Koska internet. Finiittiverbittömän koska X konstruktion syntaksi ja variaatio

Tutkielmassani käsittelen finiittiverbittömän koska X konstruktion variaatiota ja variaation hyväksyttävyyttä. Konstruktiokieliopin näkemysten mukaisesti käsittelen rakennetta itsenäisenä kielen yksikkönä ja tutkin sen rajoja ja suhdetta muihin konstruktioihin. Perustan päätelmäni kahteen toisiaan tukevaan aineistoon: verkosta kerättyyn 400 virkkeen tekstikorpusaineistoon ja kyselytestiaineistoon. Konstruktion prototyyppi on nominatiivimuotoinen lyhyt koska NP, jonka paikka on virkkeen lopussa. Konstruktio on kuitenkin produktiivinen, ja siinä esiintyy paljon variaatiota. Koska-rakenteet muodostavat jatkumon, jonka toisessa päässä ovat prototyyppiset koska NP:t ja toisessa päässä yleiskieliset koska-sivulauseet. Näiden väliin jäävät epäprototyyppiset koska X konstruktiot ja koska X fragmentit. Koska X fragmentit ovat finiittiverbittömiä lausemaisia konstruktioita, joiden verbittömyys on kontekstissaan tunnusmerkitöntä. Kielenkäyttäjät pitävät hyväksyttävimpinä kiteytyneitä konstrukteja. Mitä tutumpi koska X konstruktio on, sitä hyväksyttävämmäksi sen toteumat arvioidaan. Suurin ero on prototyyppisissä koska NP konstrukteissa, joita koska X konstruktiota käyttävät pitävät erittäin luontevina mutta muut kielenkäyttäjät epäkieliopillisina. Koska X konstruktio sopii kielenkäyttäjien mielestä epämuodolliseen tyyliin ja leikillisiin asiayhteyksiin. Konstruktiossa ei ole kyse ellipsistä tai lyhentämisestä, vaan sillä on oma tehtävänsä kielessä. Koska X kantaa omaa merkitystä, joka ei ole välitettävissä muilla rakenteilla. Se ilmaisee ironiaa, jonka ymmärtäminen perustuu kirjoittajan ja vastaanottajan yhteiseen tietoon. Koska X voi myös toimia referoinnin keinona, joka osoittaa väheksyvää suhtautumista referoidun ryhmän tai henkilön perusteluihin.

Caracterização de suspensões de nanopartículas poliméricas estudadas como carreadores de fármacos através de técnicas de espalhamento de luz e de raios-x a baixo ângulo

O objetivo do presente estudo foi caracterizar suspensões de nanopartículas (NP) de poli(-caprolactona) (PCL) contendo indometacina. Nanocápsulas com fármaco (NC1) foram submetidas a tratamento térmico para verificar se uma relaxação das cadeias poliméricas que recobrem o núcleo pudesse ocorrer. Resultados similares de grau de despolarização da luz espalhada ( c0), determinado por Espalhamento de luz estático, e pH, para as NC que passaram ou não por tratamento térmico, sugeriram que essa relaxação não ocorreu. NC, nanoemulsão (NE) e nanoesfera (NS) apresentaram  c0 que caracterizam espécies esféricas. No entanto, a suspensão NC3 (com fármaco em excesso) apresentou valores de  c0 que sugeriram a presença de nanocristais de fármaco em suspensão. A partir disto, analisou-se a intensidade de luz espalhada por NC0 (NC sem fármaco), NC1, NC3 e nanodispersões com e sem fármaco (ND1 e ND0, respectivamente). Houve uma queda brusca nas intensidades médias de luz espalhada pela NC3 e ND1 após armazenagem, evidenciada também através da normalização das intensidades pela Razão de Rayleigh. Estes dados, associados à redução do teor de fármaco após armazenagem, apenas para NC3 e ND1, levaram à conclusão de que são formados nanocristais de fármaco durante o preparo destas NP e que estes se depositam ao longo do tempo, reduzindo a intensidade de luz espalhada pelas amostras. Por outro lado, a NC1, quando formulada, não apresentou nanocristais nem houve formação desses após armazenagem Houve grande semelhança entre os espectros de Espalhamento de raios-X a baixo ângulo (SAXS) de NS e ND, atribuída à presença do tensoativo monostearato de sorbitano disperso nestas NP. Este tensoativo não foi identificado em espectros de SAXS de NC e NE. O mesmo perfil de espectro foi obtido para NC contendo diferentes concentrações de PCL, levando à conclusão de que as NC devem manter sua organização em nível molecular independente da concentração de PCL utilizada

Variable parameter and double sampling (X)over-bar charts in the presence of correlation: The Markov chain approach

The general assumption under which the (X) over bar chart is designed is that the process mean has a constant in-control value. However, there are situations in which the process mean wanders. When it wanders according to a first-order autoregressive (AR (1)) model, a complex approach involving Markov chains and integral equation methods is used to evaluate the properties of the (X) over bar chart. In this paper, we propose the use of a pure Markov chain approach to study the performance of the (X) over bar chart. The performance of the chat (X) over bar with variable parameters and the (X) over bar with double sampling are compared. (C) 2011 Elsevier B.V. All rights reserved.

Gráficos de controle de EWMA e de X-barra para monitoramento de processos autocorrelacionados

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

Gráficos de controle X para processos robustos

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Economic design of (X)over-bar and R charts under Weibull shock models

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

Joint (X)over-bar and R charts with variable sample sizes and sampling intervals

Recent studies have shown that the (X) over bar chart with variable sampling intervals (VSI) and/or with variable sample sizes (VSS) detects process shifts faster than the traditional (X) over bar chart. This article extends these studies for processes that are monitored by both the (X) over bar and R charts. A Markov chain model is used to determine the properties of the joint (X) over bar and R charts with variable sample sizes and sampling intervals (VSSI). The VSSI scheme improves the joint (X) over bar and R control chart performance in terms of the speed with which shifts in the process mean and/or variance are detected.

An adaptive chart for monitoring the process mean and variance

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.

Synthetic control chart for monitoring the pprocess mean and variance

In this article, we consider the synthetic control chart with two-stage sampling (SyTS chart) to control the process mean and variance. During the first stage, one item of the sample is inspected; if its value X, is close to the target value of the process mean, then the sampling is interrupted. Otherwise, the sampling goes on to the second stage, where the remaining items are inspected and the statistic T = Sigma [x(i) - mu(0) + xi sigma(0)](2) is computed taking into account all items of the sample. The design parameter is function of X-1. When the statistic T is larger than a specified value, the sample is classified as nonconforming. According to the synthetic procedure, the signal is based on Conforming Run Length (CRL). The CRL is the number of samples taken from the process since the previous nonconforming sample until the occurrence of the next nonconforming sample. If the CRL is sufficiently small, then a signal is generated. A comparative study shows that the SyTS chart and the joint X and S charts with double sampling are very similar in performance. However, from the practical viewpoint, the SyTS chart is more convenient to administer than the joint charts.

(X)over-bar charts with variable parameters

Varying the parameters of the (X) over bar chart has been explored extensively in recent years. In this paper, we extend the study of the (X) over bar chart with variable parameters to include variable action limits. The action limits establish whether the control should be relaxed or not. When the (X) over bar falls near the target, the control is relaxed so that there will be more time before the next sample and/or the next sample will be smaller than usual. When the (X) over bar falls far from the target but not in the action region, the control is tightened so that there is less time before the next sample and/or the next sample will be larger than usual. The goal is to draw the action limits wider than usual when the control is relaxed and narrower than usual when the control is tightened. This new feature then makes the (X) over bar chart more powerful than the CUSUM scheme in detecting shifts in the process mean.

Economic design of two-stage (X)over-bar charts: the Markov chain approach

When the (X) over bar chart is in use, samples are regularly taken from the process, and their means are plotted on the chart. In some cases, it is too expensive to obtain the X values, but not the values of a correlated variable Y. This paper presents a model for the economic design of a two-stage control chart, that is. a control chart based on both performance (X) and surrogate (Y) variables. The process is monitored by the surrogate variable until it signals an out-of-control behavior, and then a switch is made to the (X) over bar chart. The (X) over bar chart is built with central, warning. and action regions. If an X sample mean falls in the central region, the process surveillance returns to the (Y) over bar chart. Otherwise. The process remains under the (X) over bar chart's surveillance until an (X) over bar sample mean falls outside the control limits. The search for an assignable cause is undertaken when the performance variable signals an out-of-control behavior. In this way, the two variables, are used in an alternating fashion. The assumption of an exponential distribution to describe the length of time the process remains in control allows the application of the Markov chain approach for developing the cost function. A study is performed to examine the economic advantages of using performance and surrogate variables. (C) 2003 Elsevier B.V. All rights reserved.

(X)OVER-BAR CHARTS WITH VARIABLE SAMPLE-SIZE

The usual practice in using a control chart to monitor a process is to take samples of size n from the process every h hours This article considers the properties of the XBAR chart when the size of each sample depends on what is observed in the preceding sample. The idea is that the sample should be large if the sample point of the preceding sample is close to but not actually outside the control limits and small if the sample point is close to the target. The properties of the variable sample size (VSS) XBAR chart are obtained using Markov chains. The VSS XBAR chart is substantially quicker than the traditional XBAR chart in detecting moderate shifts in the process.

The non-central chi-square chart with two-stage samplings

In this paper, we consider the non-central chi-square chart with two stage samplings. During the first stage, one item of the sample is inspected and, depending on the result, the sampling is either interrupted, or it goes on to the second stage, where the remaining sample items are inspected and the non-central chi-square statistic is computed. The proposed chart is not only more sensitive than the joint (X) over bar and R charts, but operationally simpler too, particularly when appropriate devices, such as go-no-go gauges, can be used to decide if the sampling should go on to the second stage or not. (c) 2004 Elsevier B.V. All rights reserved.

VSSI (X)over-bar charts with sampling at fixed times

A standard (X) over bar chart for controlling the process mean takes samples of size no at specified, equally-spaced, fixed-time points. This article proposes a modification of the standard (X) over bar chart that allows one to take additional samples, bigger than no, between these fixed times. The additional samples are taken from the process when there is evidence that the process mean moved from target. Following the notation proposed by Reynolds (1996a) and Costs (1997) we shortly call the proposed (X) over bar chart as VSSIFT (X) over bar chart: where VSSIFT means variable sample size and sampling intervals with fixed times. The (X) over bar chart with the VSSIFT feature is easier to be administered than a standard VSSI (X) over bar chart that is not constrained to sample at the specified fixed times. The performances of the charts in detecting process mean shifts are comparable.

Economic design of (X)over-bar charts with variable parameters: the Markov chain approach

This paper presents an economic design of (X) over bar control charts with variable sample sizes, variable sampling intervals, and variable control limits. The sample size n, the sampling interval h, and the control limit coefficient k vary between minimum and maximum values, tightening or relaxing the control. The control is relaxed when an (X) over bar value falls close to the target and is tightened when an (X) over bar value falls far from the target. A cost model is constructed that involves the cost of false alarms, the cost of finding and eliminating the assignable cause, the cost associated with production in an out-of-control state, and the cost of sampling and testing. The assumption of an exponential distribution to describe the length of time the process remains in control allows the application of the Markov chain approach for developing the cost function. A comprehensive study is performed to examine the economic advantages of varying the (X) over bar chart parameters.