Thesis (M.Soc.Sc.)--University of Hong Kong, 1989. / Also available in print.

京都大学 / 0048 / 新制・課程博士 / 博士(情報学) / 甲第19340号 / 情博第592号 / 新制||情||103(附属図書館) / 32342 / 京都大学大学院情報学研究科社会情報学専攻 / (主査)教授 多々納 裕一, 教授 矢守 克也, 教授 守屋 和幸 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DGAM

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The progress of a unimolecular chemical degradation reaction is used in representing a component failure mechanism. The component is said to fail when the concentration of reaction product accumulates beyond an acceptable level. The process of accumulating reaction product is modelled as a Markov pure birth process which, in turn, is used in developing the failure time distribution. The model is analyzed under the assumption that the reaction rate is constant. Also, the initial state and the final state of the degradation process are assumed to be Poisson variables. Based on numerical examples, it is found that the failure model can be described as a three-parameter or two-parameter Weibull distribution. / Master of Science

This study can be classified under the subject of equipment item replacement analysis. Simultaneous failure, SF, of the components of an equipment item, EI, is the topic of this thesis. Examination of the possibility of designing components of an EI for SF is one objective of the study. The motive for this objective is the belief that SF designs of EI minimize the total cost of acquiring and operating the EI. Examination of the strength of materials reveals that design life of components is not sufficiently flexible to realize SF, and design requirements can predetermine design life. This is true for mechanical components such as links, gears, and bearings. Hence it was concluded that SF of the mechanical components of an EI, cannot be easily achieved. The second objective of the study was to formulate a model for optimizing the design of the components of an EI with life considerations. A mechanical reliability model was first modified by the inclusion of fatigue stress-strength relationships, and the theory of Cumulative Damage. By mathematical manipulation to suit the principles of Lagrange's Method of Undetermined Multipliers, an optimization model has been developed. This model enables system and component design constraints and requirements to be included in the optimization process. / Master of Science

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ABSTRACT Assessing the impact of herbivory on plant growth and reproduction is important to predict the success of biocontrol of invasive plants. Leaf area production is most important, as photosynthesis provides the foundation for all plant growth and fitness. High levels of defoliation generally reduce the productivity of plants. However, leaf area production fluctuates during the season and compensational growth may occur, which both complicate accurate estimations of defoliation impacts. Under field conditions the interaction with neighbouring species and the availability of soil nutrients need to be assessed in order to gauge long term effects of weed invasions on natural environments. In this thesis I have investigated seasonal leaf area dynamics in Buddleia davidii following repeated artificial defoliation, to quantify compensational leaf production and to understand the regulatory mechanisms involved. The impact of defoliation on photosynthesis, seed production, germination and nitrogen translocation patterns were analysed. Finally, possible facilitation between B. davidii and a native nitrogen fixer, Coriaria arborea, and the impact of B. davidii on soil nutrient availability were investigated. In defoliated B. davidii, increased node production (34%), leaf size (35%) and leaf longevity (12%) resulted in 52% greater total emergent leaf area in the short term. However, with time and diminishing tissue resources the compensation declined. No upregulation of photosynthesis was observed in pre-existing leaves. Compensational leaf area production occurred at the expense of reproduction but the germination capacity of individual seeds was unaffected. In B. davidii, nitrogen reserves are stored in old leaves. Thus, the defoliation-induced decline in tissue reserves led to changes in the remobilisation pattern and increased the importance of soil uptake but biomass production especially that of roots had declined significantly (39%). Slight facilitation effects from the neighbouring nitrogen fixer and VA-mycorrhizae were observed on B. davidii in the field, while its impact on soil chemistry during spring was negligible. Defoliation of B. davidii resulted in priority allocation of resources to compensational leaf growth and a concomitant reduction in flower and seed production. The compensational leaf production greatly increased the demand for nitrogen, while continued leaf removal decreased the pool of stored nitrogen. This led to changes in nitrogen remobilisation and an increased importance of root uptake. However, the significant decline in root growth will likely impair adequate nutrient uptake from the soil, which is especially important where B. davidii invades nutrient poor habitats and will increase the success of biocontrol of the species. While mycorrhizae increase nutrient accessibility for B. davidii, it is likely that the additional stress of defoliation will negate the small facilitative effects from nitrogen-fixing species like C. arborea. This research provides new insights into the mechanisms regulating leaf area dynamics at the shoot level and systemic physiological responses to defoliation in plants, such as nitrogen translocation. The compensation in leaf area production was considerable but only transitory and thus, the opportunity to alleviate effects of leaf loss though adjustment of light capture limited. However, to ascertain that photosynthesis at whole plant level does not increase after defoliation, more detailed measurements especially on new grown leaves are necessary. In general, defoliation had greatly reduced plant growth and performance so that an optimistic outlook for controlling this species can be given. Conclusions about the wider impacts of B. davidii on soil chemistry and community function will require further research.

Joint modeling of longitudinal and survival data has received much attention and is becoming increasingly useful. In clinical studies, longitudinal biomarkers are used to monitor disease progression and to predict survival. These longitudinal measures are often missing at failure times and may be prone to measurement errors. In previous studies these two types of data are frequently analyzed separately where a mixed effects model is used for longitudinal data and a survival model is applied to event outcomes. The argument in favor of a joint model has been the efficient use of the data as the survival information goes into modeling the longitudinal process and vice versa. In this thesis, we present joint maximum likelihood methods, a two stage approach and time dependent covariate methods that link longitudinal data to survival data. First, we use simulation studies to explore and assess the performance of these methods with bias, accuracy and coverage probabilities. Then, we focus on four time dependent methods considering models that are unadjusted and adjusted for time. Finally, we consider mediation analysis for longitudinal and survival data. Mediation analysis is introduced and applied in a research framework based on genetic variants, longitudinal measures and disease risk. We implement accelerated failure time regression using the joint maximum likelihood approach (AFT-joint) and an accelerated failure time regression model using the observed longitudinal measures as time dependent covariates (AFT-observed) to assess the mediated effect. We found that the two stage approach (TSA) performed best at estimating the link parameter. The joint maximum likelihood methods that used the predicted values of the longitudinal measures, similar to the TSA, provided larger estimates. The time dependent covariate methods that used the observed longitudinal measures in the survival analysis underestimated the true estimates. The mediation results showed that the AFT-joint and the AFT-observed underestimated the mediated effect. Comparison of the methods in Framingham Heart Study data revealed similar patterns. We recommend adjusting for time when estimating the association parameter in time dependent Cox and logistic models. Additional work is needed for estimating the mediated effect with longitudinal and survival data.

Medical cost has received increasing interest recently in Biostatistics and public health. Statistical analysis and inference of life time medical cost have been challenging by the fact that the survival times are censored on some study subjects and their subsequent cost are unknown. Huang (2002) proposed the calibration regression model which is a semiparametric regression tool to study the medical cost associated with covariates. In this thesis, an inference procedure is investigated using empirical likelihood ratio method. The unadjusted and adjusted empirical likelihood confidence regions are constructed for the regression parameters. We compare the proposed empirical likelihood methods with normal approximation based method. Simulation results show that the proposed empirical likelihood ratio method outperforms the normal approximation based method in terms of coverage probability. In particular, the adjusted empirical likelihood is the best one which overcomes the under coverage problem.