What about me? Avoiding fatigue and gaining personal time in the work to leisure transition in work-life balance initiatives

Fatigue and overwork are problems experienced by numerous employees in many industry sectors. Focusing on improving work-life balance can frame the ‘problem’ of long work hours to resolve working time duration issues. Flexible work options through re-organising working time arrangements is key to developing an organisational response for delivering work-life balance and usually involves changing the internal structure of work time. This study examines the effect of compressed long weekly working hours and the consequent ‘long break’ on work-life balance. Using Spillover theory and Border theory, this research considers organisational and personal determinants of overwork and fatigue. It concludes compressed long work hours with a long break provide better work-life balance. Further, a long break allows gaining ‘personal time’ and overcoming fatigue.

Use of Probe Vehicles to Increase Traffic Estimation Accuracy in Brisbane

Traffic congestion is an increasing problem with high costs in financial, social and personal terms. These costs include psychological and physiological stress, aggressivity and fatigue caused by lengthy delays, and increased likelihood of road crashes. Reliable and accurate traffic information is essential for the development of traffic control and management strategies. Traffic information is mostly gathered from in-road vehicle detectors such as induction loops. Traffic Message Chanel (TMC) service is popular service which wirelessly send traffic information to drivers. Traffic probes have been used in many cities to increase traffic information accuracy. A simulation to estimate the number of probe vehicles required to increase the accuracy of traffic information in Brisbane is proposed. A meso level traffic simulator has been developed to facilitate the identification of the optimal number of probe vehicles required to achieve an acceptable level of traffic reporting accuracy. Our approach to determine the optimal number of probe vehicles required to meet quality of service requirements, is to simulate runs with varying numbers of traffic probes. The simulated traffic represents Brisbane’s typical morning traffic. The road maps used in simulation are Brisbane’s TMC maps complete with speed limits and traffic lights. Experimental results show that that the optimal number of probe vehicles required for providing a useful supplement to TMC (induction loop) data lies between 0.5% and 2.5% of vehicles on the road. With less probes than 0.25%, little additional information is provided, while for more probes than 5%, there is only a negligible affect on accuracy for increasingly many probes on the road. Our findings are consistent with on-going research work on traffic probes, and show the effectiveness of using probe vehicles to supplement induction loops for accurate and timely traffic information.

Exercise and lymphoedema : Is it good or bad?

Being physically active during and following treatment for breast cancer has been associated with a range of benefits including improved fitness and function, body composition and immune function and reductions in stress, depression and anxiety, as well as the number and severity of treatment-related side-effects such as nausea, fatigue and pain, all of which contribute to improvements in quality of life. There is also emerging evidence linking active lifestyles with improved survival. Therefore, there is little doubt that participating in regular exercise following breast cancer is ‘good’. Unfortunately, research investigating the role of exercise for women considered at high-risk of lymphoedema or who have developed lymphedema following breast cancer is lacking. For fear of initiating or exacerbating lymphoedema, these women have traditionally been cautioned rather than encouraged to be regularly active. However, recent preliminary findings suggest that being inactive may increase risk of developing lymphedema, and that for those with lymphoedema, participation in an exercise program does not exacerbate the condition. This presentation will address what we know about the role of exercise following a breast cancer diagnosis and will provide some practical recommendations about becoming and staying regularly active following breast cancer, for those with and without lymphoedema.

Limits of spherical blur determined with an adaptive optics mirror

We extended an earlier study (Vision Research, 45, 1967–1974, 2005) in which we investigated limits at which induced blur of letter targets becomes noticeable, troublesome and objectionable. Here we used a deformable adaptive optics mirror to vary spherical defocus for conditions of a white background with correction of astigmatism; a white background with reduction of all aberrations other than defocus; and a monochromatic background with reduction of all aberrations other than defocus. We used seven cyclopleged subjects, lines of three high-contrast letters as targets, 3–6 mm artificial pupils, and 0.1–0.6 logMAR letter sizes. Subjects used a method of adjustment to control the defocus component of the mirror to set the 'just noticeable', 'just troublesome' and 'just objectionable' defocus levels. For the white-no adaptive optics condition combined with 0.1 logMAR letter size, mean 'noticeable' blur limits were ±0.30, ±0.24 and ±0.23 D at 3, 4 and 6 mm pupils, respectively. White-adaptive optics and monochromatic-adaptive optics conditions reduced blur limits by 8% and 20%, respectively. Increasing pupil size from 3–6 mm decreased blur limits by 29%, and increasing letter size increased blur limits by 79%. Ratios of troublesome to noticeable, and of objectionable to noticeable, blur limits were 1.9 and 2.7 times, respectively. The study shows that the deformable mirror can be used to vary defocus in vision experiments. Overall, the results of noticeable, troublesome and objectionable blur agreed well with those of the previous study. Attempting to reduce higher-order aberrations or chromatic aberrations, reduced blur limits to only a small extent.

Intelligent prognostics of machinery health utilising suspended condition monitoring data

The ability to forecast machinery failure is vital to reducing maintenance costs, operation downtime and safety hazards. Recent advances in condition monitoring technologies have given rise to a number of prognostic models for forecasting machinery health based on condition data. Although these models have aided the advancement of the discipline, they have made only a limited contribution to developing an effective machinery health prognostic system. The literature review indicates that there is not yet a prognostic model that directly models and fully utilises suspended condition histories (which are very common in practice since organisations rarely allow their assets to run to failure); that effectively integrates population characteristics into prognostics for longer-range prediction in a probabilistic sense; which deduces the non-linear relationship between measured condition data and actual asset health; and which involves minimal assumptions and requirements. This work presents a novel approach to addressing the above-mentioned challenges. The proposed model consists of a feed-forward neural network, the training targets of which are asset survival probabilities estimated using a variation of the Kaplan-Meier estimator and a degradation-based failure probability density estimator. The adapted Kaplan-Meier estimator is able to model the actual survival status of individual failed units and estimate the survival probability of individual suspended units. The degradation-based failure probability density estimator, on the other hand, extracts population characteristics and computes conditional reliability from available condition histories instead of from reliability data. The estimated survival probability and the relevant condition histories are respectively presented as “training target” and “training input” to the neural network. The trained network is capable of estimating the future survival curve of a unit when a series of condition indices are inputted. Although the concept proposed may be applied to the prognosis of various machine components, rolling element bearings were chosen as the research object because rolling element bearing failure is one of the foremost causes of machinery breakdowns. Computer simulated and industry case study data were used to compare the prognostic performance of the proposed model and four control models, namely: two feed-forward neural networks with the same training function and structure as the proposed model, but neglected suspended histories; a time series prediction recurrent neural network; and a traditional Weibull distribution model. The results support the assertion that the proposed model performs better than the other four models and that it produces adaptive prediction outputs with useful representation of survival probabilities. This work presents a compelling concept for non-parametric data-driven prognosis, and for utilising available asset condition information more fully and accurately. It demonstrates that machinery health can indeed be forecasted. The proposed prognostic technique, together with ongoing advances in sensors and data-fusion techniques, and increasingly comprehensive databases of asset condition data, holds the promise for increased asset availability, maintenance cost effectiveness, operational safety and – ultimately – organisation competitiveness.

Within- and between-day repeatability and variability in children's physiological responses during submaximal treadmill exercise

The purpose of this study was to verify within- and between-day repeatability and variability in children's oxygen uptake (VO^sub 2^), gross economy (GE; VO^sub 2^ divided by speed) and heart rate (HR) during treadmill walking based on self-selected speed (SS). Fourteen children (10.1 ± 1.4 years) undertook three testing sessions over 2 days in which four walking speeds, including SS were tested. Within- and between-day repeatability were assessed using the Bland and Altman method, and coefficients of variability (CV) were determined for each child across exercise bouts and averaged to obtain a mean group CV value for VO^sub 2^, GE, and HR per speed. Repeated measures analysis of variance showed no statistically significant differences in within- or between-day CV for VO^sub 2^, GE, or HR at any speed. Repeatability within- and between-day for VO^sub 2^, GE, and HR for all speeds was verified. These results suggest that submaximal VO^sub 2^ during treadmill walking is stable and reproducible at a range of speeds based on children's SS.

Inequalities in cardiovascular disease mortality : the role of behavioural, physiological and social risk factors

Background: While the relationship between socioeconomic disadvantage and cardiovascular disease (CVD) is well established, the role that traditional cardiovascular risk factors play in this association remains unclear. We examined the association between education attainment and CVD mortality and the extent to which behavioural, social and physiological factors explained this relationship. Methods: Adults (n=38 355) aged 40-69 years living in Melbourne, Australia were recruited in 1990-1994. Subjects with baseline CVD risk factor data ascertained through questionnaire and physical measurement were followed for an average of 9.4 years with CVD deaths verified by review of medical records and autopsy reports. Results: CVD mortality was higher for those with primary education only compared to those who had completed tertiary education, with a hazard ratio (HR) of 1.66 (95% confidence interval [CI] 1.11-2.49) after adjustment for age, country of birth and gender. Those from the lowest educated group had a more adverse cardiovascular risk factor profile compared to the highest educated group, and adjustment for these risk factors reduced the HR to 1.18 (95% CI 0.78-1.77). In analysis of individual risk factors, smoking and waist circumference explained most of the difference in CVD mortality between the highest and lowest education groups. Conclusions: Most of the excess CVD mortality in lower socioeconomic groups can be explained by known risk factors, particularly smoking and overweight. While targeting cardiovascular risk factors should not divert efforts from addressing the underlying determinants of health inequalities, it is essential that known risk factors are addressed effectively among lower socioeconomic groups.

Condition-based prognosis of machine health

Modern machines are complex and often required to operate long hours to achieve production targets. The ability to detect symptoms of failure, hence, forecasting the remaining useful life of the machine is vital to prevent catastrophic failures. This is essential to reducing maintenance cost, operation downtime and safety hazard. Recent advances in condition monitoring technologies have given rise to a number of prognosis models that attempt to forecast machinery health based on either condition data or reliability data. In practice, failure condition trending data are seldom kept by industries and data that ended with a suspension are sometimes treated as failure data. This paper presents a novel approach of incorporating historical failure data and suspended condition trending data in the prognostic model. The proposed model consists of a FFNN whose training targets are asset survival probabilities estimated using a variation of Kaplan-Meier estimator and degradation-based failure PDF estimator. The output survival probabilities collectively form an estimated survival curve. The viability of the model was tested using a set of industry vibration data.

Four Ideas to Improve the Human Condition

A collection of four progressive ideas targeted for the improvement of the human condition has been compiled in this book. They were derived from the first attempted MEDP Australian Summit. Although the Summit itself did not meet expectations for a variety of reasons, the four ideas contained herein are gems derived from the Summit processes.