Fast Initial Response Research Articles

Psoriasis localization patterns in the Swiss Psoriasis Registry (SDNTT) over 11 years: an analysis by sex and age

Real-world data on anatomically localized psoriasis and its response to systemic therapy across different age-groups and sexes is limited. This study aimed to evaluate the severity and distribution of psoriasis over time in female and male patients receiving systemic therapies, categorized by age within the Swiss psoriasis registry (SDNTT). Patient-data was obtained over 11 years through the SDNTT. The localized Psoriasis Area and Severity Index (locPASI) of the head, trunk, upper and lower extremities was analyzed over two years following the start of systemic non-/biologic treatment. A total of 316 female and 517 male patients were analyzed. Male patients had a higher baseline locPASI for legs, trunk and arms (p < 0.001), but not for the head (p = 0.961). The locPASI for the head in younger female patients (18–40 years) had a higher score than those aged 55 + (p = 0.022) and after two years, middle aged (41–54) showed a lower score compared to younger patients (p = 0.045). Younger male patients revealed a lower score after two years of therapy in the leg- and arm-area compared to older (p = 0.018 and p = 0.048, respectively). Female patients on non-biologics had a fast initial response, converging with male patients’ scores over 24 months. Over 75% locPASI reduction was observed for female head-area (81.4%), male trunk (82.7%) and legs (76.1%). Absolute locPASI ≤ 2 was achieved 3–6 months for all locations with interleukin (IL)-17, IL-12/23 and IL-23-inhibitors, except for the legs of male patients on anti-IL-17 and female patients on anti-IL-12/23 and -IL-23. After two years, male patients did not achieve a locPASI ≤ 2 for any biologic-treatment in the legs, nor for the arms on anti-TNF-α. Significant disparities in localized PASI were observed between female and male patients. The age, sex and severity of distinct localizations should be considered to optimize treatment goals.

Enhancing the Performance of CUSUM Schemes for Process Mean Monitoring: A Generalized Fast Initial Response Approach

Enhancing the Performance of CUSUM Schemes for Process Mean Monitoring: A Generalized Fast Initial Response Approach

Bidirectional DC-DC Converter and Improved Electrical Vehicle Dynamic Response Control

Background: In automotive applications where bidirectional power flow is necessary to lighten the power system, dual active bridge (DAB) converters are frequently employed. Variations in the required output voltage, erratic input voltage, and shifting loads all have an impact on this converter. As a result, converter performance has to be improved. To increase efficiency, the current stress of the DC-DC converter must be optimised. This paper proposes a control scheme for the coupled inductor bidirectional DC-DC (CIB DC-DC) converter utilising both model predictive control (MPC) and a proportional-integral (PI) controller. The integration of these control techniques aims to enhance the performance and efficiency of the converter. Methods: The MPC algorithm is employed to predict the converter's future behaviour based on a dynamic model, taking into account system constraints and performance criteria. By optimising the control action over a finite time horizon, the MPC algorithm ensures an optimal response, considering the current state and anticipated changes. Additionally, a PI controller is incorporated to augment the control strategy. The proportional component of the PI controller enables a fast initial response to the error between the desired and actual converter outputs. The integral component eliminates steady-state errors and provides robustness against disturbances, resulting in improved overall system performance. Results: The proposed control scheme is implemented and evaluated through simulations and experimental tests on a prototype converter. The results demonstrate the effectiveness of the combined MPC and PI controller approach. Conclusion: The coupled inductor bidirectional DC-DC (CIB DC-DC) converter using MPC can provide precise control of power flow between two voltage domains, enabling efficient bidirectional power transfer. The predictive capabilities of MPC allow it to adapt to varying load conditions and respond quickly to changes, ensuring stable operation and accurate regulation of voltage and current. Overall, the coupled inductor bidirectional DC-DC converter controlled using MPC over PI offers improved performance, efficiency, and flexibility compared to traditional control methods. MPC can handle the complex dynamics response and non-linear characteristics of the converter, making it suitable for bi-directional vehicle charging applications, where precise control and high efficiency can be achieved.

Age and fast initial response predict omalizumab retreatment in chronic urticaria

Age and fast initial response predict omalizumab retreatment in chronic urticaria

Generalized new exponentially weighted moving average control charts (NEWMA) for monitoring process dispersion

PurposeThis work aims to focuse on improving the performance of the new exponentially weighted moving average (NEWMA) scheme for monitoring process dispersion. The authors use the generalized time-varying fast initial response (GFIR) to further enhance the detection ability of variability NEWMA control charts at the process startup. The performance of the proposed chart and other schemes discussed in this article are evaluated; and compared using the average run length (ARL) and standard deviation run length (SDRL) measures. It is observed that the ARL of the proposed scheme is quicker in detecting small and moderate shifts in the process dispersion than its counterparts. The real-life application of the proposed scheme is presented.Design/methodology/approachThe dynamic parameter of GFIR is used to enhance the detection ability of variability NEWMA control charts. The authors apply GFIR to the control limit of variability NEWMA scheme. This further narrows the control limit, hence enabling it to swiftly detect small and moderate changes in process dispersion.FindingsThe authors present the performance comparisons by examining the ARL properties of the proposed chart and its counterparts. The performance comparison shows that the proposed chart is highly sensitive in detecting small and intermediate process shifts. The real-life application presented also supports the study’s conclusion from the simulation studies. The performance comparison of the proposed chart and its counterparts shows that the proposed scheme is efficient in detecting process abnormalities, especially at the startup.Originality/valueIn terms of the control limits, the proposed chart is the generalized variability NEWMA control chart in which all the previously proposed NEWMA variant schemes can be obtained. Also, the newly proposed control scheme is more efficient in detecting small or moderate persistent shifts in the process dispersion.

Dynamic generalized fast initial response for EWMA control schemes for monitoring processes with startup and post‐startup problems

Dynamic generalized fast initial response for EWMA control schemes for monitoring processes with startup and post‐startup problems

Generalization of time‐varying fast initial response for exponentially weighted moving average control charts

AbstractFor monitoring processes, exponentially weighted moving average (EWMA) control charts have been found to be very powerful and reliable tools. Meanwhile, some processes require great attention at the startup to ascertain that the processes are free from any disturbances. In this article, we propose a generalized time‐varying fast initial response for EWMA control charts. This chart is a special case of the previously proposed time‐varying EWMA control chart. The proposed scheme is highly sensitive in detecting small shifts in a given process, particularly at the process startup. The proposed chart is compared with its counterparts using the zero state average run length. We also show an application of the proposed EWMA chart to a real‐life situation.

Enhanced CUSUM and dual CUSUM mean charts

The CUSUM (C) and dual C (DC) charts are well-known because of their sensitive nature against small-to-moderate shifts that occur in the process parameter(s). In this study, we propose new auxiliary information based (AIB) control charts for monitoring the process mean, which include Brownian motion-based C (BC), new BC (NBC), NBC with Crosier C (NBCC), dual NBC (DNBC) and dual NBCC (DNBCC) charts. In addition, fast initial response feature is also added to the proposed charts. We use Monte Carlo simulations to compute the run length properties of the control charts. Based on detailed run length comparisons, it is found that the proposed AIB charts are uniformly better than the existing AIB charts in terms of expected weighted and relative average run length benchmarks. An important concern all around the world is the quality and safety of drinking water. Several water quality tests are available that provide required information about the health of the waterway. Here, a water quality test dataset is taken on two variables, namely pH and total solids, and the proposed AIB charts are used to monitor the mean level of pH in the water.

Design Tukey’s Control Chart and mix with CUSUM Control Chart

Statistical process control is a collection of valuable tools for detecting alteration in a process. It has wide application in many areas field and other fields where variation is being monitored. The variation may be a natural cause variation or a particular cause variation. Statistical process control deals with the monitoring process to detect disturbances in the process. These disturbances may be from the process mean or variance. This study proposes efficient charts for detecting early shifts in dispersion parameters by applying the Fast Initial Response feature. We propose and compare the performance of different cumulative sum (CUSUM)control charts for phase II monitoring of location based on mean and median. The (CUSUM) control chart, which is a method of data analysis based on John Tukey's principles control chart (TCC), is used to compare the proposed charts with their existing counterparts is used to evaluate new charts to existing charts using performance measures such as average run length, the standard deviation of run length, additional quadratic loss, relative average run length, and performance comparison . The proposed charts detect early shifts in the process dispersion faster and have better overall. This article is a similar effort to design an improved charting structure in the form of mixed or using Tukey -CUSUM chart together, to show the process control chart., and drawing the Average Run Length ARL value.

On designing the two one-sided mixed EWMA-CUSUM monitoring schemes for the coefficient of variation with an application to the sintering process

The Coefficient of Variation (CV) has been applied to different fields to measure the relative variation of a variable. To monitor the quality characteristic of various manufacturing or service processes, the memory-type monitoring scheme has become an important role in practice. In this study, we investigate the performance of the memory-type scheme, a mixed structure of the exponentially weighted moving average (EWMA) and cumulative sum (CUSUM), for monitoring the CV, denoted as the MEC-γ2 scheme. Moreover, another MEC-γ2 scheme with a new resetting technology, denoted as MREC-γ2 scheme, is constructed to improve the monitoring scheme’s sensitivity to large shifts in a process. The fast initial response (FIR) feature is incorporated into the proposed schemes to enhance the efficiency. The simulation results, based on the run length (RL) properties, show the superiority of the proposed monitoring schemes against some competitors, including the EWMA-γ2 and CUSUM-γ2. Finally, an illustrative example is presented using a real dataset from the sintering process.

Enhanced CUSUM control charts for monitoring Coefficient of Variation: A case study in Textile industry

The recent blooming developments of Artificial Intelligence (AI), Internet of Things (IoT), and Data Science (DS) have put Smart Manufacturing (SM) into a new context. This leads to more attractions on control charts as one of the useful tools that contribute to the success in SM by anomaly detection (AD) approach. Coefficient of variation (CV) is a recent popular statistic that is used in the quality control of SM. In this paper, we propose investigating the performance of Cumulative sum (CUSUM) control charts monitoring CV with a fast initial response (FIR) strategy. The chart parameters are also optimized according to the random shift size in a given interval with the proposed Nelder-Mead optimization algorithm. The numerical results show that the performance of FIR CUSUM-γ2 charts are greater than the initial CUSUM-γ2 ones. An example in monitoring yarn quality at the spinning mill with the design of FIR CUSUM-γ2 charts is also proposed. These findings are useful for practitioners as well as managers and researchers. The proposed design of FIR CUSUM-γ2 charts could be applied in other processes of various domains such as finance, business, industrial processes, etc.

The use of fast initial response features on the homogeneously weighted moving average chart with estimated parameters under the effect of measurement errors

AbstractFast initial response (FIR) features are generally used to improve the sensitivity of memory‐type control charts by shrinking time‐varying control limits in the earlier stage of the monitoring regime. This paper incorporates FIR features to increase the sensitivity of the homogeneously weighted moving average (HWMA) monitoring schemes with and without measurement errors under constant as well as linearly increasing variance scenarios. The robustness and the performance of the HWMA monitoring schemes are investigated in terms of numerous run‐length properties assuming that the underlying process parameters are known and unknown. It is found that the FIR features improves the performance of the HWMA monitoring scheme as compared to the standard no FIR feature HWMA scheme, and at the same time, it is observed that the simultaneous use of a recently proposed FIR feature and multiple measurements significantly reduces the negative effect of measurement errors. An illustrative example on the volume of milk in bottles is used to demonstrate a real‐life application.

Univariate fast initial response statistical process control with taut strings

We present a novel real-time univariate monitoring scheme for detecting a sustained departure of a process mean from some given standard assuming a constant variance. Our proposed stopping rule is based on the total variation of a nonparametric taut string estimator of the process mean and is designed to provide a desired average run length for an in-control situation. Compared to the more prominent CUSUM fast initial response (FIR) methodology and allowing for a restart following a false alarm, the proposed two-sided taut string (TS) scheme produces a significant reduction in average run length for a wide range of changes in the mean that occur at or immediately after process monitoring begins. A decision rule for when to choose our proposed TS chart compared to the CUSUM FIR chart that takes into account both false alarm rate and average run length to detect a shift in the mean is proposed and implemented. Supplementary materials are available online.

Safety of live attenuated herpes zoster vaccine in Australian adults 70–79 years of age: an observational study using active surveillance

ObjectivesTo assess the safety of live attenuated herpes zoster vaccine live (ZVL) through cumulative analysis of near real-time, participant-based active surveillance from Australia’s AusVaxSafety system.Design and settingZVL was funded in...

Directionally sensitive MCUSUM mean charts

AbstractIn the context of a disease outbreak detection, a prime interest is to only detect increases in the process mean. It is thus desirable to have a directionally sensitive multivariate chart that can effectively detect either increases or decreases in the process mean vector. In this paper, with a suitable transformation that truncates multivariate observations either above or below the process mean vector, we propose one‐sided and two one‐sided MCUSUM charts for monitoring the mean of a multivariate normal process. Among the proposed charts, the one‐sided MCUSUM charts are directionally sensitive, while the two one‐sided MCUSUM charts are directionally insensitive. In addition, the fast initial response feature is also incorporated into the proposed charts to enhance their sensitivities against initial process shifts. The run length characteristics of these control charts are computed with the Monte Carlo simulation. Based on the run length comparisons, it is found that the proposed charts are more sensitive than the existing charts when detecting moderate‐to‐large shifts in the process mean. The proposed charts are also applied on real datasets to support the theory.

Distribution‐free triple EWMA control chart for monitoring the process location using the Wilcoxon rank‐sum statistic with fast initial response feature

AbstractThe exponentially weighted moving average (EWMA) control chart is a memory‐type chart known to be more efficient in detecting small and moderate shifts in the process parameter. The double EWMA (DEWMA) chart is an extension of the EWMA chart that is more effective than the latter in the detection of small‐to‐moderate shifts. This paper proposes a new distribution‐free (or nonparametric) triple EWMA (TEWMA) control chart based on the Wilcoxon rank‐sum (W) statistic to improve the detection ability in the process location parameter. Moreover, a new fast initial response (FIR) feature is added to further improve the sensitivity of the new TEWMA chart. The performance of the proposed TEWMA chart with and without FIR features is compared to those of the existing EWMA and DEWMA W charts. It is observed that the TEWMA chart with and without FIR features is superior to the competing charts in most situations. A real‐life illustration is provided to show the application and implementation of the new chart.

New CUSUM and dual CUSUM mean charts

AbstractThe CUSUM (C) charts are well recognized as a potentially advanced process monitoring tools because of their sensitivity against small and moderate shifts. In this paper, we first improve the sensitivity of the Brownian motion–based C (BC) chart with an appropriate transformation, named new BC (NBC) chart, for monitoring moderate and large shifts in the mean of a normal process. Then, using the control charting structure of the Crosier C (CC) chart, we propose the NBCC (NBC with CC structure) chart. In addition, for efficiently detecting a mean shift within an interval, dual version of these control charts are also proposed, named the dual NBC (DNBC) and dual NBCC (DNBCC) charts. Moreover, the fast initial response feature is also incorporated into the proposed charts. Using the Monte Carlo simulation, the run length properties of the proposed charts are computed. The run length performances of the existing and proposed charts are compared using the extra quadratic loss and integral relative average run length as performance criterion. It turns out that the NBC and NBCC (DNBC and DNBCC) charts are uniformly more sensitive than the C, CC, and NBC (dual C and dual CC) charts when detecting the mean shifts in small, moderate, and large intervals, where the DNBCC chart outperforms all considered charts. The proposed charts are also applied on real data sets to support the proposed theory.

Enhanced EWMA charts for monitoring the process coefficient of variation

AbstractThe coefficient of variation (CV) is an important quality characteristic when the process variance is a function of the process mean for a production process. In this paper, we develop an auxiliary information–based (AIB) estimator for estimating the squared CV, along with its approximated mean and variance. This estimator is then used to devise new one‐sided EWMA charts for monitoring the increases or decreases in the squared CV of a normal process, named the AIB‐EWMA CV charts. In addition, the sensitivities of these control charts are also enhanced with the fast initial response feature. The Monte Carlo simulation method is used to compute the run length characteristics of the proposed CV charts. Based on detailed run length comparisons, it is found that the proposed AIB‐EWMA CV charts are uniformly and substantially better than the existing EWMA CV charts when detecting different kinds of upward/downward shifts in the squared CV. The proposed charts are also applied to a real dataset to support the proposed theory.

The Successes and Failures of the Initial COVID-19 Pandemic Response in Romania.

In the context of the COVID-19 pandemic, countries around the world varied in the strength and timeliness of their responses. In Romania, specific challenges were faced with regards to managing the spread and limiting the impact of the disease, ranging from healthcare infrastructure to demographic and sociocultural aspects. As the country has a sizeable diaspora, major difficulties were faced when large numbers of individuals from highly affected areas returned to Romania. However, the fast implementation of control measures successfully averted a surge in the number of COVID-19 cases. This delayed the overburdening of an already challenged healthcare system during the initial phases of the epidemic. Furthermore, early control was facilitated by the exploitation of communication channels that penetrated all layers of society, from ordinary citizens to governmental authorities and high-ranking religious figures. The management of the COVID-19 crisis in Romania illustrates the importance of a fast initial response which takes into account the role played by sociocultural aspects in the context of an epidemic. As the challenges faced by Romania are not unique, these results could inform future public health strategies worldwide.

An efficient approach of designing distribution-free exponentially weighted moving average schemes with dynamic fast initial response for joint monitoring of location and scale

ABSTRACT An EWMA scheme with the fast initial response (FIR) feature usually detects an initial out-of-control (OOC) situation much quicker compared to a standard EWMA scheme. Researches on EWMA schemes with the FIR feature primarily focussed on their OOC performance, ignoring the fact that the FIR feature may increase the number of false alarms at the early stage of monitoring. In this paper, we introduce the concept of an EWMA scheme with a dynamic FIR feature that restricts initial false alarms and also improves OOC performance. We discuss a data-dependent approach based on Kernel density estimation for the design parameters. We use the proposed concept in designing distribution-free FIR-based EWMA schemes for joint monitoring of location and scale. Simulation results demonstrate that our proposed procedure generally performs well under various continuous distributions. We illustrate the application of our proposed schemes using two sets of data arriving out of the manufacturing sector.