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.

The notion of complementarity is fundamental to economics, as reflected in the large and growing number of studies that invoke alternate conceptions of this idea. Though complementarity has been studied for many years, its connection with theory of supermodularity is far more recent. Taking advantage of these techniques, the first three chapters of this dissertation study aspects of interest in network markets; endogenous information acquisition; and some insights into the comparison of player's equilibrium strategies. The last chapter applies this methodology to econometric identification.Chapter one provides a thorough analysis of oligopolistic markets with positive demand-side network externalities and perfect compatibility. With a general complementarity structure on the model primitives allowing for products with low or high stand-alone values, a nontrivial fulfilled-expectations equilibrium exists. We formalize the concept of industry viability, investigate its determinants, and show that viability is always enhanced by having more firms in the market and/or by technological progress.The second chapter studies covert information acquisition in common value Bayesian games of strategic complementarities. Using the supermodular stochastic order to arrange the structures of information increasingly in terms of preferences, we provide novel, easily interpretable conditions under which the value of information is globally convex, and study the implications in terms of the equilibrium configuration. Our analysis also enlightens the effect of information on players' behavior.Chapter three proposes a simple approach to compare players' equilibrium choices in asymmetric games with strategic complementarities. We offer three applications of our idea to industrial organization and behavioral economics.The last chapter studies (nonparametric) partial identification of treatment response with social interactions. It imposes economically driven monotone conditions to the primitives of the model, i.e., the structural equations, and shows that they imply shape restrictions on the distribution of potential outcomes by means of monotone comparative statics. We propose precise conditions that validate counterfactual predictions in models with multiple equilibria. Under three sets of assumptions, we identify sharp distributional bounds (in terms of stochastic dominance) on the potential outcomes given observable data. We illustrate our results by studying the effect of police per-capita on crime rates in New York state.

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Between the end of the nineteenth and the first half of the twentieth century, criminal anthropology was a very influential theory for criminologists throughout the western world. Proposed by the Italian alienist Cesare Lombroso, its theoretical core centred on the figure of the “criminal man,” a character atavistic instinct forced to live a life of crime. By filling a gap in the literature, this work deals with the historical and sociological circumstances in which criminal anthropology emerged and prospered, and concentrates on the impact Lombroso’s theory had on the development of scientific policing in Italy since the beginning of the twentieth century. A detailed account of the causes that favoured the rise of Lombroso’s scientific police provides an explanation for the appeal criminal anthropology exerted on western political elites. In Italy, the Lombrosian approach left his mark on the development of highly specific forensic tools like fingerprinting, and this had a strong impact on their utilisation by fascist authorities as the account of a famous case of identity fraud occurred in Italy in 1927 revealed. As a result, it is argued that the production of Lombrosian scientific policing was shaped by the wider cultural and social goals of the actors involved, as it is of any other form of knowledge. By choosing to sideline Lombrosian techniques, fascist authorities favoured the exploitation of un-scientific methods of crime prevention that, it is argued, were not perceived as inferior, anachronistic, or unreliable. Such a choice was dictated by specific social goals that favoured the implementation of constitutional anthropology on Lombrosian science of the deviance. Finally, it is suggested that this socio-historical reading of the Italian case could cast more light on the complex relationship between totalitarianism, technology, and forms public surveillance.

The present thesis examined the effects of anger on the completeness and accuracy of eyewitness free and cued recall and recognition performance. Anger was revealed by a recent survey as the emotion experienced by the majority of eyewitnesses to crime, so is particularly important in this context. Previous literature has tended to use generic concepts such as ‘emotion’ or ‘stress’ to investigate emotion effects, but this thesis sought to examine the effect of the specific emotion of anger on memory. Experiment 1 tested theoretical predictions regarding the effects of anger on encoding and retrieval processes. In line with these predictions, angry participants provide more complete descriptions of a perpetrator compared to neutral participants. However, angry participants provide less complete descriptions of the perpetrator’s actions than their neutral counterparts. This pattern of results was replicated throughout all experiments in this thesis. Experiment 2 revealed that anger has no effect on the completeness and accuracy of victim descriptions. Experiment 3 found that the pattern of anger effects observed for a younger adult sample were also found when older adults were tested. This prompted a statistical comparison of younger and older adults which found very few age effects and no interactions between age of the participant, experience of anger and the category of detail recalled. The final experiment thoroughly investigated the effects of anger on participants’ ability to recognise the perpetrator from a photographic lineup. The main findings of this thesis suggest that while angry eyewitnesses may be able to provide a more complete description of the perpetrator, they may be less able to describe what he did, and less able to accurately recognise him from a lineup than neutral eyewitnesses. These findings are discussed in terms of cognitive and meta-cognitive models of encoding and retrieval.

The purpose of this thesis is to provide insight into the potential benefits that Radio Frequency Identification (RFID) technology may provide USSOCOM and other commands in the Global War on Terror. This thesis will explain the basic concept behind RFID, and cite some of the current day applications of today that are revolutionizing the civilian sector. More importantly, this thesis will introduce conceptual security applications that could benefit USSOCOM today, highlighting the possible successes and downfalls that these applications might include. / US Army (USA) author

Master of Science / Department of Mechanical and Nuclear Engineering / Dale Schinstock / Developments in farm related technology have increased the importance of mapping individual plants in the field. An automated mapping system allows the size of these fields to scale up without being hindered by time-intensive, manual surveying. This research focuses on the development of a mapping system which uses geo-located images of the field to automatically locate plants and determine their coordinates. Additionally, this mapping process is capable of differentiating between groupings of plants by using Quick Response (QR) codes. This research applies to green plants that have been grown into seedlings before being planted, known as transplants, and for fields that are planted in nominally straight rows. The development of this mapping system is presented in two stages. First is the design of a robotic platform equipped with a Real Time Kinematic (RTK) receiver that is capable of traversing the field to capture images. Second is the post-processing pipeline which converts the images into a field map. This mapping system was applied to a field at the Land Institute containing approximately 25,000 transplants. The results show the mapped plant locations are accurate to within a few inches, and the use of QR codes is effective for identifying plant groups. These results demonstrate this system is successful in mapping large fields. However, the high overall complexity makes the system restrictive for smaller fields where a simpler solution may be preferable.