pinecone/core-2020-05-10-deduplication huggingface dataset:
Ocean Rossby waves as a triggering mechanism for primary Madden-Julian events

The Madden–Julian Oscillation (MJO) is sporadic, with episodes of cyclical activity interspersed with inactive periods. However, it remains unclear what may trigger a Madden–Julian (MJ) event which is not immediately preceded by any MJO activity: a ‘primary’ MJ event. A combination of case-studies and composite analysis is used to examine the extent to which the triggering of primary MJ events might occur in response to ocean dynamics. The case-studies show that such events can be triggered by the arrival of a downwelling oceanic equatorial Rossby wave, which is shown to be associated with a deepening of the mixed layer and positive sea-surface temperature (SST) anomalies of the order of 0.5–1 °C. These SST anomalies are not attributable to forcing by surface fluxes which are weak for the case-studies analysed. Furthermore, composite analysis suggests that such forcing is consistently important for triggering primary events. The relationship is much weaker for successive events, due to the many other triggering mechanisms which operate during periods of cyclical MJO activity. This oceanic feedback mechanism is a viable explanation for the sporadic and broadband nature of the MJO. Additionally, it provides hope for forecasting MJ events during periods of inactivity, when MJO forecasts generally exhibit low skill
Upper-Ocean Singular Vectors of the North Atlantic Climate with Implications for Linear Predictability and Variability

The limits of predictability of the meridional overturning circulation (MOC) and upper-ocean temperatures due to errors in ocean initial conditions and model parametrizations are investigated in an idealized configuration of an ocean general circulation model (GCM). Singular vectors (optimal perturbations) are calculated using the GCM, its tangent linear and adjoint models to determine an upper bound on the predictability of North Atlantic climate. The maximum growth time-scales of MOC and upper-ocean temperature anomalies, excited by the singular vectors, are 18.5 and 13 years respectively and in part explained by the westward propagation of upper-ocean anomalies against the mean flow. As a result of the linear interference of non-orthogonal eigenmodes of the non-normal dynamics, the ocean dynamics are found to actively participate in the significant growth of the anomalies. An initial density perturbation of merely \(0.02 kg m^{−3}\) is found to lead to a 1.7 Sv MOC anomaly after 18.5 years. In addition, Northern Hemisphere upper-ocean temperature perturbations can be amplified by a factor of 2 after 13 years. The growth of upper-ocean temperature and MOC anomalies is slower and weaker when excited by the upper-ocean singular vectors than when the deep ocean is perturbed. This leads to the conclusion that predictability experiments perturbing only the atmospheric initial state may overestimate the predictability time. Interestingly, optimal MOC and upper-ocean temperature excitations are only weakly correlated, thus limiting the utility of SST observations to infer MOC variability. The excitation of anomalies in this model might have a crucial impact on the variability and predictability of Atlantic climate. The limit of predictability of the MOC is found to be different from that of the upper-ocean heat content, emphasizing that errors in ocean initial conditions will affect various measures differently and such uncertainties should be carefully considered in decadal prediction experiments.Earth and Planetary Science
Blending a probabilistic nowcasting method with a high-resolution numerical weather prediction ensemble for convective precipitation forecasts

A seamless prediction of convective precipitation for a continuous range of lead times from 0Ã¢Â�Â�8 h requires the application of different approaches. Here, a nowcasting\ud
method and a high-resolution numerical weather prediction ensemble are combined to provide probabilistic precipitation forecasts. For the nowcast, an existing deterministic extrapolation technique was modified by the local Lagrangian method to calculate the probability of exceeding a threshold value in radar reflectivity. Numerical forecasts were obtained from an experimental high-resolution ensemble\ud
that provides 20 different deterministic forecasts of synthetic radar reflectivity. Probabilistic information was calculated by different approaches from the ensemble output. The probabilistic forecasts based on the ensemble were calibrated with the reliability diagram statistics method. The skill of the probabilistic nowcasts and forecasts was evaluated using three quality measures. Finally, a seamless probabilistic forecast was generated as an additive combination of nowcast and forecast, using a weighting function based on their relative skills. The skill of the seamless forecast was greater than or equal to that of the nowcast or ensemble forecast in all quality measures and at all lead times
A regulated localization scheme for ensemble-based Kalman filters

Localization is an essential element of ensemble-based Kalman filters in large-scale systems. Two localization methods are commonly used: Covariance localization and domain localization. The former applies a localizing weight to the forecast covariance matrix while the latter splits the assimilation into local regions in which independent assimilation updates are performed.The domain localization is usually combined with a weighting of the observation error covariance matrix, resulting in a similar localization effect to that of covariance localized filters. In order to improve the performance of domain localization with weighting of the observation errors, a regulated localization scheme is introduced. Twin experiments with the Lorenz-96 model demonstrate that the regulated localization can lead to a significant reduction of the estimation errors as well as increased stability of the assimilation process. In addition, the numerical experiments point out that the combination of covariance localization with a serial processing of observations during the analysis step can destabilize the assimilation process
Inter-domain cooperation for mesoscale atmospheric laboratories: The Mesoscale Alpine Programme as a rich study case.

The development and conduct of the Mesoscale Alpine Programme (MAP) is summarized – a 10-year\ud
research initiative shared by meteorological services, research laboratories and university institutes from 14 countries. The\ud
overall investment is estimated to have exceeded 35 million Euros. Inventories are given of scientific results as published\ud
in some 220 peer-reviewed publications and over 40 completed PhD theses spanning a full decade. The close linkage\ud
between field measurements and various types of simulation experiments is emphasized by considering the role of natural laboratories (observing the atmosphere) and numerical laboratories (undertaking simulation experiments). Various forms of cooperation between laboratories and different domains such as nations, institutions and generations are used as the\ud
structuring elements of the survey. A comparison with surveys from other large field programmes helps to put the overall\ud
achievements of MAP into perspective
Design and analysis for the Gaussian process model

The article of record as published may be located at http://dx.doi.org/10.1002/qre.1044In an effort to speed the development of new products and processes, many companies are turning to computer simulations to avoid the time and expense of building prototypes.  These computer simulations are often complex, taking hours to complete one run.  If there are many variables affecting the ressults of the simulation, then it makes sense to design an experiment to gain the most information possible from a limited number of computer simulation runs.  The researcher can use the results of these runs to build a surrogate model of the computer simulation model.  The absence of noise is the key difference between computer simulation experiments and experiments in the real world.  Since there is no variability in the results of computer experiments, optimal designs, which are based on reducing the variance of some statistic, have questionable utility.  Replication, usually a "good thing", is clearly undesirable in computer experiments.  Thus, a new approach to experimentation is necessary
Designing Experiments for Nonlinear Models - An Introduction

The article of record as published may be found at http://dx.doi.org/10.1002/qre.1063We illustrate the construction of Bayesian D-optimal designs for nonlinear models and compare the relative efficiency
of standard designs with these designs for several models and prior distributions on the parameters. Through a
relative efficiency analysis, we show that standard designs can perform well in situations where the nonlinear model
is intrinsically linear. However, if the model is nonlinear and its expectation function cannot be linearized by simple
transformations, the nonlinear optimal design is considerably more efficient than the standard design
A Survey of Quality Engineering-ManagementJournals by Bibliometric Indicators

This paper analyses some of the most popular scientific journals in the Quality field from the point of view of three bibliometric indicators: the Hirsch (h) index for journals, the total number of citations and the h-spectrum. In particular, h-spectrum is a novel tool based on h, making it possible to (i) identify a reference profile of the typical authors of a journal; (ii) compare different journals; and (iii) provide a rough indication of their ‘bibliometric positioning' in the scientific community. Results of this analysis can be helpful for guiding potential authors and members of the scientific community in the Quality Engineering/Management area. A large amount of empirical data are presented and discusse
Application of six sigma methodology to reduce defects of a grinding process

Six Sigma is a data-driven leadership approach using specific tools and methodologies that lead to fact-based decision making. This paper deals with the application of the Six Sigma methodology in reducing defects in a fine grinding process of an automotive company in India. The DMAIC (Define–Measure–Analyse–Improve–Control) approach has been followed here to solve the underlying problem of reducing process variation and improving the process yield. This paper explores how a manufacturing process can use a systematic methodology to move towards world-class quality level. The application of the Six Sigma methodology resulted in reduction of defects in the fine grinding process from 16.6 to 1.19%. The DMAIC methodology has had a significant financial impact on the profitability of the company in terms of reduction in scrap cost, man-hour saving on rework and increased output. A saving of approximately US$2.4 million per annum was reported from this project
Feature Evaluation for Effective Bearing Prognostics.

International audienceRolling element bearing failure is one of the foremost causes of breakdown in rotating machinery. It is not uncommon to replace a defected/used bearing with a new one that has shorter remaining useful life than the defected one. Thus, prognostics of bearing plays critical role for increased availability and reduced cost. Effective prognostics highly depend on the quality of the extracted features. Diagnostics is basically a classification problem, whereas the prognostics is the process of forecasting the future health states. The quality of the features for classification has been studied thoroughly. However, evaluation of the quality of features for prognostics is a relatively new problem. This paper presents an evaluation method for the goodness of the features for prognostics and presents results on bearings run until failure in a lab environment
Warranty Claim Forecasting Based On Weighted Maximum Likelihood Estimation

Warranty claims reported in recent months might carry more up-to-date information than those reported in earlier months. Using weighted maximum likelihood estimation for estimating model parameters might therefore lead to better performance of warranty forecasting models than maximum likelihood estimation. This paper examines this issue and also presents comparison of the forecasting performance of the parametric models such as Poisson processes and ARIMA models and non-parametric models such as artificial neural networks. It shows that mixed non-homogenous Poisson process models can lead to better forecasting results than other competing methods. The paper also shows that the models built with the weighted maximum likelihood estimation yield smaller error than those based on the maximum likelihood estimation
Synthetic phase II Shewhart-type attributes control charts when process parameters are estimated

The performance of attributes control charts (such as c and np charts) is usually evaluated under the assumption of known
process parameters (i.e., the nominal proportion of nonconforming units or the nominal number of nonconformities). However,
in practice, these process parameters are rarely known and have to be estimated from an in-control phase I data set. In this paper,
we derive the run length properties of the phase II synthetic c and np charts with estimated parameters, and we investigate the
numbermof phase I samples that would be necessary for these charts in order to obtain similar in-control average run lengths as
in the known parameters case. We also propose new specific chart parameters that allow these charts to have approximately the
same in-control average run lengths as the ones obtained in the known parameter case.China Scholarship Council No [2011]6032.SARChI chair at the University of Pretoria, South Africa.http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)1099-1638hb201
A comparison of normal approximation rules for attribute control charts

Control charts, known for more than 80 years, have been important tools for business and industrial manufactures. Among many different types of control charts, the attribute control chart (np-chart or p-chart) is one of the most popular methods to monitor the number of observed defects in products, such as semiconductor chips, automobile engines, and loan applications. The attribute control chart requires that the sample size n is sufficiently large and the defect rate p is not too small so that the normal approximation to the binomial works well. Some rules for the required values for n and p are available in the textbooks of quality control and mathematical statistics. However, these rules are considerably different and hence it is less clear which rule is most appropriate in practical applications. In this paper, we perform a comparison of five frequently used rules for n and p required for the normal approximation to the binomial. Based on this result, we also refine the existing rules to develop a new rule that has a reliable performance. Datasets are analyzed for illustration.
Improved Phase I control charts for monitoring times between events

In many situations, the times between certain events are observed and monitored instead of the number of events particularly when the events occur rarely. In this case, it is common to assume that the times between events follow an exponential distribution. Control charts are one of the main tools of statistical process control and monitoring. Control charts are used in phase I to assist operating personnel in bringing the process into a state of statistical control. In this paper, phase I control charts are considered for the observations from an exponential distribution with an unknown mean. A simulation study is carried out to compare the in-control robustness and out-of-control performance of the proposed chart. It is seen that the proposed charts are considerably more in-control robust than two competing charts and have comparable out-of control propertiesSouth African Research Chairs Initiative (SARChI) and University of Pretoria, in South Africa.http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)1099-16382016-06-30hb201
Distribution-free Phase II CUSUM control chart for joint monitoring of location and scale

Mukherjee and Chakraborti1 proposed a single distribution-free (nonparametric) Shewhart-type
chart based on the Lepage2 statistic for simultaneously monitoring both the location and the scale
parameters of a continuous distribution when both of these parameters are unknown. In the present work,
we consider a single distribution-free CUSUM chart, based on the Lepage2 statistic, referred to as the
CUSUM-Lepage (denoted by CL) chart. The proposed chart is distribution-free (nonparametric) and
therefore, the in control (denoted IC) properties of the chart remain invariant and known for all
continuous distributions. Control limits are tabulated for implementation of the proposed chart in practice.
The IC and out of control (denoted OOC) performance properties of the chart are investigated through
simulation studies in terms of the average, the standard deviation, the median and some percentiles of the
run length distribution. Detailed comparison with a competing Shewhart-type chart is presented. Several
existing CUSUM charts are also considered in the performance comparison. The proposed CL chart is
found to perform very well in the location-scale models. We also examine the effect of the choice of the
reference value (k) of CUSUM chart on the performance of the CL chart. The proposed chart is illustrated
with a real data set. Summary and conclusions are presented.http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)1099-16382016-02-28hb201
On the Effectiveness of Profile Monitoring to Enhance Functional Performance of Particleboards

This paper explores connection between profile monitoring and functional performance of manufactured products. In particular, the empirical relationship between the vertical density profile of the particleboards and their functional performances (the internal bond and the surface soundness) is studied. Results based on a real case study showed that the profile shape clearly affects the final performance of the panel, and thus profile monitoring is really worth to keep the final quality of the product at its target level. This result motivates the second objective of the paper, which consists of comparing performance of two (parametric and nonparametric) approaches for vertical density profile monitoring
Use of Petri nets to model the maintenance of wind turbines

This is the peer reviewed version of the following article: LEIGH, J.M. and DUNNETT, S.J., 2014. Use of Petri nets to model the maintenance of wind turbines. Quality and Reliability Engineering International, doi: 10.1002/qre.1737, which has been published in final form at http://dx.doi.org/10.1002/qre.1737. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.With large expansion plans for the offshore wind turbine industry there has never been a greater need for effective operations and maintenance. The two main problems with the current operations and maintenance of an offshore wind turbine are the cost and availability. In this work a simulation model has been produced of the maintenance process for a wind turbine with the aim of developing a procedure that can be used to optimise the process. This initial model considers three types of maintenance; periodic, conditional and corrective and also considers the weather in order to determine the accessibility of the turbine. Petri nets have been designed to simulate each type of maintenance and weather conditions. It has been found that Petri nets are a very good method to model the maintenance process due to their dynamic modelling and adaptability and their ability to test optimisation techniques. Due to their versatility Petri net models are developed for both system hardware and the maintenance processes and these are combined in an efficient and concise manner
Formal Specification and Quantitative Analysis of a Constellation of
  Navigation Satellites

Navigation satellites are a core component of navigation satellite based
systems such as GPS, GLONASS and Galileo which provide location and timing
information for a variety of uses. Such satellites are designed for operating
on orbit to perform tasks and have lifetimes of 10 years or more. Reliability,
availability and maintainability (RAM) analysis of systems has been
indispensable in the design phase of satellites in order to achieve minimum
failures or to increase mean time between failures (MTBF) and thus to plan
maintenance strategies, optimise reliability and maximise availability. In this
paper, we present formal models of both a single satellite and a navigation
satellite constellation and logical specification of their reliability,
availability and maintainability properties respectively. The probabilistic
model checker PRISM has been used to perform automated analysis of these
quantitative properties
Monitoring the process mean when standards are unknown : a classic problem revisited

One of the most common applications in statistical process monitoring is the use of
control charts to monitor a process mean. In practice, this is often done with a
Shewhart X chart along with a Shewhart R (or an S) chart. Thus two charts are
typically used together, as a scheme, each using the 3-sigma limits. Moreover, the
process mean and standard deviation are often unknown and need to be estimated
before monitoring can begin. We show that there are three major issues with this
monitoring scheme described in most textbooks. The first issue is not accounting for
the effects of parameter estimation, which is known to degrade chart performance.
The second issue is the implicit assumption that the charting statistics are both
normally distributed and, accordingly, using the 3-sigma limits. The third issue is
multiple testing, since two charts are used, in this scheme, at the same time. We
illustrate the deleterious effects of these issues on the in-control properties of the
(X,R) charting scheme and present a method for finding the correct charting
constants taking proper account of these issues. Tables of the new charting constants
are provided for some commonly used nominal in-control average run-length
(ICARL0) values and different sample sizes. This will aid in implementing the (X,R)
charting scheme correctly in practice. Examples are given along with a summary and
some conclusions.http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)1099-16382017-03-31hb2016Statistic
A generally weighted moving average signed-rank control chart

The idea of process monitoring emerged so as to preserve and improve the quality of a
manufacturing process. In this regard, control charts are widely accepted tools in the manufacturing sector for
monitoring the quality of a process. However, a specific distributional assumption for any process is
restrictive and often criticised. Distribution-free control charts are efficient alternatives when information on
the process distribution is partially or completely unavailable. In this article, we propose a distribution-free
generally weighted moving average (GWMA) control chart based on the Wilcoxon signed-rank (SR) statistic.
Extensive simulation is done to study the performance of the proposed chart. The performance of the proposed
chart is then compared to a number of existing control charts including the parametric GWMA chart for
subgroup averages, a recently proposed GWMA chart based on the sign statistic and an exponentially
weighted moving average (EWMA) chart based on the signed-rank statistic. The simulation results reveal that
the proposed chart performs just as well and in many cases better than the existing charts, and therefore can
serve as a useful alternative in practice.Research of the first author was supported in part by STATOMET at the University of Pretoria, South Africa and National Research Foundation through the SARChI Chair at the University of Pretoria, South Africa.http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)1099-16382017-12-31hb2017Statistic