6σ管理中需要澄清的几个问题

6σ管理作为组织实施连续质量改进的一种经营策略,已引起企业界和学术界的极大兴趣,并在一些企业中得到很好应用。正确实施6σ,需要对其基本概念、假设条件、相关约束等有一个较为全面的了解和认识。本文探讨了6σ文献和应用过程中需要关注的几个问题,其中包括6σ的基本概念和特点;分布假设;过程均值1.5σ漂移的可行性;过程能力指数;短期波动和长期波动;6σ改进和6σ设计等。     

Analysis of aging-related protein interactome and cross-network module comparisons across tissues provide new insights into aging

Delaying the human aging process and thus eliminating the risk factors for age-related diseases is one of the prime objectives. While various aging-associated genes and proteins have been characterized, which provide a significant understanding of the human aging process, a significant success in regulating aging is not achieved yet. Understanding how aging proteins interact with each other and also with other proteins could provide important insights into the underlying mechanisms governing the aging process. Therefore, in this work, information of gene expression was included to the static aging-related protein interactome to understand the network-based relationships among aging-related essential (AE) proteins, aging-related non-essential (ANE) proteins, and housekeeping-proteins that could regulate or influence aging. Comprehensive analyses provided various systems-level insights into the regulatory characteristics of aging; for example, (i) network-based correlation analysis predicted functional relationships among AE proteins and ANE proteins; (ii) network variability analysis predicted aging to affect different tissues in strikingly different ways by differentially regulating various regulatory interactions; (iii) cross-network comparisons identified two aging-related modules to be significantly conserved across most of the tissues. Overall, the findings obtained during this study could be helpful for researchers to delay, prevent, or even reverse various aspects of the aging.     

超声心动图联合动态心电图检查对急性心肌梗死诊断和预后评估的价值

为探讨超声心动图联合动态心电图检查在急性心肌梗死(AMI)患者诊断和预后评估中的应用价值,本研究选取2016年6月~2018年6月我院收治并确诊的100例AMI患者作为观察组,另选取同期100例非冠心病患者作为对照组。以AMI患者心源性死亡为终点事件,将观察组分为死亡组(n=15)和存活组(n=85)。所有患者均采用飞利浦IU-Elite及EPIQ5彩色多普勒超声诊断仪进行超声心动图检查,采用DMS-3004A进行标准的12导联动态心电图检查。比较各组患者心率震荡指标[震荡起始(TO)、震荡斜率(TS)]、心率变异性指标[NN间期标准差(SDNN)、QT离散度(QTd)、经心率校正的QT离散度(QTcd)]及心功能指标[左心室舒张末期内径(LVEDD)、左心室射血分数(LVEF)]水平的变化。结果显示,观察组TO、QTd、QTcd、LVEDD均明显高于对照组,TS、SDNN、LVEF均明显低于对照组,差异有统计学意义(P<0.05)。死亡组TS、SDNN均明显低于存活组,差异有统计学意义(P<0.05);死亡组TO高于存活组,QTd、QTcd、LVEF、LVEDD均低于存活组,但差异无统计学意义(P>0.05)。本研究结果表明,AMI患者心率震荡(HRT)明显减弱甚至消失,HRV、LVEF明显降低,LVEDD、QTd明显增大,QT间期明显延长。超声心动图和动态心电图联合检查对AMI患者诊断及预后评估均有重要的临床应用价值。     

Right Spread序的若干封闭性质

§1Introductionandmotivation Itisalwaysofinteresttocomparethevariabilityoftworandomvariablesinreliability theoryandeconomics.Thecomparisonsbasedonmomentssuchasvarianceandstandard deviationarenotveryinformativealthoughsuchmeasuresaresimpletouse.Meanwhile,sincethesecomparisonsarestaticones,andhencetheirapplicationsinsomefields,e.g.,reliabilityandfinance,arerestricted.Inthesefields,however,somedynamicalcomparisons areneeded.Asaresult,severalstochasticordersthataremorerefinedhavebeen establishedi…     

Variability in the noise-induced modes of climate dynamics

New features of noise-induced climate variability are revealed on the basis of the three-dimensional model derived by Saltzman and Maasch. It is shown that the climate system can be highly noise excitable and it possesses the large-amplitude fluctuations even in those regions where its akin deterministic model does not contain any self-sustained oscillations. Intermittency in small- and large amplitude climate fluctuations between different basins of attraction of a limit cycle and stable equilibria substantially influencing the climate state (from warm to cold and vice versa) are found at various noise intensities. Suddenly occurring jumps between the basins of attraction of two stable equilibria corresponding to the warm and cold climate states are statistically confirmed under a certain diapason of noise intensities. The climate system undergoes transitions between its equilibria in the presence of noise in its prognostic variables. In addition, such transitions become more likely with increasing the noise intensity.     

On the Variability of Heart Rate Variability—Evidence from Prospective Study of Healthy Young College Students

Heart rate variability (HRV) has been widely used as indices for autonomic regulation, including linear analyses, entropy and multi-scale entropy based nonlinear analyses, and however, it is strongly influenced by the conditions under which the signal is being recorded. To investigate the variability of healthy HRV under different settings, we recorded electrocardiograph (ECG) signals from 56 healthy young college students (20 h for each participant) at campus using wearable single-lead ECG device. Accurate R peak to R peak (RR) intervals were extracted by combing the advantages of five commonly used R-peak detection algorithms to eliminate data quality influence. Thorough and detailed linear and nonlinear HRV analyses were performed. Variability of HRV metrics were evaluated from five categories: (1) different states of daily activities; (2) different recording time period in the same day during free-running daily activities; (3) body postures of sitting and lying; (4) lying on the left, right and back; and (5) gender influence. For most of the analyzed HRV metrics, significant differences (p < 0.05) were found among different recording conditions within the five categories except lying on different positions. Results suggested that the standardization of ECG data collection and HRV analysis should be implemented in HRV related studies, especially for entropy and multi-scale entropy based analyses. Furthermore, this preliminary study provides reference values of HRV indices under various recording conditions of healthy young subjects that could be useful information for different applications (e.g., health monitoring and management).     

Sample Entropy as a Tool to Assess Lumbo-Pelvic Movements in a Clinical Test for Low-Back-Pain Patients

Low back pain (LBP) obviously reduces the quality of life but is also the world’s leading cause of years lived with disability. Alterations in motor response and changes in movement patterns are expected in LBP patients when compared to healthy people. Such changes in dynamics may be assessed by the nonlinear analysis of kinematical time series recorded from one patient’s motion. Since sample entropy (SampEn) has emerged as a relevant index measuring the complexity of a given time series, we propose the development of a clinical test based on SampEn of a time series recorded by a wearable inertial measurement unit for repeated bending and returns (b and r) of the trunk. Twenty-three healthy participants were asked to perform, in random order, 50 repetitions of this movement by touching a stool and another 50 repetitions by touching a box on the floor. The angular amplitude of the b and r movement and the sample entropy of the three components of the angular velocity and acceleration were computed. We showed that the repetitive b and r “touch the stool” test could indeed be the basis of a clinical test for the evaluation of low-back-pain patients, with an optimal duration of 70 s, acceptable in daily clinical practice.     

Theil Entropy as a Non-Lineal Analysis for Spectral Inequality of Physiological Oscillations

Theil entropy is a statistical measure used in economics to quantify income inequalities. However, it can be applied to any data distribution including biological signals. In this work, we applied different spectral methods on heart rate variability signals and cellular calcium oscillations previously to Theil entropy analysis. The behavior of Theil entropy and its decomposable property was investigated using exponents in the range of [−1, 2], on the spectrum of synthetic and physiological signals. Our results suggest that the best spectral decomposition method to analyze the spectral inequality of physiological oscillations is the Lomb–Scargle method, followed by Theil entropy analysis. Moreover, our results showed that the exponents that provide more information to describe the spectral inequality in the tested signals were zero, one, and two. It was also observed that the intra-band component is the one that contributes the most to total inequality for the studied oscillations. More in detail, we found that in the state of mental stress, the inequality determined by the Theil entropy analysis of heart rate increases with respect to the resting state. Likewise, the same analytical approach shows that cellular calcium oscillations present on developing interneurons display greater inequality distribution when inhibition of a neurotransmitter system is in place. In conclusion, we propose that Theil entropy is useful for analyzing spectral inequality and to explore its origin in physiological signals.     

Seasonal variability of cloud optical depth over northwestern China derived from CERES/MODIS satellite measurements

The seasonal variability of cloud optical depth over northwestern China derived from Clouds and the Earth＇s Radiant Energy System （CERES） Single Scanner Footprint （SSF） Aqua Moderate Resolution Imaging Spectroradiometer （MODIS） Edition IB data from July 2002 to June 2004 is presented. The regions of interest are those with Asia monsoon influence, the Tianshan and Qilian Mountains, and the Taklimakan Desert. The results show that the instantaneous measurements presented here are much higher than the previous results derived from International Satellite Cloud Climatology Project （ISCCP） D2 monthly mean data. Generally the measurements of cloud optical depth are the highest in summer and the lowest in winter, however, Taklimakan Desert has the lowest measurements in autumn. The regional variation is quite significant over northwestern China.     

Why Do Big Data and Machine Learning Entail the Fractional Dynamics?

Fractional-order calculus is about the differentiation and integration of non-integer orders. Fractional calculus (FC) is based on fractional-order thinking (FOT) and has been shown to help us to understand complex systems better, improve the processing of complex signals, enhance the control of complex systems, increase the performance of optimization, and even extend the enabling of the potential for creativity. In this article, the authors discuss the fractional dynamics, FOT and rich fractional stochastic models. First, the use of fractional dynamics in big data analytics for quantifying big data variability stemming from the generation of complex systems is justified. Second, we show why fractional dynamics is needed in machine learning and optimal randomness when asking: “is there a more optimal way to optimize?”. Third, an optimal randomness case study for a stochastic configuration network (SCN) machine-learning method with heavy-tailed distributions is discussed. Finally, views on big data and (physics-informed) machine learning with fractional dynamics for future research are presented with concluding remarks.