Distributed, renewable generation-units and more efficient use of electric power,demands a developed electric grid. This grid is often referred to as the “Smart Grid”. This future electric system rise expectations among actors in the existing electricmarket. International organizations share visions of opportunities for electricityconsumers and new actors, ready to enter the market. At the same time, electricutility companies may question the need for some of these upcoming changes, as theyare sometimes expected to lead the development. This paper therefore seeks to investigate the business opportunities related to SmartGrids. This is done throughout interviews, literature-studies and analysis usinginnovation-development theories. Primary focus of the paper is directed towards theelectric utility companies, secondary towards electric consumers. Business opportunities for utility companies are found to be primary associated withoperation and maintenance aspects of the grid. Developed control-systems andupdated maintenance strategies may increase the overall system knowledge and thequality of the distribution. Cost-reduction activities as these are probably results ofthe revenue-cap regulation controlling grid companies. Future consumers may benefit,socially and economically, from solutions visualizing and controlling different electriccomponents in the real estate. At the same time the development of Smart Grids is a rather slow process, partlybecause of large amount of system-components and partly because of existing rulesand laws concerning the electric system. In order to identify future opportunitieswithin Smart Grids, it is crucial to gain knowledge of the system and to satisfy realneeds or problems.

This paper considers the quantitative and qualitative observation-based assessment of neighbourhoodhealth in three Toronto neighbourhoods. In so doing, the paper addresses prevailing challenges andopportunities in a range of forms of neighbourhood health. The Bay Street Corridor, Kensington-Chinatown,and Regent Park were studied as exemplar cases of neighbourhoods in varying degrees of degradation. Theresearch involved a robust evaluation of raw statistical analysis, census data, anecdotal evidence, and annualreports to demonstrate the spatiality of crime, real estate, and socio-economic opportunity. Results from thisanalysis have demonstrated that certain neighbourhood characteristics are prone to higher or lower crime rates,structural neglect, real estate speculation, and proprietary action. ‘Fear proves itself’ in as far as neighbourhoodaction provides impetus to create defensible spaces to increase neighbourhood engagement and ownership and toprotect against neglect and crime. What is clear from these descriptions is that neighbourhood health issignificantly associated with endogenous behaviour; positing the important role that community advocacy playsin stewarding neighbourhood health. Understanding neighbourhood health requires an appreciation of localsocial assets, and how these indigenous resources articulate opportunities for sustainable urban development.The longevity of cities involves building neighbourhoods for people and providing a stable structure thatpromotes and perpetuates sustainable city living. By exploring themes in urban sociology, psychology of place,and place belonging, the investigation highlights

How to allocate resources in the health care sector is academically dealt with within the subject of health economics. Economic evaluations are within this area used to compare the costs and effects of medical interventions with the purpose to help decision makers decide how to allocate resources. Oxygen deficiency in the foetus during birth can lead to severe life long injuries in the child. In high-risk deliveries, it is therefore considered necessary to use foetal surveillance with a scalp electrode and the choice is between surveillance with internal cardiotocography (CTG) and surveillance with ST analysis. The standard procedure is in most hospitals currently CTG, which records the foetal heart rate and the uterine contractions. The second strategy, in this thesis referred to as ST analysis, complements CTG with foetal electrocardiography(ECG) and ST analysis. The objective of this report is to from a societal perspective determine the cost-effectiveness of using ST analysis in complicated deliveries, compared to the use of CTG alone. A cost-utility analysis was performed based on a probabilistic decision model incorporating the relevant strategies and outcomes. The costs and effects of the two different treatment strategies were compared in a decision tree. Discounted costs and quality-adjusted life-years (QALYs) were measured and simulated over a life-time perspective. The analysis resulted in an incremental effect of 0.005 QALYs for the ST analysis strategy, when compared to the CTG strategy. ST analysis was also associated with a €30 lower cost. Thus, CTG is dominated by the ST analysis strategy. The probability that ST analysis is the cost-effective alternative is high for all values of willingness-to-pay for a QALY, which means that a decision to implement the ST analysis strategy based on the results of this thesis would be surrounded by a low degree of uncertainty.

Introduction Movies are considered entertainment goods. Entertainment is one of the experience industries. Intangibility, perishability and heterogeneity are the most significant characteristics of the movie industry. An emotional reaction of consumers cannot be calculated in the same sense that most other physical goods can. If the movie succeeded in meeting the expectations, ticket price decreases will not necessarily indicate further purchases in the future. There are high risks and uncertainty in the movie industry. Purpose The purpose of this paper is to define through a hedonic price theory establishing whether the recognition is a significant factor to the global success of movies. The global success of the movies is determined by the global box office revenue. There are eight independent variables tested in this paper: global movie popularity, global popularity of the directors, global popularity of the authors, fame (determined by wining Academy Award), major studios, sequel, family genre and animation genre. Only one control variable, which is year of release, is included. Method The paper uses empirical model and the data set along with the results of the empirical analysis to achieve the purpose. Only secondary data were collected for the paper. Conclusion To reduce uncertainty in movie industry box office revenue, according to the data collected, recognition is significant to the consumers’ willingness to pay. The willingness to pay is determined by the global box office revenue. Only four independent variables, namely: sequels, Academy Award, the global popularity of the directors and the global popularity of the authors of the original script, are significant recognition factors to the global box office revenue. Movie producers shall be aware that consumers have to make sure utility gained from the consumption exceeds the costs in order to make purchases. Based on the sample collected, it can be summarized that consumers of the movie industry in general rely on previous consumption and recognition to reduce risks and uncertainty in terms of making purchases.

This dissertation aims at developing and testing a new method that can better capture preferences for multistate health profiles. The motivation arose from the failure of the QALY (Quality-Adjusted Life Year) model in adequately capturing preferences in multistate health profiles. The current QALY-based technique captures preferences for multistate health profiles by evaluating each health state in the profile independently of other states. As the past literature showed, this additive independence condition does not hold in practice and hence such approach is inadequate. To address this issue, this study proposes a novel approach to measure preferences for multistate health profiles by looking at two consecutive health states at a time. It hypothesizes that an evaluation of the future health state is dependent or "conditioned" on the level of the preceding, or current, health state. Characteristics of the current health state that are suspected to impact the resulting conditional preference scores for future health state are systematically explored in a carefully designed empirical study. The interested factors include duration of the current health state, direction of change and amplitude of change between the current and future health states. A 2

No description available.

Risk attitudes are important for human decision making, especially in scenarios where huge wins or losses are possible, as exemplified by planetary rover navigation, oilspill response, and business applications. Decision-theoretic planners therefore need to take risk aspects into account to serve their users better. However, most existing decision-theoretic planners use simplistic planning objectives that are risk-neutral. The thesis research is the first comprehensive study of how to incorporate risk attitudes into decision-theoretic planners and solve large-scale planning problems represented as Markov decision process models. The thesis consists of three parts. The first part of the thesis work studies risk-sensitive planning in case where exponential utility functions are used to model risk attitudes. I show that existing decision-theoretic planners can be transformed to take risk attitudes into account. Moreover, different versions of the transformation are needed if the transition probabilities are implicitly given, namely, temporally extended probabilities and probabilities given in a factored form. The second part of the thesis work studies risk-sensitive planning in case where general nonlinear utility functions are used to model risk attitudes. I show that a state-augmentation approach can be used to reduce a risk-sensitive planning problem to a risk-neutral planning problem with an augmented state space. I further use a functional interpretation of value functions and approximation methods to solve the planning problems efficiently with value iteration. I also show an exact method for solving risk-sensitive planning problems where one-switch utility functions are used to model risk attitudes. The third part of the thesis work studies risk sensitive planning in case where arbitrary rewards are used. I propose a spectrum of conditions that can be used to constrain the utility function and the planning problem so that the optimal expected utilities exist and are finite. I prove that the existence and finiteness properties hold for stationary plans, where the action to perform in each state does not change over time, under different sets of conditions.

The current dominate layered networking architecture, in which each layer is designed and operated independently, results in inefficient and inflexible resource use in wireless networks due to the nature of the wireless medium, such as time-varying channel fading, mutual interference, and topology variations. In this thesis, we focus on resource allocation and scheduling in wireless orthogonal frequency division multiplexing (OFDM) networks based on joint physical and medium access control (MAC) layer optimization. To achieve orders of magnitude gains in system performance, we use two major mechanisms in resource management: exploiting the time variance and frequency selectivity of wireless channels through adaptive modulation, coding, as well as packet scheduling and regulating resource allocation through network economics. With the help of utility functions that capture the satisfaction level of users for a given resource assignment, we establish a utility optimization framework for resource allocation in OFDM networks, in which the network utility at the level of applications is maximized subject to the current channel conditions and the modulation and coding techniques employed in the network. Although the nonlinear and combinatorial nature of the cross-layer optimization challenges algorithm development, we propose novel efficient dynamic subcarrier assignment (DSA) and adaptive power allocation (APA) algorithms that are proven to achieve the optimal or near-optimal performance with very low complexity. Based on a holistic design principle, we design max-delay-utility (MDU) scheduling, which senses both channel and queue information. The MDU scheduling can simultaneously improve the spectral efficiency and provide right incentives to ensure that all applications can receive their different required quality of service (QoS). To facilitate the cross-layer design, we also deeply investigate the mechanisms of channel-aware scheduling, such as efficiency, fairness, and stability. First, using extreme value theory, we analyze the impact of multiuser diversity on throughput and packet delay. Second, we reveal a generic relationship between a specific convex utility function and a type of fairness. Third, with rigorous proofs, we provide a method to design cross-layer scheduling algorithms that allow the queueing stability region at the network layer to approach the ergodic capacity region at the physical layer.

Utility mining algorithms have recently been proposed to discover high utility itemsets from a quantitative database. Factors such as profits or prices are concerned in measuring the utility values of purchased items for revealing more useful knowledge to managers. Nearly all the existing algorithms are performed in a batch way to extract high utility itemsets. In real-world applications, transactions may, however, be inserted, deleted or modified in a database. The batch mining procedure requires more computational time for rescanning the whole updated database to maintain the up-to-date knowledge. In the first part of this thesis, two algorithms for data insertion and data deletion are respectively proposed for efficiently updating the discovered high utility itemsets based on pre-large concepts. The proposed algorithms firstly partition itemsets into three parts with nine cases according to whether they are large (high), pre-large or small transaction-weighted utilization in the original database. Each part is then performed by its own procedure to maintain and update the discovered high utility itemsets. Based on the pre-large concepts, the original database only need to be rescanned for much fewer itemsets in the maintenance process of high utility itemsets. Besides, the risk of privacy threats usually exists in the process of data collection and data dissemination. Sensitive or personal information are required to be kept as private information before they are shared or published. Privacy-preserving utility mining (PPUM) has thus become an important issue in recent years. In the second part of this thesis, two evolutionary privacy-preserving utility mining algorithms to hide sensitive high utility itemsets in data sanitization for inserting dummy transactions and deleting transactions are respectively proposed. The two evolutionary privacy-preserving utility mining algorithms find appropriate transactions for insertion and deletion in the data-sanitization process. They adopt a flexible evaluation function with three factors. Different weights are assigned to the three factors depending on users¡¦ preference. The maintenance algorithms proposed in the first part of this thesis are also used in the GA-based approach to reduce the cost of rescanning databases, thus speeding up the evaluation process of chromosomes. Experiments are conducted as well to evaluate the performance of the proposed algorithms.

One of the petroleum industry?s goals is to reduce the environmental impact of oil and gas operations in environmentally sensitive areas. To achieve this, a number of Environmentally Friendly Drilling (EFD) technologies have been developed to varying degrees. For example, the use of an elevated platform as an alternative to the gravel pad is less intrusive and leads to a more environmentally friendly approach to drilling operations. Elevated drilling platforms will require the use of piles. Another alternative to the gravel pad is the use of composite mats. Since the demand of low impact technologies for drill site construction has rapidly increased, the parametric study for the feasibility of using pile foundations and composite mats is conducted in this research. Even though a number of EFD technologies have already been developed to varying degrees, few have been integrated into a field demonstrable drilling system (i.e., combination of technologies) compatible with ecologically sensitive areas. In general, it is difficult to select the best combination of EFD technologies for a given site because there are many possible combinations and many different evaluation criteria. The proposed technology evaluation method is based on a systems analysis that can be used for integrating current and new EFD technologies into an optimal EFD system. An optimization scheme is suggested based on a combination of multi-attribute utility theory and exhaustively enumerating all possible technology combinations to provide a quantitative rationale and suggest the best set of systems according to a set of criteria, with the relative importance of the different criteria defined by the decision-maker. In this research, the sensitivity of the optimal solution to the weight factors and the effects of the uncertainty of input scores are also discussed using a case study. An application of the proposed approach is described by conducting a case study in Green Lake at McFaddin, TX. The main purpose of this case study is to test the proposed technology evaluation protocol in a real site and then to refine the protocol. This research describes the results of the case study which provided a more logical and comprehensive approach that maximized the economic and environmental goals of both the landowner and the oil company leaseholder.