The network structure of a regional tourism organization: Application of he social network theory in one region of Queensland

Regional tourism organizations (RTOs) plays a central role in planning, coordinating and marketing tourism in many areas, including Queensland, Australia. RTOs rely on interaction with a network of other organizations for their efficient functioning. This paper describes an exploratory case study that develops a method for use of social network analysis techniques to analyse the inter-organizational network in one RTO region in Queensland. Results indicate that differences exist in the structure of inter-organizational links between commercial tourism organizations and planning organizations, between tourism organizations and other sectoral clusters, and between organizations at local, regional and state levels. The results highlight areas or improvement in the role and responsibilities of RTOs in Queensland.

Filtering duplicate items over distributed data streams

In recent years many real time applications need to handle data streams. We consider the distributed environments in which remote data sources keep on collecting data from real world or from other data sources, and continuously push the data to a central stream processor. In these kinds of environments, significant communication is induced by the transmitting of rapid, high-volume and time-varying data streams. At the same time, the computing overhead at the central processor is also incurred. In this paper, we develop a novel filter approach, called DTFilter approach, for evaluating the windowed distinct queries in such a distributed system. DTFilter approach is based on the searching algorithm using a data structure of two height-balanced trees, and it avoids transmitting duplicate items in data streams, thus lots of network resources are saved. In addition, theoretical analysis of the time spent in performing the search, and of the amount of memory needed is provided. Extensive experiments also show that DTFilter approach owns high performance.

Typing mineral deposits using their grades and tonnages in an artificial neural network

A test of the ability of a probabilistic neural network to classify deposits into types on the basis of deposit tonnage and average Cu, Mo, Ag, Au, Zn, and Pb grades is conducted. The purpose is to examine whether this type of system might serve as a basis for integrating geoscience information available in large mineral databases to classify sites by deposit type. Benefits of proper classification of many sites in large regions are relatively rapid identification of terranes permissive for deposit types and recognition of specific sites perhaps worthy of exploring further. Total tonnages and average grades of 1,137 well-explored deposits identified in published grade and tonnage models representing 13 deposit types were used to train and test the network. Tonnages were transformed by logarithms and grades by square roots to reduce effects of skewness. All values were scaled by subtracting the variable's mean and dividing by its standard deviation. Half of the deposits were selected randomly to be used in training the probabilistic neural network and the other half were used for independent testing. Tests were performed with a probabilistic neural network employing a Gaussian kernel and separate sigma weights for each class (type) and each variable (grade or tonnage). Deposit types were selected to challenge the neural network. For many types, tonnages or average grades are significantly different from other types, but individual deposits may plot in the grade and tonnage space of more than one type. Porphyry Cu, porphyry Cu-Au, and porphyry Cu-Mo types have similar tonnages and relatively small differences in grades. Redbed Cu deposits typically have tonnages that could be confused with porphyry Cu deposits, also contain Cu and, in some situations, Ag. Cyprus and kuroko massive sulfide types have about the same tonnages. Cu, Zn, Ag, and Au grades. Polymetallic vein, sedimentary exhalative Zn-Pb, and Zn-Pb skarn types contain many of the same metals. Sediment-hosted Au, Comstock Au-Ag, and low-sulfide Au-quartz vein types are principally Au deposits with differing amounts of Ag. Given the intent to test the neural network under the most difficult conditions, an overall 75% agreement between the experts and the neural network is considered excellent. Among the largestclassification errors are skarn Zn-Pb and Cyprus massive sulfide deposits classed by the neuralnetwork as kuroko massive sulfides—24 and 63% error respectively. Other large errors are the classification of 92% of porphyry Cu-Mo as porphyry Cu deposits. Most of the larger classification errors involve 25 or fewer training deposits, suggesting that some errors might be the result of small sample size. About 91% of the gold deposit types were classed properly and 98% of porphyry Cu deposits were classes as some type of porphyry Cu deposit. An experienced economic geologist would not make many of the classification errors that were made by the neural network because the geologic settings of deposits would be used to reduce errors. In a separate test, the probabilistic neural network correctly classed 93% of 336 deposits in eight deposit types when trained with presence or absence of 58 minerals and six generalized rock types. The overall success rate of the probabilistic neural network when trained on tonnage and average grades would probably be more than 90% with additional information on the presence of a few rock types.

Use of a probabilistic neural network to reduce costs of selecting construction rock

Rocks used as construction aggregate in temperate climates deteriorate to differing degrees because of repeated freezing and thawing. The magnitude of the deterioration depends on the rock's properties. Aggregate, including crushed carbonate rock, is required to have minimum geotechnical qualities before it can be used in asphalt and concrete. In order to reduce chances of premature and expensive repairs, extensive freeze-thaw tests are conducted on potential construction rocks. These tests typically involve 300 freeze-thaw cycles and can take four to five months to complete. Less time consuming tests that (1) predict durability as well as the extended freeze-thaw test or that (2) reduce the number of rocks subject to the extended test, could save considerable amounts of money. Here we use a probabilistic neural network to try and predict durability as determined by the freeze-thaw test using four rock properties measured on 843 limestone samples from the Kansas Department of Transportation. Modified freeze-thaw tests and less time consuming specific gravity (dry), specific gravity (saturated), and modified absorption tests were conducted on each sample. Durability factors of 95 or more as determined from the extensive freeze-thaw tests are viewed as acceptable—rocks with values below 95 are rejected. If only the modified freeze-thaw test is used to predict which rocks are acceptable, about 45% are misclassified. When 421 randomly selected samples and all four standardized and scaled variables were used to train aprobabilistic neural network, the rate of misclassification of 422 independent validation samples dropped to 28%. The network was trained so that each class (group) and each variable had its own coefficient (sigma). In an attempt to reduce errors further, an additional class was added to the training data to predict durability values greater than 84 and less than 98, resulting in only 11% of the samples misclassified. About 43% of the test data was classed by the neural net into the middle group—these rocks should be subject to full freeze-thaw tests. Thus, use of the probabilistic neural network would meanthat the extended test would only need be applied to 43% of the samples, and 11% of the rocks classed as acceptable would fail early.

Subordinate-manager gender combination and perceived leadership style influence on emotions, self-esteem and organizational commitment

A theoretical model was developed to investigate the relationships among subordinate-manager gender combinations, perceived leadership style, experienced frustration and optimism, organization-based self-esteem and organizational commitment. The model was tested within the context of a probabilistic structural model, a discrete Bayesian network, using cross-sectional data from a global pharmaceutical company. The Bayesian network allowed forward inference to assess the relative influence of gender combination and leadership style on the emotions, self-esteem and commitment consequence variables. Further, diagnostics from backward inference were used to assess the relative influence of variables antecedent to organizational commitment. The results showed that gender combination was independent of leadership style and had a direct impact on subordinates' levels of frustration and optimism. Female manager-female subordinate had the largest probability of optimism, while male manager teamed with a male subordinate had the largest probability of frustration. Furthermore, having a female manager teamed up with a male subordinate resulted in the lowest possibility of frustration. However, the findings show that the gender issue is not simply female managers versus male managers, but is concerned with the interaction of the subordinate-manager gender combination and leadership style in a nonlinear manner. (C) 2003 Elsevier Inc. All rights reserved.

History and habit in the mobilization of ICT resources

Evidence demonstrates that the digital divide is deepening despite strategies mobilized worldwide to reduce it. In disadvantaged communities, beyond training and infrastructural issues, there often lies a range of cultural and historically formed relationships that affect people's adoption of ICTs. This article presents an analysis of local resident's engagement with their council's pilot project to develop a computer facility in their community center. We ask, to what extent can people in poor urban communities, once trained, be expected to volunteer to work on furthering community education and development in ICTs in their local area? Findings indicate four patterns of individual engagement with the computer project: reflexive, utilitarian, distributive, and nonparticipatory. It is argued that local people engaged with the intervention in historically patterned and locally distinctive ways that served immediate personal and pragmatic ends. They did not adopt the long-term strategic goals of the council or university.

The role of psychological climate in facilitating employee adjustment during organizational change

The current research tested a theoretical model of employee adjustment during organizational change based on Lazarus and Folkman's (1984) cognitive-phenomenological framework. The model hypothesized that psychological climate variables would act as coping resources and predict improved adjustment during change. Two variations of this model were tested using survey data from two different organizational samples: 779 public hospital employees and 877 public sector employees. Confirmatory factor analyses and structural equation analyses were conducted in order to evaluate the models. Results showed that employees whose perceptions of the organization and environment in which they were working (that is, psychological climate) were more positive, were more likely to appraise change favourably and report better adjustment in terms of higher job satisfaction, psychological well-being, and organizational commitment, and lower absenteeism and turnover intentions.

Shadowing experiences and the extension of communities of practice: A case study of women education managers

Recent developments in workplace learning have focused on relational and social network views of learning that introduce practitioners to the norms, values and assumptions of the workplace as well as the learning processes through which knowledge is acquired. This article reports on a qualitative study of a mentoring programme designed to assist women education managers gain promotion by broadening their networks and stimulating insights into the senior management positions for which they were being prepared. The findings are that members reflexively assess and reassess goals and values to demystify knowledge and resolved cognitive dissonance in these processes. Moreover, this article shows that women participants learn from the networks, and that the networks learn from the participant in a reciprocal and informal way. The article concludes that organizational learning programmes must focus on enabling such networks to flourish.

A probabilistic switch model of information flow in social networks: Application for virtual circuits to organizational design

Network building and exchange of information by people within networks is crucial to the innovation process. Contrary to older models, in social networks the flow of information is noncontinuous and nonlinear. There are critical barriers to information flow that operate in a problematic manner. New models and new analytic tools are needed for these systems. This paper introduces the concept of virtual circuits and draws on recent concepts of network modelling and design to introduce a probabilistic switch theory that can be described using matrices. It can be used to model multistep information flow between people within organisational networks, to provide formal definitions of efficient and balanced networks and to describe distortion of information as it passes along human communication channels. The concept of multi-dimensional information space arises naturally from the use of matrices. The theory and the use of serial diagonal matrices have applications to organisational design and to the modelling of other systems. It is hypothesised that opinion leaders or creative individuals are more likely to emerge at information-rich nodes in networks. A mathematical definition of such nodes is developed and it does not invariably correspond with centrality as defined by early work on networks.

Applying network analysis to the conservation of habitat trees in urban environments: a case study from Brisbane, Australia

In Australia more than 300 vertebrates, including 43 insectivorous bat species, depend on hollows in habitat trees for shelter, with many species using a network of multiple trees as roosts, We used roost-switching data on white-striped freetail bats (Tadarida australis; Microchiroptera: Molossidae) to construct a network representation of day roosts in suburban Brisbane, Australia. Bats were caught from a communal roost tree with a roosting group of several hundred individuals and released with transmitters. Each roost used by the bats represented a node in the network, and the movements of bats between roosts formed the links between nodes. Despite differences in gender and reproductive stages, the bats exhibited the same behavior throughout three radiotelemetry periods and over 500 bat days of radio tracking: each roosted in separate roosts, switched roosts very infrequently, and associated with other bats only at the communal roost This network resembled a scale-free network in which the distribution of the number of links from each roost followed a power law. Despite being spread over a large geographic area (> 200 km(2)), each roost was connected to others by less than three links. One roost (the hub or communal roost) defined the architecture of the network because it had the most links. That the network showed scale-free properties has profound implications for the management of the habitat trees of this roosting group. Scale-free networks provide high tolerance against stochastic events such as random roost removals but are susceptible to the selective removal of hub nodes. Network analysis is a useful tool for understanding the structural organization of habitat tree usage and allows the informed judgment of the relative importance of individual trees and hence the derivation of appropriate management decisions, Conservation planners and managers should emphasize the differential importance of habitat trees and think of them as being analogous to vital service centers in human societies.

Intra-colonial response to Acroporid "White syndrome" lesions in tabular Acropora spp. (Scleractinia)

'White syndrome' is considered to be the most prevalent coral disease on the Great Barrier Reef, characterised by rapid rates of lesion progression and high levels of colony mortality. This study investigated the production and translocation of photoassimilates towards white syndrome lesions (WSLs) and artificially inflicted lesions in healthy and diseased colonies of tabular Acropora spp. to determine the intra-colonial response to white syndrome using C-14 labelling. Translocation of C-14 labelled photoassimilates was preferentially orientated away from active WSLs, with minimal C-14 activity observed in the lesion borders, whilst artificial lesions (ALs) created directly opposite WSL borders showed significantly higher C-14 activity, suggesting active translocation of photoassimilates for tissue regeneration. Transport of photoassimilates in healthy coral colonies was preferentially oriented towards ALs with a higher perimeter-area ratio, although translocation towards WSL boundaries was minimal even though the lesion perimeter was often the width of the colony (> 200 cm). We suggest that the preferential orientation of photoassimilates away from WSLs may represent a deliberate strategy by the colony to induce a 'shutdown reaction' in order to preserve intra-colonial resources within areas of the colony that are more likely to survive and recover.

An FPGA network architecture for accelerating 3DES - CBC

This paper presents a DES/3DES core that will support cipher block chaining (CBC) and also has a built in keygen that together take up about 10% of the resources in a Xilinx Virtex II 1000-4. The core will achieve up to 200Mbit/s of encryption or decryption. Also presented is a network architecture that will allow these CBC capable 3DES cores to perform their processing in parallel.