The impact of project announcements on property values: An empirical analysis

The study examines the property value impacts of an announcement of a project which has potential environmental impacts as distinct from other studies that address costs associated with under-construction and the operating impacts of developments. The hypothesis is that the announcement of a proposed project with potential environmental impact creates uncertainty in the property market of the affected area, and this impact is greater on properties closer to the project than those farther from it. The results of the study confirm the hypothesis and indicate that the marginal willingness to pay for properties within a 5 km distance declined by AU$17,020 per km proximity to the proposed heavy vehicle route, after the proposed route was announced. The results support the need for more holistic measurement of cost–benefit analysis of projects and provide a basis for improved consideration by policy makers of the rights of affected parties.

Non-parametric consistency test for multiple-sensing-modality data fusion

Fusing data from multiple sensing modalities, e.g. laser and radar, is a promising approach to achieve resilient perception in challenging environmental conditions. However, this may lead to \emph{catastrophic fusion} in the presence of inconsistent data, i.e. when the sensors do not detect the same target due to distinct attenuation properties. It is often difficult to discriminate consistent from inconsistent data across sensing modalities using local spatial information alone. In this paper we present a novel consistency test based on the log marginal likelihood of a Gaussian process model that evaluates data from range sensors in a relative manner. A new data point is deemed to be consistent if the model statistically improves as a result of its fusion. This approach avoids the need for absolute spatial distance threshold parameters as required by previous work. We report results from object reconstruction with both synthetic and experimental data that demonstrate an improvement in reconstruction quality, particularly in cases where data points are inconsistent yet spatially proximal.

Scalable Bayesian Computation for intractable likelihoods in image analysis

The inverse temperature hyperparameter of the hidden Potts model governs the strength of spatial cohesion and therefore has a substantial influence over the resulting model fit. The difficulty arises from the dependence of an intractable normalising constant on the value of the inverse temperature, thus there is no closed form solution for sampling from the distribution directly. We review three computational approaches for addressing this issue, namely pseudolikelihood, path sampling, and the approximate exchange algorithm. We compare the accuracy and scalability of these methods using a simulation study.

Assessing specific deterrence effects of increased speeding penalties using four measures of recidivism

Traffic law enforcement sanctions can impact on road user behaviour through general and specific deterrence mechanisms. The manner in which specific deterrence can influence recidivist behaviour can be conceptualised in different ways. While any reduction in speeding will have road safety benefits, the ways in which a ‘reduction’ is determined deserves greater methodological attention and has implications for countermeasure evaluation more generally. The primary aim of this research was to assess the specific deterrent impact of penalty increases for speeding offences in Queensland, Australia, in 2003 on two cohorts of drivers detected for speeding prior to and after the penalty changes were investigated. Since the literature is relatively silent on how to assess recidivism in the speeding context, the secondary research aim was to contribute to the literature regarding ways to conceptualise and measure specific deterrence in the speeding context. We propose a novel way of operationalising four measures which reflect different ways in which a specific deterrence effect could be conceptualised: (1) the proportion of offenders who re-offended in the follow up period; (2) the overall frequency of re-offending in the follow up period; (3) the length of delay to re-offence among those who re-offended; and (4) the average number of re-offences during the follow up period among those who re-offended. Consistent with expectations, results suggested an absolute deterrent effect of penalty changes, as evidenced by significant reductions in the proportion of drivers who re-offended and the overall frequency of re-offending, although effect sizes were small. Contrary to expectations, however, there was no evidence of a marginal specific deterrent effect among those who re-offended, with a significant reduction in the length of time to re-offence and no significant change in the average number of offences committed. Additional exploratory analyses investigating potential influences of the severity of the index offence, offence history, and method of detection revealed mixed results. Access to additional data from various sources suggested that the main findings were not influenced by changes in speed enforcement activity, public awareness of penalty changes, or driving exposure during the study period. Study limitations and recommendations for future research are discussed with a view to promoting more extensive evaluations of penalty changes and better understanding of how such changes may impact on motorists’ perceptions of enforcement and sanctions, as well as on recidivist behaviour.

The Arctic Summer Cloud Ocean Study (ASCOS): overview and experimental design

The climate in the Arctic is changing faster than anywhere else on earth. Poorly understood feedback processes relating to Arctic clouds and aerosol–cloud interactions contribute to a poor understanding of the present changes in the Arctic climate system, and also to a large spread in projections of future climate in the Arctic. The problem is exacerbated by the paucity of research-quality observations in the central Arctic. Improved formulations in climate models require such observations, which can only come from measurements in situ in this difficult-to-reach region with logistically demanding environmental conditions. The Arctic Summer Cloud Ocean Study (ASCOS) was the most extensive central Arctic Ocean expedition with an atmospheric focus during the International Polar Year (IPY) 2007–2008. ASCOS focused on the study of the formation and life cycle of low-level Arctic clouds. ASCOS departed from Longyearbyen on Svalbard on 2 August and returned on 9 September 2008. In transit into and out of the pack ice, four short research stations were undertaken in the Fram Strait: two in open water and two in the marginal ice zone. After traversing the pack ice northward, an ice camp was set up on 12 August at 87°21' N, 01°29' W and remained in operation through 1 September, drifting with the ice. During this time, extensive measurements were taken of atmospheric gas and particle chemistry and physics, mesoscale and boundary-layer meteorology, marine biology and chemistry, and upper ocean physics. ASCOS provides a unique interdisciplinary data set for development and testing of new hypotheses on cloud processes, their interactions with the sea ice and ocean and associated physical, chemical, and biological processes and interactions. For example, the first-ever quantitative observation of bubbles in Arctic leads, combined with the unique discovery of marine organic material, polymer gels with an origin in the ocean, inside cloud droplets suggests the possibility of primary marine organically derived cloud condensation nuclei in Arctic stratocumulus clouds. Direct observations of surface fluxes of aerosols could, however, not explain observed variability in aerosol concentrations, and the balance between local and remote aerosols sources remains open. Lack of cloud condensation nuclei (CCN) was at times a controlling factor in low-level cloud formation, and hence for the impact of clouds on the surface energy budget. ASCOS provided detailed measurements of the surface energy balance from late summer melt into the initial autumn freeze-up, and documented the effects of clouds and storms on the surface energy balance during this transition. In addition to such process-level studies, the unique, independent ASCOS data set can and is being used for validation of satellite retrievals, operational models, and reanalysis data sets.

Separable spatiotemporal priors for convex reconstruction of time-varying 3D point clouds

Reconstructing 3D motion data is highly under-constrained due to several common sources of data loss during measurement, such as projection, occlusion, or miscorrespondence. We present a statistical model of 3D motion data, based on the Kronecker structure of the spatiotemporal covariance of natural motion, as a prior on 3D motion. This prior is expressed as a matrix normal distribution, composed of separable and compact row and column covariances. We relate the marginals of the distribution to the shape, trajectory, and shape-trajectory models of prior art. When the marginal shape distribution is not available from training data, we show how placing a hierarchical prior over shapes results in a convex MAP solution in terms of the trace-norm. The matrix normal distribution, fit to a single sequence, outperforms state-of-the-art methods at reconstructing 3D motion data in the presence of significant data loss, while providing covariance estimates of the imputed points.

Customised osteotomy guides and endoprosthetic reconstruction for periacetabular tumours

Purpose We sought to analyse clinical and oncological outcomes of patients after guided resection of periacetabular tumours and endoprosthetic reconstruction of the remaining defect. Methods From 1988 to 2008, we treated 56 consecutive patients (mean age 52.5 years, 41.1 % women). Patients were followed up either until death or February 2011 (mean follow up 5.5 years, range 0.1–22.5, standard deviation ± 5.3). Kaplan–Meier analysis was used to estimate survival rates. Results Disease-specific survival was 59.9 % at five years and 49.7 % at ten and 20 years, respectively. Wide resection margins were achieved in 38 patients, whereas 11 patients underwent marginal and seven intralesional resection. Survival was significantly better in patients with wide or marginal resection than in patients with intralesional resection (p = 0.022). Survival for patients with secondary tumours was significantly worse than for patients with primary tumours (p = 0.003). In 29 patients (51.8 %), at least one reoperation was necessary, resulting in a revision-free survival of 50.5 % at five years, 41.1 % at ten years and 30.6 % at 20 years. Implant survival was 77.0 % at five years, 68.6 % at ten years and 51.8 % at 20 years. A total of 35 patients (62.5 %) experienced one or more complications after surgery. Ten of 56 patients (17.9 %) experienced local recurrence after a mean of 8.9 months. The mean postoperative Musculoskeletal Tumor Society (MSTS) score was 18.1 (60.1 %). Conclusion The surgical approach assessed in this study simplifies the process of tumour resection and prosthesis implantation and leads to acceptable clinical and oncological outcomes.

Improved linear cryptanalysis of reduced-round SIMON-32 and SIMON-48

In this paper we analyse two variants of SIMON family of light-weight block ciphers against variants of linear cryptanalysis and present the best linear cryptanalytic results on these variants of reduced-round SIMON to date. We propose a time-memory trade-off method that finds differential/linear trails for any permutation allowing low Hamming weight differential/linear trails. Our method combines low Hamming weight trails found by the correlation matrix representing the target permutation with heavy Hamming weight trails found using a Mixed Integer Programming model representing the target differential/linear trail. Our method enables us to find a 17-round linear approximation for SIMON-48 which is the best current linear approximation for SIMON-48. Using only the correlation matrix method, we are able to find a 14-round linear approximation for SIMON-32 which is also the current best linear approximation for SIMON-32. The presented linear approximations allow us to mount a 23-round key recovery attack on SIMON-32 and a 24-round Key recovery attack on SIMON-48/96 which are the current best results on SIMON-32 and SIMON-48. In addition we have an attack on 24 rounds of SIMON-32 with marginal complexity.

Exploring the role of energy, trade and financial development in explaining economic growth in South Africa: A revisit

South Africa is an emerging and industrializing economy which is experiencing remarkable progress. We contend that amidst the developments in the economy, the role of energy, trade openness and financial development are critical. In this article, we revisit the pivotal role of these factors. We use the ARDL bounds [72], the Bayer and Hanck [11] cointegration techniques, and an extended Cobb–Douglas framework, to examine the long-run association with output per worker over the sample period 1971–2011. The results support long-run association between output per worker, capital per worker and the shift parameters. The short-run elasticity coefficients are as follows: energy (0.24), trade (0.07), financial development (−0.03). In the long-run, the elasticity coefficients are: trade openness (0.05), energy (0.29), and financial development (−0.04). In both the short-run and the long-run, we note the post-2000 period has a marginal positive effect on the economy. The Toda and Yamamoto [91] Granger causality results show that a unidirectional causality from capital stock and energy consumption to output; and from capital stock to trade openness; a bidirectional causality between trade openness and output; and absence (neutrality) of any causality between financial development and output thus indicating that these two variables evolve independent of each other.

The case for “n«all”: Why the Big Data revolution will probably happen differently in the mining sector

Big Data and predictive analytics have received significant attention from the media and academic literature throughout the past few years, and it is likely that these emerging technologies will materially impact the mining sector. This short communication argues, however, that these technological forces will probably unfold differently in the mining industry than they have in many other sectors because of significant differences in the marginal cost of data capture and storage. To this end, we offer a brief overview of what Big Data and predictive analytics are, and explain how they are bringing about changes in a broad range of sectors. We discuss the “N=all” approach to data collection being promoted by many consultants and technology vendors in the marketplace but, by considering the economic and technical realities of data acquisition and storage, we then explain why a “n « all” data collection strategy probably makes more sense for the mining sector. Finally, towards shaping the industry’s policies with regards to technology-related investments in this area, we conclude by putting forward a conceptual model for leveraging Big Data tools and analytical techniques that is a more appropriate fit for the mining sector.

Virtues of marginally metallic oxides

Transition-metal oxides at the metal-insulator boundary, especially those belonging to the perovskite family, exhibit fascinating phenomena such as insulator-metal transitions controlled by composition, high-temperature superconductivity and giant magnetoresistance (GMR), Interestingly, many of these marginally metallic oxides obey the established criteria for metallicity and have a finite density of states at the Fermi;level. The perovskite manganates exhibiting GMR, on the other hand, are unusual in that they possess very high resistivities in the 'metallic' state and show no significant density of states at the Fermi level, Marginal metallicity in oxide systems is a problem of great complexity and contemporary interest and its understanding is of crucial significance to the diverse phenomena exhibited by these materials.

Osmolality electrolyte and carbohydrate type and oral rehydration solutions: A controlled study to compare the efficacy of two commercially available solutions (osmolalities 240 mmol/L and 340 mmol/L)

An open-label, inpatient study was undertaken to compare the efficacy of two oral rehydration solutions (ORS) given randomly to children aged 1-10 years who had acute gastroenteritis with mild or moderate dehydration (n = 45). One solution contained 60 mmol/L sodium and 1.8% glucose, total osmolality 240 mosm/l (gastrolyte, Rhone-poulenc, Rorer) and the other contained 26 mmol/l sodium, 2.7% glucose and 3.6% sucrose, total osmolality 340 mOsm/l (Glucolyte, Gilseal). Analysis of data indicated that Gastrolyte therapy resulted in significantly fewer episodes and volume of vomiting over all time periods in comparison to Glucolyte and significantly less stool volume during the first 8 h and in the 0-24 h period. The differences between treatments in degree of dehydration at each follow-up period, duration of diarrhea, and duration of hospital stay were not significant. No adverse drug reactions occurred. Six patients received intravenous rehydration treatment and were considered treatment failures. We conclude that oral rehydration therapy is safe and efficacious in the management of dehydration in acute diarrhoea and that the lower osmolar rehydration solution has clinically marginal advantages.

UVB irradiation alters the activities and kinetic properties of the enzymes of energy metabolism in rat lens during aging

Ultraviolet (UV) radiation is one of the major risk factors of cataract (loss of eye-lens transparency). The influence of UVB radiation (300 nm, 100 mu W cm(-2)) on the activity and apparent kinetic constants (K-m and V-max) of rat lens hexokinase (HK;EC2.7.1.1), phosphofructokinase (PFK;EC2.7.1.11), isocitrate dehydrogenase (ICDH;EC1.1.1.41) and malate dehydrogenase (MDH;EC1.1.1.37) of energy metabolism has been investigated by irradiating the lens homogenate of three-and 12-month-old rats. In the three-month-old group specific activities of HK and PFK are reduced by 56 and 43 %, respectively, and there is no change in ICDH and MDH activities after a 24 h exposure. On the other hand, in the 12-month-old group the decreases are 72, 71, 24 and 16 % for HK, PFK. ICDH and MDH, respectively. UVB irradiation increases the apparent K-m of HK and PFK (in both age groups), whereas the K-m of ICDH and MDH is not altered. While the decrease in V-max of these enzymes due to UVB exposure is only marginal in three-month-old rats, it is more pronounced (significant) in 12-month-old rats. A similar decrease in enzyme activities of HK and PFK is also observe upon UVB exposure of the intact rat lens. The photoinduced changes in energy metabolism may in turn have a bearing on lens transparency, particularly at an older age.

Australian journalism students’ professional views and news consumption: Results from a representative study

Journalism education’s role in shaping students’ professional views has been a topic of interest among scholars for the past decade in particular. Increasing numbers of studies are concerned with examining students’ backgrounds and views in order to identify what role exposure to the tertiary environment may play in socializing them into the industry. This study reports on the results of the largest survey of Australian journalism students undertaken to date, with a sample size of 1884 students. The study finds that time spent studying journalism appears to be related to changes in role perceptions and news consumption. Final-year students are significantly more likely to support journalism’s watchdog role and to reject consumer-oriented and ‘loyal’ roles. They also consume more news than first-year students. On the other hand, journalism education appears to have little impact on views of controversial practices, with only marginal differences between final- and first-year students.

Efficient parameter estimation in longitudinal data analysis using a hybrid GEE method

The method of generalized estimating equations (GEEs) provides consistent estimates of the regression parameters in a marginal regression model for longitudinal data, even when the working correlation model is misspecified (Liang and Zeger, 1986). However, the efficiency of a GEE estimate can be seriously affected by the choice of the working correlation model. This study addresses this problem by proposing a hybrid method that combines multiple GEEs based on different working correlation models, using the empirical likelihood method (Qin and Lawless, 1994). Analyses show that this hybrid method is more efficient than a GEE using a misspecified working correlation model. Furthermore, if one of the working correlation structures correctly models the within-subject correlations, then this hybrid method provides the most efficient parameter estimates. In simulations, the hybrid method's finite-sample performance is superior to a GEE under any of the commonly used working correlation models and is almost fully efficient in all scenarios studied. The hybrid method is illustrated using data from a longitudinal study of the respiratory infection rates in 275 Indonesian children.