Measurement of stable changes of self-management skills after rehabilitation: a latent state-trait analysis of the Health Education Impact Questionnaire (heiQ™)

To assess stable effects of self-management programs, measurement instruments should primarily capture the attributes of interest, for example, the self-management skills of the measured persons. However, measurements of psychological constructs are always influenced by both aspects of the situation (states) and aspects of the person (traits). This study tests whether the Health Education Impact Questionnaire (heiQ™), an instrument assessing a wide range of proximal outcomes of self-management programs, is primarily influenced by person factors instead of situational factors. Furthermore, measurement invariance over time, changes in traits and predictors of change for each heiQ™ scale were examined.

Australian SMEs waste to landfill

Landfill waste has a negative impact on the environment and small and medium sized enterprises (SMEs) are believed to be significant contributors. There is little government or scholarly research, however, quantifying the collective volume of waste SMEs send to landfill. Where studies do exist they measure total volumes (landfill and recycling combined) and/or do not distinguish between specific waste streams (e.g. wood) and subcategories (e.g. dust). This paper contributes to knowledge by giving insight into the collective volume of waste of 404 SMEs, reconceptualising SME waste into subcategories and by measuring landfill volumes. It presents findings from these 404 Australian SMEs which found that, in descending order, cardboard, paper, plastic wrap, wood dust and particleboard were the subcategories these SMEs sent to landfill in the greatest volumes. It also argues that this reconceptualisation, and associated data collection protocols, have the potential to enable scholars and policy makers to determine the waste subcategories to which SMEs contribute most, formulate targeted interventions and research or evaluate environmental outcomes. © 2014 © 2014 Environment Institute of Australia and New Zealand Inc.

Effects of patient-, environment- and medication-related factors on high-alert medication incidents.

To measure the rate of medication incidents associated with the prescription and administration of high-alert medications and to identify patient-, environment- and medication-related factors associated with these incidents.

Prediction of pedestrians routes within a built environment in normal conditions

Modelling and prediction of pedestrian routing behaviours within known built environments has recently attracted the attention of researchers across multiple disciplines, owing to the growing demand on urban resources and requirements for efficient use of public facilities. This study presents an investigation into pedestrians' routing behaviours within an indoor environment under normal, non-panic situations. A network-based method using constrained Delaunay triangulation is adopted, and a utility-based model employing dynamic programming is developed. The main contribution of this study is the formulation of an appropriate utility function that allows an effective application of dynamic programming to predict a series of consecutive waypoints within a built environment. The aim is to generate accurate sequence waypoints for the pedestrian walking path using only structural definitions of the environment as defined in a standard CAD format. The simulation results are benchmarked against those from the A* algorithm, and the outcome positively indicates the usefulness of the proposed method in predicting pedestrians' route selection activities. © 2014 Elsevier Ltd. All rights reserved.

Condition monitoring of induction motors : A review and an application of an ensemble of hybrid intelligent models

In this paper, a review on condition monitoring of induction motors is first presented. Then, an ensemble of hybrid intelligent models that is useful for condition monitoring of induction motors is proposed. The review covers two parts, i.e.; (i) a total of nine commonly used condition monitoring methods of induction motors; and (ii) intelligent learning models for condition monitoring of induction motors subject to single and multiple input signals. Based on the review findings, the Motor Current Signature Analysis (MCSA) method is selected for this study owing to its online, non-invasive properties and its requirement of only single input source; therefore leading to a cost-effective condition monitoring method. A hybrid intelligent model that consists of the Fuzzy Min-Max (FMM) neural network and the Random Forest (RF) model comprising an ensemble of Classification and Regression Trees is developed. The majority voting scheme is used to combine the predictions produced by the resulting FMM-RF ensemble (or FMM-RFE) members. A benchmark problem is first deployed to evaluate the usefulness of the FMM-RFE model. Then, the model is applied to condition monitoring of induction motors using a set of real data samples. Specifically, the stator current signals of induction motors are obtained using the MCSA method. The signals are processed to produce a set of harmonic-based features for classification using the FMM-RFE model. The experimental results show good performances in both noise-free and noisy environments. More importantly, a set of explanatory rules in the form of a decision tree can be extracted from the FMM-RFE model to justify its predictions. The outcomes ascertain the effectiveness of the proposed FMM-RFE model in undertaking condition monitoring tasks, especially for induction motors, under different environments. © 2014 Elsevier Ltd. All rights reserved.

A review on computational intelligence methods for controlling traffic signal timing

Urban traffic as one of the most important challenges in modern city life needs practically effective and efficient solutions. Artificial intelligence methods have gained popularity for optimal traffic light control. In this paper, a review of most important works in the field of controlling traffic signal timing, in particular studies focusing on Q-learning, neural network, and fuzzy logic system are presented. As per existing literature, the intelligent methods show a higher performance compared to traditional controlling methods. However, a study that compares the performance of different learning methods is not published yet. In this paper, the aforementioned computational intelligence methods and a fixed-time method are implemented to set signals times and minimize total delays for an isolated intersection. These methods are developed and compared on a same platform. The intersection is treated as an intelligent agent that learns to propose an appropriate green time for each phase. The appropriate green time for all the intelligent controllers are estimated based on the received traffic information. A comprehensive comparison is made between the performance of Q-learning, neural network, and fuzzy logic system controller for two different scenarios. The three intelligent learning controllers present close performances with multiple replication orders in two scenarios. On average Q-learning has 66%, neural network 71%, and fuzzy logic has 74% higher performance compared to the fixed-time controller.

Estimating capability index in multivariate processes using bootstrap sequential sampling procedures

Capability indices in both univariate and multivariate processes are extensively employed in quality control to assess the quality status of production batches before their release for operational use. It is traditionally a measure of the ratio of the allowable process spread and the actual spread. In this paper, we will adopt a bootstrap and sequential sampling procedures to determine the optimal sample size for estimating a multivariate capability index introduced by Pearns et. al. [12]. Bootstrap techniques have the distinct advantage of placing very minimum requirement on the distributions of the underlying quality characteristics, thereby rendering them more relevant under a wide variety of situations. Finally, we provide several numerical examples where the sequential sampling procedures are evaluated and compared.

EEG signal classification for BCI applications by wavelets and interval type-2 fuzzy logic systems

The nonlinear, noisy and outlier characteristics of electroencephalography (EEG) signals inspire the employment of fuzzy logic due to its power to handle uncertainty. This paper introduces an approach to classify motor imagery EEG signals using an interval type-2 fuzzy logic system (IT2FLS) in a combination with wavelet transformation. Wavelet coefficients are ranked based on the statistics of the receiver operating characteristic curve criterion. The most informative coefficients serve as inputs to the IT2FLS for the classification task. Two benchmark datasets, named Ia and Ib, downloaded from the brain-computer interface (BCI) competition II, are employed for the experiments. Classification performance is evaluated using accuracy, sensitivity, specificity and F-measure. Widely-used classifiers, including feedforward neural network, support vector machine, k-nearest neighbours, AdaBoost and adaptive neuro-fuzzy inference system, are also implemented for comparisons. The wavelet-IT2FLS method considerably dominates the comparable classifiers on both datasets, and outperforms the best performance on the Ia and Ib datasets reported in the BCI competition II by 1.40% and 2.27% respectively. The proposed approach yields great accuracy and requires low computational cost, which can be applied to a real-time BCI system for motor imagery data analysis.

Intelligent cuckoo search optimized traffic signal controllers for multi-intersection network

Traffic congestion in urban roads is one of the biggest challenges of 21 century. Despite a myriad of research work in the last two decades, optimization of traffic signals in network level is still an open research problem. This paper for the first time employs advanced cuckoo search optimization algorithm for optimally tuning parameters of intelligent controllers. Neural Network (NN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) are two intelligent controllers implemented in this study. For the sake of comparison, we also implement Q-learning and fixed-time controllers as benchmarks. Comprehensive simulation scenarios are designed and executed for a traffic network composed of nine four-way intersections. Obtained results for a few scenarios demonstrate the optimality of trained intelligent controllers using the cuckoo search method. The average performance of NN, ANFIS, and Q-learning controllers against the fixed-time controller are 44%, 39%, and 35%, respectively.

Individual prevention courses for occupational skin diseases: changes in and relationships between proximal and distal outcomes

BACKGROUND: To treat people with occupational contact dermatitis, the German Accident Prevention and Insurance Association in the Health and Welfare Services offers 2-day individual prevention (IP) seminars. OBJECTIVES: We investigated whether there are short-term and medium-term changes in proximal (e.g. behaviour) and distal (e.g. symptoms) outcomes after an IP seminar, whether changes in proximal outcomes are associated with changes in distal outcomes, and whether subgroups can be identified that benefit in particular. PATIENTS/MATERIALS/METHODS: In a prospective study, 502 participants of 85 IP courses completed the health education impact questionnaire (heiQ™) and skin symptom questionnaire (Skindex-29) at the start of the course, immediately thereafter, and after 6 months. Change was assessed according to standardized effect size. Regression techniques were used to analyse associations between proximal and distal outcomes. RESULTS: After 6 months, participants showed improved self-management skills and preventive behaviour, and less fear of job loss, disease-related symptoms, and emotional distress. Significant associations between proximal and distal outcomes were found. Participants who felt more limited by their skin disease showed greater effects. CONCLUSIONS: The results are consistent with the assumption that IP courses provide a range of benefits for people with occupational contact dermatitis. Changes in distal outcomes may be influenced by changes in proximal outcomes.

Sound the alarm: health and safety risks associated with alarm response for salaried and retained metropolitan firefighters

Responding to an emergency alarm poses a significant risk to firefighters' health and safety, particularly to cardiovascular health, physical and psychological stress, and fatigue. These risks have been largely categorised for salaried firefighters working 'on station'. Less is known about the factors that contribute to these risks for the vast number of non-salaried personnel who serve in retained roles, often deploying from home. The present study investigated the alarm response procedure for Australian metropolitan fire fighters, identifying common and divergent sources of risk for salaried and retained staff. There were significant differences in procedure between the two workgroups and this resulted in differences in risk profile between groups. Sleep and fatigue, actual response to the alarm stimulus, work-life balance and trauma emerged as sources of risk experienced differently by salaried and retained firefighters. Key findings included reports of fatigue in both groups, but particularly in the case of retained firefighters who manage primary employment as well as their retained position. This also translated into a poor sense of work-life balance. Both groups reported light sleep, insufficient sleep or fragmented sleep as a result of alarm response. In the case of salaried firefighters, this was associated with being woken on station when other appliances are called. There were risks from physical and psychological responses to the alarm stimulus, and reports of sleep inertia when driving soon after waking. The findings of this study highlight the common and divergent risks for these workgroups, and could be used in the ongoing management of firefighters' health and safety.

Patient physiological status at the emergency department-ward interface and emergency calls for clinical deterioration during early hospital admission

AIMS: To examine the relationship between physiological status at the emergency department-ward interface and emergency calls (medical emergency team or cardiac arrest team activation) during the first 72 hours of hospital admission. BACKGROUND: Ward adverse events are related to abnormal physiology in emergency department however the relationship between physiology at the emergency department-ward interface and ward adverse events is unknown. DESIGN: Descriptive and exploratory design. METHODS: The study involved 1980 patients at three hospitals in Melbourne Australia: i) 660 randomly selected adults admitted via the emergency department to medical or surgical wards during 2012 and who had an emergency call; and ii) 1320 adults without emergency calls matched for gender, triage category, usual residence, admitting unit and age. RESULTS/FINDINGS: The median age was 78 years and 48·8% were males. The median time to the first emergency call was 18·8 hours and ≥1 abnormal parameters were documented in 34·9% of patients during the last hour of ED care and 47·1% of patients during first hour of ward care. Emergency calls were significantly more common in patients with heart rate and conscious state abnormalities during the last hour of emergency care and abnormal oxygen saturation, heart rate or respiratory rate during the first hour of ward care. Medical emergency team afferent limb failure occurred in 55·3% patients with medical emergency team activation criteria during first hour of ward care. CONCLUSION: The use of physiological status at the emergency department-ward interface to guide care planning and reasons for and outcomes of medical emergency team afferent limb failure are important areas for future research.

A unified mixed-integer programming model for simultaneous fluence weight and aperture optimization in VMAT, tomotherapy, and cyberknife

All rights reserved. In this paper, we propose and study a unified mixed-integer programming model that simultaneously optimizes fluence weights and multi-leaf collimator (MLC) apertures in the treatment planning optimization of VMAT, Tomotherapy, and CyberKnife. The contribution of our model is threefold: (i) Our model optimizes the fluence and MLC apertures simultaneously for a given set of control points. (ii) Our model can incorporate all volume limits or dose upper bounds for organs at risk (OAR) and dose lower bound limits for planning target volumes (PTV) as hard constraints, but it can also relax either of these constraint sets in a Lagrangian fashion and keep the other set as hard constraints. (iii) For faster solutions, we propose several heuristic methods based on the MIP model, as well as a meta-heuristic approach. The meta-heuristic is very efficient in practice, being able to generate dose- and machinery-feasible solutions for problem instances of clinical scale, e.g., obtaining feasible treatment plans to cases with 180 control points, 6750 sample voxels and 18,000 beamlets in 470 seconds, or cases with 72 control points, 8000 sample voxels and 28,800 beamlets in 352 seconds. With discretization and down-sampling of voxels, our method is capable of tackling a treatment field of 8000-64,000cm3, depending on the ratio of critical structure versus unspecified tissues.