Residual Charts Research Articles

Web-based surveillance of respiratory infection outbreaks: retrospective analysis of Italian COVID-19 epidemic waves using Google Trends.

Large-scale diagnostic testing has been proven insufficient to promptly monitor the spread of the Coronavirus disease 2019. Electronic resources may provide better insight into the early detection of epidemics. We aimed to retrospectively explore whether the Google search volume has been useful in detecting Severe Acute Respiratory Syndrome Coronavirus outbreaks early compared to the swab-based surveillance system. The Google Trends website was used by applying the research to three Italian regions (Lombardy, Marche, and Sicily), covering 16 million Italian citizens. An autoregressive-moving-average model was fitted, and residual charts were plotted to detect outliers in weekly searches of five keywords. Signals that occurred during periods labelled as free from epidemics were used to measure Positive Predictive Values and False Negative Rates in anticipating the epidemic wave occurrence. Signals from "fever," "cough," and "sore throat" showed better performance than those from "loss of smell" and "loss of taste." More than 80% of true epidemic waves were detected early by the occurrence of at least an outlier signal in Lombardy, although this implies a 20% false alarm signals. Performance was poorer for Sicily and Marche. Monitoring the volume of Google searches can be a valuable tool for early detection of respiratory infectious disease outbreaks, particularly in areas with high access to home internet. The inclusion of web-based syndromic keywords is promising as it could facilitate the containment of COVID-19 and perhaps other unknown infectious diseases in the future.

Linear and Non Linear Recognition for the Sorption of 60Co and 152+154Eu Radionuclides onto Bio CuO Nanocomposite

A novel nanocomposite was prepared by hybridizing polyacrylic acid/maleic acid with nano copper oxide (PAACMA/CuO) for the sorption of 60Co (II) and 152+154Eu (III) radionuclides from an aqueous solution. Nano-CuO was biochemically produced by hydrolysing its salt in the presence of the Aspergillus terreus fungus. The PAACMA/CuO nanocomposite was characterized using a variety of analytical techniques. The optimum sorption conditions (pH 4.5 for 60Co and pH 3.53 for 152+154Eu, 24 h of equilibrium time at 20 oC) were applied. The kinetic mechanism of the sorption reaction was controlled by pseudo second order based on residual charts, coefficient of determination (R2), and corrected Akaike information Criterion (AICc). The sorption reaction mechanism was controlled by Langmuir model for linear regression using the coefficient of determination and the Dubinin-Radushkevich D-R model for the AICc and residual plots error functions. The reaction mechanism throughout non-linear regression was controlled by the D-R model due to the coefficient of determination, AICc, and residual charts. The PAACMA/CuO nanocomposite had a mono-layer adsorption capacity of 11.04 mg g− 1 for Co (II) and 21.54 mg g− 1 for Eu (III). According to desorption studies, Co (II) and Eu (III) could be recovered by 0.1 mol L− 1 EDTA with efficiencies 55.46% and 95.044%, respectively. According to thermodynamic studies, the sorption of Co (II) and Eu (III) on the prepared composite was endothermic and spontaneous.

Early Detection of SARS-CoV-2 Epidemic Waves: Lessons from the Syndromic Surveillance in Lombardy, Italy.

We evaluated the performance of the exponentially weighted moving average (EWMA) model for comparing two families of predictors (i.e., structured and unstructured data from visits to the emergency department (ED)) for the early detection of SARS-CoV-2 epidemic waves. The study included data from 1,282,100 ED visits between 1 January 2011 and 9 December 2021 to a local health unit in Lombardy, Italy. A regression model with an autoregressive integrated moving average (ARIMA) error term was fitted. EWMA residual charts were then plotted to detect outliers in the frequency of the daily ED visits made due to the presence of a respiratory syndrome (based on coded diagnoses) or respiratory symptoms (based on free text data). Alarm signals were compared with the number of confirmed SARS-CoV-2 infections. Overall, 150,300 ED visits were encoded as relating to respiratory syndromes and 87,696 to respiratory symptoms. Four strong alarm signals were detected in March and November 2020 and 2021, coinciding with the onset of the pandemic waves. Alarm signals generated for the respiratory symptoms preceded the occurrence of the first and last pandemic waves. We concluded that the EWMA model is a promising tool for predicting pandemic wave onset.

An Improved Hidden Markov Model for Monitoring the Process with Autocorrelated Observations

With the development of intelligent manufacturing, automated data acquisition techniques are widely used. The autocorrelations between data that are collected from production processes have become more common. Residual charts are a good approach to monitoring the process with data autocorrelation. An improved hidden Markov model (IHMM) for the prediction of autocorrelated observations and a new expectation maximization (EM) algorithm is proposed. A residual chart based on IHMM is employed to monitor the autocorrelated process. The numerical experiment shows that, in general, IHMMs outperform both conventional hidden Markov models (HMMs) and autoregressive (AR) models in quality shift diagnosis, decreasing the cost of missing alarms. Moreover, the times taken by IHMMs for training and prediction are found to be much less than those of HMMs.

High-Density UAV-LiDAR in an Integrated Crop-Livestock-Forest System: Sampling Forest Inventory or Forest Inventory Based on Individual Tree Detection (ITD)

Lidar point clouds have been frequently used in forest inventories. The higher point density has provided better representation of trees in forest plantations. So we developed a new approach to fill this gap in the integrated crop-livestock-forest system, the sampling forest inventory, which uses the principles of individual tree detection applied under different plot arrangements. We use a UAV-lidar system (GatorEye) to scan an integrated crop-livestock-forest system with Eucalyptus benthamii seed forest plantations. On the high density UAV-lidar point cloud (>1400 pts. m2), we perform a comparison of two forest inventory approaches: Sampling Forest Inventory (SFI) with circular (1380 m2 and 2300 m2) and linear (15 trees and 25 trees) plots and Individual Tree Detection (ITD). The parametric population values came from the approach with measurements taken in the field, called forest inventory (FI). Basal area and volume estimates were performed considering the field heights and the heights measured in the LiDAR point clouds. We performed a comparison of the variables number of trees, basal area, and volume per hectare. The variables by scenarios were submitted to analysis of variance to verify if the averages are considered different or equivalent. The RMSE (%) were calculated to explain the deviation between the measured volume (filed) and estimated volume (LiDAR) values of these variables. Additionally, we calculated rRMSE, Standard error, AIC, R2, Bias, and residual charts. The basal area values ranged from 7.40 m2 ha−1 (C1380) to 8.14 m2 ha−1 281 (C2300), about −5.9% less than the real value (8.65 m2 ha−1). The C2300 scenario was the only one whose confidence interval (CI) limits included the basal area real. For the total stand volume, the ITD scenario was the one that presented the closer values (689.29 m3) to the real total value (683.88 m3) with the real value positioned in the CI. Our findings indicate that for the stand conditions under study, the SFI approach (C2300) that considers an area of 2300 m2 is adequate to generate estimates at the same level as the ITD approach. Thus, our study should be able to assist in the selection of an optimal plot size to generate estimates with minimized errors and gain in processing time.

New Approaches for Monitoring Image Data.

In this paper, we develop new techniques for monitoring image processes under a fairly general setting with spatially correlated pixels in the image. Monitoring and handling the pixels directly is infeasible due to an extremely high image resolution. To overcome this problem, we suggest control charts that are based on regions of interest. The regions of interest cover the original image which leads to a dimension reduction. Nevertheless, the data are still high-dimensional. We consider residual charts based on the generalized likelihood ratio approach. Existing control statistics typically depend on the inverse of the covariance matrix of the process, involving high computing times and frequently generating instable results in a high-dimensional setting. As a solution of this issue, we suggest two further control charts that can be regarded as modifications of the generalized likelihood ratio statistic. Within an extensive simulation study, we compare the newly proposed control charts using the median run length as a performance criterion.

Monitoring wine fermentation deviations using an ATR-MIR spectrometer and MSPC charts

Despite the winemaker’s efforts, deviations such as bacterial spoilage can occur during wine alcoholic fermentation resulting in economic losses and low quality wines. When a deviation is suspected, samples are usually sent to an oenological laboratory for the off-line analysis of specific quality control parameters. The use of ATR-MIR as a fast analytical tool to monitor the fermentation process could be very useful, as getting real-time information of the process allows making readjustments before the process ends. In this study, we aimed at detecting white wine spoilage during alcoholic fermentation due to the action of lactic bacteria using a portable ATR-MIR instrument and MSPC charts. A total of 33 small-scale alcoholic fermentations were conducted (25 in normal operation conditions (NOC) and 8 simulating a bacterial spoilage with the addition of lactic bacteria (MLF)) to evaluate the capability of the MSPC charts to detect deviations from NOC. MSPC control charts were developed based on Q residuals and Hotelling’s T2 statistics. Time-wise unfolding was applied to the original three-way data to build different PCA models, obtaining very satisfactory results: MLF samples were detected before the end of alcoholic fermentation in the Q residuals charts after 80 hours and Hotelling T2 chart could also differentiate the samples after 100 hours.

On the bivariate control charts for the mean of autocorrelated process

The major problem in analysing control charts is to work with autocorrelated data. This problem can be solved by fitting a suitable model to data and using the control chart for the residuals. In most papers, the average run length (ARL) criterion has been used to evaluate the effect of autocorrelation on the performance of control chart. But, in this paper, we compare the efficiency of standardised and modified control charts with residuals charts in identifying changes in the mean process in the presence of autocorrelation using the false positive rate and the false negative rate criteria, and also the comparison between the results of this criterion and the results of ARL criterion is discussed.

Deep recurrent neural network‐based residual control chart for autocorrelated processes

AbstractWith the growth of automation in process industries, there is correlation in the process variables. Deep learning has achieved many great successes in image and visual analysis. This paper concentrates on developing a deep recurrent neural network (RNN) model to characterize process variables at vary time lags, and then a residual chart is developed to detect mean shifts in autocorrelated processes. The experiment results indicate that the RNN‐based residual chart outperforms other typical methods (eg, autoregressive [AR]‐based control chart, back propagation network [BPN]‐based residual chart). This paper provides guideline for deep learning technique employed as an effective tool in autocorrelated process control.

Residual Chart with Hidden Markov Model to Monitoring the Auto-Correlated Processes

Autocorrelations exist in real production extensively, and special statistical tools are needed for process monitoring. Residual charts based on autoregressive integrated moving average (ARIMA) models are typically used. However, ARIMA models need a quite amount of experience, which sometimes causes inconveniences in the implementation. With a good performance under less experience or even none, hidden Markov models (HMMs) were proposed. Since ARIMA models have many different performances in positive and negative autocorrelations, it is interesting and essential to study how HMMs affect the performances of residual charts in opposite autocorrelations, which has not been studied yet. Therefore, we extend HMMs to negatively auto-correlated observations. The cross-validation method is used to select the relatively optimal state number. The experiment results show that HMMs are more stable than Auto-Regressive of order one (AR(1) models) in both cases of positive and negative autocorrelations. For detecting abnormalities, the performance of HMMs approach is much better than AR(1) models under positive autocorrelations while under negative autocorrelations both methods have similar performances.

Monitoring of biodiesel content and adulterant presence in methyl and ethyl biodiesels of jatropha in blends with mineral diesel using MIR spectrometry and multivariate control charts

In this work, fast and efficient analytical methodologies were developed for quality monitoring of the content of jatropha methyl and ethyl biodiesels in blends with mineral diesel, and to monitor the presence of adulterants using mid-infrared (MIR) spectrometry allied to the method of multivariate control charts based on net analyte signal (NAS). The methodology of multivariate control charts consists of dividing the spectral signal of calibration samples into three vectors, namely NAS, interference and residual, generating three charts. The NAS chart monitors the quality of the analyte of interest in blends (biodiesel content, in this case), while the interference chart monitors the diesel quality, i.e., monitors the presence of adulterants or contaminants in the binary mixtures. The residual chart monitors the information or variations not modeled by the NAS and interference vectors. The validation of control charts in both models was done in two ways: (i) in relation to the biodiesel content in the biodiesel/diesel blends and (ii) in relation to the presence of contaminants in blends. In relation to the biodiesel content, sets of samples with biodiesel content in and out of control of the quality specifications were prepared for validation. In relation to the presence of adulterants, validation was performed by applying other sets of samples adulterated by the partial substitution of biodiesel in the blends by soybean oil and used frying oil (UFO) in the range of 0–30%, and by partial substitution of diesel by residual automotive lubricant oil (RALO) and gasoline, also in the range of 0–30%. The results of the application of multivariate control charts demonstrated satisfactory monitoring for the identification of samples of B7 blends in or out of quality specifications and identification of the presence of adulterants in blends. That is, the proposed methodologies correctly identified samples as in- or out-of-control. Therefore, the methodologies were shown to be effective for use by the supervisory board of such blends of these biofuels.

Quality Control of Biodiesel Content of B7 Blends of Methyl Jatropha and Methyl Crambe Biodiesels Using Mid-Infrared Spectroscopy and Multivariate Control Charts Based on Net Analyte Signal

In this work, mid-infrared spectroscopy and multivariate control charts based on the net analyte signal (NAS) were applied for quality control of B7 (7% biodiesel and 93% diesel) biodiesel/diesel blends of jatropha oil methyl esters (JOME) and crambe oil methyl esters (COME) relative to the biodiesel content. The construction of the multivariate control charts was based on the decomposition of the spectral signal of samples prepared within quality specifications. Three charts were generated: (i) a NAS chart to monitor the content of the biodiesel in the blend, (ii) an interference chart to monitor the diesel content, and (iii) a residual chart to monitor any systematic variations. For each chart developed, statistical limits were established using the same samples prepared under normal operating conditions, with biodiesel content between 6.5% and 7.5%, considering the range of ±0.5% (v/v) permitted by the Brazilian National Agency of Petroleum, Natural Gas, and Biofuels. To validate the control charts, new in-control and out-of-control samples relative to the biodiesel content were prepared. Of the two sets of out-of-control samples, the biodiesel content of one set (D) was below 6.5% (v/v) and the other set (E) was above 7.5%. The third set (C) of validation samples was prepared with biodiesel content within control of the quality specifications. The analytical methodology developed for each model (JOME and COME) was rapid, efficient, and able to correctly monitor the compliant and noncompliant samples relative to the biodiesel content in B7 biodiesel/diesel blends, demonstrating that this methodology is an alternative route to be used by the supervisory bodies for quality control of these types of biodiesel.

EWMA control charts for detecting changes in the mean of a long-memory process

In this paper EWMA control charts are introduced for detecting changes in the mean of a long-memory process. Besides the modified EWMA scheme the EWMA residual chart is also considered. The control design of the charts is calculated for an ARFIMA(p,d,q) process. In order to assess the introduced charts, the average run length is used as a performance criterion. Using an extensive simulation study, the control charts are compared with each other. The target process is assumed to be an ARFIMA(1,d,1) process.

Multivariate control charts based on NAS and mid‐infrared spectroscopy for quality control of B5 blends of methyl soybean biodiesel in diesel

In this work, mid‐infrared spectroscopy and multivariate control charts based on net analyte signal were applied for quality control of B5 blends of biodiesel/diesel (5% biodiesel/95% diesel). Control charts were constructed using instrumental signal decomposition, generating three charts: the net analyte signal chart for monitoring the analyte of interest (methyl soybean biodiesel); the interference chart, which corresponds to the contribution of all other compounds in the diesel sample (diesel); and the residual chart, which corresponds to non‐systematic variations. Statistical limits were established for each developed chart, using samples inside quality specifications (normal operation conditions). To validate multivariate control charts, new samples were analyzed. The new samples represented samples in‐control and samples out‐of‐control in relation to the content of biodiesel, adulterated biodiesel with severe vegetable oils and adulterated diesel with residual automotive lubricant oil, kerosene, and gasoline. The results obtained show an excellent distinction between the samples inside and out of the quality specifications, with 91% and 100% correctly classified, respectively, which demonstrates that the methodology developed is a viable alternative for quality monitoring of this type of fuel. Copyright © 2015 John Wiley & Sons, Ltd.

Monitoring of process mean and variance simultaneously by joint <span style="text-decoration: overline">X</span> and R chart for serial correlation

It is assumed that the presence of the assignable cause may shift either process mean or standard deviation from their target value. But in the design of joint X and R charts, it is assumed that, both mean and standard deviation of the process change simultaneously. So, for simultaneous monitoring of the process mean and standard deviation, a modified joint X and R chart is suggested in this paper, which can be more useful at the shop floor level. The design of modified joint X and R chart is based upon the sum of chi-squares theory. The performance of joint chart is measured in terms of average run lengths (ARLs) and compared with the joint Shewhart chart and residuals charts, suggested by Karaoglan and Bayhan (2011) for sample size of ten. It is observed that the joint modified X and R chart outperforms all the residual charts at the highest level of correlation, i.e., Φ = 0.95. The simplicity and robustness in the design of the modified joint X and R chart makes it suitable for the industries where the data are highly correlated.

Sequentielle Überwachung von Finanzzeitreihen anhand von Residuenkarten

In dem vorliegenden Beitrag steht die sequentielle Analyse von Finanzzeitreihen im Vordergrund. Der Erwartungswert und die Varianz von Zeitreihen sollen simultan uberwacht werden. Zunachst werden konventionelle Kontrollkarten als wesentliches Werkzeug der statistischen Prozesskontrolle mit einer zuvor eingefuhrten charakteristischen Gros e kombiniert. Diese charakteristische Gros e ist ein Vektor, der die Residuen eines angepassten Modells und deren Quadrate beinhaltet. Des Weiteren mussen die entsprechenden Prozeduren anhand einer Simulationsstudie kalibriert werden. Aus erdem wird die Anwendung dieser Prozeduren mittels eines empirischen Beispiels verdeutlicht. Im Rahmen der empirischen Analyse wird der Deutsche Aktienindex in den Jahren 2006 bis 2008 wahrend der Finanzkrise untersucht. Das wesentliche Ziel ist hierbei die Identifikation von strukturellen Veranderungen im Erwartungswert bzw. in der Varianz des Aktienindex anhand von Prognosen aus dem jeweils angepassten Modell. Dabei werden die lineare Regression mit Zeitreihenfehlern angewendet auf den betrachteten Aktienindex sowie das autoregressive Modell bedingter Heteroskedastizitat angewendet auf logarithmierte Renditen in Betracht gezogen. Diese Strukturbruche werden in Form von Signalen der betrachteten Residuenkarten ersichtlich. Auf diese Weise lassen sich strukturelle Schwankungen auf dem Kapitalmarkt wahrend wirtschaftlicher Krisen visualisieren.

The use of a CUSUM residual chart to monitor respiratory syndromic data

This article reports a surveillance mechanism that can be used to monitor syndromic data on respiratory syndrome. The data used for illustration are the daily counts of respiratory-syndrome visits sampled from the National Health Insurance Research Database in Taiwan. The population size is 160 000. A regression model with an autoregressive-integrated-moving-average error term is fitted to the data and then CUmulative SUM (CUSUM) residual charts are plotted to detect aberrations in the frequency of visits to a walk in clinic. Day-of-the-week, seasonal, and holiday effects are considered in the regression model. It is shown that a CUSUM residual chart can be used to detect abnormal increases in daily counts of respiratory-syndrome visits.

On monitoring shift in the mean processes with vector autoregressive residual control charts of individual observation

In this paper, we purpose a monitoring scheme of individual observation that can be modeled as a first-order vector autoregressive process and develop it to mean square successive difference matrix based on residuals. Due to the effect of shift in mean process on VAR residual charts, we investigate the T 2 -type charts performance of residual and control charts as an individual observation. Then we compare both T 2 -type charts using upper control limit approximation with α=0.005 (fix) based simulation. From the results of comparison, it shows that the chart for a small to moderate level of shift in the process mean vector gives a better performance than the charts.

Detection of shift in process mean withX¯chart for autocorrelated data

Purpose – The purpose of this paper is to study the performance of the X‐bar chart on the basis of average run lengths (ARLs) for the positively correlated data. The ARLs at various sets of parameters of the X‐bar chart are computed by simulation. The performance of the chart at the various shifts in the process mean is compared with the X‐bar chart suggested by Zang and residual chart proposed by Zang. The optimal schemes suggested in this paper are also compared with variable parameters (VP) chart and double sampling (DS) X‐bar chart suggested by Costa and Machado.Design/methodology/approach – Positively correlated observations having normal distribution are generated with the help of the MATLAB software. The performance of the X‐bar chart in terms of ARLs at the various shifts in the process mean is compared with the X‐bar chart suggested by Zang and residual chart proposed by Zang. The optimal schemes are also compared with VP X‐bar chart and DS X‐bar chart suggested by Costa and Machado.Findings – Th...

Analysis of chromite processing plant data by first order autoregressive model

Many mineral processing data can be monitored by a time series model. This research presents results of analysis and simulations of a chromite processing plant data determined by time series model. The plant data obtained by shift to shift include feed grade, concentrate grade, tailing grade, Cr/Fe ratio in concentrate. All the chromite processing data were found stationary over time. The autocorrelation was high for feed grade and Cr/Fe ratio. Weaker autocorrelation was observed for concentrate grade and tailing grade. Autoregressive integrated moving average (ARIMA, 1,0,0) or first order autoregressive (AR, 1) model, was found to fit all data very well. The models obtained have been also shown to be used for the near future estimation of these data. The time constant which is an indicator of sampling frequency of the data sets were determined. It was found that sampling frequency was enough for concentrate and tailing grade and their original values can be used in process control charts for monitoring. On the other hand, the sampling frequency should be reduced for feeding grade and Cr/Fe ratio for the same aims hence ARIMA residual charts were more suitable to monitor their values.