Fracture response of fibre-reinforced materials with macro/microcrack damage using the Boundary Element Technique

The Boundary Element Method (BEM) incorporating the Embedded Cell Approach (ECA) has been used to analyse the effects of constituent material properties, fibre spatial distribution and microcrack damage on the localised behaviour of transversely fractured, unidirectional fibre-reinforced composites. Three specific composites, i.e., glass fibre reinforced polyester, carbon fibre reinforced epoxy and a glass-carbon hybrid, are considered. The geometrical structures examined were perfectly periodic, uniformly spaced fibre arrangements in square and hexagonal embedded cells. In addition, numerical simulations were also conducted using embedded cells containing randomly distributed fibres. The models involve both elastic fibres and matrix, with the interfaces between the different phases being fully bonded. The results indicate that the constituent material properties (two phase composite) and spatial distribution have a significant effect on the localised stress distributions around the primary crack tip. However, the strain energy release rate associated with crack propagation is predominantly influenced by the material composition. The three-phase hybrid composite exhibited an apparent intermediate fracture toughness value, compared to the all-glass and all-carbon models. Furthermore, the strain energy release rate for the macrocrack lowers as it enters a zone of localised damage (microcracking). The presence of microcracks relaxes the stress field, which can result in a significant reduction in the energetics of the primary crack.     

Personal telecommunication radiation safety and the precautionary principle

The approach to risk management for RF and microwave radiation has been to establish guidelines for maximum permissible levels of exposure. These guidelines are based on evaluations of the relevant scientific literature and, with safety margins to account for uncertainties and gaps in scientific knowledge, offer a significant level of protection against established hazards of RF and microwave radiation for short-term exposures. One can argue, on this basis, that the precautionary principle - specifically, safety factors -have been used to guide the making and promulgation of existing permissible-exposure standards for RF and microwave radiation. Therefore, discussions on the appropriateness of the precautionary principle for RF radiation may be "much ado about nothing". Clearly, this approach to "pre caution" neither has stifled technological development, nor has it helped to enforce the acceptance of zero risk, but it provided a basis for action when science was unable to give a clear answer.     

Surface effects in nanocrystalline silicon

Exposure to ammonia (NH₃) increases the dark current (DC) in nanocrystalline silicon. Light soaking (LS) for short periods also enhances the dark current, which remains at a high value for a long time. Pumping alone is unable to restore the initial annealed state, but annealing brings it back. The final state obtained by LS and NH₃ exposure depends on the order in which they are performed. Evaporated selenium (Se) deposited on nanocrystalline silicon decreases the DC. These effects cannot be explained entirely by the presence of a-Si : H alone, in our sample. DC and photoluminescence (PL) measurements indicate the presence of two types of center in our sample, which behave differently when exposed to NH₃.     

Intelligent process analytics for CRM

Many organisations use decision models in their processes such as tables or trees to provide decision support to their operational divisions. For example, in fault management, customer contact centre operators usually use a decision model in the form of prescribed interviews. Based on the answers given by customers, the operator navigates through the decision model to reach an assessment of the problem. In order to achieve customer satisfaction and operational excellence, it is very important to constantly monitor the performance of a decision model not only on an overall level, but also on the level of individual decisions. In this paper we present a configurable business process analytics tool, known as the intelligent Universal Service Management System, that constantly monitors decision data and is capable of optimising the decisions based on high-level business objectives. We explain the various features of the software and show how it can be used to optimise decision processes. We also show how we can easily provide a customised version to monitor the performance of provision processes.     

Profile-Driven Component Placement for Cluster-Based Online Services

The growth of the Internet and of various intranets has spawned a wealth of online services, most of which are implemented on local-area clusters using remote invocation (for example, remote procedure call/remote method invocation) among manually placed application components. Component placement can be a significant challenge for large-scale services, particularly when application resource needs are workload dependent. Automatic component placement has the potential to maximize overall system throughput. The key idea is to construct (offline) a mapping between input workload and individual-component resource consumption. Such mappings, called component profiles, then support high-performance placement. Preliminary results on an online auction benchmark based on J2EE (Java 2 Platform, Enterprise Edition) suggest that profile-driven tools can identify placements that achieve near-optimal overall throughput.     

Valorisation of waste fractions from autohydrolysis of selected lignocellulosic materials

Non‐isothermal processing of lignocellulosic materials in aqueous media (autohydrolysis reaction) under mild conditions leads to solutions containing valuable chemicals (oligosaccharides, sugars and acetic acid) and other, undesired, compounds (belonging to the extractive and acid‐soluble lignin fractions) which have to be removed in further purification treatments. Liquors obtained by non‐isothermal autohydrolysis of Eucalyptus globulus wood and corncobs under a variety of operational conditions were extracted with ethyl acetate in order to remove non‐saccharide components, and the suitability of the fraction dissolved in the organic phase was assayed for possible utilisation as an antioxidant. The yield and antioxidant activity of ethyl acetate extracts (measured by the α,α‐diphenyl‐β‐picrylhydrazyl (DPPH) radical scavenging capacity) showed a strong dependence on the autohydrolysis conditions. The antioxidant activity of extracts obtained under selected operational conditions compared well with synthetic antioxidants. © 2003 Society of Chemical Industry     

Minimax estimation of deterministic parameters in linear models with a random model matrix

We consider the problem of estimating an unknown deterministic parameter vector in a linear model with a random model matrix, with known second-order statistics. We first seek the linear estimator that minimizes the worst-case mean-squared error (MSE) across all parameter vectors whose (possibly weighted) norm is bounded above. We show that the minimax MSE estimator can be found by solving a semidefinite programming problem and develop necessary and sufficient optimality conditions on the minimax MSE estimator. Using these conditions, we derive closed-form expressions for the minimax MSE estimator in some special cases. We then demonstrate, through examples, that the minimax MSE estimator can improve the performance over both a Baysian approach and a least-squares method. We then consider the case in which the norm of the parameter vector is also bounded below. Since the minimax MSE approach cannot account for a nonzero lower bound, we consider, in this case, a minimax regret method in which we seek the estimator that minimizes the worst-case difference between the MSE attainable using a linear estimator that does not know the parameter vector, and the optimal MSE attained using a linear estimator that knows the parameter vector. For analytical tractability, we restrict our attention to the scalar case and develop a closed-form expression for the minimax regret estimator.     

Velocity selective filters recursively implemented in the spatiotemporal domain.

Energy-based methods for motion estimation in image sequences process the input data either in the spatiotemporal or in the frequency domain. In both cases, the algorithms already described in the literature often require a huge number of elementary operations. In this paper, we describe a class of velocity selective filters which yield an accurate detection of the edges moving in the sequence. We first present a filtering scheme based on a convolution operation computed on a finite size neighborhood and describe its properties in the spatiotemporal and frequency domains. Then, we show that filters with similar properties can be implemented recursively, i.e., as convolutions computed on infinite-size neighborhoods. As an example, we finally show the filters' responses in the case of two superimposed translational motions.