999 resultados para Automatic weight assignment


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This reports a study that seeks to explore the experience of students majoring in technology and design in an undergraduate education degree. It examines their experiences in finding and using information for a practical assignment. In mapping the variation of the students' experience, the study uses a qualitative, interpretive approach to analyse the data, which was collected via one-to-one interviews. The analysis yielded five themes through which technology education students find and use information: interaction with others; experience (past and new); formal educational learning; the real world; and incidental occurrences. The intentions and strategies that form the students' approaches to finding and using information are discussed. So too are the implications for teaching practice.

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Background Food neophobia, the rejection of unknown or novel foods, may result in poor dietary patterns. This study investigates the cross-sectional relationship between neophobia in children aged 24 months and variety of fruit and vegetable consumption, intake of discretionary foods and weight. Methods Secondary analysis of data from 330 parents of children enrolled in the NOURISH RCT (control group only) and SAIDI studies was performed using data collected at child age 24 months. Neophobia was measured at 24 months using the Child Food Neophobia Scale (CFNS). The cross-sectional associations between total CFNS score and fruit and vegetable variety, discretionary food intake and BMI (Body Mass Index) Z-score were examined via multiple regression models; adjusting for significant covariates. Results At 24 months, more neophobic children were found to have lower variety of fruits (β=-0.16, p=0.003) and vegetables (β=-0.29, p<0.001) but have a greater proportion of daily energy from discretionary foods (β=0.11, p=0.04). There was no significant association between BMI Z-score and CFNS score. Conclusions Neophobia is associated with poorer dietary quality. Results highlight the need for interventions to (1) begin early to expose children to a wide variety of nutritious foods before neophobia peaks and (2) enable health professionals to educate parents on strategies to overcome neophobia.

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The commercialization of aerial image processing is highly dependent on the platforms such as UAVs (Unmanned Aerial Vehicles). However, the lack of an automated UAV forced landing site detection system has been identified as one of the main impediments to allow UAV flight over populated areas in civilian airspace. This article proposes a UAV forced landing site detection system that is based on machine learning approaches including the Gaussian Mixture Model and the Support Vector Machine. A range of learning parameters are analysed including the number of Guassian mixtures, support vector kernels including linear, radial basis function Kernel (RBF) and polynormial kernel (poly), and the order of RBF kernel and polynormial kernel. Moreover, a modified footprint operator is employed during feature extraction to better describe the geometric characteristics of the local area surrounding a pixel. The performance of the presented system is compared to a baseline UAV forced landing site detection system which uses edge features and an Artificial Neural Network (ANN) region type classifier. Experiments conducted on aerial image datasets captured over typical urban environments reveal improved landing site detection can be achieved with an SVM classifier with an RBF kernel using a combination of colour and texture features. Compared to the baseline system, the proposed system provides significant improvement in term of the chance to detect a safe landing area, and the performance is more stable than the baseline in the presence of changes to the UAV altitude.

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This thesis undertakes an empirical investigation to identify factors that influence the decision to undertake weight loss behaviour using the nationally representative HILDA dataset. Although many factors influenced the decision, the findings suggested that body weight satisfaction was the greatest determinant of weight loss dieting. This thesis therefore conducted a further empirical study to analyse the determinants of body weight satisfaction. A rank-hypothesis was found to better predict variation in body weight satisfaction levels than the absolute value of the individual's Body Mass Index (BMI) or the relative-norm hypothesis, which are commonly reported in the literature.

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It is commonplace to use digital video cameras in robotic applications. These cameras have built-in exposure control but they do not have any knowledge of the environment, the lens being used, the important areas of the image and do not always produce optimal image exposure. Therefore, it is desirable and often necessary to control the exposure off the camera. In this paper we present a scheme for exposure control which enables the user application to determine the area of interest. The proposed scheme introduces an intermediate transparent layer between the camera and the user application which combines the information from these for optimal exposure production. We present results from indoor and outdoor scenarios using directional and fish-eye lenses showing the performance and advantages of this framework.

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It’s commonly assumed that psychiatric violence is motivated by delusions, but here the concept of a reversed impetus is explored, to understand whether delusions are formed as ad-hoc or post-hoc rationalizations of behaviour or in advance of the actus reus. The reflexive violence model proposes that perceptual stimuli has motivational power and this may trigger unwanted actions and hallucinations. The model is based on the theory of ecological perception, where opportunities enabled by an object are cues to act. As an apple triggers a desire to eat, a gun triggers a desire to shoot. These affordances (as they are called) are part of the perceptual apparatus, they allow the direct recognition of objects – and in emergencies they enable the fastest possible reactions. Even under normal circumstances, the presence of a weapon will trigger inhibited violent impulses. The presence of a victim will also, but under normal circumstances, these affordances don’t become violent because negative action impulses are totally inhibited, whereas in psychotic illness, negative action impulses are treated as emergencies and bypass frontal inhibitory circuits. What would have been object recognition becomes a blind automatic action. A range of mental illnesses can cause inhibition to be bypassed. At its most innocuous, this causes both simple hallucinations (where the motivational power of an object is misattributed). But ecological perception may have the power to trigger serious violence also –a kind that’s devoid of motives or planning and is often shrouded in amnesia or post-rational delusions.

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Genomes of 82 Acinetobacter baumannii global clones 1 (GC1) and 2 (GC2) isolates were sequenced and different forms of the locus predicted to direct synthesis of the outer core (OC) of the lipooligosaccharide were identified. OCL1 was in all GC2 genomes, whereas GC1 isolates carried OCL1, OCL3 or a new locus, OCL5. Three mutants in which an insertion sequence (ISAba1 or ISAba23) interrupted OCL1 were identified. Isolates with OCL1 intact produced only lipooligosaccharide, while the mutants produced lipooligosaccharide of reduced molecular weight. Thus, the assignment of the OC locus as that responsible for the synthesis of the OC is correct.

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Background Food security exists when all people, at all times, have physical, economic and socially acceptable access to safe, sufficient, and adequately nutritious food in order to meet their dietary needs for an active and healthy life. For high income countries and those experiencing the nutrition transition, food security is not only about the quantity of available food but also the nutritional quality as related to over- and under-nutrition. Vietnam is currently undergoing this nutrition transition, and as a result the relationship between food insecurity, socio-demographic factors and weight status is complex. The primary objective of this study was to therefore measure the prevalence of household food insecurity in a disadvantaged urban district in Ho Chi Minh City (HCMC) in Vietnam using a more comprehensive tool. This study also aims to examine the relationships between food insecurity and socio-demographic factors, weight status, and food intakes. Methods A cross-sectional study was conducted using multi-stage sampling. Adults who were mainly responsible for cooking were interviewed in 250 households. Data was collected on socioeconomic and demographic factors using previously validated tools. Food security was assessed using the Latin American and Caribbean Household Food Security Scale (ELCSA) tool and households were categorized as food secure or mildly, moderately or severely food insecure. Questions regarding food intake were based on routinely used and validated questions in HCMC, weight status was self-reported. Results Cronbach’s alpha coefficient was 0.87, showing the ELCSA had a good internal reliability. Approximately 34.4% of households were food insecure. Food insecurity was inversely related to total household income (OR = 0.09, 95% CI = 0.04 - 0.22) and fruit intakes (OR = 2.2, 95% CI 1.31 - 4.22). There was no association between weight and food security status. Conclusions Despite rapid industrialization and modernization, food insecurity remains an important public health issue in large urban areas of HCMC, suggesting that strategies to address food insecurity should be implemented in urban settings, and not just rural locations. Fruit consumption among food insecure households may be compromised because of financial difficulties, which may lead to poorer health outcomes particularly related to non-communicable disease prevention and management.

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Affect is an important feature of multimedia content and conveys valuable information for multimedia indexing and retrieval. Most existing studies for affective content analysis are limited to low-level features or mid-level representations, and are generally criticized for their incapacity to address the gap between low-level features and high-level human affective perception. The facial expressions of subjects in images carry important semantic information that can substantially influence human affective perception, but have been seldom investigated for affective classification of facial images towards practical applications. This paper presents an automatic image emotion detector (IED) for affective classification of practical (or non-laboratory) data using facial expressions, where a lot of “real-world” challenges are present, including pose, illumination, and size variations etc. The proposed method is novel, with its framework designed specifically to overcome these challenges using multi-view versions of face and fiducial point detectors, and a combination of point-based texture and geometry. Performance comparisons of several key parameters of relevant algorithms are conducted to explore the optimum parameters for high accuracy and fast computation speed. A comprehensive set of experiments with existing and new datasets, shows that the method is effective despite pose variations, fast, and appropriate for large-scale data, and as accurate as the method with state-of-the-art performance on laboratory-based data. The proposed method was also applied to affective classification of images from the British Broadcast Corporation (BBC) in a task typical for a practical application providing some valuable insights.

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Robustness to variations in environmental conditions and camera viewpoint is essential for long-term place recognition, navigation and SLAM. Existing systems typically solve either of these problems, but invariance to both remains a challenge. This paper presents a training-free approach to lateral viewpoint- and condition-invariant, vision-based place recognition. Our successive frame patch-tracking technique infers average scene depth along traverses and automatically rescales views of the same place at different depths to increase their similarity. We combine our system with the condition-invariant SMART algorithm and demonstrate place recognition between day and night, across entire 4-lane-plus-median-strip roads, where current algorithms fail.

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Objective The objective of this study was to evaluate weight-related risk perception in early pregnancy and to compare this perception between women commencing pregnancy healthy weight and overweight. Study design Pregnant women (n=664) aged 29±5 (mean±s.d.) years were recruited from a metropolitan teaching hospital in Australia. A self-administered questionnaire was completed at around 16 weeks of gestation. Height measured at baseline and self-reported pre-pregnancy weight were used to calculate body mass index. Cross-sectional analysis was conducted. Differences between groups were assessed using chi-squared tests for categorical variables and t-tests or Mann–Whitney U tests for continuous variables depending on distribution. Result Excess gestational weight gain (GWG) during pregnancy was more important in leading to health problems for women or their child compared with pre-pregnancy weight. Personal risk perception for complications was low for all women, although overweight women had slightly higher scores than healthy-weight women (2.4±1.0 vs 2.9±1.0; P<0.001). All women perceived their risk for complications to be below that of an average pregnant woman. Conclusion Women should be informed of the risk associated with their pre-pregnancy weight (in the case of maternal overweight) and excess GWG. If efforts to raise risk awareness are to result in preventative action, this information needs to be accompanied by advice and appropriate support on how to reduce risk.

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To analyse and compare standing thoracolumbar curves in normal weight participants and participants with obesity, using an electromagnetic device, and to analyse the measurement reliability. Material and Methods. Cross-sectional study was carried out. 36 individuals were divided into two groups (normal-weight and participants with obesity) according to their waist circumference. The reference points (T1–T8–L1–L5 and both posterior superior iliac spines) were used to perform a description of thoracolumbar curvature in the sagittal and coronal planes. A transformation from the global coordinate system was performed and thoracolumbar curves were adjusted by fifth-order polynomial equations. The tangents of the first and fifth lumbar vertebrae and the first thoracic vertebra were determined from their derivatives. The reliability of the measurement was assessed according to the internal consistency of the measure and the thoracolumbar curvature angles were compared between groups. Results. Cronbach’s alpha values ranged between 0.824 (95% CI: 0.776–0.847) and 0.918 (95% CI: 0.903–0.949). In the coronal plane, no significant differences were found between groups; however, in sagittal plane, significant differences were observed for thoracic kyphosis. Conclusion. There were significant differences in thoracic kyphosis in the sagittal plane between two groups of young adults grouped according to their waist circumference.

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Semantic priming occurs when a subject is faster in recognising a target word when it is preceded by a related word compared to an unrelated word. The effect is attributed to automatic or controlled processing mechanisms elicited by short or long interstimulus intervals (ISIs) between primes and targets. We employed event-related functional magnetic resonance imaging (fMRI) to investigate blood oxygen level dependent (BOLD) responses associated with automatic semantic priming using an experimental design identical to that used in standard behavioural priming tasks. Prime-target semantic strength was manipulated by using lexical ambiguity primes (e.g., bank) and target words related to dominant or subordinate meaning of the ambiguity. Subjects made speeded lexical decisions (word/nonword) on dominant related, subordinate related, and unrelated word pairs presented randomly with a short ISI. The major finding was a pattern of reduced activity in middle temporal and inferior prefrontal regions for dominant versus unrelated and subordinate versus unrelated comparisons, respectively. These findings are consistent with both a dual process model of semantic priming and recent repetition priming data that suggest that reductions in BOLD responses represent neural priming associated with automatic semantic activation and implicate the left middle temporal cortex and inferior prefrontal cortex in more automatic aspects of semantic processing.

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To understand factors that affect brain connectivity and integrity, it is beneficial to automatically cluster white matter (WM) fibers into anatomically recognizable tracts. Whole brain tractography, based on diffusion-weighted MRI, generates vast sets of fibers throughout the brain; clustering them into consistent and recognizable bundles can be difficult as there are wide individual variations in the trajectory and shape of WM pathways. Here we introduce a novel automated tract clustering algorithm based on label fusion - a concept from traditional intensity-based segmentation. Streamline tractography generates many incorrect fibers, so our top-down approach extracts tracts consistent with known anatomy, by mapping multiple hand-labeled atlases into a new dataset. We fuse clustering results from different atlases, using a mean distance fusion scheme. We reliably extracted the major tracts from 105-gradient high angular resolution diffusion images (HARDI) of 198 young normal twins. To compute population statistics, we use a pointwise correspondence method to match, compare, and average WM tracts across subjects. We illustrate our method in a genetic study of white matter tract heritability in twins.

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Automatic labeling of white matter fibres in diffusion-weighted brain MRI is vital for comparing brain integrity and connectivity across populations, but is challenging. Whole brain tractography generates a vast set of fibres throughout the brain, but it is hard to cluster them into anatomically meaningful tracts, due to wide individual variations in the trajectory and shape of white matter pathways. We propose a novel automatic tract labeling algorithm that fuses information from tractography and multiple hand-labeled fibre tract atlases. As streamline tractography can generate a large number of false positive fibres, we developed a top-down approach to extract tracts consistent with known anatomy, based on a distance metric to multiple hand-labeled atlases. Clustering results from different atlases were fused, using a multi-stage fusion scheme. Our "label fusion" method reliably extracted the major tracts from 105-gradient HARDI scans of 100 young normal adults. © 2012 Springer-Verlag.