Nonlinear site response analyses are generally preferred over equivalent linear analyses for soft soil sites subjected to high-intensity input ground motions. However, both nonlinear and equivalent linear analyses often result in large induced shear strains (3-10%) at soft sites, and these large strains may generate unusual characteristics in the predicted surface ground motions. One source of the overestimated shear strains may be attributed to unrealistically low shear strengths implied by commonly used modulus reduction curves. Therefore, modulus reduction and damping curves can be modified at shear strains greater than 0.1% to provide a more realistic soil model for site response. However, even after these modifications, nonlinear and equivalent linear site response analyses still may generate unusual surface acceleration time histories and Fourier amplitude spectra at soft soil sites when subjected to high-intensity input ground motions. As part of this work, equivalent linear and nonlinear 1D site response analyses for the well-known Treasure Island site demonstrate the challenges associated with accurately modeling large shear strains, and subsequent surface response, at soft soil sites. Accounting for the uncertainties associated with the shear wave velocity profile is an important part of a properly executed site response analyses. Surface wave data from Grenoble, France and Mirandola, Italy have been used to determine shear wave velocity (Vs) profiles from inversion of surface wave data. Furthermore, Vs profiles from inversion have been used to determine boundary, median and statistically-based randomly generated profiles. The theoretical dispersion curves from the inversion analyses as well as the boundary, median and randomly generated Vs profiles are compared with experimentally measured surface wave data. It is found that the median theoretical dispersion curve provides a satisfactory fit to the experimental data, but the boundary type theoretical dispersion curves do not. Randomly generated profiles result in some theoretical dispersion curves that fit the experimental data, and many that do not. Site response analyses revealed that the greater variability in the response spectra and amplification factors were determined from the randomly generated Vs profiles than the inversion or boundary Vs profiles.

In recent years, the call for business actors to be part of collaborations addressing sustainable development has become more common. There is a consensus that no single sector alone can solve the environmental problems and poverty conditions challenging humanity. However, it is not clear if these cross-sector collaborations thrive when disasters can strike any time and when some actors are richer than others. Through a series of experiments involving threshold public goods games with stochastic shocks, this dissertation contains three related papers exploring different facets of the persistence of cooperation. The experiments were conducted in Sweden, the Philippines and South Africa, countries with varying disaster risk exposures and income structures. Cooperation in the face of disaster explores the effects of different types of uncertainties on cooperation, particularly when there is a risk for repeated disasters (i.e. losses resulting from inadequate cooperation). The results show that cooperation persists when we do not know when disasters may strike (i.e. timing), as well as when there are uncertainties on what is required to avoid the disaster (i.e. threshold) and which losses will be incurred (i.e. impact). Conditional cooperation and disaster uncertainty explores the mechanism behind the persistence of cooperation, as it investigates if conditionality continues to prevail in the face of disaster. The findings show that conditionality and free-riding attenuates while unconditional cooperation accelerates. Cooperating in an unequal and uncertain world explores what happens when inequality enters the picture. The findings reveal that cooperation remains the same when there is inequality and increases in the presence of uncertainty. The effect of uncertainty is stronger than inequality, with high unconditional cooperation and low freeriding. / <p>Diss. Stockholm :  Stockholm School of Economics, 2015</p>

Functional materials with controllable phase transitions have been widely used in devices for information storage (e.g. hard-disk, CD-ROM, memory) and energy storage (e.g. battery, shape memory alloy). One of the important issues to design such materials is to realize the desirable phase transition processes, in which atomistic simulation can be used for the prediction of materials properties. The accuracy of the prediction is largely dependent on searching the true value of the transition rate, which is determined by the minimum energy barrier between stable states, i.e. the saddle point on a potential energy surface (PES). Although a number of methods that search for saddle points on a PES have been developed, they intend to locate only one saddle point with the maximum energy along the transition path at a time. In addition, they do not consider the input uncertainty associated with the calculation of potential energy. To overcome the limitations, in this dissertation, new saddle point search methods are developed to provide a global view of energy landscape with improved efficiency and robustness. First, a concurrent search algorithm for multiple phase transition pathways is developed. The algorithm is able to search multiple local minima and saddle points simultaneously without prior knowledge of initial and final stable configurations. A new representation of transition paths based on parametric Bézier curves is introduced. A curve subdivision scheme is developed to dynamically locate all the intermediate local minima and saddle points along the transition path. Second, a curve swarm search algorithm is developed to exhaustively locate the local minima and saddle points within a region concurrently. The algorithm is based on the flocking of multiple groups of curves. A collective potential model is built to simulate the communication activities among curves. Third, a hybrid saddle-point search method using stochastic kriging models is developed to improve the efficiency of the search algorithm as well as to incorporate model-form uncertainty and numerical errors associated with density functional theory calculation. These algorithms are demonstrated by predicting the hydrogen diffusion process in FeTiH and body-centered iron Fe8H systems.

A field development plan is an important document used to tell the share holders and investors that every aspect of the project has been carefully evaluated. The field development plan should include the objectives of the development, petroleum engineering data, operating and maintenance principles, description of engineering facilities, cost and manpower estimates, project planning and budget proposal. But to arrive at decisions concerning the contents of the field development plan many concept and ideas would have to be screened so that the best ideas and concepts are carried forward for detailed analysis. This screening process can be daunting as there is no limit to the number of viable concepts and ideas. To add to this, for a new field there is hardly ever enough data to fully characterize the reservoir at the time the field development plan is being formulated because there are only a handful of wells in the reservoir. This lack of information about the reservoir introduces uncertainty in the analysis done during the screening process of the concept selection and can have a significant impact on the quality of the project. In this work, we present a simple integrated asset model that can be used in conjunction with a proposed framework at the concept screening and selection phase of a project to evaluate the impact of uncertainty in the input variables on key project drivers. The model can be used to screen multiple concepts to arrive at a few promising concepts that point the direction for detailed studies. The application of the model is demonstrated with synthetic cases formulated for a deep water field which is at the concept selection phase. In the demonstration, we investigated how uncertainty in the reservoir thickness (NTG) and the degree of heterogeneity affect the optimal choices for initial facility size, the number of rigs and the number of pre drilled wells. / text

This research addresses the infrastructure maintenance scheduling problems under budget uncertainty. Infrastructure agencies usually face budget uncertainties that will eventually lead to suboptimal planning if maintenance decisions are made without taking the uncertainty into consideration. It is important for decision makers to adopt maintenance scheduling policies that take future budget uncertainty into consideration. The author proposes a multistage, stochastic linear programming model to address this problem. The author also develops solution procedures using the augmented Lagrangian decomposition algorithm and scenario reduction method. A case study exploring the computational characteristics of the proposed methods is conducted and the benefit of using the stochastic programming approach is discussed. In the case study, the road network in Dallas District is used with data taken from the Texas Department of Transportation’s Pavement Management Information System. The case study results reveal that the stochastic programming solutions tend to allocate more resources to preventive maintenance than deterministic solutions that ignore the uncertainty information. The proposed methodology can help decision makers effectively obtain optimal maintenance plan under budget uncertainty. / text

Guided by a conceptual framework regarding how relationships experience points of transition, this research explored individuals’ perceptions of their online romantic relationship’s transition from a casual to serious relationship in comparison to how individuals in face-to-face romantic relationships experience points of transition. Participants were asked to answer questions regarding their perceptions of relational characteristics during different points in their relational transition. Perceptions regarding intimacy, relationship uncertainty, partner interference, directness of communication, topic avoidance, turmoil, deception and met expectations were assessed. Additionally, individuals in both online and face-to-face relationships responded to questions regarding their relationship status, commitment, length, proximity and other demographic questions. Results indicated that individuals in online relationships perceive more intimacy and less uncertainty prior to a transition while perceiving less intimacy and more uncertainty after a transition than face-to-face relationships. Relationships uncertainty was associated with topic avoidance and turmoil in online romantic relationships. Further results and the relevance of perceptions of relational characteristics on online transitioning relationships are discussed. / text

Firms are believed to engage in strategic political management (SPM) in attempts to shape public policy in favorable ways and enhance their economic returns. Extant research has broadly considered the motivations leading to corporate political activity, focusing on the effects of market power using metrics such as firm size and industry concentration to investigate this phenomenon. More recently, scholars have proposed a more nuanced perspective on the subject, suggesting that different types of SPM may exist. For example, both Baysinger (1984) and Oliver and Holzinger (2008) have distinguished between corporate political strategies designed to maintain or alter the firm’s political environment. In this study, I seek to more critically explore this distinction. I propose that at least two different types of SPM exist: defensive SPM, which is directed at protecting existing competitive advantage, and offensive SPM, which is focused on creating new forms of competitive advantage. I further propose that the threats and opportunities in a firm’s regulatory environment are important motivators of these different types of SPM. In the context of the natural gas industry in Texas from 1999-2009, I find that the degree of regulatory uncertainty in the firm’s political environment influences it to engage in defensive SPM. I also find that the size of the firm’s asset inventory influences it to engage in offensive SPM. Furthermore, I find that regulatory uncertainty negatively moderates the relationship between the size of a firm’s asset inventory and its likelihood of investing in offensive SPM. / text

This dissertation applies a mixed-methods model across three cases of climate risk in order to examine the rhetorical dynamics of uncertainties. I argue that a rhetorical approach to uncertainties can effectively scaffold civic agency in risk communication by translating conflicting interests and creating sites of public participation. By tracing the networks of scientists and their artifacts through cases of climate risk, I demonstrate how the performances of scientific ethos and their material-discursive technologies facilitate the personalization of risk as a form of scientific prudence, and thus a channel to feasible political action. I support these claims through a rhetorical model of translation, which hybridizes methods from discourse analysis and Actor-Network Theory (ANT) in order assemble a data-driven and corpus-based approach to rhetorical analysis. From this rhetorical perspective uncertainties expand on our notions of risk because they reveal associations between scientific inquiries, probability assessments, and the facilitation of political dialogues. In each case, the particular insight of the model reveals a range of rhetorical potentials in climate risk that can be confronted through uncertainties.

The purpose of this study was to describe the relationships among common signs/symptoms (depression, fatigue, declines in cognitive function) in women with multiple sclerosis (MS) using a modified version of Braden's Self Help Theoretical Model and evaluate whether depression, fatigue, declines in cognitive function and uncertainty, enabling skills (self-control in this study) and self-management (coping in this study) influence quality of life outcomes in women with MS. MS is one of the most common causes of disability among young adults and is the most prevalent neurological disease among young and middle-aged adults in certain parts of the world. Although research had previously been undertaken with regards to the common symptoms of MS, uncertainty, enabling skills, self-management and quality of life, there had been no studies undertaken that involved all of them. This descriptive study was the first to explore relationships among common symptoms of MS, uncertainty, enabling skills, self-management and quality of life in an MS population using Braden's Learned Response Chronic Illness Self Help Model. A cross-sectional descriptive study was conducted with 106 participants. Measurement tools utilized in the study included: 1) Demographic and Illness Characteristics, 2) The Modified Fatigue Impact Scale, 3) Perceived Deficits Questionnaire, 4) Patient Health Questionnaire-9, 5) Mishel's Uncertainty in Illness Scale- Adult, 6) Rosenbaum's Self-Control Scale- Modified, 7) COPE Inventory-Brief, and 8) SF-36 Health Status Questionnaire. Data analysis involved descriptive statistics, correlations and linear regression to answer the specific aims. The study findings indicate that relationships exist among depression, fatigue, declines in cognitive function, uncertainty, enabling skills and self-management in women with MS. The study findings also indicate that depression, fatigue, declines in cognitive function, uncertainty, enabling skills and self-management influence quality of life outcomes in women with MS. Finally, while only a first study, the research findings indicate using a modified version of Braden's Learned Response Chronic Illness Self Help Model (LRCISHM) is appropriate in a population of women with MS. Future research involving women with MS who meet the inclusion criteria across the contiguous United States as well as male military veterans with MS is recommended. Research involving this modified version of Braden's LRCISHM as well as research incorporating disability levels is recommended. Research to develop interventions to improve quality of life outcomes and minimize distress is also recommended.

Water distribution system modeling can be used as a basis of planning and operation decisions. However, model accuracy and uncertainty will impact the model based decisions. Model prediction uncertainty results from uncertainty in model parameters that are determined through calibration or are based upon modeler judgment. The focus of this dissertation is the effect of uncertainties on water quality model estimates and calibration. The dissertation is centered around three journal articles and a technical note.In the first paper, the effect of parameter uncertainty on water quality in a distribution system under steady and unsteady conditions was analyzed by Monte Carlo simulation (MCS). Sources of uncertainties for water quality include decay coefficients, pipe diameter and roughness, and nodal spatial and temporal demands. The effect of individual parameter is discussed, as well as the combined effect of the parameters. It also describes the effect of flow patterns.A general calibration model is developed in the second paper for identifying wall decay coefficients. The problem is solved using the SFLA optimization algorithm that is coupled with hydraulic and water quality simulation models using the EPANET toolkit. The methodology is applied on two application networks. The study presents the effect of different field conditions such as the network with or without tanks, altering disinfectant injection policies, changing measurement locations, and varying the number of global wall decay coefficient on the estimated parameters. The numerical study also discusses whether the complexity of the system can be captured with fewer than the actual number of field parameters and if the number of the measurement locations is sufficient.The third paper conducts a study that considers a full calibration assessment for a water quality model in the distribution systems. The calibration process begins with estimating the the best fit wall decay coefficients. Next, the uncertainties involved with estimated parameters are calculated. Finally, the study assesses the model prediction uncertainties for critical demand conditions due to the parameter uncertainties. Various conditions are evaluated including the effects of different measurement errors and different measurement conditions on the uncertainty levels of estimated parameters as well as on the model predictions.Fourth paper presents study in which a booster disinfectant is introduced within a distribution system to maintain disinfectant residuals and avoid high dosages at water sources. Assuming that first order reaction kinetics apply to chlorine decay, an integer linear programming optimization problem is posed to booster locations and their injection rates. The formulation avoids long water quality simulations by adding constraints requiring the concentrations at the beginning and end of the design period to be the same. The optimization problem is divided into two levels. The upper level selects the booster locations using a genetic algorithm, if more than a few boosters are included, or enumeration, if the number of boosters and/or potential locations is relatively small. Given a set of boosters from the upper level, the lower level minimizes the chlorine mass to be injected to maintain required residuals. The approach is applied to the Brushy Plains system for alternative numbers of allowable boosters.