Baseline study of alcohol dependence among general drivers and drunk driving offenders in Guangzhou, China

This study aimed to investigate drink driving in a sample of general drivers and convicted drunk driving offenders in Guangzhou, China. The study also aimed to explore some potential factors that impact on alcohol-related driving behaviour. Samples of 406 general drivers and 101 drunk driving offenders were recruited between May and October 2012. A survey was used to collect information about demographic characteristics, knowledge, attitudes and practices related to drink driving. The Alcohol Use Disorders Identification Test (AUDIT) was used to assess possible drinking problems. The average age reported for starting to drink alcohol for both groups of participants was around 19 years old. The mean AUDIT score of general drivers was 7.4 (SD = 5.4) representing a low level of alcohol problems, and for convicted drunk driving offenders was 11.1 (SD = 5.9) representing a medium level of alcohol problems (significant difference between means, t = 5.75, p < 0.001). AUDIT scores indicated that a substantial proportion (65%) of the offenders had medium to high levels of alcohol use disorders, compared with 38.5% among general drivers. Offenders who knew the drunk driving legal limit had a lower AUDIT score (M = 9.8, SD = 5.16) than those who did not know it (M = 12.2, SD = 6.257, t = -1.987. p = 0.05). In addition, offenders who were novice drivers (licensed less than 2 years) had a higher AUDIT score (M = 16.4, SD = 7.6) than the other three driver experience categories used.

Role of particle size and composition in metal adsorption by solids deposited on urban road surfaces

Despite common knowledge that the metal content adsorbed by fine particles is relatively higher compared to coarser particles, the reasons for this phenomenon has gained little research attention. The research study discussed in the paper investigated the variations in metal content for different particle sizes of solids associated with pollutant build-up on urban road surfaces. Data analysis confirmed that parameters favourable for metal adsorption to solids such as specific surface area, organic carbon content, effective cation exchange capacity and clay forming minerals content decrease with the increase in particle size. Furthermore, the mineralogical composition of solids was found to be the governing factor influencing the specific surface area and effective cation exchange capacity. There is high quartz content in particles >150µm compared to particles <150µm. As particle size reduces below 150µm, the clay forming minerals content increases, providing favourable physical and chemical properties that influence adsorption.

Dependence of residual amplitude noise in electro-optic phase modulators on the intensity distribution of the incident field

Our results demonstrate that photorefractive residual amplitude modulation (RAM) noise in electro-optic modulators (EOMs) can be reduced by modifying the incident beam intensity distribution. Here we report an order of magnitude reduction in RAM when beams with uniform intensity (flat-top) profiles, generated with an LCOS-SLM, are used instead of the usual fundamental Gaussian mode (TEM00). RAM arises from the photorefractive amplified scatter noise off the defects and impurities within the crystal. A reduction in RAM is observed with increasing intensity uniformity (flatness), which is attributed to a reduction in space charge field on the beam axis. The level of RAM reduction that can be achieved is physically limited by clipping at EOM apertures, with the observed results agreeing well with a simple model. These results are particularly important in applications where the reduction of residual amplitude modulation to 10^-6 is essential.

Phase composition of the rapidly quenched melt of YBa2Cu3O7-y+20 mol% Y2BaCuO5

Samples of YBa2Cu3O7-y+20 mol% Y2BaCuO5, with thicknesses ranging between 50-250 μm, have been melt processed and rapidly quenched from temperatures between 985 and 1100°C by immersing them in liquid nitrogen. The phase composition and microstructures of these samples have been characterised using a combination of X-ray diffractometry, optical microscopy and scanning electron microscopy with energy dispersive X-ray spectroscopy. The quenched melt of samples quenched from temperatures greater than 985°C appears relatively homogeneous but consists of Ba2Cu3Ox (BC1.5) and BaCu2O2 (BC2) regions. At about 985°C, BaCuO2 (BC1) crystallises from the melt and most of the BC1.5 decomposes into BC1 and CuO or into BC1 and BC2. The crystallisation of BC1 induces segregation of elements in the melt and this is very significant for the melt texturing of YBCO.

Age-dependence of the average and equivalent refractive indices of the crystalline lens

Lens average and equivalent refractive indices are required for purposes such as lens thickness estimation and optical modeling. We modeled the refractive index gradient as a power function of the normalized distance from lens center. Average index along the lens axis was estimated by integration. Equivalent index was estimated by raytracing through a model eye to establish ocular refraction, and then backward raytracing to determine the constant refractive index yielding the same refraction. Assuming center and edge indices remained constant with age, at 1.415 and 1.37 respectively, average axial refractive index increased (1.408 to 1.411) and equivalent index decreased (1.425 to 1.420) with age increase from 20 to 70 years. These values agree well with experimental estimates based on different techniques, although the latter show considerable scatter. The simple model of index gradient gives reasonable estimates of average and equivalent lens indices, although refinements in modeling and measurements are required.

Carrot red leaf and carrot mottle viruses : observations on the composition of the particles in single and mixed infections

Particles of carrot red leaf virus (CRLV; luteovirus group) purified from chervil (Anthriscus cerefolium) contain a single ssRNA species of mol. wt. about 1.8 x 106 and a major protein of mol. wt. about 25000. CRLV acts as a helper for aphid transmission of carrot mottle virus (CMotV; ungrouped) from mixedly infected plants. Virus preparations purified from such plants possess the infectivity of both viruses but contain particles indistinguishable from those of CRLV; some of the particles are therefore thought to consist of CMotV RNA packaged in CRLV coat protein. When RNA from such preparations was electrophoresed in agarose/polyacrylamide gels, CMotV infectivity was associated with an RNA band that migrated ahead of the CRLV RNA band and had an estimated mol. wt. of about 1.5 x 106, similar to that previously found for the infective ssRNA extracted directly from Nicotiana clevelandii leaves infected with CMotV alone. Preparations of dsRNA from CMotV-infected N. clevelandii leaves contained two species: one of mol. wt. about 3.2 x 106, presumably the replicative form of the infective ssRNA, and the other, mol. wt. about 0.9 x 106, of unknown origin and function. The infective agent in buffer extracts of CMotV-infected N. clevelandii was resistant to RNase (although the enzyme acted as a reversible inhibitor of infection at high concentrations) and is therefore not unprotected RNA. It may be protected within the approximately 52 nm enveloped structures previously reported.

Developing a dashboard for real-time data stream composition and visualization

Technological advances have led to an influx of affordable hardware that supports sensing, computation and communication. This hardware is increasingly deployed in public and private spaces, tracking and aggregating a wealth of real-time environmental data. Although these technologies are the focus of several research areas, there is a lack of research dealing with the problem of making these capabilities accessible to everyday users. This thesis represents a first step towards developing systems that will allow users to leverage the available infrastructure and create custom tailored solutions. It explores how this notion can be utilized in the context of energy monitoring to improve conventional approaches. The project adopted a user-centered design process to inform the development of a flexible system for real-time data stream composition and visualization. This system features an extensible architecture and defines a unified API for heterogeneous data streams. Rather than displaying the data in a predetermined fashion, it makes this information available as building blocks that can be combined and shared. It is based on the insight that individual users have diverse information needs and presentation preferences. Therefore, it allows users to compose rich information displays, incorporating personally relevant data from an extensive information ecosystem. The prototype was evaluated in an exploratory study to observe its natural use in a real-world setting, gathering empirical usage statistics and conducting semi-structured interviews. The results show that a high degree of customization does not warrant sustained usage. Other factors were identified, yielding recommendations for increasing the impact on energy consumption.

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.

Variability of metal composition and concentrations in road dust in the urban environment

Urban road dust comprises of a range of potentially toxic metal elements and plays a critical role in degrading urban receiving water quality. Hence, assessing the metal composition and concentration in urban road dust is a high priority. This study investigated the variability of metal composition and concentrations in road dust in 4 different urban land uses in Gold Coast, Australia. Samples from 16 road sites were collected and tested for selected 12 metal species. The data set was analyzed using both univariate and multivariate techniques. Outcomes of the data analysis revealed that the metal concentrations in road dust differ considerably within and between different land uses. Iron, aluminum, magnesium and zinc are the most abundant in urban land uses. It was also noted that metal species such as titanium, nickel, copper and zinc have the highest concentrations in industrial land use. The study outcomes revealed that soil and traffic related sources as key sources of metals deposited on road surfaces.

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.

The salt dependence of DNA recognition by NF-kappaB p50 : a detailed kinetic analysis of the effects on affinityand specificity

The binding kinetics of NF-kappaB p50 to the Ig-kappaB site and to a DNA duplex with no specific binding site were determined under varying conditions of potassium chloride concentration using a surface plasmonresonance biosensor. Association and dissociation rate constants were measured enabling calculation of the dissociation constants. Under previously established high affinity buffer conditions, the k a for both sequences was in the order of 10(7) M-1s-1whilst the k d values varied 600-fold in a sequence-dependent manner between 10(-1) and 10(-4 )s-1, suggesting that the selectivity of p50 for different sequences is mediated primarily through sequence-dependent dissociation rates. The calculated K D value for the Ig-kappaB sequence was 16 pM, whilst the K D for the non-specific sequence was 9.9 nM. As the ionic strength increased to levels which are closer to that of the cellular environment, the binding of p50 to the non-specific sequence was abolished whilst the specific affinity dropped to nanomolar levels. From these results, a mechanism is proposed in which p50 binds specific sequences with high affinity whilst binding non-specific sequences weakly enough to allow efficient searching of the DNA.

Elemental composition and potential toxicity of airborne particles at some urban schools

Although both the size and chemical composition of ambient particles are important parameters in determining their toxicities, their relative contributions are unclear (Heal et al., 2012). Children are particularly at risk to the detrimental health effects that have been linked to long term exposure to airborne particles (See e.g. Ruckerl et al., 2011). However, there is currently limited understanding of the health effects in children due to long term exposure to airborne particles. Schools are locations within an urban environment where children experience significant exposure to vehicle emissions, and to date there is limited information assessing children’s exposure at school. This study is a part of a large project aimed at gaining a holistic picture of the exposure of children to traffic related pollutants. In the current paper, results from the investigation of the elemental composition of airborne particle at urban schools are presented.

Entrepreneurial bricolage and young firm performance : the moderating effect of team composition

Many young firms face significant resource constraints during attempts to develop and grow. One promising theory that explicitly links to resource constraints is bricolage: a construct developed by Levi Strauss (1967). Bricolage aligns with notions of resourcefulness: using what’s on hand, through making do, and recombining resources for new or novel purposes. In this paper we further theorize and test the moderating effects of ownership team composition on bricolage and firm performance. Our findings suggest that team size, strong network ties, and functionality enhance the effects of bricolage in young firm performance.

Clinical dyslipidaemia is associated with changes in the lipid composition and inflammatory properties of apolipoprotein-B-containing lipoproteins from women with type 2 diabetes

The aim of this study was to use lipidomics to determine if the lipid composition of apolipoprotein-B-containing lipoproteins is modified by dyslipidaemia in type 2 diabetes and if any of the identified changes potentially have biological relevance in the pathophysiology of type 2 diabetes. VLDL and LDL from normolipidaemic and dyslipidaemic type 2 diabetic women and controls were isolated and quantified with HPLC and mass spectrometry. A detailed molecular characterisation of VLDL triacylglycerols (TAG) was also performed using the novel ozone-induced dissociation method, which allowed us to distinguish vaccenic acid (C18:1 n-7) from oleic acid (C18:1 n-9) in specific TAG species. Lipid class composition was very similar in VLDL and LDL from normolipidaemic type 2 diabetic and control participants. By contrast, dyslipidaemia was associated with significant changes in both lipid classes (e.g. increased diacylglycerols) and lipid species (e.g. increased C16:1 and C20:3 in phosphatidylcholine and cholesteryl ester and increased C16:0 [palmitic acid] and vaccenic acid in TAG). Levels of palmitic acid in VLDL and LDL TAG correlated with insulin resistance, and VLDL TAG enriched in palmitic acid promoted increased secretion of proinflammatory mediators from human smooth muscle cells. We showed that dyslipidaemia is associated with major changes in both lipid class and lipid species composition in VLDL and LDL from women with type 2 diabetes. In addition, we identified specific molecular lipid species that both correlate with clinical variables and are proinflammatory. Our study thus shows the potential of advanced lipidomic methods to further understand the pathophysiology of type 2 diabetes.