The use of strategic visioning to enhance local tourism planning in periphery communities

Tourism has had a profound impact upon destinations worldwide, and although this impact has been positive for many destinations, there are numerous examples where tourism has adversely impacted upon the environment and social fabric of the destination community (Coccossis 1996; Murphy 1985). The negative impacts of tourism have been attributed, among other things, to inadequate or non-existent planning for development (Gunn 1994; Hall2000). This has led to increased calls for tourism planning to offset some of the negative impacts that tourism can have on the destination community. While a number of approaches have been advocated, a collaborative philosophy, based on the principles of sustainability, is more likely to result in acceptable and successful policies and programmes for tourism destinations (Farrell1986; Jamal & Getz 1995; Maitland 2002; Minca & Getz 1995). Such an approach focuses on cooperation and broader based participation in tourism planning and decision-making between stakeholders to lead to agreement on planning directions and goals, with one of the primary objectives of collaborative arrangements being to develop a strategic vision for a destination (Bramwell & Lane 2000). [Extract from introduction]

A new integrable model of strongly correlated electrons with Hubbard-like interaction

A new completely integrable model of strongly correlated electrons is proposed which describes two competitive interactions: one is the correlated one-particle hopping, the other is the Hubbard-like interaction. The integrability follows from the fact that the Hamiltonian is derivable from a one-parameter family of commuting transfer matrices. The Bethe ansatz equations are derived by algebraic Bethe ansatz method.

The effects of normalisation on the ability of business end-users to detect data anomalies: An experimental evaluation

With the proliferation of relational database programs for PC's and other platforms, many business end-users are creating, maintaining, and querying their own databases. More importantly, business end-users use the output of these queries as the basis for operational, tactical, and strategic decisions. Inaccurate data reduce the expected quality of these decisions. Implementing various input validation controls, including higher levels of normalisation, can reduce the number of data anomalies entering the databases. Even in well-maintained databases, however, data anomalies will still accumulate. To improve the quality of data, databases can be queried periodically to locate and correct anomalies. This paper reports the results of two experiments that investigated the effects of different data structures on business end-users' abilities to detect data anomalies in a relational database. The results demonstrate that both unnormalised and higher levels of normalisation lower the effectiveness and efficiency of queries relative to the first normal form. First normal form databases appear to provide the most effective and efficient data structure for business end-users formulating queries to detect data anomalies.