Ranking Influential Nodes of Fake News Spreading on Mobile Social Networks

Online fake news can generate a negative impact on both users and society. Due to the concerns with spread of fake news and misinformation, assessing the network influence of online users has become an important issue. This study quantifies the influence of nodes by proposing an algorithm based on information entropy theory. Dynamic process of influence of nodes is characterized on mobile social networks (MSNs). Weibo (i.e., the Chinese version of microblogging) users are chosen to build the real network and quantified influence of them is analyzed according to the model proposed in this paper. MATLAB is employed to simulate and validate the model. Results show the comprehensive influence of nodes increases with the rise of two factors: the number of nodes connected to them and the frequency of their interaction. Indirect influence of nodes becomes stronger than direct influence when the network scope rises. This study can help relevant organizations effectively oversee the spread of online fake news on MSNs

Experiences of Public-Private Partnership Concession Uncertainties in the Federal Capital Territory Administration of Nigeria

Structuring successful public-private partnership (P3) concession contracts is a problem for both the Federal Capital Territory Administration (FCTA) of Nigeria and private concessionaires that partner with the government to finance the delivery of public goods in the territory. Researchers have estimated the percentage of P3 concession failures in Nigeria to be between 50% and 60%. The purpose of this study is to explore concessionaires’ lived experiences involving P3 concession uncertainties, opportunities, and barriers while they partner with the FCTA of Nigeria to deliver public goods. The key research question examined the meaning of P3 concession uncertainty to concessionaires with the FCTA of Nigeria, and to what extent these choices, options, and experiences affected project values. This phenomenological study was conducted through online interviews with 18 senior management staff of companies that have or have had P3 concessions with the FCTA since 2011. Interview data were transcribed and coded manually for efficiency before importing codes into NVivo for analysis. All concessionaires recounted experiencing both barriers and opportunities during their concessions with the FCTA. Each participant reported being unable to freely negotiate concession contract terms and structures. Useful recommendations and study results may inform and improve P3 collaborations for more efficient and effective infrastructural development. Recommendations will aid policymakers in crafting better P3 contracts and public policies that might escalate successful projects, which may boost development from Public Infrastructure delivery and improve the living standards of the society leading to positive social change

Modelling and design of the eco-system of causality for real-time systems

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University London.The purpose of this research work is to propose an improved method for real-time sensitivity analysis (SA) applicable to large-scale complex systems. Borrowed from the EventTracker principle of the interrelation of causal events, it deploys the Rank Order Clustering (ROC) method to automatically group every relevant system input to parameters that represent the system state (i.e. output). The fundamental principle of event modelling is that the state of a given system is a function of every acquirable piece of knowledge or data (input) of events that occur within the system and its wider operational environment unless proven otherwise. It therefore strives to build the theoretical and practical foundation for the engineering of input data. The event modelling platform proposed attempts to filter unwanted data, and more importantly, include information that was thought to be irrelevant at the outset of the design process. The underpinning logic of the proposed Event Clustering technique (EventiC) is to build causal relationship between the events that trigger the inputs and outputs of the system. EventiC groups inputs with relevant corresponding outputs and measures the impact of each input variable on the output variables in short spans of time (relative real-time). It is believed that this grouping of relevant input-output event data by order of its importance in real-time is the key contribution to knowledge in this subject area. Our motivation is that components of current complex and organised systems are capable of generating and sharing information within their network of interrelated devices and systems. In addition to being an intelligent recorder of events, EventiC could also be a platform for preliminary data and knowledge construction. This improvement in the quality, and at times the quantity of input data, may lead to improved higher level mathematical formalism. It is hoped that better models will translate into superior controls and decision making. It is therefore believed that the projected outcome of this research work can be used to predict, stabilize (control), and optimize (operational research) the work of complex systems in the shortest possible time. For proof of concept, EventiC was designed using the MATLAB package and implemented using real-time data from the monitoring and control system of a typical cement manufacturing plant. The purpose for this deployment was to test and validate the concept, and to demonstrate whether the clusters of input data and their levels of importance against system performance indicators could be approved by industry experts. EventiC was used as an input variable selection tool for improving the existing fuzzy controller of the plant. Finally, EventiC was compared with its predecessor EventTracker using the same case study. The results revealed improvements in both computational efficiency and the quality of input variable selection