This thesis introduces a new integrated algorithm for the detection of lane-level irregular driving. To date, there has been very little improvement in the ability to detect lane level irregular driving styles, mainly due to a lack of high performance positioning techniques and suitable driving pattern recognition algorithms. The algorithm combines data from the Global Positioning System (GPS), Inertial Measurement Unit (IMU) and lane information using advanced filtering methods. The vehicle state within a lane is estimated using a Particle Filter (PF) and an Extended Kalman Filter (EKF). The state information is then used within a novel Fuzzy Inference System (FIS) based algorithm to detect different types of irregular driving. Simulation and field trial results are used to demonstrate the accuracy and reliability of the proposed irregular driving detection method.

Typically utilized by larger corporations, social media marketing and strategy is lacking in small and medium-sized nonprofit organizations. Although these organizations are beginning to incorporate this form of online communication, there is still a need to understand the best practices and proper tools to enhance an organization's presence on the web. Cases on Strategic Social Media Utilization in the Nonprofit Sector brings together cases and chapters in order to examine both the practical and theoretical components of creating an online social community for nonprofit organizations. The technologies discussed in this publication provide organizations with the necessary cost-effective tools for fundraising, marketing, and civic engagement. This publication is an essential reference source for practitioners, academicians, researchers, and advanced-level students interested in learning how to effectively use social media technologies in the nonprofit sector.

This thesis introduces a new integrated algorithm for the detection of lane-level irregular driving. To date, there has been very little improvement in the ability to detect lane level irregular driving styles, mainly due to a lack of high performance positioning techniques and suitable driving pattern recognition algorithms. The algorithm combines data from the Global Positioning System (GPS), Inertial Measurement Unit (IMU) and lane information using advanced filtering methods. The vehicle state within a lane is estimated using a Particle Filter (PF) and an Extended Kalman Filter (EKF). The state information is then used within a novel Fuzzy Inference System (FIS) based algorithm to detect different types of irregular driving. Simulation and field trial results are used to demonstrate the accuracy and reliability of the proposed irregular driving detection method.

The notion of a lifestyle system leading to zero waste is obviously appealing, and a strategy of total reuse and recycling of: waste material is often advocated. However, there is a growing realization that the recycling process itself produces waste, and the environmental and economic cost of recycling and reusing certain materials invalidates the zero waste approach as a universally viable solution. Thus, solutions must be found to deal with the part of waste that it is not practicable to recycle or reuse. The energy content of municipal waste (whether raw or classified) is about 10MJ kg-1. If the total amount of waste material in any region is around 30 million tons per year or about 1000 kg/ s, the total energy is thus 10,000MJ /s = 10,000 MW. At an electricity generation efficiency of 20%, this could provide 2000 MW plus about 6000MWof district heating. This energy source is largely biomass, which is carbon dioxide neutral, and thus does not contribute to the total atmospheric greenhouse gases. The present work includes many aspects of municipal solid waste combustion, such as the effects of moisture, particle size and ash content effects on solid particle during process rates (moisture evaporation, volatile release, and char burning rate). The COMMENT code has developed to reveal much detailed information on the combustion processes. Through experimental and numerical investigations, the combustion process of simulated MSW in bed can be better understood and the experiment results can be used to amend the mathematics model and be consulted by the application in the project. The results from modeling can show the combustion process, and make us deeply know how the heat transfers in the fuel and gas yields from fuel. At the same time, the simulation can predict the maximum temperature of waste incineration and the trend concerning combustion.