Microstructural-Based Modeling of Electrical Percolation In Polymer Nanocomposites

Polymer nanocomposites (PNCs) represent a radical alternative to conventional filled polymers or polymer blends. In contrast to conventional composites, where the included phase is on the order of micrometers, PNCs are defined as those that have discrete constituents on the order of a few hundred nanometers. The value of PNCs is not solely based on tailoring mechanical properties, as in traditional composite design and manufacture, but rather on the potential for the design and optimization of multi-functional properties. There is major interest in these polymeric materials embedded with a conductive nanoscale filler. This is due to the possibility of designing a composite that retains the easy processing of plastics but can take advantage of a conductive nanoscale phase; providing electrical conductivity in additional to structural reinforcement. The challenge to modeling multi-functional properties of PNCs is that, traditionally, different models have been applied to model different properties. Mechanical properties are most often modeled using mean-field models from micromechanics; properties depend on the microstructural arrangement of the included phase, phase properties and volume fraction. Electrical conductivity has primarily been modeled using percolation theory and power-law models; properties depend on theoretical or simulation based estimates of a percolation threshold, phase properties and volume fraction. However, models of both properties should ideally be built on specific microstructural descriptions as well as include a probabilistic framework to capture percolation effects. In this work a modeling framework, developed for predicting composite mechanical properties, is investigated for its applicability in modeling effective electrical composite properties. The basis for using a micromechanics approach for predictions of conductivity is presented as well as how the model is adapted to model conductivity. A comparison of the adapted and original micromechanics approach is presented for deterministic microstructures. The modeling framework is subsequently used to predict the effective electrical conductivity of a model PNC. Using the micromechanical parameters of interface thickness and properties, the model can be adjusted to correspond with observed experimental and is useful in suggesting underlying mechanisms

Direct-form adaptive equalization for underwater acoustic communication

Submitted in partial fulfillment of the requirements for the degree of Master of Science at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution June 2012Adaptive equalization is an important aspect of communication systems in various environments. It is particularly important in underwater acoustic communication systems, as the channel has a long delay spread and is subject to the effects of time- varying multipath fading and Doppler spreading. The design of the adaptation algorithm has a profound influence on the performance of the system. In this thesis, we explore this aspect of the system. The emphasis of the work presented is on applying concepts from inference and decision theory and information theory to provide an approach to deriving and analyzing adaptation algorithms. Limited work has been done so far on rigorously devising adaptation algorithms to suit a particular situation, and the aim of this thesis is to concretize such efforts and possibly to provide a mathematical basis for expanding it to other applications. We derive an algorithm for the adaptation of the coefficients of an equalizer when the receiver has limited or no information about the transmitted symbols, which we term the Soft-Decision Directed Recursive Least Squares algorithm. We will demonstrate connections between the Expectation-Maximization (EM) algorithm and the Recursive Least Squares algorithm, and show how to derive a computationally efficient, purely recursive algorithm from the optimal EM algorithm. Then, we use our understanding of Markov processes to analyze the performance of the RLS algorithm in hard-decision directed mode, as well as of the Soft-Decision Directed RLS algorithm. We demonstrate scenarios in which the adaptation procedures fail catastrophically, and discuss why this happens. The lessons from the analysis guide us on the choice of models for the adaptation procedure. We then demonstrate how to use the algorithm derived in a practical system for underwater communication using turbo equalization. As the algorithm naturally incorporates soft information into the adaptation process, it becomes easy to fit it into a turbo equalization framework. We thus provide an instance of how to use the information of a turbo equalizer in an adaptation procedure, which has not been very well explored in the past. Experimental data is used to prove the value of the algorithm in a practical context.Support from the agencies that funded this research- the Academic Programs Office at WHOI and the Office of Naval Research (through ONR Grant #N00014-07-10738 and #N00014-10-10259)

Graphical model driven methods in adaptive system identification

Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution September 2016Identifying and tracking an unknown linear system from observations of its inputs and outputs is a problem at the heart of many different applications. Due to the complexity and rapid variability of modern systems, there is extensive interest in solving the problem with as little data and computation as possible. This thesis introduces the novel approach of reducing problem dimension by exploiting statistical structure on the input. By modeling the input to the system of interest as a graph-structured random process, it is shown that a large parameter identification problem can be reduced into several smaller pieces, making the overall problem considerably simpler. Algorithms that can leverage this property in order to either improve the performance or reduce the computational complexity of the estimation problem are developed. The first of these, termed the graphical expectation-maximization least squares (GEM-LS) algorithm, can utilize the reduced dimensional problems induced by the structure to improve the accuracy of the system identification problem in the low sample regime over conventional methods for linear learning with limited data, including regularized least squares methods. Next, a relaxation of the GEM-LS algorithm termed the relaxed approximate graph structured least squares (RAGS-LS) algorithm is obtained that exploits structure to perform highly efficient estimation. The RAGS-LS algorithm is then recast into a recursive framework termed the relaxed approximate graph structured recursive least squares (RAGSRLS) algorithm, which can be used to track time-varying linear systems with low complexity while achieving tracking performance comparable to much more computationally intensive methods. The performance of the algorithms developed in the thesis in applications such as channel identification, echo cancellation and adaptive equalization demonstrate that the gains admitted by the graph framework are realizable in practice. The methods have wide applicability, and in particular show promise as the estimation and adaptation algorithms for a new breed of fast, accurate underwater acoustic modems. The contributions of the thesis illustrate the power of graphical model structure in simplifying difficult learning problems, even when the target system is not directly structured.The work in this thesis was supported primarily by the Office of Naval Research through an ONR Special Research Award in Ocean Acoustics; and at various times by the National Science Foundation, the WHOI Academic Programs Office and the MIT Presidential Fellowship Program

Epidermal Growth Factor–PEG Functionalized PAMAM-Pentaethylenehexamine Dendron for Targeted Gene Delivery Produced by Click Chemistry

Aim of this study was the site-specific conjugation of an epidermal growth factor (EGF)-polyethylene glycol (PEG) chain by click chemistry onto a poly(amido amine) (PAMAM) dendron, as a key step toward defined multifunctional carriers for targeted gene delivery. For this purpose, at first propargyl amine cored PAMAM dendrons with ester ends were synthesized. The chain terminal ester groups were then modified by oligoamines with different secondary amino densities. The oligoamine-modified PAMAM dendrons were well biocompatible, as demonstrated in cytotoxicity assays. Among the different oligoamine-modified dendrons, PAMAM-pentaethylenehexamine (PEHA) dendron polyplexes displayed the best gene transfer ability. Conjugation of PAMAM-PEHA dendron with PEG spacer was conducted via click reaction, which was performed before amidation with PEHA. The resultant PEG-PAMAM-PEHA copolymer was then coupled with EGF ligand. pDNA transfections in HuH-7 hepatocellular carcinoma cells showed a 10-fold higher efficiency with the polyplexes containing conjugated EGF as compared to the ligand-free ones, demonstrating the concept of ligand targeting. Overall gene transfer efficiencies, however, were moderate, suggesting that additional measures for overcoming subsequent intracellular bottlenecks in delivery have to be taken

Current use of complementary and conventional medicine for treatment of pediatric patients with gastrointestinal disorders

Infants, children, and adolescents are at risk of experiencing a multitude of gastrointestinal disorders (GID). These disorders can adversely affect the quality of life or be life-threatening. Various interventions that span the conventional and complementary therapeutic categories have been developed. Nowadays, parents increasingly seek complementary options for their children to use concurrently with conventional therapies. Due to the high prevalence and morbidity of diarrhea, constipation, and irritable bowel syndrome (IBS) in children, in this review, we decided to focus on the current state of the evidence for conventional and complementary therapies used for the treatment of these diseases in children. Diarrhea treatment focuses on the identification of the cause and fluid management. Oral rehydration with supplementation of deficient micronutrients, especially zinc, is well established and recommended. Some probiotic strains have shown promise in reducing the duration of diarrhea. For the management of constipation, available clinical trials are insufficient for conclusive recommendations of dietary modifications, including increased use of fruit juice, fiber, and fluid. However, the role of laxatives as conventional treatment is becoming more established. Polyethylene glycol is the most studied, with lactulose, milk of magnesia, mineral oil, bisacodyl, and senna presenting as viable alternatives. Conventional treatments of the abdominal pain associated with IBS are poorly studied in children. Available studies investigating the effectiveness of antidepressants on abdominal pain in children with IBS were inconclusive. At the same time, probiotics and peppermint oil have a fair record of benefits and safety. The overall body of evidence indicates that a careful balance of conventional and complementary treatment strategies may be required to manage gastrointestinal conditions in children

Current trends in drug metabolism and pharmacokinetics.

Pharmacokinetics (PK) is the study of the absorption, distribution, metabolism, and excretion (ADME) processes of a drug. Understanding PK properties is essential for drug development and precision medication. In this review we provided an overview of recent research on PK with focus on the following aspects: (1) an update on drug-metabolizing enzymes and transporters in the determination of PK, as well as advances in xenobiotic receptors and noncoding RNAs (ncRNAs) in the modulation of PK, providing new understanding of the transcriptional and posttranscriptional regulatory mechanisms that result in inter-individual variations in pharmacotherapy; (2) current status and trends in assessing drug-drug interactions, especially interactions between drugs and herbs, between drugs and therapeutic biologics, and microbiota-mediated interactions; (3) advances in understanding the effects of diseases on PK, particularly changes in metabolizing enzymes and transporters with disease progression; (4) trends in mathematical modeling including physiologically-based PK modeling and novel animal models such as CRISPR/Cas9-based animal models for DMPK studies; (5) emerging non-classical xenobiotic metabolic pathways and the involvement of novel metabolic enzymes, especially non-P450s. Existing challenges and perspectives on future directions are discussed, and may stimulate the development of new research models, technologies, and strategies towards the development of better drugs and improved clinical practice