High Sigma Research Articles

A High dimensional and high sigma statistical model for SRAM read access yield estimation

A High dimensional and high sigma statistical model for SRAM read access yield estimation

A-118 Sigma Metric Performance of Chemistry Assays on 4 Instrument Systems

Abstract Background Chemistry panels play a crucial role in determining a person’s general health and well-being. These tests evaluate electrolyte balance and the status of several major organs. The Comprehensive Metabolic Panel (CMP) and Lipid Panel constitute approximately 87% of the blood tests prescribed in medical laboratories. Laboratories monitor assay performance by performing proficiency testing using quality controls (QC). College of American Pathologists (CAP) data is useful for laboratories to compare their proficiency testing performance to other laboratories and instrument systems. Sigma metrics serve as a valuable tool for laboratories to assess the quality and accuracy of their tests. Sigma scores vary from 0 to 6, with the 6 score designated as world-class performance, 5 as excellent, 4 as good, 3 as marginal, and 2 as poor. The analytical performance of 16 clinical assays from Abbott, Beckman, Roche, and Siemens instrument systems were compared using Sigma metrics. Methods Sigma metrics of 16 chemistry analytes were compared across 4 instruments system using CAP data from three reports (C-A 2023, C-C 2023 and C-B 2022). The 4 systems evaluated were Abbott Alinity ci, Beckman AU, Roche COBAS c500/c303 and Siemens Atellica CH. Method imprecision was estimated for each assay using the cumulative coefficient of variation or standard deviation for each assay at a single QC concentration and calculating the average sigma. Since sigma levels can be concentration-dependent, an equivalent CAP sample concentration was chosen for each instrument system evaluation. Method bias was estimated by evaluating each method QC mean to the peer group mean. Sigma values were calculated for each method as Sigma = (TEa - |Bias|) / Precision using the allowable total error from 2014 Clinical Laboratory Improvement Amendments (CLIA) proposed acceptance limits. Average Sigma values were generated for each analyte and graded as an optimal Sigma score. Results The sigma scores varied across assays and instrument systems. Abbott demonstrated the best overall performance. Specifically, the Abbott Alinity system had the highest number (7/16, 44%) of assays at 5 sigma or higher, with Siemens showing the lowest number of assays at 5 Sigma or higher (1/16, 6%). Of the 16 assays evaluated, Abbott has the fewest assays at or below the 3 sigma threshold (5/16) when compared to Roche (7/16), Beckman (7/16), and Siemens (9/16). Sigma scores were comparable between the Abbott ARCHITCT and Alinity clinical chemistry systems. Conclusions Sigma metrics are useful in assessing the performance of clinical assays. Assays with high Sigma have better performance due to lower imprecision and higher accuracy. None of the instrument systems demonstrated optimal six sigma performance for all analytes. However, Abbott demonstrated the best overall performance.

A Novel Approach for Routinely Assessing Laboratory Sigma Metrics for a Broad Range of Automated Assays.

Sigma metrics have been adapted for the clinical laboratory to incorporate observed accuracy, precision, and total error allowed. The higher the Sigma level for a process, the better performance that process has. A limitation of studies assessing Sigma metrics is that they are performed on a small number of well-controlled systems. An algorithm was developed to extract QC data and derive the Sigma metric for 115 analytes from sites connected to the QuidelOrtho E-Connectivity® database. The median of these results was then used to derive the Sigma metric for each assay. In this analysis, 79 out of 115 (68.7%) of the assays assessed achieved 6 Sigma or better and 98 out of 115 (85.2%) achieved 5 Sigma or better. This study has demonstrated a methodology that can be used to condense Sigma metrics from hundreds of analyzers into a single metric of assay quality. Because these analyzers are running in working laboratories from around the world, this analysis can serve as a baseline for understanding the assay performance achieved in the presence of variabilities such as lab-to-lab, instrument-to-instrument, material handling, environmental conditions, and reagent lot. The significant number of assays demonstrating high Sigma levels did so despite this variation. The ability of the methods reported here to include hundreds of analyzers represents a novel approach for assessing Sigma metrics in clinical laboratories.

Applying sigma metrics to assess quality control processes in the transfusion transmissible infection screening laboratory of a blood center.

In the field of healthcare, quality and efficiency are of paramount importance to ensure the provision of safe and reliable diagnostic services. Blood screening laboratories play a critical role in detecting and preventing the spread of infectious diseases, ensuring the safety of blood transfusions, and supporting medical diagnoses. To enhance the quality of processes in such laboratories, the Six Sigma methodology has gained significant recognition for its ability to systematically identify and minimize variations, thereby improving overall efficiency and reducing errors. This retrospective study aims to explore the application of Six Sigma metrics in the context of blood screening laboratories, providing an in-depth analysis of its implementation, benefits, and challenges. The performance of three serology assays, i.e., anti-HCV, HIV Ag/Ab combo, and HBsAg, using internal quality control (IQC) daily data extracted from six Alinity i instruments (Abbott GmbH, Germany), from February to April 2023, was evaluated. Mean, standard deviation (SD), and coefficient of variation (CV%) was calculated for positive controls. Bias was calculated using peer group data. Sigma metrics were calculated using allowable total error (TEa %) based on difference between the observed mean of the positive control and the s/co cut-off of assay. The observed CV% for positive controls was ≤10%. The TEa% ranged from 66% to 79% for the analytes using the observed mean. All the assays showed Six Sigma performance (σ>6) with and without bias. The study observed that the serology assays showed very high sigma values (σ>6) and thus, simplified statistical quality control (SQC) design based on Westgard Sigma rules could be implemented without compromising blood safety.

Accurate Multi-segment Probability Density Estimation Through Moment Matching

The impact of process variations continues to grow as transistor feature size shrinks. Such variations in transistor parameters lead to variations and unpredictability in circuit output and may ultimately cause them to violate specifications leading to circuit failure. In fact, timely failures in critical circuits may lead to catastrophic failures in the entire chip. As such, statistical modeling of circuit behavior is becoming increasingly important. However, existing statistical circuit simulation approaches fail to accurately and efficiently analyze the high sigma behavior of probabilistic circuit output. To this end, we propose PDM (Piecewise Distribution Model) - a piecewise distribution modeling approach via moment matching using maximum entropy to model the high sigma behavior of analog/mixed-signal (AMS) circuit probability distributions. PDM is independent of the number of input dimensions and matches region specific probabilistic moments which allows for significantly greater accuracy compared to other moment matching approaches. PDM also utilizes Spearman’s rank correlation coefficient to select the optimal approximation for the tail of the distribution. Experiments on a known mathematical distribution and various circuits obtain accurate results up to 4.8 sigma with 2-3 orders of speedup relative to Monte Carlo. PDM also demonstrates better accuracy while compared against other state-of-the-art statistical modeling approaches, such as maximum entropy, importance sampling, and subset simulation.

Energetic ion enhancements in sheaths driven by interplanetary coronal mass ejections

We analyze here an energetic proton enhancement in a sheath ahead of a slow interplanetry coronal mass ejection (ICME) detected by Parker Solar Probe on June 30, 2021 at the heliospheric distance of 0.76 AU. The shock was likely quasi-parallel and had a high Mach number. However, the proton fluxes were not enhanced at the shock but about an hour later. The fluxes stayed elevated with a sporadic behaviour throughout the sheath. We suggest that some mechanism internal to the sheath was responsible for the energization. The observations show enhanced levels of magnetic field fluctuations in the sheath and frequent presence of highly reduced magnetic helicity structures (sigma _{m}) at various time scales, representing either small-scale flux ropes or Alfvénic fluctuations that could have contributed to the energization. The correlation between the energetic proton fluxes and normalized fluctuation amplitudes/occurrence of high sigma _{m} structures was generally weak or negligible. The most striking feature of the sheath was a strong enhancement of density (up to 50 cm−3) that implies the importance of compressive acceleration in the sheath. A statistical analysis of ion enhancements of 73 sheaths detected by ACE at {sim} 1 AU reveals that this sheath was peculiar as in ICME-driven sheaths preceded by strong shocks the ion fluxes typically peak at the shock and strongly decline through the sheath.

Thalamic activity during scalp slow waves in humans

Slow waves are major pacemakers of NREM sleep oscillations. While slow waves themselves are mainly generated by cortical neurons, it is not clear what role thalamic activity plays in the generation of some oscillations grouped by slow waves, and to what extent thalamic activity during slow waves is itself driven by corticothalamic inputs. To address this question, we simultaneously recorded both scalp EEG and local field potentials from six thalamic nuclei (bilateral anterior, mediodorsal and ventral anterior) in fifteen epileptic patients (age-range: 17–64 years, 7 females) undergoing Deep Brain Stimulation Protocol and assessed the temporal evolution of thalamic activity relative to scalp slow waves using time-frequency analysis. We found that thalamic activity in all six nuclei during scalp slow waves is highly similar to what is observed on the scalp itself. Slow wave downstates are characterized by delta, theta and alpha activity and followed by beta, high sigma and low sigma activity during subsequent upstates. Gamma activity in the thalamus is not significantly grouped by slow waves. Theta and alpha activity appeared first on the scalp, but sigma activity appeared first in the thalamus. These effects were largely independent from the scalp region in which SWs were detected and the precise identity of thalamic nuclei. Our results suggest that while small thalamocortical neuron assemblies may initiate cortical oscillations, especially in the sleep spindle range, the large-scale neuronal activity in the thalamus which is detected by field potentials is principally driven by global cortical activity, and thus it is highly similar to what is observed on the scalp.

Multirule procedures vs moving average algorithms for IQC: An appropriate comparison reveals how best to combine their strengths

BackgroundMoving Average Algorithms (MAA) have been widely recommended for use in Patient Based Real Time Quality Control applications (PBRTQC) to supplement or replace traditional Internal Quality Control (IQC) techniques. A recent “proof of concept” study recommends applying MAAs to IQC data to replace traditional IQC procedures because they “outperform Westgard Rules,” which is a current standard of practice for IQC. MethodsWe generated power curves for multi-rule procedures with 2 and 4 control measurements per QC event, as well as a Simple Moving Average having block sizes of 5, 10, and 20 control measurements. We also assessed time to detection in terms of the Average Number of QC Events required to detect different sizes of systematic errors. ResultsAs expected, the more control measurements included in the control technique, the better the error detection. However, when QC performance is considered on the Sigma Scale, high Sigma methods require only 1 or 2 control measurements to detect medically important systematic errors. MAAs have very low ability to detect error at the first few QC events following shift, so they suffer a lag phase in detecting medically important errors. MAAs are most useful for methods having 4.0 Sigma performance or less. Even then, large systematic shifts are more quickly detected by simple single and multirule procedures. ConclusionsChoice of control techniques (rules, means, ranges, etc.) should consider the Sigma-metric of the method. For methods having Sigmas of 4 or greater, traditional single rule and multirule procedures with Ns up to 4 are most effective; below 4 Sigma, a multirule coupled with a Simple Moving Average (SMA) rule with Ns of 4 to 8 can improve error detection.

Rhythmicity in heart rate and its surges usher a special period of sleep, a likely home for PGO waves.

High amplitude electroencephalogram (EEG) events, like unitary K-complex (KC), are used to partition sleep into stages and hence define the hypnogram, a key instrument of sleep medicine. Throughout sleep the heart rate (HR) changes, often as a steady HR increase leading to a peak, what is known as a heart rate surge (HRS). The hypnogram is often unavailable when most needed, when sleep is disturbed and the graphoelements lose their identity. The hypnogram is also difficult to define during normal sleep, particularly at the start of sleep and the periods that precede and follow rapid eye movement (REM) sleep. Here, we use objective quantitative criteria that group together periods that cannot be assigned to a conventional sleep stage into what we call REM0 periods, with the presence of a HRS one of their defining properties. Extended REM0 periods are characterized by highly regular sequences of HRS that generate an infra-low oscillation around 0.05 Hz. During these regular sequence of HRS, and just before each HRS event, we find avalanches of high amplitude events for each one of the mass electrophysiological signals, i.e. related to eye movement, the motor system and the general neural activity. The most prominent features of long REM0 periods are sequences of three to five KCs which we label multiple K-complexes (KCm). Regarding HRS, a clear dissociation is demonstrated between the presence or absence of high gamma band spectral power (55–95 Hz) of the two types of KCm events: KCm events with strong high frequencies (KCmWSHF) cluster just before the peak of HRS, while KCm between HRS show no increase in high gamma band (KCmNOHF). Tomographic estimates of activity from magnetoencephalography (MEG) in pre-KC periods (single and multiple) showed common increases in the cholinergic Nucleus Basalis of Meynert in the alpha band. The direct contrast of KCmWSHF with KCmNOHF showed increases in all subjects in the high sigma band in the base of the pons and in three subjects in both the delta and high gamma bands in the medial Pontine Reticular Formation (mPRF), the putative Long Lead Initial pulse (LLIP) for Ponto-Geniculo-Occipital (PGO) waves.

A Spline-High Dimensional Model Representation for SRAM Yield Estimation in High Sigma and High Dimensional Scenarios

Traditional Static Random-Access Memory (SRAM) yield estimation through Monte Carlo analysis is an extremely time-consuming process since it runs millions of expensive transistor-level simulations to get the yield results with the specified precision, especially for the large-scale circuits. In this paper, we develop an efficient yield analysis framework by integrating our novel performance metamodel into a state-of-art importance sampling method. The performance meta-model, named Spline-High Dimensional Model Representation (SP-HDMR), is used to substitute the expensive transistor-level simulations in yield estimation. The proposed SP-HDMR model provides a high computationally efficient formula expansion. It uses spline functions as the kernels to describe the various relations between the process parameters and SRAM read access delay. And an adaptive sampling method with sparsity analysis is developed to support SP-HDMR modeling. The experiments on the 40nm SRAM circuits validate the accuracy and the efficiency of the proposed yield analysis framework based on our SP-HDMR model with 1.3X $\sim 5\text{X}$ speedup over the other state-of-art methods within 9% relative error.

Sigma Metrics: A Powerful Tool for Performance Evaluation and Quality Control Planning in a Clinical Biochemistry Laboratory

Introduction: Any error in the laboratory testing processes can affect the diagnosis and patient management. Six Sigma is a data driven quality management system for identifying and reducing errors and variations in clinical laboratory processes. Aim: This study was carried out to estimate Sigma metrics of various biochemical analytes in order to evaluate performance of quality control and implement optimum quality control strategy for each analyte. Quality goal index (QGI) was also calculated to identify the problems of inaccuracy and imprecision for parameters having lower sigma values. Materials and Methods: This retrospective, observational study was conducted at the Central Clinical Biochemistry Laboratory of Seth GS Medical College and KEM hospital in Mumbai for a period of six months (July 2019 to December 2019). Sigma metrics for 20 analytes was calculated by using internal quality control and external quality control data. Further, QGI was calculated for analytes having sigma value of <4 to identify imprecision or inaccuracy. Statistical analysis was performed using Microsoft office excel 2010 software. Results: Total protein, Glucose, Urea, Triglyceride (TAG), High Density Lipoprotein (HDL), and Low Density Lipoprotein (LDL) for normal (L1) and pathological (L2) controls achieved excellent performance (>6 sigma). Westgard rule (13s) with two control measurement (N2) per QC event and run size (R1000) i.e. 1000 patient samples between consecutive QC events was adopted for these analytes. For analytes with sigma value of 4-6, more rules (sigma 4-5: Westgardrules-13s/22s/R4s/41s, N4 and R200 and for sigma value 5-6: 13S/22s/R4s, N2 and R450) were adopted. The sigma values of six analytes (Creatinine, Sodium, Potassium, Calcium, Chloride, Inorganic phosphate) were <4 at one or more QC levels. For these analytes, strict QC procedures (Westgard rules-13s/22s/R4s/41s/6x, N4 and R45) were incorporated. QGI of these analytes was <0.8 which indicated the problem of imprecision. Staff training programs and review of standard operating procedures were done for these analytes to improve method performance. Conclusion: Sigma Metrics estimation helps in designing optimum QC protocols, minimising unnecessary QC runs and reducing the cost for analytes having high sigma metrics. Focused and effective QC strategy for analytes having low sigma helps in improving the performance of those analytes.

MOEO-EED: A multi-objective equilibrium optimizer with exploration–exploitation​ dominance strategy

The work proposes multi-objective variants of the recently-proposed equilibrium optimizer (EO) using an archive to obtain Pareto optimal solutions and a crowding distance approach to preserve the diversity among the non-dominated solutions. However, due to the use of constant values for the controlling parameters of EO, the exploration and exploitation of multi-objective EO (MOEO) are not accelerated, so the first variant is proposed with a number of linear and non-linear equations to generate increasing and decreasing values proportional to the number of iterations that will eventually improve exploratory and exploitative behaviors of MOEO. In the second proposed variant of MOEO with exploration–exploitation​ dominance strategy (MOEO-EED), solutions are updated according to the number of dominated solutions. If a solution has a high number of dominated solutions, it will go through fewer abrupt changes, while others will undergo major changes. In addition, a novel strategy known as a Gaussian-based mutation (G) strategy is proposed to use the Gaussian distribution to generate two different step sizes: small step sizes that are generated under a small sigma value for the Gaussian distribution to promote the exploitation capability, and high step sizes under a high sigma value to increase the exploration operator. The tradeoff between those two-step sizes is predefined through experimentation. This strategy is integrated to generate the third variant, namely MOEO-EED-G. In the fourth variant (OMOEO-EED-G), the tradeoff between the best solution selected from the archive and its opposite is achieved with a probability to increase the diversity and accelerate the convergence of MOEO-EED-G. Finally, the efficacy of the proposed algorithms is tested on four benchmark multi-objective functions to show that the proposed algorithms, especially OMOEO-EED-G, are superior to selected state-of-the-art multi-objective algorithms.

Double Stage Gaussian Filter for Better Underwater Image Enhancement

Major issue in marine environment imaging is the expulsion of hazy scenes caused by natural phenomena such as absorption and scattering in underwater images. The visual appearance of the underwater image needs to be improved. In this paper, a double stage Gaussian filter (DSGF) and fusion-based approach to enhance the underwater images is proposed. In the proposed two stage process for image fusion, first section deals with applying the DSGF with high, low, and medium sigma values on the individual R, G and B components of input RGB images and second section deals with applying colour component wise contrast limited adaptive histogram followed by median filtering. The outputs of these two sections are fused using principal component analysis fusion technique. Fused image is post-processed using the colour contrast correction for better visibility results. Proposed method is compared with single stage Gaussian filter where only one stage of Gaussian filtering is applied for reduced computation, Adaptive histogram equalization and histogram specification methods. Proposed method results in quality images obtained through gradual intensity improvement and contrast correction process. Qualitative and quantitative analysis are performed on the enhanced underwater images to observe the effectiveness of the algorithm used. The performance measures such as entropy, SSIM, and AMBE unveil the verification of the proposed findings.

Sigma Metrics used to Evaluate the Performance of Internal Quality Control in a Clinical Biochemistry Laboratory.

The performance of 17 routine chemical detection methods was evaluated by the Sigma (σ) index, and separate quality control standards were established according to the sigma values of different detection methods. The internal quality control (IQC) and external quality assessment (EQA) data of 17 assays in the biochemical laboratory of our hospital were collected from January to June 2019. Referring to the total allowed error (TEa) standards established in the Health Industry Standards of the People's Republic of China (WS/T 403-2012), the sigma metric of each assay was calculated, the performance level for inspection was evaluated, the quality goal index (QGI) was calculated for items with analysis performance < 5 sigma, and the main causes of poor performance were determined to guide quality improvement. For level 1 internal quality control (IQC), five assays (AMY, Crea, UA, TP, and Na) showed a performance of ≥ 6 sigma levels. Five assays (GGT, LDH, ALP, K, and Ca) had a performance lower than 3 sigma. For level 2 IQC, nine assays (ALT, AST, CK, AMY, Crea, UA, TP, Na, and Mg) achieved 6 sigma, and four assays (GGT, LDH, ALP, and K) achieved less than 3 sigma. Among the 12 assays with a sigma value < 5, the precision of 1 assay should be improved first, the accuracy of 6 assays should be improved next, and both the precision and the accuracy of 5 assays should be improved. The sigma metric is the best tool for evaluating the performance of different test methods. Assays with high sigma values can be evaluated with single-rule quality control, while assays with low values should be evaluated with strict quality control rules.

Sleep EEG functional connectivity varies with age and sex, but not general intelligence

Variations in the anatomical and functional connectivity between brain areas underlie both healthy and pathological variation in psychological measures. Largely independent from external stimuli, the sleep EEG is particularly well suited to measure individual variations in functional brain connectivity. In this study of 172 healthy individuals (17–69 years old), we show that functional connectivity between distant brain areas—reflected by the weighted phase lag index of the sleep EEG—is strongly affected by the age and sex of participants. Both NREM and REM connectivity in the theta and beta range increased with age, whereas a decrease was seen in the sigma range. Connectivity was substantially greater in females than in males in the high sigma frequency range, but an opposite pattern was seen in the alpha/low sigma and beta range. General intelligence was not significantly associated with connectivity in either sex. Our results confirm strong age effects on sleep spindle-frequency activity, which loses synchrony as a function of aging. Furthermore, we found support for a vigilance state–independent age-related increase in high beta power, previously demonstrated in waking EEG studies. The results highlight that future studies establishing sleep EEG connectivity measures as psychological or psychiatric biomarkers should take into account that sleep EEG synchronization is strongly affected by age and sex, and clinical thresholds must be adjusted accordingly.

Infraslow oscillations in human sleep spindle activity

BackgroundIt has previously been reported that EEG sigma (10–15 Hz) activity during sleep exhibits infraslow oscillations (ISO) with a period of 50 s. However, a detailed analysis of the ISO of individually identified sleep spindles is not available. New methodWe investigated basic properties of ISO during baseline sleep of 34 healthy young human participants using new and established methods. The analyses focused on fast sleep spindle and sigma activity (13–15 Hz) in NREM stage 2 and slow wave sleep (SWS). To describe ISO in sigma activity we analyzed power of power of the EEG signal. For the study of ISO in sleep spindle activity we applied a new method in which the EEG signal was reduced to a spindle on/off binary square signal. Its spectral properties were contrasted to that of a square signal wherein the same spindles and also the inter spindle intervals were permutated randomly. This approach was validated using surrogate data with imposed ISO modulation. ResultsWe confirm the existence of ISO in sigma activity albeit with a frequency below the previously reported 0.02 Hz. These ISO are most prominent in the high sigma band and over the centro-parieto-occipital regions. A similar modulation is present in spindle activity. ISO in sleep spindles are most prominent in the centro-parieto-occipital regions, left hemisphere and second half of the night independent of the number of spindles. ConclusionsThe comparison of spectral properties of binary event signals and permutated event signals is effective in detecting slow oscillatory phenomena.

Differentiation of intrapancreatic accessory spleen from small hypervascular neuroendocrine tumor of the pancreas: textural analysis on contrast-enhanced computed tomography.

Intrapancreatic accessory spleens (IPASs) are usually misdiagnosed as pancreatic neuroendocrine tumors (PNETs). Texture analysis is valuable in tumor detection, diagnosis, and staging. To identify the potential of texture features in differentiating IPASs from small hypervascular PNETs. Twenty-one patients with PNETs and 13 individuals with IPASs who underwent pretreatment dynamic contrast-enhanced computed tomography (CT) were retrospectively analyzed. The routine imaging features-such as location, size, margin, cystic or solid appearance, enhancement degree and pattern, and lymph node enlargement-were recorded. Texture features, such as entropy, skewness, kurtosis, and uniformity, on contrast-enhanced images were analyzed. Receiver operating characteristic (ROC) analysis was performed to differentiate IPASs from PNETs. No significant differences were observed in margin, enhancement degree (arterial and portal phase), lymph node enlargement, or size between PNETs and IPASs (all P > 0.05). However, IPASs usually showed heterogeneous enhancement at the arterial phase and the same degree of enhancement as the spleen at the portal phase, both of which were greater than those of PNETs (69% vs. 35%, P = 0.06; 100% vs. 29%, P = 0.04). Entropy and uniformity were significantly different between IPASs and PNETs at moderate (1.5) and high sigma values (2.5) (both P < 0.01). ROC analysis showed that uniformity at moderate and high sigma had the highest area under the curve (0.82 and 0.89) with better sensitivity (85.0-95.0%) and acceptable specificity (75.0-83.3%) for differentiating IPASs from PNETs. Texture parameters have potential in differentiating IPASs from PNETs.

An Efficient Non-Gaussian Sampling Method for High Sigma SRAM Yield Analysis

Yield 1 analysis of SRAM is a challenging issue, because the failure rates of SRAM cells are extremely small. In this article, an efficient non-Gaussian sampling method of cross entropy optimization is proposed for estimating the high sigma SRAM yield. Instead of sampling with the Gaussian distribution in existing methods, a non-Gaussian distribution, i.e., a joint one-dimensional generalized Pareto distribution and ( n -1)-dimensional Gaussian distribution, is taken as the function family of practical distribution, which is proved to be more suitable to fit the ideal distribution in the view of extreme failure event. To minimize the cross entropy between practical and ideal distributions, a sequential quadratic programing solver with multiple starting points strategy is applied for calculating the optimal parameters of practical distributions. Experimental results show that the proposed non-Gaussian sampling is a 2.2--4.1× speedup over the Gaussian sampling, on the whole, it is about a 1.6--2.3× speedup over state-of-the-art methods with low- and high-dimensional cases without loss of accuracy

Planning Risk-Based SQC Schedules for Bracketed Operation of Continuous Production Analyzers.

To minimize patient risk, "bracketed" statistical quality control (SQC) is recommended in the new CLSI guidelines for SQC (C24-Ed4). Bracketed SQC requires that a QC event both precedes and follows (brackets) a group of patient samples. In optimizing a QC schedule, the frequency of QC or run size becomes an important planning consideration to maintain quality and also facilitate responsive reporting of results from continuous operation of high production analytic systems. Different plans for optimizing a bracketed SQC schedule were investigated on the basis of Parvin's model for patient risk and CLSI C24-Ed4's recommendations for establishing QC schedules. A Sigma-metric run size nomogram was used to evaluate different QC schedules for processes of different sigma performance. For high Sigma performance, an effective SQC approach is to employ a multistage QC procedure utilizing a "startup" design at the beginning of production and a "monitor" design periodically throughout production. Example QC schedules are illustrated for applications with measurement procedures having 6-σ, 5-σ, and 4-σ performance. Continuous production analyzers that demonstrate high σ performance can be effectively controlled with multistage SQC designs that employ a startup QC event followed by periodic monitoring or bracketing QC events. Such designs can be optimized to minimize the risk of harm to patients.

Using Kano's model to incorporate Six Sigma analysis in customer satisfaction measurement

The Six Sigma approach is a statistical concept that analyses a process in terms of defects and has been well recognised as an important tool for continuous improvement. Since sigma level can be used as a measure of the number of dissatisfied customers (defects), it can be assumed that there is a direct relationship between satisfaction ratings and sigma levels. However, higher sigma levels for certain attributes do not necessarily translate to correspondingly higher customer satisfaction scores, while some attributes with higher satisfaction scores may not have a very high sigma level. The main goal of this paper is to incorporate the concept of Six Sigma analysis in customer satisfaction measurement by introducing the principles of Kano's customer satisfaction model in order to derive important information for the selection of strategic actions. The proposed approach has been implemented for the evaluation of customer satisfaction from Greek mobile service providers.