Ordered mesoporous silica as supports for immobilization of biocatalyst

Mesoporous materials are of great interest to the materials community because of their potential applications for catalysis,separation of large molecules,medical implants,semiconductors,magnetoelectric devices.The thesis entitled 'Ordered Mesoporous Silica as supports for immobilization of Biocatalyst' presents how the pore size can be tuned without the loss in ordered structure for the entrapment of an industially important biocatalyst-amylase.Immobilization of enzymes on ordered mesoporous material has triggered new ooportunities for stabilizing enzymes with improved intrinsic and operational stabilities.

Vanadia Supported on Rice Husk Silica Promoted Rare Earth Oxides

The selective oxidation of alkylaromatics is one of the main processes since the reaction products are important as intermediates in numerous industrial organic chemicals. Side-chain oxidation of alkyl aromatic compounds catalyzed by heterogeneous catalysts using cleaner peroxide oxidants is an especially attractive goal since classical synthetic laboratory procedures preferably use permanganate or acid dichromate as stoichiometric oxidants. In spite of many studies, there are very few which use hydrogen peroxide as a source of oxygen in the C-H activation of alkanes. Eflective utilization of ethylbenzene, available in the xylene stream of the petrochemical industry to more value added products is a promising one in chemical industry. The oxidation products of ethylbenzene are widely employed as intermediates in organic, steroid and resin synthesis.

Size-dependent surface plasmon resonance in silver silica nanocomposites

Silver silica nanocomposites were obtained by the sol–gel technique using tetraethyl orthosilicate (TEOS) and silver nitrate (AgNO3) as precursors. The silver nitrate concentration was varied for obtaining composites with different nanoparticle sizes. The structural and microstructural properties were determined by x-ray diffractometry (XRD), Fourier transform infrared spectroscopy (FTIR) and transmission electron microscopy (TEM). X-ray photoelectron spectroscopic (XPS) studies were done for determining the chemical states of silver in the silica matrix. For the lowest AgNO3 concentration, monodispersed and spherical Ag crystallites, with an average diameter of 5 nm, were obtained. Grain growth and an increase in size distribution was observed for higher concentrations. The occurrence of surface plasmon resonance (SPR) bands and their evolution in the size range 5–10 nm is studied. For decreasing nanoparticle size, a redshift and broadening of the plasmon-related absorption peak was observed. The observed redshift and broadening of the SPR band was explained using modified Mie scattering theory

Sequential interactions of Silver-silica nanocomposite (Ag-SiO2NC) with cell wall, metabolism and genetic stability of Psedomonas aeruginosa, a multiple antibiotic resistant bacterium

The study was carried out to understand the effect of silver-silica nanocomposite (Ag-SiO2NC) on the cell wall integrity, metabolism and genetic stability of Pseudomonas aeruginosa, a multiple drugresistant bacterium. Bacterial sensitivity towards antibiotics and Ag-SiO2NC was studied using standard disc diffusion and death rate assay, respectively. The effect of Ag-SiO2NC on cell wall integrity was monitored using SDS assay and fatty acid profile analysis while the effect on metabolism and genetic stability was assayed microscopically, using CTC viability staining and comet assay, respectively. P. aeruginosa was found to be resistant to β-lactamase, glycopeptidase, sulfonamide, quinolones, nitrofurantoin and macrolides classes of antibiotics. Complete mortality of the bacterium was achieved with 80 μgml-1 concentration of Ag-SiO2NC. The cell wall integrity reduced with increasing time and reached a plateau of 70 % in 110 min. Changes were also noticed in the proportion of fatty acids after the treatment. Inside the cytoplasm, a complete inhibition of electron transport system was achieved with 100 μgml-1 Ag-SiO2NC, followed by DNA breakage. The study thus demonstrates that Ag-SiO2NC invades the cytoplasm of the multiple drug-resistant P. aeruginosa by impinging upon the cell wall integrity and kills the cells by interfering with electron transport chain and the genetic stability

Development of Hierarchical Clustering Techniques for Gridded Data from Mixed Data Sequences

Knowledge discovery in databases is the non-trivial process of identifying valid, novel potentially useful and ultimately understandable patterns from data. The term Data mining refers to the process which does the exploratory analysis on the data and builds some model on the data. To infer patterns from data, data mining involves different approaches like association rule mining, classification techniques or clustering techniques. Among the many data mining techniques, clustering plays a major role, since it helps to group the related data for assessing properties and drawing conclusions. Most of the clustering algorithms act on a dataset with uniform format, since the similarity or dissimilarity between the data points is a significant factor in finding out the clusters. If a dataset consists of mixed attributes, i.e. a combination of numerical and categorical variables, a preferred approach is to convert different formats into a uniform format. The research study explores the various techniques to convert the mixed data sets to a numerical equivalent, so as to make it equipped for applying the statistical and similar algorithms. The results of clustering mixed category data after conversion to numeric data type have been demonstrated using a crime data set. The thesis also proposes an extension to the well known algorithm for handling mixed data types, to deal with data sets having only categorical data. The proposed conversion has been validated on a data set corresponding to breast cancer. Moreover, another issue with the clustering process is the visualization of output. Different geometric techniques like scatter plot, or projection plots are available, but none of the techniques display the result projecting the whole database but rather demonstrate attribute-pair wise analysis

Catalysis by Enzymes immobilized on Tuned Mesoporous Silica

Mesoporous silica nanoparticles provide a non-invasive and biocompatible delivery platform for a broad range of applications in therapeutics, pharmaceuticals and diagnosis. Additionally, mesoporous silica materials can be synthesized together with other nanomaterials to create new nanocomposites, opening up a wide variety of potential applications. The ready functionalization of silica materials makes them ideal candidates for bioapplications and catalysis. These properties of mesoporous silica like high surface areas, large pore volumes and ordered pore networks allow them for higher loading of drugs or biomolecules. Comparative studies have been made to evaluate the different procedures; much of the research to date has involved quick exploration of new methods and supports. Requirements for different enzymes may vary, and specific conditions may be needed for a particular application of an immobilized enzyme such as a highly rigid support. In this endeavor, mesoporous silica materials having different pore size were synthesized and easily modified with active functional groups and were evaluated for the immobilization of enzymes. In this work, Aspergillus niger glucoamylase, Bovine liver catalase, Candida rugosa lipase were immobilized onto support by adsorption and covalent binding. The structural properties of pure and immobilized supports are analyzed by various characterization techniques and are used for different reactions of industrial applications.

Utilization of waste expanded polystyrene: Blends with silica-filled natural rubber

Expanded polystyrene (EPS) constitutes a considerable part of thermoplastic waste in the environment in terms of volume. In this study, this waste material has been utilized for blending with silica-reinforced natural rubber (NR). The NR/EPS (35/5) blends were prepared by melt mixing in a Brabender Plasticorder. Since NR and EPS are incompatible and immiscible a method has been devised to improve compatibility. For this, EPS and NR were initially grafted with maleic anhydride (MA) using dicumyl peroxide (DCP) to give a graft copolymer. Grafting was confirmed by Fourier Transform Infrared Spectroscopy (FTIR) spectroscopy. This grafted blend was subsequently blended with more of NR during mill compounding. Morphological studies using Scanning Electron Microscopy (SEM) showed better dispersion of EPS in the compatibilized blend compared to the noncompatibilized blend. By this technique, the tensile strength, elongation at break, modulus, tear strength, compression set and hardness of the blend were found to be either at par with or better than that of virgin silica filled NR compound. It is also noted that the thermal properties of the blends are equivalent with that of virgin NR. The study establishes the potential of this method for utilising waste EPS

Hierarchical Object Recognition Using Libraries of Parameterized Model Sub-Parts

This thesis describes the development of a model-based vision system that exploits hierarchies of both object structure and object scale. The focus of the research is to use these hierarchies to achieve robust recognition based on effective organization and indexing schemes for model libraries. The goal of the system is to recognize parameterized instances of non-rigid model objects contained in a large knowledge base despite the presence of noise and occlusion. Robustness is achieved by developing a system that can recognize viewed objects that are scaled or mirror-image instances of the known models or that contain components sub-parts with different relative scaling, rotation, or translation than in models. The approach taken in this thesis is to develop an object shape representation that incorporates a component sub-part hierarchy- to allow for efficient and correct indexing into an automatically generated model library as well as for relative parameterization among sub-parts, and a scale hierarchy- to allow for a general to specific recognition procedure. After analysis of the issues and inherent tradeoffs in the recognition process, a system is implemented using a representation based on significant contour curvature changes and a recognition engine based on geometric constraints of feature properties. Examples of the system's performance are given, followed by an analysis of the results. In conclusion, the system's benefits and limitations are presented.

A Hierarchical Cache Coherent Protocol

As the number of processors in distributed-memory multiprocessors grows, efficiently supporting a shared-memory programming model becomes difficult. We have designed the Protocol for Hierarchical Directories (PHD) to allow shared-memory support for systems containing massive numbers of processors. PHD eliminates bandwidth problems by using a scalable network, decreases hot-spots by not relying on a single point to distribute blocks, and uses a scalable amount of space for its directories. PHD provides a shared-memory model by synthesizing a global shared memory from the local memories of processors. PHD supports sequentially consistent read, write, and test- and-set operations. This thesis also introduces a method of describing locality for hierarchical protocols and employs this method in the derivation of an abstract model of the protocol behavior. An embedded model, based on the work of Johnson[ISCA19], describes the protocol behavior when mapped to a k-ary n-cube. The thesis uses these two models to study the average height in the hierarchy that operations reach, the longest path messages travel, the number of messages that operations generate, the inter-transaction issue time, and the protocol overhead for different locality parameters, degrees of multithreading, and machine sizes. We determine that multithreading is only useful for approximately two to four threads; any additional interleaving does not decrease the overall latency. For small machines and high locality applications, this limitation is due mainly to the length of the running threads. For large machines with medium to low locality, this limitation is due mainly to the protocol overhead being too large. Our study using the embedded model shows that in situations where the run length between references to shared memory is at least an order of magnitude longer than the time to process a single state transition in the protocol, applications exhibit good performance. If separate controllers for processing protocol requests are included, the protocol scales to 32k processor machines as long as the application exhibits hierarchical locality: at least 22% of the global references must be able to be satisfied locally; at most 35% of the global references are allowed to reach the top level of the hierarchy.

A Detailed Look at Scale and Translation Invariance in a Hierarchical Neural Model of Visual Object Recognition

The HMAX model has recently been proposed by Riesenhuber & Poggio as a hierarchical model of position- and size-invariant object recognition in visual cortex. It has also turned out to model successfully a number of other properties of the ventral visual stream (the visual pathway thought to be crucial for object recognition in cortex), and particularly of (view-tuned) neurons in macaque inferotemporal cortex, the brain area at the top of the ventral stream. The original modeling study only used ``paperclip'' stimuli, as in the corresponding physiology experiment, and did not explore systematically how model units' invariance properties depended on model parameters. In this study, we aimed at a deeper understanding of the inner workings of HMAX and its performance for various parameter settings and ``natural'' stimulus classes. We examined HMAX responses for different stimulus sizes and positions systematically and found a dependence of model units' responses on stimulus position for which a quantitative description is offered. Interestingly, we find that scale invariance properties of hierarchical neural models are not independent of stimulus class, as opposed to translation invariance, even though both are affine transformations within the image plane.

Hierarchical Mixtures of Experts and the EM Algorithm

We present a tree-structured architecture for supervised learning. The statistical model underlying the architecture is a hierarchical mixture model in which both the mixture coefficients and the mixture components are generalized linear models (GLIM's). Learning is treated as a maximum likelihood problem; in particular, we present an Expectation-Maximization (EM) algorithm for adjusting the parameters of the architecture. We also develop an on-line learning algorithm in which the parameters are updated incrementally. Comparative simulation results are presented in the robot dynamics domain.

Global and dominance with concentric hierarchical stimuli and orientation classification task. 'Dominancia global y local con est??mulos conc??ntricos y tareas de clasificaci??n de la orientaci??n'

Resumen tomado de la publicaci??n. Resumen tambi??n en ingl??s

Hierarchical Prediction Machines and Big Data Analytics

An emerging consensus in cognitive science views the biological brain as a hierarchically-organized predictive processing system. This is a system in which higher-order regions are continuously attempting to predict the activity of lower-order regions at a variety of (increasingly abstract) spatial and temporal scales. The brain is thus revealed as a hierarchical prediction machine that is constantly engaged in the effort to predict the flow of information originating from the sensory surfaces. Such a view seems to afford a great deal of explanatory leverage when it comes to a broad swathe of seemingly disparate psychological phenomena (e.g., learning, memory, perception, action, emotion, planning, reason, imagination, and conscious experience). In the most positive case, the predictive processing story seems to provide our first glimpse at what a unified (computationally-tractable and neurobiological plausible) account of human psychology might look like. This obviously marks out one reason why such models should be the focus of current empirical and theoretical attention. Another reason, however, is rooted in the potential of such models to advance the current state-of-the-art in machine intelligence and machine learning. Interestingly, the vision of the brain as a hierarchical prediction machine is one that establishes contact with work that goes under the heading of 'deep learning'. Deep learning systems thus often attempt to make use of predictive processing schemes and (increasingly abstract) generative models as a means of supporting the analysis of large data sets. But are such computational systems sufficient (by themselves) to provide a route to general human-level analytic capabilities? I will argue that they are not and that closer attention to a broader range of forces and factors (many of which are not confined to the neural realm) may be required to understand what it is that gives human cognition its distinctive (and largely unique) flavour. The vision that emerges is one of 'homomimetic deep learning systems', systems that situate a hierarchically-organized predictive processing core within a larger nexus of developmental, behavioural, symbolic, technological and social influences. Relative to that vision, I suggest that we should see the Web as a form of 'cognitive ecology', one that is as much involved with the transformation of machine intelligence as it is with the progressive reshaping of our own cognitive capabilities.

Reanalyzing fourth grade math student achievement in Chile : applying hierarchical linear models (HLMs). 'Reanalizando el rendimiento de los alumnos de cuarto grado en matemáticas en Chile: una aplicación de modelos jerárquicos lineales (HLM)'.

Resumen tomado de la publicación

Hierarchical modeling of entangled polymers

We demonstrate that it is possible to link multi-chain molecular dynamics simulations with the tube model using a single chain slip-links model as a bridge. This hierarchical approach allows significant speed up of simulations, permitting us to span the time scales relevant for a comparison with the tube theory. Fitting the mean-square displacement of individual monomers in molecular dynamics simulations with the slip-spring model, we show that it is possible to predict the stress relaxation. Then, we analyze the stress relaxation from slip-spring simulations in the framework of the tube theory. In the absence of constraint release, we establish that the relaxation modulus can be decomposed as the sum of contributions from fast and longitudinal Rouse modes, and tube survival. Finally, we discuss some open questions regarding possible future directions that could be profitable in rendering the tube model quantitative, even for mildly entangled polymers