The purpose of this paper is to study nth order integral equations. The integrals studied in this paper are of the Riemann type.

This study is to discuss the correlation between artificial buying desire and some of its influential variables, different price promotion on nth good and different buying motivation. For the purpose of study, 360 students of college degree and master degree in Taiwan are categories into five groups based on their buying motivation. They are (1) pursuit convenience group ;(2) pursuit fashion and convenience group; (3) pursuit fashion group; (4) pursuit relationship group and (5) unknowing motivation group. The conclusions of this study are presented as follows: 1. Consumers who face different price promotion on nth good in the convenience store have significant difference. 2. Consumers who have different buying motivation in the convenience store probably have significant difference. 3. Consumers of different buying motivation who have same promotion situation in the convenience store probably have significant difference. The conclusions above are significant to the management in the following aspects. 1. For students of college degree and master degree in Taiwan, nearly 20% still can be developed. 2. Convenience store which uses different price promotion on nth good should seriously consider its effect. 3. Convenience store should design different promotion activities for different consumer of different buying motivation, and try to separate them.

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<p> The very sophisticated control systems of today are built around computers. It is felt that an improved form of cost function in vector or matrix form is needed to fully and most easily utilize the computer's advantages. After defining a vector cost function G , the problem of adapting and learning simplifies to the solution of a partial difference equation. Total system properties are easily defined as matrix arrays which are "learned" in an adapting and "learning" control loop.</p> <p> The relative merits of open and closed loop adaptive systems were investigated. The Nth order adaptive control system was finally chosen to be closed loop after developing two criterion equations in two unknowns which, if satisfied guaranteed improved system sensitivity with the closed loop configuration.</p> <p> Finally, several simple examples are given in experiment form to demonstrate the applicability of the proposed control system techniques.</p> / Thesis / Master of Engineering (MEngr)

Time series data mining is one branch of data mining. Time series analysis and prediction have always played an important role in human activities and natural sciences. A Pseudo-Periodical time series has a complex structure, with fluctuations and frequencies of the times series changing over time. Currently, Pseudo-Periodicity of time series brings new properties and challenges to time series analysis and prediction. This thesis proposes two original computational approaches for time series analysis and prediction: Moving Average of nth-order Difference (MANoD) and Series Features Extraction (SFE). Based on data-driven methods, the two original approaches open new insights in time series analysis and prediction contributing with new feature detection techniques. The proposed algorithms can reveal hidden patterns based on the characteristics of time series, and they can be applied for predicting forthcoming events. This thesis also presents the evaluation results of proposed algorithms on various pseudo-periodical time series, and compares the predicting results with classical time series prediction methods. The results of the original approaches applied to real world and synthetic time series are very good and show that the contributions open promising research directions.

The bison industry has limited resources for increasing market share. Exploring how consumers react to information about bison and discovering what people know about bison is important to determine the most efficient way to increase market share and ensure the sustainability of the bison industry. <p> This thesis examines the impact of three different information treatments on willingness-to-pay for bison. The three treatments are a nutritional comparison chart of negatively-perceived nutrients, a bison taste testimonial from a chef and a statement concerning the absence of growth hormones and antibiotics in the processed bison product used in the research. The hypothesis tested is that nutritional information about bison would elicit the greatest increase in willingness-to-pay for the processed bison product. <p> A random nth-price auction was conducted in December 2002 in Guelph, Ontario with 57 participants to elicit willingness-to-pay values for the processed bison product. Participants initial bids for the processed bison product were elicited without being given any information and a second round of bidding was conducted once participants had reviewed an information treatment. The mean difference in the bids between round two and round one are $0.221 for the nutritional comparison treatment, $0.210 for the taste testimonial treatment and $0.185 for the natural aspects treatment. ANOVA results indicate no statistically significant difference between the mean difference in bids between the three treatments. Further analysis with a regression model using the difference in bids as the dependent variable, dummy variables representing treatment types and survey data for the other relevant independent variables, shows that the coefficient for the nutritional comparison treatment is not significantly different from zero. Therefore, the hypothesis that nutritional information about bison would elicit the greatest increase in willingness-to-pay for the processed bison product has been rejected. The other independent variables examined in the regression are not significant. <p> This thesis does not clearly indicate which information treatment would be the most effective for the bison industry to utilize in a bison information campaign. However, each information treatment did increase the group mean willingness-to-pay so any information relevant to consumers about bison may be beneficial in increasing market share for bison products. Industry participants may need to work together to simultaneously increase awareness, distribution and consumption of bison products to ensure the sustainability of the bison industry.

The bison industry has limited resources for increasing market share. Exploring how consumers react to information about bison and discovering what people know about bison is important to determine the most efficient way to increase market share and ensure the sustainability of the bison industry. <p> This thesis examines the impact of three different information treatments on willingness-to-pay for bison. The three treatments are a nutritional comparison chart of negatively-perceived nutrients, a bison taste testimonial from a chef and a statement concerning the absence of growth hormones and antibiotics in the processed bison product used in the research. The hypothesis tested is that nutritional information about bison would elicit the greatest increase in willingness-to-pay for the processed bison product. <p> A random nth-price auction was conducted in December 2002 in Guelph, Ontario with 57 participants to elicit willingness-to-pay values for the processed bison product. Participants initial bids for the processed bison product were elicited without being given any information and a second round of bidding was conducted once participants had reviewed an information treatment. The mean difference in the bids between round two and round one are $0.221 for the nutritional comparison treatment, $0.210 for the taste testimonial treatment and $0.185 for the natural aspects treatment. ANOVA results indicate no statistically significant difference between the mean difference in bids between the three treatments. Further analysis with a regression model using the difference in bids as the dependent variable, dummy variables representing treatment types and survey data for the other relevant independent variables, shows that the coefficient for the nutritional comparison treatment is not significantly different from zero. Therefore, the hypothesis that nutritional information about bison would elicit the greatest increase in willingness-to-pay for the processed bison product has been rejected. The other independent variables examined in the regression are not significant. <p> This thesis does not clearly indicate which information treatment would be the most effective for the bison industry to utilize in a bison information campaign. However, each information treatment did increase the group mean willingness-to-pay so any information relevant to consumers about bison may be beneficial in increasing market share for bison products. Industry participants may need to work together to simultaneously increase awareness, distribution and consumption of bison products to ensure the sustainability of the bison industry.

Time series data mining is one branch of data mining. Time series analysis and prediction have always played an important role in human activities and natural sciences. A Pseudo-Periodical time series has a complex structure, with fluctuations and frequencies of the times series changing over time. Currently, Pseudo-Periodicity of time series brings new properties and challenges to time series analysis and prediction. This thesis proposes two original computational approaches for time series analysis and prediction: Moving Average of nth-order Difference (MANoD) and Series Features Extraction (SFE). Based on data-driven methods, the two original approaches open new insights in time series analysis and prediction contributing with new feature detection techniques. The proposed algorithms can reveal hidden patterns based on the characteristics of time series, and they can be applied for predicting forthcoming events. This thesis also presents the evaluation results of proposed algorithms on various pseudo-periodical time series, and compares the predicting results with classical time series prediction methods. The results of the original approaches applied to real world and synthetic time series are very good and show that the contributions open promising research directions.

Neutron-induced fission has been studied at different excitation energies of the compound nucleus by measurements on the two fissioning systems, 252Cf* and 239U*. For the first time, the light fission fragment yields from the reaction 251Cf(nth, f) have been measured with high resolution. This experiment was performed with the recoil mass spectrometer LOHENGRIN at ILL in Grenoble, France. When the results from this work, where the compound nucleus is at thermal excitation, are compared to the spontaneous fission of 252Cf, enhanced emission yields as well as an increased mean kinetic energy is observed around A = 115. This suggests the existence of an additional super-deformed fission mode in 252Cf. The reaction 238U(n, f) was studied using the 2E-technique with a double Frisch grid ionization chamber. Fission fragment mass, energy and angular distributions were determined for incident neutron energies between 0.9 and 2.0 MeV. The experiments were performed at the Van de Graaff accelerator of IRMM in Geel, Belgium. This is the first measurement of the mass distribution for incident neutron energies around 0.9 MeV. The motivation for studying 238U(n, f) was to verify theoretical predictions of the mass distribution at the vibrational resonance in the fission cross section at 0.9 MeV. However, the predicted changes in fission fragment distributions could not be confirmed. A precise modelling of the fission process for the minor actinides becomes very important for future generation IV and accelerator driven nuclear reactors. Since fission fragment distributions depend on the excitation of the fissioning system, so does the number of delayed neutrons, which are one of the safety parameters in a reactor.