A modified two-stage method for parameter estimation in sinusoidal models of correlated gene expression profiles

A two-stage method by Seber and Wild (2003) used to fit nonlinear regression models with correlated errors by using residuals obtained from the ordinary least square estimation has been shown by Pukdee et al. (2018) to underestimate the standard errors of parameter estimates in sinusoidal models, leading to poor coverage probabilities. In order to improve inferential statistics, a modified two-stage method is developed using residuals from the one-way ANOVA model to estimate variance components in the iterative estimation procedure and compared with the two-stage, conditional least squares and generalized least squares methods. A simulation study shows that the proposed method has similar successful convergence rates as the two-stage and conditional least squares methods but produces more reliable point and interval estimates. Although very little difference is seen between estimates produced by generalized least squares and the proposed method, the latter has a consistently higher successful convergence rate, and consequently is more likely to produce a result than the former, and this difference in rates becomes substantial when the model complexity increases

Improved methods for the analysis of circadian rhythms in correlated gene expression data

Circadian clocks regulate biological behaviours, such as sleeping and waking times, that recur naturally on an approximately 24-hour cycle. These clocks tend to be influenced by a variety of external factors, sometimes to the extent that it can have an impact on health. As an example in pharmacology, the effects of chemicals on the circadian rhythm in patients can be key in clarifying the relationship of drug efficacy and toxicity with dosing times. While pre-clinical experiments conducted to elucidate these effects may produce correlated data measured over time, such as gene expression profiles, existing methods for fitting parametric nonlinear regression models are however inadequate and can lead to unreliable, inconsistent parameter estimates and invalid inference. A de-trending method is widely used as a pre-processing step to address the non-stationary problem in the data before fitting models based on the assumption of independence. However, as it is unclear that this approach properly accounts for the correlation structure, alternative methods that specifically model the correlation in the data based on conditional least squares and a two-stage estimation procedure are proposed and evaluated. A simulation study covering a wide range of scenarios and models show that the proposed methods more efficient and robust to model mis-specification than de-trending and, furthermore, they lead to reduced bias in estimation of the circadian period and more reliable confidence intervals

High-speed roll-to-roll manufacturing of graphene using a concentric tube CVD reactor

We present the design of a concentric tube (CT) reactor for roll-to-roll chemical vapor deposition (CVD) on flexible substrates, and its application to continuous production of graphene on copper foil. In the CTCVD reactor, the thin foil substrate is helically wrapped around the inner tube, and translates through the gap between the concentric tubes. We use a bench-scale prototype machine to synthesize graphene on copper substrates at translation speeds varying from 25 mm/min to 500 mm/min, and investigate the influence of process parameters on the uniformity and coverage of graphene on a continuously moving foil. At lower speeds, high-quality monolayer graphene is formed; at higher speeds, rapid nucleation of small graphene domains is observed, yet coalescence is prevented by the limited residence time in the CTCVD system. We show that a smooth isothermal transition between the reducing and carbon-containing atmospheres, enabled by injection of the carbon feedstock via radial holes in the inner tube, is essential to high-quality roll-to-roll graphene CVD. We discuss how the foil quality and microstructure limit the uniformity of graphene over macroscopic dimensions. We conclude by discussing means of scaling and reconfiguring the CTCVD design based on general requirements for 2-D materials manufacturing.National Science Foundation (U.S.). Science, Engineering, and Education for Sustainability (Postdoctoral Fellowship Award 1415129

Conformal Robotic Stereolithography

Additive manufacturing by layerwise photopolymerization, commonly called stereolithography (SLA), is attractive due to its high resolution and diversity of materials chemistry. However, traditional SLA methods are restricted to planar substrates and planar layers that are perpendicular to a single-axis build direction. Here, we present a robotic system that is capable of maskless layerwise photopolymerization on curved surfaces, enabling production of large-area conformal patterns and the construction of conformal freeform objects. The system comprises an industrial six-axis robot and a custom-built maskless projector end effector. Use of the system involves creating a mesh representation of the freeform substrate, generation of a triangulated toolpath with curved layers that represents the target object to be printed, precision mounting of the substrate in the robot workspace, and robotic photopatterning of the target object by coordinated motion of the robot and substrate. We demonstrate printing of conformal photopatterns on spheres of various sizes, and construction of miniature three-dimensional objects on spheres without requiring support features. Improvement of the motion accuracy and development of freeform toolpaths would enable construction of polymer objects that surpass the size and support structure constraints imparted by traditional SLA systems.American Society for Engineering Education. National Defense Science and Engineering Graduate FellowshipNational Institute of Mental Health (U.S.) (University of Michigan Microfluidics in Biomedical Sciences Training Program. 5T32-EB005582)Singapore-MIT Alliance for Research and Technology (SMART

The Advanced Compton Telescope

The Advanced Compton Telescope (ACT), the next major step in gamma-ray astronomy, will probe the fires where chemical elements are formed by enabling high-resolution spectroscopy of nuclear emission from supernova explosions. During the past two years, our collaboration has been undertaking a NASA mission concept study for ACT. This study was designed to (1) transform the key scientific objectives into specific instrument requirements, (2) to identify the most promising technologies to meet those requirements, and (3) to design a viable mission concept for this instrument. We present the results of this study, including scientific goals and expected performance, mission design, and technology recommendations

The Advanced Compton Telescope Mission

The Advanced Compton Telescope (ACT), the next major step in gamma-ray astronomy, will probe the fires where chemical elements are formed by enabling high-resolution spectroscopy of nuclear emission from supernova explosions. During the past two years, our collaboration has been undertaking a NASA mission concept study for ACT. This study was designed to (1) transform the key scientific objectives into specific instrument requirements, (2) to identify the most promising technologies to meet those requirements, and (3) to design a viable mission concept for this instrument. We present the results of this study, including scientific goals and expected performance, mission design, and technology recommendations.Comment: NASA Vision Mission Concept Study Report, final version. (A condensed version of this report has been submitted to AIAA.

Towards scale‐up of graphene production via nonoxidizing liquid exfoliation methods

Graphene, the two‐dimensional form of carbon, has received a great deal of attention across academia and industry due to its extraordinary electrical, mechanical, thermal, chemical, and optical properties. In view of the potential impact of graphene on numerous and diverse applications in electronics, novel materials, energy, transport, and healthcare, large‐scale graphene production is a challenge that must be addressed. In the past decade, top–down production has demonstrated high potential for scale‐up. This review features the recent progress made in top–down production methods that have been proposed for the manufacturing of graphene‐based products. Fabrication methods such as liquid‐phase mechanical, chemical and electrochemical exfoliation of graphite are outlined, with a particular focus on nonoxidizing routes for graphene production. Analysis of exfoliation mechanisms, solvent considerations, key advantages and issues, and important production characteristics including production rate and yield, where applicable, are outlined. Future challenges and opportunities in graphene production are also highlighted

Nonparametric regression and mixture models

Nonparametric regression estimation has become popular in the last 50 years. A commonly used nonparametric method for estimating the regression curve is the kernel estimator, exemplified by the Nadaraya- Watson estimator. The first part of thesis concentrates on the important issue of how to make a good choice of smoothing parameter for the Nadaraya- Watson estimator. In this study three types of smoothing parameter selectors are investigated: cross-validation, plug-in and bootstrap. In addition, two situations are examined: the same smoothing parameter and different smoothing parameters are employed for the estimates of the numerator and the denominator. We study the asymptotic bias and variance of the Nadaraya- Watson estimator when different smoothing parameters are used. We propose various plug-in methods for selecting smoothing parameter including a bootstrap smoothing parameter selector. The performances of the proposed selectors are investigated and also compared with cross-validation via a simulation study. Numerical results demonstrate that the proposed plug-in selectors outperform cross-validation when data is bivariate normal distributed. Numerical results also suggest that the proposed bootstrap selector with asymptotic pilot smoothing parameter compares favourably with cross-validation. We consider a circular-circular parametric regression model proposed by Taylor (2009), including parameter estimation and inference. In addition, we investigate diagnostic tools for circular regression which can be generally applied. A final thread is related to mixture models, in particular a mixture of linear regression models and a mixture of circular-circular regression models where there is unobserved group membership of the observation. We investigate methods for selecting starting values for EM algorithm which is used to fit mixture models and also the distributions of these values. Our experiments suggest that the proposed method compares favourably with the common method in mixture linear regression models.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Robust Synthesis and Continuous Manufacturing of Carbon Nanotube Forests and Graphene Films.

Successful translation of the outstanding properties of carbon nanotubes (CNTs) and graphene to commercial applications requires highly consistent methods of synthesis, using scalable and cost-effective machines. This thesis presents robust process conditions and a series of process operations that will enable integrated roll-to-roll (R2R) CNT and graphene growth on flexible substrates. First, a comprehensive study was undertaken to establish the sources of variation in laboratory CVD growth of CNT forests. Statistical analysis identified factors that contribute to variation in forest height and density including ambient humidity, sample position in the reactor, and barometric pressure. Implementation of system modifications and user procedures reduced the variation in height and density by 50% and 54% respectively. With improved growth, two new methods for continuous deposition and patterning of catalyst nanoparticles for CNT forest growth were developed, enabling the diameter, density and pattern geometry to be tailored through the control of process parameters. Convective assembly of catalyst nanoparticles in solution enables growth of CNT forests with density 3-fold higher than using sputtered catalyst films with the same growth parameters. Additionally, laser printing of magnetic ink character recognition toner provides a large scale patterning method, with digital control of the pattern density and tunable CNT density via laser intensity. A concentric tube CVD reactor was conceptualized, designed and built for R2R growth of CNT forests and graphene on flexible substrates helically fed through the annular gap. The design enables downstream injection of the hydrocarbon source, and gas consumption is reduced 90% compared to a standard tube furnace. Multi-wall CNT forests are grown continuously on metallic and ceramic fiber substrates at 33 mm/min. High quality, uniform bi- and multi-layer graphene is grown on Cu and Ni foils at 25 - 495 mm/min. A second machine for continuous forest growth and delamination was developed; and forest-substrate adhesion strength was controlled through CVD parameters. Taken together, these methods enable uniform R2R processing of CNT forests and graphene with engineered properties. Last, it is projected that foreseeable improvements in CNT forest quality and density using these methods will result in electrical and thermal properties that exceed state-of-the-art bulk materials.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/97837/1/polsene_1.pd