Civil infrastructure systems are the foundation of economic growth and prosperity in all nations. In recent years, infrastructure rehabilitation has been a focus of attention in North America and around the world. A large percentage of existing infrastructure assets is deteriorating due to harsh environmental conditions, insufficient capacity, and age. Ideally, an assets management system would include functions such as condition assessment, deterioration modeling, repair modeling, life-cycle cost analysis, and asset prioritization for repair along a planning horizon. While many asset management systems have been introduced in the literature, few or no studies have reported on the performance of either optimization or heuristic tools on large-scale networks of assets. This research presents an extensive comparison between heuristic and genetic-algorithm optimization methods for handling large-scale rehabilitation programs. Heuristic and optimization fund-allocation approaches have been developed for three case studies obtained from the literature related to buildings, pavements, and bridges with different life cycle cost analysis (LCCA) formulations. Large-scale networks were constructed for comparing the efficiency of heuristic and optimization approaches on large-scale rehabilitation programs. Based on extensive experiments with various case studies on different network sizes, the heuristic technique proved its practicality for handling various network sizes while maintaining the same efficiency and performance levels. The performance of the genetic algorithm optimization approach decreased with network size and model complexity. The optimization technique can provide a high performance level, given enough processing time.

The death of a spouse is one of the most profound and life-altering events adults will ever experience. While the experience of spousal bereavement is traumatic at any time, there is evidence to support the fact that young women who are widowed experience unique challenges. The purpose of studying young widows was to understand the meaning of spousal bereavement for individual participants. The research tradition of phenomenology was chosen to inform the study, and the guiding question became what is the lived experience of spousal bereavement for young women? The study participants were five women who were under the age of 45 at the time of their husbands death. The experiences of these young widows were illuminated through stories and reflections on the journey of a young widow. Five themes emerged from their experiences. Young widows grieve both the loss of a companion and the death of their dreams. Accompanying these losses can be the challenges of single parenthood, the need for career, financial and lifestyle changes, and the readjustment to life as a single adult. Increased understanding from the perspective of the bereaved widow, along with strategies and interventions for nurses working with this group of women, will provide nurses and health care professionals with skills to better assist this client population.

In the past decade, the availability of digitally encoded, downloadable music has increased dramatically, pushed mainly by the release of the now famous MP3 compression format (Fraunhofer-Gesellschaft, 1994). Online sales of music in the US doubled in 2005, according to a recent news article (*), while the number of files exchanged on P2P platforms is much higher, but hard to estimate. The existing and coming informational flood in digital music prompts the need for sophisticated content-based information retrieval. Query-by-Humming is a prototypical technique aimed at locating pieces of music by melody; automatic annotation algorithms seek to enable finer search criteria, such as instruments, genre, or meter. Score transcription systems strive for an abstract, compressed form of a piece of music understandable by composers and musicians. Much research still has to be performed to achieve these goals. This thesis connects essential knowledge about music and human auditory perception with signal processing algorithms to solve the specific problem of pitch estimation. The designed algorithm obtains an estimate of the magnitude spectrum via STFT and models the harmonic structure of each pitch contained in the magnitude spectrum with Gaussian density mixtures, whose parameters are subsequently estimated via an Expectation-Maximization (EM) algorithm. Heuristics for EM initialization are formulated mathematically. The system is implemented in MATLAB, featuring a GUI that provides for visual (spectrogram) and numerical (console) verification of results. The algorithm is tested using an array of data ranging from single to triple superposed instrument recordings. Its advantages and limitations are discussed, and a brief outlook over potential future research is given. (*) "Online and Wireless Music Sales Tripled in 2005"; Associated Press; January 19, 2006

The so-called heuristics have been widely used in solving combinatorial optimization problems because they provide a simple but effective way to find an approximate solution. These technologies are very useful for users who do not need the exact solution but who care very much about the response time. For every existing heuristic algorithm has its pros and cons, a hyper-heuristic clustering algorithm based on the diversity detection and improvement detection operators to determine when to switch from one heuristic algorithm to another is presented to improve the clustering result in this paper. Several well-known datasets are employed to evaluate the performance of the proposed algorithm. Simulation results show that the proposed algorithm can provide a better clustering result than the state-of-the-art heuristic algorithms compared in this paper, namely, k-means, simulated annealing, tabu search, and genetic k-means algorithm.

Little is known about how people evaluate credence attributes, that is, those attributes which the consumer often cannot fully evaluate even after purchasing and consuming the product. And yet consumers struggle to evaluate quality in several important product categories dominated by credence attributes such as food safety, medical services, legal services, and pharmaceuticals, among others. The dissertation explores the processes by which people form quality evaluations of services high in credence attributes and the consequences of those evaluations. Drawing on the service quality, dual-process social information processing, expert-novice and risk literatures, I develop a conceptual model to illustrate how skill and motivation moderate the ways people seek and integrate observable information to infer unobservable quality. The influence of quality evaluations on outcome, satisfaction, value, and loyalty is mapped. The model is tested in the context of a classic credence service, health care services with two large datasets using structural equation modeling. Study 1 draws on an existing patient satisfaction database (6,280 records) to measure the sources and consequences of quality evaluations. Study 2 validates Study 1 findings and extends those findings to show the moderating roles of product expertise and perceived risk on quality evaluation processes. The second study is tested with 1,379 consumers (patients) drawn from an online consumer panel. The research suggests service quality in this context refers narrowly to the attributes of the core product (here, the physician‘s medical competence); interpersonal and organizational quality are associated with value, satisfaction and loyalty, rather than overall quality. Two paths to quality evaluations appear to exist. In the first, consumers integrate evidence of the physician‘s capabilities, practices, and prior outcomes to reach evaluations of technical quality. In the second path, consumers rely on a trust heuristic in which observed interpersonal and organizational quality signals are used to build trust in the physician; that trust, in turn, influences perceptions of technical quality. The trust heuristic appears to be used when the stakes are low and, counterintuitively, when the stakes are high, just when superior evaluations are most needed.

The purpose of the inverse-transport problems that we address is to reconstruct the material distribution inside an unknown object undergoing a nondestructive evaluation. We assume that the object is subjected to incident beams of photons or particles and that the exiting radiation is measured with detectors around the periphery of the object. In the present work we focus on problems in which radiation can undergo significant scattering within the optically thick object. We develop a set of reconstruction strategies to infer the material distribution inside such objects. When we apply these strategies to a set of neutron-tomography test problems we find that the results are substantially superior to those obtained by previous methods. We first demonstrate that traditional analytic methods such as filtered back projection (FBP) methods do not work for very thick, highly scattering problems. Then we explore deterministic optimization processes, using the nonlinear conjugate gradient iterative updating scheme to minimize an objective functional that characterizes the misfits between forward predicted measurements and actual detector readings. We find that while these methods provide more information than the analytic methods such as FBP, they do not provide sufficiently accurate solutions of problems in which the radiation undergoes significant scattering. We proceed to present some advances in inverse transport methods. Our strategies offer several advantages over previous reconstruction methods. First, our optimization procedure involves the systematic use of both deterministic and stochastic methods, using the strengths of each to mitigate the weaknesses of the other. Another key feature is that we treat the material (a discrete quantity) as the unknown, as opposed to individual cross sections (continuous variables). This changes the mathematical nature of the problem and greatly reduces the dimension of the search space. In our hierarchical approach we begin by learning some characteristics of the object from relatively inexpensive calculations, and then use knowledge from such calculations to guide more sophisticated calculations. A key feature of our strategy is dimension-reduction schemes that we have designed to take advantage of known and postulated constraints. We illustrate our approach using some neutron-tomography model problems that are several mean-free paths thick and contain highly scattering materials. In these problems we impose reasonable constraints, similar to those that in practice would come from prior information or engineering judgment. Our results, which identify exactly the correct materials and provide very accurate estimates of their locations and masses, are substantially better than those of deterministic minimization methods and dramatically more efficient than those of typical stochastic methods.

Software testing plays a necessary role in software development and maintenance. This activity is performed to support quality assurance. It is very common to design a number of testing suite to test their programs manually for most test engineers. To design test data manually is an expensive and labor-wasting process. Base on this reason, how to generate software test data automatically becomes a hot issue. Most researches usually use the meta-heuristic search methods like genetic algorithm or simulated annealing to gain the test data. In most circumstances, test engineers will generate the test suite first if they have a new program. When they debug or change some code to become a new one, they still design another new test suite to test it. Nearly no people will reserve the first test data and reuse it. In this research, we want to discuss whether it is useful to store the original test data.

In this paper, the heuristic rules are proposed to derive the rephasing strategy of laterals and distribution transformers to improve the three phase unbalance of distribution systems. The distribution feeder network has been obtained by retrieving the attribute data of distribution components from the database of outage management system (OMS) in Taipower. The topology process and node reduction have also been executed to identify the network configuration and to prepare the input data for load flow analysis. With the monthly energy consumption of customers served by each transformer, which has been retrieved from the Customer Information System (CIS), the hourly loading of each distribution transformer can be derived. By performing the three phase load flow analysis, the three phase currents and neutral current of each primary trunk line section and each lateral can be calculated. The heuristic rule is employed to determine the phase adjustment strategy laterals and distribution transformers for rephasing to achieve three phase balancing. In order to demonstrate the effectiveness of the proposed methodology for three phase balancing, two practical distribution feeders in Taipower Fengshan District are selected for simulation. After rephasing the distribution transformers and laterals proposed by this paper, the three phase currents and netural of the test feeders have been collected. By companing to the neutral current before rephasing, it is found that the neutral current of test feeders have been reduced significantly and there phase balancing has been obtained by executing the proposed strategy derived using the heuristic rule.

Unbalanced operation of distribution feeders not only affects equipment utilization, voltage level and system protection, but it also increases extra energy losses. This leads to a deterioration of service quality, reliability and operation efficiency of a distribution system. This dissertation analyzes the problems of unbalanced three-phase distribution feeders, and offers potential solutions. Due to the voluminous data involved in a distribution system, analyzing the system by retrieving system data from paper maps is tedious and difficult. Thus, this dissertation uses data from the already constructed Outage Management System (OMS) of Taiwan Power Company (Taipower) to support distribution feeder three-phase unbalance analysis. The distribution feeder network was obtained by retrieving the connectivity table and attribute data of distribution components from the database of OMS. The topology process and node reduction were executed to identify the network configuration and to prepare the input data for load flow analysis. The hourly loading of each distribution transformer was derived using data of monthly energy consumption of customers served by each transformer, as retrieved from the Customer Information System (CIS), and the typical daily load patterns of customer classes. By performing three-phase load flow analysis, phase currents and neutral current of each primary trunk line section and each lateral could be calculated. Finally, an expert system is proposed to establish the rephasing strategy of laterals and distribution transformers to improve the imbalance of the three phases of the unbalanced distribution feeders. The heuristic rules adopted by distribution engineers are incorporated in the knowledge base of the expert system in the problem-solving process. The neutral current reduction algorithm is developed to support the inference engine to derive the rephasing strategy to reduce the neutral current of distribution feeder. In doing so, customer service interruption due to unexpected tripping of low energy over current relay (LCO) can be prevented, and furthermore the customer service interruption costs and labor costs to implement the rephasing strategy can be justified by the reduced power loss. To demonstrate the effectiveness of this proposed methodology in improving the three-phase unbalance of the distribution feeders, two actual distribution feeders in the Taipower Fengshan District were selected for computer simulation. After Taipower engineers implement the proposed rephasing strategies, the data of phase currents and neutral current of test feeders were collected from the SCADA system of Distribution Dispatch Control Center (DDCC). By comparing to the neutral current of test feeders before rephasing, it is concluded that the proposed rephasing strategy is effective in achieving three-phase balance of the distribution feeders after executing the rephasing of laterals and distribution transformers.

Section Steel usually is a order-oriented production and not easy to resell. The material cost is large percentages of overall production cost. Hence, the key to boost efficient management is to increase the material output rate. In other words, we need to publish a efficient and reasonable cutting plan before production. And the cutting plan can cope with change to meet the market demand. The cutting plan designing is a one-dimensional cutting stock problem, and also is a typical bin packing problem. In this study we examine a combined heuristic approach for this problem. The proposed approach combines two themes of solving method¡Ga neighborhood search algorithm with threshold accepting techniques, and a Branch and Bound method. The performance of the combined heuristic approach was verified by running several benchmarking problems and the results were reported. Experimental results indicate that the proposed solving process can effectively search the feasible region and avoid being trapped in local optimal.