Multi-step ahead direct prediction for the machine condition prognosis using regression trees and neuro-fuzzy systems

This paper presents an approach to predict the operating conditions of machine based on classification and regression trees (CART) and adaptive neuro-fuzzy inference system (ANFIS) in association with direct prediction strategy for multi-step ahead prediction of time series techniques. In this study, the number of available observations and the number of predicted steps are initially determined by using false nearest neighbor method and auto mutual information technique, respectively. These values are subsequently utilized as inputs for prediction models to forecast the future values of the machines’ operating conditions. The performance of the proposed approach is then evaluated by using real trending data of low methane compressor. A comparative study of the predicted results obtained from CART and ANFIS models is also carried out to appraise the prediction capability of these models. The results show that the ANFIS prediction model can track the change in machine conditions and has the potential for using as a tool to machine fault prognosis.

FDR and victims of family violence: Ensuring a safe process and outcomes

Family dispute resolution (FDR) is a positive first-stop process for family law matters, particularly those relating to disputes about children. FDR provides the parties with flexibility within a positive, structured and facilitated framework for what are often difficult and emotional negotiations. However, there are a range of issues that arise for victims of family violence in FDR that can make it a dangerous and unsafe process for them unless appropriate precautions are taken. This article discusses the nature of FDR and identifies the many positive aspects of it for women participants. The article then considers the nature and dynamic of family violence in order to contextualise the discussion that follows regarding concerns for the safety of participants in the FDR process. Finally, it offers some suggestions about how Australia could approach FDR differently to make it safer for victims of family violence.

Fault diagnosis of induction motor based on decision trees and adaptive neuro-fuzzy inference

This paper presents a fault diagnosis method based on adaptive neuro-fuzzy inference system (ANFIS) in combination with decision trees. Classification and regression tree (CART) which is one of the decision tree methods is used as a feature selection procedure to select pertinent features from data set. The crisp rules obtained from the decision tree are then converted to fuzzy if-then rules that are employed to identify the structure of ANFIS classifier. The hybrid of back-propagation and least squares algorithm are utilized to tune the parameters of the membership functions. In order to evaluate the proposed algorithm, the data sets obtained from vibration signals and current signals of the induction motors are used. The results indicate that the CART–ANFIS model has potential for fault diagnosis of induction motors.

Why do family firms congregate in certain industries?

We propose that family firm involvement and performance across industries is not random and is related to specific industry conditions. Using the population of listed companies on the Taiwan Stock Exchange over the period 1997-2007 we find that family firms are more involved in industries with more fixed assets, consistent with the long-term view of family owners, and in industry conditions that make it potentially easier for family owners to consume private benefits of control. Overall, we document a positive relationship between family firm involvement and performance, which indicates a net advantage for family firm shareholders in industries where family firms congregate. However, we also find that family firm performance is negatively affected when family firms use more debt and maintain a higher control wedge than their industry counterparts.

Seeing the Wood for the Trees: A Critical Evaluation of Methods to Estimate the Parameters of Stochastic Differential Equations

Maximum-likelihood estimates of the parameters of stochastic differential equations are consistent and asymptotically efficient, but unfortunately difficult to obtain if a closed-form expression for the transitional probability density function of the process is not available. As a result, a large number of competing estimation procedures have been proposed. This article provides a critical evaluation of the various estimation techniques. Special attention is given to the ease of implementation and comparative performance of the procedures when estimating the parameters of the Cox–Ingersoll–Ross and Ornstein–Uhlenbeck equations respectively.

Measurement and correlates of caregiver self-efficacy amongst family caregivers of persons with dementia living in Shanghai, China

Background: Caring for family members with dementia can be a long-term, burdensome task resulting in physical and emotional distress and impairment. Research has demonstrated significantly lower levels of selfefficacy among family caregivers of people with dementia (CGs) than caregivers of relatives with non-dementia diseases. Intervention studies have also suggested that the mental and physical health of dementia CGs could be improved through the enhancement of their self-efficacy. However, studies are limited in terms of the influences of caregiver self-efficacy on caregiver behaviour, subjective burden and health-related quality of life. Of particular note is that there are no studies on the applicability of caregiver self-efficacy in the social context of China. Objective: The purpose of this thesis was to undertake theoretical exploration using Bandura’s (1997) self-efficacy theory to 1) revise the Revised Caregiving Self-Efficacy Scale (C-RCSES) (Steffen, McKibbin, Zeiss, Gallagher-Thompson, & Bandura, 2002), and 2) explore determinants of caregiver self-efficacy and the role of caregiver self-efficacy and other conceptual constructs (including CGs’ socio-demographic characteristics, CRs’ impairment and CGs’ social support) in explaining and predicting caregiver behaviour, subjective burden and health-related quality of life among CGs in China. Methodology: Two studies were undertaken: a qualitative elicitation study with 10 CGs; and a cross-sectional survey with 196 CGs. In the first study, semi-structured interviews were conducted to explore caregiver behaviours and corresponding challenges for their performance. The findings of the study assisted in the development of the initial items and domains of the Chinese version of the Revised Caregiving Self-Efficacy Scale (C-RCSES). Following changes to items in the scale, the second study, a cross-sectional survey with 196 CGs was conducted to evaluate the psychometric properties of C-RCSES and to test a hypothesised self-efficacy model of family caregiving adapted from Bandura’s theory (1997). Results: 35 items were generated from the qualitative data. The content validity of the C-RCSES was assessed and ensured in Study One before being used for the cross-sectional survey. Eight items were removed and five subscales (caregiver self-efficacy for gathering information about treatment, symptoms and health care; obtaining support; responding to problematic behaviours; management of household, personal and medical care; and controlling upsetting thoughts about caregiving) were identified after principal component factor analysis on the cross-sectional survey data. The reliability of the scale is acceptable: the Cronbach’s alpha coefficients for the whole scale and for each subscale were all over .80; and the fourweek test-retest reliabilities for the whole scale and for each subscale ranged from .64 to .85. The concurrent, convergent and divergent validity were also acceptable. CGs reported moderate levels of caregiver self-efficacy. Furthermore, the level of self-efficacy for management of household, personal and medical care was relatively high in comparison to those of the other four domains of caregiver self-efficacy. Caregiver self-efficacy was also significantly influenced by CGs’ socio-demographic characteristics and the caregiving external factors (CR impairment and social support that CGs obtained). The level of caregiver behaviour that CGs reported was higher than that reported in other Chinese research. CGs’ socio-demographics significantly influenced caregiver behaviour, whereas caregiver self-efficacy did not influence caregiver behaviour. Regarding the two external factors, CGs who cared for highly impaired relatives reported high levels of caregiver behaviour, but social support did not influence caregiver behaviour. Regarding caregiver subjective burden and health-related quality of life, CGs reported moderate levels of subjective burden, and their level of healthrelated quality of life was significantly lower than that of the general population in China. The findings also indicated that CGs’ subjective burden and health-related quality of life were influenced by all major factors in the hypothesised model, including CGs’ socio-demographics, CRs’ impairment, social support that CGs obtained, caregiver self-efficacy and caregiver behaviour. Of these factors, caregiver self-efficacy and social support significantly improved their subjective burden and health-related quality of life; whereas caregiver behaviour and CRs’ impairment were detrimental to CGs, such as increasing subjective burden and worsening health-related quality of life. Conclusion: While requiring further exploration, the qualitative study was the first qualitative research conducted in China to provide an in-depth understanding of CGs’ caregiving experience, including their major caregiver behaviours and the corresponding challenges. Meanwhile, although the C-RCSES needs further psychometric testing, it is a useful tool for assessing caregiver self-efficacy in Chinese populations. Results of the qualitative and quantitative study provide useful information for future studies regarding the explanatory power of caregiver self-efficacy to caregiver behaviour, subjective burden and health-related quality of life. Additionally, integrated with Bandura’s theory, the findings from the quantitative study also suggested a further study exploring the role of outcome expectations in caregiver behaviour, subjective burden and healthrelated quality of life.

Genetic and gene expression analyses of the polycystic ovary syndrome candidate gene fibrillin-3 and other fibrillin family members in human ovaries

Several studies have demonstrated an association between polycystic ovary syndrome (PCOS) and the dinucleotide repeat microsatellite marker D19S884, which is located in intron 55 of the fibrillin-3 (FBN3) gene. Fibrillins, including FBN1 and 2, interact with latent transforming growth factor (TGF)-β-binding proteins (LTBP) and thereby control the bioactivity of TGFβs. TGFβs stimulate fibroblast replication and collagen production. The PCOS ovarian phenotype includes increased stromal collagen and expansion of the ovarian cortex, features feasibly influenced by abnormal fibrillin expression. To examine a possible role of fibrillins in PCOS, particularly FBN3, we undertook tagging and functional single nucleotide polymorphism (SNP) analysis (32 SNPs including 10 that generate non-synonymous amino acid changes) using DNA from 173 PCOS patients and 194 controls. No SNP showed a significant association with PCOS and alleles of most SNPs showed almost identical population frequencies between PCOS and control subjects. No significant differences were observed for microsatellite D19S884. In human PCO stroma/cortex (n = 4) and non-PCO ovarian stroma (n = 9), follicles (n = 3) and corpora lutea (n = 3) and in human ovarian cancer cell lines (KGN, SKOV-3, OVCAR-3, OVCAR-5), FBN1 mRNA levels were approximately 100 times greater than FBN2 and 200–1000-fold greater than FBN3. Expression of LTBP-1 mRNA was 3-fold greater than LTBP-2. We conclude that FBN3 appears to have little involvement in PCOS but cannot rule out that other markers in the region of chromosome 19p13.2 are associated with PCOS or that FBN3 expression occurs in other organs and that this may be influencing the PCOS phenotype.