The objective of this research was to develop fluorescence-based tools that are suitable for performing rapid, accurate and direct characterization of natural organic matter (NOM) and colloidal/particulate substances present in natural water. Most available characterization methods are neither suitable for characterizing all the major NOM fractions such as protein-, humic acid-, fulvic acid- and polysaccharide-like substances as well as colloidal/particulate matter present in natural water nor are they suitable for rapid analyses. The individual and combined contributions of these NOM fractions and colloidal/particulate matter present in natural water contribute to membrane fouling, disinfection by-products formation and undesirable biological growth in drinking water treatment processes and distribution systems. The novel techniques developed in this research therefore, provide an avenue for improved understanding of these negative effects and proactive implementation of control and/or optimization strategies. The fluorescence excitation-emission matrix (EEM) method was used for characterization of NOM and colloidal/particulate matter present in water. Unlike most NOM and colloidal/particulate matter characterization techniques, this method can provide fast and consistent analyses with high instrumental sensitivity. The feasibility of using this method for monitoring NOM at very low concentration levels was also demonstrated with an emphasis on optimizing the instrument parameters necessary to obtain reproducible fluorescence signals. Partial least squares regression (PLS) was used to develop calibration models by correlating the fluorescence EEM intensities of water samples that contained surrogate NOM fractions with their corresponding dissolved organic carbon (DOC) concentrations. These fluorescence-based calibration models were found to be suitable for identifying/monitoring the extent of the relative changes that occur in different NOM fractions and the interactions between polysaccharide- and protein-like NOM in water treatment processes and distribution systems. Principal component analysis (PCA) of fluorescence EEMs was identified as a viable tool for monitoring the performance of biological filtration as a pre-treatment step, as well as ultrafiltration (UF) and nanofiltration (NF) membrane systems. The principal components (PCs) extracted in this approach were related to the major membrane foulant groups such as humic substances (HS), protein-like and colloidal/particulate matter in natural water. The PC score plots generated using the fluorescence EEMs obtained after just one hour of UF or NF operation could be related to high fouling events likely caused by elevated levels of colloidal/particulate-like material in the biofilter effluents. This fluorescence EEM-based PCA approach was sensitive enough to be used at low organic carbon levels present in NF permeate and has potential as an early detection method to identify high fouling events, allowing appropriate operational countermeasures to be taken. This fluorescence EEM-based PCA approach was also used to extract information relevant to reversible and irreversible membrane fouling behaviour in a bench-scale flat sheet cross flow UF process consisting of cycles of permeation and back-washing. PC score-based analysis revealed that colloidal/particulate matter mostly contributed to reversible fouling, while HS and protein-like matter were largely responsible for irreversible fouling. This method therefore has potential for monitoring modes of membrane fouling in drinking water treatment applications. The above approach was further improved by utilizing the evolution of the PC scores over the filtration time and relating these to membrane fouling by the use of PC scores??? balanced-based differential equations. Using these equations the proposed fluorescence-based modeling approach was capable of forecasting UF fouling behaviours with good accuracy based solely on fluorescence data obtained at time = 15 min from the initiation of the filtration process. In addition, this approach was tested experimentally as a basis for optimization by modifying the UF back-washing times with the objective of minimizing energy consumption and maximizing water production. Preliminary optimization results demonstrated the potential of this approach to reduce power consumption by significant percentages. This approach was also useful for identifying the fouling components of the NOM that were contributing to reversible and irreversible membrane fouling. Grand River water (Southwestern Ontario, Canada) was used as the natural water source for developing the techniques presented in this thesis. Future research focusing on testing these methods for monitoring of membrane fouling and treatment processes in large-scale drinking water treatment facilities that experience different sources of raw water would be useful for identifying the limitation of these techniques and areas for improvements.

Les motivacions entorn al programari lliure han estat sempre un tema de gran interès, sent la pregunta més obvia, "perquè les persones treballen de forma gratuïta?". Les motivacions dels desenvolupadors han estat establertes (per exemple, von Hippel (2001), Lerner and Tirole (2002)). De la mateixa manera que ho han estat per a les empreses grans i petites que adopten programari lliure basat en models de negoci (per exemple, Lakhani and von Hippel, 2003; Fitzgerald, 2006; Krishnamurthy, 2004). No obstant això, un nombre cada vegada més elevat de les PIMES amb estratègies que no estan directament relacionades amb aquest model de negoci estan contribuint a les comunitats de programari lliure. En aquest estudi s'investiga les motivacions d'aquestes empreses des d'un punt de vista de comportament mitjançant un model d'investigació basat en TPB (Theory of Planned Behavior). Hem demostrat que factors com la "obertura" d'una PIME, la importància percebuda del programari lliure, els desenvolupadors (empleats) d'una PIME, juntament amb l'ambient extern, podrien influir en la decisió d'una PIME a participar en comunitats de programari lliure. A més, hem demostrat que es poden identificar algunes diferències entre empreses d'alta base tecnològica i empreses amb poca base tecnològica. Aquestes conclusions poden ajudar governs nacionals o regionals per millorar el disseny de polítiques per tal d'incentivar l'ús i la participació de les empreses en les comunitats de programari lliure. Especialment ara, degut a la forta crisi econòmica que pateix Europa, el programari lliure pot ser una solució adequada per a fomentar la innovació. / Motivations in FLOSS have always been a subject of great interest, by starting with the most obvious question, “why people work for free?”. The motivations of developers have been well established (eg von Hippel (2001), Lerner and Tirole 2002). The same exists also for big and small companies adopting FLOSS based Business Models (eg Lakhani and von Hippel, 2003; Fitzgerald 2006; Krishnamurthy, 2004). However an increasing number of SMEs with strategies not directly related to the Business Model are contributing to FLOSS communities. In this study we try to investigate these motivations under a behavioral perspective by using a research model based on TPB (Theory of Planned Behavior). We demonstrated that factors like the “openness” of a SME, the perceived importance of FLOSS, the developers (employees) of a SME along with the external environment of a SME, could influence the decision of a SME to participate in FLOSS communities. Also, we have demonstrated that some differences can be identified between high tech firms and non high tech firms. These findings can help national or regional governments to design better policies in order to better promote the use and the participation of firms to FLOSS communities. Especially now, in times of heavy economical crisis in Europe, FLOSS can be an adequate solution to foster innovation.

Kath. Universiẗat, Diss.--Eichstätt-Ingolstadt, 2007.

Submitted by Bruna Rodrigues (bruna92rodrigues@yahoo.com.br) on 2016-10-06T11:48:12Z No. of bitstreams: 1 DissMHC.pdf: 1077783 bytes, checksum: c81f777131e6de8fb219b8c34c4337df (MD5) / Approved for entry into archive by Marina Freitas (marinapf@ufscar.br) on 2016-10-20T13:58:41Z (GMT) No. of bitstreams: 1 DissMHC.pdf: 1077783 bytes, checksum: c81f777131e6de8fb219b8c34c4337df (MD5) / Approved for entry into archive by Marina Freitas (marinapf@ufscar.br) on 2016-10-20T13:58:47Z (GMT) No. of bitstreams: 1 DissMHC.pdf: 1077783 bytes, checksum: c81f777131e6de8fb219b8c34c4337df (MD5) / Made available in DSpace on 2016-10-20T13:58:52Z (GMT). No. of bitstreams: 1 DissMHC.pdf: 1077783 bytes, checksum: c81f777131e6de8fb219b8c34c4337df (MD5) Previous issue date: 2016-04-29 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / This paper presents a comparative study of the predictive power of four suitable regression methods for situations in which data, arranged in the planning matrix, are very poorly multicolinearity and / or high dimensionality, wherein the number of covariates is greater the number of observations. In this study, the methods discussed are: principal component regression, partial least squares regression, ridge regression and LASSO. The work includes simulations, wherein the predictive power of each of the techniques is evaluated for di erent scenarios de ned by the number of covariates, sample size and quantity and intensity ratios (e ects) signi cant, highlighting the main di erences between the methods and allowing for the creating a guide for the user to choose which method to use based on some prior knowledge that it may have. An application on real data (not simulated) is also addressed. / Este trabalho apresenta um estudo comparativo do poder de predi c~ao de quatro m etodos de regress~ao adequados para situa c~oes nas quais os dados, dispostos na matriz de planejamento, apresentam s erios problemas de multicolinearidade e/ou de alta dimensionalidade, em que o n umero de covari aveis e maior do que o n umero de observa c~oes. No presente trabalho, os m etodos abordados s~ao: regress~ao por componentes principais, regress~ao por m nimos quadrados parciais, regress~ao ridge e LASSO. O trabalho engloba simula c~oes, em que o poder preditivo de cada uma das t ecnicas e avaliado para diferentes cen arios de nidos por n umero de covari aveis, tamanho de amostra e quantidade e intensidade de coe cientes (efeitos) signi cativos, destacando as principais diferen cas entre os m etodos e possibilitando a cria c~ao de um guia para que o usu ario possa escolher qual metodologia usar com base em algum conhecimento pr evio que o mesmo possa ter. Uma aplica c~ao em dados reais (n~ao simulados) tamb em e abordada

Made available in DSpace on 2016-08-19T12:56:41Z (GMT). No. of bitstreams: 1 Dissertacao Marcelo Jose.pdf: 1590491 bytes, checksum: 7be3e83649dd910e0afe9a5a25de4e73 (MD5) Previous issue date: 2013-10-22 / The ethanol has continuously gained interests in many countries including Brazil due to the PROÁLCOOL program. The experimental determination of properties of ethanol and other fuels through official methods is very time consuming as well as tedious process. The estimation of these properties with the help of computational tools can be very useful. In the present work, the methods of partial least squares regression (PLS) and artificial neural network multilayer (ANN) were used to estimate one of the most important properties of fuel ethanol, density, using official quality parameters for ethanol, collected from LAPQAP/UFMA laboratory corresponding to 12 years (period: 2002-2013) of analyzes. A careful analysis of the data was performed to obtain a set of variables and data that best represents satisfactory performance of the two models. The estimates of both approaches were compared and validated. The predictive ability of the network obtained was very good for the parameters studied, consistent with the accuracy of the experimental measurements. The low mean square error, the randomness, the zero mean and the constant variance, obtained for the residues, indicated the suitability of the models, suggesting their use to estimate (predict) the density of ethanol. Results indicated that the model ANN was adequate, and the value of NMSE (normalized mean square error) of 0.0012, less than the PLS model of 0.2221. The result achieved is less than the range of measurement uncertainty of the equipment responsible for testing the density proving that the model used has satisfactory performance. / O etanol tem alcançado crescente interesse em muitos países, principalmente, no Brasil devido ao programa PROÁLCOOL. A determinação experimental das propriedades deste biocombustível e de outros combustíveis por meio de métodos oficiais é muito demorada, bem como é considerado um tedioso processo. A estimativa dessas propriedades com a ajuda de ferramentas computacionais pode ser de grande utilidade. No presente trabalho, os métodos de regressão por mínimos quadrados parciais (PLS) e redes neurais artificiais de múltiplas camadas (RNA) foram usados para estimar uma das mais importantes propriedades do etanol combustível, massa específica, utilizando parâmetros de qualidade oficiais de etanol, oriundos de análises realizadas no laboratório LAPQAP/UFMA, durante 12 anos (período: 2002-2013). Inicialmente, uma análise cuidadosa dos dados foi realizada a fim de selecionar um conjunto de variáveis e dados que melhor representasse um desempenho satisfatório dos dois modelos estudados. As estimativas de ambas as abordagens foram comparadas e validadas. A capacidade preditiva da rede neural obtida foi considerada muito boa para os parâmetros estudados, e compatível com a precisão das medidas experimentais. O baixo erro quadrático médio, a aleatoriedade, a média nula e a variância constante, obtida para os resíduos, evidenciaram a adequabilidade dos modelos usados, sugerindo a utilização destes modelos para estimar (predizer) a massa específica do etanol. Resultados indicaram que o modelo de RNA foi adequado, sendo o valor de NMSE (erro quadrático médio normalizado) de 0,0012, valor este, muito inferior ao modelo de PLS de 0,2221. Este resultado alcançado é inferior aos valores da faixa de incerteza de medição do equipamento responsável pelo ensaio experimental da massa específica, comprovando que o modelo utilizado possui desempenho considerado muito bom.

The paper describes predictive modelling of biomarker data stemming from patients suffering from multiple sclerosis. Improvements of multivariate analyses of the data are investigated with the goal of increasing the capability to assign samples to correct subgroups from the data alone. The effects of different preceding scalings of the data are investigated and combinations of multivariate modelling methods and variable selection methods are evaluated. Attempts at merging the predictive capabilities of the method combinations through voting-procedures are made. A technique for improving the result of PLS-modelling, called bagging, is evaluated. The best methods of multivariate analysis of the ones tried are found to be Partial least squares (PLS) and Support vector machines (SVM). It is concluded that the scaling have little effect on the prediction performance for most methods. The method combinations have interesting properties – the default variable selections of the multivariate methods are not always the best. Bagging improves performance, but at a high cost. No reasons for drastically changing the work flows of the biomarker data analysis are found, but slight improvements are possible. Further research is needed.

Trots att det inom forskningsvärlden propageras för formativ bedömning, kan man i dagsläget notera en mycket stor variation gällande införlivandet av, samt effekter av, formativ bedömning i skolor. Metoder för att kartlägga formativ bedömningspraxis fordras för att kunna särskilja på ”god” respektive ”mindre god” formativ bedömningspraxis. Syftet med föreliggande uppsats var att, med hjälp av en elevenkät och multivariata projektionsmetoder såsom PCA och PLS-DA, kartlägga, och särskilja, formativ bedömningspraxis hos sex olika gymnasieklasser som genomfört kursen kemi 2. Ett sekundärt syfte var även att, med samma verktyg, försöka karakterisera och särskilja frekvenser av olika genomförda undervisningsmoment inom samma kurs och klasser. Studien visade, på ett grafiskt och illustrativt sätt, en stor variation av upplevelser av formativ bedömning inom de tillfrågade klasserna. Vidare visade sig PCA vara ett utmärkt verktyg för att identifiera elevsvar som låg utanför den ”normala” variationen. Genom en PLS-DA-analys påvisades en skillnad i frekvenser av genomförda undervisningsmoment mellan två kommunala och en privat skola – även om dessa resultat bör tolkas med en viss försiktighet.

Due to the invasive nature of quantification techniques, baseline pigment data for coral-dwelling zooxanthellae are not known. In an attempt to develop a model for non-invasive estimation of zooxanthellae pigment concentrations from corals, field samples were taken from Porites rus and P. lutea in Apra Harbor, Guam. In-situ reflectance spectra (R400-R800) from 22 coral colonies were collected. “Coral truthing” was accomplished by extracting corresponding tissue core samples. Subsequent analysis to quantify the concentrations of 6 zooxanthellae pigments (µg cm-2) was performed using HPLC. Trials of multiple linear regressions were attempted (EJ Hochberg) and found inappropriate, despite previous success. The multivariate calibration technique partial least squares regression (PLS-R) is an excellent tool in the case of co-linear variables. Thus, PLS-R was attempted for chlorophyll c2 and peridinin after demonstration of co-linearity. This may be an appropriate approach for development of bio-optical models to estimate zooxanthellae pigment concentrations. Further, the dinoflagellate diagnostic pigment peridinin may be of great value for reef-scale remote sensing of changes in coral status in the future.

Background: Joint stability has been experimentally and clinically linked to mechanisms of knee injury and joint degeneration. The only dynamic, and perhaps most important, regulators of knee joint stability are contributions from muscular contractions. In participants with unstable knees, such as anterior cruciate ligament (ACL) injured, a range of neuromuscular adaptations has been observed including quadriceps weakness and increased co-activation of adjacent musculature. This co-activation is seen as a compensation strategy to increase joint stability. In fact, despite increased co-activation, instability persists and it remains unknown whether observed adaptations are the result of injury induced quadriceps weakness or the mechanical instability itself. Furthermore, there exists conflicting evidence on how and which of the neuromuscular adaptations actually improve and/or reduce knee joint stability. Purpose: The overall aim of this thesis is therefore to elucidate the role of injury and muscle weakness on muscular contributions to knee joint stability by addressing two main objectives: (1) to further our understanding of individual muscle contribution to internal knee joint moments; and (2) to investigate neuromuscular adaptations, and their effects on knee joint moments, caused by either ACL injury and experimental voluntary quadriceps inhibition (induced by pain). Methods: The relationship between individual muscle activation and internal net joint moments was quantified using partial least squares regression models. To limit the biomechanical contributions to force production, surface electromyography (EMG) and kinetic data was elicited during a weight-bearing isometric force matching task. A cross-sectional study design determined differences in individual EMG-moment relationships between ACL deficient and healthy controls (CON) groups. A crossover placebo controlled study design determined these differences in healthy participants with and without induced quadriceps muscle pain. Injections of hypertonic saline (5.8%) to the vastus medialis induced muscle pain. Isotonic saline (0.9%) acted as control. Effect of muscle pain on muscle synergies recruited for the force matching task, lunging and squatting tasks was also evaluated. Synergies were extracted using a concatenated non-negative matrix factorization framework. Results/Discussion: In CON, significant relationships of the rectus femoris and tensor fascia latae to knee extension and hip flexion; hamstrings to hip extension and knee flexion; and gastrocnemius and hamstrings to knee rotation were identified. Vastii activation was independent of moment generation, suggesting mono-articular vastii activate to produce compressive forces, essentially bracing the knee, so that bi-articular muscles crossing the hip can generate moments for the purpose of sagittal plane movement. Hip ab/adductor muscles modulate frontal plane moments, while hamstrings and gastrocnemius support the knee against externally applied rotational moments. Compared to CON, ACL had 1) stronger relationships between rectus femoris and knee extension, semitendinosus and knee flexion, and gastrocnemius and knee flexion moments; and 2) weaker relationships between biceps femoris and knee flexion, gastrocnemius and external knee rotation, and gluteus medius and hip abduction moments. Since the knee injury mechanism, is associated with shallow knee flexion angles, valgus alignment and rotation, adaptations after ACL injury are suggested to improve sagittal plane stability, but reduce frontal and rotational plane stability. During muscle pain, EMG-moment relationships of 1) semitendinosus and knee flexor moments were stronger compared to no pain, while 2) rectus femoris and tensor fascia latae to knee extension moments and 3) semitendinosus and lateral gastrocnemius to knee internal rotation moments were reduced. Results support the theory that adaptations to quadriceps pain reduces knee extensor demand to protect the joint and prevent further pain; however, changes in non-painful muscles reduce rotational plane stability. Individual muscle synergies were identified for each moment type: flexion and extension moments were respectively accompanied by dominant hamstring and quadriceps muscle synergies while co-activation was observed in muscle synergies associated with abduction and rotational moments. Effect of muscle pain was not evident on muscle synergies recruited for the force matching task. This may be due to low loading demands and/or a subject-specific redistribution of muscle activation. Similarly, muscle pain did not affect synergy composition in lunging and squatting tasks. Rather, activation of the extensor dominant muscle synergy and knee joint dynamics were reduced, supporting the notion that adaptive response to pain is to reduce the load and risk of further pain and/or injury. Conclusion: This thesis evaluated the interrelationship between muscle activation and internal joint moments and the effect of ACL injury and muscle pain on this relationship. Findings indicate muscle activation is not always dependent on its anatomical orientation as previous works suggest, but rather on its role in maintaining knee joint stability especially in the frontal and transverse loading planes. In tasks that are dominated by sagittal plane loads, hamstring and quadriceps will differentially activate. However, when the knee is required to resist externally applied rotational and abduction loads, strategies of global co-activation were identified. Contributions from muscles crossing the knee for supporting against knee adduction loads were not apparent. Alternatively hip abductors were deemed more important regulators of knee abduction loads. Both muscle pain and ACL groups demonstrated changes in muscle activation that reduced rotational stability. Since frontal plane EMG-moment changes were not present during muscle pain, reduced relationships between hip muscles and abduction moments may be chronic adaptions by ACL that facilitate instability. Findings provide valuable insight into the roles muscles play in maintaining knee joint stability. Rehabilitative/ preventative exercise interventions should focus on neuromuscular training during tasks that elicit rotational and frontal loads (i.e. side cuts, pivoting maneuvers) as well as maintaining hamstring balance, hip abductor and plantarflexor muscle strength in populations with knee pathologies and quadriceps muscle weakness.

Multivariate statistical process control (MSPC) techniques play an important role in industrial batch process monitoring and control. This research illustrates the capabilities and limitations of existing MSPC technologies, with a particular focus on partial least squares (PLS).In modern industry, batch processes often operate over relatively large spaces, with many chemical and physical systems displaying nonlinear performance. However, the linear PLS model cannot predict nonlinear systems, and hence non-linear extensions to PLS may be required. The nonlinear PLS model can be divided into Type I and Type II nonlinear PLS models. In the Type I Nonlinear PLS method, the observed variables are appended with nonlinear transformations. In contrast to the Type I nonlinear PLS method, the Type II nonlinear PLS method assumes a nonlinear relationship within the latent variable structure of the model. Type I and Type II nonlinear multi-way PLS (MPLS) models were applied to predict the endpoint value of the product in a benchmark simulation of a penicillin batch fermentation process. By analysing and comparing linear MPLS, and Type I and Type II nonlinear MPLS models, the advantages and limitations of these methods were identified and summarized. Due to the limitations of Type I and II nonlinear PLS models, in this study, Neural Network PLS (NNPLS) was proposed and applied to predict the final product quality in the batch process. The application of the NNPLS method is presented with comparison to the linear PLS method, and to the Type I and Type II nonlinear PLS methods. Multi-way NNPLS was found to produce the most accurate results, having the added advantage that no a-priori information regarding the order of the dynamics was required. The NNPLS model was also able to identify nonlinear system dynamics in the batch process. Finally, NNPLS was applied to build the controller and the NNPLS method was combined with the endpoint control algorithm. The proposed controller was able to be used to keep the endpoint value of penicillin and biomass concentration at a set-point.