Recrystallized parylene as a mask for silicon chemical etching

This paper presents the first use of recrystallized parylene as masking material for silicon chemical etch. Recrystallized parylene was obtained by melting parylene C at 350°C for 2 hours. The masking ability of recrystallized parylene was tested in HNA (hydrofluoric acid, nitric acid and acetic acid) solution of various ratios, KOH (potassium hydroxide) solution and TMAH (tetramethylammonium hydroxide) at different temperatures and concentrations. It is found that interface between parylene and the substrate can be attacked, which results in undercuts. Otherwise, recrystallized parylene exhibited good adhesion to silicon, complete protection of unexposed silicon and silicon etching rates comparable to literature data

Bayesian Lattice Filters for Time-Varying Autoregression and Time-Frequency Analysis

Modeling nonstationary processes is of paramount importance to many scientific disciplines including environmental science, ecology, and finance, among others. Consequently, flexible methodology that provides accurate estimation across a wide range of processes is a subject of ongoing interest. We propose a novel approach to model-based time-frequency estimation using time-varying autoregressive models. In this context, we take a fully Bayesian approach and allow both the autoregressive coefficients and innovation variance to vary over time. Importantly, our estimation method uses the lattice filter and is cast within the partial autocorrelation domain. The marginal posterior distributions are of standard form and, as a convenient by-product of our estimation method, our approach avoids undesirable matrix inversions. As such, estimation is extremely computationally efficient and stable. To illustrate the effectiveness of our approach, we conduct a comprehensive simulation study that compares our method with other competing methods and find that, in most cases, our approach performs superior in terms of average squared error between the estimated and true time-varying spectral density. Lastly, we demonstrate our methodology through three modeling applications; namely, insect communication signals, environmental data (wind components), and macroeconomic data (US gross domestic product (GDP) and consumption).Comment: 49 pages, 16 figure

Hierarchical nonlinear, multivariate, and spatially-dependent time-frequency functional methods

Notions of time and frequency are important constituents of most scientific inquiries, providing complimentary information. In an era of "big data," methodology for analyzing functional and/or image data is increasingly important. This dissertation develops methodology at the cross-section of time-frequency analysis and functional data and consists of three distinct, but related, contributions. First, we propose nonparametric methodology for nonlinear multivariate time-frequency functional data. In particular, we consider polynomial nonlinear functional data models that accommodate higher dimensional functional covariates, including time-frequency images, along with their interactions. The necessary dimension reduction for model estimation proceeds through carefully chosen basis expansions (empirical orthogonal functions) and feature-extraction stochastic search variable selection (SSVS). Properties of the methodology are examined through an extensive simulation study. Finally, we illustrate the approach through an application that attempts to characterize spawning behavior of shovelnose sturgeon in terms of high-density depth and temperature profiles. The second contribution proposes model-based time-frequency estimation through Bayesian lattice filter time-varying autoregressive models. In this context, we take a fully Bayesian approach and allow both the autoregressive coefficients and innovation variance to vary over time. Importantly, our model is estimated within the partial autocorrelation domain (i.e., through the partial autocorrelation coefficients). Additionally, all of the full conditional distributions required for our algorithm are of standard form and thus can be easily implemented using a Gibbs sampler. Further, as a by-product of the lattice filter recursions, our approach avoids undesirable matrix inversions. As such, estimation is computationally efficient and stable. We conduct a comprehensive simulation study that compares our method with other competing methods and find that, in most cases, our approach performs superior in terms of average squared error between the estimated and true time-varying spectral density. Lastly, we demonstrate our methodology through several real case studies. The final project of the dissertation develops models that accommodate spatially dependent functional responses with spatially dependent image predictors. The methodology is motivated by a soil science study that seeks to model spatially correlated water content functionals as a function of electro-conductivity images. The water content curves are measured at different locations within the study field and at various depths, whereas the electro-conductivity images are spatially referenced images of wavelength by depth. Estimation is facilitated by taking a Bayesian approach, where the necessary dimension reduction for model implementation proceeds using basis function expansions along with SSVS. Finally, the methodology is illustrated through an application to our motivating data.Includes bibliographical references (pages 122-133

Text difficulty in extensive reading: Reading comprehension and reading motivation

This study investigates the effects of the text difficulty of extensive reading materials on the reading comprehension and reading motivation of English as a foreign language (EFL) vocational high school students in Taiwan. Two experimental groups were assigned, on an individual basis, to read graded readers at either one level below (‘i-1’) or one level beyond (‘i+1’) their current level, while a control group followed their regular curriculum. The results showed that after treatment, the ‘i-1’ group improved their overall comprehension and the subset of literal comprehension. They also outperformed the ‘i+1’ group on the same measures. For reading motivation, the ‘i+1’ group’s overall motivation was promoted. Both groups enhanced their reading engagement, while only the ‘i-1’ group inhibited reading avoidance. Moreover, the ‘i+1’ group outperformed the ‘i-1’ group in the perception of self-efficacy. Overall, the ‘i-1’ level yielded better effects on reading comprehension; the ‘i+1’ level, on reading motivation

Stock Assessment of Ballot's saucer scallop (Ylistrum balloti) in Queensland

This assessment estimates the status of saucer scallops in the main fishing grounds of the Queensland Southern Inshore fishery. The stock assessment data inputs included total harvests, standardised catch rates and fishery independent density estimates.   Analyses suggested that spawning biomass in 2019 fell to around 17 per cent of the unfished level. The report presents recommendations on fishing effort levels to begin rebuilding the stock to levels consistent with 40 per cent of unfished biomass

Perfect simulation from unbiased simulation

We show that any application of the technique of unbiased simulation becomes perfect simulation when coalescence of the two coupled Markov chains can be practically assured in advance. This happens when a fixed number of iterations is high enough that the probability of needing any more to achieve coalescence is negligible; we suggest a value of $10^{-20}$. This finding enormously increases the range of problems for which perfect simulation, which exactly follows the target distribution, can be implemented. We design a new algorithm to make practical use of the high number of iterations by producing extra perfect sample points with little extra computational effort, at a cost of a small, controllable amount of serial correlation within sample sets of about 20 points. Different sample sets remain completely independent. The algorithm includes maximal coupling for continuous processes, to bring together chains that are already close. We illustrate the methodology on a simple, two-state Markov chain and on standard normal distributions up to 20 dimensions. Our technical formulation involves a nonzero probability, which can be made arbitrarily small, that a single perfect sample point may have its place taken by a "string" of many points which are assigned weights, each equal to $\pm 1$, that sum to~$1$. A point with a weight of $-1$ is a "hole", which is an object that can be cancelled by an equivalent point that has the same value but opposite weight $+1$.Comment: 17 pages, 4 figures; for associated R scripts, see https://github.com/George-Leigh/PerfectSimulatio

Capturing episodic impacts of environmental signals

Environmental scientists frequently rely on time series of explanatory variables to explain their impact on an important response variable. However, sometimes, researchers are less interested in raw observations of an explanatory variable than in derived indices induced by episodes embedded in its time series. Often these episodes are intermittent, occur within a specific limited memory, persist for varying durations, at varying levels of intensity, and overlap important periods with respect to the response variable. We develop a generic, parametrised, family of weighted indices extracted from an environmental signal called IMPIT indices. To facilitate their construction and calibration, we developed a user friendly app in Shiny R referred to as IMPIT-a. We construct examples of IMPIT indices extracted from the Southern Oscillation Index and sea surface temperature signals. We illustrate their applications to two fished species in Queensland waters (i.e., snapper and saucer scallop) and wheat yield in New South Wales.Comment: 27 page

Transforming growth factor-β1 induces matrix metalloproteinase-9 and cell migration in astrocytes: roles of ROS-dependent ERK- and JNK-NF-κB pathways

<p>Abstract</p> <p>Background</p> <p>Transforming growth factor-β (TGF-β) and matrix metalloproteinases (MMPs) are the multifunctional factors during diverse physiological and pathological processes including development, wound healing, proliferation, and cancer metastasis. Both TGF-β and MMPs have been shown to play crucial roles in brain pathological changes. Thus, we investigated the molecular mechanisms underlying TGF-β1-induced MMP-9 expression in brain astrocytes.</p> <p>Methods</p> <p>Rat brain astrocytes (RBA-1) were used. MMP-9 expression was analyzed by gelatin zymography and RT-PCR. The involvement of signaling molecules including MAPKs and NF-κB in the responses was investigated using pharmacological inhibitors and dominant negative mutants, determined by western blot and gene promoter assay. The functional activity of MMP-9 was evaluated by cell migration assay.</p> <p>Results</p> <p>Here we report that TGF-β1 induces MMP-9 expression and enzymatic activity via a TGF-β receptor-activated reactive oxygen species (ROS)-dependent signaling pathway. ROS production leads to activation of extracellular signal-regulated kinase 1/2 (ERK1/2) and c-Jun-N-terminal kinase (JNK) and then activation of the NF-κB transcription factor. Activated NF-κB turns on transcription of the MMP-9 gene. The rat MMP-9 promoter, containing a NF-κB <it>cis</it>-binding site, was identified as a crucial domain linking to TGF-β1 action.</p> <p>Conclusions</p> <p>Collectively, in RBA-1 cells, activation of ERK1/2- and JNK-NF-κB cascades by a ROS-dependent manner is essential for MMP-9 up-regulation/activation and cell migration induced by TGF-β1. These findings indicate a new regulatory pathway of TGF-β1 in regulating expression of MMP-9 in brain astrocytes, which is involved in physiological and pathological tissue remodeling of central nervous system.</p