The association between student characteristics and the development of clinical reasoning in a graduate-entry, PBL medical programme

This study sought to assess the extent to which the entry characteristics of students in a graduate-entry medical programme predict the subsequent development of clinical reasoning ability. Subjects comprised 290 students voluntarily recruited from three successive cohorts of the University of Queensland's MBBS Programme. Clinical reasoning was measured once a year over a period of three years using two methods, a set of 10 Clinical Reasoning Problems (CRPs) and the Diagnostic Thinking Inventory (DTI). Data on gender, age at entry into the programme, nature of primary degree, scores on selection criteria (written examination plus interview) and academic performance in the first two years of the programme were recorded for each student, and their association with clinical reasoning skill analysed using univariate and multivariate analysis. Univariate analysis indicated significant associations between CRP score, gender and primary degree with a significant but small association between DTI and interview score. Stage of progression through the programme was also an important predictor of performance on both indicators. Subsequent multivariate analysis suggested that female gender is a positive predictor of CRP score independently of the nature of a subject's primary degree and stage of progression through the programme, although these latter two variables are interdependent. Positive predictors of clinical reasoning skill are stage of progression through the MBBS programme, female gender and interview score. Although the nature of a student's primary degree is important in the early years of the programme, evidence suggests that by graduation differences between students' clinical reasoning skill due to this factor have been resolved.

New Governance and the EU Courts : The Experimentalist Architecture of Judicial Decision-Making

The dissertation examines the role of the EU courts in new governance. New governance has raised unprecedented interest in the EU in recent years. This is manifested in a plethora of instruments and actors at various levels that challenge more traditional forms of command-and-control regulation. New governance and political experimentation more generally is thought to sap the ability of the EU judiciary to monitor and review these experiments. The exclusion of the courts is then seen to add to the legitimacy problem of new governance. The starting point of this dissertation is the observation that the marginalised role of the courts is based on theoretical and empirical assumptions which invite scrutiny. The theoretical framework of the dissertation is deliberative democracy and democratic experimentalism. The analysis of deliberative democracy is sustained by an attempt to apply theoretical concepts to three distinctive examples of governance in the EU. These are the EU Sustainable Development Strategy, the European Chemicals Agency, and the Common Implementation Strategy for the Water Framework Directive. The case studies show numerous disincentives and barriers to judicial review. Among these are questions of the role of courts in shaping governance frameworks, the reviewability of science-based measures, the standing of individuals before the courts, and the justiciability of soft law. The dissertation analyses the conditions of judicial review in each governance environment and proposes improvements. From a more theoretical standpoint it could be said that each case study presents a governance regime which builds on legislation that lays out major (guide)lines but leaves details to be filled out at a later stage. Specification of detailed standards takes place through collaborative networks comprising members from national administrations, NGOs, and the Commission. Viewed this way, deliberative problem-solving is needed to bring people together to clarify, elaborate, and revise largely abstract and general norms in order to resolve concrete and specific problems and to make law applicable and enforceable. The dissertation draws attention to the potential of peer review included there and its profound consequences for judicial accountability structures. It is argued that without this kind of ongoing and dynamic peer review of accountability in governance frameworks, judicial review of new governance is difficult and in some cases impossible. This claim has implications for how we understand the concept of soft law, the role of the courts, participation rights, and the legitimacy of governance measures more generally. The experimentalist architecture of judicial decision-making relies upon a wide variety of actors to provide conditions for legitimate and efficient review.

Computational algorithms inspired by biological processes and evolution

In recent times computational algorithms inspired by biological processes and evolution are gaining much popularity for solving science and engineering problems. These algorithms are broadly classified into evolutionary computation and swarm intelligence algorithms, which are derived based on the analogy of natural evolution and biological activities. These include genetic algorithms, genetic programming, differential evolution, particle swarm optimization, ant colony optimization, artificial neural networks, etc. The algorithms being random-search techniques, use some heuristics to guide the search towards optimal solution and speed-up the convergence to obtain the global optimal solutions. The bio-inspired methods have several attractive features and advantages compared to conventional optimization solvers. They also facilitate the advantage of simulation and optimization environment simultaneously to solve hard-to-define (in simple expressions), real-world problems. These biologically inspired methods have provided novel ways of problem-solving for practical problems in traffic routing, networking, games, industry, robotics, economics, mechanical, chemical, electrical, civil, water resources and others fields. This article discusses the key features and development of bio-inspired computational algorithms, and their scope for application in science and engineering fields.

Flexibility in Food Extraction Techniques in Urban Free-Ranging Bonnet Macaques, Macaca radiata

Non-human primate populations, other than responding appropriately to naturally occurring challenges, also need to cope with anthropogenic factors such as environmental pollution, resource depletion, and habitat destruction. Populations and individuals are likely to show considerable variations in food extraction abilities, with some populations and individuals more efficient than others at exploiting a set of resources. In this study, we examined among urban free-ranging bonnet macaques, Macaca radiata (a) local differences in food extraction abilities, (b) between-individual variation and within-individual consistency in problem-solving success and the underlying problem-solving characteristics, and (c) behavioral patterns associated with higher efficiency in food extraction. When presented with novel food extraction tasks, the urban macaques having more frequent exposure to novel physical objects in their surroundings, extracted food material from PET bottles and also solved another food extraction task (i.e., extracting an orange from a wire mesh box), more often than those living under more natural conditions. Adults solved the tasks more frequently than juveniles, and females more frequently than males. Both solution-technique and problem-solving characteristics varied across individuals but remained consistent within each individual across the successive presentations of PET bottles. The macaques that solved the tasks showed lesser within-individual variation in their food extraction behavior as compared to those that failed to solve the tasks. A few macaques appropriately modified their problem-solving behavior in accordance with the task requirements and solved the modified versions of the tasks without trial-and-error learning. These observations are ecologically relevant - they demonstrate considerable local differences in food extraction abilities, between-individual variation and within-individual consistency in food extraction techniques among free-ranging bonnet macaques, possibly affecting the species' local adaptability and resilience to environmental changes.

Multi-Temporal Satellite Image Analysis Using Gene Expression Programming

This paper discusses an approach for river mapping and flood evaluation to aid multi-temporal time series analysis of satellite images utilizing pixel spectral information for image classification and region-based segmentation to extract water covered region. Analysis of Moderate Resolution Imaging Spectroradiometer (MODIS) satellite images is applied in two stages: before flood and during flood. For these images the extraction of water region utilizes spectral information for image classification and spatial information for image segmentation. Multi-temporal MODIS images from ``normal'' (non-flood) and flood time-periods are processed in two steps. In the first step, image classifiers such as artificial neural networks and gene expression programming to separate the image pixels into water and non-water groups based on their spectral features. The classified image is then segmented using spatial features of the water pixels to remove the misclassified water region. From the results obtained, we evaluate the performance of the method and conclude that the use of image classification and region-based segmentation is an accurate and reliable for the extraction of water-covered region.

Studies and Evaluation of EIT Image Reconstruction in EIDORS with Simulated Boundary Data

Simulated boundary potential data for Electrical Impedance Tomography (EIT) are generated by a MATLAB based EIT data generator and the resistivity reconstruction is evaluated with Electrical Impedance Tomography and Diffuse Optical Tomography Reconstruction Software (EIDORS). Circular domains containing subdomains as inhomogeneity are defined in MATLAB-based EIT data generator and the boundary data are calculated by a constant current simulation with opposite current injection (OCI) method. The resistivity images reconstructed for different boundary data sets and images are analyzed with image parameters to evaluate the reconstruction.