The Development of Reliability Analysis Toolkit (RAT) For Analyzing Plant Maintenance Data

Reliability analysis is an important tool for engineers in assessing the performance of existing operational system of the plant. Major loss can be eliminated and therefore increase the plant profitability. The aim of this study is to develop a toolkit that will assist the engineers in performing the reliability analysis. The main issues found during the research are namely improper methodology used by the engineers and inadequate of time to perform the analysis. In order to solve these issues, a computer based toolkit called as Reliability Analysis Toolkit (RAT) is programmed using the combination of Microsoft Excel, Macro and Visual Basic for the interface. The toolkit (RAT) is developed using two type of analysis which is exploratory and inferential analysis. In exploratory analysis, the plant field maintenance data is processed into graphical charts such as Pareto and trend chart to help the engineer in identifying the critical equipment/systems. In Inferential analysis, the plant field maintenance data is analyzed for independent and identically distributed data (IID) validation whether life data analysis (LDA) may be used or not in the reliability analysis. In this step, Laplace trend test and serial correlation test are used to test the IID assumption. The toolkit will also provides the engineer with the reliability measures for the analysis namely MTBF and failure rate. After the toolkit (RAT) is developed, a case study is conducted to validate the result generated by the toolkit. The results from the toolkit are compared with the result obtain analytically via Excel Spreadsheets. From the demonstrated results, the toolkit shows the ability to analyze the plant field data since the results obtain are exactly similar with analytical results. This study achieved the objective and will expedite the analysis process so that the major plant issues can be addressed timely and appropriately

Building a Generic Value Creation Model For the Sri Lankan National Education System

This research was an attempt to build a generic value creation model architecture which can be used by any organisation without business v. public or profit v non-profit differences, by way of: a synthesis of literature in 6 streams of management related to value creation; operationalise it using data collected through an exploratory study in the System of General School Education in Sri Lanka; and, test the operationalised model in the same context through a confirmatory study. The study was a mixed-method one, using in its exploratory phase interviews as its data collection instrument, and in its subsequent confirmatory phase, questionnaires as its data collection instruments. Data analysis methodologies used to test hypotheses were structured equation modelling and multiple regression analysis. The operationalisation validated the model building assumptions, and the final research results showed that the proposed model can be used in a national-scale public education context to measure value creation

Learning processes in interactive CALL systems: Linking automatic feedback, system logs, and learning outcomes

Interactive digital tools increasingly used for language learning can provide detailed system logs (e.g., number of attempts, responses submitted), and thereby a window into the user’s learning processes. To date, SLA researchers have made little use of such data to understand the relationships between learning conditions, processes, and outcomes. To fill this gap, we analyzed and interpreted detailed logs from an ICALL system used in a randomized controlled field study where 205 German learners of English in secondary school received either general or specific corrective feedback on grammar exercises. In addition to explicit pre-/post-test results, we derived 19 learning process variables from the system log. Exploratory factor analysis revealed three latent factors underlying these process variables: effort, accuracy focus, and time on task. Accuracy focus and finish time (a process variable that did not load well on any factors) significantly predicted pre-/post-test gain scores with a medium effect size. We then clustered learners based on their process patterns and found that the specific feedback group tended to demonstrate particular learning processes and that these patterns moderate the advantage of specific feedback. We discuss the implications of analyzing system logs for SLA, CALL, and education researchers and call for more collaboration

Using exploratory factor analysis in information system (IS) research

This paper is part of a field study that explored the impact of Information System implementation on Organisational Performance by examining the concept of IS effectiveness and by exploring how businesses arrive at the conclusion that the undertaking is successful or unsuccessful. Many statistical techniques have been used for the inference of conclusions. This paper will explain in brief the methodology followed and the exploratory factor analysis (EFA) conducted for the measurement of the construct if IS effectiveness. Following all tests on correlations and a number of extraction methods the final solution comprised 13 factors representing the independent variables and 4 factors representing the dependent variables. The results from our analysis provide insight into the IS evaluation field of research and provide new scales for the measurement of IS effectiveness

Identifying reliable traits across laboratory mouse exploration arenas: A meta-analysis

This study is a meta-analysis of 367 mice from a collection of behaviour neuroscience and behaviour genetic studies run in the same lab in Zurich, Switzerland. We employed correlation-based statistics to confirm and quantify consistencies in behaviour across the testing environments. All 367 mice ran exactly the same behavioural arenas: the light/dark box, the null maze, the open field arena, an emergence task and finally an object exploration task. We analysed consistency of three movement types across those arenas (resting, scanning, progressing), and their relative preference for three zones of the arenas (home, transition, exploration). Results were that 5/6 measures showed strong individual-differences consistency across the tests. Mean inter-arena correlations for these five measures ranged from +.12 to +.53. Unrotated principal component factor analysis (UPCFA) and Cronbach’s alpha measures showed these traits to be reliable and substantial (32-63% of variance across the five arenas). UPCFA loadings then indicate which tasks give the best information about these cross-task traits. One measure (that of time spent in “intermediate” zones) was not reliable across arenas. Conclusions centre on the use of individual differences research and behavioural batteries to revise understandings of what measures in one task predict for behaviour in others. Developing better behaviour measures also makes sound scientific and ethical sense