Cumulative sum analysis score and phacoemulsification competency learning curve

AIM： To use the cumulative sum analysis score（CUSUM） to construct objectively the learning curve of phacoemulsification competency.METHODS： Three second-year residents and an experienced consultant were monitored for a series of 70 phacoemulsification cases each and had their series analysed by CUSUM regarding posterior capsule rupture（PCR） and best-corrected visual acuity. The acceptable rate for PCR was 〈5%（lower limit h） and the unacceptable rate was 〉10%（upper limit h）. The acceptable rate for bestcorrected visual acuity worse than 20/40 was 〈10%（lower limit h） and the unacceptable rate was 〉20%（upper limit h）. The area between lower limit h and upper limit h is called the decision interval. RESULTS： There was no statistically significant difference in the mean age, sex or cataract grades between groups. The first trainee achieved PCR CUSUM competency at his 22 nd case. His best-corrected visual acuity CUSUM was in the decision interval from his third case and stayed there until the end, never reaching competency. The second trainee achieved PCR CUSUM competency at his 39^ th case. He could reach best-corrected visual acuity CUSUM competency at his 22 ^nd case. The third trainee achieved PCR CUSUM competency at his 41 st case. He reached bestcorrected visual acuity CUSUM competency at his 14 ^th case.CONCLUSION： The learning curve of competency in phacoemulsification is constructed by CUSUM and in average took 38 cases for each trainee to achieve it.

Learning curve for a surgeon in robotic pancreaticoduodenectomy through a“G”-shaped approach:A cumulative sum analysis

BACKGROUND Robotic pancreaticoduodenectomy(RPD)can achieve similar surgical results to open and PD;however,RPD has a long learning curve and operation time(OT).To address this issue,we have summarized a surgical path to shorten the surgical learning curve and OT.AIM To investigate the effective learning curve of a“G”-shaped surgical approach in RPD for patients.METHODS A total of 60 patients,who received“G”-shaped RPD(GRPD)by a single surgeon in the First Hospital of Shanxi Medical University from May 2017 to April 2020,were included in this study.The OT,demographic data,intraoperative blood loss,complications,hospitalization time,and pathological results were recorded,and the cumulative sum(CUSUM)analysis was performed to evaluate the learning curve for GRPD.RESULTS According to the CUSUM analysis,the learning curve for GRPD was grouped into two phases:The early and late phases.The OT was 480±81.65 min vs 331±76.54 min,hospitalization time was 22±4.53 d vs 17±6.08 d,and blood loss was 308±54.78 mL vs 169.2±35.33 mL in the respective groups.Complications,including pancreatic fistula,bile leakage,reoperation rate,postoperative death,and delayed gastric emptying,were significantly decreased after this surgical technique.CONCLUSION GRPD can improve the learning curve and operative time,providing a new method for shortening the RPD learning curve.

Learning curve for magnetic resonance imaging/ultrasound fusion prostate biopsy in detecting prostate cancer using cumulative sum analysis

Background:Targeted magnetic resonance(MR)with ultrasound(US)fusion-guided biopsy has been shown to improve detection of prostate cancer.The implementation of this approach requires integration of skills from radiologists and urologists.Objective methods for assessment of learning curves,such as cumulative sum(CUSUM)analysis,may be helpful in identifying the presence and duration of a learning curve.The aim of this study is to determine the learning curve for MR/US fusion-guided biopsy in detecting clinically significant prostate cancer using CUSUM analysis.Materials and methods:Retrospective analysis was performed in this institutional review board-approved study.Two urologists implemented an MR/US fusion-guided prostate biopsy program between March 2015 and September 2017.The primary outcome measure was cancer detection rate(CDR)stratified by Prostate Imaging Reporting and Data System(PI-RADS)scores assigned on the MR imaging.Cumulative sum analysis quantified actual cancer detection versus a predetermined target satisfactory CDR of MR/US fusion biopsies in a sequential case-by-case basis.For this analysis,satisfactory performance was defined as>80%CDR in patients with Pl-RADS 5,>50%in PI-RADS 4,and<20%in Pl-RADS 1-3.Results:Complete data were available for MR/US fusion-guided biopsies performed on 107 patients.The CUSUM learning curve analysis demonstrated intermittent underperformance until approximately 50 cases.After this inflection point,there was consistently good performance,evidence that no further learning curve was being encountered.Conclusions:At a new center implementing MR/US fusion-guided prostate biopsy,the learning curve was approximately 50 cases before a consistently high performance for prostate cancer detection.

Short-term warning and integrity monitoring algorithm for coal mine shaft safety

A new short-term warning and integrity monitoring algorithm was proposed for coal mine shaft safety. The Kalman filter (KF) model was used to extract real global positioning system (GPS) kinematic deformation information. The short-term warning model was built by using the two-side cumulative sum (CUSUM) test, which further improves the warning system reliability. Availability (the minimum warning deformation, MWD), false alarm rate (the average run length, ARL), missed rate (the warning delay, WD) and the relationships among them were analyzed and the method choosing warning parameters is given. A test of a deformation simulation platform shows that the warning algorithm can be effectively used for steep deformation warning. A field experiment of the Malan mine shaft in Shanxi coal area illustrates that the proposed algorithm can detect small dynamic changes and the corresponding occurring time. At given warning thresholds (MWD is 15 mm and ARL is 1000),the detected deformations of two consecutive days’ deformation sequences with the algorithm occur at the 705th epoch (705 s) and the 517th epoch (517 s), respectively.

An Early Stage Detecting Method against SYN Flooding Attacks

Existing detection methods against SYN flooding attacks are effective only at the later stages when attacking signatures are obvious.In this paper an early stage detecting method(ESDM) is proposed.The ESDM is a simple but effective method to detect SYN flooding attacks at the early stage.In the ESDM the SYN traffic is forecasted by autoregressive integrated moving average model, and non-parametric cumulative sum algorithm is used to find the SYN flooding attacks according to the forecasted traffic.Trace-driven simulations show that ESDM is accurate and efficient to detect the SYN flooding attacks.

A cumulative standard deviation sum based method for high resistance fault identification and classification in power transmission lines

High resistance fault poses an enormous challenge to the existing algorithms of fault detection and fault classification.In this paper,the standard deviation and accumulation method are employed to perform the fault detection and classification.It is primarily built in two stages.Firstly,the standard deviations for the measured current’s signals of the local and remote terminals is computed to extract the fault feature.Secondly,the cumulative approach is used to enlarge the fault feature to perform the high resistance fault.The proposed scheme is known as Standard Deviation Index(SDI),and it is obtained for the three phases and zero sequence.The proposed algorithm has been tested through different fault circumstances such as multiple faults locations,fault resistances,and fault inception time.Moreover,far-end faults with high-resistance,faults happened nearby the terminal,faults considering variable loading angle,sudden load change,different sampling frequency,bad signaling and a fault occurred in the presence of series compensation are also discussed.The results show that the proposed scheme performed remarkably well regarding the fault with resistance up to 1.5kΩand can be detected within a millisecond after the fault inception.Additionally,the computational simplicity that characterizes the processes makes it more efficient and suitable for domain applications.

Testing conditional independence with data missing at random

It is known that conditional independence is a quite basic assumption in many fields of statistics. How to test its validity is of great importance and has been extensively studied by the literature. Nevertheless, all of the existing methods focus on the case that data are fully observed, but none of them seems having taken into account of the scenario when missing data are present. Motivated by this, this paper develops two testing statistics to handle such a situation relying on the idea of inverse probability weighted and augmented inverse probability weighted techniques. The asymptotic distributions of the proposed statistics are also derived under the null hypothesis. The simulation studies indicate that both testing statistics perform well in terms of size and power.

Comparative analysis of interdecadal precipitation variability over central North China and sub Saharan Africa

The interdecadal variability of precipitation over sub-Saharan Africa (SSA) and central North China (CNC) is examined and compared in this study. Previous studies have found that the two regions have similar interdecadal dry–wet evolution in the past 100 years. The results show obvious decadal precipitation fluctuations in the two regions. In CNC, a persistent negative precipitation anomaly is detected from the early 1970s to the 2000s. In SSA, a negative precipitation anomaly is apparent since the late 1970s, while a distinct upward trend is found since the 1990s although the precipitation anomaly is still negative. Significant correlation is found between the decadal precipitation anomalies in SSA and the SST modes (Atlantic Multidecadal Oscillation (AMO) and Pacific Decadal Oscillation (PDO)), as well as the North Atlantic Oscillation (NAO), while in CNC the decadal precipitation is influenced by the NAO and PDO. The EOF results show that the total explained variance of the first four EOFs in SSA is smaller than that of CNC. The fourth and third modes in SSA are significantly associated with the AMO and PDO respectively, while the first, third, and fourth modes are significantly associated with the NAO. The first mode in CNC is significantly associated with the NAO. The first mode of the precipitation anomaly in SSA fluctuates out of phase with that in CNC, while in-phase changes are apparent among the third and the fourth modes.

A Nonparametric Adaptive CUSUM Method and Its Application in Source-End Defense against SYN Flooding Attacks

Combating DDoS attacks at their sources is still in its infancy. In tttis paper, a noaparametric adaptive CUSUM （cumulative sum） method is presented, which is proven efficient in detecting SYN flooding attacks close to their sources. Different from other CUSUM methods, this new method has two distinct features： （1） its detection threshold can adapt itself to various traffic conditions and （2） it can timely detect the end of an attack within a required delay. Trace-driven simulations are conducted to validate the efficacy of this method in detecting SYN flooding attacks, and the results show that the nonparametric adaptive CUSUM method excels in detecting low-rate attacks.

A Comparative Assessment of Temperature Data from Different Sources for Dehradun, Uttarakhand, India

A comparative study of extreme temperature parameters from different sources is carried out by examining standardized anomalies, trends, correlation, and equivalence of datasets. Maximum temperature （Tmax） and minimum temperature （Wmln） for Dehradun, from two different sources such as computed and gridded data from Climatic Research Unit （CRU） and observed data from India Meteorological Department （IMD） are used for 1901-2012. The CRU data are compared initially with IMD, by graphical assessment of standardized anomalies. Subsequently, change points are identified by using Cumulative Sum （CUSUM）-chart technique for trend analysis. The magnitude and significance of trends are determined by applying Sen＇s slope test, and on the basis of these, trends are compared. Further, correlation analysis is carried out and datasets are tested for equivalence by using Wileoxon-Mann Whitney test. The result shows that annual standardized anomalies of CRU data follow the pattern of annual standardized anomalies of IMD data. The CRU data exhibit similar trends and are well correlated with IMD dataset. Moreover, CRU anomaly data are identical with IMD anomaly data in the recent decades. High resolution gridded CRU data have open access and may be more useful due to its spatio-temporal continuity for land areas of the world.

Fault tolerant control in an unmanned bicycle robot via sliding mode theory

In this work,a new active fault tolerant control(FTC)is developed for an unmanned bicycle robot based on an integration between a sliding mode control(SMC),fault detection(FD),and fault estimation(FE)via a residual signal.A sliding surface in accordance with the fault tolerant sliding mode control(FTSMC)is designed for the bicycle robot to get multiple exciting features such as fast transient response with finite time convergence,small overshoot and quick stabilisation in the presence of an actuator fault.To obtain an effective performance for the FTSMC,a fault estimation system is employed and in order to attain estimation,an extended Kalman filter(EKF)as an estimator and a change detection algorithm called cumulative sum(CUSUM)as a residual evaluation function are developed.The innovative features of the proposed approach,that is FTSMC,are verified when compared with the other up‐to‐date control techniques like fault tolerant model‐based predictive control with feedback linearisation(FTMPC+FBL)and fault tolerant linear quadratic regulator with feedback linearisation(FTLQR+FBL)on an unmanned bicycle robot.

Determining the component-based operative time learning curve for robotic-assisted radical prostatectomy

Objectives:To determine the learning curve(LC)of total operative time and the discrete components of the robotic-assisted radical prostatectomy(RARP)for a recent robotic fellowship-trained urologic surgeon.Materials and methods:We performed a retrospective analysis of RARP procedures performed by a single new attending surgeon from August 2015 to April 2019.Patients'demographics and operative details were assessed.Total operative time was divided and prospectively recorded in 7 parts:(a)docking robot,(b)dissecting seminal vesicles(SVs)(c)dissecting endopelvic fascia(EPF),(d)incising bladder neck(BN),(e)completing the dissection,(f)lymph node dissection,and(g)urethrovesical anastomosis(UVA)and robot undocking.Cumulative sum analysis was used to ascertain the LC for total operative time and the 7 parts of the procedure.Results:One hundred twenty consecutive RARPs were performed.The LC was overcome at 25 cases for total operative time,13 cases for docking the robot,33 cases for dissecting SVs,31 cases for dissecting EPF,46 cases for incising BN,38 cases for prostate dissection,25 cases for lymph node dissection,and 52 cases for UVA.Total operative time was decreased 22.8%(p<0.0001)and time for robot docking,dissecting SVs,dissecting EPF,incising BN,completing prostate dissection,lymph node dissection,and UVA were decreased 16.7%,30.5%,29.5%,36.2%,37.3%,32.2%,and 26.9%,respectively(all p<0.05).Conclusions:We observed a 25-case LC for a fellowship-trained urologist to achieve stable operative performance of RARP surgery.Procedural components demonstrated variable LCs including the UVA that required upward of 52 cases.