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The residential building sector & transportation sector has received significant attention in recent years due to carbon emissions affecting the livelihoods of humans. Electric vehicles (EVs) are poised to play a vital role in reducing dependency on non-renewable fuels and the transportation sector's environmental implications. Building integrated Photovoltaics (BIPV), on the other hand, will be able to cut down the energy requirements and contribute to building sustainability. This paper evaluates the energy, economic, and environmental performances of BIPV with EV charging system systems for a building in Canberra, Australia. BIPV with EV charging can assist in fulfilling building energy needs, charging electric vehicles, and reducing grid reliance. Different load scenarios were studied to optimize system size. Different panel layouts with orientation have been studied for the 5.0kWp BIPV system, each module rating 355kWp. The annual energy output of the BIPV system was found to vary between 8.56 MWh for layout one and 7.08MWh for layout 3. It was found that the payback back of the proposed BIPV system is 4.46 years, LCOE of 0.074AUD/kWh and EV charging cost of 0.95AUD/100 km. The BIPV with EV charging system could save approximately 160,198 kgCO 2 e in GHG emissions over its lifespan. • The estimated annual energy for the BIPV system with Layout 1 is 8.56MWh. • The energy supplied to the grid was found to be positive in the BIPV system. • The performance ratio of the proposed layouts for the BIPV system is 81.7 %. [ABSTRACT FROM AUTHOR]

This paper evaluates Integrated-Demand Responsive Transport (I-DRT) as a solution to the challenges faced by traditional public transport (PT) systems in low-demand urban areas. The study investigates the implications of replacing local PT with I-DRT in low-demand urban areas. A multi-objective model, incorporating operational cost, environmental impact, passengers' travel time, and inequity is used to simulate the I-DRT performance. The analysis compares the performance of I-DRT and existing local bus lines in Belconnen, Canberra, Australia, based on number of utilised vehicles, operational cost, fuel consumption, average travel time, individual passenger travel time, delay, and inequity in delay distribution. [ABSTRACT FROM AUTHOR]

• The Canberra distance presents the most relevant maps in the analysis of the observed satellite features. • The effect of adding Planet Nine's moon has a significant impact on the output of the visualisation techniques. • The Planet Nine and Satellite X disclose an important character in reducing the spread of emerging clusters. This paper studies the characteristics of the large natural satellites in the solar system using clustering techniques. A variety of distances such as the Canberra, Arc-Cosine, Jaccard, Euclidean, Lorentzian and Manhattan are used to unravel the relationships between the moons emerging from their attributes. In a first phase, 19 large moons are selected and measured under the light of 9 orbital and physical characteristics. Two visualisation methods, namely the hierarchical clustering and multidimensional scaling, are used to analyse the observed satellite features. The Canberra distance presents a superior performance and leads to the most relevant maps. In a second phase, the influence of adding the hypothetical Satellite X, that is proposed to orbit Planet Nine, is examined. The effect of adding Planet Nine's moon has a significant impact on the output of the two visualisation techniques. Adding this speculative object leads to a better discrimination of the clusters. Finally, the case of mixing some planets (including Planet Nine) with satellites (consisting of Satellite X) is tested leading to identical conclusions. Indeed, the numerical results reveal that including Planet Nine and its moon decrease the spread of emerging clusters in comparison with the case of their exclusion. [ABSTRACT FROM AUTHOR]

• Generalization of the periodic table of elements. • Inclusion of fractional order elements. • 2- and 3-dimensional maps of elements organized accordingly to their features. This paper combines the synergies of three mathematical and computational generalizations. The concepts of fractional calculus, memristor and information visualization extend the classical ideas of integro-differential calculus, electrical elements and data representation, respectively. The study embeds these notions in a common framework, with the objective of organizing and describing the "continuum" of fractional order elements (FOE). Each FOE is characterized by its behavior, either in the time or in the frequency domains, and the differences between the FOE are captured by a variety of distinct indices, such as the Arccosine, Canberra, Jaccard and Sørensen distances. The dissimilarity information is processed by the multidimensional scaling (MDS) computational algorithm to unravel possible clusters and to allow a direct pattern visualization. The MDS yields 3-dimensional loci organized according to the FOE characteristics both for linear and nonlinear elements. The new representation generalizes the standard Cartesian 2-dimensional periodic table of elements. [ABSTRACT FROM AUTHOR]

Food tourism events have become a large focus for economic development in rural and regional areas. Tourism has been a key driver of food festivals, where they play a role in attracting visitors and generating expenditure in the local economy. While these types of events are generally badged as enhancing sustainability, the criteria and tests for these claims are rarely specified. In this paper we explore how the sustainability of food tourism festivals can be considered and evaluated in economic terms, using a case study of the Truffle Festival — Canberra Region in Australia. A zonal travel cost analysis was conducted to measure consumer surplus, providing potential inputs into individual economic welfare and public good evaluation tests. The results estimate that the event generated consumer surpluses of $754/person and more than $8 million overall in 2016, highlighting the importance of artisan food experiences for tourism events. To further implement food experiences as an economic development strategy further consideration and linkages with agriculture, environment and community are required to ensure they are sustainable. [ABSTRACT FROM AUTHOR]

• A detailed assessment of energy performances of BIPV/T-DSF was performed. • Three types of PV glazing and three types of ventilation modes were assessed. • The model with a naturally-ventilated air cavity showed the lowest consumptions. • The façade with Perovskite cells has the highest conversion of solar radiation. • Both DSFs with natural and mechanical ventilation collect useful thermal energy. Being the interface between indoor and outdoor, building envelope significantly influences indoor heating and cooling loads and thus affects building energy consumption. This paper presents the results of numerical simulation for the performance prediction of building-integrated photovoltaic/thermal double-skin facade (BIPV/T-DSF). Different BIPV materials (amorphous silicon PV, dye-sensitized solar cell and Perovskite based solar cells) were considered as the exterior cladding of a North-facing facade of an office building located in Australia. The performance assessment involved the selection of three climates in Australia, represented by the cities of Darwin, Sydney and Canberra. The air cavity created between the outer skin and the inner one were alternatively assessed in the non-ventilated, naturally-ventilated and mechanically-ventilated modes of operation, while a full sensitivity analysis was performed in order to assess the influence of different design parameters, such as internal skin's thermal transmittance, cavity depth, ventilation louvres' opening ratio and cavity ventilation rate. By comparing the different operational modes and BIPV technologies, it was found that naturally-ventilated DSF integrating the Perovskite-based solar cell could be the optimal configuration achieving the highest savings. Total annual energy savings of 34.1%, 86% and 106% annual were reached respectively in Darwin, Sydney and Canberra, in comparison to conventional technologies. In addition, this façade technology could further reduce the building's heating and cooling loads by harvesting thermal energy generated within the air cavity. The study finally demonstrated that the harvested electrical and thermal energy from the façade could cover a significant share of building's energy consumption. [ABSTRACT FROM AUTHOR]

[Display omitted] • Bifacial solar modules on rooftops offer up to 22.6% energy yield gains. • Rooftop reflectivity plays a crucial role in maximizing the bifacial potential. • Higher optimal tilt angles for bifacial modules, influenced by rooftop reflectivity. • Parallel intra-module interconnection supports achieving high bifacial yield gains. • Module-level optimizers enhance bifacial energy generation potential by up to 1.4% Bifacial solar modules have emerged as a promising technology in utility-scale photovoltaic systems, experiencing significant growth and capturing a substantial market share worldwide, as reported by the International Technology Roadmap for Photovoltaic (ITRPV) 2023. Despite this progress, the potential of bifacial modules in rooftop applications remains largely unexplored. This paper aims to address this knowledge gap by conducting a comprehensive study utilizing Monte Carlo Ray Tracing techniques coupled with detailed electrical modelling. The primary objective of this study is to investigate the viability of implementing bifacial solar modules on rooftops by examining the potential energy yield gains. By conducting a detailed analysis on a representative rooftop in Canberra, Australia, real-world conditions, and variations are incorporated, providing a more accurate assessment of the energy yield gains achievable in such settings. The simulation results reveal that the implementation of bifacial solar modules on rooftops within Australia can result in energy yield gains of up to 22.6%. These findings demonstrate the considerable potential of bifacial technology in maximizing solar energy production in rooftop applications. The analysis shows significant implications of module and system design on the potential gain. For example, electrical optimisation of individual modules in a system accounted for 1.4% of the bifacial gain. The analysis considers full annual time-step simulation, typical mechanical mounting components, installation orientations and module characteristics, ensuring practical relevance and reliability of the results. [ABSTRACT FROM AUTHOR]

Abstract Digital pulse-shape discrimination (PSD) of scintillator detectors has gained popularity as an efficient technique for the discrimination between different types of particles. In this paper, we have investigated the use of simple distance metrics for digital PSD of neutron and gamma-rays in liquid scintillator detectors. The distance metrics are commonly used in the field of pattern recognition and offer the advantage of a simple and effective performance. Our method is based on quantifying the difference between a sampled detector pulse and an ideal step pulse. For this purpose, some of the common distance metrics including Euclidean, City-block, Canberra, root mean square (RMS) and Hellinger were experimentally examined through the discrimination of neutrons and gamma-rays in a liquid scintillator detector and their performances were compared to that of the conventional charge-comparison method. It was found that the City-Block distance metric exhibits a very simple and effective PSD approach by producing a figure-of-merit (FOM) of 0.72 against a FOM = 0.54 for the charge-comparison method in the very low light output range of below 160 keVee (electron equivalent energy) and a mean FOM value of 1.23 over the whole examined light output range of 60–1000 keVee. Highlights • Distance metrics are proposed for digital discrimination of neutrons and gamma-rays. • Five metrics of Euclidean, City-block, Canberra, RMS and Hellinger were experimentally examined. • The City-Block distance metric exhibits a very simple and effective PSD approach. [ABSTRACT FROM AUTHOR]

• Review of the most commonly used distance measures in machine learning • Analysis of their main properties, applications and key aspects to consider • The similarity analysis shows a high degree of correlation between all the measures • Evaluation of classification and clustering performance, noise tolerance and runtime • Canberra distance shows the best overall performance and the highest tolerance to noise Many machine learning and data mining tasks are based on distance measures, so a large amount of literature addresses this aspect somehow. Due to the broad scope of the topic, this paper aims to provide an overview of the use of these measures in the most common machine learning problems, pointing out those aspects to consider to choose the most appropriate measure for a particular task. For this purpose, the most recent works addressing the subject were reviewed and seven of the most commonly used measures were analyzed, investigating in detail their main properties and applications. Different experiments were carried out to study their relationships and compare their performance. The degradation of the results in the presence of noise was also considered, as well as the execution time required by each measure. [ABSTRACT FROM AUTHOR]

With the rapid evolution of the Internet of Things (IoT), network advancement has significantly influenced the increasing number of devices and advanced enhancements linked with it. Indeed the increasing number prevalence and sophistication of emerging cyber-attacks have highlighted the necessity for designing robust security application. In this paper, the Remora-based Deep Maxout Network model is Proposed. Here, the input data is acquired and forwarded to the pre-processing phase, wherein the missing value imputation approach is employed for creating a complete dataset. Later, the pre-processed data is then subjected to dimension transformation from the transformed data; the Convolutional Neural Network features are extracted, followed by feature selection based on Canberra distance. Here, detection is carried out using a Deep Maxout Network whose weights and training parameters are modified using the Remora Optimization Algorithm. However, the proposed model has delivered superior results with a high testing accuracy of 0.945 [ABSTRACT FROM AUTHOR]

• The paper focuses on the need for muscle artifact detection for efficient analysis. • An Entropy based M-DDTW technique has been proposed for muscle artifact (MA) detection. • An accuracy of 95 % and a Performance Index of 90 % has been achieved in identifying MAs. • Reduces the computational time amd improves performance of EMD and EEMD based artifact extraction techniques. The exclusion of artifacts plays an indispensable role in the processing of Electroencephalographic (EEG) recordings. This work highlights one such inescapable artifactual event known as muscle artifacts (MA) and its detection methodology. These are high-frequency signals that are recurrently present in EEG and generally recorded via Electromyogram. The paper presents an entropy based Manhattan derivative dynamic time warping (M-DDTW) technique for MA epoch detection. Manhattan (City block) and Canberra distance have been proposed as the distance to be optimized by using them with dynamic time warping (DTW) and derivative dynamic time warping (DDTW) technique. The study reduces the computational time and improves the performance by utilizing entropy for reference generation and identifies the optimal threshold value for each technique. The results for the optimal threshold have been validated on the real EEG dataset. It was observed that the proposed entropy based M-DDTW technique exhibits the highest performance of 90 % and an accuracy of 95 % at an optimal threshold surpassing state of the art techniques. The testing of qualitative performance and time consumption has been done using traditional mode decomposition methods. The proposed Entropy based M-DDTW technique along with EEMD showed a noteworthy performance compared to other techniques. Overall the combination of entropy with time warping based local distance variation appears to be an adequate solution for muscle artifact detection. [ABSTRACT FROM AUTHOR]

• This paper proposes a new system for ear biometric identification. • It selects the best combination of local and global spectral features using GA. • The global features are extracted by applying the Gabor-Zernike operator. • The local phase quantization operator is used for extracting local features. Identification systems based on biometric features are becoming increasingly important. One of the most common biometric features is the ear. The accuracy of these systems is heavily dependent on the characteristics extracted from them. In this paper, an appropriate combination of local and global features in the frequency domain is extracted as unique features of the ear region. In the proposed approach, at first the image quality is improved by Contrast-limited Adaptive Histogram Equalization. Then, the global features of the ear region are extracted by applying the Gabor-Zernike operator to the whole image and its non-overlapping blocks. In addition, to extract of local features, the local phase quantization operator is used on the original image of the ear region. Then, the optimum combination of global and local features is selected using Genetic Algorithm. Finally, the nearest neighbor classifier with Canberra distance is used to identify users. The proposed approach is evaluated using three databases, i.e. USTB-1, IIT125 and IIT221. The recognition rate of 100%, 99.2% and 97.13%, is reported on these databases, respectively. The obtained results show that the proposed approach performs better than existing ear recognition methods. [ABSTRACT FROM AUTHOR]

Abstract: The “Big Dry”, a prolonged dry period in Australia from 1997 to 2009, dried out much of the Murray-Darling Basin (MDB) and resulted in large agricultural losses and degraded river ecosystems. Climate projections are that dry conditions in the MDB are likely to be more regular and severe than ever before, and recent policy initiatives are likely to reduce consumptive water use and redirect water to ecosystem management. This paper aims to develop an understanding of the interactions between water policy and irrigation practices by deriving lessons from drought management in irrigated agriculture of the MDB during the Big Dry, and furthermore, to draw out lessons to enhance the preparedness of irrigated agriculture for a future drier climate and reduced water availability. Reviews of irrigation farmers’ practices, attitudes and capacity to manage during prolonged droughts in the MDB, and the evolution of agricultural water policy in Australia since 1990 were made. It is clear that farmers could be better prepared to deal with a drier climate if their water management practices, e.g. irrigation methods and soil moisture measuring tools are improved, if the impediments to the uncertainty of water allocation and low water availability could be overcome, and if well-targeted research and extension could assist farmers to use water more wisely. It is also clear that Australian water policy could be better prepared in terms of assisting irrigated agriculture to deal with a drier climate. Key areas are reduction of barriers and distortions to water trading, optimizing the environmental water allocation, and seeking mutual benefits between environmental water allocation and irrigated agriculture, improvement of the cost-effectiveness of investments in water supply infrastructure, facilitating carryover and capacity sharing at larger scales, and provision of accurate, accessible and useful water information at different scales. An approach to irrigation practice and water policy is proposed based on past experience and potential opportunities. The approach is a set of linked strategies for more robust agricultural production and a more sustainable environment under a drier climate and reduced water availability. [Copyright &y& Elsevier]

Water markets have featured prominently in the reallocation of water rights to restore freshwater ecosystem health. Incentive-based water rights acquisition and transactions have emerged as a market-oriented policy approach to reallocate water resources from existing uses to enhance the provision, regulation, and sustainability of freshwater ecosystem services. This paper develops a conceptual framework to examine factors enabling and constraining successful policy reform and implementation in market-based environmental water allocation. This analysis distills and extends the findings and lessons of a September 2007 workshop in Brisbane, Australia on environmental water transactions. Two case studies were selected in water stressed basins – the Columbia (U.S.) and Murray-Darling (Australia) Basins – where transactional approaches to environmental water allocation first emerged. The case studies draw upon practitioner perspectives and previous policy and economic analysis in two regions where shared political economic and physical conditions lend a strong analytical basis for comparison. A common set of policy and regulatory reforms has occurred in both cases – albeit in different forms and via distinct paths – to develop three enabling conditions: (1) establishment of rights to and limits on freshwater extraction and alteration; (2) recognition of the environment as a legitimate water use; and (3) authority to transfer existing water rights to an environmental purpose. However, these elements of policy reform are necessary but not sufficient for effective implementation; a second set of driving forces, barriers, and adaptations explains the ability to achieve larger scale ecological outcomes. These conditions include the physical, social and economic factors driving demand for environmental water allocation; administrative procedures, organizational development and institutional capacity to effect transfers; and adaptive mechanisms to overcome legal, cultural, economic, and environmental barriers. The case study analysis suggests that environmental water transactions can play an important role in establishing environmental water allocations, although water markets require ongoing institutional capacity and adaptive governance. The conceptual framework and empirical lessons generated through this cross-case comparison provide the basis for an expanded research agenda to evaluate the design and performance of market-oriented reforms as implementation experience accrues and new programs emerge in diverse ecological and political economic settings. [Copyright &y& Elsevier]

• Reflecting the personalization of stock time series by weighting the time series. • Utilizing dynamic time warping to cope with time shifts and warpings. • Embedding Canberra distance for eliminating the impact of singularities. The stock market is a very important financial market, and the prediction of the stock has always been of great interest to many investors. Nowadays, many methods for predicting stocks have been developed and one of the most commonly adopted strategies is to seek similar stocks through historical data to make predictions. The key to this strategy is the construction of a reasonable similarity measurement. In this paper, for accurately describing the similarity between a pair of time series, a novel similarity measurement is proposed, which is named as the dynamic multi-perspective personalized similarity measurement (DMPSM). Specifically, the segmented stock series are weighted according to the principle that the closer to current data, the more weight will be given. Then, Canberra distance is embedded into the dynamic time warping (DTW) to measure the similarity between any pair of time series. By this way, the DMPSM can not only reflect the personalization of stock time series, but also eliminate the impact of singularities and apply to one-to-many matching. To validate the efficiency of DMPSM, experiments utilized 285 stocks from the Shanghai Stock Exchange and the results demonstrated the superiority of the proposed approach over similarity measurements, including Euclidean distance, Canberra distance and DTW. [ABSTRACT FROM AUTHOR]

This paper presents a new technique for mapping regional salt sources that has major implications for salinity management in southeastern Australia. This was achieved by analyzing a regional mosaic of airborne gamma-ray emission derivatives and verified by existing airborne electromagnetic and drilling data. A significant correlation was found between aeolian (windblown) materials, upland salts and gamma-ray signatures. This is consistent with the conceptual model that much of the salt in the upland areas of the Murray–Darling Basin is sourced from deposited aeolian materials that have been derived from deflationary events in salt-bearing landscapes in the western arid part of the basin. From gamma-ray emissions, and based on an observed relationship with borehole salinity, concentrated aeolian salt source deposits contained about 0.7% potassium and 10 ppm thorium. Using this signature on normalized data, an Euclidean distance algorithm provided mapping and information relating to salt-mobility pathways over a wide region. The resulting gamma-ray salt source model (GSM) facilitates focussed management of salinity infiltration zones in catchments across the basin. [Copyright &y& Elsevier]

• Climate change and building load variations may impact optimised building designs. • A method using climate models and optimisation is used to optimise future buildings. • A case study is conducted on two cities in Australia: Canberra and Brisbane. • Results show that optimising for future climates can save energy for Canberra. • For Brisbane, savings are small and the present-optimised design seems acceptable. Building energy optimisation is generally performed under present climate conditions with fixed simulation parameters (e.g. internal loads). However, climate change and variations in simulation parameters over the building's life span may impact the optimised design. A key question is whether a particular energy-optimised design under present climate conditions would remain energy-optimised in the future. Accordingly, in this paper, a new simulation-based optimisation method is developed, which uses climate models and Ant Colony Optimisation to compare the energy-optimised designs under present and future climates. To demonstrate its potential, this method is applied to a typical office building in two Australian cities, Brisbane and Canberra. The results show that optimising under future climate conditions can lead to different optimal building designs. For Brisbane, the energy difference between optimising under present and future climate conditions is small, but in Canberra the cooling load is increased by up to 6%. This suggests that optimising the studied office building under present climate conditions is acceptable for Brisbane, while considering future climate may yield some savings in Canberra. Results also show that the energy-optimised building configuration for both future and present climates in Brisbane is less sensitive to changes in the load scenario than in Canberra. [ABSTRACT FROM AUTHOR]

• We use electrochemical impedance spectroscopy to characterize beverages. • The similarities between the data are measured by the Euclidean and Canberra distances. • The multidimensional scaling is adopted for clustering and visualization. • The results are compared with the ones obtained by standard processes. This paper compares the results of standard chemical analytical processes and electrochemical impedance spectroscopy (EIS) in the characterization of different beverages, namely ground coffee, soluble coffee, coffee substitutes, barley, cow milk, vegetable drinks, tea, plant infusions and plant mixtures. For the two approaches, the similarities between the experimental data are assessed by means of the Euclidean and Canberra distances. The resulting information is processed by means of the multidimensional scaling (MDS) clustering and visualization algorithm. The results of the chemical analytical processes and EIS reveal identical clusters for the two adopted distances. Furthermore, the robustness of the experimental and computational scheme are assessed by means of the Procrustes technique. The results confirm the effectiveness of combining the EIS and MDS. [ABSTRACT FROM AUTHOR]

Comparing structural similarities among complex networks is an important task in several scientific and social science applications. Existing techniques for quantifying network similarity range from network-centric methods that consider global network topology to node-centric methods that consider local node-level sub-structures. In this paper, we address the research gap between computationally expensive network-centric approaches and myopic node-centric network comparison methods by introducing a novel approach to quantify network similarity based on hierarchical graph decomposition. The approach adequately captures both global and local topology and is motivated by the observation that networks from diverse domains such as physical, chemical, biological and social systems exhibit an inherent structural hierarchy that emerges from local dyadic and triadic interactions. The proposed algorithm, N etwork S imilarity via graph D ecomposition (NSD), extracts network signatures from hierarchical decomposition of networks and uses Canberra distance to quantify the similarity between signatures. We use two well-known graph decomposition methods to expose network hierarchy resulting in two variations of NSD. We find that our approach groups similar networks better than competing algorithms. Experimentation using 40 real-world networks, 15 massive networks, and 30 large synthetic networks establishes that the proposed methodology is effective, scalable, sensitive and applicable to wide variety of networks. [ABSTRACT FROM AUTHOR]