957 resultados para Sierpinski network, generalized Sierpinski network, fractal dimension
Resumo:
Cancer can be defined as a deregulation or hyperactivity in the ongoing network of intracellular and extracellular signaling events. Reverse phase protein microarray technology may offer a new opportunity to measure and profile these signaling pathways, providing data on post-translational phosphorylation events not obtainable by gene microarray analysis. Treatment of ovarian epithelial carcinoma almost always takes place in a metastatic setting since unfortunately the disease is often not detected until later stages. Thus, in addition to elucidation of the molecular network within a tumor specimen, critical questions are to what extent do signaling changes occur upon metastasis and are there common pathway elements that arise in the metastatic microenvironment. For individualized combinatorial therapy, ideal therapeutic selection based on proteomic mapping of phosphorylation end points may require evaluation of the patient's metastatic tissue. Extending these findings to the bedside will require the development of optimized protocols and reference standards. We have developed a reference standard based on a mixture of phosphorylated peptides to begin to address this challenge.
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This paper examines a buffer scheme to mitigate the negative impacts of power-conditioned loads on network voltage and transient stabilities. The scheme is based on the use of battery energy-storage systems in the buffers. The storage systems ensure that protected loads downstream of the buffers can ride through upstream voltage sags and swells. Also, by controlling the buffers to operate in either constant impedance or constant power modes, power is absorbed or injected by the storage systems. The scheme thereby regulates the rotor-angle deviations of generators and enhances network transient stability. A computational method is described in which the capacity of the storage systems is determined to achieve simultaneously the above dual objectives of load ride-through and stability enhancement. The efficacy of the resulting scheme is demonstrated through numerical examples.
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Toxicity is a major concern for anti-neoplastic drugs, with much of the existing pharmacopoeia being characterized by a very narrow therapeutic index. 'Network-targeted' combination therapy is a promising new concept in cancer therapy, whereby therapeutic index might be improved by targeting multiple nodes in a cell's signaling network, rather than a single node. Here, we examine the potential of this novel approach, illustrating how therapeutic benefit could be achieved with smaller doses of the necessary agents.
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Social Networks (SN) users have various privacy requirements to protect their information; to address this issue, a six-stage thematic analysis of scholarly articles related to SN user privacy concerns were synthesized. Then this research combines mixed methods research employing the strengths of quantitative and qualitative research to investigate general SN users, and thus construct a new set of ?ve primary and Twenty-?ve secondary SN user privacy requirements. Such an approach has been rarely used to examine the privacy requirements. Factor analysis results show superior agreement with theoretical predictions and signi?cant improvement over previous alternative models of SN user privacy requirements. This research presented here has the potential to provide for the development of more sophisticated privacy controls which will increase the ability of SN users to: specify their rights in SNs and to determine the protection of their own SN data.
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A novel gray-box neural network model (GBNNM), including multi-layer perception (MLP) neural network (NN) and integrators, is proposed for a model identification and fault estimation (MIFE) scheme. With the GBNNM, both the nonlinearity and dynamics of a class of nonlinear dynamic systems can be approximated. Unlike previous NN-based model identification methods, the GBNNM directly inherits system dynamics and separately models system nonlinearities. This model corresponds well with the object system and is easy to build. The GBNNM is embedded online as a normal model reference to obtain the quantitative residual between the object system output and the GBNNM output. This residual can accurately indicate the fault offset value, so it is suitable for differing fault severities. To further estimate the fault parameters (FPs), an improved extended state observer (ESO) using the same NNs (IESONN) from the GBNNM is proposed to avoid requiring the knowledge of ESO nonlinearity. Then, the proposed MIFE scheme is applied for reaction wheels (RW) in a satellite attitude control system (SACS). The scheme using the GBNNM is compared with other NNs in the same fault scenario, and several partial loss of effect (LOE) faults with different severities are considered to validate the effectiveness of the FP estimation and its superiority.
Resumo:
Discussions of public diplomacy in recent years have paid a growing amount of attention to networks. This network perspective is understood to provide insights into various issues of public diplomacy, such as its effects, credibility, reputation, identity and narratives. This paper applies the network idea to analyse China’s Confucius Institutes initiative. It understands Confucius Institutes as a global network and argues that this network structure has potential implications for the operation of public and cultural diplomacy that are perhaps underestimated in existing accounts of Chinese cultural diplomacy. In particular, it is noted that the specific setup of Confucius Institutes requires the engagement of local stakeholders, in a way that is less centralised and more networked than comparable cultural diplomacy institutions. At the same time, the development of a more networked for of public cultural diplomacy is challenged in practice by both practical issues and the configuration of China’s state-centric public diplomacy system informed by the political constitution of the Chinese state.
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This thesis explored traffic characteristics at the aggregate level for area-wide traffic monitoring of large urban area. It focused on three aspects: understanding a macroscopic network performance under real-time traffic information provision, measuring traffic performance of a signalised arterial network using available data sets, and discussing network zoning for monitoring purposes in the case of Brisbane, Australia. This work presented the use of probe vehicle data for estimating traffic state variables, and illustrated dynamic features of regional traffic performance of Brisbane. The results confirmed the viability and effectiveness of area-wide traffic monitoring.
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Abstract: Social network technologies, as we know them today have become a popular feature of everyday life for many people. As their name suggests, their underlying premise is to enable people to connect with each other for a variety of purposes. These purposes however, are generally thought of in a positive fashion. Based on a multi-method study of two online environments, Habbo Hotel and Second Life, which incorporate social networking functionality, we she light on forms of what can be conceptualized as antisocial behaviours and the rationales for these. Such behaviours included: scamming, racist/homophobic attacks, sim attacks, avatar attacks, non-conformance to contextual norms, counterfeiting and unneighbourly behaviour. The rationales for sub behaviours included: profit, fun, status building, network disruption, accidental acts and prejudice. Through our analysis we are able to comment upon the difficulties of defining antisocial behaviour in such environments, particularly when such environments are subject to interpretation vis their use and expected norms. We also point to the problems we face in conducting our public and private lives given the role ICTs are playing in the convergence of these two spaces and also the convergence of ICTs themselves.
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This paper presents a case study for the application of a Linear Engineering Asset Renewal decision support software tool (LinEAR) at a water distribution network in Australia. This case study examines how the LinEAR can assist water utilities to minimise their total pipeline management cost, to make a long-term budget based on mathematically predicted expenditure, and to present calculated evidence for supporting their expenditure requirements. The outcomes from the study on pipeline renewal decision support demonstrate that LinEAR can help water utilities to improve the decision process and save renewal costs over a long-term by providing an optimum renewal schedules. This software can help organisation to accumulate technical knowledge and prediction future impact of the decision using what-if analysis.
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This thesis introduces advanced Demand Response algorithms for residential appliances to provide benefits for both utility and customers. The algorithms are engaged in scheduling appliances appropriately in a critical peak day to alleviate network peak, adverse voltage conditions and wholesale price spikes also reducing the cost of residential energy consumption. Initially, a demand response technique via customer reward is proposed, where the utility controls appliances to achieve network improvement. Then, an improved real-time pricing scheme is introduced and customers are supported by energy management schedulers to actively participate in it. Finally, the demand response algorithm is improved to provide frequency regulation services.
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This paper introduces a new method to automate the detection of marine species in aerial imagery using a Machine Learning approach. Our proposed system has at its core, a convolutional neural network. We compare this trainable classifier to a handcrafted classifier based on color features, entropy and shape analysis. Experiments demonstrate that the convolutional neural network outperforms the handcrafted solution. We also introduce a negative training example-selection method for situations where the original training set consists of a collection of labeled images in which the objects of interest (positive examples) have been marked by a bounding box. We show that picking random rectangles from the background is not necessarily the best way to generate useful negative examples with respect to learning.
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Supervisory Control and Data Acquisition systems (SCADA) are widely used to control critical infrastructure automatically. Capturing and analyzing packet-level traffic flowing through such a network is an essential requirement for problems such as legacy network mapping and fault detection. Within the framework of captured network traffic, we present a simple modeling technique, which supports the mapping of the SCADA network topology via traffic monitoring. By characterizing atomic network components in terms of their input-output topology and the relationship between their data traffic logs, we show that these modeling primitives have good compositional behaviour, which allows complex networks to be modeled. Finally, the predictions generated by our model are found to be in good agreement with experimentally obtained traffic.