65,116 research outputs found
Recreational Demand, Travel Cost Method and Flow Fixed Costs
No full textThe Travel Cost Method (TCM) is one of the most frequently used approaches to estimating the use values of recreational sites. The first aim of this chapter is to discuss the role played by a category of fixed costs that we term annual flow fixed costs, that is annual fixed expenses related to the recreational activity, on the consumer decision-making process under different time horizons. These considerations are important when, as is often the case, a recreational user incurs substantial fixed costs directly related to the recreational activity on an annual basis, regardless of the number of trips undertaken (for example, an annual site licence). Second, we develop a simple model to account for the effect of flow fixed costs on consumer surplus and behaviour in a full annual perspective
Deep learning-based structural health monitoring for damage detection on a large space antenna
No full textDue to the stringent requirements imposed by state-of-the-art technologies, most of modern spacecrafts are now equipped with very large substructures such as antennas, deployable booms and solar arrays. However, while the size of these elements increases, their mass is limited by the rocket maximum take-off weight and, therefore, they result to be lightweight and very flexible. A natural concern derived from this trend is that these structures are now more susceptible to structural damages during launch phase or operational life (impacts, transient thermal states and fatigue). Since the degradation of some structural elements would naturally result in a modification of the system dynamical behaviour, damage detection processes are usually performed by comparing the dynamical responses or the structural model matrices (i.e. stiffness, mass and damping) of the undamaged model with those of the damaged structure. However, for large structures, the presence of local damage does not generally induce substantial change in the global dynamic which makes the local failures difficult to detect. A methodology for structural damage detection based on data-driven techniques with Deep Neural Network (DNN) is hereby proposed for the study of a large in-orbit flexible system. The deep architecture evaluated in this work is composed by stacking several neural network layers with different functions. In order to generate data for training and testing the machine learning model, different damage scenarios are generated via Finite Element commercial code and, based on the extracted modal parameters, the fully coupled 3D equations for the flexible spacecraft are integrated to test typical profiles of attitude manoeuvres. The DNN model is trained using the sensor-measured time series responses, where each of the former has been associated with the label of the corresponding damage scenario
Deep Learning for local damage identification in large space structures via sensor-measured time responses
No full textDue to the stringent requirements imposed by state-of-the-art technologies, most of modern spacecrafts are now equipped with very large substructures such as antennas, deployable booms and solar arrays. However, while the size of these elements increases, their mass is limited by the rocket maximum take-off weight and, therefore, they result to be lightweight and very flexible. A natural concern derived from this trend is that these structures are now more susceptible to possible structural damages during launch phase or operational life (impacts, transient thermal states and fatigue). In fact, for the increasing importance of the issue, damage detection in aerospace structures has been studied extensively in the last decades, employing both data and model-driven solutions. Since the degradation of some structural elements would naturally result in a modification of the system dynamical behaviour, damage detection processes are usually performed by comparing the dynamical responses or the structural model matrices (i.e. stiffness, mass and damping) of the undamaged model with those of the damaged structure. However, for large structures the presence of local damage does not generally induce substantial change in the global dynamic which makes the local failures difficult to detect. A methodology for structural damage detection based on data-driven techniques with Deep Neural Network (DNN) is hereby proposed for the study of a large in-orbit flexible system. DNNs are widely used in a plethora of supervised learning problems, with applications in various contexts, given their great generalization capability. The deep architecture evaluated in this work is composed by stacking several neural network layers with different functions. The core of the processing is carried out by the Long Short-Term Memory neural network layers, which can be considered among the most advanced machine learning techniques for time series prediction. They are combined with non-linear activations layer and fully-connected blocks. The resulting deep model is put in use to detect the structural damages, formulating the problem as a sequence-to-label classification one. In order to generate data for training and testing the machine learning model, different damage scenarios are generated via Finite Element (FE) commercial code and, based on the extracted modal parameters, the fully coupled 3D equations for the flexible spacecraft are integrated to test typical profiles of attitude manoeuvres. The DNN model is trained using the sensor-measured time series responses, where each of the former has been associated with the label of the corresponding damage scenario. Finally, the effectiveness of the proposed DNN model will be verified numerically simulating typical mission scenarios. Insights of its performance in presence of modelling uncertainties and noisy measurements will be given
Common-Property Resource Exploitation: A Real Options Approach
No full textAgricultural land and forestlands can have multiple uses and generate multiple sources
of utility. Although landowners benefit from most of them, society can benefit from others because
of their intrinsic characteristics as common-property resources and customary practice. In many
Italian territories, the picking of mushrooms is allowed on privately owned agricultural land and in
forests. The management of these resources is challenging due to the emerging conflicts between
landowners and users. In addition, the pressure exerted by users gives rise to issues on stock
preservation, thus contributing to putting biodiversity at risk in contexts already heavily jeopardized
by modern agriculture. Through the years, regulation established the primacy of the landowner’s
right, introduced a permit fee for users, and set limits on the resource stock to be collected daily.
Nonetheless, the relationship between public and private interests in common-property resource
exploitation is still controversial. In this paper, we investigate and model a right holder’s decision
whether to exploit a common-property resource according to their actual status of being an actual or
potential user. The model is developed within the real options valuation framework. In detail, we
investigate the entry/exit decision on the exploitation of the resource by considering the uncertainty
that affects the resource stock, the entry/exist costs, and the number of rival users
Plasticity in primary somatosensory cortex in young and older humans
No full textCortical plasticity effects on motor or somatosensory cortex are induced by the Paired Associative Stimulation (PAS) protocol, and long-term potentiation (LTP) - depression (LTD) mechanisms were proposed on the basis of independent evidences. PAS consists of pairing a repetitive peripheral electrical stimulation with cortical Transcranial Magnetic Stimulation (TMS). The effects of this protocol on motor or somatosensory cortex are valuable by changes in amplitude of motor evoked potentials (Stefan et al., 2000) or somatosensory evoked potentials (Wolters et al., 2005; Litvak et al., 2007), respectively. Since age-dependent effects have been shown for motor cortical plasticity (Tecchio et al., 2008; Florian et al., 2008), the aim of the present study is to investigate the same effects in somatosensory cortex
Preface: At the Origin of Circularity
No full textThe planning of a "sustainable" development path for urban areas is a challenge both difficult and promising at the same time. In fact, heavily anthropized areas present the highest environmental and social criticalities and, nevertheless, are an ever-coveted source of opportunities and well-being. The book collects contributions on evaluation models in decision-making processes for the construction of circular urban systems in the digital era, with particular attention to the improvement of social and individual well-being
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