258 resultados para Marginal upland environments


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Purpose – The purpose of this paper is to discuss residents’ views of social and physical environments in a co-housing and in a senior housing setting in Finland. Also, the study aims to point out important connections between well-being and built environment. Design/methodology/approach – The data include interviews and survey responses gathered in the cases. The results and analysis are presented at different case study levels, with the discussion and conclusions following this. Findings – The findings show that the physical environment and common areas have an important role to activate residents. When well-designed common areas exist, a higher level of engagement can be achieved by getting residents involved in the planning and running of activities. Research limitations/implications – This paper discusses residents’ experiences in two Finnish housing settings and it focuses on the housing market in Finland. Practical implications – The findings encourage investors and housing operators to design and invest common areas which could activate residents and create social contacts. Also, investors have to pay attention to the way these developments are managed. Originality/value – This study is the first to investigate the Finnish co-housing setting and compare social and physical environments in a co-housing and a senior house.

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Dynamic Bayesian Networks (DBNs) provide a versatile platform for predicting and analysing the behaviour of complex systems. As such, they are well suited to the prediction of complex ecosystem population trajectories under anthropogenic disturbances such as the dredging of marine seagrass ecosystems. However, DBNs assume a homogeneous Markov chain whereas a key characteristics of complex ecosystems is the presence of feedback loops, path dependencies and regime changes whereby the behaviour of the system can vary based on past states. This paper develops a method based on the small world structure of complex systems networks to modularise a non-homogeneous DBN and enable the computation of posterior marginal probabilities given evidence in forwards inference. It also provides an approach for an approximate solution for backwards inference as convergence is not guaranteed for a path dependent system. When applied to the seagrass dredging problem, the incorporation of path dependency can implement conditional absorption and allows release from the zero state in line with environmental and ecological observations. As dredging has a marked global impact on seagrass and other marine ecosystems of high environmental and economic value, using such a complex systems model to develop practical ways to meet the needs of conservation and industry through enhancing resistance and/or recovery is of paramount importance.

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We propose a family of multivariate heavy-tailed distributions that allow variable marginal amounts of tailweight. The originality comes from introducing multidimensional instead of univariate scale variables for the mixture of scaled Gaussian family of distributions. In contrast to most existing approaches, the derived distributions can account for a variety of shapes and have a simple tractable form with a closed-form probability density function whatever the dimension. We examine a number of properties of these distributions and illustrate them in the particular case of Pearson type VII and t tails. For these latter cases, we provide maximum likelihood estimation of the parameters and illustrate their modelling flexibility on simulated and real data clustering examples.

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One underappreciated consequence of the aging population phenomenon is that we are now experiencing what is arguably the most age-diverse workforce in modern history (Hanks & Icenogle, 2001; Newton, 2006; Toossi, 2004). As our workforce continues to age, shifts in the age demographic composition (i.e., the age diversity) of organizations and their subunits will become more apparent (Roth, Wegge, & Schmidt, 2007). Several factors have influenced and will continue to drive this trend. For example, in Western countries, younger people entering the workforce are more educated than ever before (Hussar & Bailey, 2013; Ryan & Siebens, 2012; Stoops, 2003) and could feasibly rise to positions of power in organizations more quickly than others have in the past (e.g., promotion rates vary as a function of age) (Rosenbaum, 1979; see also Clemens, 2012 conceptualization of the "fast track effect"). Furthermore, older workers are increasingly delaying retirement beyond the normative retirement age (Baltes & Rudolph, 2012; Burtless, 2012; Flynn, 2010), and already retired individuals are seeking re-employment in bridge employment roles in higher numbers than before (e.g., Adams & Rau, 2004; Kim & Feldman, 2000; Weckerle & Shultz, 1999).

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Urbanization is becoming increasingly important in terms of climate change and ecosystem functionality worldwide. We are only beginning to understand how the processes of urbanization influence ecosystem dynamics and how peri-urban environments contribute to climate change. Brisbane in South East Queensland (SEQ) currently has the most extensive urban sprawl of all Australian cities. This leads to substantial land use changes in urban and peri-urban environments and the subsequent gaseous emissions from soils are to date neglected for IPCC climate change estimations. This research examines how land use change effects methane (CH4) and nitrous oxide (N2O) fluxes from peri-urban soils and consequently influences the Global Warming Potential (GWP) of rural ecosystems in agricultural use undergoing urbanization. Therefore, manual and fully automated static chamber measurements determined soil gas fluxes over a full year and an intensive sampling campaign of 80 days after land use change. Turf grass, as the major peri-urban land cover, increased the GWP by 415 kg CO2-e ha 1 over the first 80 days after conversion from a well-established pasture. This results principally from increased daily average N2O emissions of 0.5 g N2O ha-1 d-1 from the pasture to 18.3 g N2O ha-1 d-1 from the turf grass due to fertilizer application during conversion. Compared to the native dry sclerophyll eucalypt forest, turf grass establishment increases the GWP by another 30 kg CO2-e ha 1. The results presented in this study clearly indicate the substantial impact of urbanization on soil-atmosphere gas exchange in form of non-CO2 greenhouse gas emissions particularly after turf grass establishment.

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All architecture embodies narratives that may either support or work against a state of good health. Neurological theory can be used to explain why salutogenic environments work, and how they can improve health outcomes.

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Reviews and synthesizes evidence to produce evidence-based recommendations on policy actions to improve food composition for NSW Health

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Reviews and synthesizes evidence to produce evidence-based recommendations on policy actions to improve food labeling for NSW Health

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Reviews and synthesizes evidence to make recommendations on policy actions improve food environments in the area of food promotion for NSW Health

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Reviews and synthesizes evidence to produce evidence-based recommendations on policy actions to improve food pricing for NSW Health

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Reviews and synthesizes evidence to produce evidence-based recommendations on policy actions to improve food provision for NSW Health

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Reviews and synthesizes nutrition policy actions to improve food retail for NSW Health

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Summaries evidence across seven domains of potential food policy action to improve food environments and food supply to prevent obesity for NSW Health

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There are some scenarios in which Unmmaned Aerial Vehicle (UAV) navigation becomes a challenge due to the occlusion of GPS systems signal, the presence of obstacles and constraints in the space in which a UAV operates. An additional challenge is presented when a target whose location is unknown must be found within a confined space. In this paper we present a UAV navigation and target finding mission, modelled as a Partially Observable Markov Decision Process (POMDP) using a state-of-the-art online solver in a real scenario using a low cost commercial multi rotor UAV and a modular system architecture running under the Robotic Operative System (ROS). Using POMDP has several advantages to conventional approaches as they take into account uncertainties in sensor information. We present a framework for testing the mission with simulation tests and real flight tests in which we model the system dynamics and motion and perception uncertainties. The system uses a quad-copter aircraft with an board downwards looking camera without the need of GPS systems while avoiding obstacles within a confined area. Results indicate that the system has 100% success rate in simulation and 80% rate during flight test for finding targets located at different locations.