Evaluating the risks of pasture and land degradation in native pastures in Queensland

The objective of the project was to develop an approach to quantify the risks of land nd pasture degradation. This objective was achieved by developing an operational model of the condition of native pastures in Queensland. The results of the project showed that: 1) historical and current pasture data can be used with models to simulate grazing lands in near real-time; 2) spatial models of production can be developed and validated with existing spatial data and monitoring systems; 3) data from graziers indicate that safe utilisation rates are 15-25% of average pasture growth; 4) relative risks of land and pasture can be quantified from simulations using actual numbers compared to safe stocking rates; and 5) case studies using the pasture growth model and models of grazing feedback on pasture and land degradation to evaluate the economic consequences of stocking rate strategies have been used in other projects (e.g. DroughtPlan: McKeon et al. 1996, Stafford Smith et al. 1996)

Instanton vacuum at finite density of quark matter

We study light quark interactions in the instanton liquid at finite quark/baryon number density analyzing chiral and diquark condensates and investigate the behaviors of quark dynamical mass and both condensates together with instanton liquid density as a function of quark chemical potential. We conclude the quark impact (estimated in the tadpole approximation) on the instanton liquid could shift color superconducting phase transition to higher values of the chemical potential bringing critical quark matter density to the values essentially higher than conventional nuclear one.Comment: Lattice 2001 (Non-zero temperature and density), 3 pages, 4 figure

A comprehensive techno-economic and power quality analysis of a remote PV-diesel system in Australia

Presently, the world is considering the integration of small, medium and large-scale PV systems into both urban and remote rural electricity networks. This sees a transition towards a 100% renewable energy based electricity supply from the current conventional fossil fuel based electricity supply. Australia has a significant electricity generation potential from solar PV resources which also encourages the uptake of PV-battery hybrid systems. Australian utilities operating in the isolated and remote areas are now seriously considering the integration of solar PV systems as a long-term solution to reduce costs and facilitate sustainable electricity generation. This would also defer expensive grid extension to supply electricity to these dispersed remote communities. This study aims to model and optimise a remote Australian PV-diesel system incorporated with high levels of PV penetration and battery storage and investigate the system power quality issues. The study includes system component optimisation and techno-economic analysis which considers the outcomes regarding the cost of energy (AUD$/kWh), fuel savings potential and environmental impacts. Power quality issues have been explored by analysing the response of fast frequency-responsive (FFR) battery storage. The overall study has found that higher levels of PV penetration integrated with the current diesel operated system provides a comprehensive and efficient electricity supply and FFR Li-ion batteries can mitigate transient power quality issues and maintain system frequency within acceptable limits

The relationship between personality traits, self-report conscientiousness the Conscientiousness Index and academic performance in undergraduate medical students

Personality traits are now accepted as being important factors within the workplace and medical education. Both cognitive ability and conscientiousness have been shown as important predictors of work-related performance within organisational research1. Conscientiousness is in an important trait within any career, but in particular within medicine where a lack of diligence can be disastrous and potentially cost lives. Within undergraduate students, objective measurement of conscientiousness is important since conscientious students are more likely to set and achieve goals. Durham University utilises the Conscientiousness Index (CI) as an objective scalar measure of conscientiousness. The CI has previously been validated against staff and peer views of student professionalism4,5. This study aimed to investigate the relationships between the Big Five personality domains (including the 6 sub-facets of conscientiousness), Conscientiousness Index scores and academic performance in medical students in order to determine whether self-report conscientiousness correlates with the Conscientiousness Index and whether a relationship with academic performance exists

The climate change risk management matrix for the grazing industry of northern Australia

The complexity, variability and vastness of the northern Australian rangelands make it difficult to assess the risks associated with climate change. In this paper we present a methodology to help industry and primary producers assess risks associated with climate change and to assess the effectiveness of adaptation options in managing those risks. Our assessment involved three steps. Initially, the impacts and adaptation responses were documented in matrices by ‘experts’ (rangeland and climate scientists). Then, a modified risk management framework was used to develop risk management matrices that identified important impacts, areas of greatest vulnerability (combination of potential impact and adaptive capacity) and priority areas for action at the industry level. The process was easy to implement and useful for arranging and analysing large amounts of information (both complex and interacting). Lastly, regional extension officers (after minimal ‘climate literacy’ training) could build on existing knowledge provided here and implement the risk management process in workshops with rangeland land managers. Their participation is likely to identify relevant and robust adaptive responses that are most likely to be included in regional and property management decisions. The process developed here for the grazing industry could be modified and used in other industries and sectors. By 2030, some areas of northern Australia will experience more droughts and lower summer rainfall. This poses a serious threat to the rangelands. Although the impacts and adaptive responses will vary between ecological and geographic systems, climate change is expected to have noticeable detrimental effects: reduced pasture growth and surface water availability; increased competition from woody vegetation; decreased production per head (beef and wool) and gross margin; and adverse impacts on biodiversity. Further research and development is needed to identify the most vulnerable regions, and to inform policy in time to facilitate transitional change and enable land managers to implement those changes

Climate Change in Queensland's Grazing Lands: II. An Assessment of the Impact on Animal Production From Native Pastures

The 160 million ha of grazing land in Queensland support approximately 10 million beef equivalents (9.8 million cattle and 10.7 million sheep) with treed and cleared native pastures as the major forage source. The complexity of these biophysical systems and their interaction with pasture and stock management, economic and social forces limits our ability to easily calculate the impact of climate change scenarios. We report the application of a systems approach in simulating the flow of plant dry matter and utilisation of forage by animals. Our review of available models highlighted the lack of suitable mechanistic models and the potential role of simple empirical relationships of utilisation and animal production derived from climatic and soil indices. Plausible climate change scenarios were evaluated by using a factorial of rainfall (f 10%) * 3260C temperature increase * doubling CO, in sensitivity studies at property, regional and State scales. Simulation of beef cattle liveweight gain at three locations in the Queensland black speargrass zone showed that a *lo% change in rainfall was magnified to be a f 15% change in animal production (liveweight gain per ha) depending on location, temperature and CO, change. Models of 'safe' carrying capacity were developed from property data and expert opinion. Climate change impacts on 'safe' carrying capacity varied considerably across the State depending on whether moisture, temperature or nutrients were the limiting factors. Without the effect of doubling CO,, warmer temperatures and +lo% changes in rainfall resulted in -35 to +70% changes in 'safe' carrying capacity depending on location. With the effect of doubling CO, included, the changes in 'safe' carrying capacity ranged from -12 to +115% across scenarios and locations. When aggregated to a whole-of-State carrying capacity, the combined effects of warmer temperature, doubling CO, and +lo% changes in rainfall resulted in 'safe' carrying capacity changes of +3 to +45% depending on rainfall scenario and location. A major finding of the sensitivity study was the potential importance of doubling CO, in mitigating or amplifying the effects of warmer temperatures and changes in rainfall. Field studies on the impact of CO, are therefore a high research priority. Keywords: climate change, Queensland, simulation, rangelands, beef production, cattle, carrying capacity, CO,, utilisatio

Anomalous specific heat in high-density QED and QCD

Long-range quasi-static gauge-boson interactions lead to anomalous (non-Fermi-liquid) behavior of the specific heat in the low-temperature limit of an electron or quark gas with a leading $T\ln T^{-1}$ term. We obtain perturbative results beyond the leading log approximation and find that dynamical screening gives rise to a low-temperature series involving also anomalous fractional powers $T^{(3+2n)/3}$. We determine their coefficients in perturbation theory up to and including order $T^{7/3}$ and compare with exact numerical results obtained in the large-$N_f$ limit of QED and QCD.Comment: REVTEX4, 6 pages, 2 figures; v2: minor improvements, references added; v3: factor of 2 error in the T^(7/3) coefficient corrected and plots update

The significance of 'the visit' in an English category-B prison: Views from prisoners, prisoners' families and prison staff

A number of claims have been made regarding the importance of prisoners staying in touch with their family through prison visits, firstly from a humanitarian perspective of enabling family members to see each other, but also regarding the impact of maintaining family ties for successful rehabilitation, reintegration into society and reduced re-offending. This growing evidence base has resulted in increased support by the Prison Service for encouraging the family unit to remain intact during a prisoner’s incarceration. Despite its importance however, there has been a distinct lack of research examining the dynamics of families visiting relatives in prison. This paper explores perceptions of the same event – the visit – from the families’, prisoners’ and prison staffs' viewpoints in a category-B local prison in England. Qualitative data was collected with 30 prisoners’ families, 16 prisoners and 14 prison staff, as part of a broader evaluation of the visitors’ centre. The findings suggest that the three parties frame their perspective of visiting very differently. Prisoners’ families often see visits as an emotional minefield fraught with practical difficulties. Prisoners can view the visit as the highlight of their time in prison and often have many complaints about how visits are handled. Finally, prison staff see visits as potential security breaches and a major organisational operation. The paper addresses the current gap in our understanding of the prison visit and has implications for the Prison Service and wider social policy

Blinded Predictions and Post Hoc Analysis of the Second Solubility Challenge Data: Exploring Training Data and Feature Set Selection for Machine and Deep Learning Models

Accurate methods to predict solubility from molecular structure are highly sought after in the chemical sciences. To assess the state of the art, the American Chemical Society organized a "Second Solubility Challenge"in 2019, in which competitors were invited to submit blinded predictions of the solubilities of 132 drug-like molecules. In the first part of this article, we describe the development of two models that were submitted to the Blind Challenge in 2019 but which have not previously been reported. These models were based on computationally inexpensive molecular descriptors and traditional machine learning algorithms and were trained on a relatively small data set of 300 molecules. In the second part of the article, to test the hypothesis that predictions would improve with more advanced algorithms and higher volumes of training data, we compare these original predictions with those made after the deadline using deep learning models trained on larger solubility data sets consisting of 2999 and 5697 molecules. The results show that there are several algorithms that are able to obtain near state-of-the-art performance on the solubility challenge data sets, with the best model, a graph convolutional neural network, resulting in an RMSE of 0.86 log units. Critical analysis of the models reveals systematic differences between the performance of models using certain feature sets and training data sets. The results suggest that careful selection of high quality training data from relevant regions of chemical space is critical for prediction accuracy but that other methodological issues remain problematic for machine learning solubility models, such as the difficulty in modeling complex chemical spaces from sparse training data sets