Catastrophes such as floods, droughts and earthquakes have caused significant human and infrastructural losses throughout history. Nevertheless, researchers struggle to quantify macroeconomic impacts, and the existing literature is ambiguous in its findings. In this study I use econometric methods on panel data from Centre for Research on the Epidemiology of Disasters (CRED), and find that hydrometeorological, climatological and geophysical events all affect economic growth negatively in the short run. Second, while events typically linked to climate change tend to cause negative growth shocks the same year they occur, geophysical disasters do not alter overall economic performance before the next year. With respect to future global warming, these dynamic differences give important insights for the understanding of how economies might be affected by climate change. However, by means of two stage least square methods, I do not find that negative economic shocks caused by weather related disasters increase the likelihood of armed civil conflicts. This latter result is in contrast to conclusions in much of the seminal conflict literature, but similar to findings in other recent cross-country studies that use the instrument variable approach.

This work deals with the control of the human immune system. A standard immune system model is modified by introducing control signals corresponding to drug cocktail and immune suppressor treatments. The ultimate objective is to use these control signals to 'cure' a chronically-ill patient. Control is challenging for this system due to nonlinearities and time delays. In fact, it is shown that fundamental aspects of the system dynamics are lost when the system is linearised; hence, control approaches involving linearisation are fruitless. Feedback linearisation and some optimal control methods are also investigated and shown to be infeasible. However, it is shown that, for certain parameter values and initial conditions related to the virus and patient, a specific open-loop control scheme using only the drug cocktail achieves the objective. It is also proven that, unfortunately, this control scheme fails for other parameter values and initial conditions. A two-stage open-loop controller that uses both control inputs is then proposed. It is shown in simulation that the two-stage controller works over a larger set of parameters and initial conditions than the single-stage controller, but a rigorous analysis of the two-stage controller remains elusive.

This study examines whether different corporate real estate (CRE) strategies affect the stock outperformance and systemic risk of various companies. The sample of 443 listed companies of 17 industries in Taiwan during 2000 to 2010 was divided into four groups for the different corporate real estate strategies. The pairwise abnormal return and systemic risk of composite and business (without the affect from real estate market) series were empirically examined and compared using a partial adjustment model. This study also conducts the two-stage least squares procedure to determine whether four CRE strategies were considered diversifiable factors when evaluating the firm¡¦s value The results do not indicate an increasingly abnormal return performance associated with the company implementing a certain CRE strategy, but companies with a stable profession and consistent adjustment strategies are considered a good diversifier by stock investors. Aggressive adjustment strategies do not diversify the systematic risk to overall industry, otherwise the scale of total assets would be considered a diversification in companies with aggressive strategies. The companies using an aggressive profession strategy to increase leverage are regarded as risky phenomen for stock investors, and companies with stable profession strategies face higher systemic risk if their book value is greater than their market value. Therefore, this study determines that CRE strategies affect companies¡¦ systematical risk.

This dissertation, which consists of three essays, studies online auctions both theoretically and empirically. The first essay studies a special online auction format used by eBay, “Buy-It- Now” (BIN) auctions, in which bidders are allowed to buy the item at a fixed BIN price set by the seller and end the auction immediately. I construct a two-stage model in which the BIN price is only available to one group of bidders. I find that bidders cutoff is lower in this model, which means, bidders are more likely to accept the BIN option, compared with the models assuming all bidders are offered the BIN. The results explain the high frequency of bidders accepting BIN price, and may also help explain the popularity of temporary BIN auctions in online auction sites, such as eBay, where BIN option is only offered to early bidders. In the second essay, I study how bidders’ risk attitude and time preference affect their behavior in Buy-It-Now auctions. I consider two cases, when both bidders enter the auction at the same time (homogenous bidders) thus BIN option is offered to both of them, and when two bidders enter the auction at two different stages (heterogenous bidders) thus the BIN option is only offered to the early bidder. Bidders’ optimal strategies are derived explicitly in both cases. In particular, given bidders’ risk attitude and time preference, the cutoff valuation, such that a bidder will accept BIN if his valuation is higher than the cutoff valuation and reject it otherwise, is calculated. I find that the cutoff valuation in the case of heterogenous bidders is lower than that in the case of homogenous bidders. The third essay focuses on the empirical modeling of the price processes of online auctions. I generalize the monotone series estimator to model the pooled price processes. Then I apply the model and the estimator to eBay auction data of a palm PDA. The results are shown to capture closely the overall pattern of observed price dynamics. In particular, early bidding, mid-auction draught, and sniping are well approximated by the estimated price curve.

Internal combustion engines have dealt with increasingly restricted emissions requirements. After-treatment devices are successful in bringing emissions into compliance, but in-cylinder combustion control can reduce their burden by reducing engine out emissions. For example, oxides of nitrogen (NOx) are diesel combustion exhaust species that are notoriously difficult to remove by after-treatment. In-cylinder conditions can be controlled for low levels of NOx, but this produces high levels of soot potentially leading to increased particulate matter (PM). The simultaneous reduction of NOx and PM can be realized through a combustion process known as low temperature combustion (LTC). In this study, the typical definition of LTC as the defeat of the inverse relationship between soot and NOx is not applicable as a return to the soot-NOx tradeoff is observed with increasing exhaust gas recirculation (EGR). It is postulated that this effect is the result of an increase in the hot ignition equivalence ratio, moving the combustion event into a slightly higher soot formation region. This is important because a simple emissions based definition of LTC is no longer helpful. In this study, the manifestation of LTC in the calculated heat release profile is investigated. The conditions classified as LTC undergo a two-stage ignition process. Two-stage ignition is characterized by an initial cool-flame reaction followed by typical hot ignition. In traditional combustion conditions, the ignition is fast enough that a cool-flame is not observed. By controlling initial conditions (pressure, temperature, and composition), the creation and duration of the cool-flame event is predictable. Further, the effect that injection timing and the exhaust gas recirculation level have on the controlling factors of the cool-flame reaction is well correlated to the duration of the cool-flame event. These two results allow the postulation that the presence of a sufficiently long cool-flame reaction indicates a combustion event that can be classified as low temperature combustion. A potential method for identifying low temperature combustion events using only the rate of heat release profile is theorized. This study employed high levels of EGR and late injection timing to realize the LTC mode of ordinary petroleum diesel fuel. Under these conditions, and based on a 90 percent reduction in nitric oxide and no increase in smoke output relative to the chosen baseline condition, a two part criteria is developed that identifies the LTC classified conditions. The criteria are as follow: the combustion event of conventional petroleum diesel fuel must show a two-stage ignition process; the first stage (cool-flame reaction) must consume at least 2 percent of the normalized fuel energy before the hot ignition commences.

Biodiesel has been suggested as an alternative fuel to the petroleum diesel fuel. It beneficially reduces regulated emission gases, but increases NOx (nitric oxide and nitrogen dioxide) Thus, the increase in NOx is the barrier for potential growth of the biodiesel fuel. In general, NOx formation is dominated by flame temperature. Interestingly, soot can play a role as a heat sink as well as a heat transfer media to high temperature gases. Thus, the cooling effect of soot may change the flame temperature and therefore, NOx emissions. In this study, emphasis is placed on the relationship between soot and NO (Nitric oxide) formation. For the experimental study, a metallic fuel additive is used since barium is known to be effective to suppress soot formation during combustion. The barium additive is applied to #2D (Number 2 diesel fuel) by volume basis: 0.1, 0.25 and 0.5 %-v, and to the palm olein oil by 0.25 %-v. All the tests are carried out in a four-cylinder medium duty diesel engine, 4045 DI diesel engine, manufactured by John Deere. For the analysis, an analytical model is used to estimate combustion temperature, NO concentration and soot emissivity. The results show that NO concentration does not have the expected trade-off relation with soot. Rather, NO concentration is found to be more strongly affected by ambient temperature and combustion characteristics than by soot. The results of the analytical model show the reasonable NO estimation and the improvement on temperature calculation. However, the model is not able to explain the detailed changes of soot emissivity by the different fuels since the emissivity correlation is developed empirically for diesel fuel.

In this essay, we use two stage data envelopment analysis (Two-stage DEA) with an application to evaluate the efficiency of six teams from Chinese Professional Baseball League (CPBL) during 2004 to 2005. In the essay, we use three methods to develop the research. First, we use the Window Analysis based on fewer DMUs situation. Second, the modification across different period has been made by Malmquist Index Analysis. Last one is Cross Efficiency; we use it for ranking efficiency performance for those teams during 2004 to 2005. According to above methods, it shows that: 1. the efficiency performance of Brother Elephants, Sinon Bulls, Chinatrust Whales, Uni Lions in the management stage is better than it in the production stage, vice versa Macoto Cobras and La new Bears in the production stage is better than management stage. 2. Each team is over optimal scale in the production stage during 2004 to 2005. 3. The result of overall performance during is shown as following place: Bulls, Elephants, Lions, Cobras, Whales, Bears; the orders in production stage are Cobras, Whales, Bulls, Bears, Elephants, Lions; the orders in management stage are Elephants, Bulls, Lions, Cobras, Bears, Whales. 4. The Malmquist Productivity Index across different period each year indicates that only La new Bears reach 4.7% according average growth rate.

Carbon dioxide is a natural refrigerant that has been considered for certain refrigeration and air conditioning applications. The coefficient of performance (COP) of carbon dioxide cycles is low compared to classical vapor compression cycles. The aim of this portion of the thesis is to present a thermodynamic analysis of carbon dioxide cycles in order to evaluate the potential performance of a refrigeration cycle using carbon dioxide. A thermodynamic model for the cycle is proposed which can simulate the operation of a carbon dioxide refrigeration cycle. This model takes into account the practical effects of the thermo-physical properties of carbon dioxide as a refrigerant in a trans-critical cycle. One and two-stage compression processes were considered for comparison purposes. A sensitivity analysis has been conducted so that cycle performance can be estimated. The effect of cycle components on system capacity and cycle performance was investigated. The second portion of the thesis deals with the concept of reheat air conditioning, and looks at the performance of different reheat cycles. The thesis looks at reheat systems that utilize different placements of the reheat coil. The overall performance of these reheat systems is then calculated. These systems require no additional electric power to reheat the air after it is cooled and dehumidified in the evaporator. Instead, they use heat from the condenser heat exchanger to reheat the air during partial load conditions. Four different reheat configurations are discussed and analyzed to determine performance levels. Visual Basic programs were written for each of the four cycles to simulate the different configurations and to evaluate key performance parameters. Graphs were developed based on these programs, where critical variables were changed to monitor trends in coefficient of performance. The thermodynamic cycle of each reheat configuration is developed, with equations presented with figures depicting the cycles. Refrigerant 134a was used in the programs throughout the reheat section of the thesis. The reheat coefficient of performance is used as the basis for cycle comparison. The relative performance of the four cycles is illustrated in the figures and explained in the Results and Discussion section at the end of chapter 3.

We consider the problem of staffing large-scale service centers with multiple customer classes and agent types operating under quality-of-service (QoS) constraints. We introduce formulations for a class of staffing problems, minimizing the cost of staffing while requiring that the long-run average QoS achieves a certain pre-specified level. The queueing models we use to define such service center staffing problems have random inter-arrival times and random service times. The models we study differ with respect to whether the arrival rates are deterministic or stochastic. In the deterministic version of the service center staffing problem, we assume that the customer arrival rates are known deterministically. It is computationally challenging to solve our service center staffing problem with deterministic arrival rates. Thus, we provide an approximation and prove that the solution of our approximation is asymptotically optimal in the sense that the gap between the optimal value of the exact model and the objective function value of the approximate solution shrinks to zero as the size of the system grows large. In our work, we also focus on doubly stochastic service center systems; that is, we focus on solving large-scale service center staffing problems when the arrival rates are uncertain in addition to the inherent randomness of the system's inter-arrival times and service times. This brings the modeling closer to reality. In solving the service center staffing problems with deterministic arrival rates, we provide a solution procedure for solving staffing problems for doubly stochastic service center systems. We consider a decision making scheme in which we must select staffing levels before observing the arrival rates. We assume that the decision maker has distributional information about the arrival rates at the time of decision making. In the presence of arrival-rate uncertainty, the decision maker's goal is to minimize the staffing cost, while ensuring the QoS achieves a given level. We show that as the system scales large in size, there is at most one key scenario under which the probability of waiting converges to a non-trivial value, i.e., a value strictly between 0 and 1. That is, the system is either over- or under-loaded in any other scenario as the size of the system grows to infinity. Exploiting this result, we propose a two-step solution procedure for the staffing problem with random arrival rates. In the first step, we use the desired QoS level to identify the key scenario corresponding to the optimal staffing level. After finding the key scenario, the random arrival-rate model reduces to a deterministic arrival-rate model. In the second step, we solve the resulting model, with deterministic arrival rate, by using the approximation model we point to above. The approximate optimal staffing level obtained in this procedure asymptotically converges to the true optimal staffing level for the random arrival-rate problem. The decision making scheme we sketch above, assumes that the distribution of the random arrival rates is known at the time of decision making. In reality this distribution must be estimated based on historical data and experience, and needs to be updated as new observations arrive. Another important issue that arises in service center management is that in the daily operation in service centers, the daily operational period is split into small decision time periods, for example, hourly periods, and then the staffing decisions need to be made for all such time periods. Thus, to achieve an overall optimal daily staffing policy, one must deal with the interaction among staffing decisions over adjacent time periods. In our work, we also build a model that handles the above two issues. We build a two-stage stochastic model with recourse that provides the staffing decisions over two adjacent decision time periods, i.e., two adjacent decision stages. The model minimizes the first stage staffing cost and the expected second stage staffing cost while satisfying a service quality constraint on the second stage operation. A Bayesian update is used to obtain the second-stage arrival-rate distribution based on the first-stage arrival-rate distribution and the arrival observations in the first stage. The second-stage distribution is used in the constraint on the second stage service quality. After reformulation, we show that our two-stage model can be expressed as a newsvendor model, albeit with a demand that is derived from the first stage decision. We provide an algorithm that can solve the two-stage staffing problem under the most commonly used QoS constraints. This work uses stochastic programming methods to solve problems arising in queueing networks. We hope that the ideas that we put forward in this dissertation lead to other attempts to deal with decision making under uncertainty for queueing systems that combine techniques from stochastic programming and analysis tools from queueing theory. / text

We consider a facility location problem with uncertainty flow customers' demands, which we refer to as stochastic flow capturing location allocation problem (SFCLAP). Potential applications include siting farmers' market, emergency shelters, convenience stores, advertising boards and so on. For this dissertation, electric vehicle charging stations siting with maximum accessibility at lowest cost would be studied. We start with placing charging stations under the assumptions of pre-determined demands and uniform candidate facilities. After this model fails to deal with different scenarios of customers' demands, a two stage flow capturing location allocation programming framework is constructed to incorporate demand uncertainty as SFCLAP. Several extensions are built for various situations, such as secondary coverage and viewing facility's capacity as variables. And then, more capacitated stochastic programming models are considered as systems optimal and user oriented optimal cases. Systems optimal models are introduced with variations which include outsourcing the overflow and alliance within the system. User oriented optimal models incorporate users' choices with system's objectives. After the introduction of various models, an approximation method for the boundary of the problem and also the exact solution method, the L-Shaped method, are presented. As the computation time in the user oriented case surges with the expansion of the network, scenario reduction method is introduced to get similar optimal results within a reasonable time. And then, several cases including testing with different number of scenarios and different sample generating methods are operated for model validation. In the last part, simulation method is operated on the authentic network of the state of Arizona to evaluate the performance of this proposed framework.