12 resultados para EVOLUTIONARY PERSPECTIVE

em Universidade do Minho


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Earthworks involve the levelling or shaping of a target area through the moving or processing of the ground surface. Most construction projects require earthworks, which are heavily dependent on mechanical equipment (e.g., excavators, trucks and compactors). Often, earthworks are the most costly and time-consuming component of infrastructure constructions (e.g., road, railway and airports) and current pressure for higher productivity and safety highlights the need to optimize earthworks, which is a nontrivial task. Most previous attempts at tackling this problem focus on single-objective optimization of partial processes or aspects of earthworks, overlooking the advantages of a multi-objective and global optimization. This work describes a novel optimization system based on an evolutionary multi-objective approach, capable of globally optimizing several objectives simultaneously and dynamically. The proposed system views an earthwork construction as a production line, where the goal is to optimize resources under two crucial criteria (costs and duration) and focus the evolutionary search (non-dominated sorting genetic algorithm-II) on compaction allocation, using linear programming to distribute the remaining equipment (e.g., excavators). Several experiments were held using real-world data from a Portuguese construction site, showing that the proposed system is quite competitive when compared with current manual earthwork equipment allocation.

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Traffic Engineering (TE) approaches are increasingly impor- tant in network management to allow an optimized configuration and resource allocation. In link-state routing, the task of setting appropriate weights to the links is both an important and a challenging optimization task. A number of different approaches has been put forward towards this aim, including the successful use of Evolutionary Algorithms (EAs). In this context, this work addresses the evaluation of three distinct EAs, a single and two multi-objective EAs, in two tasks related to weight setting optimization towards optimal intra-domain routing, knowing the network topology and aggregated traffic demands and seeking to mini- mize network congestion. In both tasks, the optimization considers sce- narios where there is a dynamic alteration in the state of the system, in the first considering changes in the traffic demand matrices and in the latter considering the possibility of link failures. The methods will, thus, need to simultaneously optimize for both conditions, the normal and the altered one, following a preventive TE approach towards robust configurations. Since this can be formulated as a bi-objective function, the use of multi-objective EAs, such as SPEA2 and NSGA-II, came nat- urally, being those compared to a single-objective EA. The results show a remarkable behavior of NSGA-II in all proposed tasks scaling well for harder instances, and thus presenting itself as the most promising option for TE in these scenarios.

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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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This study considers the Certified Public Accountants (CPA) partners of the audit firms as the core producers of these knowledge-based organisations and investigates the effects of audit firm-level of CPA partners’ experience in audit quality. Audit quality was measured through the audit clients’ earnings quality. The main findings suggested that the audit firm-level of CPA partners’ general experience is positively associated with audit quality. Also, the audit firm-level of CPA partners’ experience acquired with prior audit firms significantly improves audit quality. Concerning the effects of audit firm-level of CPA partners’ experience acquired in the current audit firm in audit quality, results were statistically insignificant. Finally, we found that changes in audit firm-level of CPA partners’ general experience also influence audit quality. We verified that the more positive the change in the audit firm stock of CPA partners’ general experience, the higher is audit quality. We interpret this finding as revealing that the year-to-year changes in audit firm’s stock of CPA partners’ general experience is the mechanism allowing these firms to achieve and maintain the critical mass of this resource and reach higher performance levels. We further showed that our main results were not driven by the engagement partner experience.

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The filamentous fungus Ashbya gossypii has been safely and successfully used for more than two decades in the commercial production of riboflavin (vitamin B2). Its industrial relevance combined with its high genetic similarity with Saccharomyces cerevisiae together promoted the accumulation of fundamental knowledge that has been efficiently converted into a significant molecular and in silico toolbox for its genetic engineering. This synergy has enabled a directed and sustained exploitation of A. gossypii as an industrial riboflavin producer. Although there is still room for optimizing riboflavin production, the recent years have seen an abundant advance in the exploration of A. gossypii for other biotechnological applications, such as the production of recombinant proteins, single cell oil and flavour compounds. Here, we will address the biotechnological potential of A. gossypii beyond riboflavin production by presenting (a) a physiological and metabolic perspective over this fungus; (b) the molecular toolbox available for its manipulation; and (c) commercial and emerging biotechnological applications for this industrially important fungus, together with the approaches adopted for its engineering.

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Natural selection favors the survival and reproduction of organisms that are best adapted to their environment. Selection mechanism in evolutionary algorithms mimics this process, aiming to create environmental conditions in which artificial organisms could evolve solving the problem at hand. This paper proposes a new selection scheme for evolutionary multiobjective optimization. The similarity measure that defines the concept of the neighborhood is a key feature of the proposed selection. Contrary to commonly used approaches, usually defined on the basis of distances between either individuals or weight vectors, it is suggested to consider the similarity and neighborhood based on the angle between individuals in the objective space. The smaller the angle, the more similar individuals. This notion is exploited during the mating and environmental selections. The convergence is ensured by minimizing distances from individuals to a reference point, whereas the diversity is preserved by maximizing angles between neighboring individuals. Experimental results reveal a highly competitive performance and useful characteristics of the proposed selection. Its strong diversity preserving ability allows to produce a significantly better performance on some problems when compared with stat-of-the-art algorithms.

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Publicado em "AIP Conference Proceedings" Vol. 1648

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The recent focus on the cystic fibrosis (CF) complex microbiome has led to the recognition that the microbes can interact between them and with the host immune system, affecting the disease progression and treatment routes. Although the main focus remains on the interactions between traditional pathogens, growing evidence supports the contribution and the role of emergent species. Understanding the mechanisms and the biological effects involved in polymicrobial interactions may be the key to improve effective therapies and also to define new strategies for disease control. This review focuses on the interactions between microbe-microbe and host-microbe, from an ecological point of view, discussing their impact on CF disease progression. There are increasing indications that these interactions impact the success of antimicrobial therapy. Consequently, a new approach where therapy is personalized to patients by taking into account their individual CF microbiome is suggested.

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This review tackles the issues related to disease burden caused by cervical cancer (CC) and its precursor (CIN) lesions in Brazil. A special focus is given to new technologies with potential to interfere with the development of CC by reducing the high-risk human papillomavirus (hr-HPV)-induced lesions that remain a major public health burden in all developing countries where organized screening programs do not exist. Globally, 85 % of all incident CC and 50 % of CC deaths occur in the developing countries. Unfortunately, most regions of Brazil still demonstrate high mortality rates, ranking CC as the second most common cancer among Brazilian women. Recently, CC screening programs have been tailored in the country to enable early detection of CC precursor lesions and thereby reduce cancer mortality. A combination of HPV testing with liquid-based cytology (LBC) seems to be a promising new approach in CC screening, with high expectation to offer an adequate control of CC burden in this country.

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Tese de Doutoramento em Estudos da Criança (Área de Especialidade Psicologia do Desenvolvimento e Educação)

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The various genetic systems (mitochondrial DNA, the Y-chromosome and the genome-wide autosomes) indicate that Africa is the most genetically diverse continent in the world and the most likely place of origin for anatomically modern humans. However, where in Africa modern humans arose and how the current genetic makeup within the continent was shaped is still open to debate. Here, we summarize the debate and focus especially on the maternally inherited mitochondrial DNA (mtDNA) and a recently revised chronology for the African mtDNA tree. We discuss the possible origin of modern humans in southern, eastern or Central Africa; the possibility of a migration from southern to eastern Africa more than 100 ka, carrying lineages within mtDNA haplogroup L0; the evidence for a climate-change-mediated population expansion in eastern Africa involving mtDNA haplogroup L3, leading to the “out-of-Africa” migration around 70–60 ka; the re-population of North Africa from the Near East around 40–30 ka suggested by mtDNA haplogroups U6 and M1; the evidence for population expansions and dispersals across the continent at the onset of the Holocene ; and the impact of the Bantu dispersals in Central, eastern and southern Africa within the last few millennia.

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"A workshop within the 19th International Conference on Applications and Theory of Petri Nets - ICATPN’1998"