Studies on Acrylonitrile Butadiene Rubber - Short Nylon Fiber Composites

Acrylonitrile butadiene rubber (NBR) matrix was reinforced with different levels of short nylon fiber loading. Cure characteristics and mechanical properties of composites in longitudinal and transverse directions have been studied. Cure time was reduced while processability, as indicated by the minimum torque, was marginally reduced with increase in fiber loading. Tensile and tear properties improved with fiber concentration and the values were higher in longitudinal direction of fiber orientation. Abrasion resistance, resilience and compression set were increased in presence of fibers. Elongation at break values showed a drastic drop on introduction of fibers. Heat build up was higher for composites.

Development of Elastomeric Hybrid Composite Based on Synthesised Manosilica and Short Nylon Fiber

Nanoscale silica was synthesized by precipitation method using sodium silicate and dilute hydrochloric acid under controlled conditions. The synthesized silica was characterized by Scanning Electron Microscopy (SEM), Transmission Electron Microscopy (TEM), BET adsorption and X-Ray Diffraction (XRD). The particle size of silica was calculated to be 13 nm from the XRD results and the surface area was found to be 295 m2/g by BET method. The performance of this synthesized nanosilica as a reinforcing filler in natural rubber (NR) compound was investigated. The commercial silica was used as the reference material. Nanosilica was found to be effective reinforcing filler in natural rubber compound. Filler-matrix interaction was better for nanosilica than the commercial silica. The synthesized nanosilica was used in place of conventional silica in HRH (hexamethylene tetramine, resorcinol and silica) bonding system for natural rubber and styrene butadiene rubber / Nylon 6 short fiber composites. The efficiency of HRH bonding system based on nanosilica was better. Nanosilica was also used as reinforcing filler in rubber / Nylon 6 short fiber hybrid composite. The cure, mechanical, ageing, thermal and dynamic mechanical properties of nanosilica / Nylon 6 short fiber / elastomeric hybrid composites were studied in detail. The matrices used were natural rubber (NR), nitrile rubber (NBR), styrene butadiene rubber (SBR) and chloroprene rubber (CR). Fiber loading was varied from 0 to 30 parts per hundred rubber (phr) and silica loading was varied from 0 to 9 phr. Hexa:Resorcinol:Silica (HRH) ratio was maintained as 2:2:1. HRH loading was adjusted to 16% of the fiber loading. Minimum torque, maximum torque and cure time increased with silica loading. Cure rate increased with fiber loading and decreased with silica content. The hybrid composites showed improved mechanical properties in the presence of nanosilica. Tensile strength showed a dip at 10 phr fiber loading in the case of NR and CR while it continuously increased with fiber loading in the case of NBR and SBR. The nanosilica improved the tensile strength, modulus and tear strength better than the conventional silica. Abrasion resistance and hardness were also better for the nanosilica composites. Resilience and compression set were adversely affected. Hybrid composites showed anisotropy in mechanical properties. Retention in ageing improved with fiber loading and was better for nanosilica-filled hybrid composites. The nanosilica also improved the thermal stability of the hybrid composite better than the commercial silica. All the composites underwent two-step thermal degradation. Kinetic studies showed that the degradation of all the elastomeric composites followed a first-order reaction. Dynamic mechanical analysis revealed that storage modulus (E’) and loss modulus (E”) increased with nanosiica content, fiber loading and frequency for all the composites, independent of the matrix. The highest rate of increase was registered for NBR rubber.

Studies on the Utilisation of Rubber Reclaim in Elastomers

The thesis describes utilisation of reclaimed rubber, Whole Tyre Reclaim (WTR) produced from bio non- degradable solid pollutant scrap and used tyres. In this study an attempt has made to optimize the substitution of virgin rubber with WTR in both natural and synthetic rubber compounds without seriously compromising the important mechanical properties. The WTR is used as potent source of rubber hydrocarbon and carbon black filler. Apart from natural rubber (NR), Butadiene rubber (BR), Styrene butadiene rubber (SBR), Acrylonitrile butadiene rubber (NBR) and Chloroprene rubber (CR) were selected for study, being the most widely used general purpose and specialty rubbers. The compatibility problem was addressed by functionalisation of WTR with maleic anhydride and by using a coupling agent Si69.The blends were systematically evaluated with respect to various mechanical properties. The thermogravimetric analyses were also carried out to evaluate the thermal stability of the blends.Mechanical properties of the blends were property and matrix dependant. Presence of reinforcing carbon black filler and curatives in the reclaimed rubber improved the mechanical properties with the exception of some of the elastic properties like heat build up, resilience, compression set. When WTR was blended with natural rubber and synthetic rubbers, as the concentration of the low molecular weight, depolymerised WfR was increased above 46-weight percent, the properties deteriorates.When WTR was blended with crystallizing rubbers such as natural rubber and chloroprene rubber, properties like tensile strength, ultimate elongation were decreased in presence of WTR. Where as in the case of blends of WTR with non-crystallizing rubbers reinforcement effect was more prominent.The effect of functionalisation and coupling agent was studied in three matrices having different levels of polarity(NBR, CR and SBR).The grafting of maleic anhydride on to WTR definitely improved the properties of its blends with NBR, CR and SBR, the effect being prominent in Chloroprene rubber.Improvement in properties of these blends could also achieved by using a coupling agent Si69. With this there is apparent plasticizing effect at higher loading of the coupling agent. The optimum concentration of Si69 was 1 phr for improved properties, though the improvements are not as significant as in the case of maleic anhydride grafting.Thermal stability of the blend was increased by using silane-coupling agent.

Study on acceleratore in rubber vulcanization with special reference to the binary systems containing substituted dithiobiurets

The thesis entitled Study on Accelerators in Rubber Vulcanization with Special Reference to the Binary Systems Containing Substituted Dithiobiurets. It includes a detailed study on the binary accelerator systems containing substituted dithiobiurets in natural rubber and NR latex and dithiobiurets in SBR and NR-SBR blends vulcanization systems. It deals with the experimental procedure adopted for the preparation of DTB-II and DTB-III; the procedure for compounding and vulcanization and determination of physical properties like modulus, tensile strength, elongation at break, hardness, compression set, heat build up etc. The results indicate that there is efficient acceleration activity of the dithiobiurets in the vulcanization of natural rubber latex containing TMT. The study of dithiobiurets in natural rubber and NR latex reveal that they are having definite accelerating effect in SBR vulcanization systems.

Curing studies of elastomer blends with special reference to NR/ NBR and NR/BR blends

There are a large number of commercial examples and property advantages of immiscible elastomer blends.73 Blends of natural rubber (NR) and polybutadiene (BR) have shown various advantages including heat stability, improved elasticity and abrasion resistance. Ethylene-propylene-diene-rubber (EPDM) blended with styrene-butadiene rubber (SBR) has shown improvements in ozone and chemical resistance with better compression set properties. Blends of EPDM and nitrile rubber (NBR) have been cited as a compromise for obtaining moderate oil and ozone resistance with improved low temperature properties. Neoprene (CR)/BR blends offer improved low temperature properties and abrasion resistance with better processing characteristics etc. However, in many of the commercial two-phase elastomer blends, segregation of the crosslinking agents, carbon black or antioxidants preferentially into one phase can result in failure to attain optimum properties. Soluble and insoluble compounding ingredients are found to be preferentially concentrated in one phase. The balance of optimum curing of both phases therefore presents a difficult problem. It has been the aim of this study to improve the performance of commercially important elastomer blends such as natural rubber (NR)/styrene-butadiene rubber (SBR) and natural rubber/polybutadiene rubber (BR) by industrially viable procedures

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

Fatiga del reanimador y calidad de las compresiones torácicas en niños con y sin vía aérea asegurada

Introducción: La calidad de las compresiones torácicas tiene importancia durante la reanimación pediátrica y se ve afectada por diversos factores como la fatiga del reanimador, esta puede verse condicionada por las características de las compresiones establecidas según la presencia o ausencia de un dispositivo avanzado en la vía aérea determinando la interrupción continuidad de las mismas. En este estudio se realizó una simulación clínica, evaluando la presencia de fatiga del reanimador frente a pacientes con y sin dispositivo avanzado de la vía aérea. Metodología: Se incluyeron 12 participantes, quienes realizaron compresiones torácicas a un simulador clínico, tanto para el caso de la maniobra 1 correspondiente a ciclos interrumpidos con el fin de proporcionar ventilaciones, como para el caso de la maniobra 2 en la que la actividad fue continua. Se midieron calidad de compresiones, VO2 max y fatiga mediante escala de Borg RPE 6-20. Resultados: La calidad de las compresiones disminuyó en ambos grupos después del minuto 2 y más rápidamente cuando fueron ininterrumpidas. La fatiga se incrementó cuando las compresiones fueron continuas. Discusión: Se evidencia una relación directamente proporcional del aumento de la fatiga en relación al tiempo de reanimación e inversamente proporcional entre la calidad de las compresiones y la sensación de cansancio, en especial después del minuto 2. Un tiempo de 2 minutos podría ser el tiempo ideal para lograr compresiones de calidad y para realizar el reemplazo de la persona que realiza las compresiones.

Forming of Learning Set for Neural Networks in Problems of Losless Data Compression

questions of forming of learning sets for artificial neural networks in problems of lossless data compression are considered. Methods of construction and use of learning sets are studied. The way of forming of learning set during training an artificial neural network on the data stream is offered.

On uniquely determining cell-wall material properties with the compression experiment

A finite element model (FEM) of the cell-compression experiment has been developed in dimensionless form to extract the fundamental cell-wall-material properties (i.e. the constitutive equation and its parameters) from experiment force-displacement data. The FEM simulates the compression of a thin-walled, liquid-filled sphere between two flat surfaces. The cell-wall was taken to be permeable and the FEM therefore accounts for volume loss during compression. Previous models assume an impermeable wall and hence a conserved cell volume during compression. A parametric study was conducted for structural parameters representative of yeast. It was shown that the common approach of assuming reasonable values for unmeasured parameters (e.g. cell-wall thickness, initial radial stretch) can give rise to nonunique solutions for both the form and constants in the cell-wall constitutive relationship. Similarly, measurement errors can also lead to an incorrectly defined cell-wall constitutive relationship. Unique determination of the fundamental wall properties by cell compression requires accurate and precise measurement of a minimum set of parameters (initial cell radius, initial cell-wall thickness, and the volume loss during compression). In the absence of such measurements the derived constitutive relationship may be in considerable error, and should be evaluated against its ability to predict the outcome of other mechanical experiments. (C) 1998 Elsevier Science Ltd. All rights reserved.

Text classification using compression-based dissimilarity measures

Arguably, the most difficult task in text classification is to choose an appropriate set of features that allows machine learning algorithms to provide accurate classification. Most state-of-the-art techniques for this task involve careful feature engineering and a pre-processing stage, which may be too expensive in the emerging context of massive collections of electronic texts. In this paper, we propose efficient methods for text classification based on information-theoretic dissimilarity measures, which are used to define dissimilarity-based representations. These methods dispense with any feature design or engineering, by mapping texts into a feature space using universal dissimilarity measures; in this space, classical classifiers (e.g. nearest neighbor or support vector machines) can then be used. The reported experimental evaluation of the proposed methods, on sentiment polarity analysis and authorship attribution problems, reveals that it approximates, sometimes even outperforms previous state-of-the-art techniques, despite being much simpler, in the sense that they do not require any text pre-processing or feature engineering.

Selecting Wavelet Filter for Compression of Spectra from Multispectral Images

The problem of selecting anappropriate wavelet filter is always present in signal compression based on thewavelet transform. In this report, we propose a method to select a wavelet filter from a predefined set of filters for the compression of spectra from a multispectral image. The wavelet filter selection is based on the Learning Vector Quantization (LVQ). In the training phase for the test images, the best wavelet filter for each spectrum has been found by a careful compression-decompression evaluation. Certain spectral features are used in characterizing the pixel spectra. The LVQ is used to form the best wavelet filter class for different types of spectra from multispectral images. When a new image is to be compressed, a set of spectra from that image is selected, the spectra are classified by the trained LVQand the filter associated to the largest class is selected for the compression of every spectrum from the multispectral image. The results show, that almost inevery case our method finds the most suitable wavelet filter from the pre-defined set for the compression.

Wavelet Filter Parameterization for Compression of Spectral Images

Vaatimus kuvatiedon tiivistämisestä on tullut entistä ilmeisemmäksi viimeisen kymmenen vuoden aikana kuvatietoon perustuvien sovellutusten myötä. Nykyisin kiinnitetään erityistä huomiota spektrikuviin, joiden tallettaminen ja siirto vaativat runsaasti levytilaa ja kaistaa. Aallokemuunnos on osoittautunut hyväksi ratkaisuksi häviöllisessä tiedontiivistämisessä. Sen toteutus alikaistakoodauksessa perustuu aallokesuodattimiin ja ongelmana on sopivan aallokesuodattimen valinta erilaisille tiivistettäville kuville. Tässä työssä esitetään katsaus tiivistysmenetelmiin, jotka perustuvat aallokemuunnokseen. Ortogonaalisten suodattimien määritys parametrisoimalla on työn painopisteenä. Työssä todetaan myös kahden erilaisen lähestymistavan samanlaisuus algebrallisten yhtälöiden avulla. Kokeellinen osa sisältää joukon testejä, joilla perustellaan parametrisoinnin tarvetta. Erilaisille kuville tarvitaan erilaisia suodattimia sekä erilaiset tiivistyskertoimet saavutetaan eri suodattimilla. Lopuksi toteutetaan spektrikuvien tiivistys aallokemuunnoksen avulla.

Lossless Compression Methods for Multispectral Images: Development and Comparisons

Main purpose of this thesis is to introduce a new lossless compression algorithm for multispectral images. Proposed algorithm is based on reducing the band ordering problem to the problem of finding a minimum spanning tree in a weighted directed graph, where set of the graph vertices corresponds to multispectral image bands and the arcs’ weights have been computed using a newly invented adaptive linear prediction model. The adaptive prediction model is an extended unification of 2–and 4–neighbour pixel context linear prediction schemes. The algorithm provides individual prediction of each image band using the optimal prediction scheme, defined by the adaptive prediction model and the optimal predicting band suggested by minimum spanning tree. Its efficiency has been compared with respect to the best lossless compression algorithms for multispectral images. Three recently invented algorithms have been considered. Numerical results produced by these algorithms allow concluding that adaptive prediction based algorithm is the best one for lossless compression of multispectral images. Real multispectral data captured from an airplane have been used for the testing.

Sentence Compression with Supervised Machine Learning

The subject of the thesis is automatic sentence compression with machine learning, so that the compressed sentences remain both grammatical and retain their essential meaning. There are multiple possible uses for the compression of natural language sentences. In this thesis the focus is generation of television program subtitles, which often are compressed version of the original script of the program. The main part of the thesis consists of machine learning experiments for automatic sentence compression using different approaches to the problem. The machine learning methods used for this work are linear-chain conditional random fields and support vector machines. Also we take a look which automatic text analysis methods provide useful features for the task. The data used for machine learning is supplied by Lingsoft Inc. and consists of subtitles in both compressed an uncompressed form. The models are compared to a baseline system and comparisons are made both automatically and also using human evaluation, because of the potentially subjective nature of the output. The best result is achieved using a CRF - sequence classification using a rich feature set. All text analysis methods help classification and most useful method is morphological analysis. Tutkielman aihe on suomenkielisten lauseiden automaattinen tiivistäminen koneellisesti, niin että lyhennetyt lauseet säilyttävät olennaisen informaationsa ja pysyvät kieliopillisina. Luonnollisen kielen lauseiden tiivistämiselle on monta käyttötarkoitusta, mutta tässä tutkielmassa aihetta lähestytään television ohjelmien tekstittämisen kautta, johon käytännössä kuuluu alkuperäisen tekstin lyhentäminen televisioruudulle paremmin sopivaksi. Tutkielmassa kokeillaan erilaisia koneoppimismenetelmiä tekstin automaatiseen lyhentämiseen ja tarkastellaan miten hyvin erilaiset luonnollisen kielen analyysimenetelmät tuottavat informaatiota, joka auttaa näitä menetelmiä lyhentämään lauseita. Lisäksi tarkastellaan minkälainen lähestymistapa tuottaa parhaan lopputuloksen. Käytetyt koneoppimismenetelmät ovat tukivektorikone ja lineaarisen sekvenssin mallinen CRF. Koneoppimisen tukena käytetään tekstityksiä niiden eri käsittelyvaiheissa, jotka on saatu Lingsoft OY:ltä. Luotuja malleja vertaillaan Lopulta mallien lopputuloksia evaluoidaan automaattisesti ja koska teksti lopputuksena on jossain määrin subjektiivinen myös ihmisarviointiin perustuen. Vertailukohtana toimii kirjallisuudesta poimittu menetelmä. Tutkielman tuloksena paras lopputulos saadaan aikaan käyttäen CRF sekvenssi-luokittelijaa laajalla piirrejoukolla. Kaikki kokeillut teksin analyysimenetelmät auttavat luokittelussa, joista tärkeimmän panoksen antaa morfologinen analyysi.

Faster techniques to evolve wavelet coefficients for better fingerprint image compression

In this article, techniques have been presented for faster evolution of wavelet lifting coefficients for fingerprint image compression (FIC). In addition to increasing the computational speed by 81.35%, the coefficients performed much better than the reported coefficients in literature. Generally, full-size images are used for evolving wavelet coefficients, which is time consuming. To overcome this, in this work, wavelets were evolved with resized, cropped, resized-average and cropped-average images. On comparing the peak- signal-to-noise-ratios (PSNR) offered by the evolved wavelets, it was found that the cropped images excelled the resized images and is in par with the results reported till date. Wavelet lifting coefficients evolved from an average of four 256 256 centre-cropped images took less than 1/5th the evolution time reported in literature. It produced an improvement of 1.009 dB in average PSNR. Improvement in average PSNR was observed for other compression ratios (CR) and degraded images as well. The proposed technique gave better PSNR for various bit rates, with set partitioning in hierarchical trees (SPIHT) coder. These coefficients performed well with other fingerprint databases as well.