Browsing by Subject "experimental design"
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Item Campus interactive interchangeable living laboratory design for student sustainable innovation at the University of Texas at Austin(2013-05) Jiang, Nan; Fajkus, MatthewUniversities are places where advanced education be taught, and also where pinner thoughts be conceived. Explorations and innovations in every field are spontaneous and highly encouraged, so does in field of Sustainable Architecture. Correspondingly, campus buildings should be considered as a carrier of knowledge, which can enlighten occupants in certain extents. Can the design of a Living Laboratory on Campus of the University of Texas at Austin facilitate student architectural sustainable explorations and innovations and contribute to campus sustainability socially and environmentally through the operation of interactive interchangeable building system? Specifically focusing on the University of Texas at Austin, this Master Design Study attempts to utilize a design process of a campus living laboratory to answer the questions above. It considered the lab as a platform for students to public their sustainable ideas and works, and get initial feedback from the occupants thereby. It would be especially benefit for university education and also for professional practice of students.Item No Control Genes Required: Bayesiian Analysis of qRT-PCR Data(PLOS One, 2013-08-19) Matz, Mikhail V.; Wright, Rachel M.; Scott, James G.Background: Model-based analysis of data from quantitative reverse-transcription PCR (qRT-PCR) is potentially more powerful and versatile than traditional methods. Yet existing model-based approaches cannot properly deal with the higher sampling variances associated with low-abundant targets, nor do they provide a natural way to incorporate assumptions about the stability of control genes directly into the model-fitting process. Results: In our method, raw qPCR data are represented as molecule counts, and described using generalized linear mixed models under Poisson-lognormal error. A Markov Chain Monte Carlo (MCMC) algorithm is used to sample from the joint posterior distribution over all model parameters, thereby estimating the effects of all experimental factors on the expression of every gene. The Poisson-based model allows for the correct specification of the mean-variance relationship of the PCR amplification process, and can also glean information from instances of no amplification (zero counts). Our method is very flexible with respect to control genes: any prior knowledge about the expected degree of their stability can be directly incorporated into the model. Yet the method provides sensible answers without such assumptions, or even in the complete absence of control genes. We also present a natural Bayesian analogue of the “classic” analysis, which uses standard data pre-processing steps (logarithmic transformation and multi-gene normalization) but estimates all gene expression changes jointly within a single model. The new methods are considerably more flexible and powerful than the standard delta-delta Ct analysis based on pairwise t-tests. Conclusions: Our methodology expands the applicability of the relative-quantification analysis protocol all the way to the lowest-abundance targets, and provides a novel opportunity to analyze qRT-PCR data without making any assumptions concerning target stability. These procedures have been implemented as the MCMC.qpcr package in R.Item Optimizing the Quality of Parts Manufactured by the Automated Plasma Cutting Process Using Response Surface Methodology(University of Texas at Austin, 2009-09-18) Asiabanpour, B.; Vejandla, D.T.; Novoa, C.; Jimenez, J.; Fischer, R.Automated plasma cutting is an effective process for building complex two-dimensional metallic parts in a short period of time. Because the plasma cutting machine has several factors or input variables to control (e.g., current, cutting speed, torch height) and a variety of part quality characteristics or response variables to satisfy (e.g., flatness, clean cut, bevel angle), it is very difficult to find an overall optimum machine setting. In this research, response surface methodology and desirability functions are used to simultaneously optimize 18 part quality characteristics. Final results identify an optimal machine configuration that facilitates the fabrication of parts with close-to-perfect quality for all responses considered.