanding. The joint assessment of parameter and conceptual model uncertainty was performed by generating the multiple realizations of random parameters from the feasible space for each calibrated model using a simple Monte Carlo approach. The multi-model averaging methods produce a higher percentage of predictive coverage than do any individual models. Using model-averaging predictions, lower optimal rates were obtained to minimize head constraint violations, which do not ensue if a single best model is used with parameter uncertainty analysis. Although accounting for all sources of uncertainty is very important in predicting environmental and management problems, the available techniques used in the literature may be too computationally demanding and, in some cases, unnecessary complex, particularly in data-poor systems. The methods presented here to account for the main sources of uncertainty provide the required practical and comprehensive uncertainty analysis and can be applied to other case studies to provide reliable and accurate predictions for groundwater management applications.

The objectives of this work were to evaluate the vegetative parameters used to estimate crop N demand and to estimate the accuracy and precision of the conceptual model of fertilization using an error propagation method. Corn plants were collected throughout the entire crop life cycle to determine the fresh and dry weight of the aboveground biomass and roots, root index, plant height, and corn grain yield. Three experiments were conducted, two under field conditions and one under greenhouse conditions. In the first field experiment in 2002, three sites were selected. The first site was the Texas A&M University (TAMU) Agricultural Experiment Station Research Farm in which a Ships clay soil was used. The second site was a cooperative farmer's land on a Weswood silt loam soil in Burleson County. These first two sites used Pioneer 32R25 as the corn hybrid. The third site was also a Ships soil in the TAMU Farm, but Dekalb 687 was the corn variety. In 2003, the second experiment was on a Ships soil in the field of TAMU Farm, and the third experiment was conducted in a greenhouse using Ships and Weswood soil. No differences in the root index and harvest index were observed, even when the Dekalb 687 hybrid was included. Variations in plant N concentration, moisture content, and yield were noted, but followed predictable patterns with time over the season. These parameters were consistent throughout the entire life cycle of the crop. The linear relationship between the fresh weight of aboveground biomass and fresh weight of roots was R2 = 0.92, the moisture content of corn plants over time was fit to a second grade polynomial with R2 = 0.98, and plant N content had a close linear relationship (R2=0.90) with the total plant dry weight, including roots, at harvest. The accuracy of the conceptual model was low under field conditions (55%), but high under greenhouse conditions (90%). Precision of the conceptual model was low both in the field (194%) and the greenhouse (115%) conditions.

The design of pump and treat systems for groundwater remediation is often aided by numerical groundwater modelling. Model predictions are uncertain, with this uncertainty resulting from unknown parameter values, model structure and future system forcings. Researchers have begun to suggest that uncertainty in groundwater model predictions is largely dominated by structural/conceptual model uncertainty and that multiple conceptual models be developed in order to characterize this uncertainty. As regulatory bodies begin to endorse the more expensive multiple conceptual model approach, it is useful to assess whether a multiple model approach provides a signi cant improvement over a conventional single model approach for pump and treat system design, supplemented with a factor of safety. To investigate this question, a case study located in Tacoma, Washington which was provided by Conestoga-Rovers & Associates (CRA) was used. Twelve conceptual models were developed to represent conceptual model uncertainty at the Tacoma, Washington site and a pump and treat system was optimally designed for each conceptual model. Each design was tested across all 12 conceptual models with no factor of safety applied, and a factor of safety of 1.5 and 2 applied. Adding a factor of safety of 1.5 decreased the risk of containment failure to 15 percent, compared to 21 percent with no factor of safety. Increasing the factor of safety from 1.5 to 2 further reduced the risk of containment failure to 9 percent, indicating that the application of a factor of safety reduces the risk of design failure at a cost directly proportional to the value of the factor of safety. To provide a relatively independent estimate of a factor of safety approach a single "best" model developed by CRA was compared against the multiple model approach. With a factor of safety of 1.5 or greater, adequate capture was demonstrated across all 12 conceptual models. This demonstrated that in this case using the single \best" model developed by CRA with a factor of safety would have been a reasonable surrogate for a multiple model approach. This is of practical importance to engineers as it demonstrates that the a conventional single model approach may be su cient. However, it is essential that the model used is a good model. Furthermore, a multiple model approach will likely be an excessive burden in cases such as pump and treat system design, where the cost of failure is low as the system can be adjusted during operation to respond to new data. This may not be the case for remedial systems with high capital costs such as permeable reactive barriers, which cannot be easily adjusted.

With increasing demand and pressures on groundwater resources, accurate and reliable groundwater prediction models are essential for sustainable groundwater management. Groundwater models are merely approximations of reality, and we are unable to either fully characterize or mathematically describe the true complexity of the hydrologic system; therefore, inherent in all models are varying degree of uncertainty. A robust management policy should consider uncertainties in both the imprecise nature of conceptual/numerical models and their parameters. This study addresses the critical question of whether the use of multiple conceptual models to explicitly account for conceptual model uncertainty improves the ability of the models to assist in management decisions. Twelve unique conceptual models, characterized by three alternative geological interpretations, two recharge estimations, and two boundary condition implementations, were formulated to estimate sustainable extraction rates from Thailand’s Thaphra Area, where increasing groundwater withdrawals may result in water level declination and saline water upconing. The models were developed with MODFLOW and calibrated using PEST with the same set of observed hydraulic head data. All of the models were found to reasonably produce predictions of the available heads data. To select the best among the alternative models, multiple criteria have been defined and applied to evaluate the quality of individual models. It was found that models perform differently with respect to different evaluation criteria, and that it is unlikely that a single inter-model comparison criterion will ever be sufficient for general use. The chosen alternative models were applied both individually and jointly to quantify uncertainty in the groundwater management context. Different model-averaging methods were assessed in terms of their ability to assist in quantifying uncertainty in sustainable yield estimation. The twelve groundwater simulation models were additionally linked with optimization techniques to determine appropriate groundwater abstraction rates in the TPA Phu Thok aquifer. The management models aim to obtain maximal yields while protecting water level decline. Despite similar performances among the calibrated models, total sustainable yield estimates vary substantially depending on the conceptual model used and range widely, by a factor of 0.6 in total, and by as much as a factor of 4 in each management area. The comparison results demonstrate that simple averaging achieves a better performance than formal and sophisticated averaging methods such as Maximum Likelihood Bayesian Model Averaging, and produce a similar performance to GLUE and combined-multiple criteria averaging methods for both validation testing and management applications, but is much simpler to implement and use, and computationally much less demanding. The joint assessment of parameter and conceptual model uncertainty was performed by generating the multiple realizations of random parameters from the feasible space for each calibrated model using a simple Monte Carlo approach. The multi-model averaging methods produce a higher percentage of predictive coverage than do any individual models. Using model-averaging predictions, lower optimal rates were obtained to minimize head constraint violations, which do not ensue if a single best model is used with parameter uncertainty analysis. Although accounting for all sources of uncertainty is very important in predicting environmental and management problems, the available techniques used in the literature may be too computationally demanding and, in some cases, unnecessary complex, particularly in data-poor systems. The methods presented here to account for the main sources of uncertainty provide the required practical and comprehensive uncertainty analysis and can be applied to other case studies to provide reliable and accurate predictions for groundwater management applications.

In this thesis work we develop and test MOGO (a Mini-Ontology GeneratOr.) MOGO automates the generation of mini-ontologies from canonicalized tables of data. This will help anyone trying to organize large amounts of existing data into a more searchable and accessible form. By using a number of different heuristic rules for selecting, enhancing, and modifying ontology elements, MOGO allows users to automatically, semi-automatically, or manually generate conceptual mini-ontologies from canonicalized tables of data. Ideally, MOGO operates fully automatically while allowing users to intervene to direct and correct when necessary so that they can always satisfactorily complete the translation of canonicalized tables into mini-ontologies. Experimental results show that MOGO is able to automatically identify the concepts, relationships, and constraints that exist in arbitrary tables of values with a relatively high level of accuracy. This automation significantly reduces the work required to translate canonicalized tables into mini-ontologies.

Peer learning is a learning strategy that enables learners to interact with others and become active participants in their learning. To design peer learning activities, a model of peer learning is necessary to provide peers with guidance. However, previous models related to peer learning have not contained systematic strategies from diagnosis to evaluation. Scaffolding is an appropriate tactic for peer learning as it includes diagnosis, specific learning strategies, and assessment procedures. Therefore, the purpose of this study was to develop and validate a model of peer learning that incorporates scaffolding strategies in order to provide a structure for designing and implementing effective peer learning, and to enhance peers' teaching skills and learners' capability to gain new knowledge. This study drew from design and development research, including model development and model revision. This process was arranged in four phases. The first phase comprised of an intensive literature review to identify related theories, the conceptualization of scaffolding, and the operationalization of scaffolding. In the second phase, the model of peer learning was developed based on the results of the literature review. The model was synthesized using the data from the literature review, which included the main elements and characteristics of scaffolding suitable for peer learning. An online education program was also developed to teach the steps in the model to peer tutors participating in a peer tutoring program, which is one type of peer learning, for the purposes of model validation. In the third phase, model validation through internal (expert review) and external (external validation interview for field evaluation) validation was implemented. Based on the outcomes of these model validation processes, in the fourth phase, guidelines for revisions were developed to improve the proposed model. This model exhibits a synthesis of scaffolding strategies that enhance peer learning, including related theories, the conceptualization of scaffolding, and the operationalization of scaffolding. This model consists of four steps: (a) knowing each other, (b) learning together, (c) checking what you learned, and (d) finalizing peer learning. According to the results of model validation using an online education program designed for peer tutors participating in a peer tutoring program, which is one type of peer learning, this model of peer learning was useful for peers in providing structure and guidance for the design of their peer learning activities and the selection of appropriate peer learning strategies for learners who had different backgrounds and skills. This model is also applicable to various subjects and fields. / Doctor of Philosophy / Peer learning is a learning strategy that enables learners to interact with others and become active participants in their learning. To design effective peer learning activities, a model of peer learning is necessary to provide peers with guidance. The purpose of this study was to develop and validate a model of peer learning that incorporates scaffolding strategies in order to provide guidance for designing and implementing effective peer learning, and to enhance peers' teaching skills and learners' ability to gain new knowledge. This study was conducted through model development and model validation. For model development, previous research and books were reviewed to identify main elements of scaffolding such as related theories, the conceptualization of scaffolding, and the operationalization of scaffolding. Based on identified elements of scaffolding, the model of peer learning was developed. This model consists of four steps: (1) knowing each other, (2) learning together, (3) checking what you learned, and (4) finalizing peer learning. According to the results of model validation using an online education program designed for peer tutors participating in a peer tutoring program, which is one type of peer learning, this model of peer learning was useful for peers in providing guidance for the design of their peer learning activities and the selection of appropriate peer learning strategies for learners who had different backgrounds and skills. This model is also applicable to various subjects and fields.

Esta dissertação trata da aeração induzida em vertedores com objetivo de mitigar os efeitos da cavitação sobre os mesmos. A análise bibliográfica do tema está calcada na introdução ao mecanismo da cavitação e no levantamento do estado da arte a partir dos trabalhos pioneiros, nos clássicos e nos recentes. Com base nos princípios da física: Conservação de Massa e Primeira Lei da Termodinâmica, foi desenvolvido um modelo matemático para aeração induzida em vertedores. O modelo proposto, após ser analisado do ponto de vista de sua consistência, foi avaliado em comparações com resultados fornecidos por outros pesquisadores, obtidos através de modelos físicos reduzidos. Os resultados obtidos pelo modelo matemático proposto correspondem a boas estimativas das grandezas envolvidas na aeração induzida em vertedores, o que credencia o modelo proposto como uma ferramenta apropriada para projetos de engenharia hidráulica e futuros desenvolvimentos científicos. / This study adress prompt aeration in spillways reaching to reduce the cavitation effects on them. References were based on introduction to cavitation machanism and, state of art survey, to leave on not only earlier studies, but also on classical and most recent ones. Based on principles of Physics, such as Mass Conservation and the First Law of Thermodynamics, a mathematical model has been developed as an example of prompt aeration in spillways. After extensive consistency analyses the proposed model has been validated by comparing different results furnished by studies on physical reduced models by other researchers. Results obtained from the mathematical model proposed here correspond to good estimates of greatnesses involved in prompt aeration is spillways and that turns the proposed model into an adequate tool for Hydraulic Engineering projects and for future scientifics stydies.

Esta dissertação trata da aeração induzida em vertedores com objetivo de mitigar os efeitos da cavitação sobre os mesmos. A análise bibliográfica do tema está calcada na introdução ao mecanismo da cavitação e no levantamento do estado da arte a partir dos trabalhos pioneiros, nos clássicos e nos recentes. Com base nos princípios da física: Conservação de Massa e Primeira Lei da Termodinâmica, foi desenvolvido um modelo matemático para aeração induzida em vertedores. O modelo proposto, após ser analisado do ponto de vista de sua consistência, foi avaliado em comparações com resultados fornecidos por outros pesquisadores, obtidos através de modelos físicos reduzidos. Os resultados obtidos pelo modelo matemático proposto correspondem a boas estimativas das grandezas envolvidas na aeração induzida em vertedores, o que credencia o modelo proposto como uma ferramenta apropriada para projetos de engenharia hidráulica e futuros desenvolvimentos científicos. / This study adress prompt aeration in spillways reaching to reduce the cavitation effects on them. References were based on introduction to cavitation machanism and, state of art survey, to leave on not only earlier studies, but also on classical and most recent ones. Based on principles of Physics, such as Mass Conservation and the First Law of Thermodynamics, a mathematical model has been developed as an example of prompt aeration in spillways. After extensive consistency analyses the proposed model has been validated by comparing different results furnished by studies on physical reduced models by other researchers. Results obtained from the mathematical model proposed here correspond to good estimates of greatnesses involved in prompt aeration is spillways and that turns the proposed model into an adequate tool for Hydraulic Engineering projects and for future scientifics stydies.