945 resultados para value networks
Resumo:
Major advances in power electronics during recent years have prompted considerable interest within the traction community. The capability of new technologies to reduce the AC railway networks' effect on power quality and improve their supply efficiency is expected to significantly decrease the cost of electric rail supply systems. Of particular interest are Static Frequency Converter (SFC), Rail Power Conditioner (RPC), High Voltage Direct Current (HVDC) and Energy Storage Systems (ESS) solutions. Substantial impacts on future feasibility of railway electrification are anticipated. Aurizon, Australia's largest heavy haul railway operator, has recently commissioned the world's first 50Hz/50Hz SFC installation and is currently investigating SFC, RPC, HVDC and ESS solutions. This paper presents a summary of current and emerging technologies with a particular focus on the potential techno-economic benefits.
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Malnutrition is a common problem in children with end-stage liver disease (ESLD), and accurate assessment of nutritional status is essential in managing these children. In a retrospective study, we compared nutritional assessment by anthropometry with that by body composition. We analyzed all consecutive measurements of total body potassium (TBK, n = 186) of children less than 3 years old with ESLD awaiting transplantation found in our database. The TBK values obtained by whole body counting of 40K were compared with reference TRK values of healthy children. The prevalence of malnutrition, as assessed by weight (weight Z score < -2) was 28%, which was significantly lower (chi-square test, p < 0.0001) than the prevalence of malnutrition (76%) assessed by TBK (< 90% of expected TRK for age). These results demonstrated that body weight underestimated the nutritional deficit and stressed the importance of measuring body composition as part of assessing nutritional status of children with ESLD.
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For point to point multiple input multiple output systems, Dayal-Brehler-Varanasi have proved that training codes achieve the same diversity order as that of the underlying coherent space time block code (STBC) if a simple minimum mean squared error estimate of the channel formed using the training part is employed for coherent detection of the underlying STBC. In this letter, a similar strategy involving a combination of training, channel estimation and detection in conjunction with existing coherent distributed STBCs is proposed for noncoherent communication in Amplify-and-Forward (AF) relay networks. Simulation results show that the proposed simple strategy outperforms distributed differential space-time coding for AF relay networks. Finally, the proposed strategy is extended to asynchronous relay networks using orthogonal frequency division multiplexing.
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The prospect of synthesizing ordered, covalently bonded structures directly on a surface has recently attracted considerable attention due to its fundamental interest and for potential applications in electronics and photonics. This prospective article focuses on efforts to synthesize and characterize epitaxial one- and two-dimensional (1D and 2D, respectively) polymeric networks on single crystal surfaces. Recent studies, mostly performed using scanning tunneling microscopy (STM), demonstrate the ability to induce polymerization based on Ullmann coupling, thermal dehalogenation and dehydration reactions. The 2D polymer networks synthesized to date have exhibited structural limitations and have been shown to form only small domains on the surface. We discuss different approaches to control 1D and 2D polymerization, with particular emphasis on the surface phenomena that are critical to the formation of larger ordered domains.
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This thesis presents a novel approach to building large-scale agent-based models of networked physical systems using a compositional approach to provide extensibility and flexibility in building the models and simulations. A software framework (MODAM - MODular Agent-based Model) was implemented for this purpose, and validated through simulations. These simulations allow assessment of the impact of technological change on the electricity distribution network looking at the trajectories of electricity consumption at key locations over many years.
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The Artificial Neural Networks (ANNs) are being used to solve a variety of problems in pattern recognition, robotic control, VLSI CAD and other areas. In most of these applications, a speedy response from the ANNs is imperative. However, ANNs comprise a large number of artificial neurons, and a massive interconnection network among them. Hence, implementation of these ANNs involves execution of computer-intensive operations. The usage of multiprocessor systems therefore becomes necessary. In this article, we have presented the implementation of ART1 and ART2 ANNs on ring and mesh architectures. The overall system design and implementation aspects are presented. The performance of the algorithm on ring, 2-dimensional mesh and n-dimensional mesh topologies is presented. The parallel algorithm presented for implementation of ART1 is not specific to any particular architecture. The parallel algorithm for ARTE is more suitable for a ring architecture.
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With the introduction of the PCEHR (Personally Controlled Electronic Health Record), the Australian public is being asked to accept greater responsibility for the management of their health information. However, the implementation of the PCEHR has occasioned poor adoption rates underscored by criticism from stakeholders with concerns about transparency, accountability, privacy, confidentiality, governance, and limited capabilities. This study adopts an ethnographic lens to observe how information is created and used during the patient journey and the social factors impacting on the adoption of the PCEHR at the micro-level in order to develop a conceptual model that will encourage the sharing of patient information within the cycle of care. Objective: This study aims to firstly, establish a basic understanding of healthcare professional attitudes toward a national platform for sharing patient summary information in the form of a PCEHR. Secondly, the studies aims to map the flow of patient related information as it traverses a patient’s personal cycle of care. Thus, an ethnographic approach was used to bring a “real world” lens to information flow in a series of case studies in the Australian healthcare system to discover themes and issues that are important from the patient’s perspective. Design: Qualitative study utilising ethnographic case studies. Setting: Case studies were conducted at primary and allied healthcare professionals located in Brisbane Queensland between October 2013 and July 2014. Results: In the first dimension, it was identified that healthcare professionals’ concerns about trust and medico-legal issues related to patient control and information quality, and the lack of clinical value available with the PCEHR emerged as significant barriers to use. The second dimension of the study which attempted to map patient information flow identified information quality issues, clinical workflow inefficiencies and interoperability misconceptions resulting in duplication of effort, unnecessary manual processes, data quality and integrity issues and an over reliance on the understanding and communication skills of the patient. Conclusion: Opportunities for process efficiencies, improved data quality and increased patient safety emerge with the adoption of an appropriate information sharing platform. More importantly, large scale eHealth initiatives must be aligned with the value proposition of individual stakeholders in order to achieve widespread adoption. Leveraging an Australian national eHealth infrastructure and the PCEHR we offer a practical example of a service driven digital ecosystem suitable for co-creating value in healthcare.
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Wireless adhoc networks transmit information from a source to a destination via multiple hops in order to save energy and, thus, increase the lifetime of battery-operated nodes. The energy savings can be especially significant in cooperative transmission schemes, where several nodes cooperate during one hop to forward the information to the next node along a route to the destination. Finding the best multi-hop transmission policy in such a network which determines nodes that are involved in each hop, is a very important problem, but also a very difficult one especially when the physical wireless channel behavior is to be accounted for and exploited. We model the above optimization problem for randomly fading channels as a decentralized control problem - the channel observations available at each node define the information structure, while the control policy is defined by the power and phase of the signal transmitted by each node. In particular, we consider the problem of computing an energy-optimal cooperative transmission scheme in a wireless network for two different channel fading models: (i) slow fading channels, where the channel gains of the links remain the same for a large number of transmissions, and (ii) fast fading channels, where the channel gains of the links change quickly from one transmission to another. For slow fading, we consider a factored class of policies (corresponding to local cooperation between nodes), and show that the computation of an optimal policy in this class is equivalent to a shortest path computation on an induced graph, whose edge costs can be computed in a decentralized manner using only locally available channel state information (CSI). For fast fading, both CSI acquisition and data transmission consume energy. Hence, we need to jointly optimize over both these; we cast this optimization problem as a large stochastic optimization problem. We then jointly optimize over a set of CSI functions of the local channel states, and a c- - orresponding factored class of control poli.
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Background The genome of a wide variety of prokaryotes contains the luxS gene homologue, which encodes for the protein S-ribosylhomocysteinelyase (LuxS). This protein is responsible for the production of the quorum sensing molecule, AI-2 and has been implicated in a variety of functions such as flagellar motility, metabolic regulation, toxin production and even in pathogenicity. A high structural similarity is present in the LuxS structures determined from a few species. In this study, we have modelled the structures from several other species and have investigated their dimer interfaces. We have attempted to correlate the interface features of LuxS with the phenotypic nature of the organisms. Results The protein structure networks (PSN) are constructed and graph theoretical analysis is performed on the structures obtained from X-ray crystallography and on the modelled ones. The interfaces, which are known to contain the active site, are characterized from the PSNs of these homodimeric proteins. The key features presented by the protein interfaces are investigated for the classification of the proteins in relation to their function. From our analysis, structural interface motifs are identified for each class in our dataset, which showed distinctly different pattern at the interface of LuxS for the probiotics and some extremophiles. Our analysis also reveals potential sites of mutation and geometric patterns at the interface that was not evident from conventional sequence alignment studies. Conclusion The structure network approach employed in this study for the analysis of dimeric interfaces in LuxS has brought out certain structural details at the side-chain interaction level, which were elusive from the conventional structure comparison methods. The results from this study provide a better understanding of the relation between the luxS gene and its functional role in the prokaryotes. This study also makes it possible to explore the potential direction towards the design of inhibitors of LuxS and thus towards a wide range of antimicrobials.
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Architects regularly employ design as a problem-solving tool in the built environment. Within the design process, architects apply design thinking to reframe problems as opportunities, take advantage of contradictory information to develop new solutions, and differentiate outcomes based on context. This research aims to investigate how design can be better positioned to develop greater differentiated value to an architect’s current service offering, and how design as a strategy could be applied as a driver of business innovation within the Australian architecture industry. The research will explore literature relating to the future of architecture, the application of design thinking, and the benefits of strategic design. The future intent of the research is to develop strategies that improve the value offering of architects, and develop design led solutions that could be applied successfully to the business of architecture.
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Synchrotron-based high-pressure x-ray diffraction measurements indicate that compressibility, a fundamental materials property, can have a size-specific minimum value. The bulk modulus of nanocrystalline titania has a maximum at particle size of 15 nm. This can be explained by dislocation behavior because very high dislocation contents can be achieved when shear stress induced within nanoparticles counters the repulsion between dislocations. As particle size decreases, compression increasingly generates dislocation networks hardened by overlap of strain fields that shield intervening regions from external pressure. However, when particles become too small to sustain high dislocation concentrations, elastic stiffening declines. The compressibility has a minimum at intermediate sizes.
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Background: A genetic network can be represented as a directed graph in which a node corresponds to a gene and a directed edge specifies the direction of influence of one gene on another. The reconstruction of such networks from transcript profiling data remains an important yet challenging endeavor. A transcript profile specifies the abundances of many genes in a biological sample of interest. Prevailing strategies for learning the structure of a genetic network from high-dimensional transcript profiling data assume sparsity and linearity. Many methods consider relatively small directed graphs, inferring graphs with up to a few hundred nodes. This work examines large undirected graphs representations of genetic networks, graphs with many thousands of nodes where an undirected edge between two nodes does not indicate the direction of influence, and the problem of estimating the structure of such a sparse linear genetic network (SLGN) from transcript profiling data. Results: The structure learning task is cast as a sparse linear regression problem which is then posed as a LASSO (l1-constrained fitting) problem and solved finally by formulating a Linear Program (LP). A bound on the Generalization Error of this approach is given in terms of the Leave-One-Out Error. The accuracy and utility of LP-SLGNs is assessed quantitatively and qualitatively using simulated and real data. The Dialogue for Reverse Engineering Assessments and Methods (DREAM) initiative provides gold standard data sets and evaluation metrics that enable and facilitate the comparison of algorithms for deducing the structure of networks. The structures of LP-SLGNs estimated from the INSILICO1, INSILICO2 and INSILICO3 simulated DREAM2 data sets are comparable to those proposed by the first and/or second ranked teams in the DREAM2 competition. The structures of LP-SLGNs estimated from two published Saccharomyces cerevisae cell cycle transcript profiling data sets capture known regulatory associations. In each S. cerevisiae LP-SLGN, the number of nodes with a particular degree follows an approximate power law suggesting that its degree distributions is similar to that observed in real-world networks. Inspection of these LP-SLGNs suggests biological hypotheses amenable to experimental verification. Conclusion: A statistically robust and computationally efficient LP-based method for estimating the topology of a large sparse undirected graph from high-dimensional data yields representations of genetic networks that are biologically plausible and useful abstractions of the structures of real genetic networks. Analysis of the statistical and topological properties of learned LP-SLGNs may have practical value; for example, genes with high random walk betweenness, a measure of the centrality of a node in a graph, are good candidates for intervention studies and hence integrated computational – experimental investigations designed to infer more realistic and sophisticated probabilistic directed graphical model representations of genetic networks. The LP-based solutions of the sparse linear regression problem described here may provide a method for learning the structure of transcription factor networks from transcript profiling and transcription factor binding motif data.
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Fillets of five fish species were irradiated at 0, 1 and 3kGy to investigate whether the K-value test of freshness can be applied to irradiated fish. Following irradiation, the fillets were stored on ice and sampled regularly for K-value analysis. Hypoxanthine (Hx) and total nucleotide content were also determined on fillets of two species. K-values of irradiated fillets were generally lower than those of unirradiated controls. Hypoxanthine levels paralleled the K-value changes. These results indicated that quality standards based on K-values or Hx levels that have been set for unirradiated species cannot be directly applied to fish that has been irradiated. Total nucleotide content did not appear to be affected by irradiation.