The goal of this research was to maintain excellent water quality at reduced chemical operations cost. Chemical usage data at the Austin water treatment plants were examined by identifying trends and investigating suspected inefficiencies. The investigation consisted in jar test experiments, plant-scale experiments, and equilibrium modeling. Lime and ferric sulfate were suspected to be added inefficiently with respect to cost while the other treatment chemicals were assessed to be added efficiently. Lime was investigated in greater depth than ferric sulfate because ferric sulfate was better characterized in its effect on finished water quality within the range of interest. The goal of lime addition is to remove hardness from the water by a process called lime softening. Hardness removal decreases corrosion in transmission lines and prevents deposition of unwanted solids in household appliances. Additionally, lime softening aids in particle removal and disinfection-by-product precursor reduction. The efficiency of lime addition was evaluated based on settled water pH and causticity goals, which serve as the operating parameters for the water treatment plants. The most efficient lime softening occurs when multiple softening goals are simultaneously achieved. First, the dissolved calcium concentration must achieve a minimum. Second, the dissolved magnesium concentration must be reduced by at least 10 mg/L as CaCO₃. Third, total alkalinity must be preserved at its maximum concentration while also achieving excellent hardness removal. Fourth, natural organic matter (NOM), which serves as a precursor for disinfection-by-products, must be removed sufficiently to achieve DBP reduction goals. Finally, the turbidity in the effluent from the settling basin must be below 2.0 NTU. Through the chemical investigation of lime based on existing scientific literature, computer modeling, jar test experiments, and full-scale testing, it was determined that the optimal condition operating condition for lime softening was a settled water pH range from 10.0 - 10.1. / text

In recent years, smartphone has become the most popular products, the literature for smartphone is relatively less, most of using Technology Acceptance Model ¡]TAM¡^ as the main research framework to explore. Influencing the consumer intension is concept of cognition ¡]perceived usefulness, perceived ease of use¡^, thereby affecting subsequent behavioral intentions, but what kind of product characteristics affect consumer¡¦s perceiving are seldom addressed. This study proposes "Involvement - Brand Loyalty Model" as the theoretical basis, with the "involvement" concept applied to smartphone users in order to explore the brand loyalty. Involvement antecedent focuses on "product utility", and explores what kind of product characteristics affect the product utility from the past literature, and then is combined with the social influence to investigate smartphone usage. Finally, the study will also compare different groups based on with or without owning a smartphone, and then provide the final analysis of the research results. The final results show that user involvement does affect the brand loyalty of the smartphone, and if the users are highly involved, regardless of whatever the user experience may be, product involvement will affect the brand involvement. Comparison with previous studies finds the similarity and the differences between those two groups. The common is the high involvement of smartphone. The differences lie in the user experience that does affect the user for the demand of products characteristics, and the impact of the social influence. The three products characteristics: convergence, innovation and network externality will definitely affect the product utility for someone who has smartphone, but we can understand that the user experience will impact convergence by analyzing the groups without smartphone. It is difficult to measure the value of convergence for someone who doesn¡¦t have smartphone, so in the study show that the convergence does not significantly affect the product utility, while the other two product characteristics ¡]innovation, network externalities¡^ are significant impact the product utility. For people who have usage experience, the social influence can directly affect their brand judgment, nevertheless without the usage experience, the social influence has impact on the brand involvement only through the product involvement.

The research area of recruitment utility analysis (RUA) models has been somewhat unexplored for decades, and has earlier been reduced to simplified mathematic formulas measuring only dollar term value. The need for more dynamic models and theories surrounding the area has been voiced numerous times, yet little has been done. The purpose of this study was to highlight this need to encourage to further research, and to examine the managerial perspective on RUA from a semi explorative perspective. Eight interviews were conducted where the managers received questions concerning the recruitment process and the method used to conduct RUA. The results of this study indicate somewhat unimpressed attitudes toward the added value of conducting RUA. The reasons for not conducting RUA were found to be due to time constraint, lack of skills and managerial support. The majority of the RUA was found to be reactive and based on gut feeling implying a need for proactive RUA hence ensuring the value of the recruitment process.

We extend Kreps and Porteus' (1978, 1979a,b) temporal utility theory to include measures of suspense for gambles that vary in the timing of uncertainty resolution. Our f$ sp t$-modification (of their theory) defines overall utility and suspense in terms of two functions: a standard utility function and an iterative function whose properties determine attitude towards temporal uncertainty resolution. Suspense, which is increasing with time delay to uncertainty resolution, is defined as the "variance" of the standard utilities of the outcome streams taken about our measure of overall utility (rather than about the standard mean utility). We explore the properties of our measures and their implications for the overall utility and suspense of various key examples. Two preliminary experiments are reported which give some support for our overall utility and suspense measures, and which suggest that risk and suspense are different concepts. Iteration theory is also discussed in some detail.

This thesis contains three essays. In the first essay, I provide new evidence on the failure of the Q theory of investment. The Q theory implies the state-by-state equivalence of stock returns and investment returns. However in the data, I find that investment and stock returns are negatively correlated. I also show that a production economy with time-to-build can explain these empirical facts. When I compute Q theory based investment returns on simulated data of the time-to-build model, they are uncorrelated with simulated stock returns, as in the data. Moreover, the model replicates the empirical negative correlation between stock returns and investment growth which some researchers have interpreted as evidence for irrational markets. In the second essay, I analyze the equilibrium effects of investment commitment on asset prices when the representative consumer has Epstein-Zin utility. Investment commitment captures the idea that long-term investment projects require not only current expenditures but also commitment to future expenditures. The general equilibrium effects of investment commitment and Epstein-Zin preferences generate endogenously time-varying first and second moments of consumption growth and stock returns. As a result, the first and second moments of excess returns are endogenously counter-cyclical, excess returns are predictable, and the equity premium increases by an order of magnitude. This paper also offers novel empirical findings regarding the predictability of returns. In the real and simulated data, the lagged investment rate helps to forecast the mean and volatility of returns. In the third essay, we embed a structural model of credit risk inside a consumption based model, which allows us to price equity and corporate debt in a single framework. Our key economic assumptions are that the first and second moments of earnings and consumption growth depend on the state of the economy which switches randomly, creating intertemporal risk, which agents prefer to resolve quickly because they have Epstein- Zin-Weil preferences. Our model generates co-movement between aggregate stock return volatility and credit spreads, consistent with the data, and potentially resolves the equity risk premium and credit spread puzzles.

This research investigated the association of street network connectivity differences across travel modes with travel behaviour – mode choice, distance traveled and number of trips. To date research on travel behaviour relationships with urban form has not developed empirical evidence on street designs as distinct networks for walking and driving. A street network having greater connectivity and continuity for the pedestrian mode of travel vis-à-vis the vehicular network, like the Fused Grid, will likely encourage more walking. This hypothesis was investigated using a quasi-experimental approach within a rational utility behavioural framework. Local travel behaviour is theorized to be affected by desire to access goods and services (broadly termed, ‘activities’) in the community where people live. Using inferential statistics, the research tested for relationships between measured street patterns and self-reported local travel by King County, WA households. The main variables were ratios (walking : driving) of network connectivity and density, in the vicinity of travel survey households. Demographics and household characteristics, as well as other behaviourally influential urban form factors (residential density, proximity of destinations, etc.), were included in regression models, allowing control for confounding factors. Findings suggest that street networks with connectivity that provides better routing for one mode of transportation over others encourage more travel by the favored mode. The regression model demonstrated that a change from a pure small-block grid to a modified grid (i.e. Fused Grid) can result in an 11.3% increase in odds of a home-based trip being walked. The modified street pattern like a Fused Grid is also associated with a 25.9% increase, over street patterns with equivalent route directness for walking and driving, in the odds a person will meet recommended levels of physical activity. Finally, the Fused Grid’s 10% increase in relative connectivity for pedestrians is associated with a 23% decrease in local vehicle travel distance (VMT), and its improved continuity is associated with increased walking trips and distance. Conclusions: Other factors being equal, residential street networks with either more direct routing for pedestrians or more pedestrian facilities relative to vehicular network are associated with improved odds of walking and reduced odds of driving.

Companies and organizations must make decisions concerning capital budgeting. Capital budgeting is a decision-making process that determines whether a firm should purchase equipment to be used on a long-term basis. The initial investment in the equipment is predicted to be returned through revenue gained by the use of the equipment over its lifetime. However, there is inherent risk associated with these investment decisions. Therefore, potential purchasers must decide whether the risk involved with investing in the equipment is justified. This dissertation addresses risk-based technology assessment for capital equipment acquisition decisions in small firms. Technology assessment, here, is concerned with understanding the uncertainty associated with assessing the value predicted in the capital budgeting process. When analyzing the risk for a given technology, we assign a probability law to its net present value. Our primary research contribution is providing an analytical framework together with a computational strategy to support capital equipment budgeting in firms where the value of candidate technologies can represent nearly all the firm’s value. Since small firms typically have limited budgets, spending for technology is always a difficult budgeting decision. The organization’s administration must decide which, if any, among the available technologies will be best for their operation. The process for acquiring technology in many small firms can be filled with challenges. Most important among them is that capital budgeting is typically a “one-off” decision. These decisions are difficult since the candidate technologies may not have operational data available. Thus, decision makers need some means to predict how the proposed technology (e.g., equipment or machinery) will be used. Hence, firms should follow techniques and procedures based on appropriate normative principles and well-established theory. Senior company executives and/or governance boards are often authorized to approve capital equipment purchases. However, these company leaders may not have adequate expertise in the operations of candidate technologies or may lack the understanding necessary to determine how new technologies may impact other company operations. Appropriate financial evaluation measures and selection criteria that incorporate risk are critical to making sound, quantitative acquisition decisions. The research reported here offers an analytical framework for comparing different technology alternatives in capital budgeting decisions. Comparison is based on the expected net present value and the risk (i.e., probability law on net present value) associated with each decision alternative. To this end, the operational characteristics of each technology alternative are connected to their potential revenue and cost streams. The framework is embedded within a computational architecture that can be customized to account for operations and technologies in specific application scenarios. One major barrier addressed by this research is overcoming the fact that new technologies typically have no historical operational data. Therefore, characterizing the uncertainty of operations (e.g., distribution of the equipment lifetime) can be very difficult. Discrete- event simulation is used to generate potential revenue and cost estimates. We demonstrate the tractability and practicality of the analytical framework and computational architecture via a healthcare technology assessment decision. Data extracted from a published journal article detailing a hospital’s technology assessment decision are used to find the risk of the medical technology using the computational architecture developed. Widely-available, no-cost software tools are employed. Results of the health care example suggest that the financial analysis in the original technology assessment was in- adequate and simplistic. Small firms may find this research particularly beneficial because potential investments can be a significant portion of a small firm’s value.

The simulation of mobile networks relies on a reliable movement generation. Random movement patterns are frequently used in simulators. In this report, the performance of the popular setdest movement generator, which is built into the ns2 open source simulator, is investigated using two statistical tests: quadrat count test and the variance to mean ratio (VMR) test. The results show a non-uniform distribution of nodes during the simulation with a bias towards placing the nodes in the center of the simulated area. We propose and implement a di erent method for random movement generation in the ns2 simulator and show that our movement generator improves the randomness of the node distribution during the simulation. The new generator was successfully tested with the ns2 simulator.

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