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Enzyme-linked immunosorbent assays (ELISAs) were developed for the detection of Ig G, Ig G4 and Ig E antibodies against Strongyloides stercoralis. A commercial ELISA ( IVD Research, USA) was also used, and the sensitivities and specificities of the four assays were determined. Serum samples from 26 patients with S. stercoralis infection and 55 patients with other infections or no infection were analysed. Sensitivities of the Ig G4, Ig G, Ig E and Ig G ( IVD) assays were 76·9%, 84·6%, 7·7% and 84·6%, respectively, while the specificities were 92·7%, 81·8%, 100% and 83·6%, respectively. If filariasis samples were excluded, the specificities of the Ig G4- ELISA and both Ig G- ELISAs increased to 100% and 98%, respectively. A significant positive correlation was observed between Ig G- and Ig G4- ELISAs ( r = 0·4828; P = 0·0125). Ig G- and Ig G- ( IVD) ELISAs ( r = 0·309) were positively correlated, but was not significant ( P = 0·124). Meanwhile there was no correlation between Ig G4- and Ig G- ( IVD) ELISAs ( r = 0·0042; P = 0·8294). Sera from brugian filariasis patients showed weak, positive correlation between the titres of antifilarial IgG4 and the optical densities of anti- Strongyloides Ig G4- ELISA ( r = 0·4544, P = 0·0294). In conclusion, the detection of both anti- Strongyloides Ig G4 and Ig G antibodies could improve the serodiagnosis of human strongyloidiasis. Furthermore, patients from lymphatic filariasis endemic areas who are serologically diagnosed with strongyloidiasis should also be tested for filariasis. [ABSTRACT FROM AUTHOR]

In this article, a robust regression technique called MM-estimator is proposed to model the responses in response surface methodology (RSM). Model fitting based on the MM-estimator allows practitioners to find a better optimal setting in dual-response surface optimization and multiple-response optimization when the responses are considerably nonnormal and/or contain some outliers. The MM-estimator is one of the robust regression techniques that can dampen the effect of the outliers. An example from the Roman catapult experiment is used to illustrate our proposal. [ABSTRACT FROM AUTHOR]

The desirability of a response that increases in a linear manner from the lower specification limit to the target value and decreases in a linear manner from the target value to the upper specification limit results in a breakpoint (nondifferentiable point) at the target value of its desirability function. Thus, modified desirability functions were proposed to solve this breakpoint problem so that the generalized reduced gradient can be applied. The modified desirability functions require the fitting of a quartic polynomial in the vicinity of the target. However, the fitting of the quartic polynomial needs to satisfy five conditions and involves numerous computations. Therefore, an approach with less computations and yet that is simple to implement is proposed in this article. This approach is applicable when the lower specification limit, the target value, and the upper specification limit of a response are available. In addition, this approach also allows practitioners to assign different degrees of importance to the responses. An example from the original paper of modified desirability functions is used to illustrate this approach. [ABSTRACT FROM AUTHOR]

The multivariate Hotelling's T2 control chart is designed to be used in a mass production for processes where data to estimate the mean vector and covariance matrix as well as the computation of control limits are available before a production run. Recent years have seen a trend in manufacturing industries to produce smaller lot sizes, a.k.a., low volume production which is a result of increased importance given to just-in-time (JIT) manufacturing techniques, synchronous manufacturing and the reduction of in-process inventory and costs. This new manufacturing environment is also referred to as short runs production or short runs. In a short runs environment, it is difficult or perhaps impossible to establish a reliable historical data set in setting valid control limits and in estimating process parameters due to the availability of insufficient data for a particular process because production runs are usually short and change frequently from one process to another. There is also a need to start charting at or very near the beginning of the run in such a case. Another problem encountered in a short runs production such as in job shops is that there are many different types of measurements so that many different control charts are needed. Standardized control charts that allow different statistics to be plotted on the same chart are extremely useful in short runs. Control charts with standard scale simplify the control charting process in a short runs environment. In this paper, we address the multivariate short runs problems for process dispersion based on individual measurements by presenting the required formulas so that the chart can be used from the start of production, whether or not prior information for estimating the chart's limit and its parameter is available. The proposed chart plots standardized statistics for multiple parts on the same chart. This paper extends the work of the authors in Ref. 13. [ABSTRACT FROM AUTHOR]

A single extreme point that plots outside of the 3σ control limits for the Shewhart charts indicate one possible out-of-control signal for the presence of assignable causes. Assignable causes can also cause unusual variation within the control limits. Runs rules have been used widely to detect patterns in successive observations. Excellent discussions about runs rules can be found in Nelson (Nelson, L. S. (1984). The Shewhart control chart—tests for special causes. Journal of Quality Technology 16:237–239.), Champ and Woodall (Champ, C. W., Woodall, W. H. (1987). Exact results for Shewhart control charts with supplementary runs rules. Technometrics 29:393–399.), Klein (Klein, M. (2000). Two alternatives to the Shewhart [Xbar] control chart. Journal of Quality Technology 32:427–431.), and Khoo (Khoo, B. C. Michael. (2003). Design of runs rules schemes. Quality Engineering 16, (27–43)). In this article, enhanced rules for the Hotelling'sχ2 chart will be suggested to increase the chart's sensitivity. [ABSTRACT FROM AUTHOR]

The index used in process capability is proposed as an optimization criterion in this article. This index can be viewed as an extension of mean squared error (MSE). Thus, the index allows practitioners to find an optimal setting with the mean responses close to their respective target values, Ti, while the standard deviations (or variance) of the responses are kept small. The index as an optimization criterion is applicable when upper specification limits, target values, and lower specification limits of mean responses are available. However, it also can be applied to mean responses with only target values and lower specification limits (or upper specification limits) in specific situations. An example will be used to illustrate this proposal. [ABSTRACT FROM AUTHOR]