High Styrene–Rubber Ionomers, an Alternative to Thermoplastic Elastomers

High styrene rubber ionomers were prepared by sulfonating styrene–butadiene rubber of high styrene content (high styrene rubber) in 1,2-dichloroethane using acetyl sulfate reagent, followed by neutralization of the precursor acids using methanolic zinc acetate. The ionomers were characterized using X-ray fluorescence spectroscopy, Fourier transform infrared spectroscopy (FTIR), nuclear magnetic resonance spectroscopy (NMR), dynamic mechanical analysis (DMA), and also by the evaluation of mechanical properties. The FTIR studies of the ionomer reveal that the sulfonate groups are attached to the benzene ring. The NMR spectra give credence to this observation. Results of DMA show an ionic transition (Ti) in addition to glass–rubber transition (Tg). Incorporation of ionic groups results in improved mechanical properties as well as retention of properties after three cycles of processing

Microwave exfoliated reduced graphene oxide epoxy nanocomposites for high performance applications

Graphene has captured the attention of scientific community due to recently emerging high performance applications. Hence, studying its reinforcing effects on epoxy resin is a significant step. In this study, microwave exfoliated reduced graphene oxide (MERGO) was prepared from natural graphite for subsequent fabrication of epoxy nanocomposites using triethylenetetramine (TETA) as a curing agent via insitu polymerization. Thermogravimetric analysis (TGA), X-ray diffraction (XRD), Raman spectroscopy, Fourier transform infrared spectroscopy (FTIR), C13 NMR spectroscopy, X-ray photoelectron spectroscopy (XPS) and ultravioletevisible (UVevis) spectroscopy were employed to confirm the simultaneous reduction and exfoliation of graphene oxide. The reinforcing effect of MERGO on epoxy resin was explored by investigating its static mechanical properties and dynamic mechanical analysis (DMA) at MERGO loadings of 0 to 0.5 phr. The micro-structure of epoxy/MERGO nanocomposites was investigated using scanning electron microscope (SEM), transmission electron microscope (TEM) and XRD techniques. The present work reports an enhancement of 32%, 103% and 85% in tensile, impact and flexural strength respectively of epoxy by the addition of even 0.25 phr MERGO. At this loading elastic and flexural moduli also increased by 10% and 65%, respectively. Single-edge-notch three-point-Bending (SEN-TPB) fracture toughness (KIC) measurements were carried out where a 63% increase was observed by the introduction of 0.25 phr MERGO. The interfacial interactions brought about by graphene also benefited the dynamic mechanical properties to a large extent in the form of a significant enhancement in storage modulus and slightly improved glass transition temperature. Considerable improvements were also detected in dielectric properties. The epoxy nanocomposite also attained an ac conductivity of 10 5 S/m and a remarkable increase in dielectric constant. The simple and cost effective way of graphene synthesis for the fabrication of epoxy/MERGO nanocomposites may be extended to the preparation of other MERGO based polymer nanocomposites. This remarkable class of materials has thrown open enormous opportunities for developing conductive adhesives and in microelectronics

Modification of Polypropylene/High Density Polyethylene Blend Using Nanokaolinite Clay and E-Glass Fibre: Preparation, Characterization and Micromechanical Modelling

Upgrading two widely used standard plastics, polypropylene (PP) and high density polyethylene (HDPE), and generating a variety of useful engineering materials based on these blends have been the main objective of this study. Upgradation was effected by using nanomodifiers and/or fibrous modifiers. PP and HDPE were selected for modification due to their attractive inherent properties and wide spectrum of use. Blending is the engineered method of producing new materials with tailor made properties. It has the advantages of both the materials. PP has high tensile and flexural strength and the HDPE acts as an impact modifier in the resultant blend. Hence an optimized blend of PP and HDPE was selected as the matrix material for upgradation. Nanokaolinite clay and E-glass fibre were chosen for modifying PP/HDPE blend. As the first stage of the work, the mechanical, thermal, morphological, rheological, dynamic mechanical and crystallization characteristics of the polymer nanocomposites prepared with PP/HDPE blend and different surface modified nanokaolinite clay were analyzed. As the second stage of the work, the effect of simultaneous inclusion of nanokaolinite clay (both N100A and N100) and short glass fibres are investigated. The presence of nanofiller has increased the properties of hybrid composites to a greater extent than micro composites. As the last stage, micromechanical modeling of both nano and hybrid A composite is carried out to analyze the behavior of the composite under load bearing conditions. These theoretical analyses indicate that the polymer-nanoclay interfacial characteristics partially converge to a state of perfect interfacial bonding (Takayanagi model) with an iso-stress (Reuss IROM) response. In the case of hybrid composites the experimental data follows the trend of Halpin-Tsai model. This implies that matrix and filler experience varying amount of strain and interfacial adhesion between filler and matrix and also between the two fillers which play a vital role in determining the modulus of the hybrid composites.A significant observation from this study is that the requirement of higher fibre loading for efficient reinforcement of polymers can be substantially reduced by the presence of nanofiller together with much lower fibre content in the composite. Hybrid composites with both nanokaolinite clay and micron sized E-glass fibre as reinforcements in PP/HDPE matrix will generate a novel class of high performance, cost effective engineering material.

Information Exchange and Fusion in Dynamic and Heterogeneous Distributed Environments

Context awareness, dynamic reconfiguration at runtime and heterogeneity are key characteristics of future distributed systems, particularly in ubiquitous and mobile computing scenarios. The main contributions of this dissertation are theoretical as well as architectural concepts facilitating information exchange and fusion in heterogeneous and dynamic distributed environments. Our main focus is on bridging the heterogeneity issues and, at the same time, considering uncertain, imprecise and unreliable sensor information in information fusion and reasoning approaches. A domain ontology is used to establish a common vocabulary for the exchanged information. We thereby explicitly support different representations for the same kind of information and provide Inter-Representation Operations that convert between them. Special account is taken of the conversion of associated meta-data that express uncertainty and impreciseness. The Unscented Transformation, for example, is applied to propagate Gaussian normal distributions across highly non-linear Inter-Representation Operations. Uncertain sensor information is fused using the Dempster-Shafer Theory of Evidence as it allows explicit modelling of partial and complete ignorance. We also show how to incorporate the Dempster-Shafer Theory of Evidence into probabilistic reasoning schemes such as Hidden Markov Models in order to be able to consider the uncertainty of sensor information when deriving high-level information from low-level data. For all these concepts we provide architectural support as a guideline for developers of innovative information exchange and fusion infrastructures that are particularly targeted at heterogeneous dynamic environments. Two case studies serve as proof of concept. The first case study focuses on heterogeneous autonomous robots that have to spontaneously form a cooperative team in order to achieve a common goal. The second case study is concerned with an approach for user activity recognition which serves as baseline for a context-aware adaptive application. Both case studies demonstrate the viability and strengths of the proposed solution and emphasize that the Dempster-Shafer Theory of Evidence should be preferred to pure probability theory in applications involving non-linear Inter-Representation Operations.

A behavior-based scheme using reinforcement learning for autonomous underwater vehicles

This paper presents a hybrid behavior-based scheme using reinforcement learning for high-level control of autonomous underwater vehicles (AUVs). Two main features of the presented approach are hybrid behavior coordination and semi on-line neural-Q_learning (SONQL). Hybrid behavior coordination takes advantages of robustness and modularity in the competitive approach as well as efficient trajectories in the cooperative approach. SONQL, a new continuous approach of the Q_learning algorithm with a multilayer neural network is used to learn behavior state/action mapping online. Experimental results show the feasibility of the presented approach for AUVs

Dynamic conditional correlation in Latin-American asset markets

In this paper we reviewed the models of volatility for a group of five Latin American countries, mainly motivated by the recent periods of financial turbulence. Our results based on high frequency data suggest that Dynamic multivariate models are more powerful to study the volatilities of asset returns than Constant Conditional Correlation models. For the group of countries included, we identified that domestic volatilities of asset markets have been increasing; but the co-volatility of the region is still moderate.

Integration of knowledge-based, qualitative and numeric tools for real time dynamic systems supervision

The proposal presented in this thesis is to provide designers of knowledge based supervisory systems of dynamic systems with a framework to facilitate their tasks avoiding interface problems among tools, data flow and management. The approach is thought to be useful to both control and process engineers in assisting their tasks. The use of AI technologies to diagnose and perform control loops and, of course, assist process supervisory tasks such as fault detection and diagnose, are in the scope of this work. Special effort has been put in integration of tools for assisting expert supervisory systems design. With this aim the experience of Computer Aided Control Systems Design (CACSD) frameworks have been analysed and used to design a Computer Aided Supervisory Systems (CASSD) framework. In this sense, some basic facilities are required to be available in this proposed framework: ·

The Dubawnt Lake palaeo-ice stream: evidence for dynamic ics sheet behaviour on the Canadian Shield and insights regarding the controls on ice-stream location and vigour

We report evidence for a major ice stream that operated over the northwestern Canadian Shield in the Keewatin Sector of the Laurentide Ice Sheet during the last deglaciation 9000-8200 (uncalibrated) yr BP. It is reconstructed at 450 km in length, 140 km in width, and had an estimated catchment area of 190000 km. Mapping from satellite imagery reveals a suite of bedforms ('flow-set') characterized by a highly convergent onset zone, abrupt lateral margins, and where flow was presumed to have been fastest, a remarkably coherent pattern of mega-scale glacial lineations with lengths approaching 13 km and elongation ratios in excess of 40:1. Spatial variations in bedform elongation within the flow-set match the expected velocity field of a terrestrial ice stream. The flow pattern does not appear to be steered by topography and its location on the hard bedrock of the Canadian Shield is surprising. A soft sedimentary basin may have influenced ice-stream activity by lubricating the bed over the downstream crystalline bedrock, but it is unlikely that it operated over a pervasively deforming till layer. The location of the ice stream challenges the view that they only arise in deep bedrock troughs or over thick deposits of 'soft' fine-grained sediments. We speculate that fast ice flow may have been triggered when a steep ice sheet surface gradient with high driving stresses contacted a proglacial lake. An increase in velocity through calving could have propagated fast ice flow upstream (in the vicinity of the Keewatin Ice Divide) through a series of thermomechanical feedback mechanisms. It exerted a considerable impact on the Laurentide Ice Sheet, forcing the demise of one of the last major ice centres.

Elimination of the Greenland Ice Sheet in a High CO2 Climate

Projections of future global sea level depend on reliable estimates of changes in the size of polar ice sheets. Calculating this directly from global general circulation models (GCMs) is unreliable because the coarse resolution of 100 km or more is unable to capture narrow ablation zones, and ice dynamics is not usually taken into account in GCMs. To overcome these problems a high-resolution (20 km) dynamic ice sheet model has been coupled to the third Hadley Centre Coupled Ocean-Atmosphere GCM (HadCM3). A novel feature is the use of two-way coupling, so that climate changes in the GCM drive ice mass changes in the ice sheet model that, in turn, can alter the future climate through changes in orography, surface albedo, and freshwater input to the model ocean. At the start of the main experiment the atmospheric carbon dioxide concentration was increased to 4 times the preindustrial level and held constant for 3000 yr. By the end of this period the Greenland ice sheet is almost completely ablated and has made a direct contribution of approximately 7 m to global average sea level, causing a peak rate of sea level rise of 5 mm yr-1 early in the simulation. The effect of ice sheet depletion on global and regional climate has been examined and it was found that apart from the sea level rise, the long-term effect on global climate is small. However, there are some significant regional climate changes that appear to have reduced the rate at which the ice sheet ablates.

High performance subgraph mining in molecular compounds

Structured data represented in the form of graphs arises in several fields of the science and the growing amount of available data makes distributed graph mining techniques particularly relevant. In this paper, we present a distributed approach to the frequent subgraph mining problem to discover interesting patterns in molecular compounds. The problem is characterized by a highly irregular search tree, whereby no reliable workload prediction is available. We describe the three main aspects of the proposed distributed algorithm, namely a dynamic partitioning of the search space, a distribution process based on a peer-to-peer communication framework, and a novel receiver-initiated, load balancing algorithm. The effectiveness of the distributed method has been evaluated on the well-known National Cancer Institute’s HIV-screening dataset, where the approach attains close-to linear speedup in a network of workstations.

Rapid, uncatalyzed ring-opening polymerization of individual macrocyclic poly(arylene thioether ketone)s under dynamic heating conditions

Uncatalyzed, ring-opening polymerization of individual macrocyclic poly(arylene thioether ketone)s (1-4) and mixtures (5) under dynamic heating conditions has been demonstrated for the first time. High-molecular-weight, film-forming products were obtained after heating of the macrocycles up to 480 degreesC, with a heating rate of 10-20 degreesC /min. Depending on the macrocyclic structure and heat treatment conditions, the polymers obtained were amorphous or semicrystalline, soluble or slightly crosslinked. NMR analyses of the soluble polymers revealed their linear, highly regular structure. According to NMR, DSC, and TGA studies, the polymers obtained do not contain any residual macrocycles. The polymers with thio-p-arylene moieties in the main chain were thermally stabile. The catalyzed ring opening polymerization of 5 carried out in diphenyl sulfone solution is also reported for comparison. Using quantum mechanical calculations of the ring opening of macrocycles, a reaction mechanism is suggested. Preparation of nanosized poly(thioether ketone) fibrils by a replication method is described.

Effects of hydrocolloid addition and high pressure processing on the rheological properties and microstructure of a commercial ostrich meat product "Yor" (Thai sausage)

"Yor" is a traditional sausage like product widely consumed in Thailand. Its textures are usually set by steaming, in this experiment ultra-high pressure was used to modify the product. Three types of hydrocolloid; carboxymethylcellulose (CMC), locust bean gum (LBG) and xanthan gum, were added to minced ostrich meat batter at concentration of 0-1% and subjected to high pressure 600 Mpa, 50 degrees C, 40 min. The treated samples were analysed for storage (G) and loss (G '') moduli by dynamic oscillatory testing as well as creep compliance for control stress measurement. Their microstructures using confocal microscopy were also examined. Hydrocolloid addition caused a significant (P < 0.05) decrease in both the G' and G '' moduli. However the loss tangent of all samples remained unchanged. Addition of hydrocolloids led to decreases in the gel network formation but appears to function as surfactant materials during the initial mixing stage as shown by the microstructure. Confocal microscopy suggested that the size of the fat droplets decreased with gum addition. The fat droplets were smallest on the addition of xanthan gum and increased in the order CMC, LBG and no added gum, respectively. Creep parameters of ostrich yors with four levels of xanthan gum addition (0.50%, 0.75%, 1.00% and 1.25%) showed an increase in the instantaneous compliance (J(0)), the retarded compliance (J(1)) and retardation time (lambda(1)) but a decrease in the viscosity (eta(0)) with increasing levels of addition. The results also suggested that the larger deformations used during creep testing might be more helpful in assessing the mechanical properties of the product than the small deformations used in oscillatory rheology. (c) 2007 Elsevier Ltd. All rights reserved.

Visual surveillance for moving vehicles

An overview is given of a vision system for locating, recognising and tracking multiple vehicles, using an image sequence taken by a single camera mounted on a moving vehicle. The camera motion is estimated by matching features on the ground plane from one image to the next. Vehicle detection and hypothesis generation are performed using template correlation and a 3D wire frame model of the vehicle is fitted to the image. Once detected and identified, vehicles are tracked using dynamic filtering. A separate batch mode filter obtains the 3D trajectories of nearby vehicles over an extended time. Results are shown for a motorway image sequence.