23 resultados para sequential

em Universidade do Minho


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This paper aims to describe the Sequential Excavation Method, used for excava-tion in underground works, as well as the related risks and preventive measures. This method has characteristics that differentiate it from other tunnelling techniques: it uses a larger number of workers and equipment; it has a high concurrency of tasks with various workers and equip-ment quite exposed to hazards; and it uses many potentially aggressive chemicals. Firstly, it is given a broad overview of this issue. Afterwards, it will be presented the results of a survey to a sample of experienced technicians, aimed at gauging the relevance of a set of guidelines relat-ing to the design and work phases, applicable to the domestic market and prepared following technical visits to works abroad.

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The weak fixation of biomaterials within the bone structure is one of the major reasons of implants failures. Calcium phosphate (CaP) coatings are used in bone tissue engineering to improve implant osseointegration by enhancing cellular adhesion, proliferation and differentiation, leading to a tight and stable junction between implant and host bone. It has also been observed that materials compatible with bone tissue either have a CaP coating or develop such a calcified surface upon implantation. Thus, the development of bioactive coatings becomes essential for further improvement of integration with the surrounding tissue. However, most of current applied CaP coatings methods (e.g. physical vapor deposition), cannot be applied to complex shapes and porous implants, provide poor structural control over the coating and prevent incorporation of bioactive organic compounds (e.g. antibiotics, growth factors) because of the used harsh processing conditions. Layer-by-layer (LbL) is a versatile technology that permits the building-up of multilayered polyelectrolyte films in mild conditions based on the alternate adsorption of cationic and anionic elements that can integrate bioactive compounds. As it is recognized in natureâ s biomineralization process the presence of an organic template to induce mineral deposition, this work investigate a ion based biomimetic method where all the process is based on LbL methodology made of weak natural-origin polyelectrolytes. A nanostructured multilayer component, with 5 or 10 bilayers, was produced initially using chitosan and chondroitin sulphate polyelectrolyte biopolymers, which possess similarities with the extracellular matrix and good biocompatibility. The multilayers are then rinsed with a sequential passing of solutions containing Ca2+ and PO43- ions. The formation of CaP over the polyelectrolyte multilayers was confirmed by QCM-D, SEM and EDX. The outcomes show that 10 polyelectrolyte bilayer condition behaved as a  better site for initiating the formation of CaP as the precipitation occur at earlier stages than in 5 polyelectrolyte bilayers one. This denotes that higher number of bilayers could hold the CaP crystals more efficiently. This work achieved uniform coatings that can be applied to any surface with access to the liquid media in a low-temperature method, which potentiates the manufacture of effective bioactive biomaterials with great potential in orthopedic applications.

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The present paper reports the precipitation process of Al3Sc structures in an aluminum scandium alloy, which has been simulated with a synchronous parallel kinetic Monte Carlo (spkMC) algorithm. The spkMC implementation is based on the vacancy diffusion mechanism. To filter the raw data generated by the spkMC simulations, the density-based clustering with noise (DBSCAN) method has been employed. spkMC and DBSCAN algorithms were implemented in the C language and using MPI library. The simulations were conducted in the SeARCH cluster located at the University of Minho. The Al3Sc precipitation was successfully simulated at the atomistic scale with the spkMC. DBSCAN proved to be a valuable aid to identify the precipitates by performing a cluster analysis of the simulation results. The achieved simulations results are in good agreement with those reported in the literature under sequential kinetic Monte Carlo simulations (kMC). The parallel implementation of kMC has provided a 4x speedup over the sequential version.

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Despite improvements over the years, accidents continue to be a scourge in the construction sector, leading to an increase in the number of journal articles addressing the issue, in an attempt to help construction industry to increase safety performance [1]. This paper aims to, helping construction industry and particulary tunneling community, describe the Portuguese approach to most typical health and safety problems in underground excavations performed with the Sequential Excavation Method (SEM). The article will address various topics, from safety management and organizational practices, to collective and personal protection equipment, to emergency planning. nt problems in safety and health matters are similar to several other countries, the paper will expose a compilation of Portuguese best practices used to solve that problems. This enunciation of best practices describes experience from most important and recognized Project Owners and Contractors in Portugal. In a second phase it will be analysed Portuguese weaknesses, identifying preventive measures, and their comparative importance, that should be adopted in Portugal in order to reduce accidents and health diseases.

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Earthworks tasks aim at levelling the ground surface at a target construction area and precede any kind of structural construction (e.g., road and railway construction). It is comprised of sequential tasks, such as excavation, transportation, spreading and compaction, and it is strongly based on heavy mechanical equipment and repetitive processes. Under this context, it is essential to optimize the usage of all available resources under two key criteria: the costs and duration of earthwork projects. In this paper, we present an integrated system that uses two artificial intelligence based techniques: data mining and evolutionary multi-objective optimization. The former is used to build data-driven models capable of providing realistic estimates of resource productivity, while the latter is used to optimize resource allocation considering the two main earthwork objectives (duration and cost). Experiments held using real-world data, from a construction site, have shown that the proposed system is competitive when compared with current manual earthwork design.

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Earthworks tasks are often regarded in transportation projects as some of the most demanding processes. In fact, sequential tasks such as excavation, transportation, spreading and compaction are strongly based on heavy mechanical equipment and repetitive processes, thus becoming as economically demanding as they are time-consuming. Moreover, actual construction requirements originate higher demands for productivity and safety in earthwork constructions. Given the percentual weight of costs and duration of earthworks in infrastructure construction, the optimal usage of every resource in these tasks is paramount. Considering the characteristics of an earthwork construction, it can be looked at as a production line based on resources (mechanical equipment) and dependency relations between sequential tasks, hence being susceptible to optimization. Up to the present, the steady development of Information Technology areas, such as databases, artificial intelligence and operations research, has resulted in the emergence of several technologies with potential application bearing that purpose in mind. Among these, modern optimization methods (also known as metaheuristics), such as evolutionary computation, have the potential to find high quality optimal solutions with a reasonable use of computational resources. In this context, this work describes an optimization algorithm for earthworks equipment allocation based on a modern optimization approach, which takes advantage of the concept that an earthwork construction can be regarded as a production line.

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The kinetics of GnP dispersion in polypropylene melt was studied using a prototype small scale modular extensional mixer. Its modular nature enabled the sequential application of a mixing step, melt relaxation, and a second mixing step. The latter could reproduce the flow conditions on the first mixing step, or generate milder flow conditions. The effect of these sequences of flow constraints upon GnP dispersion along the mixer length was studied for composites with 2 and 10 wt.% GnP. The samples collected along the first mixing zone showed a gradual decrease of number and size of GnP agglomerates, at a rate that was independent of the flow conditions imposed to the melt, but dependent on composition. The relaxation zone induced GnP re-agglomeration, and the application of a second mixing step caused variable dispersion results that were largely dependent on the hydrodynamic stresses generated.

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This article compiles the main topics addressed by management systems (MSs) literature concerningMSs integration by performing a systematic literature review. In this paper, it is intended to present themain limitations of non-integratedmanagement systems (IMSs), the main motivations driving an IMS implementation, the major resistances faced, the most common resultant benefits, the suitable guidelines and standards and the critical success factors. In addition, this paper addresses the issues concerning integration strategies and models, the integration levels or degrees achieved by an IMS and the audit function in an integrated context. The motivations that drive companies to integrate their management subsystems, the obstacles faced and the benefits collected may have internal or external origins. The publishing of standards guiding companies on how to integrate their management subsystems has been done mainly at a national level. There are several models that could be used in order to support companies in their management subsystems integration processes, and a sequential or an all-in strategy may be adopted. Four audit typologies can be distinguished, and the adoption of any of these typologies should consider resource availability and audit team know-how, among other features.

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Tese de Doutoramento em Estudos da Criança (área de especialização em Comunicação Visual e Expressão Plástica)

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Dissertação de mestrado integrado em Psicologia

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Tese de Doutoramento em Engenharia Industrial e de Sistemas (PDEIS)

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A search for high-mass resonances decaying into τ+τ− final states using proton-proton collisions at s√=8 TeV produced by the Large Hadron Collider is presented. The data were recorded with the ATLAS detector and correspond to an integrated luminosity of 19.5-20.3 fb−1. No statistically significant excess above the Standard Model expectation is observed; 95% credibility upper limits are set on the cross section times branching fraction of Z′ resonances decaying into τ+τ− pairs as a function of the resonance mass. As a result, Z′ bosons of the Sequential Standard Model with masses less than 2.02 TeV are excluded at 95% credibility. The impact of the fermionic couplings on the Z′ acceptance is investigated and limits are also placed on a Z′ model that exhibits enhanced couplings to third-generation fermions.

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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.

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Dissertação de mestrado integrado em Engenharia e Gestão Industrial

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Dissertação de mestrado em Engenharia Mecatrónica