Lifetime and fleece quality traits associated with the occurrence of entangled mohair staples

Staple entanglement in mohair fleeces occurs when adhesions form between longer and faster growing fibres and shorter and slower growing fibres. This results in accentuated crimp of the longer fibres and an "apparently" reduced staple length. The appearance in the fleece of Angora goats of staple entanglements can lead to the downgrading of the mohair to poorer style and shorter length grades, resulting in up to 60% price reductions. This study examined how staple entanglement score (SES) is related to lifetime factors of Angora goats, and how this relationship can be explained by variations in animal size and fleece attributes. SES was scored using a five-point scale: 5, long free fibres easily separated as no adhesions; 4, some adhesions between fibres; 3, some effort to separate fibres as many adhesions; 2, many adhesions, staple fibres entangled, shortening of staple; 1, very entangled and shortened staple. Measurements were made over 9 shearing periods on a population of Angora castrated males (wethers) goats representing the current range and diversity of genetic origins in Australia, including South African, Texan and interbred admixtures of these and Australian sources. Data on genetic origin, sire, dam, date of birth, dam age, birth weight, birth parity, weaning weight, live weight, fleece growth and fleece attributes were recorded. Two restricted maximum likelihood (REML) models were developed to relate SES with age, animal lifetime factors, fleece quality attributes and live weight. One model allowed fleece quality and live weight traits in the model and the other excluded these traits. Staple entanglement was almost eliminated in mohair harvested from goats shorn every 3. months but was common in mohair from goats shorn twice or once per year. SES was less in goats of Texan genetic background, and was generally less in winter grown mohair. SES was higher for mohair with low fibre curvature (FC, 10°/mm) and a high clean washing yield (CWY, 90%) compared with mohair with low FC and lower CWY (80%), and compared with all mohair with high FC (18°/mm). The response of SES to shearing regime, genetic background, shearing season, age of goat and a response to dam age were almost identical whether or not an adjustment was made for CWY and FC. There was a moderate amount of variability due to sires and individuals. We can conclude that a large part of these effects observed, namely breed, dam age, sire, and a component of the FC and CWY effects, are genetic. Mohair producers can manage the genetic effects by careful selection of sires, especially avoiding those with low CWY or high FC, and avoiding sires with higher levels of staple entanglement or that have produced progeny with higher levels of staple entanglement. Also, unidentified environmental effects are affecting staple entanglement, although a lack of a live weight change effect on entanglement indicates that this effect might not be due to nutrition. © 2013 Elsevier B.V.

Analytical model to locate the fluidisation interface in a solid-gas vacuum fluidised bed

Vacuum fluidised beds have a distinct advantage of being operated with reduced mass consumption of the fluidising media. However, a low quality of fluidisation reduces the opportunity to utilise the bubbling regime in vacuum fluidised beds. Fluidisation maps are often used to depict the interface between the quiescent, bubbling and slugging regimes inside a fluidised bed. Such maps have been obtained by visual observations of the fluidisation interface in transparent fluidised beds. For beds which are visually inaccessible fluidisation maps are difficult to obtain. The present work therefore attempts to model the interface travel in a vacuum fluidised bed. The pressure gradient due to the bed weight has been determined to be a main contributor for fluidisation/defluidisation under vacuum. A simple analytical model based on the pressure gradient (PG model) is developed to predict the interface location in a vacuum fluidised bed. For a segregated bed, the Gibilaro-Rowe (GR) model is modified and used to predict the jetsam layer growth along with the fluidisation interface. The predictions are compared with the experimental data for minimally and highly segregated particles and it is seen that for non-segregated powders the predictions are quite accurate. Lack of sufficient knowledge of bubble characteristics, however, impeded accurate prediction of the jetsam growth especially at high flow rates. However, an approximate complete fluidisation interface is successfully predicted using the GR-PG model. © 2014 Elsevier B.V.

Modelling and power quality analysis of a grid-connected solar PV system

Increased concern about global warming coupled with the escalating demand of energy has driven the conventional power system to be more reliable one by integrating Renewable Energies (RE) in to grid. Over the recent years, integration of solar PV forming a gridconnected PV is considered as one of the most promisingtechnologies to the developed countries like Australia to meet the growing demand of energy. This rapid increase in grid connected photovoltaic (PV) systems has made the supply utilities concerned about the drastic effects that have to be considered on the distribution network in particular voltage fluctuations, harmonic distortions and the Power factor for sustainable power generation. However, irrespective of thefact that the utility grid can accommodate the variability of load or irregular solar irradiance, it is essential to study the impact of grid connected PV systems during higher penetration levels as the intermittent nature of solar PV adversely effects the grid characteristics in meeting the load demand. Hence, keeping this in track, this paper examines the grid-connected PV system considering a residential network of Geelong region (38◦.09' S and 144◦.21’ E) and explores the level of impacts considering summer load profile with a change in the level of integrations. Initially, a PV power system network model is developed in Matlab-Simulink environment and the simulations are carried out to explore the impacts of solar PV penetration at low voltage distribution network considering power quality (PQ) issues such as voltage fluctuations, harmonics distortion at different load conditions.

A framework for mobile elearning (mLearning) with analytical decision model

Mobile eLearning (mLearning) can create a revolution in eLearning with the popularity of smart mobile devices and Application. However, contents are the king to make this revolution happen. Moreover, for an effective mLearning system, analytical aspects such as, quality of contents, quality of results, performance of learners, needs to be addressed. This paper presents a framework for personal mLearning. In this paper, we have used graph-based model called bipartite graph for content authentication and identification of the quality of results. Furthermore, we have used statistical estimation process for trustworthiness of weights in the bipartite graph using confidence interval and hypothesis test as analytical decision model tool.

Demo abstract: wind measurements for water quality studies in urban reservoirs

Water quality monitoring and prediction are critical for ensuring the sustainability of water resources which are essential for social security, especially for countries with limited land like Singapore. For example, the Singapore government identified water as a new growth sector and committed in 2006 to invest S$ 330 million over the following five years for water research and development [1]. To investigate the water quality evolution numerically, some key water quality parameters at several discrete locations in the reservoir (e.g., dissolved oxygen, chlorophyll, and temperature) and some environmental parameters (e.g., the wind distribution above water surface, air temperature and precipitation) are used as inputs to a three-dimensional hydrodynamics-ecological model, Estuary Lake and Coastal Ocean Model - Computational Aquatic Ecosystem Dynamics Model (ELCOM-CAEDYM) [2]. Based on the calculation in the model, we can obtain the distribution of water quality in the whole reservoir. We can also study the effect of different environmental parameters on the water quality evolution, and finally predict the water quality of the reservoir with a time step of 30 seconds. In this demo, we introduce our data collection system which enables water quality studies with real-time sensor data.

Associations of diet quality with health-related quality of life in older Australian men and women

This study investigated associations between diet quality measures and quality of life two years later. Adults 55-65 years participating in the Wellbeing, Eating and Exercise for a Long Life (WELL) study in Victoria, Australia (n = 1150 men and n = 1307 women) completed a postal survey including a 111-item food frequency questionnaire in 2010. Diet quality in 2010 was assessed via the dietary guideline index (DGI), recommended food score (RFS) and Mediterranean diet score (MDS). The RAND 36-item survey assessed health-related quality of life in 2012. Associations were assessed using logistic regression adjusted for covariates. In men, DGI and RFS were associated with better reported energy (OR = 1.79, CI: 1.25, 2.55 and OR = 1.56, CI: 1.11, 2.19 respectively), and DGI was additionally associated with better general health (OR = 1.54, 95% CI: 1.08, 2.20), and overall mental component summary scale (OR = 1.51, CI: 1.07, 2.15) in the fully adjusted model. In women, associations between two indices of diet quality (DGI, RFS) physical function (OR = 1.66, CI: 1.19, 2.31 and OR = 1.70, CI: 1.21, 2.37 respectively) and general health (OR = 1.83, CI: 1.32, 2.54 and OR = 1.54, CI: 1.11, 2.14 respectively) were observed. DGI was also associated with overall physical component summary score (OR = 1.56, CI: 1.12, 2.17). Additional associations between emotional wellbeing and DGI (OR = 1.40, CI: 1.01, 1.93) and RFS (OR = 1.44, CI: 1.04, 1.99), and MDS and energy (OR = 1.53, CI: 1.11, 2.10) were observed in the fully adjusted model, in women only. Older adults with better quality diets report better health-related quality of life, with additional associations with emotional wellbeing observed in women.

A new computerized adaptive test advancing the measurement of health-related quality of life (HRQoL) in children: the Kids-CAT

Purpose: Assessing health-related quality of life (HRQoL) via Computerized Adaptive Tests (CAT) provides greater measurement precision coupled with a lower test burden compared to conventional tests. Currently, there are no European pediatric HRQoL CATs available. This manuscript aims at describing the development of a HRQoL CAT for children and adolescents: the Kids-CAT, which was developed based on the established KIDSCREEN-27 HRQoL domain structure. Methods: The Kids-CAT was developed combining classical test theory and item response theory methods and using large archival data of European KIDSCREEN norm studies (n = 10,577–19,580). Methods were applied in line with the US PROMIS project. Item bank development included the investigation of unidimensionality, local independence, exploration of Differential Item Functioning (DIF), evaluation of Item Response Curves (IRCs), estimation and norming of item parameters as well as first CAT simulations. Results: The Kids-CAT was successfully built covering five item banks (with 26–46 items each) to measure physical well-being, psychological well-being, parent relations, social support and peers, and school well-being. The Kids-CAT item banks proved excellent psychometric properties: high content validity, unidimensionality, local independence, low DIF, and model conform IRCs. In CAT simulations, seven items were needed to achieve a measurement precision between.8 and.9 (reliability). It has a child-friendly design, is easy accessible online and gives immediate feedback reports of scores. Conclusions: The Kids-CAT has the potential to advance pediatric HRQoL measurement by making it less burdensome and enhancing the patient–doctor communication.

Erratum to: A new computerized adaptive test advancing the measurement of health-related quality of life (HRQoL) in children: the Kids-CAT

Abstract
Purpose
Assessing health-related quality of life (HRQoL) via Computerized Adaptive Tests (CAT) provides greater measurement precision coupled with a lower test burden compared to conventional tests. Currently, there are no European pediatric HRQoL CATs available. This manuscript aims at describing the development of a HRQoL CAT for children and adolescents: the Kids-CAT, which was developed based on the established KIDSCREEN-27 HRQoL domain structure.
Methods
The Kids-CAT was developed combining classical test theory and item response theory methods and using large archival data of European KIDSCREEN norm studies (n=10,577–19,580). Methods were applied in line with the US PROMIS project. Item bank development included the investigation of unidimensionality, local independence, exploration of Differential Item Functioning (DIF), evaluation of Item Response Curves (IRCs), estimation and norming of item parameters as well as first CAT simulations.
Results
The Kids-CAT was successfully built covering five item banks (with 26–46 items each) to measure physical well-being, psychological well-being, parent relations, social support and peers, and school well-being. The Kids-CAT item banks proved excellent psychometric properties: high content validity, unidimensionality, local independence, low DIF, and model conform IRCs. In CAT simulations, seven items were needed to achieve a measurement precision between .8 and .9 (reliability). It has a child-friendly design, is easy accessible online and gives immediate feedback reports of scores.
Conclusions
The Kids-CAT has the potential to advance pediatric HRQoL measurement by making it less burdensome and enhancing the patient–doctor communication.

Evidencing learning outcomes: a multi-level, multi-dimensional course alignment model

This conceptual framework proposes a multi-level, multi-dimensional course alignment model to implement a contextualised constructive alignment of rubric design that authentically evidences and assesses learning outcomes. By embedding quality control mechanisms at each level for each dimension, this model facilitates the development of an aligned curriculum. The use of rubrics is a crucial theme for many higher education institutions owing to the binding requirement by universities to provide evidence to quality assurance agencies. The success of evidencing learning outcomes through rubrics, however, is only one piece of the puzzle. The other is the contextualised constructive alignment of intertwined factors. Despite the significance of embedding these factors, there has been little, if any, systematic framework in this area. The two key instrumental forces underpinning the conception of this model are: seeking external accreditation and the implementation of programme enhancement thus realising the strategic agenda for an Australian university.

A modified fuzzy min-max neural network for data clustering and its application to power quality monitoring

When no prior knowledge is available, clustering is a useful technique for categorizing data into meaningful groups or clusters. In this paper, a modified fuzzy min-max (MFMM) clustering neural network is proposed. Its efficacy for tackling power quality monitoring tasks is demonstrated. A literature review on various clustering techniques is first presented. To evaluate the proposed MFMM model, a performance comparison study using benchmark data sets pertaining to clustering problems is conducted. The results obtained are comparable with those reported in the literature. Then, a real-world case study on power quality monitoring tasks is performed. The results are compared with those from the fuzzy c-means and k-means clustering methods. The experimental outcome positively indicates the potential of MFMM in undertaking data clustering tasks and its applicability to the power systems domain.

Sensor placement and measurement of wind for water quality studies in urban reservoirs

We study the water quality in an urban district, where the surface wind distribution is an essential input but undergoes high spatial and temporal variations due to the impact of surrounding buildings. In this work, we develop an optimal sensor placement scheme to measure the wind distribution over a large urban reservoir using a limited number of wind sensors. Unlike existing solutions that assume Gaussian process of target phenomena, this study measures the wind that inherently exhibits strong non-Gaussian yearly distribution. By leveraging the local monsoon characteristics of wind, we segment a year into different monsoon seasons that follow a unique distribution respectively. We also use computational fluid dynamics to learn the spatial correlation of wind. The output of sensor placement is a set of the most informative locations to deploy the wind sensors, based on the readings of which we can accurately predict the wind over the entire reservoir in real time. Ten wind sensors are deployed. The in-field measurement results of more than 3 months suggest that the proposed sensor placement and spatial prediction scheme provides accurate wind measurement that outperforms the state-of-the-art Gaussian model based on interpolation-based approaches.

Improving the quality of prediction intervals through optimal aggregation

Neural networks (NNs) are an effective tool to model nonlinear systems. However, their forecasting performance significantly drops in the presence of process uncertainties and disturbances. NN-based prediction intervals (PIs) offer an alternative solution to appropriately quantify uncertainties and disturbances associated with point forecasts. In this paper, an NN ensemble procedure is proposed to construct quality PIs. A recently developed lower-upper bound estimation method is applied to develop NN-based PIs. Then, constructed PIs from the NN ensemble members are combined using a weighted averaging mechanism. Simulated annealing and a genetic algorithm are used to optimally adjust the weights for the aggregation mechanism. The proposed method is examined for three different case studies. Simulation results reveal that the proposed method improves the average PI quality of individual NNs by 22%, 18%, and 78% for the first, second, and third case studies, respectively. The simulation study also demonstrates that a 3%-4% improvement in the quality of PIs can be achieved using the proposed method compared to the simple averaging aggregation method.

Franchisee-based brand equity: The role of brand relationship quality and brand citizenship behavior

Despite the evidence that brand management is core to the success of franchising businesses, limited empirical work has focused on branding in such business-to-business (B2B) exchanges. Integrating social exchange theory and the identity-based brand management framework, this study proposes that brand relationship quality is crucial in promoting franchisee brand citizenship behavior that can enhance brand equity attributable to franchisees, thereby advancing a model of '. franchisee-based brand equity' (FBBE). Survey results from 352 franchisees in franchised B2B exchanges suggest that brand relationship quality promotes brand citizenship behavior, thereby enhancing FBBE. Additionally, moderated mediation analysis indicates that the indirect effect of brand relationship quality on FBBE via brand citizenship behavior is stronger when franchisor competence is high. However, franchisor-franchisee relationship duration has no moderating effects on these relationships. The findings of this study have implications for franchising practitioners that are interested in understanding the role of brand relationship management in promoting franchisee brand citizenship behavior and FBBE.

Application of air quality combination forecasting to Bogota

The bulk of existing work on the statistical forecasting of air quality is based on either neural networks or linear regressions, which are both subject to important drawbacks. In particular, while neural networks are complicated and prone to in-sample overfitting, linear regressions are highly dependent on the specification of the regression function. The present paper shows how combining linear regression forecasts can be used to circumvent all of these problems. The usefulness of the proposed combination approach is verified using both Monte Carlo simulation and an extensive application to air quality in Bogota, one of the largest and most polluted cities in Latin America. © 2014 Elsevier Ltd.

Evaluating virtuality in teams : a conceptual model

Major parts of teams deployed in contemporary organisations to some extent possess the attributes of virtuality. In this context, at high levels of virtuality, team requirements become quite different from those of face-to-face teams. Thus, evaluating virtuality in teams is a key prerequisite for assessing its requirements and defining the applicable managerial policies. Nonetheless, the concept of virtuality in teams has remained elusive within the extant literature. To address this, this paper deploys a qualitative meta-analysis to synthesise the relevant findings drawing upon an exhaustive review of the literature. The study conceptualises the phenomenon of virtuality through proposing a conceptual framework for evaluating the level of virtuality in teams. This paper contributes to the field by offering further insight into the concept of virtuality and directs future investigations by providing the ground for validating the presented conceptual model.