Supporting working parents: findings from the AHRC’s national inquiry into the prevalence of pregnancy and return to work discrimination in the workplace

In July 2014 the Australian Human Rights Commission (AHRC) released the findings of its national review into pregnancy and return to work discrimination in the workplace1 which it conducted following a request from the Commonwealth Attorney-General’s Department.2 The review comes 15 years after the commission’s first inquiry into pregnancy discrimination in the workplace.3Federal law has prohibited pregnancy discrimination in the workplace since the Sex Discrimination Act 1984 (Cth) (SDA) came into force.4 It is now unlawful in every state and territory.5 Discrimination on the basis of breastfeeding and family or carer’s responsibilities is also prohibited.6 Since 2009 the Fair Work Act 2009 (Cth) (FW Act) has prohibited workplace discrimination based on pregnancy and family or carer’s responsibilities7 and the Act gives employees additional entitlements relating to their parental and caring responsibilities. Male and female employees who are the primary caregiver for a child are entitled to 12 months unpaid parental leave upon the birth or adoption of the child and can request an additional 12 months leave.8 Upon returning to work, they can request flexible working conditions9 and they are protected from adverse action, such as dismissal, for exercising these rights.10 Yet despite these legal protections, the findings of the national review show that employees continue to experience discrimination during pregnancy, when taking parental leave and upon re-entering the workforce. This note presents the main findings from the surveys and consultations that were held with employers and employees as part of the review and the review’s recommendations for addressing the prevalence of what it terms ‘pregnancy/return to work discrimination’.

Out of the frying pan, into the fire : Cantrills Filmnotes as Prestige Machine

This examination of the gaps and ambiguities linked to Cantrills Filmnotes (CF), an Australian publication on experimental film, offers a case study on the production and ownership of Pierre Bourdieu’s 'cultural capital' in film art at the margins, witnessed first-hand. CF emerged at the intersection between the street and the academy, spanning that period from the 70s till its abandonment in 2000 during which, it is argued here, it migrated from the former to the latter. This examination surveils, in retrospect, for whose benefit was the magazine's accumulation of power, status and prestige exercised, in whose service was it exacted? CF’s manifesto-like editorial rhetoric was often directed at perceived shortcomings of those institutions servicing film art in Australia. What is revealed when such a critical eye focuses on the production of Cantrills Filmnotes (CF) itself? CF's cultural production has a further dimension of both taking on and taking place inside a colonial mind-set, a cultural cringe often the subject of editorial commentary, elucidating a practice residing at the geographic margins of a marginal arts practice. The founders and editors of CF, the married couple Corinne and Arthur Cantrill both suffered and benefited from CF’s impact on this international field of art production.

Association between attributions of responsibility for motor vehicle crashes, depressive symptoms, and return to work.

Perceptions surrounding the underlying causes of accidents and injuries may be a key mechanism influencing postaccident health and functional outcomes among people injured in road crashes. In particular, attributions of responsibility may influence rates of postcrash depressive symptomatology and return-to-work.

Prediction interval-based neural network modelling of polystyrene polymerization reactor - a new perspective of data-based modelling

In this paper, prediction interval (PI)-based modelling techniques are introduced and applied to capture the nonlinear dynamics of a polystyrene batch reactor system. Traditional NN models are developed using experimental datasets with and without disturbances. Simulation results indicate that traditional NNs cannot properly handle disturbances in reactor data and demonstrate a poor forecasting performance, with an average MAPE of 22% in the presence of disturbances. The lower upper bound estimation (LUBE) method is applied for the construction of PIs to quantify uncertainties associated with forecasts. The simulated annealing optimization technique is employed to adjust NN parameters for minimization of an innovative PI-based cost function. The simulation results reveal that the LUBE method generates quality PIs without requiring prohibitive computations. As both calibration and sharpness of PIs are practically and theoretically satisfactory, the constructed PIs can be used as part of the decision-making and control process of polymerization reactors. © 2014 The Institution of Chemical Engineers.

Structural classification of proteins through amino acid sequence using interval type-2 fuzzy logic system

This paper introduces a new multi-output interval type-2 fuzzy logic system (MOIT2FLS) that is automatically constructed from unsupervised data clustering method and trained using heuristic genetic algorithm for a protein secondary structure classification. Three structure classes are distinguished including helix, strand (sheet) and coil which correspond to three outputs of the MOIT2FLS. Quantitative properties of amino acids are used to characterize the twenty amino acids rather than the widely used computationally expensive binary encoding scheme. Amino acid sequences are parsed into learnable patterns using a local moving window strategy. Three clustering tasks are performed using the adaptive vector quantization method to derive an equal number of initial rules for each type of secondary structure. Genetic algorithm is applied to optimally adjust parameters of the MOIT2FLS with the purpose of maximizing the Q3 measure. Comprehensive experimental results demonstrate the strong superiority of the proposed approach over the traditional methods including Chou-Fasman method, Garnier-Osguthorpe-Robson method, and artificial neural network models.

Use of motivational interviewing to improve return-to-work and work-related outcomes: a review

Work-based return-to-work (RTW) interventions can help to reduce the duration and cost of work disability, and in turn, prevent the negative effects of long-term sickness absence. However, there are a number of complex cognitive, affective and behavioural factors that can impact an individual's confidence, motivation and willingness to RTW that need to be addressed to facilitate effective outcomes. This literature review investigates evidence for the use of motivational interviewing (MI) for improving return-to-work (RTW) and employment outcomes. Whilst evidence for the efficacy of MI in clinical settings to motivate health behaviour change is strong, more research is needed to determine whether MI can be usefully applied to improve RTW and other work-related outcomes. © 2014 The Author(s).

Prediction interval-based modelling of polymerization reactor: a new modelling strategy for chemical reactors

Precise and reliable modelling of polymerization reactor is challenging due to its complex reaction mechanism and non-linear nature. Researchers often make several assumptions when deriving theories and developing models for polymerization reactor. Therefore, traditional available models suffer from high prediction error. In contrast, data-driven modelling techniques provide a powerful framework to describe the dynamic behaviour of polymerization reactor. However, the traditional NN prediction performance is significantly dropped in the presence of polymerization process disturbances. Besides, uncertainty effects caused by disturbances present in reactor operation can be properly quantified through construction of prediction intervals (PIs) for model outputs. In this study, we propose and apply a PI-based neural network (PI-NN) model for the free radical polymerization system. This strategy avoids assumptions made in traditional modelling techniques for polymerization reactor system. Lower upper bound estimation (LUBE) method is used to develop PI-NN model for uncertainty quantification. To further improve the quality of model, a new method is proposed for aggregation of upper and lower bounds of PIs obtained from individual PI-NN models. Simulation results reveal that combined PI-NN performance is superior to those individual PI-NN models in terms of PI quality. Besides, constructed PIs are able to properly quantify effects of uncertainties in reactor operation, where these can be later used as part of the control process. © 2014 Taiwan Institute of Chemical Engineers.

An interval type-2 fuzzy logic system-based method for prediction interval construction

This paper introduces a new non-parametric method for uncertainty quantification through construction of prediction intervals (PIs). The method takes the left and right end points of the type-reduced set of an interval type-2 fuzzy logic system (IT2FLS) model as the lower and upper bounds of a PI. No assumption is made in regard to the data distribution, behaviour, and patterns when developing intervals. A training method is proposed to link the confidence level (CL) concept of PIs to the intervals generated by IT2FLS models. The new PI-based training algorithm not only ensures that PIs constructed using IT2FLS models satisfy the CL requirements, but also reduces widths of PIs and generates practically informative PIs. Proper adjustment of parameters of IT2FLSs is performed through the minimization of a PI-based objective function. A metaheuristic method is applied for minimization of the non-linear non-differentiable cost function. Performance of the proposed method is examined for seven synthetic and real world benchmark case studies with homogenous and heterogeneous noise. The demonstrated results indicate that the proposed method is capable of generating high quality PIs. Comparative studies also show that the performance of the proposed method is equal to or better than traditional neural network-based methods for construction of PIs in more than 90% of cases. The superiority is more evident for the case of data with a heterogeneous noise. © 2014 Elsevier B.V.

A new method for deriving priority weights by extracting consistent numerical-valued matrices from interval-valued fuzzy judgement matrix

It is important to derive priority weights from interval-valued fuzzy preferences when a pairwise comparative mechanism is used. By focusing on the significance of consistency in the pairwise comparison matrix, two numerical-valued consistent comparison matrices are extracted from an interval fuzzy judgement matrix. Both consistent matrices are derived by solving the linear or nonlinear programming models with the aid of assessments from Decision Makers (DMs). An interval priority weight vector from the extracted consistent matrices is generated. In order to retain more information hidden in the intervals, a new probability-based method for comparison of the interval priority weights is introduced. An algorithm for deriving the final priority interval weights for both consistent and inconsistent interval matrices is proposed. The algorithm is also generalized to handle the pairwise comparison matrix with fuzzy numbers. The comparative results from the five examples reveal that the proposed method, as compared with eight existing methods, exhibits a smaller degree of uncertainty pertaining to the priority weights, and is also more reliable based on the similarity measure. © 2014 Elsevier Inc. All rights reserved.

Does fire influence the landscape-scale distribution of an invasive mesopredator?

Predation and fire shape the structure and function of ecosystems globally. However, studies exploring interactions between these two processes are rare, especially at large spatial scales. This knowledge gap is significant not only for ecological theory, but also in an applied context, because it limits the ability of landscape managers to predict the outcomes of manipulating fire and predators. We examined the influence of fire on the occurrence of an introduced and widespread mesopredator, the red fox (Vulpes vulpes), in semi-arid Australia. We used two extensive and complimentary datasets collected at two spatial scales. At the landscape-scale, we surveyed red foxes using sand-plots within 28 study landscapes - which incorporated variation in the diversity and proportional extent of fire-age classes - located across a 104 000 km2 study area. At the site-scale, we surveyed red foxes using camera traps at 108 sites stratified along a century-long post-fire chronosequence (0-105 years) within a 6630 km2 study area. Red foxes were widespread both at the landscape and site-scale. Fire did not influence fox distribution at either spatial scale, nor did other environmental variables that we measured. Our results show that red foxes exploit a broad range of environmental conditions within semi-arid Australia. The presence of red foxes throughout much of the landscape is likely to have significant implications for native fauna, particularly in recently burnt habitats where reduced cover may increase prey species' predation risk.

Optimal fire histories for biodiversity conservation

Fire is used as a management tool for biodiversity conservation worldwide. A common objective is to avoid population extinctions due to inappropriate fire regimes. However, in many ecosystems, it is unclear what mix of fire histories will achieve this goal. We determined the optimal fire history of a given area for biological conservation with a method that links tools from 3 fields of research: species distribution modeling, composite indices of biodiversity, and decision science. We based our case study on extensive field surveys of birds, reptiles, and mammals in fire-prone semi-arid Australia. First, we developed statistical models of species' responses to fire history. Second, we determined the optimal allocation of successional states in a given area, based on the geometric mean of species relative abundance. Finally, we showed how conservation targets based on this index can be incorporated into a decision-making framework for fire management. Pyrodiversity per se did not necessarily promote vertebrate biodiversity. Maximizing pyrodiversity by having an even allocation of successional states did not maximize the geometric mean abundance of bird species. Older vegetation was disproportionately important for the conservation of birds, reptiles, and small mammals. Because our method defines fire management objectives based on the habitat requirements of multiple species in the community, it could be used widely to maximize biodiversity in fire-prone ecosystems. © 2014 Society for Conservation Biology.

Why do some species have geographically varying responses to fire history?

A capacity to predict the effects of fire on biota is critical for conservation in fire-prone regions as it assists managers to anticipate the outcomes of different approaches to fire management. The task is complicated because species' responses to fire can vary geographically. This poses challenges, both for conceptual understanding of post-fire succession and fire management. We examine two hypotheses for why species may display geographically varying responses to fire. 1) Species' post-fire responses are driven by vegetation structure, but vegetation - fire relationships vary spatially (the 'dynamic vegetation' hypothesis). 2) Regional variation in ecological conditions leads species to select different post-fire ages as habitat (the 'dynamic habitat' hypothesis). Our case study uses data on lizards at 280 sites in a ~ 100 000 km2 region of south-eastern Australia. We compared the predictive capacity of models based on 1) habitat associations, with models based on 2) fire history and vegetation type, and 3) fire history alone, for four species of lizards. Habitat association models generally out-performed fire history models in terms of predictive capacity. For two species, habitat association models provided good discrimination capacity even though the species showed geographically varying post-fire responses. Our results support the dynamic vegetation hypothesis, that spatial variation in relationships between fire and vegetation structure results in regional variation in fauna-fire relationships. These observations explain how the widely recognised 'habitat accommodation' model of animal succession can be conceptually accurate yet predictively weak. © 2014 The Authors.

A new interval-based method for handling non-monotonic information

The focus of this paper is on handling non-monotone information in the modelling process of a single-input target monotone system. On one hand, the monotonicity property is a piece of useful prior (or additional) information which can be exploited for modelling of a monotone target system. On the other hand, it is difficult to model a monotone system if the available information is not monotonically-ordered. In this paper, an interval-based method for analysing non-monotonically ordered information is proposed. The applicability of the proposed method to handling a non-monotone function, a non-monotone data set, and an incomplete and/or non-monotone fuzzy rule base is presented. The upper and lower bounds of the interval are firstly defined. The region governed by the interval is explained as a coverage measure. The coverage size represents uncertainty pertaining to the available information. The proposed approach constitutes a new method to transform non-monotonic information to interval-valued monotone system. The proposed interval-based method to handle an incomplete and/or non-monotone fuzzy rule base constitutes a new fuzzy reasoning approach.

Extending scheduling algorithms to minimise the impact of bounded uncertainty using interval programming

 This research proposed a new methodology to extend algorithms to accept interval-based uncertain parameters. The methodology is applied on scheduling algorithms, including heuristic and meta-heuristic algorithms and produced optimal results with higher accuracy. The research outcomes are effective for decision making process using uncertain or predicted data.