In etwa 2-10% aller Krebsleiden findet man eine Metastase vor bei unbekanntem Primärtumor, der mit der üblichen Diagnostik nicht bestimmt werden kann. Meist handelt es sich hierbei um ein metastasierendes Adenokarzinom. Als Hilfsmittel bei der immunhistochemischen Differenzierung solcher Metastasen ist ein statistisches Verfahren entwickelt worden. Dabei können Wahrscheinlichkeitsangaben für die mögliche Organlokalisation des Primärtumors auf der Grundlage von immunhistologischen Färbeergebnissen mit 7 verschiedenen Tumormarkern (CEA, CK7, CK20, ER, GCDFP-15, Surfactant A, Vimentin) geliefert werden. Das histologische Untersuchungsmaterial umfaßte 313 Adenokarzinommetastasen mit bekanntem Primärtumor in Mamma, Ovar, Lunge, Niere, Kolon, Magen und Pankreas. Unter der Annahme einer ausreichenden Diagnosesicherheit bei einer Zuordnungswahrscheinlichkeit von >=90% konnten mit Hilfe des Verfahrens 46% der Metastasen ihrem Primärtumor zugeordnet werden. Die Methode erreicht dabei eine Spezifität von 95%. Mamma- und Lungenadenokarzinommetastasen wurden vor allem aufgrund des positiven Färbeergebnisses für ihren spezifischen Marker GCDFP-15 bzw. Surfactant A differenziert. Da die Diagnose hierbei unabhängig von den übrigen Ergebnissen gestellt werden konnte, wird die Anwendung des Verfahrens besonders bedeutsam für Metastasen, die ihren organspezifischen Marker nicht exprimieren, bzw. für Kolon-, Nieren- und Ovarialkarzinommetastasen, die zum Teil charakteristische Markerspektren aufweisen und damit recht gut zu differenzieren waren. Eine Differenzierung von Magen- und Pankreaskarzinommetastasen war dagegen aufgrund der sehr ähnlichen Markerprofile nicht möglich. Das vorgestellte Programm läßt sich nicht nur als Diagnosehilfe für den Immunhistologen nutzen, sondern läßt auch eine bessere Beurteilung der diagnostischen Wertigkeit verwendeter Markerkombinationen zu, so dass evtl. eine effektivere Antikörper-Auswahl getroffen werden kann. / Cancer of unknown primary origin is a common clinical syndrome and accounts for 2-10% of all cancer diagnoses. Most of these cases are related to a metastatic adenocarcinoma. We have developed and tested a statistical method which can serve as a diagnostic tool for the immunhistochemical differentiation of such metastases. Based on the different expression patterns of 7 tumor markers (CEA, CK 7, CK20, GCDFP-15, Vimentin, Surfactant A), the probability for a certain primary cancer site is calculated. To test the method, 313 metastases of adenocarcinoma with primary sites in the breast, the ovary, the kidney, the colon, the stomach, the pancreas and the lung were examined. Taking the diagnosis to be reliable if the method identifies a certain cancer site with a probability of 90 %, we find that we could differentiate 46 % of the metastases with a specificity of 95 %. Metastases of breast and lung carcinoma were identified mainly based on the expression of their specific markers Surfactant A and GCDFP-15. The procedure becomes especially useful, if a specific marker does not exist, or if the staining result is negative. Metastases of carcinoma from the kidney, the colon and the ovary could be differentiated, because they often exhibit specific expression patterns. In contrast, metastases from stomach and pancreas carcinoma have very similar immunhistochemical properties. Here, a differentiation was not possible. The suggested method can help immunhistochemists not only in the diagnosis, but also to estimate the diagnostic value of certain combinations of tumor markers.

<p>This thesis is concerned with detection of transient signal families and detectors in nonlinear static sensor systems. The detection problems are treated within the framework of likelihood ratio based binary hypothesis testing.</p><p>An analytical solution to the noncoherent detection problem is derived, which in contrast to the classical noncoherent detector, is optimal for wideband signals. An optimal detector for multiple transient signals with unknown arrival times is also derived and shown to yield higher detection performance compared to the classical approach based on the generalized likelihood ratio test.</p><p>An application that is treated in some detail is that of ultrasonic nondestructive testing, particularly pulse-echo detection of defects in elastic solids. The defect detection problem is cast as a composite hypothesis test and a methodology, based on physical models, for designing statistically optimal detectors for cracks in elastic solids is presented. Detectors for defects with low computational complexity are also formulated based on a simple phenomenological model of the defect echoes. The performance of these detectors are compared with the physical model-based optimal detector and is shown to yield moderate performance degradation.</p><p>Various aspects of optimal detection in static nonlinear sensor systems are also treated, in particular the stochastic resonance (SR) phenomenon which, in this context, implies noise enhanced detectability. Traditionally, SR has been quantified by means of the signal-to-noise ratio (SNR) and interpreted as an increase of a system's information processing capability. Instead of the SNR, rigorous information theoretic distance measures, which truly can support the claim of noise enhanced information processing capability, are proposed as quantifiers for SR. Optimal detectors are formulated for two static nonlinear sensor systems and shown to exhibit noise enhanced detectability.</p>

This thesis is concerned with detection of transient signal families and detectors in nonlinear static sensor systems. The detection problems are treated within the framework of likelihood ratio based binary hypothesis testing. An analytical solution to the noncoherent detection problem is derived, which in contrast to the classical noncoherent detector, is optimal for wideband signals. An optimal detector for multiple transient signals with unknown arrival times is also derived and shown to yield higher detection performance compared to the classical approach based on the generalized likelihood ratio test. An application that is treated in some detail is that of ultrasonic nondestructive testing, particularly pulse-echo detection of defects in elastic solids. The defect detection problem is cast as a composite hypothesis test and a methodology, based on physical models, for designing statistically optimal detectors for cracks in elastic solids is presented. Detectors for defects with low computational complexity are also formulated based on a simple phenomenological model of the defect echoes. The performance of these detectors are compared with the physical model-based optimal detector and is shown to yield moderate performance degradation. Various aspects of optimal detection in static nonlinear sensor systems are also treated, in particular the stochastic resonance (SR) phenomenon which, in this context, implies noise enhanced detectability. Traditionally, SR has been quantified by means of the signal-to-noise ratio (SNR) and interpreted as an increase of a system's information processing capability. Instead of the SNR, rigorous information theoretic distance measures, which truly can support the claim of noise enhanced information processing capability, are proposed as quantifiers for SR. Optimal detectors are formulated for two static nonlinear sensor systems and shown to exhibit noise enhanced detectability.

Algebraic equations and functions play an important role in various mathematical topics, including algebra, trigonometry, linear programming and calculus. Accordingly, various documents, such as the most recent Swedish curriculum (Lpf 94) for upper secondary school and the course syllabi in mathematics, specify what the students should learn in Mathematics Course B. They should be able to solve quadratic equations and apply this knowledge in solving problems, explain the properties of a function, as well as be able to set up, interpret and use some nonlinear functions as models for real processes. To implement these recommendations, it is crucial to understand the students’ way of experiencing quadratic equations and functions, and describe the meaning these have for the students in relation to the possibility they have to their experience of them. The aim of this thesis is to analyse, understand and explain the relation between the handled and learned content, which consists of second-degree equations and quadratic functions, in classroom practice. This means that content is the research object and not the teacher’s conceptions or knowledge of, or about this content. This restriction implies that the handled and learned contents are central in this study and will be analysed from different perspectives. The study includes two teachers and 45 students in two different classes. The data consist of video-recordings of lessons, individual sessions, interviews and the teachers’/researcher’s review of the individual sessions. The students’ tests also constituted an important part of the data collection. When analysing the data, concepts relating to variation theory have been used as analytical tools. Data have been analysed in respect of the teachers’ focus on the lesson content, which aspects are ignored and which patterns of dimensions of variations are constituted when the contents are handled by the teachers in the classroom. Also, data have been analysed in respect of the students’ focus when they solve different exercises in a test situation. It can be shown that the meaning of parameters, the unknown quantity in an equation and the function’s argument change several times when the teacher presents the content in the classroom and when the students solve different exercises. It can also be shown that the teachers and the students develop complicated patterns of variation during the lessons and that the ways in which the teachers open up dimensions of variation play an important role in the learning process. The results indicate that there is a convergent variation leading the students to improve their learning. By focusing on some aspects of the objects of learning and create convergent variations, it is possible for the students to understand the difference between various interpretations of these aspects and thereafter focus on the interpretation that fits in a certain context. Furthermore, this variation leads the students to make generalisations in each object of learning (equations and functions) and between these objects of learning. These generalisations remain over time, despite working with new objects of learning. An important result in this study is that the implicit or explicit arguments of a function can make it possible to discern an equation from a function despite the fact that they are constituted by the same algebraic expression. / Ekvationer och funktioner har en viktig roll i olika matematiska moment, som exempelvis algebra, trigonometri, programmering och analys. Under gymnasiets matematikkurs B förväntas det att eleverna ska lära sig lösa andragradsekvationer och vad som kännetecknar en funktion samt att de ska kunna tolka och använda en andragradsfunktion. Trots det ökade intresset för medborgare med djupare matematiska kunskaper redovisas ständigt larmrapporter från landets tekniska högskolor och universitet om allt sämre matematikkunskaper hos de nyantagna studenterna. För att förstå elevernas problem med och i matematik behövs ökad kunskap om elevernas lärande i relation till vad det är i innehållet som behandlas i klassrummet. Syftet med denna studie är att analysera, söka förstå och förklara relationen mellan vad som framställs i matematiskt innehåll rörande andragradsekvationer och andragradsfunktioner i klassrumspraktiken och elevernas lärande av detsamma. Fokus ligger på relationen mellan det framställda och det lärda innehållet och inte på att analysera lärarnas uppfattningar eller deras kunskap i ämnet. Denna begränsning innebär att det är innehållet som är det centrala i min studie och som kommer att analyseras ur olika perspektiv. 45 elever och två lärare deltog i undersökningen. Data består av videoinspelade lektioner, lärarnas individuella genomgång, sekvenser när lärarna tillsammans med mig tittade på och diskuterade den individuella genomgången samt intervjuer med eleverna. Elevernas prov utgör en viktig del i samlandet av data. Det variationsteoretiska perspektivet ger mig teoretiska begrepp som fungerar som analysverktyg för att tolka det empiriska materialet i min studie. Tillämpningen av variationsteori har gjort det möjligt att analysera lärandet ur två perspektiv, nämligen vad som erbjuds och vad som erfars i ett innehåll. I det erbjudna lärandeobjektet har lärarnas undervisningshandlingar analyserats som uttryck för de aspekter, delar och helheter som eleverna erbjuds att urskilja samt deras relation till varandra. Det framställda innehållet i läromedlet har analyserats utifrån samma princip, det vill säga genom att identifiera fokuserade aspekter, delar och helheter samt deras relation till varandra. Därefter har analysen fokuserat på att identifiera de variationer som öppnas upp eller begränsas i lärarens och läromedlets framställning av objekten för lärande. På så sätt kunde de aspekter som är möjliga att urskilja utifrån framställningen av lärandeobjekten identifieras och relateras till mönster av variation. Elevernas erfarande har studerats som uttryck för de aspekter, delar och helheter som urskiljs när de löser olika uppgifter samt hur dessa aspekter relateras till varandra. De aspekter som blir urskiljda och sättet på vilket detta görs, har gjort det möjligt att identifiera vilka aspekter som är kritiska för elevernas lärande. Resultaten visar att komplexa dimensioner av variation öppnas upp i det innehåll som eleverna erbjuds. Det förefaller vara vad som här kallas för konvergenta variationer som leder till ett mer fullständigt lärande. Det är denna variation som gör det möjligt för eleverna att göra generaliseringar inom varje objekt för lärande (ekvationer och funktioner) och mellan dessa lärande objekt. Dessa generaliseringar kvarstår, trots att man arbetar med nya lärandeobjekt. Dessutom kan det konstateras att parametrar, den obekanta storheten i en ekvation och funktionens argument är kritiska aspekter i elevens lärande och att meningen med dem ändras flera gånger när lärare presenterar innehållet i klassrummet och när eleverna löser olika uppgifter. Vidare demonstreras att huruvida funktionens argument framträder i explicit eller implicit form kan ha avgörande betydelse för om läraren i sin framställning av lärandeobjekten och elever i sitt erfarande av dem skiljer eller inte skiljer en funktion från en ekvation.

A solution to the vehicle state estimation problem is given using the Kalman filtering and the Particle filtering theories. Vehicle states are necessary for an active or a semi-active suspension control system, which is intended to enhance ride comfort, road handling and stability of the vehicle. Due to a lack of information on road disturbances, conventional estimation techniques fail to provide accurate estimates of all the required states. The proposed estimation algorithm, named Supervisory Kalman Filter (SKF), consists of a Kalman filter with an extra update step which is inspired by the particle filtering technique. The extra step, called a supervisory layer, operates on the portion of the state vector that cannot be estimated by the Kalman filter. First, it produces N randomly generated state vectors, the particles, which are distributed based on the Kalman filter’s last updated estimate. Then, a resampling stage is implemented to collect the particles with higher probability. The effectiveness of the SKF is demonstrated by comparing its estimation results with that of the Kalman filter and the particle filter when a test vehicle is passing over a bump. The estimation results confirm that the SKF precisely estimates those states of the vehicle that cannot be estimated by either the Kalman filter or the particle filter, without any direct measurement of the road disturbance inputs. Once the vehicle states are provided, a suspension control law, the Skyhook strategy, processes the current states and adjusts the damping forces accordingly to provide a better and safer ride for the vehicle passengers. This thesis presents a novel systematic and practical methodology for the design and implementation of the Skyhook control strategy for vehicle’s semi-active suspension systems. Typically, the semi-active control strategies (including the Skyhook strategy) have switching natures. This makes the design process difficult and highly dependent on extensive trial and error. The proposed methodology maps the discontinuous control system model to a continuous linear region, where all the time/frequency design techniques, established in the conventional control system theory, can be applied. If the semiactive control law is designed to satisfy ride and stability requirements, an inverse mapping offers the ultimate control law. The effectiveness of the proposed methodology in the design of a semi-active suspension control system for a Cadillac SRX 2005 is demonstrated by real-time road tests. The road tests results verify that the use of the newly developed systematic design methodology reduces the required time and effort in real industrial problems.

A solution to the vehicle state estimation problem is given using the Kalman filtering and the Particle filtering theories. Vehicle states are necessary for an active or a semi-active suspension control system, which is intended to enhance ride comfort, road handling and stability of the vehicle. Due to a lack of information on road disturbances, conventional estimation techniques fail to provide accurate estimates of all the required states. The proposed estimation algorithm, named Supervisory Kalman Filter (SKF), consists of a Kalman filter with an extra update step which is inspired by the particle filtering technique. The extra step, called a supervisory layer, operates on the portion of the state vector that cannot be estimated by the Kalman filter. First, it produces N randomly generated state vectors, the particles, which are distributed based on the Kalman filter’s last updated estimate. Then, a resampling stage is implemented to collect the particles with higher probability. The effectiveness of the SKF is demonstrated by comparing its estimation results with that of the Kalman filter and the particle filter when a test vehicle is passing over a bump. The estimation results confirm that the SKF precisely estimates those states of the vehicle that cannot be estimated by either the Kalman filter or the particle filter, without any direct measurement of the road disturbance inputs. Once the vehicle states are provided, a suspension control law, the Skyhook strategy, processes the current states and adjusts the damping forces accordingly to provide a better and safer ride for the vehicle passengers. This thesis presents a novel systematic and practical methodology for the design and implementation of the Skyhook control strategy for vehicle’s semi-active suspension systems. Typically, the semi-active control strategies (including the Skyhook strategy) have switching natures. This makes the design process difficult and highly dependent on extensive trial and error. The proposed methodology maps the discontinuous control system model to a continuous linear region, where all the time/frequency design techniques, established in the conventional control system theory, can be applied. If the semiactive control law is designed to satisfy ride and stability requirements, an inverse mapping offers the ultimate control law. The effectiveness of the proposed methodology in the design of a semi-active suspension control system for a Cadillac SRX 2005 is demonstrated by real-time road tests. The road tests results verify that the use of the newly developed systematic design methodology reduces the required time and effort in real industrial problems.

Across the globe, people are everyday audiences of advertising images, which have become integrated in our life worlds. Advertising images are entangled with interesting moral conflicts. This study analyses the decision-processes of advertising self-regulators, who are in the midst of such moral conflicts, with the purpose of showing how and why they decide if advertising images are acceptable or not. Two organizations based in different countries are included in the study; The Advertising Standards Authority (ASA) in the United Kingdom and The Trade Ethical Council against Sexism in Advertising (ERK) in Sweden. The empirical material consists of interviews with 38 people, images and text documents, from the two mentioned self-regulatory bodies, and some (participant) observation. The study focuses on cases of potentially offensive advertisements. The material is primarily analysed using the theory of worlds of worth, developed by Luc Boltanski and Laurent Thévenot. The thesis argues that advertising self-regulation is about ascertaining, and making compromises between, conventions of morality. The study demonstrates the pattern of how the contextual circumstances influence the moral decisions that are made. It is shown that a decisive feature of the decisions is to conceptualize the general public in a justified way. This means that decision-makers picture the public as types of people who hold one or a combination of moral logics, and assume that they use these to interpret and evaluate advertising images. How these publics are defined depends on how the settings of the different advertising images are collectively interpreted by the decision-makers. The thesis argues more generally that to understand people’s values we must look at conflict situations in which current morals surface, such as the ways they appear in relation to advertising images.

No description available.

This study presents different aspects on the use of deterministic methods including Artificial Neural Networks (ANNs), and linear and nonlinear regression, as well as probabilistic methods including Bayesian inference and Monte Carlo methods to develop reliable solutions for challenging problems in geotechnics. This study addresses the theoretical and computational advantages and limitations of these methods in application to: 1) prediction of the stiffness and strength of stabilized organic soils, 2) determination of unknown foundations for bridges vulnerable to scour, and 3) uncertainty quantification for one-dimensional diffusion processes. ANNs were successfully implemented in this study to develop nonlinear models for the mechanical properties of stabilized organic soils. ANN models were able to learn from the training examples and then generalize the trend to make predictions for the stiffness and strength of stabilized organic soils. A stepwise parameter selection and a sensitivity analysis method were implemented to identify the most relevant factors for the prediction of the stiffness and strength. Also, the variations of the stiffness and strength with respect to each factor were investigated. A deterministic and a probabilistic approach were proposed to evaluate the characteristics of unknown foundations of bridges subjected to scour. The proposed methods were successfully implemented and validated by collecting data for bridges in the Bryan District. ANN models were developed and trained using the database of bridges to predict the foundation type and embedment depth. The probabilistic Bayesian approach generated probability distributions for the foundation and soil characteristics and was able to capture the uncertainty in the predictions. The parametric and numerical uncertainties in the one-dimensional diffusion process were evaluated under varying observation conditions. The inverse problem was solved using Bayesian inference formulated by both the analytical and numerical solutions of the ordinary differential equation of diffusion. The numerical uncertainty was evaluated by comparing the mean and standard deviation of the posterior realizations of the process corresponding to the analytical and numerical solutions of the forward problem. It was shown that higher correlation in the structure of the observations increased both parametric and numerical uncertainties, whereas increasing the number of data dramatically decreased the uncertainties in the diffusion process.

Mémoire numérisé par la Division de la gestion de documents et des archives de l'Université de Montréal