Spatial and Temporal Changes in Soil Organic Carbon Assessment using an Integrated Geostatistical and Compositional Data Analysis Approach

The Irish and UK governments, along with other countries, have made a commitment to limit the concentrations of greenhouse gases in the atmosphere by reducing emissions from the burning of fossil fuels. This can be achieved (in part) through increasing the sequestration of CO2 from the atmosphere including monitoring the amount stored in vegetation and soils. A large proportion of soil carbon is held within peat due to the relatively high carbon density of peat and organic-rich soils. This is particularly important for a country such as Ireland, where some 16% of the land surface is covered by peat. For Northern Ireland, it has been estimated that the total amount of carbon stored in vegetation is 4.4Mt compared to 386Mt stored within peat and soils. As a result it has become increasingly important to measure and monitor changes in stores of carbon in soils. The conservation and restoration of peat covered areas, although ongoing for many years, has become increasingly important. This is summed up in current EU policy outlined by the European Commission (2012) which seeks to assess the relative contributions of the different inputs and outputs of organic carbon and organic matter to and from soil. Results are presented from the EU-funded Tellus Border Soil Carbon Project (2011 to 2013) which aimed to improve current estimates of carbon in soil and peat across Northern Ireland and the bordering counties of the Republic of Ireland.
Historical reports and previous surveys provide baseline data. To monitor change in peat depth and soil organic carbon, these historical data are integrated with more recently acquired airborne geophysical (radiometric) data and ground-based geochemical data generated by two surveys, the Tellus Project (2004-2007: covering Northern Ireland) and the EU-funded Tellus Border project (2011-2013) covering the six bordering counties of the Republic of Ireland, Donegal, Sligo, Leitrim, Cavan, Monaghan and Louth. The concept being applied is that saturated organic-rich soil and peat attenuate gamma-radiation from underlying soils and rocks. This research uses the degree of spatial correlation (coregionalization) between peat depth, soil organic carbon (SOC) and the attenuation of the radiometric signal to update a limited sampling regime of ground-based measurements with remotely acquired data. To comply with the compositional nature of the SOC data (perturbations of loss on ignition [LOI] data), a compositional data analysis approach is investigated. Contemporaneous ground-based measurements allow corroboration for the updated mapped outputs. This provides a methodology that can be used to improve estimates of soil carbon with minimal impact to sensitive habitats (like peat bogs), but with maximum output of data and knowledge.

Towards utilizing Model-Driven Engineering of Composite Applications for business performance analysis

Composite Applications on top of SAPs implementation of SOA (Enterprise SOA) enable the extension of already existing business logic. In this paper we show, based on a case study, how Model-Driven Engineering concepts are applied in the development of such Composite Applications. Our Case Study extends a back-end business process which is required for the specific needs of a demo company selling wine. We use this to describe how the business centric models specifying the modified business behaviour of our case study can be utilized for business performance analysis where most of the actions are performed by humans. In particular, we apply a refined version of Model-Driven Performance Engineering that we proposed in our previous work and motivate which business domain specifics have to be taken into account for business performance analysis. We additionally motivate the need for performance related decision support for domain experts, who generally lack performance related skills. Such a support should offer visual guidance about what should be changed in the design and resource mapping to get improved results with respect to modification constraints and performance objectives, or objectives for time.

The Development of the Contextual Analysis of Human Remains in Ireland

The colonial experience has been a dominant factor in the production of culture in Ireland, including narratives of the past. In the context of nineteenth century British imperialism, physical anthropology and archaeology were just two of a number of scientific discourses recruited to rationalise and justify colonialist policies. Legitimation was in part provided by racialised and sectarian conceptualisations of local populations in both past and present. After the partition of the island in the early twentieth century, racialised notions of the Irish population were embraced by both nationalist movements (green and orange) on the island. Changes came with the impact of processual archaeology and the appearance of bioarchaeology in the early 1980s, the latter directly influenced by the North American tradition. The last two decades have seen considerable achievements in bioarchaeology in Ireland. The profile of the discipline has been raised, and despite the impact of the recent economic downturn, the number of archaeologists gaining the necessary specialist skills has finally reached critical mass. The focus in Irish bioarchaeology is now on synthetic and thematic projects, and a number of initiatives are currently underway which will go some way towards furthering understanding of the past populations of Ireland.

Root Cause Analysis by a Combined Sparse Classification and Monte Carlo Approach

Classification methods with embedded feature selection capability are very appealing for the analysis of complex processes since they allow the analysis of root causes even when the number of input variables is high. In this work, we investigate the performance of three techniques for classification within a Monte Carlo strategy with the aim of root cause analysis. We consider the naive bayes classifier and the logistic regression model with two different implementations for controlling model complexity, namely, a LASSO-like implementation with a L1 norm regularization and a fully Bayesian implementation of the logistic model, the so called relevance vector machine. Several challenges can arise when estimating such models mainly linked to the characteristics of the data: a large number of input variables, high correlation among subsets of variables, the situation where the number of variables is higher than the number of available data points and the case of unbalanced datasets. Using an ecological and a semiconductor manufacturing dataset, we show advantages and drawbacks of each method, highlighting the superior performance in term of classification accuracy for the relevance vector machine with respect to the other classifiers. Moreover, we show how the combination of the proposed techniques and the Monte Carlo approach can be used to get more robust insights into the problem under analysis when faced with challenging modelling conditions.

Global Sensitivity Analysis for MAP Inference in Graphical Models

We study the sensitivity of a MAP configuration of a discrete probabilistic graphical model with respect to perturbations of its parameters. These perturbations are global, in the sense that simultaneous perturbations of all the parameters (or any chosen subset of them) are allowed. Our main contribution is an exact algorithm that can check whether the MAP configuration is robust with respect to given perturbations. Its complexity is essentially the same as that of obtaining the MAP configuration itself, so it can be promptly used with minimal effort. We use our algorithm to identify the largest global perturbation that does not induce a change in the MAP configuration, and we successfully apply this robustness measure in two practical scenarios: the prediction of facial action units with posed images and the classification of multiple real public data sets. A strong correlation between the proposed robustness measure and accuracy is verified in both scenarios.

The Meaning of Drug Use among Homeless Young People:A longitudinal pathways analysis

A strong link between drug use and homelessness has long since been documented in the international literature. However, much of the research has concentrated on the direction of the relationship between drug use and homelessness, seeking to establish drug use as a cause or consequence of homelessness, with far less attention to the intersection of drug and homeless ‘careers’. This paper examines the drug and homeless pathways of young people who are participants in a qualitative longitudinal study of homeless youth in Dublin, Ireland. The findings highlight downward drug transitions as associated with exiting homelessness and continued or escalated consumption as associated with remaining homeless. Analyses of the meanings young people attach to drug use over time reveal the importance of housing as an enabler to engaging with treatment and as assisting the process of becoming and remaining drug free. Young people who remained homeless did not accept their situations, as ‘acculturation’ accounts would suggest; rather, they aspired to changing their situations. However, they also face strong barriers to accessing housing which in turn hamper their efforts to address the matter of their drug use. The implications for how the homeless/drug use ‘nexus’ is conceptualised and understood, as well as implications for policy, are discussed.

Multimodal Critical Discourse Analysis (MCDA)

Critical Discourse Analysis (CDA) has probably made the most comprehensive attempt to develop a theory of the inter-connectedness of discourse, power and ideology and is specifically concerned with the role that discourse plays in main-taining and legitimizing inequality in society. While CDA’s general thrust has been towards the analysis of linguistic structures, some critical discourse analysts have begun to focus on multimodal discourses because of the increasingly impor-tant role these play in many social and political contexts. Still, a great deal of CDA analysis has remained largely monomodal. The principal aim of this chapter is therefore to address this situation and demonstrate in what ways CDA can be deployed to analyse the ways that ideological discourses can be communicated, naturalised and legitimated beyond the linguistic level. The chapter also offers a rationale for a multimodal approach based on Halliday’s Systemic Functional Linguistics (SFL), by which it is directly informed