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Grain boundaries with [111] tilt axes are common in polycrystalline face centered cubic metals. For copper (111) films, emergent grain boundaries close to surface have tilt axes that are shifted away from [111] that are lower in energy than the corresponding truncated bulk boundaries. Geometrical analysis and atomic calculations were used to study the driving force for this same relaxation phenomenon in representative fcc elemental metals. We show that the reduction in boundary energy scales with the elimination of energetically costly boundary core facets. We find that for a wide range of misorientation angles low energy core-shifted boundaries are also favored in Al, Ni, Au and Pt and discuss the significance for electromigration and other metal properties.

Grain boundary energies in different elements are correlated. The proportional scaling constants relating the energies of crystallography-equivalent boundaries in any two f.c.c. elements are nearly constant, with the notable exception of aluminum where these constants are known to vary significantly. However, the origins of the exceptional behavior of aluminum are not understood. Previously, we reported that for fcc metals there is a preference for boundaries to shift their tilt axis across the (1-10) plane towards [112] and to ultimately form low energy [112] core shifted boundaries (CSBs). By comparing grain boundary energies in copper and aluminum with different tilt axis in (1-10) plane, we now report the existence of a well-defined scaling behavior for the case of low angle boundaries. In contrast, the scaling constant for high angle boundaries is essentially fixed regardless of their tilt axis shift. This results in a gradual change in the scaling constants from low angle to high angle boundaries, which is responsible for the apparent exceptional scaling behavior found in aluminum. An analysis of structure evolution during core shifting points to the significance of boundary-core dissociation, a form of correlated relaxation of individual atoms at boundaries, in controlling the scaling of the boundary energies.

We prove a generalized form of the Granger-Johansen representation theorem (GJRT) for finite or infinite order integrated auto-regressive time series on Banach space, Comment: 29 pages, no figures, Preprint for article submitted to Journal of Time Series Econometrics

Two-dimensional layered semiconductors have recently emerged as attractive building blocks for next-generation low-power non-volatile memories. However, challenges remain in the controllable sub-micron fabrication of bipolar resistively switching circuit components from these novel materials. Here we report on the scalable experimental realisation of lateral on-dielectric memtransistors from monolayer single-crystal molybdenum disulfide (MoS2) utilising a focused helium ion beam. Site-specific irradiation with the probe of a helium ion microscope (HIM) allows for the creation of charged defects in the MoS2 lattice. The reversible drift of these locally seeded defects in the applied electric field modulates the resistance of the semiconducting channel, enabling versatile memristive functionality on the nanoscale. We find the device can reliably retain its resistance ratios and set biases for hundreds of switching cycles at sweep frequencies of up to 2.9 V/s with relatively low drain-source biases. We also demonstrate long-term potentiation and depression with sharp habituation that promises application in future neuromorphic architectures. This work advances the down-scaling progress of memristive devices without sacrificing key performance parameters such as power consumption or its applicability for synaptic emulation., Comment: 37 pages, 5 main figures, 14 supplementary figures

Precise tunability of electronic properties of 2D nanomaterials is a key goal of current research in this field of materials science. Chemical modification of layered transition metal dichalcogenides leads to the creation of heterostructures of low-dimensional variants of these materials. In particular, the effect of oxygen-containing plasma treatment on molybdenum disulfide (MoS$_2$) has long been thought to be detrimental to the electrical performance of the material. Here we show that the mobility and conductivity of MoS$_2$ can be precisely controlled and improved by systematic exposure to oxygen:argon plasma, and characterise the material utilising advanced spectroscopy and microscopy. Through complementary theoretical modelling which confirms conductivity enhancement, we uncover the role of a two-dimensional phase of molybdenum trioxide (2D-MoO$_3$) in improving the electronic behaviour of the material. Deduction of the beneficial role of MoO$_3$ will serve to open the field to new approaches with regard to the tunability of 2D semiconductors by their low-dimensional oxides in nano-modified heterostructures., Comment: 11 pages, 5 figures

This is a presentation given at the 2016 Planet Talks series at the Womadelaide World Music Festival.

This is a presentation given at the 2016 Planet Talks series at the Womadelaide World Music Festival.

Appendix B. Analysis of the largest direct and indirect waste multipliers for 14 waste types. (PDF 129 kB)

Climate change is leading to an increased frequency and severity of heat waves. Spells of several consecutive days of unusually high temperatures have led to increased mortality rates for the more vulnerable in the community. The problem is compounded by the escalating energy costs and increasing peak electrical demand as people become more reliant on air conditioning. Domestic air conditioning is the primary determinant of peak power demand which has been a major driver of higher electricity costs. This report presents the findings of multidisciplinary research which develops a national framework to evaluate the potential impacts of heat waves. It presents a technical, social and economic approach to adapt Australian residential buildings to ameliorate the impact of heat waves in the community and reduce the risk of its adverse outcomes. Through the development of a methodology for estimating the impact of global warming on key weather parameters in 2030 and 2050, it is possible to re-evaluate the size and anticipated energy consumption of air conditioners in future years for various climate zones in Australia. Over the coming decades it is likely that mainland Australia will require more cooling than heating. While in some parts the total electricity usage for heating and cooling may remain unchanged, there is an overall significant increase in peak electricity demand, likely to further drive electricity prices. Through monitoring groups of households in South Australia, New South Wales and Queensland, the impact of heat waves on both thermal comfort sensation and energy consumption for air conditioning has been evaluated. The results show that households are likely to be able to tolerate slightly increased temperature levels indoors during periods of high outside temperatures. The research identified that household electricity costs are likely to rise above what is currently projected due to the impact of climate change. Through a number of regulatory changes to both household design and air conditioners, this impact can be minimised. A number of proposed retrofit and design measures are provided, which can readily reduce electricity usage for cooling at minimal cost to the household. Using a number of social research instruments, it is evident that households are willing to change behaviour rather than to spend money. Those on lower income and elderly individuals are the least able to afford the use of air conditioning and should be a priority for interventions and assistance. Increasing community awareness of cost effective strategies to manage comfort and health during heat waves is a high priority recommended action. Overall, the research showed that a combined approach including behaviour change, dwelling modification and improved air conditioner selection can readily adapt Australian households to the impact of heat waves, reducing the risk of heat related deaths and household energy costs.

Environmental context Nitrate (NO3−) and ammonium (NH4+) are the most important soil nitrogen forms for plant growth. However, conventional extraction techniques may introduce artefacts affecting the measurement of plant-available N concentrations following sampling and sample preparation processes. This is the first study of the DGT technique being used to measure NO3-N and NH4-N in a wide range of soils, compared with conventional KCl extraction, and examined different factors that contribute to the plant-availability of these ions in soils. The knowledge would help to optimise soil nitrogen management practices, increase economic benefits and reduce environmental impacts. Rationale The availability of soil nitrogen for plant uptake can be affected by numerous soil factors such as soil texture, moisture and organic matter content, temperature and microbial activity. Conventional extraction techniques may affect the measurement of plant-available N concentrations following sampling and sample preparation processes, including drying, sieving, homogenising, freezing and thawing. The diffusive gradients in thin-films (DGT) technique can overcome some limitations of the conventional extraction techniques and has been used to successfully estimate the plant-available fractions of nutrients, such as P, K, Zn, Cu and Mn in soils. Therefore, it is important to evaluate the use of DGT for measuring NO3− and NH4+ in a wide variety of soils and examine the factors that contribute to the plant-availability of these ions in soils. Methodology The experiment evaluated the ability of the DGT technique to measure NO3-N and NH4-N in soils using binding layers containing A520E anion exchange resin or Microlite® PrCH cation exchange resin, respectively. The DGT results were compared to those from conventional KCl extraction. Results The A520E- and PrCH-DGTs showed good detection limits for NO3-N (6.90 µg L−1) and NH4-N (6.23 µg L−1) and were able to measure potentially available NO3-N and NH4-N in unfertilised soils. The mass of NO3-N and NH4-N that accumulated on the DGT device increased linearly across soil concentrations ranging from 5 to 300 mg kg−1 NO3-N (depending on soil type) and 5–300 mg kg−1 NH4-N; which is equivalent to fertiliser rates of 75–450 kg ha−1 N. DGTs were used to measure potentially available NO3-N and NH4-N in ten soils with various physical and chemical properties. The DGT results were compared with conventional KCl extraction used to determine soil mineral N. DGT and KCl extraction measured values were significantly correlated with each other for NO3-N (R2 = 0.53; P-value

Refereed/Peer-reviewed Aim: The island species–area relationship (ISAR) assumes that the area of islands is homogeneous, or scales with environmental heterogeneity across an archipelago, which is not always the case. We compare the performance of models that adjust or substitute for island area with measures of habitat diversity, island age and resource availability for two taxonomic groups. Location: Five hotspot archipelagos (Azores, Galapagos, Hawaii, Cape Verde, Canary Islands). Taxa: Vascular plants, birds. Methods: We used the mathematical framework of the power law to compare relevant models, treating the one containing only area as a null model against which others were compared. Data were collated from databases and the literature. Models were compared using linear regression within archipelagos and via mixed effect models with archipelago as a random effect. Results: Weighting of island area by habitat diversity and resource availability systematically improved statistical significance and model fits versus the area only power law. Models including island age did not show the same systematic improvement in model fits. For vascular plants, weighting islands by resource availability (energy and water) performed better than weighting by habitat diversity, although for birds these weightings performed equally well. Main Conclusions: Given that islands within archipelagos are fairly heterogeneous in climate, topography and geology, it is worth accounting for this in ISARs. Our results suggest that, for islands in volcanic hotspot archipelagos, this is best done by using direct measures of habitat diversity and resource availability rather than using island age as a proxy. We, therefore, recommend using direct measures, rather than proxies, when investigating the drivers of biodiversity patterns on islands.

An efficient use of solar energy production requires reliable forecast information on surface solar irradiance. This article aims at providing a Model Output Statistics (MOS) method of improving solar irradiance forecasts from Weather Research and Forecasting (WRF) model. The WRF model was used to produce one year of day ahead solar irradiance forecasts covering Reunion Island with an horizontal resolution of 3 km. These forecasts are refined with a Kalman filter using hourly high quality ground measurements. Determination of the relevant data inputs for the Kalman filter method is realized with a bias error analysis. The solar zenith angle and the clear sky index, among others, are used for this analysis. Accuracy of the method is evaluated with a comprehensive testing procedure using different error metrics. The Kalman filtering appears to be a viable method in order to improve the solar irradiance forecasting. Refereed/Peer-reviewed

The species-area relationship (SAR) is widely applied in ecology. Mathematically, it is usually expressed as either a semi-log or power-law relationship, with the former being introduced by Gleason and the latter by Arrhenius. We here resolve the dispute about which form of the SAR to prefer by introducing a novel model that smoothly transforms between the Gleason semi-log (GSL) and Arrhenius power law (APL) forms. The model introduced has the form of lnq (S) = a + z ln A, with lnq being a generalized logarithmic function, which is a linear map (y = x) for q = 0 and a logarithmic map (y = ln x) for q = 1 and q can take any intermediate value between 0 and 1. We applied this model to 100 datasets (mostly islands), linking species richness to island area. The APL was the preferred model in 68% of head-to-head comparisons with the GSL. Both models were supported in 40% of cases. In just under half (44%) of the cases, an intermediate model best explained the data. The results demonstrate the utility of a simple intermediate SAR model. Visualizing the profile of the range of model fits for all q ∈ [0, 1] (a q chart) allows us to gain extra insight into SARs not yielded by head-to-head comparisons of GSL and APL. The mathematics related to the generalized logarithmic function introduced here should have applications to other areas of ecological modelling.

This study proposes a novel 10-point numerical scale to evaluate long-term thermal comfort satisfaction. The available scale that verbally evaluates the short-term thermal comfort is thermal sensation vote (TSV) and this has substituted for the long-term thermal comfort assessment. However, the TSV has been questioned by many studies due to the ambiguous, widely different interpretation of each score and unequal perceived gaps between the scores. The paper argues the biological similarities between pain and temperature perceptions and proposes a numerical thermal comfort scale like that of long-term pain perception. Participants in a survey rated their long-term summer thermal comfort in the natural ventilation mode through the numerical and TSV scales. Ratings were compared after converting to degree hours based on monitoring temperature data for the same summer. The results show that the numerical scale provides an opportunity for respondents to express their long-term thermal comfort satisfaction level. Scoring by the numerical scale was convenient and clear to the respondents. The verbal scores of TSV, had different interpretations for individuals as each point was attributed to more than two values of degree hours. The score ‘Neutral’ was significantly ambiguous for assessing thermal comfort. Refereed/Peer-reviewed

Aim: The General Dynamic Model of island biogeography (GDM) explains patterns of species richness on volcanic hotspot archipelagos with respect to island age (T) and area (A), most commonly employing what we here call semi‐log ATT² and LogATT² models. These can be considered direct extensions of semi‐log and power law species‐area relationships (SARs), with a hump‐shaped function of T modelling drivers of biodiversity over and above area per se. We compare these and related models and investigate their conceptual and mathematical connections. Location: The Azores, Galapagos, Hawaii, Marquesas, Society Islands, Cape Verde Islands, Canary Islands and Aeolian archipelagos. Taxa: Vascular plants, birds, bryophytes, arthropods and molluscs. Methods: We gathered species richness data from databases and published sources, resulting in 34 datasets for various taxa. We used the Akaike information criterion corrected for small sample size (AICc) to compare the relative performance of models. We analysed the data at archipelago level and across archipelagos by using mixed effect models. We also calculated the relative time of expected peak diversity (Tpeak) from parameter estimates. Results: In almost three‐fourth of the cases the best model overall was one that contained only A or T rather than both predictors. There was greater variance between taxa than between archipelagos. Better dispersed taxa tended to reach peak diversity earlier. The most generally applicable of the models including T were the LogATT² and LogTT² models. These have important, previously unrecognized mathematical properties. Main Conclusions: SARs and species–time relationships should always be considered as potentially more parsimonious options. Considering the LogATT² model as a modification of the power law S = cAz, where a lognormal function in T replaces the constant, c allows the construction of an extended framework for the GDM with incorporation of additional predictors such as measures of environmental heterogeneity and application beyond hotspot archipelagos. Refereed/Peer-reviewed

This paper evaluates the application of a family of VAR-mGARCH (Vector AutoRegressive with multivariate AutoRegressive Conditional Heteroskedasticity) volatility models to the problem of modeling (i.e. generating correct in-sample scenarios) and forecasting in a probabilistic way three univariate but mutually dependent wind speed hourly series. The evaluation starts from the consideration that optimal modeling and optimal forecasting are not necessarily attained by the same models, and that they are assessed in different ways. The proposed VAR-mGARCH family, originated in Finance, consists of the VAR-BEKK (VAR - Baba, Engle, Kraft and Kroner) model and the VAR-DCC (VAR - Dynamic Conditional Correlation) model, the latter seen as the simplified and more scalable version of the former. These models were never used in wind speed studies before. Both models will be used with Gaussian innovations, and the VAR-DCC model will be also used with Student's t innovations. These models, which are able to dynamically couple volatilities, are compared to two benchmark model sets, the first set consisting of three independent univariate AR-GARCH (i.e. non-vector) models, the second set consisting of an individual VAR model without the GARCH sector. These benchmark models cannot dynamically couple volatilities. Both benchmark model sets will be used with Gaussian innovations. The time series used for the evaluation are taken from three North-American wind metering sites located at few tens of kilometers from each other. In order to highlight the usefulness of choosing autoregressive coupled volatility schemes for modeling and forecasting spatially and temporally varying wind speed data, a controlled variance electricity generation portfolio example of Markowitz type, typical of Energy Finance, is also discussed at some length. In all, it is concluded that the VAR-DCC model with Student's t innovations is the most balanced choice for this data set when the three goals of modeling, forecasting and scalability in the number of considered sites are taken into account at once. Refereed/Peer-reviewed

A Photovoltaic/Thermal (PV/T) system combines PV and thermal collector, which is considered promising technology especially for building integrated PV/T system. The PV/T cooling systems using water, water-PCM and nanofluid/nano-PCM moves through the cooling pipes were investigated, in this study. However, this paper focuses on testing different PV/T systems (conventional PV, water-based PVT, water-nanofluid PVT, and nanofluid/nano-PCM) under the same conditions and environment using one artificial neural network (ANN) based Multi-Layer Perceptron (MLP) system. Also, investigate the differences in the efficiency of these systems on both thermal and electrical when using only one simulation system (MLP). The proposed ANN approach proved that using of nanofluid/nano-PCM was enhanced the electrical efficiency from 8.07% to 13.32% and its thermal efficiency reached 72%. Also, the voltage was improved significantly. Many measurement methods were used for validating the results of the proposed ANN model like the Mean Absolute Error (MAE), Mean Square Error (MSE), Correlation (R), and coefficient of determination (R 2 ). The proposed ANN model achieved a final MSE of 0.0229 in the training phase and 0.0282 in the cross-validation phase. The sensitivity analysis showed that the influence of solar irradiation and Amb-temp almost has a constant effect on electrical efficiency. However, the Ambient temperature had a significant impact on thermal efficiency. The results of the network were consistent with the experimental results of the current study and published works. Refereed/Peer-reviewed

Current and newly built buildings will inevitably experience the effects of climate change, therefore, the design and performance of these buildings should consider weather data that includes some of the effects of climate change, instead of only using historical weather data. However, climate change weather data suitable for buildings performance simulation are typically unavailable. This research presents a method to integrate climate change features into historical weather data to make suitable climate change weather data available for buildings performance simulation. The method separates hourly dry bulb temperature into three time series components to simplify the integration of three climate change features. The method adjusts the maximum and minimum monthly averages, the number of days with maximum temperature above a specified threshold, and the number of consecutive occurrences of days with maximum temperature above a specified threshold (heatwave). The research also presents the adjustment of monthly averages of global solar irradiation. Under the climate–changed weather conditions, the annual heating thermal energy decreases by 21%–22%, the annual cooling thermal energy increases by 29%–31%, and the combined heating and cooling thermal energy decreases by 4%–5% compared to the heating and cooling thermal energy under the current weather conditions. The results indicate that climate–changed weather data is necessary as historical weather data is insufficient for accurately assessing the energy performance of a building. Refereed/Peer-reviewed

We develop a new probabilistic forecasting method for global horizontal irradiation (GHI) by extending our previous bootstrap method to a case of an exponentially decaying heteroscedastic model for tracking dynamics in solar radiance. Our previous method catered for the global systematic variation in variance of solar radiation, whereas our new method also caters for the local variation in variance. We test the performance of our new probabilistic forecasting method against our old probabilistic forecasting method at three locations: Adelaide, Darwin, and Mildura. These locations are chosen to represent three distinct climates. The prediction interval coverage probability, prediction interval normalized averaged width and Winkler score results from our new probabilistic forecasting method are encouraging. Our new method performs better than our previous method at Adelaide and Mildura, regions with a higher proportion of clear-sky days, whereas our previous method performs better than our new method at Darwin, a region with a lower proportion of clear-sky days. These results suggest that the ideal probabilistic forecasting method might be climate specific.

At present, solar energy resources are becoming more and more popular as a substitute for conventional energy sources, in order to reduce greenhouse gas emissions. In order to further improve the utilization rate of solar energy resources, storage, as an important supplement, is more and more widely used. One of the most important research directions is to determine the necessary size of storage capacity by mathematic modeling. This paper will describe a new and efficient decision-making process to determine a series of storage capacities for a grid-connected PV system. The benefit of using a decision-making model is that it can easily determine the most appropriate storage capacity for the system. The paper will show here that the decision-making model will not only find the most effectiveness storage, but also find the relationship between storage capacities and the utilization rates of renewable energy. The case study shows that using 141.25 kWh storage increases the utilization rate of renewable energy in demand by 3.605%. And it would recover the storage cost by 4.66 years in Australia market. Refereed/Peer-reviewed

Privacy enhancing technologies (PETs) in mobile platforms typically restrict undesired information flow based on its sensitivity. However, sensitivity is often regarded as dichotomous and inflexible to the ever-changing contexts. Improving the effectiveness of PETs requires a better understanding of these contexts. In this paper, we examine the influence of contextual factors in users’ mobile usage based on Nissenbaum’s framework of contextual integrity. Specifically, we conducted a user study (n = 2889) to investigate the influence of relevance of information types on the willingness of disclosure towards typical groups of recipient. While the results suggest a significant relationship between information relevance (of different information) and willingness to disclose (to different recipients), closer examination reveals the relationship is not always clear-cut, and there is a potential influence of recipient. Therefore, incorporating the recipient factor can serve as a potential improvement to the existing approach in privacy management in the mobile device. Refereed/Peer-reviewed

This article proposes a new hybrid least squares-support vector machine and artificial bee colony algorithm (ABC-LS-SVM) for multi-hour ahead forecasting of global solar radiation (GHI) data. The framework performs on training the least squares-support vector machine (LS-SVM) model by means of the ABC algorithm using the measured data. ABC is developed for free parameters optimization for the LS-SVM model in a search space so as to boost the forecasting performance. The developed ABC-LS-SVM approach is verified on an hourly scale on a database of five years of measurements. The measured data were collected from 2013 to 2017 at the Applied Research Unit for Renewable Energy (URAER) in Ghardaia, south of Algeria. Several combinations of input data have been tested to model the desired output. Forecasting results of 12 h ahead GHI with the ABC-LS-SVM model led to the root-mean-square error (RMSE) equal to 116.22 Wh/m2, Correlation coefficient r = 94.3%. With the classical LS-SVM, the RMSE error equals to 117.73 Wh/m2 and correlation coefficient r = 92.42%; for cuckoo search algorithm combined with LS-SVM, the RMSE = 116.89 Wh/m2 and r = 93.78%. The results achieved reveal that the proposed hybridization scheme provides a more accurate performance compared to cuckoo search-LS-SVM and the stand-alone LS-SVM.

There is an increasing demand for cooling cities because of its importance on human health and the quality of life in outdoor urban spaces. However, the development of methods in improving outdoor thermal comfort and zoning cities based on outdoor thermal comfort is still challenging. In this work, we propose a new approach to cities zoning from the lens of outdoor thermal comfort in the arid climate of the city of Mashhad, Iran, and investigate the impacts of urban form characteristics on pedestrian thermal comfort. The effects of complex urban form parameters including height to width (H/W) ratio, canyon orientation, tree canopy cover, and building surface materials on the thermal comfort of pedestrians were studied in the arid climate of Mashhad. Microclimate simulation and analysis is conducted in ENVI-met software, and ArcMap is used to calculate Mashhad urban heat islands. Path analysis in SPSS presents an urban form formulation, which predicts approximate outdoor thermal comfort condition in current and future urban context of Mashhad and other cities with the same climate. We finally demonstrate the use of our research method as an alternative method for all cities: urban heat island (UHI) zoning can be used as a substitute for urban form zonings based on outdoor thermal comfort, especially in large cities where data collection on urban form can be difficult due to limited time and resources. Refereed/Peer-reviewed

As the frequency and intensity of heatwaves are growing, strategies to improve our resilience are becoming more vital. Policies to increase heat stress resilience are mostly isolated across different disciplines and government departments. A holistic approach would be necessary that mitigates the numerous negative impacts of heatwaves on public health, urban infrastructure and services through adaptation to heatwaves in the realm of public health, building and construction industry, and urban planning and infrastructure. This paper reviews the research on heat stress adaptation measures, before presenting recommendations for a range of integrated policy measures to increase the heat stress resilience of urban populations in Australian cities. The recommended policy measures include information dissemination, incentives and disincentives, promotion, demonstration and regulations. The paper concludes by identifying directions for further research and reinforcing the multiple benefits that can result from the implementation of heat stress resilience policies and strategies. Refereed/Peer-reviewed

Particle size is a fundamental variable in the dust aerosol cycle. A dust layer effective particle size is strongly related to its’ radiative forcing and the extent of its impact on an area of interest. However, in-situ measurements of dust particle size are costly, spatially sparse and time-consuming. This paper presents a simple empirically derived model to estimate effective dust diameter using infrared band brightness temperature difference of the 8.7 and 12.0 μm wavelength bands retrieved from the SEVIRI (Spinning Enhanced Visible and InfaRed Imager) radiometer onboard Meteosat satellites. Three case studies were used to test the model. The results showed consistency between the model and in-situ aircraft fly through sampling of dust particle size. The use of the model is demonstrated by presenting an analysis of a severe dust storm over Western Asia. This model is expected to contribute to addressing the discrepancies between the current particle size retrieval techniques and aircraft measurements. There are many potential applications for this model including providing an independent reference data for atmospheric dust models, forecasting the impact of dust storms on horizontal visibility as wellas the solar energy system performance over regions prone to dust storms. Refereed/Peer-reviewed

Outdoor thermal discomfort discourages outdoor living at the cost of increased demand for indoor air-conditioning. The resulted waste heat from air-conditioning makes a feedback loop with increased outdoor heat stress. Local and seasonal climate expectations, comfort perceptions, demographic specifications, activity choices and socio-cultural norms can affect the adaptation of public life to outdoor heat stress. This paper explores limits of outdoor thermal adaptation in Sydney, Melbourne and Adelaide. Outdoor activity observation and microclimate measurement were conducted in 10 selected public spaces between February 2013 and March 2014. Results indicate that before the outdoor thermal environment of UTCI = 22–34°C, there is no significant decline in outdoor living and people adapted their outdoor activities, clothing and activity rate to achieve thermal comfort. Beyond this neutral thermal threshold, outdoor thermal adaptation shifted towards dismissal of optional and social activities and modification of necessary activities. Space configurations, local climate expectations and flexible activity choices may extend outdoor thermal adaptation by the critical zero-activity thresholds of 48°C. Thereafter, outdoor activity prevention can become the dominant thermal adaptation strategy. Localized limits of outdoor thermal adaptation are to be addressed to facilitate more liveable and healthy cities in the context of climate change. Refereed/Peer-reviewed

In Australia, heatwaves are the deadliest natural hazard and a major driver of peak electricity demand. The disproportionately high peak demand increases electricity prices, causes occasional blackouts and exacerbates energy poverty, all of which limit one's ability to use air conditioning. Meanwhile, increased energy efficiency of dwellings may decrease their heat stress resistance. This paper challenges whether the current Australian Nationwide Energy Rating Scheme encourages heat stress resistance. Cooling consumption, peak cooling demand and the risk of indoor overheating were assessed for a typical single-storey home in Adelaide and Sydney. Design options between 6 and 8 stars, plus two traditional, energy-inefficient homes were simulated with the AccuRate energy simulation program. This paper demonstrates that the NatHERS does not directly encourage heat stress resistance in new homes and can even deliver buildings with worse heat resistance and increased reliance on air conditioning than traditional, energy-inefficient buildings. This overreliance on air conditioning can present a public health hazard, consequently, the integration of heat stress resistance in the Nationwide House Energy Rating Scheme is recommended. Recommendations for the analysis and assessment of heat stress resistance are provided. Refereed/Peer-reviewed

Photovoltaic (PV) solar power, which is considered as the most competitive clean energy source, contributes to a significant percentage of electricity production in many developed countries. However, accurate PV power forecasting is necessary due to its high variation that can be caused by several factors. Hence, the intermittent nature of PV production represents a major challenge to integrate PV systems into the electric grid. The scope of this paper deals with this issue through developing a new integrated PV power forecasting model. The proposed model is based on the use of a new decomposition methodology, named Iterative Filtering for decomposing PV power into different intrinsic functions (IMFs), then Extreme Learning Machine (ELM) is used as essence predictor. To this end, the proposed IF-ELM model is evaluated on three solar PV power plants installed at three different sites, with different climatic conditions. Direct and recursive IF-ELM methodologies are examined for multi-step ahead forecasting in a very short time-scale (up to 60 min). Overall, the forecasting results show high precision performance for the studied forecasting horizons- in terms of different statistical metrics compared to stand-alone models. Also, the proposed IF method shows its high performance when compared to the recently developed decomposition method. complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) in improving the forecasting accuracy of a single model. Forecasting results with the IF-ELM model led to an error in nRMSE that is less than 10% and a Correlation Coefficient greater than 98% over all forecasting horizons. Refereed/Peer-reviewed

Electrical energy storage can be used to store excess power generated by domestic rooftop PV systems, rather than exporting it to the grid and then buying back energy at a higher price. We have used one-minute PV generation and electrical load data from thirty-eight low-energy homes to simulate the operation of energy storage, and to calculate the impact on the amount and cost of imported electricity. The payback period for energy storage systems depends on factors including the cost of energy storage, the cost of electricity, the price paid for exported energy, the power generated by the PV system, and how and when energy is used by the household. We calculate the payback period for various configurations. Decreasing feed-in tariffs and the decreasing cost of energy storage will lead to an uptake of energy storage system over the next few years. While storage can be used to reduce household electricity cost, it does not lead directly to reductions in CO 2 emissions. However, household energy storage will enable greater use of rooftop PV, and ultimately can be used to match household demand to variable supply from local and centralised renewable energy sources. Refereed/Peer-reviewed

This study aims to test the appropriateness of multivariate skew-t copula and checkerboard copula of maximum entropy in generating monthly rainfall total data. The generation of synthetic data is important, as it provides hypothetical data in areas for which data availability remains limited. Three selected meteorological stations in Kelantan, Malaysia, Stesen Pertanian Melor, Rumah Pam Salor, and Ladang Lepan Kabu, are considered in this study. Monthly rainfall total data for the driest and wettest months in the year are tested in this study. For these three stations, the identified month with the least total of rainfall received (driest) is May, while the month with the highest total of rainfall received (wettest) is November. The data is fitted to gamma distribution with the corresponding parameters estimated. The observed data will be transformed to be in unit uniform using the gamma marginal. The resulting data is compared to simulated uniform data generated using multivariate skew-t copula and checkerboard copula of maximum entropy models based on the correlation values of the observed and simulated data. Next, the Kolmogorov-Smirnov test is used to assess the fit between the observed and generated data. The results show that the values of simulated correlation coefficients do not differ much for gamma distribution, multivariate skew-t, and maximum entropy approaches. This implies that the multivariate skew-t and maximum entropy may be used to generate monthly rainfall total for cases in which actual data is unavailable. Refereed/Peer-reviewed

Heatwaves have been subject to significant attention in Australia and globally due to their negative impacts on the ecosystem, infrastructure, human health and social life. Measures to increase resilience to heatwaves, however, are mostly isolated in different disciplines. This paper proposes a framework integrating urban and infrastructure planning, building design, public health and social research to comprehensively assess heat stress resilience. The proposed framework can assist decision makers in the evaluation of different policy changes addressing heat stress resilience and contribute to more comprehensive and effective heatwave management. An online survey was undertaken with a representative sample (N = 393) from Adelaide, South Australia, to explore heat stress resistance of the built environment, adaptation and heat-related, self-reported health problems. The study established the magnitudes of the secondary negative impacts of heatwaves on public health and daily routines. The findings identify a low level of resistance to heat stress by the built environment. It was concluded that community education along with focused building and planning regulations has the potential to significantly increase heat stress resilience. Refereed/Peer-reviewed

The current deep concerns on energy independence and global society's security at the face of climate change have empowered the new "green energy" paradigm and led to a rapid development of new methodology for modeling sustainable energy resources. However, clean renewables such as wind and solar energies are inherently intermittent, and their integration into a electric power grid require accurate and reliable estimation of uncertainties. And, if probabilistic forecasting of wind power is generally well developed, probabilistic forecasting of solar power is still in its infancy. In this paper we propose a new data-driven method for constructing a full predictive density of solar radiance based on a nonparametric bootstrap. We illustrate utility of the new bootstrapped statistical ensembles for probabilistic one-hour ahead forecasting in Mildura, Australia. We show that the new approach delivers sharp and calibrated ensembles of one-hour forecasts, and is computationally inexpensive and easily tractable. Refereed/Peer-reviewed

Although heatwave-related excess mortality and morbidity have been widely studied, results are not comparable spatially and often longitudinally because of different heatwave definitions applied. The excess heat factor (EHF) quantifies heatwave intensity relative to the local climate, enabling cross-regional comparisons. Previous studies have shown a strong relationship between EHFs and daily mortality during severe heatwaves. An extensive study about the relationship between EHFs and daily morbidity compared to the currently applied heatwave thresholds in Adelaide has not yet been undertaken. This paper analyzes the association of EHFs with daily morbidity between 2008 and 2014 in the Adelaide metropolitan region, South Australia, and probes three different approaches to calculate the EHF. The EHF is found to differentiate days with heatwave-related excess morbidity significantly better than other widely used weather parameters, resulting in fewer days per year with heatwave alerts than using previously proposed methods. The volume of excess morbidity can be predicted by the EHF more reliably with a model proposed for the SA Ambulance Service to support their heatwave preparation plan. Refereed/Peer-reviewed

Waste input-output (WIO) model is a suitable method to explore the nexus between economic activities and waste management. Contemporary research that typically explores this nexus follows two main aspects: either they consider Final demand as a whole, or they identify the nexus between households,with different types of socio-demographic indicators and household waste generation. However, it is complex to apply the WIO model from the perspective of household consumption- a major component of Final demand d because of a lack of economic and environmental data related to household consumption.This paper proposes a new perspective, applying the WIO model to assess the nexus between different patterns of household consumption and indirect waste generation and treatment. This novelty is to combine macro- and micro-economic and environmental data related to Australian industrialsectors, different patterns of household consumption (Mosaic data), and direct waste generation into the WIO model for exploring this nexus in two scenarios. Results indicate that the total amount of indirectwaste generation caused by B05 (couples without children who spend the majority of their time at the office) are 99.24 kg more than that of D16 (couples without children who are retired and stay at home)for scenario I. The correlation coefficients for differences of output of economy and indirect waste generation between B05 and D16 are 0.9796 and 0.9773 in scenarios I and II, respectively. Sensitivity analysis indicates the change of the amount of direct waste generation in a reasonable range cannot dramatically affect the major economic activities and waste generation. This research suggests a different perspective of household consumption to estimate indirect waste generation through a WIO model to provide more reliable information for waste management in the supply chain. Refereed/Peer-reviewed

With the recent rapid increase in the use of roof top photovoltaic solar systems worldwide,and also, more recently, the dramatic escalation in building grid connected solar farms, especially in Australia, the need for more accurate methods of very short-term forecasting has become a focus of research. The International Energy Agency Tasks 46 and 16 have brought together groups of experts to further this research. In Australia, the Australian Renewable Energy Agency is funding consortia to improve the five minute forecasting of solar farm output, as this is the time scale of the electricity market. The first step in forecasting of either solar radiation or output from solar farms requires there presentation of the inherent seasonality. One can characterise the seasonality in climate variables by using either a multiplicative or additive modelling approach. The multiplicative approach with respect to solar radiation can be done by calculating the clearness index, or alternatively estimating the clear sky index. The clearness index is defined as the division of the global solar radiation by the extraterrestrial radiation, a quantity determined only via astronomical formulae. To form the clear sky index one divides the global radiation by a clear sky model. For additive de-seasoning, one subtracts some form of a mean function from the solar radiation. That function could be simply the long term average at the time steps involved, or more formally the addition of terms involving a basis of the function space. An appropriate way to perform this operation is by using a Fourier series set of basis functions. This article will show that for various reasons the additive approach is superior.Also, the differences between the representation for solar energy versus solar farm output will be demonstrated. Finally, there is a short description of the subsequent steps in short-term forecasting. Refereed/Peer-reviewed

Outdoor thermal comfort is influenced by people’s climate expectations, perceptions and adaptation capacity. Varied individual response to comfortable or stressful thermal environments results in a deviation between actual outdoor thermal activity choices and those predicted by thermal comfort indices. This paper presents a passive activity observation (PAO) method for estimating contextual limits of outdoor thermal adaptation. The PAO method determines which thermal environment result in statistically meaningful changes may occur in outdoor activity patterns, and it estimates thresholds of outdoor thermal neutrality and limits of thermal adaptation in public space based on activity observation and microclimate field measurement. Applications of the PAO method have been demonstrated in Adelaide, Melbourne and Sydney, where outdoor activities were analysed against outdoor thermal comfort indices between 2013 and 2014. Adjusted apparent temperature (aAT), adaptive predicted mean vote (aPMV), outdoor standard effective temperature (OUT_SET), physiological equivalent temperature (PET) and universal thermal comfort index (UTCI) are calculated from the PAO data. Using the PAO method, the high threshold of outdoor thermal neutrality was observed between 24 °C for optional activities and 34 °C for necessary activities (UTCI scale). Meanwhile, the ultimate limit of thermal adaptation in uncontrolled public spaces is estimated to be between 28 °C for social activities and 48 °C for necessary activities. Normalised results indicate that city-wide high thresholds for outdoor thermal neutrality vary from 25 °C in Melbourne to 26 °C in Sydney and 30 °C in Adelaide. The PAO method is a relatively fast and localised method for measuring limits of outdoor thermal adaptation and effectively informs urban design and policy making in the context of climate change. Refereed/Peer-reviewed

In this study, previously established arsenic (As) in vivo–in vitro correlations (IVIVC) were assessed for their validity using an independent data set comprising As relative bioavailability (RBA) and bioaccessibility values for 13 herbicide- and mine-impacted soils. The validation process established the correlation between As RBA (swine model) and bioaccessibility (five in vitro assays), determined whether correlations differed significantly from previous relationships and assessed model bias and error. The capacity of in vitro assays to predict As RBA was demonstrated by the strength of IVIVC; goodness of fit ranged from 0.53 (DIN-I) to 0.74 (UBM-I). When compared to previous IVIVC (Juhasz et al. Environ. Sci. Technol. 2009, 43, 9487; Juhasz et al. J. Hazard. Mater. 2011, 197, 161), there was no significant difference (P < 0.01) in the slope and y-intercept for IVG-G, UBM-G, and UBM-I indicating the consistency of these assays for predicting As RBA. However, variability in model bias and prediction error was observed with significantly lower (P < 0.01) error determined for IVG-G suggesting that As RBA predictions using IVG-G may be more robust compared to UBM-G and UBM-I. In contrast, differences in the slope and/or y-intercept were observed for SBRC-I, IVG-I, PBET-G, PBET-I, DIN-G, and DIN-I suggesting that these methodologies may not be suitable for predicting As RBA. Refereed/Peer-reviewed

New electricity generation technologies are often assessed using simple metrics such as average return, break-even energy prices or levelised cost of electricity. These simple metrics do not always capture the full economic value of a technology, particularly those that can react quickly and efficiently to changes in demand. In a wholesale spot market, opportunities exist to capture greater revenues, as currently achieved by peak power plants. This study demonstrates the use of two complementary methods to determine how storage should be operated to maximise revenue. First, the authors formulate and solve the problem as a linear program. The results indicate that there are distinct control modes. They then use Pontryagin's principle to confirm that the optimal operating strategy has three distinct control modes: (i) store all collected power, without generating, (ii) generate using collected power only and (iii) generate at maximum capacity using both collected and stored power. The mode that should be used at any instant depends only on the spot price of electricity relative to a pair of critical prices. These critical prices depend on the total energy that will be collected and the turnaround efficiency of the storage system Refereed/Peer-reviewed

As the frequency and intensity of heatwaves are growing in Australia, strategies to combat heat are becoming more vital. Cities are exposed to urban heat islands (UHIs) due to excess urbanisation. In this study, a definition of urban heatwave (UHW) is conceptualised to investigate the combined impacts of heatwaves and UHIs. To quantify the negative impacts of UHW, indicators-such as excess morbidity, electricity and water consumption-are considered. The intensity of UHWs is calculated using the unit of excess heat factor (EHF), developed by the Australian Bureau of Meteorology. EHF enables the comparability of UHWs in different geographical locations. Using the indicators and the intensity of UHWs, a calculation method to quantify heatwave resilience at a precincts scale is proposed. The study summarises the assumed influential factors of precinct heatwave resilience based on the existing literature and propose a "cool retrofitting toolkit" (CRT). CRT creates the framework to assess the adaptation to and mitigation of UHWs available to retrofit existing precincts, and to evaluate potential retrofitting strategies in terms of energy and carbon efficiency, financial affordability and perceived acceptability by population. This study illuminates the importance of climate, function, built environment and population characteristics-conscious retrofitting. Refereed/Peer-reviewed

The proliferation of non-scheduled generation from renewable electrical energy sources such concentrated solar power (CSP) presents a need for enabling scheduled generation by incorporating energy storage, either via directly coupled Thermal Energy Storage (TES) or Electrical Storage Systems (ESS) distributed within the electrical network or grid. The challenges for 100% renewable energy generation are: to minimise capitalisation cost and to maximise energy dispatch capacity. The aims of this review article are twofold: to review storage technologies and to survey the most appropriate optimisation techniques to determine optimal operation and size of storage of a system to operate in the Australian National Energy Market (NEM). Storage technologies are reviewed to establish indicative characterisations of energy density, conversion efficiency, charge/discharge rates and costings. A partitioning of optimisation techniques based on methods most appropriate for various time scales is performed: from “whole of year”, seasonal, monthly, weekly and daily averaging to those best suited matching the NEM bid timing of five minute dispatch bidding, averaged on the half hour as the trading settlement spot price. Finally, a selection of the most promising research directions and methods to determine the optimal operation and sizing of storage for renewables in the grid is presented.

This paper introduces a practical supply chain optimisation model that incorporates both economic and carbon emission objectives. The proposed model is implemented to examine the possible economic and environmental trade-offs for various carbon-pricing and fuel-pricing scenarios in an actual case company representing the discrete, durable parts manufacturing sector. Analysis of the numerical results provides important managerial implications and policy insights. For industry practitioners, the findings can assist in identifying the critical activities along the supply chain on which to focus in order to minimise the cost implications of a carbon-pricing regulation. For related policy-makers, the findings provide insights on how carbon should be priced to make meaningful impacts on emissions reduction while matching variations in fuel prices. Refereed/Peer-reviewed

Compiling, deploying and utilising large-scale databases that integrate environmental and economic data have traditionally been labour- and cost-intensive processes, hindered by the large amount of disparate and misaligned data that must be collected and harmonised. The Australian Industrial Ecology Virtual Laboratory (IELab) is a novel, collaborative approach to compiling large-scale environmentally extended multi-region input-output (MRIO) models.The utility of the IELab product is greatly enhanced by avoiding the need to lock in an MRIO structure at the time the MRIO system is developed. The IELab advances the idea of the "mother-daughter" construction principle, whereby a regionally and sectorally very detailed "mother" table is set up, from which "daughter" tables are derived to suit specific research questions. By introducing a third tier - the "root classification" - IELab users are able to define their own mother-MRIO configuration, at no additional cost in terms of data handling. Customised mother-MRIOs can then be built, which maximise disaggregation in aspects that are useful to a family of research questions.The second innovation in the IELab system is to provide a highly automated collaborative research platform in a cloud-computing environment, greatly expediting workflows and making these computational benefits accessible to all users.Combining these two aspects realises many benefits. The collaborative nature of the IELab development project allows significant savings in resources. Timely deployment is possible by coupling automation procedures with the comprehensive input from multiple teams. User-defined MRIO tables, coupled with high performance computing, mean that MRIO analysis will be useful and accessible for a great many more research applications than would otherwise be possible. By ensuring that a common set of analytical tools such as for hybrid life-cycle assessment is adopted, the IELab will facilitate the harmonisation of fragmented, dispersed and misaligned raw data for the benefit of all interested parties. © 2014 Elsevier B.V.

Separation models predict diffuse horizontal irradiance from other meteorological parameters such as the global horizontal irradiance or zenith angle. From a mathematical point of view, the separation modeling problem is a regression, where the regressors are observed or computed variables and the regressand is the unobserved diffuse fraction. The most successful minute-resolution separation model prior to 2016 was proposed by Engerer, which is constructed using a trend component (cloud enhancement) and a main effect (logistic function). Subsequently, the Starke model published in 2018 further improved the Engerer model. It is herein shown that the logistic-function type of model, and many other separation models, can be transformed into a (condition-based) multiple linear regression problem. Under this transformation framework, two new models are proposed, which strictly dominate the performance of the Engerer model and the Starke model, at all 7 test sites across the continental United States, making them probably the most accurate separation models to date. The new models are also tested at 5 European sites with unseen data (i.e., not involved during model parameter fitting); their performance again dominates all benchmarking models. The new separation models leverage half-hourly satellite-derived diffuse fraction. Since satellite data are available globally, the satellite-augmented separation models have universal applicability. However, despite their good performance, empirical separation models suffer from a series of issues. Hence, models driven by atmospheric physics are the “true gems” that one should pursue.Separation models predict diffuse horizontal irradiance from other meteorological parameters such as the global horizontal irradiance or zenith angle. From a mathematical point of view, the separation modeling problem is a regression, where the regressors are observed or computed variables and the regressand is the unobserved diffuse fraction. The most successful minute-resolution separation model prior to 2016 was proposed by Engerer, which is constructed using a trend component (cloud enhancement) and a main effect (logistic function). Subsequently, the Starke model published in 2018 further improved the Engerer model. It is herein shown that the logistic-function type of model, and many other separation models, can be transformed into a (condition-based) multiple linear regression problem. Under this transformation framework, two new models are proposed, which strictly dominate the performance of the Engerer model and the Starke model, at all 7 test sites across the continental United States, making th...

To assess the viability of proposed solar installations, knowledge of global solar radiation is not sufficient. For stationary photovoltaic plant, we require global radiation series, but also the contemporaneous diffuse radiation series. Alternatively, for concentrated solar thermal, we need global and direct normal solar radiation. In this paper, we investigate whether one can simply use a model for predicting diffuse radiation using multiple predictions derived by our research team, the Boland-Ridley-Lauret (BRL) model, to give delineations of both diffuse and direct or if we need to use another model for direct or develop a new direct normal statistical model. Refereed/Peer-reviewed

Refereed/Peer-reviewed This paper reviews the current literature around the environmental impacts of dietary recommendations. The focus of the review is on collating evidence relating to environmental impacts of the dietary advice found in the World Health Organisation guidelines, and environmental impact literature: reducing the consumption of fat, reducing the consumption of meat-based protein and animal-based foods, and increasing the consumption of fruit and vegetables. The environmental impact of reducing dietary fat intake is unclear, although reducing consumption of the food category of edible fats and oils appears to have little impact. However most, but not all, studies support environmental benefits of a reduced consumption of animal-based foods and increased consumption of fruit and vegetables. In general, it appears that adhering to dietary guidelines reduces impact on the environment, but further study is required to examine the environmental impacts of animal-based foods, and fruit and vegetable intake in depth.

With ever increasing demand for electricity and the huge potential of renewable energy, an increasing number of renewable-energy sources are being used to generate electricity. However, due to the intermittency of renewable-energy generation, many researchers try to overcome the variable nature of renewable energy. A hybrid renewable-energy system is one possible way to introduce smoothing of the supply. Many hybrid renewable-energy studies focus on system optimization and management. This paper mainly researches the performance prediction accuracy of a hybrid solar and wind system. Through a mixed autoregressive and dynamical system model, we test the predictability of the hybrid system and compare it with individual solar and wind series forecasting. After error analysis, the predictability of the hybrid system shows a better performance than solar or wind for Adelaide global solar radiation and Starfish Hill wind farm data. The prediction errors were reduced by 13% to more than 30% according to various error analyses. This result indicates an advantage of the hybrid solar and wind system compared to solar and wind systems taken individually. Refereed/Peer-reviewed

International audience; For some locations both global and diffuse solar radiation are measured. However, for many locations, only global radiation is measured, or inferred from satellite data. For modelling solar energy applications, the amount of radiation on a tilted surface is needed. Since only the direct component on a tilted surface can be calculated from direct on some other plane using trigonometry, we need to have diffuse radiation on the horizontal plane available. There are regression relationships for estimating the diffuse on a tilted surface from diffuse on the horizontal. Models for estimating the diffuse on the horizontal from horizontal global that have been developed in Europe or North America have proved to be inadequate for Australia [13]. Boland et al. [2] developed a validated model for Australian conditions. Boland et al. [3] detailed our recent advances in developing the theoretical framework for the use of the logistic function instead of piecewise linear or simple nonlinear functions and was the first step in identifying the means for developing a generic model for estimating diffuse from global and other predictors. We have developed a multiple predictor model, which is much simpler than previous models, and uses hourly clearness index, daily clearness index, solar altitude, apparent solar time and a measure of persistence of global radiation level as predictors. This model performs marginally better than currently used models for locations in the Northern Hemisphere and substantially better for Southern Hemisphere locations. We suggest it can be used as a universal model.