http://hdl.handle.net/11343/159 2019-05-23T03:24:01Z http://hdl.handle.net/11343/224111 The career aspirations and development of government secondary school assistant principals in Victoria, Australia Thompson, Kenneth Henry In the context of international concerns about the possible lack of quality candidates for the top leadership positions in schools, this study focuses on those a heartbeat away from the top job, that is, assistant principals. It examines the career intentions and professional development of 48 assistant principals in government secondary schools in a defined geographical region in the state of Victoria, Australia. A mixed methods and cross sectional approach are taken to investigate the key research questions: What are the leadership aspirations and career intentions of Assistant principals in Government Secondary Schools? What development opportunities do they engage in? How effective are they and what impact do they have? What is their own job satisfaction and how do they rate the job satisfaction of principals in general? How do they rate their own preparedness for the principalship? How strategic is the school-based planning for leadership succession? What would need to change to increase their interest in the principalship? Consistent with similar studies, only a minority of participants intended to apply for principal positions and the interest could be conditional. Those who were interested in the role were attracted to the dynamic nature of the role, whereas those who did not intend to apply were mindful of matters such as the stress and time demands of the role. There was a willingness to lead challenging schools among those willing to lead. The research also found that relatively few participants were undertaking the professional development available through the region and state. However, there were more levels of participation in a broader range of programs offered by a range of organisations. The most effective forms of professional development were identified and discussed with in house and/ or planned activities being seen as more effective than unplanned and /or external programs being judged as less effective. While some schools were taking a relatively strategic approach to staff development, most had room to improve by better rewarding and recognising staff members and being able to replace sudden vacancies, as well as providing stretch assignments to develop particular employees, to name some examples. Assistant principals were found to be relatively satisfied with their jobs. The most common thing that needed to change to change the mind of those not seeking the principalship was for the department to be more supportive of principals. Unlike a previous study, no link was found between the perception assistant principals have of the job satisfaction of principals on the one hand and their career intentions. Participants provided a long list of opportunities that would help prepare them for principal leadership as well as hindrances. But a feeling of preparedness was quite a different thing to being willing. Less than half of the participants linked their career development needs to their professional development plans, whereas almost all linked their professional development plans to the school strategic plan. Recommendations for future research, including a re-testing of key assumptions that are currently made. Further research recommendations were also made in areas such as: the predictive reliability of career intentions, the impact of professional learning and any link between preparedness for the principalship and career intentions. Recommendations for future practice included working with researchers on the research priorities. © 2019 Dr. Kenneth Henry Thompson 2019-01-01T00:00:00Z http://hdl.handle.net/11343/224110 Genetic and early-life environmental influences on dental health in childhood: a study of twins Silva, Mihiri Through toothache, infection and tooth loss, dental diseases have a major impact on the health and well-being of children. Dental caries and hypomineralised second primary molars (HSPM) are two common diseases of childhood. Both are complex conditions, which are likely to be influenced by genetic and environmental factors. Although the cause of dental caries is generally well understood, persistently high rates in childhood suggests that more effective preventive strategies are needed. Targeting upstream factors, from early in life, before clinical signs occur and before caries risk is programmed may provide a novel target for caries prevention. In contrast, HSPM is a relatively poorly understood condition of unknown aetiology that is thought to be triggered during tooth formation, in the prenatal and early life period. Understanding prenatal and early life influences on dental health is hampered by a lack of prospective longitudinal studies. The aim of this project was to use a prospective longitudinal study of twin children to (1) investigate the contribution of genetics, shared and non-shared environment to the variation in risk of dental caries and HSPM; (2) to identify specific shared and non-shared factors from the pre- and perinatal period that are likely to be associated with disease; and (3) apply twin statistical methods to explore a potentially causal relationship between body mass index (BMI) and dental caries. Data collected from questionnaires, medical records, biosamples and dental examinations through the course of the study were used in this thesis. In order to evaluate the relative contribution of genetic and environmental factors to the variation in risk of the binary outcomes, dental caries and HSPM, case-wise concordances were derived and compared in monozygotic (MZ) and dizygotic (DZ) twins. Various general and twin specific regression based statistical methods (including within/between analyses) were applied to evaluate the association between the outcomes and a range of exposures. Finally, within/between pair analysis was applied to determine the strength and direction of a potential causal relationship between dental caries and BMI. As detailed in Chapter Four of this thesis, HSPM was found to be common, affecting 19.8% of the children in study, and the variation in risk was predominantly due to environmental factors. Dizygosity, infantile eczema, vitamin D levels at birth, in vitro fertilization, maternal smoking beyond the first trimester of pregnancy and higher socioeconomic status were risk factors for HSPM. As detailed in Chapter Five, dental caries affected 32.2% of children, with 24.1% of children showing signs of advanced dental caries. Environmental factors predominated over genetics in determining variation in dental caries risk. Water fluoridation, maternal obesity and HSPM were important risk factors for dental caries. Analysis of BMI and dental caries at six years of age revealed that although the relationship is heavily influenced by confounders, an inverse causal relationship is likely. However, due to the cross-sectional nature of the study, it was not possible to determine which is the determinant and which is the outcome. The findings of this thesis suggest that dental caries and HSPM risk are heavily influenced by environmental factors, some of which are modifiable and arise as early as the prenatal period. With replication in other longitudinal cohorts, these findings may provide novel targets for population-based preventive strategies to improve the oral health of children. © 2018 Dr. Mihiri Jacintha Silva 2018-01-01T00:00:00Z http://hdl.handle.net/11343/224109 Natural Regression of Frailty Among Community-Dwelling Older Adults: A Systematic Review and Meta-Analysis Ofori-Asenso, R; Lee Chin, K; Mazidi, M; Zomer, E; Ilomaki, J; Ademi, Z; Bell, JS; Liew, D http://hdl.handle.net/11343/224108 Privacy-preserving machine learning and data aggregation for Internet of Things Lyu, Lingjuan The proliferation of Internet of Things (IoT) devices has contributed to the emergence of participatory sensing (PS) and collaborative learning (CL), where multiple participants collect and report their data to a cloud service to analyse the union of the collected data in the server-based framework. While in the decentralized framework, multiple participants collaboratively train a more accurate global model or multiple local models. However, the possibility of the cloud service or any participant being semi-honest or malicious pose a serious challenge of preserving the participants' privacy. Privacy-preserving machine learning and data aggregation aim to discover or derive useful statistics without compromising privacy. This thesis systematically investigates state-of-the-art techniques for privacy-preserving machine learning and data aggregation in a range of IoT applications. Extensive theoretical and experimental results are provided to support the following primary contributions. First, we explore three privacy-preserving machine learning applications. Examples include collaborative anomaly detection, human activity recognition and decentralized collaboration in a biomedical domain. We tackle security challenges in collaborative anomaly detection with a two-stage scheme called RG+RT: in the first stage, participants individually perturb their data by passing through a nonlinear function called repeated Gompertz (RG); in the second stage, the perturbed data are projected to a lower dimension using a participant-specific uniform random transformation (RT) matrix. The nonlinear RG function is designed to mitigate maximum a posteriori (MAP) estimation attacks, while random transformation resists independent component analysis (ICA) attacks. For human activity recognition, a similar two-stage scheme called RG+RP is proposed, the difference lies in the second stage, where participants project their perturbed data to a lower dimension in an (almost) distance-preserving manner, using a random projection (RP) matrix. The random projection can both resist ICA attacks and maintain model accuracy. These proposed two-stage randomisation schemes are assessed in terms of their recovery resistance to MAP estimation attacks. Preliminary theoretical analysis as well as experimental results on synthetic and real-world datasets indicate that both RG+RT and RG+RP exhibit better recovery resistance to MAP estimation attacks than most state-of-the-art techniques, meanwhile high utility is guaranteed. To mitigate the inherent limitations in the centralized framework, and investigate the applicability of the decentralized framework, we study the decentralized collaboration in a biomedical domain. In particular, we develop an efficient Decentralized Privacy-Preserving Centroid Classifier (DPPCC) considering three practical scenarios, where distributed differential privacy (DDP) is combined with distributed exponential ElGamal cryptosystem to preserve privacy and maintain utility. We realize DDP using discrete Gaussian mechanism without any restriction on ε as in the traditional Gaussian mechanism, and only the encrypted noisy model parameters or test results are shared among all parties. It ensures each party learns nothing but the noisy sum of local statistics. Second, we examine privacy-preserving data aggregation in smart grid application. To this end, we propose a multi-level aggregation framework based on fog architecture, which © 2018 Dr. Lingjuan Lyu 2018-01-01T00:00:00Z