In multivariate statistical process control it is generally assumed that the process variables follow a multivariate normal distribution with mean vector " and covariance matrix •, but this is rarely satisfied in practice. Some robust control charts have been developed to monitor the mean and variance of univariate processes, or the mean vector " of multivariate processes, but the development of robust multivariate charts for monitoring • has not been adequately addressed. The control charts that are most affected by departures from normality are actually the charts for • not the charts for ". In this article, the robust design of several MEWMA-type control charts for monitoring • is investigated. In particular, the robustness and efficiency of different MEWMA-type control charts are compared for the in-control and out-of-control cases over a variety of multivariate distributions. Additionally, the total extra quadratic loss is proposed to evaluate the overall performance of control charts for multivariate processes. / Ph. D.

A performance analysis of three different Minimax control charts is performed with respect to their Chi-Square control chart counterparts under several different conditions. A unique control chart must be constructed for each process described by a unique combination of quality characteristic mean vector and associated covariance matrix. The three different charts under consideration differ in the number of quality characteristic variables of concern. In each case, without loss of generality the in-control quality characteristic mean vector is assumed to have zero entries and the associated covariance matrix is assumed to have non-negative entries. The performance of the Chi-Square and Minimax charts are compared under different values of the sample size, the probability of a Type I error, and selected shifts in the quality characteristic mean vector. Minimax and Chi-Square charts that are compared share identical in-control average run lengths (ARL) making the out-of-control ARL the appropriate performance measure. A combined Tausworthe pseudorandom number generator is used to generate the out-of-control mean vectors. Issues regarding multivariate uniform pseudorandom number generation are addressed. / Master of Science

abstract: Mobile ad hoc networks (MANETs) have attracted attention for mission critical applications. This dissertation investigates techniques of statistical monitoring and control for overhead reduction in a proactive MANET routing protocol. Proactive protocols transmit overhead periodically. Instead, we propose that the local conditions of a node should determine this transmission decision. While the goal is to minimize overhead, a balance in the amount of overhead transmitted and the performance achieved is required. Statistical monitoring consists of techniques to determine if a characteristic has shifted away from an in-control state. A basic tool for monitoring is a control chart, a time-oriented representation of the characteristic. When a sample deviates outside control limits, a significant change has occurred and corrective actions are required to return to the in-control state. We investigate the use of statistical monitoring of local conditions in the Optimized Link State Routing (OLSR) protocol. Three versions are developed. In A-OLSR, each node uses a Shewhart chart to monitor betweenness of its two-hop neighbourhood. Betweenness is a social network metric that measures a node's influence; betweenness is larger when a node has more influence. Changes in topology are associated with changes in betweenness. We incorporate additional local node conditions including speed, density, packet arrival rate, and number of flows it forwards in A+-OLSR. Response Surface Methodology (RSM) is used to optimize timer values. As well, the Shewhart chart is replaced by an Exponentially Weighted Moving Average (EWMA) chart, which is more sensitive to small changes in the characteristic. It is known that control charts do not work as well in the presence of correlation. Hence, in A*-OLSR the autocorrelation in the time series is removed and an Auto-Regressive Integrated Moving Average (ARIMA) model found; this removes the dependence on node speed. A*-OLSR also extends monitoring to two characteristics concurrently using multivariate cumulative sum (MCUSUM) charts. The protocols are evaluated in simulation, and compared to OLSR and its variants. The techniques for statistical monitoring and control are general and have great potential to be applied to the adaptive control of many network protocols. / Dissertation/Thesis / Ph.D. Computer Science 2012

abstract: The complexity of supply chains (SC) has grown rapidly in recent years, resulting in an increased difficulty to evaluate and visualize performance. Consequently, analytical approaches to evaluate SC performance in near real time relative to targets and plans are important to detect and react to deviations in order to prevent major disruptions. Manufacturing anomalies, inaccurate forecasts, and other problems can lead to SC disruptions. Traditional monitoring methods are not sufficient in this respect, because com- plex SCs feature changes in manufacturing tasks (dynamic complexity) and carry a large number of stock keeping units (detail complexity). Problems are easily confounded with normal system variations. Motivated by these real challenges faced by modern SC, new surveillance solutions are proposed to detect system deviations that could lead to disruptions in a complex SC. To address supply-side deviations, the fitness of different statistics that can be extracted from the enterprise resource planning system is evaluated. A monitoring strategy is first proposed for SCs featuring high levels of dynamic complexity. This presents an opportunity for monitoring methods to be applied in a new, rich domain of SC management. Then a monitoring strategy, called Heat Map Contrasts (HMC), which converts monitoring into a series of classification problems, is used to monitor SCs with both high levels of dynamic and detail complexities. Data from a semiconductor SC simulator are used to compare the methods with other alternatives under various failure cases, and the results illustrate the viability of our methods. To address demand-side deviations, a new method of quantifying forecast uncer- tainties using the progression of forecast updates is presented. It is illustrated that a rich amount of information is available in rolling horizon forecasts. Two proactive indicators of future forecast errors are extracted from the forecast stream. This quantitative method re- quires no knowledge of the forecasting model itself and has shown promising results when applied to two datasets consisting of real forecast updates. / Dissertation/Thesis / Doctoral Dissertation Industrial Engineering 2015

Successfully implementing continuous improvement and quality methodologies have been challenging over the past century. Challenges occur in managing change in the organization, which can be effectively addressed through relationship building in concert with strong leadership. Even with solid leadership, change management, and relationship building, initiatives have challenges quantifying and identifying when to intervene in order to keep them on track for success. Creating a quantitative model using a 3-parameter logit s-curve, Blythe S-curve, to illustrate the growth and decay of the handoff of projects to the process owner will allow leadership the knowledge and direction to keep the initiative moving in a positive direction. Leveraging the Hotelling T2 multivariate charts on the residuals derived from the Blythe S-curve will give definitive statistical evidence on when to intervene on projects in the earliest possible time frame. Leaders in organizations implementing and actively using change management, relationship building, the Blythe S-curve model, and Hotelling T2 multivariate charts to drive quality and continuous improvement methodologies in their organization will improve the likelihood of success.

CNPq - Conselho Nacional de Desenvolvimento Científico e Tecnológico / A Lei nº 13.033/2014 estabelece a adição de 7 ± 0,5% (v/v) de biodiesel ao óleo diesel utilizado no sistema viário e proíbe a adição de óleos vegetais ou quaisquer solventes nesta mistura. No entanto, há casos de adição irregular de óleos vegetais e/ou residuais no diesel devido, principalmente, ao baixo custo destas matérias-primas em comparação com o produto final ou contaminado durante seu transporte e armazenamento. Assim, são necessárias análises que forneçam respostas imediatas e eficientes para garantir a qualidade dos combustíveis comercializados. Nesse sentido, o presente trabalho propõe o monitoramento da qualidade das misturas biodiesel/diesel (7% de biodiesel e 93% de diesel) usando a Espectrometria no Infravermelho Médio associada a Cartas de Controle Multivariadas baseadas no sinal analítico líquido (NAS). Os biodieseis foram produzidos a partir de óleos de macaúba e algodão usando metanol e etanol. Para cada modelo, foram desenvolvidas três cartas (gráficos): a Carta NAS, que corresponde ao analito de interesse (nesse caso o biodiesel), a carta interferente, relacionadas às contribuições de outros componentes na amostra (diesel), e a carta resíduo correspondendo à variação não sistemática nos espectros (ruído instrumental). Foram analisadas 1508 amostras utilizadas na calibração e validação de seis modelos (biodieseis etílicos e metílicos de algodão, biodieseis metílicos da amêndoa da macaúba, biodieseis metílicos e etílicos do mesocarpo da macaúba, e também de B7 comercial). Na etapa de calibração foram usadas 103 amostras dentro das especificações de qualidade, amostras estas usadas para estabelecerem os limites estatísticos para cada carta. A etapa de validação se deu com amostras dentro e fora das especificações de qualidade (1405 amostras). A validação com amostras fora dos padrões de qualidade foi feita de duas maneiras: em relação ao teor de biodiesel no diesel e à presença de adulterantes no biodiesel, no diesel e na mistura. A presença de adulterantes se deu por substituição parcial do biodiesel por óleos de soja, milho e residual, substituição parcial do diesel por óleo lubrificante, querosene e gasolina e por adição direta dos adulterantes citados na mistura biodiesel/diesel (B7) na faixa de 3,5 a 43,5% (v/v) correspondendo a uma faixa de adulteração de 0,2 a 30% (v/v) na mistura. Assim, foi possível separar as amostras conformes e não conformes tanto em relação ao teor de biodiesel no diesel quanto à presença de adulterantes, uma vez que as amostras dentro das especificações ficaram dentro dos limites estabelecidos e as amostras fora das especificações saíram do limite em pelo menos uma das cartas. Desta maneira, os resultados mostraram que o método descrito neste trabalho é uma alternativa viável, eficiente e rápida no controle de qualidade de biocombustível. / The Law No. 13.033/2014 establishes the addition of 7 ± 0.5% (v/v) of biodiesel to the diesel used in the road system and prohibits the addition of vegetable oils or any solvents to this mixture. However, there are cases of irregular addition of vegetable and/or waste oils in diesel, mainly due to the low cost of these raw materials when compared to the final or contaminated product during transportation and storage. Thus, analyzes are necessary to provide immediate and efficient responses in order to ensure the quality of the marketed fuels. In this sense, the present study proposed the quality monitoring of biodiesel/diesel blends (7% biodiesel and 93% diesel) through the use of the Medium Infrared Spectrometry associated with Multivariate Control Charts based on the liquid analytical signal (NAS). Biodiesel was produced from macaúba (acrocomia aculeate) and cotton oils using methanol and ethanol. For each model, three charts were developed: the NAS Chart, which corresponds to the analyte of interest (in this case biodiesel); the interfering chart, related to the contributions of other components in the sample (diesel); and the waste chart, corresponding to non-systematic variation in spectra (instrumental noise). A total of 1508 samples were used for the calibration and validation of the six models (ethyl and cotton biodiesel, macaúba almond (acrocomia aculeate) methyl biodiesel, macaúba mesocarp (acrocomia aculeate) methyl and biodiesel, and also commercial B7 biodiesel). At the calibration stage, 103 samples were used within the quality specifications, these samples were used to establish the statistical limits for each chart. The validation step was carried out with samples within and out of the quality specifications (1405 samples). The validation with non-standard quality samples was carried out in two ways: in relation to the biodiesel content in diesel and the presence of adulterants in biodiesel, diesel and blend. The presence of adulterants was due to the partial replacement of biodiesel by soybean, corn and residual oils; the partial replacement of diesel by lubricating oil, kerosene and gasoline; and by direct addition of the adulterants mentioned in the biodiesel/diesel blend (B7) in the range of 3.5 to 43.5% (v/v) corresponding to an adulteration range of 0.2 to 30% (v/v) in the blend. Thus, it was possible to separate the conforming and non-conforming samples, both regarding the biodiesel content in diesel and the presence of adulterants, since the samples within the specifications were within the established limits, and the samples out of the specifications have exceeded the limit in least one of the charts. Thus, the results have showed that the method described in this study is a viable, efficient and fast alternative to the biofuel quality control. / Tese (Doutorado)

It is often important to detect an increase in the frequency of some event. Particular attention is given to medical events such as mortality or the incidence of a given disease, infection or birth defect. Observations are regularly taken in which either an incidence occurs or one does not. This dissertation contains the result of an investigation of prospective monitoring techniques in two distinct surveillance situations. In the first situation, the observations are assumed to be the results of independent Bernoulli trials. Some have suggested adapting the scan statistic to monitor such rates and detect a rate increase as soon as possible after it occurs. Other methods could be used in prospective surveillance, such as the Bernoulli cumulative sum (CUSUM) technique. Issues involved in selecting parameters for the scan statistic and CUSUM methods are discussed, and a method for computing the expected number of observations needed for the scan statistic method to signal a rate increase is given. A comparison of these methods shows that the Bernoulli CUSUM method tends to be more effective in detecting increases in the rate. In the second situation, the incidence information is available at multiple locations. In this case the individual sites often report a count of incidences on a regularly scheduled basis. It is assumed that the counts are Poisson random variables which are independent over time, but the counts at any given time are possibly correlated between regions. Multivariate techniques have been suggested for this situation, but many of these approaches have shortcomings which have been demonstrated in the quality control literature. In an attempt to remedy some of these shortcomings, a new control chart is recommended based on a multivariate exponentially weighted moving average. The average run-length performance of this chart is compared with that of the existing methods. / Ph. D.

Submitted by Maike Costa (maiksebas@gmail.com) on 2016-05-16T13:15:08Z No. of bitstreams: 1 arquivo total.pdf: 6619153 bytes, checksum: ab30577da64fe07f90d28f172fea6661 (MD5) / Made available in DSpace on 2016-05-16T13:15:08Z (GMT). No. of bitstreams: 1 arquivo total.pdf: 6619153 bytes, checksum: ab30577da64fe07f90d28f172fea6661 (MD5) Previous issue date: 2015-04-06 / Statistical process control is becoming increasingly important in the pharmaceutical industry, since it can act on the product quality assurance and monitoring of production efficiency. Regulatory agencies worldwide already include multivariate statistical process control on their recommendations. In this work we perform three applications of multivariate control charts to detect small variations in monitoring pharmaceutical process. The studies were performed in the Laboratório Farmacêutico do Estado de Pernambuco Governador Miguel Arraes - LAFEPE using process data and addressing real problems of the industry routine. The first one consists in the elaboration of the Annual Product Review using multivariate control charts based on Principal Component Analysis, accompanied by a simple and unique methodology for identification of multivariate signal out of control, which was performed by decomposition of T2 statistics based on the loadings of VARIMAX rotation. The method developed has allowed the sector responsible for the Quality Assurance industry to verify the variables that most impact the process. The second application consists in monitoring of the degradation of captopril in tablets by High Performance Liquid Chromatograph measurements. For this aim multivariate control charts based on residuals of principal component analysis were built. The weighting of the retention time of the chromatogram in the region of captopril disulfide highlights the residual chart for revealing slight changes in the chromatographic standards. The third and last application consists in monitoring of the degradation of captopril in tablets by near infrared and middle infrared measurements. For this aim T2 control charts were built by using a variable selection method and a CUSUM-COT scheme. Both the techniques showed to be able for detection of expired samples and Middle infrared was able to identify slight changes during degradation process. Keywords: Monitoring of Process, Multivariate Control Charts, Annual Prod / O controle estatístico de processos vem ganhando importância na indústria farmacêutica, uma vez que pode atuar na garantia da qualidade do produto e no monitoramento da eficiência da produção. Agências regulatórias no mundo já incluem o controle estatístico de processos multivariado em suas recomendações. Neste trabalho realizamos três aplicações de cartas de controle multivariadas para detectar pequenas variações em processos na indústria farmacêutica. Os estudos foram realizados no Laboratório Farmacêutico do Estado de Pernambuco Governador Miguel Arraes - LAFEPE, empregando dados do processo e abordando problemas reais da rotina da indústria. A primeira aplicação consiste na elaboração da Revisão Periódica de Produtos empregando cartas de controle multivariadas baseadas em Análise de Componentes Principais, acompanhada de uma metodologia simples e original para identificação do sinal multivariado fora de controle por meio da decomposição da estatística T2 com base nos pesos da rotação VARIMAX. O procedimento desenvolvido permitiu monitorar e diagnosticar os desvios no processo produtivo, além de permitir ao setor responsável pela Garantia da Qualidade verificar as variáveis que mais impactaram o processo. A segunda aplicação é demonstrada em estudos de estabilidade de medicamentos empregando Cromatografia Líquida de Alta Eficiência. No estudo de estabilidade a carta de resíduos das componentes principais permitiu monitorar o processo de degradação de comprimidos de captopril. A ponderação do cromatograma na região do dissulfeto de captopril sensibiliza a carta de resíduos para revelar alterações sutis no padrão cromatográfico. A terceira aplicação envolve estudos de estabilidade empregando a espectroscopia no infravermelho próximo e médio. Nesta aplicação, conclui-se que ambas as técnicas são eficientes em detectar amostras vencidas. O Processo de degradação do captopril pode ser acompanhado pela carta de resíduos baseada nos espectros do infravermelho próximo. No infravermelho médio, a carta T2 construída com um método de seleção de variáveis e um esquema CUSUM-COT também foi eficiente em detectar as pequenas alterações durante o processo de degradação.

Orientador: Ronei Jesus Poppi / Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Química / Made available in DSpace on 2018-08-22T08:18:21Z (GMT). No. of bitstreams: 1 Avila_ThiagoCarvalhode_M.pdf: 7831860 bytes, checksum: 010f2295e00f097a9ecfaf3f498a7069 (MD5) Previous issue date: 2013 / Resumo: Este trabalho visou o uso de Espectroscopia Raman e de Quimiometria para monitoramento e controle da fermentação de glicose por Saccharomyces cerevisiae. Na primeira etapa, foi utilizada calibração multivariada baseada no método dos Mínimos Quadrados Parciais (PLS) para quantificação de glicose, etanol, glicerol, ácido acético e células. Os modelos foram desenvolvidos baseados nos valores de concentração obtidos pelos métodos de referência, cromatografia líquida de alta eficiência ¿ HPLC e espectrofotometria UV/Vis. Tanto na etapa de calibração quanto na de validação, a otimização foi realizada com eliminação de amostras anômalas, baseada nos valores de leverage, resíduos e escores. Na segunda etapa, cartas de controle multivariadas foram usadas para identificação de falhas em bateladas durante o processo de fermentação. Foram construídos modelos MPCA (Análise de Componentes Principais Multimodo) a partir de bateladas NOC (Condições Normais de Operação). As cartas de controle multivariadas foram aplicadas em dois modos de desdobramento dos dados obtidos durante o monitoramento, um preservando a direção das bateladas e outro a direção do tempo. As falhas estudadas foram temperatura, mudança no substrato e contaminação do sistema. No modo de desdobramento por bateladas, a carta de controle Q foi eficiente para detecção das falhas estudas, fato comprovado pela classificação correta de três bateladas NOC como dentro de controle. No entanto, a carta de controle T2 não foi capaz de identificar as falhas estudadas corretamente como fora de controle. O modo de desdobramento pelo tempo também apresentou classificações corretas das falhas estudadas / Abstract: This work aims the use of Raman Spectroscopy and Chemometrics in the monitoring and control in the fermentation of the glucose by Saccharomyces cerevisiae. In the first step, it was applied the multivariate calibration based on Partial Least Squares (PLS) for the quantification of glucose, ethanol, glycerol, acetic acid and cells. The developed of calibration models was performed against the concentration values obtained by the reference methods, High Performance Liquid Chromatography and UV/Vis spectrophotometer. The optimization of the calibration and validation steps, the elimination of outliers was performed based on the values of leverage, residues and scores. In the second step, multivariate control charts were used for identification of batch-fault during the fermentation process. Multi-way Principal Component Analysis (MPCA) models were developed from batch NOC (Normal Operation Conditions). The multivariate control charts were based on two modes of unfolding the multi-way data, obtained during monitoring, one preserving the direction of the batch and another the direction of time. The fault studied were temperature, changes in the substrate and contamination of the system. In unfolding batch mode, the chart Q was effective for detection of the faults studied, proven by the correctly classification of 3 NOC batches as in control. However, the chart T2 failed to identify faults studied. The unfolding in time mode, also presented correct classifications of the faults studied / Mestrado / Quimica Analitica / Mestre em Química

Submitted by JOSIANE SANTOS DE OLIVEIRA (josianeso) on 2017-05-19T12:43:37Z No. of bitstreams: 1 Davenilcio Luiz de. Souza_.pdf: 539499 bytes, checksum: cf86851f0b7523f3b7d78589539fdbcb (MD5) / Made available in DSpace on 2017-05-19T12:43:37Z (GMT). No. of bitstreams: 1 Davenilcio Luiz de. Souza_.pdf: 539499 bytes, checksum: cf86851f0b7523f3b7d78589539fdbcb (MD5) Previous issue date: 2017-03-13 / Nenhuma / Os crimes de lavagem de dinheiro têm provocado grandes perdas aos países e a seus sistemas financeiros, o volume de dados em transações digitais representa dificuldade para a detecção deste tipo de ilícito. As auditorias em dados financeiros mostram-se limitadas na identificação de fraudes, pois em grande parte, ainda são realizadas com dados coletados por amostragem e incapazes de identificar as situações de delito em tempo real. Este trabalho, visando auxiliar no atendimento a esta lacuna, tem por objetivo propor um método estatístico de monitoramento por Cartas de Controle multivariadas, com base na Lei de Benford, para a detecção de suspeitas de fraude em lançamentos financeiros, entre eles os devidos à lavagem de dinheiro. Foi definido um modelo conceitual com distribuição de probabilidades representando dados oriundos de lançamentos financeiros, e adotada a suposição de que aderem a distribuição da Lei de Benford. Posteriormente foi considerada a distribuição empírica, estimada a partir dos próprios dados e dois procedimentos foram testados para verificar as suspeitas de fraude por lavagem de dinheiro utilizando a avaliação dos primeiros dígitos significativos: A Carta de Controle multivariada _2 e a Carta de Controle multivariada T2 de Hotelling. Foram simulados dados com auxílio do software R-Project até a ocorrência do 50.000o sinal. Foram avaliados casos simulados e reais, com o fim de exemplificar a operação do método. A partir da simulação, as duas Cartas de Controle testadas foram avaliadas quanto ao ARL, isto é, o número médio de observações até sinalizar que a série passou a operar em um estado fora de controle, o que significa a suspeita de lançamentos fraudulentos. Após aplicação do método de análise retrospectiva, com base nas proporções dos primeiros dígitos de Benford em lançamentos financeiros da campanha para Prefeito em 2016, não foram evidenciadas suspeitas de fraude nos dados obtidos junto ao sítio do Tribunal Superior Eleitoral (TSE). Em um conjunto de dados de uma instituição financeira, foram observados sinais de divergência entre as frequências dos primeiros dígitos nos lançamentos e nos valores esperados, porém os pontos além dos limites de controleidentificados encontram-se em um período próximo nas três análises realizadas, concentrando os dados de investigação para a auditoria financeira. A contribuição acadêmica deu-se pelo desenvolvimento de um modelo de aplicação de Cartas de Controle multivariadas e da Lei de Benford, com uma abordagem inovadora do controle estatístico de processos voltado à área financeira, utilizando recurso computacional acessível, de fácil processamento, confiável e preciso, que permite aprimoramento por novas abordagens acadêmicas. No que tange à contribuição à sociedade, se dá pelo uso do modelo por entidades que atuam com movimentações financeiras e pela comunidade, em dados de organizações civis e estatais divulgados nos canais de informação, de modo a proporcionar a prática cidadã pelo acesso à análise e a constatação da idoneidade dos fatos e dos dados. / Large losses are generated in the countryes financial systems, by money laundering. The volume of financial data is big issue to identify digital crime and money laundering. Audits in financial data have limitations in detecting fraud, in large part it is still performed in a traditional way, data are collected by sampling and often unable to identify a real-time crime situation. This research is aiming to serve in addressing this gap, to propose an monitoring statistical method, from multivariate control chart based on Benford’s law for detecting suspicious of fraud in financial data, including those due to money laundering. It was initially defined as a conceptual model in order to determine the type of probability distribution that represents data from financial launches. It was adopted an assumption that this type of data adheres to the Benford’s Law distribution. Subsequently, an empirical distribution was obtained, estimated from the own data. Two procedures were tested to verify a suspected money laundering fraud through the significant first-digit assessment: The Multivariate 2 Control Chart and the Multivariate Hotelling’s T2 Control Chart. Data were simulated using the R-Project software until the occurrence of the 50.000o signal. Finally, the simulation procedures were applied to real data in order to exemplify the method operationally. From the simulation, the two Control Charts tested were evaluated for ARL, that is, average number of observations until the signaling that the series started to operate in an out-of-control state, which it means suspicious of fraudulent launches. The application of the retrospective analysis method in the financial launchings of county’s campaign from 2016 Elections in five capitals of Brazil, based on the expected proportions from the first digit given by Benford’s Law, no suspicions fraud were evidenced in the data obtained from the site of Tribunal Superior Eleitoral (TSE). Considering the application in a set of data from a financial institution, signs of divergence between the frequencies of the first digits of the entries and the expected values were observed, but these points beyond the identified limits are close in all three analyzes. Indicating the period of the data which ones the audit will focus in a further investigation. Academic contribution is identified by developing a multivariate Control Chart together the Benford’s law in an application model with an innovative approach to the statistical process control aimed at the financial area,using accessible, easy to process, reliable and accurate computational resources that allow improvement through new academic approaches. As regard to the contribution to society, it is given the opportunity of applying the model by financial entities and the community in the data of civil and state organizations, disclosed in the information channels in order to provide access to analysis and verification of the suitability of facts and data by citizen practice.