This thesis explores effects of uncertainty on firm investment that are described in estimates of firm level investment specifications which include proxies for uncertainty over expected future firm profitability. A panel data set of UK firms covering the period 1987-2000 is used to estimate firm level investment specifications. Within year volatility in stock returns - a common proxy for firm specific uncertainty in previous literature - is compared with covariance measures between stock returns and market returns representing un-diversifiable risk from the CAPM; and with alternative uncertainty proxies based on volatility in I/B/E/S securities analysts' forecasts of earnings per share. Within estimates of firm level investment specifications, the thesis investigates the sensitivity of coefficients on uncertainty terms to the choice of underlying investment specification: error correction model between the natural logarithms of capital and sales; or the Hayashi (1982) Q model of investment. Coefficients on stock return volatility measures of uncertainty terms are found to vary significantly between estimates of error correction and average q specifications. Differences between coefficients estimated on uncertainty terms across estimates of these two investment specifications are supported with simulated data. Uncertainty measures based on volatility in I/B/E/S securities analysts' forecasts of earnings per share are found to be much more informative of investment behaviour than within year stock return volatility in estimates of both error correction and average q specifications. Coefficients on I/B/E/S uncertainty proxies imply more consistent investment-uncertainty relationships across estimates of error correction and average q specifications for the UK panel data set.

The study of metacognition is rooted in the observation of behaviors under states of uncertainty (e.g., Smith et al., 1995). Individuals who are more responsive to uncertainty tend to show greater interference effects in a Stroop color-word naming task compared to those who are less responsive to uncertainty (Washburn, Smith, & Taglialatela, 2005). Individual differences in Stroop interference also have been shown to reflect relative differences in response competition (Washburn, 1994) and rule-maintenance ability (Kane & Engle, 2003). Why would individuals who respond to uncertainty most adaptively be characterized by the worst attention-control skills? The current study was designed to measure the individual contribution of sensitivity to response competition and rule maintenance ability to the pre-established relationship between Stroop interference and uncertainty responsiveness. Though participants performed as expected in both tasks, the previously reported relationship between Stroop interference and uncertainty responsiveness was not observed.

A research report submitted to the Faculty of Geosciences, University of the Witwatersrand for the degree of Master of Science Johannesburg, 2015 / Quantifying or estimating emission uncertainty for particulate matter from stationary sources in South Africa. The estimation of measurement uncertainty with regards to hazardous air pollution emissions from stationary sources is currently the most uncertain element associated with obtaining relevant, valid stack emission data in South Africa. This project is aimed at developing an appropriate method to evaluate the uncertainty associated with particulate matter measurements conducted for stationary source emissions in the South African context. A series of In-Stack measurements were taken in accordance with recognized international methodology (ISO 9096:1992, and 2003) on two different industrial processes, representing a best and worst case scenario. A comparison between the two scenarios was made in an attempt to establish what components of the sampling technique have the greatest error. The effect of cumulative errors in the sampling train as well as external factors that may influence the results were evaluated and included in the final estimate of uncertainty. Some of the factors used included the sampling location, industrial process and external environmental factors. The overarching goal of this project was to establish an estimate of the cumulative uncertainty on the final emission values obtained, inclusive of both analytical, field sampling and process related variables that may result in a cumulative error associated with quantifying stationary source particulate matter emission values. The results of the study found that the estimated combined expanded uncertainty for both sets of data was calculated to be between 62 – 72%. Upon closer analysis of the data it was ascertained that the data obtained were inadequate and the calculation of the uncertainty of the results both with the compliant and non-compliant sampling campaigns revealed that the variability of the results was too great for both scenarios to make any statistically valid observations or conclusions about the data. In lieu of this, and considering the significant costs, time and labour involved in order to obtain enough data to enable adequate quantification of an uncertainty budget for the results obtained, the author has developed an alternative tool for assessing the quality and reliability of reported emission figures. The author has developed what he has named a sampling suitability matrix, this tool although subjective in nature will add significant value (in the authors opinion) to the interpretation of the quality and reliability of the final emission results reported. The intention of this tool is to be incorporated as supplementary information into all emission reports in future. This will enable the plant operator and regulator to assess the quality of reported data and final emission results, thus assisting in establishing whether the plant is in compliance with their Air Emission License (AEL) requirements or not.

Autonomous aerial, terrestrial, and marine vehicles provide a platform for several applications including cargo transport, information gathering, surveillance, reconnaissance, and search-and-rescue. To enable such applications, two main technical problems are commonly addressed.On the one hand, the motion-planning problem addresses optimal motion to a destination: an application example is the delivery of a package in the shortest time with least fuel. Solutions to this problem often assume that all relevant information about the environment is available, possibly with some uncertainty. On the other hand, the information gathering problem addresses the maximization of some metric of information about the environment: application examples include such as surveillance and environmental monitoring. Solutions to the motion-planning problem in vehicular autonomy assume that information about the environment is available from three sources: (1) the vehicle’s own onboard sensors, (2) stationary sensor installations (e.g. ground radar stations), and (3) other information gathering vehicles, i.e., mobile sensors, especially with the recent emphasis on collaborative teams of autonomous vehicles with heterogeneous capabilities. Each source typically processes the raw sensor data via estimation algorithms. These estimates are then available to a decision making system such as a motion- planning algorithm. The motion-planner may use some or all of the estimates provided. There is an underlying assumption of “separation� between the motion-planning algorithm and the information about environment. This separation is common in linear feedback control systems, where estimation algorithms are designed independent of control laws, and control laws are designed with the assumption that the estimated state is the true state. In the case of motion-planning, there is no reason to believe that such a separation between the motion-planning algorithm and the sources of estimated environment information will lead to optimal motion plans, even if the motion planner and the estimators are themselves optimal. The goal of this dissertation is to investigate whether the removal of this separation, via interactive motion-planning and sensing, can significantly improve the optimality of motion- planning. The major contribution of this work is interactive planning and sensing. We consider the problem of planning the path of a vehicle, which we refer to as the actor, to traverse a threat field with minimum threat exposure. The threat field is an unknown, time- variant, and strictly positive scalar field defined on a compact 2D spatial domain – the actor’s workspace. The threat field is estimated by a network of mobile sensors that can measure the threat field pointwise. All measurements are noisy. The objective is to determine a path for the actor to reach a desired goal with minimum risk, which is a measure sensitive not only to the threat exposure itself, but also to the uncertainty therein. A novelty of this problem setup is that the actor can communicate with the sensor network and request that the sensors position themselves in a procedure we call sensor reconfiguration such that the actor’s risk is minimized. This work continues with a foundation in motion planning in time-varying fields where waiting is a control input. Waiting is examined in the context of finding an optimal path with considerations for the cost of exposure to a threat field, the cost of movement, and the cost of waiting. For example, an application where waiting may be beneficial in motion-planning is the delivery of a package where adverse weather may pose a risk to the safety of a UAV and its cargo. In such scenarios, an optimal plan may include “waiting until the storm passes.� Results on computational efficiency and optimality of considering waiting in path- planning algorithms are presented. In addition, the relationship of waiting in a time- varying field represented with varying levels of resolution, or multiresolution is studied. Interactive planning and sensing is further developed for the case of time-varying environments. This proposed extension allows for the evaluation of different mission windows, finite sensor network reconfiguration durations, finite planning durations, and varying number of available sensors. Finally, the proposed method considers the effect of waiting in the path planner under the interactive planning and sensing for time-varying fields framework. Future work considers various extensions of the proposed interactive planning and sensing framework including: generalizing the environment using Gaussian processes, sensor reconfiguration costs, multiresolution implementations, nonlinear parameters, decentralized sensor networks and an application to aerial payload delivery by parafoil.

Zhang, Lianmin. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2013. / Includes bibliographical references (leaves 130-139). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstracts also in Chinese.

In distribution networks, the traditional way to eliminate network stresses caused by increasing generation and demand is to reinforce the primary network assets. A cheaper alternative is active network management (ANM) which refers to real-time network control to resolve power flow, voltage, fault current and security issues. However, there are two limitations in ANM. First, previous ANM strategies investigated generation side and demand side management separately. The generation side management evaluates the value from ANM in terms of economic generation curtailment. It does not consider the potential benefits from integrating demand side response such as economically shifting flexible load over time. Second, enhancing generation side management with load shifting requires the prediction of network stress whose accuracy will decrease as the lead time increases. The uncertain prediction implies the potential failure of reaching expected operational benefits. However, there is very limited investigation into the trade-offs between operational benefit and its potential risk. In order to tackle the challenges, there are two aspects of research work in this thesis. 1) Enhanced ANM. It proposes the use of electric vehicles (EVs) as responsive demand to complement generation curtailment strategies in relieving network stress. This is achieved by shifting flexible EV charging demand over time to absorb excessive wind generation when they cannot be exported to the supply network. 2) Uncertainty management. It adopts Sharpe Ratio and Risk Adjust Return On Capital concepts from financial risk management to help the enhanced ANM make operational decisions when both operational benefit and its associated risk are considered. Copula theory is applied to further integrate correlations of forecasting errors between nodal power injections (caused by wind and load forecasting) into uncertainty management. The enhanced ANM can further improve network efficiency of the existing distribution networks to accommodate increasing renewable generation. The cost-benefit assessment informs distribution network operators of the trade-off between investment in ANM strategy and in the primary network assets, thus helping them to make cost-effective investment decisions. The uncertainty management allows the impact of risks that arise from network stress prediction on the expected operational benefits to be properly assessed, thus extending the traditional deterministic cost-benefit assessment to cost-benefit-risk assessment. Moreover, it is scalable to other systems in any size with low computational burden, which is the major contribution of this thesis.

This paper examines the statistical mechanical and thermodynamical consequences of variable phase-space volume element $h_I=?igtriangleup x_i?igtriangleup p_i$. Varying $h_I$ leads to variations in the amount of measured entropy of a system but the maximum entropy remains constant due to the uncertainty principle. By taking $h_u ightarrow 0^+$ an infinite unobservable entropy is attained leading to an infinite unobservable energy per particle and an unobservable chemical equilibrium between all particles. The amount of heat fluxing though measurement apparatus is formulated as a function of $h_I$ for systems in steady state equilibrium as well as the number of measured particles or sub-particles so any system can be described as unitary or composite in number. Some example systems are given using variable $h_I$.

This dissertation focuses on the identification of the dynamics of risk aversion (price of risk) and economic uncertainties (amount of risk) and their effects on both domestic and international asset markets. In the first essay, I study the differences between global equity return comovements and global bond return comovements and use a consistent and flexible asset pricing framework to motivate and quantify the role of various economic determinants in explaining the comovement difference. This study contributes to the recent debate on how shocks transmit across countries, and documents that the ``risk compensation'' channel plays a major role in affecting international comovements. In the second essay, I find that fundamental shocks (consumption growth) and cash flow shocks (dividend growth) comove procyclically. This new stylized fact helps explain the ``Duffee Puzzle'' (Duffee, 2005): stock returns and consumption growth covary procyclically, whereas the conventional wisdom and extant consumption-based asset pricing models suggest that returns respond to fundamental shocks more significantly in a bad economic environment. This research contributes to an under-explored area in the consumption-based asset pricing literature: the dynamics of the ``amount of risk''. I then explore the asset pricing implications of this procyclical source of amount of risk in a consumption-based workhorse model that allows for time-varying risk aversion. In my joint paper with Geert Bekaert and Eric Engstrom, we develop a new measure of time-varying risk aversion that is consistent with a dynamic no-arbitrage asset pricing model, using a wide range of observed asset moments, macro and option data. In addition, our findings formally support the close relationship between variance risk premium and risk aversion (as suggested in the literature), and propose a financial proxy to economic uncertainty, which is a more significant predictor of future economic growth than VIX and true economic uncertainty.

Surface metrology has been widely used in manufacturing for many years. There has been a wide range of techniques applied for measuring surface topography. A photometric stereo technique is one of the best ways for the analysis of three-dimensional (3D) surface textural patterns. Many published works are concerned the developed approach for recovering the 3D profiles from surface normal. This research not only presents a methodology used to retrieve the profiles of surface roughness standards but also investigates the uncertainty estimation of textural measurement determined by the photometric stereo method. Various input quantities have been studied such as pixel error from recovered 3D surface textural patterns, the power of light source which involved with surface roughness average (Ra) value and the effect of room temperature. The surface roughness standards were utilized as the reference value. In term of increasing accuracy of the reference value, a contact method (stylus instrument) was used to calibrate them. Illumination angles of light source had some influence on the measurement results. A coordinate measuring machine (CMM) was used for holding the light source in order to study the effects of tilt and slant angles. The effect of tilt and slant angles were investigated. The results of these experiments successfully indicated that the angle used in photometric stereo method played an important role to the accuracy level of the roughness measurement results. The surface roughness specimen manufactured by a Computer Numerical Control (CNC) was applied to validate the capability of the photometric stereo system.

My final thesis exhibition, Effluvia and Aporia, explores impermanence, loss and uncertainty. I use materials and images in a poetic way, where there is a link between what the work is and what it means. I use looped videos with images of water, light, and dissolving clay to invite a meditative state. I also use materials like tissue paper, paper-mache, and paper thin porcelain tiles to invite fragility and complexity into the viewer's experience. I am concerned with creating an interactive environment that allows for a multiplicity of responses and interpretations from each viewer depending on their unique perceptions. I am interested in impermanence, loss and uncertainty as themes because I find that they are ever present in life. My intention is to explore these ideas and create an experience that allows for the viewer to reflect on them as well.