EcoHomeHelper: An Expert System to Empower End-Users in Climate Change Action

Climate change has been a popular topic for a number of years now. Computer Science has contributed to aiding humanity in reducing energy requirements and consequently global warming. Much of this work is through calculators which determine a user's carbon footprint. However there are no expert systems which can offer advice in an efficient and time saving way. There are many publications which do offer advice on reducing greenhouse gas (GHG) emissions but to find the advice the reader seeks will involve reading a lot of irrelevant material. This work built an expert system (which we call EcoHomeHelper) and attempted to show that it is useful in changing people's behaviour with respect to their GHG emissions and that they will be able to find the information in a more efficient manner. Twelve participants were used. Seven of which used the program and five who read and attempted to find advice by reading from a list. The application itself has current implementations and the concept further developed, has applications for the future.Comment: Contains links to the actual thesis on this topi

A Mobile-Based Group Quiz System to Promote Collaborative Learning and Facilitate Instant Feedback

In this paper we develop and evaluate a mobile-based questioning-answering system (MQAS) that complements traditional learning which can be used as a tool to encourage teachers to give their students mobile-based weekly group quizzes. These quizzes can provide teachers with valid information about the progress of their students and can also motivate students to work in a collaborative manner in order to facilitate the integration of their knowledge. We describe the architecture and experiences with the system

A Crash Risk Assessment Model for Road Curves

A comprehensive model to assess crash risks and reduce driver’s exposure to risks on road curves is still unavailable. We aim to create a model that can assist a driver to negotiate road curves safely. The overall model uses situation awareness, ubiquitous data mining and driver behaviour modelling concepts to assess crash risks on road curves. However, only the risk assessment model, which is part of the overall model, is presented in the paper. Crash risks are assessed using the predictions and a risk assessment scale that is created based on driver behaviours on road curves. This paper identifies the contributing factors from which we assess crash risk level. Five risk levels are defined and the contributing factors for each crash risk level are used to determine risk. The contributing factors are identified from a set of insurance crash records using link analysis. The factors will be compared with the actual factors of the driving context in order to determine the risk level

Metabolic control in type 2 diabetes correlates weakly with patient adherence to oral hypoglycaemic treatment.

Introduction: Patient adherence to treatment is viewed as essential to good metabolic control in diabetes. Our primary objective was to determine if self-reported patient adherence correlated strongly with metabolic control. Our secondary objective was to determine the natural grouping of factors which influence adherence. Materials and Methods: Data were collected using a questionnaire set with 5-point Likert scales. Primary analysis was done using Spearman's correlation coefficient between self-reported composite adherence scores and HbA1c. Secondary analysis was done using exploratory factor analysis. Results: The primary analysis suggests that patient adherence to the treatment regime is weakly correlated to metabolic control. Calculated Spearman's rho was 0.197, with a two-tailed P value of 0.027. The secondary analysis demonstrates the natural clustering of factors that influence patient adherence to treatment. A 6-factor solution was found to account for most of the variance in the data. We also found that feelings of frustration, anxiety, and depression were associated with a lack of knowledge about diabetes treatment. In addition, belief in traditional medicine correlated strongly with ethnicity. Conclusion: A good treatment regime for type 2 diabetes mellitus influences metabolic outcome far more than patient adherence

Assessing Crash Risks on Curves

In Queensland, curve related crashes contributed to 63.44% of fatalities, and 25.17% required hospitalisation. In addition, 51.1% of run-off-road crashes occurred on obscured or open-view road curves (Queensland Transport, 2006). This paper presents a conceptual framework for an in-vehicle system, which assesses crash risk when a driver is manoeuvring on a curve. Our approach consists of using Intelligent Transport Systems (ITS) to collect information about the driving context. The driving context corresponds to information about the environment, driver, and vehicle gathered from sensor technology. Sensors are useful to detect drivers’ high-risk situations such as curves, fogs, drivers’ fatigue or slippery roads. However, sensors can be unreliable, and therefore the information gathered from them can be incomplete or inaccurate. In order to improve the accuracy, a system is built to perform information fusion from past and current driving information. The integrated information is analysed using ubiquitous data mining techniques and the results are later used in a Coupled Hidden Markov Model (CHMM), to learn and classify the information into different risk categories. CHMM is used to predict the probability of crash on curves. Based on the risk assessment, our system provides appropriate intervention to the driver. This approach could allow the driver to have sufficient time to react promptly. Hence, this could potentially promote safe driving and decrease curve related injuries and fatalities