Explaining tourists´ support for environmental protection

Any movement towards sustainable tourism is dependent not only upon the industry and other key stakeholders but also the demand side, namely the tourists. Yet, there is a limited literature from the demand point of view. In this area, contributions to an understanding of tourists’ support to sustainable development are necessary. This paper analyzes the main determinants in tourist behavior regarding the environmental considerations when they are making decisions about their holiday plans. General literature on this issue highlights the need to consider socio-economic variables of the individual as well as the attributes related of their style of living. If the econometric model takes into account all these variables simultaneously, then the linkage between contextual changes and tourists´ behaviour is enriched and it may be estimated more accurately. In this sense, a multilevel approach using a random-intercept logistic models is proposed, since tourists belong to a country are affected by the same contextual variables. The analysis comprises a joint dataset composed by microdata belong to the survey Attitudes of Europeans Towards Tourism, which corresponds to Flash Eurobarometer 281, macrodata from Eurostat (GDP in pps and GDP growth) and additional variables profiles from the 2005 Environmental Sustainability Index. Country-specific effects are calculated across the EU-27 countries, which corroborated that attitudes to the sustainable tourism are heterogeneous geo-graphically. The higher the level of GDP, the lower the level of tourists´ support. These results could be explained because tourists of richer countries already have to pay more tax for envi-ronmental protection. Age, gender and educational attainment are relevant. Motivations for travelling, size of the community, type of the destination, and environmental sustainability indi-cators of the place of residence are also important factors.

A multi-view framework for generating mobile apps

This paper demonstrates a multi-view framework for Rapid APPlication Tool (RAPPT). RAPPT enables rapid development of mobile applications. It employs a multilevel approach to mobile application development: a Domain Specific Visual Language to define the high level structure of mobile apps, a Domain Specific Textual Language to define behavioural concepts, and concrete source code for fine grained improvements.

Multilevel load frequency control (a perturbational approach)

A two-level control scheme for the load frequency control of a multi-area power system utilizing certain possible beneficial aspects of interconnections is described in this paper. The problem is identified as the determination of the necessary equivalent perturbation on the control distribution matrix to provide the corrective control.

A combined evolutionary search and multilevel optimisation approach to graph-partitioning

The graph-partitioning problem is to divide a graph into several pieces so that the number of vertices in each piece is the same within some defined tolerance and the number of cut edges is minimised. Important applications of the problem arise, for example, in parallel processing where data sets need to be distributed across the memory of a parallel machine. Very effective heuristic algorithms have been developed for this problem which run in real-time, but it is not known how good the partitions are since the problem is, in general, NP-complete. This paper reports an evolutionary search algorithm for finding benchmark partitions. A distinctive feature is the use of a multilevel heuristic algorithm to provide an effective crossover. The technique is tested on several example graphs and it is demonstrated that our method can achieve extremely high quality partitions significantly better than those found by the state-of-the-art graph-partitioning packages.

Predictors of pre- and post-competition affective states in male martial artists : a multilevel interactional approach

The aims of this study were to examine (a) the effects of competition-related and competition-extraneous concerns on affective states; (b) the relationships of primary and secondary appraisal with affective states and (c) the main and moderating effects of personality traits on pre- and post-competition affects. Thirty-nine male elite martial artists were assessed on 12 affective states, concerns and dimensions of primary and secondary appraisal at five random times a day across 1 week before and 3 days after a competition. On the competition day, they were assessed 1 h before and immediately after the contest. Competitive trait anxiety, neuroticism and extraversion were measured at the start of the study. The competition was the most significant and stressful event experienced in the examined period and had a pervasive influence on athletes' affective states. All examined appraisal and personality factors were somewhat associated with pre- and post-competition affective states. Competitive trait anxiety was a key moderator of the relationship between cognitive appraisal and affective states. This study supports the idea that cognitive appraisal and situational and personality factors exert main and interactive effects on athletes' pre- and post-competition affects. These factors need to be accounted for in planning of emotion regulation interventions.

Modelling multilevel data in multimedia : A hierarchical factor analysis approach

Multimedia content understanding research requires rigorous approach to deal with the complexity of the data. At the crux of this problem is the method to deal with multilevel data whose structure exists at multiple scales and across data sources. A common example is modeling tags jointly with images to improve retrieval, classification and tag recommendation. Associated contextual observation, such as metadata, is rich that can be exploited for content analysis. A major challenge is the need for a principal approach to systematically incorporate associated media with the primary data source of interest. Taking a factor modeling approach, we propose a framework that can discover low-dimensional structures for a primary data source together with other associated information. We cast this task as a subspace learning problem under the framework of Bayesian nonparametrics and thus the subspace dimensionality and the number of clusters are automatically learnt from data instead of setting these parameters a priori. Using Beta processes as the building block, we construct random measures in a hierarchical structure to generate multiple data sources and capture their shared statistical at the same time. The model parameters are inferred efficiently using a novel combination of Gibbs and slice sampling. We demonstrate the applicability of the proposed model in three applications: image retrieval, automatic tag recommendation and image classification. Experiments using two real-world datasets show that our approach outperforms various state-of-the-art related methods.

A Bayesian nonparametric approach to multilevel regression

Regression is at the cornerstone of statistical analysis. Multilevel regression, on the other hand, receives little research attention, though it is prevalent in economics, biostatistics and healthcare to name a few. We present a Bayesian nonparametric framework for multilevel regression where individuals including observations and outcomes are organized into groups. Furthermore, our approach exploits additional group-specific context observations, we use Dirichlet Process with product-space base measure in a nested structure to model group-level context distribution and the regression distribution to accommodate the multilevel structure of the data. The proposed model simultaneously partitions groups into cluster and perform regression. We provide collapsed Gibbs sampler for posterior inference. We perform extensive experiments on econometric panel data and healthcare longitudinal data to demonstrate the effectiveness of the proposed model

A Fully Bayesian Approach for Combining Multilevel Failure Information in Fault Tree Quantification and Corresponding Optimal Resource Allocation

This paper presents a fully Bayesian approach that simultaneously combines basic event and statistically independent higher event-level failure data in fault tree quantification. Such higher-level data could correspond to train, sub-system or system failure events. The full Bayesian approach also allows the highest-level data that are usually available for existing facilities to be automatically propagated to lower levels. A simple example illustrates the proposed approach. The optimal allocation of resources for collecting additional data from a choice of different level events is also presented. The optimization is achieved using a genetic algorithm.

The Complex Unified Evolutionary Approach to the Creation of the Multilevel Distributed Control System of a Gas-transport Company

The objects of a large-scale gas-transport company (GTC) suggest a complex unified evolutionary approach, which covers basic building concepts, up-to-date technologies, models, methods and means that are used in the phases of design, adoption, maintenance and development of the multilevel automated distributed control systems (ADCS).. As a single methodological basis of the suggested approach three basic Concepts, which contain the basic methodological principles and conceptual provisions on the creation of distributed control systems, were worked out: systems of the lower level (ACS of the technological processes based on up-to-date SCADA), of the middle level (ACS of the operative-dispatch production control based on MES-systems) and of the high level (business process control on the basis of complex automated systems ERP).

Design considerations in an indirectly coupled multilevel motor controller

Permanent magnet (PM) motors utilising ironless stator structures have been incorporated into a wide variety of applications where high efficiency and stringent torque control are required. With recent developments in magnetic materials, improved design strategies, and power outputs of up to 40kW, PM motors have become an attractive candidate for traction drives in electric and hybrid electric vehicles. However, due to their large air gaps and ironless stators these motors can have inductances as low as 2μH, imposing increased requirements on the converter to minimise current ripple. Multilevel converters with n cells can effectively increase the motor inductance by a factor of n2 and are an excellent approach to minimise the motor ripple current. Furthermore by indirectly coupling the outputs of each cell, improvements in converter input and cell ripple current can also be realised. This paper examines the issues in designing a high current indirectly coupled multilevel motor controller for an ironless BLDC traction drive and highlights the limitations of the common ladder core structure.

A state-wide systems approach to embedding the learning and teaching of sustainability in teacher education

Education systems have a key role to play in preparing future citizens to engage in sustainable living practices and help create a more sustainable world. Many schools throughout Australia have begun to develop whole-school approaches to sustainability education that are supported by national and state policies and curriculum frameworks. Preservice teacher education, however, lags behind in building the capacity of new teachers to initiate and implement such approaches (ARIES, 2010). This proposed project seeks to develop a state-wide systems approach to embedding Education for Sustainability (EfS) in teacher education that is aligned with the Australian National Curriculum and the aspirations for EfS in the Melbourne Declaration and other national documents. Representatives from all teacher education institutions and other agents of change in the Queensland education system will be engaged in a multilevel systems approach, involving collaboration at the state, institutional and course levels, to develop curriculum practices that reflect a shared vision of EfS.