Media and information literacy : policy and strategy guidelines

In the evolving knowledge societies of today, some people are overloaded with information, others are starved for information. Everywhere, people are yearning to freely express themselves,to actively participate in governance processes and cultural exchanges. Universally, there is a deep thirst to understand the complex world around us. Media and Information Literacy (MIL) is a basis for enhancing access to information and knowledge, freedom of expression, and quality education. It describes skills, and attitudes that are needed to value the functions of media and other information providers, including those on the Internet, in societies and to find, evaluate and produce information and media content; in other words, it covers the competencies that are vital for people to be effectively engaged in all aspects of development.

Estimation of biodiesel properties from chemical composition – An artificial neural network (ANN) approach

Biodiesel, produced from renewable feedstock represents a more sustainable source of energy and will therefore play a significant role in providing the energy requirements for transportation in the near future. Chemically, all biodiesels are fatty acid methyl esters (FAME), produced from raw vegetable oil and animal fat. However, clear differences in chemical structure are apparent from one feedstock to the next in terms of chain length, degree of unsaturation, number of double bonds and double bond configuration-which all determine the fuel properties of biodiesel. In this study, prediction models were developed to estimate kinematic viscosity of biodiesel using an Artificial Neural Network (ANN) modelling technique. While developing the model, 27 parameters based on chemical composition commonly found in biodiesel were used as the input variables and kinematic viscosity of biodiesel was used as output variable. Necessary data to develop and simulate the network were collected from more than 120 published peer reviewed papers. 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 and learning algorithm were 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 coefficient of determination (R2), root mean squared (RMS) and maximum average error percentage (MAEP) between predicted and experimental results. This study found high predictive accuracy of the ANN in predicting fuel properties of biodiesel and has demonstrated the ability of the ANN model to find a meaningful relationship between biodiesel chemical composition and fuel properties. Therefore the model developed in this study can be a useful tool to accurately predict biodiesel fuel properties instead of undertaking costly and time consuming experimental tests.

Geo-locked photo sharing on mobile devices

We introduce the idea of geo-locking through a mobile phone based photo sharing application called Picalilly (figure 1). Using its geo-locking feature, Picalilly allows its users to manually define geographical boundaries for sharing photos -- limiting sharing within user-defined boundaries as well as facilitating open sharing between strangers within such boundaries. To explore the potential of geo-locking, we carried out a small scale field trial of Picalilly involving two groups of students, who were part of a two-week long introduction program at a university. Our preliminary results show that Picalilly facilitated 1) sharing of 'places' and 2) localized explorations.

Evaluation of a pilot-scale oil extraction from microalgae for biodiesel production

Biodiesel derived from microalgae is one of a suite of potential solutions to meet the increasing demand for a renewable, carbon-neutral energy source. However, there are numerous challenges that must be addressed before algae biodiesel can become commercially viable. These challenges include the economic feasibility of harvesting and dewatering the biomass and the extraction of lipids and their conversion into biodiesel. Therefore, it is essential to find a suitable extraction process given these processes presently contribute significantly to the total production costs which, at this stage, inhibit the ability of biodiesel to compete financially with petroleum diesel. This study focuses on pilot-scale (100 kg dried microalgae) solvent extraction of lipids from microalgae and subsequent transesterification to biodiesel. Three different solvents (hexane, isopropanol (IPA) and hexane + IPA (1:1)) were used with two different extraction methods (static and Soxhlet) at bench-scale to find the most suitable solvent extraction process for the pilot-scale. The Soxhlet method extracted only 4.2% more lipid compared to the static method. However, the fatty acid profiles of different extraction methods with different solvents are similar, suggesting that none of the solvents or extraction processes were biased for extraction of particular fatty acids. Considering the cost and availability of the solvents, hexane was chosen for pilot-scale extraction using static extraction. At pilot-scale the lipid yield was found to be 20.3% of total biomass which is 2.5% less than from bench scale. Extracted fatty acids were dominated by polyunsaturated fatty acids (PUFAs) (68.94±0.17%) including 47.7±0.43 and 17.86±0.42% being docosahexaenoic acid (DHA) (C22:6) and docosapentaenoic acid (DPA) (C22:5, ω-3), respectively. These high amounts of long chain poly unsaturated fatty acids are unique to some marine microalgae and protists and vary with environmental conditions, culture age and nutrient status, as well as with cultivation process. Calculated physical and chemical properties of density, viscosity of transesterified fatty acid methyl esters (FAMEs) were within the limits of the biodiesel standard specifications as per ASTM D6751-2012 and EN 14214. The calculated cetane number was, however, significantly lower (17.8~18.6) compared to ASTM D6751-2012 or EN 14214-specified minimal requirements. We conclude that the obtained microalgal biodiesel would likely only be suitable for blending with petroleum diesel to a maximum of 5 to 20%.

“My hands are plunged in oranges”…: an experiential account of a working process

This presentation incorporated the live performance throughout, by the author, of movement from “The All Weather Project” by Liz Roche. Movement sections are indicated by italics. “I am going to start by dancing for you… Movement: Live performance of solo approximately 10 minutes in duration This is the introduction... Through my PhD research, I am examining the choreographic process from the perspective of the independent contemporary dancer, through embodying this role as a researcher/participant. My methodological frameworks, which utilise video documentation and journal writing, could be characterised as ethnographic, multi-modal embodied theorising, leading to “multi-dimensional theorising” (I adopt this term from Susan Melrose). In this way, I am unwinding the embodied practice of dancing, through the co-existent layers of experience, towards forming a theoretical understanding of the issues that arise for the dancer. The issues that I have identified as relevant to my research are those relating to the dancer’s ‘moving identity’ or way of moving, as a mutable and adaptable form that must alter and re-adjust to each different choreographic engram or movement vocabulary, that she/he encounters. I am examining this interplay between stability and change. I also reflect on the impact of destabilisation and flux on the dancer’s identity in a wider sense, as she/he relates outwardly to signifying factors within the social strata. Today I am going to bring you through a reflection on the working process of a dance piece as experienced from the inside. By doing so, I hope to capture and elucidate the multi-dimensional layers which existed for me within this process. Through displaying these fragments together, I endeavour to invoke the ‘totality’ of the experience...

Can’t we all just get along? A qualitative investigation of the cognitive processes motivating driver aggression.

Although driver aggression has been identified as contributing to crashes, current understanding of the fundamental causes of the behaviour is poor. Two key reasons for this are evident. Firstly, existing research has been largely atheoretical, with no unifying conceptual framework guiding investigation. Secondly, emphasis on observable behaviours has resulted in limited knowledge of the underlying thought processes that motivate behaviour. Since driving is fundamentally a social situation, requiring drivers to interpret on-road events, insight regarding these perception and appraisal processes is paramount in advancing understanding of the underlying causes. Thus, the current study aimed to explore the cognitive appraisal processes involved in driver aggression, using a conceptual model founded on the General Aggression Model (Anderson & Bushman, 2002). The present results reflect the first of several studies testing this model. Participants completed 3 structured driving diaries to explore perceptions and cognitions. Thematic analysis of diaries identified several cognitive themes. The first, ‘driving etiquette’ concerned an implied code of awareness and consideration for other motorists, breaches of which were strongly associated with reports of anger and frustration. Such breaches were considered intentional; attributed to dispositional traits of another driver, and precipitated the second theme, ‘justified retaliation’. This theme showed that drivers view their aggressive behaviour as warranted, to convey criticism towards another motorist’s etiquette violation. However, the third theme, ‘superiority’ suggested that those refraining from an aggressive response were motivated by a desire to perceive themselves as ‘better’ than the offending motorists. Collectively, the themes indicate deep-seated and complex thought patterns underlying driver aggression, and suggest the behaviour will be challenging to modify. Implications of these themes in relation to the proposed model will be discussed, and continued research will explore these cognitive processes further, to examine their interaction with person-related factors.

Heat maps for aggregating bioacoustic annotations

In our large library of annotated environmental recordings of animal vocalizations, searching annotations by label can return thousands of results. We propose a heat map of aggregated annotation time and frequency bounds, maintaining the shape of the annotations as they appear on the spectrogram. This compactly displays the distribution of annotation bounds for the user's query, and allows them to easily identify unusual annotations. Key to this is allowing zero values on the map to be differentiated from areas where there are single annotations.

Developing creative services industries in Hangzhou, China

Creativity is changing the People’s Republic of China according to Li Wuwei (2011), a leading Chinese economist and policy advisor. The nation is learning to embrace a “third industrial revolution” (Rifkin, 2011) while banking the economic capital of the carbon-dependent manufacturing economy. Urbanisation is also driving change and consumer culture (Gerth, 2010). Most of China’s high-value creative service industries are found in the large urban centres of Beijing, Shanghai, Guangzhou and Shenzhen in the coastal provinces. China’s second-tier cities, including Hangzhou in Zhejiang province, are also seeking to make capital out of culture, albeit with different strategies than the coastal hubs. The Hangzhou metropolitan area is the fourth largest in China, with 8.8 million residents. Zhejiang province was once known as the “land of rice and fish.” However, with the increased emphasis on productivity in China’s economic reforms since 1978, the province became an economic heavyweight, characterised by small and medium-sized enterprises often working together to produce complementary products...

A Monte-Carlo tree search in argumentation

Monte-Carlo Tree Search (MCTS) is a heuristic to search in large trees. We apply it to argumentative puzzles where MCTS pursues the best argumentation with respect to a set of arguments to be argued. To make our ideas as widely applicable as possible, we integrate MCTS to an abstract setting for argumentation where the content of arguments is left unspecified. Experimental results show the pertinence of this integration for learning argumentations by comparing it with a basic reinforcement learning.

Sustainability into the engineering curriculum

Higher education institutions have made some progress towards Engineering Education for Sustainable Development (EESD). There is however a ‘time lag dilemma’ facing engineering educators, where the pace of traditional curriculum renewal may not be sufficient to keep up with potential market,regulatory and institutional shifts.

A new registration method based on Log-Euclidean tensor metrics and its application to genetic studies

In structural brain MRI, group differences or changes in brain structures can be detected using Tensor-Based Morphometry (TBM). This method consists of two steps: (1) a non-linear registration step, that aligns all of the images to a common template, and (2) a subsequent statistical analysis. The numerous registration methods that have recently been developed differ in their detection sensitivity when used for TBM, and detection power is paramount in epidemological studies or drug trials. We therefore developed a new fluid registration method that computes the mappings and performs statistics on them in a consistent way, providing a bridge between TBM registration and statistics. We used the Log-Euclidean framework to define a new regularizer that is a fluid extension of the Riemannian elasticity, which assures diffeomorphic transformations. This regularizer constrains the symmetrized Jacobian matrix, also called the deformation tensor. We applied our method to an MRI dataset from 40 fraternal and identical twins, to revealed voxelwise measures of average volumetric differences in brain structure for subjects with different degrees of genetic resemblance.

Mapping genetic influences on brain fiber architecture with high angular resolution diffusion imaging (HARDI)

We report the first 3D maps of genetic effects on brain fiber complexity. We analyzed HARDI brain imaging data from 90 young adult twins using an information-theoretic measure, the Jensen-Shannon divergence (JSD), to gauge the regional complexity of the white matter fiber orientation distribution functions (ODF). HARDI data were fluidly registered using Karcher means and ODF square-roots for interpol ation; each subject's JSD map was computed from the spatial coherence of the ODFs in each voxel's neighborhood. We evaluated the genetic influences on generalized fiber anisotropy (GFA) and complexity (JSD) using structural equation models (SEM). At each voxel, genetic and environmental components of data variation were estimated, and their goodness of fit tested by permutation. Color-coded maps revealed that the optimal models varied for different brain regions. Fiber complexity was predominantly under genetic control, and was higher in more highly anisotropic regions. These methods show promise for discovering factors affecting fiber connectivity in the brain.

Quantitative genetic modeling of lateral ventricular shape and volume using multi-atlas fluid image alignment in twins

Despite substantial progress in measuring the 3D profile of anatomical variations in the human brain, their genetic and environmental causes remain enigmatic. We developed an automated system to identify and map genetic and environmental effects on brain structure in large brain MRI databases . We applied our multi-template segmentation approach ("Multi-Atlas Fluid Image Alignment") to fluidly propagate hand-labeled parameterized surface meshes into 116 scans of twins (60 identical, 56 fraternal), labeling the lateral ventricles. Mesh surfaces were averaged within subjects to minimize segmentation error. We fitted quantitative genetic models at each of 30,000 surface points to measure the proportion of shape variance attributable to (1) genetic differences among subjects, (2) environmental influences unique to each individual, and (3) shared environmental effects. Surface-based statistical maps revealed 3D heritability patterns, and their significance, with and without adjustments for global brain scale. These maps visualized detailed profiles of environmental versus genetic influences on the brain, extending genetic models to spatially detailed, automatically computed, 3D maps.