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.

Biocompatibility, SiH lenses and the impact of hydration on comfort

Silicone hydrogel (SiH) contact lenses have been available for over a decade. During that time, these highly innovative materials and designs have continually improved and now represent a major percentage of fits within the global contact lens market.1 Their high oxygen transmissibility has drastically reduced the incidence of hypoxia-related conditions such as corneal edema, limbal hyperaemia, and corneal vascularisation.2,3 However, there remain significant challenges in the quest for the ideal contact lens. The silicone material used in SiH contact lenses is inherently more hydrophobic than the non-silicone hydrogel materials. SiH lens manufacturers must find ways to overcome lens surface hydrophobicity since it can create issues in terms of lens wettability and surface deposition. Achieving ideal lens water content presents yet another challenge since increasing water content in a silicone hydrogel lens can reduce oxygen transmissibility. This is because increasing water content results in decreased silicone content in the lens and silicone is a better transmitter of oxygen than water.

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.