Young at heart, old at work? Relations between age, (meta-)stereotypes, self-categorization, and retirement attitudes

The aims of this study were to examine how workers' negative age stereotypes (i.e., denying older workers' ability to develop) and negative meta-stereotypes (i.e., beliefs that the majority of colleagues feel negative about older workers) are related to their attitudes towards retirement (i.e., occupational future time perspective and intention to retire), and whether the strength of these relationships is influenced by workers' self-categorization as an “older” person. Results of a study among Dutch taxi drivers provided mixed support for the hypotheses. Negative meta-stereotypes, but not negative age stereotypes, were associated with fewer perceived opportunities until retirement and, in turn, a stronger intention to retire. Self-categorization moderated the relationships between negative age (meta-)stereotypes and occupational future time perspective. However, contrary to expectations, the relations were stronger among workers with a low self-categorization as an older person in comparison with workers with a high self-categorization in this regard. Overall, results highlight the importance of psychosocial processes in the study of retirement intentions and their antecedents.

Links between emotional job demands and occupational well-being: Age differences depend on type of demand

In the growing health care sector, meeting emotional job demands is crucial to organizational outcomes but may negatively affect employees’ well-being. Drawing on the emotional aging literature, we predicted that two common emotional job demands, display demands (expressing positive, negative, and neutral emotions toward clients) and sensitivity demands (knowing what the client is feeling), affect older health care workers’ occupational well-being differently than young workers, as indicated by their job satisfaction and need for recovery. Survey data from employees of senior care homes (N = 141, aged between 17 and 62 years) confirmed the moderating role of age for links between emotional job demands and occupational well-being indicators. Emotional display demands were generally positively associated with emotional dissonance; however, the association between demands to display neutral emotions and emotional dissonance was stronger among young compared with older employees. In contrast, among older but not young employees, emotional dissonance was negatively associated with job satisfaction, and emotional sensitivity demands were positively associated with need for recovery. These findings suggest that age may confer both advantages (facing neutral display demands) and vulnerabilities (facing emotional dissonance and sensitivity demands) in managing emotional job demands.

Reducing age bias and turnover intentions by enhancing intergenerational contact quality in the workplace: The role of opportunities for generativity and development

Based on socio-emotional selectivity and self-categorization theories, we developed and tested a model on how the interplay between employee age and opportunities for generativity and development predicts age bias and turnover intentions via intergenerational contact quality in the workplace. We hypothesized indirect effects of opportunities for generativity on outcomes through intergenerational contact quality among older workers only, whereas we expected that the indirect effects of opportunities for development are stronger for young compared with older workers. Data came from 321 employees in Belgium who responded to an online questionnaire. Results showed that age moderated the relationships of opportunities for generativity and development with intergenerational contact quality consistent with the expected patterns. Furthermore, age moderated the indirect effects of opportunities for generativity and development on age bias through intergenerational contact quality, but not on turnover intentions. Implications for future research and practical suggestions for managing intergenerational contact at work are discussed.

Older workers' age as a moderator of the relationship between career adaptability and job satisfaction

Research on career adaptability and its relationships with work outcomes has so far primarily focused on the cohort of younger workers and largely neglected older workers. We investigated the relationship between career adaptability and job satisfaction in a sample of 577 older workers from Australia (M age = 59.6 years, SD = 2.4, range 54–66 years), who participated in a 4-wave substudy of the 45 and Up Study. Based on socioemotional selectivity theory, we examined older workers’ chronological age (as a proxy for retirement proximity) and motivation to continue working after traditional retirement age as moderators of the relationship between career adaptability and job satisfaction. We hypothesized that the positive relationship between career adaptability and job satisfaction is stronger among relatively younger workers and workers with a high motivation to continue working compared to relatively older workers and workers with a low motivation to continue working. Results showed that older workers’ age, but not their motivation to continue working, moderated the relationship between career adaptability and job satisfaction consistent with the expected pattern. Implications for future research on age and career adaptability as well as ideas on how to maintain and improve older workers’ career adaptability and job satisfaction are discussed.

NEESTIMATOR v2: re-implementation of software for the estimation of contemporary effective population size (N-e) from genetic data

NeEstimator v2 is a completely revised and updated implementation of software that produces estimates of contemporary effective population size, using several different methods and a single input file. NeEstimator v2 includes three single-sample estimators (updated versions of the linkage disequilibrium and heterozygote-excess methods, and a new method based on molecular coancestry), as well as the two-sample (moment-based temporal) method. New features include the following: (i) an improved method for accounting for missing data; (ii) options for screening out rare alleles; (iii) confidence intervals for all methods; (iv) the ability to analyse data sets with large numbers of genetic markers (10000 or more); (v) options for batch processing large numbers of different data sets, which will facilitate cross-method comparisons using simulated data; and (vi) correction for temporal estimates when individuals sampled are not removed from the population (Plan I sampling). The user is given considerable control over input data and composition, and format of output files. The freely available software has a new JAVA interface and runs under MacOS, Linux and Windows.

The validity of the GENEActiv wrist-worn accelerometer for measuring adult sedentary time in free living

OBJECTIVES Based on self-reported measures, sedentary time has been associated with chronic disease and mortality. This study examined the validity of the wrist-worn GENEactiv accelerometer for measuring sedentary time (i.e. sitting and lying) by posture classification, during waking hours in free living adults. DESIGN Fifty-seven participants (age=18-55 years 52% male) were recruited using convenience sampling from a large metropolitan Australian university. METHODS Participants wore a GENEActiv accelerometer on their non-dominant wrist and an activPAL device attached to their right thigh for 24-h (00:00 to 23:59:59). Pearson's Correlation Coefficient was used to examine the convergent validity of the GENEActiv and the activPAL for estimating total sedentary time during waking hours. Agreement was illustrated using Bland and Altman plots, and intra-individual agreement for posture was assessed with the Kappa statistic. RESULTS Estimates of average total sedentary time over 24-h were 623 (SD 103) min/day from the GENEActiv, and 626 (SD 123) min/day from the activPAL, with an Intraclass Correlation Coefficient of 0.80 (95% confidence intervals 0.68-0.88). Bland and Altman plots showed slight underestimation of mean total sedentary time for GENEActiv relative to activPAL (mean difference: -3.44min/day), with moderate limits of agreement (-144 to 137min/day). Mean Kappa for posture was 0.53 (SD 0.12), indicating moderate agreement for this sample at the individual level. CONCLUSIONS The estimation of sedentary time by posture classification of the wrist-worn GENEActiv accelerometer was comparable to the activPAL. The GENEActiv may provide an alternative, easy to wear device based measure for descriptive estimates of sedentary time in population samples

Physical activity in mid-age and older women: Lessons from the Australian longitudinal study on women’s health

The Australian Longitudinal Study on Women’s Health (ALSWH) commenced in Australia in 1996 when researchers recruited approximately 40,000 women in three birth cohorts: 1973–1978, 1946–1951, and 1921–1926. Since then participants have completed surveys on a wide range of health issues, at approximately three-year intervals. This overview describes changes in physical activity (PA) over time in the mid-age and older ALSWH cohorts, and summarizes the findings of studies published to date on the determinants of PA, and its associated health outcomes in Australian women. The ALSWH data show a significant increase in PA during mid-age, and a rapid decline in activity levels when women are in their 80s. The study has demonstrated the importance of life stages and key life events as determinants of activity, the additional benefits of vigorous activity for mid-age women, and the health benefits of ‘only walking’ for older women. ALSWH researchers have also drawn attention to the benefits of activity in terms of a wide range of physical and mental health outcomes, as well as overall vitality and well-being. The data indicate that maintaining a high level of PA throughout mid and older age will not only reduce the risk of premature death, but also significantly extend the number of years of healthy life.

Age-based demographics of the pearl perch Glaucosoma scapulare (Ramsay, 1881)

Summary: This research represents the first age-based demographic assessment of pearl perch, Glaucosoma scapulare (Ramsay, 1881), a highly valued species endemic to coastal waters off central eastern Australia. The study was conducted across the species' distribution that encompasses two state jurisdictions (Queensland in the north and New South Wales in the south) using data collected approximately 10 years apart in each state. Estimates of age were made by counting annuli (validated using marginal increment ratios) in sectioned sagittal otoliths. The maximum estimated age was 19 years. Pearl perch attained approx. 12 cm fork length (FL) after one year, 21 cm FL after 2 years and 29 cm FL after 3 years. Fish from the southern end of the species' distribution grew significantly more slowly than those from the northern part of its range. Commercial landings in the north were characterized by greater proportions of larger (>40 cm FL) and older (>6 years) fish than those in the south, with landings mainly of fish between 3 and 6 years of age. The observed variations in age-based demographics of pearl perch highlight the need for a better understanding of patterns of movement and reproduction in developing a model of population dynamics and life-history for this important species. There is a clear need for further, concurrent, age-based studies on pearl perch in the northern and southern parts of its distribution to support the conclusions of the present study based on data collected a decade apart. © 2013 Blackwell Verlag GmbH.

Nonparametric traversability estimation in partially occluded and deformable terrain

Terrain traversability estimation is a fundamental requirement to ensure the safety of autonomous planetary rovers and their ability to conduct long-term missions. This paper addresses two fundamental challenges for terrain traversability estimation techniques. First, representations of terrain data, which are typically built by the rover’s onboard exteroceptive sensors, are often incomplete due to occlusions and sensor limitations. Second, during terrain traversal, the rover-terrain interaction can cause terrain deformation, which may significantly alter the difficulty of traversal. We propose a novel approach built on Gaussian process (GP) regression to learn, and consequently to predict, the rover’s attitude and chassis configuration on unstructured terrain using terrain geometry information only. First, given incomplete terrain data, we make an initial prediction under the assumption that the terrain is rigid, using a learnt kernel function. Then, we refine this initial estimate to account for the effects of potential terrain deformation, using a near-to-far learning approach based on multitask GP regression. We present an extensive experimental validation of the proposed approach on terrain that is mostly rocky and whose geometry changes as a result of loads from rover traversals. This demonstrates the ability of the proposed approach to accurately predict the rover’s attitude and configuration in partially occluded and deformable terrain.

When time ran out : Coming of age in the Third Reich 1925-1982

Memoir based on diaries kept by sculptor Zeller as a boy; Nazi periods in Berlin; primary and secondary school; pogrom (November 1938); emigration to England via Holland; visit to Berlin in 1982

Analysing the Impact of Learning Inputs - Application to Terrain Traversability Estimation

Data-driven approaches such as Gaussian Process (GP) regression have been used extensively in recent robotics literature to achieve estimation by learning from experience. To ensure satisfactory performance, in most cases, multiple learning inputs are required. Intuitively, adding new inputs can often contribute to better estimation accuracy, however, it may come at the cost of a new sensor, larger training dataset and/or more complex learning, some- times for limited benefits. Therefore, it is crucial to have a systematic procedure to determine the actual impact each input has on the estimation performance. To address this issue, in this paper we propose to analyse the impact of each input on the estimate using a variance-based sensitivity analysis method. We propose an approach built on Analysis of Variance (ANOVA) decomposition, which can characterise how the prediction changes as one or more of the input changes, and also quantify the prediction uncertainty as attributed from each of the inputs in the framework of dependent inputs. We apply the proposed approach to a terrain-traversability estimation method we proposed in prior work, which is based on multi-task GP regression, and we validate this implementation experimentally using a rover on a Mars-analogue terrain.

Estimation of cosmological parameters using adaptive importance sampling

We present a Bayesian sampling algorithm called adaptive importance sampling or population Monte Carlo (PMC), whose computational workload is easily parallelizable and thus has the potential to considerably reduce the wall-clock time required for sampling, along with providing other benefits. To assess the performance of the approach for cosmological problems, we use simulated and actual data consisting of CMB anisotropies, supernovae of type Ia, and weak cosmological lensing, and provide a comparison of results to those obtained using state-of-the-art Markov chain Monte Carlo (MCMC). For both types of data sets, we find comparable parameter estimates for PMC and MCMC, with the advantage of a significantly lower wall-clock time for PMC. In the case of WMAP5 data, for example, the wall-clock time scale reduces from days for MCMC to hours using PMC on a cluster of processors. Other benefits of the PMC approach, along with potential difficulties in using the approach, are analyzed and discussed.

Bayesian mixture model estimation of aerosol particle size distributions

In this paper, we examine approaches to estimate a Bayesian mixture model at both single and multiple time points for a sample of actual and simulated aerosol particle size distribution (PSD) data. For estimation of a mixture model at a single time point, we use Reversible Jump Markov Chain Monte Carlo (RJMCMC) to estimate mixture model parameters including the number of components which is assumed to be unknown. We compare the results of this approach to a commonly used estimation method in the aerosol physics literature. As PSD data is often measured over time, often at small time intervals, we also examine the use of an informative prior for estimation of the mixture parameters which takes into account the correlated nature of the parameters. The Bayesian mixture model offers a promising approach, providing advantages both in estimation and inference.

Sweetcorn colour change and consumer perception associated with increasing zeaxanthin for the amelioration of age-related macular degeneration

Age-related macular degeneration (AMD) is the leading cause of blindness in the developed world. Increasing dietary intake of lutein- and zeaxanthin-rich foods is a potential means of preventing, or at least slowing the progression of AMD. Zeaxanthin levels in tropical super-sweetcorn was increased from 1.1 to 11.9 µg/g FW through conventional breeding and selection, associated with both an increase in the proportion of zeaxanthin relative to other carotenoids, and a general increase in carotenoid synthesis. Increasing zeaxanthin was associated with a colour shift from traditional ‘canary-yellow’ kernels to a golden-orange colour. Kernel colour was most closely correlated (r2=69%) with an increase in beta-arm carotenoid concentration. Consumer analysis revealed that prior to any knowledge of zeaxanthin-related health benefit, consumers would readily purchase both yellow and gold cobs. Once the health benefit was explained, this extended to deep-gold cobs. Colour difference between regular yellow sweetcorn and high-zeaxanthin sweetcorn could potentially be used as a visual means of differentiating high-zeaxanthin sweetcorn in the marketplace.