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The use of spectral data is seen as a fast and non-destructive method capable of monitoring pasture biomass. Although there is great potential in this technique, both end users and sensor manufacturers are uncertain about the necessary sensor specifications and achievable accuracies in an operational scenario. This study presents a straightforward parametric method able to accurately retrieve the hyperspectral signature of perennial ryegrass ( Lolium perenne ) canopies from multispectral data collected within a two-year period in Australia and the Netherlands. The retrieved hyperspectral data were employed to generate optimal indices and continuum-removed spectral features available in the scientific literature. For performance comparison, both these simulated features and a set of currently employed vegetation indices, derived from the original band values, were used as inputs in a random forest algorithm and accuracies of both methods were compared. Our results have shown that both sets of features present similar accuracies (root mean square error (RMSE) ≈490 and 620 kg DM/ha) when assessed in cross-validation and spatial cross-validation, respectively. These results suggest that for pasture biomass retrieval solely from top-of-canopy reflectance (ranging from 550 to 790 nm), better performing methods do not rely on the use of hyperspectral or, yet, in a larger number of bands than those already available in current sensors.

Cheese is a nutritious dairy product and a valuable commodity. Internationally, cheddar cheese is produced and consumed in large quantities, and it is the main cheese variety that is exported from Australia. Despite its importance, the analytical methods to that are used to determine cheese quality rely on traditional approaches that require time, are invasive, and which involve potentially hazardous chemicals. In contrast, spectroscopic techniques can rapidly provide molecular information and are non-destructive, fast, and chemical-free methods. Combined with partner recognition methods (chemometrics), they can identify small changes in the composition or condition of cheeses. In this work, we combined FTIR and Raman spectroscopies with principal component analysis (PCA) to investigate the effects of aging in commercial cheddar cheeses. Changes in the amide I and II bands were the main spectral characteristics responsible for classifying commercial cheddar cheeses based on the ripening time and manufacturer using FTIR, and bands from lipids, including β'-polymorph of fat crystals, were more clearly determined through changes in the Raman spectra.

Calcium (Ca) and magnesium (Mg) are essential for growth of sugarcane leaves and roots, as well as respiration and nitrogen metabolism, respectively. To assist farmers decide suitable application rates of lime and Mg fertiliser, respectively, the Australian sugarcane industry established the Six-Easy-Steps nutrient management guidelines based on topsoil (0-0.3 m) Ca (cmol(+) kg -1 ) and Mg (cmol(+) kg -1 ). Given the heterogeneous nature of soil, digital soil mapping (DSM) methods can be employed to allow for the precise application rate to be determined. In this study, we examine statistical models (i.e., ordinary kriging [OK], linear mixed model [LMM], quantile regression forests [QRF], support vector machine [SVM], and Cubist regression kriging [CubistRK]) to predict topsoil and subsoil (0.6-0.9) Ca and Mg, employing digital data in combination (i.e., proximal sensing electromagnetic induction (EMI) [e.g., 1mPcon, 1mHcon, etc.], gamma-ray [γ-ray] spectrometry [i.e., TC, K, U and Th] and digital elevation model [DEM] derivatives). We also investigate various sampling designs (i.e., spatial coverage [SCS], feature space coverage [FSCS], conditioned Latin hypercube [cLHS] and simple random sampling [SRS]) and calibration sample size (i.e., n = 180, 150, 120, 90, 60 and 30). The predictions are assessed using Lin's concordance correlation coefficient (LCCC) and ratio of performance to interquartile distance (RPIQ) with an independent validation dataset (i.e., n = 40). The best results were for prediction of subsoil Mg, utilising CubistRK (LCCC = 0.82) with the largest calibration sample size (n = 180), followed by LMM (0.79), SVM (0.76), QRF (0.70) and OK (0.65). This was generally the case for topsoil and subsoil Ca. We also conclude that no single sampling design was universally better, and 180 samples were necessary for predicting topsoil Ca and Mg with moderate agreement (0.65 < LCCC < 0.80). However, with FSCS, a minimum of 120 samples were enough to calibrate a CubistRK model and achieve substantial (LCCC > 0.80) agreement for predicting subsoil Ca and Mg. With respect to soil use and management according to the Six-Easy-Steps, the sandy soil in the north and south require large application rate of lime (3.5 t/ha) and Mg (125 kg/ha), respectively. Conversely, varying amounts of fertiliser rates of lime (2.0, 1.5 and 1 t/ha) and Mg (50 kg/ha) were recommended where Vertosols were previously mapped., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

The levels of trace elements in whole blood and plasma have been widely used for assessing nutritional status and monitoring exposure and can vary widely in populations from different geographical regions. In this study, whole blood samples ( n = 120) and plasma samples ( n = 120) were obtained from healthy donors attending the Red Cross Blood Bank (Queensland Red Cross Blood Service), which provided information for age and sex. There were 71 males (age range: 19-73 years) and 49 females (age range: 18-72 years) for whole blood samples, and 59 males (age range: 19-81 years) and 61 females (age range: 19-73 years) for plasma samples. The main aim of the study was to provide information on blood reference levels of 21 trace elements (Ag, Al, As, Bi, Br, Cd, Co, Cr, Cu, Hg, I, Mn, Mo, Ni, Pb, Sb, Se, Tl, U, V, Zn) in Queensland. The study also aimed to assess differences in trace element blood levels between males and females and the effect of age. The trace element levels in blood samples were analysed using inductively coupled plasma mass spectrometry (ICP-MS) and the standard reference materials of Seronorm (Trace Elements Whole Blood) and UTAK (Trace Elements Serum) were used for quality control and assurance. The study found wide variations of trace element levels in whole blood and plasma, and generally the levels were comparable to other countries. No detectable levels were found for Bi, Cr, U and V in whole blood, but V levels were found in plasma samples. There were significant differences between males and females for whole blood Cu ( p < 0.001), I ( p = 0.009), Tl ( p = 0.016) and Zn ( p = 0.016). Significant differences were also found for plasma Cu ( p < 0.001) and Se ( p = 0.003) between males and females. There were trends of increased levels of blood Pb, Se and Zn with age. The study has provided further information on a wide range of trace elements in blood as reference levels for Queensland and Australia which are currently lacking.

This study focuses on methylamphetamine (MA) seizures made by the Australian Federal Police (AFP) to investigate the use of chemical profiling in an intelligence perspective. Correlation coefficients were used to obtain a similarity degree between a population of linked samples and a population of unlinked samples. Although it was demonstrated that a general framework can be followed for the use of any forensic case data in an intelligence-led perspective, threshold values have to be re-evaluated for each type of illicit drug investigated. Unlike the results obtained in a previous study on 3,4-methylenedioxymethylamphetamine (MDMA) seizures, chemical profiles of MA samples coming from the same seizure showed relative inhomogeneity, limiting their ability to link seizures. Different hypotheses were investigated to obtain a better understanding of this inhomogeneity although no trend was observed. These findings raise an interesting discussion in regards to the homogeneity and representativeness of illicit drug seizures (for intelligence purposes). Further, it also provides some grounds to discuss the initial hypotheses and assumptions that most forensic science studies are based on., (Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.)

This paper introduces an innovative framework for wind power prediction that focuses on the future of energy forecasting utilizing intelligent deep learning and strategic feature engineering. This research investigates the application of a state-of-the-art deep learning model for wind energy prediction to make extremely short-term forecasts using real-time data on wind generation from New South Wales, Australia. In contrast with typical approaches to wind energy forecasting, this model relies entirely on historical data and strategic feature engineering to make predictions, rather than relying on meteorological parameters. A hybrid feature engineering strategy that integrates features from several feature generation techniques to obtain the optimal input parameters is a significant contribution to this work. The model's performance is assessed using key metrics, yielding optimal results with a Mean Absolute Error (MAE) of 8.76, Mean Squared Error (MSE) of 139.49, Root Mean Squared Error (RMSE) of 11.81, R-squared score of 0.997, and Mean Absolute Percentage Error (MAPE) of 4.85%. Additionally, the proposed framework outperforms six other deep learning and hybrid deep learning models in terms of wind energy prediction accuracy. These findings highlight the importance of advanced data analysis for feature generation in data processing, pointing to its key role in boosting the precision of forecasting applications. [ABSTRACT FROM AUTHOR]

Mass-timber construction is becoming more prevalent in Australia and around the world. However, as these mass-timber buildings become taller, it is important to consider robust detailing against accidental damages to major load-bearing elements and thereby avoid potential progressive collapse scenarios. In order to better understand the robustness of typical mass-timber buildings, this study analyzed a typical 10-story midrise mass-timber building subjected to different progressive collapse scenarios. A scenario-independent approach was considered in the analysis, which includes the sudden removal of a load-bearing glulam column from the midrise mass-timber building. It was observed that when internal ties and bracings were provided, the robustness of the building was not affected as there was an alternative load path created. The orthogonal tie beam and diagonal bracing can transfer about 60% and 20% of the axial loads of the removed column to the adjacent columns, respectively. It was found that the building becomes vulnerable when no internal tie or bracing is available to transfer the accidental load. In this scenario, higher stiffness of the cross-laminated timber panel-to-panel joint can be adopted to improve the robustness of mass-timber building. [ABSTRACT FROM AUTHOR]

A new inter‐basin teleconnection analysis method is proposed to comprehensively investigate the connection among global oceans. The strong negative correlation (−0.59) between sea surface temperature (SST) along Leeuwin Current (LC) and East Australia Current (EAC) is discovered and named as Australia Boundary Current Dipole (ABCD). This new inter‐basin air‐sea coupled mode exhibits remarkable interannual variability, with a peak phase in austral summer. The related SST anomalies are mainly generated by anomalous Australia High induced opposite alongshore surface winds to enhance/suppress the heat transport of the two warm currents, in turn, reinforce the wind anomalies by heating/cooling the overlying atmosphere and changing regional pressure anomalies. The meridional ocean advections control the anomalous SST, indicating the essential role of coastal Bjerknes feedback. The kinetic energy analysis further demonstrates that ABCD represents the reverse variation pattern of LC and EAC. Plain Language Summary: The interactions between different oceans have strong impact on the global climate change. We proposed a new method aims to reveal global oceans interactions and thus discovered the opposite change features between the sea surface temperature (SST) off the west and east coasts of Australia. The coastal SST anomalies are accompanied by the synchronous evolution of alongshore surface winds, coastal boundary currents, and sea level pressure, demonstrating it as an intervein climate pattern and we name it as Australia Boundary Current Dipole (ABCD). The anomalous Australia High, evaporation anomalies and the heat transport of Leeuwin Current (LC) and East Australia Current (EAC) contribute to the evolution of ABCD. ABCD also indicates reverse variability of LC and EAC. Our results reveal the existence and generation mechanism of ABCD for the first time, which advances our knowledge of air‐sea interactions and may provide new insights into the research of marine ecology. Key Points: A new method is proposed to reveal inter‐basin teleconnectionA new inter‐basin climate pattern between Indian and Pacific Oceans is discovered and named Australia Boundary Current Dipole (ABCD)ABCD represents the reverse variability feature of Leeuwin Current and East Australia Current [ABSTRACT FROM AUTHOR]

Most studies of sea level budget only indicate the stationary causes for the global mean sea level (GMSL) rise over the course of a time span of interest, providing limited information on temporary changes in the GMSL budget. In this contribution, we present an instantaneous budget of the GMSL for the period of 2003–2015, which offers new insights on the time evolution of the GMSL budget. We use a space-state model to compute the instantaneous rates of GMSL and their contributing sources (barystatic and steric contributions), to investigate the causes of the GMSL instantaneous budget, which accounts for and quantifies low-frequency variations in GMSL rise. Combining the barystatic GMSL instantaneous rates (estimated from satellite gravimetry) along with a particular choice of steric GMSL instantaneous rates (inferred from an Argo product) achieves closure of the GMSL instantaneous budget with a standard deviation of 0.4 mm yr−1. We find that the barystatic GMSL rate is primarily responsible for the striking fluctuations in the GMSL instantaneous rates between 2009 and 2015. For example, over only 20 months (July 2010 to February 2012), the GMSL (barystatic) rate increases from 0.32 (0.71) to 6.12 (4.55) mm yr−1. These strong fluctuations mainly result from hydrology and mountain glacier mass variations rather than mass loss in ice sheet, for instance, Australia slowed ∼1.2 mm yr−1 equivalent contribution to GMSL rise over the year 2010, which is approximately four times the linear rate of Antarctica mass loss. [ABSTRACT FROM AUTHOR]

Prey spectra (the number and composition of captured arthropods) represent a crucial aspect of carnivorous plant ecology, yet remain poorly studied. Traditional morphology-based approaches for prey identification are time-intensive, require specialists with considerable knowledge of arthropod taxonomy, and are hampered by high numbers of unidentifiable (i.e., heavily digested) prey items. We examined prey spectra of three species of closely-related annual Drosera (Droseraceae, sundews) from tropical northern Australia using a novel DNA metabarcoding approach with in-situ macro photography as a plausibility control and to facilitate prey quantity estimations. This new method facilitated accurate analyses of carnivorous plant prey spectra (even of heavily digested prey lacking characteristic morphological features) at a taxonomic resolution and level of completeness far exceeding morphology-based methods and approaching the 100% mark at arthropod order level. Although the three studied species exhibited significant differences in detected prey spectra, little prey specialisation was observed and habitat or plant population density variations were likely the main drivers of prey spectra dissimilarity. [ABSTRACT FROM AUTHOR]

High-impedance faults are a challenging problem in power distribution systems. They often do not trigger protection devices and can result in serious hazards such as igniting fires when in contact with vegetation. The current research field dedicated to studying these faults is extensive but suffers from a constraining bottleneck of a lack of real experimental data. Many works set to detect and localize such faults rely on high-impedance fault low-fidelity models, and the lack of public data sets makes it impractical to have objective performance benchmarks. This letter describes and proposes a format for a data set of more than 900 vegetation high-impedance faults funded by the Victorian Government in Australia recorded in high-sampling resolution. The original data set is public, but it was made available through an obscure format that limits its accessibility. The presented format in this letter uses the standard hierarchical data format (HDF5), which makes it easily accessible in many languages such as MATLAB, Python, C++, and more. The data set compiler and visualizer script are also provided in the work repository1. [ABSTRACT FROM AUTHOR]

Overhead wiring structures (OWS) provide physical support to overhead electrical wires that power trains. They are typically spaced at 50 to 70 m. In a rail network, tens of thousands of these structures are required. Although they are simple structures, due to their numbers; design, construction and maintenance often involve large capital investments. Their reliability is also crucial to a safe operating rail network. This paper presents a review of OWS in Australia. Electrification of train services began in the 1910s, making some of the OWS over 100 years old. Descriptions in this article include their structural forms, design, construction, assessment and maintenance. It follows with a structural assessment carried out on a century-old riveted OWS built in the 1910s. This OWS was decommissioned in a recent railway renewal project which allowed the assessments to carry out. The assessment provides insights into hundreds of similar aged OWS still being used today. Assessments carried out consisted of tensile tests, corrosion depth measurements, radiographic imaging, scanning electron microscopy (SEM) and energy dispersive spectroscopy (EDX). Crevice corrosion is common in locations where moisture accumulated. Material properties were similar to modern-day Grade 250 steel with satisfactory ductility. Corrosion depths were less than those predicted. Samples of riveted connection showed no sign of deterioration within connected plates. This study may provide insights into structural design, construction and maintenance of similar structures in Australia and abroad. [ABSTRACT FROM AUTHOR]

Using continuous data from a dense seismic array in Southwestern China, we tracked S‐wave microseism sources generated by two storms in Southeast Australia and the North Atlantic. In the frequency band 0.1–0.2 Hz, the beam strengths for SV‐ and SH‐wave sources can reach 29% and 18% of P‐wave, respectively. We found that the strong SH‐wave is only observed when the seismic sources are near the thick sedimentary regions whereas P‐ and SV‐wave is irrespective of that. It appears that in addition to a three‐dimensional structure that is needed for conversion from P‐wave to SH‐wave, amplitude enhancement by a sedimentary layer is essential for the detection of SH‐wave. Plain Language Summary: The interactions of ocean waves in the opposite propagation directions generate an equivalent vertical force in the ocean, creating dominant seismic noise in the frequency band 0.1–0.3 Hz. This forcing mechanism can explain the observations of P or Rayleigh waves but cannot explain the observations of SH or Love waves. In this study, from precise locations of SH‐wave sources in Southeast Australia and the North Atlantic, it is shown that SH‐wave sources are only found near a region of the thick sedimentary layer at the sea bottom. Amplification of waves in a thick sedimentary layer seems to be required to excite observable SH‐wave in seismic noise. Key Points: A large, dense seismic array in Southwestern China detects clean S‐wave signals in seismic noiseStrong S‐wave signals were tracked for the storms in Southeast Australia and the North AtlanticThe SH‐wave sources were only observed near thick sediment at the sea bottom, suggesting an amplitude enhancement by sediment is essential [ABSTRACT FROM AUTHOR]

Hourly river flow pattern monitoring and simulation is the indispensable precautionary task for river engineering sustainability, water resource management, flood risk mitigation, and impact reduction. Reliable river flow forecasting is highly emphasized to support major decision-makers. This research paper adopts a new implementation approach for the application of a river flow prediction model for hourly prediction of the flow of Mary River in Australia; a novel data-intelligent model called emotional neural network (ENN) was used for this purpose. A historical dataset measured over a 4-year period (2011–2014) at hourly timescale was used in building the ENN-based predictive model. The results of the ENN model were validated against the existing approaches such as the minimax probability machine regression (MPMR), relevance vector machine (RVM), and multivariate adaptive regression splines (MARS) models. The developed models are evaluated against each other for validation purposes. Various numerical and graphical performance evaluators are conducted to assess the predictability of the proposed ENN and the competitive benchmark models. The ENN model, used as an objective simulation tool, revealed an outstanding performance when applied for hourly river flow prediction in comparison with the other benchmark models. However, the order of the model, performance wise, is ENN > MARS > RVM > MPMR. In general, the present results of the proposed ENN model reveal a promising modeling strategy for the hourly simulation of river flow, and such a model can be explored further for its ability to contribute to the state-of-the-art of river engineering and water resources monitoring and future prediction at near real-time forecast horizons. [ABSTRACT FROM AUTHOR]

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We report on aerobic "environmental" bacteria isolated from European honey bees (Apis mellifera). We determined the number of culturable aerobic bacteria in the gut of nurse bees sampled from locations around Australia. Bees from healthy colonies had 107–108 aerobic bacteria per g of bee gut, while bees from colonies with chalkbrood consistently had significantly fewer bacteria (104–105 bacteria per g). When colonies recovered from chalkbrood, bacterial numbers returned to normal levels, suggesting that counting aerobic bacteria in the gut could be used to predict an outbreak of the disease. Furthermore, Western Australian bees from the "Better Bees" program (bred to promote hygienic behaviour) had significantly higher numbers of aerobic gut bacteria compared to regular bees from healthy colonies. Bacteria with the ability to inhibit the chalkbrood pathogen were found in most bees from regular colonies (> 60%) but only in a few "Better Bees" (10%). Phylogenetic analysis of aerobic bacterial isolates that inhibited the chalkbrood pathogen revealed a close relationship (>97% sequence identity) to the genera Bacillus, Klebsiella, Pantoea, Hafnia, and Enterobacter (bacteria that have previously been isolated from honey bees), but we also isolated Maccrococcus and Frigoribacterium species (bacteria that were not previously identified in bees). Finally, we investigated the ability of bacteria to inhibit the chalkbrood fungus Ascosphaera apis. Mass spectroscopy analysis revealed that the bee gut isolates Frigoribacterium sp. and Bacillus senegalensis produce gluconic acid. We further found that this simple sugar is involved in chalkbrood fungal hyphal lysis and cytoplasmic leakage. Our findings suggest that "environmental" gut bacteria may help bees to control the chalkbrood pathogen. [ABSTRACT FROM AUTHOR]

The global ionosphere map (GIM) is not capable of serving precise positioning and navigation for single frequency receivers in Australia due to sparse International GNSS Service (IGS) stations located in the vast land. This study proposes an approach to represent Australian total electron content (TEC) using the spherical cap harmonic analysis (SCHA) and artificial neural network (ANN). The new Australian TEC maps are released with an interval of 15 min for longitude and latitude in 0.5° × 0.5°. The validation results show that the Australian Ionospheric Maps (AIMs) well represent the hourly and seasonally ionospheric electrodynamic features over the Australian continent; the accuracy of the AIMs improves remarkably compared to the GIM and the model built only by the SCHA. The residual of the AIM is inversely proportional to the level of solar radiation. During the equinoxes and solstices in a solar minimum year, the residuals are 2.16, 1.57, 1.68, and 1.98 total electron content units (TECUs, 1 TECU = 1016 electron/m2), respectively. Furthermore, the AIM has a strong capability in capturing the adequate electrodynamic evolutions of the traveling ionospheric disturbances under severe geomagnetic storms. The results demonstrate that the ANN-aided SCHA method is an effective approach for mapping and investigating the TEC maps over Australia. [ABSTRACT FROM AUTHOR]

Satellite remote sensing of vegetation at regional to global scales is undertaken at considerable variations in solar zenith angle (SZA) across space and time, yet the extent to which these SZA variations matter for the retrieval of phenology remains largely unknown. Here we examined the effect of seasonal and spatial variations in SZA on retrieving vegetation phenology from time series of the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) across a study area in southeastern Australia encompassing forest, woodland, and grassland sites. The vegetation indices (VI) data span two years and are from the Advanced Himawari Imager (AHI), which is onboard the Japanese Himawari-8 geostationary satellite. The semi-empirical RossThick-LiSparse-Reciprocal (RTLSR) bidirectional reflectance distribution function (BRDF) model was inverted for each spectral band on a daily basis using 10-minute reflectances acquired by H-8 AHI at different sun-view geometries for each site. The inverted RTLSR model was then used to forward calculate surface reflectance at three constant SZAs (20°, 40°, 60°) and one seasonally varying SZA (local solar noon), all normalised to nadir view. Time series of NDVI and EVI adjusted to different SZAs at nadir view were then computed, from which phenological metrics such as start and end of growing season were retrieved. Results showed that NDVI sensitivity to SZA was on average nearly five times greater than EVI sensitivity. VI sensitivity to SZA also varied among sites (biome types) and phenological stages, with NDVI sensitivity being higher during the minimum greenness period than during the peak greenness period. Seasonal SZA variations altered the temporal profiles of both NDVI and EVI, with more pronounced differences in magnitude among NDVI time series normalised to different SZAs. When using VI time series that allowed SZA to vary at local solar noon, the uncertainties in estimating start, peak, end, and length of growing season introduced by local solar noon varying SZA VI time series, were 7.5, 3.7, 6.5, and 11.3 days for NDVI, and 10.4, 11.9, 6.5, and 8.4 days for EVI respectively, compared to VI time series normalised to a constant SZA. Furthermore, the stronger SZA dependency of NDVI compared with EVI, resulted in up to two times higher uncertainty in estimating annual integrated VI, a commonly used remote-sensing proxy for vegetation productivity. Since commonly used satellite products are not generally normalised to a constant sun-angle across space and time, future studies to assess the sun-angle effects on satellite applications in agriculture, ecology, environment, and carbon science are urgently needed. Measurements taken by new-generation geostationary (GEO) satellites offer an important opportunity to refine this assessment at finer temporal scales. In addition, studies are needed to evaluate the suitability of different BRDF models for normalising sun-angle across a broad spectrum of vegetation structure, phenological stages and geographic locations. Only through continuous investigations on how sun-angle variations affect spatiotemporal vegetation dynamics and what is the best strategy to deal with it, can we achieve a more quantitative remote sensing of true signals of vegetation change across the entire globe and through time. [ABSTRACT FROM AUTHOR]

This paper presents the results from a study on the application of an artificial neural network (ANN) model for regional flood frequency analysis (RFFA). The study was conducted using stream flow data from 88 gauging stations across New South Wales (NSW) in Australia. Five different models consisting of three to eight predictor variables (i.e., annual rainfall, drainage area, fraction forested area, potential evapotranspiration, rainfall intensity, river slope, shape factor and stream density) were tested. The results show that an ANN model with a higher number of predictor variables does not always improve the performance of RFFA models. For example, the model with three predictor variables performs considerably better than the models using a higher number of predictor variables, except for the one which contains all the eight predictor variables. The model with three predictor variables exhibits smaller median relative error values for 2- and 20-year return periods compared to the model containing eight predictor variables. However, for 5-, 10-, 50- and 100-year return periods, the model with eight predictor variables shows smaller median relative error values. The proposed ANN modelling framework can be adapted to other regions in Australia and abroad. [ABSTRACT FROM AUTHOR]

Electricity load forecasting plays an essential role in improving the management efficiency of power generation systems. A large number of load forecasting models aiming at promoting the forecasting effectiveness have been put forward in the past. However, many traditional models have no consideration for the significance of data preprocessing and the constraints of individual forecasting models. Moreover, most of them only focus on the forecasting accuracy but ignore the forecasting stability, resulting in nonoptimal performance in practical applications. This paper presents a novel hybrid model that combines an advanced data preprocessing strategy, a deep neural network, and an avant-garde multi-objective optimization algorithm, overcoming the defects of traditional models and thus improving the forecasting performance effectively. In order to evaluate the validity of the proposed hybrid model, the electricity load data sampled in 30-min intervals from Queensland, Australia are used as a case to study. The experiments show that the new proposed model is obviously superior to all other traditional models. Furthermore, it provides an effective technical forecasting means for smart grid management. [ABSTRACT FROM AUTHOR]

Airborne particulate matter (PM) is a major health risk in urban settings. Ammonia (NH3) from vehicle exhaust is an under-recognised ingredient in the formation of inorganic PM and there remains a shortage of data to properly quantify the role of NH3 from vehicles in PM formation. An Open-path Fourier transform infra-red (OP-FTIR) spectrometer measured atmospheric NH3, carbon monoxide (CO) and carbon dioxide (CO2) at high temporal resolution (5 min) in Western Sydney over 11 months. The oxides of nitrogen (NO2 and NO; NOx) and sulphur dioxide (SO2) were measured at an adjacent air quality monitoring station. NH3 levels were maxima in the morning and evening coincident with peak traffic. During peak traffic NH3:CO ratio ranged from 0.018 to 0.022 ppbv:ppbv. Results were compared with the Greater Metropolitan Region 2008 (GMR2008) emissions inventory. Measured NH3:CO was higher during peak traffic times than the GMR2008 emissions estimates, indicating an underestimation of vehicle NH3 emissions in the inventory. Measurements also indicated the urban atmosphere was NH3 rich for the formation of ammonium sulphate ((NH4)2SO4) particulate was SO2 limited while the formation of ammonium nitrate (NH4NO3) was NH3 limited. Any reduction in NOx emissions with improved catalytic converter efficiency will be accompanied by an increase in NH3 production and potentially with an increase in NH4NO3 particulate. [ABSTRACT FROM AUTHOR]

Volumetric prefabricated building construction is growing in most developed countries; for example, in Sweden the market share of prefabricated building systems in the housing industry was more than 80%. However, in Australia only approximately 3–4% of new building constructions are prefabricated buildings in a year. A major hindrance to the growth of prefab construction in Australia is that systems are developed under commercial and confidential conditions. There are limited publicly-available research and case studies for certifiers, regulators, engineers and academia to provide independent information on the performance, advantages and disadvantages of prefabricated building systems. Independent designers and structural engineers are relying on the strength of the structural and non-structural element, as well as the connections of the prefabricated building systems. This strength is estimated from the "commercial-in-confidence" test of individual components by manufactures, and it might result in undesired outcomes in design. This paper provides an overview of available literature on structural performance, benefits, constraints and challenges of prefabricated building systems. This paper also highlights the research needed on the prefabricated building systems such as full-scale tests, numerical modelling, hybrid simulations, case studies and social and economic assessments. Being supported by sound academic research will increase the market demand for prefabricated building systems in Australia as well as in other countries. [ABSTRACT FROM AUTHOR]