El fitoplancton es uno de los compuestos orgánicos de las aguas naturales y su diagnóstico es importante para evaluar el estado ecológico de los ecosistemas acuáticos, entre ellos las aguas costeras y de transición. El enriquecimiento de nutrientes antropogénicos y las alteraciones en la cadena de alimentación, incluyendo la reducción de consumidores de fitoplancton, produce un aumento espectacular de las existencias de fitoplancton. Esto ha causado cambios significativos en los ciclos de nutrientes de las áreas costeras, en la calidad del agua, en la biodiversidad y en el estado global del ecosistema. La caracterización de las comunidades fitoplanctónicas en ecosistemas acuáticos mediante el método de los recuentos microscópicos por epifluorescencia, es una tarea costosa en tiempo, material y recursos humanos altamente cualificados. El objetivo de este trabajo es, sin pretender sustituir a los recuentos con el microscopio sino complementarlos, poner a punto una técnica mediante espectrofotometría que disminuya estos costes, realizando medidas de espectros de absorción en el rango del visible en las muestras. Para llevar a cabo este trabajo se han tomado muestras en cinco zonas de la costa mediterránea de España. Estas zonas corresponden a ecosistemas acuáticos en los que influyen tanto las aguas continentales como las del mar Mediterráneo, es decir, zonas costeras influidas por aguas continentales (plumas continentales) y zonas continentales influidas por aguas marinas (estuarios). Las muestras tomadas presentan un gradiente de salinidad, en función de una mayor o menor influencia continental y también en función de la capa superficial de menor salinidad que yace sobre las aguas salinas más densas. En estas muestras con distintas salinidades también existen unas diferencias cualitativas y cuantitativas de la composición fitoplanctónica. / Martínez Guijarro, MR. (2010). DETERMINACIÓN DE COMUNIDADES FITOPLACTÓNICAS MEDIANTE ESPECTROSCOPÍA VISIBLE Y SU RELACIÓN CON LOS RECUENTOS POR MICROSCOPIA DE EPIFLUORESCENCIA [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/7106 / Palancia

In 2011 one-third of South African organisations did not intend to adopt cloud computing services because IT decision-maker lacked understanding of the related concepts and benefits (Goldstuck, 2011). This research develops a media-oriented model to examine the adoption of these services in South Africa. The model uses the technology acceptance model (TAM) and innovation diffusion theory (IDT) to develop variables that are considered determinants of adoption including trialability, complexity of understanding, perceived risk, perceived ease of use and perceived usefulness.An electronic survey was sent to 107 IT decision-makers. Over 80% of the respondents were C-suite executives. The Partial Least Squares (PLS) method was chosen to depict and test the proposed model. PLS is superior to normal regression models and is a second generation technique. The data analysis included evaluating and modifying the model, assessing the new measurement model, testing the hypotheses of the model structure and presenting the structural model.The research found that media, experts and word of mouth mitigate perceived risks including bandwidth, connectivity and power. Furthermore, trialability and perceived usefulness were affected by social influence, as well as influencing adoption. The results enable service providers and marketers to develop product roadmaps and pinpoint media messages. / Dissertation (MBA)--University of Pretoria, 2012. / Gordon Institute of Business Science (GIBS) / unrestricted

The study employed multi-variate methods to generate vulnerability maps for acid sulfate soils (AS) in the Norrbotten county of Sweden. In this study, the relationships between the reclassified datasets and each biogeochemical element was carefully evaluated with ANOVA Kruskal Wallis and PLS analysis. The sta-tistical results of ANOVA Kruskall-Wallis provided us a useful knowledge of the relationships of the preliminary vulnerability ranks in the classified datasets ver-sus the amount of each biogeochemical element. Then, the statistical knowledge and expert knowledge were used to generate the final vulnerability ranks of AS soils in the classified datasets which were the input independent variables in PLS analyses. The results of Kruskal-Wallis one way ANOVA and PLS analyses showed a strong correlation of the higher levels total Cu2+, Ni2+ and S to the higher vulnerability ranks in the classified datasets. Hence, total Cu2+, Ni2+ and S were chosen as the dependent variables for further PLS analyses. In particular, the Variable Importance in the Projection (VIP) value of each classified dataset was standardized to generate its weight. Vulnerability map of AS soil was a result of a lineal combination of the standardized values in the classified dataset and its weight. Seven weight sets were formed from either uni-variate or multi-variate PLS analyses. Accuracy tests were done by testing the classification of measured pH values of 74 soil profiles with different vulnerability maps and evaluating the areas that were not the AS soil within the groups of medium to high AS soil probability in the land-cover and soil-type datasets. In comparison to the other weight sets, the weight set of multi-variate PLS analysis of the matrix of total Ni2+& S or total Cu2+& S had the robust predictive performance. Sensitivity anal-ysis was done in the weight set of total Ni2+& S, and the results of sensitivity analyses showed that the availability of ditches, and the change in the terrain sur-faces, the altitude level, and the slope had a high influence to the vulnerability map of AS soils. The study showed that using multivariate analysis was a very good approach methodology for predicting the probability of acid sulfate soil.

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.

Hybrit development AB är ett bolag som strävar mot att kunna producera fossilfritt stål genom att reducera järnmalmspellets med hjälp av vätgas. Därför har Hybrit utfört experimentella kampanjer där genomförbarheten av att reducera järnmalmspellets med hjälp av vätgas undersökts och studerats. Vid produktion av järn och stål måste produktkvalitén tas i beaktan. Reduktionsprocessen karaktäriseras av en mängd olika process- och kvalitetsparametrar, där kvalitetsparametrarna beskriver produktkvalitén. Det är av intresse att studera hur processparametrarna påverkar produktkvalitén. Processparametrarna kan mätas vid vilket tidpunkt som helst genom olika sensorer. Produktkvalitén kan bestämmas först efter att järnmalmspelletsen är färdigreducerad. Därför präglas processen av en tidsfördröjning mellan mätningen av processparametrarna och labanalysemätningarna av kvalitetsparametrarna. På grund av tidsfördröjningen är det av intresse att kunna prediktera produktkvalitén utifrån processparametrarna. Om det går att prediktera produktkvalitén, är det av vikt att kunna avgöra prediktionens giltighet.  Examensarbetets syfte är att identifiera hur reduktionsprocessparametrarna påverkar reducerade järnets kvalitetsparametrar. En processövervakningsmetod som passar för processövervakning ska testas och undersökas utifrån hur metoden kan användas för att avgöra prediktionens giltighet. Processövervakningen ska användas för att avgöra om processen befinner sig i ett processläge som bidrar till en någorlunda korrekt och lämplig prediktion av produktkvalitén.  För analys av data användes 65 processparametrar och 6 kvalitetsparametrar. Den multivariata analysmetoden Partial Least Squares (PLS) användes för att nå syftet med examensarbetet. Via PLS skapades en modell som kunde beskriva vilka processparametrar som påverkade kvalitetsparametrarna samt hur processparametrarna påverkade kvalitetsparametrarna. PLS-modellen kunde prediktera kvalitetsparametrarna någorlunda korrekt och lämpligt, givet att processen befinner sig inom ramen för modellen och att det är en hög förklaringsgrad för kvalitetsparametern som predikteras. Kvalitetsparametern Y6-1 predikterades sämre eftersom förklaringsgraden för Y6-1 var låg. Processövervakningsmetoden som testades och undersöktes var PLS-övervakning. För att undersöka hur PLS-övervakning kan användas för att avgöra prediktions giltighet, användes tre processövervakningsverktyg. Dessa var X-scores processövervakning, Hotelling T2 och SPE. Resultatet var att PLS-övervakning kunde angiva hur processen förhåller sig till modellen. Observationerna som avvek i PLS-övervakningen predikterades sämre. Därmed kunde information om prediktionens giltighet genom PLS-övervakning erhållas. Att tillämpa PLS-övervakning för att avgöra prediktionens giltighet är en större framgång. Detta på grund av att information om produktkvalitén innan reduktionsprocessen är genomförd kan användas för att säkerställa produktion med tillfredställande kvalitet. Att tillämpa multivariata processövervakningsmetoder för att övervaka de predikterade kvalitetsparametrarna kan vara av intresse för framtida studier. Detta då processövervakningen kan användas för att minimera den interna variationen hos kvalitetsparametrarna. / Hybrit development AB strives to produce fossil-free steel by using hydrogen for the direct reduction process of iron ore pellets. To achieve that goal, Hybrit has carried out experimental campaigns where the feasibility of direct reduction using hydrogen gas has been investigated and studied. The quality of the reduced iron must be considered when producing iron and steel. The reduction process is characterized by a variety of process- and quality parameters. Because the quality parameters describe the quality of the product, it is of interest to study how the process parameters affect the quality parameters. The process parameters can be measured at any time through various sensors around the reactor in which the iron ore pellets are reduced. While the quality of the product can only be determined after the iron ore pellets have been completely reduced. Therefore, the process is characterized by a time delay between the measurement of the process parameters and the measurement of the quality parameters, where the reduced iron must be analyzed in a laboratory before the quality parameters can be measured. Because of the time delay, it is of interest to be able to predict the quality of the product based on the process parameters. If it is possible to predict the quality, then it is of importance to be able to determine the validity of the prediction.  The aim of this master thesis is to identify how the reduction process parameters affect the quality parameters of the reduced iron. A process monitoring method suitable for monitoring the process need be tested and investigated based on how the method can be used to determine the validity of the prediction. The process monitoring will be used to determine whether the process is in a process state that contributes to a reasonably accurate and appropriate prediction of the quality of the product.  65 process parameters and 6 quality parameters were used for the analysis of how the reduction process parameters affect the quality parameters of the reduced iron. The multivariate analysis method Partial Least Squares (PLS) was used to achieve the aim of the thesis. A multivariate model which could describe how the process parameters affect the quality parameters was created through PLS. The PLS-model was able to predict the quality parameters reasonably correctly and appropriately, given that the process is within the scope of the model and that the explanatory power is high for the quality parameter that is predicted. The quality parameter Y6-1 could not be predicted reasonably correct as the explanatory power for Y6-1 was low. The process monitoring method tested and investigated was PLS monitoring. Three process monitoring tools were used when PLS monitoring was investigated based on how they can be used to determine the validity of the prediction. These tools were X-scores process monitoring, Hotelling T2 and SPE. The result was that PLS monitoring could indicate how the process relates to the model. Observations that deviated in the PLS monitoring could not be predicted correctly. Thus, information about the validity of the prediction through PLS monitoring could be obtained. Applying PLS monitoring to determine the validity of the prediction is a greater success. This is because information about the quality of the product before the reduction process is completed can be used to ensure production with a satisfactory product quality. Applying multivariate process monitoring methods to monitor the predicted quality parameters may be of interest for future studies. This is because the process monitoring can be used to minimize the internal variation of the quality parameters.

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 / Research in facial age estimation has been active for over a decade. This is due to its numerous applications. Recently, convolutional neural networks (CNNs) have been used in an attempt to solve this age old problem. For this purpose, researchers have proposed various CNN architectures. Unfortunately, most of the proposed techniques have been based on relatively ‘shallow’ networks. In this work, we leverage the capability of an off-the-shelf deep CNN model, namely the VGG-Face model, which has been trained on millions of face images. Interestingly, despite being a simple approach, features extracted from the VGG-Face model, when reduced and fed into linear regressors, outperform most of the state-of-the-art CNNs. e.g. on both FGNET-AD and Morph II benchmark databases. Furthermore, contrary to using the last fully connected (FC) layer of the trained model, we evaluate the activations from different layers of the architecture. In fact, our experiments show that generic features learnt from intermediate layer activations carry more ageing information than the FC layers.