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Contemporary challenges in public sector reporting
No full textSardesai, AV ORCiD: 0000-0001-6794-4549Public sector accounting scholarship has witnessed enormous developments over the last decades. One area of scholarship is public sector accountability and public service accounting and reporting. Accountability in the public sector is a different, complex, chameleon-like and multifaced concept, encompassing several dimensions. With multiple stakeholders, the public sector requires a broader set of accountability forms, which goes beyond the scope of financial aspects, to include political, public, managerial, bureaucratic, professional, and personal accountability. In contemporary times the public sector needs to consider accounting and reporting on global and critical issues like climate change, sustainability, modern-day slavery, social inequality, taxation avoidance, biodiversity and ecological accounts. This chapter provides an overview of several developments in the past decades and reviews seven reporting frameworks that have been used for public sector reporting
Person-centred rhetoric in chronic care: A review of health policies
No full textBaldwin, AE ORCiD: 0000-0002-6325-4142; Byrne, AJ ORCiD: 0000-0002-8679-8310; Harvey, CL ORCiD: 0000-0001-9016-8840; Willis, EM ORCiD: 0000-0001-7576-971XPurpose: The purpose of the paper is to explore how the national, state and organisational health policies in Australia support the implementation of person-centred care in managing chronic care conditions. Design/methodology/approach: A qualitative content analysis was performed regarding the national, state and organisational Queensland Health policies using Elo and Kyngas' (2008) framework. Findings: Although the person-centred care as an approach is well articulated in health policies, there is still no definitive measure or approach to embedding it into operational services. Complex funding structures and competing priorities of the governments and the health organisations carry the risk that person-centred care as an approach gets lost in translation. Three themes emerged: the patient versus the government; health care delivery versus the political agenda; and health care organisational processes versus the patient. Research limitations/implications: Given that person-centred care is the recommended approach for responding to chronic health conditions, further empirical research is required to evaluate how programs designed to deliver person-centred care achieve that objective in practice. Practical implications: This research highlights the complex environment in which the person-centred approach is implemented. Short-term programmes created specifically to focus on person-centred care require the right organisational infrastructure, support and direction. This review demonstrates the need for alignment of policies related to chronic disease management at the broader organisational level. Originality/value: Given the introduction of the nurse navigator program to take up a person-centred care approach, the review of the recent policies was undertaken to understand how they support this initiative. © 2020, Emerald Publishing Limited
Assessment of oxygenated fuels for lowering NOx emissions of a diesel engine
No full textThe supply of petroleum sources is finite, non-renewable and, at the current rate of consumption, it will become severely depleted by 2050. Furthermore, the use of petroleum fuel increases greenhouse gas (GHG) emissions, leading to global warming which is harmful. Thus, there is an urgent need to find alternative sources of energy that are renewable, cost effective and can be produced in a sustainable manner. Non-edible feedstock biodiesels are a promising alternative fuel for reducing most petroleum fuel related environmental problems. They are attracting increasing attention due to their abundant availability and similar physicochemical properties as petroleum-derived diesel. This study carefully investigated six major non-edible vegetable oils (papaya seed oil, stone fruit kernel oil, jatropha oil, rapeseed oil, beauty leaf tree oil and waste cooking oil), that are locally available, out of 350 oil-bearing crops that could be potentially used to produce biodiesel. Four multiple criteria decision analysis (MCDA) methods with twelve physicochemical properties of biodiesel feedstocks and three different weightage (%) determination methods were used to rank these six feedstocks, with the view to find the best performing biodiesel feedstocks. The overall results show that the stone fruit kernel oil (SFO) was ranked as the best performing feedstock on the basis of engine performance amongst the six locally available feedstocks examined, papaya seed oil (PSO) came out as the second best, and the waste cooking oil was the worst performing biodiesel.
Alkali catalysed transesterification reaction is the most widely used method for producing biodiesel from oil/animal fats due to its higher conversion efficiency in a short reaction time (30-60 min). The current study was undertaken to optimise the transesterification process for PSO and SFO with the view to increasing the efficiency of biodiesel conversion. A response surface method (RSM) based Box-Behnken design was employed to optimise biodiesel conversion processes for both PSO and SFO. Biodiesel conversion efficiencies of 96.5% and 95.8% were found for PSO and SFO at their respective optimum operating conditions.
These PSO and SFO biodiesels were evaluated using a 4-cylinder, 4-stroke Kubota diesel engine. In general, both PSO and SFO blends decreased engine performance slightly compared to diesel as expected, however, SFO biodiesel blends gave about 3% better performance compared to PSO blends. On the other hand, PSO blends (20%) decreased most of the engine emissions by up to 34% except for an increase of about 5% in nitrogen oxide (NOx) compared to diesel. These emission performances are up to 14% better than the corresponding SFO emissions. Although the SFO biodiesel blends have slightly better engine performance than PSO biodiesel blends, the PSO biodiesel blends proved to be a better overall choice due to their excellent environmentally friendly attributes as they can reduce exhaust emissions to a great extent. Therefore, PSO was chosen subsequently to develop interactive relationships between three operating parameters of PSO, namely biodiesel blends, engine load, and engine speed and four responses of brake power (BP), torque, brake specific fuels consumption (BSFC), and brake thermal efficiency (BTE) for engine testing and emissions behaviour.
Analysis of variance (ANOVA) and a statistical regression model show that load and speed were the two most important parameters that affect all four responses. The biodiesel blends parameter had a significant effect on BSFC. The engine load and engine speed were the two most important parameters that affect four of the responses (NOx, hydrocarbon (HC), particulate matter (PM) and carbon monoxide (CO)). In-cylinder peak pressures for PSO biodiesel blends were higher than for diesel irrespective of engine speed. Heat release rates of PSO biodiesel blends were found to be lower than for diesel due to lower ignition delays and lower caloric values of biodiesel. The maximum cylinder temperatures of PSO biodiesel blends were higher (3.73%) than that of diesel. To minimise the exhaust emissions, PSO biodiesel blends were mixed with two oxygenated additives, namely diethylene glycol dimethyl ether (diglyme) and n-butanol, to make ternary blends. These blends were tested for both engine performance and emissions. The addition of oxygenated additives increased the BP, torque and BTE values of PSO biodiesel ternary blends and it lowered the average BSFC by 0.5% and 17.7% compared with diesel and PSO blends (20%), respectively. PSO-diglyme-diesel ternary blend performed better than all other binary blends as well as the PSO-n-butanol-diesel ternary blend. The average reductions of HC, CO, NOx and PM of PSO-diglyme-diesel ternary blends compared with diesel were 32.4%, 61%, 0.64% and 47.4% respectively, whereas a 2.8% increase in carbon dioxide (CO2) emission was observed. The average increase of NOx, and CO2 for PSO blends (20%) compared with diesel were 4.1% and 4.5%, respectively.
In conclusion, this study provided a solid base of new knowledge regarding biodiesel feedstock selection and optimisation techniques for PSO and SFO, assessed the suitability of PSO and SFO as alternatives to petroleum diesel and analysed how the emissions from these biodiesels could be reduced. These are very useful information for engine manufacturers, Government, stakeholders and policy makers to eliminate the lack of awareness of using second-generation biodiesel in Australi
A reduced tillering trait shows small but important yield gains in dryland wheat production
No full textTausz, M ORCiD: 0000-0001-8205-8561; Tausz-Posch, S ORCiD: 0000-0002-1213-7907Reducing the number of tillers per plant using a tiller inhibition (tin) gene has been considered as an important trait for wheat production in dryland environments. We used a spatial analysis approach with a daily time-step coupled radiation and transpiration efficiency model to simulate the impact of the reduced-tillering trait on wheat yield under different climate change scenarios across Australia's arable land. Our results show a small but consistent yield advantage of the reduced-tillering trait in the most water-limited environments both under current and likely future conditions. Our climate scenarios show that whilst elevated [CO2 ] (e[CO2 ]) alone might limit the area where the reduced-tillering trait is advantageous, the most likely climate scenario of e[CO2 ] combined with increased temperature and reduced rainfall consistently increased the area where restricted tillering has an advantage. Whilst long-term average yield advantages were small (ranged from 31 to 51 kg ha-1 yr-1 ), across large dryland areas the value is large (potential cost-benefits ranged from AUD 23 to 60 MIL yr-1 ). It seems therefore worthwhile to further explore this reduced-tillering trait in relation to a range of different environments and climates, because its benefits are likely to grow in future dry environments where wheat is grown around the world
Robust malware defense in industrial IoT applications using machine learning with selective adversarial samples
No full textImam, T ORCiD: 0000-0002-8864-4155Industrial Internet of Things (IIoT) deploys edge devices to act as intermediaries between sensors and actuators and application servers or cloud services. Machine learning models have been widely used to thwart malware attacks in such edge devices. However, these models are vulnerable to adversarial attacks where attackers craft adversarial samples by introducing small perturbations to malware samples to fool a classifier to misclassify them as benign applications. Literature on deep learning networks proposes adversarial retraining as a defense mechanism where adversarial samples are combined with legitimate samples to retrain the classifier. However, existing works select such adversarial samples in a random fashion which degrades the classifier's performance. This work proposes two novel approaches for selecting adversarial samples to retrain a classifier. One, based on the distance from malware cluster center, and the other, based on a probability measure derived from a kernel based learning (KBL). Our experiments show that both of our sample selection methods outperform the random selection method and the KBL selection method improves detection accuracy by 6%. Also, while existing works focus on deep neural networks with respect to adversarial retraining, we additionally assess the impact of such adversarial samples on other classifiers and our proposed selective adversarial retraining approaches show similar performance improvement for these classifiers as well. The outcomes from the study can assist in designing robust security systems for IIoT applications
The shock doctrine and industrial relations
No full textColley, LK ORCiD: 0000-0001-7692-5868Crises require swift policy responses, but can provide an opportunity for political leaders to introduce reforms that might otherwise prove unpopular (Colley and Head 2014). They provide an opportunity to advance neoliberal economic policies that could not be progressed through democratic means but where a sweeping crisis provides a pretext to override the expressed wishes of voters (Friedman 1962, cited in Klein 2007). Policy shifts are also possible in democratic contexts, where there is widespread acceptance of a policy problem and a government can provide a compelling alternative (Kingdon 2003; Sabatier 2007). In her book Shock Doctrine, Naomi Klein (2007) provides numerous examples, such as Hurricane Katrina in New Orleans, where states’ crisis response and recovery plans advanced capitalism and corporate goals at a time when victims were unable to regroup and resist. Not all crises need lead to outcomes like this. The response of the Malaysian government to the 1997 Asian financial crisis, for example, was to incorporate the peak council representatives of labour into the national decision-making process, through a new national economic planning body — representatives it had previously threatened to jail, from a movement it had previously legislated against (Campbell 2001). So crises can be critical junctures in reshaping a country’s political economy. While the state will frequently serve the interests of capital, it is still an actor in its own right. The interests of political leaders might not always coincide with those of capital. And there may be times when capital, and even the state, sees benefits from accommodation with labour in response to a crisis. So it was that the post-war settlement through most of Western Europe was a mostly Keynesian accommodation.
This current crisis, then, could have gone either way. The fact that the Australian Council of Trade Unions (ACTU) played a critical role in the introduction of the JobKeeper wage subsidy scheme, and the reported daily interactions between the ACTU secretary and the relevant federal minister (Maley 2020) could have indicated a major realignment between the state and unions, and that the government had come to accept a legitimate role for unions. Similarly, some may have thought that the introduction of JobKeeper, doubling of unemployment benefits and provision of free child care may have signalled a recognition of the legitimacy of Keynesian approaches that repudiated austerity
It is not all about being sweet: Differences in floral traits and insect visitation among hybrid carrot cultivars
No full textBrown, PH ORCiD: 0000-0001-6272-5507 0000-0002-4033-6719Cytoplasmically male-sterile (CMS) carrot cultivars suffer from low pollination rates. In this study, insect visitation varied more than eightfold between 17 CMS carrot cultivars in a field-based cultivar evaluation trial. The visitation rates of honey bees, nectar scarabs, muscoid flies, and wasps each significantly differed among these cultivars. No significant difference in visitation rates was observed among cultivars of different CMS type (brown-anther or petaloid) or flower colour, but cultivars of Berlicumer root type had significantly higher insect visitation rates than Nantes. Six cultivars were further compared in regard to selected umbel traits: as umbel diameter increased, so did the visitation of soldier beetles, while that of honey bees decreased. Finally, nectar of these six cultivars was analysed for sugar content, which revealed monosaccharides to be the most common sugars in all. There was high variation in the levels of sugars from individual umbellets but no significant difference in nectar sugar composition among cultivars, suggesting that nectar sugar composition is of minor importance regarding pollinator attraction to hybrid CMS carrot umbels. © 2020 by the authors. Licensee MDPI, Basel, Switzerland
A novel framework for optimised ensemble classifiers
No full textJan, M ORCiD: 0000-0002-5066-4118Ensemble classifiers are created by combining multiple single classifiers to achieve higher classification accuracy. Ensemble classifiers benefit from the ‘perturb and combine’ strategy, where an input data is perturbed to generate sub-samples and base classifiers are trained on generated sub-samples. All trained base classifiers are then suitably combined, and an ensemble decision is formed. One common strategy of perturbing input data is through clustering. Data clusters are generated from the input, and base classifiers are trained on generated data clusters. Such ensemble classifiers are also called clustering-based ensemble classifiers as they utilise clustering algorithms to generate a perturbed input training space.
Clustering has been very applicable when it comes to generating ensemble classifiers, however it has certain limitations. One key limitation is that clustering algorithms require the number of data clusters in advance. Most of the existing ensemble approaches use a fixed number of data clusters, that are generated for various datasets, and normally searched through a process of trial and error. Additionally, since clustering works independently of data classes, class imbalances may occur in the data clusters, and data clusters may miss data samples from certain classes. Therefore, not all data clusters are suitable for the training of base classifiers, and redundant or imbalanced data clusters, should be dealt with appropriately. Besides the number of data clusters problem, the choice and type of base classifiers utilised to train on generated data clusters also have significant impact on the ensemble classifier’s performance. The use of all base classifiers to generate an ensemble classifier is not an ideal strategy, so an appropriate classifier selection methodology must be adopted to select the subset of base classifiers that can maximise the ensemble classifier’s accuracy.
In this thesis several novel ensemble classifier methods have been proposed to mitigate the limitations and improve accuracy of ensemble classifiers. The first ensemble method is based on a novel strategy of incorporating an evolutionary algorithm to dynamically search for the upper bound of clustering. The second ensemble classifier method incorporates an evolutionary algorithm in two phases by optimising the pool of data clusters rather than a single upper bound and optimising the pool of base classifiers. The third ensemble classifier method is based on a hybrid approach that solves the problem of dimensionality and uses reduced dimensions data to generate an optimised ensemble classifier. The fourth ensemble classifier method is based on a novel cluster balancing strategy that solves the problem of class imbalances by balancing data clusters. The fifth ensemble classifier method contains a novel strategy to find the optimal value of clusters for each data class through the incorporation of cluster validation strategies. The sixth ensemble classifier method is based on a novel classifier selection strategy that selects classifiers from the pool based on accuracy and diversity comparisons. The seventh, and final ensemble classifier method, uses a novel pairwise diversity measure to select classifiers from the pool based on increasing accuracy and diversity. The proposed ensemble methods were evaluated on several benchmark datasets. These datasets are used by other researchers and allow a comparative analysis. In most cases an ensemble classifier’s accuracy was used as a metric to measure the performance, and in other cases different diversity measures were used. Statistical significance testing was also conducted to further validate the efficacy of the results and p-values were reported.
The results and analysis presented in this thesis show that the proposed ensemble methods not only achieved classification accuracy better than existing state-of-the-art ensemble methods, but also provide a platform for future research. It was found through experimentation that upper bounds of clustering follow a logarithmic relation with the number of data samples each dataset has. Moreover, through extensive experimentation, it was proved that not all base classifiers should be selected to generate the ensemble, and only a subset of base classifiers is required to generate an ensemble classifier that can achieve the highest classification accuracy. Through the incorporation of optimisation, it was also proved that no preference is given to a specific base classifier and the type of base classifier is dependent on the characteristics of the dataset. Silhouette analysis proved to be an effective cluster validation metric to determine the optimal number of data clusters. Finally, balancing data clusters proved to be effective not only in terms of classification accuracy, but also confirmed that each dataset has different spatial characteristics which, when exploited appropriately, can contribute to overall ensemble classifier accuracy
Bridging the gap between reading theory and innovating teacher practice
No full textKnight, BA ORCiD: 0000-0001-6627-378XExperienced teachers possess wide-ranging knowledge about how best to effectively teach vulnerable children to read. Reading research also provides extensive information on what constitutes best-practice instruction. This paper reports on an ARC research grant that aimed to bridge the gap between theory and practice involving researchers and teachers of Prep (Foundation) to Year 3 working together to develop a set of principles towards optimising reading instruction for all students, and particularly at-risk readers. Now freely available for use by interested parties, the principles document is intended as a resource for ongoing use and exploration by educators and researchers.
This paper first discusses the vital need for and the challenges of optimising early literacy learning of at-risk Anglophone students; then details the collaborative research that established the set of principles. It includes discussion of the strengths and challenges of the research, ways forward for enhancing the use of the principles, and models of collaborative knowledge building into the future
Systems, economics, and neoliberal politics: Theories to understand missed nursing care
No full textWillis, EM ORCiD: 0000-0001-7576-971XThe phenomenon of missed nursing care is endemic across all sectors. Nurse leaders have drawn attention to the implications of missed care for patient outcomes, with calls to develop clear political, methodological, and theoretical approaches. As part of this call, we describe three structural theories that inform frameworks of missed care: systems theory, economic theory, and neoliberal politics. The final section provides commentary on the strengths and limitations of these three theories, in the light of structuration theory and calls to balance this research agenda by reinstating nurse agency and examining the interactions between nurses as agents and the health systems as structures. The paper argues that a better understanding of variations in structure–agency interaction across the healthcare system might lead to more effective interventions at strategic leverage points. © 2020 John Wiley & Sons Australia, Lt