153 resultados para Individual fishery quotas (IFQ)
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We study the influence of the choice of template in tensor-based morphometry. Using 3D brain MR images from 10 monozygotic twin pairs, we defined a tensor-based distance in the log-Euclidean framework [1] between each image pair in the study. Relative to this metric, twin pairs were found to be closer to each other on average than random pairings, consistent with evidence that brain structure is under strong genetic control. We also computed the intraclass correlation and associated permutation p-value at each voxel for the determinant of the Jacobian matrix of the transformation. The cumulative distribution function (cdf) of the p-values was found at each voxel for each of the templates and compared to the null distribution. Surprisingly, there was very little difference between CDFs of statistics computed from analyses using different templates. As the brain with least log-Euclidean deformation cost, the mean template defined here avoids the blurring caused by creating a synthetic image from a population, and when selected from a large population, avoids bias by being geometrically centered, in a metric that is sensitive enough to anatomical similarity that it can even detect genetic affinity among anatomies.
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Background Understanding how different socioeconomic indicators are associated with transport modes provide insight into which interventions might contribute to reducing socioeconomic inequalities in health. The purpose of this study was to examine associations between neighbourhood-level socioeconomic disadvantage, individual-level socioeconomic position (SEP) and usual transport mode. Methods This investigation included 11,036 residents from 200 neighbourhoods in Brisbane, Australia. Respondents self-reported their usual transport mode (car or motorbike, public transport, walking or cycling). Indicators for individual-level SEP were education, occupation, and household income; and neighbourhood disadvantage was measured using a census-derived index. Data were analysed using multilevel multinomial logistic regression. High SEP respondents and residents of the most advantaged neighbourhoods who used a private motor vehicle as their usual form of transport was the reference category. Results Compared with driving a motor vehicle, the odds of using public transport were higher for white collar employees (OR1.68, 95%CrI 1.41-2.01), members of lower income households (OR 1.71 95%CrI 1.25-2.30), and residents of more disadvantaged neighbourhoods (OR 1.93, 95%CrI 1.46-2.54); and lower for respondents with a certificate-level education (OR 0.60, 95%CrI 0.49-0.74) and blue collar workers (OR 0.63, 95%CrI 0.50-0.81). The odds of walking for transport were higher for the least educated (OR 1.58, 95%CrI 1.18-2.11), those not in the labour force (OR 1.94, 95%CrI 1.38-2.72), members of lower income households (OR 2.10, 95%CrI 1.23-3.64), and residents of more disadvantaged neighbourhoods (OR 2.73, 95%CrI 1.46-5.24). The odds of cycling were lower among less educated groups (OR 0.31, 95% CrI 0.19-0.48). Conclusion The relationships between socioeconomic characteristics and transport modes are complex, and provide challenges for those attempting to encourage active forms of transportation. Further work is required exploring the individual- and neighbourhood-level mechanisms behind transport mode choice, and what factors might influence individuals from different socioeconomic backgrounds to change to more active transport modes.
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The impairing effect from sleepiness is a major contributor to road crashes. The ability of a sleepy driver to perceive their level of sleepiness is an important consideration for road safety as well as the type of sleepiness countermeasure used by drivers as some sleepiness countermeasures are more effective than others. The aims of the current study were to determine the extent that the signs of driver sleepiness were associated with sleepy driving behaviours, as well as determining which individual factors (demographic, work, driving, and sleep-related factors) were associated with using a roadside or in-vehicle sleepiness countermeasure. A sample of 1518 Australian drivers from the Australian State of New South Wales and the neighbouring Australian Capital Territory took part in the study. The participants’ experiences with the signs of sleepiness were reasonably extensive. A number of the early signs of sleepiness (e.g., yawning, frequent eye blinks) were related with continuing to drive while sleepy, with the more advanced signs of sleepiness (e.g., difficulty keeping eyes open, dreamlike state of consciousness) associated with having a sleep-related close call. The individual factors associated with using a roadside sleepiness countermeasure included age (being older), education (tertiary level), difficulties getting to sleep, not continuing to drive while sleepy, and having experienced many signs of sleepiness. The results suggest that these participants have a reasonable awareness and experience with the signs of driver sleepiness. Factors related to previous experiences with sleepiness were associated with implementing a roadside countermeasure. Nonetheless, the high proportions of drivers performing sleepy driving behaviours, suggest that concerted efforts are needed with road safety campaigns regarding the dangers of driving while sleepy.
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This study utilizes a multilevel approach to both estimate the relative importance of individual, subunit, and organization effects on entrepreneurial intentions in academia, as well as to investigate specific factors within the subunit effect and their interactions with other levels. Using a dataset of 2,652 researchers from 386 departments in 24 European universities, our findings reveal that intra-university differences, caused by the influence of the department, should not be ignored when studying academic entrepreneurship. Whereas researchers’ entrepreneurial intentions are mostly influenced by individual differences, department membership explains more variation than the university as a whole. Furthermore, drawing upon organizational culture literature, we identify a department’s adhocracy culture, characterized by flexibility and an external orientation, to be positively related to entrepreneurial intentions. Finally, consistent with trait activation theory, we find that strong adhocracy cultures reinforce the positive association between proactive personality and entrepreneurial intentions. This effect is further intensified when the university also has a technology transfer office with a substantial size. Our results have relevant implications for both academics and practitioners, including university managers, department heads and policy makers.
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Theories of individual attitudes toward IT include task technology fit (TTF), technology acceptance model (TAM), unified theory of acceptance and use of technology (UTAUT), cognitive fit, expectation disconfirmation, and computer self-efficacy. Examination of these theories reveals three main concerns. First, the theories mostly ‘‘black box’’ (or omit) the IT artifact. Second, appropriate mid-range theory is not developed to contribute to disciplinary progress and to serve the needs of our practitioner community. Third, theories are overlapping but incommensurable. We propose a theoretical framework that harmonizes these attitudinal theories and shows how they can be specialized to include relevant IS phenomenon.
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Introduction Clinically, the Cobb angle method measures the overall scoliotic curve in the coronal plane but does not measure individual vertebra and disc wedging. The contributions of the vertebrae and discs in the growing scoliotic spine were measured to investigate coronal plane deformity progression with growth. Methods A 0.49mm isotropic 3D MRI technique was developed to investigate the level-by-level changes that occur in the growing spine of a group of Adolescent Idiopathic Scoliosis (AIS) patients, who received two to four sequential scans (spaced 3-12 months apart). The coronal plane wedge angles of each vertebra and disc in the major curve were measured to capture any changes that occurred during their adolescent growth phase. Results Seventeen patients had at least two scans. Mean patient age was 12.9 years (SD 1.5 years). Sixteen were classified as right-sided major thoracic Lenke Type 1 (one left sided). Mean standing Cobb angle at initial presentation was 31° (SD 12°). Six received two scans, nine three scans and two four scans, with 65% showing a Cobb angle progression of 5° or more between scans. Overall, there was no clear pattern of deformity progression of individual vertebrae and discs, nor between patients who progressed and those who didn’t. There were measurable changes in the wedging of the vertebrae and discs in all patients. In sequential scans, change in direction of wedging was also seen. In several patients there was reverse wedging in the discs that counteracted increased wedging of the vertebrae such that no change in overall Cobb angle was seen. Conclusion Sequential MRI data showed complex patterns of deformity progression. Changes to the wedging of individual vertebrae and discs may occur in patients who have no increase in Cobb angle measure; the Cobb method alone may be insufficient to capture the complex mechanisms of deformity progression.
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INTRODUCTION. Clinically, the Cobb angle method measures the overall scoliotic curve in the coronal plane but does not measure individual vertebra and disc wedging. The contributions of the vertebrae and discs in the growing scoliotic spine were measured to investigate coronal plane deformity progression with growth. METHODS. A 0.49mm isotropic 3D MRI technique was developed to investigate the level-by-level changes that occur in the growing spine of a group of Adolescent Idiopathic Scoliosis (AIS) patients, who received two to four sequential scans (spaced 3-12 months apart). The coronal plane wedge angles of each vertebra and disc in the major curve were measured to capture any changes that occurred during their adolescent growth phase. RESULTS. Seventeen patients had at least two scans. Mean patient age was 12.9 years (SD 1.5 years). Sixteen were classified as right-sided major thoracic Lenke Type 1 (one left sided). Mean standing Cobb angle at initial presentation was 31° (SD 12°). Six received two scans, nine three scans and two four scans, with 65% showing a Cobb angle progression of 5° or more between scans. Overall, there was no clear pattern of deformity progression of individual vertebrae and discs, nor between patients who progressed and those who didn’t. There were measurable changes in the wedging of the vertebrae and discs in all patients. In sequential scans, change in direction of wedging was also seen. In several patients there was reverse wedging in the discs that counteracted increased wedging of the vertebrae such that no change in overall Cobb angle was seen. CONCLUSION. Sequential MRI data showed complex patterns of deformity progression. Changes to the wedging of individual vertebrae and discs may occur in patients who have no increase in Cobb angle measure; the Cobb method alone may be insufficient to capture the complex mechanisms of deformity progression.
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Clinically, the Cobb angle method measures the overall scoliotic curve in the coronal plane but does not measure individual vertebra and disc wedging. The contributions of the vertebrae and discs in the growing scoliotic spine were measured to investigate coronal plane deformity progression with growth. Sequential MRI data in this project showed complex patterns of deformity progression. Changes to the wedging of individual vertebrae and discs may occur in patients who have no increase in Cobb angle measure; the Cobb method alone may be insufficient to capture the complex mechanisms of deformity progression.
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A theoretical basis is required for comparing key features and critical elements in wild fisheries and aquaculture supply chains under a changing climate. Here we develop a new quantitative metric that is analogous to indices used to analyse food-webs and identify key species. The Supply Chain Index (SCI) identifies critical elements as those elements with large throughput rates, as well as greater connectivity. The sum of the scores for a supply chain provides a single metric that roughly captures both the resilience and connectedness of a supply chain. Standardised scores can facilitate cross-comparisons both under current conditions as well as under a changing climate. Identification of key elements along the supply chain may assist in informing adaptation strategies to reduce anticipated future risks posed by climate change. The SCI also provides information on the relative stability of different supply chains based on whether there is a fairly even spread in the individual scores of the top few key elements, compared with a more critical dependence on a few key individual supply chain elements. We use as a case study the Australian southern rock lobster Jasus edwardsii fishery, which is challenged by a number of climate change drivers such as impacts on recruitment and growth due to changes in large-scale and local oceanographic features. The SCI identifies airports, processors and Chinese consumers as the key elements in the lobster supply chain that merit attention to enhance stability and potentially enable growth. We also apply the index to an additional four real-world Australian commercial fishery and two aquaculture industry supply chains to highlight the utility of a systematic method for describing supply chains. Overall, our simple methodological approach to empirically-based supply chain research provides an objective method for comparing the resilience of supply chains and highlighting components that may be critical.
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Reduced economic circumstances havemoved management goals towards higher profit, rather than maximum sustainable yields in several Australian fisheries. The eastern king prawn is one such fishery, for which we have developed new methodology for stock dynamics, calculation of model-based and data-based reference points and management strategy evaluation. The fishery is notable for the northward movement of prawns in eastern Australian waters, from the State jurisdiction of New South Wales to that of Queensland, as they grow to spawning size, so that vessels fishing in the northern deeper waters harvest more large prawns. Bioeconomic fishing data were standardized for calibrating a length-structured spatial operating model. Model simulations identified that reduced boat numbers and fishing effort could improve profitability while retaining viable fishing in each jurisdiction. Simulations also identified catch rate levels that were effective for monitoring in simple within-year effort-control rules. However, favourable performance of catch rate indicators was achieved only when a meaningful upper limit was placed on total allowed fishing effort. Themethods and findings will allow improved measures for monitoring fisheries and inform decision makers on the uncertainty and assumptions affecting economic indicators.
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We derive a new method for determining size-transition matrices (STMs) that eliminates probabilities of negative growth and accounts for individual variability. STMs are an important part of size-structured models, which are used in the stock assessment of aquatic species. The elements of STMs represent the probability of growth from one size class to another, given a time step. The growth increment over this time step can be modelled with a variety of methods, but when a population construct is assumed for the underlying growth model, the resulting STM may contain entries that predict negative growth. To solve this problem, we use a maximum likelihood method that incorporates individual variability in the asymptotic length, relative age at tagging, and measurement error to obtain von Bertalanffy growth model parameter estimates. The statistical moments for the future length given an individual's previous length measurement and time at liberty are then derived. We moment match the true conditional distributions with skewed-normal distributions and use these to accurately estimate the elements of the STMs. The method is investigated with simulated tag-recapture data and tag-recapture data gathered from the Australian eastern king prawn (Melicertus plebejus).
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The fundamental aim in fisheries management is to determine an optimal fishing effort for sustainably harvesting from a replenishable resource. The current management objective of Australia's Northern Prawn Fishery is to maximize the long-term net economic return following Australian government policy, resulting in an average recent catch of tiger prawn species of about 1,250 tons only. However, the maximum sustainable catch stated from different studies is around 3,000-4,700 tons. We also evaluated the net profit assuming that there was no buyback scheme in 2005 and the fishing fleet was kept at 89 vessels since 2005 and concluded that 40% more catch on average (2006-2009) and an additional total profit of A$ 17 million ( excluding crew cost) could have been gained in addition to the many millions of dollars of savings in the buyback scheme. These findings have great implications for future management in Australia and elsewhere because there is a grave concern of overfishing worldwide.
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Estimation of von Bertalanffy growth parameters has received considerable attention in fisheries research. Since Sainsbury (1980, Can. J. Fish. Aquat. Sci. 37: 241-247) much of this research effort has centered on accounting for individual variability in the growth parameters. In this paper we demonstrate that, in analysis of tagging data, Sainsbury's method and its derivatives do not, in general, satisfactorily account for individual variability in growth, leading to inconsistent parameter estimates (the bias does not tend to zero as sample size increases to infinity). The bias arises because these methods do not use appropriate conditional expectations as a basis for estimation. This bias is found to be similar to that of the Fabens method. Such methods would be appropriate only under the assumption that the individual growth parameters that generate the growth increment were independent of the growth parameters that generated the initial length. However, such an assumption would be unrealistic. The results are derived analytically, and illustrated with a simulation study. Until techniques that take full account of the appropriate conditioning have been developed, the effect of individual variability on growth has yet to be fully understood.
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Although subsampling is a common method for describing the composition of large and diverse trawl catches, the accuracy of these techniques is often unknown. We determined the sampling errors generated from estimating the percentage of the total number of species recorded in catches, as well as the abundance of each species, at each increase in the proportion of the sorted catch. We completely partitioned twenty prawn trawl catches from tropical northern Australia into subsamples of about 10 kg each. All subsamples were then sorted, and species numbers recorded. Catch weights ranged from 71 to 445 kg, and the number of fish species in trawls ranged from 60 to 138, and invertebrate species from 18 to 63. Almost 70% of the species recorded in catches were "rare" in subsamples (less than one individual per 10 kg subsample or less than one in every 389 individuals). A matrix was used to show the increase in the total number of species that were recorded in each catch as the percentage of the sorted catch increased. Simulation modelling showed that sorting small subsamples (about 10% of catch weights) identified about 50% of the total number of species caught in a trawl. Larger subsamples (50% of catch weight on average) identified about 80% of the total species caught in a trawl. The accuracy of estimating the abundance of each species also increased with increasing subsample size. For the "rare" species, sampling error was around 80% after sorting 10% of catch weight and was just less than 50% after 40% of catch weight had been sorted. For the "abundant" species (five or more individuals per 10 kg subsample or five or more in every 389 individuals), sampling error was around 25% after sorting 10% of catch weight, but was reduced to around 10% after 40% of catch weight had been sorted.