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Kriging is a useful method for developing metamodels for product design optimization. The most popular kriging method, known as ordinary kriging, uses a constant mean in the model. In this article, a modified kriging method is proposed, which has an unknown mean model. Therefore, it is called blind kriging. The unknown mean model is identified from experimental data using a Bayesian variable selection technique. Many examples are presented, which show remarkable improvement in prediction using blind kriging over ordinary kriging. Moreover, a blind kriging predictor is easier to interpret and seems to be more robust against mis-specification in the correlation parameters.

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In many experiments, some of the factors exist only within the level of another factor. Such factors are often called nested factors. A factor within which other factors are nested is called a branching factor. For example, suppose we want to experiment with two processing methods. The factors involved in these two methods can be different. Thus, in this experiment the processing method is a branching factor and the other factors are nested within the branching factor. Design and analysis of experiments with branching and nested factors are challenging and have not received much attention in the literature.Motivated by a computer experiment in a machining process, we develop optimal Latin hypercube designs and kriging methods that can accommodate branching and nested factors. Through the application of the proposed methods, optimal machining conditions and tool edge geometry are attained, which resulted in a remarkable improvement in the machining process.

The cytotoxic effect of lidocaine and amitriptyline in cultured dorsal root ganglia cells and the nerve degeneration in the rat sciatic nerve model seem, at least in part, to be mediated by apoptosis but seem efficiently blocked by an inhibitor of p38 MAPK, making it conceivable that coinjection might be useful in preventing local anesthetic-induced neurotoxicity.