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Advances in microchannel heating and cooling based on fine scale heterogeneity of thermal conductivity; Theory, Optimization, and Experimental Validation
No full textA major advance in microchannel heating and cooling technology is described and has the potential to change the way thermal processing of microscale fluidic samples is achieved. The microchannel is one of the key building blocks of micro total analysis systems (�TAS), which are already being released to market in the form of disposable test kits for biometrics and disease identification. However, significant cost reductions and other advances in microchannel heating and cooling technology are still needed and can be expected to improve the accuracy with which quantitative analysis can be performed. In part, this is because the fidelity of many existing and potential microfluidic applications is critically dependent on precise temperature control. The scientific objective of the study was to understand and then manipulate the physics of thermal transport within fine scale composite materials containing an embedded microchannel. In essence, it was necessary that the composition of the composite materials be manipulated in a precise way that established a particular nonlinear pattern of temperature along the length of the microchannel. In practice, this required the development of a systematic inverse solution process that could identify an acceptable composition to exactly match the desired pattern of temperature variation. As a demonstration of capability, fully operational prototypes comprised of patterned copper laminated within polyimide and acrylic polymers were designed, fabricated, and tested. In terms of conceptual advances, this is the first time that composite materials of this type have been used for microchannel heating AND cooling. From an analytical perspective, the critical advance was the development of a robust simulation framework consisting of an outer optimization loop and an inner finite element analysis (FEA) loop, which was used to iteratively identify the optimal copper pattern for each of several applications. In terms of experimental advances, the key fabrication steps included UV laser micromachining, acid etching, microchannel formation, and hot vacuum lamination. To avoid any ambiguity or artifact, microchannel temperatures were painstakingly measured at each of several flow rates using fine thermocouples embedded in the microchannel wall. For the most part, the measured temperatures were within 1 �C of the expected values based on simulation, with spatial temperature gradients of up to +/-94 �C/mm (or equivalently +/-188 �C/s at 2 l/min flow rate) within the range of 16 �C to 92 �C, using a resistively heated source at 100 �C and a thermoelectrically cooled sink at 2 �C. The temperature profiles studied here included two highly nonlinear cyclical profiles applicable to 1) continuous-flow polymerase chain reaction (PCR) and 2) thermally mediated counter-flow separation in conjunction with electrophoresis
Multigrid solution of distributed optimal control problems constrained by semilinear elliptic PDEs
No full textOptimization constrained by partial differential equations (PDEs) is a research area in which the scientific and engineering communities have seen a growing interest over the last decade. The recent rise in interest was fostered by the tremendous increase in computing power over the last twenty years. This can be attributed both to the tremendous advances in high-performance computing technologies and to its wide range of applicability. However, just growth in computing power is insufficient for tackling PDE-constrained optimization problems and there is always a need for ever-increasing efficient algorithms. The objective of this dissertation is to develop, analyze and implement multigrid preconditioners for the linear systems arising in the Newton-Krylov solution process for the nonlinear PDE constraints. Our main focus is on semilinear elliptic constraints with no additional control or state constraints. We analyze the preconditioners both theoretically and numerically. In this work we show that the multigrid preconditioners are of optimal order (p = 2) for piecewise linear finite element approximations. We also study control-constrained optimal control problems constrained by linear-elliptic equations. This problem is non-trivial from the optimization point of view as the KKT system is now a complementarity system. We employ semismooth Newton methods (SSNMs) to solve this problem efficiently. The multigrid preconditioners discussed here are extensions of preconditioners developed previously for the unconstrained case. We present some new results and techniques that yield optimal order multigrid preconditioners, at least for the case when the control is discretized using piecewise-constant finite elements
Development Of Advanced Sandwich Core Topologies Using Fused Deposition Modeling And Electroforming Processes
No full textNew weight efficient materials are needed to enhance the performance of vehicle systems allowing increased speed, maneuverability and fuel economy. This work leveraged a multi-length-scale composite approach combined with hybrid material methodology to create new state-of-the-art additive manufactured sandwich core material. The goal of the research was to generate a new material to expands material space for strength versus density. Fused-Deposition-Modeling (FDM) was used to remove geometric manufacturing constraints, and electrodepositing was used to generate a high specific-strength, bio-inspired hybrid material. Microtension samples (3mm x 1mm with 250�m x 250�m gage) were used to investigate the electrodeposited coatings in the transverse (TD) and growth (GD) directions. Three bath chemistries were tested: copper, traditional nickel sulfamate (TNS) nickel, and nickel deposited with a platinum anode (NDPA). NDPA shows tensile strength exceeding 1600 MPa, significantly beyond the literature reported values of 60MPa. This strengthening was linked to grain size refinement into the sub-30nm range, in addition to grain texture refinement resulting in only 17% of the slip systems for nickel being active. Anisotropy was observed in nickel deposits, which was linked to texture evolution inside of the coating. Microsample testing guided the selection of 15�m layer of copper deposition followed by a 250 �m NDPA layer. Classical formulas for structural collapse were used to guide an experimental parametric study to establish a weight/volume efficient strut topology. Length, diameter and thickness were all investigated to determine the optimal column topology. The most optimal topology exists when Eulerian buckling, shell micro buckling and yielding failure modes all exist in a single geometric topology. Three macro-scale sandwich topologies (pyramidal, tetrahedral, and strut-reinforced-tetrahedral (SRT) were investigated with respect to strength-per-unit-weight. The topologies were optimized across length scales using texture on the nano-scale microsamples on the micro-scale, and the parametric column study on the meso-scale. The results showed that additive manufacturing as a viable method for removing geometric constraints observed by other manufacturing methods. The SRT was the most optimized topology showing the highest strength-per-unit-weight. The final topology sits in a best-of-both areas of material space exceeding the commercially available honeycombs strength per relative density by 1670%
Modelling and Estimation of Characteristics of the Rainfall Distribution
No full textRainfall varies in space and time in a highly irregular manner and is described naturally in terms of a stochastic process. A characteristic of rainfall statistics is that they depend strongly on the space-time scales over which rain data are averaged. A spectral model of precipitation has been developed based on a stochastic differential equation of fractional order, which allows concise description of the second moment statistics over any space-time averaging scale. The model is thus capable of providing a unified description of both radar and rain gauge data. We test the model with radar and gauge data collected contemporaneously at the NASA TRMM ground validation sites located near Melbourne, Florida. Understanding precipitation is an essential component of climate modeling. Part of the calibration process for the recently launched GPM satellite involves comparison with radar observations. Ensuring that the radars are well calibrated is an import part of this process. We have used the developed stochastic model to explore sampling error for gauge and radar derived estimates of rain rates. This allows us to detect the presence and estimate the magnitude of any retrieval errors for the radar or gauge. We also formulated a standard linear regression analysis approach to the intercomparison of radar and gauge rain rate estimates in terms of the appropriate observed and model-derived quantities
Using an Intelligent Interviewer to Conduct Cognitive Assessments
No full textCognitive assessments are conducted in one of two ways; a trained professional will conduct a face-to-face interview, collecting the answers from the interviewee during the interaction; or a computer will present an inventory as a list of questions and multiple-choice answers. While a trained professional can provide an individualized assessment there is also concern that the interviewee can appear different while interacting with different assessors. Although conducting the assessment using a computer provides a consistent and cost-effective way of conducting an inventory this static process affects interpretation because it does not provide a means of clarification either for the user or the computer: the user to clarify ambiguous wording, or the computer to clarify potential conflicting responses from the user. This dissertation researches an Intelligent Interviewer conducting a cognitive assessment, specifically a learning style inventory. The Intelligent Interviewer was presented to the participants in two forms: an Embodied Interviewer, where the user and computer conversed vocally and visually; and a Text Interviewer, where the user and computer conversed by typing and reading text. Both the subjective and objective evaluation metrics were studied to determine the user experience, effectiveness, and efficiency of the Intelligent Interviewer
Transcutaneous monitoring of respiratory gases in preterm neonates
No full textThe past decades have seen a widespread application of transcutaneous monitoring in neonatology. The noninvasive approach overcomes the painful nature of blood sampling. Our work focused on the technique of simultaneous noninvasive measurement of tcpO2 (transcutaneous partial pressure of oxygen) and tcpCO2 (transcutaneous partial pressure of carbon dioxide). A small sampling chamber was built connecting the carbon dioxide and oxygen sensors to measure the gases diffusing out of the skin. The measurements were based on the initial diffusion rate instead of the mass-transfer equilibrium which allows for a faster response. By using the new method, physical activities, food intake and breathing patterns were shown to have a strong effect on the amount of carbon dioxide and oxygen evolving from the human skin. The measurement parameters such as sampler area, location, position and procedure timing were optimized using design of experiments. The measurements were validated and correlated against an FDA approved commercial transcutaneous monitor (the Radiometer TCM 4.0). The experimental setup was further modified and automated, and then approved for clinical trials at the University of Maryland, School of Medicine in the Neonatal Intensive Care Unit (NICU). The results obtained from a premature baby in the NICU were correlated to the Arterial Blood Gas (ABG) analysis. The noninvasive and rate-based monitoring technique was also extended to monitoring dissolved oxygen and carbon dioxide in cell cultures. We have shown that by measuring the initial diffusion rate we were able to determine the partial pressures of the two gases in the culture. The technique could be readily automated and measurements could be made in minutes. It was tested in demonstration experiments by growing mammalian cells in a T flask and a spinner flask at 37 degree C.The results were validated using optical sensor systems. A dynamic theoretical model based on a three dimensional unit cell representation of the experimental system that includes a single blood vessel was constructed in order to gain a better fundamental understanding of the factors that determine the performance of the new approach. The model employed the finite element numerical method and accounted for the fluid mechanics, mass transfer and reaction kinetics of the system. The model was implemented using COMSOL Multiphysics engineering simulation software
Developing a Cybersecurity Text Corpus and its Application for Augmenting Semantic Text Similarity
No full textThe growing use of cyber-services automatically impart great importance to cybersecurity. The Internet is a primary source of information regarding software flaws, vulnerabilities, cyber-attacks and exploits. This information is available through vulnerability databases, news articles, security bulletins and blogs. Variety of applications and security systems like Intrusion Detection Systems (IDS), Intrusion Prevention System (IPS), etc. can take advantage of this information for consolidating their infrastructure. The lack of availability of ready text corpus of high quality security information from various sources makes it difficult for these applications to use this information. To overcome this problem our work focuses on building a multi-genre corpus of security text using information retrieved from multiple internet based sources; National Vulnerabilities Database, Wikipedia articles, security blogs, security bulletins and scholarly papers. The system builds a text classifier from the initial high quality data which is used to classify and accommodate new data from these sources into the corpus. This corpus can be used by variety of applications like IDS or IPS, in variety of ways like assertion into knowledge base or extraction of named entities. Our work explores one of the applications of generating the semantic text similarity model for cybersecurity text. We use the multi-genre cybersecurity text corpus for creating the word co-occurrence model. This model can extract the synonymity between the different security terms. For example, the words 'virus' and 'malware' that have same context are scored for their degree of similarity. The word co-occurrence model is then extended to generate a semantic text similarity model.The text similarity model extracts the semantic text similarity between different security texts like titles of the papers, vulnerability descriptions, blog paragraphs, etc. The system also develops a combined text similarity model from cybersecurity similarity model and generic text similarity model. This model can be used in document mining for matching security text, clustering documents describing similar vulnerabilities and so on
The Relation of Motivation and Self-efficacy to Consistency of Condom Use: The Role of Sex Under the Influence of Alcohol
No full textYouth ages 15 to 24 account for half of the estimated 20 million new cases of Sexually Transmitted Infections (STIs) that occur each year (CDC, 2013). Consistent condom use is an important intervention target to prevent STI contraction among youth. The present study evaluated whether motivation and self-efficacy to use condoms predict subsequent consistency of condom use in a community sample (N=166) of female youth (ages 13-21) at increased risk for STI contraction. Additionally, the current study investigated if these relations are moderated by frequency of sex under the influence. Exploratory analyses investigated these relations in sex events with steady boyfriends only separately from sex events with other guys only. Data were analyzed from Project SAFE, a randomized controlled clinical trial investigating the efficacy of a Computer-Assisted Motivational Intervention compared to Didactic Educational Counseling for sexual risk and protective behaviors. Both motivation and self-efficacy to use condoms demonstrated a significant positive linear relation with subsequent consistency of condom use. However, there was limited evidence for the moderating effect of frequency of sex under the influence. There was some indication that the pattern of results differed in exploratory analyses including only sex events with steady boyfriends compared to sex events with other guys. Analyses were re-estimated including past consistency of condom use as a covariate. In these analyses there was some evidence for a negative main effect of frequency of sex under the influence. These finding have implications for the development of tailored interventions and future research
Entropy rate estimators and their application to blind source separation
No full textThe characterization of a signal is one of the key issues in statistical signal processing and is vital to the success of many methods. Thus, the goal of characterization techniques is to achieve a desirable balance between flexibility and maintaining a simple form, so as to allow for generalization and efficient implementation. In this dissertation, we propose a new density estimation technique, which uses the principle of maximum entropy with Gaussian kernels. By jointly using global and local measuring functions, the new density estimation method enjoys a high level of flexibility while providing a simple parametric form for the estimated density function. When considering a signal with sample dependence, the signal cannot be fully characterized by its density function. To address this issue, we propose the use of entropy rate to provide a powerful tool for data analysis, since it accounts for all types of diversity: higher-order statistics, sample dependence, noncircularity for a complex-valued process, and dependence across processes for a vector-valued process. We propose entropy rate estimators for real-valued processes based on both Markovian and invertible source models. We extend the entropy rate estimator to complex-valued and vector-valued processes by using a widely linear whitening filter and vector autoregressive model, respectively. The new entropy rate estimators are shown to exploit all these statistical properties effectively. These entropy rate estimators can be used in a wide array of applications in statistical signal processing. In this work, we mainly focus on: model order selection, real-valued independent component analysis, complex-valued independent component analysis, and independent vector analysis. Detecting the number of latent signals in a given number of observations, or order detection, is a key issue in many signal processing problems. Many of the commonly used methods to solve this problem are based on information theoretic criteria. In order to facilitate the use of information theoretic criteria, these approaches address the issue of dependent samples by downsampling the dataset. However, downsampling the data decreases the sample size, which can cause degradation in the accuracy of the order estimation. Using an entropy rate estimator, we write the likelihood based on the entire data set and use it in an information theoretic framework. We propose two entropy-rate-based order estimation methods and show the performance improvement they offer with examples. In addition, the consistency of the entropy-rate-based methods is shown when the sample size goes to infinity. By assuming latent sources are statistically independent, independent component analysis separates underlying sources from a given linear mixture. Since in many applications, latent sources are both non-Gaussian and have sample dependence, it is desirable to exploit both properties jointly. In this work, we propose to use the mutual information rate to construct a general framework for analysis and derivation of algorithms that account for both types of diversity. We introduce four algorithms for the problem of independent component analysis, and show that their performance can approach the Cramer-Rao lower bound. Complex-valued signals frequently arise in applications such as communications, radar, and biomedicine, as well as processing when real-valued signals are in a transform domain. Independent component analysis, when implemented in the complex domain, enjoys the addition of yet another type of diversity, noncircularity of the sources---underlying components. Using the new entropy rate estimator, we develop a new complex-valued independent component analysis algorithm, which exploits all three types of diversity, non-Gaussianity, sample dependence, and noncircularity. In addition, we establish the general form of the Fisher information matrix and Cramer-Rao lower bound and show the identification conditions for the complex-valued independent component analysis problem. Experimental results show that the new method accounts for all three types of diversity effectively and provides desirable performance for a wide variety of sources. Additionally, the entropy rate estimator for vector-valued processes is used for the independent vector analysis problem, which is a generalization of independent component analysis to multiple datasets and adds the use of one more diversity, the dependence across multiple datasets. Using mutual information rate, we propose a new method that takes all these types of diversity into account
The role of sexual selection in darter speciation
No full textSexual selection results from the competition over mating opportunities in natural populations. Just as natural selection favors traits that increase survival and fecundity, sexual selection favors traits that increase mating opportunities and fertilization success. These two processes cause changes within populations but are also important in driving divergence between populations. Speciation by divergent natural selection, also called ecological speciation, is now a widely accepted driver of diversity; however the role of sexual selection is less concrete. Speciation by sexual selection is thought to occur via divergence in female mating preferences between populations followed by coevolution of male mating traits. These differences eventually lead to behavioral reproductive isolation as female preferences in one population no longer correspond to male traits in another. Although verbal and quantitative models demonstrate the plausibility of speciation by sexual selection, empirical examples are relatively few and evidence often comes from comparative datasets that rely on proxy measures of divergent sexual selection. Additionally, natural and sexual selection often interact making it difficult to distinguish speciation by sexual selection alone. Here, I address the potential for speciation by natural selection, by sexual selection, or by a combination of the two in a group of freshwater fishes commonly called darters (Etheostoma). My results suggest a primary role for sexual selection in early darter divergence. Interestingly, however, trait divergence and the evolution of behavioral isolation in darters appear to involve divergence in male aggressive responses and male mate preferences, respectively. Further, differences in the sexual environment predict total reproductive isolation between species while differences in the ecological environment do not. The influence of male behavior was unexpected given the overwhelming focus on divergence in female mate preferences in speciation by sexual selection models. Additionally, the potential for divergence in sexual selection to promote reproductive barriers outside behavioral isolation is surprising. Taken together, my results suggest that early darter divergence is driven by sexual selection and imply that the definition of speciation by sexual selection should be expanded to encompass the multiple mechanisms of sexual selection and how they might contribute to all reproductive barriers