404 resultados para Landscape Ecological Classification
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This study investigated how and to what degree “hybrid photography”—the simultaneous use of indexical and fictional properties and strategies— innovates the representation of animals within animalcentric, ecocentric frameworks. Design theory structured this project’s Practice-led, Visual research methodology framework. Grounded theory processes articulated emerging categories of hybrid photography through systematically and comparatively treating animal photography works for reflexive analysis. Design theory then applied and clarified categories, developing practice that re-visualised shark perspectives as new ecological discourse. Shadows, a creative practice installation, realised a full-scale photographic investigation into shark and marine animal realities of a specific environment—Heron Island and Gladstone, Great Barrier Reef—facing ecological crisis from dredging and development at Gladstone Harbour. Works rendered and explored hybrid photography’s capacity for illuminating nonhuman animals, in particular, sharks, and comprise 65% of this project’s weighting. This exegetical paper offers a definition, strategies and evaluation of hybrid photography in unsettling animal perspectives as effective ecological discourse, and comprises 35%.
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This thesis is a population-based epidemiological study to explore the spatial and temporal pattern of malaria, and to assess the relationship between socio-ecological factors and malaria in Yunnan, China. Geospatial and temporal approaches were applied; the high risk areas of the disease were identified; and socio-ecological drivers of malaria were assessed. These findings will provide important evidence for the control and prevention of malaria in China and other countries with a similar situation of endemic malaria.
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Regrowing forests on cleared land is a key strategy to achieve both biodiversity conservation and climate change mitigation globally. Maximizing these co-benefits, however, remains theoretically and technically challenging because of the complex relationship between carbon sequestration and biodiversity in forests, the strong influence of climate variability and landscape position on forest development, the large number of restoration strategies possible, and long time-frames needed to declare success. Through the synthesis of three decades of knowledge on forest dynamics and plant functional traits combined with decision science, we demonstrate that we cannot always maximize carbon sequestration by simply increasing the functional trait diversity of trees planted. The relationships between plant functional diversity, carbon sequestration rates above-ground and in the soil are dependent on climate and landscape positions. We show how to manage ‘identities’ and ‘complementarities’ between plant functional traits in order to achieve systematically maximal co-benefits in various climate and landscape contexts. We provide examples of optimal planting and thinning rules that satisfy this ecological strategy and guide the restoration of forests that are rich in both carbon and plant functional diversity. Our framework provides the first mechanistic approach for generating decision-making rules that can be used to manage forests for multiple objectives, and supports joined carbon credit and biodiversity conservation initiatives, such as Reducing Emissions from Deforestation and forest Degradation REDD+. The decision framework can also be linked to species distribution models and socio-economic models in order to find restoration solutions that maximize simultaneously biodiversity, carbon stocks and other ecosystem services across landscapes. Our study provides the foundation for developing and testing cost-effective and adaptable forest management rules to achieve biodiversity, carbon sequestration and other socio-economic co-benefits under global change.
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Although there is an increasing recognition of the impacts of climate change on communities, residents often resist changing their lifestyle to reduce the effects of the problem. By using a landscape architectural design medium, this paper argues that public space, when designed as an ecological system, has the capacity to create social and environmental change and to increase the quality of the human environment. At the same time, this ecological system can engage residents, enrich the local economy, and increase the social network. Through methods of design, research and case study analysis, an alternative master plan is proposed for a sustainable tourism development in Alacati, Turkey. Our master plan uses local geographical, economic and social information within a sustainable landscape architectural design scheme that addresses the key issues of ecology, employment, public space and community cohesion. A preliminary community empowerment model (CEM) is proposed to manage the designs. The designs address: the coexistence of local agricultural and sustainable energy generation; state of the art water management; and the functional and sustainable social and economic interrelationship of inhabitants, NGOs, and local government.
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‘Sustainability’ provides the dominant frame within which environmental policy debate occurs, notwithstanding its divergent meanings. However, how different discourses combine to shape understanding of the environment, the causes of environmental issues, and the responses required, is less clear cut. Drawing primarily on the approach to critical discourse analysis (CDA) developed by Fairclough, this paper explores the way in which neoliberal and ecologically modern discourses combine to shape environmental policy. Environmental scholars have made relatively little use of this approach to CDA to date, despite the significant interest in the discursive aspects of environmental issues, and its wide use in other areas of policy interest. Using the case of environmental policy-making in Victoria, Australia, this paper illustrates how neoliberalism and weak ecological modernization represented sustainability in ways that seriously limited the importance of environmental issues.
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Recent welfare reform in Australia has been constructed around the now-familiar principle of paid work and willingness to work as the fundamental marker of social citizenship. Beginning with the long-term unemployed in Australia in the mid 1990s, the scope of welfare reform has now extended to include people with a disability – which is a category of income support that has been growing in Australia. From the national government’s point of view this growth is a financial concern as it seeks to move as many people as possible into paid work to support the costs of an ageing population (DEWR, 2005). In doing so, the government has changed the meaning of disability in terms of eligibility for financial support from the state, and at the same time redefined the role of people with a disability with regard to work, and the role of the state with regard to the disabled. This has been a matter of some political contention in Australia.
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Textual document set has become an important and rapidly growing information source in the web. Text classification is one of the crucial technologies for information organisation and management. Text classification has become more and more important and attracted wide attention of researchers from different research fields. In this paper, many feature selection methods, the implement algorithms and applications of text classification are introduced firstly. However, because there are much noise in the knowledge extracted by current data-mining techniques for text classification, it leads to much uncertainty in the process of text classification which is produced from both the knowledge extraction and knowledge usage, therefore, more innovative techniques and methods are needed to improve the performance of text classification. It has been a critical step with great challenge to further improve the process of knowledge extraction and effectively utilization of the extracted knowledge. Rough Set decision making approach is proposed to use Rough Set decision techniques to more precisely classify the textual documents which are difficult to separate by the classic text classification methods. The purpose of this paper is to give an overview of existing text classification technologies, to demonstrate the Rough Set concepts and the decision making approach based on Rough Set theory for building more reliable and effective text classification framework with higher precision, to set up an innovative evaluation metric named CEI which is very effective for the performance assessment of the similar research, and to propose a promising research direction for addressing the challenging problems in text classification, text mining and other relative fields.
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The detection and correction of defects remains among the most time consuming and expensive aspects of software development. Extensive automated testing and code inspections may mitigate their effect, but some code fragments are necessarily more likely to be faulty than others, and automated identification of fault prone modules helps to focus testing and inspections, thus limiting wasted effort and potentially improving detection rates. However, software metrics data is often extremely noisy, with enormous imbalances in the size of the positive and negative classes. In this work, we present a new approach to predictive modelling of fault proneness in software modules, introducing a new feature representation to overcome some of these issues. This rank sum representation offers improved or at worst comparable performance to earlier approaches for standard data sets, and readily allows the user to choose an appropriate trade-off between precision and recall to optimise inspection effort to suit different testing environments. The method is evaluated using the NASA Metrics Data Program (MDP) data sets, and performance is compared with existing studies based on the Support Vector Machine (SVM) and Naïve Bayes (NB) Classifiers, and with our own comprehensive evaluation of these methods.
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Objectives To describe the intervention protocol for the first multilevel ecological intervention for physical activity in retirement communities that addresses individual, interpersonal and community influences on behavior change. Design A cluster randomized controlled trial design was employed with two study arms: a physical activity intervention and an attention control successful aging condition. Setting Sixteen continuing care retirement communities in San Diego County. Participants Three hundred twenty older adults, aged 65 years and older, are being recruited to participate in the trial. In addition, peer leaders are being recruited to lead some study activities, especially to sustain the intervention after study activities ceased. Intervention Participants in the physical activity trial receive individual, interpersonal and community intervention components. The individual level components include pedometers, goal setting and individual phone counseling. The interpersonal level components include group education sessions and peer-led activities. The community level components include resource audits and enumeration, tailored walking maps, and community improvement projects. The successful aging group receives individual and group attention about successful aging topics. Measurements The main outcome is light to moderate physical activity, measured objectively by accelerometry. Other objective outcomes included physical functioning, blood pressure, physical fitness, and cognitive functioning. Self report measures include depressive symptoms and health related quality of life. Results The intervention is being delivered successfully in the communities and compliance rates are high. Conclusion Ecological Models call for interventions that address multiple levels of the model. Previous studies have not included components at each level and retirement communities provide a model environment to demonstrate how to implement such an intervention.
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BACKGROUND Pandemic influenza A (H1N1) has a significant public health impact. This study aimed to examine the effect of socio-ecological factors on the transmission of H1N1 in Brisbane, Australia. METHODOLOGY We obtained data from Queensland Health on numbers of laboratory-confirmed daily H1N1 in Brisbane by statistical local areas (SLA) in 2009. Data on weather and socio-economic index were obtained from the Australian Bureau of Meteorology and the Australian Bureau of Statistics, respectively. A Bayesian spatial conditional autoregressive (CAR) model was used to quantify the relationship between variation of H1N1 and independent factors and to determine its spatiotemporal patterns. RESULTS Our results show that average increase in weekly H1N1 cases were 45.04% (95% credible interval (CrI): 42.63-47.43%) and 23.20% (95% CrI: 16.10-32.67%), for a 1 °C decrease in average weekly maximum temperature at a lag of one week and a 10mm decrease in average weekly rainfall at a lag of one week, respectively. An interactive effect between temperature and rainfall on H1N1 incidence was found (changes: 0.71%; 95% CrI: 0.48-0.98%). The auto-regression term was significantly associated with H1N1 transmission (changes: 2.5%; 95% CrI: 1.39-3.72). No significant association between socio-economic indexes for areas (SEIFA) and H1N1 was observed at SLA level. CONCLUSIONS Our results demonstrate that average weekly temperature at lag of one week and rainfall at lag of one week were substantially associated with H1N1 incidence at a SLA level. The ecological factors seemed to have played an important role in H1N1 transmission cycles in Brisbane, Australia.
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Capacity to produce data for performance analysis in sports has been enhanced in the last decade with substantial technological advances. However, current performance analysis methods have been criticised for the lack of a viable theoretical framework to assist on the development of fundamental principles that regulate performance achievement. Our aim in this paper is to discuss ecological dynamics as an explanatory framework for improving analysis and understanding of competitive performance behaviours. We argue that integration of ideas from ecological dynamics into previous approaches to performance analysis advances current understanding of how sport performance emerges from continuous interactions between individual players and teams. Exemplar data from previous studies in association football are presented to illustrate this novel perspective on performance analysis. Limitations of current ecological dynamics research and challenges for future research are discussed in order to improve the meaningfulness of information presented to coaches and managers.
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Object classification is plagued by the issue of session variation. Session variation describes any variation that makes one instance of an object look different to another, for instance due to pose or illumination variation. Recent work in the challenging task of face verification has shown that session variability modelling provides a mechanism to overcome some of these limitations. However, for computer vision purposes, it has only been applied in the limited setting of face verification. In this paper we propose a local region based intersession variability (ISV) modelling approach, and apply it to challenging real-world data. We propose a region based session variability modelling approach so that local session variations can be modelled, termed Local ISV. We then demonstrate the efficacy of this technique on a challenging real-world fish image database which includes images taken underwater, providing significant real-world session variations. This Local ISV approach provides a relative performance improvement of, on average, 23% on the challenging MOBIO, Multi-PIE and SCface face databases. It also provides a relative performance improvement of 35% on our challenging fish image dataset.
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The production of culture is today a matter of ‘user generated content’ and young people are vital participants as ‘prosumers’, i.e. both producers and consumers, of cultural products. Among other things, they are busy creating fan works (stories, pictures, films) based on already published material. Using the genre fan fiction as a point of departure, this article explores the drivers behind net communities organised around fan culture and argues that fan fiction sites can in many aspects be regarded as informal learning settings. By turning to the rhetoric principle of imitatio, the article shows how in the collective interactive processes between readers and writers such fans develop literacies and construct gendered identities.