Annotation: This thesis deals with the development of a small company by means of franchising. This franchising concept is based on the critical analysis of the chosen companies. In the theoretical part I processed franchising generally. The practical part contents the characteristic of the chosen company CZECH RENT A SKI s.r.o. This company deals with a winter equipment rental. I processed this concept based on the analysis of the company. The last part contains the schedule of the implementation of this concept.

Made available in DSpace on 2016-04-04T18:29:41Z (GMT). No. of bitstreams: 1 Renato Marchi.pdf: 1336693 bytes, checksum: bcdd5e2f052a29bb390cf3fc87b8af07 (MD5) Previous issue date: 2008-02-12 / Bipolar disorder (BD) is associated with ps ychosocial and family relationships disturbing, mortality and economic burden high rates . The treatment aims the acute episodes and prevents new episodes. There are high rates of non-adherence in BD. The objective of this study was to develop and validate a clinical rating scale capable to predict the patient compliance to treatment in BD in both gend ers. The procedure involved the search in pertinent scientific literature for reports of factors of non-adherence of bipolar patients, analysis of patients during pilot studies and contact with professionals who deal with those patients in order to develop a comprehensive list of possible symptoms. This procedure was followed by items' selection and testing of the preliminary form of the clinical rating scale. The scale was analyzed statistically. Reliability study showed a high level of internal consistency. Factor analysis revealed five factors related to the clinical treatment: behaviors and beliefs of the patient, therapeutic alliance, therapeutic procedures, association of psychotherapy interventions and adverse effects of drug therapy. Predictive validation showed that items' factors were able to measure the non - adherence to treatment. It was concluded that the Clinical Rating Scale to Predict the Compliance to Treatment in Bipolar Disorder can be considered a valid instrument to predict the patient compliance to medical treatment . / O Transtorno Bipolar do Humor (TBH) est? associado a altas taxas de desajustes psicossociais e familiares, mortalidade e preju?zos econ?micos. O tratamento visa o controle de epis?dios agudos e preven??o de novos epis?dios. As taxas de n?o - ades?o ao tratamento s?o altas em TBH. Este trabalho teve por objetivo elaborar e validar uma escala de avalia??o clinica , capaz de indicar a probabilidade de ades?o ao tratamento medico clinico dos pacientes bipolares de ambos os g?neros. O procedimento foi constitu?do de levantamento dos fatores ligados a n?o-ades?o ao tratamento em TBH na literatura pertinente, analise de pacientes bipolares durante estudo piloto, analise por juizes, sele??o dos itens e aplica??o da escala em sua fase inicial, para subseq?ente an?lise psicom?trica. A analise de precis?o do instrumento evidenciou n?vel satisfat?rio de consist?ncia interna. Extra?ram-se cinco fatores de acordo com a analise fatorial explorat?ria, ligados ao tratamento medico clinico: atitudes e cren?as do paciente, alian?a terap?utica, procedimentos terap?uticos, associa??o de interven??es psicoter?picas e efeitos adversos dos psicof?rmacos. A valida??o preditiva revelou que os itens referentes a tais fatores medem a n?o-ades?o ao tratamento. Conclui -se que a ECPAT-TBH pode ser considerada um instrumento v?lido para prever a ades?o ao tratamento m?dico.

<div>Supervised classification methods based on deep learning have achieved great success in many domains and tasks that are previously unimaginable. Such approaches build on learning paradigms that require hundreds of examples in order to learn to classify objects or events. Thus, their immediate application to the domains with few or no observations is limited. This is because of the lack of ability to rapidly generalize to new categories from a few examples or from high-level descriptions of categories. This can be attributed to the significant gap between the way machines represent knowledge and the way humans represent categories in their minds and learn to recognize them. In this context, this research represents categories as semantic trees in a high-level attribute space and proposes an approach to utilize these representations to conduct N-Shot, Few-Shot, One-Shot, and Zero-Shot Learning (ZSL). This work refers to this paradigm as the problem of general classification (GCP) and proposes a unified framework for GCP referred to as the Few and Zero-Shot Technique (FAZT). FAZT framework is an end-to-end approach that uses trainable 3D convolutional neural networks and recurrent neural networks to simultaneously optimize for both the semantic and the classification tasks. Lastly, the problem of systematically obtaining semantic attributes by utilizing domain-specific ontologies is presented. The proposed framework is validated in the domains of hand gesture and action/activity recognition, however, this research can be applied to other domains such as video understanding, the study of human behavior, emotion recognition, etc. First, an attribute-based dataset for gestures is developed in a systematic manner by relying on literature in gestures and semantics, and crowdsourced platforms such as Amazon Mechanical Turk. To the best of our knowledge, this is the first ZSL dataset for hand gestures (ZSGL dataset). Next, our framework is evaluated in two experimental conditions: 1. Within-category (to test the attribute recognition power) and 2. Across-category (to test the ability to recognize an unknown category). In addition, we conducted experiments in zero-shot, one-shot, few-shot and continuous learning conditions in both open-set and closed-set scenarios. Results showed that our framework performs favorably on the ZSGL, Kinetics, UIUC Action, UCF101 and HMDB51 action datasets in all the experimental conditions.<br></div><div><br></div>