Practice-based film education for children: teaching and learning for creativity, citizenship and participation

Practice-oriented film education aimed at children has been hailed for various reasons: at a personal level, as a means of providing tools for self-expression, for developing creativity and communication skills. And at a social level, it is argued that children must now become competent producers, in addition to critical consumers, of audiovisual content so they can take part in the global public sphere that is arguably emerging. This chapter discusses how the challenges posed by introducing children to filmmaking (i.e. digital video) are being met at three civil associations in Mexico: La Matatena AC, which seeks to enrich the children’s lives by means of the aesthetic experience filmmaking can bring them. Comunicaciòn Comunitaria, concerned with the impact filmmaking can have on the community, preserving cultural memory and enabling participation. And Juguemos a Grabar, with a focus on urban regeneration through the cultural industries.

Content-aware compression for big textual data analysis

A substantial amount of information on the Internet is present in the form of text. The value of this semi-structured and unstructured data has been widely acknowledged, with consequent scientific and commercial exploitation. The ever-increasing data production, however, pushes data analytic platforms to their limit. This thesis proposes techniques for more efficient textual big data analysis suitable for the Hadoop analytic platform. This research explores the direct processing of compressed textual data. The focus is on developing novel compression methods with a number of desirable properties to support text-based big data analysis in distributed environments. The novel contributions of this work include the following. Firstly, a Content-aware Partial Compression (CaPC) scheme is developed. CaPC makes a distinction between informational and functional content in which only the informational content is compressed. Thus, the compressed data is made transparent to existing software libraries which often rely on functional content to work. Secondly, a context-free bit-oriented compression scheme (Approximated Huffman Compression) based on the Huffman algorithm is developed. This uses a hybrid data structure that allows pattern searching in compressed data in linear time. Thirdly, several modern compression schemes have been extended so that the compressed data can be safely split with respect to logical data records in distributed file systems. Furthermore, an innovative two layer compression architecture is used, in which each compression layer is appropriate for the corresponding stage of data processing. Peripheral libraries are developed that seamlessly link the proposed compression schemes to existing analytic platforms and computational frameworks, and also make the use of the compressed data transparent to developers. The compression schemes have been evaluated for a number of standard MapReduce analysis tasks using a collection of real-world datasets. In comparison with existing solutions, they have shown substantial improvement in performance and significant reduction in system resource requirements.

Biomethane production from macroalgae

Irish brown seaweeds have been identified as a potential bio-resource with potentially high specific methane yields. Anaerobic digestion is deemed the most feasible technology due to its commercial viability for handling such wet feedstock. However, the biomethane potential of seaweed is highly dependent on its chemical composition which can vary by species type, cultivation method, and time of harvest. This study aims to investigate and optimize the process for the production of biomethane from Irish brown seaweeds focusing on the key technology bottlenecks including for seaweed characterization, biomethane potential assessment, optimization of long-term anaerobic digestion and suitable pre-treatment technologies to enhance potential gas yields. Laminaria digitata and Ascophyllum nodosum were tested for seasonal variation. From the characterization and batch digestion of L. digitata, August was found to be the optimal month for harvest due to high organic matter content, low level of ash and ultimately highest biomethane yield. The specific methane yield of 53 m3 CH4 t-1 wwt in August was 4.5 times higher than the yield in December (12 m3 CH4 t-1 wwt), with ash content the key factor in seasonal variation. For A. nodosum, the optimal harvest month was October with polyphenol content found to be a more influential factor than ash. The gross energy yields from both species were evaluated in the range of 116-200 GJ ha-1 yr-1. Continuous digestion trials were subsequently designed for S. latissima and L. digitata to optimize the key digestion parameters. Results from mono-digestion and co-digestion with dairy slurry revealed that both seaweeds could be digested at maximum biomethane efficiency to a loading rate of 4 kg VS m-3 d-1. Accumulation of salt in the digesters was a concern for long term digestion and it was reasoned that suitable pretreatment may be required prior to digestion. Various pre-treatments were subsequently tested on L. digitata to enhance the gas yield. It was found that maceration after hot water washing yielded 25% more specific methane and up to 54% salt removal as compared to untreated L. digitata. The experiments undertaken aim to assist in providing a basic guideline for feasible design and operation of seaweed digesters in Ireland.