Groundwater is a major source of water supply, and aquifers form major storage reservoirs as well as water conveyance systems, worldwide. The viability of groundwater as a source of water to the world's population is threatened by overexploitation and contamination. The rational management of water resource systems requires an understanding of their response to existing and planned schemes of exploitation, pollution prevention and/or remediation. Such understanding requires the collection of data to help characterize the system and monitor its response to existing and future stresses. It also requires incorporating such data in models of system makeup, water flow and contaminant transport. As the collection of subsurface characterization and monitoring data is costly, it is imperative that the design of corresponding data collection schemes is cost-effective. A major benefit of new data is its potential to help improve one's understanding of the system, in large part through a reduction in model predictive uncertainty and corresponding risk of failure. Traditionally, value-of-information or data-worth analyses have relied on a single conceptual-mathematical model of site hydrology with prescribed parameters. Yet there is a growing recognition that ignoring model and parameter uncertainties render model predictions prone to statistical bias and underestimation of uncertainty. This has led to a recent emphasis on conducting hydrologic analyses and rendering corresponding predictions by means of multiple models. We develop a theoretical framework of data worth analysis considering model uncertainty, parameter uncertainty and potential sample value uncertainty. The framework entails Bayesian Model Averaging (BMA) with emphasis on its Maximum Likelihood version (MLBMA). An efficient stochastic optimization method, called Differential Evolution Method (DEM), is explored to aid in the design of optimal sampling schemes aiming at maximizing data worth. A synthetic case entailing generated log hydraulic conductivity random fields is used to illustrate the procedure. The proposed data worth analysis framework is applied to field pneumatic permeability data collected from unsaturated fractured tuff at the Apache Leap Research Site (ALRS) near Superior, Arizona.

In recent decades, extreme meteorological events have become more frequent and more severe. Flooding, heavy precipitation and droughts, in particular, are a few of these extreme events that can cause widespread property damage and loss of life. The climate is always changing and there is a general agreement that the changes will be more amplified and occur more rapidly due to anthropogenic influences. As a result, it is expected that the societal and economic impacts of heavy precipitation, floods, and droughts will increase as the climate continues to rapidly change. For these reasons, continued research to improve extreme precipitation predictions and long-term projections is vital. With improved projections, society will be able to improve their efforts to prepare for and implement better management practices to effectively adapt to the changing climate and help reduce the impacts of a changing climate. A great deal of progress has already been made in extreme precipitation research in relation to climate change. Overall, the tendency for dry areas to get drier and wet areas to get wetter has been identified. However, much of the work has focused on the daily timescale, and much less is known about sub-daily precipitation. It is becoming increasingly more important to consider this time scale because of evidence that climate change could have more of an impact on sub-daily (e.g., 3-hourly) rather than daily precipitation. To complicate the matter, there is still a need to evaluate the performance of global climate models in reproducing the precipitation statistics at the sub-daily time scales. The goal of this work is to evaluate the projected changes in precipitation at both the daily and sub-daily time scales and, more specifically, understand whether daily or sub-daily precipitation extremes will change more through the end of this century. However, to understand future projections it is first vital to analyze model accuracy and determine how well global climate models can reproduce the extreme precipitation statistics across the historical past. This is accomplished by comparing the historical runs for each model to observations during the same time period using several different methods, including a skill score analysis, using Taylor diagrams to visualize accuracy, and meridional plots that show intermodel variability. The results from this analysis show model performance for daily extreme precipitation is higher than that of the 3-hourly extreme precipitation. Although there are few models that do an adequate job of producing reliable results at the sub-daily time scale, there is an overall significant increase in skill as the temporal resolution becomes coarser. Variability also exists among models, with sub-daily precipitation having more widespread variability across every latitude, but daily precipitation has a wider range in potential extreme precipitation that is focused more in the tropics. Model performance also varies by season, resulting in higher performance and less variability among models for individual seasons. These results also point to several models that consistently perform well for both sub-daily and daily extreme precipitation, but it is still worth remembering that there is no guarantee that a good performance during the historical period ensures a good performance in the futures as well. The next part of the work focuses on the models with the highest performance in reproducing the observations. From there, it was possible to determine locations with the greatest changes in precipitation, the magnitude of changes, and whether sub-daily or daily extreme precipitation will be impacted more by climate change. Overall, extreme precipitation at both sub-daily and daily times scales is projected to increase globally. At the regional scale, precipitation is projected to primarily increase in the tropics, with smaller changes towards the poles. Areas of decreases in precipitation vary by model with the exception of a decrease in precipitation near the tropical Pacific Ocean that is seen in almost every model.

Conceptual modeling allows abstraction, communication and consensus building in system development. It is challenging to expand and improve the accuracy of models in an iterative process, producing models able to facilitate analysis. Modeling and analysis can be especially challenging in early Requirements Engineering (RE), where high-level system requirements are discovered. In this stage, hard-to-measure non-functional requirements are critical; understanding the interactions between systems and stakeholders is a key to system success. Goal models have been introduced as a means to ensure stakeholder needs are met in early RE. Because of the high-level, social nature of early RE models, it is important to provide procedures which prompt stakeholder involvement (interaction) and model improvement (iteration). Most current approaches to goal model analysis require quantitative or formal information that is hard to gather in early RE, or produce analysis results automatically over models. Approaches are needed which balance automated analysis over complex models with the need for interaction and iteration. This work develops a framework for iterative, interactive analysis for early RE using agent-goal models. We survey existing approaches for goal model analysis, providing guidelines using domain characteristics to advise on procedure selection. We define requirements for an agent-goal model framework specific to early RE analysis, using these requirements to evaluate the appropriateness of existing work and to motivate and evaluate the components of our analysis framework. We provide a detailed review of forward satisfaction procedures, exploring how different model interpretations affect analysis results. A survey of agent-goal variations in practice is used to create a formal definition of the i* modeling framework which supports sensible syntax variations. This definition is used to precisely define analysis procedures and concepts throughout the work. The framework consists of analysis procedures, implemented in the OpenOME requirements modeling tool, which allow users to ask “What if?” and “Is this goal achievable, and how?” questions. Visualization techniques are introduced to aid analysis understanding. Consistency checks are defined over the interactive portion of the framework. Implementation, performance and potential optimizations are described. Group and individual case studies help to validate framework effectiveness in practice. Contributions are summarized in light of the requirements for early RE analysis. Finally, limitations and future work are described.

Conceptual modeling allows abstraction, communication and consensus building in system development. It is challenging to expand and improve the accuracy of models in an iterative process, producing models able to facilitate analysis. Modeling and analysis can be especially challenging in early Requirements Engineering (RE), where high-level system requirements are discovered. In this stage, hard-to-measure non-functional requirements are critical; understanding the interactions between systems and stakeholders is a key to system success. Goal models have been introduced as a means to ensure stakeholder needs are met in early RE. Because of the high-level, social nature of early RE models, it is important to provide procedures which prompt stakeholder involvement (interaction) and model improvement (iteration). Most current approaches to goal model analysis require quantitative or formal information that is hard to gather in early RE, or produce analysis results automatically over models. Approaches are needed which balance automated analysis over complex models with the need for interaction and iteration. This work develops a framework for iterative, interactive analysis for early RE using agent-goal models. We survey existing approaches for goal model analysis, providing guidelines using domain characteristics to advise on procedure selection. We define requirements for an agent-goal model framework specific to early RE analysis, using these requirements to evaluate the appropriateness of existing work and to motivate and evaluate the components of our analysis framework. We provide a detailed review of forward satisfaction procedures, exploring how different model interpretations affect analysis results. A survey of agent-goal variations in practice is used to create a formal definition of the i* modeling framework which supports sensible syntax variations. This definition is used to precisely define analysis procedures and concepts throughout the work. The framework consists of analysis procedures, implemented in the OpenOME requirements modeling tool, which allow users to ask “What if?” and “Is this goal achievable, and how?” questions. Visualization techniques are introduced to aid analysis understanding. Consistency checks are defined over the interactive portion of the framework. Implementation, performance and potential optimizations are described. Group and individual case studies help to validate framework effectiveness in practice. Contributions are summarized in light of the requirements for early RE analysis. Finally, limitations and future work are described.

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A study of two-phase flow in annular channels with annular gaps of less than 1 mm is useful for the design and safety analysis of high power density systems such as accelerator targets and nuclear reactor cores. Though much work has been done on pressure drop in two-phase flow, designers rely mostly on empirical models and correlations; hence, it is valuable to study their applicability for different channel sizes, geometries, and gas qualities. The pressure drop along a concentric annular test section was measured for cases of either constant quality or variable quality along its length (such as in sub-cooled and flow boiling). A porous tube was used to inject gas along the inner surface of the annular channel, thereby simulating the case of flow boiling along the inner surface. The data were compared to predictions of various models and correlations. Additionally, the effect of wall vibrations on the pressure drop was examined. Experiments were conducted by imposing vibrations of known amplitudes and frequencies on the outer tube of the annulus. Wall vibrations were thought to be important for flow in microchannels where the vibration amplitudes may be significant compared to the channel hydraulic diameter. The results obtained in this investigation indicate that the pressure drop correlation given by Beattie and Whalley provides the best agreement with the data for both porous tube gas injection (i.e. variable quality) and constant quality two-phase flow within the narrow annulus. Furthermore, the results show that there is a minimal effect of vibrations on two-phase pressure drop over the range of frequencies and amplitudes studied.

This study is focused on how characters of management control system affect organizational climate and the how the relation between those two factors guide performance in different organizations. With four diffent case studies in Taiwan, this study found three types of reaction in management control system, organizational climate and organizational performance: mediator model, moderating model and interactive model. This study showed that the relationship among management control system, organizational climate and performance are not in a given type; there would be various possible mixes in their relationship. In mediator model, management control system is an important factor to improve performance and the awareness of organizational climate in employee directly affected the guiding effects of management control system to organizational performance. According to the previous finding, an environment makes the employee have better awareness about organizational climate will help managers to reach the performance goals with management control systems. In moderating model, organizational climate moderate the linkeage between managemeant control system and the awareness of performance in employee. It is not ony the factor to adjust effects of management control system but also change managers¡¦ designs of management control system and moderate the relationsip on management control system and performance. Organizational performance could be improved when managers design indicators and controlling mechanism with considered interactive effects in organizational climate. This study also found that industrial characters will affect operational process of management control system and awareness of organizational climate in employee. Due to professional knowledge is an important factor for employee in service industry to provide servive to their customers, attendance rate and safety of customers are two important indicators to management control system in such industry. According to this reaesch, the case in service industry put more focus on this performance item than the cases in manufacturing industry, so the study found the characters of industrial technology will also a factor to affect the design of management control system

This research aims at the specifics of Computer Aided Design (CAD) and Computer Aided Manufacturing (CAM) market. Through discussion of the digital model’s path since geometric construction till completion of a product the major trends are derived. Evolution of hardware for CAM challenges software developers to come up with new solutions. Growing numbers of CAD/CAM software proved to be considerable factor for incompatibility of data formats among systems. Therefore universal data exchange standards were claimed to enable communication. Also a few gaps of functionality among CAD/CAM software products encourages the development of new application, based on research. The set of functions to be integrated into application is supported theoretically according to researched information and tested practically during the realization of software. The most successful standards and tools are selected as the basis of new software. IGES (The Initial Graphics Exchange Specification) standard along with NURBS (Non-Uniform Rational BSpline) curves and surfaces is used for description of geometric data and OpenGL is used as drawing tool. Model analysis and modification for CAM includes following function description and realization: calculation of volume, non-uniform region scaling, 2D packing, curve control point reduction, curve conversion to lines and arcs. Testing results proves that most of the theoretic assumptions are correct and a development of such or similar software is truly... [to full text]

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