Description:
This research explores the extent to which hosts, local industries and destination regions influence VFR (Visiting Friends and Relatives) travellers’ purpose of visit. Whilst VFR travellers are often assumed to travel for the purpose of visiting friends and relatives, this research, based in the Sunshine Coast Australia, indicates that VFR is only one reason that motivates VFR travellers to travel. The simplistic view that VFRs are travellers whose purpose of visit is only VFR is challenged by these research findings showing that 26.6% of visitors who stayed with friends and relatives stated a Non-VFR purpose of visit. Clearly, if purpose of visit is used as a sole measurement for considering the size of VFR travel then a significant portion of VFR travellers will be missed. Interestingly, visitors staying with relatives were more inclined than friends to be travelling with this purpose in mind. Whilst 80.6% of visitors staying with relatives reported VFR to be their purpose of visit, only 63.4% of visitors staying with friends were travelling for VFR purposes. VFR travellers were also found to be significantly more likely than Non-VFRs to rely on word of mouth than other sources in terms of planning their trip. As such, the role that the local host has in influencing, not only the purpose of trip to begin with, but the activities undertaken during the visit is also critical. This relationship that VFR travellers have with industries, the destination, and the host are all interlinked and can influence the purpose, or purposes, of the trip. A VFR purpose of visit model is proposed to explain the relationships between these elements, also demonstrating that VFR is only one type of purpose of visit amongst VFR travellers.
Description:
This research explores the extent to which hosts, local industries and destination regions influence VFR (Visiting Friends and Relatives) travellers’ purpose of visit. Whilst VFR travellers are often assumed to travel for the purpose of visiting friends and relatives, this research, based in the Sunshine Coast Australia, indicates that VFR is only one reason that motivates VFR travellers to travel. The simplistic view that VFRs are travellers whose purpose of visit is only VFR is challenged by these research findings showing that 26.6% of visitors who stayed with friends and relatives stated a Non-VFR purpose of visit. Clearly, if purpose of visit is used as a sole measurement for considering the size of VFR travel then a significant portion of VFR travellers will be missed. Interestingly, visitors staying with relatives were more inclined than friends to be travelling with this purpose in mind. Whilst 80.6% of visitors staying with relatives reported VFR to be their purpose of visit, only 63.4% of visitors staying with friends were travelling for VFR purposes. VFR travellers were also found to be significantly more likely than Non-VFRs to rely on word of mouth than other sources in terms of planning their trip. As such, the role that the local host has in influencing, not only the purpose of trip to begin with, but the activities undertaken during the visit is also critical. This relationship that VFR travellers have with industries, the destination, and the host are all interlinked and can influence the purpose, or purposes, of the trip. A VFR purpose of visit model is proposed to explain the relationships between these elements, also demonstrating that VFR is only one type of purpose of visit amongst VFR travellers.
Description:
Visiting Friends and Relatives (VFR) Travel represents a large and significant component of tourism. However, despite this, VFR Travel has been largely ignored by tourism marketing practitioners, and has remained under-researched in many domains. VFR Travel tends to be overlooked in tourism marketing campaigns, in part because these travellers have been regarded as ones who cannot be influenced, and in part because of their perceived minimal economic impact. However, VFR travellers are not always attracted solely by the hosts. The attractiveness of the destination can also have an influencing role on VFR trips. Whilst little research has been done to examine these issues, this paper utilises a whole tourism systems model to examine the linkages between VFR travellers and destination regions. A comparative analysis compares VFR research undertaken in two different regions in Australia. Through this approach, it is shown that VFRs are often influenced to embark on VFR trips because of the attractiveness of a destination. As such, whilst any region can attract VFRs because of the hosts, the attractiveness of a region as a tourist destination can influence the length of stay and visitor spend and, as such, enhance the impact of VFR Travel on a local economy.
Description:
Visiting Friends and Relatives (VFR) Travel represents a large and significant component of tourism. However, despite this, VFR Travel has been largely ignored by tourism marketing practitioners, and has remained under-researched in many domains. VFR Travel tends to be overlooked in tourism marketing campaigns, in part because these travellers have been regarded as ones who cannot be influenced, and in part because of their perceived minimal economic impact. However, VFR travellers are not always attracted solely by the hosts. The attractiveness of the destination can also have an influencing role on VFR trips. Whilst little research has been done to examine these issues, this paper utilises a whole tourism systems model to examine the linkages between VFR travellers and destination regions. A comparative analysis compares VFR research undertaken in two different regions in Australia. Through this approach, it is shown that VFRs are often influenced to embark on VFR trips because of the attractiveness of a destination. As such, whilst any region can attract VFRs because of the hosts, the attractiveness of a region as a tourist destination can influence the length of stay and visitor spend and, as such, enhance the impact of VFR Travel on a local economy.
Description:
Souvenirs are a popular commodity for purchase by tourists. Souvenirs can serve as an emotional connection for people to bring back fond memories of touristic experiences. They can also be gifts for friends and relatives to showcase and share touristic events when the tourist returns back home. In recent years, research has been undertaken on the importance of souvenirs and the aspect of authenticity. However, the relationship between country of origin, more specifically the ‘Made in...’ labels, and souvenirs has attracted little research interest. Importantly, despite the importance of souvenirs to tourism, a content analysis has not been undertaken. Such research seems important in order to guide future research and hold important implications for tourism practitioners. Accordingly, this research involves a content analysis of the relationship between authenticity and souvenirs. Key themes are highlighted and future research identified.
Description:
Souvenirs are a popular commodity for purchase by tourists. Souvenirs can serve as an emotional connection for people to bring back fond memories of touristic experiences. They can also be gifts for friends and relatives to showcase and share touristic events when the tourist returns back home. In recent years, research has been undertaken on the importance of souvenirs and the aspect of authenticity. However, the relationship between country of origin, more specifically the ‘Made in...’ labels, and souvenirs has attracted little research interest. Importantly, despite the importance of souvenirs to tourism, a content analysis has not been undertaken. Such research seems important in order to guide future research and hold important implications for tourism practitioners. Accordingly, this research involves a content analysis of the relationship between authenticity and souvenirs. Key themes are highlighted and future research identified.
Description:
The problem of cluster analysis is formulated as a problem of nonsmooth, nonconvex optimization, and an algorithm for solving the cluster analysis problem based on the nonsmooth optimization techniques is developed. The issues of applying this algorithm to large data sets are discussed and a feature selection procedure is demonstrated. The algorithm is then applied to a hospital data set to generate new knowledge about different patterns of patients resource consumption.
Description:
In this paper global optimisation-based techniques are studied in order to increase the accuracy of medical diagnosis and prognosis with FNA image data from the Wisconsin Diagnostic and Prognostic Breast Cancer databases. First we discuss the problem of determining the most informative features for the classification of cancerous cases in the databases under consideration. Then we apply a technique based on convex and global optimisation to breast cancer diagnosis. It allows the classification of benign cases and malignant ones and the subsequent diagnosis of patients with very high accuracy. The third application of this technique is a method that calculates centres of clusters to predict when breast cancer is likely to recur in patients for which cancer has been removed. The technique achieves higher accuracy with these databases than reported elsewhere in the literature.
Description:
In this paper global optimisation-based techniques are studied in order to increase the accuracy of medical diagnosis and prognosis with FNA image data from the Wisconsin Diagnostic and Prognostic Breast Cancer databases. First we discuss the problem of determining the most informative features for the classification of cancerous cases in the databases under consideration. Then we apply a technique based on convex and global optimisation to breast cancer diagnosis. It allows the classification of benign cases and malignant ones and the subsequent diagnosis of patients with very high accuracy. The third application of this technique is a method that calculates centres of clusters to predict when breast cancer is likely to recur in patients for which cancer has been removed. The technique achieves higher accuracy with these databases than reported elsewhere in the literature.
Description:
In this paper the problem of localization of wireless sensor network is formulated as an unconstrained nonsmooth optimization problem. We minimize a distance objective function which incorporates unknown sensor nodes and nodes with known positions (anchors) in contrast to popular semidefinite programming (SDP) methods which use artificial objective functions. We study the main properties of the objective function in this problem and design an algorithm for its minimization. Our algorithm is a derivative-free discrete gradient method that allows one to find a near global solution. The algorithm can handle a large number of sensors in the network. This paper contains the theory of our proposed formulation and algorithm while experimental results are included in later work.
Description:
In this paper the problem of localization of wireless sensor network is formulated as an unconstrained nonsmooth optimization problem. We minimize a distance objective function which incorporates unknown sensor nodes and nodes with known positions (anchors) in contrast to popular semidefinite programming (SDP) methods which use artificial objective functions. We study the main properties of the objective function in this problem and design an algorithm for its minimization. Our algorithm is a derivative-free discrete gradient method that allows one to find a near global solution. The algorithm can handle a large number of sensors in the network. This paper contains the theory of our proposed formulation and algorithm while experimental results are included in later work.
Description:
There are many improvements to operation that can be made to a water distribution system once it has been constructed and placed in ground. Pipes and associated storages and pumps are typically designed to meet average peak daily demands, offer some capacity for growth, and also allow for some deterioration of performance over time. However, the 'as constructed' performance of the pipeline is invariably different to what was designed on paper, and this is particularly so for anything other than design flows, such as during times of water restrictions when there are significantly reduced flows. Because of this, there remain significant benefits to owners and operators for the adaptive and global optimisation of such systems. The present paper uses the Ouyen subsystem of the Northern Mallee Pipeline, in Victoria, as a case study for the development of an optimisation model. This has been done with the intent of using this model to reduce costs and provide better service to customers on this system. The Ouyen subsystem consists of 1600 km of trunk and distribution pipeline servicing an area of 456,000 Ha. The system includes 2 fixed speed pumps diverting water from the Murray River at Liparoo into two 150 ML balancing storages at Ouyen, 4 variable speed pumps feeding water from the balancing storages into the pipeline system, 2 variable speed pressure booster pumps and 5 town balancing storages. When considering all these components of the system, power consumption becomes an important part of the overall operation. The present paper considers a global optimisation model to minimise power consumption while maintaining reasonable performance of the system. The main components of the model are described including the network structure and the costs functions associated with the system. The final model presents the cost functions associated with the pump scheduling, including the penalties descriptions associated with maintaining appropriate storages levels and pressure bounds within the water distribution network.
Description:
Clustering in gene expression data sets is a challenging problem. Different algorithms for clustering of genes have been proposed. However due to the large number of genes only a few algorithms can be applied for the clustering of samples. k-means algorithm and its different variations are among those algorithms. But these algorithms in general can converge only to local minima and these local minima are significantly different from global solutions as the number of clusters increases. Over the last several years different approaches have been proposed to improve global search properties of k-means algorithm and its performance on large data sets. One of them is the global k-means algorithm. In this paper we develop a new version of the global k-means algorithm: the modified global k-means algorithm which is effective for solving clustering problems in gene expression data sets. We present preliminary computational results using gene expression data sets which demonstrate that the modified k-means algorithm improves and sometimes significantly results by k-means and global k-means algorithms.
Description:
Clustering in gene expression data sets is a challenging problem. Different algorithms for clustering of genes have been proposed. However due to the large number of genes only a few algorithms can be applied for the clustering of samples. k-means algorithm and its different variations are among those algorithms. But these algorithms in general can converge only to local minima and these local minima are significantly different from global solutions as the number of clusters increases. Over the last several years different approaches have been proposed to improve global search properties of k-means algorithm and its performance on large data sets. One of them is the global k-means algorithm. In this paper we develop a new version of the global k-means algorithm: the modified global k-means algorithm which is effective for solving clustering problems in gene expression data sets. We present preliminary computational results using gene expression data sets which demonstrate that the modified k-means algorithm improves and sometimes significantly results by k-means and global k-means algorithms.
Description:
We reduce the supervised classification to solving a nonsmooth optimization problem. The proposed method allows one to solve classification problems for databases with arbitrary number of classes. Numerical experiments have been carried out with databases of small and medium size. We present their results and provide comparison of these results with ones obtained by other algorithms of classification based on the optimization techniques. Results of numerical experiments show effectiveness of the proposed algorithms.
Description:
We reduce the supervised classification to solving a nonsmooth optimization problem. The proposed method allows one to solve classification problems for databases with arbitrary number of classes. Numerical experiments have been carried out with databases of small and medium size. We present their results and provide comparison of these results with ones obtained by other algorithms of classification based on the optimization techniques. Results of numerical experiments show effectiveness of the proposed algorithms.
Description:
The operation of a water distribution system is a complex task which involves scheduling of pumps, regulating water levels of storages, and providing satisfactory water quality to customers at required flow and pressure. Pump scheduling is one of the most important tasks of the operation of a water distribution system as it represents the major part of its operating costs. In this paper, a novel approach for modeling of pump scheduling to minimize energy consumption by pumps is introduced which uses pump's start/end run times as continuous variables. This is different from other approaches where binary integer variables for each hour are typically used which is considered very impractical from an operational perspective. The problem is formulated as a nonlinear programming problem and a new algorithm is developed for its solution. This algorithm is based on the combination of the grid search with the Hooke-Jeeves pattern search method. The performance of the algorithm is evaluated using literature test problems applying the hydraulic simulation model EPANet.
Description:
Pumping cost constitutes the main part of the overall operating cost of water distribution systems. There are different optimization formulations of the pumping cost minimization problem including those with application of continuous and integer programming approaches. To date mainly various metaheuristics have been applied to solve this problem. However, the comprehensive comparison of those metaheuristics has not been done. Such a comparison is important to identify strengths and weaknesses of different algorithms which reflects on their performance. In this paper, we present a methodology for comparative analysis of widely used metaheuristics for solving the pumping cost minimization problem. This methodology includes the following comparison criteria: (a) the "optimal solution" obtained; (b) the efficiency; and (c) robustness. Algorithms applied are: particle swarm optimization, artificial bee colony and firefly algorithms. These algorithms were applied to one test problem available in the literature. The results obtained demonstrate that the artificial bee colony is the most robust and the firefly is the most efficient and accurate algorithm for this test problem. Funding :ARC
Description:
The k-means algorithm and its variations are known to be fast clustering algorithms. However, they are sensitive to the choice of starting points and inefficient for solving clustering problems in large data sets. Recently, in order to resolve difficulties with the choice of starting points, incremental approaches have been developed. The modified global k-means algorithm is based on such an approach. It iteratively adds one cluster center at a time. Numerical experiments show that this algorithm considerably improve the k-means algorithm. However, this algorithm is not suitable for clustering very large data sets. In this paper, a new version of the modified global k-means algorithm is proposed. We introduce an auxiliary cluster function to generate a set of starting points spanning different parts of the data set. We exploit information gathered in previous iterations of the incremental algorithm to reduce its complexity.
Description:
In this paper the problem of finding piecewise linear boundaries between sets is considered and is applied for solving supervised data classification problems. An algorithm for the computation of piecewise linear boundaries, consisting of two main steps, is proposed. In the first step sets are approximated by hyperboxes to find so-called “indeterminate” regions between sets. In the second step sets are separated inside these “indeterminate” regions by piecewise linear functions. These functions are computed incrementally starting with a linear function. Results of numerical experiments are reported. These results demonstrate that the new algorithm requires a reasonable training time and it produces consistently good test set accuracy on most data sets comparing with mainstream classifiers.