The bioreactor landfill is an emerging concept for solid waste management that has gained significant attention in the last decade. This technology employs specific operational practices to enhance the microbial decomposition processes in landfills. However, the unsupervised management and lack of operational guidelines for the bioreactor landfill, specifically leachate manipulation and recirculation processes, usually results in less than optimal system performance. Therefore, these limitations have led to the development of SMART (Sensor-based Monitoring and Remote-control Technology), an expert control system that utilizes real-time monitoring of key system parameters in the management of bioreactor landfills. SMART replaces conventional open-loop control with a feedback control system that aids the human operator in making decisions and managing complex control issues. The target from this control system is to provide optimum conditions for the biodegradation of the refuse, and also, to enhance the performance of the bioreactor in terms of biogas generation. SMART includes multiple cascading logic controllers and mathematical calculations through which the quantity and quality of the recirculated solution are determined. The expert system computes the required quantities of leachate, buffer, supplemental water, and nutritional amendments in order to provide the bioreactor landfill microbial consortia with their optimum growth requirements. Soft computational methods, particularly fuzzy logic, were incorporated in the logic controllers of SMART so as to accommodate the uncertainty, complexity, and nonlinearity of the bioreactor landfill processes. Fuzzy logic was used to solve complex operational issues in the control program of SMART including: (1) identify the current operational phase of the bioreactor landfill based on quantifiable parameters of the leachate generated and biogas produced, (2) evaluate the toxicological status of the leachate based on certain parameters that directly contribute to or indirectly indicates bacterial inhibition, and (3) predict biogas generation rates based on the operational phase, leachate recirculation, and sludge addition. The later fuzzy logic model was upgraded to a hybrid model that employed the learning algorithm of artificial neural networks to optimize the model parameters. SMART was applied to a pilot-scale bioreactor landfill prototype that incorporated the hardware components (sensors, communication devices, and control elements) and the software components (user interface and control program) of the system. During a one-year monitoring period, the feasibility and effectiveness of the SMART system were evaluated in terms of multiple leachate, biogas, and waste parameters. In addition, leachate heating was evaluated as a potential temperature control tool in bioreactor landfills. The pilot-scale implementation of SMART demonstrated the applicability of the system. SMART led to a significant improvement in the overall performance of the BL in terms of methane production and leachate stabilization. Temperature control via recirculation of heated leachate achieved high degradation rates of organic matter and improved the methanogenic activity.

This thesis involves modeling, guidance, control, and flight simulations of a canard controlled guided missile. The autopilot is designed by a pole placement technique. Designed autopilot is used with the guidance systems considered in the thesis. Five different guidance methods are applied in the thesis, one of which is the famous proportional navigation guidance. The other four guidance methods are different fuzzy logic guidance systems designed considering different types of guidance inputs. Simulations are done against five different target types and the performances of the five guidance methods are compared and discussed.

Fishing is a significant source of food, and constitutes an important source of income in Turkey. Due to the large extent required to analyse the distribution of fish stocks, information derived from satellites play an important role in fisheries applications. Chlorophyll concentration and sea surface temperature (SST) are the most significant parameters which define the fish habitat. The accuracy of these parameters in the Black Sea taken from two different satellites, namely Sea-viewing Wide Field-of-views Sensor (SeaWIFS) and Moderate Resolution Imaging Spectroradiometer (MODIS) are evaluated. Results indicate that both satellites give good estimates of SST but the algorithms overestimate the chlorophyll concentration values. MODIS products are used in the subsequent analyses due to their high correlation with in-situ measurements relative to SeaWIFS products. The cause of the overestimation of chlorophyll concentration is further examined and a general description of environmental variability in Black Sea is done using MODIS products. Anchovy, the most important commercial fish in Turkey, has been selected as the target specie of the study. Level 3 weekly average MODIS chlorophyll and SST products are processed using remote sensing (RS) and geographic information systems (GIS) integration to estimate potential favorable zones for pelagic fish aggregations. Two different decision rules are employed to generate fish stock maps, simple additive weigthing (SAW) and fuzzy additive weigthing (FSAW). The resultant maps are used to visualize the general distribution of Anchovy in Turkish Seas from May 2000 to May 2001. The resultant thematic fish stock maps generated by FSAW analysis represents the uncertainity in the environment better than the ones generated by SAW analysis.

The eulachon (Thaleichthys pacificus), a small anadromous smelt (Family Osmeridae) found only along the Northwest Pacific Coast, is poorly understood. Many spawning populations have suffered declines but as their historic status is relatively unknown and the fisheries poorly documented, it is difficult to study the contributing factors. This thesis provides a survey of eulachon fisheries throughout its geographical range and three analyses aimed at improving our understanding of past and present fisheries, coast-wide abundance status, and the factors which may be impacting these populations. An in-depth view of the Nuxalk Nation eulachon fishery on the Bella Coola River, Central Coast, BC, is provided. The majority of catches were used for making eulachon grease, a food item produced by First Nations by fermenting, then cooking the fish to release the grease. Catch statistics were kept yearly from 1945-1989 but have since, rarely been recorded. Using traditional and local ecological knowledge, catches were reconstructed based on estimated annual grease production. Run size trends were also created using local Fisheries Officers and Nuxalk interview comments. A fuzzy logic expert system was designed to estimate the relative abundance of fifteen eulachon systems. The expert system uses catch data to determine the exploitation status of a fishery and combines it with other data sources (e.g., CPUE) to estimate an abundance status index. The number of sources depended on the existing data and varied from one to eight. Using designed heuristic rules and by adjusting weighting parameters a final index was produced. Results suggest that there have been recent and extended declines in several eulachon rivers particularly the Klamath, California; Bella Coola, BC; Wannock, BC; and Kitimat, BC. Seven of the fifteen abundance time-series were used to evaluate the potential relationships between the declines and some of the factors that impact eulachon. Results suggest increases in shrimp and hake catches, seal and sea lion abundance, and sea surface temperatures were weakly associated with the declines. But contrary to expectations, adult hake biomass showed a positive association with four eulachon relative abundance time-series, suggesting that common environmental factors influenced both species.

No description available.

No description available.

The demand for imported fruit in China has increased dramatically since it first appeared in Chinese markets around 1993. Although imported fruit is much more expensive than domestically produced fruit and people’s income is much lower in China than in developed countries, imported fruit still attracts many willing buyers. Conventional concepts such as meeting basic needs or increasing consumer awareness of the importance of fresh fruit to a healthy lifestyle cannot adequately explain this phenomenon, as there is an abundance of fresh, cheap, local produce available in almost every Chinese market. There must be other factors influencing buying behaviour. To explore these factors and to examine the dynamics of the market for imported fruit, this research adopted a mixed qualitative-quantitative methodology guided by the paradigm of phenomenology. The research examined the characteristics of imported fruit itself, criteria imposed by Chinese buyers on these characteristics, the intended uses of imported fruit and their associated consumption values. To shed light on the possible influence of socio-economic factors, the research also compared buyers from two Chinese cities, Guangzhou and Urumqi. The former is one of the most developed cities in China and the latter is regarded as among the more backward and conservative cities in China. The research identified ten attributes that appeal to Chinese buyers. Six relate to the fruit’s physical attributes: that it has better appearance and packaging, lower chemical residues, better or different taste, and freshness. The remaining four relate to symbolic attributes associated with the fruit: that it represents achievement, wealth, personality and social status. Five intended uses of imported fruit were identified: for gifts, self-consumption, children, aged parents and patients. Four consumption values underlying these intentions were also identified: symbolism, concern for health, meeting basic needs and hedonism. However, the research revealed that no single combination of intended use and consumption value drives the demand for imported fruit in the Chinese market. Most frequently, it is a mix of hedonic and symbolic values behind a range of different intended uses that stimulates demand. Pursuing hedonic and symbolic values also leads to the visual quality of imported fruit generally being the most appealing attribute to Chinese buyers, a pattern common to both Guangzhou and Urumqi. These findings make a significant contribution to empirical knowledge about Chinese consumer behaviour. Results provide valuable insights into the interrelationships among product attributes, intended uses, consumption values and cultural values, and would give essential guidance to the development of strategies to market imported fruit in China. The research also examined limitations of current analytical approaches to the study of consumer behaviour. It demonstrated that approaches based on neural networks and fuzzy logic could be used independently or combined with conventional statistical methods to improve the explanation of consumer behaviour in this case. A comparison was carried out between the most popular form of neural networks (feedforward networks) and multivariate statistical methods in terms of their ability to predict behavioural intention through consumers’ attitudes towards products. Results demonstrated that neural networks were capable of capturing nonlinear aspects of complex relationships and producing better predictions than conventional statistical models. To explore consumer cognitive patterns, the research also compared K-means clustering with a Self-organizing Map (SOM) neural network in terms of the ability to cluster consumers on the basis of perceptions towards imported fruit attributes. Results indicate a superior outcome when K-means is used in conjunction with SOM in clustering analysis: using SOM to determine the ‘natural’ numbers of clusters and using K-means to do clustering. Finally, to quantitatively evaluate the impact of consumption values, this research develops a new approach that combines Means-end Chain theory with fuzzy logic theory. Given the global importance of the Chinese market, the successful application of neural networks and fuzzy logic in this study of the behaviour of Chinese consumers purchasing imported fruit could have wider ramifications. If the approach were proven in other applications, it could significantly improve the ability to understand the demand for consumer goods in China.

No description available.

The demand for imported fruit in China has increased dramatically since it first appeared in Chinese markets around 1993. Although imported fruit is much more expensive than domestically produced fruit and people’s income is much lower in China than in developed countries, imported fruit still attracts many willing buyers. Conventional concepts such as meeting basic needs or increasing consumer awareness of the importance of fresh fruit to a healthy lifestyle cannot adequately explain this phenomenon, as there is an abundance of fresh, cheap, local produce available in almost every Chinese market. There must be other factors influencing buying behaviour. To explore these factors and to examine the dynamics of the market for imported fruit, this research adopted a mixed qualitative-quantitative methodology guided by the paradigm of phenomenology. The research examined the characteristics of imported fruit itself, criteria imposed by Chinese buyers on these characteristics, the intended uses of imported fruit and their associated consumption values. To shed light on the possible influence of socio-economic factors, the research also compared buyers from two Chinese cities, Guangzhou and Urumqi. The former is one of the most developed cities in China and the latter is regarded as among the more backward and conservative cities in China. The research identified ten attributes that appeal to Chinese buyers. Six relate to the fruit’s physical attributes: that it has better appearance and packaging, lower chemical residues, better or different taste, and freshness. The remaining four relate to symbolic attributes associated with the fruit: that it represents achievement, wealth, personality and social status. Five intended uses of imported fruit were identified: for gifts, self-consumption, children, aged parents and patients. Four consumption values underlying these intentions were also identified: symbolism, concern for health, meeting basic needs and hedonism. However, the research revealed that no single combination of intended use and consumption value drives the demand for imported fruit in the Chinese market. Most frequently, it is a mix of hedonic and symbolic values behind a range of different intended uses that stimulates demand. Pursuing hedonic and symbolic values also leads to the visual quality of imported fruit generally being the most appealing attribute to Chinese buyers, a pattern common to both Guangzhou and Urumqi. These findings make a significant contribution to empirical knowledge about Chinese consumer behaviour. Results provide valuable insights into the interrelationships among product attributes, intended uses, consumption values and cultural values, and would give essential guidance to the development of strategies to market imported fruit in China. The research also examined limitations of current analytical approaches to the study of consumer behaviour. It demonstrated that approaches based on neural networks and fuzzy logic could be used independently or combined with conventional statistical methods to improve the explanation of consumer behaviour in this case. A comparison was carried out between the most popular form of neural networks (feedforward networks) and multivariate statistical methods in terms of their ability to predict behavioural intention through consumers’ attitudes towards products. Results demonstrated that neural networks were capable of capturing nonlinear aspects of complex relationships and producing better predictions than conventional statistical models. To explore consumer cognitive patterns, the research also compared K-means clustering with a Self-organizing Map (SOM) neural network in terms of the ability to cluster consumers on the basis of perceptions towards imported fruit attributes. Results indicate a superior outcome when K-means is used in conjunction with SOM in clustering analysis: using SOM to determine the ‘natural’ numbers of clusters and using K-means to do clustering. Finally, to quantitatively evaluate the impact of consumption values, this research develops a new approach that combines Means-end Chain theory with fuzzy logic theory. Given the global importance of the Chinese market, the successful application of neural networks and fuzzy logic in this study of the behaviour of Chinese consumers purchasing imported fruit could have wider ramifications. If the approach were proven in other applications, it could significantly improve the ability to understand the demand for consumer goods in China.

Thesis (M.S.)--University of Texas at El Paso, 2007. / Title from title screen. Vita. CD-ROM. Includes bibliographical references. Also available online.