926 resultados para Multivariate Statistical Process Monitoring


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Tese (doutorado)—Universidade de Brasília, Faculdade de Ciências da Saúde, Programa de Pós-Graduação em Ciências da Saúde, 2016.

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A presente dissertação de mestrado tem como finalidade primordial mapear a intervenção técnica com base processual, junto de uma família beneficiária de rendimento social de inserção. Mais especificamente aferir as problemáticas e vulnerabilidades da família ao longo do acompanhamento, confrontando com a avaliação diagnóstica e a elaboração de todo o acompanhamento. A investigação segue uma metodologia de estudo de caso com análise documental de um processo acompanhado há 18 anos, cruzando as evidências da intervenção aferindo avaliação diagnostica, a conceção do plano de intervenção e respetivo processo de monitorização, acompanhamento e avaliação. O presente estudo apresenta a necessidade de estruturação de instrumentos de monitorização de todo acompanhamento e uma reflexão sobre as implicações da medida relativamente ao comportamento das famílias beneficiária.

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The macrofauna assemblages of a XVIIth century shipwreck off southern Portugal were studied and compared with those of nearby natural reefs and sandy bottoms, by underwater visual census. A total of 11 173 specimens of 224 different fauna taxa and 12 phyla were recorded. Natural reefs had the highest density of specimens (35 122 / 1000 m2) followed by the shipwreck (21 392 / 1000 m2) and the sandy bottoms (3771 / 1000 m2). Three biodiversity indices were estimated (Margalef, Shannon- Wiener and Pielou), with the natural reefs showing the highest values. However, the shipwreck presented values relatively similar to those of the natural reefs for the Shannon-Wiener and Pielou indices. The three habitats were clearly distinguishable by multivariate statistical analysis, with the average dissimilarity between sand and shipwreck, and between sand and natural reefs being much higher than that between the shipwreck and the natural reefs. The shipwreck had higher abundances of some commercially important species, such as the pouting Trisopterus luscus, European conger Conger conger, and common spider crab Maja squinado, as well as some vulnerable and threatened species such as the pink seafan Eunicella verrucosa. The results presented emphasize the importance of this habitat on the southern Portuguese coast.

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Antigen design is generally driven by the need to obtain enhanced stability,efficiency and safety in vaccines.Unfortunately,the antigen modification is rarely proceeded in parallel with analytical tools development characterization.The analytical tools set up is required during steps of vaccine manufacturing pipeline,for vaccine production modifications,improvements or regulatory requirements.Despite the relevance of bioconjugate vaccines,robust and consistent analytical tools to evaluate the extent of carrier glycosylation are missing.Bioconjugation is a glycoengineering technology aimed to produce N-glycoprotein in vivo in E.coli cells,based on the PglB-dependent system by C. jejuni,applied for production of several glycoconjugate vaccines.This applicability is due to glycocompetent E. coli ability to produce site-selective glycosylated protein used,after few purification steps, as vaccines able to elicit both humoral and cell-mediate immune-response.Here, S.aureus Hla bioconjugated with CP5 was used to perform rational analytical-driven design of the glycosylation sites for the glycosylation extent quantification by Mass Spectrometry.The aim of the study was to develop a MS-based approach to quantify the glycosylation extent for in-process monitoring of bioconjugate production and for final product characterization.The three designed consensus sequences differ for a single amino-acid residue and fulfill the prerequisites for engineered bioconjugate more appropriate from an analytical perspective.We aimed to achieve an optimal MS detectability of the peptide carrying the consensus sequences,complying with the well-characterized requirements for N-glycosylation by PglB.Hla carrier isoforms,bearing these consensus sequences allowed a recovery of about 20 ng/μg of periplasmic protein glycosylated at 40%.The SRM-MS here developed was successfully applied to evaluate the differential site occupancy when carrier protein present two glycosites.The glycosylation extent in each glycosite was determined and the difference in the isoforms were influenced either by the overall source of protein produced and by the position of glycosite insertion.The analytical driven design of the bioconjugated antigen and the development of accurate,precise and robust analytical method allowed to finely characterize the vaccine.

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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

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Traditionally, an (X) over bar chart is used to control the process mean and an R chart is used to control the process variance. However, these charts are not sensitive to small changes in the process parameters. The adaptive ($) over bar and R charts might be considered if the aim is to detect small disturbances. Due to the statistical character of the joint (X) over bar and R charts with fixed or adaptive parameters, they are not reliable in identifing the nature of the disturbance, whether it is one that shifts the process mean, increases the process variance, or leads to a combination of both effects. In practice, the speed with which the control charts detect process changes may be more important than their ability in identifying the nature of the change. Under these circumstances, it seems to be advantageous to consider a single chart, based on only one statistic, to simultaneously monitor the process mean and variance. In this paper, we propose the adaptive non-central chi-square statistic chart. This new chart is more effective than the adaptive (X) over bar and R charts in detecting disturbances that shift the process mean, increase the process variance, or lead to a combination of both effects. Copyright (c) 2006 John Wiley & Sons, Ltd.

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This work aims to investigate the efficiency of digital signal processing tools of acoustic emission signals in order to detect thermal damages in grinding processes. To accomplish such a goal, an experimental work was carried out for 15 runs in a surface grinding machine operating with an aluminum oxide grinding wheel and ABNT 1045 Steel as work material. The acoustic emission signals were acquired from a fixed sensor placed on the workpiece holder. A high sampling rate data acquisition system working at 2.5 MHz was used to collect the raw acoustic emission instead of the root mean square value usually employed. Many statistical analyses have shown to be effective to detect burn, such as the root mean square (RMS), correlation of the AE, constant false alarm rate (CFAR), ratio of power (ROP) and mean-value deviance (MVD). However, the CFAR, ROP, Kurtosis and correlation of the AE have been presented more sensitive than the RMS. Copyright © 2006 by ABCM.

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Purpose - The aim of this paper is to present a synthetic chart based on the non-central chi-square statistic that is operationally simpler and more effective than the joint X̄ and R chart in detecting assignable cause(s). This chart will assist in identifying which (mean or variance) changed due to the occurrence of the assignable causes. Design/methodology/approach - The approach used is based on the non-central chi-square statistic and the steady-state average run length (ARL) of the developed chart is evaluated using a Markov chain model. Findings - The proposed chart always detects process disturbances faster than the joint X̄ and R charts. The developed chart can monitor the process instead of looking at two charts separately. Originality/value - The most important advantage of using the proposed chart is that practitioners can monitor the process by looking at only one chart instead of looking at two charts separately. © Emerald Group Publishing Limted.

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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

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Objective: Processes occurring in the course of psychotherapy are characterized by the simple fact that they unfold in time and that the multiple factors engaged in change processes vary highly between individuals (idiographic phenomena). Previous research, however, has neglected the temporal perspective by its traditional focus on static phenomena, which were mainly assessed at the group level (nomothetic phenomena). To support a temporal approach, the authors introduce time-series panel analysis (TSPA), a statistical methodology explicitly focusing on the quantification of temporal, session-to-session aspects of change in psychotherapy. TSPA-models are initially built at the level of individuals and are subsequently aggregated at the group level, thus allowing the exploration of prototypical models. Method: TSPA is based on vector auto-regression (VAR), an extension of univariate auto-regression models to multivariate time-series data. The application of TSPA is demonstrated in a sample of 87 outpatient psychotherapy patients who were monitored by postsession questionnaires. Prototypical mechanisms of change were derived from the aggregation of individual multivariate models of psychotherapy process. In a 2nd step, the associations between mechanisms of change (TSPA) and pre- to postsymptom change were explored. Results: TSPA allowed a prototypical process pattern to be identified, where patient's alliance and self-efficacy were linked by a temporal feedback-loop. Furthermore, therapist's stability over time in both mastery and clarification interventions was positively associated with better outcomes. Conclusions: TSPA is a statistical tool that sheds new light on temporal mechanisms of change. Through this approach, clinicians may gain insight into prototypical patterns of change in psychotherapy.

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Syndromic surveillance (SyS) systems currently exploit various sources of health-related data, most of which are collected for purposes other than surveillance (e.g. economic). Several European SyS systems use data collected during meat inspection for syndromic surveillance of animal health, as some diseases may be more easily detected post-mortem than at their point of origin or during the ante-mortem inspection upon arrival at the slaughterhouse. In this paper we use simulation to evaluate the performance of a quasi-Poisson regression (also known as an improved Farrington) algorithm for the detection of disease outbreaks during post-mortem inspection of slaughtered animals. When parameterizing the algorithm based on the retrospective analyses of 6 years of historic data, the probability of detection was satisfactory for large (range 83-445 cases) outbreaks but poor for small (range 20-177 cases) outbreaks. Varying the amount of historical data used to fit the algorithm can help increasing the probability of detection for small outbreaks. However, while the use of a 0·975 quantile generated a low false-positive rate, in most cases, more than 50% of outbreak cases had already occurred at the time of detection. High variance observed in the whole carcass condemnations time-series, and lack of flexibility in terms of the temporal distribution of simulated outbreaks resulting from low reporting frequency (monthly), constitute major challenges for early detection of outbreaks in the livestock population based on meat inspection data. Reporting frequency should be increased in the future to improve timeliness of the SyS system while increased sensitivity may be achieved by integrating meat inspection data into a multivariate system simultaneously evaluating multiple sources of data on livestock health.

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This paper proposes an architecture for machining process and production monitoring to be applied in machine tools with open Computer numerical control (CNC). A brief description of the advantages of using open CNC for machining process and production monitoring is presented with an emphasis on the CNC architecture using a personal computer (PC)-based human-machine interface. The proposed architecture uses the CNC data and sensors to gather information about the machining process and production. It allows the development of different levels of monitoring systems with mininium investment, minimum need for sensor installation, and low intrusiveness to the process. Successful examples of the utilization of this architecture in a laboratory environment are briefly described. As a Conclusion, it is shown that a wide range of monitoring solutions can be implemented in production processes using the proposed architecture.