Through the rapid expansion of information technologies, Learning Management Systems have become one of the most important innovations for delivery of education. Successful implementation and management of these systems are primarily based on the instructors&#039 / adoption. However, too few researches have been conducted to evaluate instructors&rsquo / adoption towards e-learning system as taking higher education as base. This study aims to understand behavioral intentions of higher education instructors towards Learning Management Systems and further to identify the influencing factors. A research model has been proposed based on the belief variables of the Technology Acceptance Model. Additionally, Application Characteristics, Individual, Social and Technological dimensions were considered to identify the effects of key variables on behavioral intention of users. A survey instrument has been developed and conducted with 224 academicians after a pilot study through its reliability and validity has been assured. Although the items of the survey instrument were based on the literature, an explanatory factor analysis was performed to strictly determine which items belong to which factors. Then, in order to assess the measurement model Convergent validity and Discriminant validity were conducted via confirmatory factor analyses. After the required prior analyses, Component based Structural Equation Modeling (Partial Least Square - PLS) was used to validate the predictive power of the proposed research model. Consequently, relationships between the influencing factors were detected and the results showed that the factors related with Belief dimension directly influenced behavioral intention of instructors. Also, the factors under the Individual, Social and Technological dimensions indirectly affected behavioral intention of users towards learning management system use. Additionally, structured and informal interviews were performed with ten instructors and the findings of the research model were explained with the opinions of system users. The indications of this research will be valuable for implementation, management and continuous improvement of learning management systems.

Ein zusammengesetzter Index ist eine aggregierte Variable, die aus individuellen Indikatoren und Gewichten besteht, wobei die Gewichte die relative Wichtigkeit jedes Indikators darstellen. Zusammengesetzte Indizes werden oft benutzt um latente Phänomene zu schreiben oder komplexe Informationen zu einer geringen Anzahl an Variablen zusammenzufassen. Es ist von großer Bedeutung richtige Gewichte für die Variablen, die einen zusammengesetzten Index bilden, zu wählen. Hauptkomponentenanalyse (PCA) ist ein populärer Ansatz um Gewichte abzuleiten, aber es ist ungeeignet, wenn informative Variationen nur kleine Varianzen der Variablen in einem zusammengesetzten Index haben. Deshalb schlägt diese Studie vor, Partial Least Squares (PLS) anzuwenden, welches die Beziehung zwischen Zielvariablen and den Variablen in einem zusammengesetzten Index ausnutzt. Unsere Simulationsstudie zeigt, dass PLS so gut wie PCA funktioniert oder erheblich es übertrifft. Zusätzlich sind in der Praxis die Variablen in einem zusammengesetzten Index häufig nicht-metrisch. Solche Variablen benötigen spezielle Verfahren, um PCA oder PLS anzuwenden. Diese Studie untersucht mehrere PCA und PLS Algorithmen für nicht-metrische Variablen in der vorliegenden Literatur und vergleicht sie durch umfangreiche Simulationsstudien, um Empfehlungen für die Praxis abzugeben. Dummy coding zeigt häufig zufriedenstellende Leistung im Vergleich zu komplizierteren Methoden. Als unsere Anwendungen betrachten wir Vermögen, Globalisierung, Geschlechtergleichheit und Korruption, indem PCA- und PLS-basierte zusammengesetzte Indizes angewendet werden. PLS erzeugt für die jeweiligen Zielvariablen massgeschnittene zusammengesetzte Indizes, die häufig bessere Leistung als PCA zeigten. Ein Vergleich zwischen PCA und PLS Gewichten und Koeffizienten zeigt, welche Variablen für die jeweiligen Zielvariablen besonders relevant sind.

The purpose of this analysis is to evaluate the usefulness of Fourier Transform Infrared (FTIR) spectroscopy in the detection of Herpes Simplex Virus 1 (hsv1) infection at an early stage. The raw absorption values were standardized to eliminate inter-sampling error. Wilcoxon-Mann-Whitney (WMW) statistic's Z score was calculated to select significant spectral regions. Partial least squares modeling was performed because of multicollinearity. Kolmogorov-Smirnov statistic showed models for healthy tissues from different time groups were not from same distribution. The additional 24 hour dataset was evaluated using the following methods. Variables were selected by WMW Z score. Difference of Composites statistic, DC, was created as a disease indicator and evaluated using area under the ROC curve, specificities, and confidence intervals using bootstrap algorithm. The specificity of DC was high, however the confidence intervals were large. Future studies are required with larger sample sizes to test this statistic's usefulness.

No description available.

The fate and behaviour of hydrophobic organic compounds (e.g. pesticides) in soils are largely controlled by sorption processes. Recent findings suggest that the chemical properties of soil organic carbon (OC) significantly control the extent of sorption of such compounds in soil systems. However, currently there is no practical tool to integrate the effects of OC chemistry into sorption predictions. Therefore, the K [subscript]oc model, which relies on the soil OC content (foc), is used for predicting soil sorption coefficients (K[subscript]d) of pesticides. The K[subscript]oc model can be expressed as K[subscript]d = K[subscript]oc × foc, where K[subscript]oc is the OC-normalized sorption coefficient for the compound. Hence, there is a need for a prediction tool that can effectively capture the role of both the chemical structural variation of OC as well as foc in the prediction approach. Infrared (IR) spectroscopy offers a potential alternative to the K[subscript]oc approach because IR spectra contain information on the amount and nature of both organic and mineral soil components. The potential of mid-infrared (MIR) spectroscopy for predicting K[subscript]d values of a moderately hydrophobic pesticide, diuron, was investigated. A calibration set of 101 surface soils from South Australia was characterized for reference sorption data (K[subscript]d and K[subscript]oc) and foc as well as IR spectra. Partial least squares (PLS) regression was employed to harness the apparent complexity of IR spectra by reducing the dimensionality of the data. The MIR-PLS model was developed and validated by dividing the initial data set into corresponding calibration and validation sets. The developed model showed promising performance in predicting K[subscript]d values for diuron and proved to be a more efficacious than the K[subscript]oc model. The significant statistical superiority of the MIR-PLS model over the K[subscript]oc model was caused by some calcareous soils which were outliers for the K[subscript]oc model. Apart from these samples, the performance of the two compared models was essentially similar. The existence of carbonate peaks in the MIR-PLS loadings of the MIR based model suggested that carbonate minerals may interfere or affect the sorption. This requires further investigation. Some other concurrent studies suggested excellent quality of prediction of soil properties by NIR spectroscopy when applied to homogenous samples. Next, therefore, the performance of visible near-infrared (VNIR) and MIR spectroscopy was thoroughly compared for predicting both foc and diuron K[subscript]d values in soils. Some eleven calcareous soils were added to the initial calibration set for an attempt to further investigate the effect of carbonate minerals on sorption. MIR spectroscopy was clearly a more accurate predictor of foc and K[subscript]d in soils than VNIR spectroscopy. Close inspection of spectra showed that MIR spectra contain more relevant and straightforward information regarding the chemistry of OC and minerals than VNIR and thus useful in modelling sorption and OC content. Moreover, MIR spectroscopy provided a better (though still not great) estimation of sorption in calcareous soils than either VNIR spectroscopy or the K[subscript]oc model. Separate research is recommended to fully explore the unusual sorption behaviour of diuron in calcareous soils. In the last experiment, two dimensional (2D) nuclear magnetic resonance/infrared heterospectral correlation analyses revealed that MIR spectra contain specific and clear signals related to most of the major NMR-derived carbon types whereas NIR spectra contain only a few broad and overlapped peaks weakly associated with aliphatic carbons. 2D heterospectral correlation analysis facilitated accurate band assignments in the MIR and NIR spectra to the NMR-derived carbon types in isolated SOM. In conclusion, the greatest advantage of the MIR-PLS model is the direct estimation of Kd based on integrated properties of organic and mineral components. In addition, MIR spectroscopy is being used increasingly in predicting various soil properties including foc, and therefore, its simultaneous use for K[subscript]d estimation is a resource-effective and attractive practice. Moreover, it has the advantage of being fast and inexpensive with a high repeatability, and unlike the K[subscript]oc approach, MIR-PLS shows a better potential for extrapolating applications in data-poor regions. Where available, MIR spectroscopy is highly recommended over NIR spectroscopy. 2D correlation spectroscopy showed promising potential for providing rich insight and clarification into the thorough study of soil IR spectra. / http://proxy.library.adelaide.edu.au/login?url= http://library.adelaide.edu.au/cgi-bin/Pwebrecon.cgi?BBID=1415416 / Thesis (Ph.D.) - University of Adelaide, School of Earth and Environmental Sciences, 2009

The fate and behaviour of hydrophobic organic compounds (e.g. pesticides) in soils are largely controlled by sorption processes. Recent findings suggest that the chemical properties of soil organic carbon (OC) significantly control the extent of sorption of such compounds in soil systems. However, currently there is no practical tool to integrate the effects of OC chemistry into sorption predictions. Therefore, the K [subscript]oc model, which relies on the soil OC content (foc), is used for predicting soil sorption coefficients (K[subscript]d) of pesticides. The K[subscript]oc model can be expressed as K[subscript]d = K[subscript]oc × foc, where K[subscript]oc is the OC-normalized sorption coefficient for the compound. Hence, there is a need for a prediction tool that can effectively capture the role of both the chemical structural variation of OC as well as foc in the prediction approach. Infrared (IR) spectroscopy offers a potential alternative to the K[subscript]oc approach because IR spectra contain information on the amount and nature of both organic and mineral soil components. The potential of mid-infrared (MIR) spectroscopy for predicting K[subscript]d values of a moderately hydrophobic pesticide, diuron, was investigated. A calibration set of 101 surface soils from South Australia was characterized for reference sorption data (K[subscript]d and K[subscript]oc) and foc as well as IR spectra. Partial least squares (PLS) regression was employed to harness the apparent complexity of IR spectra by reducing the dimensionality of the data. The MIR-PLS model was developed and validated by dividing the initial data set into corresponding calibration and validation sets. The developed model showed promising performance in predicting K[subscript]d values for diuron and proved to be a more efficacious than the K[subscript]oc model. The significant statistical superiority of the MIR-PLS model over the K[subscript]oc model was caused by some calcareous soils which were outliers for the K[subscript]oc model. Apart from these samples, the performance of the two compared models was essentially similar. The existence of carbonate peaks in the MIR-PLS loadings of the MIR based model suggested that carbonate minerals may interfere or affect the sorption. This requires further investigation. Some other concurrent studies suggested excellent quality of prediction of soil properties by NIR spectroscopy when applied to homogenous samples. Next, therefore, the performance of visible near-infrared (VNIR) and MIR spectroscopy was thoroughly compared for predicting both foc and diuron K[subscript]d values in soils. Some eleven calcareous soils were added to the initial calibration set for an attempt to further investigate the effect of carbonate minerals on sorption. MIR spectroscopy was clearly a more accurate predictor of foc and K[subscript]d in soils than VNIR spectroscopy. Close inspection of spectra showed that MIR spectra contain more relevant and straightforward information regarding the chemistry of OC and minerals than VNIR and thus useful in modelling sorption and OC content. Moreover, MIR spectroscopy provided a better (though still not great) estimation of sorption in calcareous soils than either VNIR spectroscopy or the K[subscript]oc model. Separate research is recommended to fully explore the unusual sorption behaviour of diuron in calcareous soils. In the last experiment, two dimensional (2D) nuclear magnetic resonance/infrared heterospectral correlation analyses revealed that MIR spectra contain specific and clear signals related to most of the major NMR-derived carbon types whereas NIR spectra contain only a few broad and overlapped peaks weakly associated with aliphatic carbons. 2D heterospectral correlation analysis facilitated accurate band assignments in the MIR and NIR spectra to the NMR-derived carbon types in isolated SOM. In conclusion, the greatest advantage of the MIR-PLS model is the direct estimation of Kd based on integrated properties of organic and mineral components. In addition, MIR spectroscopy is being used increasingly in predicting various soil properties including foc, and therefore, its simultaneous use for K[subscript]d estimation is a resource-effective and attractive practice. Moreover, it has the advantage of being fast and inexpensive with a high repeatability, and unlike the K[subscript]oc approach, MIR-PLS shows a better potential for extrapolating applications in data-poor regions. Where available, MIR spectroscopy is highly recommended over NIR spectroscopy. 2D correlation spectroscopy showed promising potential for providing rich insight and clarification into the thorough study of soil IR spectra. / http://proxy.library.adelaide.edu.au/login?url= http://library.adelaide.edu.au/cgi-bin/Pwebrecon.cgi?BBID=1415416 / Thesis (Ph.D.) - University of Adelaide, School of Earth and Environmental Sciences, 2009

In order to meet tight product quality specifications for chemical batch processes, it is vital to monitor and control product quality throughout the batch duration. However, the frequent lack of in situ sensors for continuous monitoring of batch product quality complicates the control problem and calls for novel control approaches. This thesis focuses on the study and application of multivariate statistical methods to control product quality in chemical batch processes. These multivariate statistical methods can be used to identify data-driven prediction models that can be integrated within a model predictive control (MPC) framework. The ideal MPC control strategy achieves end-product quality specifications by performing trajectory tracking during the batch operating time. However, due to the lack of in-situ sensors, measurements of product quality are usually obtained by laboratory assays and are, therefore, inherently intermittent. This thesis proposes a new approach to realise trajectory tracking control of batch product quality in those situations where only intermittent measurements are available. The scope of this methodology consists of: 1) the identification of a partial least squares (PLS) model that works as an estimator of product quality, 2) the transformation of the PLS model into a recursive formulation utilising a moving window technique, and 3) the incorporation of the recursive PLS model as a predictor into a standard MPC framework for tracking the desired trajectory of batch product quality. The structure of the recursive PLS model allows a straightforward incorporation of process constraints in the optimisation process. Additionally, a method to incorporate a nonlinear inner relation within the proposed PLS recursive model is introduced. This nonlinear inner relation is a combination of feedforward artificial neural networks (ANNs) and linear regression. Nonlinear models based on this method can predict product quality of highly nonlinear batch processes and can, therefore, be used within an MPC framework to control such processes. The use of linear regression in addition to ANNs within the PLS model reduces the risk of overfitting and also reduces the computational e↵ort of the optimisation carried out by the controller. The benefits of the proposed modelling and control methods are demonstrated using a number of simulated batch processes.

Partial Least Squares is both a regression method and a tool for variable selection, that is especially appropriate for models based on numerous (possibly correlated) variables. While being a well established modeling tool in chemometrics, this thesis adapts PLS to financial data to predict the movements of the business cycle represented by the OECD Composite Leading Indicators. High-dimensional data is used, and a model with automated variable selection through a genetic algorithm is developed to forecast different economic regions with good results in out-of-sample tests. / Partial Least Squares är både en regressionsmetod och ett verktyg för variabelselektion som är specielltlämpligt för modeller baserade på en stor mängd (möjligtvis korrelerade) variabler.Medan det är en väletablerad modelleringsmetod inom kemimetri, anpassar den häruppsatsen PLS till finansiell data för att förutspå rörelserna av konjunkturen,representerad av OECD's Composite Leading Indicator. Högdimensionella dataanvänds och en model med automatiserad variabelselektion via en genetiskalgoritm utvecklas för att göra en prognos av olika ekonomiska regioner medgoda resultat i out-of-sample-tester

Chatbots are very widely used nowadays. However, much of the research on Chatbots have had a technology focus or has been limited to studies of adoption. To take advantage of the potential associated with chatbots, research that addresses the issues online users face when interacting with such programs is needed. The study described in this paper used the task-to technology fit theory to address the question of how individual characteristics and task/technology requirements influence the performance and utilization of chatbots. This paper used the quantitative methodology over two sets of data collected independently from two different populations. The first dataset of 100 respondents was obtained firstly through a structured questionnaire administered at Linnaeus University Campus in Växjö. The respondents are students in the university who use chatbots regularly. A second dataset was also collected from 20 participants through a practical test experiment with three different chatbots (Eliza, Rose, and Watson). The result and the data were then recorded through an online interview via the zoom application. The two datasets were analyzed quantitatively using comparative factor analysis with the aid of Smart PLS software. While few variables provided little support for the claims, the majority of the variables show strong support for the importance of task–technology fit, as a measure of chatbot utilization and performance based on individual characteristics as well as the task/technology requirements.

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.