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.     

An introduction of the condition class space with continuous value discretization and rough set theory

The granularity of an information system has an incumbent effect on the efficacy of the analysis from many machine learning algorithms. An information system contains a universe of objects characterized and categorized by condition and decision attributes. To manage the concomitant granularity, a level of continuous value discretization (CVD) is often undertaken. In the case of the rough set theory (RST) methodology for object classification, the granularity contributes to the grouping of objects into condition classes with the same condition attribute values. This article exposits the effect of a level of CVD on the subsequent condition classes constructed, with the introduction of the condition class space—the domain within which the condition classes exist. This domain elucidates the association of the condition classes to the related decision outcomes—reflecting the inexactness incumbent when a level of CVD is undertaken. A series of measures is defined that quantify this association. Throughout this study and without loss of generality, the findings are made through the RST methodology. This further offers a novel exposition of the relationship between all the condition attributes and the RST‐related reducts (subsets of condition attributes). © 2006 Wiley Periodicals, Inc. Int J Int Syst 21: 173–191, 2006.     

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.     

Learning in multiple model adaptive control switch

Including a learning mechanism in SMMAC (switched, multiple-model adaptive control) avoids the need for a priori knowledge of the model set of the plant to control and leads to a significant performance improvement with respect to the sole inclusion of an adaptive control channel in combination with switched fixed local controllers.     

Fault detection for control of wastewater treatment plants.

Interest in real-time model-based control is increasing as more and more facilities are being asked to meet stricter effluent requirements while at the same time minimizing costs. It has been identified that biological process models and automated process control technologies are being used at wastewater treatments plants throughout the world and that great potential for optimising biotreatment may exist with the integration of these two technology areas. According to our experience, wastewater treatment plants are indeed looking for ways to successfully integrate their modelling knowledge into their process control structure; however, there are practical aspects of this integration that must be addressed if the benefits of this integration are to be realised. This paper discusses the practical aspects of monitoring, filtering and analysing real sensor data with an aim to produce a reliable real-time data stream that might be used within a model-based control structure. Several real case study examples are briefly discussed in this paper.     

Comparison of total and faecal coliforms as faecal indicator in eutrophicated surface water.

The aim of this study was to evaluate the use of total coliforms (TC) and faecal coliforms (FC) using a membrane filtration method for precise monitoring of faecal pollution in Korean surface water. The samples were collected in Korea from both main rivers and their tributaries. Presumptive TC * FC were enumerated. The ratios of presumptive FC to TC were not constant, but varied widely, and TC were difficult to enumerate because of overgrowth by background colonies. For FC this was not the case. Seven hundred and three purified strains of presumptive TC * FC and their background colonies were biotyped using API 20E. Among 272 presumptive TC, non-faecal related species, Aeromonas hydrophila dominated (34.6%) and E. coli accounted for only 5.1%. In contrast, E. coli made up 89% of the 209 presumptive FC. Furthermore, of 164 background colonies on Endo Agar LES, 54.9% was A. hydrophila, while background colonies on m-FC Agar were few (58 strains), and despite their atypical colony appearance, most of them were biotyped as enteric bacteria. These results reveal that the detection of FC rather than TC using m-FC Agar is more appropriate for faecal pollution monitoring in eutrophicated surface water located in a temperate region.     

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.