Child abuse monitor system model: a health care critical knowledge monitor system

The Childhood protection is a subject with high value for the society, but, the Child Abuse cases are difficult to identify. The process from suspicious to accusation is very difficult to achieve. It must configure very strong evidences. Typically, Health Care services deal with these cases from the beginning where there are evidences based on the diagnosis, but they aren’t enough to promote the accusation. Besides that, this subject it’s highly sensitive because there are legal aspects to deal with such as: the patient privacy, paternity issues, medical confidentiality, among others. We propose a Child Abuses critical knowledge monitor system model that addresses this problem. This decision support system is implemented with a multiple scientific domains: to capture of tokens from clinical documents from multiple sources; a topic model approach to identify the topics of the documents; knowledge management through the use of ontologies to support the critical knowledge sensibility concepts and relations such as: symptoms, behaviors, among other evidences in order to match with the topics inferred from the clinical documents and then alert and log when clinical evidences are present. Based on these alerts clinical personnel could analyze the situation and take the appropriate procedures.

Sistema de predição de avarias em máquinas de unidades fabris globalmente dispersas

Tese de Doutoramento Ramo Engenharia Industrial e de Sistemas

Desenvolvimento de rede neuronal artificial para o diagnóstico de doenças neurodegenerativas

Dissertação de mestrado integrado em Engenharia Eletrónica Industrial e Computadores

Um modelo para a gestão do conhecimento organizacional no contexto dos serviços partilhados com recurso à utilização do e-Learning

Tese de Doutoramento em Tecnologias e Sistemas de Informação

Redes de inovação em pequenas e médias empresas: um estudo de caso no setor têxtil

Dissertação de mestrado em Engenharia Industrial

Sistema de aquisição e comunicação para sensores magnetoelétricos

Dissertação de mestrado integrado em Engenharia Eletrónica Industrial e Computadores

O Espelho do Índio: A estética da afetividade no autorretrato da criança indígena na Fotopintura

Tese de Doutoramento em Estudos da Criança - Especialidade Comunicação Visual e Expressão Plástica

Updating of the hierarchical rock mass rating (HRMR) system and a new subsystem developed for weathered granite formations

The RMR system is still very much applied in rock mechanics engineering context. It is based on the evaluation of six weights to obtain a final rating. To obtain the final rating a considerable amount of information is needed concerning the rock mass which can be difficult to obtain in some projects or project stages at least with accuracy. In 2007 an alternative classification scheme based on the RMR, the Hierarchical Rock Mass Rating (HRMR) was presented. The main feature of this system was the adaptation to the level of knowledge existent about the rock mass to obtain the classification of the rock mass since it followed a decision tree approach. However, the HRMR was only valid for hard rock granites with low fracturing degrees. In this work, the database was enlarged with approximately 40% more cases considering other types of granite rock masses including weathered granites and based on this increased database the system was updated. Granite formations existent in the north of Portugal including Porto city are predominantly granites. Some years ago a light rail infrastructure was built in the city of Porto and surrounding municipalities whi h involved considerable challenges due to the high heterogeneity levels of the granite formations and the difficulties involved in their geomechanical characterization. In this work it is intended to provide also a contribution to improve the characterization of these formations with special emphasis to the weathered horizons. A specific subsystem applicable to the weathered formations was developed. The results of the validation of these systems are presented and show acceptable performances in identifying the correct class using less information than with the RMR system.

Methanogens, sulphate and heavy metals: a complex system

Anaerobic digestion (AD) is a well-established technology used for the treatment of wastes and wastewaters with high organic content. During AD organic matter is converted stepwise to methane-containing biogasa renewable energy carrier. Methane production occurs in the last AD step and relies on methanogens, which are rather sensitive to some contaminants commonly found in wastewaters (e.g. heavy metals), or easily outcompeted by other groups of microorganisms (e.g. sulphate reducing bacteria, SRB). This review gives an overview of previous research and pilot-scale studies that shed some light on the effects of sulphate and heavy metals on methanogenesis. Despite the numerous studies on this subject, comparison is not always possible due to differences in the experimental conditions used and parameters explained. An overview of the possible benefits of methanogens and SRB co-habitation is also covered. Small amounts of sulphide produced by SRB can precipitate with metals, neutralising the negative effects of sulphide accumulation and free heavy metals on methanogenesis. Knowledge on how to untangle and balance sulphate reduction and methanogenesis is crucial to take advantage of the potential for the utilisation of biogenic sulphide as a metal detoxification agent with minimal loss in methane production in anaerobic digesters.

Off-line simulation inspires insight: a neurodynamics approach to efficient robot task learning

There is currently an increasing demand for robots able to acquire the sequential organization of tasks from social learning interactions with ordinary people. Interactive learning-by-demonstration and communication is a promising research topic in current robotics research. However, the efficient acquisition of generalized task representations that allow the robot to adapt to different users and contexts is a major challenge. In this paper, we present a dynamic neural field (DNF) model that is inspired by the hypothesis that the nervous system uses the off-line re-activation of initial memory traces to incrementally incorporate new information into structured knowledge. To achieve this, the model combines fast activation-based learning to robustly represent sequential information from single task demonstrations with slower, weight-based learning during internal simulations to establish longer-term associations between neural populations representing individual subtasks. The efficiency of the learning process is tested in an assembly paradigm in which the humanoid robot ARoS learns to construct a toy vehicle from its parts. User demonstrations with different serial orders together with the correction of initial prediction errors allow the robot to acquire generalized task knowledge about possible serial orders and the longer term dependencies between subgoals in very few social learning interactions. This success is shown in a joint action scenario in which ARoS uses the newly acquired assembly plan to construct the toy together with a human partner.

Desenvolvimento e actualização do controlo de um equipamento de prototipagem rápida

Dissertação de mestrado integrado em Engenharia Mecânica

Sistema de apoio à decisão clínica: quality of life system

Dissertação de mestrado integrado em Engenharia e Gestão de Sistemas de Informação

Development of an intelligent earthwork optimization system

Tese de Doutoramento em Engenharia Civil.

Longitudinal evaluation, acceptability and long-term retention of knowledge on a horizontally integrated organic and functional systems course

Undergraduate medical education is moving from traditional disciplinary basic science courses into more integrated curricula. Integration models based on organ systems originated in the 1950s, but few longitudinal studies have evaluated their effectiveness. This article outlines the development and implementation of the Organic and Functional Systems (OFS) courses at the University of Minho in Portugal, using evidence collected over 10 years. It describes the organization of content, student academic performance and acceptability of the courses, the evaluation of preparedness for future courses and the retention of knowledge on basic sciences. Students consistently rated the OFS courses highly. Physician tutors in subsequent clinical attachments considered that students were appropriately prepared. Performance in the International Foundations of Medicine examination of a self-selected sample of students revealed similar performances in basic science items after the last OFS course and 4 years later, at the moment of graduation. In conclusion, the organizational and pedagogical approaches of the OFS courses achieve high acceptability by students and result in positive outcomes in terms of preparedness for subsequent training and long-term retention of basic science knowledge.

Foreign acquisition and internal organization

NIPE WP 05/2016