Should athletes return to sport after applying Ice? A systematic review of the effect of local 3 cooling on functional performance

Applying ice or other forms of topical cooling is a popular method of treating sports injuries. It is commonplace for athletes to return to competitive activity, shortly or immediately after the application of a cold treatment. In this article, we examine the effect of local tissue cooling on outcomes relating to functional performance and to discuss their relevance to the sporting environment. A computerized literature search, citation tracking and hand search was performed up to April, 2011. Eligible studies were trials involving healthy human participants, describing the effects of cooling on outcomes relating to functional performance. Two reviewers independently assessed the validity of included trials and calculated effect sizes. Thirty five trials met the inclusion criteria; all had a high risk of bias. The mean sample size was 19. Meta-analyses were not undertaken due to clinical heterogeneity. The majority of studies used cooling durations >20 minutes. Strength (peak torque/force) was reported by 25 studies with approximately 75% recording a decrease in strength immediately following cooling. There was evidence from six studies that cooling adversely affected speed, power and agility-based running tasks; two studies found this was negated with a short rewarming period. There was conflicting evidence on the effect of cooling on isolated muscular endurance. A small number of studies found that cooling decreased upper limb dexterity and accuracy. The current evidence base suggests that athletes will probably be at a performance disadvantage if they return to activity immediately after cooling. This is based on cooling for longer than 20 minutes, which may exceed the durations employed in some sporting environments. In addition, some of the reported changes were clinically small and may only be relevant in elite sport. Until better evidence is available, practitioners should use short cooling applications and/or undertake a progressive warm up prior to returning to play.

Hospital outpatient responses to potential driving impairment due to prescribed medications

Driving and using prescription medicines that have the potential to impair driving is an emerging research area. To date it is characterised by a limited (although growing) number of studies and methodological complexities that make generalisations about impairment due to medications difficult. Consistent evidence has been found for the impairing effects of hypnotics, sedative antidepressants and antihistamines, and narcotic analgesics, although it has been estimated that as many as nine medication classes have the potential to impair driving (Alvarez & del Rio, 2000; Walsh, de Gier, Christopherson, & Verstraete, 2004). There is also evidence for increased negative effects related to concomitant use of other medications and alcohol (Movig et al., 2004; Pringle, Ahern, Heller, Gold, & Brown, 2005). Statistics on the high levels of Australian prescription medication use suggest that consumer awareness of driving impairment due to medicines should be examined. One web-based study has found a low level of awareness, knowledge and risk perceptions among Australian drivers about the impairing effects of various medications on driving (Mallick, Johnston, Goren, & Kennedy, 2007). The lack of awareness and knowledge brings into question the effectiveness of the existing countermeasures. In Australia these consist of the use of ancillary warning labels administered under mandatory regulation and professional guidelines, advice to patients, and the use of Consumer Medicines Information (CMI) with medications that are known to cause impairment. The responsibility for the use of the warnings and related counsel to patients primarily lies with the pharmacist when dispensing relevant medication. A review by the Therapeutic Goods Administration (TGA) noted that in practice, advice to patients may not occur and that CMI is not always available (TGA, 2002). Researchers have also found that patients' recall of verbal counsel is very low (Houts, Bachrach, Witmer, Tringali, Bucher, & Localio, 1998). With healthcare observed as increasingly being provided in outpatient conditions (Davis et al., 2006; Vingilis & MacDonald, 2000), establishing the effectiveness of the warning labels as a countermeasure is especially important. There have been recent international developments in medication categorisation systems and associated medication warning labels. In 2005, France implemented a four-tier medication categorisation and warning system to improve patients' and health professionals' awareness and knowledge of related road safety issues (AFSSAPS, 2005). This warning system uses a pictogram and indicates the level of potential impairment in relation to driving performance through the use of colour and advice on the recommended behaviour to adopt towards driving. The comparable Australian system does not indicate the severity level of potential effects, and does not provide specific guidelines on the attitude or actions that the individual should adopt towards driving. It is reliant upon the patient to be vigilant in self-monitoring effects, to understand the potential ways in which they may be affected and how serious these effects may be, and to adopt the appropriate protective actions. This thesis investigates the responses of a sample of Australian hospital outpatients who receive appropriate labelling and counselling advice about potential driving impairment due to prescribed medicines. It aims to provide baseline data on the understanding and use of relevant medications by a Queensland public hospital outpatient sample recruited through the hospital pharmacy. It includes an exploration and comparison of the effect of the Australian and French medication warning systems on medication user knowledge, attitudes, beliefs and behaviour, and explores whether there are areas in which the Australian system may be improved by including any beneficial elements of the French system. A total of 358 outpatients were surveyed, and a follow-up telephone survey was conducted with a subgroup of consenting participants who were taking at least one medication that required an ancillary warning label about driving impairment. A complementary study of 75 French hospital outpatients was also conducted to further investigate the performance of the warnings. Not surprisingly, medication use among the Australian outpatient sample was high. The ancillary warning labels required to appear on medications that can impair driving were prevalent. A subgroup of participants was identified as being potentially at-risk of driving impaired, based on their reported recent use of medications requiring an ancillary warning label and level of driving activity. The sample reported previous behaviour and held future intentions that were consistent with warning label advice and health protective action. Participants did not express a particular need for being advised by a health professional regarding fitness to drive in relation to their medication. However, it was also apparent from the analysis that the participants would be significantly more likely to follow advice from a doctor than a pharmacist. High levels of knowledge in terms of general principles about effects of alcohol, illicit drugs and combinations of substances, and related health and crash risks were revealed. This may reflect a sample specific effect. Emphasis is placed in the professional guidelines for hospital pharmacists that make it essential that advisory labels are applied to medicines where applicable and that warning advice is given to all patients on medication which may affect driving (SHPA, 2006, p. 221). The research program applied selected theoretical constructs from Schwarzer's (1992) Health Action Process Approach, which has extended constructs from existing health theories such as the Theory of Planned Behavior (Ajzen, 1991) to better account for the intention-behaviour gap often observed when predicting behaviour. This was undertaken to explore the utility of the constructs in understanding and predicting compliance intentions and behaviour with the mandatory medication warning about driving impairment. This investigation revealed that the theoretical constructs related to intention and planning to avoid driving if an effect from the medication was noticed were useful. Not all the theoretical model constructs that had been demonstrated to be significant predictors in previous research on different health behaviours were significant in the present analyses. Positive outcome expectancies from avoiding driving were found to be important influences on forming the intention to avoid driving if an effect due to medication was noticed. In turn, intention was found to be a significant predictor of planning. Other selected theoretical constructs failed to predict compliance with the Australian warning label advice. It is possible that the limited predictive power of a number of constructs including risk perceptions is due to the small sample size obtained at follow up on which the evaluation is based. Alternately, it is possible that the theoretical constructs failed to sufficiently account for issues of particular relevance to the driving situation. The responses of the Australian hospital outpatient sample towards the Australian and French medication warning labels, which differed according to visual characteristics and warning message, were examined. In addition, a complementary study with a sample of French hospital outpatients was undertaken in order to allow general comparisons concerning the performance of the warnings. While a large amount of research exists concerning warning effectiveness, there is little research that has specifically investigated medication warnings relating to driving impairment. General established principles concerning factors that have been demonstrated to enhance warning noticeability and behavioural compliance have been extrapolated and investigated in the present study. The extent to which there is a need for education and improved health messages on this issue was a core issue of investigation in this thesis. Among the Australian sample, the size of the warning label and text, and red colour were the most visually important characteristics. The pictogram used in the French labels was also rated highly, and was salient for a large proportion of the sample. According to the study of French hospital outpatients, the pictogram was perceived to be the most important visual characteristic. Overall, the findings suggest that the Australian approach of using a combination of visual characteristics was important for the majority of the sample but that the use of a pictogram could enhance effects. A high rate of warning recall was found overall and a further important finding was that higher warning label recall was associated with increased number of medication classes taken. These results suggest that increased vigilance and care are associated with the number of medications taken and the associated repetition of the warning message. Significantly higher levels of risk perception were found for the French Level 3 (highest severity) label compared with the comparable mandatory Australian ancillary Label 1 warning. Participants' intentions related to the warning labels indicated that they would be more cautious while taking potentially impairing medication displaying the French Level 3 label compared with the Australian Label 1. These are potentially important findings for the Australian context regarding the current driving impairment warnings about displayed on medication. The findings raise other important implications for the Australian labelling context. An underlying factor may be the differences in the wording of the warning messages that appear on the Australian and French labels. The French label explicitly states "do not drive" while the Australian label states "if affected, do not drive", and the difference in responses may reflect that less severity is perceived where the situation involves the consumer's self-assessment of their impairment. The differences in the assignment of responsibility by the Australian (the consumer assesses and decides) and French (the doctor assesses and decides) approaches for the decision to drive while taking medication raises the core question of who is most able to assess driving impairment due to medication: the consumer, or the health professional? There are pros and cons related to knowledge, expertise and practicalities with either option. However, if the safety of the consumer is the primary aim, then the trend towards stronger risk perceptions and more consistent and cautious behavioural intentions in relation to the French label suggests that this approach may be more beneficial for consumer safety. The observations from the follow-up survey, although based on a small sample size and descriptive in nature, revealed that just over half of the sample recalled seeing a warning label about driving impairment on at least one of their medications. The majority of these respondents reported compliance with the warning advice. However, the results indicated variation in responses concerning alcohol intake and modifying the dose of medication or driving habits so that they could continue to drive, which suggests that the warning advice may not be having the desired impact. The findings of this research have implications for current countermeasures in this area. These have included enhancing the role that prescribing doctors have in providing warnings and advice to patients about the impact that their medication can have on driving, increasing consumer perceptions of the authority of pharmacists on this issue, and the reinforcement of the warning message. More broadly, it is suggested that there would be benefit in a wider dissemination of research-based information on increased crash risk and systematic monitoring and publicity about the representation of medications in crashes resulting in injuries and fatalities. Suggestions for future research concern the continued investigation of the effects of medications and interactions with existing medical conditions and other substances on driving skills, effects of variations in warning label design, individual behaviours and characteristics (particularly among those groups who are dependent upon prescription medication) and validation of consumer self-assessment of impairment.

Design and rationale of a Prospective, Observational European Multicenter study on the efficacy of acute surgical decompression after traumatic Spinal Cord Injury: the SCI-POEM study

Objectives:Despite many years of research, there is currently no treatment available that results in major neurological or functional recovery after traumatic spinal cord injury (tSCI). In particular, no conclusive data related to the role of the timing of decompressive surgery, and the impact of injury severity on its benefit, have been published to date. This paper presents a protocol that was designed to examine the hypothesized association between the timing of surgical decompression and the extent of neurological recovery in tSCI patients.Study design: The SCI-POEM study is a Prospective, Observational European Multicenter comparative cohort study. This study compares acute (<12 h) versus non-acute (>12 h, <2 weeks) decompressive surgery in patients with a traumatic spinal column injury and concomitant spinal cord injury. The sample size calculation was based on a representative European patient cohort of 492 tSCI patients. During a 4-year period, 300 patients will need to be enrolled from 10 trauma centers across Europe. The primary endpoint is lower-extremity motor score as assessed according to the 'International standards for neurological classification of SCI' at 12 months after injury. Secondary endpoints include motor, sensory, imaging and functional outcomes at 3, 6 and 12 months after injury.Conclusion:In order to minimize bias and reduce the impact of confounders, special attention is paid to key methodological principles in this study protocol. A significant difference in safety and/or efficacy endpoints will provide meaningful information to clinicians, as this would confirm the hypothesis that rapid referral to and treatment in specialized centers result in important improvements in tSCI patients.Spinal Cord advance online publication, 17 April 2012; doi:10.1038/sc.2012.34.

An ingenious decision support system for remote health monitoring of pregnant women

Maternal deaths have been a critical issue for women living in rural and remote areas. The need to travel long distances, the shortage of primary care providers such as physicians, specialists and nurses, and the closing of small hospitals have been problems identified in many rural areas. Some research work has been undertaken and a few techniques have been developed to remotely measure the physiological condition of pregnant women through sophisticated ultrasound equipment. There are numerous ways to reduce maternal deaths, and an important step is to select the right approaches to achieving this reduction. One such approach is the provision of decision support systems in rural and remote areas. Decision support systems (DSSs) have already shown a great potential in many health fields. This thesis proposes an ingenious decision support system (iDSS) based on the methodology of survey instruments and identification of significant variables to be used in iDSS using statistical analysis. A survey was undertaken with pregnant women and factorial experimental design was chosen to acquire sample size. Variables with good reliability in any one of the statistical techniques such as Chi-square, Cronbach’s á and Classification Tree were incorporated in the iDSS. The decision support system was developed with significant variables such as: Place of residence, Seeing the same doctor, Education, Tetanus injection, Baby weight, Previous baby born, Place of birth, Assisted delivery, Pregnancy parity, Doctor visits and Occupation. The ingenious decision support system was implemented with Visual Basic as front end and Microsoft SQL server management as backend. Outcomes of the ingenious decision support system include advice on Symptoms, Diet and Exercise to pregnant women. On conditional system was sent and validated by the gynaecologist. Another outcome of ingenious decision support system was to provide better pregnancy health awareness and reduce long distance travel, especially for women in rural areas. The proposed system has qualities such as usefulness, accuracy and accessibility.

Bayesian hierarchical models in statistical quality control methods to improve healthcare in hospitals

Quality oriented management systems and methods have become the dominant business and governance paradigm. From this perspective, satisfying customers’ expectations by supplying reliable, good quality products and services is the key factor for an organization and even government. During recent decades, Statistical Quality Control (SQC) methods have been developed as the technical core of quality management and continuous improvement philosophy and now are being applied widely to improve the quality of products and services in industrial and business sectors. Recently SQC tools, in particular quality control charts, have been used in healthcare surveillance. In some cases, these tools have been modified and developed to better suit the health sector characteristics and needs. It seems that some of the work in the healthcare area has evolved independently of the development of industrial statistical process control methods. Therefore analysing and comparing paradigms and the characteristics of quality control charts and techniques across the different sectors presents some opportunities for transferring knowledge and future development in each sectors. Meanwhile considering capabilities of Bayesian approach particularly Bayesian hierarchical models and computational techniques in which all uncertainty are expressed as a structure of probability, facilitates decision making and cost-effectiveness analyses. Therefore, this research investigates the use of quality improvement cycle in a health vii setting using clinical data from a hospital. The need of clinical data for monitoring purposes is investigated in two aspects. A framework and appropriate tools from the industrial context are proposed and applied to evaluate and improve data quality in available datasets and data flow; then a data capturing algorithm using Bayesian decision making methods is developed to determine economical sample size for statistical analyses within the quality improvement cycle. Following ensuring clinical data quality, some characteristics of control charts in the health context including the necessity of monitoring attribute data and correlated quality characteristics are considered. To this end, multivariate control charts from an industrial context are adapted to monitor radiation delivered to patients undergoing diagnostic coronary angiogram and various risk-adjusted control charts are constructed and investigated in monitoring binary outcomes of clinical interventions as well as postintervention survival time. Meanwhile, adoption of a Bayesian approach is proposed as a new framework in estimation of change point following control chart’s signal. This estimate aims to facilitate root causes efforts in quality improvement cycle since it cuts the search for the potential causes of detected changes to a tighter time-frame prior to the signal. This approach enables us to obtain highly informative estimates for change point parameters since probability distribution based results are obtained. Using Bayesian hierarchical models and Markov chain Monte Carlo computational methods, Bayesian estimators of the time and the magnitude of various change scenarios including step change, linear trend and multiple change in a Poisson process are developed and investigated. The benefits of change point investigation is revisited and promoted in monitoring hospital outcomes where the developed Bayesian estimator reports the true time of the shifts, compared to priori known causes, detected by control charts in monitoring rate of excess usage of blood products and major adverse events during and after cardiac surgery in a local hospital. The development of the Bayesian change point estimators are then followed in a healthcare surveillances for processes in which pre-intervention characteristics of patients are viii affecting the outcomes. In this setting, at first, the Bayesian estimator is extended to capture the patient mix, covariates, through risk models underlying risk-adjusted control charts. Variations of the estimator are developed to estimate the true time of step changes and linear trends in odds ratio of intensive care unit outcomes in a local hospital. Secondly, the Bayesian estimator is extended to identify the time of a shift in mean survival time after a clinical intervention which is being monitored by riskadjusted survival time control charts. In this context, the survival time after a clinical intervention is also affected by patient mix and the survival function is constructed using survival prediction model. The simulation study undertaken in each research component and obtained results highly recommend the developed Bayesian estimators as a strong alternative in change point estimation within quality improvement cycle in healthcare surveillances as well as industrial and business contexts. The superiority of the proposed Bayesian framework and estimators are enhanced when probability quantification, flexibility and generalizability of the developed model are also considered. The empirical results and simulations indicate that the Bayesian estimators are a strong alternative in change point estimation within quality improvement cycle in healthcare surveillances. The superiority of the proposed Bayesian framework and estimators are enhanced when probability quantification, flexibility and generalizability of the developed model are also considered. The advantages of the Bayesian approach seen in general context of quality control may also be extended in the industrial and business domains where quality monitoring was initially developed.

Omega-3 polyunsaturated fatty acids in the treatment of kidney disease

After more than 25 years of published investigation, including randomized controlled trials, the role of omega-3 polyunsaturated fatty acids in the treatment of kidney disease remains unclear. In vitro and in vivo experimental studies support the efficacy of omega-3 polyunsaturated fatty acids on inflammatory pathways involved with the progression of kidney disease. Clinical investigations have focused predominantly on immunoglobulin A (IgA) nephropathy. More recently, lupus nephritis, polycystic kidney disease, and other glomerular diseases have been investigated. Clinical trials have shown conflicting results for the efficacy of omega-3 polyunsaturated fatty acids in IgA nephropathy, which may relate to varying doses, proportions of eicosapentaenoic acid and docosahexaenoic acid, duration of therapy, and sample size of the study populations. Meta-analyses of clinical trials using omega-3 polyunsaturated fatty acids in IgA nephropathy have been limited by the quality of available studies. However, guidelines suggest that omega-3 polyunsaturated fatty acids should be considered in progressive IgA nephropathy. Omega-3 polyunsaturated fatty acids decrease blood pressure, a known accelerant of kidney disease progression. Well-designed, adequately powered, randomized, controlled clinical trials are required to further investigate the potential benefits of omega-3 polyunsaturated fatty acids on the progression of kidney disease and patient survival.

An ingenuous Decision Support System (iDSS) approach for remote area pregnant women

This paper addresses development of an ingenious decision support system (iDSS) based on the methodology of survey instruments and identification of significant variables to be used in iDSS using statistical analysis. A survey was undertaken with pregnant women and factorial experimental design was chosen to acquire sample size. Variables with good reliability in any one of the statistical techniques such as Chi-square, Cronbach’s α and Classification Tree were incorporated in the iDSS. The ingenious decision support system was implemented with Visual Basic as front end and Microsoft SQL server management as backend. Outcome of the ingenious decision support system include advice on Symptoms, Diet and Exercise to pregnant women.

Baby-friendly hospital accreditation, in-hospital care practices and breastfeeding : a population based survey

OBJECTIVES: To investigate the effect of Baby-Friendly Hospital Initiative (BFHI) accreditation and hospital care practices on breastfeeding rates at 1 and 4 months. METHODS: All women who birthed in Queensland, Australia, from February 1 to May 31, 2010, received a survey 4 months postpartum. Maternal, infant, and hospital characteristics; pregnancy and birth complications; and infant feeding outcomes were measured. RESULTS: Sample size was 6752 women. Breastfeeding initiation rates were high (96%) and similar in BFHI-accredited and nonaccredited hospitals. After adjustment for significant maternal, infant, clinical, and hospital variables, women who birthed in BFHI-accredited hospitals had significantly lower odds of breastfeeding at 1 month (adjusted odds ratio 0.72, 95% confidence interval 0.58–0.90) than those who birthed in non–BFHI-accredited hospitals. BFHI accreditation did not affect the odds of breastfeeding at 4 months or exclusive breastfeeding at 1 or 4 months. Four in-hospital practices (early skin-to-skin contact, attempted breastfeeding within the first hour, rooming-in, and no in-hospital supplementation) were experienced by 70% to 80% of mothers, with 50.3% experiencing all 4. Women who experienced all 4 hospital practices had higher odds of breastfeeding at 1 month (adjusted odds ratio 2.20, 95% confidence interval 1.78–2.71) and 4 months (adjusted odds ratio 2.93, 95% confidence interval 2.40–3.60) than women who experienced fewer than 4. CONCLUSIONS: When breastfeeding-initiation rates are high and evidence-based practices that support breastfeeding are common within the hospital environment, BFHI accreditation per se has little effect on both exclusive or any breastfeeding rates.C

Adaptive radiotherapy for virally mediated head and neck cancer

Purpose: Virally mediated head and neck cancers (VMHNC) often present with nodal involvement, and are generally considered radioresponsive, resulting in the need for a re-planning CT during radiotherapy (RT) in a subset of patients. We sought to identify a high-risk group based on nodal size to be evaluated in a future prospective adaptive RT trial. Methodology: Between 2005-2010, 121 patients with virally-mediated, node positive nasopharyngeal (EBV positive) or oropharyngeal (HPV positive) cancers, receiving curative intent RT were reviewed. Patients were analysed based on maximum size of the dominant node with a view to grouping them in varying risk categories for the need of re-planning. The frequency and timing of the re-planning scans were also evaluated. Results: Sixteen nasopharyngeal and 105 oropharyngeal tumours were reviewed. Twenty-five (21%) patients underwent a re-planning CT at a median of 22 (range, 0-29) fractions with 1 patient requiring re-planning prior to the commencement of treatment. Based on the analysis, patients were subsequently placed into 3 groups; ≤35mm (Group 1), 36-45mm (Group 2), ≥46mm (Group 3). Re-planning CT’s were performed in Group 1- 8/68 (11.8%), Group 2- 4/28 (14.3%), Group 3- 13/25 (52%). Sample size did not allow statistical analysis to detect a significant difference or exclusion of a lack of difference between the 3 groups. Conclusion: In this series, patients with VMHNC and nodal size > 46mm appear to be a high-risk group for the need of re-planning during a course of definitive radiotherapy. This finding will now be tested in a prospective adaptive RT study.

Adaptive radiotherapy for virally mediated head and neck cancer

Purpose: Virally mediated head and neck cancers (VMHNC) often present with nodal involvement, and are generally considered radioresponsive, resulting in the need for a re-planning CT during radiotherapy (RT) in a subset of patients. We sought to identify a high-risk group based on nodal size to be evaluated in a future prospective adaptive RT trial. Methodology: Between 2005-2010, 121 patients with virally-mediated, node positive nasopharyngeal (EBV positive) or oropharyngeal (HPV positive) cancers, receiving curative intent RT were reviewed. Patients were analysed based on maximum size of the dominant node with a view to grouping them in varying risk categories for the need of re-planning. The frequency and timing of the re-planning scans were also evaluated. Results: Sixteen nasopharyngeal and 105 oropharyngeal tumours were reviewed. Twenty-five (21%) patients underwent a re-planning CT at a median of 22 (range, 0-29) fractions with 1 patient requiring re-planning prior to the commencement of treatment. Based on the analysis, patients were subsequently placed into 3 groups; ≤35mm (Group 1), 36-45mm (Group 2), ≥46mm (Group 3). Re-planning CT’s were performed in Group 1- 8/68 (11.8%), Group 2- 4/28 (14.3%), Group 3- 13/25 (52%). Sample size did not allow statistical analysis to detect a significant difference or exclusion of a lack of difference between the 3 groups. Conclusion: In this series, patients with VMHNC and nodal size > 46mm appear to be a high-risk group for the need of re-planning during a course of definitive radiotherapy. This finding will now be tested in a prospective adaptive RT study.

Asset health prediction using the explicit hazard model

The ability to estimate the asset reliability and the probability of failure is critical to reducing maintenance costs, operation downtime, and safety hazards. Predicting the survival time and the probability of failure in future time is an indispensable requirement in prognostics and asset health management. In traditional reliability models, the lifetime of an asset is estimated using failure event data, alone; however, statistically sufficient failure event data are often difficult to attain in real-life situations due to poor data management, effective preventive maintenance, and the small population of identical assets in use. Condition indicators and operating environment indicators are two types of covariate data that are normally obtained in addition to failure event and suspended data. These data contain significant information about the state and health of an asset. Condition indicators reflect the level of degradation of assets while operating environment indicators accelerate or decelerate the lifetime of assets. When these data are available, an alternative approach to the traditional reliability analysis is the modelling of condition indicators and operating environment indicators and their failure-generating mechanisms using a covariate-based hazard model. The literature review indicates that a number of covariate-based hazard models have been developed. All of these existing covariate-based hazard models were developed based on the principle theory of the Proportional Hazard Model (PHM). However, most of these models have not attracted much attention in the field of machinery prognostics. Moreover, due to the prominence of PHM, attempts at developing alternative models, to some extent, have been stifled, although a number of alternative models to PHM have been suggested. The existing covariate-based hazard models neglect to fully utilise three types of asset health information (including failure event data (i.e. observed and/or suspended), condition data, and operating environment data) into a model to have more effective hazard and reliability predictions. In addition, current research shows that condition indicators and operating environment indicators have different characteristics and they are non-homogeneous covariate data. Condition indicators act as response variables (or dependent variables) whereas operating environment indicators act as explanatory variables (or independent variables). However, these non-homogenous covariate data were modelled in the same way for hazard prediction in the existing covariate-based hazard models. The related and yet more imperative question is how both of these indicators should be effectively modelled and integrated into the covariate-based hazard model. This work presents a new approach for addressing the aforementioned challenges. The new covariate-based hazard model, which termed as Explicit Hazard Model (EHM), explicitly and effectively incorporates all three available asset health information into the modelling of hazard and reliability predictions and also drives the relationship between actual asset health and condition measurements as well as operating environment measurements. The theoretical development of the model and its parameter estimation method are demonstrated in this work. EHM assumes that the baseline hazard is a function of the both time and condition indicators. Condition indicators provide information about the health condition of an asset; therefore they update and reform the baseline hazard of EHM according to the health state of asset at given time t. Some examples of condition indicators are the vibration of rotating machinery, the level of metal particles in engine oil analysis, and wear in a component, to name but a few. Operating environment indicators in this model are failure accelerators and/or decelerators that are included in the covariate function of EHM and may increase or decrease the value of the hazard from the baseline hazard. These indicators caused by the environment in which an asset operates, and that have not been explicitly identified by the condition indicators (e.g. Loads, environmental stresses, and other dynamically changing environment factors). While the effects of operating environment indicators could be nought in EHM; condition indicators could emerge because these indicators are observed and measured as long as an asset is operational and survived. EHM has several advantages over the existing covariate-based hazard models. One is this model utilises three different sources of asset health data (i.e. population characteristics, condition indicators, and operating environment indicators) to effectively predict hazard and reliability. Another is that EHM explicitly investigates the relationship between condition and operating environment indicators associated with the hazard of an asset. Furthermore, the proportionality assumption, which most of the covariate-based hazard models suffer from it, does not exist in EHM. According to the sample size of failure/suspension times, EHM is extended into two forms: semi-parametric and non-parametric. The semi-parametric EHM assumes a specified lifetime distribution (i.e. Weibull distribution) in the form of the baseline hazard. However, for more industry applications, due to sparse failure event data of assets, the analysis of such data often involves complex distributional shapes about which little is known. Therefore, to avoid the restrictive assumption of the semi-parametric EHM about assuming a specified lifetime distribution for failure event histories, the non-parametric EHM, which is a distribution free model, has been developed. The development of EHM into two forms is another merit of the model. A case study was conducted using laboratory experiment data to validate the practicality of the both semi-parametric and non-parametric EHMs. The performance of the newly-developed models is appraised using the comparison amongst the estimated results of these models and the other existing covariate-based hazard models. The comparison results demonstrated that both the semi-parametric and non-parametric EHMs outperform the existing covariate-based hazard models. Future research directions regarding to the new parameter estimation method in the case of time-dependent effects of covariates and missing data, application of EHM in both repairable and non-repairable systems using field data, and a decision support model in which linked to the estimated reliability results, are also identified.

Designing a new robust on-line secondary path modeling technique for feedforward active noise control systems

Several approaches have been introduced in the literature for active noise control (ANC) systems. Since the filtered-x least-mean-square (FxLMS) algorithm appears to be the best choice as a controller filter, researchers tend to improve performance of ANC systems by enhancing and modifying this algorithm. This paper proposes a new version of the FxLMS algorithm, as a first novelty. In many ANC applications, an on-line secondary path modeling method using white noise as a training signal is required to ensure convergence of the system. As a second novelty, this paper proposes a new approach for on-line secondary path modeling on the basis of a new variable-step-size (VSS) LMS algorithm in feed forward ANC systems. The proposed algorithm is designed so that the noise injection is stopped at the optimum point when the modeling accuracy is sufficient. In this approach, a sudden change in the secondary path during operation makes the algorithm reactivate injection of the white noise to re-adjust the secondary path estimate. Comparative simulation results shown in this paper indicate the effectiveness of the proposed approach in reducing both narrow-band and broad-band noise. In addition, the proposed ANC system is robust against sudden changes of the secondary path model.

An optimized online secondary path modeling method for single-channel feedback ANC systems

This paper proposes a new method for online secondary path modeling in feedback active noise control (ANC) systems. In practical cases, the secondary path is usually time varying. For these cases, online modeling of secondary path is required to ensure convergence of the system. In literature the secondary path estimation is usually performed offline, prior to online modeling, where in the proposed system there is no need for using offline estimation. The proposed method consists of two steps: a noise controller which is based on an FxLMS algorithm, and a variable step size (VSS) LMS algorithm which is used to adapt the modeling filter with the secondary path. In order to increase performance of the algorithm in a faster convergence and accurate performance, we stop the VSS-LMS algorithm at the optimum point. The results of computer simulation shown in this paper indicate effectiveness of the proposed method.

A new feedback ANC system approach

We propose a new active noise control (ANC) technique. The technique has a feedback structure to have a simple configuration in practical implementation. In this approach, the secondary path is modelled online to ensure convergence of the system as the secondary paths are practically time varying or non-linear. The proposed method consists of two steps: a noise controller which is based on a modified FxLMS algorithm, and a new variable step size (VSS) LMS algorithm which is used to adapt the modelling filter with the secondary path. The proposed algorithm stops injection of the white noise at the optimum point and reactivate the injection during the operation, if needed, to maintain performance of the system. Eliminating continuous injection of the white noise increases the performance of the proposed method significantly and makes it more desirable for practical ANC systems. The computer simulations are presented to show the effectiveness of the proposed method.

Bayesian computation via empirical likelihood

Approximate Bayesian computation has become an essential tool for the analysis of complex stochastic models when the likelihood function is numerically unavailable. However, the well-established statistical method of empirical likelihood provides another route to such settings that bypasses simulations from the model and the choices of the approximate Bayesian computation parameters (summary statistics, distance, tolerance), while being convergent in the number of observations. Furthermore, bypassing model simulations may lead to significant time savings in complex models, for instance those found in population genetics. The Bayesian computation with empirical likelihood algorithm we develop in this paper also provides an evaluation of its own performance through an associated effective sample size. The method is illustrated using several examples, including estimation of standard distributions, time series, and population genetics models.