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Abstract Objective Compare and analyze clinical data of total laparoscopic pancreaticoduodenectomy (TLPD) cases for surgeons with / without first assistant experience (FAE) in TLPD. Probe influence of FAE in TLPD on the learning curve for an operator. Methods The clinical data of 239 patients, that underwent TLPD performed by two surgeons between January 2017 and January 2022) in our department, were consecutively collected and divided into two groups (A and B). Group A cases were operated by Surgeon A, with FAE of 57 TLPDs in our department prior to initial TLPD as an operator. Group B cases were operated by Surgeon B with no FAE of TLPD. Cumulative sum (CUSUM) method developed learning curves. Clinical data and both surgeons’ learning curves were statistically compared between both groups. Results Between both groups, no statistically significant variations were observed for pre-operative health conditions. Reduced surgical duration, blood loss and transfusion volume during surgery, together with reductions in major post–operative complication rates and reduced hospital/ICU stays were identified within Group A, having statistically significant variations. The technical plateau phases of the learning curves were approximately 25–41 cases and 35–51 cases, for Surgeon A and Surgeon B, respectively. Conclusion FAE in TLPD can accelerate the learning curve of TLPD for an operator, with safer surgical procedures and enhanced post–operative recovery.

Monitoring techniques based on the multivariate coefficients of variation (MCV) have received a great deal of attention in quality control. Numerous studies have shown that adaptively changing the charting parameters based on the past sample information can improve the performance of a chart in detecting process changes. In view of the performance benefits of adaptive strategies, two one-sided cumulative sum (CUSUM) charts are proposed to monitor the MCV and a variable sampling interval (VSI) strategy is incorporated into these charts. By using the Markov chain method, the formulas for calculating the average and standard deviation of time to signal measures of the VSI CUSUM MCV control charts are derived. Then for the known and unknown shift sizes, the optimization algorithms for obtaining the charting parameters are presented. The superiority of the proposed control charts is confirmed by comparing their performance with two existing MCV charts. Finally, an example using real investment data demonstrates the practical application of the proposed charts.

In the context of big data, multivariate processes must often be monitored in a timely and accurate manner. Usually, the distribution of process variables is unknown. This paper proposes a new strategy for multivariate process monitoring when the distribution of a process variable is unknown. We address monitoring by means of a rank-based method that is completely nonparametric. We also discuss the optimal strategy of parameters. A simulation study demonstrates that the proposed method is efficient in detecting shifts for multivariate processes. A real data example is presented to illustrate the performance of the proposed method. [ABSTRACT FROM AUTHOR]

Two methods of structural break detection in a piecewise generalized model of autoregressive conditional heteroscedasticity are considered. The first method is based on Kolmogorov–Smirnov statistics and is called the KS method. The second one is based on the cumulative sums and is called the KL method. In this paper, the KS and KL methods are compared under the assumption of Student's conditional distribution of random errors. The results of our Monte Carlo experiments are as follows: the KL method is inferior to the KS method both in terms of the average probability of errors of the first type and in terms of the average power of detecting a structural break. [ABSTRACT FROM AUTHOR]

This article studies multiple structural breaks in large contemporaneous covariance matrices of high-dimensional time series satisfying an approximate factor model. The breaks in the second-order moment structure of the common components are due to sudden changes in either factor loadings or covariance of latent factors, requiring appropriate transformation of the factor models to facilitate estimation of the (transformed) common factors and factor loadings via the classical principal component analysis. With the estimated factors and idiosyncratic errors, an easy-to-implement CUSUM-based detection technique is introduced to consistently estimate the location and number of breaks and correctly identify whether they originate in the common or idiosyncratic error components. The algorithms of Wild Binary Segmentation for Covariance (WBS-Cov) and Wild Sparsified Binary Segmentation for Covariance (WSBS-Cov) are used to estimate breaks in the common and idiosyncratic error components, respectively. Under some technical conditions, the asymptotic properties of the proposed methodology are derived with near-optimal rates (up to a logarithmic factor) achieved for the estimated breaks. Monte Carlo simulation studies are conducted to examine the finite-sample performance of the developed method and its comparison with other existing approaches. We finally apply our method to study the contemporaneous covariance structure of daily returns of S&P 500 constituents and identify a few breaks including those occurring during the 2007–2008 financial crisis and the recent coronavirus (COVID-19) outbreak. An R package " BSCOV " is provided to implement the proposed algorithms. [ABSTRACT FROM AUTHOR]

The classical memory Exponential Weighted Moving Average (EWMA) and Cumulative Sum (CUSUM) control charts (CCs) are important to detect small‐to‐moderate sizes of shift in the process location and/or dispersion parameters. To make the memory control chart (CC) more efficient for broad range of shift, the score functions in particular Huber and Bi‐square functions are incorporated in the classical memory CCs. These score functions help to formulate the parameters of memory CCs and are called Adaptive EWMA (AEWMA) and Adaptive CUSUM (ACUSUM). The AEWMA and ACUSUM are capable to handle various sizes (i.e., small, moderate, and large) of shift. The aim of this study is to utilize Hampel function as the alternative of the mentioned score functions, and propose a new AEWMA CC, symbolized as AEWMAHample. To develop the proposed AEWMAHample CC, the standard EWMA statistic and Hampel function are incorporated in the classical EWMA CC structure. The proposed AEWMAHample CC is also efficient to handle certain sizes of shift in the process location parameter. Performance evaluation procedures, like average run length is considered for a particular shift. Similarly, the performance comparison index, relative average run length, and extra quadratic loss function are estimated over a range of shift. All these performance measures are calculated from run length (RL) characteristic of the proposed control chart. An algorithm in MATLAB is developed based on Monte Carlo simulation procedure to generate the RLs. Some existing control charts including EWMA, CUSUM, MEC, MCE, ACUSUMc(1)${\rm{ACUSUM}}_{\rm{c}}^{(1)}$, ACUSUMc(2)${\rm{ACUSUM}}_{\rm{c}}^{(2)}$, IACCUSUM, AEWMA∅hu${\rm{AEWMA}}_{{\emptyset }_{{\rm{hu}}}}$, and AEWMA∅bs${\rm{AEWMA}}_{{\emptyset }_{{\rm{bs}}}}$ are taken for comparison reason. For specific sizes of shift, the proposed AEWMAHample CC reveals the superiority. Besides, to address the practical significance for practitioners and quality engineers, the real‐life and simulated data examples are considered. [ABSTRACT FROM AUTHOR]

Introduction: Over the past decade, an increase has been seen in robotics used for hernia repair, specifically robotic abdominal wall reconstruction (rAWR). However, the learning curve for rAWR can be steep and presently, little is understood regarding the optimal case volume required to achieve proficiency. The aim of our study was to review skill acquisition and describe the learning curve for rAWR. Methods: A retrospective, single-surgeon case series of consecutive patients who underwent rAWR from 2018 to 2022. The primary outcome was operative time, obtained from console time identified through the MyIntutive application. A one-sided cumulative sum analysis (CUSUM) curve for the total operative time was derived based on the mean operative time of chronological procedures (207 min). Results: 185 patients underwent rAWR between 2018 and 2022. These patients were more likely to be female, Caucasian, and have undergone two previous hernia repairs. ASA complexity increased over time with ASA 3 being predominant from 2020 onwards. The median hernia length was 15.0 cm and the median width was 7 cm. Average operative time was 207.8 min and decreased over time. The CUSUM analysis identified four phases of skill acquisition with the following case volumes: Initial Learning Curve (0–20), Stabilization Phase (21–55), Second Learning Curve (56–70), 4) Skill Proficiency (> 70). Conclusion: In the early learning curve of rAWR, operative time decreased consistently after 70 cases, with an initial inflection after 20 cases. We identified varying stages of skill acquisition that are likely typical of a surgeon as they would progress through the learning curve of advanced robotic surgery. Future studies are needed to confirm the optimal case volume for determining the skill level for the performance of rAWR. [ABSTRACT FROM AUTHOR]

‎In this paper‎, ‎a new generalized likelihood ratio (GLR) control chart based on sequentially probability ratio test (SPRT) is introduced to monitor the directional mean of von Mises distribution‎. ‎Different window size of past samples are utilized to construct the GLR chart statistic‎, ‎and the performance of this chart in detecting a wide range of parameter shift is evaluated‎. ‎A simulation study is carried out to investigate the performance of the proposed control chart in comparison with cumulative sum (CUSUM) control chart‎. ‎To guide practitioners‎, ‎a real example is provided‎.

Purpose The learning curve cumulative summation (LC-CUSUM) test is commonly used as a quantitative assessment of the individual learning process. This study aimed to evaluate the skill acquisition process for performing ultrasound staging of endometrial cancer using the LC-CUSUM test. Methods Sixty-seven ultrasound examinations performed by two operators were evaluated using the LC-CUSUM test according to their rate of success or failure to correctly stage myometrial invasion, serosa involvement, and pelvic and para-aortic lymph node involvement. The reference standard was the pathologic result. After the learning phase, the cumulative summation (CUSUM) test was applied to assess performance maintenance. Results The processes achieved satisfactory performance in the majority of the cases according to the established definitions. Operator 1 reached adequate performance within the 30th procedure for all the locations considered, while operator 2 performed fewer than 30 ultrasound examinations by the audit time. The CUSUM test confirmed that the target quality was preserved after the learning phase. Moreover, the ultrasound staging for endometrial cancer was highly accurate. Conclusion Using the LC-CUSUM test made it possible to monitor the achievement and maintenance of a satisfactory performance quantitatively. The LC-CUSUM test could be a valuable instrument to survey diagnostic pathways in gynecological ultrasonography quantitatively.

Purpose: Every procedure, no matter how difficult, has a learning curve. However, no one discusses the learning curve for simple operations. This idea constantly comes up when there is a demanding surgical method that challenges habits. The aim of this study is to use cumulative sum analysis to estimate the learning curve of the demanding laparoscopic complete mesocolic excision+central vascular ligation technique for right sided colon cancer. Materials and Methods: This retrospective study involved patients older than 18 years and with right-sided colon cancer admitted to Ankara University School of Medicine, Department of General Surgery between January 2015 and June 2022. The cumulative sum (CUSUM) chart based on operation duration and quantity of lymph nodes harvested was utilized to determine the learning curve. Results: 53 patients included in this study. CUSUM analysis based on operation duration showed that Surgeon had passed the learning phase and was considered proficient after 21 cases. And the CUSUM analysis based on the lymph nodes harvested revealed that surgeon gained proficiency in 25th case. In terms of demographic, oncological, and operative data, there was no difference between the the groups defined by the phases according to the CUSUM analysis based on both operation duration and lymph node count. Conclusion: Surgeons can become familiar with laparoscopic complete mesocolic excision for right sided colon cancer and perform it after approximately 21 to 25 operations.

Abstract Purpose As transanal total mesorectal excision (taTME) is performed worldwide, the optimization of existing training and guidance programs to enhance new taTME learners’ competence in performing this procedure is warranted. This study aimed to evaluate the taTME learning curve in patients with mid-low rectal cancer. Methods Patients who underwent taTME for mid-low rectal cancer between October 2015 and August 2021 at a single center were included. A cumulative sum (CUSUM) learning curve analysis was performed with the total operation time as the study outcome. The learning curve was analyzed using risk-adjusted CUSUM analysis, with postoperative complications and anastomotic leakage (AL) as outcomes. Results In total, 104 consecutive patients were included in this study. The CUSUM learning curve for total operative time started declining after 42 cases (309.1 ± 84.4 vs. 220.2 ± 46.4, P < 0.001). The risk-adjusted CUSUM (RA-CUSUM) learning curve for postoperative complications fluctuated in cases 44–75 and declined significantly after case 75. The RA-CUSUM learning curve for AL declined after 68 cases. Conclusions taTME had learning curves of 42, 75, and 68 cases for total operative time, postoperative complications, and AL, respectively. A surgeon may require 42 and 75 cases to achieve “proficiency” and “mastery” in taTME procedures, respectively.

The global navigation satellite system (GNSS), as a high-time resolution and high-precision measurement technology, has been widely used in the field of deformation monitoring. Owing to the influence of uncontrollable factors, there are inevitably some abnormal data in the GNSS monitoring series. Thus, it is necessary to detect and identify abnormal data in the GNSS monitoring series to improve the accuracy and reliability of the deformation disaster law analysis and warning. Many methods can be used to detect abnormal data, among which the statistical process control theory, represented by the cumulative sum (CUSUM), is widely used. CUSUM usually constructs statistics and determines control limits based on the threshold criteria of the average run length (ARL) and then uses the control limits to identify abnormal data in CUSUM statistics. However, different degrees of the 'trailing' phenomenon exist in the interval of abnormal data identified by the algorithm, leading to a higher false alarm rate. Therefore, we propose an improved CUSUM method that uses breaks for additive season and trend (BFAST) instead of ARL-based control limits to identify abnormal data in CUSUM statistics to improve the accuracy of identification. The improved CUSUM method is used to detect abnormal data in the GNSS coordinate series. The results show that compared with CUSUM, the improved CUSUM method shows stronger robustness, more accurate detection of abnormal data, and a significantly lower false alarm rate. [ABSTRACT FROM AUTHOR]

Objective: Compare and analyze clinical data of total laparoscopic pancreaticoduodenectomy (TLPD) cases for surgeons with / without first assistant experience (FAE) in TLPD. Probe influence of FAE in TLPD on the learning curve for an operator. Methods: The clinical data of 239 patients, that underwent TLPD performed by two surgeons between January 2017 and January 2022) in our department, were consecutively collected and divided into two groups (A and B). Group A cases were operated by Surgeon A, with FAE of 57 TLPDs in our department prior to initial TLPD as an operator. Group B cases were operated by Surgeon B with no FAE of TLPD. Cumulative sum (CUSUM) method developed learning curves. Clinical data and both surgeons' learning curves were statistically compared between both groups. Results: Between both groups, no statistically significant variations were observed for pre-operative health conditions. Reduced surgical duration, blood loss and transfusion volume during surgery, together with reductions in major post–operative complication rates and reduced hospital/ICU stays were identified within Group A, having statistically significant variations. The technical plateau phases of the learning curves were approximately 25–41 cases and 35–51 cases, for Surgeon A and Surgeon B, respectively. Conclusion: FAE in TLPD can accelerate the learning curve of TLPD for an operator, with safer surgical procedures and enhanced post–operative recovery. [ABSTRACT FROM AUTHOR]

Profile monitoring has received much attention from the applications in statistical process control. It is a method for monitoring the stability of a functional relationship between a response variable and one or more explanatory variables over the time axis. General linear profiles monitoring is very important since the linear relationship between a response variable and explanatory variables is easy to characterize besides it is simple and flexible. In addition, most of general linear profiles monitoring techniques assume normality of random error variables. However, the normality of random error variables is not satisfied in certain applications. This causes the existing monitoring methods for general linear profiles both inadequate and inefficient. Based on the log‐linear modelling, in this paper, we develop a non‐parametric control chart for Phase II monitoring of general linear profiles where normality of random error variables is not assumed. The proposed charting method applies the CUSUM (cumulative sum) to the Pearson chi‐square test for the Wilcoxon‐type rank‐based estimators of coefficient parameters and an estimator of random error variance. Effectiveness of the developed control chart is assessed and compared with that of two existing control charts based on the criterion of ARL (average run length). An industrial production process example is also applied to illustrate how the proposed control chart can be used in practice. [ABSTRACT FROM AUTHOR]

The active quickest detection problem with unknown postchange parameters is studied under the sampling control constraint, where there are p local streams in a system but one is only able to take observations from one and only one of these p local streams at each time instant. The objective is to raise a correct alarm as quickly as possible once the change occurs subject to both false alarm and sampling control constraints. Here we assume that exactly one of the p local streams is affected, and the postchange distribution involves unknown parameters. In this context, we propose an efficient greedy cyclic sampling–based quickest detection algorithm and show that our proposed algorithm is asymptotically optimal in the sense of minimizing the detection delay under both false alarm and sampling control constraints. Numerical studies are conducted to show the effectiveness and applicability of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Background: There is few study evaluating the relationship between endoscopic submucosal dissection (ESD) resection speed and the lesion characteristics of gastric neoplasia. We investigated the learning curve of consecutive ESDs using cumulative sum (CUSUM) analysis. Methods: A total of 356 ESDs performed by a single endoscopist were grouped chronologically into three learning periods. The ESD procedure was defined to be fast when resection speed was > 9.0 cm2/hour. The CUSUM method was used to assess the number of ESDs required for achieving proficiency and mastery. Results: Mean resection speed was significantly faster in Phase III (15.1 cm2/hour) compared to those in Phase I (9.3 cm2/hour) and II (11.4 cm2/hour) (p < 0.001). Tumors in the stomach's upper and middle third location were significantly associated with difficulty in attaining the fast resection speed (odds ratios, 0.05 and 0.36) compared to the lower third location. The number of ESDs required to achieve a competency for fast resection was 15 for tumors in the lower third of the stomach and 98 for those in the upper/middle third location, respectively. In the lower third location of the tumor, the CUSUM curve revealed that 75 cases were needed to achieve proficiency and 174 cases to achieve mastery. However, mastery was not achieved in ESD for the upper/middle third tumor during the study period. Conclusion: The time required to achieve relevant competency in gastric ESD depends on the tumor location. [ABSTRACT FROM AUTHOR]

We present a new CUSUM procedure for sequential change-point detection in self- and mutually-exciting point processes (specifically, Hawkes networks) using discrete events data. Hawkes networks have become a popular model in statistics and machine learning, primarily due to their capability in modeling irregularly observed data where the timing between events carries a lot of information. The problem of detecting abrupt changes in Hawkes networks arises from various applications, including neuroengineering, sensor networks, and social network monitoring. Despite this, there has not been an efficient online algorithm for detecting such changes from sequential data. To this end, we propose an online recursive implementation of the CUSUM statistic for Hawkes processes, which is computationally and memory-efficient and can be decentralized for distributed computing. We first prove theoretical properties of this new CUSUM procedure, then show the improved performance of this approach over existing methods, including the Shewhart procedure based on count data, the generalized likelihood ratio statistic, and the standard score statistic. This is demonstrated via simulation studies and an application to population code change-detection in neuroengineering. [ABSTRACT FROM AUTHOR]

Many modern applications of online changepoint detection require the ability to process high-frequency observations, sometimes with limited available computational resources. Online algorithms for detecting a change in mean often involve using a moving window, or specifying the expected size of change. Such choices affect which changes the algorithms have most power to detect. We introduce an algorithm, Functional Online CuSUM (FOCuS), which is equivalent to running these earlier methods simultaneously for all sizes of windows, or all possible values for the size of change. Our theoretical results give tight bounds on the expected computational cost per iteration of FOCuS, with this being logarithmic in the number of observations. We show how FOCuS can be applied to a number of different changes in mean scenarios, and demonstrate its practical utility through its state-of-the-art performance at detecting anomalous behaviour in computer server data. [ABSTRACT FROM AUTHOR]

Purpose: The learning curve cumulative summation (LC-CUSUM) test is commonly used as a quantitative assessment of the individual learning process. This study aimed to evaluate the skill acquisition process for performing ultrasound staging of endometrial cancer using the LC-CUSUM test. Methods: Sixty-seven ultrasound examinations performed by two operators were evaluated using the LC-CUSUM test according to their rate of success or failure to correctly stage myometrial invasion, serosa involvement, and pelvic and para-aortic lymph node involvement. The reference standard was the pathologic result. After the learning phase, the cumulative summation (CUSUM) test was applied to assess performance maintenance. Results: The processes achieved satisfactory performance in the majority of the cases according to the established definitions. Operator 1 reached adequate performance within the 30th procedure for all the locations considered, while operator 2 performed fewer than 30 ultrasound examinations by the audit time. The CUSUM test confirmed that the target quality was preserved after the learning phase. Moreover, the ultrasound staging for endometrial cancer was highly accurate. Conclusion: Using the LC-CUSUM test made it possible to monitor the achievement and maintenance of a satisfactory performance quantitatively. The LC-CUSUM test could be a valuable instrument to survey diagnostic pathways in gynecological ultrasonography quantitatively. [ABSTRACT FROM AUTHOR]

In this paper, a new generalized likelihood ratio (GLR) control chart based on sequentially probability ratio test (SPRT) is introduced to monitor the directional mean of von Mises distribution. Different window size of past samples are utilized to construct the GLR chart statistic, and the performance of this chart in detecting a wide range of parameter shift is evaluated. A simulation study is carried out to investigate the performance of the proposed control chart in comparison with cumulative sum (CUSUM) control chart. To guide practitioners, a real example is provided. [ABSTRACT FROM AUTHOR]

Statistical process control (SPC) charts have been widely used in the field of educational measurement. The cumulative sum (CUSUM) is an established SPC method to detect aberrant responses for educational assessments. There are many studies that investigated the performance of CUSUM in different test settings. This paper describes the CUSUM procedure and shows how it can be used to monitor the testtaking process and detect aberrant responses. It aims to provide an accessible guide for the CUSUM control chart. [ABSTRACT FROM AUTHOR]

Manufacturing and energy sectors provide vast amounts of maintenance data and information which can be used proactively for performance monitoring and prognostic analysis which lead to improve maintenance planning and scheduling activities. This leads to reduced unplanned shutdowns, maintenance costs and any fatal events that could affect the operations of the overall system. Performance and condition monitoring are among the most used strategies for prognostic and health management (PHM), in which different methods and techniques can be implemented to analyse maintenance and online data. Offshore wind turbines (WTs) are complex systems increasingly needing maintenance. This study proposes a performance monitoring system to monitor the performance of the WT power generation process by exploiting artificial neural networks (ANN) composed of different network designs and training algorithms, using simulated supervisory control and data acquisition (SCADA) data. The performance monitoring is based on different operating modes of the same type of wind turbine. The degradation models were developed based on the generated active power resulting from different degradation levels of the gearbox, which is a critical component of the WTs. The deviations of the wind power curves for all operating modes over time are monitored in terms of the resulting power residuals and are modelled using ANN with a unique network architecture. The monitoring process uses the recursive form of the cumulative summation (CUSUM) change detection algorithm to detect the state change point in which the gearbox efficiency is degraded by evaluating the power residuals predicted by the ANN model. To increase the monitoring effectiveness, a second ANN model was developed to predict the gearbox efficiency to monitor any failure that could happen once the efficiency degrades below a threshold. The results show a high degree of accuracy in power and efficiency prediction in addition to monitoring the abnormal state or deviations of the power generation process resulting from the degraded gearbox efficiency and their corresponding time slots. The developed monitoring method can be a valuable tool to provide maintenance experts with alarms and insights into the general state of the power generation process, which can be used for further maintenance decision-making. [ABSTRACT FROM AUTHOR]

We generalize the Homm and Breitung (2012) CUSUM-based procedure for the real-time detection of explosive autoregressive episodes in financial price data to allow for time-varying volatility. Such behavior can heavily inflate the false positive rate (FPR) of the CUSUM-based procedure to spuriously signal the presence of an explosive episode. Our modified procedure involves replacing the standard variance estimate in the CUSUM statistics with a nonparametric kernel-based spot variance estimate. We show that the sequence of modified CUSUM statistics has a joint limiting null distribution which is invariant to any time-varying volatility present in the innovations and that this delivers a real-time monitoring procedure whose theoretical FPR is controlled. Simulations show that the modification is effective in controlling the empirical FPR of the procedure, yet sacrifices only a small amount of power to detect explosive episodes, relative to the standard procedure, when the shocks are homoskedastic. An empirical illustration using Bitcoin price data is provided. [ABSTRACT FROM AUTHOR]

Thermal protection in marine electrical propulsion motors is commonly implemented by installing temperature sensors on the windings of the motor. An alarm is issued once the temperature reaches the alarm limit, while the motor shuts down once the trip limit is reached. Field experience shows that this protection scheme in some cases is insufficient, as the motor may already be damaged before reaching the trip limit. In this paper, we develop a machine learning algorithm to predict overheating, based on past data collected from a class of identical vessels. All methods were implemented to comply with real-time requirements of the on-board protective systems with minimal need for memory and computational power. Our two-stage overheating detection algorithm first predicts the temperature in a normal state using linear regression fitted to regular operation motor performance measurements, with exponentially smoothed predictors accounting for time dynamics. Then it identifies and monitors temperature deviations between the observed and predicted temperatures using an adaptive cumulative sum (CUSUM) procedure. Using data from a real fault case, the monitor alerts between 60 to 90 min before failure occurs, and it is able to detect the emerging fault at temperatures below the current alarm limits. [ABSTRACT FROM AUTHOR]

Hospitals are among the most energy-intensive buildings in the service industry. Developing energy management strategies could lead to important energy savings, and it must pass through detailed analyses of each specific activity's energy requirement. The present study aims to find a healthcare facility's main energy drivers and develop an offline monitoring method applicable to future healthcare energy requirements. A Multiple Linear Regression model has been realised to define the standard energy consumption based on the year 2019, allowing a Cumulative Sum of differences control chart including the 2020 energy consumption data. The proposed method allows finding variations between actual and standard building energy demands, a useful tool to monitor the effectiveness of energy system control strategies. [ABSTRACT FROM AUTHOR]

As a result of the severe energy shortage and the greenhouse effect, experts worldwide have been devoted to solving energy management problems. Smart grid construction is an essential technology for mastering energy allocation. Smart grids enable end users to adjust their energy consumption via incentive measures, reduce the frequency of power supply instability, and improve energy efficiency. Non-intrusive load monitoring (NILM) is a vital technology for smart grid construction. One of the fundamental steps of NILM is event detection. Proper event detection can increase the accuracy of load identification. Among traditional methods, especially the event detection method developed with the CUSUM method, although the accuracy is reasonable, the precision, recall, and f1 score are not relatively better. Thus, there is an opportunity to improve the performance of CUSUM. Additionally, many studies focus on the step-like event, but the long-transient event is often overlooked in event detection. Therefore, in this study, it was observed that when the transient current deviates from the steady-state current, the transient current can be regarded as a key indicator for event detection. With this observation, a method is proposed to convert the root mean square (RMS) current into a cumulative sum (CUSUM) diagram method and identify turning points representing events from the CUSUM geometry. Once the slope of the turning point has been determined, event detection is achieved. Compared with traditional methods, the proposed method is easy to implement, its recognition rate can reach around 98%, and the window length is reduced from 5 s to 3 s.

The control chart is a fundamental tool in statistical process control (SPC), widely employed in manufacturing and construction industries for process monitoring with the primary objective of maintaining quality standards and improving operational efficiency. Control charts play a crucial role in identifying special cause variations and guiding the process back to statistical control. While Shewhart control charts excel at detecting significant shifts, EWMA and CUSUM charts are better suited for detecting smaller to moderate shifts. However, the effectiveness of all these control charts is compromised when the underlying distribution deviates from normality. In response to this challenge, this study introduces a robust mixed EWMA-CUSUM control chart tailored for monitoring processes characterized via symmetric but non-normal distributions. The key innovation of the proposed approach lies in the integration of a robust estimator, based on order statistics, that leverages the generalized least square (GLS) technique developed by Lloyd. This integration enhances the chart’s robustness and minimizes estimator variance, even in the presence of non-normality. To demonstrate the effectiveness of the proposed control chart, a comprehensive comparison is conducted with several well-known control charts. Results of the study clearly show that the proposed chart exhibits superior sensitivity to small and moderate shifts in process parameters when compared to its predecessors. Through a compelling illustrative example, a real-life application of the enhanced performance of the proposed control chart is provided in comparison to existing alternatives.

The emergence of large sensor networks and the Internet of Things has reinvigorated interest into distributed quickest change detection. Important shortcomings of existing approaches are ease of design, flexibility in communication, and applicability to larger networks. The new approach proposed in this work features local sensors that can be triggered multiple times, i.e., can reset and continue monitoring after transmitting their decisions. With larger sensor networks as a focus, the system allows for multiple simultaneous transmissions to a fusion center within bandwidth limitations. The proposed system uses the cumulative-sum procedure at local sensors to binarize local decisions, which are then transmitted to the fusion center that also employs cumulative-sum quickest detection. Test overdesign due to sequential test overshoot is avoided, and global and local thresholds are chosen to meet a desired false alarm rate constraint using numerical computation of expected delay performance. The system compares favourably to several existing methods while offering greater flexibility in the amount of fusion center communication.

BackgroundTill now there are very few reports about surgical results of Uniportal-VATS esophagectomy and no one about long-term outcomes. This study is the first comparing surgical and oncological outcomes of Uniportal-VATS with open McKeown esophagectomy, with the largest reported series and longest oncological follow-up.MethodsThe prospectively collected clinical, surgical and oncological data of 75 patients, undergone McKeown esophagectomy at our Thoracic Surgery Department, from January 2012 to August 2022, were retrospectively analyzed. Nineteen patients underwent esophagectomy by thoracotomy and reconstruction according to McKeown technique while 56 by Uniportal-VATS approach. Gastric tubulization was performed totally laparoscopic or through a mini-laparatomic access and cervical anastomosis was made according to Orringer's technique.ResultsThe mean operative thoracic time was similar in both accesses (102.34 ± 15.21 min in Uniportal-VATS vs. 115.56 ± 23.12 min in open, p: 0.646), with a comparable number of mediastinal nodes retrieved (Uniportal-VATS:13.40 ± 8.12 vs. open:15.00 ± 6.86, p: 0.275). No case needed conversion from VATS to open. The learning curve in Uniportal-VATS was completed after 34 cases, while the Mastery was reached after 40. Both approaches were comparable in terms of minor post-operative complications (like pneumonia, lung atelectasis, anemization, atrial fibrillation, anastomotic-leak, left vocal cord palsy, chylothorax), while the number of re-operation for major complications (bleeding or mediastinitis) was higher in open group (21.0% vs. 3.6%, p: 0.04). Both techniques were also effective in terms of surgical radicality and local recurrence but VATS approach allowed a significantly lower chest tube length (11.89 ± 9.55 vs. 25.82 ± 24.37 days, p: 0.003) and post-operative stay (15.63 ± 11.69 vs. 25.53 ± 23.33, p: 0.018). The 30-day mortality for complications related to surgery was higher in open group (p: 0.002). The 2-, 5- and 8-year survival of the whole series was 72%, 50% and 33%, respectively. Combined 2- and 5-year OS in Uniportal-VATS group was 76% and 47% vs. 62% and 62% in open group, respectively (Log-rank, p: 0.286; Breslow-Wilcoxon: p: 0.036). No difference in DFS was recorded between the two approaches (5 year-DFS in Uniportal-VATS: 86% vs. 72%, p: 0.298). At multivariate analysis, only pathological stage independently affected OS (p: 0.02), not the surgical approach (p: 0.276).ConclusionsUniportal-VATS seems to be a safe, feasible and effective technique for performing McKeown esophagectomy, with equivalent surgical and long-term oncological results to standard thoracotomy, but with a faster and unharmed recovery, and a quite short learning curve.

The present study set out to build a machine learning model to incorporate conventional quality control (QC) rules, exponentially weighted moving average (EWMA), and cumulative sum (CUSUM) with random forest (RF) algorithm to achieve better performance and to evaluate the performances the models using computer simulation to aid laboratory professionals in QC procedure planning.

(1) Background: Hand-assisted laparoscopic total gastrectomy (LTG) for patients with gastric cancer (GC) has been established as the standard surgical treatment at our center. This study aims to quantify the learning curve for surgeons performing minimally invasive total gastrectomy at a high-volume single center. (2) Methods: One hundred and eighteen consecutive patients who underwent minimally invasive total gastrectomy between January 2014 and December 2020 at a single high-volume center were included and reviewed retrospectively. Risk-adjusted cumulative sum analysis (RA-CUSUM) was used to monitor the surgical outcomes for patients with different risks of postoperative mortality using varying-coefficient logistic regression models. Patients were ordered by the sequential number of the procedure performed and divided into two groups according to the degree of surgeon proficiency as determined by RA-CUSUM analysis (group A: 45; group B: 73 patients). Age, gender, body mass index (BMI), tumor location, pathology, and comorbidities were compared while primary endpoints comprised surgical parameters, postoperative course, and survival outcomes. (3) Results: Forty-four cases were required for the completion of the learning curve. During this time, the mean operating time decreased. Hand-assisted laparoscopic total gastrectomy performed after a learning curve was associated with a shorter median operating time (OT) (360 min vs. 289 min, <0.001), and a reduced length of stay (A = 18.0 vs. B = 14.0 days) (p = 0.154), while there was a trend toward less major complications (Clavien–Dindo (CD) 3–5 within 90 days (12 (26.67%) vs. 10 (13.70%) p = 0.079). Our results showed no difference in anastomotic leakage between the two groups (group A vs. group B, 3 (6.67%) vs. 4 (5.48%) p = 0.99). Similarly, 30-day (0 (0%) vs. 1 (1.7%), p = 0.365) and 90-day mortality (1 (2.08%) vs. 2 (3.39%), p = 0.684) were comparable. Following multivariate analysis, the level of surgical proficiency was not a significant prognostic factor for overall survival. (4) Conclusions: A minimum of 44 cases are required for experienced laparoscopic surgeons to achieve technical competence for performing LTG. While operation time decreased after completion of the learning curve, quality criteria such as achievement of R0 resection, anastomotic leakage, and perioperative mortality remained unaltered. Of note, the level of surgical training showed no significant impact on the 2 year OS or DFS. [ABSTRACT FROM AUTHOR]

In the manufacturing industry, process surveillance plays an important role in improving product reliability. Many monitoring procedures have been devised to improve the reliability of a product in the literature. Due to cascading nature of multistage processes, the quality variable of the final stage can be influenced by the quality variable in the previous stages. Furthermore, the ordinary least squares method produces biased estimates if there are outliers in the historical data. The existence of cascade property in the multistage process and the presence of influential observations (outliers) in the historical data make the analysis of control schemes more challenging. Therefore, it is essential to take into account the impact of effective covariates and outliers in the historical dataset. In this paper, a cumulative sum (CUSUM) and two exponentially weighted moving average (EWMA) charts have been developed to monitor a two‐stage‐dependent process. The proportional hazard (PH) model has been applied to model the relationship among the incoming and outgoing variables of the two stage process. To deal with the deleterious effects of outliers on the results, a robust regression technique known as the M‐estimation has been implemented and the performance of the proposed monitoring procedures has been analyzed through Monte Carlo simulations. Lastly, two real‐life applications of the proposed control schemes are presented. [ABSTRACT FROM AUTHOR]

Purposes: Little research exists on potential learning curve for male sling procedures. We aimed to perform a learning curve analysis of a single surgeon's experience of sling placement evaluating multiple outcomes and using the cumulative sum failure methodology. Methods: The study included 65 consecutive patients that underwent implantation of a fixed transobturator sling (TiLOOP Male) for post-radical prostatectomy stress incontinence at our institution from January 2013 to December 2018. Dichotomous outcomes evaluated with cumulative sum failure analysis included 12-months continence defined based on Patient Global Impression of Improvement (PGI-I) questionnaire (primary outcome), 24 h pad test and, 24 h pad use, operative time (⩽/>60 min), and complications (yes/no). Univariate and multivariate logistic regression analyses were performed to evaluate the association of the procedures' chronological sequence number with the outcomes. Results: Cumulative sum failure curves revealed a clear and lengthy learning curve effect for most of subjective and quantitative continence outcomes and for operative time. For the primary outcome (at least much improved at PGI-I), 62 procedures were required to overcome the learning curve. Accordingly, multivariate analyses showed that the sequence number was statistically significant for predicting failures based on PGI-I (adjusted OR 0.95; 95% CI: 0.91–0.99; p = 0.02), objective outcomes, and operative time. Conclusions: An evident and lengthy learning curve was observed in our series of male sling placement to achieve the end level of proficiency, independently from case-mix. Individualized structured training on male sling surgery will benefit patients treated in the initial surgeon's experience. Surgical experience should be considered when reporting studies on male slings. [ABSTRACT FROM AUTHOR]

Recently, model‐assisted designs, including the Bayesian optimal interval (BOIN) design with optimal thresholds to determine the dose for the next cohort, have been proposed for cancer phase I studies. Model‐assisted designs are useful because of their good performance as model‐based designs in addition to their algorithm‐based simplicity. In BOIN, escalation and de‐escalation based on boundaries can be understood as a type of change point detection based on a sequential test procedure. Notably, the sequential test procedure is used in a wide range of fields and is known for its application to control charts, statistical monitoring methods used for detecting abnormalities in manufacturing processes. In control charts, abnormalities are detected if the control chart statistics are observed to be outside of the optimal boundaries. The cumulative sum (CUSUM) statistic, which is developed for control chart applications, derives higher power under the same erroneous judgment rate. Hence, it is expected that a more efficient model‐assisted design can be achieved by the application of CUSUM statistics. In this study, a model‐assisted design based on the CUSUM statistic is proposed. In the proposed design, the dose for the next cohort is decided by CUSUM statistics calculated from the counts of the dose‐limiting toxicity and pre‐defined boundaries, based on the CUSUM control chart scheme. Intensive simulation shows that our proposed method performs better than BOIN, and other representative model‐assisted designs, including modified toxicity probability interval (mTPI) and Keyboard, in terms of controlling over‐dosing rates while maintaining similar performance in the determination of maximum tolerated dose. [ABSTRACT FROM AUTHOR]

This article considers changepoint detection in serially correlated categorical time series. While changepoint aspects in correlated sequences of continuous random variables have been extensively explored in the literature, changepoint methods for independent categorical time series are only now coming into vogue. This study extends changepoint methods by developing techniques for correlated categorical time series. Here, a cumulative sum type test is devised to test for a single changepoint in a correlated categorical data sequence. Our categorical series is constructed from a latent Gaussian process through clipping techniques. A sequential parameter estimation method is proposed to estimate the parameters in this model. The methods are illustrated via simulations and applied to a real categorized rainfall time series from Albuquerque, New Mexico. [ABSTRACT FROM AUTHOR]

A double moving average (DMA) control chart has been proposed in the literature for monitoring the process mean. Several studies have also been done based on this scheme. Unfortunately, the variance of DMA statistic that has been used in these studies is not correct. In this article, we provide the correct variance of the DMA chart and through a simulation study, we evaluate its performance. It is shown that the DMA chart is more effective than the moving average (MA) chart in detecting small shifts in the process mean and vice versa for moderate to large shifts. Moreover, the DMA chart, even with a small value of span w, has similar, and in some cases better, detection ability than the EWMA and CUSUM charts, especially for moderate and large shifts. [ABSTRACT FROM AUTHOR]

Purpose: As transanal total mesorectal excision (taTME) is performed worldwide, the optimization of existing training and guidance programs to enhance new taTME learners' competence in performing this procedure is warranted. This study aimed to evaluate the taTME learning curve in patients with mid-low rectal cancer. Methods: Patients who underwent taTME for mid-low rectal cancer between October 2015 and August 2021 at a single center were included. A cumulative sum (CUSUM) learning curve analysis was performed with the total operation time as the study outcome. The learning curve was analyzed using risk-adjusted CUSUM analysis, with postoperative complications and anastomotic leakage (AL) as outcomes. Results: In total, 104 consecutive patients were included in this study. The CUSUM learning curve for total operative time started declining after 42 cases (309.1 ± 84.4 vs. 220.2 ± 46.4, P < 0.001). The risk-adjusted CUSUM (RA-CUSUM) learning curve for postoperative complications fluctuated in cases 44–75 and declined significantly after case 75. The RA-CUSUM learning curve for AL declined after 68 cases. Conclusions: taTME had learning curves of 42, 75, and 68 cases for total operative time, postoperative complications, and AL, respectively. A surgeon may require 42 and 75 cases to achieve "proficiency" and "mastery" in taTME procedures, respectively. [ABSTRACT FROM AUTHOR]

The analysis of record-breaking events is of interest in fields such as climatology, hydrology or anthropology. In connection with the record occurrence, we propose three distribution-free statistics for the changepoint detection problem. They are CUSUM-type statistics based on the upper and/or lower record indicators observed in a series. Using a version of the functional central limit theorem, we show that the CUSUM-type statistics are asymptotically Kolmogorov distributed. The main results under the null hypothesis are based on series of independent and identically distributed random variables, but a statistic to deal with series with seasonal component and serial correlation is also proposed. A Monte Carlo study of size, power and changepoint estimate has been performed. Finally, the methods are illustrated by analyzing the time series of temperatures at Madrid, Spain. The R package RecordTest publicly available on CRAN implements the proposed methods. [ABSTRACT FROM AUTHOR]