Environmental management decision-making in certified hotels

This paper analyses environmental decision-making against two axes, motivations and decision-making processes, to understand the reasons for pro-environmental behaviour by the managements of Spanish Eco-management and Audit Scheme (EMAS)-certified hotels. Mixed methods were used to study perceptions of EMAS and reasons for being certified, with current and lapsed EMAS-certified firms triangulated against expert interviews and documentary evidence. Four groups of hotels were differentiated: Strategic hotels (22%) (with high levels of integrated environmental management), Followers (48%), Greenwashers (11%) and Laggers (19%) (with low levels of integrated environmental management). Most hotels were found to be internally driven in their purpose and ad hoc in their decision-making, with limited understanding of externally driven benefits and motivation for more systematic management systems. This questions the success of EMAS as both a continuous improvement management and as a market-based regulation tool for hotels. Few hotels overall related high environmental standards to the possibilities of gaining market advantage: most wished to avoid legal challenges. The paper also illustrates the ways in which hotels opportunistically switch certification systems to get what they see as a better deal. © 2011 Taylor & Francis

Autonomous clustering using rough set theory

This paper proposes a clustering technique that minimises the need for subjective human intervention and is based on elements of rough set theory. The proposed algorithm is unified in its approach to clustering and makes use of both local and global data properties to obtain clustering solutions. It handles single-type and mixed attribute data sets with ease and results from three data sets of single and mixed attribute types are used to illustrate the technique and establish its efficiency

Motivation profiles in sport: A self-determination theory perspective

The present study examined the link between motivation profiles among adult sports participants and the outcomes of enjoyment, effort, positive and negative affect, attitude toward sport participation, intention to continue sport participation, satisfaction, and persistence in sport. Two samples of participants (n = 590 and n = 555) completed the Sport Motivation Scale and a range of self-report measures to assess the outcome variables. Exploratory cluster analyses applied to Sample 1 and confirmatory cluster analysis applied to Sample 2 identified two clusters of sport participants. The first comprised participants with high scores on both non self-determined and self-determined motives. The second comprised participants with high scores on self-determined motives but low scores on non self- determined motives. Participants in the first cluster scored higher on all outcome variables. The results are discussed with reference to a more in-depth understanding of the motivation dynamics of sport participation based on Self-Determination Theory

Holistic corpus-based dialectology

This paper is concerned with sketching future directions for corpus-based dialectology. We advocate a holistic approach to the study of geographically conditioned linguistic variability, and we present a suitable methodology, 'corpusbased dialectometry', in exactly this spirit. Specifically, we argue that in order to live up to the potential of the corpus-based method, practitioners need to (i) abandon their exclusive focus on individual linguistic features in favor of the study of feature aggregates, (ii) draw on computationally advanced multivariate analysis techniques (such as multidimensional scaling, cluster analysis, and principal component analysis), and (iii) aid interpretation of empirical results by marshalling state-of-the-art data visualization techniques. To exemplify this line of analysis, we present a case study which explores joint frequency variability of 57 morphosyntax features in 34 dialects all over Great Britain

An effective non-parametric method for globally clustering genes from expression profiles

Clustering is widely used in bioinformatics to find gene correlation patterns. Although many algorithms have been proposed, these are usually confronted with difficulties in meeting the requirements of both automation and high quality. In this paper, we propose a novel algorithm for clustering genes from their expression profiles. The unique features of the proposed algorithm are twofold: it takes into consideration global, rather than local, gene correlation information in clustering processes; and it incorporates clustering quality measurement into the clustering processes to implement non-parametric, automatic and global optimal gene clustering. The evaluation on simulated and real gene data sets demonstrates the effectiveness of the algorithm. <br /

Prior knowledge based mining functional modules from Yeast PPI networks with gene ontology

<p>Abstract</p> <p>Background</p> <p>In the literature, there are fruitful algorithmic approaches for identification functional modules in protein-protein interactions (PPI) networks. Because of accumulation of large-scale interaction data on multiple organisms and non-recording interaction data in the existing PPI database, it is still emergent to design novel computational techniques that can be able to correctly and scalably analyze interaction data sets. Indeed there are a number of large scale biological data sets providing indirect evidence for protein-protein interaction relationships.</p> <p>Results</p> <p>The main aim of this paper is to present a prior knowledge based mining strategy to identify functional modules from PPI networks with the aid of Gene Ontology. Higher similarity value in Gene Ontology means that two gene products are more functionally related to each other, so it is better to group such gene products into one functional module. We study (i) to encode the functional pairs into the existing PPI networks; and (ii) to use these functional pairs as pairwise constraints to supervise the existing functional module identification algorithms. Topology-based modularity metric and complex annotation in MIPs will be used to evaluate the identified functional modules by these two approaches.</p> <p>Conclusions</p> <p>The experimental results on Yeast PPI networks and GO have shown that the prior knowledge based learning methods perform better than the existing algorithms.</p

Subtypes of children with attention disabilities.

Subtypes of children with attentional problems were investigated using cluster analysis. Subjects were 9-year-old-elementary school children (N = 443). The test battery administered to these children comprised a comprehensive set of common attention tests, covering different aspects of attentional functioning, and a test of reading comprehension. Cluster analysis of these data yielded eight stable and reproducible clus¬ters. The test profiles of two subgroups were indicative of distinct attentional problems. One group ap¬peared deficient in speed of processing, the other in attentional control. A third subgroup showed a reading deficit. Two additional clusters had very poor and excellent performance on the whole battery, respec¬tively. Finally, three clusters were found with minor variations approximating average performance. The internal validity, that is, the adequacy and stability of the cluster solution, appeared to be reasonably good, as indicated by a variety of measures. The long-term stability over an 18-month period was also checked and found to be satisfactory

Exploring the constraint profile of winter sports resort tourist segments

Many studies have confirmed the importance of market segmentation both theoretically and empirically. Surprisingly though, no study has so far addressed the issue from the perspective of leisure constraints. Since different consumers face different barriers, we look at participation in leisure activities as an outcome of the negotiation process that winter sports resort tourists go through, to balance between related motives and constraints. This empirical study reports the findings on the applicability of constraining factors in segmenting the tourists who visit winter sports resorts. Utilizing data from 1,391 tourists of winter sports resorts in Greece, five segments were formed based on their constraint, demographic and behavioral profile. Our findings indicate that such segmentation sheds light on factors that could potentially limit the full utilization of the market. To maximize utilization, we suggest customizing marketing to the profile of each distinct winter sports resort tourist segment that emerge