The Eastern stock of Atlantic bluefin tuna (Thunnus thynnus thynnus) has experienced a steep decline, 74.2%, in recent decades, mainly driven by large unreported catches and growth of the capture-based aquaculture of this species in the Mediterranean. This study addresses the potential food-web effects on trophic linkages in the ecosystem through the removal of both small pelagic fish species and wild bluefin tuna (BFT) for capture-based aquaculture operations. An Ecopath model of the Southern Catalan Sea (Western Mediterranean) was modified to include a BFT farm supplied entirely by the area modeled (Coll et al, 2006). Six scenarios were developed to simulate possible changes to the system using Ecosim, including the continued growth of aquaculture operations, as well as changes to the total allowable catch for BFT as set by ICCAT. Species not directly connected with the production of BFT showed large fluctuations in biomass and yield as a result of these simulations. Using these models, increases in biomass of lower trophic level functional groups were observed with reductions in biomass from other top trophic level predators. Jellyfish and benthopelagic fish exhibited an increase in biomass, the largest being 8.76% for jellyfish and 69.76% for benthopelagic fish, while wild BFT biomass decreased 87.26%. Atlantic bonito and swordfish showed similar rates of decline in biomass levels. These outcomes stress that fishing at top trophic levels can have unforeseen outcomes on the structure of the ecosystem, due to the complexity of the food web. Capture-based aquaculture of Atlantic bluefin tuna can be expected to increase along the proposed scenario levels, due to the decision not to place this species on Appendix I of CITES during the CITES meeting of 2010. This study suggests that farming activity has additional impacts on the ecosystems that should be taken into account when evaluating the suitability of this industry and projecting the trends towards the future. Results suggest that, in the case of the Western Mediterranean Sea, increasing BFT farming activities will likely contribute towards further degradation of an already highly exploited marine ecosystem. The development of hatchery technology to close the life cycle and produce juvenile BFT for stocking cages as well as using artificial diets during the grow out stage are required to minimize long-term impacts and support industry expansion.

The importance of attribute vector ambiguity has been largely overlooked by the machine learning community. A pattern recognition problem can be solved in many ways within the scope of machine learning. Neural Networks, Decision Tree Algorithms such as C4.5, Bayesian Classifiers, and Instance Based Learning are the main algorithms. All listed solutions fail to address ambiguity in the attribute vector. The research reported shows, ignoring this ambiguity leads to problems of classifier scalability and issues with instance collection and aggregation. The Algorithm presented accounts for both ambiguity of the attribute vector and class label thus solving both issues of scalability and instance collection. The research also shows that when applied to sanitized data sets, suitable for traditional instance based learning, the presented algorithm performs equally as well.

The ultimate goal of most prognostic systems is accurate prediction of the remaining useful life (RUL) of individual systems or components based on their use and performance. This class of prognostic algorithms is termed Degradation-Based, or Type III Prognostics. As equipment degrades, measured parameters of the system tend to change; these sensed measurements, or appropriate transformations thereof, may be used to characterize degradation. Traditionally, individual-based prognostic methods use a measure of degradation to make RUL estimates. Degradation measures may include sensed measurements, such as temperature or vibration level, or inferred measurements, such as model residuals or physics-based model predictions. Often, it is beneficial to combine several measures of degradation into a single parameter. Selection of an appropriate parameter is key for making useful individual-based RUL estimates, but methods to aid in this selection are absent in the literature. This dissertation introduces a set of metrics which characterize the suitability of a prognostic parameter. Parameter features such as trendability, monotonicity, and prognosability can be used to compare candidate prognostic parameters to determine which is most useful for individual-based prognosis. Trendability indicates the degree to which the parameters of a population of systems have the same underlying shape. Monotonicity characterizes the underlying positive or negative trend of the parameter. Finally, prognosability gives a measure of the variance in the critical failure value of a population of systems. By quantifying these features for a given parameter, the metrics can be used with any traditional optimization technique, such as Genetic Algorithms, to identify the optimal parameter for a given system. An appropriate parameter may be used with a General Path Model (GPM) approach to make RUL estimates for specific systems or components. A dynamic Bayesian updating methodology is introduced to incorporate prior information in the GPM methodology. The proposed methods are illustrated with two applications: first, to the simulated turbofan engine data provided in the 2008 Prognostics and Health Management Conference Prognostics Challenge and, second, to data collected in a laboratory milling equipment wear experiment. The automated system was shown to identify appropriate parameters in both situations and facilitate Type III prognostic model development.

This thesis work is motivated by the potential and promise of image fusion technologies in the multi sensor image fusion system and applications. With specific focus on pixel level image fusion, the process after the image registration is processed, we develop graphic user interface for multi-sensor image fusion software using Microsoft visual studio and Microsoft Foundation Class library. In this thesis, we proposed and presented some image fusion algorithms with low computational cost, based upon spatial mixture analysis. The segment weighted average image fusion combines several low spatial resolution data source from different sensors to create high resolution and large size of fused image. This research includes developing a segment-based step, based upon stepwise divide and combine process. In the second stage of the process, the linear interpolation optimization is used to sharpen the image resolution. Implementation of these image fusion algorithms are completed based on the graphic user interface we developed. Multiple sensor image fusion is easily accommodated by the algorithm, and the results are demonstrated at multiple scales. By using quantitative estimation such as mutual information, we obtain the experiment quantifiable results. We also use the image morphing technique to generate fused image sequence, to simulate the results of image fusion. While deploying our pixel level image fusion algorithm approaches, we observe several challenges from the popular image fusion methods. While high computational cost and complex processing steps of image fusion algorithms provide accurate fused results, they also makes it hard to become deployed in system and applications that require real-time feedback, high flexibility and low computation ability

Child maltreatment (CM) is a major public health problem in the United States which affects millions of children each year. Because parents are responsible for the majority of substantiated CM reports, behavioral parent training is recommended as the primary prevention strategy. In recent years, researchers and clinicians have begun work examining the relevance and effectiveness of making cultural adaptations to parent training programs. The purpose of this study was to explore the need for systemic cultural adaptations to SafeCare®, an evidence-based parent-training child maltreatment prevention program. SafeCare is currently implemented in nine states and SafeCare providers are serving families representing a wide array of cultures and ethnicities. Eleven SafeCare providers, representing six states, participated in individual, semi-structured interviews to determine what, if any, cultural adaptations were in place in the field and whether there was a need for systematic culture-specific or general cultural adaptations to the SafeCare model. The interviews provided evidence that, across sites and populations, adaptations are being made when implementing SafeCare with diverse families. Providers expressed a need to make the language/reading levels of the model materials more relevant for all the populations served. Overall, however, providers found the model to be flexible and amenable to working with families of various cultures and ethnicities. Providers recommended against systematic adaptations of the model for specific ethnic groups.

Thesis (M.S.)--University of Florida, 2001. / Title from first page of PDF file. Document formatted into pages; contains x, 61 p.; also contains graphics. Vita. Includes bibliographical references (p. 58-60).

Thesis (Doctoral)--Universität Osnabrücks, 2006. / Includes abstract and bibliographical references (p. 216-231).

As technology has advanced, corporations, government entities, and institutions of higher education have all begun experimenting with online classes and training. In colleges and universities around the world, everything from individual online classes to entire online degree programs are now offered. While many researchers and educators support this trend, many are concerned with whether online education is truly comparable to traditional, live instruction. The goal of this study was to evaluate an online version of a floral design course in comparison to the traditional version of the class. There were 140 students in the sample, including both the online and traditional classes. All were students at Texas A&M University in College Station, Texas. During the spring semester of 2003, the experimental group was enrolled in the online version of the course, while the control group was enrolled in the traditional version of the course. Students in both groups were asked to fill out surveys at the beginning and end of the semester to collect background information and to evaluate the course. Their floral designs were evaluated at the beginning and end of the class in order to measure design skill, and grades earned in the class were also collected at the end of the semester for comparison purposes. Statistically significant differences were noted in class grades, with traditional students outperforming the Web-based students in lecture points, lab points, and overall course grades. No statistically significant differences were noted in terms of student course satisfaction. In addition, students in the traditional class outperformed Web-based students in design skills. Besides class differences in performance, variables such as gender and distance course preparedness seemed to affect the outcome of some measures. Overall, females outperformed males in both classes. In the Web-based class, students found to be more prepared for distance learning courses fared better than students who were not as prepared. These results may indicate that certain students may do better in an online course than others, and it may be possible to screen these students in advance in order to maximize success in the online classroom.

This thesis presents the development of a hydrologic model in the vector environment. Establishing spatial relationship between flow elements is the key for flow routing techniques. Such a relationship is called hydrologic topology, making each flow element know which other elements are upstream and which are downstream. Based on the hydrologic topology established for the flow elements, tools were developed for flow network navigation, drainage area estimation, flow length calculation and drainage divide determination. To apply the tools, data required might be obtained from different sources, which may lead to certain problems that have to do with wrong flow direction of stream lines and, mismatches in location of stream lines with respect to the corresponding drainage area polygons. Procedures to detect such inconsistencies and to correct them have been developed and are presented here. Data inconsistencies correction and parameter computation methods form the basis for the development of a routing model, which would be referred as hydrologic model. The hydrologic model consists of an overland flow routing module, two options for channel routing and a reservoir routing module. Two case studies have been presented to show the application of the tools developed.

The undesirable expansion of concrete because of a reaction between alkalis and certain type of reactive siliceous aggregates, known as alkali-silica reactivity (ASR), continues to be a major problem across the entire world. The renewed interest to minimize distress resulting from ASR has emphasized the need to develop predictable modeling of concrete ASR behavior under field conditions. Current test methods are either incapable or need long testing periods in which to only offer rather limited predictive estimates of ASR behavior in a narrow and impractical band of field conditions. Therefore, an attempt has been made to formulate a robust performance approach based upon basic properties of aggregate and concrete ASR materials derived from dilatometry and a kinetic-based mathematical expressions for ASR behavior. Because ASR is largely an alkali as well as a thermally activated process, the use of rate theory (an Arrhenius relationship between temperature and the alkali solution concentration) on the dilatometer time-expansion relationship, provides a fundamental aggregate ASR material property known as “activation energy.” Activation energy is an indicator of aggregate reactivity which is a function of alkalinity, particle size, crystallinity, calcium concentration, and others. The studied concrete ASR material properties represent a combined effects of mixture related properties (e.g., water-cementitious ratio, porosity, presence of supplementary cementitious materials, etc.) and maturity. Therefore, the proposed performance-based approach provides a direct accountability for a variety of factors that affect ASR, such as aggregate reactivity (activation energy), temperature, moisture, calcium concentration, solution alkalinity, and water-cementitious material ratio. Based on the experimental results, the following conclusion can be drawn concerning the performance-based approach to evaluate ASR potential of aggregate and concrete using dilatometer method; (i) the concept of activation energy can be used to represent the reactivity of aggregate subjected to ASR, (ii) the activation energy depends on the reactivity of aggregate and phenomenological alkalinity of test solution, and (iii) The proposed performance-based model provides a means to predict ASR expansion development in concrete.