13 resultados para Mathematical and statistical techniques

em Universidade do Minho


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Tese de Doutoramento em Tecnologias e Sistemas de Informação

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BACKGROUND: Fine-needle aspiration cytology (FNAC) of serous membrane effusions may fulfil a challenging role in the diagnostic analysis of both primary and metastatic disease. From this perspective, liquid-based cytology (LBC) represents a feasible and reliable method for empowering the performance of ancillary techniques (ie, immunocytochemistry and molecular testing) with high diagnostic accuracy. METHODS: In total, 3171 LBC pleural and pericardic effusions were appraised between January 2000 and December 2013. They were classified as negative for malignancy (NM), suspicious for malignancy (SM), or positive for malignancy (PM). RESULTS: The cytologic diagnoses included 2721 NM effusions (2505 pleural and 216 pericardic), 104 SM effusions (93 pleural and 11 pericardic), and 346 PM effusions (321 pleural and 25 pericardic). The malignant pleural series included 76 unknown malignancies (36 SM and 40 PM effusions), 174 metastatic lesions (85 SM and 89 PM effusions), 14 lymphomas (3 SM and 11 PM effusions), 16 mesotheliomas (5 SM and 11 SM effusions), and 3 myelomas (all SM effusions). The malignant pericardic category included 20 unknown malignancies (5 SM and 15 PM effusions), 15 metastatic lesions (1 SM and 14 PM effusions), and 1 lymphoma (1 PM effusion). There were 411 conclusive immunocytochemical analyses and 47 molecular analyses, and the authors documented 88% sensitivity, 100% specificity, 98% diagnostic accuracy, 98% negative predictive value, and 100% positive predictive value for FNAC. CONCLUSIONS: FNAC represents a primary diagnostic tool for effusions and a reliable approach with which to determine the correct follow-up. Furthermore, LBC is useful for ancillary techniques, such as immunocytochemistry and molecular analysis, with feasible diagnostic and predictive utility.

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Commercial stents, especially metallic ones, present several disadvantages, and this gives rise to the necessity of producing or coating stents with different materials, like natural polymers, in order to improve their biocompatibility and minimize the disadvantages of metallic ones. This review paper discusses some applications of natural-based polymers in stents, namely polylactic acid (PLA) for stent development and chitosan for biocompatible coatings of stents . Furthermore, some effective stent functionalization techniques will be discussed, namely Layer by Layer (LBL) technique.

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This study aims to develop an innovative carbon fibre reinforced polymer (CFRP) laminate with a U configuration to address strengthening interventions, where the increment of both flexural and shear capacity of reinforced concrete (RC) elements is required. This strengthening solution combines the near surface mounted (NSM) and embedded through section (ETS) techniques in the same application, since these techniques have already evidenced high performance on flexural and shear strengthening of RC beams using FRP systems, respectively. In fact, the proposed hybrid technique aims to mobilize the advantages provided by these two strengthening techniques by using an innovative CFRP laminate. The strengthening efficacy of this new hybrid NSM/ETS technique was numerically assessed and compared to the corresponding efficiency of NSM and ETS techniques applied separately for the flexural and shear strengthening of RC beams, respectively. The numerical models are described and the main relevant results are presented and discussed.

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This work was supported by FCT (Fundação para a Ciência e Tecnologia) within Project Scope (UID/CEC/00319/2013), by LIP (Laboratório de Instrumentação e Física Experimental de Partículas) and by Project Search-ON2 (NORTE-07-0162- FEDER-000086), co-funded by the North Portugal Regional Operational Programme (ON.2 - O Novo Norte), under the National Strategic Reference Framework, through the European Regional Development Fund.

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There is an increasing interest in thin and flexible energy storage devices to meet modern society needs for applications such as, radio frequency sensing, interactive packaging and other consumer products. Printed batteries comply these requirements and are an excellent alternative to conventional batteries for many applications. Flexible and micro-batteries are also included in the area of printed batteries whenever fabricated by printing technologies. The main characteristics, advantages, disadvantages, developments, and printing techniques of printed batteries are presented and discussed in this review. The state-of-art takes into account both the research and industrial levels. In the academic one, the research progress of printed batteries is summarized divided in lithium-ion battery (Li-ion), zinc-manganese dioxide (Zn-MnO2), and other battery types with emphasis on the different materials for anode, cathode and separator as well as in the battery design. With respect to the industrial state-of-art, materials, device formulations and manufacturing techniques are presented. Finally, the prospects and challenges of printed batteries are discussed.

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This paper presents a systematic study for the production of poly(vinylidene fluoride-hexafluoropropylene), P(VDF-HFP), porous films using solvent evaporation (SE) and non-solvent induced phase separation (NIPS) techniques. Parameters such as volume fraction of the copolymer solution, film thickness, time exposure to air, non-solvent and temperature of the coagulation bath were investigated on the morphology, crystallization and mechanical properties of the samples. Films with different porous morphologies including homogeneous pore sizes, macrovoids and spherulites were obtained depending on the processing conditions, which in turn affect the wettability and mechanical properties of the material. Knowing that the phase content of the films also depends on the processing conditions, this paper shows that P(VDF-HFP) films with tailored porous morphology, electroactive phase content, hydrophobicity, cristallinity and mechanical properties can be achieved for a specific application using the adequate SE and NIPS techniques conditions.

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Propolis is a chemically complex biomass produced by honeybees (Apis mellifera) from plant resins added of salivary enzymes, beeswax, and pollen. The biological activities described for propolis were also identified for donor plants resin, but a big challenge for the standardization of the chemical composition and biological effects of propolis remains on a better understanding of the influence of seasonality on the chemical constituents of that raw material. Since propolis quality depends, among other variables, on the local flora which is strongly influenced by (a)biotic factors over the seasons, to unravel the harvest season effect on the propolis chemical profile is an issue of recognized importance. For that, fast, cheap, and robust analytical techniques seem to be the best choice for large scale quality control processes in the most demanding markets, e.g., human health applications. For that, UV-Visible (UV-Vis) scanning spectrophotometry of hydroalcoholic extracts (HE) of seventy-three propolis samples, collected over the seasons in 2014 (summer, spring, autumn, and winter) and 2015 (summer and autumn) in Southern Brazil was adopted. Further machine learning and chemometrics techniques were applied to the UV-Vis dataset aiming to gain insights as to the seasonality effect on the claimed chemical heterogeneity of propolis samples determined by changes in the flora of the geographic region under study. Descriptive and classification models were built following a chemometric approach, i.e. principal component analysis (PCA) and hierarchical clustering analysis (HCA) supported by scripts written in the R language. The UV-Vis profiles associated with chemometric analysis allowed identifying a typical pattern in propolis samples collected in the summer. Importantly, the discrimination based on PCA could be improved by using the dataset of the fingerprint region of phenolic compounds ( = 280-400m), suggesting that besides the biological activities of those secondary metabolites, they also play a relevant role for the discrimination and classification of that complex matrix through bioinformatics tools. Finally, a series of machine learning approaches, e.g., partial least square-discriminant analysis (PLS-DA), k-Nearest Neighbors (kNN), and Decision Trees showed to be complementary to PCA and HCA, allowing to obtain relevant information as to the sample discrimination.

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Dissertação de mestrado em Engenharia Industrial

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A summary is presented of ATLAS searches for gluinos and first- and second-generation squarks in final states containing jets and missing transverse momentum, with or without leptons or b-jets, in the s√=8 TeV data set collected at the Large Hadron Collider in 2012. This paper reports the results of new interpretations and statistical combinations of previously published analyses, as well as a new analysis. Since no significant excess of events over the Standard Model expectation is observed, the data are used to set limits in a variety of models. In all the considered simplified models that assume R-parity conservation, the limit on the gluino mass exceeds 1150 GeV at 95% confidence level, for an LSP mass smaller than 100 GeV. Furthermore, exclusion limits are set for left-handed squarks in a phenomenological MSSM model, a minimal Supergravity/Constrained MSSM model, R-parity-violation scenarios, a minimal gauge-mediated supersymmetry breaking model, a natural gauge mediation model, a non-universal Higgs mass model with gaugino mediation and a minimal model of universal extra dimensions.

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Mathematical and computational models play an essential role in understanding the cellular metabolism. They are used as platforms to integrate current knowledge on a biological system and to systematically test and predict the effect of manipulations to such systems. The recent advances in genome sequencing techniques have facilitated the reconstruction of genome-scale metabolic networks for a wide variety of organisms from microbes to human cells. These models have been successfully used in multiple biotechnological applications. Despite these advancements, modeling cellular metabolism still presents many challenges. The aim of this Research Topic is not only to expose and consolidate the state-of-the-art in metabolic modeling approaches, but also to push this frontier beyond the current edge through the introduction of innovative solutions. The articles presented in this e-book address some of the main challenges in the field, including the integration of different modeling formalisms, the integration of heterogeneous data sources into metabolic models, explicit representation of other biological processes during phenotype simulation, and standardization efforts in the representation of metabolic models and simulation results.

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Dissertação de mestrado integrado em Engenharia Civil