Effectiveness of 3D Geoelectrical Resistivity Imaging using Parallel 2D Profiles

Acquisition geometry for 3D geoelectrical resistivity imaging in which apparent resistivity data of a set of parallel 2D profiles are collated to 3D dataset was evaluated. A set of parallel 2D apparent resistivity data was generated over two model structures. The models, horst and trough, simulate the geological environment of a weathered profile and refuse dump site in a crystalline basement complex respectively. The apparent resistivity data were generated for Wenner–alpha, Wenner–beta, Wenner–Schlumberger, dipole–dipole, pole–dipole and pole–pole arrays with minimum electrode separation, a (a = 2, 4, 5 and 10 m) and inter-line spacing, L (L = a, 2a, 2.5a, 4a, 5a and 10a). The 2D apparent resistivity data for each of the arrays were collated to 3D dataset and inverted using a full 3D inversion code. The 3D imaging capability and resolution of the arrays for the set of parallel 2D profiles are presented. Grid orientation effects are observed in the inversion images produced. Inter-line spacing of not greater than four times the minimum electrode separation gives reasonable inverse models. The resolution of the inverse models can be greatly improved if the 3D dataset is built by collating sets of orthogonal 2D profile

Partial mixture model for tight clustering of gene expression time-course

Background: Tight clustering arose recently from a desire to obtain tighter and potentially more informative clusters in gene expression studies. Scattered genes with relatively loose correlations should be excluded from the clusters. However, in the literature there is little work dedicated to this area of research. On the other hand, there has been extensive use of maximum likelihood techniques for model parameter estimation. By contrast, the minimum distance estimator has been largely ignored. Results: In this paper we show the inherent robustness of the minimum distance estimator that makes it a powerful tool for parameter estimation in model-based time-course clustering. To apply minimum distance estimation, a partial mixture model that can naturally incorporate replicate information and allow scattered genes is formulated. We provide experimental results of simulated data fitting, where the minimum distance estimator demonstrates superior performance to the maximum likelihood estimator. Both biological and statistical validations are conducted on a simulated dataset and two real gene expression datasets. Our proposed partial regression clustering algorithm scores top in Gene Ontology driven evaluation, in comparison with four other popular clustering algorithms. Conclusion: For the first time partial mixture model is successfully extended to time-course data analysis. The robustness of our partial regression clustering algorithm proves the suitability of the ombination of both partial mixture model and minimum distance estimator in this field. We show that tight clustering not only is capable to generate more profound understanding of the dataset under study well in accordance to established biological knowledge, but also presents interesting new hypotheses during interpretation of clustering results. In particular, we provide biological evidences that scattered genes can be relevant and are interesting subjects for study, in contrast to prevailing opinion

Introducing SaMip: The Social Assistance and Minimum Income Protection Interim Dataset

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Implementation of an evidence-based practice nursing handover tool in intensive care using the knowledge-to-action framework

Background Miscommunication during handover has been linked to adverse patient events and is an international patient safety priority. Despite the development of handover resources, s tandardised handover tools for nursing team leader s in intensive care are limited. Aims The study aim was to implement and evaluate an evidence- based electronic minimum dataset for nursing team leader shift -to-shift handover in the intensive care unit using the k nowledge- to-action framework. Methods This study was conducted in a 21- bed medical/surgical intensive care unit in Queensland, Australia. Senior registered nurses involved in team leader handover were recruited. Three phases of the knowledge- to-action framework (select, tailor and implement interventions, monitor knowledge use and evaluate outcomes ) guided the implementation and evaluation process. A post -implementation practice audit and survey were carried out to determine nursing team leader use and perceptions of the electronic minimum dataset three months after implementation. Results are presented using descriptive statistics ( median, IQR, frequency and percentage) . Results Overall (86%, n=49) , team leader s used the electronic minimum dataset for handover and communication regarding patient plan increased . K ey content items however were absent from handovers and additional documentation was required alongside the minimum dataset to conduct handover. Of the team leader s surveyed (n=35), those receiving handover perceived the electronic minimum dataset more Page 4 of 24 positive ly than team leader s giving handover (n=35) . Benefits to using the electronic minimum dataset included the pat ient content (48%), suitability for short -stay patients (16%), decreased time updating (12%) and print ing the tool (12%) . Almost half of the participants however, found the minimum dataset contained irrelevant information, reported difficulties navigating and locating relevant information and pertinent information was missing. Suggestions for improvement focused on modifications to the electronic handover interface. Linking evidence to action Prior to developing and implementing electronic handover tools , adequate infrastructure is required to support knowledge translation and ensure clinician and organisational needs are met

Retirement Choice Simulation in Household Settings with Heterogeneous Pension Plans

This paper estimates a structured life cycle model of family retirement decision using a unique historical dataset back simulated from Living in Ireland survey. Our model takes the advantages of the dataset and models retirement decisions in terms of monetary and leisure incentives, which reflect the complex welfare system in Ireland. The household extension version of the model adapts a collective modelling approach, where the intra-household bargaining is considered. We further incorporate complimentary leisure, which allows us to analyse the interactions of spouses' retirement timing. This methodology enables us to capture the dynamics of retirement and tax-benefit policies and can be used to simulate the effect of policy reform on household retirement behaviours. The paper, in addition, applies the model to assess individual budgetary implications and the labour market impact of rising the minimum retirement age. Our simulation shows that increasing the minimum age for state pension entitlement to 70 would only delay the retirement by less than 2 years according to the individual based model. When we consider the intra-household bargaining and the higher preference of leisure found in the dual career households, the effect of postponing retirement further declines. The result suggests barely postponing the minimum retirement age for state pension without redefining the occupation and private pension rules will only have limited impact for household retirement behaviour in Ireland.retirement, choice modelling, microsimulation