Piggery: from environmental pollution to a climate change solution

Pig farms are a vital component of rural economies in Australia. However, disposal of effluent leads to many environmental problems. This case study of the Berrybank Farm piggery waste management system in Victoria estimates greenhouse gas (GHG) benefits from three different activities. Analysis reveals that the capturing and combusting of methane from piggery effluent could save between 4859 and 5840 tCO2e yr−1 ofGHGemissions. Similarly, using methane for replacing fuels for electricity generation could save another 800 tCO2e/yr of GHGs. Likewise, by utilizing the biogas wastes to replace inorganic fertilizers there could be a further saving of 1193 to 1375 tCO2e yr−1 of GHG, depending on the type of fertilizers the waste replaces. Therefore, a well-managed piggery farm with 15,000 pigs could save 6,852 to 8,015 tCO2e/yr, which equates to the carbon sequestrated from 6,800 to 8,000 spotted gum trees (age = 35 year) in their above plus below-ground biomass. Implementation of similar project in suitable areas in Australia could have significant environmental and financial benefits

ERAWATCH Country Report 2008 - An Assessment of Research System and Policies: Greece

The main objective of ERAWATCH country reports 2008 is to characterise and assess the performance of national research systems and related policies in a structured manner that is comparable across countries. The reports are produced for each EU Member State to support the mutual learning process and the monitoring of Member States' efforts by DG Research in the context of the Lisbon Strategy and the European Research Area. In order to do so, the system analysis focuses on key processes relevant for system performance. Four policy-relevant domains of the research system are distinguished, namely resource mobilisation, knowledge demand, knowledge production and knowledge circulation. The reports are based on a synthesis of information from the ERAWATCH Research Inventory and other important available information sources.JRC.J.3-Knowledge for Growt

Reducing Spatial Data Complexity for Classification Models

Intelligent data analytics gradually becomes a day-to-day reality of today's businesses. However, despite rapidly increasing storage and computational power current state-of-the-art predictive models still can not handle massive and noisy corporate data warehouses. What is more adaptive and real-time operational environment requires multiple models to be frequently retrained which fiirther hinders their use. Various data reduction techniques ranging from data sampling up to density retention models attempt to address this challenge by capturing a summarised data structure, yet they either do not account for labelled data or degrade the classification performance of the model trained on the condensed dataset. Our response is a proposition of a new general framework for reducing the complexity of labelled data by means of controlled spatial redistribution of class densities in the input space. On the example of Parzen Labelled Data Compressor (PLDC) we demonstrate a simulatory data condensation process directly inspired by the electrostatic field interaction where the data are moved and merged following the attracting and repelling interactions with the other labelled data. The process is controlled by the class density function built on the original data that acts as a class-sensitive potential field ensuring preservation of the original class density distributions, yet allowing data to rearrange and merge joining together their soft class partitions. As a result we achieved a model that reduces the labelled datasets much further than any competitive approaches yet with the maximum retention of the original class densities and hence the classification performance. PLDC leaves the reduced dataset with the soft accumulative class weights allowing for efficient online updates and as shown in a series of experiments if coupled with Parzen Density Classifier (PDC) significantly outperforms competitive data condensation methods in terms of classification performance at the comparable compression levels

Analytic Approach for Controlling Realistic Quantum Chaotic Systems

An analytic approach for controlling quantum states, which was originally applied to fully random matrix systems [T. Takami and H. Fujisaki, Phys. Rev. E 75, 036219 (2007)], is extended to deal with more realistic quantum systems with a banded random matrix (BRM). The validity of the new analytic field is confirmed by directly solving the Schroedinger equation with a BRM interaction. We find a threshold of the width of the BRM for the quantum control to be successful.Comment: 4 pages with 4 PostScript figures, to appear in the Proceedings of ICCMSE 2007 in a section of Symposium 8 "Quantum Control and Light-Matter Interactions: Recent Computational and Theoretical Results

Fast cooling of trapped ions using the dynamical Stark shift gate

A laser cooling scheme for trapped ions is presented which is based on the fast dynamical Stark shift gate, described in [Jonathan etal, PRA 62, 042307]. Since this cooling method does not contain an off resonant carrier transition, low final temperatures are achieved even in traveling wave light field. The proposed method may operate in either pulsed or continuous mode and is also suitable for ion traps using microwave addressing in strong magnetic field gradients.Comment: 4 pages 5 figure

ERAWATCH COUNTRY REPORTS 2011: Greece

The main objective of the ERAWATCH Annual Country Reports is to characterise and assess the performance of national research systems and related policies in a structured manner that is comparable across countries. EW Country Reports 2011 identify the structural challenges faced by national innovation systems. They further analyse and assess the ability of the policy mix in place to consistently and efficiently tackle these challenges. The annex of the reports gives an overview of the latest national policy efforts towards the enhancement of European Research Area and further assess their efficiency to achieve the targets. These reports were originally produced in November - December 2011, focusing on policy developments over the previous twelve months. The reports were produced by the ERAWATCH Network under contract to JRC-IPTS. The analytical framework and the structure of the reports have been developed by the Institute for Prospective Technological Studies of the Joint Research Centre (JRC-IPTS) and Directorate General for Research and Innovation with contributions from ERAWATCH Network Asbl.JRC.J.2-Knowledge for Growt

A Spot Modeling Evolutionary Algorithm for Segmenting Microarray Images

cDNA microarrays is one of the most fundamental and powerful tools in biotechnology. Despite its relatively late discovery in 1995, it has since been utilized in many biomedical applications such as cancer research, infectious disease diagnosis and treatment, toxicology research, pharmacology research, and agricultural development. The reason for its broa