Purpose: With the growing importance of performance measurement and management, this exploratory study intends to examine the practices of maintenance managers with regards to maintenance measures, as used in their organizations. In this process, the study attempts to uncover the relevant maintenance performance dimensions from the perspectives of the surveyed managers. In addition, the mediating effect of information availability on the main performance measures utilization is studied. Design/methodology/approach: The research at hand is survey-based. It utilizes the responses of a sample of ninety-five (95) experienced maintenance managers to identify the most relevant maintenance performance measures. Factor analysis is then utilized to uncover the important dimensions of performance, as seen by the respondents. Additionally, using the Partial Least Squares method, several models were studied. Findings: The findings of this exploratory research appear to suggest that maintenance managers are beginning to broaden their perspective with regard to performance management. While machine and plant-related performance measures are still emphasized, maintenance managers are slowly moving toward a wider organizational orientation. While the manufacturing organizations are becoming more and more customer-oriented open systems, the maintenance function of these organizations is still, for the most part, operating under the semi-open system orientation. Overall, it appears that an emerging maintenance strategy is slowly taking shape. Research limitations/implications: For the most part, performance measures and measurement related to maintenance have not received enough attention from researchers. Therefore, the literature dealing with the different facets of performance in maintenance has not been forthcoming. The study attempts to fill this apparent gap in the literature. This is important, as maintenance managers are being asked to contribute to the achievement of the competitive strategies of their organizations. Therefore, they must quickly learn how to view maintenance from a coherent strategic organizational perspective. Such a perspective should help in integrating the maintenance, resources, capabilities, and technical know-how in order to serve the strategic goal of their organization. The research at hand is limited to a sample from Portugal. Therefore, the results and conclusions must be interpreted accordingly. Practical implications: As maintenance managers struggle to move from a machine-orientation to a more organizational-wide strategic orientation, they are often left with many questions and few answers. This study attempts to bring this problem to the spotlight so that it can receive more systematic empirical and practical research. In this context, the role of maintenance managers in the process of organizational strategy formulation should be examined. Originality/value: The study presented in this article has practical, as well as theoretical contributions. It deals with an area of performance measurement, which so far has been relatively ignored. It uses a system orientation (closed vs open), in addition to the strategic orientation (single vs multi-faceted strategy) in order to shed some light on the need to have consistency between the nature of the system and its strategic objective.

DNA methylation plays a key role in disease analysis, especially for studies that compare known large scale differences in CpG sites, such as cancer/normal studies or between-tissues studies. However, before any analysis can be done, data normalization and preprocessing of methylation data are required. A useful data preprocessing pipeline for large scale comparisons is Functional Normalization (FunNorm), (Fortin et al., 2014) implemented in the minfi package in R. In FunNorm, the univariate quantiles of the methylated and unmethylated signal values in the raw data are used to preprocess the data. However, although FunNorm has been shown to outperform other preprocessing and data normalization processes for these types of studies, it does not account for the correlation between the methylated and unmethylated signals into account; the focus of this paper is to improve upon FunNorm by taking this correlation into account. The concept of a bivariate quantile is used in this study as an attempt to take the correlation between the methylated and unmethylated signals into consideration. From the bivariate quantiles found, the partial least squares method is then used on these quantiles in this preprocessing. The raw datasets used for this research were collected from the European Molecular Biology Laboratory - European Bioinformatics Institute (EMBL-EBI) website. The results from this preprocessing algorithm were then compared and contrasted to the results from FunNorm. Drawbacks, limitations and future research are then discussed. / Thesis / Master of Science (MSc)

This dissertation contributes to IS research by explaining user intentions while using AR features in mobile social networking and retail app contexts. It consists of three essays, which use partial least squares modeling to analyze different consumer behavior models. The first essay examines the influence of quality, human, and environmental factors on AR reuse intention in a mobile social networking context. The second essay introduces position relevance, a new construct essential to AR research in e-commerce, and it looks at the influence of this construct and app involvement on user purchase intention, while using view-in-room features on mobile retail apps. The third essay examines the influence of service quality and visual quality on recommendation intention of mobile retail apps while using view-in-room features compared to shopping without using these AR features.

Purpose: With the growing importance of performance measurement and management, this exploratory study intends to examine the practices of maintenance managers with regards to maintenance measures, as used in their organizations. In this process, the study attempts to uncover the relevant maintenance performance dimensions from the perspectives of the surveyed managers. In addition, the mediating effect of information availability on the main performance measures utilization is studied. Design/methodology/approach: The research at hand is survey-based. It utilizes the responses of a sample of ninety-five (95) experienced maintenance managers to identify the most relevant maintenance performance measures. Factor analysis is then utilized to uncover the important dimensions of performance, as seen by the respondents. Additionally, using the Partial Least Squares method, several models were studied. Findings: The findings of this exploratory research appear to suggest that maintenance managers are beginning to broaden their perspective with regard to performance management. While machine and plant-related performance measures are still emphasized, maintenance managers are slowly moving toward a wider organizational orientation. While the manufacturing organizations are becoming more and more customer-oriented open systems, the maintenance function of these organizations is still, for the most part, operating under the semi-open system orientation. Overall, it appears that an emerging maintenance strategy is slowly taking shape. Research limitations/implications: For the most part, performance measures and measurement related to maintenance have not received enough attention from researchers. Therefore, the literature dealing with the different facets of performance in maintenance has not been forthcoming. The study attempts to fill this apparent gap in the literature. This is important, as maintenance managers are being asked to contribute to the achievement of the competitive strategies of their organizations. Therefore, they must quickly learn how to view maintenance from a coherent strategic organizational perspective. Such a perspective should help in integrating the maintenance, resources, capabilities, and technical know-how in order to serve the strategic goal of their organization. The research at hand is limited to a sample from Portugal. Therefore, the results and conclusions must be interpreted accordingly. Practical implications: As maintenance managers struggle to move from a machine-orientation to a more organizational-wide strategic orientation, they are often left with many questions and few answers. This study attempts to bring this problem to the spotlight so that it can receive more systematic empirical and practical research. In this context, the role of maintenance managers in the process of organizational strategy formulation should be examined. Originality/value: The study presented in this article has practical, as well as theoretical contributions. It deals with an area of performance measurement, which so far has been relatively ignored. It uses a system orientation (closed vs open), in addition to the strategic orientation (single vs multi-faceted strategy) in order to shed some light on the need to have consistency between the nature of the system and its strategic objective.

Despite considerable advances in missing data imputation methods over the last three decades, the problem of missing data remains largely unsolved. Many techniques have emerged in the literature as candidate solutions. These techniques can be categorised into two classes: statistical methods of data imputation and computational intelligence methods of data imputation. Due to the longstanding use of statistical methods in handling missing data problems, it takes quite some time for computational intelligence methods to gain profound attention even though these methods have analogous accuracy, in comparison to other approaches. The merits of both these classes have been discussed at length in the literature, but only limited studies make significant comparison to these classes. This thesis contributes to knowledge by firstly, conducting a comprehensive comparison of standard statistical methods of data imputation, namely, mean substitution (MS), regression imputation (RI), expectation maximization (EM), tree imputation (TI) and multiple imputation (MI) on missing completely at random (MCAR) data sets. Secondly, this study also compares the efficacy of these methods with a computational intelligence method of data imputation, ii namely, a neural network (NN) on missing not at random (MNAR) data sets. The significance difference in performance of the methods is presented. Thirdly, a novel procedure for handling missing data is presented. A hybrid combination of each of these statistical methods with a NN, known here as the post-processing procedure, was adopted to approximate MNAR data sets. Simulation studies for each of these imputation approaches have been conducted to assess the impact of missing values on partial least squares structural equation modelling (PLS-SEM) based on the estimated accuracy of both structural and measurement parameters. The best method to deal with particular missing data mechanisms is highly recognized. Several significant insights were deduced from the simulation results. It was figured that for the problem of MCAR by using statistical methods of data imputation, MI performs better than the other methods for all percentages of missing data. Another unique contribution is found when comparing the results before and after the NN post-processing procedure. This improvement in accuracy may be resulted from the neural network¿s ability to derive meaning from the imputed data set found by the statistical methods. Based on these results, the NN post-processing procedure is capable to assist MS in producing significant improvement in accuracy of the approximated values. This is a promising result, as MS is the weakest method in this study. This evidence is also informative as MS is often used as the default method available to users of PLS-SEM software. / Minister of Higher Education Malaysia and University Utara Malaysia

No description available.

<p>The overall focus of this thesis is the implementation of a process monitoring system in a real manufacturing environment that utilizes multivariate analysis techniques to assess the state of the process. The process in question was the medium-high volume manufacturing of discrete aluminum parts using relatively simple machining processes involving the use of two tools. This work can be broken down into three main sections.</p><p>The first section involved the modeling of temperatures and thermal expansion measurements for real-time thermal error compensation. Thermal expansion of the Z-axis was measured indirectly through measurement of the two quality parameters related to this axis with a custom gage that was designed for this part. A compensation strategy is proposed which is able to hold the variation of the parts to ±0.02mm, where the tolerance is ±0.05mm.</p><p>The second section involved the modeling of the process data from the parts that included vibration, current, and temperature signals from the machine. The modeling of the process data using Principal Component Analysis (PCA), while unsuccessful in detecting minor simulated process faults, was successful in detecting a miss-loaded part during regular production. Simple control charts using Hotelling's T^2 statistic and Squared Prediction Error are illustrated. The modeling of quality data from the process data of good parts using Projection to Latent Structures by Partial Least Squares (PLS) data did not provide very accurate fits to the data; however, all of the predictions are within the tolerance specifications.</p><p>The final section discusses the implementation of a process monitoring system in both manual and automatic production environments. A method for the integration and storage of process data with Mitutoyo software MCOSMOS and MeasurLink® is described. All of the codes to perform multivariate analysis and process monitoring were written using Matlab.</p> / Thesis / Master of Applied Science (MASc)

Yes / Age and gender classification are two important problems that recently gained popularity in the research community, due to their wide range of applications. Research has shown that both age and gender information are encoded in the face shape and texture, hence the active appearance model (AAM), a statistical model that captures shape and texture variations, has been one of the most widely used feature extraction techniques for the aforementioned problems. However, AAM suffers from some drawbacks, especially when used for classification. This is primarily because principal component analysis (PCA), which is at the core of the model, works in an unsupervised manner, i.e., PCA dimensionality reduction does not take into account how the predictor variables relate to the response (class labels). Rather, it explores only the underlying structure of the predictor variables, thus, it is no surprise if PCA discards valuable parts of the data that represent discriminatory features. Toward this end, we propose a supervised appearance model (sAM) that improves on AAM by replacing PCA with partial least-squares regression. This feature extraction technique is then used for the problems of age and gender classification. Our experiments show that sAM has better predictive power than the conventional AAM.

Individuals are considered the weakest link when it comes to securing a personal computer system. All the technological solutions can be in place, but if individuals do not make appropriate security protection decisions they introduce holes that technological solutions cannot protect. This study investigates what personal characteristics influence differences in individual security behaviors, defined as behaviors to protect against security threats, by adapting Protection Motivation Theory into an information security context. This study developed and validated an instrument to measure individual security behaviors. It then tested the differences in these behaviors using the security research model, which built from Protection Motivation Theory, and consisted of perceived security vulnerability, perceived security threat, security self-efficacy, response efficacy, and protection cost. Participants, representing a sample population of home computer users with ages ranging from 20 to 83, provided 279 valid responses to surveys. The behaviors studied include using anti-virus software, utilizing access controls, backing up data, changing passwords frequently, securing access to personal computers, running software updates, securing wireless networks, using care when storing credit card information, educating others in one's house about security behaviors, using caution when following links in emails, running spyware software, updating a computer's operating system, using firewalls, and using pop-up blocking software. Testing the security research model found different characteristics had different impacts depending on the behavior studied. Implications for information security researchers and practitioners are provided, along with ideas for future research. / Ph. D.

A continuous improvement project (CIP) is a structured improvement project using a team of people "typically representing different departments or units in the organization" working to improve a process or work area over a relatively short period of time, such as a few days or up to several months. A CIP may use different improvement methodologies and tools, and may thus be defined according to the improvement approach. For instance, an organization adopting Lean as an improvement approach is likely to have CIPs implementing Lean tools, such as 5S or value stream mapping. These projects may be referred to as Lean projects in general, although they may also represent accelerated improvement projects such as Kaizen events, Kaizen blitz, or rapid improvement projects. Alternatively, an organization utilizing Six Sigma as an improvement approach may have Six Sigma projects that use the Define-Measure-Analyze-Improve-Control (DMAIC) process and statistical tools. Some organizations adopt an integrated improvement approach, such as Lean Six Sigma, and therefore may have CIPs with an even broader set of tools from which to choose. Lastly, many organizations may have an improvement approach not characterized by any single set of improvement processes and tools, and thus, may be thought of generally as process improvement, or quality improvement, projects using a traditional methodology as plan-do-study/check-act (PDSA or PDCA). In this dissertation, all of these types of improvement projects are referred as CIPs. Since the 1980s, hospitals have been using CIPs to address some of the problems in hospitals, such as quality in healthcare delivery, internal process efficiency, communication and coordination, and the cost of services. Some hospitals have achieved significant improvements, such as reducing the turnaround time for clinical laboratory results by 60 percent and reducing instrumentation decontaminations and sterilization cycle time by 70 percent. However, as with many other companies, hospitals often experience difficulty achieving their desired level of improvements with CIPs. Therefore, the purpose of this dissertation is to identify the critical success factors (CSFs) related to CIP success. In order to achieve this goal, five objectives were achieved: creating a methodology to assess the maturity or evolution of a research field (manuscript #1), identifying a comprehensive list of CSFs for CIPs (manuscript #2), assessing the maturity of the published literature on CIPs in hospitals (manuscript #3), identifying the most important factors related to CIPs in hospitals (manuscript #4) , and conducting an empirical investigation to define the CSFs for CIPs in hospital settings (manuscript #5 and #6). This investigation was conducted in three phases: research framing, variable reduction, and model development and testing. During these phases, the researcher used the following methodologies and data collection tools: systematic literature review, maturity framework (developed as part of this dissertation), expert study, retrospective survey questionnaire, exploratory factor analysis, partial-least squares structural equation modeling, and regression modeling. A maturity framework with nine dimensions was created (manuscript #1) and applied in order to identify a list of 53 factors related to CIP in general, involving any organization (manuscript #2). Additionally, the maturity framework was used to assess the literature available on CIPs in hospitals, considering only the authorship characteristic dimension (manuscript #3). Considering the frequency of new authors per year, the relative new integration of research groups, and the limited set of predominant authors, the research field, or area, of CIPs in hospitals is one with opportunities for improving maturity. Using the systematic literature review from manuscript #3, the list of 53 factors, and the list of predominant authors, a review of the literature was conducted, along with an expert study to more fully characterize the importance of various factors (manuscript #4). A conclusion from this particular work was that it is not possible to reduce the list of 53 factors based on these results, thus, a field study using the complete comprehensive list of factors was determined to have stronger practical implications. A field study was conducted to identify factors most related to CIP perceived success (manuscript #5) and CIP goal achievement (manuscript #6). The final results and practical implications of this dissertation consist in the identification of the following CSFs for CIP success in hospitals: Goal Characteristics, Organizational Processes, Improvement Processes, and Team Operation. These CSFs include several specific factors that, to the researcher's knowledge, have not been previously studied in empirical investigations: goal development process, organizational policies and procedures, CIP progress reporting, and CIP technical documentation. Practitioners involved with CIPs, such as CIP leaders, facilitators, stakeholders/customers, and continuous improvement managers/leaders, can utilize these results to increase the likelihood of success by considering these factors in planning and conducting CIPs. / Ph. D.