842 resultados para distribution networks


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Large arrays and networks of carbon nanotubes, both single- and multi-walled, feature many superior properties which offer excellent opportunities for various modern applications ranging from nanoelectronics, supercapacitors, photovoltaic cells, energy storage and conversation devices, to gas- and biosensors, nanomechanical and biomedical devices etc. At present, arrays and networks of carbon nanotubes are mainly fabricated from the pre-fabricated separated nanotubes by solution-based techniques. However, the intrinsic structure of the nanotubes (mainly, the level of the structural defects) which are required for the best performance in the nanotube-based applications, are often damaged during the array/network fabrication by surfactants, chemicals, and sonication involved in the process. As a result, the performance of the functional devices may be significantly degraded. In contrast, directly synthesized nanotube arrays/networks can preclude the adverse effects of the solution-based process and largely preserve the excellent properties of the pristine nanotubes. Owing to its advantages of scale-up production and precise positioning of the grown nanotubes, catalytic and catalyst-free chemical vapor depositions (CVD), as well as plasma-enhanced chemical vapor deposition (PECVD) are the methods most promising for the direct synthesis of the nanotubes.

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Integration of rooftop photovoltaics (PVs) in residential networks at moderate penetration levels is becoming a reality in many countries including Australia. Despite the technical challenges in properly accommodating PV units, one of the major benefits is the ability of PV units to extend useful life time of distribution transformers. This effect is not quantified in the existing literature. This paper carries out an analysis into the impacts of rooftop PVs at different penetration levels on the performance of distribution transformers and residential networks. This paper presents a methodology to quantify the benefit of the distribution transformer life extension brought about by customer-owned rooftop PV units. The proposed methodology is applied to a real distribution system with various scenarios, including different penetration levels. The results show the distribution transformer loss-of-life function, as a function of the rooftop PV penetration level, is monotonically decreasing function which saturates after a certain penetration level. The best life improvements occur with transformers that are highly loaded and the presence of a significant PV installation may support the deferral of transformer upgrades.

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Digital innovation is transforming the media and entertainment industries. The professionalization of YouTube’s platform is paradigmatic of that change. The 100 original channel initiative launched in late 2011 was designed to transform YouTube’s brand through production of a high volume of quality premium video content that would more deeply engage its audience base and in the process attract big advertisers. An unanticipated by-product has been the rapid growth of a wave of aspiring next-generation digital media companies from within the YouTube ecosystem. Fuelled by early venture capital some have ambitious goals to become global media corporations in the online video space. A number of larger MCNs (Multi-Channel Networks) - BigFrame, Machinima, Fullscreen, AwesomenessTV, Maker Studios , Revision3 and DanceOn - have attracted interest from media incumbents like Warner Brothers, DreamWorks, Discovery, Bertlesmann, Comcast and AMC, and two larger MCNs Alloy and Break Media have merged. This indicates that a shakeout is underway in these new online supply chains, after rapid initial growth. The higher profile MCNs seek to rapidly develop scale economies in online distribution and facilitate audience growth for their member channels, helping channels optimize monetization, develop sustainable business models and to facilitate producer-collaboration within a growing online community of like-minded content creators. Some MCNs already attract far larger online audiences than any national TV network. The speed with which these developments have occurred is reminiscent of the 1910s, when Hollywood studios first emerged and within only a few years replaced the incumbent film studios as the dominant force within the film industry.

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The World Health Organization identifies road trauma as a major public health issue in every country; most notably among low-to-middle income countries. More than 90% of all road fatalities occur in these countries, although they have only 48% of all registered vehicles [1]. Unprecedented focus has been placed on reducing the global road trauma burden through the United Nations Decade of Action for Road Safety (2011-2020). China is rapidly transitioning from a nation of bicycle riders and pedestrians to one where car ownership and use is increasing. This transition presents important public health, mobility, and safety challenges. Rapid motorisation has resulted in an increased road trauma burden, shouldered disproportionately among the population. Vulnerable road users (bicyclists, pedestrians, and motorcyclists) are of particular concern, representing 70% of all road-related fatalities [1]. Furthermore, those at greatest risk of sustaining a crash-related disability are: male, older, less educated, and earning a lower income [2] and residing in urban areas [3], with higher fatality rates in north-western poorer provinces [3]. Speeding is a key factor in road crashes in China [1, 4] and is one of two risk factors targeted in the Bloomberg Philanthropies-funded Global Road Safety Program operating in two Chinese cities over five year [5] to which the first author has provided expert advice. However, little evidence exists to help understand the factors underpinning speeding behaviour. Previous research conducted by the authors in Beijing and Hangzhou explored personal, social, and legal factors relating to speeding to assist in better understanding the motivations for non-compliance with speed limits. Qualitative and quantitative research findings indicated that speeding is relatively common, including self-reported travel speeds of greater than 30 km/hour above posted speed limits [6], and that the road safety laws and enforcement practices may, in some circumstances, contribute to this [7]. Normative factors were also evident; the role of friends, family members and driving instructors were influential. Additionally, using social networks to attempt to avoid detection and penalty was reported, thereby potentially reinforcing community perceptions that speeding is acceptable [8, 9]. The authors established strong collaborative links with the Chinese Academy of Sciences and Zhejiang Police College to conduct this research. The first author has worked in both institutions for extended time periods and recognises that research must include an understanding of culturally-relevant issues if road safety is to improve in China. Future collaborations to assist in enhancing our understanding of such issues are welcomed. References [1] World Health Organization. (2009). Global status report on road safety: Time for action; Geneva. [2] Chen, H., Du, W., & Li, N. (2013). The socioeconomic inequality in traffic-related disability among Chinese adults: the application of concentration index. Accident Analysis & Prevention, 55(101-106). [3] Wang, S. Y., Li, Y. H., Chi, G. B., Xiao, S. Y., Ozanne-Smith, J., Stevenson, M., & Phillips, M. (2008). Injury-related fatalities in China: an under-recognised public-health problem. The Lancet (British edition), 372(9651), 1765-1773. [4] He, J., King, M. J., Watson, B., Rakotonirainy, A., & Fleiter, J. J. (2013). Speed enforcement in China: National, provincial and city initiatives and their success. Accident Analysis & Prevention, 50, 282-288. [5] Bhalla, K., Li, Q., Duan, L., Wang, Y., Bishai, D., & Hyder, A. A. (2013). The prevalence of speeding and drink driving in two cities in China: a mid project evaluation of ongoing road safety interventions. Injury, 44, 49-56. doi:10.1016/S0020-1383(13)70213-4. [6] Fleiter, J. J., Watson, B., & Lennon, A. (2013). Awareness of risky behaviour among Chinese drivers. Peer-reviewed paper presented at 23rd Canadian Multidisciplinary Road Safety Conference, Montréal, Québec. [7] Fleiter, J. J., Watson, B., Lennon, A., King, M. J., & Shi, K. (2009). Speeding in Australia and China: A comparison of the influence of legal sanctions and enforcement practices on car drivers. Peer-reviewd paper presented at Australasian Road Safety Research Policing Education Conference, Sydney. [8] Fleiter, J. J., Watson, B., Lennon, A., King, M. J., & Shi, K. (2011). Social influences on drivers in China. Journal of the Australasian College of Road Safety, 22(2), 29-36. [9] Fleiter, J. J., Watson, B., Guan, M. Q., Ding, J. Y., & Xu, C. (2013). Characteristics of Chinese Drivers Attending a Mandatory Training Course Following Licence Suspension. Peer-reviewed paper presented at Road Safety on Four Continents, Beijing, China.

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Re-supplying loads on outage through cross-connect from adjacent feeders in a distribution system may cause voltage drop and hence require load shedding. However, the surplus PV generated in some of the LV feeders can prevent load shedding, and improve reliability. In order to measure these effects, this paper proposes the application of Direct Load Flow method[1] in reliability evaluation of distribution systems with PV units. As part of this study, seasonal impacts on load consumption together with surplus PV output power injection to higher voltage networks are also considered. New indices are proposed to measure yearly expected energy export, from LV to MV and from MV to higher voltage network.

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A theoretical basis is required for comparing key features and critical elements in wild fisheries and aquaculture supply chains under a changing climate. Here we develop a new quantitative metric that is analogous to indices used to analyse food-webs and identify key species. The Supply Chain Index (SCI) identifies critical elements as those elements with large throughput rates, as well as greater connectivity. The sum of the scores for a supply chain provides a single metric that roughly captures both the resilience and connectedness of a supply chain. Standardised scores can facilitate cross-comparisons both under current conditions as well as under a changing climate. Identification of key elements along the supply chain may assist in informing adaptation strategies to reduce anticipated future risks posed by climate change. The SCI also provides information on the relative stability of different supply chains based on whether there is a fairly even spread in the individual scores of the top few key elements, compared with a more critical dependence on a few key individual supply chain elements. We use as a case study the Australian southern rock lobster Jasus edwardsii fishery, which is challenged by a number of climate change drivers such as impacts on recruitment and growth due to changes in large-scale and local oceanographic features. The SCI identifies airports, processors and Chinese consumers as the key elements in the lobster supply chain that merit attention to enhance stability and potentially enable growth. We also apply the index to an additional four real-world Australian commercial fishery and two aquaculture industry supply chains to highlight the utility of a systematic method for describing supply chains. Overall, our simple methodological approach to empirically-based supply chain research provides an objective method for comparing the resilience of supply chains and highlighting components that may be critical.

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In this paper, we present an improved load distribution strategy, for arbitrarily divisible processing loads, to minimize the processing time in a distributed linear network of communicating processors by an efficient utilization of their front-ends. Closed-form solutions are derived, with the processing load originating at the boundary and at the interior of the network, under some important conditions on the arrangement of processors and links in the network. Asymptotic analysis is carried out to explore the ultimate performance limits of such networks. Two important theorems are stated regarding the optimal load sequence and the optimal load origination point. Comparative study of this new strategy with an earlier strategy is also presented.

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Structural and rheological features of a series of molecular hydrogels formed by synthetic bile salt analogues have been scrutinized. Among seven gelators, two are neutral compounds, while the others are cationic systems among which one is a tripodal steroid derivative. Despite the fact that the chemical structures are closely related, the variety of physical characteristics is extremely large in the structures of the connected fibers (either plain cylinders or ribbons), in the dynamical modes for stress relaxation of the associated SAFINs, in the scaling laws of the shear elasticity (typical of either cellular solids or fractal floc-like assemblies), in the micron-scale texture and the distribution of ordered domains (spherulites, crystallites) embedded in a random mesh, in the type of nodal zones (either crystalline-like, fiber entanglements, or bundles), in the evolution of the distribution and morphology of fibers and nodes, and in the sensitivity to added salt. SANS appears to be a suitable technique to infer all geometrical parameters defining the fibers, their interaction modes, and the volume fraction of nodes in a SAFIN. The tripodal system is particularly singular in the series and exhibits viscosity overshoots at the startup of shear flows, an “umbrella-like” molecular packing mode involving three molecules per cross section of fiber, and scattering correlation peaks revealing the ordering and overlap of 1d self-assembled polyelectrolyte species.

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The quality of species distribution models (SDMs) relies to a large degree on the quality of the input data, from bioclimatic indices to environmental and habitat descriptors (Austin, 2002). Recent reviews of SDM techniques, have sought to optimize predictive performance e.g. Elith et al., 2006. In general SDMs employ one of three approaches to variable selection. The simplest approach relies on the expert to select the variables, as in environmental niche models Nix, 1986 or a generalized linear model without variable selection (Miller and Franklin, 2002). A second approach explicitly incorporates variable selection into model fitting, which allows examination of particular combinations of variables. Examples include generalized linear or additive models with variable selection (Hastie et al. 2002); or classification trees with complexity or model based pruning (Breiman et al., 1984, Zeileis, 2008). A third approach uses model averaging, to summarize the overall contribution of a variable, without considering particular combinations. Examples include neural networks, boosted or bagged regression trees and Maximum Entropy as compared in Elith et al. 2006. Typically, users of SDMs will either consider a small number of variable sets, via the first approach, or else supply all of the candidate variables (often numbering more than a hundred) to the second or third approaches. Bayesian SDMs exist, with several methods for eliciting and encoding priors on model parameters (see review in Low Choy et al. 2010). However few methods have been published for informative variable selection; one example is Bayesian trees (O’Leary 2008). Here we report an elicitation protocol that helps makes explicit a priori expert judgements on the quality of candidate variables. This protocol can be flexibly applied to any of the three approaches to variable selection, described above, Bayesian or otherwise. We demonstrate how this information can be obtained then used to guide variable selection in classical or machine learning SDMs, or to define priors within Bayesian SDMs.

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Predicting temporal responses of ecosystems to disturbances associated with industrial activities is critical for their management and conservation. However, prediction of ecosystem responses is challenging due to the complexity and potential non-linearities stemming from interactions between system components and multiple environmental drivers. Prediction is particularly difficult for marine ecosystems due to their often highly variable and complex natures and large uncertainties surrounding their dynamic responses. Consequently, current management of such systems often rely on expert judgement and/or complex quantitative models that consider only a subset of the relevant ecological processes. Hence there exists an urgent need for the development of whole-of-systems predictive models to support decision and policy makers in managing complex marine systems in the context of industry based disturbances. This paper presents Dynamic Bayesian Networks (DBNs) for predicting the temporal response of a marine ecosystem to anthropogenic disturbances. The DBN provides a visual representation of the problem domain in terms of factors (parts of the ecosystem) and their relationships. These relationships are quantified via Conditional Probability Tables (CPTs), which estimate the variability and uncertainty in the distribution of each factor. The combination of qualitative visual and quantitative elements in a DBN facilitates the integration of a wide array of data, published and expert knowledge and other models. Such multiple sources are often essential as one single source of information is rarely sufficient to cover the diverse range of factors relevant to a management task. Here, a DBN model is developed for tropical, annual Halophila and temperate, persistent Amphibolis seagrass meadows to inform dredging management and help meet environmental guidelines. Specifically, the impacts of capital (e.g. new port development) and maintenance (e.g. maintaining channel depths in established ports) dredging is evaluated with respect to the risk of permanent loss, defined as no recovery within 5 years (Environmental Protection Agency guidelines). The model is developed using expert knowledge, existing literature, statistical models of environmental light, and experimental data. The model is then demonstrated in a case study through the analysis of a variety of dredging, environmental and seagrass ecosystem recovery scenarios. In spatial zones significantly affected by dredging, such as the zone of moderate impact, shoot density has a very high probability of being driven to zero by capital dredging due to the duration of such dredging. Here, fast growing Halophila species can recover, however, the probability of recovery depends on the presence of seed banks. On the other hand, slow growing Amphibolis meadows have a high probability of suffering permanent loss. However, in the maintenance dredging scenario, due to the shorter duration of dredging, Amphibolis is better able to resist the impacts of dredging. For both types of seagrass meadows, the probability of loss was strongly dependent on the biological and ecological status of the meadow, as well as environmental conditions post-dredging. The ability to predict the ecosystem response under cumulative, non-linear interactions across a complex ecosystem highlights the utility of DBNs for decision support and environmental management.

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Public key authentication is the verification of the identity-public key binding, and is foundational to the security of any network. The contribution of this thesis has been to provide public key authentication for a decentralised and resource challenged network such as an autonomous Delay Tolerant Network (DTN). It has resulted in the development and evaluation of a combined co-localisation trust system and key distribution scheme evaluated on a realistic large geographic scale mobility model. The thesis also addresses the problem of unplanned key revocation and replacement without any central authority.

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The problem of scheduling divisible loads in distributed computing systems, in presence of processor release time is considered. The objective is to find the optimal sequence of load distribution and the optimal load fractions assigned to each processor in the system such that the processing time of the entire processing load is a minimum. This is a difficult combinatorial optimization problem and hence genetic algorithms approach is presented for its solution.

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Deterministic models have been widely used to predict water quality in distribution systems, but their calibration requires extensive and accurate data sets for numerous parameters. In this study, alternative data-driven modeling approaches based on artificial neural networks (ANNs) were used to predict temporal variations of two important characteristics of water quality chlorine residual and biomass concentrations. The authors considered three types of ANN algorithms. Of these, the Levenberg-Marquardt algorithm provided the best results in predicting residual chlorine and biomass with error-free and ``noisy'' data. The ANN models developed here can generate water quality scenarios of piped systems in real time to help utilities determine weak points of low chlorine residual and high biomass concentration and select optimum remedial strategies.

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Artificial neural networks (ANNs) have shown great promise in modeling circuit parameters for computer aided design applications. Leakage currents, which depend on process parameters, supply voltage and temperature can be modeled accurately with ANNs. However, the complex nature of the ANN model, with the standard sigmoidal activation functions, does not allow analytical expressions for its mean and variance. We propose the use of a new activation function that allows us to derive an analytical expression for the mean and a semi-analytical expression for the variance of the ANN-based leakage model. To the best of our knowledge this is the first result in this direction. Our neural network model also includes the voltage and temperature as input parameters, thereby enabling voltage and temperature aware statistical leakage analysis (SLA). All existing SLA frameworks are closely tied to the exponential polynomial leakage model and hence fail to work with sophisticated ANN models. In this paper, we also set up an SLA framework that can efficiently work with these ANN models. Results show that the cumulative distribution function of leakage current of ISCAS'85 circuits can be predicted accurately with the error in mean and standard deviation, compared to Monte Carlo-based simulations, being less than 1% and 2% respectively across a range of voltage and temperature values.