Reciprocal Relationships Between Collective Efficacy and Team Performance in Women's Ice Hockey.

This study examined reciprocal relationships between collective efficacy and team performance over a season of competition in women's intercollegiate ice hockey within weekends where the opponent was constant for 2 games. Collective efficacy beliefs within 12 teams were assessed prior to both games for at least 7 weekends. Team performance indexes produced an overall measure of performance for each game. The average influence of Saturday collective efficacy on Saturday performance was moderate and positive after controlling for Friday performance. The average influence of Friday performance on Saturday collective efficacy was small and positive after removing the influence of Friday collective efficacy from Friday performance. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Heterogeneity in Youth Symptom Trajectories Following Psychiatric Crisis: Predictors and Placement Outcome.

The authors examined heterogeneity in symptom trajectories among youths following psychiatric crises as well as the psychosocial correlates and placement outcomes associated with identified trajectories. Using semiparametric mixture modeling with 156 youths approved for psychiatric hospitalization, the authors identified 5 trajectories based on symptoms over the 16 months following crisis: high improved, high unimproved, borderline improved, borderline unimproved, and subclinical. Membership in unimproved symptom groups was associated with less suicidality, younger age, more youth hopelessness, and more caregiver empowerment. Improved symptom group membership predicted long-term decreases in days in out-of-home placements. More important, and in contrast with general impressions from the existing literature, findings suggest that a substantive proportion of youths with serious emotional disturbance sustain high levels of symptomatology following intensive mental health services. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Extracting Light from Polymer Light‐Emitting Diodes Using Stamped Bragg Gratings

In this paper, we describe a method for increasing the external efficiency of polymer light‐emitting diodes (LEDs) by coupling out waveguided light with Bragg gratings. We numerically model the waveguide modes in a typical LED structure and demonstrate how optimizing layer thicknesses and reducing waveguide absorption can enhance the grating outcoupling. The gratings were created by a soft‐lithography technique that minimizes changes to the conventional LED structure. Using one‐dimensional and two‐dimensional gratings, we were able to increase the forward‐directed emission by 47 % and 70 %, respectively, and the external quantum efficiency by 15 % and 25 %.     

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.     

Parameter Uncertainty and Policy Intensity: Some Extensions and Suggestions for Further Work

The effects of parameter uncertainty on optimal policy have been a matter of interest for academics, and even for some policymakers, for a long time. Two lines of literature have developed analytical results on this matter. The first line uses static models and the second dynamic models. In this dynamic line most of the results are confined to models with a single state and a single control variable. In this paper we want to encourage the analysis of more general dynamic cases. To do so, the results in the dynamic line are extended from one-state and one-control finite horizon models to models with a pair of control variables. We then discuss some of the hurdles which must be surmounted for the results to be made more general and suggests some lines for further research. JEL classification: C61; E61     

Measurement and Identification of the Parameters of SPICE-Models of a Bipolar Transistor

A method of measuring and identifying the static parameters of a bipolar transistor is considered. The characteristic of the transistor, from which the parameters of the model are determined, is chosen depending on what group the calculated parameters belong to. The characteristics are measured in such a way that the equations of the model describing them can be reduced to the simplest form.