Novel Melt Transurethane Process for Cycloaliphatic Polyurethanes and Their Self-Organization for Nano-Materials

In this introduction part, importance has been given to the elastomeric properties of polyurethanes. Emphasis has been laid to this property based on microphase separation and how this could be modified by modifying the segment lengths, as well as the structure of the segments. Implication was also made on the mechanical and thermal properties of these copolymers based on various analytical methods usually used for characterization of polymers. A brief overview of the challenges faced by the polyurethane chemistry was also done, pointing to the fact that though polyurethane industry is more than 75 years old, still a lot of questions remain unanswered, that too mostly in the synthesis of polyurethanes. A major challenge in this industry is the utilization of more environmental friendly “Green Chemistry Routes” for the synthesis of polyurethanes which are devoid of any isocyanates or harsh solvents.The research work in this thesis was focused to develop non-isocyanate green chemical process for polyurethanes and also self-organize the resultant novel polymers into nano-materials. The thesis was focused on the following three major aspects:(i) Design and development of novel melt transurethane process for polyurethanes under non-isocyanate and solvent free melt condition. (ii) Solvent induced self-organization of the novel cycloaliphatic polyurethanes prepared by the melt transurethane process into microporous templates and nano-sized polymeric hexagons and spheres. (iii) Novel polyurethane-oligophenylenevinylene random block copolymer nano-materials and their photoluminescence properties. The second chapter of the thesis gives an elaborate discussion on the “Novel Melt Transurethane Process ” for the synthesis of polyurethanes under non-isocyanate and solvent free melt condition. The polycondensation reaction was carried out between equimolar amounts of a di-urethane monomer and a diol in the presence of a catalyst under melt condition to produce polyurethanes followed by the removal of low boiling alcohol from equilibrium. The polymers synthesized through this green chemical route were found to be soluble (devoid of any cross links), thermally stable and free from any isocyanate entities. The polymerization reaction was confirmed by various analytical techniques with specific references to the extent of reaction which is the main watchful point for any successful polymerization reaction. The mechanistic aspects of the reaction were another point of consideration for the novel polymerization route which was successfully dealt with by performing various model reactions. Since this route was successful enough in synthesizing polyurethanes with novel structures, they were employed for the solvent induced self-organization which is an important area of research in the polymer world in the present scenario. Chapter three mesmerizes the reader with multitudes of morphologies depending upon the chemical backbone structure of the polyurethane as well as on the nature and amount of various solvents employed for the self-organization tactics. The rationale towards these morphologies-“Hydrogen Bonding ” have been systematically probed by various techniques. These polyurethanes were then tagged with luminescent 0ligo(phenylene vinylene) units and the effects of these OPV blocks on the morphology of the polyurethanes were analyzed in chapter four. These blocks have resulted in the formation of novel “Blue Luminescent Balls” which could find various applications in optoelectronic devices as well as delivery vehicles.

Queues with inter- ruption in Random/ Markovian Environment

In many situations probability models are more realistic than deterministic models. Several phenomena occurring in physics are studied as random phenomena changing with time and space. Stochastic processes originated from the needs of physicists.Let X(t) be a random variable where t is a parameter assuming values from the set T. Then the collection of random variables {X(t), t ∈ T} is called a stochastic process. We denote the state of the process at time t by X(t) and the collection of all possible values X(t) can assume, is called state space

TU Graz: Course: 707.000 Web Science and Web Technology: Lecture 6: Network Evolution and Process

In this class, we will discuss the nature of network evolution and some selected network processes. We will discuss graph generation algorithms that generate networks with different interesting characteristics. Optional : The Structure and Function of Complex Networks (chapter 8), M.E.J. Newman, SIAM Review 45 167--256 (2003); Optional: Emergence of Scaling in Random Networks, A.L. Barabasi and R. Albert, Science 286, 509 (1999)

Medical image registration based on random line sampling

One of the key aspects in 3D-image registration is the computation of the joint intensity histogram. We propose a new approach to compute this histogram using uniformly distributed random lines to sample stochastically the overlapping volume between two 3D-images. The intensity values are captured from the lines at evenly spaced positions, taking an initial random offset different for each line. This method provides us with an accurate, robust and fast mutual information-based registration. The interpolation effects are drastically reduced, due to the stochastic nature of the line generation, and the alignment process is also accelerated. The results obtained show a better performance of the introduced method than the classic computation of the joint histogram

Systolic random number generation for genetic algorithms

A parallel hardware random number generator for use with a VLSI genetic algorithm processing device is proposed. The design uses an systolic array of mixed congruential random number generators. The generators are constantly reseeded with the outputs of the proceeding generators to avoid significant biasing of the randomness of the array which would result in longer times for the algorithm to converge to a solution. 1 Introduction In recent years there has been a growing interest in developing hardware genetic algorithm devices [1, 2, 3]. A genetic algorithm (GA) is a stochastic search and optimization technique which attempts to capture the power of natural selection by evolving a population of candidate solutions by a process of selection and reproduction [4]. In keeping with the evolutionary analogy, the solutions are called chromosomes with each chromosome containing a number of genes. Chromosomes are commonly simple binary strings, the bits being the genes.

Random number generation as an index of controlled processing

Random number generation (RNG) is a functionally complex process that is highly controlled and therefore dependent on Baddeley's central executive. This study addresses this issue by investigating whether key predictions from this framework are compatible with empirical data. In Experiment 1, the effect of increasing task demands by increasing the rate of the paced generation was comprehensively examined. As expected, faster rates affected performance negatively because central resources were increasingly depleted. Next, the effects of participants' exposure were manipulated in Experiment 2 by providing increasing amounts of practice on the task. There was no improvement over 10 practice trials, suggesting that the high level of strategic control required by the task was constant and not amenable to any automatization gain with repeated exposure. Together, the results demonstrate that RNG performance is a highly controlled and demanding process sensitive to additional demands on central resources (Experiment 1) and is unaffected by repeated performance or practice (Experiment 2). These features render the easily administered RNG task an ideal and robust index of executive function that is highly suitable for repeated clinical use.

Neural networks for identification of nonlinear systems under random piecewise polynomial disturbances

The problem of identification of a nonlinear dynamic system is considered. A two-layer neural network is used for the solution of the problem. Systems disturbed with unmeasurable noise are considered, although it is known that the disturbance is a random piecewise polynomial process. Absorption polynomials and nonquadratic loss functions are used to reduce the effect of this disturbance on the estimates of the optimal memory of the neural-network model.

From symmetry breaking to Poisson Point Process in 2D Voronoi Tessellations: the generic nature of hexagons

We bridge the properties of the regular triangular, square, and hexagonal honeycomb Voronoi tessellations of the plane to the Poisson-Voronoi case, thus analyzing in a common framework symmetry breaking processes and the approach to uniform random distributions of tessellation-generating points. We resort to ensemble simulations of tessellations generated by points whose regular positions are perturbed through a Gaussian noise, whose variance is given by the parameter α2 times the square of the inverse of the average density of points. We analyze the number of sides, the area, and the perimeter of the Voronoi cells. For all valuesα >0, hexagons constitute the most common class of cells, and 2-parameter gamma distributions provide an efficient description of the statistical properties of the analyzed geometrical characteristics. The introduction of noise destroys the triangular and square tessellations, which are structurally unstable, as their topological properties are discontinuous in α = 0. On the contrary, the honeycomb hexagonal tessellation is topologically stable and, experimentally, all Voronoi cells are hexagonal for small but finite noise withα <0.12. For all tessellations and for small values of α, we observe a linear dependence on α of the ensemble mean of the standard deviation of the area and perimeter of the cells. Already for a moderate amount of Gaussian noise (α >0.5), memory of the specific initial unperturbed state is lost, because the statistical properties of the three perturbed regular tessellations are indistinguishable. When α >2, results converge to those of Poisson-Voronoi tessellations. The geometrical properties of n-sided cells change with α until the Poisson- Voronoi limit is reached for α > 2; in this limit the Desch law for perimeters is shown to be not valid and a square root dependence on n is established. This law allows for an easy link to the Lewis law for areas and agrees with exact asymptotic results. Finally, for α >1, the ensemble mean of the cells area and perimeter restricted to the hexagonal cells agree remarkably well with the full ensemble mean; this reinforces the idea that hexagons, beyond their ubiquitous numerical prominence, can be interpreted as typical polygons in 2D Voronoi tessellations.

Random walks systems with killing on Z

We study random walks systems on Z whose general description follows. At time zero, there is a number N >= 1 of particles at each vertex of N, all being inactive, except for those placed at the vertex one. Each active particle performs a simple random walk on Z and, up to the time it dies, it activates all inactive particles that it meets along its way. An active particle dies at the instant it reaches a certain fixed total of jumps (L >= 1) without activating any particle, so that its lifetime depends strongly on the past of the process. We investigate how the probability of survival of the process depends on L and on the jumping probabilities of the active particles.

NONHOMOGENEOUS RANDOM WALKS SYSTEMS ON Z

We consider a random walks system on Z in which each active particle performs a nearest-neighbor random walk and activates all inactive particles it encounters. The movement of an active particle stops when it reaches a certain number of jumps without activating any particle. We prove that if the process relies on efficient particles (i.e. those particles with a small probability of jumping to the left) being placed strategically on Z, then it might survive, having active particles at any time with positive probability. On the other hand, we may construct a process that dies out eventually almost surely, even if it relies on efficient particles. That is, we discuss what happens if particles are initially placed very far away from each other or if their probability of jumping to the right tends to I but not fast enough.

Billiards in a General Domain with Random Reflections

We study stochastic billiards on general tables: a particle moves according to its constant velocity inside some domain D R(d) until it hits the boundary and bounces randomly inside, according to some reflection law. We assume that the boundary of the domain is locally Lipschitz and almost everywhere continuously differentiable. The angle of the outgoing velocity with the inner normal vector has a specified, absolutely continuous density. We construct the discrete time and the continuous time processes recording the sequence of hitting points on the boundary and the pair location/velocity. We mainly focus on the case of bounded domains. Then, we prove exponential ergodicity of these two Markov processes, we study their invariant distribution and their normal (Gaussian) fluctuations. Of particular interest is the case of the cosine reflection law: the stationary distributions for the two processes are uniform in this case, the discrete time chain is reversible though the continuous time process is quasi-reversible. Also in this case, we give a natural construction of a chord ""picked at random"" in D, and we study the angle of intersection of the process with a (d - 1) -dimensional manifold contained in D.

Perpendicularly self-oriented and shape-controlled L1(0)-FePt nanorods directly synthesized by a temperature-modulated process

The synthesis and self-assembly of tetragonal phase-containing L1(0)-Fe(55)Pt(45) nanorods with high coercive field is described. The experimental procedure resulted in a tetragonal/cubic phase ratio close to 1:1 for the as-synthesized nanoparticles. Using different surfactant/solvent proportions in the process allowed control of particle morphology from nanospheres to nanowires. Monodisperse nanorods with lengths of 60 +/- 5 nm and diameters of 2-3 nm were self-assembled in a perpendicular oriented array onto a substrate surface using hexadecylamine as organic spacer. Magnetic alignment and properties assigned, respectively, to the shape anisotropy and the tetragonal phase suggest that the self-assembled materials are a strong candidate to solve the problem of random magnetic alignment observed in FePt nanospheres leading to applications in ultrahigh magnetic recording (UHMR) systems capable of achieving a performance of the order of terabits/in(2).

Machine Learning Algorithms in Heavy Process Manufacturing

In a global economy, manufacturers mainly compete with cost efficiency of production, as the price of raw materials are similar worldwide. Heavy industry has two big issues to deal with. On the one hand there is lots of data which needs to be analyzed in an effective manner, and on the other hand making big improvements via investments in cooperate structure or new machinery is neither economically nor physically viable. Machine learning offers a promising way for manufacturers to address both these problems as they are in an excellent position to employ learning techniques with their massive resource of historical production data. However, choosing modelling a strategy in this setting is far from trivial and this is the objective of this article. The article investigates characteristics of the most popular classifiers used in industry today. Support Vector Machines, Multilayer Perceptron, Decision Trees, Random Forests, and the meta-algorithms Bagging and Boosting are mainly investigated in this work. Lessons from real-world implementations of these learners are also provided together with future directions when different learners are expected to perform well. The importance of feature selection and relevant selection methods in an industrial setting are further investigated. Performance metrics have also been discussed for the sake of completion.

A Higher order and stable method for the numerical integration of Random Differential Equations

Trabalho apresentado no Congresso Nacional de Matemática Aplicada à Indústria, 18 a 21 de novembro de 2014, Caldas Novas - Goiás

Undersampled Critical Branching Processes on Small-World and Random Networks Fail to Reproduce the Statistics of Spike Avalanches

The power-law size distributions obtained experimentally for neuronal avalanches are an important evidence of criticality in the brain. This evidence is supported by the fact that a critical branching process exhibits the same exponent t~3=2. Models at criticality have been employed to mimic avalanche propagation and explain the statistics observed experimentally. However, a crucial aspect of neuronal recordings has been almost completely neglected in the models: undersampling. While in a typical multielectrode array hundreds of neurons are recorded, in the same area of neuronal tissue tens of thousands of neurons can be found. Here we investigate the consequences of undersampling in models with three different topologies (two-dimensional, small-world and random network) and three different dynamical regimes (subcritical, critical and supercritical). We found that undersampling modifies avalanche size distributions, extinguishing the power laws observed in critical systems. Distributions from subcritical systems are also modified, but the shape of the undersampled distributions is more similar to that of a fully sampled system. Undersampled supercritical systems can recover the general characteristics of the fully sampled version, provided that enough neurons are measured. Undersampling in two-dimensional and small-world networks leads to similar effects, while the random network is insensitive to sampling density due to the lack of a well-defined neighborhood. We conjecture that neuronal avalanches recorded from local field potentials avoid undersampling effects due to the nature of this signal, but the same does not hold for spike avalanches. We conclude that undersampled branching-process-like models in these topologies fail to reproduce the statistics of spike avalanches.