Practice links [Issue 37, April 2010]

Practice Links is a free e-publication for practitioners working in Irish social services, voluntary and nongovernmental sectors. Practice Links was created to enable practitioners to keep up-to-date with new publications, electronic resources and conference opportunities. Issue 37 reports on some preliminary results from a survey by IASW Southern Region on supervision practices.

Transport energy demand: techno-economic modelling and scenarios for Irish climate policy

The case for energy policy modelling is strong in Ireland, where stringent EU climate targets are projected to be overshot by 2015. Policy targets aiming to deliver greenhouse gas and renewable energy targets have been made, but it is unclear what savings are to be achieved and from which sectors. Concurrently, the growth of personal mobility has caused an astonishing increase in CO2 emissions from private cars in Ireland, a 37% rise between 2000 and 2008, and while there have been improvements in the efficiency of car technology, there was no decrease in the energy intensity of the car fleet in the same period. This thesis increases the capacity for evidenced-based policymaking in Ireland by developing techno-economic transport energy models and using them to analyse historical trends and to project possible future scenarios. A central focus of this thesis is to understand the effect of the car fleet‘s evolving technical characteristics on energy demand. A car stock model is developed to analyse this question from three angles: Firstly, analysis of car registration and activity data between 2000 and 2008 examines the trends which brought about the surge in energy demand. Secondly, the car stock is modelled into the future and is used to populate a baseline “no new policy” scenario, looking at the impact of recent (2008-2011) policy and purchasing developments on projected energy demand and emissions. Thirdly, a range of technology efficiency, fuel switching and behavioural scenarios are developed up to 2025 in order to indicate the emissions abatement and renewable energy penetration potential from alternative policy packages. In particular, an ambitious car fleet electrification target for Ireland is examined. The car stock model‘s functionality is extended by linking it with other models: LEAP-Ireland, a bottom-up energy demand model for all energy sectors in the country; Irish TIMES, a linear optimisation energy system model; and COPERT, a pollution model. The methodology is also adapted to analyse trends in freight energy demand in a similar way. Finally, this thesis addresses the gap in the representation of travel behaviour in linear energy systems models. A novel methodology is developed and case studies for Ireland and California are presented using the TIMES model. Transport Energy

Early biomarkers to predict grade of encephalopathy following hypoxic ischaemic injury

The standard early markers for identifying and grading HIE severity, are not sufficient to ensure all children who would benefit from treatment are identified in a timely fashion. The aim of this thesis was to explore potential early biomarkers of HIE. Methods: To achieve this a cohort of infants with perinatal depression was prospectively recruited. All infants had cord blood samples drawn and biobanked, and were assessed with standardised neurological examination, and early continuous multi-channel EEG. Cord samples from a control cohort of healthy infants were used for comparison. Biomarkers studied included; multiple inflammatory proteins using multiplex assay; the metabolomics profile using LC/MS; and the miRNA profile using microarray. Results: Eighty five infants with perinatal depression were recruited. Analysis of inflammatory proteins consisted of exploratory analysis of 37 analytes conducted in a sub-population, followed by validation of all significantly altered analytes in the remaining population. IL-6 and IL-6 differed significantly in infants with a moderate/severely abnormal vs. a normal-mildly abnormal EEG in both cohorts (Exploratory: p=0.016, p=0.005: Validation: p=0.024, p=0.039; respectively). Metabolomic analysis demonstrated a perturbation in 29 metabolites. A Cross- validated Partial Least Square Discriminant Analysis model was developed, which accurately predicted HIE with an AUC of 0.92 (95% CI: 0.84-0.97). Analysis of the miRNA profile found 70 miRNA significantly altered between moderate/severely encephalopathic infants and controls. miRNA target prediction databases identified potential targets for the altered miRNA in pathways involved in cellular metabolism, cell cycle and apoptosis, cell signaling, and the inflammatory cascade. Conclusion: This thesis has demonstrated that the recruitment of a large cohortof asphyxiated infants, with cord blood carefully biobanked, and detailed early neurophysiological and clinical assessment recorded, is feasible. Additionally the results described, provide potential alternate and novel blood based biomarkers for the identification and assessment of HIE.

Self-neglect: development and evaluation of a self-neglect (SN-37) measurement instrument

Aim: To develop and evaluate the psychometric properties of an instrument for the measurement of self-neglect (SN).Conceptual Framework: An elder self-neglect (ESN) conceptual framework guided the literature review and scale development. The framework has two key dimensions physical/psycho-social and environmental and seven sub dimensions which are representative of the factors that can contribute to intentional and unintentional SN. Methods: A descriptive cross-sectional design was adopted to achieve the research aim. The study was conducted in two phases. Phase 1 involved the development of the questionnaire content and structure. Phase 2 focused on establishing the psychometric properties of the instrument. Content validity was established by a panel of 8 experts and piloted with 9 health and social care professionals. The instrument was subsequently posted with a stamped addressed envelope to 566 health and social care professionals who met specific eligibility criteria across the four HSE areas. A total of 341 questionnaires were returned, a response rate of 60% and 305 (50%) completed responses were included in exploratory factor analysis (EFA). Item and factor analyses were performed to elicit the instruments underlying factor structure and establish preliminary construct validity. Findings: Item and factor analyses resulted in a logically coherent, 37 items, five factor solution, explaining 55.6% of the cumulative variance. The factors were labelled: ‘Environment’, ‘Social Networks’, ‘Emotional and Behavioural Liability’, ‘Health Avoidance’ and ‘Self-Determinism’. The factor loadings were >0.40 for all items on each of the five subscales. Preliminary construct validity was supported by findings. Conclusion: The main outcome of this research is a 37 item Self-Neglect (SN-37) measurement instrument that was developed by EFA and underpinned by an ESN conceptual framework. Preliminary psychometric evaluation of the instrument is promising. Future work should be directed at establishing the construct and criterion related validity of the instrument.

Current methods and advances in forecasting of wind power generation

Wind power generation differs from conventional thermal generation due to the stochastic nature of wind. Thus wind power forecasting plays a key role in dealing with the challenges of balancing supply and demand in any electricity system, given the uncertainty associated with the wind farm power output. Accurate wind power forecasting reduces the need for additional balancing energy and reserve power to integrate wind power. Wind power forecasting tools enable better dispatch, scheduling and unit commitment of thermal generators, hydro plant and energy storage plant and more competitive market trading as wind power ramps up and down on the grid. This paper presents an in-depth review of the current methods and advances in wind power forecasting and prediction. Firstly, numerical wind prediction methods from global to local scales, ensemble forecasting, upscaling and downscaling processes are discussed. Next the statistical and machine learning approach methods are detailed. Then the techniques used for benchmarking and uncertainty analysis of forecasts are overviewed, and the performance of various approaches over different forecast time horizons is examined. Finally, current research activities, challenges and potential future developments are appraised.

Design history: exploring corporate communities

A design history is a narrative involving a multitude of social groups, interpretive flexibility, and eventual stabilization of shared understanding. Design history surfaces the practices that help shape and define engagements and can increase not only our theoretical understanding of what design is, but also our capacity to realize this understanding in practice. We use a design history perspective to examine how corporate technology initiatives establish and support open source communities and the crafting of relevant design practices that enable their advancement. We foster an evolving expression of design research that treats artifacts not as stable objects to be singularly evaluated, but as evolving systems contingent on historical trajectories.

At the Vanguard of Design Science: First Impressions and Early Findings from Ongoing Research

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