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The purpose of this study is to research the influence of wire’s surface topography on interwire contact performance of simple spiral strand.

The mechanical model of the simple spiral strand imposed by a tensile load is first established, into which the surface topography, Poisson’s ratio effect and radial deformation are incorporated simultaneously. Meanwhile, the Gaussian and non-Gaussian rough surfaces of the steel wires are obtained with the fast Fourier transform (FFT) and digital filter technology. Then, the rough interwire contact performance of the simple spiral strand is calculated by using conjugate gradient method and FFT.

As compared with smooth wire surface, both the longitudinal orientation for the Gaussian wire surface and large kurtosis or small skewness for the non-Gaussian surface yield a small contact pressure and stress.

This study conducts detailed discussion of the influence of wire’s surface topography on the interwire contact performance for the simple spiral strand and gives a beneficial reference for the design and application of a wire rope.

Control charts and process capability calculations remain fundamental techniques for statistical process control. However, it has long been realized that the accuracy of these calculations can be significantly affected when sampling from a non‐Gaussian population. Many quality practitioners are conscious of these problems but are not aware of the effects such problems might have on the integrity of their results. Considers non‐normality with respect to the use of traditional control charts and process capability calculations, so that users may be aware of the errors that are involved when sampling from a non‐Gaussian population. Use is made of the Johnson system of distributions as a simulation technique to investigate the effects of non‐normality of control charts and process control calculations. An alternative technique is suggested for process capability calculations which alleviates the problems of non‐normality while retaining computational efficiency.

This chapter reviews qualitative research on parenting children with disabilities published over the last 50 years to explore whether shifts in academic discourse and changes in professional training have affected research on parenting and/or the experiences of parents who are the subject of such research.

An extensive literature search was conducted, and 78 peer-reviewed, qualitative studies on the experience of parenting a child with a disability were included in the sample. Themes were extracted from the reviewed literature and compared across decades.

The findings of the present review suggest that some aspects of the parenting experience have changed very little. In particular, parents continue to experience negative reactions such as stress and anomie, especially early in their children’s lives, and socially imposed barriers such as unhelpful professionals, and a lack of needed services continue to create problems and inspire an entrepreneurial response. In addition, stigmatizing encounters with others continue to be a common occurrence. In contrast to earlier decades, studies conducted in more recent years have begun to use the social model of disability as an analytic frame and also increasingly report that parents are questioning and challenging the concept of “normal” itself.

Additional improvements are needed in professional education and services to reduce the negative reactions experienced by parents of children with disabilities.

The findings of this meta-analysis can serve as a guide to future research on parenting children with disabilities.

This study aims to monitor the overall equipment effectiveness (OEE) indicator that is one of the best indicators used to monitor the performance of the company by the multivariate control chart.

To improve continually the performance of a company, many research studies tend to apply Lean six sigma approach. It is one of the best ways used to reduce the variability in the process by using the univariate control chart to know the trend of the variable and make the action before process deviation. Nevertheless, and when the need is to monitor two or more correlated characteristics simultaneously, the univariate control chart will be unable to do it, and the multivariate control chart will be the best way to successfully monitor the correlated characteristics.

For this study, the authors have applied the multivariate control chart to control the OEE performance rate which is composed by the quality rate, performance rate and availability rate, and the relative work from which the authors have adopted the same methodology (Hadian and Rahimifard, 2019) was done for project monitoring, which is done by following different indicators such as cost, and time; the results of this work shows that by applying this tool, all project staff can meet the project timing with the cost already defined at the beginning of the project. The idea of monitoring the OEE rate comes because the OEE contains the three correlated indicators, we can’t do the monitoring of the OEE just by following one of the three because data change and if today we have the performance and quality rate are stable, and the availability is not, tomorrow we can another indicator impacted and, in this case, the univariate control chart can’t response to our demand. That’s why we have choose the multivariate control chart to prevent the trend of OEE performance rate. Otherwise, and according to production planning work, they try to prevent the downtime by switching to other references, but after applying the OEE monitoring using the multivariate control chart, the company can do the monitoring of his ability to deliver the good product at time to meet customer demand.

The application was done per day, it will be good to apply it per shift in order to have the ability to take the fast reaction in case of process deviation. The other perspective point we can have is to supervise the process according to the control limits found and see if the process still under control after the implementation of the Multivariate control chart at the OEE Rate and if we still be able to meet customer demand in terms of Quantity and Quality of the product by preventing the process deviation using multivariate control chart.

The implication of this work is to provide to the managers the trend of the performance of the workshop by measuring the OEE rate and by following if the process still under control limits, if not the reaction plan shall be established before the process become out of control.

The OEE indicator is one of the effective indicators used to monitor the ability of the company to produce good final product, and the monitoring of this indicator will give the company a visibility of the trend of performance. For this reason, the authors have applied the multivariate control chart to supervise the company performance. This indicator is composed by three different rates: quality, performance and availability rate, and because this tree rates are correlated, the authors have tried to search the best tool which will give them the possibility to monitor the OEE rate. After literature review, the authors found that many works have used the multivariate control chart, especially in the field of project: to monitor the time and cost simultaneously. After that, the authors have applied the same approach to monitor the OEE rate which has the same objective : to monitor the quality, performance and availability rate in the same time.

The purpose of this paper is to model interest rates from observed financial market data through a new approach to the Cox–Ingersoll–Ross (CIR) model. This model is popular among financial institutions mainly because it is a rather simple (uni-factorial) and better model than the former Vasicek framework. However, there are a number of issues in describing interest rate dynamics within the CIR framework on which focus should be placed. Therefore, a new methodology has been proposed that allows forecasting future expected interest rates from observed financial market data by preserving the structure of the original CIR model, even with negative interest rates. The performance of the new approach, tested on monthly-recorded interest rates data, provides a good fit to current data for different term structures.

To ensure a fitting close to current interest rates, the innovative step in the proposed procedure consists in partitioning the entire available market data sample, usually showing a mixture of probability distributions of the same type, in a suitable number of sub-sample having a normal/gamma distribution. An appropriate translation of market interest rates to positive values has been introduced to overcome the issue of negative/near-to-zero values. Then, the CIR model parameters have been calibrated to the shifted market interest rates and simulated the expected values of interest rates by a Monte Carlo discretization scheme. We have analysed the empirical performance of the proposed methodology for two different monthly-recorded EUR data samples in a money market and a long-term data set, respectively.

Better results are shown in terms of the root mean square error when a segmentation of the data sample in normally distributed sub-samples is considered. After assessing the accuracy of the proposed procedure, the implemented algorithm was applied to forecast next-month expected interest rates over a historical period of 12 months (fixed window). Through an error analysis, it was observed that our algorithm provides a better fitting of the predicted expected interest rates to market data than the exponentially weighted moving average model. A further confirmation of the efficiency of the proposed algorithm and of the quality of the calibration of the CIR parameters to the observed market interest rates is given by applying the proposed forecasting technique.

This paper has the objective of modelling interest rates from observed financial market data through a new approach to the CIR model. This model is popular among financial institutions mainly because it is a rather simple (uni-factorial) and better model than the former Vasicek model (Section 2). However, there are a number of issues in describing short-term interest rate dynamics within the CIR framework on which focus should be placed. A new methodology has been proposed that allows us to forecast future expected short-term interest rates from observed financial market data by preserving the structure of the original CIR model. The performance of the new approach, tested on monthly data, provides a good fit for different term structures. It is shown how the proposed methodology overcomes both the usual challenges (e.g. simulating regime switching, clustered volatility and skewed tails), as well as the new ones added by the current market environment (particularly the need to model a downward trend to negative interest rates).

The purpose of this paper is to improve the tribological properties of Polyamide 1010 (PA 1010) in rolling friction with traction.

PA1010 composites filled with zinc oxide whiskers (ZnOw) were prepared by hot compression molding. The compressive properties of the composites were measured with an electronic material tester and the tribological behavior in rolling friction with traction of nylon composites was studied with a two‐roller contact rolling tester.

The results indicate that the compression modulus of composites increases with the rising content of ZnOw. Both the ultimate compression strength and the compression yield strength of composites increase to the maximum value when the content of ZnOw is 15 wt%. Both the traction coefficient and the slip ratio of each composite were influenced by the traction load and the normal load. In addition, the ZnOw proportion affected the slip ratio of the composites. The experimental results demonstrate that composites including 10 wt% and 15 wt% ZnOw exhibit lighter wear and lower slip ratio. The wear rate of the nylon composites is increased as the normal load increases due to the rising acting pressure against the nylon composites. The rising traction load also causes inflation in the wear rate of the composites.

The tests in the paper were carried out according to the conditions of tramcars in mining.

PA 1010 composites filled with ZnOw presented the preferable mechanical and tribological properties of PA1010, which can be used in the driving wheel of tramcars in mining and other components requiring high traction coefficient.

The paper studied the tribological properties of PA 1010 composites including ZnOw with special dimensional structure.

This paper aims to contribute to knowledge by investigating the return behaviour of seven global real estate investment trusts (REITs) with respect to the appropriate distributional fit that captures tail and shape characteristics. The study adds to the knowledge of distributional properties of seven global REITs by using the generalised lambda distribution (GLD), which captures fairly well the higher moments of the returns.

This is an empirical study with GLD through three rival methods of fitting tail and shape properties of seven REIT return data from January 2008 to November 2017. A post-Global Financial Crisis (GFC) (from July 2009) period fits from the same methods are juxtaposed for comparison.

The maximum likelihood estimates outperform the methods of moment matching and quantile matching in terms of goodness-of-fit in line with extant literature; for the post-GFC period as against the full-sample period. All three methods fit better in full-sample period than post-GFC period for all seven countries for the Region 4 support dynamics. Further, USA and Singapore possess the strongest and stronger infinite supports for both time regimes.

The REITs markets, however, developed, are of wide varied sizes. This makes comparison less than ideal. This is mitigated by a univariate analysis rather than multivariate one.

This paper is a reminder of the inadequacy of the normal distribution, as well as the mean, variance, skewness and kurtosis measures, in describing distributions of asset returns. Investors and policymakers may look at the location and scale of GLD for decision-making about REITs.

The novelty of this work lies with the data used and the detailed analysis and for the post-GFC sample.

To simulate the dynamic feature of bulk granular material (such as agricultural products) during sudden braking of a truck by applying discrete element method.

The bulk granular material was modeled by the discrete element approach, in which the spherical elements were used to represent the granular particles; the interaction between two in‐adhesive particles was modeled by Hertz for normal interaction, and by Mindlin and Deresiewicz for tangential interaction; the interaction between two particles with adhesion was modeled by the JKR theory for normal interaction, and by Thornton's theory for the tangential interaction. Different initial conditions (braking speeds/accelerations) were considered. The dynamic system was numerically solved by the central difference based explicit time integration, and the dynamic impact forces were recorded to further analysis.

The computation predicted that the resultant dynamic force acting upon the front wall behaves in four stages, i.e. increasing, plateau, sharp increasing and drop with damped fluctuation. It was observed that, the shorter the breaking time is, the faster the force reaches its peak, and the greater the peak value is. The phenomenon was in good agreement with physical principals and common knowledge.

It is an application of the discrete element method and, therefore, no important contribution is made to advance the methodology.

The proposed modeling approach may serve as a useful tool for advanced design of trucks.

This paper is the first to apply the advanced discrete element method to the problem concerned.

This article investigates the effect of modeling extreme events on the calculation of minimum capital risk requirements for three LIFFE futures contracts. The use of internal models will be permitted under the European Community Capital Adequacy Directive II and will be widely adopted in the near future for determining capital adequacies. Close scrutiny of competing models is required to avoid a potentially costly misallocation of capital resources, to ensure the safety of the financial system. The authors propose a semi‐parametric approach, for which extreme risks are modeled using a generalized Pareto distribution, and smaller risks are characterized by the empirically observed distribution function. The primary finding of comparing the capital requirements based on this approach with those calculated from both the unconditional density and from a conditional density (a GARCH(1,1) model), is that for both in‐sample and out‐of‐sample tests, the extreme value approach yields superior results. This is attributable to the fact that the other two models do not explicitly model the tails of the return distribution.