An analysis of students' travel motivations and images of China as a tourist destination

Despite China's rapid growth in inbound tourism, the nature of its Canadian tourist market has been insufficiently studied. In response to this need, the objectives of this study are to identify China's destination image in Canadian students' minds, their possible internal motivations for visiting China as well as examining demographic influences on people's destination image formation. The study reviews image formation process and travel motivation categorisation, discusses their relationship, and implements Baloglu and McCleary's (1999) perceptual and affective image formation model and "push and pull factors" theory as its framework. A self-administered survey was applied to 424 undergraduate students in a Canadian university in early 2004. Exploratory factor analyses were conducted to identify perceived images and travel motivation. Summated means were calculated to illustrate the affective attitudes. A series of f-test and ANOVA tests were employed to examine the influence of demographics. An open-ended question format was adopted to analyse other images, motivations and visitation barriers that students may have. Findings demonstrate that cultural and natural attractions are the predominant image which the Canadian students have of China'; some stereotypes and negative images still influence the students' perception; travel service quality is largely unknown; increasing knowledge and seeking excitement and fun are the significant motivators in the likelihood of the Canadian students choosing to visit China; and personal interests may be a factor that significantly influences an individual's destination image and travel motivation. Raising awareness and increasing familiarity through promotion are suggested as methods to create a positive destination image of China.

Tourist travel via Grand Trunk Railway System : and connections, including Niagara Falls and Gorge, the Highlands of Ontario, comprising Georgian Bay, Muskoka Lakes ; St. Lawrence River, Montreal, Quebec, the Saguenay River, the Rangeley Lakes, White Mountains, and the Atlantic Sea-Coast /

Previous editions of this work have appeared under the title: Gateways of tourist travel.

Niagara Resort and Tourist Association Records, 1975-1976, 1978-1983, 1985, 1987-1989, n.d.

Ontario Editorial Bureau (O.E.B.)

Cuban Tourist Commission Air Certificate, 1955

A certificate from the Cuban Tourist Commission "in recognition of his success in crossing the Channel to Cuba from the United States in the Sixth Annual All Women International Air Race". The certificate was awarded to Dorothy Rungeling on July 11, 1955.

Assessing the Reproducibility of Clustering of Molecular Dynamics Conformations on Self-Organizing Maps

The goal of most clustering algorithms is to find the optimal number of clusters (i.e. fewest number of clusters). However, analysis of molecular conformations of biological macromolecules obtained from computer simulations may benefit from a larger array of clusters. The Self-Organizing Map (SOM) clustering method has the advantage of generating large numbers of clusters, but often gives ambiguous results. In this work, SOMs have been shown to be reproducible when the same conformational dataset is independently clustered multiple times (~100), with the help of the Cramérs V-index (C_v). The ability of C_v to determine which SOMs are reproduced is generalizable across different SOM source codes. The conformational ensembles produced from MD (molecular dynamics) and REMD (replica exchange molecular dynamics) simulations of the penta peptide Met-enkephalin (MET) and the 34 amino acid protein human Parathyroid Hormone (hPTH) were used to evaluate SOM reproducibility. The training length for the SOM has a huge impact on the reproducibility. Analysis of MET conformational data definitively determined that toroidal SOMs cluster data better than bordered maps due to the fact that toroidal maps do not have an edge effect. For the source code from MATLAB, it was determined that the learning rate function should be LINEAR with an initial learning rate factor of 0.05 and the SOM should be trained by a sequential algorithm. The trained SOMs can be used as a supervised classification for another dataset. The toroidal 10×10 hexagonal SOMs produced from the MATLAB program for hPTH conformational data produced three sets of reproducible clusters (27%, 15%, and 13% of 100 independent runs) which find similar partitionings to those of smaller 6×6 SOMs. The χ^2 values produced as part of the C_v calculation were used to locate clusters with identical conformational memberships on independently trained SOMs, even those with different dimensions. The χ^2 values could relate the different SOM partitionings to each other.