The degree of usability from selected DVD menus and their navigational systems

The purpose of this research is to investigate the usability of DVD interfaces via their menus and navigation, inspired by Donald Norman who has had a pivotal role in user-centred design and usability. The paper encompasses theoretical aspects of interactivity, usability and DVD technology. A usability test was administered with the DVDs chosen. The results from the usability test were the main focus in this research. Such results were supportive of Normanrsquos claims, as participants experienced varying degrees of usability issues. Furthermore, the findings were used to develop a set of guidelines and recommendations designers could follow. If these were adhered to, it would have significantly alleviated the difficulty the participants had in interacting with the DVDs.

The degree of job satisfaction experienced by site-based construction professionals

The construction industry worldwide is having problems attracting and retaining skilled workers. This study analyses a series of factors affecting job satisfaction of site-based construction professionals employed in medium to large scale metropolitan construction firms in Melbourne, Australia. The industry survey carried out identified salary as the strongest determinant of job satisfaction. However, many respondents reported being dissatisfied with pay levels when compared to other industries and the number of work hours expected. The greatest causes of dissatisfaction were related to difficulties in maintaining a work-life balance. The indicator "Variety, interest and challenge" was the most frequently cited positive aspect of a career in construction. Given the shortage of skilled construction workers in Australia, it is important for companies to maximise the retention of site-based construction professionals and ensure that key job satisfaction indicators are met.

Community reintegration of ex-prisoners : type and degree of change in variables influencing successful reintegration

Community reintegration of ex-prisoners is an important issue in efforts to reduce recidivism. The present study examined the multiple, complex, and dynamic nature of variables influencing successful reintegration by assessing the type and degree of change in reintegration variables over time. Participants were 79 adult prisoners (54 male, 25 female) who completed a prerelease questionnaire 1 month before their release, which focused on prison-related variables, participant background, and anticipated conditions upon release. A postrelease questionnaire was administered to the same participants at 1-4 weeks and 3-4 months postrelease, focusing on the quality of life conditions experienced following release. Results indicate that current health ratings and several indicators of drug use were significantly different over the three measurement phases. Ratings of employment and housing stability, finance, and social support were unchanged over the postrelease period. Theoretical implications of the present investigation for reintegration theory are discussed, together with practical applications.

Effect of the degree of crystallinity on the electrochemical behavior of Mg65Cu25Y10 and Mg70Zn25Ca5 bulk metallic glasses

The effect of varying the percent crystallinity on the electrochemical behavior of Mg65Cu25Y10 and Mg70Zn25Ca5 bulk metallic glasses was studied. The alloys were heat-treated to achieve desired microstructures ranging from fully amorphous to fully crystalline, providing a systematic basis for subsequent testing. Potentiodynamic experiments in 0.01 M sodium chloride (NaCl) were used, whereby both the amorphous and partially crystallized samples were observed to have more noble corrosion potentials and lower anodic kinetics. However, this was accompanied by more rapid cathodic kinetics relative to their fully crystalline counterparts, meaning that corrosion rates were not significantly lower in the amorphous state. To describe the electrochemical response as a function of the degree of crystallinity, differential scanning calorimetry (DSC), scanning electron microscopy, x-ray diffraction (XRD), and electrical conductivity measurements were undertaken, where it was found that crystallinity alone is not necessarily the controlling factor and microchemistry that evolves upon devitrification, plays a key role in the electrochemical response of these materials.

An algorithm for determination of rank and degree of contribution of sMRI volumetric features in depression detection

Brain volume changes at structural level appear to have utmost importance in depression biomarkers studies. However, these brain volumetric findings have very minimal utilization in depression detection studies at individual level. Thus, this paper presents an evaluation of volumetric features to identify the relevant/optimal features for the detection of depression. An algorithm is presented for determination of rank and degree of contribution (DoC) of structural magnetic resonance imaging (sMRI) volumetric features. The algorithm is based on the frequencies of each feature contribution toward the desired accuracy limit. Forty-four volumetric features from various brain regions were adopted for evaluation. From DoC analysis, the DoC of each volumetric feature for depression detection is calculated and the features that dominate the contribution are determined.

Degree of contribution (DoC) feature selection algorithm for structural brain MRI volumetric features in depression detection

Accurate detection of depression at an individual level using structural magnetic resonance imaging (sMRI) remains a challenge. Brain volumetric changes at a structural level appear to have importance in depression biomarkers studies. An automated algorithm is developed to select brain sMRI volumetric features for the detection of depression.

Methods of construction of OWA operators from data

This paper investigates the problem of obtaining the weights of the ordered weighted aggregation (OWA) operators from observations. The problem is formulated as a restricted least squares and uniform approximation problems. We take full advantage of the linearity of the problem. In the former case, a well known technique of non-negative least squares is used. In a case of uniform approximation, we employ a recently developed cutting angle method of global optimisation. Both presented methods give results superior to earlier approaches, and do not require complicated nonlinear constructions. Additional restrictions, such as degree of orness of the operator, can be easily introduced

Multi-wavelets from B-spline super-functions with approximation order

Approximation order is an important feature of all wavelets. It implies that polynomials up to degree p−1 are in the space spanned by the scaling function(s). In the scalar case, the scalar sum rules determine the approximation order or the left eigenvectors of the infinite down-sampled convolution matrix H determine the combinations of scaling functions required to produce the desired polynomial. For multi-wavelets the condition for approximation order is similar to the conditions in the scalar case. Generalized left eigenvectors of the matrix H_f; a finite portion of H determines the combinations of scaling functions that produce the desired superfunction from which polynomials of desired degree can be reproduced. The superfunctions in this work are taken to be B-splines. However, any refinable function can serve as the superfunction. The condition of approximation order is derived and new, symmetric, compactly supported and orthogonal multi-wavelets with approximation orders one, two, three and four are constructed.

New methods for objective evaluation of fabric pilling by frequency domain image processing

Fabric pilling is a serious problem for the apparel industry. Resistance to pilling is normally tested by simulated accelerated wear and manual assessment of degree of pilling based on a visual comparison of the sample to a set of test images. A number of automated systems based on image analysis have been developed. The authors propose new methods of image analysis based on the two-dimensional wavelet transform to objectively measure the pilling intensity in sample images. Initial work employed the detail coefficients of the two-dimensional discrete wavelet transform (2DDWT) as a measure of the pilling intensity of woven/knitted fabrics.

This method is shown to be robust to image translation and brightness variation. Using the approximation coefficients of the 2DDWT, the method is extended to non-woven pilling image sets. Wavelet texture analysis (WTA) combined with principal components analysis are shown to produce a richer texture description of pilling for analysis and classification. Finally, employing the two-dimensional dual-tree complex wavelet transform as the basis for the WTA feature vector is shown to produce good automated classification on a range of standard pilling image sets.

Fuzzy neural networks for control of dynamic systems

This thesis provides a unified and comprehensive treatment of the fuzzy neural networks as the intelligent controllers. This work has been motivated by a need to develop the solid control methodologies capable of coping with the complexity, the nonlinearity, the interactions, and the time variance of the processes under control. In addition, the dynamic behavior of such processes is strongly influenced by the disturbances and the noise, and such processes are characterized by a large degree of uncertainty. Therefore, it is important to integrate an intelligent component to increase the control system ability to extract the functional relationships from the process and to change such relationships to improve the control precision, that is, to display the learning and the reasoning abilities. The objective of this thesis was to develop a self-organizing learning controller for above processes by using a combination of the fuzzy logic and the neural networks. An on-line, direct fuzzy neural controller using the process input-output measurement data and the reference model with both structural and parameter tuning has been developed to fulfill the above objective. A number of practical issues were considered. This includes the dynamic construction of the controller in order to alleviate the bias/variance dilemma, the universal approximation property, and the requirements of the locality and the linearity in the parameters. Several important issues in the intelligent control were also considered such as the overall control scheme, the requirement of the persistency of excitation and the bounded learning rates of the controller for the overall closed loop stability. Other important issues considered in this thesis include the dependence of the generalization ability and the optimization methods on the data distribution, and the requirements for the on-line learning and the feedback structure of the controller. Fuzzy inference specific issues such as the influence of the choice of the defuzzification method, T-norm operator and the membership function on the overall performance of the controller were also discussed. In addition, the e-completeness requirement and the use of the fuzzy similarity measure were also investigated. Main emphasis of the thesis has been on the applications to the real-world problems such as the industrial process control. The applicability of the proposed method has been demonstrated through the empirical studies on several real-world control problems of industrial complexity. This includes the temperature and the number-average molecular weight control in the continuous stirred tank polymerization reactor, and the torsional vibration, the eccentricity, the hardness and the thickness control in the cold rolling mills. Compared to the traditional linear controllers and the dynamically constructed neural network, the proposed fuzzy neural controller shows the highest promise as an effective approach to such nonlinear multi-variable control problems with the strong influence of the disturbances and the noise on the dynamic process behavior. In addition, the applicability of the proposed method beyond the strictly control area has also been investigated, in particular to the data mining and the knowledge elicitation. When compared to the decision tree method and the pruned neural network method for the data mining, the proposed fuzzy neural network is able to achieve a comparable accuracy with a more compact set of rules. In addition, the performance of the proposed fuzzy neural network is much better for the classes with the low occurrences in the data set compared to the decision tree method. Thus, the proposed fuzzy neural network may be very useful in situations where the important information is contained in a small fraction of the available data.