Making apartments affordable: Moving from speculative to deliberative development

Urban planning policies in Australia presuppose apartments as the new dominant housing type, but much of what the market has delivered is criticised as over-development, and as being generic, poorly-designed, environmentally unsustainable and unaffordable. Policy responses to this problem typically focus on planning regulation and construction costs as the primary issues needing to be addressed in order to increase the supply of quality, affordable apartment housing. In contrast, this paper uses Ball’s (1983) ‘structures of provision’ approach to outline the key processes informing apartment development and identifies a substantial gap in critical understanding of how apartments are developed in Australia. This reveals economic problems not typically considered by policymakers. Using mainstream economic analysis to review the market itself, the authors found high search costs, demand risk, problems with exchange, and lack of competition present key barriers to achieving greater affordability and limit the extent to which ‘speculative’ developers can respond to the preferences of would be owner-occupiers of apartments. The existing development model, which is reliant on capturing uplift in site value, suits investors seeking rental yields in the first instance and capital gains in the second instance, and actively encourages housing price inflation. This is exacerbated by lack of density restrictions, such as have existed in inner Melbourne for many years, which permits greater yields on redevelopment sites. The price of land in the vicinity of such redevelopment sites is pushed up as landholders' expectation of future yield is raised. All too frequently existing redevelopment sites go back onto the market as vendors seek to capture the uplift in site value and exit the project in a risk free manner...

A new method for the in vivo identification of mechanical properties in arteries from cine MRI images: Theoretical framework and validation

Quantifying the stiffness properties of soft tissues is essential for the diagnosis of many cardiovascular diseases such as atherosclerosis. In these pathologies it is widely agreed that the arterial wall stiffness is an indicator of vulnerability. The present paper focuses on the carotid artery and proposes a new inversion methodology for deriving the stiffness properties of the wall from cine-MRI (magnetic resonance imaging) data. We address this problem by setting-up a cost function defined as the distance between the modeled pixel signals and the measured ones. Minimizing this cost function yields the unknown stiffness properties of both the arterial wall and the surrounding tissues. The sensitivity of the identified properties to various sources of uncertainty is studied. Validation of the method is performed on a rubber phantom. The elastic modulus identified using the developed methodology lies within a mean error of 9.6%. It is then applied to two young healthy subjects as a proof of practical feasibility, with identified values of 625 kPa and 587 kPa for one of the carotid of each subject.

Linking spatial stock dynamics and economics: Evaluation of indicators and fishery management for the travelling eastern king prawn (Melicertus plebejus)

Reduced economic circumstances havemoved management goals towards higher profit, rather than maximum sustainable yields in several Australian fisheries. The eastern king prawn is one such fishery, for which we have developed new methodology for stock dynamics, calculation of model-based and data-based reference points and management strategy evaluation. The fishery is notable for the northward movement of prawns in eastern Australian waters, from the State jurisdiction of New South Wales to that of Queensland, as they grow to spawning size, so that vessels fishing in the northern deeper waters harvest more large prawns. Bioeconomic fishing data were standardized for calibrating a length-structured spatial operating model. Model simulations identified that reduced boat numbers and fishing effort could improve profitability while retaining viable fishing in each jurisdiction. Simulations also identified catch rate levels that were effective for monitoring in simple within-year effort-control rules. However, favourable performance of catch rate indicators was achieved only when a meaningful upper limit was placed on total allowed fishing effort. Themethods and findings will allow improved measures for monitoring fisheries and inform decision makers on the uncertainty and assumptions affecting economic indicators.

Standard errors and covariance matrices for smoothed rank estimators

A 'pseudo-Bayesian' interpretation of standard errors yields a natural induced smoothing of statistical estimating functions. When applied to rank estimation, the lack of smoothness which prevents standard error estimation is remedied. Efficiency and robustness are preserved, while the smoothed estimation has excellent computational properties. In particular, convergence of the iterative equation for standard error is fast, and standard error calculation becomes asymptotically a one-step procedure. This property also extends to covariance matrix calculation for rank estimates in multi-parameter problems. Examples, and some simple explanations, are given.

Stock-recruitment relationships of the tiger prawns (Penaeus esculentus and Penaeus semisulcatus) in the Australian Northern Prawn Fishery

This paper investigates the stock-recruitment and equilibrium yield dynamics for the two species of tiger prawns (Penaeus esculentus and Penaeus semisulcatus) in Australia's most productive prawn fishery: the Northern Prawn Fishery. Commercial trawl logbooks for 1970-93 and research surveys are used to develop population models for these prawns. A population model that incorporates continuous recruitment is developed. Annual spawning stock and recruitment indices are then estimated from the population model. Spawning stock indices represent the abundance of female prawns that are likely to spawn; recruitment indices represent the abundance of all prawns less than a certain size. The relationships between spawning stock and subsequent recruitment (SRR), between recruitment and subsequent spawning stock (RSR), and between recruitment and commercial catch were estimated through maximum-likelihood models that incorporated autoregressive terms. Yield as a function of fishing effort was estimated by constraining to equilibrium the SRR and RSR. The resulting production model was then used to determine maximum sustainable yield (MSY) and its corresponding fishing effort (f(MSY)). Long-term yield estimates for the two tiger prawn species range between 3700 and 5300 t. The fishing effort at present is close to the level that should produce MSY for both species of tiger prawns. However, current landings, recruitment and spawning stock are below the equilibrium values predicted by the models. This may be because of uncertainty in the spawning stock-recruitment relationships, a change in carrying capacity, biased estimates of fishing effort, unreliable catch statistics, or simplistic assumptions about stock structure. Although our predictions of tiger prawn yields are uncertain, management will soon have to consider new measures to counteract the effects of future increases in fishing effort.

Kinetic analysis of nitroxide radical coupling reactions mediated by CuBr

In a previous paper, we described the room temperature rapid, selective, reversible, and near quantitative Cu-activated nitroxide radical coupling (NRC) technique to prepare 3-arm polystyrene stars. In this work, we evaluated the Cu-activation mechanism, either conventional atom transfer or single electron transfer (SET), through kinetic simulations. Simulation data showed that one can describe the system by either activation mechanism. We also found through simulations that bimolecular radical termination, regardless of activation mechanism, was extremely low and could be considered negligible in an NRC reaction. Experiments were carried out to form 2- and 3-arm PSTY stars using two ligands, PMDETA and Me6TREN, in a range of solvent conditions by varying the ratio of DMSO to toluene, and over a wide temperature range. The rate of 2- or 3-arm star formation was governed by the choice of solvent and ligand. The combination of Me6TREN and toluene/DMSO showed a relatively temperature independent rate, and remarkably reached near quantitative yields for 2-arm star formation after only 1 min at 25 °C.

Rapid, selective, and reversible Nitroxide Radical Coupling (NRC) reactions at amibient temperature

High activation of polystyrene with bromine end groups (PSTY-Br) to their incipient radicals occurred in the presence of Cu(I)Br, Me6TREN, and DMSO solvent. These radicals were then trapped by nitroxide species leading to coupling reactions between PSTY-Br and nitroxides that were ultrafast and selective in the presence of a diverse range of functional groups. The nitroxide radical coupling (NRC) reactions have the attributes of a “click” reaction with near quantitative yields of product formed, but through the reversibility of this reaction, it has the added advantage of permitting the exchange of chemical functionality on macromolecules. Conditions were chosen to facilitate the disproportionation of Cu(I)Br to the highly activating nascent Cu(0) and deactivating Cu(II)Br2 in the presence of DMSO solvent and Me6TREN ligand. NRC at room temperature gave near quantitative yields of macromolecular coupling of low molecular weight polystyrene with bromine chain-ends (PSTY-Br) and nitroxides in under 7 min even in the presence of functional groups (e.g., −≡, −OH, −COOH, −NH2, =O). Utilization of the reversibility of the NRC reaction at elevated temperatures allowed the exchange of chain-end groups with a variety of functional nitroxide derivatives. The robustness and orthogonality of this NRC reaction were further demonstrated using the Cu-catalyzed azide/alkyne “click” (CuAAC) reactions, in which yields greater than 95% were observed for coupling between PSTY-N3 and a PSTY chain first trapped with an alkyne functional TEMPO (PSTY-TEMPO-≡).

Domain Adaptation on the Statistical Manifold

In this paper, we tackle the problem of unsupervised domain adaptation for classification. In the unsupervised scenario where no labeled samples from the target domain are provided, a popular approach consists in transforming the data such that the source and target distributions be- come similar. To compare the two distributions, existing approaches make use of the Maximum Mean Discrepancy (MMD). However, this does not exploit the fact that prob- ability distributions lie on a Riemannian manifold. Here, we propose to make better use of the structure of this man- ifold and rely on the distance on the manifold to compare the source and target distributions. In this framework, we introduce a sample selection method and a subspace-based method for unsupervised domain adaptation, and show that both these manifold-based techniques outperform the cor- responding approaches based on the MMD. Furthermore, we show that our subspace-based approach yields state-of- the-art results on a standard object recognition benchmark.

A novel approach to 6-DOF adaptive trajectory tracking control of an AUV in the presence of parameter uncertainties

In this paper, the trajectory tracking control of an autonomous underwater vehicle (AUVs) in six-degrees-of-freedom (6-DOFs) is addressed. It is assumed that the system parameters are unknown and the vehicle is underactuated. An adaptive controller is proposed, based on Lyapunov׳s direct method and the back-stepping technique, which interestingly guarantees robustness against parameter uncertainties. The desired trajectory can be any sufficiently smooth bounded curve parameterized by time even if consist of straight line. In contrast with the majority of research in this field, the likelihood of actuators׳ saturation is considered and another adaptive controller is designed to overcome this problem, in which control signals are bounded using saturation functions. The nonlinear adaptive control scheme yields asymptotic convergence of the vehicle to the reference trajectory, in the presence of parametric uncertainties. The stability of the presented control laws is proved in the sense of Lyapunov theory and Barbalat׳s lemma. Efficiency of presented controller using saturation functions is verified through comparing numerical simulations of both controllers.

The use of an acetoacetyl-CoA synthase in place of a -ketothiolase enhances poly-3-hydroxybutyrate production in sugarcane mesophyll cells

Engineering the production of polyhydroxyalkanoates (PHAs) into high biomass bioenergy crops has the potential to provide a sustainable supply of bioplastics and energy from a single plant feedstock. One of the major challenges in engineering C-4 plants for the production of poly[(R)-3-hydroxybutyrate] (PHB) is the significantly lower level of polymer produced in the chloroplasts of mesophyll (M) cells compared to bundle sheath (BS) cells, thereby limiting the full PHB yield-potential of the plant. In this study, we provide evidence that the access to substrate for PHB synthesis may limit polymer production in M chloroplasts. Production of PHB in M cells of sugarcane is significantly increased by replacing -ketothiolase, the first enzyme in the bacterial PHA pathway, with acetoacetyl-CoA synthase. This novel pathway enabled the production of PHB reaching an average of 6.3% of the dry weight of total leaf biomass, with levels ranging from 3.6 to 11.8% of the dry weight (DW) of individual leaves. These yields are more than twice the level reported in PHB-producing sugarcane containing the -ketothiolase and illustrate the importance of producing polymer in mesophyll plastids to maximize yield. The molecular weight of the polymer produced was greater than 2x10(6)Da. These results are a major step forward in engineering a high biomass C-4 grass for the commercial production of PHB.

Physio-chemical assessment of beauty leaf (Calophyllum inophyllum) as second-generation biodiesel feedstock

Recently, second-generation (non-vegetable oil) feedstocks for biodiesel production are receiving significant attention due to the cost and social effects connected with utilising food products for the production of energy products. The Beauty leaf tree (Calophyllum inophyllum) is a potential source of non-edible oil for producing second-generation biodiesel because of its suitability for production in an extensive variety of atmospheric condition, easy cultivation, high fruit production rate, and the high oil content in the seed. In this study, oil was extracted from Beauty leaf tree seeds through three different oil extraction methods. The important physical and chemical properties of these extracted Beauty leaf oils were experimentally analysed and compared with other commercially available vegetable oils. Biodiesel was produced using a two-stage esterification process combining of an acid catalysed pre-esterification process and an alkali catalysed transesterification process. Fatty acid methyl ester (FAME) profiles and important physicochemical properties were experimentally measured and estimated using equations based on the FAME analysis. The quality of Beauty leaf biodiesels was assessed and compared with commercially available biodiesels through multivariate data analysis using PROMETHEE-GAIA software. The results show that mechanical extraction using a screw press produces oil at a low cost, however, results in low oil yields compared with chemical oil extraction. High pressure and temperature in the extraction process increase oil extraction performance. On the contrary, this process increases the free fatty acid content in the oil. A clear difference was found in the physical properties of Beauty leaf oils, which eventually affected the oil to biodiesel conversion process. However, Beauty leaf oils methyl esters (biodiesel) were very consistent physicochemical properties and able to meet almost all indicators of biodiesel standards. Overall this study found that Beauty leaf is a suitable feedstock for producing second-generation biodiesel in commercial scale. Therefore, the findings of this study are expected to serve as the basis for further development of Beauty leaf as a feedstock for industrial scale second-generation biodiesel production.

Independent replication and meta-analysis for endometriosis risk loci

Endometriosis is a complex disease that affects 6-10% of women in their reproductive years and 20-50% of women with infertility. Genome-wide and candidate-gene association studies for endometriosis have identified 10 independent risk loci, and of these, nine (rs7521902, rs13394619, rs4141819, rs6542095, rs1519761, rs7739264, rs12700667, rs1537377, and rs10859871) are polymorphic in European populations. Here we investigate the replication of nine SNP loci in 998 laparoscopically and histologically confirmed endometriosis cases and 783 disease-free controls from Belgium. SNPs rs7521902, rs13394619, and rs6542095 show nominally significant (p <.05) associations with endometriosis, while the directions of effect for seven SNPs are consistent with the original reports. Association of rs6542095 at the IL1A locus with 'All' (p =.066) and 'Grade-B' (p =.01) endometriosis is noteworthy because this is the first successful replication in an independent population. Meta-analysis with the published results yields genome-wide significant evidence for rs7521902, rs13394619, rs6542095, rs12700667, rs7739264, and rs1537377. Notably, three coding variants in GREB1 (near rs13394619) and CDKN2B-AS1 (near rs1537377) also showed nominally significant associations with endometriosis. Overall, this study provides important replication in a uniquely characterized independent population, and indicates that the majority of the original genome-wide association findings are not due to chance alone. © The Author(s) 2015.

Zygosity diagnosis in the absence of genotypic data: An approach using latent class analysis

For zygosity diagnosis in the absence of genotypic data, or in the recruitment phase of a twin study where only single twins from same-sex pairs are being screened, or to provide a test for sample duplication leading to the false identification of a dizygotic pair as monozygotic, the appropriate analysis of respondents' answers to questions about zygosity is critical. Using data from a young adult Australian twin cohort (N = 2094 complete pairs and 519 singleton twins from same-sex pairs with complete responses to all zygosity items), we show that application of latent class analysis (LCA), fitting a 2-class model, yields results that show good concordance with traditional methods of zygosity diagnosis, but with certain important advantages. These include the ability, in many cases, to assign zygosity with specified probability on the basis of responses of a single informant (advantageous when one zygosity type is being oversampled); and the ability to quantify the probability of misassignment of zygosity, allowing prioritization of cases for genotyping as well as identification of cases of probable laboratory error. Out of 242 twins (from 121 like-sex pairs) where genotypic data were available for zygosity confirmation, only a single case was identified of incorrect zygosity assignment by the latent class algorithm. Zygosity assignment for that single case was identified by the LCA as uncertain (probability of being a monozygotic twin only 76%), and the co-twin's responses clearly identified the pair as dizygotic (probability of being dizygotic 100%). In the absence of genotypic data, or as a safeguard against sample duplication, application of LCA for zygosity assignment or confirmation is strongly recommended.

Class-specific sparse codes for representing activities

In this paper we investigate the effectiveness of class specific sparse codes in the context of discriminative action classification. The bag-of-words representation is widely used in activity recognition to encode features, and although it yields state-of-the art performance with several feature descriptors it still suffers from large quantization errors and reduces the overall performance. Recently proposed sparse representation methods have been shown to effectively represent features as a linear combination of an over complete dictionary by minimizing the reconstruction error. In contrast to most of the sparse representation methods which focus on Sparse-Reconstruction based Classification (SRC), this paper focuses on a discriminative classification using a SVM by constructing class-specific sparse codes for motion and appearance separately. Experimental results demonstrates that separate motion and appearance specific sparse coefficients provide the most effective and discriminative representation for each class compared to a single class-specific sparse coefficients.

Open source computer-vision based guidance system for UAVs on-board decision making

The use of UAVs for remote sensing tasks; e.g. agriculture, search and rescue is increasing. The ability for UAVs to autonomously find a target and perform on-board decision making, such as descending to a new altitude or landing next to a target is a desired capability. Computer-vision functionality allows the Unmanned Aerial Vehicle (UAV) to follow a designated flight plan, detect an object of interest, and change its planned path. In this paper we describe a low cost and an open source system where all image processing is achieved on-board the UAV using a Raspberry Pi 2 microprocessor interfaced with a camera. The Raspberry Pi and the autopilot are physically connected through serial and communicate via MAVProxy. The Raspberry Pi continuously monitors the flight path in real time through USB camera module. The algorithm checks whether the target is captured or not. If the target is detected, the position of the object in frame is represented in Cartesian coordinates and converted into estimate GPS coordinates. In parallel, the autopilot receives the target location approximate GPS and makes a decision to guide the UAV to a new location. This system also has potential uses in the field of Precision Agriculture, plant pest detection and disease outbreaks which cause detrimental financial damage to crop yields if not detected early on. Results show the algorithm is accurate to detect 99% of object of interest and the UAV is capable of navigation and doing on-board decision making.