Indiana University-Purdue University Indianapolis (IUPUI) / Up-to-date and accurate information on soil properties is important for precision farming and environmental management. The spatial information of soil properties allows adjustments of fertilizer applications to be made based on knowledge of local field conditions, thereby maximizing agricultural productivity and minimizing the risk of environmental pollution. While conventional soil sampling procedures are labor-intensive, time-consuming and expensive, remote sensing techniques provide a rapid and efficient tool for mapping soil properties. This study aimed at examining the capacity of hyperspectral reflectance data for mapping soil organic matter (SOM), total nitrogen (N) and total phosphorus (P). Soil samples collected from Eagle Creek Watershed, Cicero Creek Watershed, and Fall Creek Watershed were analyzed for organic matter content, total N and total P; their corresponding spectral reflectance was measured in the laboratory before and after oven drying and in the field using Analytical Spectral Devices spectrometer. Hyperion images for each of the watersheds were acquired, calibrated and corrected and Hyperion image spectra for individual sampled sites were extracted. These hyperspectral reflectance data were related to SOM, total N and total P concentration through partial least squares (PLS) regressions. The samples were split into two datasets: one for calibration, and the other for validation. High PLS performance was observed during the calibration for SOM and total N regardless of the type of the reflectance spectra, and for total P with Hyperion image spectra. The validation of PLS models was carried out with each type of reflectance to assess their predictive power. For laboratory reflectance spectra, PLS models of SOM and total N resulted in higher R2 values and lower RMSEP with oven-dried than those with field-moist soils. The results demonstrate that soil moisture degrades the performance of PLS in estimating soil constituents with spectral reflectance. For in-situ field spectra, PLS estimated SOM with an R2 of 0.74, N with an R2 of 0.79, and P with an R2 of 0.60. For Hyperion image spectra, PLS predictive models yielded an R2 of 0.74 between measured and predicted SOM, an R2 of 0.72 between measured and predicted total N, and an R2 of 0.67 between measured and predicted total P. These results reveal slightly decreased model performance when shifting from laboratory-measured spectra to satellite image spectra. Regardless of the spectral data, the models for estimating SOM and total N consistently outperformed those for estimating total P. These results also indicate that PLS is an effective tool for remotely estimating SOM, total N and P in agricultural soils, but more research is needed to improve the predictive power of the model when applied to satellite hyperspectral imagery.

A Kaizen event (KE) may be defined as a structured improvement project that uses a cross-functional team and specific improvement goals to improve a targeted work area or process in an accelerated time frame. KEs, also known as Rapid Improvement Events (RIEs), have been utilized within hospitals to achieve beneficial operations, stakeholder (i.e., social), financial, and clinical outcomes. Due to their potential to achieve positive results in a rapid timeframe, understanding the determinants of KE success within a hospital environment is a valuable research undertaking. To date there has been limited rigorous empirical quantitative research focused on identifying success factors (SFs) influencing socio-technical outcomes of hospital-based KEs. Hence, this empirical research study seeks to determine the critical success factors (CSFs) for KEs in hospitals. For the first phase of this research work, a comprehensive systematic literature review (SLR) was conducted to identify the success factors (SFs) for KEs in hospitals as reported in the literature. This SLR resulted in the identification of 54 unique success factors mapping to four broad success factor categories, KE Task Design, KE Team Design, Organization, and KE Process. Thereafter, the second phase, which involved the variable reduction process, was performed to determine the strength of effect, or importance, of the SFs in order to determine a feasible number of SFs to include in further empirical work. Two robust methods were applied; a Meta-synthesis Evaluation and an Expert Survey, to query the SFs and to determine high priority factors for the empirical study. As a result, a total of 30 factors were finalized for empirical study. Next, the last phase, the empirical study to investigate and determine the CSFs for KEs in hospitals, was executed using a retrospective field study survey research design. Specifically, a survey questionnaire was designed to elicit feedback on perceptual measures from targeted hospital KE facilitators/leaders on the criticality of SFs on socio-technical outcomes for KEs in hospitals. Sixty usable responses were obtained, which were subjected to Exploratory Factor Analysis (EFA) and Partial Least Squares-Structural Equation Modeling (PLS-SEM), which were used to identify latent factor constructs and to determine the significance of the SFs, respectively. The results of this study identified seven significant direct relationships. Kaizen Event Design Characteristics (KEDC) and Target Area Buy-in (TABI) were found to have significant direct effects with both dependent variables, Performance Impact (PI) and Growth in Kaizen Capabilities (KCG). In addition, KEDC also had a significant direct relationship with Performance Culture (PC) and Team Dynamics (TD), respectively. Also, PC has a significant direct relationship with TD. Furthermore, Logistic Regression was utilized to test the SFs impact on the one objective technical outcome measure in the study, Goal Attainment (GOALATT). This analysis revealed one significant negative relationship occurring between TD and GOALATT. Overall, the study's findings provide evidence-based results for informing hospital managers, leaders, and continuous improvement practitioners on the key factors or value-added practices that can be adopted in their hospital KE initiatives to achieve beneficial socio-technical outcomes, as well as overall hospital KE success. Furthermore, this research can enable academia/researchers to strategize more confirmatory analysis approaches for theory validation and generalizability. / Doctor of Philosophy / The focus of this research study is to identify the most significant factors for Kaizen events (KEs) in hospitals, referred herein as critical success factors (CSFs). A KE may be defined as a structured improvement project that uses a cross-functional team and specific improvement goals to improve a targeted work area or process in an accelerated time frame. The aim of the study is to ultimately improve KE practice in hospitals through increased understanding of CSFs that can be planned or designed into KE processes to increase the likelihood of successful event outcomes. Various research formulation, development, and testing techniques are applied to frame the research study according to the aims and objectives and to achieve targeted research outcomes. The overall research design encompasses a retrospective study approach, performing a large-scale field study using a survey questionnaire to empirically identify the CSFs for KEs in hospitals. To help frame the research, a systematic literature review (SLR) along with bibliometric analyses were conducted. To help refine and select the success factors for empirical study, a meta-synthesis evaluation and an expert survey study were conducted. Exploratory factor analysis (EFA) and partial least squares-structural equation modeling (PLS-SEM) along with mediation analyses (MA) were performed to identify key factors, determine the significance of those factors, and to understand the influential relationships of those factors to hospital KE success. Results from this study aim to inform healthcare managers, healthcare improvement practitioners, researchers, and other relevant stakeholders about the critical components needed to achieve hospital KE success. The dissertation is documented according to a "manuscript style," using a journal/conference paper format to organize and report on the key findings and results obtained from the investigation. The Introduction chapter is provided to introduce the research study topic, study significance, indicate the overall research aims and objectives, present the overall research approach and design methodology, and to enumerate the main publication outputs and outcomes from this dissertation work. The Conclusions chapter summarizes the overall research outcomes, key study findings, study limitations, and provides areas for future research.

Wastewater treatment plants (WWTPs) face increasingly stringent effluent quality constraints as a result of rising environmental concerns. Efficient operation of the secondary clarification process is essential to be able to meet these strict regulations. Treatment plants can benefit greatly from making better use of available resources through improved automation and implementing more process systems engineering techniques to enhance plant performance. As such, the primary objective of this research is to utilize data-driven modeling techniques to obtain a representative model of a simplified secondary clarification unit in a WWTP. First, a deterministic subspace-based identification approach is used to estimate a linear state-space model of the secondary clarification process that can accurately predict process dynamics, with the ultimate objective of motivating the use of the subspace model in a model predictive control (MPC) framework for closed-loop control of the clarification process. To this end, a low-order subspace model which relates a set of typical measured outputs from a secondary clarifier to a set of typical inputs is identified and subsequently validated on simulated data obtained via Hydromantis's WWTP simulation software, GPS-X. Results illustrate that the subspace model is able to approximate the nonlinear process behaviour well and can effectively predict the dynamic output trajectory for various candidate input profiles, thus establishing its candidacy for use in MPC. Subsequently, a framework for forecasting the occurrence of sludge bulking--and consequently clarification failure--based on an engineered interaction variable that aims to capture the relationship between key input variables is proposed. Partial least squares discriminant analysis (PLS-DA) is used to discriminate between process conditions associated with clarification failure versus effective clarification. Preliminary results show that PLS-DA models augmented with the interaction variable demonstrate improved predictions and higher classification accuracy. / Thesis / Master of Applied Science (MASc)

No description available.

<p>In spite of numerous benefits that are suggested for consumers’ utilizing integrated personal health record (PHR) systems, research has shown that these systems are not yet popular or well known to consumers. Therefore, research is needed to understand what would rise adoption rates for these systems. Hence, the main objective of this dissertation is to develop and empirically validate a theoretical model for explaining consumers’ intention to use integrated PHR systems.</p> <p>In developing the theoretical model of this dissertation, theories of information systems adoption were integrated with Self-Determination Theory (SDT), which is a well established theory from the Psychology literature that explains the mechanism through which individuals become more self-determined, i.e., motivated to take more active (rather than passive) roles in undertaking different behaviours. Taking such an active role by consumers, in the context of personal health management, is suggested to be necessary for realizing the full benefits of integrated PHR systems.</p> <p>The proposed theoretical model was validated using the PLS approach to structural equation modeling, on data collected from a cross-sectional survey involving 159 participants with no prior experience in using PHR systems. A stratified random sampling was employed to draw a representative sample of the Canadian population. The results show that consumers with higher levels of self-determination in managing their health are more likely to adopt integrated PHR systems since they have more positive perceptions regarding the use of such systems. Further, such self-determination is fueled by autonomy support from consumers’ physicians as well as consumers’ personality trait of autonomy orientation.</p> <p>This study advances the theoretical understanding of integrated PHR system adoption, and it contributes to practice by providing insightful implications for designing, promotion, and facilitating the use of integrated PHR systems among consumers.</p> / Doctor of Philosophy (PhD)

Sustainability of the built environment is one of the most significant challenges facing the construction industry, and presents significant opportunities to affect change. The absence of quantifiable and holistic sustainability measures for the built environment has hindered their application. As a result, a sustainability performance metric (SPM) framework was conceptually formulated by employing sustainability objectives and function statements a-priori to identify the correlated sustainability indicators that need to be captured equally, with respect to the environment, the economy, and society. Projection to Latent Structures (PLS), a latent variable method, was adopted to mathematically formulate the metric. Detached single-family housing was used to demonstrate the application of SPM. Datasets were generated using Athena Impact Estimator, EnergyPlus, Building Information Modelling (BIM), Socioeconomic Input/Output models, among others. Results revealed that a holistic metric, such as the SPM is necessary to obtain a sustainable design, where qualitative or univariate considerations may result in the contrary. A building envelope coefficient of performance (BECOP) metric based on an idealized system was also developed to measure the energy efficiency of the building envelope. Results revealed the inefficiencies in the current building envelope construction technologies and the missed opportunities for saving energy. Furthermore, a decision-making tool, which was formulated using the PLS utilities, was shown to be effective and necessary for early stages of the design for energy efficiency. / Thesis / Doctor of Science (PhD) / Sustainability of the built environment is a significant challenge facing the industry, and presents opportunities to affect changes. The absence of holistic sustainability measures has hindered their application. As a result, a sustainability performance metric (SPM) framework was formulated by employing sustainability objectives and function statements a-priori to identify the indicators that need to be captured. Projection to Latent Structures was adopted to mathematically formulate the metric. A housing prototype was used to demonstrate the application of the SPM utilizing a bespoke dataset. Results revealed that holistic metric, such as the SPM is necessary for achieving sustainable designs. A building envelope coefficient of performance metric was also developed to measure the energy efficiency of the building envelope. Results revealed the inefficiencies in the current building envelope technologies and identified missed opportunities. Furthermore, a decision-making tool was formulated and shown to be effective and necessary for design for energy efficiency.

Lean Management and Lean Six Sigma (LM/LSS) are improvement methodologies that have been utilized to achieve better performance outcomes at organizational and operational levels. Although there has been evidence of breakthrough improvement across diverse organizational settings, LM/LSS remains an early-stage improvement methodology in public sector organizations, specifically within local government organizations (LGOs). Some LGOs have benefited from LM/LSS and reported significant improvements, such as reducing process time by up to 90% and increasing financial savings by up to 57%. While the success of LM/LSS can lead to satisfactory outcomes, the risk of failure can also result in a tremendous waste of financial and non-financial resources. Evidence from the literature indicates that the failure to achieve the expected outcomes is likely due to the lack of attention paid to critical success factors (CSFs) that are crucial for LM/LSS success. Furthermore, research in this research area regarding characterizing and statistically examining the CSFs associated with LM/LSS in such organizational settings has been limited. Hence, the aim of this research is to provide a comprehensive investigation of the success factors for LM/LSS in LGOs. The initial stage of this dissertation involved analyzing the scientific literature to identify and characterize the CSFs associated with LM/LSS in LGOs through a systematic literature review (SLR). This effort identified a total of 47 unique factors, which were grouped into 5 categories, including organization, process, workforce knowledge, communications, task design, and team design. The next stage of this investigation focused on identifying a more focused set of CSFs. This involved evaluating the strength of the effect (or importance) of the factors using two integrated approaches: meta-synthesis and expert assessment. This process concluded with a total of 29 factors being selected for the empirical field study. The final stage included designing and implementing an online survey questionnaire to solicit LGOs' experience on the presence of factors during the development and/or implementation of LM/LSS and their impact on social-technical system outcomes. Once the survey was concluded, an exploratory factor analysis (EFA) was conducted to identify the underlying latent variables, followed by using a partial least square-structural equation model (PLS-SEM) to determine the significance of the factors on outcomes. The EFA identified three endogenous and five exogenous latent variables. The results of the PLS-SEM model identified four significant positive relationships. Based on the results from the structural paths, the antecedent Improvement Readiness (IR) and Change Awareness (CA) were significant and had a positive influence on Transformation Success (TS). For the outcome Deployment Success (DS), Sustainable Improvement Infrastructure (SII) was the only significant exogenous variable and had the highest positive impact among all significant predictor constructs. Furthermore, Measurement-Based Improvement (MBI) was significant and positively influenced Improvement Project Success (IPS). Findings from this dissertation could serve as a foundation for researchers looking to further advance the maturity of this research area based on the evidence presented in this work. Additionally, this work could be used as guidelines for practitioners in developing implementation processes by considering the essential factors to maximize the success of LM/LSS implementation. Given the diversity of functional areas and processes within LGO contexts, it is also possible that other public sector organizations could benefit from these findings. / Doctor of Philosophy / Lean Management and Lean Six Sigma (LM/LSS) is an improvement methodology that is used by businesses and organizations to improve how they work and achieve better results. LM/LSS has been especially helpful in various organizations; however, the implementation of this improvement methodology has been limited by many challenges for public sector organizations, especially local government organizations (LGOs). The overall aim of this dissertation is to improve the success of LM/LSS implementation within the context of LGOs. More specifically, this dissertation systematically studied the critical success factors associated with LM/LSS success. Different research approaches, including research formulation, development, and testing techniques, were conducted to achieve the aim of this dissertation. Publications related to LM/LSS in LGOs have been rigorously analyzed to identify a comprehensive list of CSFs. To identify the most important factors, a meta-synthesis evaluation and expert survey assessment have been conducted. Following the refinement of the factors, a large-scale field study using a survey questionnaire has been designed and distributed to LGOs. Once the survey concluded, statistical methods that included Exploratory Factor Analysis (EFA) and Partial Least Squares-Structural Equation Modeling (PLS-SEM) were conducted. The former was used to identify the underlying latent variables, while the latter was conducted to examine the influence of the factors on social and technical outcomes. This dissertation could be used as a reference guideline helping practitioners to increase the success of LM/LSS implementation in LGOs. This dissertation can also guide scholars to potential research avenues that could advance this research area.

Yes / Automatic facial age progression (AFAP) has been an active area of research in recent years. This is due to its numerous applications which include searching for missing. This study presents a new method of AFAP. Here, we use an Active Appearance Model (AAM) to extract facial features from available images. An ageing function is then modelled using Sparse Partial Least Squares Regression (sPLS). Thereafter, the ageing function is used to render new faces at different ages. To test the accuracy of our algorithm, extensive evaluation is conducted using a database of 500 face images with known ages. Furthermore, the algorithm is used to progress Ben Needham’s facial image that was taken when he was 21 months old to the ages of 6, 14 and 22 years. The algorithm presented in this paper could potentially be used to enhance the search for missing people worldwide.

BACKGROUND:In a globalized word, prevention of infectious diseases is a major challenge. Rapid detection of viable virus particles in water and other environmental samples is essential to public health risk assessment, homeland security and environmental protection. Current virus detection methods, especially assessing viral infectivity, are complex and time-consuming, making point-of-care detection a challenge. Faster, more sensitive, highly specific methods are needed to quantify potentially hazardous viral pathogens and to determine if suspected materials contain viable viral particles. Fourier transform infrared (FTIR) spectroscopy combined with cellular-based sensing, may offer a precise way to detect specific viruses. This approach utilizes infrared light to monitor changes in molecular components of cells by tracking changes in absorbance patterns produced following virus infection. In this work poliovirus (PV1) was used to evaluate the utility of FTIR spectroscopy with cell culture for rapid detection of infective virus particles.RESULTS:Buffalo green monkey kidney (BGMK) cells infected with different virus titers were studied at 1 - 12 hours post-infection (h.p.i.). A partial least squares (PLS) regression method was used to analyze and model cellular responses to different infection titers and times post-infection. The model performs best at 8 h.p.i., resulting in an estimated root mean square error of cross validation (RMSECV) of 17 plaque forming units (PFU)/ml when using low titers of infection of 10 and 100 PFU/ml. Higher titers, from 103 to 106 PFU/ml, could also be reliably detected.CONCLUSIONS:This approach to poliovirus detection and quantification using FTIR spectroscopy and cell culture could potentially be extended to compare biochemical cell responses to infection with different viruses. This virus detection method could feasibly be adapted to an automated scheme for use in areas such as water safety monitoring and medical diagnostics.

The purpose of this research was to identify key determinants of service provider effectiveness and how it impacts outsourced security success. As environments have become more robust and dynamic, many organizations have made the decision to leverage external security expertise and have outsourced many of their information technology security functions to Managed Security Service Providers (MSSPs). Information Systems Outsourcing, at its core, is when a customer chooses to outsource certain information technology functions or services to a service provider and engages in a legally binding agreement. While legal contracts govern many aspects of an outsourcing arrangement, it cannot serve as the sole source of determining the outcome of a project. Organizations are viewing outsourcing success as an attainment of net benefits achieved through the use of a service provider. The effectiveness of the service provider has an impact on a company’s ability to meet business objectives and adhere to service level agreements. Many empirical studies have focused on outsourcing success, but few have focused on service provider effectiveness, which can serve as a catalyst to outsourcing success. For this research, Agency Theory (AT) was proposed as a foundation for developing the research model which included key areas of focus in information asymmetry, the outsourcing contract, moral hazard, trust, service provider effectiveness, and security outsourcing success. Agency Theory helped uncover several hypotheses deemed germane to service provider effectiveness and provided insight into helping understand the principal-agent paradigm that exists with security outsourcing. Confirmatory Factor Analysis (CFA) and Partial Least Squares-Structured Equation Modeling (PLS-SEM) were used with SmartPLS to analyze the data and provided clarity and validation for the research model and helped uncover key determinants of service provider effectiveness. The statistical results showed support for information asymmetry, contract, and trust, all of which were mediated through service provider effectiveness. The results also showed that service provider effectiveness is directly correlated to increasing security outsourcing success. This concluded that the research model showed significant results to support 4 of the 5 hypotheses proposed and helped uncover key findings on how security outsourcing success can be impacted. This research served as an original contribution to information security while viewing outsourcing success from the perspective of the client, security services, and customer expectations.