The place attachment of visitors to Ningaloo Marine Park, north-western Australia

Place attachment has been the subject of a rich, growing body of research in natural resource management, with a particular emphasis on visitors to national parks and forests. Understanding how such attachments are formed as well as their strength has been of central interest. Most of this work has been terrestrially based, with little attention to marine settings. The question of whether the same predominant elements of place attachment – physical environment, activities, social ties and emotional connection – apply to marine national parks remains to be answered. Ningaloo Marine Park in north-western Australia provided the focus for this study. Place attachment is of great interest in this Marine Park because the results of a recent visitor survey showed that 55% of respondents had visited previously and of this, 44% stayed at the same location, suggesting strong place attachment. Photo-elicitation, where photographs are taken by visitors and provide the basis for a subsequent interview, was used to explore this phenomenon. A total of 30 visitors agreed to participate, with up to eight photographs taken by each. Fieldwork was undertaken in July 2009 with preliminary results suggesting that place attachment at the Marine Park has the same four elements as those found in terrestrial-based studies. There were, however, distinct marine components for each element. For the physical environment, coastal vistas were enjoyed because of the feelings of remoteness and isolation they promoted as well as their majestic beauty. Activities centred on those that were uniquely marine, such as snorkelling, diving and fishing. Social ties were evidenced in the bonding of visitors with others who shared similar marine-based past-times such as boating. Regarding emotional connection, families enjoyed being able to visit a location where everybody was happy because all members were able to do their own activities and have a pleasurable experience. Participants were also asked about place-specific behaviours. Place-protective behaviours were obvious from the responses provided and included picking up rubbish and telling others if they were doing the ‘wrong’ thing. Place protection was also evident in the responses regarding perceptions of management. The dominant response related to increasing the presence of management to enforce rules and regulations, particularly for fishing

Engineering Treatment of Hazardous Wastewaters Utilizing Dye-sensitized Photooxidation

Studies were conducted to determine the applicability of photooxidation for the degradation of selected hazardous and refractory organic compounds. These photochemical oxidation reactions occur through the transfer of energy from electronically excited sensitizer molecules which attain excited states by absorbing visible light energy. Optimum conditions for photooxidation were established based on sensitizer concentration and reaction pH for four polynuclear aromatic pollutants. The rate of photooxidation was found to be independent of the initial substrate concentration for methylene blue-sensitized reactions, and dependent on substrate concentration for solutions without a sensitizing dye. Photolysis of substrate mixtures established acridine and anthracene as photochemically active substrates. Photochemical reaction data suggest predictable trends in substrate reactivity based on pKa values of both sensitizer and substrate, initial substrate concentration and light absorbance characteristics. The photoproducts formed during the photolysis of acridine were found to be more toxic than the parent compound. These reaction products appear to be atable and warrant further study

Soil Phase Photodegradation of Toxic Organics at Contaminated Disposal Sites for Soil Renovation and Groundwater Quality Protection

Accurate assessment of the potential for contaminated soil remediation requires detailed knowledge of the fate of waste constituents within the soil environment. For many non-biodegradable organics compounds, photochemical degradation may provide a potential pathway for the removal of such compounds from soil surfaces. A study was conducted to evaluate the rate of photodegradation of ten hazardous organic compounds from three soils, silica gel, and four soil minerals (kaolinite, montmorillonite, illite, and calcite) under conditions of controlled irradiation. In addition, the effect of siz amendment treatments (methylene blue, riboflavin, hydrogen peroxide, diethylamine, peat moss, and silica gel) on the rates of compound loss was also investigated. Soil and mineral samples were spiked with various combinations of m-cresol, quinoline, biphenyl, dibenzo[a]furan, fluorene, pentachlorophenol, phenanthrene, anthracene, 9H-carbazole and pyrene at either 500 or 1000 mg/kg initial soil concentration of each chemical. Amendments were applied to the soils and minerals and duplicate samples were irradiated in petri dishes under ultraviolet or visible light while spike controls were inclubated in the dark. Linear regression of soil/mineral contaminant concentration data showed that first order kinetic modeling best described the degradation process. Significant loss of anthracene occurred on all surfaces tested althrough the rate of loss varied with surface type and, for some surfaces, with the spiking solution concentration and chemical mixtures. Anthracene loss from silica gel was the msot rapid of all reactions observed. Skumpah soil, a light colored alkaline soil, yielded the greatest reduction in contaminant concentrations found in the soil studies. Calcium kaolinite displaed the most rapid kinetics of the mineral surfaces tested. Loss of the other test compounds was observed from only some of the surfaces investigated. Anthraquinone and fluorenone were identified as the major degradation products of the photoreaction of anthracene and fluorene. Under the conditions of this study, soild and mineral type, as well as surface renewal via mixing, were found to have more effect on degradation rates than any of the amendments that were tested

Participatory ecology for \u27Agriculture of the Middle\u27: Developing tools and partnerships to bridge gaps among science, people and policy in landscape change

Based on findings of this project, the adaptive landscape changes needed to significantly incorporate perennial vegetation strategies into Iowa\u27s Corn Belt-dominated agriculture are possible if a coordinated strategy of change is coupled across three scales: field/individual, landscape/community, and regional/institutional

Predicting the Difficulty of Pure, Strict, Epistatic Models: Metrics for Simulated Model Selection

Background: Algorithms designed to detect complex genetic disease associations are initially evaluated using simulated datasets. Typical evaluations vary constraints that influence the correct detection of underlying models (i.e. number of loci, heritability, and minor allele frequency). Such studies neglect to account for model architecture (i.e. the unique specification and arrangement of penetrance values comprising the genetic model), which alone can influence the detectability of a model. In order to design a simulation study which efficiently takes architecture into account, a reliable metric is needed for model selection. Results: We evaluate three metrics as predictors of relative model detection difficulty derived from previous works: (1) Penetrance table variance (PTV), (2) customized odds ratio (COR), and (3) our own Ease of Detection Measure (EDM), calculated from the penetrance values and respective genotype frequencies of each simulated genetic model. We evaluate the reliability of these metrics across three very different data search algorithms, each with the capacity to detect epistatic interactions. We find that a model’s EDM and COR are each stronger predictors of model detection success than heritability. Conclusions: This study formally identifies and evaluates metrics which quantify model detection difficulty. We utilize these metrics to intelligently select models from a population of potential architectures. This allows for an improved simulation study design which accounts for differences in detection difficulty attributed to model architecture. We implement the calculation and utilization of EDM and COR into GAMETES, an algorithm which rapidly and precisely generates pure, strict, n-locus epistatic models

A New Perennial Legume to Combat Dryland Salinity in South-Western Australia

Dryland salinity has devastated large tracts of productive land in Australia. This has resulted from the clearing of native perennial vegetation and its replacement with annual crops and pastures. As annual plants are shallow rooted and only use water during their winter-spring growing season, unutilised rainwater leaks into groundwater tables which rise and bring stored salt to the soil surface. The adoption of deep rooted perennial pasture plants that increase the water use can help to manage dryland salinity whilst maintaining productivity. However, new plants are needed as few perennial pasture options currently exist. Preliminary research into the potential of hairy canary clover (Dorycnium hirsutum (L.) Ser.) to increase water use is presented

Experimental Implementation of Discrete Time Quantum Random Walk on an NMR Quantum Information Processor

We present an experimental implementation of the coined discrete time quantum walk on a square using a three qubit liquid state nuclear magnetic resonance (NMR) quantum information processor (QIP). Contrary to its classical counterpart, we observe complete interference after certain steps and a periodicity in the evolution. Complete state tomography has been performed for each of the eight steps making a full period. The results have extremely high fidelity with the expected states and show clearly the effects of quantum interference in the walk. We also show and discuss the importance of choosing a molecule with a natural Hamiltonian well suited to NMR QIP by implementing the same algorithm on a second molecule. Finally, we show experimentally that decoherence after each step makes the statistics of the quantum walk tend to that of the classical random walk.Comment: revtex4, 8 pages, 6 figures, submitted to PR

Multifactor Dimensionality Reduction Reveals a Three-Locus Epistatic Interaction Associated with Susceptibility to Pulmonary Tuberculosis

Background: Identifying high-order genetics associations with non-additive (i.e. epistatic) effects in population-based studies of common human diseases is a computational challenge. Multifactor dimensionality reduction (MDR) is a machine learning method that was designed specifically for this problem. The goal of the present study was to apply MDR to mining high-order epistatic interactions in a population-based genetic study of tuberculosis (TB). Results: The study used a previously published data set consisting of 19 candidate single-nucleotide polymorphisms (SNPs) in 321 pulmonary TB cases and 347 healthy controls from Guniea-Bissau in Africa. The ReliefF algorithm was applied first to generate a smaller set of the five most informative SNPs. MDR with 10-fold cross-validation was then applied to look at all possible combinations of two, three, four and five SNPs. The MDR model with the best testing accuracy (TA) consisted of SNPs rs2305619, rs187084, and rs11465421 (TA = 0.588) in PTX3, TLR9 and DC-Sign, respectively. A general 1000-fold permutation test of the null hypothesis of no association confirmed the statistical significance of the model (p = 0.008). An additional 1000-fold permutation test designed specifically to test the linear null hypothesis that the association effects are only additive confirmed the presence of non-additive (i.e. nonlinear) or epistatic effects (p = 0.013). An independent information-gain measure corroborated these results with a third-order epistatic interaction that was stronger than any lower-order associations. Conclusions: We have identified statistically significant evidence for a three-way epistatic interaction that is associated with susceptibility to TB. This interaction is stronger than any previously described one-way or two-way associations. This study highlights the importance of using machine learning methods that are designed to embrace, rather than ignore, the complexity of common diseases such as TB. We recommend future studies of the genetics of TB take into account the possibility that high-order epistatic interactions might play an important role in disease susceptibility