Specific recognition of ligands including metal ions by proteins is the key of many crucial biological functions and systems. Accurate prediction of the binding strength not only sheds light on the mechanism of the molecular recognition but also provides the most important prerequisite of drug discovery. Computational modeling of molecular binding has gained a great deal of attentions in the last few decades since the advancement of computer power and availability of high-resolution crystal structures. However there still exist two major challenges in the field of molecular modeling, i.e. sampling issue and accuracy of the models. In this work, I have dedicated to tackling these two problems with a noval polarizable force field which is believed to produce more accurate description of molecular interactions than classic non-polarizable force fields. We first developed the model for divalent cations Mg²⁺ and Ca²⁺, deriving the parameters from quantum mechanics. To understand the hydration thermodynamics of these ions we have performed molecular dynamics simulations using our AMOEBA force field. Both the water structures around ions and the solvation free energies were in great accordance with experiment data. We have also simulated and calculated the binding free energies of a series of benzamidine-like inhibitors to trypsin using explicit solvent approach by free energy perturbation. The calculated binding free energies are well within the accuracy of experimental measurement and the direction of change is predicted correctly in all cases. Finally, we computed the hydration free energies of a few organic molecules and automated the calculation procedure. / text

Purpose:This thesis is examining two of the most common valuation methods put into practice on firms of different sizes in order to see if the market capitalization has any impact on said valuations. Relevance: Despite the widespread use of the intrinsic valuation methods both in academia and the professional world the amount of coverage concerning real life usage and analysis seems to be somewhat lacking. The numerous studies that cover the pros and cons of different valuation models and their supposed accuracy towards current stock prices. The studies rarely try to analyze whether or not the invisible hand of the market treats the firms differently depending on the market capitalization. Method: In this thesis the Free Cash Flow to Equity and Dividend Discount Model have been applied to 10 different firms of different sizes. The 10 firms were from a market capitalization perspective viewed as  5 “large”  and 5 “small”. For comparison matter, for each of the “large”  firms there was one corresponding “small” firm that operates in a similar line of business. The future growth projections were based on historical data and for the discount rate the Capital Asset Pricing Model (CAPM) was used. Conclusion: The two valuation models showed remarkably similar results, even when applied to firms of greatly different market capitalizations. Within the constraints and delimitations of this thesis, the conclusion is that according to Free Cash Flow to Equity model and Dividend Discount Model models the market does not value the firms differently with regards to market capitalization. In fact the divergencies in terms of absolute numbers of the valuations as a whole only show a 1% percentage unit difference in the Dividend Discount Model and a 2% percentage unit of difference in the Free Cash Flow to Equity model between the large and small cap segments.

The human cognitive biases that result in anthropomorphism, the moral confusion surrounding the status of robots, and wider societal concerns related to the deployment of artificial intelligence at scale all motivate the study of robot transparency --- the design of robots such that they may be fully understood by humans. Based on the hypothesis that robot transparency leads to better (in the sense of more accurate) mental models of robots, I investigate how humans perceive and understand a robot when they encounter it, both in online video and direct physical encounter. I also use Amazon Mechanical Turk as a platform to facilitate online experiments with larger population samples. To improve transparency I use a visual real-time transparency tool providing a graphical representation of the internal processing and state of a robot. I also describe and deploy a vocalisation algorithm for transparency. Finally, I modify the form of the robot with a simple bee-like cover, to investigate the effect of appearance on transparency. I find that the addition of a visual or vocalised representation of the internal processing and state of a robot significantly improves the ability of a naive observer to form an accurate model of a robot's capabilities, intentions and purpose. This is a significant result across a diverse, international population sample and provides a robust result about humans in general, rather than one geographic, ethnic or socio-economic group in particular. However, all the experiments were unable to achieve a Mental Model Accuracy (MMA) of more than 59%, indicating that despite improved transparency of the internal state and processing, naive observers' models remain inaccurate, and there is scope for further work. A vocalising, or 'talking', robot greatly increases the confidence of naive observers to report that they understand a robot's behaviour when observed on video. Perhaps we might be more easily deceived by talking robots than silent ones. A zoomorphic robot is perceived as more intelligent and more likeable than a very similar mechanomorphic robot, even when the robots exhibit almost identical behaviour. A zoomorphic form may attract closer visual attention, and whilst this results in an improved MMA, it also diverts attention away from transparency measures, reducing their efficacy to further increase MMA. The trivial embellishment of a robot to alter its form has significant effects on our understanding and attitude towards it. Based on the concerns that motivate this work, together with the results of the robot transparency experiments, I argue that we have a moral responsibility to make robots transparent, so as to reveal their true machine nature. I recommend the inclusion of transparency as a fundamental design consideration for intelligent systems, particularly for autonomous robots. This research also includes the design and development of the 'Instinct' reactive planner, developed as a controller for a mobile robot of my own design. Instinct provides facilities to generate a real-time 'transparency feed'--- a real-time trace of internal processing and state. Instinct also controls agents within a simulation environment, the 'Instinct Robot World'. Finally, I show how two instances of Instinct can be used to achieve a second order control architecture.

This master thesis deals with bankruptcy prediction models for construction companies doing business in Czech Republic. Terms important for understanding the issue are defined in the theoretical part. In analytical part, there are five current bankruptcy prediction models tested on the analysed sample and resulted accuracy compared with original ones. On the basis of knowledges acquired, there is developed a brand-new bankruptcy prediction model.

La réussite du développement des produits industriels a un enjeu économique considérable pour l’entreprise. Les décisions sur les concepts et sur l’architecture du produit ont un impact considérable sur le coût global du cycle de vie du produit. Les industriels sont alors de plus en plus encouragés à adopter des méthodes permettant de rationaliser les décisions de conception. Ces méthodes doivent être en adéquation avec le niveau de connaissance acquis sur les alternatives de conception, la nature des décisions à prendre et les outils d’évaluation disponibles. Ainsi, deux méthodes d’aide à la décision ont été développées dans cette thèse en fonction de la phase de conception étudiée.Une première méthode est proposée pour la phase amont de conception dans laquelle l’objectif de l’entreprise est de réduire le nombre de concepts proposés initialement pour concentrer ses efforts et ses ressources sur les concepts les plus prometteurs. Durant cette phase, les produits sont définis d’une manière très imprécise. Les concepteurs doivent alors faire beaucoup d'hypothèses lorsqu’ils proposent des modèles de comportement destinés à évaluer les concepts. Ces modèles, qui expriment une connaissance de nature explicite, sont insuffisants pour faire des choix de concepts. Pour remédier à cette difficulté, nous nous orientons dans notre étude vers la connaissance implicite (ou subjective), obtenue au travers de l'expérience et du savoir-faire acquis par les concepteurs. Afin d’intégrer cette connaissance dans la prise de décision, elle est d’abord formalisée au travers d’outils comme les fonctions de préférence. La méthode globale d’aide à la décision proposé permet de combiner à la fois cette connaissance implicite et la connaissance explicite (représentée par les modèles de comportement) pour évaluer chaque concept en termes d’aptitude à respecter les exigences minimales de validation et du niveau d’adéquation avec les objectifs de conception. Les objectifs et préférences des décideurs sont, quant à eux, structurés et formalisés au travers du modèle OIA (Observation-Interprétation-Agrégation) développé auparavant dans le laboratoire I2M-IMC.Une deuxième méthode a été proposée pour la phase aval de conception qui a pour objectif de fixer les paramètres du produit final (choix des matériaux, dimensions, etc.) après que le concept ait été choisi. Durant cette phase, le produit est défini d’une manière plus précise et les modèles de comportement sont plus représentatifs des phénomènes physiques pertinents du concept étudié. Ces modèles restent cependant basés sur des hypothèses qui conduisent parfois à une remise en question de leur exactitude, surtout pour certains paramètres de conception. Fixer les paramètres du produit en se basant uniquement sur ces modèles de comportement nécessite la prise en compte de leur niveau exactitude. Nous avons donc développé un indicateur d’exactitude de ces modèles qui intègre, d’une part, une évaluation objective qui est la mesure d’écart entre le modèle et un comportement de référence (supposé exact) et, d’autre part, sur une évaluation subjectif qui se base sur la mesure de distance avec la solution de référence (solution prototypée) et sur des fonctions de confiances établies par les concepteurs. La combinaison d’une évaluation objective et subjective de l’exactitude permet d’obtenir à la fin un indicateur général d’exactitude capable de couvrir l’intégralité de l’espace de conception. L’indicateur ainsi obtenu est utilisé dans une méthode d’aide à la décision qui qualifie chaque solution candidate en terme de risque engendré par les l’exactitude des modèles et de degré de satisfaction des objectifs de conception (en se basant la modèle OIA). / The successful development of industrial products has a considerable economic challenge for the company. Decisions on concepts and product architecture have a significant impact on the overall cost of the product life cycle. Manufacturers are then increasingly encouraged to adopt methods to streamline design decisions. These methods must be consistent with the level of knowledge acquired on alternative design, the nature of decision making and assessment tools available. Thus, both methods of decision support have been developed in this thesis based on the study design phase.A first method is proposed for the upstream design stage in which the goal of the company is to reduce the number of concepts initially proposed to focus its efforts and resources on the most promising concepts. During this phase, the products are defined very loosely. Designers must then make a lot of assumptions when proposing role models to assess the concepts. These models, which express a knowledge of explicit nature, are insufficient to make choices concepts. To overcome this difficulty, we are moving in our study to the implicit knowledge (or subjective), obtained through the experience and expertise acquired by the designers. To integrate this knowledge in decision-making, it is first formalized through tools such as preferably functions. The overall approach to decision support proposed for combining both the implicit knowledge and explicit knowledge (represented by the role models) to assess each concept in terms of ability to meet the minimum requirements for validation and level fitness with the design goals. The objectives and preferences of decision makers are, in turn, structured and formalized through the OIA model (Observation-Interpretation-Aggregation) previously developed in the laboratory I2M-BMI.A second method was proposed for the downstream design phase that aims to set the parameters of the final product (choice of materials, dimensions, etc.) after the concept was chosen. During this phase, the product is defined in a more precise manner and behavior patterns are more representative of the relevant physical phenomena of the concept studied. These models are however based on assumptions which sometimes lead to a questioning their accuracy, especially for some design parameters. Fix the product parameters based solely on these patterns of behavior requires taking into account their accuracy level. We have therefore developed an accuracy indicator of these models that integrates on the one hand, an objective assessment which is the measure of difference between the model and a reference behavior (assumed correct) and, secondly, on a subjective assessment that is based on the distance measurement with the reference solution (prototyped solution) and trusts functions established by the designers. The combination of objective and subjective assessment of the accuracy provides at the end a general indicator of accuracy able to cover the entire design space. The indicator thus obtained is used in a method of decision support that qualifies each candidate solution in terms of risk posed by the accuracy of the models and level of satisfaction of design objectives (based the model OIA) .

The result of the diploma thesis is a photogrammetrically evaluated georeferenced 3D model of an object with its environment, located in the AdMaS complex. The work describes in detail all phases of creating a 3D model of the object from the selection and calibration of the camera to editing the 3D model. Discuss about software and methods for evaluating 3D models. The thesis deals with the optimal setting of ContectCapture software. The accuracy of the resulting 3D model is tested by the methodology according to ČSN 013410 on the basis of control measurements.