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The purpose of this paper is to study the relationship between reported sigma levels and actual failure rates (FRs) of gamma-distributed processes. The added complexity of the non-normality behavior of the gamma distribution is analyzed for the case of the cycle time (CT) of a real procurement process from the oil and gas industry. Then, recommendations and guidelines for the application of Six Sigma methodology for the case study are proposed.

Sensitivity analysis is conducted to study the relationship between gamma distribution parameters and FRs considering different quality levels. Then, adjustments for implementing Six Sigma programs for gamma processes are proposed. These adjustments consist of first determining the appropriate probability distribution, the standard CT and the due date, followed by setting performance zones and improvement strategies on target gamma parameters that yield the minimal FR.

For gamma-distributed processes, simply reporting the sigma level is not sufficient to capture the main characteristics of the process. These characteristics include process FR, mean setting, shape, spread and amount of variation reduction (i.e. improvement effort) required. That is why caution must be exercised when dealing with one-sided non-normal quality characteristics such as CT.

To the authors’ knowledge, this is the first time that the Six Sigma performance has been evaluated for gamma processes to analyze the link between Six Sigma FRs and gamma distribution parameters leading to the development of a modified Six Sigma methodology for non-normal processes.

This paper deals with the buckling analysis of prismatic folded plate structures supported on diaphragms at two opposite edges. The analysis is carried out using variable thickness finite strips based on Mindlin‐Reissner assumptions which allow for transverse shear deformation effects. The theoretical formulation is presented for a family of C(0) strips and the accuracy and relative performance of the strips are examined. Results are presented for a series of problems including plates and stiffened panels. In a companion paper these accurate and inexpensive finite strips are used in the context of structural shape optimization.

The purpose of this paper is to compute reliability, availability and maintainability (RAM) indices to measure and improve the performance of an automated croissant production line under real working conditions. Based on this study, the authors demonstrate how RAM analysis is very useful for deciding maintenance intervals and for planning and organizing the adequate maintenance strategy.

The present work is carried out by analyzing failure and repair data based on statistical techniques. Descriptive statistics of the failure and repair data at workstation and line level were carried out. Trend and serial correlation tests validated the assumption of independence and identical distribution of the failure data were conducted. Moreover, the reliability and maintainability of both the croissant production line and its workstations have been estimated at different mission times with their best fit distribution.

The main objectives of the applied method are to understand the nature of the failure patterns, and to estimate the reliability and maintainability characteristics of the croissant production system in precise quantitative terms. The analysis identifies the critical points of the production line that require further improvement through effective maintenance strategy.

This study is anticipated to serve as an illuminating effort in conducting a complete RAM analysis and its effect on the performance of the system that works under real conditions. The advantage of the methodology is the continuous monitoring of the production process through appropriate indices, the utilization of which leads to a continuous cycle of improvement.

Reliability plays an important role in the execution of the maintenance improvement and the understanding of its concepts is essential to predict the type of maintenance according to the equipment state. Thereby, a computational tool was developed and programming with VBA in Excel® for reliability and failure analysis in a mining context. The paper aims to discuss these issues.

The developed approach use the modeling of stochastic processes, such as the renewal process, the non-homogeneous Poisson process and less conventional method as the Bayesian approach, by considering Jeffreys non-informative prior. The resolution gives the best associated model, the parameters estimation, the mean time between failure and the reliability estimate. This approach is validated with the reliability analysis of inter-failure times from underground rock bolters subsystems, over a two-year period.

Results show that Weibull and lognormal probability distribution fit to the most subsystems inter-failure times. The study revealed that the bolting head, the rock drill, the screen handler, the electric/electronic system, the hydraulic system, the drilling feeder and the structural consume the most repair frequency. The hydraulic and electric/electronic subsystems represent the lowest reliability after 50 operation hours.

For the first time, this case study defines practical failures and reliability information for rock bolter subsystems based on real operation data. This paper is useful to the comparative evaluation of rock bolter by detecting the weakest elements and understanding failure patterns in the individual observation subsystems on the overall machine performance.

Application of financial risk models in the emerging markets poses special challenges. A fundamental challenge is to accurately model the return distributions which are particularly fat tailed and skewed. Value-at-Risk (VaR) measures based on the Extreme Value Theory (EVT) have been suggested, but typically data histories are limited, making it hard to test and apply EVT. The chapter addresses issues in (i) modeling the VaR measure in the presence of structural breaks in an economy, (ii) the choice of stable innovation distribution with volatility clustering effects, (iii) modeling the tails of the empirical distribution, and (iv) fixing the cut-off point for isolating extreme observations. Pakistan offers an instructive case since its equity market exhibits high volatility and incidence of extreme returns. The recent Global Financial Crisis has been another source of extreme returns. The confluence of the two sources of volatility provides us with a rich data set to test the VaR/EVT model rigorously and examine practical challenges in its application in an emerging market.

The traditional predictor of technical inefficiency proposed by Jondrow, Lovell, Materov, and Schmidt (1982) is a conditional expectation. This chapter explores whether, and by how much, the predictor can be improved by using auxiliary information in the conditioning set. It considers two types of stochastic frontier models. The first type is a panel data model where composed errors from past and future time periods contain information about contemporaneous technical inefficiency. The second type is when the stochastic frontier model is augmented by input ratio equations in which allocative inefficiency is correlated with technical inefficiency. Compared to the standard kernel-smoothing estimator, a newer estimator based on a local linear random forest helps mitigate the curse of dimensionality when the conditioning set is large. Besides numerous simulations, there is an illustrative empirical example.

ALTHOUGH the lowering cloud of increased charges for stamps and fewer collections and deliveries of mail faces the Post Office it will still, according to Mr. Jackson, general secretary of the Union of Post Office Workers, suffer a deficit of £130 million by 1973. The context in which he made his remarks to the union delegates, assembled at a conference in Bournemouth, is of special interest to this journal's readers.

Travel time at inter-stops is a set of important parameters in bus timetabling, which is usually assumed to be normal (log-normal) random variable in literature. With the development of digital technology and big data analytics ability in the bus industry, practitioners prefer to generate deterministic travel time based on the on-board GPS data under maximum probability rule and mean value rule, which simplifies the optimization procedure, but performs poorly in the timetabling practice due to the loss of uncertain nature on travel time. The purpose of this study is to propose a GPS-data-driven bus timetabling approach with consideration of the spatial-temporal characteristic of travel time.

The authors illustrate that the real-life on-board GPS data does not support the hypothesis of normal (log-normal) distribution on travel time at inter-stops, thereby formulating the travel time as a scenario-based spatial-temporal matrix, where K-means clustering approach is utilized to identify the scenarios of spatial-temporal travel time from daily observation data. A scenario-based robust timetabling model is finally proposed to maximize the expected profit of the bus carrier. The authors introduce a set of binary variables to transform the robust model into an integer linear programming model, and speed up the solving process by solution space compression, such that the optimal timetable can be well solved by CPLEX.

Case studies based on the Beijing bus line 628 are given to demonstrate the efficiency of the proposed methodology. The results illustrate that: (1) the scenario-based robust model could increase the expected profits by 15.8% compared with the maximum probability model; (2) the scenario-based robust model could increase the expected profit by 30.74% compared with the mean value model; (3) the solution space compression approach could effectively shorten the computing time by 97%.

This study proposes a scenario-based robust bus timetabling approach driven by GPS data, which significantly improves the practicality and optimality of timetable, and proves the importance of big data analytics in improving public transport operations management.

Blast‐induced traumatic brain injury (TBI) is a signature injury of the current military conflicts. Among the different types of TBI, diffuse axonal injury (DAI) plays an important role since it can lead to devastating effects in the inflicted military personnel. To better understand the potential causes associated with DAI, this paper aims to investigate a transient non‐linear dynamics finite element simulation of the response of the brain white matter to shock loading.

Brain white matter is considered to be a heterogeneous material consisting of fiber‐like axons and a structure‐less extracellular matrix (ECM). The brain white matter microstructure in the investigated corpus callosum region of the brain is idealized using a regular hexagonal arrangement of aligned equal‐size axons. Deviatoric stress response of the axon and the ECM is modeled using a linear isotropic viscoelastic formulation while the hydrostatic stress response is modeled using a shock‐type equation of state. To account for the stochastic character of the brain white matter microstructure and shock loading, a parametric study is carried out involving a factorial variation of the key microstructural and waveform parameters.

The results obtained show that the extent of axon undulations and the strength of axon/ECM bonding profoundly affect the spatial distribution and magnitude of the axonal axial normal and shear stresses (the stresses which can cause diffuse axonal injury).

The present approach enables a more accurate determination of the mechanical behavior of brain white matter when subjected to a shock.

In a variable‐pitch propeller in combination, a hub, a plurality o£ blades arranged on said hub for adjustment about their longitudinal axis, means displaceable by the action of a hydraulic pressure medium and operatively connected to said blades, an operation of said means effecting an adjustment of the blades and thus a variation of the propeller pitch, a source of hdyraulic pressure supplying liquid under pressure to said blade‐adjusting means, an additional source of hydraulic pressure also adapted to supply liquid under pressure to said blade‐adjusting means, a member determining the magnitude of the hydraulic pressure acting upon the blade adjusting means, a governing member operated by the action of centrifugal forces and a change‐over member, both these members controlling the distribution of the hydraulic pressure medium to said blade‐adjusting means, a further member adapted to interrupt the supply of hydraulic pressure medium to said blade‐adjusting means when a limit of a normal predetermined pitch range is reached, auxiliary valve means for the hydraulic‐pressure medium, which permit a further actuation of said blade‐adjusting means for the purpose of moving the blades beyond that predetermined pitch range only after increased hydraulic adjusting pressure has been able to open said auxiliary valve means, and means connecting said governing member and said change‐over member in such a way that they can be adjusted by a deliberate manual operation for the purpose of connecting said additional source of hydraulic pressure to said other source of hy‐draulic pressure, said member determining the magnitude of the hydraulic pressure acting on the blade‐adjusting means being thereby acted upon in such a way as to increase the hydraulic pressure, so that a greater quantity of pressure medium and an increased hydraulic pressure are available for adjusting the blades beyond the said normal pre‐determined pitch range.