A Multidimensional Analysis of Adaptation in a Developing Country Context

Econometric analyses of European datasets suggest that income aspirations increase with current income. This finding is consistent with the adaptation hypothesis - the notion that individual aspirations adjust to reflect personal circumstances and living conditions. We add to these existing studies in two ways: we investigate the relationship between aspirations and living conditions within a developing country rather than a developed country context, thereby extending the analysis to individuals with considerably poorer living conditions; and we expand the analysis to look not only at income but also at educational and health aspirations. Like earlier studies we find that income aspirations increase with both the individual’s own actual income and the incomes of those around them. We also find a positive relationship between actual and aspired to education. However, with respect to health, we find that people aspire to more rather than less health when surrounded by others who are ill.adaptation, aspirations, poverty, well-being, education, health

Somatic Cell Counts (Leucocyte Counts) A Standard of Milk Acceptability

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Students and ICT: an analysis of student reaction to the use of computer technology in language learning

Thispaperdiscussesthereactionofstudentsinthreeuniversitiesto the use of information and communications technology(ICT) in their language learning experience. Although thefindings apply to the language-learning context, there are moregeneric implications for the wider area of computer enhancedlearning. The study uses qualitative and quantitative datacollected as part of a doctoral investigation into computerbasedlanguage-learning environments. The paper considersone main research question: are students resistant, radical orreluctant users of the technology, and why? It examines howand why students use the Web, e-mail and CALL packages toenhance their learning. This study shows that students aregenerally not unsympathetic towards it, although some of thefactors that affectthe level of student use of the technology, suchas course relevance and access of computers, are often outsidetheir control

Classification of Material Mixtures in Volume Data for Visualization and Modeling

Material classification is a key stop in creating computer graphics models and images from volume data, We present a new algorithm for identifying the distribution of different material types in volumetric datasets such as those produced with Magnetic Resonance Imaging (NMI) or Computed Tomography (CT). The algorithm assumes that voxels can contain more than one material, e.g. both muscle and fat; we wish to compute the relative proportion of each material in the voxels. Other classification methods have utilized Gaussian probability density functions to model the distribution of values within a dataset. These Gaussian basis functions work well for voxels with unmixed materials, but do not work well where the materials are mixed together. We extend this approach by deriving non-Gaussian "mixture" basis functions. We treat a voxel as a volume, not as a single point. We use the distribution of values within each voxel-sized volume to identify materials within the voxel using a probabilistic approach. The technique reduces the classification artifacts that occur along boundaries between materials. The technique is useful for making higher quality geometric models and renderings from volume data, and has the potential to make more accurate volume measurements. It also classifies noisy, low-resolution data well

Partial-volume Bayesian classification of material mixtures in MR volume data using voxel histograms

The authors present a new algorithm for identifying the distribution of different material types in volumetric datasets such as those produced with magnetic resonance imaging (MRI) or computed tomography (CT). Because the authors allow for mixtures of materials and treat voxels as regions, their technique reduces errors that other classification techniques can create along boundaries between materials and is particularly useful for creating accurate geometric models and renderings from volume data. It also has the potential to make volume measurements more accurately and classifies noisy, low-resolution data well. There are two unusual aspects to the authors' approach. First, they assume that, due to partial-volume effects, or blurring, voxels can contain more than one material, e.g., both muscle and fat; the authors compute the relative proportion of each material in the voxels. Second, they incorporate information from neighboring voxels into the classification process by reconstructing a continuous function, ρ(x), from the samples and then looking at the distribution of values that ρ(x) takes on within the region of a voxel. This distribution of values is represented by a histogram taken over the region of the voxel; the mixture of materials that those values measure is identified within the voxel using a probabilistic Bayesian approach that matches the histogram by finding the mixture of materials within each voxel most likely to have created the histogram. The size of regions that the authors classify is chosen to match the sparing of the samples because the spacing is intrinsically related to the minimum feature size that the reconstructed continuous function can represent