Advances in monitoring the human dimension of natural resource systems: an example from the Great Barrier Reef

The aim of this paper is to demonstrate the feasibility and potential utility of decision-centric social-economic monitoring using data collected from Great Barrier Reef (Reef) region. The social and economic long term monitoring program (SELTMP) for the Reef is a novel attempt to monitor the social and economic dimensions of social-ecological change in a globally and nationally important region. It represents the current status and condition of the major user groups of the Reef with the potential to simultaneously consider trends, interconnections, conflicts, dependencies and vulnerabilities. Our approach was to combine a well-established conceptual framework with a strong governance structure and partnership arrangement that enabled the co-production of knowledge. The framework is a modification of the Millennium Ecosystem Assessment and it was used to guide indicator choice. Indicators were categorised as; (i) resource use and dependency, (ii) ecosystem benefits and well-being, and (iii) drivers of change. Data were collected through secondary datasets where existing and new datasets were created where not, using standard survey techniques. Here we present an overview of baseline results of new survey data from commercial-fishers (n =210), marine-based tourism operators (n =119), tourists (n =2877), local residents (n =3181), and other Australians (n =2002). The indicators chosen describe both social and economic components of the Reef system and represent an unprecedented insight into the ways in which people currently use and depend on the Reef, the benefits that they derive, and how they perceive, value and relate to the Reef and each other. However, the success of a program such as the SELTMP can only occur with well-translated cutting-edge data and knowledge that are collaboratively produced, adaptive, and directly feeds into current management processes. We discuss how data from the SELTMP have already been incorporated into Reef management decision-making through substantial inclusion in three key policy documents

Experimental Evidence of Giant Electron - Gamma Bursts Generated by Extensive Atmospheric Showers in Thunderclouds

The existence of a new phenomena - giant electron-gamma bursts is established. The bursts are generated in thunderclouds as a result of the combined action of runaway breakdown and extensive atmosphere showers (RB-EAS). The experiments were fulfilled at the Tien Shan Mountain Scientific Station using EAS-Radio installation. This specially constructed installation consists of a wide spread EAS trigger array and a high time resolution radiointerferometer.Comment: 30 pages, 16 figure

Using smart meters to estimate low voltage losses

Losses on low voltage networks are often substantial. For example, in the UK they have been estimated as being 4% of the energy supplied by low voltage networks. However, the breakdown of the losses to individual conductors and their split over time are poorly understood as generally only the peak demands and average loads over several months have been recorded. The introduction of domestic smart meters has the potential to change this. How domestic smart meter readings can be used to estimate the actual losses is analysed. In particular, the accuracy of using 30 minute readings compared with 1 minute readings, and how this accuracy could be improved, were investigated. This was achieved by assigning the data recorded by 100 smart meters with a time resolution of 1 minute to three test networks. Smart meter data from three sources were used in the investigation. It was found that 30 minute resolution data underestimated the losses by between 9% and 24%. By fitting an appropriate model to the data, it was possible to reduce the inaccuracy by approximately 50%. Having a smart meter time resolution of 10 minutes rather than 30 gave little improvement to the accuracy

On the Benefits of Transparent Compression for Cost-Effective Cloud Data Storage

International audienceInfrastructure-as-a-Service (IaaS) cloud computing has revolutionized the way we think of acquiring computational resources: it allows users to deploy virtual machines (VMs) at large scale and pay only for the resources that were actually used throughout the runtime of the VMs. This new model raises new challenges in the design and development of IaaS middleware: excessive storage costs associated with both user data and VM images might make the cloud less attractive, especially for users that need to manipulate huge data sets and a large number of VM images. Storage costs result not only from storage space utilization, but also from bandwidth consumption: in typical deployments, a large number of data transfers between the VMs and the persistent storage are performed, all under high performance requirements. This paper evaluates the trade-off resulting from transparently applying data compression to conserve storage space and bandwidth at the cost of slight computational overhead. We aim at reducing the storage space and bandwidth needs with minimal impact on data access performance. Our solution builds on BlobSeer, a distributed data management service specifically designed to sustain a high throughput for concurrent accesses to huge data sequences that are distributed at large scale. Extensive experiments demonstrate that our approach achieves large reductions (at least 40%) of bandwidth and storage space utilization, while still attaining high performance levels that even surpass the original (no compression) performance levels in several data-intensive scenarios

A look at the other 90 per cent: Investigating British Sign Language vocabulary knowledge in deaf children from different language learning backgrounds

In this study we present new data on deaf children's receptive and expressive vocabulary knowledge in British Sign Language (BSL) from a sample consisting of children with deaf parents, children with hearing parents, and children with additional needs. Their performance on three BSL vocabulary tasks was compared with (previously reported findings from) a sample of deaf fluent signers. We use these data to assess the effects of some key demographic/ child variables on deaf signing children's vocabulary and discuss findings in the relation to the meaning of 'normative' data and samples for this heterogeneous population. Findings show no effect of the presence of additional disabilities on participants' scores for any of the three tasks. As expected, chronological age is the most significant factor in performance on all vocabulary tasks while the number of deaf relatives only becomes statistically significant for the form recall task. This study contributes to the field of sign language assessment by seeking to identify key variables in heterogeneity and how these variables affect signed vocabulary acquisition with the long-term objective of informing intervention