Social engineering in social networking sites : affect-based model

While social engineering represents a real and ominous threat to many organizations, companies, governments, and individuals, social networking sites (SNSs), have been identified as among the most common means of social engineering attacks. Owing to factors that reduce the ability of users to detect social engineering tricks and increase the ability of attackers to launch them, SNSs seem to be perfect breeding ground for exploiting the vulnerabilities of people, and the weakest link in security. This work will contribute to the knowledge of social engineering by identifying different entities and subentities that affect social engineering based attacks in SNSs. Moreover, this paper includes an intensive and comprehensive overview of different aspects of social engineering threats in SNSs.

Hybrid model for motorway travel time estimation considering increased detector spacing

Travel time estimation and prediction on motorways has long been a topic of research. Prediction modeling generally assumes that the estimation is perfect. No matter how good is the prediction modeling- the errors in estimation can significantly deteriorate the accuracy and reliability of the prediction. Models have been proposed to estimate travel time from loop detector data. Generally, detectors are closely spaced (say 500m) and travel time can be estimated accurately. However, detectors are not always perfect, and even during normal running conditions few detectors malfunction, resulting in increase in the spacing between the functional detectors. Under such conditions, error in the travel time estimation is significantly large and generally unacceptable. This research evaluates the in-practice travel time estimation model during different traffic conditions. It is observed that the existing models fail to accurately estimate travel time during large detector spacing and congestion shoulder periods. Addressing this issue, an innovative Hybrid model that only considers loop data for travel time estimation is proposed. The model is tested using simulation and is validated with real Bluetooth data from Pacific Motorway Brisbane. Results indicate that during non free flow conditions and larger detector spacing Hybrid model provides significant improvement in the accuracy of travel time estimation.

The development of a carrying capacity assessment model for the Australian socio-environmental context

A key aim of this research was to highlight how society's understanding of constraints to the productive capacity of its resource base is vital to its long-term survival. This was achieved through the development of an online model, the Carrying Capacity Dashboard. The Dashboard was developed to estimate how much land Australian populations require for the production of their food, textiles, timber and liquid fuel. Findings reveal that Australia's estimated carrying capacity is currently over 40 million people but longer-term and more regional analyses suggest a much smaller number. Carrying capacity assessment also indicates that optimal resource security is to be found in balancing both small and large-scale self-sufficiency.

Model-predictive control and closed-loop stability considerations for nonlinear plants described by local ARX-type models

In this paper, a model-predictive control (MPC) method is detailed for the control of nonlinear systems with stability considerations. It will be assumed that the plant is described by a local input/output ARX-type model, with the control potentially included in the premise variables, which enables the control of systems that are nonlinear in both the state and control input. Additionally, for the case of set point regulation, a suboptimal controller is derived which has the dual purpose of ensuring stability and enabling finite-iteration termination of the iterative procedure used to solve the nonlinear optimization problem that is used to determine the control signal.

Embedding EfS in teacher education : an introductory guide to using the systems change model

Welcome to this introductory guide on using a systems change model to embed Education for Sustainability (EfS) into teacher education. Pressing sustainability issues such as climate change, biodiversity loss and depletion of non-renewable resources pose new challenges for education. The importance of education in preparing future citizens to engage in sustainable living practices and help create a more sustainable world is widely acknowledged. As a result many universities around the world are beginning to recognize the need to integrate EfS into their teacher education programs. However, evidence indicates that there is little or no core EfS knowledge or pedagogy in pre-service teacher courses available to student teachers in a thorough and systematic fashion. Instead efforts are fragmented and individually or, at best, institutionally-based and lacking a systems approach to change, an approach that is seen as essential to achieving a sustainable society (Henderson & Tilbury, 2004). The result is new teachers are graduating without the necessary knowledge or skills to teach in ways that enable them to prepare their students to cope well with the new and emerging challenges their communities face. This guide has been prepared as part of a teaching and learning research project that applied a systems change approach to embedding the learning and teaching of sustainability into pre-service teacher education. The processes, outcomes and lessons learnt from this project are presented here as a guide for navigating pathways to systemic change in the journey of re-orienting teacher education towards sustainability. The guide also highlights how a systems change approach can be used to successfully enact change within a teacher education system. If you are curious about how to introduce and embed EfS into teacher education – or have tried other models and are looking for a more encompassing, transformative approach – this guide is designed to help you. The material presented in this guide is designed to be flexible and adaptive. However you choose to use the content, our aim is to help you and your students develop new perspectives, promote discussion and to engage with a system-wide approach to change.

The mouse model of Chlamydia genital tract infection: a review of infection, disease, immunity and vaccine development

Chlamydia trachomatis is the most common sexually transmitted bacterial infection worldwide. The impact of this pathogen on human reproduction has intensified research efforts to better understand chlamydial infection and pathogenesis. Whilst there are animal models available that mimic the many aspects of human chlamydial infection, the mouse is regarded as the most practical and widely used of the models. Studies in mice have greatly contributed to our understanding of the host-pathogen interaction and provided an excellent medium for evaluating vaccines. Here we explore the advantages and disadvantages of all animal models of chlamydial genital tract infection, with a focus on the murine model and what we have learnt from it so far.

An Artificial Neutral Network (ANN) model for predicting biodiesel kinetic viscosity as a function of temperature and chemical compositions

An Artificial Neural Network (ANN) is a computational modeling tool which has found extensive acceptance in many disciplines for modeling complex real world problems. An ANN can model problems through learning by example, rather than by fully understanding the detailed characteristics and physics of the system. In the present study, the accuracy and predictive power of an ANN was evaluated in predicting kinetic viscosity of biodiesels over a wide range of temperatures typically encountered in diesel engine operation. In this model, temperature and chemical composition of biodiesel were used as input variables. In order to obtain the necessary data for model development, the chemical composition and temperature dependent fuel properties of ten different types of biodiesels were measured experimentally using laboratory standard testing equipments following internationally recognized testing procedures. The Neural Networks Toolbox of MatLab R2012a software was used to train, validate and simulate the ANN model on a personal computer. The network architecture was optimised following a trial and error method to obtain the best prediction of the kinematic viscosity. The predictive performance of the model was determined by calculating the absolute fraction of variance (R2), root mean squared (RMS) and maximum average error percentage (MAEP) between predicted and experimental results. This study found that ANN is highly accurate in predicting the viscosity of biodiesel and demonstrates the ability of the ANN model to find a meaningful relationship between biodiesel chemical composition and fuel properties at different temperature levels. Therefore the model developed in this study can be a useful tool in accurately predict biodiesel fuel properties instead of undertaking costly and time consuming experimental tests.