Norms and non-governmental advocacy on conventional arms control : dynamics and governance.
AuthorAnders, Nils H.
SupervisorGreene, Owen J.
Non-governmental advocacy actors
Multilateral arms control
Norm emergence and diffusion
Arms transfer controls
Arms brokering controls
Tracing of small arms
The University of Bradford theses are licenced under a Creative Commons Licence.
InstitutionUniversity of Bradford
DepartmentDepartment of Peace Studies
MetadataShow full item record
AbstractClear changes occurred in the field of conventional arms control in the last two decades. States adopted a multitude of norms on especially small arms control in various multilateral control instruments. In addition, non-governmental advocacy actors often established themselves as active participants in control debates with governments. The changes are surprising because they took place in the security sphere and therewith in an area traditionally understood to be the exclusive domain of governments. This research project investigates the significance of the changes for the traditional understanding of security governance. Specifically, it investigates the emergence of control norms and the role and policy impact of non-governmental actors in the promotion of the norms. It asks whether the normative changes and significance of nongovernmental actors therein challenge the understanding of security governance that underpins many established approaches to international relations theory.
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Optimal Multi-Drug Chemotherapy Control Scheme for Cancer Treatment. Design and development of a multi-drug feedback control scheme for optimal chemotherapy treatment for cancer. Evolutionary multi-objective optimisation algorithms were used to achieve the optimal parameters of the controller for effective treatment of cancer with minimum side effects.Hossain, M. Alamgir; Majumder, Md A.A.; Algoul, Saleh (University of BradfordSchool of Computing, Informatics and Media, 2013-01-23)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.
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