127 resultados para Exponential financial models


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ABSTRACT: Financing is a critical factor in ensuring the optimal development and delivery of a mental health system. The primary method of financing worldwide is tax-based. However many low income countries depend on out-of-pocket payments. There is a report on Irish Health Care funding but none that deals exclusively with mental health care. This paper analyses the various financial models that exist globally with respect to financing the mental health sector, examines the impact of various models on service users, especially in terms of relative ‘financial burden’ and provides a more detailed examination of the current mental health funding situation in Ireland After extensive internet and hardcopy research on the above topics, the findings were analysed and a number of recommendations were reached. Mental health service should be free at the point of delivery to achieve universal coverage. Government tax-based funding or mandatory social insurance with government top-ups, as required, appears the optimal option, although there is no one funding system applicable everywhere. Out-of-pocket funding can create a crippling financial burden for service users. It is important to employ improved revenue collection systems, eliminate waste, provide equitable resource distribution, ring fence mental health funding and cap the number of visits, where necessary. Political, economic, social and cultural factors play a role in funding decisions and this can be clearly seen in the context of the current economic recession in Ireland. Only 33% of the Irish population has access to free public health care and the number health insurance policy holders has dramatically declined, resulting in increased out-of-pocket payments. This approach risks negatively impacting on the social determinants of health, increasing health inequalities and negatively affecting economic productivity. It is therefore important the Irish government examines other options to provide funding for mental health services.

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The present dissertation aims at contributing to a strategic view over the use of innovative medicines in the portuguese private health sector, as a way of being complementary to public system that nowadays finances most of the innovative medicines. With the rationalization of expenses in the public health sector, pharmaceutical companies tend to look for opportunities of expansion to the private sector. The creation of innovative financial models for the private sector to cover innovative treatments is the proposed way of surpassing the restriction on the NHS sales of innovative medicines. This can be both for differentiating private health providers from the NHS or for the creation of premium services that can be differentiated from other private providers of the portuguese market.

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Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para obtenção do grau de Mestre em Engenharia Electrotécnica e Computadores

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A Work Project, presented as part of the requirements for the Award of a Masters Degree in Finance from the NOVA – School of Business and Economics

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Dissertação apresentada para a obtenção do Grau de Mestre em Genética Molecular e Biomedicina, pela Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia

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Dissertation presented to obtain the PhD degree in Biology/Molecular Biology by Universidade Nova de Lisboa, Instituto de Tecnologia Química e Biológica

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A Work Project, presented as part of the requirements for the Award of a Masters Degree in Management from the NOVA – School of Business and Economics

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A Work Project, presented as part of the requirements for the Award of a Masters Degree in Management from the NOVA – School of Business and Economics

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A Work Project, presented as part of the requirements for the Award of a Masters Degree in Finance from the NOVA – School of Business and Economics

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This paper develops the model of Bicego, Grosso, and Otranto (2008) and applies Hidden Markov Models to predict market direction. The paper draws an analogy between financial markets and speech recognition, seeking inspiration from the latter to solve common issues in quantitative investing. Whereas previous works focus mostly on very complex modifications of the original hidden markov model algorithm, the current paper provides an innovative methodology by drawing inspiration from thoroughly tested, yet simple, speech recognition methodologies. By grouping returns into sequences, Hidden Markov Models can then predict market direction the same way they are used to identify phonemes in speech recognition. The model proves highly successful in identifying market direction but fails to consistently identify whether a trend is in place. All in all, the current paper seeks to bridge the gap between speech recognition and quantitative finance and, even though the model is not fully successful, several refinements are suggested and the room for improvement is significant.

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H3 is a fast-food chain that introduced the concept of gourmet hamburgers in the Portuguese market. This case-study illustrates its financing strategy that supported an exponential growth represented by opening 33 restaurants within approximately 3 years of its inception. H3 is now faced with the challenge of structuring its foreign ventures and change its financial approach. The main covered topics are the options an entrepreneur has for financing a new venture and how it evolves along the life cycle and different business approaches, namely franchising. It aims to be used as a learning tool in courses such as entrepreneurial finance.

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Tese de doutoramento em Filosofia

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The basic motivation of this work was the integration of biophysical models within the interval constraints framework for decision support. Comparing the major features of biophysical models with the expressive power of the existing interval constraints framework, it was clear that the most important inadequacy was related with the representation of differential equations. System dynamics is often modelled through differential equations but there was no way of expressing a differential equation as a constraint and integrate it within the constraints framework. Consequently, the goal of this work is focussed on the integration of ordinary differential equations within the interval constraints framework, which for this purpose is extended with the new formalism of Constraint Satisfaction Differential Problems. Such framework allows the specification of ordinary differential equations, together with related information, by means of constraints, and provides efficient propagation techniques for pruning the domains of their variables. This enabled the integration of all such information in a single constraint whose variables may subsequently be used in other constraints of the model. The specific method used for pruning its variable domains can then be combined with the pruning methods associated with the other constraints in an overall propagation algorithm for reducing the bounds of all model variables. The application of the constraint propagation algorithm for pruning the variable domains, that is, the enforcement of local-consistency, turned out to be insufficient to support decision in practical problems that include differential equations. The domain pruning achieved is not, in general, sufficient to allow safe decisions and the main reason derives from the non-linearity of the differential equations. Consequently, a complementary goal of this work proposes a new strong consistency criterion, Global Hull-consistency, particularly suited to decision support with differential models, by presenting an adequate trade-of between domain pruning and computational effort. Several alternative algorithms are proposed for enforcing Global Hull-consistency and, due to their complexity, an effort was made to provide implementations able to supply any-time pruning results. Since the consistency criterion is dependent on the existence of canonical solutions, it is proposed a local search approach that can be integrated with constraint propagation in continuous domains and, in particular, with the enforcing algorithms for anticipating the finding of canonical solutions. The last goal of this work is the validation of the approach as an important contribution for the integration of biophysical models within decision support. Consequently, a prototype application that integrated all the proposed extensions to the interval constraints framework is developed and used for solving problems in different biophysical domains.

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The workforce in organizations today is becoming increasingly diverse. Consequently the role of diversity management is heavily discussed with respect to the question how diversity influences the productivity of a group. Empirical studies show that on one hand there is a potential for increasing productivity but on the other hand it might be as well that conflicts arise due to the heterogeneity of the group. Usually according empirical studies are based on interviews, questionnaires and/or observations. These methods imply that answers are highly selective and filtered. In order to make the invisible visible, to have access to mental models of team members the paper will present an empirical study on the self-understanding of groups based on an innovative research method, called “mind-scripting”.

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Dissertação apresentada para obtenção do Grau de Doutor em Matemática, Estatística, pela Universidade Nova de Lisboa, faculdade de Ciências e Tecnologia