Measuring Influence and Topic Dependent Interactions in Social Media Networks Based on a Counting Process Modeling Framework.

Data extracted from social media platforms, such as Twitter, are both large in scale and complex in nature, since they contain both unstructured text, as well as structured data, such as time stamps and interactions between users. Some key questions for such platforms are (i) to determine influential users, in the sense that they generate interactions between members of the platform and (ii) identifying important interactions between nodes in the corresponding user network. Regarding the first question, common measures used both in the academic literature and by companies that provide analytics services are primarily variants of the popular web-search PageRank algorithm applied to networks that capture connections between users. In this work, we develop a modeling framework using multivariate interacting counting processes to capture the detailed actions that users undertake on such platforms, namely posting original content, reposting and/or mentioning other users’ postings. Based on the proposed model, we also derive a novel influence mea- sure. We discuss estimation of the model parameters through maximum likelihood and establish their asymptotic properties. The proposed model and the accompanying influence measure are illustrated on a data set covering a five year period of the Twitter actions of the members of the US Senate, as well as mainstream news organizations and media personalities. We then turn our attention to the problem of identifying important interactions both globally and also based on the particular topics under discussion. We modify the previously introduced modeling framework, so that topic dependent interactions can also be identified. We extend our previous algorithm to accommodate the new framework and also establish asymptotic properties of the key model parameters. We illustrate the results on the same Twitter data set.PhDStatisticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113379/1/donggeng_1.pd

Delineating hurricane vulnerable populations in Orleans Parish, Louisiana

Since settlement first began, equality issues between different social classes have been evident in the location of where residents settled in New Orleans. This research seeks to answer the question: What socioeconomic indicators are prevalent in the areas most-at-risk to flooding which could inhabit populations least able to evacuate? I will use Census 2000 block group data from the socioeconomic sample data (SF3) collected in 2000, along with other economic and GIS data from the New Orleans region to statistically represent the distribution of risk by selecting indicators predicted to be in flood zones from a classification tree analysis. Then, the results are shown in a spatial manner to identify the location of the most vulnerable people to a hurricane based on a set of indicators. The ADvanced CIRCulation Model (ADCIRC) hurricane storm surge modeling (1) Hurricane Pam and (2) Hurricane Pam—85 percent strength, two hurricane disaster exercises, hurricane probability estimates, and resampled LIDAR elevation data will be used as the base maps to characterize the areas that will flood first during a hurricane. The overlaying of the physical and social layers will identify the most socioeconomically vulnerable people in the first-to-flood areas to show where evacuation planning is essential. Recommendations for successfully evacuating residents are then discussed

Quantitative maritime security assessment: a 2020 vision

Maritime security assessment is moving towards a proactive risk-based regime. This opens the way for security analysts and managers to explore and exploit flexible and advanced risk modelling and decision-making approaches in maritime transport. In this article, following a review of maritime security risk assessment, a generic quantitative security assessment methodology is developed. Novel mathematical models for security risk analysis and management are outlined and integrated to demonstrate their use in the developed framework. Such approaches may be used to facilitate security risk modelling and decision making in situations where conventional quantitative risk analysis techniques cannot be appropriately applied. Finally, recommendations on further exploitation of advances in risk and uncertainty modelling technology are suggested with respect to maritime security risk quantification and management

Optimisation of Mobile Communication Networks - OMCO NET

The mini conference “Optimisation of Mobile Communication Networks” focuses on advanced methods for search and optimisation applied to wireless communication networks. It is sponsored by Research & Enterprise Fund Southampton Solent University. The conference strives to widen knowledge on advanced search methods capable of optimisation of wireless communications networks. The aim is to provide a forum for exchange of recent knowledge, new ideas and trends in this progressive and challenging area. The conference will popularise new successful approaches on resolving hard tasks such as minimisation of transmit power, cooperative and optimal routing

A Hybrid Analytic Network Process and Artificial Neural Network (ANP-ANN) model for urban Earthquake vulnerability assessment

© 2018 by the authors. Vulnerability assessment is one of the prerequisites for risk analysis in disaster management. Vulnerability to earthquakes, especially in urban areas, has increased over the years due to the presence of complex urban structures and rapid development. Urban vulnerability is a result of human behavior which describes the extent of susceptibility or resilience of social, economic, and physical assets to natural disasters. The main aim of this paper is to develop a new hybrid framework using Analytic Network Process (ANP) and Artificial Neural Network (ANN) models for constructing a composite social, economic, environmental, and physical vulnerability index. This index was then applied to Tabriz City, which is a seismic-prone province in the northwestern part of Iran with recurring devastating earthquakes and consequent heavy casualties and damages. A Geographical Information Systems (GIS) analysis was used to identify and evaluate quantitative vulnerability indicators for generating an earthquake vulnerability map. The classified and standardized indicators were subsequently weighed and ranked using an ANP model to construct the training database. Then, standardized maps coupled with the training site maps were presented as input to aMultilayer Perceptron (MLP) neural network for producing an Earthquake VulnerabilityMap (EVM). Finally, an EVMwas produced for Tabriz City and the level of vulnerability in various zones was obtained. South and southeast regions of Tabriz City indicate low to moderate vulnerability, while some zones of the northeastern tract are under critical vulnerability conditions. Furthermore, the impact of the vulnerability of Tabriz City on population during an earthquake was included in this analysis for risk estimation. A comparison of the result produced by EVM and the Population Vulnerability (PV) of Tabriz City corroborated the validity of the results obtained by ANP-ANN. The findings of this paper are useful for decision-makers and government authorities to obtain a better knowledge of a city's vulnerability dimensions, and to adopt preparedness strategies in the future for Tabriz City. The developed hybrid framework of ANP and ANN Models can easily be replicated and applied to other urban regions around the world for sustainability and environmental management

Delay Hierarchy Propagation Model

Construction projects are always exposed to delay. Research has shown that most projects encounter delays and this problem is a global one. Previous research related to delays in construction projects have been dedicated to measuring and ranking the direct delays that have occurred. These types of delay are past delays and have already affected many aspects of the project's performance. This type of research is of the reactive type and handles delays after they have happened. The objective of this research is to model the construction project delays that can be used to predict the level of delays that the project could face during its future life. The proposed Delay Hierarchy Propagation Model (DHPM) is the first attempt to model delays in the construction project. This model is an innovative predictive approach to anticipate the future encountered delays before they become real. The model assumes that the direct delay is generated from earlier events or aspects that are found before the direct delay occurs; these events are called the root delay causes. These root delay causes need to be analysed, measured and managed in order to prevent or mitigate the effect of a later direct delay in the project life. The direct delays were analysed by a cause-effect technique to extract a set of root delay causes. The model assumes that the root delay causes will influence the project resources supply rate. The resource shortage then leads to activity delay and, hence possible delay to the whole project. The DHPM consists of two interrelated models: a Resource Shortage Possibility (RSP) model and the Predicting Project Delay model (PPD) model. The RSP model objective is to predict the possibility of resource shortage, whilst the PPD model objectives are to predict the project finish time and to define the critical areas for the project to delay using the output of the RSP model as input. The RSP model was verified through interview questionnaires with a number of selected personnel from the construction industry. The Delphi method was used to enhance the questionnaire results. The RSP model calculations used a combination of fuzzy logic, analytical hierarchy process (AHP) and multi-attribute theory to obtain the model output. A prototype computer program was introduced. The prototype computer program was then tested on a real construction project. The application of the RSP model showed that it is viable. The PPD model used probabilistic networking to predict the finish time of the project. The model introduced two new terms that can be used to define the most critical activities and the possible resource influence to delay. The comparison between PPD and the classical critical path method (CPM), programme evaluation and review technique (PERT) and Monte Carlo simulation revealed that the proposed model provides new information required to enhance delay management by project management staff

Modelling the Interactions between Information and Communication Technologies and Travel Behaviour

The growing capabilities and widespread proliferation of information and communication technologies (ICT) into virtually every aspect of lifestyle, combined with the continuing challenges faced by transport systems, has ensured ongoing interest in the interactions between ICT and travel behaviour. Yet, despite more than three decades of efforts to understand these relationships, few point of consensus have so far emerged, partly due to the rapidly evolving character of ICT, and partly due to the inherent complexity of such interactions. This thesis seeks to develop novel understandings of such interactions by introducing a number of extensions to the existing modelling frameworks. This is achieved through three interrelated research objectives which seek to explore the topic from macro, micro, and temporal perspectives. The macro perspective takes the form of a structural equation analysis of the relationships between ICT use and travel behaviour across four countries: Canada, the United States, the United Kingdom, and Norway, with the data for the latter three obtained by pooling separate datasets on ICT use and travel behaviour. The micro perspective seeks to develop a microeconomic model of an individual maximising utility through joint choice of activities, including in-travel activities, ICT use, as well as the choice of travel mode, timing and route, with the decisions motivated by contribution towards satisfaction, productivity, and consumption. The model is subsequently tested in the empirical contexts of rail business travel time, business travel time valuation, and conceptualisation of the ICT and travel behaviour interaction scenarios reported elsewhere in the literature. The final, temporal perspective analyses the comparatively least explored topic of evolution in the relationships between ICT use and travel behaviour over time. This is achieved by analysing repeated cross-sectional data using structural equation modelling, and interpreted with reference to the theory of diffusion of innovations. The thesis also discusses a number of potential research, policy and industrial applications of its empirical and theoretical contributions.Open Acces

A survey on gas leakage source detection and boundary tracking with wireless sensor networks

Gas leakage source detection and boundary tracking of continuous objects have received a significant research attention in the academic as well as the industries due to the loss and damage caused by toxic gas leakage in large-scale petrochemical plants. With the advance and rapid adoption of wireless sensor networks (WSNs) in the last decades, source localization and boundary estimation have became the priority of research works. In addition, an accurate boundary estimation is a critical issue due to the fast movement, changing shape, and invisibility of the gas leakage compared with the other single object detections. We present various gas diffusion models used in the literature that offer the effective computational approaches to measure the gas concentrations in the large area. In this paper, we compare the continuous object localization and boundary detection schemes with respect to complexity, energy consumption, and estimation accuracy. Moreover, this paper presents the research directions for existing and future gas leakage source localization and boundary estimation schemes with WSNs