Did socio-ecological factors drive the spatiotemporal patterns of pandemic influenza A (H1N1)?

BACKGROUND Pandemic influenza A (H1N1) has a significant public health impact. This study aimed to examine the effect of socio-ecological factors on the transmission of H1N1 in Brisbane, Australia. METHODOLOGY We obtained data from Queensland Health on numbers of laboratory-confirmed daily H1N1 in Brisbane by statistical local areas (SLA) in 2009. Data on weather and socio-economic index were obtained from the Australian Bureau of Meteorology and the Australian Bureau of Statistics, respectively. A Bayesian spatial conditional autoregressive (CAR) model was used to quantify the relationship between variation of H1N1 and independent factors and to determine its spatiotemporal patterns. RESULTS Our results show that average increase in weekly H1N1 cases were 45.04% (95% credible interval (CrI): 42.63-47.43%) and 23.20% (95% CrI: 16.10-32.67%), for a 1 °C decrease in average weekly maximum temperature at a lag of one week and a 10mm decrease in average weekly rainfall at a lag of one week, respectively. An interactive effect between temperature and rainfall on H1N1 incidence was found (changes: 0.71%; 95% CrI: 0.48-0.98%). The auto-regression term was significantly associated with H1N1 transmission (changes: 2.5%; 95% CrI: 1.39-3.72). No significant association between socio-economic indexes for areas (SEIFA) and H1N1 was observed at SLA level. CONCLUSIONS Our results demonstrate that average weekly temperature at lag of one week and rainfall at lag of one week were substantially associated with H1N1 incidence at a SLA level. The ecological factors seemed to have played an important role in H1N1 transmission cycles in Brisbane, Australia.

Photocatalysis on supported gold and silver nanoparticles under ultraviolet and visible light irradiation

Studies of the optical properties and catalytic capabilities of noble metal nanoparticles (NPs), such as gold (Au) and silver (Ag), have formed the basis for the very recent fast expansion of the field of green photocatalysis: photocatalysis utilizing visible and ultraviolet light, a major part of the solar spectrum. The reason for this growth is the recognition that the localised surface plasmon resonance (LSPR) effect of Au NPs and Ag NPs can couple the light flux to the conduction electrons of metal NPs, and the excited electrons and enhanced electric fields in close proximity to the NPs can contribute to converting the solar energy to chemical energy by photon-driven photocatalytic reactions. Previously the LSPR effect of noble metal NPs was utilized almost exclusively to improve the performance of semiconductor photocatalysts (for example, TiO2 and Ag halides), but recently, a conceptual breakthrough was made: studies on light driven reactions catalysed by NPs of Au or Ag on photocatalytically inactive supports (insulating solids with a very wide band gap) have demonstrated that these materials are a class of efficient photocatalysts working by mechanisms distinct from those of semiconducting photocatalysts. There are several reasons for the significant photocatalytic activity of Au and Ag NPs. (1) The conduction electrons of the particles gain the irradiation energy, resulting in high energy electrons at the NP surface which is desirable for activating molecules on the particles for chemical reactions. (2) In such a photocatalysis system, both light harvesting and the catalysing reaction take place on the nanoparticle, and so charge transfer between the NPs and support is not a prerequisite. (3) The density of the conduction electrons at the NP surface is much higher than that at the surface of any semiconductor, and these electrons can drive the reactions on the catalysts. (4) The metal NPs have much better affinity than semiconductors to many reactants, especially organic molecules. Recent progress in photocatalysis using Au and Ag NPs on insulator supports is reviewed. We focus on the mechanism differences between insulator and semiconductor-supported Au and Ag NPs when applied in photocatalytic processes, and the influence of important factors, light intensity and wavelength, in particular estimations of light irradiation contribution, by calculating the apparent activation energies of photo reactions and thermal reactions.

An Artificial Neutral Network (ANN) model for predicting biodiesel kinetic viscosity as a function of temperature and chemical compositions

An Artificial Neural Network (ANN) is a computational modeling tool which has found extensive acceptance in many disciplines for modeling complex real world problems. An ANN can model problems through learning by example, rather than by fully understanding the detailed characteristics and physics of the system. In the present study, the accuracy and predictive power of an ANN was evaluated in predicting kinetic viscosity of biodiesels over a wide range of temperatures typically encountered in diesel engine operation. In this model, temperature and chemical composition of biodiesel were used as input variables. In order to obtain the necessary data for model development, the chemical composition and temperature dependent fuel properties of ten different types of biodiesels were measured experimentally using laboratory standard testing equipments following internationally recognized testing procedures. The Neural Networks Toolbox of MatLab R2012a software was used to train, validate and simulate the ANN model on a personal computer. The network architecture was optimised following a trial and error method to obtain the best prediction of the kinematic viscosity. The predictive performance of the model was determined by calculating the absolute fraction of variance (R2), root mean squared (RMS) and maximum average error percentage (MAEP) between predicted and experimental results. This study found that ANN is highly accurate in predicting the viscosity of biodiesel and demonstrates the ability of the ANN model to find a meaningful relationship between biodiesel chemical composition and fuel properties at different temperature levels. Therefore the model developed in this study can be a useful tool in accurately predict biodiesel fuel properties instead of undertaking costly and time consuming experimental tests.

Axial elongation associated with biomechanical factors during near work

Purpose: To investigate the changes occurring in the axial length, choroidal thickness and anterior biometrics of the eye during a 10 minute near task performed in downward gaze. Methods: Twenty young adult subjects (10 emmetropes and 10 myopes) participated in this study. To measure ocular biometrics in downward gaze, an optical biometer was inclined on a custom built, height and tilt adjustable table. Baseline measures were collected after each subject performed a distance primary gaze control task for 10 mins, to provide wash-out period for prior visual tasks before each of three different accommodation/gaze conditions. These other three conditions included a near task (2.5 D) in primary gaze, and a near (2.5 D) and a far (0 D) accommodative task in downward gaze (25°), all for 10 mins duration. Immediately after, and then 5 and 10 mins from the commencement of each trial, measurements of ocular biometrics (e.g. anterior biometrics, axial length, choroidal thickness and retinal thickness) were obtained. Results: Axial length increased with accommodation and was significantly greater for downward gaze with accommodation (mean change ± SD 23 ± 13 µm at 10 mins) compared to primary gaze with accommodation (mean change 8 ± 15 µm at 10 mins) (p < 0.05). A small amount of choroidal thinning was also found during accommodation that was statistically significant in downward gaze (13 ± 14 µm at 10 mins, p < 0.05). Accommodation in downward gaze also caused greater changes in anterior chamber depth and lens thickness compared to accommodation in primary gaze. Conclusion: Axial length, choroidal thickness and anterior eye biometrics change significantly during accommodation in downward gaze as a function of time. These changes appear to be due to the combined influence of biomechanical factors (i.e. extraocular muscle forces, ciliary muscle contraction) associated with near tasks in downward gaze.

Strain engineering of selective chemical adsorption on monolayer MoS2

Nanomaterials are prone to influence by chemical adsorption because of their large surface to volume ratios. This enables sensitive detection of adsorbed chemical species which, in turn, can tune the property of the host material. Recent studies discovered that single and multi-layer molybdenum disulfide (MoS2) films are ultra-sensitive to several important environmental molecules. Here we report new findings from ab inito calculations that reveal substantially enhanced adsorption of NO and NH3 on strained monolayer MoS2 with significant impact on the properties of the adsorbates and the MoS2 layer. The magnetic moment of adsorbed NO can be tuned between 0 and 1 μB; strain also induces an electronic phase transition between half-metal and metal. Adsorption of NH3 weakens the MoS2 layer considerably, which explains the large discrepancy between the experimentally measured strength and breaking strain of MoS2 films and previous theoretical predictions. On the other hand, adsorption of NO2, CO, and CO2 is insensitive to the strain condition in the MoS2 layer. This contrasting behavior allows sensitive strain engineering of selective chemical adsorption on MoS2 with effective tuning of mechanical, electronic, and magnetic properties. These results suggest new design strategies for constructing MoS2-based ultrahigh-sensitivity nanoscale sensors and electromechanical devices.

What factors influence nurses’ assessment practices? Development of the Barriers to Nurses’ use of Physical Assessment Scale

Aim To develop and psychometrically test the Barriers to Nurses’ use of Physical Assessment Scale. Background There is growing evidence of failure to recognise hospitalised patients at risk of clinical deterioration, in part due to inadequate physical assessment by nurses. Yet, little is known about the barriers to nurses’ use of physical assessment in the acute hospital setting and no validated scales have been published. Design Instrument development study. Method Scale development was based on a comprehensive literature review, focus groups, expert review and psychometric evaluation. The scale was administered to 434 acute care registered nurses working at a large Australian teaching hospital between June and July 2013. Psychometric analysis included factor analysis, model fit statistics and reliability testing. Results The final scale was reduced to 38 items representing seven factors, together accounting for 57.7% of the variance: (1) reliance on others and technology, (2) lack of time and interruptions, (3) ward culture, (4) lack of confidence, (5) lack of nursing role models, (6) lack of influence on patient care, and; (7) specialty area. Internal reliability ranged from .70 to .86. Conclusion Findings provide initial evidence for the validity and reliability of the Barriers to Nurses’ use of Physical Assessment Scale and point to the importance of understanding the organisational determinants of nurses’ assessment practices. The new scale has potential clinical and research applications to support nursing assessment in acute care settings.

Toxic state–corporate crimes, Neo-liberalism and green criminology : the hazards and legacies of the oil, chemical and mineral industries

This paper uses examples from the history and practices of multi-national and large companies in the oil, chemical and asbestos industries to examine their legal and illegal despoiling and destruction of the environment and impact on human and non-human life. The discussion draws on the literature on green criminology and state-corporate crime and considers measures and arrangements that might mitigate or prevent such damaging acts. This paper is part of ongoing work on green criminology and crimes of the economy. It places these actions and crimes in the context of a global neo-liberal economic system and considers and critiques the distorting impact of the GDP model of ‘economic health’ and its consequences for the environment.

Critical success factors for the implementation of a knowledge management system in a knowledge-based engineering firm

This research identifies factors that are crucial to the success of a knowledge management system (KMS) implementation in a prominent Australian engineering consultancy firm. The study employs the Delphi method to solicit the opinions of experienced market leaders in the Australian construction industry, and then benchmarks the organisational profile of the consultancy firm against the Delphi findings. From this comparative case study, recommendations are made pertaining to the organisational and cultural changes required within the consultancy firm in order to improve its readiness to successfully implement a KMS.

NRM engagement between remote dryland communities and government agencies : success factors from Australia

The management of dryland environments involves the interaction of multiple government agencies and citizens, and is required to respond to a wide range of responsibilities and aspirations for a given region. This paper focuses on the characteristics of engagement between management agencies and citizens in a dryland region, presented here as a series of success factors. These factors are based on empirical research carried out in the Lake Eyre Basin in Australia, one of the world’s largest inwardly draining basins. The results reinforce generic and dryland-specific factors supporting successful community engagement. The former, such as building trust, working in partnership, supporting community champions, and maintaining transparency, are necessary but insufficient for achieving successful community engagement in the case study region. In addition, community engagement in the case study region also required respecting the extreme conditions and extraordinary variability of the Basin and committing to longer timeframes even if the outcomes of community engagement are slow to accrue, in order to take advantage of opportunities in more prosperous times.

Asking for something different from our donors : factors influencing persuasion success

BACKGROUND Demand for plasma-derived products, and consequently plasmapheresis donors, continues to rise. This study aims to identify the factors that facilitate the persuasion success of conversations with whole blood (WB) donors to convert to plasmapheresis donation within a voluntary non-remunerated context. METHOD Surveys were sent to WB donors after a plasmapheresis conversion conversation with an Agency staff member: in center (sample 1) or via a call center (sample 2). Participants reported the number of donor initiated and Blood Collection Agency (BCA) initiated conversations about plasma, experienced in the prior 12 months. Perceptions of the most recent conversation, donor oriented and conversion oriented were also reported. The BCA provided WB donation history for the prior five years. Participants’ intentions to make a first plasmapheresis donation were captured and any subsequent plasmapheresis donation was objectively recorded. RESULTS Conversion rates were higher for in-center than call center based conversations. For both samples, path analyses revealed that intentions are associated with conversion. Prior WB donations are negatively associated, while donor initiated and donor orientated conversations are positively associated with conversion intentions. Results for agent initiated conversations and conversion orientated conversations were mixed across samples. CONCLUSION Converting suitable WB donors to plasmapheresis is best achieved early in the donor’s career using face-to-face conversations with collection center staff. BCAs should facilitate donor initiated conversations through promotional campaigns that encourage donors to approach staff. Conversations that focus on donors’ needs and welfare more effectively encourage conversion intentions than those perceived as pushing the requirements of the BCA.

Different in vitro activation methods for latent transforming growth factors (TGF)–β : considerable exogenous factors to promote higher mesenchymal-origin cell proliferation in a bioprocessing platform

Regenerative medicine includes two efficient techniques, namely tissue-engineering and cell-based therapy in order to repair tissue damage efficiently. Most importantly, huge numbers of autologous cells are required to deal these practices. Nevertheless, primary cells, from autologous tissue, grow very slowly while culturing in vitro; moreover, they lose their natural characteristics over prolonged culturing period. Transforming growth factors-beta (TGF-β) is a ubiquitous protein found biologically in its latent form, which prevents it from eliciting a response until conversion to its active form. In active form, TGF-β acts as a proliferative agent in many cell lines of mesenchymal origin in vitro. This article reviews on some of the important activation methods-physiochemical, enzyme-mediated, non-specific protein interaction mediated, and drug-induced- of TGF-β, which may be established as exogenous factors to be used in culturing medium to obtain extensive proliferation of primary cells.

‘I came to this country for a better life’ : factors mediating employment trajectories among young people who migrated to Australia as refugees during adolescence

Located at the intersection of two vulnerable groups in the contemporary labour market, young people who migrate as refugees during adolescence face a unique constellation of opportunities and challenges that shape their employment trajectories. Yet the tendency for research to focus on the early years of refugee settlement means that we have an inadequate understanding the factors that mediate their employment decisions, experiences and outcomes. Based on interviews with 51 young people, this article explores how aspirations, responsibilities, family, education and networks are understood to influence the employment trajectories of adolescent refugee migrants. While this article draws attention to the complex and dynamic range of challenges and constraints that these young people negotiate in the pursuit of satisfying and sustainable employment, what also emerges is an optimistic and determined cohort who, even as they at times unsuccessfully prepare for and navigate the labour market, maintain high hopes for a better life.

Estimation of biodiesel properties from chemical composition – An artificial neural network (ANN) approach

Biodiesel, produced from renewable feedstock represents a more sustainable source of energy and will therefore play a significant role in providing the energy requirements for transportation in the near future. Chemically, all biodiesels are fatty acid methyl esters (FAME), produced from raw vegetable oil and animal fat. However, clear differences in chemical structure are apparent from one feedstock to the next in terms of chain length, degree of unsaturation, number of double bonds and double bond configuration-which all determine the fuel properties of biodiesel. In this study, prediction models were developed to estimate kinematic viscosity of biodiesel using an Artificial Neural Network (ANN) modelling technique. While developing the model, 27 parameters based on chemical composition commonly found in biodiesel were used as the input variables and kinematic viscosity of biodiesel was used as output variable. Necessary data to develop and simulate the network were collected from more than 120 published peer reviewed papers. The Neural Networks Toolbox of MatLab R2012a software was used to train, validate and simulate the ANN model on a personal computer. The network architecture and learning algorithm were optimised following a trial and error method to obtain the best prediction of the kinematic viscosity. The predictive performance of the model was determined by calculating the coefficient of determination (R2), root mean squared (RMS) and maximum average error percentage (MAEP) between predicted and experimental results. This study found high predictive accuracy of the ANN in predicting fuel properties of biodiesel and has demonstrated the ability of the ANN model to find a meaningful relationship between biodiesel chemical composition and fuel properties. Therefore the model developed in this study can be a useful tool to accurately predict biodiesel fuel properties instead of undertaking costly and time consuming experimental tests.

A data analytics case study assessing factors affecting pavement deflection values

Road networks are a national critical infrastructure. The road assets need to be monitored and maintained efficiently as their conditions deteriorate over time. The condition of one of such assets, road pavement, plays a major role in the road network maintenance programmes. Pavement conditions depend upon many factors such as pavement types, traffic and environmental conditions. This paper presents a data analytics case study for assessing the factors affecting the pavement deflection values measured by the traffic speed deflectometer (TSD) device. The analytics process includes acquisition and integration of data from multiple sources, data pre-processing, mining useful information from them and utilising data mining outputs for knowledge deployment. Data mining techniques are able to show how TSD outputs vary in different roads, traffic and environmental conditions. The generated data mining models map the TSD outputs to some classes and define correction factors for each class.

The tertiary debate : a case study analysis of factors considered when applying for university entry by traditional age school leavers in Brisbane

Many interacting factors contribute to a student's choice of a university. This study takes a systems perspective of the choice and develops a Bayesian Network to represent and quantify these factors and their interactions. The systems model is illustrated through a small study of traditional school leavers in Australia, and highlights similarities and differences between universities' perceptions of student choices, students' perceptions of factors that they should consider and how students really make choices. The study shows the range of information that can be gained from this approach, including identification of important factors and scenario assessment.