Single line for assembly just-in-sequence multiple models

Programa Doutoral em Líderes para as Indústrias Tecnológicas

Implementação de um novo processo produtivo adequado à introdução faseada de múltiplos produtos

Dissertação de mestrado integrado em Engenharia e Gestão Industrial

Para um (novo) modelo de intervenção penal na União Europeia: uma reflexão a partir do princípio de legalidade como limite material de atuação da Procuradoria Europeia

Tese de Doutoramento em Ciências Jurídicas (área de especialização em Ciências Jurídicas - Públicas)

Development of a methodology to incorporate risk and uncertainty in electricity power planning

Dissertação de mestrado em Engenharia Industrial

Melhoria do desempenho de uma secção de montagem final de uma empresa usando ferramentas Lean Production

Dissertação de mestrado integrado em Engenharia e Gestão Industrial

PCBA external workbench evaluation procedure

Dissertação de mestrado integrado em Mechanical Engineering

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.

Non emergency patients transport - a mixed integer linear programming

This work presents a model and a heuristic to solve the non-emergency patients transport (NEPT) service issues given the new rules recently established in Portugal. The model follows the same principle of the Team Orienteering Problem by selecting the patients to be included in the routes attending the maximum reduction in costs when compared with individual transportation. This model establishes the best sets of patients to be transported together. The model was implemented in AMPL and a compact formulation was solved using NEOS Server. A heuristic procedure based on iteratively solving problems with one vehicle was presented, and this heuristic provides good results in terms of accuracy and computation time.

Effects of residues on the degradation of PHA produced from mixed microbial cultures and processed in extrusion

Thermal degradation upon melting is one of the major drawbacks reported for polyhydroxyalkanoates (PHA). However, the role of residues originating from the fermentation and the extraction steps on the thermal stability of this class of biopolymers still needs to be clarified. In the particular case of PHA produced from mixed microbial cultures (MMC), this topic is even less documented in the literature. Here, two polyhydroxy(butyrate-co-valerate) (PHBV) produced from MMC enriched in PHA accumulating organisms and fed with cheese whey were studied. A micro extrusion line is used to produce filaments and assess the processability and the degradation of processed PHBV. The prototype micro extrusion line allows for studying grams of materials. The two PHBV contain 18 mol% HV. PHBV was recovered with 11 wt% residues, and further submitted to a purification procedure resulting in a second biopolyester containing less than 2 wt% impurities. The thermorheological characterization of the two PHBV is first presented, together with their semicrystalline properties. Then the processing windows of the two biopolyesters are presented. Finally, the properties of extruded filaments are reported and the thermomechanical degradation of PHBV is extensively studied. The structure was assessed by wide angle X-ray diffraction, mechanical and rheological properties are reported, thermal properties are studied with differential scanning calorimetry and thermogravimetric analysis, whereas Fourier Transform Infrared spectroscopy was used to assess the impact of the extrusion on PHBV chemical structure. All results obtained with the two PHBV are compared to assess the effects of residues on both PHBV processability and degradation.

Lideranças curriculares intermédias no contexto da avaliação externa das escolas em Portugal

Tese de Doutoramento em Ciências da Educação (área de especialização em Desenvolvimento Curricular).

Estudo das tecnologias de interatividade no processo de compra em contexto das lojas de vestuário on-line

Dissertação de mestrado em Design de Comunicação de Moda

An integrated model for simulation of construction phasing of arch concrete dams

Dissertação de mestrado integrado em Civil Engineering

Tapping the wealth of microbial data in high-throughput metabolic model reconstruction

Genome-scale metabolic models are valuable tools in the metabolic engineering process, based on the ability of these models to integrate diverse sources of data to produce global predictions of organism behavior. At the most basic level, these models require only a genome sequence to construct, and once built, they may be used to predict essential genes, culture conditions, pathway utilization, and the modifications required to enhance a desired organism behavior. In this chapter, we address two key challenges associated with the reconstruction of metabolic models: (a) leveraging existing knowledge of microbiology, biochemistry, and available omics data to produce the best possible model; and (b) applying available tools and data to automate the reconstruction process. We consider these challenges as we progress through the model reconstruction process, beginning with genome assembly, and culminating in the integration of constraints to capture the impact of transcriptional regulation. We divide the reconstruction process into ten distinct steps: (1) genome assembly from sequenced reads; (2) automated structural and functional annotation; (3) phylogenetic tree-based curation of genome annotations; (4) assembly and standardization of biochemistry database; (5) genome-scale metabolic reconstruction; (6) generation of core metabolic model; (7) generation of biomass composition reaction; (8) completion of draft metabolic model; (9) curation of metabolic model; and (10) integration of regulatory constraints. Each of these ten steps is documented in detail.

In-line rheo-optical microstructural characterization of complex polymer systems

Tese de Doutoramento em Ciência e Engenharia de Polímeros e Compósitos.

On modified simple reacting spheres kinetic model for chemically reactive gases

Versão dos autores para esta publicação.