![]() ![]() The town of Sharon, Massachusetts, created a Governance Study Committee to recommend changes to municipal by-laws and governance within the town, particularly with an eye to elevating civic engagement among residents. I am a member of that committee, and in one phase of our work we sought to confer with officials in similar communities across the state to learn from best practices elsewhere. Quicker and less expensive trials are very welcome to speed up obtaining results and have become common practice. This finding allowed the efficiency of estimates of probabilities and relative risks to be improved and permitted a substantial reduction of sample size for treatments comparison (typically less than 4,000), in view of those needed by the binomial outcome. The comparisons of small proportions led to very large (20,000 to 30,000) trial sizes.īy using data from large trials, the Survival platform in JMP Pro clearly showed that the distribution of V is very close to the lognormal distribution. The parameters under comparison were the proportion of these events. The outcomes were PPH (V> 500 mL) and severe PPH (V> 1000 mL). Three large clinical trials were conducted in the past two decades by collecting blood loss volume data (V) for more than 70,000 deliveries. To assess this burden, the WHO conducted studies to find methods for the prevention and treatment of PPH. Postpartum hemorrhage (PPH) is a major cause of maternal death in low-resource countries, accounting for 661,000 deaths worldwide between 20. Focusing on the quality of the candidate optima (measured as a percentage of the maximum of the generating function) in addition to the prediction variance at these locations, we quantify the marginal impact of common choices facing scientists, including run size, space-filling vs optimal designs, the inclusion of replicates, and analysis approaches (full model, backward AICc, SVEM-FS, SVEM-Lasso, and a neural network used in the SVEM framework). Framed in the setting of liquid nanoparticle formulation optimization for in vivo gene therapy, we use a modular JSL simulation to explore combinations of design and analysis options in JMP Pro, highlighting the ease and performance of the new SVEM options in Generalized Regression. ![]() Power is no longer a useful metric to compare designs, and analyzing results is far more challenging. Sometimes also considering unconstrained process factors, these experiments require modifications of the typical design and analysis methods. Whether measured as a proportion of total volume or as a molar ratio, increasing the amount of one factor necessarily leads to a decrease in the total amount of the other factors. In mixture experiments, the factors are constrained to sum to a constant. ![]() This dashboard became our daily driver to quickly find faults/process drifts and achieve high yield standards. The dashboard’s correlation coefficient matrix between inputs and outputs compares the correlation before and after a detected changepoint. Multiple changepoints are handled using phase columns and correlations with input parameters, template changeovers, and PM or hardware upgrade activities. Our team used changepoint detection and correlation matrix to create an interactive dashboard that collects changepoint time stamps for output parameters and creates a phase in the existing control charts for input parameters. These process drifts are often subtle, gradual and interdependent on other parameters that traditional control charts fail to detect. Process drifts such as robot wafer placement errors, wafer alignment variation, master-template alignment errors, and measurement metrology variability can cause excursions that result in significant yield loss. Magic Leap’s lithography double-side imprinting process requires precise wafer placement, highly accurate photoresist dispension, and master-template to wafer alignment. This paper presents how Magic Leap’s Eyepiece Manufacturing team leverages the changepoint detection and correlation matrix functions in the multivariate control charts to easily detect process drift, diagnose issues, and detect the exact moment when an issue occurred - a previously impossible functionality. ![]()
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