CUSUM Statistical Monitoring of M/M/1 Queues and Extensions

<div><p>Many production and service systems can be modeled as queueing systems. Their operational efficiency and performance are often measured using queueing performance metrics (QPMs), such as average cycle time, average waiting length, and throughput rate. These metrics need to be quantitatively evaluated and monitored in real time to continuously improve the system performance. However, QPMs are often highly stochastic, and hence are difficult to monitor using existing methods. In this article, we propose the cumulative sum (CUSUM) schemes to efficiently monitor the performance of typical queueing systems based on different sampling schemes. We use M/M/1 queues to illustrate how to design the CUSUM chart and compare their performance with several alternative methods. We demonstrate that the performance of CUSUM is superior, responding faster to many shift patterns through extensive numerical studies. We also briefly discuss the extensions of CUSUM charts to more general queues, such as M/G/1, G/G/1, or M/M/c queues. We use case studies to demonstrate the applications of our approach. Supplementary materials for this article are available online.</p></div

Modeling Regression Quantile Process Using Monotone B-Splines

<p>Quantile regression as an alternative to conditional mean regression (i.e., least-square regression) is widely used in many areas. It can be used to study the covariate effects on the entire response distribution by fitting quantile regression models at multiple different quantiles or even fitting the entire regression quantile process. However, estimating the regression quantile process is inherently difficult because the induced conditional quantile function needs to be monotone at all covariate values. In this article, we proposed a regression quantile process estimation method based on monotone B-splines. The proposed method can easily ensure the validity of the regression quantile process and offers a concise framework for variable selection and adaptive complexity control. We thoroughly investigated the properties of the proposed procedure, both theoretically and numerically. We also used a case study on wind power generation to demonstrate its use and effectiveness in real problems. Supplementary materials for this article are available online.</p

Nonparametric Modeling and Prognosis of Condition Monitoring Signals Using Multivariate Gaussian Convolution Processes

<p>Condition monitoring (CM) signals play a critical role in assessing the remaining useful life of in-service components. In this article, an alternative view on modeling CM signals is proposed. This view draws its roots from multitask learning and is based on treating each CM signal as an individual task. Each task is then expressed as a convolution of a latent function drawn from a Gaussian process (GP), and the transfer of knowledge is achieved through sharing these latent functions between historical and in-service CM signals. Aside from being nonparametric, the flexible and individualistic approach in our model can account for heterogeneity in the data and automatically infer the commonalities between the new testing observations and CM signals in the historical dataset. The robustness and advantageous features of the proposed method are demonstrated through numerical studies and a case study with real-world data in the application to find the remaining useful life prediction of automotive lead-acid batteries. Technical details and additional numerical results are available in the supplementary materials.</p

Delineation of the orbitofrontal cortex (OFC).

<p>Sample views of the OFC (in blue) at different coronal levels (left column) and transaxial levels (right column). Transaxial views were automatically reconstructed synchronously when the delineation was performed on consecutive coronal slices.</p

Datasheet1_Association of remnant cholesterol with CVD incidence: a general population cohort study in Southwest China.pdf

BackgroundEmerging evidence has indicated that remnant cholesterol (RC) could predict cardiovascular disease (CVD) incidence. Nevertheless, the relationship between RC and CVD risk, especially within the general Chinese population, remains scarce.ObjectiveThe present research aimed to assess whether RC concentrations and CVD outcomes in general Chinese adults are related.MethodsThe Cox proportional hazard model was established to explore the relationship between RC and the outcomes of CVD and CVD subgroups. A restricted cubic spline (RCS) was utilized to investigate the dose–response connection between RC and the risk of CVD outcomes, and the ROC curve was used to calculate the corresponding cutoff values. Moreover, stratified analysis was conducted to investigate the potential effect modification in the association between RC and CVD outcomes.ResultsSignificant positive associations were found between elevated categorical RC and increased risk of CVD (HR Q4, 1.80; 95% CI 1.15–2.79; P-value = 0.008), atherosclerotic cardiovascular disease (HR Q4, 2.00; 95% CI 1.22–3.27; P-value = 0.007), stroke (HR Q4, 1.66; 95% CI 1.02–2.69; P-value = 0.040), and ischemic stroke (HR Q4, 1.87, 95% CI 1.08–3.25; P-value = 0.034), respectively. Our study suggested that the incidence of CVD outcomes increased when RC levels were above 0.75 mmol/L. Importantly, the CVD risks related to RC were more likely to be those found in subjects aged above 60 years, women, subjects with BMI 2, and subjects with hypertension and unhealthy diet patterns.ConclusionsAberrant high level of RC is associated with elevated CVD risk, independent of low-density lipoprotein cholesterol (LDL-C). Our data reveal urgent primary prevention for subjects with high RC levels to a low incidence of CVD, especially for the elderly, women, and those with hypertension and unhealthy diet patterns.</p

Prevalence of metabolic syndrome in China: An up-dated cross-sectional study

<div><p>Metabolic syndrome (MS) is an increasing public health concern because of rapid lifestyle changes. Although there have been previous studies on the prevalence of MS in China, the prevalence may have changed with lifestyle changes over the last decade. To update this prevalence, we performed a cross-sectional survey among adults over 18 years old across China from May 2013 to July 2014. Participants underwent questionnaires and provided blood and urine samples for analysis. MS was defined according to the criteria of the China Diabetes Society. A total of 12570 individuals (45.2% men) with an average age of 48.8±15.3 (18–96) years were selected and invited to participate in the study. In total, 9310 (40.7% men) individuals completed the investigation, with a response rate of 74.1%. The prevalence of MS in China was 14.39% [95% confidence interval (CI): -3.75–32.53%], and the age-adjusted prevalence was 9.82% (95% CI: 9.03–10.61%; 7.78% in men and 6.76% in women; 7.39% in rural residents and 6.98% in urban residents). The highest prevalence occurred among adults aged 50–59 years (1.95%, 95% CI: 1.40–2.50%), and the lowest prevalence occurred among adults aged 40–49 years (0.74%, 95% CI: 0.38–1.10%); the prevalence was the highest in the south region and lowest in the east region (4.46% and 1.23%, respectively). The results of logistic regression analyses showed that age, urolithiasis, hyperuricemia, coronary artery disease, thiazide drugs intake, family history of diabetes and hypertension were all significantly associated with an increased risk of metabolic syndrome (OR>1). In addition, education, vitamin D intake and family history of urolithiasis are all protective factors (OR<1). Our results indicate that there was a high prevalence of MS in Chinese adults. Compared to the previous study 10 years ago, some preventive strategies have worked; however, further work on the prevention and treatment of MS remains necessary.</p></div

Prevalence of MS and 95% CI among urban and rural residents.

<p>Prevalence of MS and 95% CI among urban and rural residents.</p

Characteristics of sample group by gender.

<p>Characteristics of sample group by gender.</p

Age-specific prevalences of metabolic syndrome among Chinese adults aged 18 years and older.

<p>Age-specific prevalences of metabolic syndrome among Chinese adults aged 18 years and older.</p

Prevalence of MS among Chinese adults aged 18 years and older.

<p>Prevalence of MS among Chinese adults aged 18 years and older.</p