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Cancer is a generic term for a large group of diseases where cells of the body lose their normal mechanisms for growth so that they grow in an uncontrolled way. One of the most common treatments of cancer is chemotherapy that aims to kill abnormal proliferating cells; however normal cells and other organs of the patients are also adversely affected. In practice, it's often difficult to maintain optimum chemotherapy doses that can maximise the abnormal cell killing as well as reducing side effects. The most chemotherapy drugs used in cancer treatment are toxic agents and usually have narrow therapeutic indices, dose levels in which these drugs significantly kill the cancerous cells are close to the levels which sometime cause harmful toxic side effects. To make the chemotherapeutic treatment effective, optimum drug scheduling is required to balance between the beneficial and toxic side effects of the cancer drugs. Conventional clinical methods very often fail to find drug doses that balance between these two due to their inherent conflicting nature. In this investigation, mathematical models for cancer chemotherapy are used to predict the number of tumour cells and control the tumour growth during treatment. A feedback control method is used so as to maintain certain level of drug concentrations at the tumour sites. Multi-objective Genetic Algorithm (MOGA) is then employed to find suitable solutions where drug resistances and drug concentrations are incorporated with cancer cell killing and toxic effects as design objectives. Several constraints and specific goal values were set for different design objectives in the optimisation process and a wide range of acceptable solutions were obtained trading off among different conflicting objectives. Abstract v In order to develop a multi-objective optimal control model, this study used proportional, integral and derivative (PID) and I-PD (modified PID with Integrator used as series) controllers based on Martin's growth model for optimum drug concentration to treat cancer. To the best of our knowledge, this is the first PID/I-PD based optimal chemotherapy control model used to investigate the cancer treatment. It has been observed that some solutions can reduce the cancer cells up to nearly 100% with much lower side effects and drug resistance during the whole period of treatment. The proposed strategy has been extended for more drugs and more design constraints and objectives.

This dissertation introduces the model of opportunistic adaptation to explain new venture growth. In established firms processes of change and adaptation usually imply a transition from one steady-state strategy to another and a problem oriented perspective as firms change in response to potential threats to their current positions. However, in the context of new ventures, adaptation is less about moving from one existent strategy to another and more about the entrepreneur’s effort to reach a steady state for the first time by continuously experimenting and combining resources in creative and innovative ways. The model of opportunistic adaptation rests on three key assumptions: 1.) new venture growth results from actions grounded in an opportunistic (proactive) logic; 2.) entrepreneurial cognition is viewed as an antecedent to all organizational actions leading to growth; 3.) the relationship between entrepreneurial cognition and action is influenced by industry and firm level attributes. The model is tested using quantitative and qualitative data on new ventures founded between 1996 and 2006 in technology intensive industries. The results provide partial support for the notion of opportunistic adaptation as a process in which entrepreneurial cognition, firm and industry related factors are closely intertwined. The results of the dissertation suggest that some aspects of entrepreneurial cognition, such as entrepreneurial schema focus have a more direct effect on actions related to new venture growth than others whose effect is strongly moderated by contextual influences such as industry growth and social network heterogeneity. This dissertation also finds that not all types of organizational actions associated with an opportunity logic lead to new venture growth. Of the three action types included in the model (fast, diverse and frequent) only action diversity was found to have a positive impact on new venture growth. Theoretical implications of the study results for both the literature on new venture growth and the literature on organizational adaptation, as well as practical implications are discussed.

In endogenous growth models, in contrast to the neoclassical growth models, government expenditure and taxation have an effect on the long run growth rate. In this thesis I examine whether the empirical evidence support the predictions of endogenous growth models or the neoclassical growth models in relation to fiscal policy. For this purpose I use panel data for fifteen European Union (EU) member and thirty-three developing countries between the years 1970 and 1999. I specifically test the following two propositions. The first proposition states that distortionary taxation decreases growth while non-distortionary taxation does not. The second, states that productive government expenditure increases growth while non-productive expenditure does not. The empirical results are quite different between European Union countries and developing countries. The results do not support endogenous growth especially for developing countries.

Diss. (sammanfattning) Alnarp : Sveriges lantbruksuniv., 2006. / Härtill 5 uppsatser.