Considerable resources worldwide are invested in the research and development of future transportation technology. The foreseen direction and therefore research of future personalised transportation is focused on Battery Electric Vehicles (BEV) or hybrid combinations that use hydrogen fuel cells. These new transport energy systems are consider most to replace the current vehicles powered by the internal combustion engine (ICE). The research work presented in this thesis mainly focuses on the development of a software control system for future BEV prototype vehicles - a generic intelligent control system (GICS). The system design adopts a modular design concept and intelligent control. The whole system consists of four modules being communication, power supply, motor driver and transmission module. Each module uses a microcontroller as the brain and builds an embedded control system within the module. The control and communication between the modules is based on a group of specific parameters and the status of a state machine. In order to effectively implement intelligent control and simplify the system structure and programming, a generic intelligent fuzzy logic model that can be configured to a specific application with a near real-time buffered communication methodology is developed. The tests made on the fuzzy control model and the near real-time buffered communication gave a very positive outcome. The implementation of the fuzzy control and the communication methodology in each of the modules results in a communication between the modules with a steady speed, better reliability and system stability. These modules link together through the communication channels and form a multi-agent collaborative system (MACS). As the controllers are designed based on the parametric concept, the system is able to be implemented to future new modules and therefore allow prototype vehicle control systems to be developed more efficiently. The MACS is based on the core components of the control system - fuzzy logic controller (FLC), Serial Communication and Analogue input control software modules. Further work is carried out as an attempt to integrate the control software with a hardware design for a generic electric propulsion drive (GEPD). This thesis therefore outlines the design and considerations in software and hardware integration in addition to the GICS. The output from this thesis being the construction of soft programming modules for embedded microcontroller based control system has been accepted and presented at two international conferences; one in Wellington, New Zealand[1] the second in Acireale, Italy[2].

Considerable resources worldwide are invested in the research and development of future transportation technology. The foreseen direction and therefore research of future personalised transportation is focused on Battery Electric Vehicles (BEV) or hybrid combinations that use hydrogen fuel cells. These new transport energy systems are consider most to replace the current vehicles powered by the internal combustion engine (ICE). The research work presented in this thesis mainly focuses on the development of a software control system for future BEV prototype vehicles - a generic intelligent control system (GICS). The system design adopts a modular design concept and intelligent control. The whole system consists of four modules being communication, power supply, motor driver and transmission module. Each module uses a microcontroller as the brain and builds an embedded control system within the module. The control and communication between the modules is based on a group of specific parameters and the status of a state machine. In order to effectively implement intelligent control and simplify the system structure and programming, a generic intelligent fuzzy logic model that can be configured to a specific application with a near real-time buffered communication methodology is developed. The tests made on the fuzzy control model and the near real-time buffered communication gave a very positive outcome. The implementation of the fuzzy control and the communication methodology in each of the modules results in a communication between the modules with a steady speed, better reliability and system stability. These modules link together through the communication channels and form a multi-agent collaborative system (MACS). As the controllers are designed based on the parametric concept, the system is able to be implemented to future new modules and therefore allow prototype vehicle control systems to be developed more efficiently. The MACS is based on the core components of the control system - fuzzy logic controller (FLC), Serial Communication and Analogue input control software modules. Further work is carried out as an attempt to integrate the control software with a hardware design for a generic electric propulsion drive (GEPD). This thesis therefore outlines the design and considerations in software and hardware integration in addition to the GICS. The output from this thesis being the construction of soft programming modules for embedded microcontroller based control system has been accepted and presented at two international conferences; one in Wellington, New Zealand[1] the second in Acireale, Italy[2].

This thesis aims to predict the probable undiscovered geothermal systems through investigation of spatial relation between geothermal occurrences and its surrounding geological phenomenon in Western Anatolia. In this context, four different public data, which are epicenter map, lineament map, Bouger gravity anomaly and magnetic anomaly maps, are utilized. In order to extract the necessary information for each map layer the raw public data is converted to a synthetic data which are directly used in the analysis. Synthetic data employed during the investigation process include Gutenberg-Richter b-value map, distance to lineaments map and distance to major grabens present in the area. Thus, these three layers including directly used magnetic anomaly maps are combined by means of Boolean logic model and Weights of Evidence method (WofE), which are multicriteria decision methods, in a Geographical Information System (GIS) environment. Boolean logic model is based on the simple logic of Boolean operators, while the WofE model depends on the Bayesian probability. Both of the methods use binary maps for their analysis. Thus, the binary map classification is the key point of the analysis. In this study three different binary map classification techniques are applied and thus three output maps were obtained for each of the method. The all resultant maps are evaluated within and among the methods by means of success indices. The findings reveal that the WofE method is better predictor than the Boolean logic model and that the third binarization approach, which is named as optimization procedure in this study, is the best estimator of binary classes due to obtained success indices. Finally, three output maps of each method are combined and the favorable areas in terms of geothermal potential are produced. According to the final maps the potential sites appear to be Aydin, Denizli and Manisa, of which first two have been greatly explored and exploited since today and thus not surprisingly found as potential in the output maps, while Manisa when compared to first two is nearly virgin.

O presente trabalho objetiva contribuir com a ligação entre teoria e prática da avaliação de políticas públicas. Para tanto, busca descrever um mecanismo de avaliação que constitua a base de um processo de aprendizagem em um sistema de políticas públicas. Entende-se por aprendizagem o fenômeno de aproveitamento das informações geradas pelos processos avaliativos de modo a responder às demandas e suportes no sentido de se reorganizar em busca dos objetivos da política pública. A descrição do mecanismo é feita a partir de uma revisão teórica sobre a modelagem de sistemas, o Modelo Lógico, a pesquisa avaliativa e a avaliação de quarta geração. Considera-se que a combinação dessas técnicas torna mais provável o aproveitamento das informações da avaliação, constituindo, desta forma, um mecanismo de aprendizagem. / This paper intends to contribute with the connection between theory and practice on public policy evaluation. Thereby, it seeks to describe an evaluation procedure that meets the requirements of a learning process in a public policy system. It defines learning process as the phenomenon of application of information generated by the evaluative process in order to meet the demands and supports towards a self rearrangement in pursuit of public police stated goals. The mechanism description is made after a theoretical revision on modeling, on Logic Model, on evaluative research and fourth generation evaluation. It also considers that the combination of these techniques’ improves the use of evaluative information, constituting, thus, a learning mechanism. / Este trabajo tiene como objetivo contribuir con la conexión entre teoría y práctica en la evaluación de políticas públicas. Para tanto, busca describir un mecanismo de evaluación que forma la base de un proceso de aprendizaje en un sistema de políticas públicas. Se entiende por aprendizaje el fenómeno de aprovechamiento de las informaciones generadas por los procesos evaluativos de modo a responder a las demandas e suportes en sentido de cambiarse en busca de los objetivos de la política pública. La descripción del mecanismo es hecha a partir de una revisión teórica sobre modelaje de sistemas, Modelo Lógico, pesquisa evaluativa y evaluación de cuarta generación. Se considera que la combinación de estas técnicas favorece el aprovechamiento de las informaciones de la evaluación, constituyendo, de esta forma, un mecanismo de aprendizaje.

O presente trabalho objetiva contribuir com a ligação entre teoria e prática da avaliação de políticas públicas. Para tanto, busca descrever um mecanismo de avaliação que constitua a base de um processo de aprendizagem em um sistema de políticas públicas. Entende-se por aprendizagem o fenômeno de aproveitamento das informações geradas pelos processos avaliativos de modo a responder às demandas e suportes no sentido de se reorganizar em busca dos objetivos da política pública. A descrição do mecanismo é feita a partir de uma revisão teórica sobre a modelagem de sistemas, o Modelo Lógico, a pesquisa avaliativa e a avaliação de quarta geração. Considera-se que a combinação dessas técnicas torna mais provável o aproveitamento das informações da avaliação, constituindo, desta forma, um mecanismo de aprendizagem. / This paper intends to contribute with the connection between theory and practice on public policy evaluation. Thereby, it seeks to describe an evaluation procedure that meets the requirements of a learning process in a public policy system. It defines learning process as the phenomenon of application of information generated by the evaluative process in order to meet the demands and supports towards a self rearrangement in pursuit of public police stated goals. The mechanism description is made after a theoretical revision on modeling, on Logic Model, on evaluative research and fourth generation evaluation. It also considers that the combination of these techniques’ improves the use of evaluative information, constituting, thus, a learning mechanism. / Este trabajo tiene como objetivo contribuir con la conexión entre teoría y práctica en la evaluación de políticas públicas. Para tanto, busca describir un mecanismo de evaluación que forma la base de un proceso de aprendizaje en un sistema de políticas públicas. Se entiende por aprendizaje el fenómeno de aprovechamiento de las informaciones generadas por los procesos evaluativos de modo a responder a las demandas e suportes en sentido de cambiarse en busca de los objetivos de la política pública. La descripción del mecanismo es hecha a partir de una revisión teórica sobre modelaje de sistemas, Modelo Lógico, pesquisa evaluativa y evaluación de cuarta generación. Se considera que la combinación de estas técnicas favorece el aprovechamiento de las informaciones de la evaluación, constituyendo, de esta forma, un mecanismo de aprendizaje.

En 2015, la chute de la personne âgée reste toujours un événement majeur, quel que soit l’angle de vue considéré. Elle est toujours associée à une forte morbi-mortalité, nombreuses incapacités, altération la qualité de vie du chuteur, mais aussi, en raison du vieillissement de la population, avec le nombre croissant de chuteurs requérant une prise en charge médicale. Cette situation repose en bonne partie sur notre incapacité à identifier la personne âgée qui est le plus à risque de chute, cette étape étant la première de toute stratégie d’intervention efficace et efficiente. Il est donc nécessaire voir obligatoire aujourd’hui de redoubler nos efforts sur l’amélioration de la prédiction de la chute. En contrepartie de nouvelles opportunités s’ouvrent à nous en raison de l’implantation et de l’informatisation des données médicales. La chute doit être considérée comme un événement chaotique et sa prédiction doit se faire via de nouveaux modèles mathématiques intégrant la particularité de ce comportement. C’est pour cette raison que des méthodes d’analyse basée sur l'intelligence artificielle semblent être une solution appropriée. C’est à partir de ce constat que nous avons émis l’hypothèse que les modèles mathématiques issus de l’intelligence artificielle devaient permettre d’atteindre une qualité de la prédiction meilleure. L’objectif principal de cette thèse est d’étudier la qualité de la prédiction de la chute, récurrente ou non, chez des personnes âgées de 65 ans et plus, en utilisant les réseaux neuronaux et un modèle de logique floue, en les comparant avec des modèles mathématiques linéaires utilisés classiquement dans la littérature. L’ensemble de nos résultats confirme notre hypothèse de départ en montrant que le choix du modèle mathématique influence la qualité de la prédiction de la chute, les modèles non linéaires, et notamment les réseaux neuronaux et les systèmes de logique flous, étant plus performants que les modèles linéaires pour la prédiction des chutes surtout lorsqu’elles sont récurrentes. / Falls in the elderly are still a major issue in 2015 because they are associated with high rate of morbidity, mortality and disability, which affect the quality of life. From the patient’s perspective, it is still associated with high morbidity, mortality and disability, which affect the quality of life. The number of fallers requiring medical and/or social care is growing up due to aging population. This fact seems paradoxical since during the recent years the knowledge about the mechanisms of falls and the quality of interventions to support fallers significantly increased. This is largely based on our inability to predict correctly the risk of falling among the elderly person, knowing that this is the first step of any efficient and effective intervention strategies. Therefore it is necessary today to double our efforts in improving the prediction of falls. Nonetheless, new opportunities and advanced technologies provide to us the possibility of computerizing of medical data and research, and also to improve prediction of falls using new approaches. A fall should be considered as a chaotic event, and its prediction should be done via new mathematical models incorporating the feature of this behaviour. Thus, the methods ofartificial intelligence-based analysis seem to be an appropriate solution to analyse complex medical data. These artificial intelligence techniques have been already used in many medical areas, but rarely in the field of fall prediction. Artificial neural networks are the most commonly used methods while other promising techniques based on fuzzy logic are less often applied.Based on this observation we have formulated the hypothesis that non-linear mathematical models using artificial intelligence are the models, which are the most likely to achieve the bestquality of the prediction. The main objective of this thesis is to study the quality of theprediction of falls, recurrent or not, among the adults aged 65 years and more,applying neuralnetworks and fuzzy logic models, and comparing them either among themselves or with the linear mathematical models conventionally employed in the literature for fall prediction. The first cross-sectional study was conducted by using a decision tree to explore the risk of recurrent falls in various combinations of fall risk factors compared to a logistic regression model. The second study was designed to examine the efficiency of artificial neural networks (Multilayer Perceptron and Neuroevolution of Augmenting Topologies) to classify recurrent and nonrecurrent fallers by using a set of clinical characteristics corresponding to risk factors measured among seniors living in the community. Finally, in the third study we compared the results of different statistical methods (linear and nonlinear) in order to identify the risk of falls using 7 clinical variables, separating the collection mode (retrospective and prospective) of the fall and its recurrence. The results confirm our hypothesis showing that the choice of the mathematical model affects the quality of fall prediction. Nonlinear models, such as neural networks and fuzzy logic systems, are more efficient than linear models for the prediction of falls especially for recurrent falls. However, the results show that the balance between different criteria used to judge the quality of the forecast (sensitivity, specificity, positive and negative predictive value, area under the curve, positive and negative likelihood ratio, and accuracy) has not been always correct, emphasizing the need to continue the development of the models whose intelligence should specifically predict the fall.

O presente trabalho objetiva contribuir com a ligação entre teoria e prática da avaliação de políticas públicas. Para tanto, busca descrever um mecanismo de avaliação que constitua a base de um processo de aprendizagem em um sistema de políticas públicas. Entende-se por aprendizagem o fenômeno de aproveitamento das informações geradas pelos processos avaliativos de modo a responder às demandas e suportes no sentido de se reorganizar em busca dos objetivos da política pública. A descrição do mecanismo é feita a partir de uma revisão teórica sobre a modelagem de sistemas, o Modelo Lógico, a pesquisa avaliativa e a avaliação de quarta geração. Considera-se que a combinação dessas técnicas torna mais provável o aproveitamento das informações da avaliação, constituindo, desta forma, um mecanismo de aprendizagem. / This paper intends to contribute with the connection between theory and practice on public policy evaluation. Thereby, it seeks to describe an evaluation procedure that meets the requirements of a learning process in a public policy system. It defines learning process as the phenomenon of application of information generated by the evaluative process in order to meet the demands and supports towards a self rearrangement in pursuit of public police stated goals. The mechanism description is made after a theoretical revision on modeling, on Logic Model, on evaluative research and fourth generation evaluation. It also considers that the combination of these techniques’ improves the use of evaluative information, constituting, thus, a learning mechanism. / Este trabajo tiene como objetivo contribuir con la conexión entre teoría y práctica en la evaluación de políticas públicas. Para tanto, busca describir un mecanismo de evaluación que forma la base de un proceso de aprendizaje en un sistema de políticas públicas. Se entiende por aprendizaje el fenómeno de aprovechamiento de las informaciones generadas por los procesos evaluativos de modo a responder a las demandas e suportes en sentido de cambiarse en busca de los objetivos de la política pública. La descripción del mecanismo es hecha a partir de una revisión teórica sobre modelaje de sistemas, Modelo Lógico, pesquisa evaluativa y evaluación de cuarta generación. Se considera que la combinación de estas técnicas favorece el aprovechamiento de las informaciones de la evaluación, constituyendo, de esta forma, un mecanismo de aprendizaje.

The thesis "Does ISS educate their students in accordance with the needs of the labor market?" deals with the relationship between tertiary education of graduates and their labor market outcomes. In the theoretical part the thesis deals with the importance of education in relation to the employability of graduates. The methodological part approaches this relationship on a case study of the Institute of Sociological Studies, Faculty of Social Sciences, Charles University. Evaluation as s research method is chosen to solve the diploma thesis. The analytical part gradually reveals the conceptual model of the evaluated program, stakeholders and their interests. The aim of the analytical part is to answer the evaluation questions about the level of work performance of graduate employability skills.

Relational data is characterised by the rich structure it encodes in the dependencies between the individual entities of a given domain. Statistical Relational Learning (SRL) combines first-order logic and probability to learn and reason over relational domains by creating parametric probability distributions over relational structures. SRL models can succinctly represent the complex dependencies in relational data and admit learning and inference under uncertainty. However, these models are significantly limited when it comes to the tractability of learning and inference. This limitation emerges from the intractability of Weighted First Order Model Counting (WFOMC), as both learning and inference in SRL models can be reduced to instances of WFOMC. Hence, fragments of first-order logic that admit tractable WFOMC, widely known as domain-liftable, can significantly advance the practicality and efficiency of SRL models. Recent works have uncovered another limitation of SRL models, i.e., they lead to unintuitive behaviours when used across varying domain sizes, violating fundamental consistency conditions expected of sound probabilistic models. Such inconsistencies also mean that conventional machine learning techniques, like training with batched data, cannot be soundly used for SRL models. In this thesis, we contribute to both the tractability and consistency of probabilistic inference in SRL models. We first expand the class of domain-liftable fragments with counting quantifiers and cardinality constraints. Unlike the algorithmic approaches proposed in the literature, we present a uniform combinatorial approach, admitting analytical combinatorial formulas for WFOMC. Our approach motivates a new family of weight functions allowing us to express a larger class of probability distributions without losing domain-liftability. We further expand the class of domain-liftable fragments with constraints inexpressible in first-order logic, namely acyclicity and connectivity constraints. Finally, we present a complete characterization for a statistically consistent (a.k.a projective) models in the two-variable fragment of a widely used class of SRL models, namely Markov Logic Networks.

Logical pluralism from historical perspective - Abstract The plurality of logics is understood as a challenge to seek a deeper understanding of the na- ture and import of logic. Two basic approaches to demarcation of logic are considered, the model-theoretic and the proof-theoretic one. Investigation of the history which led to these two appraoches identifies the postion of logic in Kant's epistemology as crucial for the devel- opment. An analogical development from Kant's conception of geometry to the plurality of geometric theories leads to a holistic view both of geometry and of logic. It furthermore proves essential to understand the pragmatic import of logic. Given the problems tied to the attempts to demarcate logic, inferentialism and logical expressivism are arrived at as jointly provid- ing the most appropriate account. These approaches are developed into a conception which stresses, in line with the historical perspective of the work, the ability of logic to develop.