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The maintenance and supply system of the Atlantic Missile Range Communications Network (AMRCN) is modeled to determine the optimum level of men and parts which must be available at Cape Kennedy, the source of supply, in order to minimize the level of failed equipment at the four remote sites. The system is simulated utilizing a DYNAMO model. DYNAMO is a compiler for translating and running continuous models (models described by a set of differential equations). Flow diagrams of the breakdowns, men, and parts and the information flow between these segments of the model are presented. The results obtained are discussed and the optimal solution noted.

Increasingly, globalization and the adoption of a market economy have made innovation fundamental for the success of professional sport organizations. Yet oligarchical league structures, isomorphic and hyper-traditional cultures, and hierarchical organizational structures can enhance or hinder organizational creativity, the beginning stage of the innovation process. Therefore, the purpose of this research is to determine the antecedents of organizational creativity in professional sport organizations. Perception of organizational creativity is theorized to be influenced by employee creativity, work environment, and the social interactions of employees. The results, based on a survey of three professional sport organizations’ front offices, indicated perceptions of a work environment with a clear vision and better work processes were associated with greater perceptions of organizational creativity. The lack of relationships between many of the factors theorized to influence organizational creativity, such as an employee’s advice network, could indicate the sport industry is unique in creativity management. This study is the beginning in understanding the first step of innovation, and the processes that influence employees’ perceptions regarding the ways in which their work environment relate to organizational creativity.

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).

Performing social network analysis (SNA) requires a set of powerful techniques to analyze structural information contained in interactions between social entities. Many SNA technologies and methodologies have been developed and have successfully provided significant insights for small-scale interactions. However, these techniques are not suitable for analyzing large social networks, which are very popular and important in various fields and have special structural properties that cannot be obtained from small networks or their analyses. There are a number of issues that need to be further studied in the design of current SNA techniques. A number of key issues can be embodied in three fundamental and critical challenges: long processing time, large computational resource requirements, and network dynamism. In order to address these challenges, we discuss an anytime-anywhere methodology based on a parallel/distributed computational framework to effectively and efficiently analyze large and dynamic social networks. In our methodology, large social networks are decomposed into intra-related smaller parts. A coarse-level of network analysis is built based on comprehensively analyzing each part. The partial analysis results are incrementally refined over time. Also, during the analyses process, network dynamic changes are effectively and efficiently adapted based on the obtained results. In order to evaluate and validate our methodology, we implement our methodology for a set of SNA metrics which are significant for SNA applications and cover a wide range of difficulties. Through rigorous theoretical and experimental analyses, we demonstrate that our anytime-anywhere methodology is / Ph. D.

Given several related critical infrastructure (CI) networks, such as power grid, transportation, and water systems, one crucial question emerges: how to model the propagation of failed facilities and predict their spread over time to the whole system? Given digital surveillance data, can we predict the impact of Influenza-Like Illness (ILI), including the percentage of outpatient doctors visits, the season duration, and peak? These two questions are related to modeling and predicting the incidence of different types of contagions. In the case of CI, the contagions are the failures of facilities. In the case of flu spread, the contagions are the infective ILI. In this thesis, in the case of CI, we give a novel model of failure cascades and use it to identify key facilities in an optimization-based approach, called HotSpots. In the case of flu spread, we develop a deep neural network, EpiDeep, to predict multiple key epidemiology metrics. In both of these applications, we use the dynamics of propagation to develop better approaches. By collaborating with Oak Ridge National Laboratory (ORNL) and working on the real CI networks provided by them, we find that HotSpots helps solve what-if scenarios. By using the digital surveillance data reported by the Centers for Disease Control and Prevention (CDC), we carry on experiments and find that EpiDeep is better than non-trivial baselines and outperforms them by up to 40%. We believe the generality of our approaches, and it can be applied to other propagation-based scenarios in infrastructure and epidemiology. / M.S. / Critical Infrastructure Systems (CIS), including the power grid, transportation, and gas systems, are essential to national security, economy, and political stability. Moreover, they are interconnected and are vulnerable to potential failures. The previous event, like 2012 Hurricane Sandy, showed how these interdependencies can lead to catastrophic disasters among the whole systems. Therefore, one crucial question emerges: Given several related CIS networks: how to model the propagation of failed facilities and predict their spread over time to the whole system? Similarly, in the case of seasonal influenza, it always remains a significant health issue for many people in every country. The time-series of the weighted Influenza-like Illness (wILI) data are provided to researchers by the US Center for Disease Control and Prevention (CDC), and researchers use them to predict several key epidemiological metrics. The question, in this case, is: Given the wILI time-series, can we predict the impact of Influenza-Like Illness (ILI) accurately and efficiently? Both of these questions are related to modeling and predicting the incidence of different types of contagions. Contagions are any infective trend which can spread inside a network, including failures of facilities, illness of human, and popular news. In the case of CIS, the contagions are the failures of facilities. In the case of flu spread, the contagions are the infective ILI. In this thesis, in the case of CI, we present a novel model of failure cascades and use it to identify critical facilities in an optimization-based approach. In the case of flu spread, we develop a deep neural network to predict multiple key epidemiology metrics. In both of these applications, we use the dynamics of propagation to create better approaches. By collaborating with ORNL and working on the real CI networks provided by them, we find that F-CAS captures the dynamics of the interconnected CI networks. In the experiments using the wILI data from CDC, we find that EpiDeep is better than non-trivial baselines and outperforms them by up to 40%. We believe the generality of our approaches, and it can be applied to other propagation-based scenarios in infrastructure and epidemiology.

Recent advances in high-throughput technique enables the generation of a large amount of omic data such as genomics, transcriptomics, proteomics, metabolomics, glycomics etc. Typically, differential expression analysis (e.g., student's t-test, ANOVA) is performed to identify biomolecules (e.g., genes, proteins, metabolites, glycans) with significant changes on individual level between biologically disparate groups (disease cases vs. healthy controls) for cancer biomarker discovery. However, differential expression analysis on independent studies for the same clinical types of patients often led to different sets of significant biomolecules and had only few in common. This may be attributed to the fact that biomolecules are members of strongly intertwined biological pathways and highly interactive with each other. Without considering these interactions, differential expression analysis could lead to biased results. Network-based methods provide a natural framework to study the interactions between biomolecules. Commonly used data-driven network models include relevance network, Bayesian network and Gaussian graphical models. In addition to data-driven network models, there are many publicly available databases such as STRING, KEGG, Reactome, and ConsensusPathDB, where one can extract various types of interactions to build knowledge-driven networks. While both data- and knowledge-driven networks have their pros and cons, an appropriate approach to incorporate the prior biological knowledge from publicly available databases into data-driven network model is desirable for more robust and biologically relevant network reconstruction. Recently, there has been a growing interest in differential network analysis, where the connection in the network represents a statistically significant change in the pairwise interaction between two biomolecules in different groups. From the rewiring interactions shown in differential networks, biomolecules that have strongly altered connectivity between distinct biological groups can be identified. These biomolecules might play an important role in the disease under study. In fact, differential expression and differential network analyses investigate omic data from two complementary perspectives: the former focuses on the change in individual biomolecule level between different groups while the latter concentrates on the change in pairwise biomolecules level. Therefore, an approach that can integrate differential expression and differential network analyses is likely to discover more reliable and powerful biomarkers. To achieve these goals, we start by proposing a novel data-driven network model (i.e., LOPC) to reconstruct sparse biological networks. The sparse networks only contains direct interactions between biomolecules which can help researchers to focus on the more informative connections. Then we propose a novel method (i.e., dwgLASSO) to incorporate prior biological knowledge into data-driven network model to build biologically relevant networks. Differential network analysis is applied based on the networks constructed for biologically disparate groups to identify cancer biomarker candidates. Finally, we propose a novel network-based approach (i.e., INDEED) to integrate differential expression and differential network analyses to identify more reliable and powerful cancer biomarker candidates. INDEED is further expanded as INDEED-M to utilize omic data at different levels of human biological system (e.g., transcriptomics, proteomics, metabolomics), which we believe is promising to increase our understanding of cancer. Matlab and R packages for the proposed methods are developed and available at Github (https://github.com/Hurricaner1989) to share with the research community. / Ph. D. / High-throughput technique such as transcriptomics, proteomics and metabolomics is widely used to generate ‘big’ data for cancer biomarker discovery. Typically, differential expression analysis is performed to identify cancer biomarkers. However, discrepancies from independent studies for the same clinical types of samples using differential expression analysis are observed. This may be attributed to that biomolecules such as genes, proteins and metabolites are members of strongly intertwined biological pathways and highly interactive with each other. Without considering these interactions, differential expression analysis could lead to biased results. In this dissertation, we propose to identify cancer biomarker candidates using network-based approaches. We start by proposing a novel data-driven network model (i.e., LOPC) to reconstruct sparse biological networks. Then we propose a novel method (i.e., wgLASSO) to incorporate prior biological knowledge from public available databases into purely data-driven network model to build biologically relevant networks. In addition, a novel differential network analysis method (i.e., dwgLASSO) is proposed to identify cancer biomarkers. Finally, we propose a novel network-based approach (i.e., INDEED) to integrate differential expression and differential network analyses. INDEED is further expanded as INDEED-M to utilize omic data at different levels of human biological system (e.g., transcriptomics, proteomics, and metabolomics) to identify cancer biomarkers from a systems biology perspective. Matlab and R packages for the proposed methods are developed and shared with the research community.

Yes / Social media have become an important interaction channel between the government and citizens in the era of the digital community. The adoption of social media in local government services offers a new channel to encourage citizen engagement in the public policy decision-making process. Moreover, communication with citizens through social media exposes large opportunities for the local government to analyse and appreciate the relationships among social media participants in the digital community to enhance public services. The purpose of this study is to understand the local government’s social media network and identify the social role in the local government’s social media network structure. Thus, this study adopted the social network analysis (SNA) approach on the Twitter data of a local government’s official account in the UK as a case study. The study revealed that the internal local government stakeholders play an important social role in the local government’s social media network. The implication of the study was discussed.