Understanding public response to a congestion charge : a random-effects ordered logit approach

Public acceptance is consistently listed as having an enormous impact on the implementation and success of a congestion charge scheme. This paper investigates public acceptance of such a scheme in Australia. Surveys were conducted in Brisbane and Melbourne, the two fastest growing Australian cities. Using an ordered logit modeling approach, the survey data including stated preferences were analyzed to pinpoint the important factors influencing people’s attitudes to a congestion charge and, in turn, to their transport mode choices. To accommodate the nature of, and to account for the resulting heterogeneity of the panel data, random effects were considered in the models. As expected, this study found that the amount of the congestion charge and the financial benefits of implementing it have a significant influence on respondents’ support for the charge and on the likelihood of their taking a bus to city areas. However, respondents’ current primary transport mode for travelling to the city areas has a more pronounced impact. Meanwhile, respondents’ perceptions of the congestion charge’s role in protecting the environment by reducing vehicle emissions, and of the extent to which the charge would mean that they travelled less frequently to the city for shopping or entertainment, also have a significant impact on their level of support for its implementation. We also found and explained notable differences across two cities. Finally, findings from this study have been fully discussed in relation to the literature.

Toowoomba's 'miracle girl' surfaces after being feared drowned

Musician Hannah Reardon-Smith is the woman who came back from the dead after she and her mother were plunged into last week's catastrophic flash flood in Toowoomba...

Amino group positions dependent morphology and coverage of electropolymerized metallophthalocyanine (M = Ni and Co) films on electrode surfaces

Electropolymerized films of teraaminometallophthalocyanines (MTAPc; M = Ni and Co) with amino groups at α- (4α-MTAPc) and β- (4β-MTAPc) positions were prepared on glassy carbon (GC) and indium tin oxide (ITO) electrodes. It was found that the electropolymerization growth rate of 4α-MTAPc was less than that of 4β-MTAPc prepared under identical conditions. Further, the surface coverage of the polymerized 4β-MTAPc film was greater than that of 4α-MTAPc polymerized film. Atomic force microscopy (AFM), X-ray diffraction (XRD) and UV–visible spectroscopic studies were carried out for the polymerized films of 4α-NiIITAPc (p-4α-NiIITAPc) and 4β-NiIITAPc (p-4β-NiIITAPc) alone because both Ni(II) and Co(II) polymerized films show similar trend in electropolymerization and surface coverage values. AFM images show that p-4α-NiIITAPc film contains islands and the thickness of this film was nearly three times less than that of p-4β-NiIITAPc. XRD patterns for the two polymerized films reveal that p-4β-NiIITAPc film was relatively more crystalline than p-4α-NiIITAPc film. Further, the compactness of these films was scrutinized from their barrier properties toward [Fe(CN)6]3−/4− redox couple. The differences in the polymerization growth rate of 4α-MTAPc and 4β-MTAPc, and the thicknesses of the resultant polymerized films suggest that unlike 4β-MTAPc one or two amino groups might have not involved in electropolymerization in the case of 4α-MTAPc. Further, the influence of surface coverage on the electrocatalytic properties of the polymerized films was studied by taking p-4β-CoIITAPc and p-4α-CoIITAPc films as examples. The electrocatalytic oxygen reduction current was almost same at both the electrodes suggesting that only the surface species were involved in the electrocatalytic reduction of oxygen.

Amino group position dependent orientation of self-assembled monomolecular films of tetraaminophthalocyanatocobalt(II) on Au surfaces

Self-assembled monomolecular films of 1,8,15,22-tetraaminophthalocyanatocobalt(II) (4α-CoIITAPc) and 2,9,16,23-tetraaminophthalocyanatocobalt(II) (4β-CoIITAPc) on Au surfaces were prepared by spontaneous adsorption from solution. These films were characterized by cyclic voltammetry and Raman spectroscopy. Both the surface coverage (Γ) and intensity of the in-plane stretching bands obtained from Raman studies vary for these monomolecular films, indicating different orientations adopted by them on Au surfaces. The 4α-CoIITAPc-modified electrode exhibits an E1/2 of 0.35 V, while the 4β-CoIITAPc-modified electrode exhibits an E1/2 of 0.19 V, corresponding to the CoII/CoIII redox couple in 0.1 M H2SO4. The Γ estimated from the charge associated with the oxidation of Co(II) gives (2.62 ± 0.10) × 10-11 mol cm-2 for 4α-CoIITAPc and (3.43 ± 0.14) × 10-10 mol cm-2 for 4β-CoIITAPc. In Raman spectral studies, the intensity ratio between in-plane phthalocyanine (Pc) stretching and the Au−N stretching was found to be 6.6 for 4β-CoIITAPc, while it was 1.6 for 4α-CoIITAPc. The obtained lower Γ and intensity ratio values suggest that 4α-CoIITAPc adopts nearly a parallel orientation on the Au surface, while the higher Γ and intensity ratio values suggest that 4β-CoIITAPc adopts a perpendicular orientation. The electrochemical reduction of dioxygen was carried out using these differently oriented Pc's in phosphate buffer solution (pH 7.2). Both the Pc's catalyze the reduction of dioxygen; however, the 4α-CoIITAPc-modified electrode greatly reduces the dioxygen reduction overpotential compared to 4β-CoIITAPc-modified and bare Au electrodes.

Factors influencing robustness and effectiveness of conditional random fields in active learning frameworks

Active learning approaches reduce the annotation cost required by traditional supervised approaches to reach the same effectiveness by actively selecting informative instances during the learning phase. However, effectiveness and robustness of the learnt models are influenced by a number of factors. In this paper we investigate the factors that affect the effectiveness, more specifically in terms of stability and robustness, of active learning models built using conditional random fields (CRFs) for information extraction applications. Stability, defined as a small variation of performance when small variation of the training data or a small variation of the parameters occur, is a major issue for machine learning models, but even more so in the active learning framework which aims to minimise the amount of training data required. The factors we investigate are a) the choice of incremental vs. standard active learning, b) the feature set used as a representation of the text (i.e., morphological features, syntactic features, or semantic features) and c) Gaussian prior variance as one of the important CRFs parameters. Our empirical findings show that incremental learning and the Gaussian prior variance lead to more stable and robust models across iterations. Our study also demonstrates that orthographical, morphological and contextual features as a group of basic features play an important role in learning effective models across all iterations.

Adsorption and mobility of metals in build-up on road surfaces

The study investigated the adsorption and bioavailability characteristics of traffic generated metals common to urban land uses, in road deposited solids particles. To validate the outcomes derived from the analysis of field samples, adsorption and desorption experiments were undertaken. The analysis of field samples revealed that metals are selectively adsorbed to different charge sites on solids. Zinc, copper, lead and nickel are adsorbed preferentially to oxides of manganese, iron and aluminium. Lead is adsorbed to organic matter through chemisorption. Cadmium and chromium form weak bonding through cation exchange with most of the particle sizes. Adsorption and desorption experiments revealed that at high metal concentrations, chromium, copper and lead form relatively strong bonds with solids particles while zinc is adsorbed through cation exchange with high likelihood of being released back into solution. Outcomes from this study provide specific guidance for the removal of metals from stormwater based on solids removal.

Effects of different surfaces on the perception of prey-generated noise by the Indian false vampire bat Megaderma lyra.

The low- and high-frequency components of a rustling sound, created when prey (freshly killed frog) was jerkily pulled on dry and wet sandy floors and asbestos, were recorded and played back to individual Indian false vampire bats (Megaderma lyra). Megaderma lyra responded with flight toward the speakers and captured dead frogs, that were kept as reward. The spectral peaks were at 8.6, 7.1 and 6.8 kHz for the low-frequency components of the sounds created at the dry, asbestos and wet floors, respectively. The spectral peaks for the high-frequency sounds created on the respective floors were at 36.8,27.2 and 23.3 kHz. The sound from the dry floor was more intense than that of from the other two substrata. Prey movements that generated sonic or ultrasonic sounds were both sufficient and necessary for the bats to detect and capture prey. The number of successful prey captures was significantly greater for the dry floor sound, especially to its high-frequency components. Bat-responses were low to the wet floor and moderate to the asbestos floor sounds. The bats did not respond to the sound of unrecorded parts of the tape. Even though the bats flew toward the speakers when the prey generated sounds were played back and captured the dead frogs we cannot rule out the possibility of M. lyra using echolocation to localize prey. However, the study indicates that prey that move on dry sandy floor are more vulnerable to predation by M. lyra.

Using extended random set to find specific patterns

With the overwhelming increase in the amount of data on the web and data bases, many text mining techniques have been proposed for mining useful patterns in text documents. Extracting closed sequential patterns using the Pattern Taxonomy Model (PTM) is one of the pruning methods to remove noisy, inconsistent, and redundant patterns. However, PTM model treats each extracted pattern as whole without considering included terms, which could affect the quality of extracted patterns. This paper propose an innovative and effective method that extends the random set to accurately weigh patterns based on their distribution in the documents and their terms distribution in patterns. Then, the proposed approach will find the specific closed sequential patterns (SCSP) based on the new calculated weight. The experimental results on Reuters Corpus Volume 1 (RCV1) data collection and TREC topics show that the proposed method significantly outperforms other state-of-the-art methods in different popular measures.

The BAD project: data mining, database and prediction of protein adsorption on surfaces

Protein adsorption at solid-liquid interfaces is critical to many applications, including biomaterials, protein microarrays and lab-on-a-chip devices. Despite this general interest, and a large amount of research in the last half a century, protein adsorption cannot be predicted with an engineering level, design-orientated accuracy. Here we describe a Biomolecular Adsorption Database (BAD), freely available online, which archives the published protein adsorption data. Piecewise linear regression with breakpoint applied to the data in the BAD suggests that the input variables to protein adsorption, i.e., protein concentration in solution; protein descriptors derived from primary structure (number of residues, global protein hydrophobicity and range of amino acid hydrophobicity, isoelectric point); surface descriptors (contact angle); and fluid environment descriptors (pH, ionic strength), correlate well with the output variable-the protein concentration on the surface. Furthermore, neural network analysis revealed that the size of the BAD makes it sufficiently representative, with a neural network-based predictive error of 5% or less. Interestingly, a consistently better fit is obtained if the BAD is divided in two separate sub-sets representing protein adsorption on hydrophilic and hydrophobic surfaces, respectively. Based on these findings, selected entries from the BAD have been used to construct neural network-based estimation routines, which predict the amount of adsorbed protein, the thickness of the adsorbed layer and the surface tension of the protein-covered surface. While the BAD is of general interest, the prediction of the thickness and the surface tension of the protein-covered layers are of particular relevance to the design of microfluidics devices.

Numerical study of turbulent fluid flow and heat transfer in lateral perforated extended surfaces

Numerical study has been performed in this study to investigate the turbulent convection heat transfer on a rectangular plate mounted over a flat surface. Thermal and fluid dynamic performances of extended surfaces having various types of lateral perforations with square, circular, triangular and hexagonal cross sections are investigated. RANS (Reynolds averaged Navier–Stokes) based modified k–ω turbulence model is used to calculate the fluid flow and heat transfer parameters. Numerical results are compared with the results of previously published experimental data and obtained results are in reasonable agreement. Flow and heat transfer parameters are presented for Reynolds numbers from 2000 to 5000 based on the fin thickness.

Cathodic corrosion of Cu substrates as a route to nanostructured Cu/M (M = Ag, Au, Pd) surfaces

The electrochemical formation of nanostructured materials is generally achieved by reduction of a metal salt onto a substrate that does not influence the composition of the deposit. In this work we report that Ag, Au and Pd electrodeposited onto Cu under conditions where galvanic replacement is not viable and hydrogen gas is evolved results in the formation of nanostructured surfaces that unexpectedly incorporate a high concentration of Cu in the final material. Under cathodic polarization conditions the electrodissolution/corrosion of Cu occurs which provides a source of ionic copper that is reduced at the surface-electrolyte interface. The nanostructured Cu/M (M = Ag, Au and Pd) surfaces are investigated for their catalytic activity for the reduction of 4 nitrophenol by NaBH4 where Cu/Ag was found to be extremely active. This work indicates that a substrate electrode can be utilized in an interesting manner t make bimetallic nanostructures with enhanced catalytic activity.

Catching drink drivers in Queensland through Random Breath Testing : what does the last decade of data tell us

Background Random Breath Testing (RBT) has proven to be a cornerstone of enforcement attempts to deter (as well as apprehend) motorists from drink driving in Queensland (Australia) for decades. However, scant published research has examined the relationship between the frequency of implementing RBT activities and subsequent drink driving apprehension rates across time. Aim This study aimed to examine the prevalence of apprehending drink drivers in Queensland over a 12 year period. It was hypothesised that an increase in breath testing rates would result in a corresponding decrease in the frequency of drink driving apprehension rates over time, which would reflect general deterrent effects. Method The Queensland Police Service provided RBT data that was analysed. Results Between the 1st of January 2000 and 31st of December 2011, 35,082,386 random breath tests (both mobile and stationary) were conducted in Queensland, resulting in 248,173 individuals being apprehended for drink driving offences. A total of 342,801 offences were recorded during this period, representing an intercept rate of .96. Of these offences, 276,711 (80.72%) were recorded against males and 66,024 (19.28%) offences committed by females. The most common drink driving offence was between 0.05 and 0.08 BAC limit. The largest proportion of offences was detected on the weekends, with Saturdays (27.60%) proving to be the most common drink driving night followed by Sundays (21.41%). The prevalence of drink driving detection rates rose steadily across time, peaking in 2008 and 2009, before slightly declining. This decline was observed across all Queensland regions and any increase in annual figures was due to new offence types being developed. Discussion This paper will further outline the major findings of the study in regards to tailoring RBT operations to increase detection rates as well as improve the general deterrent effect of the initiative.

Rough Fuzzy Control of SVC for Power System Stability Enhancement

Modern power systems have become more complex due to the growth in load demand, the installation of Flexible AC Transmission Systems (FACTS) devices and the integration of new HVDC links into existing AC grids. On the other hand, the introduction of the deregulated and unbundled power market operational mechanism, together with present changes in generation sources including connections of large renewable energy generation with intermittent feature in nature, have further increased the complexity and uncertainty for power system operation and control. System operators and engineers have to confront a series of technical challenges from the operation of currently interconnected power systems. Among the many challenges, how to evaluate the steady state and dynamic behaviors of existing interconnected power systems effectively and accurately using more powerful computational analysis models and approaches becomes one of the key issues in power engineering. The traditional computing techniques have been widely used in various fields for power system analysis with varying degrees of success. The rapid development of computational intelligence, such as neural networks, fuzzy systems and evolutionary computation, provides tools and opportunities to solve the complex technical problems in power system planning, operation and control.

Sources and transport pathways of common heavy metals to urban road surfaces

Heavy metals that are built-up on urban impervious surfaces such as roads are transported to urban water resources through stormwater runoff. Therefore, it is essential to understand the predominant pathways of heavy metals to the build-up on roads in order to develop suitable pollution mitigation strategies to protect the receiving water environment. The study presented in this paper investigated the sources and transport pathways of manganese, lead, copper, zinc and chromium, which are heavy metals commonly present in urban road build-up. It was found that manganese and lead are contributed to road build-up primarily by direct deposition due to the re-suspension of roadside soil by wind turbulence, while traffic is the predominant source of copper, zinc and chromium to the atmosphere and road build-up. Atmospheric deposition is also the major transport pathway for copper and zinc, and for chromium, direct deposition by traffic sources is the predominant pathway.