Barriers to information technology governance adoption : a preliminary empirical investigation

The adoption of IT Governance (ITG) continues to be an important topic for research. Many researchers have focused their attention on how these practices are currently being implemented in the many diverse areas and industries. Literature shows that a majority of these studies have only been based on industries and organizations in developed countries. There exist very few researches that look specifically within the context of a developing country. Furthermore, there seems to be a lack of research on identifying the barriers or inhibitors to IT Governance adoption within the context of an emerging yet still developing Asian country. This research sets out to justify, substantiate and improve on a priori model developed to study the barriers to the adoption of ITG practice using qualitative data obtained through a series of semi-structured interviews conducted on organizations in Malaysia.

Nursing Sans Frontieres: A Three Year Case Study of Multi-State Registration of Support Nursing Practice Using Information Technology

Objective: To highlight the registration issues for nurses who wish to practice nationally, particularly those practicing within the telehealth sector. Design: As part of a national clinical research study, applications were made to every state and territory for mutual recognition of nursing registration and fee waiver for telenursing cross boarder practice for a period of three years. These processes are described using a case study approach. Outcome: The aim of this case study was to achieve registration in every state and territory of Australia without paying multiple fees by using mutual recognition provisions and the cross-border fee waiver policy of the nurse regulatory authorities in order to practice telenursing. Results: Mutual recognition and fee waiver for cross-border practice was granted unconditionally in two states: Victoria (Vic) and Tasmania (Tas), and one territory: the Northern Territory (NT). The remainder of the Australian states and territories would only grant temporary registration for the period of the project or not at all, due to policy restrictions or nurse regulatory authority (NRA) Board decisions. As a consequence of gaining fee waiver the annual cost of registration was a maximum of $145 per annum as opposed to the potential $959 for initial registration and $625 for annual renewal. Conclusions: Having eight individual nurses Acts and NRAs for a population of 265,000 nurses would clearly indicate a case for over regulation in this country. The structure of regulation of nursing in Australia is a barrier to the changing and evolving role of nurses in the 21st century and a significant factor when considering workforce planning.

Spatially stratified sampling using auxiliary information for geostatistical mapping

This paper presents a method of spatial sampling based on stratification by Local Moran’s I i calculated using auxiliary information. The sampling technique is compared to other design-based approaches including simple random sampling, systematic sampling on a regular grid, conditional Latin Hypercube sampling and stratified sampling based on auxiliary information, and is illustrated using two different spatial data sets. Each of the samples for the two data sets is interpolated using regression kriging to form a geostatistical map for their respective areas. The proposed technique is shown to be competitive in reproducing specific areas of interest with high accuracy.

Prediction of cis/trans isomerization in proteins using PSI-BLAST profiles and secondary structure information

Background The majority of peptide bonds in proteins are found to occur in the trans conformation. However, for proline residues, a considerable fraction of Prolyl peptide bonds adopt the cis form. Proline cis/trans isomerization is known to play a critical role in protein folding, splicing, cell signaling and transmembrane active transport. Accurate prediction of proline cis/trans isomerization in proteins would have many important applications towards the understanding of protein structure and function. Results In this paper, we propose a new approach to predict the proline cis/trans isomerization in proteins using support vector machine (SVM). The preliminary results indicated that using Radial Basis Function (RBF) kernels could lead to better prediction performance than that of polynomial and linear kernel functions. We used single sequence information of different local window sizes, amino acid compositions of different local sequences, multiple sequence alignment obtained from PSI-BLAST and the secondary structure information predicted by PSIPRED. We explored these different sequence encoding schemes in order to investigate their effects on the prediction performance. The training and testing of this approach was performed on a newly enlarged dataset of 2424 non-homologous proteins determined by X-Ray diffraction method using 5-fold cross-validation. Selecting the window size 11 provided the best performance for determining the proline cis/trans isomerization based on the single amino acid sequence. It was found that using multiple sequence alignments in the form of PSI-BLAST profiles could significantly improve the prediction performance, the prediction accuracy increased from 62.8% with single sequence to 69.8% and Matthews Correlation Coefficient (MCC) improved from 0.26 with single local sequence to 0.40. Furthermore, if coupled with the predicted secondary structure information by PSIPRED, our method yielded a prediction accuracy of 71.5% and MCC of 0.43, 9% and 0.17 higher than the accuracy achieved based on the singe sequence information, respectively. Conclusion A new method has been developed to predict the proline cis/trans isomerization in proteins based on support vector machine, which used the single amino acid sequence with different local window sizes, the amino acid compositions of local sequence flanking centered proline residues, the position-specific scoring matrices (PSSMs) extracted by PSI-BLAST and the predicted secondary structures generated by PSIPRED. The successful application of SVM approach in this study reinforced that SVM is a powerful tool in predicting proline cis/trans isomerization in proteins and biological sequence analysis.