379 resultados para Intelligent Environments


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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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Early detection of (pre-)signs of ulceration on a diabetic foot is valuable for clinical practice. Hyperspectral imaging is a promising technique for detection and classification of such (pre-)signs. However, the number of the spectral bands should be limited to avoid overfitting, which is critical for pixel classification with hyperspectral image data. The goal was to design a detector/classifier based on spectral imaging (SI) with a small number of optical bandpass filters. The performance and stability of the design were also investigated. The selection of the bandpass filters boils down to a feature selection problem. A dataset was built, containing reflectance spectra of 227 skin spots from 64 patients, measured with a spectrometer. Each skin spot was annotated manually by clinicians as "healthy" or a specific (pre-)sign of ulceration. Statistical analysis on the data set showed the number of required filters is between 3 and 7, depending on additional constraints on the filter set. The stability analysis revealed that shot noise was the most critical factor affecting the classification performance. It indicated that this impact could be avoided in future SI systems with a camera sensor whose saturation level is higher than 106, or by postimage processing.

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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.

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This thesis is a development of a methodology to predict the radio transmitter signal attenuation, via vertical density profiling of digitised objects, through the use of Light Detection and Ranging (LiDaR) measurements. The resulting map of indexed signal attenuation is useful for dynamic radio transmitter placement within the geospatial data set without expensive and tedious radio measurements.

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Multimetric ecological condition assessment has become an important biodiversity management tool. This study was the first to examine the reliability of these ecological surrogates across variable environments, and the implications for surrogate efficacy. It was demonstrated that through strategic application and design of the multimetric ecological condition index, the effects of environmental gradients and disturbance regimes can be mitigated, and that ecological condition assessment may serve as a scientifically rigorous approach for conservation planning.

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Accurately quantifying total greenhouse gas emissions (e.g. methane) from natural systems such as lakes, reservoirs and wetlands requires the spatial-temporal measurement of both diffusive and ebullitive (bubbling) emissions. Traditional, manual, measurement techniques provide only limited localised assessment of methane flux, often introducing significant errors when extrapolated to the whole-of-system. In this paper, we directly address these current sampling limitations and present a novel multiple robotic boat system configured to measure the spatiotemporal release of methane to atmosphere across inland waterways. The system, consisting of multiple networked Autonomous Surface Vehicles (ASVs) and capable of persistent operation, enables scientists to remotely evaluate the performance of sampling and modelling algorithms for real-world process quantification over extended periods of time. This paper provides an overview of the multi-robot sampling system including the vehicle and gas sampling unit design. Experimental results are shown demonstrating the system’s ability to autonomously navigate and implement an exploratory sampling algorithm to measure methane emissions on two inland reservoirs.