Assessing of circuit breaker restrike risks using computer simulation and wavelet analysis

A breaker restrike is an abnormal arcing phenomenon, leading to a possible breaker failure. Eventually, this failure leads to interruption of the transmission and distribution of the electricity supply system until the breaker is replaced. Before 2008, there was little evidence in the literature of monitoring techniques based on restrike measurement and interpretation produced during switching of capacitor banks and shunt reactor banks in power systems. In 2008 a non-intrusive radiometric restrike measurement method and a restrike hardware detection algorithm were developed by M.S. Ramli and B. Kasztenny. However, the limitations of the radiometric measurement method are a band limited frequency response as well as limitations in amplitude determination. Current restrike detection methods and algorithms require the use of wide bandwidth current transformers and high voltage dividers. A restrike switch model using Alternative Transient Program (ATP) and Wavelet Transforms which support diagnostics are proposed. Restrike phenomena become a new diagnostic process using measurements, ATP and Wavelet Transforms for online interrupter monitoring. This research project investigates the restrike switch model Parameter „A. dielectric voltage gradient related to a normal and slowed case of the contact opening velocity and the escalation voltages, which can be used as a diagnostic tool for a vacuum circuit-breaker (CB) at service voltages between 11 kV and 63 kV. During current interruption of an inductive load at current quenching or chopping, a transient voltage is developed across the contact gap. The dielectric strength of the gap should rise to a point to withstand this transient voltage. If it does not, the gap will flash over, resulting in a restrike. A straight line is fitted through the voltage points at flashover of the contact gap. This is the point at which the gap voltage has reached a value that exceeds the dielectric strength of the gap. This research shows that a change in opening contact velocity of the vacuum CB produces a corresponding change in the slope of the gap escalation voltage envelope. To investigate the diagnostic process, an ATP restrike switch model was modified with contact opening velocity computation for restrike waveform signature analyses along with experimental investigations. This also enhanced a mathematical CB model with the empirical dielectric model for SF6 (sulphur hexa-fluoride) CBs at service voltages above 63 kV and a generalised dielectric curve model for 12 kV CBs. A CB restrike can be predicted if there is a similar type of restrike waveform signatures for measured and simulated waveforms. The restrike switch model applications are used for: computer simulations as virtual experiments, including predicting breaker restrikes; estimating the interrupter remaining life of SF6 puffer CBs; checking system stresses; assessing point-on-wave (POW) operations; and for a restrike detection algorithm development using Wavelet Transforms. A simulated high frequency nozzle current magnitude was applied to an Equation (derived from the literature) which can calculate the life extension of the interrupter of a SF6 high voltage CB. The restrike waveform signatures for a medium and high voltage CB identify its possible failure mechanism such as delayed opening, degraded dielectric strength and improper contact travel. The simulated and measured restrike waveform signatures are analysed using Matlab software for automatic detection. Experimental investigation of a 12 kV vacuum CB diagnostic was carried out for the parameter determination and a passive antenna calibration was also successfully developed with applications for field implementation. The degradation features were also evaluated with a predictive interpretation technique from the experiments, and the subsequent simulation indicates that the drop in voltage related to the slow opening velocity mechanism measurement to give a degree of contact degradation. A predictive interpretation technique is a computer modeling for assessing switching device performance, which allows one to vary a single parameter at a time; this is often difficult to do experimentally because of the variable contact opening velocity. The significance of this thesis outcome is that it is a non-intrusive method developed using measurements, ATP and Wavelet Transforms to predict and interpret a breaker restrike risk. The measurements on high voltage circuit-breakers can identify degradation that can interrupt the distribution and transmission of an electricity supply system. It is hoped that the techniques for the monitoring of restrike phenomena developed by this research will form part of a diagnostic process that will be valuable for detecting breaker stresses relating to the interrupter lifetime. Suggestions for future research, including a field implementation proposal to validate the restrike switch model for ATP system studies and the hot dielectric strength curve model for SF6 CBs, are given in Appendix A.

Supply chain management systems benefits : the expectation-confirmation theory perspective

Organizations adopt a Supply Chain Management System (SCMS) expecting benefits to the organization and its functions. However, organizations are facing mounting challenges to realizing benefits through SCMS. Studies suggest a growing dissatisfaction among client organizations due to an increasing gap between expectations and realization of SCMS benefits. Further, reflecting the Enterprise System studies such as Seddon et al. (2010), SCMS benefits are also expected to flow to the organization throughout its lifecycle rather than being realized all at once. This research therefore proposes to derive a lifecycle-wide understanding of SCMS benefits and realization to derive a benefit expectation management framework to attain the full potential of an SCMS. The primary research question of this study is: How can client organizations better manage their benefit expectations of SCM systems? The specific research goals of the current study include: (1) to better understand the misalignment of received and expected benefits of SCM systems; (2) to identify the key factors influencing SCM system expectations and to develop a framework to manage SCMS benefits; (3) to explore how organizational satisfaction is influenced by the lack of SCMS benefit confirmation; and (4) to explore how to improve the realization of SCM system benefits. Expectation-Confirmation Theory (ECT) provides the theoretical underpinning for this study. ECT has been widely used in the consumer behavior literature to study customer satisfaction, post-purchase behavior and service marketing in general. Recently, ECT has been extended into Information Systems (IS) research focusing on individual user satisfaction and IS continuance. However, only a handful of studies have employed ECT to study organizational satisfaction on large-scale IS. The current study will enrich the research stream by extending ECT into organizational-level analysis and verifying the preliminary findings of relevant works by Staples et al. (2002), Nevo and Chan (2007) and Nevo and Wade (2007). Moreover, this study will go further trying to operationalize the constructs of ECT into the context of SCMS. The empirical findings of the study commence with a content analysis, through which 41 vendor reports and academic reports are analyzed yielding sixty expected benefits of SCMS. Then, the expected benefits are compared with the benefits realized at a case organization in the Fast Moving Consumer Goods industry sector that had implemented a SAP Supply Chain Management System seven years earlier. The study develops an SCMS Benefit Expectation Management (SCMS-BEM) Framework. The comparison of benefit expectations and confirmations highlights that, while certain benefits are realized earlier in the lifecycle, other benefits could take almost a decade to realize. Further analysis and discussion on how the developed SCMS-BEM Framework influences ECT when applied in SCMS was also conducted. It is recommended that when establishing their expectations of the SCMS, clients should remember that confirmation of these expectations will have a long lifecycle, as shown in the different time periods in the SCMS-BEM Framework. Moreover, the SCMS-BEM Framework will allow organizations to maintain high levels of satisfaction through careful mitigation and confirming expectations based on the lifecycle phase. In addition, the study reveals that different stakeholder groups have different expectations of the same SCMS. The perspective of multiple stakeholders has significant implications for the application of ECT in the SCMS context. When forming expectations of the SCMS, the collection of organizational benefits of SCMS should represent the perceptions of all stakeholder groups. The same mechanism should be employed in the measurements of received SCMS benefits. Moreover, for SCMS, there exists interdependence of the satisfaction among the various stakeholders. The satisfaction of decision-makers or the authorized staff is not only driven by their own expectation confirmation level, it is also influenced by the confirmation level of other stakeholders‘ expectations in the organization. Satisfaction from any one particular stakeholder group can not reflect the true satisfaction of the client organization. Furthermore, it is inferred from the SCMS-BEM Framework that organizations should place emphasis on the viewpoints of the operational and management staff when evaluating the benefits of SCMS in the short and middle term. At the same time, organizations should be placing more attention on the perspectives of strategic staff when evaluating the performance of the SCMS in the long term.

Many paths lead to discovery : analogical retrieval of cancer therapies

This paper addresses the issue of analogical inference, and its potential role as the mediator of new therapeutic discoveries, by using disjunction operators based on quantum connectives to combine many potential reasoning pathways into a single search expression. In it, we extend our previous work in which we developed an approach to analogical retrieval using the Predication-based Semantic Indexing (PSI) model, which encodes both concepts and the relationships between them in high-dimensional vector space. As in our previous work, we leverage the ability of PSI to infer predicate pathways connecting two example concepts, in this case comprising of known therapeutic relationships. For example, given that drug x TREATS disease z, we might infer the predicate pathway drug x INTERACTS WITH gene y ASSOCIATED WITH disease z, and use this pathway to search for drugs related to another disease in similar ways. As biological systems tend to be characterized by networks of relationships, we evaluate the ability of quantum-inspired operators to mediate inference and retrieval across multiple relations, by testing the ability of different approaches to recover known therapeutic relationships. In addition, we introduce a novel complex vector based implementation of PSI, based on Plate’s Circular Holographic Reduced Representations, which we utilize for all experiments in addition to the binary vector based approach we have applied in our previous research.

Marginal reversible jump Markov chain Monte Carlo with application to motor unit number estimation

Motor unit number estimation (MUNE) is a method which aims to provide a quantitative indicator of progression of diseases that lead to loss of motor units, such as motor neurone disease. However the development of a reliable, repeatable and fast real-time MUNE method has proved elusive hitherto. Ridall et al. (2007) implement a reversible jump Markov chain Monte Carlo (RJMCMC) algorithm to produce a posterior distribution for the number of motor units using a Bayesian hierarchical model that takes into account biological information about motor unit activation. However we find that the approach can be unreliable for some datasets since it can suffer from poor cross-dimensional mixing. Here we focus on improved inference by marginalising over latent variables to create the likelihood. In particular we explore how this can improve the RJMCMC mixing and investigate alternative approaches that utilise the likelihood (e.g. DIC (Spiegelhalter et al., 2002)). For this model the marginalisation is over latent variables which, for a larger number of motor units, is an intractable summation over all combinations of a set of latent binary variables whose joint sample space increases exponentially with the number of motor units. We provide a tractable and accurate approximation for this quantity and also investigate simulation approaches incorporated into RJMCMC using results of Andrieu and Roberts (2009).

Prediction of transcriptional regulatory interactions in bacteria : a comparative genomics approach

Exponential growth of genomic data in the last two decades has made manual analyses impractical for all but trial studies. As genomic analyses have become more sophisticated, and move toward comparisons across large datasets, computational approaches have become essential. One of the most important biological questions is to understand the mechanisms underlying gene regulation. Genetic regulation is commonly investigated and modelled through the use of transcriptional regulatory network (TRN) structures. These model the regulatory interactions between two key components: transcription factors (TFs) and the target genes (TGs) they regulate. Transcriptional regulatory networks have proven to be invaluable scientific tools in Bioinformatics. When used in conjunction with comparative genomics, they have provided substantial insights into the evolution of regulatory interactions. Current approaches to regulatory network inference, however, omit two additional key entities: promoters and transcription factor binding sites (TFBSs). In this study, we attempted to explore the relationships among these regulatory components in bacteria. Our primary goal was to identify relationships that can assist in reducing the high false positive rates associated with transcription factor binding site predictions and thereupon enhance the reliability of the inferred transcription regulatory networks. In our preliminary exploration of relationships between the key regulatory components in Escherichia coli transcription, we discovered a number of potentially useful features. The combination of location score and sequence dissimilarity scores increased de novo binding site prediction accuracy by 13.6%. Another important observation made was with regards to the relationship between transcription factors grouped by their regulatory role and corresponding promoter strength. Our study of E.coli ��70 promoters, found support at the 0.1 significance level for our hypothesis | that weak promoters are preferentially associated with activator binding sites to enhance gene expression, whilst strong promoters have more repressor binding sites to repress or inhibit gene transcription. Although the observations were specific to �70, they nevertheless strongly encourage additional investigations when more experimentally confirmed data are available. In our preliminary exploration of relationships between the key regulatory components in E.coli transcription, we discovered a number of potentially useful features { some of which proved successful in reducing the number of false positives when applied to re-evaluate binding site predictions. Of chief interest was the relationship observed between promoter strength and TFs with respect to their regulatory role. Based on the common assumption, where promoter homology positively correlates with transcription rate, we hypothesised that weak promoters would have more transcription factors that enhance gene expression, whilst strong promoters would have more repressor binding sites. The t-tests assessed for E.coli �70 promoters returned a p-value of 0.072, which at 0.1 significance level suggested support for our (alternative) hypothesis; albeit this trend may only be present for promoters where corresponding TFBSs are either all repressors or all activators. Nevertheless, such suggestive results strongly encourage additional investigations when more experimentally confirmed data will become available. Much of the remainder of the thesis concerns a machine learning study of binding site prediction, using the SVM and kernel methods, principally the spectrum kernel. Spectrum kernels have been successfully applied in previous studies of protein classification [91, 92], as well as the related problem of promoter predictions [59], and we have here successfully applied the technique to refining TFBS predictions. The advantages provided by the SVM classifier were best seen in `moderately'-conserved transcription factor binding sites as represented by our E.coli CRP case study. Inclusion of additional position feature attributes further increased accuracy by 9.1% but more notable was the considerable decrease in false positive rate from 0.8 to 0.5 while retaining 0.9 sensitivity. Improved prediction of transcription factor binding sites is in turn extremely valuable in improving inference of regulatory relationships, a problem notoriously prone to false positive predictions. Here, the number of false regulatory interactions inferred using the conventional two-component model was substantially reduced when we integrated de novo transcription factor binding site predictions as an additional criterion for acceptance in a case study of inference in the Fur regulon. This initial work was extended to a comparative study of the iron regulatory system across 20 Yersinia strains. This work revealed interesting, strain-specific difierences, especially between pathogenic and non-pathogenic strains. Such difierences were made clear through interactive visualisations using the TRNDifi software developed as part of this work, and would have remained undetected using conventional methods. This approach led to the nomination of the Yfe iron-uptake system as a candidate for further wet-lab experimentation due to its potential active functionality in non-pathogens and its known participation in full virulence of the bubonic plague strain. Building on this work, we introduced novel structures we have labelled as `regulatory trees', inspired by the phylogenetic tree concept. Instead of using gene or protein sequence similarity, the regulatory trees were constructed based on the number of similar regulatory interactions. While the common phylogentic trees convey information regarding changes in gene repertoire, which we might regard being analogous to `hardware', the regulatory tree informs us of the changes in regulatory circuitry, in some respects analogous to `software'. In this context, we explored the `pan-regulatory network' for the Fur system, the entire set of regulatory interactions found for the Fur transcription factor across a group of genomes. In the pan-regulatory network, emphasis is placed on how the regulatory network for each target genome is inferred from multiple sources instead of a single source, as is the common approach. The benefit of using multiple reference networks, is a more comprehensive survey of the relationships, and increased confidence in the regulatory interactions predicted. In the present study, we distinguish between relationships found across the full set of genomes as the `core-regulatory-set', and interactions found only in a subset of genomes explored as the `sub-regulatory-set'. We found nine Fur target gene clusters present across the four genomes studied, this core set potentially identifying basic regulatory processes essential for survival. Species level difierences are seen at the sub-regulatory-set level; for example the known virulence factors, YbtA and PchR were found in Y.pestis and P.aerguinosa respectively, but were not present in both E.coli and B.subtilis. Such factors and the iron-uptake systems they regulate, are ideal candidates for wet-lab investigation to determine whether or not they are pathogenic specific. In this study, we employed a broad range of approaches to address our goals and assessed these methods using the Fur regulon as our initial case study. We identified a set of promising feature attributes; demonstrated their success in increasing transcription factor binding site prediction specificity while retaining sensitivity, and showed the importance of binding site predictions in enhancing the reliability of regulatory interaction inferences. Most importantly, these outcomes led to the introduction of a range of visualisations and techniques, which are applicable across the entire bacterial spectrum and can be utilised in studies beyond the understanding of transcriptional regulatory networks.

Engaging complexity through collaborative brand design

In this research we used inductive reasoning through design to understand how stakeholders in the Waterfall Way (New South Wales, Australia) perceive the relationships between themselves and the place they live in. This paper describes a collaborative design methodology used to release information about local identities, which guided the regional brand exercise. The methodology is explicit about the uncertainties and complexities of the design process and of its reception system. As such, it aims to engage with local stakeholders and experts in order to help elicit tacit knowledge and identify system patterns and trends that would possibly not be visible if a top-down expert-based process was used. Through collective design, local people were drawn together in search for a symbol to represent the meaning attached to their places/region in relation to sustainable tourism activity.

End-of-life care in the intensive care setting : a descriptive exploratory qualitative study of nurses' beliefs and practices

Background: End-of-life care is a significant component of work in intensive care. Limited research has been undertaken on the provision of end-of-life care by nurses in the intensive care setting. The purpose of this study was to explore the end-of-life care beliefs and practices of intensive care nurses. Methods: A descriptive exploratory qualitative research approach was used to invite a convenience sample of five intensive care nurses from one hospital to participate in a semi-structured interview. Interview transcripts were analysed using an inductive coding approach. Findings: Three major categories emerged from analysis of the interviews: beliefs about end-of-life care, end-of-life care in the intensive care context and facilitating end-of-life care. The first two categories incorporated factors contributing to the end-of-life care experiences and practices of intensive care nurses. The third category captured the nurses’ end-of-life care practices. Conclusions: Despite the uncertainty and ambiguity surrounding end-of-life care in this practice context, the intensive care setting presents unique opportunities for nurses to facilitate positive end-of-life experiences and nurses valued their participation in the provision of end-of-life care. Care of the family was at the core of nurses’ end-of-life care work and nurses play a pivotal role in supporting the patient and their family to have positive and meaningful experiences at the end-of-life.Variation in personal beliefs and organisational support may influence nurses’ experiences and the care provided to patients and their families. Strategies to promote an organisational culture supportive of quality end-of-life care practices, and to mentor and support nurses in the provision of this care are needed.

Why renal nurses cope so well with their workplace stressors

Background: Previous studies have found significant stressors experienced by nurses working in haemodialysis units yet renal nurses appear to report less burnout than other nurses. Objectives: This study aims to undertake an inductive process to better understand the stressors and the coping strategies used by renal nurses that may lead to resilience. Method: Sixteen haemodialysis nurses from a metropolitan Australian hospital and two satellite units participated in open-ended interviews. Data were analysed from a grounded theory methodology. Measures of burnout and resilience were also obtained. Results: Two major categories of stressors emerged. First, due to prolonged patient contact, family-like relationships developed that lead to the blurring of boundaries. Second, participants experienced discrimination from both patients and staff. Despite these stressors, the majority of participants reported low burnout and moderately high-to-high levels of resilience. The major coping strategy that appeared to promote resilience was emotional distancing, while emotional detachment appeared to promote burn-out. Conclusion: Assisting nurses to use emotional distancing, rather than emotional detachment strategies to engender a sense of personal achievement may promote resilience.

Bayesian algorithms with applications

Advances in algorithms for approximate sampling from a multivariable target function have led to solutions to challenging statistical inference problems that would otherwise not be considered by the applied scientist. Such sampling algorithms are particularly relevant to Bayesian statistics, since the target function is the posterior distribution of the unobservables given the observables. In this thesis we develop, adapt and apply Bayesian algorithms, whilst addressing substantive applied problems in biology and medicine as well as other applications. For an increasing number of high-impact research problems, the primary models of interest are often sufficiently complex that the likelihood function is computationally intractable. Rather than discard these models in favour of inferior alternatives, a class of Bayesian "likelihoodfree" techniques (often termed approximate Bayesian computation (ABC)) has emerged in the last few years, which avoids direct likelihood computation through repeated sampling of data from the model and comparing observed and simulated summary statistics. In Part I of this thesis we utilise sequential Monte Carlo (SMC) methodology to develop new algorithms for ABC that are more efficient in terms of the number of model simulations required and are almost black-box since very little algorithmic tuning is required. In addition, we address the issue of deriving appropriate summary statistics to use within ABC via a goodness-of-fit statistic and indirect inference. Another important problem in statistics is the design of experiments. That is, how one should select the values of the controllable variables in order to achieve some design goal. The presences of parameter and/or model uncertainty are computational obstacles when designing experiments but can lead to inefficient designs if not accounted for correctly. The Bayesian framework accommodates such uncertainties in a coherent way. If the amount of uncertainty is substantial, it can be of interest to perform adaptive designs in order to accrue information to make better decisions about future design points. This is of particular interest if the data can be collected sequentially. In a sense, the current posterior distribution becomes the new prior distribution for the next design decision. Part II of this thesis creates new algorithms for Bayesian sequential design to accommodate parameter and model uncertainty using SMC. The algorithms are substantially faster than previous approaches allowing the simulation properties of various design utilities to be investigated in a more timely manner. Furthermore the approach offers convenient estimation of Bayesian utilities and other quantities that are particularly relevant in the presence of model uncertainty. Finally, Part III of this thesis tackles a substantive medical problem. A neurological disorder known as motor neuron disease (MND) progressively causes motor neurons to no longer have the ability to innervate the muscle fibres, causing the muscles to eventually waste away. When this occurs the motor unit effectively ‘dies’. There is no cure for MND, and fatality often results from a lack of muscle strength to breathe. The prognosis for many forms of MND (particularly amyotrophic lateral sclerosis (ALS)) is particularly poor, with patients usually only surviving a small number of years after the initial onset of disease. Measuring the progress of diseases of the motor units, such as ALS, is a challenge for clinical neurologists. Motor unit number estimation (MUNE) is an attempt to directly assess underlying motor unit loss rather than indirect techniques such as muscle strength assessment, which generally is unable to detect progressions due to the body’s natural attempts at compensation. Part III of this thesis builds upon a previous Bayesian technique, which develops a sophisticated statistical model that takes into account physiological information about motor unit activation and various sources of uncertainties. More specifically, we develop a more reliable MUNE method by applying marginalisation over latent variables in order to improve the performance of a previously developed reversible jump Markov chain Monte Carlo sampler. We make other subtle changes to the model and algorithm to improve the robustness of the approach.

Kernel analysis on Grassmann manifolds for action recognition

Modelling video sequences by subspaces has recently shown promise for recognising human actions. Subspaces are able to accommodate the effects of various image variations and can capture the dynamic properties of actions. Subspaces form a non-Euclidean and curved Riemannian manifold known as a Grassmann manifold. Inference on manifold spaces usually is achieved by embedding the manifolds in higher dimensional Euclidean spaces. In this paper, we instead propose to embed the Grassmann manifolds into reproducing kernel Hilbert spaces and then tackle the problem of discriminant analysis on such manifolds. To achieve efficient machinery, we propose graph-based local discriminant analysis that utilises within-class and between-class similarity graphs to characterise intra-class compactness and inter-class separability, respectively. Experiments on KTH, UCF Sports, and Ballet datasets show that the proposed approach obtains marked improvements in discrimination accuracy in comparison to several state-of-the-art methods, such as the kernel version of affine hull image-set distance, tensor canonical correlation analysis, spatial-temporal words and hierarchy of discriminative space-time neighbourhood features.

Towards a typology of structural arrangements for shared services : evidence from the higher education sector

Shared services are increasingly prevalent in practice, their introduction potentially entailing substantive and highly consequential organizational redesign. Yet, attention to the structural arrangements of shared services has been limited. This study explores types of structural arrangements for shared services that are observed in practice, and the salient dimensions along which those types can be usefully differentiated. Through inductive attention to the shared services literature, and content analysis of 36 secondary case studies of shared services in the higher education sector, three salient dimensions emerged: (1) the existence or not of a separate organizational entity, (2) an intra- or inter-organizational sharing boundary, and (3) involvement or not of a third party. Each dimension being dichotomous yields 23 combinations, or eight shared services structural arrangement types. Each of the eight structural arrangement types is defined and demonstrated through case examples. The typology offers clarity around shared services structural arrangements. It can serve as a useful analytical tool for researchers investigating the phenomenon further, and for practitioners considering the introduction or further development of shared services arrangements. Important follow on research is suggested too.

Extending and validating the is-impact model in Saudi Arabia : accounting for computer network quality

Organizations from every industry sector seek to enhance their business performance and competitiveness through the deployment of contemporary information systems (IS), such as Enterprise Systems (ERP). Investments in ERP are complex and costly, attracting scrutiny and pressure to justify their cost. Thus, IS researchers highlight the need for systematic evaluation of information system success, or impact, which has resulted in the introduction of varied models for evaluating information systems. One of these systematic measurement approaches is the IS-Impact Model introduced by a team of researchers at Queensland University of technology (QUT) (Gable, Sedera, & Chan, 2008). The IS-Impact Model is conceptualized as a formative, multidimensional index that consists of four dimensions. Gable et al. (2008) define IS-Impact as "a measure at a point in time, of the stream of net benefits from the IS, to date and anticipated, as perceived by all key-user-groups" (p.381). The IT Evaluation Research Program (ITE-Program) at QUT has grown the IS-Impact Research Track with the central goal of conducting further studies to enhance and extend the IS-Impact Model. The overall goal of the IS-Impact research track at QUT is "to develop the most widely employed model for benchmarking information systems in organizations for the joint benefit of both research and practice" (Gable, 2009). In order to achieve that, the IS-Impact research track advocates programmatic research having the principles of tenacity, holism, and generalizability through extension research strategies. This study was conducted within the IS-Impact Research Track, to further generalize the IS-Impact Model by extending it to the Saudi Arabian context. According to Hofsted (2012), the national culture of Saudi Arabia is significantly different from the Australian national culture making the Saudi Arabian culture an interesting context for testing the external validity of the IS-Impact Model. The study re-visits the IS-Impact Model from the ground up. Rather than assume the existing instrument is valid in the new context, or simply assess its validity through quantitative data collection, the study takes a qualitative, inductive approach to re-assessing the necessity and completeness of existing dimensions and measures. This is done in two phases: Exploratory Phase and Confirmatory Phase. The exploratory phase addresses the first research question of the study "Is the IS-Impact Model complete and able to capture the impact of information systems in Saudi Arabian Organization?". The content analysis, used to analyze the Identification Survey data, indicated that 2 of the 37 measures of the IS-Impact Model are not applicable for the Saudi Arabian Context. Moreover, no new measures or dimensions were identified, evidencing the completeness and content validity of the IS-Impact Model. In addition, the Identification Survey data suggested several concepts related to IS-Impact, the most prominent of which was "Computer Network Quality" (CNQ). The literature supported the existence of a theoretical link between IS-Impact and CNQ (CNQ is viewed as an antecedent of IS-Impact). With the primary goal of validating the IS-Impact model within its extended nomological network, CNQ was introduced to the research model. The Confirmatory Phase addresses the second research question of the study "Is the Extended IS-Impact Model Valid as a Hierarchical Multidimensional Formative Measurement Model?". The objective of the Confirmatory Phase was to test the validity of IS-Impact Model and CNQ Model. To achieve that, IS-Impact, CNQ, and IS-Satisfaction were operationalized in a survey instrument, and then the research model was assessed by employing the Partial Least Squares (PLS) approach. The CNQ model was validated as a formative model. Similarly, the IS-Impact Model was validated as a hierarchical multidimensional formative construct. However, the analysis indicated that one of the IS-Impact Model indicators was insignificant and can be removed from the model. Thus, the resulting Extended IS-Impact Model consists of 4 dimensions and 34 measures. Finally, the structural model was also assessed against two aspects: explanatory and predictive power. The analysis revealed that the path coefficient between CNQ and IS-Impact is significant with t-value= (4.826) and relatively strong with â = (0.426) with CNQ explaining 18% of the variance in IS-Impact. These results supported the hypothesis that CNQ is antecedent of IS-Impact. The study demonstrates that the quality of Computer Network affects the quality of the Enterprise System (ERP) and consequently the impacts of the system. Therefore, practitioners should pay attention to the Computer Network quality. Similarly, the path coefficient between IS-Impact and IS-Satisfaction was significant t-value = (17.79) and strong â = (0.744), with IS-Impact alone explaining 55% of the variance in Satisfaction, consistent with results of the original IS-Impact study (Gable et al., 2008). The research contributions include: (a) supporting the completeness and validity of IS-Impact Model as a Hierarchical Multi-dimensional Formative Measurement Model in the Saudi Arabian context, (b) operationalizing Computer Network Quality as conceptualized in the ITU-T Recommendation E.800 (ITU-T, 1993), (c) validating CNQ as a formative measurement model and as an antecedent of IS Impact, and (d) conceptualizing and validating IS-Satisfaction as a reflective measurement model and as an immediate consequence of IS Impact. The CNQ model provides a framework to perceptually measure Computer Network Quality from multiple perspectives. The CNQ model features an easy-to-understand, easy-to-use, and economical survey instrument.

Examination of distracted driving and yellow light running : analysis of simulator data

Driving on an approach to a signalized intersection while distracted is particularly dangerous, as potential vehicular conflicts and resulting angle collisions tend to be severe. Given the prevalence and importance of this particular scenario, the decisions and actions of distracted drivers during the onset of yellow lights are the focus of this study. Driving simulator data were obtained from a sample of 58 drivers under baseline and handheld mobile phone conditions at the University of Iowa - National Advanced Driving Simulator. Explanatory variables included age, gender, cell phone use, distance to stop-line, and speed. Although there is extensive research on drivers’ responses to yellow traffic signals, the examination has been conducted from a traditional regression-based approach, which does not necessary provide the underlying relations and patterns among the sampled data. In this paper, we exploit the benefits of both classical statistical inference and data mining techniques to identify the a priori relationships among main effects, non-linearities, and interaction effects. Results suggest that novice (16-17 years) and young drivers’ (18-25 years) have heightened yellow light running risk while distracted by a cell phone conversation. Driver experience captured by age has a multiplicative effect with distraction, making the combined effect of being inexperienced and distracted particularly risky. Overall, distracted drivers across most tested groups tend to reduce the propensity of yellow light running as the distance to stop line increases, exhibiting risk compensation on a critical driving situation.

Interpersonal coupling in rowing : the mediating role of the environment

Evidence currently supports the view that intentional interpersonal coordination (IIC) is a self-organizing phenomenon facilitated by visual perception of co-actors in a coordinative coupling (Schmidt, Richardson, Arsenault, & Galantucci, 2007). The present study examines how apparent IIC is achieved in situations where visual information is limited for co-actors in a rowing boat. In paired rowing boats only one of the actors, [bow seat] gets to see the actions of the other [stroke seat]. Thus IIC appears to be facilitated despite the lack of important visual information for the control of the dyad. Adopting a mimetic approach to expert coordination, the present study qualitatively examined the experiences of expert performers (N=9) and coaches (N=4) with respect to how IIC was achieved in paired rowing boats. Themes were explored using inductive content analysis, which led to layered model of control. Rowers and coaches reported the use of multiple perceptual sources in order to achieve IIC. As expected(Kelso, 1995; Schmidt & O’Brien, 1997; Turvey, 1990), rowers in the bow of a pair boat make use of visual information provided by the partner in front of them [stroke]. However, this perceptual information is subordinate to perception Motor Learning and Control S111 of the relationship between the boat hull and water passing beside it. Stroke seat, in the absence of visual information about his/her partner, achieves coordination by picking up information about the lifting or looming of the boat’s stern along with water passage past the hull. In this case it appears that apparent or desired IIC is supported by the perception of extra-personal variables, in this case boat behavior; as this perceptual information source is used by both actors. To conclude, co-actors in two person rowing boats use multiple sources of perceptual information for apparent IIC that changes according to task constraints. Where visual information is restricted IIC is facilitated via extra-personal perceptual information and apparent IIC switches to intentional extra-personal coordination.

Vibrational spectroscopic study of the mineral penkvilksite Na2TiSi4O11·2H2O – a mineral used for the uptake of radionuclides

We have used vibrational spectroscopy to study the formula and molecular structure of the mineral penkvilksite Na 2TiSi 4O 11·2H 2O. Penkvilksite is a mineral which may be used in the uptake of radioactive elements. Both Raman and infrared spectroscopies identify a band at 3638 cm−1 attributed to an OH-stretching vibration of hydroxyl units. The inference is that OH units are involved in the structure of penkvilksite. The formula may be well written as Na 2TiSi 4O 10(OH)2·H 2O. The mineral is characterised by a very intense Raman band at 1085 cm−1 and a broad infrared band at 1080 cm−1 assigned to SiO-stretching vibrations. Raman bands at 620, 667 and 711 cm−1 are attributed to SiO and TiO chain bonds. Water-stretching vibrations are observed as Raman bands at 3197, 3265, 3425 and 3565 cm−1. Vibrational spectroscopy enables aspects of the molecular structure of the mineral penkvilksite to be ascertained. Penkvilksite is a mineral which can incorporate actinides and lanthanides from radioactive waste.