Standard “indifference-zone” procedures that allocate computer resources to infer the best of a finite set of simulated systems are designed with a statistically conservative, least favorable configuration assumption consider the probability of correct selection (but not the opportunity cost) and assume that the cost of simulating each system is the same. Recent Bayesian work considers opportunity cost and shows that an average case analysis may be less conservative but assumes a known output variance, an assumption that typically is violated in simulation. This paper presents new two-stage and sequential selection procedures that integrate attractive features of both lines of research. They are derived assuming that the simulation output is normally distributed with unknown mean and variance that may differ for each system. We permit the reduction of either opportunity cost loss or the probability of incorrect selection and allow for different replication costs for each system. The generality of our formulation comes at the expense of difficulty in obtaining exact closed-form solutions. We therefore derive a bound for the expected loss associated potentially incorrect selections, then asymptotically minimize that bound. Theoretical and empirical results indicate that our approach compares favorably with indifference-zone procedures.

Homozygous mutations of the glucocerebrosidase gene (GBA) cause Gaucher disease (GD), and heterozygous mutations of GBA are a major risk factor for Parkinson's disease (PD). This study examined the impact of GBA mutations on the longitudinal clinical course of PD patients by retrospective cohort design. GBA-coding regions were fully sequenced in 215 PD patients and GD-associated GBA mutations were identified in 19 (8.8%) PD patients. In a retrospective cohort study, time to develop dementia, psychosis, wearing-off, and dyskinesia were examined. Survival time analysis followed a maximum 12-year observation (median 6.0 years), revealing that PD patients with GD-associated mutations developed dementia and psychosis significantly earlier than those without mutations (p < 0.001 and p = 0.017, respectively). Adjusted hazard ratios of GBA mutations were 8.3 for dementia (p < 0.001) and 3.1 for psychosis (p = 0.002). No statistically significant differences were observed for wearing-off and dyskinesia between the groups. N-isopropyl-p[(123)I] iodoamphetamine single-photon emission tomography pixel-by-pixel analysis revealed that regional cerebral blood flow was reduced in the bilateral parietal cortex, including the precuneus of GD-associated mutant PD patients, compared with matched PD controls without mutations.

The efficacy of locally implanted antibiotic-calcium hydroxyapatite ceramic composites was investigated for the treatment of experimentally produced, implantrelated osteomyelitis in rats. High concentrations of antibiotics were detected at the site of infection and bacteria were eradicated without removal of the metal implants. Parenteral antibiotics and surgical debridement, alone or in combination with antibiotic-impregnated acrylic bone cement, all failed to eradicate the infections.

Crystal engineering is the planning of the properties and functions of crystalline materials by using preorganized molecules. [1] This process involves the control of crystal the effective size of a molecule, which is defined as the end-toend distance projected on the center line of the channel. Figure 3 shows the distribution of this distance for a large (AET) and optimal pore size (AFI). In the large-pore structure we observe for the linear isomer a very broad distribution, reflecting all possible conformations (curled and stretched) of this isomer. Hence, the effective size of the linear and branched isomer is nearly identical. If we reduce the size of the channel, this distribution is dominated by the stretched conformations, which increases the effective size of the linear isomers. Because the effective size of the linear isomer is large, these molecules are expelled at high pressure.An important difference between the concept of inverse shape selectivity and our entropic explanation is the role of the zeolite. Inverse shape selectivity indicates that one should look at those zeolites, which have an optimal ™match∫ for the branched isomer. In our mechanism the role of the zeolite is to provide an environment in which the length differences between the linear and branched isomers are maximum, which translates into an optimal pore diameter. For a given pressure, the maximum selectivity is determined by the relative effective sizes of the alkane molecules. The details of the channel structure are in this mechanism of secondary importance. This situation suggests that we can ™optimize∫ any zeolite structure by tuning its diameter. In Figure 4 we have performed this optimization for several known zeolite structures by changing the pore diameters by a simple scaling factor. Of course, such an optimization cannot be performed in practice, but does illustrate our point that irrespective of the details of the zeolite a similar optimal selectivity is obtained for nearly identical channel dimensions. At lower temperatures or higher pressures the entropy effect is more pronounced and a better selectivity could be expected. The results shown in Figure 4 are at lower temperatures than those in Figure 1 (403 K versus 577 K). The data at these lower temperatures give significantly higher selectivities. A similar effect can be expected from an increase of the pressure.Two conclusions of practical importance can be drawn from our work. We have shown that there is a thermodynamic limit to the maximum selectivity that can be obtained for these types of reactions. This limit implies that any novel zeolite Figure 4. Normalized 2,2-DMB/n-C 6 yield ratios (with respect to the FAU selectivity) for some ™optimized∫ pore structures at T 403 K and P 1000 kPa. The size of MOR-(black, pore too small), AFI-(red, optimal), AET-(green, too wide), and DON-type (blue, too wide) channels was adjusted by scaling the coordinates. The open symbols represent the zeolite structures before resizing. MOR was first made circular before the scaling was...

Although simulation is widely used to select the best of several alternative system designs, and common random numbers is an important tool for reducing the computation effort of simulation experiments, there are surprisingly few tools available to help a simulation practitioner select the best system when common random numbers are employed. This paper presents new two-stage procedures that use common random numbers to help identify the best simulated system. The procedures allow for screening and attempt to allocate additional replications to improve the value of information obtained during the second stage, rather than determining the number of replications required to provide a given probability of correct selection guarantee. The procedures allow decision makers to reduce either the expected opportunity cost associated with potentially selecting an inferior system, or the probability of incorrect selection. A small empirical study indicates that the new procedures outperform several procedures with respect to several criteria, and identifies potential areas for further improvement.Multiple Selection, Ranking and Selection, Discrete-Event Simulation, Common Random Numbers, Missing Data, Bayesian Statistics