Levene tests of homogeneity of variance for general block and treatment designs

This article develops a weighted least squares version of Levene's test of homogeneity of variance for a general design, available both for univariate and multivariate situations. When the design is balanced, the univariate and two common multivariate test statistics turn out to be proportional to the corresponding ordinary least squares test statistics obtained from an analysis of variance of the absolute values of the standardized mean-based residuals from the original analysis of the data. The constant of proportionality is simply a design-dependent multiplier (which does not necessarily tend to unity). Explicit results are presented for randomized block and Latin square designs and are illustrated for factorial treatment designs and split-plot experiments. The distribution of the univariate test statistic is close to a standard F-distribution, although it can be slightly underdispersed. For a complex design, the test assesses homogeneity of variance across blocks, treatments, or treatment factors and offers an objective interpretation of residual plot.

Sensitivity and specificity of pooled faecal culture and serology as flock-screening tests for detection of ovine paratuberculosis in Australia

The flock-level sensitivity of pooled faecal culture and serological testing using AGID for the detection of ovine Johne's disease-infected flocks were estimated using non-gold-standard methods. The two tests were compared in an extensive field trial in 296 flocks in New South Wales during 1998. In each flock, a sample of sheep was selected and tested for ovine Johne's disease using both the AGID and pooled faecal culture. The flock-specificity of pooled faecal culture also was estimated from results of surveillance and market-assurance testing in New South Wales. The overall flock-sensitivity of pooled faecal culture was 92% (95% CI: 82.4 and 97.4%) compared to 61% (50.5 and 70.9%) for serology (assuming that both tests were 100% specific). In low-prevalence flocks (estimated prevalence

Characterisation of chronic lateral epicondylalgia using the McGill pain questionnaire, visual analog scales, and quantitative sensory tests

Aims: To characterise chronic lateral epicondylalgia using the McGill Pain Questionnaire, Visual Analog Scales for pain and function, and Quantitative Sensory Tests; and to examine the relationship between these tests in a population with chronic lateral epicondylalgia. Method: Fifty-six patients (29 female, 27 male) diagnosed with unilateral lateral epicondylalgia of 18.7 months (mean) duration (range 1-300), with a mean age of 50.7 years (range 27-73) participated in this study. Each participant underwent assessment with the McGill Pain Questionnaire (MPQ), Visual Analog Scales (VAS) for pain and function. and Quantitative Sensory Tests (QST) including thermal and pressure pain thresholds, pain free grip strength, and neuromeningeal tissue testing via the upper limb tension test 2b (ULTT 2b). Results: Moderate correlation (r = .338-.514, p = .000-.013) was found between all indices of the MPQ and VAS for pain experienced in the previous 24 hours and week. Thermal pain threshold was found to be significantly higher in males. A significant poor to moderate correlation was found between the Pain Rating Index (PRI) in the sensory category of the MPQ and ULTT2b scores (r = .353, p = .038). There was no other significant correlation between MPQ and QST data. Pain free grip strength was poorly yet significantly correlated with duration of pathology (r = 318, p = .038). Conclusion: The findings of this study are in agreement with others (Melzack and Katz, 1994) regarding the multidimensional nature of pain, in a condition conventionally conceived as a musculoskeletal pain state. The findings also suggest that utilisation of only one pain measurement tool is unlikely to provide a thorough clinical picture of pain experienced with chronic lateral epicondylalgia.

A study of five cervicocephalic relocation tests in three different subject groups

Objective: To compare head relocation accuracy in traumatic ( whiplash), insidious onset neck pain patients and asymptomatic subjects when targeting a natural head posture (NHP) and complex predetermined positions. Design: A case-control study. Setting: University-based musculoskeletal research clinic. Participants: Sixty-three volunteers divided into three groups of similar gender and age: Group 1 (n=21) an asymptomatic group; group 2 (n=20) insidious onset neck pain; group 3 (n=22) a history of whiplash injury. Intervention: Five randomly ordered tests designed to detect relocation accuracy of the head. Outcome measures: A 3-Space Fastrak system measured the mean absolute relocation error of three trials of each relocation test. Results: A significant difference was found between groups in one of the tests targeting the NHP (p=0.001). Post-hoc pairwise comparisons revealed a significant difference (pless than or equal to0.05) between the asymptomatic group and each symptomatic group. The difference between the symptomatic groups just failed to reach significance (p=0.07). None of the other four tests revealed significant differences. Conclusion: The test of targeting the NHP indicates that relocation inaccuracy exists in patients with neck pain with a trend to suggest that the deficit may be greater in whiplash patients. Tests employing unfamiliar postures or more complex movement were not successful in differentiating subject groups.

Polymerase chain reaction tests for the identification of Ross River, Kunjin and Murray Valley encephalitis virus infections in horses

Objective To develop and validate specific, sensitive and rapid diagnostic tests using RT-PCR for the detection of Ross River virus (RRV), Kunjin virus (KV) and Murray Valley encephalitis virus (MVEV) infections in horses. Methods Primer sets based on nucleotide sequence encoding the envelope glycoprotein E2 of RRV and on the nonstructural protein 5 (NS5) of KV and MVEV were designed and used in single round PCRs to test for the respective viruses in infected cell cultures and, in the case of RRV, in samples of horse blood and synovial fluid. Results The primer pairs designed for each of the three viruses amplified a product of expected size from prototype viruses that were grown in cell culture. The identity of each of the products was confirmed by nucleotide sequencing indicating that in the context used the RT-PCRs were specific. RRV was detected in serums from 8 horses for which there were clinical signs consistent with RRV infection such that an acute-phase serum sample was taken and submitted for RRV serology testing. The RRV RT-PCR was analytically sensitive in that it was estimated to detect as little as 50 TCID50 of RRV per mL of serum and was specific in that the primer pairs did not amplify other products from the 8 serum samples. The RRV primers also detected virus in three independent mosquito pools known to contain RRV by virus isolation in cell culture. Samples from horses suspected to be infected with KV and MVEV were not available. Conclusion Despite much anecdotal and serological evidence for infection of horses with RRV actual infection and associated clinical disease are infrequently confirmed. The availability of a specific and analytically sensitive RT-PCR for the detection of RRV provides additional opportunities to confirm the presence of this virus in clinical samples. The RTPCR primers for the diagnosis of KV and MVEV infections were shown to be specific for cell culture grown viruses but the further validation of these tests requires the availability of appropriate clinical samples from infected horses.

The case for emotional intelligence in organizational research

Since its popularization by Goleman (1995), the concept of emotional intelligence has been the subject of ongoing controversy, so it is understandable that the model we proposed, which includes emotional intelligence as a moderator variable, would attract its share of criticism.

Is emotional intelligence training for leaders justified?

In this paper, we present the results of a qualitative study of subordinate perceptions of leaders. The study represents a preliminary test of a model based on Affective Events Theory, which posits that leaders who are seen to be effective shape the affective events that determine employees' attitudes and behaviours in the workplace. Within this framework, we argue that effective leaders ameliorate employees' hassles by providing frequent, small emotional uplifts. The resulting positive affective states are then proposed to lead to more positive employee attitudes and behaviours, and more positive regard for the leader. Importantly, leaders who demonstrate these ameliorating behaviours are likely to require high levels of emotional intelligence, defined in terms of the ability to recognise, understand, and manage emotions in self and others. To investigate this model, we conducted interviews and focus groups with 10 leaders and 24 employees. Results confirmed that these processes do indeed exist in the workplace. In particular, leaders who were seen by employees to provide continuous small emotional uplifts were consistently held to be the most effective. Study participants were especially affected by negative events (or hassles). Leaders who failed to deal with hassles or, worse still, were the source of hassles, were consistently seen to be less effective. We conclude with a discussion of implications for practicing managers, and suggest that our exploratory findings provide justification for emotional intelligence training as a means to improve leader perceptions and effectiveness. [Abstract from author]

An ecosystem of products and systems for ambient intelligence – the AAL4ALL users perspective

Developed societies are currently facing severe demographic changes: the world is getting older at an unprecedented rate. In 2000, about 420 million people, or approximately 7 percent of the world population, were aged 65 or older. By 2050, that number will be nearly 1.5 billion people, about 16 percent of the world population. This demographic trend will be also followed by an increase of people with physical limitations. New challenges will be raised to the traditional health care systems, not only in Portugal, but also in all other European states. There is an urgent need to find solutions that allow extending the time people can live in their preferred environment by increasing their autonomy, self-confidence and mobility. AAL4ALL presents an idea for an answer through the development of an ecosystem of products and services for Ambient Assisted Living (AAL) associated to a business model and validated through large scale trial. This paper presents the results of the first survey developed within the AAL4ALL project: the users’ survey targeted at the Portuguese seniors and pre-seniors. This paper is, thus, about the lives of the Portuguese population aged 50 and over.

The need for standardized tests to evaluate the reliability of data transport in wireless medical systems

Wireless medical systems are comprised of four stages, namely the medical device, the data transport, the data collection and the data evaluation stages. Whereas the performance of the first stage is highly regulated, the others are not. This paper concentrates on the data transport stage and argues that it is necessary to establish standardized tests to be used by medical device manufacturers to provide comparable results concerning the communication performance of the wireless networks used to transport medical data. Besides, it suggests test parameters and procedures to be used to produce comparable communication performance results.

A Caregiver Support Platform within the Scope of an Ambient Assisted Living Ecosystem

The Ambient Assisted Living (AAL) area is in constant evolution, providing new technologies to users and enhancing the level of security and comfort that is ensured by house platforms. The Ambient Assisted Living for All (AAL4ALL) project aims to develop a new AAL concept, supported on a unified ecosystem and certification process that enables a heterogeneous environment. The concepts of Intelligent Environments, Ambient Intelligence, and the foundations of the Ambient Assisted Living are all presented in the framework of this project. In this work, we consider a specific platform developed in the scope of AAL4ALL, called UserAccess. The architecture of the platform and its role within the overall AAL4ALL concept, the implementation of the platform, and the available interfaces are presented. In addition, its feasibility is validated through a series of tests.

Comparative analysis between statistical and artificial intelligence models in business failure prediction

A growing number of predicting corporate failure models has emerged since 60s. Economic and social consequences of business failure can be dramatic, thus it is not surprise that the issue has been of growing interest in academic research as well as in business context. The main purpose of this study is to compare the predictive ability of five developed models based on three statistical techniques (Discriminant Analysis, Logit and Probit) and two models based on Artificial Intelligence (Neural Networks and Rough Sets). The five models were employed to a dataset of 420 non-bankrupt firms and 125 bankrupt firms belonging to the textile and clothing industry, over the period 2003–09. Results show that all the models performed well, with an overall correct classification level higher than 90%, and a type II error always less than 2%. The type I error increases as we move away from the year prior to failure. Our models contribute to the discussion of corporate financial distress causes. Moreover it can be used to assist decisions of creditors, investors and auditors. Additionally, this research can be of great contribution to devisers of national economic policies that aim to reduce industrial unemployment.

Comparative Analysis between Statistical and Artificial Intelligence Models in Business Failure Prediction

A growing number of predicting corporate failure models has emerged since 60s. Economic and social consequences of business failure can be dramatic, thus it is not surprise that the issue has been of growing interest in academic research as well as in business context. The main purpose of this study is to compare the predictive ability of five developed models based on three statistical techniques (Discriminant Analysis, Logit and Probit) and two models based on Artificial Intelligence (Neural Networks and Rough Sets). The five models were employed to a dataset of 420 non-bankrupt firms and 125 bankrupt firms belonging to the textile and clothing industry, over the period 2003–09. Results show that all the models performed well, with an overall correct classification level higher than 90%, and a type II error always less than 2%. The type I error increases as we move away from the year prior to failure. Our models contribute to the discussion of corporate financial distress causes. Moreover it can be used to assist decisions of creditors, investors and auditors. Additionally, this research can be of great contribution to devisers of national economic policies that aim to reduce industrial unemployment.

Applications of artificial intelligence to condition-based maintenance

In this paper we discuss interesting developments of expert systems for machine diagnosis and condition-based maintenance. We review some elements of condition-based maintenance and its applications, expert systems for machine diagnosis, and an example of machine diagnosis. In the last section we note some problems to be resolved so that expert systems for machine diagnosis may gain wider acceptance in the future.