The prevalence of batch and batch-like operations, in conjunction with the continued resurgence of artificial intelligence techniques for clustering and classification applications, has increasingly motivated the exploration of the applicability of deep learning for modeling and feedback control of batch and batch-like processes. To this end, the present study seeks to evaluate the viability of artificial intelligence in general, and neural networks in particular, toward process modeling and control via a case study. Nonlinear autoregressive with exogeneous input (NARX) networks are evaluated in comparison with subspace models within the framework of model-based control. A batch polymethyl methacrylate (PMMA) polymerization process is chosen as a simulation test-bed. Subspace-based state-space models and NARX networks identified for the process are first compared for their predictive power. The identified models are then implemented in model predictive control (MPC) to compare the control performance for both modeling approaches. The comparative analysis reveals that the state-space models performed better than NARX networks in predictive power and control performance. Moreover, the NARX networks were found to be less versatile than state-space models in adapting to new process operation. The results of the study indicate that further research is needed before neural networks may become readily applicable for the feedback control of batch processes. / Thesis / Master of Applied Science (MASc)

Present trends in communication links between devices have opted for wireless instead of wired solutions. As a consequence, unlicensed bands have seen a rise in the interference level as more and more devices are introduced into the market place that take advantage of these free bands for their communication needs. Under these conditions, the receiver's ability to recognize and identify the presence of interference becomes increasingly important. In order for the receiver to make an optimal decision on the signal-of-interest, it has to be aware of the type (modulation) of interference as well as how the received signals are affected (channel) by these impediments in order to appropriately mitigate them. This dissertation addresses the blind (unaided) identification of the signal modulations and the channel in a Multiple Input Multiple Output (MIMO) system. The method presented herein takes advantage of the modulation induced periodicities of the signals in the system and uses higher-order cyclostationary statistics to extract the signal and channel unknowns. This method can be used to identify more signals in the system than antenna elements at the receiver (overloaded case). This dissertation presents a system theoretic analysis of the problem as well as describes the development of an algorithm that can be used in the identification of the channel and the modulation of the signals in the system. Linear and non-linear receivers are examined at the beginning of the manuscript in order to review the a priori information that is needed for each receiver configuration to function properly. / Ph. D.

In the Commonwealth of Virginia, gifted education plans are submitted to the state every five years for state approval. The plans must indicate the use of a minimum of four criteria out of the eight criteria provided by the Commonwealth in the identification process. The concept of using multiple criteria stems from research. Research has shown that the criteria used in the identification of gifted students affect the number of identified students as well as the proportions of the underrepresented (Donovan & Cross, 2002). Research has also shown that the use of multiple criteria leads to a higher proportion of underrepresented students identified (Callahan, Hunsaker, Adams, Moore, and Bland, 1995). The purpose of this study was to compare the gifted identification criteria used within the Commonwealth of Virginia's public school divisions and analyze the effects of the criteria on the percentages of underrepresented gifted within the divisions. In this study, the researcher analyzed the numbers of each minority in the total populations against the total gifted minority populations to identify those divisions that were proportional for traditionally underrepresented minorities. All aspects of the gifted identification process for each division were then analyzed. The aspects were then used to compare the proportional divisions to the non-proportional divisions for commonalities in the identification process. Findings revealed that there were no divisions with reported minorities that were proportional in all traditionally underrepresented ethnicities. In addition, no one specific standardized measure was successfully used in identifying non-traditionally gifted minorities in all ethnic groups. The implication that can be drawn from this research is that despite all attempts to put research into practice by using multiple criteria in the identification of the gifted, there is no one criterion that ensures the proportional identification of underrepresented minorities. / Ph. D.

Identification of thermoacoustic dynamics that exhibit limit cycle behavior is needed to gain a better intuitive feel of the system, to design complex control strategies, and to validate modeling efforts. Limit cycle oscillations arise in thermoacoustic systems due to the coupling between a nonlinear heat release process and the acoustic dynamics of the combustor. This response arises in lean premixed gaseous power generating turbines and is a concern due to the detrimental effect of the pressure oscillations on the structural integrity of the combustor. Due to the volatile environment intrinsic in the combustor, multiple sensing apparatuses are not available. Therefore, in the current study, an identification approach is assessed considering only a single output from the thermoacoustic system. As a means to further investigate the thermoacoustic limit cycle behavior, a scaled version of the industry-based turbine was constructed. By anchoring a flame halfway from end-to-end of a closed-open tube, a similar nonlinear response is achieved. A harmonic balance technique that linearly incorporates the nonlinearity is developed which uses frequency entrainment to offer sufficient information for the identification. Its validity is assessed on a model, which is based on known dynamics of the thermoacoustic system. The structure of the identification algorithm is based on a two-mode acoustic model with both dynamics and nonlinearity in the feedback loop. The limitations of using only a two-mode identification structure for a system with more than two modes is discussed as well as future efforts that may alleviate this problem. / Master of Science

Master of Science / Department of Statistics / John E. Boyer Jr / In order to impose fair taxes on properties, it is required that appraisers annually estimate prices of all the properties in each of the counties in Kansas. The Department of Revenue of Kansas oversees the quality of work of appraisers in each county. The Department of Revenue uses ratio data which is appraisal price divided by sale price for those parcels which are sold during the year as a basis for evaluating the work of the appraisers. They know that there are outliers in these ratio data sets and these outliers can impact their evaluations of the county appraisers. The Department of Revenue has been using a simple box plot procedure to identify outliers for the previous 10 years. Staff members have questioned whether there might be a need for improvement in the procedure. They considered the possibility of tuning the procedure to depend on distributions and sample sizes. The methodology as a possible solution was suggested by Iglewicz et al. (2007). In this report, we examine the new methodology and attempt to apply it to ratio data sets provided by the Department of Revenue.

Digitized by Kansas State University Libraries

Call number: LD2668 .T4 1937 D41

Le Campylobacter est le plus important agent d'entérites bactériennes dans les pays industrialisés et en voie de développement. Des études récentes suggèrent que l'eau non-traitée est une source sous-estimée d'infections sporadiques chez l'humain. Dans le cadre du volet environnemental du projet CampyloGIS, mon projet principal consistait en l'étude de la prévalence et de la quantité du Campylobacter retrouvé dans les eaux environnementales de l'Estrie. Trente-deux sites d'échantillonnage d'eau ont été sélectionnés dans les 7 MRC de l'Estrie pour être échantillonnés hebdomadairement du 17 juillet 2005 au 08 juillet 2007. Globalement, 1071/2481 (43%), 1481/2471 (60%) et 1463/2471 (59%) échantillons d'eau étaient respectivement positifs pour Campylobacter spp., les coliformes thermotolérants et E. coli . Il y avait une faible corrélation entre la prévalence hebdomadaire du Campylobacter spp. et des coliformes thermotolérants (rho de Spearman = 0,27; P = 0,008) et entre la quantité de ces deux microorganismes (tau-b de Kendall = 0,233; P < 0,0001). Également, plus de 150 échantillons d'eau de puits privés situés en Estrie ont été gracieusement analysés au cours de ce projet. Cinq échantillons d'eau de puits de surface sur 53 étaient positifs pour C. jejuni et seulement deux d'entre eux étaient aussi positifs pour les coliformes thermotolérants. Ces résultats suggèrent que les indicateurs de pollution fécale, comme les coliformes thermotolérants, ne sont pas suffisants pour correctement évaluer la présence et/ou la quantité du Campylobacter dans l'eau environnementale. Mon deuxième projet consistait en le développement d'une approche moléculaire d'identification à l'espèce de C. jejuni, C. coli, C. lari, C. upsaliensis et C. fetus basée sur la PCR hippurate, la PCR 16S et la PCR-RFLP. Présentement, l'approche moléculaire permettrait d'éviter la mauvaise identification de 526/1950 isolats (27%) préalablement mal identifiés par les tests biochimiques seuls. Jusqu'à présent, l'approche moléculaire ne permettrait toutefois pas d'identifier correctement à l'espèce 35/1950 isolats (1,8%) de Campylobacter. Cette approche moléculaire a permis de confirmer la nécessité d'utiliser plus d'une technique afin de s'assurer de l'identification à l'espèce adéquate du Campylobacter. Finalement, la présence soutenue et la forte concentration occasionnelle du Campylobacter détecté dans les eaux environnementales de l'Estrie, ont porté à se questionner sur l'utilité de développer une méthode efficace de détection et de quantification du Campylobacter dans l'eau. De cette réflexion et des difficultés d'exécution qu'entraîne l'utilisation de la méthode MPN comme outil quantitatif, est né mon troisième projet, soit un protocole de RT-PCR semi-quantitative précédée d'un enrichissement pour la quantification de C. jejuni, C. coli et C. lari dans l'eau. Ce projet a permis de constater qu'un enrichissement de 16 h permettait d'augmenter, par un facteur relativement constant, le nombre de C. jejuni contenu dans un échantillon d'eau naturellement contaminé et ce, peut importe la condition cellulaire initiale occasionnée par un séjour dans l'eau. De plus, les résultats obtenus via le protocole de RT-PCR semi-quantitative sont comparables à la quantification obtenue via la méthode MPN (rho de Spearman = 0,84; P < 0,001), ce qui permet de croire que ce protocole simple pourrait éventuellement être un choix envisageable pour la détection et la quantification du Campylobacter dans l'eau.

Thesis (MTh)--Stellenbosch University, 2015. / ENGLISH ABSTRACT: Social media are technological media (websites) that create a digital environment for networking between network users who interface and share information with each other. Social media network users use these media on different platforms, such as social networking sites – to share information and network with offline and online friends; wikis – to share, modify, create and disseminate information; and blogs – to create content and interface with network followers. These three platforms of social media disseminate information that influences the self-identity formation of young people. The self-identity formation of young people is both discovered and developed. The discovery of self-identity entails an understanding of humanity as God’s created being – created for His purpose and mission. The developed identity entails that young people go through different phases of life that shape their self-identity formation. These different phases are influenced by different social institutions such as social media. Social media as a source of information dissemination influence young people’s self-identity by creating a digital continent where all sorts of uncensored information is disseminated. This digital continent is used for whatsoever purpose. This fact – that social media create a complex digital continent that influences self-identity formation – is what led to this research study to investigate how do social media influence the self-identity formation of young people? To investigate this research question, this research employed conceptual analysis to give conceptual clarity of words and concept. In situating this research study within the field of practical theology, Osmer’s theological framework was adopted to understudy the research title, social media as a source of self-identity formation: Challenges and opportunities for youth ministry. Youth ministry guides young people to discover their self-identity in God and equips them to influence their friends (offline and online friends) and fellow network users. Social media as a digital continent also creates platforms for youth ministry to incarnate and influence young people’s self-identity by disseminating biblical and theological information that has the potential to help young people create a healthy self-identity formation. / AFRIKAANSE OPSOMMING: Sosiale media is tegnologiese media (webblaaie) wat ’n digitale omgewing skep vir netwerking tussen netwerkgebruikers wat ’n koppelvlak skep vir hulle om met mekaar inligting te deel. Sosiale netwerkgebruikers gebruik hierdie media op verskillende platforms, soos sosiale netwerkingsruimtes – om met aflyn- en aanlynvriende inligting te deel en met hulle te netwerk; wiki’s – om inligting te deel, te modifiseer, te skep en te versprei; en webjoernale (blogs) – om inhoud te skep en netwerkvolgers van ’n koppelvlak te voorsien. Hierdie drie sosiale media platforms versprei inligting wat die selfidentiteitsvorming van jong mense beïnvloed. Die selfidentiteit van jong mense word beide ontdek en ontwikkel. Die ontdekking van selfidentiteit behels ’n begrip van mensheid as God se geskape wese – geskep vir Sy doel en missie. Die ontwikkelde identiteit behels dat jong mense deur verskillende lewensfases gaan wat hulle selfidentiteitsvorming vorm. Hierdie verskillende fases word deur verskillende maatskaplike instellings, soos sosiale media, beïnvloed. Die sosiale media as ’n bron van inligtingsverspreiding beïnvloed jong mense se selfidentiteit deur ’n digitale vasteland te skep waar ’n groot verskeidenheid van ongesensureerde inligting versprei word. Hierdie digitale vasteland word vir wat ook al doel gebruik. Hierdie feit – dat sosiale media ’n digitale vasteland vir invloed skep – is wat tot hierdie navorsing gelei het, naamlik om ondersoek in te stel na hoe jeugbediening jong mense se selfidentiteitsvorming op sosiale media as ’n bron van selfidentiteitsvorming: Uitdagings en geleenthede vir die jeugbediening. Die jeugbediening lei jong mense in die ontdekking van hulle selfidentiteit in God en rus hulle toe om hulle vriende (aflyn- en aanlynvriende) en medenetwerkgebruikers te beïnvloed. Die sosiale media as ’n digitale vasteland skep ook ’n platform vir die jeugbediening om jong mense se selfidentiteit te inkarneer en te beïnvloed deur Bybelse en teologiese inligting te versprei wat die potensiaal het om jong mense by te staan om ’n gesonde selfidentiteit te skep.

Living cells and their components play a key role within biotechnology industry. Cell cultures and their products of interest are used for the design of vaccines as well as in the agro-alimentary field. In order to ensure optimal working of such bioprocesses, the understanding of the complex mechanisms which rule them is fundamental. Mathematical models may be helpful to grasp the biological phenomena which intervene in a bioprocess. Moreover, they allow prediction of system behaviour and are frequently used within engineering tools to ensure, for instance, product quality and reproducibility. Mathematical models of cell cultures may come in various shapes and be phrased with varying degrees of mathematical formalism. Typically, three main model classes are available to describe the nonlinear dynamic behaviour of such biological systems. They consist of macroscopic models which only describe the main phenomena appearing in a culture. Indeed, a high model complexity may lead to long numerical computation time incompatible with engineering tools like software sensors or controllers. The first model class is composed of the first principles or white box models. They consist of the system of mass balances for the main species (biomass, substrates, and products of interest) involved in a reaction scheme, i.e. a set of irreversible reactions which represent the main biological phenomena occurring in the considered culture. Whereas transport phenomena inside and outside the cell culture are often well known, the reaction scheme and associated kinetics are usually a priori unknown, and require special care for their modelling and identification. The second kind of commonly used models belongs to black box modelling. Black boxes consider the system to be modelled in terms of its input and output characteristics. They consist of mathematical function combinations which do not allow any physical interpretation. They are usually used when no a priori information about the system is available. Finally, hybrid or grey box modelling combines the principles of white and black box models. Typically, a hybrid model uses the available prior knowledge while the reaction scheme and/or the kinetics are replaced by a black box, an Artificial Neural Network for instance. Among these numerous models, which one has to be used to obtain the best possible representation of a bioprocess? We attempt to answer this question in the first part of this work. On the basis of two simulated bioprocesses and a real experimental one, two model kinds are analysed. First principles models whose reaction scheme and kinetics can be determined thanks to systematic procedures are compared with hybrid model structures where neural networks are used to describe the kinetics or the whole reaction term (i.e. kinetics and reaction scheme). The most common artificial neural networks, the MultiLayer Perceptron and the Radial Basis Function network, are tested. In this work, pure black box modelling is however not considered. Indeed, numerous papers already compare different neural networks with hybrid models. The results of these previous studies converge to the same conclusion: hybrid models, which combine the available prior knowledge with the neural network nonlinear mapping capabilities, provide better results. From this model comparison and the fact that a physical kinetic model structure may be viewed as a combination of basis functions such as a neural network, kinetic model structures allowing biological interpretation should be preferred. This is why the second part of this work is dedicated to the improvement of the general kinetic model structure used in the previous study. Indeed, in spite of its good performance (largely due to the associated systematic identification procedure), this kinetic model which represents activation and/or inhibition effects by every culture component suffers from some limitations: it does not explicitely address saturation by a culture component. The structure models this kind of behaviour by an inhibition which compensates a strong activation. Note that the generalization of this kinetic model is a challenging task as physical interpretation has to be improved while a systematic identification procedure has to be maintained. The last part of this work is devoted to another kind of biological systems: proteins. Such macromolecules, which are essential parts of all living organisms and consist of combinations of only 20 different basis molecules called amino acids, are currently used in the industrial world. In order to allow their functioning in non-physiological conditions, industrials are open to modify protein amino acid sequence. However, substitutions of an amino acid by another involve thermodynamic stability changes which may lead to the loss of the biological protein functionality. Among several theoretical methods predicting stability changes caused by mutations, the PoPMuSiC (Prediction Of Proteins Mutations Stability Changes) program has been developed within the Genomic and Structural Bioinformatics Group of the Université Libre de Bruxelles. This software allows to predict, in silico, changes in thermodynamic stability of a given protein under all possible single-site mutations, either in the whole sequence or in a region specified by the user. However, PoPMuSiC suffers from limitations and should be improved thanks to recently developed techniques of protein stability evaluation like the statistical mean force potentials of Dehouck et al. (2006). Our work proposes to enhance the performances of PoPMuSiC by the combination of the new energy functions of Dehouck et al. (2006) and the well known artificial neural networks, MultiLayer Perceptron or Radial Basis Function network. This time, we attempt to obtain models physically interpretable thanks to an appropriate use of the neural networks.