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Rockbursts are hazardous phenomena of sudden and violent rock failure in deep underground excavations under high geostress conditions, which poses a serious threat to geotechnical engineering. The occurrence of rockbursts is influenced by a combination of factors. Therefore, it is necessary to find an efficient method to assess rockburst grades. In this paper, we propose a novel method that enhances the VIKOR method using a novel combination of weight and generalized weighted Mahalanobis distance. The combination weights of the evaluation indicators were calculated using game theory by combining subjective experience and objective data statistical characteristics. By introducing the generalized weighted Mahalanobis distance, the VIKOR method is improved to address the issues of inconsistent dimensions, different importance, and inconsistent correlation among indicators. The proposed method can deal with the complexity of the impact factors of rockburst evaluation and classify the rockburst intensity level. The results show that the accuracy of the improved VIKOR method with the distance formula is higher than that of the unimproved VIKOR method; the evaluation accuracy of the improved VIKOR method with the generalized weighted Mahalanobis distance is 91.67%, which outperforms the improved VIKOR methods with the Euclidean and Canberra distances. This assessment method can be easily implemented and does not depend on the discussion of the rockburst occurrence mechanism, making it widely applicable for engineering rockburst evaluation. [ABSTRACT FROM AUTHOR]

Most of the studies on rainwater harvesting analysis present the outcomes for particular cities, representing a single set of results for a specific city. However, in reality, significant spatial and weather variabilities may exist, due to which presenting only one set of results for a particular city would be misleading. This paper presents the potential weather and spatial variabilities on the expected water savings and supply reliability through the domestic rainwater tank for an inland city. An earlier-developed daily water balance model, eTank, was used for the calculations of annual water savings and reliability. An Australian inland city, Canberra, was selected as a case study and relevant daily rainfall data were collected from the Australian Bureau of Meteorology website. For the analysis of spatial variation, two rain gauge stations within the city of Canberra were selected. For each station, from the historical data, three years were selected as dry, average and wet years. For each weather condition, annual water savings and reliabilities were calculated for different demands with different tank sizes up to 10,000 L connected with different roof sizes. Then, variations in annual water savings and reliabilities among different weather conditions, as well as among different stations, were evaluated. It was found that, with regard to annual water savings, a maximum variation of 68.6% can be expected between dry and wet weather; however, only a 15.4% maximum spatial variation is expected among the selected stations. Regarding reliability, a maximum variation of 123% is expected between dry and wet weather. Whereas, only a 17% spatial variation is expected among the selected stations. Such a study will provide valuable insights for rainwater tank users and stakeholders on potential variabilities due to weather and spatial differences. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has deeply influenced the economy of all nations, and Mexico is no exception. This effect is distinct for different groups of the population, with variable socio-economic and demographic characteristics, that live in regions of contrasting features. An efficient apparatus for measuring expenditure variations and tendency is the National Household Income and Expenditure Survey performed by the National Institute of Statistics and Geography in accordance with the international standardization established at the Canberra Conference; the latter permits, every two years, for different surveys to be comparable in time and space. Hence, in Mexico, there are three surveys for 2018, 2020, and 2022. The results of the comparison indicate that different groups of the Mexican population have experienced the pandemic with multiple impacts, apparently without large proportional variation regarding expenditure but with greater variation in time-use conditions. Moreover, it became apparent that even though the lower strata of Mexico's population have fewer proportional variations concerning income after the COVID-19 outburst, they must incorporate more family members into working activities to achieve a survival line; the latter results in the abandonment of school from various young household members that will negatively impact the quality of their lives in the long term. [ABSTRACT FROM AUTHOR]

The urban densification agenda for the Australian Capital Territory announced in 2012 as well as the climate-related impacts (such as flood and rising temperature) have made the role of green infrastructure in this city more critical than at any other time. The Living Infrastructure Plan for Canberra was proposed by the local government in 2019 and it is currently being developed. However, there is a lack of understanding of the variety of urban green infrastructure characters, associated urban green space types, and their contributions to the green infrastructure plan. Therefore, this research aims to study the existing types of urban green infrastructure and green spaces in Canberra and investigate their potential sociocultural and ecological contributions that need to be considered in developing green infrastructure plans. To achieve this, we employed a socioecological approach and a mix of methods including a literature review, a review of policy documents, semi-structured interviews with experts, geospatial data, and field observation. Ten main urban green infrastructures were identified in Canberra and associated green spaces and their ecosystem services were discussed. Finally, a framework was delineated to suggest recommendations for the identified urban green infrastructure types with the aim of improving the green infrastructure planning practice by enhancing, preserving, and reinforcing green spaces. The findings provide a foundation for coordinating green infrastructure decision making and suggest a framework for designing high-quality and multifunctional green spaces. The study concludes that further investigation is required to comprehend the diversity of urban green infrastructures and their ecosystem services, co-benefits, synergies, and trade-offs. [ABSTRACT FROM AUTHOR]

In this study, we analyze a signal segmentation-specific pain duration transfer task by applying knowledge transfer from short-term (phasic) pain stimuli to long-term (tonic) pain stimuli. To this end, we focus on the physiological signals of the X-ITE Pain Database. We evaluate different distance-based segment selection approaches with the aim of identifying individual segments of the corresponding tonic stimuli that lead to the best classification performance. The phasic domain is used to train the classification model. In the first main step, we compute class-specific prototypes for the phasic domain. In the second main step, we compute the distances between all segments of the tonic samples and each prototype. The segment with the lowest distance to the prototypes is then fed to the classifier. Our analysis includes the evaluation of a variety of distance metrics, namely the Euclidean, Bray–Curtis, Canberra, Chebyshev, City-Block and Wasserstein distances. Our results show that in combination with most of the metrics used, the distance-based selection of one individual segment outperforms the naive approach in which the tonic stimuli are fed to the phasic domain-based classification model without any adaptation. Moreover, most of the evaluated distance-based segment selection approaches lead to outcomes that are close to the classification performance, which is obtained by focusing on the respective best segments. For instance, for the trapezius (TRA) signal, in combination with the electric pain domain, we obtained an averaged accuracy of 68.0%, while the naive approach led to 66.0%. For the thermal pain domain, in combination with the electrodermal activity (EDA) signal, we obtained an averaged accuracy of 59.6%, outperforming the naive approach, which led to 53.2%. [ABSTRACT FROM AUTHOR]

Cloud computing is currently the most cost-effective means of providing commercial and consumer IT services online. However, it is prone to new flaws. An economic denial of sustainability attack (EDoS) specifically leverages the pay-per-use paradigm in building up resource demands over time, culminating in unanticipated usage charges to the cloud customer. We present an effective approach to mitigating EDoS attacks in cloud computing. To mitigate such distributed attacks, methods for detecting them on different cloud computing smart grids have been suggested. These include hard-threshold, machine, and deep learning, support vector machine (SVM), K-nearest neighbors (KNN), random forest (RF) tree algorithms, namely convolutional neural network (CNN), and long short-term memory (LSTM). These algorithms have greater accuracies and lower false alarm rates and are essential for improving the cloud computing service provider security system. The dataset of nine injection attacks for testing machine and deep learning algorithms was obtained from the Cyber Range Lab at the University of New South Wales (UNSW), Canberra. The experiments were conducted in two categories: binary classification, which included normal and attack datasets, and multi-classification, which included nine classes of attack data. The results of the proposed algorithms showed that the RF approach achieved accuracy of 98% with binary classification, whereas the SVM model achieved accuracy of 97.54% with multi-classification. Moreover, statistical analyses, such as mean square error (MSE), Pearson correlation coefficient (R), and the root mean square error (RMSE), were applied in evaluating the prediction errors between the input data and the prediction values from different machine and deep learning algorithms. The RF tree algorithm achieved a very low prediction level (MSE = 0.01465) and a correlation R2 (R squared) level of 92.02% with the binary classification dataset, whereas the algorithm attained an R2 level of 89.35% with a multi-classification dataset. The findings of the proposed system were compared with different existing EDoS attack detection systems. The proposed attack mitigation algorithms, which were developed based on artificial intelligence, outperformed the few existing systems. The goal of this research is to enable the detection and effective mitigation of EDoS attacks. [ABSTRACT FROM AUTHOR]

The ionospheric effects of six intense geomagnetic storms with Dst index ≤ −100 nT that occurred in 2012 were studied at a low-latitude station, Darwin (Geomagnetic coordinates, 21.96° S, 202.84° E), a low-mid-latitude station, Townsville (28.95° S, 220.72° E), and a mid-latitude station, Canberra (45.65° S, 226.30° E), in the Australian Region, by analyzing the storm–time variations in the critical frequency of the F2-region (foF2). Out of six storms, a storm of 23–24 April did not produce any ionospheric effect. The storms of 30 September–3 October (minimum Dst = −122 nT) and 7–10 October (minimum Dst = −109 nT) are presented as case studies and the same analysis was done for the other four storms. The storm of 30 September–3 October, during its main phase, produced a positive ionospheric storm at all three stations with a maximum percentage increase in foF2 (∆foF2%) of 45.3% at Canberra whereas during the recovery phase it produced a negative ionospheric storm at all three stations with a maximum ∆foF2% of −63.5% at Canberra associated with a decrease in virtual height of the F-layer (h'F). The storm of 7–10 October produced a strong long-duration negative ionospheric storm associated with an increase in h'F during its recovery phase at all three stations with a maximum ∆foF2% of −65.1% at Townsville. The negative ionospheric storms with comparatively longer duration were more pronounced in comparison to positive storms and occurred only during the recovery phase of storms. The storm main phase showed positive ionospheric storms for two storms (14–15 July and 30 September–3 October) and other three storms did not produce any ionospheric storm at the low-latitude station indicating prompt penetrating electric fields (PPEFs) associated with these storms did not propagate to the low latitude. The positive ionospheric storms during the main phase are accounted to PPEFs affecting ionospheric equatorial E × B drifts and traveling ionospheric disturbances due to joule heating at the high latitudes. The ionospheric effects during the recovery phase are accounted to the disturbance dynamo electric fields and overshielding electric field affecting E × B drifts and the storm-induced circulation from high latitudes toward low latitudes leading to changes in the natural gas composition [O/N2] ratio. [ABSTRACT FROM AUTHOR]

In GeoSurf satellite constellations, any transmitter/receiver, wherever it is located, is linked to a satellite with zenith paths. We have studied the tropospheric attenuation predicted for some reference sites (Canberra, Holmdel, Pasadena, Robledo, and Spino d'Adda), which also set the meridian along which we have considered sites with latitudes ranging between 60° N and 60° S. At the annual probability of 1% of an average year, in the latitude between 30° N and 30° S, there are no significant differences between GEO slant paths and GeoSurf zenith paths. On the contrary, at 0.1% and 0.01% annual probabilities, large differences are found for latitudes greater than 30° N or 30° S. For comparing the tropospheric attenuation in GeoSurf paths with that expected in LEO highly variable slant paths, we have considered, as reference, a LEO satellite constellation orbiting in circular at 817 km. GeoSurf zenith paths "gain" several dBs compared to LEO slant paths. The more static total clear-sky attenuation (water vapor, oxygen, and clouds) in both GEO and LEO slant paths shows larger values than GeoSurf zenith paths. Both for rain and clear-sky attenuations, Northern and Southern Hemispheres show significant differences. [ABSTRACT FROM AUTHOR]

Expectiles have gained considerable attention in recent years due to wide applications in many areas. In this study, the k-nearest neighbours approach, together with the asymmetric least squares loss function, called ex- k N N , is proposed for computing expectiles. Firstly, the effect of various distance measures on ex- k N N in terms of test error and computational time is evaluated. It is found that Canberra, Lorentzian, and Soergel distance measures lead to minimum test error, whereas Euclidean, Canberra, and Average of (L 1 , L ∞) lead to a low computational cost. Secondly, the performance of ex- k N N is compared with existing packages er-boost and ex-svm for computing expectiles that are based on nine real life examples. Depending on the nature of data, the ex- k N N showed two to 10 times better performance than er-boost and comparable performance with ex-svm regarding test error. Computationally, the ex- k N N is found two to five times faster than ex-svm and much faster than er-boost, particularly, in the case of high dimensional data. [ABSTRACT FROM AUTHOR]