Diverse urban plantings managed with sufficient resource availability can increase plant productivity and arthropod diversity

Buildings structures and surfaces are explicitly being used to grow plants, and these “urban plantings” are generally designed for aesthetic value. Urban plantings also have the potential to contribute significant “ecological values” by increasing urban habitat for animals such as arthropods and by increasing plant productivity. In this study, we evaluated how the provision of these additional ecological values is affected by plant species richness; the availability of essential resources for plants, such as water, light, space; and soil characteristics. We sampled 33 plantings located on the exterior of three buildings in the urban center of Brisbane, Australia (subtropical climatic region) over 2, 6 week sampling periods characterized by different temperature and rainfall conditions. Plant cover was estimated as a surrogate for productivity as destructive sampling of biomass was not possible. We measured weekly light levels (photosynthetically active radiation), plant CO2 assimilation, soil CO2 efflux, and arthropod diversity. Differences in plant cover were best explained by a three-way interaction of plant species richness, management water regime and sampling period. As the richness of plant species increased in a planter, productivity and total arthropod richness also increased significantly—likely due to greater habitat heterogeneity and quality. Overall we found urban plantings can provide additional ecological values if essential resources are maintained within a planter such as water, light and soil temperature. Diverse urban plantings that are managed with these principles in mind can contribute to the attraction of diverse arthropod communities, and lead to increased plant productivity within a dense urban context.

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.

The potential availability of roosting sites for lesser short-tailed bats (Mystacina tuberculata) on Kapiti Island, New Zealand: Implications for a translocation

Lesser short-tailed bats (Mystacina tuberculata) have recently been translocated to Kapiti Island in an attempt to form a new population of this threatened species. However, the island's vegetation is regenerating, and there was doubt that the forests provided enough large trees with cavities for bats to roost in. This study measured the availability of tree-trunk cavities of the right size for potential roost sites on Kapiti Island, and assessed if habitat restoration would be required to increase the translocation's chance of success. First, trees with cavities accessible to us were sampled in six of Kapiti Island's forest types. Size variables known to affect roost site selection by lesser short-tailed bats at the tree and cavity level were measured. Trees were classified as containing cavities that could potentially provide suitable roosts if their values for all variables measured fell within the range of roosts used by lesser short-tailed bats in natural populations. Roosts were classified as suitably sized for solitary bats or for colonies, using measurements from both types of roosts in natural populations. Second, the density of these potential roost cavities was calculated. Cavities of a size potentially suitable for colonies were found in four of the six forest types at densities ranging from 3.2 +/- 3.2 SE to 52.4 +/- 14.0 trees per ha. Density of potential solitary roosts was much higher. Not all potential cavities will be suitable because they may be damp, poorly insulated, or have an unsuitable microclimate. Nevertheless, our estimates indicated that the two most extensive forest types each contained thousands of potential cavities of a size suitable for colonies of lesser short-tailed bats. In addition, there were tens of thousands of cavities large enough to shelter solitary bats. Roost habitat restoration appears unnecessary to assist translocated Mystacina tuberculata on Kapiti Island.

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.

Patterns of service use among persons with Schizophrenia and other psychotic disorders

Objective: This study assessed 12-month service use patterns among people with psychotic disorders and sought to identify determinants of service use. Methods: As part of a large two-phase Australian study of psychotic disorders, structured interviews were conducted with a stratified random sample of adults who screened positive for psychosis. Demographic characteristics, social functioning, symptoms, mental health diagnoses, and use of psychiatric and nonpsychiatric services were assessed. Data were analyzed for 858 persons who had an ICD-10 diagnosis of a psychotic disorder and who had been hospitalized for less than six months during the previous year. Results: People with psychotic disorders had high levels of use of health services, both in absolute terms and relative to people with nonpsychotic disorders. Those with psychotic disorders were estimated to have an average of one contact with health services per week. Use of psychiatric inpatient services was associated with parenthood, higher symptom levels, recent attempts at suicide or self-harm, personal disability, medication status, and frequency of alcohol consumption. Services provided by general practitioners (family physicians) were more likely to be obtained by older people, women, people with greater availability of friends, those with fewer negative symptoms, and those whose service needs were unmet by other sources. People who were high users of health services also reported having more contact with a range of non-health agencies. Conclusions: The predictors of service use accounted for small proportions of the variance in overall use of health services. The role of general practitioners in providing and monitoring treatment programs and other psychosocial interventions needs to be acknowledged and enhanced.

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.

What roles do contemporaneous and cumulative incomes play in the income–child health gradient for young children? Evidence from an Australian Panel

The literature to date shows that children from poorer households tend to have worse health than their peers, and the gap between them grows with age. We investigate whether and how health shocks (as measured by the onset of chronic conditions) contribute to the income–child health gradient and whether the contemporaneous or cumulative effects of income play important mitigating roles. We exploit a rich panel dataset with three panel waves called the Longitudinal Study of Australian children. Given the availability of three waves of data, we are able to apply a range of econometric techniques (e.g. fixed and random effects) to control for unobserved heterogeneity. The paper makes several contributions to the extant literature. First, it shows that an apparent income gradient becomes relatively attenuated in our dataset when the cumulative and contemporaneous effects of household income are distinguished econometrically. Second, it demonstrates that the income–child health gradient becomes statistically insignificant when controlling for parental health and health-related behaviours or unobserved heterogeneity.

Measuring the regional availability of biomass for biofuels and the potential for microalgae

Biomass is an important energy resource for producing bioenergy and growing the global economy whilst minimising greenhouse gas emissions. Many countries, like Australia have a huge amount of biomass with the potential for bioenergy, but non-edible feedstock resources are significantly under-exploited. Hence it is essential to map the availability of these feedstocks to identify the most appropriate bioenergy solution for each region and develop supply chains for biorefineries. Using Australia as a case study,we present the spatial availability and opportunities for second and third generation feedstocks. Considerations included current land use, the presence of existing biomass industries and climatic conditions. Detailed information on the regional availability of biomass was collected from government statistics, technical reports and energy assessments as well as from academic literature. Second generation biofuels have the largest opportunity in New South Wales, Queensland and Victoria (NSW, QLD and VIC) and the regions with the highest potential for microalgae are Western Australia and Northern Territory (WA, NT), based on land use opportunity cost and climate. The approach can be used in other countries with a similar climate. More research is needed to overcome key technical and economic hurdles.

The prevalence of anisometropia, amblyopia and strabismus in schoolchildren of Shiraz, Iran

PURPOSE To determine the prevalence of amblyopia, anisometropia, and strabismus in schoolchildren of Shiraz, Iran. MATERIALS AND METHODS A random cluster sampling was used in a cross-sectional study on schoolchildren in Shiraz. Cycloplegic refraction was performed in elementary and middle school children and high school students had non-cylcoplegic refraction. Uncorrected visual acuity (UCVA) and best corrected visual acuity (BCVA) were recorded for each participant. Anisometropia was defined as spherical equivalent (SE) refraction difference 1.00D or more between two eyes. Amblyopia was distinguished as a reduction of BCVA to 20/30 or less in one eye or 2-line interocular optotype acuity differences in the absence of pathological causes. Cover test was performed for investigating of strabismus. RESULTS Mean age of 2638 schoolchildren was 12.5 years (response rate = 86.06%). Prevalence of anisometropia was 2.31% (95% confidence interval [CI], 1.45 to 3.16). 2.29% of schoolchildren (95% CI, 1.46 to 3.14) were amblyopic. The prevalence of amblyopia in boys and girls was 2.32% and 2.26%, respectively (p = 0.945). Anisometropic amblyopia was found in 58.1% of the amblyopic subjects. The strabismus prevalence was 2.02% (95% CI, 1.18 to 2.85). The prevalence of exotropia and esotropia was 1.30% and 0.59%, respectively. CONCLUSIONS Results of this study showed that the prevalence of anisometropia, amblyopia, and strabismus are in the mid range. The etiology of amblyopia was often refractive, mostly astigmatic, and non-strabismic. Exotropia prevalence increased with age and was the most common strabismus type.

Prevalence of refractive errors among schoolchildren in Shiraz, Iran

PURPOSE To determine the prevalence of refractive errors in Shiraz schoolchildren by age and gender. METHODS For this cross-sectional study, random cluster sampling was carried out from students of the 2008-2009 academic year. After the initial interview, ophthalmic examinations including tests of visual acuity, non-cycloplegic and cycloplegic refraction and binocular vision were performed. Myopia was defined as a spherical equivalent < or =-0.50 dioptre (D), hyperopia as > or =+2.0 D, and astigmatism as a cylinder refraction > or =0.75 D. All values for school grade and gender were directly standardized based on the total student population in the 2008-2009 school year. RESULTS A total of 2130 students were sampled, of which 1872 participated in the study (response rate = 87.88%). The prevalence of uncorrected, best-corrected, presenting and spectacle corrected visual acuity of 6/12 or worse in the better eye was 6.46%, 0%, 1.49% and 0.9%, respectively. The prevalence rates of myopia, hyperopia and astigmatism were 4.35% (95% confidence interval [CI]: 2.89-5.82%), 5.04% (95% CI: 3.50-6.58%), and 11.27% (95% CI: 9.81-12.74%), respectively. Anisometropia was detected in 2.58% of schoolchildren. The prevalence of hyperopia significantly decreased with age (P = 0.021). CONCLUSIONS Compared with other reported rates, the prevalence of myopia in the schoolchildren of Shiraz is similar to that in most places excluding East Asian countries, and that of hyperopia is in the mid range.

Prevalence and risk factors of refractive errors among schoolchildren in Shiraz, Iran

Purpose: To determine the prevalence and risk factors of refractive errors among schoolchildren in Shiraz, Iran. Methods: In a cross-sectional study, using random cluster sampling, 3065 Shiraz schoolchildren were selected in this study. The participants totaled 2683; 1872 elementary and middle school and 811 high school students. For the primary and middle schoolchildren, cycloplegic refraction and for the high school students, non-cycloplegic autorefraction was measured. Myopia, defined as spherical equivalent (SE) refraction -0.50 diopter (D) or worse, hyperopia as SE +2.00D and +0.50D or more for cycloplegic and noncycloplegic refractions respectively, and astigmatism as cylinder -0.75D or worse. Results: The prevalence of refractive errors in elementary and middle school students was: myopia 4.35 % (95% confidence interval (CI), 2.89 -5.81), hyperopia 5.04 % (95%CI, 3.49 -6.58) and astigmatism 11.79 % (95%CI, 10.21 -13.38). For high school students, these rates were 22.4 % (95%CI, 18.44 -26.36), 10.52 % (95%CI, 6.75 -14.29) and 20.99% (95%CI, 16.55 -25.44), respectively.The prevalence of myopia increased with age in primary and middle school students (OR=1.15, 95% CI, 0.98 to1.33, p=0.073). Conclusions: The result of this study indicated a relatively low prevalence of refractive errors among schoolchildren in Shiraz according to the protocol by "Refractive Error Study in Children" (RESC) in other investigations.

External knowledge and query strategies in active learning: A study in clinical information extraction

This paper presents a new active learning query strategy for information extraction, called Domain Knowledge Informativeness (DKI). Active learning is often used to reduce the amount of annotation effort required to obtain training data for machine learning algorithms. A key component of an active learning approach is the query strategy, which is used to iteratively select samples for annotation. Knowledge resources have been used in information extraction as a means to derive additional features for sample representation. DKI is, however, the first query strategy that exploits such resources to inform sample selection. To evaluate the merits of DKI, in particular with respect to the reduction in annotation effort that the new query strategy allows to achieve, we conduct a comprehensive empirical comparison of active learning query strategies for information extraction within the clinical domain. The clinical domain was chosen for this work because of the availability of extensive structured knowledge resources which have often been exploited for feature generation. In addition, the clinical domain offers a compelling use case for active learning because of the necessary high costs and hurdles associated with obtaining annotations in this domain. Our experimental findings demonstrated that 1) amongst existing query strategies, the ones based on the classification model’s confidence are a better choice for clinical data as they perform equally well with a much lighter computational load, and 2) significant reductions in annotation effort are achievable by exploiting knowledge resources within active learning query strategies, with up to 14% less tokens and concepts to manually annotate than with state-of-the-art query strategies.

Aspect-based opinion mining from product reviews using conditional random fields

Product reviews are the foremost source of information for customers and manufacturers to help them make appropriate purchasing and production decisions. Natural language data is typically very sparse; the most common words are those that do not carry a lot of semantic content, and occurrences of any particular content-bearing word are rare, while co-occurrences of these words are rarer. Mining product aspects, along with corresponding opinions, is essential for Aspect-Based Opinion Mining (ABOM) as a result of the e-commerce revolution. Therefore, the need for automatic mining of reviews has reached a peak. In this work, we deal with ABOM as sequence labelling problem and propose a supervised extraction method to identify product aspects and corresponding opinions. We use Conditional Random Fields (CRFs) to solve the extraction problem and propose a feature function to enhance accuracy. The proposed method is evaluated using two different datasets. We also evaluate the effectiveness of feature function and the optimisation through multiple experiments.