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<p>Meta-analysis is a method of synthesizing results of different studies conducted to answer a specific question. Meta-analysis applications have been published in a wide range of disciplines including medicine, education, psychology and many others. However, for many years, only pair-wise and direct comparisons have been made using standard meta-analysis methods. It is only recently that network meta-analysis emerged enabling the comparison of multiple treatments based on estimates from different studies. With network meta-analysis, the relative efficacy (or safety) of a particular intervention versus competing interventions can be obtained even in the absence of head-to-head evidence via a common comparator.</p> <p>An increasing number of methodologies related to network meta-analysis, assessments of underlying assumptions, and strategies for presentation of results have been proposed by several researchers. But only few simulation studies have been done to investigate different characteristics of this emerging statistical method. Hierarchical Bayesian meta-analysis model is commonly used in network meta-analysis to estimate effect of each intervention relative to every other. This model facilitates the calculation of the rank probabilities of a set of alternative treatments. However, various factors can determine the performance of the model which needs to be considered before using results for decision.</p> <p>This project aimed to investigate how the Bayesian hierarchical model estimates the rank probability of the best overall most effective treatment (i.e., the treatment ranked first) under different scenarios for modelling a binary outcome. Different network geometries, numbers of studies per comparison, sets of probabilities of success for treatments and sample sizes were investigated in our simulation study for binary outcome.</p> <p>Our simulation study showed that the estimates of treatments under consideration can be affected by network structures. Similar geometries affect the estimate in similar ways. Unbalanced number of studies per comparison influenced estimates of treatments in the geometries we considered. When a superior treatment is involved in the network, the hierarchical Bayesian mixed treatment model correctly identified it regardless of network patterns, number of studies and individual study sample size.</p> / Master of Science (MSc)

Highway bridges are among the most vulnerable and expensive components in transportation networks. In response, the Government of Ontario has allocated $26 billion in the next 10 years to address issues pertaining to aging bridge and deteriorating highway infrastructure in the province. Although several approaches have been developed to guide their rehabilitation, most bridge rehabilitation approaches are focused on the component level (individual bridge) in a relative isolation of other bridges in the network. The current study utilizes a complex network theoretic approach to quantify the topological characteristics of the Ontario Bridge Network (OBN) and subsequently evaluate the OBN robustness and vulnerability characteristics. These measures are then integrated in the development of a Multi Scale Bridge Classification (MSBC) approach—an innovative classification approach that links the OBN component level data (i.e., Bridge Condition Index and year of construction, etc.) to the corresponding dynamic network-level measures. The novel approach calls for a paradigm shift in the strategy governing classifying and prioritizing bridge rehabilitation projects based on bridge criticality within the entire network, rather than only the individual bridge’s structural conditions. The model was also used to identify the most critical bridges in the OBN under different disruptions to facilitate rapid implementation of the study results. / Thesis / Master of Applied Science (MASc)

Yes / As a global strategic reserve resource, rare earth has been widely used in important industries, such as military equipment and biomedicine. However, through existing analyses based on the total volume of rare earth trade, the competition and dependency behind the trade cannot be revealed. In this paper, based on the principle of trade preference and import similarity, we construct dependency and competition networks and use complex network analysis to study the evolution of the global rare earth trade network from 2002 to 2018. The main conclusions are as follows: the global rare earth trade follows the Pareto principle, and the trade network shows a scale-free distribution. China has become the largest country in both import and export of rare earth trade in the world since 2017. In the dependency network, China has become the most dependent country since 2006. The result of community division shows that China has separated from the American community and formed new communities with the Association of Southeast Asian Nations (ASEAN) countries. The United States of America has formed a super-strong community with European and Asian countries. In the competition network, the distribution of competition intensity follows a scale-free distribution. Most countries are faced with low-intensity competition, but competing countries are relatively numerous. The competition related to China has increased significantly. The competition source of the United States of America has shifted from Mexico to China. China, the USA, and Japan have been the cores of the competition network. / This work was supported by the Ministry of Education of the People’s Republic of China Humanities and Social Sciences Youth Foundation (Grant No. 22YJC910014), the Social Sciences Planning Youth Project of Anhui Province (Grant No. AHSKQ2022D138), and the Innovation Development Research Project of Anhui Province (Grant No. 2021CX053).

No / A new scheme is presented for partitioning a network having a specific number of nodes and degree of connectivity such that the number of operations required to find a constrained path between a source node and destination node, averaged over all source-destination pairs, is minimised. The scheme can speed up the routing function, possibly by orders of magnitude under favourable conditions, at the cost of a sub-optimal solution.

No / Along with the tremendous expansion of information technology and networking, the number of malicious attacks which cause disruption to business processes has concurrently increased. Despite such attacks, the aim for network administrators is to enable these systems to continue delivering the services they are intended for. Currently, many research efforts are directed towards securing network further whereas, little attention has been given to the quantification of network security which involves assessing the vulnerability of these systems to attacks. In this paper, a method is devised to quantify the security level of IT networks. This is achieved by electronically scanning the network using the vulnerability scanning tool (Nexpose) to identify the vulnerability level at each node classified according to the common vulnerability scoring system standards (critical, severe and moderate). Probabilistic approach is then applied to calculate an overall security risk level of sub networks and entire network. It is hoped that these metrics will be valuable for any network administrator to acquire an absolute risk assessment value of the network. The suggested methodology has been applied to a computer network of an existing UK organization with 16 nodes and a switch.

No / Traditional Chinese medicine (TCM) investigates the clinical diagnosis and treatment regularities in a typical schema of personalized medicine, which means that individualized patients with same diseases would obtain distinct diagnosis and optimal treatment from different TCM physicians. This principle has been recognized and adhered by TCM clinical practitioners for thousands of years. However, the underlying mechanisms of TCM personalized medicine are not fully investigated so far and remained unknown. This paper discusses framework of TCM personalized medicine in classic literatures and in real-world clinical settings, and investigates the underlying mechanisms of TCM personalized medicine from the perspectives of network medicine. Based on 246 well-designed outpatient records on insomnia, by evaluating the personal biases of manifestation observation and preferences of herb prescriptions, we noted significant similarities between each herb prescriptions and symptom similarities between each encounters. To investigate the underlying mechanisms of TCM personalized medicine, we constructed a clinical phenotype network (CPN), in which the clinical phenotype entities like symptoms and diagnoses are presented as nodes and the correlation between these entities as links. This CPN is used to investigate the promiscuous boundary of syndromes and the co-occurrence of symptoms. The small-world topological characteristics are noted in the CPN with high clustering structures, which provide insight on the rationality of TCM personalized diagnosis and treatment. The investigation on this network would help us to gain understanding on the underlying mechanism of TCM personalized medicine and would propose a new perspective for the refinement of the TCM individualized clinical skills.