Does road network density matter in optimally locating facilities?

Optimal location on the transport infrastructure is the preferable requirement for many decision making processes. Most studies have focused on evaluating performances of optimally locate p facilities by minimizing their distances to a geographically distributed demand (n) when p and n vary. The optimal locations are also sensitive to geographical context such as road network, especially when they are asymmetrically distributed in the plane. The influence of alternating road network density is however not a very well-studied problem especially when it is applied in a real world context. This paper aims to investigate how the density level of the road network affects finding optimal location by solving the specific case of p-median location problem. A denser network is found needed when a higher number of facilities are to locate. The best solution will not always be obtained in the most detailed network but in a middle density level. The solutions do not further improve or improve insignificantly as the density exceeds 12,000 nodes, some solutions even deteriorate. The hierarchy of the different densities of network can be used according to location and transportation purposes and increase the efficiency of heuristic methods. The method in this study can be applied to other location-allocation problem in transportation analysis where the road network density can be differentiated. 

How does the use of different road networks effect the optimal location of facilities in rural areas?

The p-median problem is often used to locate P service facilities in a geographically distributed population. Important for the performance of such a model is the distance measure. Distance measure can vary if the accuracy of the road network varies. The rst aim in this study is to analyze how the optimal location solutions vary, using the p-median model, when the road network is alternated. It is hard to nd an exact optimal solution for p-median problems. Therefore, in this study two heuristic solutions are applied, simulating annealing and a classic heuristic. The secondary aim is to compare the optimal location solutions using dierent algorithms for large p-median problem. The investigation is conducted by the means of a case study in a rural region with an asymmetrically distributed population, Dalecarlia. The study shows that the use of more accurate road networks gives better solutions for optimal location, regardless what algorithm that is used and regardless how many service facilities that is optimized for. It is also shown that the simulated annealing algorithm not just is much faster than the classic heuristic used here, but also in most cases gives better location solutions.

How does data quality in a network affect heuristic solutions?

To have good data quality with high complexity is often seen to be important. Intuition says that the higher accuracy and complexity the data have the better the analytic solutions becomes if it is possible to handle the increasing computing time. However, for most of the practical computational problems, high complexity data means that computational times become too long or that heuristics used to solve the problem have difficulties to reach good solutions. This is even further stressed when the size of the combinatorial problem increases. Consequently, we often need a simplified data to deal with complex combinatorial problems. In this study we stress the question of how the complexity and accuracy in a network affect the quality of the heuristic solutions for different sizes of the combinatorial problem. We evaluate this question by applying the commonly used p-median model, which is used to find optimal locations in a network of p supply points that serve n demand points. To evaluate this, we vary both the accuracy (the number of nodes) of the network and the size of the combinatorial problem (p). The investigation is conducted by the means of a case study in a region in Sweden with an asymmetrically distributed population (15,000 weighted demand points), Dalecarlia. To locate 5 to 50 supply points we use the national transport administrations official road network (NVDB). The road network consists of 1.5 million nodes. To find the optimal location we start with 500 candidate nodes in the network and increase the number of candidate nodes in steps up to 67,000 (which is aggregated from the 1.5 million nodes). To find the optimal solution we use a simulated annealing algorithm with adaptive tuning of the temperature. The results show that there is a limited improvement in the optimal solutions when the accuracy in the road network increase and the combinatorial problem (low p) is simple. When the combinatorial problem is complex (large p) the improvements of increasing the accuracy in the road network are much larger. The results also show that choice of the best accuracy of the network depends on the complexity of the combinatorial (varying p) problem.

Copper-induced trafficking of the Cu-ATPase: a key mechanism for copper homeostasis

The Menkes protein (MNK) and Wilson protein (WND) are transmembrane, CPX-type Cu-ATPases with six metal binding sites (MBSs) in the N-terminal region containing the motif GMXCXXC. In cells cultured in low copper concentration MNK and WND localize to the transGolgi network but in high copper relocalize either to the plasma membrane (MNK) or a vesicular compartment (WND). In this paper we investigate the role of the MBSs in Cu-transport and trafficking. The copper transport activity of MBS mutants of MNK was determined by their ability to complement a strain of Saccharomyces cerevisiae deficient in CCC2 (Deltaccc2), the yeast MNK/WND homologue. Mutants (CXXC to SXXS) of MBS1, MBS6, and MBSs1-3 were able to complement Deltaccc2 while mutants of MBS4-6, MBS5-6 and all six MBS inactivated the protein. Each of the inactive mutants also failed to display Cu-induced trafficking suggesting a correlation between trafficking and transport activity. A similar correlation was found with mutants of MNK in which various MBSs were deleted, but two constructs with deletion of MBS5-6 were unable to traffic despite retaining 25% of copper transport activity. Chimeras in which the N-terminal MBSs of MNK were replaced with the corresponding MBSs of WND were used to investigate the region of the molecules that is responsible for the difference in Cu-trafficking of MNK and WND. The chimera which included the complete WND N-terminus localized to a vesicular compartment, similar to WND in elevated copper. Deletions of various MBSs of the WND N-terminus in the chimera indicate that a targeting signal in the region of MBS6 directs either WND/MNK or WND to a vesicular compartment of the cell.

Predictors of the strain experienced by professional Australian footballers

The purpose of this model was to examine whether both the generic dimensions of the Job stress model (JSM) and job-specific stressors would be predictive of the strain experienced by professional Australian footballers. A football-specific stressor scale questionnaire was distributed and football specific stressors and their impact were identified.

Employer satisfaction, job-match and future hiring intentions for employees with a disability

This paper is based on survey responses from 656 employers who used disability employment services to employ someone with a disability. Relationships between employer satisfaction and employer perceptions of job-match and future hiring intentions toward people who have a disability are outlined and discussed. Employers' perceptions of the job-match process were found to be an important determinant of their perceptions of work performance and employer satisfaction. Comparative ratings on employer satisfaction for employees with and without a disability were seen as an important indicator of future hiring intentions toward people with a disability. Findings reported in this paper provide important information on which to base strategies for improving future employment outcomes for people who have a disability.

Which work characteristics predict employee outcomes for the public-sector employee? An examination of generic and occupation-specific characteristics

The wide-ranging changes that have occurred in the public sector over recent years have placed increasing demands on public-sector employees. A survey of employees within a relatively commercially-oriented public-sector organization in Australia was used to test a demand-oriented generic model of employee well-being and a variety of situation-specific variables. The presence of support at work and the amount of control an employee had over their job were found to be key predictors of employee-level outcomes. Perceptions of pay and the perception of a lack of human resources (HR) were also found to predict employee outcome variables. The results emphasize the impact that middle managers and HR managers can have in terms of reducing the detrimental employee effects that can be caused by the introduction of new public management (NPM) and the potential for a positive impact on employees. In particular, public-sector managers can use the design of jobs and the development of social support mechanisms, such as employee assistance programmes, to maintain, if not improve, the quality of working life experienced by their employees. More broadly, this study has found that the job strain model is a useful tool in a public-sector environment and is likely to be of increasing utility with the continuing introduction or consolidation of NPM over time. Managing these issues in the new public sector could be a key means of protecting the key resource of the Australian public sector - the employees.

Speed control and policing in a cellular mobile network: speedNet

In this paper, we describe SpeedNet, a GSM network variant which resembles an ad hoc wireless mobile network where base stations keep track of the velocities of mobile users (cars). SpeedNet is intended to track mobile users and their speed passively for both speed policing and control of traffic. The speed of the vehicle is controlled in a speed critical zone by means of an electro-mechanical control system, suitably referred to as VVLS (Vehicular Velocity Limiting System). VVLS is mounted on the vehicle and responds to the command signals generated by the base station. It also determines the next base station to handoff, in order to improve the connection reliability and bandwidth efficiency of the underlying network. Robust Extended Kalman Filter (REKF) is used as a passive velocity estimator of the mobile user with the widely used proportional and integral controller speed control. We demonstrate through simulation and analysis that our prediction algorithm can successfully estimate the mobile user’s velocity with low system complexity as it requires two closest mobile base station measurements and also it is robust against system uncertainties due to the inherent deterministic nature in the mobility model.

Factors affecting patch occupancy by the white-browed treecreeper Climacteris Affinis in an agricultural landscape in north-west Victoria, Australia

The survival of habitat-dependent fauna within agricultural mosaics depends on their ability to occupy remnant habitat patches and move through the modified landscape. In north-west Victoria, Australia, less than 10% of the pre-European extent of Selah Casuarina pauper woodland remains intact due to agricultural development. The White-browed Treecreeper Climacteris affinis, is a small, insectivorous passerine that, in this region, preferentially inhabits Selah woodland. To assess the ability of C. affinis to persist in an agricultural landscape, 30 woodland sites in the Millewa landscape (34°30'S, 141°30'E) were surveyed, and patterns of patch occupancy used to examine the influence of spatial characteristics, landscape context and grazing by stock on the suitability of remnants as C. affinis habitat. Sites occupied by C. affinis were larger and less likely to be grazed by stock than vacant patches. The area-dependency of patch occupancy represents a step-threshold: C. affinis were not detected in remnants with less than 18.5 ha of Selah woodland but above this threshold, density was not correlated with patch area. Measures of patch isolation, the existence of linking linear "corridors" and tree density were not reliable indicators of patch occupancy. The presence of the species in remnants entirely surrounded by agricultural land suggests they are capable of crossing up to 450 m of cultivated land to prospect for habitat. The extensive network of linear vegetation and the numerous small remnants and scattered trees appear to facilitate movements of C. affinis in this landscape. Increasing the size of existing remnants, creating new habitat to expand the area of occupancy and maintaining landscape connectivity are priorities for the long-term management of this threatened species.

Fault detection in a cold forging process through feature extraction with a neural network

This paper investigates the application of neural networks to the recognition of lubrication defects typical to an industrial cold forging process employed by fastener manufacturers. The accurate recognition of lubrication errors, such as coating not being applied properly or damaged during material handling, is very important to the quality of the final product in fastener manufacture. Lubrication errors lead to increased forging loads and premature tool failure, as well as to increased defect sorting and the re-processing of the coated rod. The lubrication coating provides a barrier between the work material and the die during the drawing operation; moreover it needs be sufficiently robust to remain on the wire during the transfer to the cold forging operation. In the cold forging operation the wire undergoes multi-stage deformation without the application of any additional lubrication. Four types of lubrication errors, typical to production of fasteners, were introduced to a set of sample rods, which were subsequently drawn under laboratory conditions. The drawing force was measured, from which a limited set of features was extracted. The neural network based model learned from these features is able to recognize all types of lubrication errors to a high accuracy. The overall accuracy of the neural network model is around 98% with almost uniform distribution of errors between all four errors and the normal condition.

Predicting the distribution of discrete spatial events using artificial neural networks

Although the development of geographic information system (GIS) technology and digital data manipulation techniques has enabled practitioners in the geographical and geophysical sciences to make more efficient use of resource information, many of the methods used in forming spatial prediction models are still inherently based on traditional techniques of map stacking in which layers of data are combined under the guidance of a theoretical domain model. This paper describes a data-driven approach by which Artificial Neural Networks (ANNs) can be trained to represent a function characterising the probability that an instance of a discrete event, such as the presence of a mineral deposit or the sighting of an endangered animal species, will occur over some grid element of the spatial area under consideration. A case study describes the application of the technique to the task of mineral prospectivity mapping in the Castlemaine region of Victoria using a range of geological, geophysical and geochemical input variables. Comparison of the maps produced using neural networks with maps produced using a density estimation-based technique demonstrates that the maps can reliably be interpreted as representing probabilities. However, while the neural network model and the density estimation-based model yield similar results under an appropriate choice of values for the respective parameters, the neural network approach has several advantages, especially in high dimensional input spaces.

Application of neural networks as an auxiliary technique in the modelling of power station

Artificial neural network (NN) is an alternative way (to conventional physical or chemical based modeling technique) to solve complex ill-defined problems. Neural networks trained from historical data are able to handle nonlinear problems and to find the relationship between input data and output data when there is no obvious one between them. Neural Networks has been successfully used in control, robotic, pattern recognition, forecasting areas. This paper presents an application of neural networks in finding some key factors eg. heat loss factor in power station modeling process. In the conventional modeling of power station, these factors such as heat loss are normally determined by experience or “rule of thumb”. To get an accurate estimation of these factors special experiment needs to be carried out and is a very time consuming process. In this paper the neural networks (technique) is used to assist this difficult conventional modeling process. The historical data from a real running brown coal power station in Victoria has been used to train the neural network model and the outcomes of the trained NN model will be used to determine the factors in the conventional energy modeling of the power stations that is under the development as a part of an on-going ARC Linkage project aiming to detail modeling the internal energy flows in the power station.

An immune network approach for web document clustering

The human immune system provides inspiration for solving a wide range of innovative problems. In this paper, we propse an immune network based approach for web document clustering. All the immune cells in the network competitively recognize the antigens (web documents) which are presented to the network one by one. The interaction between immune cells and an antigen leads to an augment of the network through the clonal selection and somatic mutation of the stimulated immune cells, while the interaction among immune cells results in a network compression. The structure of the immune network is well maintained by learning and self-regularity. We use a public web document data set to test the effectiveness of our method and compare it with other approaches. The experimental results demonstrate that the most striking advantage of immune-based data clustering is its adaptation in dynamic environment and the capability of finding new clusters automatically.

A self-learning system for identifying harmful network information

Network information identification is a “hot” topic currently. This paper designs a self-learning system using neural network algorithm for identifying the harmful network messages of both Chinese and English languages. The system segments the message into words and creates key word vector which characterizes the harmful network information. The BP algorithm is taken advantage of to train the neural network. The result of training and studying of the neural network can be applied onto many network applications based on message identification. The result of experiments demonstrates that our system has a high degree of accuracy.

Designing educational tasks for success in industry: a reflection on the evolution of business communication studies in tertiary education

This paper is the result of a "Rip Van Winkle" experience I had concerning the teaching of Business Communication. The paper focuses on the remarkable expansion in the curriculum of the traditional "Business Communication" or "Business Writing" course offered by many tertiary institutions around the world. Based on 25 years of personal observation and experience in a number of educational settings, the paper will trace the increasing sophistication and complexity of the study of business communication from one that covered little more than lessons in the design of hardcopy memos, letters, and reports to one that now covers a broad spectrum of topics such as "emotional intelligence," "intercultural communication," "effective public speaking," as well as the effects of purpose and audience on the design of a wide variety of business communications.

An example of an effective task that involves a number of on the job activities is provided in the form of a ready to use assignment that is applicable in a number of contexts.