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.

Rapid economic evaluations of investment alternatives in the oil and gas industry are typically contingent on fast and credible evaluations of reservoir models to make future forecasts. It is often important to also quantify inherent risks and uncertainties in these evaluations. These ideally require several full-scale numerical simulations which is time consuming, impractical, if not impossible to do with conventional (Finite Difference) simulators in real life situations. In this research, the aim will be to improve on the efficiencies associated with these tasks. This involved exploring the applications of Fast Marching Methods (FMM) in both conventional and unconventional reservoir characterization problems. In this work, we first applied the FMM for rapidly ranking multiple equi-probable geologic models. We demonstrated the suitability of drainage volume, efficiently calculated using FMM, as a surrogate parameter for field-wide cumulative oil production (FOPT). The probability distribution function (PDF) of the surrogate parameter was point-discretized to obtain 3 representative models for full simulations. Using the results from the simulations, the PDF of the reservoir performance parameter was constructed. Also, we investigated the applicability of a higher-order-moment-preserving approach which resulted in better uncertainty quantification over the traditional model selection methods. Next we applied the FMM for a hydraulically fractured tight oil reservoir model calibration problem. We specifically applied the FMM geometric pressure approximation as a proxy for rapidly evaluating model proposals in a two-stage Markov Chain Monte Carlo (MCMC) algorithm. Here, we demonstrated the FMM-based proxy as a suitable proxy for evaluating model proposals. We obtained results showing a significant improvement in the efficiency compared to conventional single stage MCMC algorithm. Also in this work, we investigated the possibility of enhancing the computational efficiency for calculating the pressure field for both conventional and unconventional reservoirs using FMM. Good approximations of the steady state pressure distributions were obtained for homogeneous conventional waterflood systems. In unconventional system, we also recorded slight improvement in computational efficiency using FMM pressure approximations as initial guess in pressure solvers.

The removal of arsenic toxicity from water is accomplished by a preliminary preoxidative step transforming the most toxic form, arsenite (As (III)), to the least toxic form, arsenate (As (V)). The potential of As (III) oxidation to As (V) was initially investigated in batch reactors using the chemoautotrophic Thiomonas arsenivorans strain b6 under varying initial As (III) and cell concentrations and at optimal pH and temperature conditions (pH 6.0 and temperature 30°C). The strain b6 completely oxidized As (III) to As (V) during exponential growth phase for lower levels of As (III) concentrations (≤ 100 mg/L) but continued into stationary phase of growth for higher levels (≥ 500 mg/L). Other important factors such as oxygen and carbon limitations during biological As (III) oxidation were also evaluated. The biokinetic parameters of the strain b6 were estimated using a Haldanesubstrate inhibition model with the aid of a non-linear estimation technique. Microbial As (III) oxidation was further investigated in continuous-flow bioreactors (CSTR and biofilm reactor) under varying As (III) loading rates. Both the reactors achieved As (III) oxidation efficiency exceeding 99% during the steady-state conditions. The reactors were also able to recover from an As (III) overloading phase establishing the resilient nature of the microorganism. The basic mass balance expressions on As (III) and biomass along with the Monod model were used to linearly estimate the biokinetic parameters in the CSTR study. However, in the biofilm study, a steady-state flux model was used to estimate the same parameters. The performance of the model was very good in simulating the transient and steady-state conditions. Finally, the potential application of one-stage and two-stage reactor systems was investigated for the near complete removal of arsenic. Activated alumina was used as the adsorbent for the As (V) produced by the biological oxidation of As (III). The two-stage reactor process performed better than the one-stage reactor system in lowering the arsenic level below the detection limit (1 mg/L) for at least eight days of operation. However, pH fluctuations and probable competition from ions such as PO43- , SO42-, and Cl- severely impacted the performance of the reactors. Further study is needed to improve the overall efficiency of the reactor systems for achieving complete removal of arsenic for a longer operating time.

Several studies have examined the effect of remittances on economic growth,poverty, education, and governance, among other factors, in developing countrieswith inconclusive results. Using annual panel data of 99 developing countries invarious empirical models, this study aim to answer the question how remittances affect a broader aspect of development using the Human Development Index asdependent variable. The findings indicate that there is a positive relationship between remittances and the level of human development in developing countries.

Hydrogen production by photosynthetic bacteria occurs under illumination in the presence of anaerobic atmosphere from the breakdown of organic substrates, which is known as photofermentation. In this study, single-stage and two-stage process development were investigated for photofermentative hydrogen production from olive mill wastewater by Rhodobacter sphaeroides O.U.001 within indoor and outdoor photobioreactors. It was proven that diluted olive mill wastewater (OMW) could be utilized for photobiological hydrogen production as a sole substrate source. However, pretreatment of the system is needed to reduce the dark color and bacteriostatic effects of OMW. In this study, several two stage processes including pretreatment of OMW followed by photofermentation were investigated to increase the hydrogen production yields in addition to the significant remediation of OMW. Explored pretreatment methods contain chemical oxidation with ozone or Fenton&rsquo / s reagent, photodegradation by UV radiation, adsorption with clay or zeolite and dark fermentation with acclimated or non-acclimated sewage sludge. Among these different two-stage processes / clay treatment method resulted the highest hydrogen production capacity. As a result of clay pretreatment, 65% of the initial color and 81% of the phenolic content were decreased. Hydrogen production capacity was 16 LH2/LOMW without pretreatment, while it was enhanced up to 29 LH2/LOMW by two-stage processes. Moreover, clay pretreatment process made it possible to utilize highly concentrated OMW (50% and 100%) media for hydrogen production and for remediation. On the aspects of environment, treatment of OMW was achieved in the present work. The final composition of the organic pollutants in the effluent of two-stage processes was below the wastewater discharge limits. The overall results obtained throughout this study may open a new opportunity for the olive oil industry and for the biohydrogen area as a result of the effective biotransformation of OMW into hydrogen gas and valuable by-products.

In this thesis a quadrotor is studied and a linear model is derived using grey-box estimation, a discipline in system identification where a model structure based on physical relations is used and the parameters are estimated using input-output measurements. From IMU measurements and measured PWM signals to the four motors, a direct approach using the prediction-error method is applied. To investigate the impact of the unknown controller the two-stage method, a closed-loop approach in system identification,  is applied as well. The direct approach was enough for estimating the model parameters. The resulting model manages to simulate the major dynamics for the vertical acceleration and the angular rates well enough  for future control design.

abstract: The large distributed electric power system is a hierarchical network involving the transportation of power from the sources of power generation via an intermediate densely connected transmission network to a large distribution network of end-users at the lowest level of the hierarchy. At each level of the hierarchy (generation/ trans- mission/ distribution), the system is managed and monitored with a combination of (a) supervisory control and data acquisition (SCADA); and (b) energy management systems (EMSs) that process the collected data and make control and actuation de- cisions using the collected data. However, at all levels of the hierarchy, both SCADA and EMSs are vulnerable to cyber attacks. Furthermore, given the criticality of the electric power infrastructure, cyber attacks can have severe economic and social con- sequences. This thesis focuses on cyber attacks on SCADA and EMS at the transmission level of the electric power system. The goal is to study the consequences of three classes of cyber attacks that can change topology data. These classes include: (i) unobservable state-preserving cyber attacks that only change the topology data; (ii) unobservable state-and-topology cyber-physical attacks that change both states and topology data to enable a coordinated physical and cyber attack; and (iii) topology- targeted man-in-the-middle (MitM) communication attacks that alter topology data shared during inter-EMS communication. Specically, attack class (i) and (ii) focus on the unobservable attacks on single regional EMS while class (iii) focuses on the MitM attacks on communication links between regional EMSs. For each class of attacks, the theoretical attack model and the implementation of attacks are provided, and the worst-case attack and its consequences are exhaustively studied. In particularly, for class (ii), a two-stage optimization problem is introduced to study worst-case attacks that can cause a physical line over ow that is unobservable in the cyber layer. The long-term implication and the system anomalies are demonstrated via simulation. For attack classes (i) and (ii), both mathematical and experimental analyses sug- gest that these unobservable attacks can be limited or even detected with resiliency mechanisms including load monitoring, anomalous re-dispatches checking, and his- torical data comparison. For attack class (iii), countermeasures including anomalous tie-line interchange verication, anomalous re-dispatch alarms, and external contin- gency lists sharing are needed to thwart such attacks. / Dissertation/Thesis / Masters Thesis Electrical Engineering 2015

In this master thesis I discuss the possibilities how General Practitioners can sell their medical practices. First of all I will mention assumptions relating to the termination of the business as well as the specifics of valuation, legal and tax issues that may General Practitioners meet in the transfer of their medical practices. In the following part I will deal with the effects of taking into account all possibilities mentioned in the theoretical part.

Heat-release in CI engines occurs in the presence of concentration and temperature gradients. Recognizing the need for a validation of chemical kinetic models in transport-affected systems, this study employs non-premixed systems to better understand complex couplings between low/high temperature oxidation kinetics and diffusive transport. This dissertation is divided into two sections. In the first section, a two-stage Lagrangian model compares model prediction of ignition delay time and experimental data from the KAUST ignition quality tester, and ignition data for liquid sprays in constant volume combustion chambers. The TSL employed in this study utilizes detailed chemical kinetics while also simulating basic mixing processes. The TSL model was found to be efficient in simulating IQT in long ignition delay time fuels; it was also effective in CVCC experiments with high injection pressures, where physical processes contributed little to ignition delay time. In section two, an atmospheric pressure counterflow burner was developed and fully validated. The counterflow burner was employed to examine the effects of molecular structure on low/high temperature reactivity of various fuels in transport-affected systems. These effects were investigated through measurement of conditions of extinction and ignition of various fuel/oxidizer mixtures. Data generated were used to validate various chemical kinetic models in diffusion flames. Where necessary, suggestions were made for improving these models. For hot flames studies, tested fuels included C3-C4 alcohols and six FACE gasoline fuels. Results for alcohols indicated that the substituted alcohols were less reactive than the normal alcohols. The ignition temperature of FACE gasoline was found to be nearly identical, while there was a slight difference in their extinction limits. Predictions by Sarathy et al. (2014) alcohol combustion model, and by the gasoline surrogate model (Sarathy et al., 2015), agreed with the experimental data. For cool diffusion flames studies, tested fuels included butane isomers, naphtha, gasolines and their surrogates. Results revealed that the addition of ozone successfully established cool flames in the fuels at low and moderate strain rates. Numerical simulations were performed to replicate the extinction limits of the cool flames of butane isomers. The model captured experimental trends for both fuels; but over-predicted their extinction limits.