999 resultados para Queueing Networks


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The mechanical properties of microfilament networks are systematically summarized at different special scales in this paper. We have presented the mechanical models of single microfilaments and microfilament networks at microscale. By adopting a coarse-grained simulation strategy, the mechanical stability of microfilaments related cellular structures are analysed. Structural analysis is conducted to microfilament networks to understand the stress relaxation under compression. The nanoscale molecular mechanisms of the microfilaments deformation is also summarized from the viewpoint of molecular dynamics simulation. This paper provides the fundaments of multiscale modelling framework for the mechanical behaviours simulation of hierarchical microfilament networks.

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This paper elaborates on the use of future wireless communication networks for autonomous city vehicles. After addressing the state of technology, the paper explains the autonomous vehicle control system architecture and the Cybercars-2 communication framework; it presents experimental tests of communication-based real-time decision making; and discusses potential applications for communication in order to improve the localization and perception abilities of autonomous vehicles in urban environments.

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Since the revisions to the International Health Regulations (IHR) in 2005, much attention has turned to how states, particularly developing states, will address core capacity requirements attached to the revised IHR. Primarily, how will states strengthen their capacity to identify and verify public health emergencies of international concern (PHEIC)? Another important but under-examined aspect of the revised IHR is the empowerment of the World Health Organization (WHO) to act upon non-governmental reports of disease outbreaks. The revised IHR potentially marks a new chapter in the powers of ‘disease intelligence’ and how the WHO may press states to verify an outbreak event. This article seeks to understand whether internet surveillance response programs (ISRPs) are effective in ‘naming and shaming’ states into reporting disease outbreaks.

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Globalization, financial deregulation, economic turmoil, and technology breakthroughs are profoundly exposing organizations to business networks. Engaging these networks requires explicit planning from the strategic level down to the operational level of an organization, which significantly affects organizational artefacts such as business services, processes, and resources. Although enterprise architecture (EA) aligns business and IT aspects of organizational systems, previous applications of EA have not comprehensively addressed a methodological framework for planning. In the context of business networks, this study seeks to explore the application of EA for business network planning where it builds upon relevant and well-established prescriptive and descriptive aspects of EA. Prescriptive aspects include integrated models of services, business processes, and resources among other organizational artefacts, at both business and IT levels. Descriptive aspects include ontological classifications of business functionality, which allow EA models to be aligned semantically to organizational artefacts and, ultimately higher-level business strategy. A prominent approach for capturing descriptive aspects of EA is business capability modelling. In order to explore and develop the illustrative extensions of EA through capability modelling, a list of requirements (capability dimensions) for business network planning will be identified and validated through a revelatory case study encompassing different business network manifestations, or situations. These include virtual organization, liquid workforce, business network orchestration, and headquarters-subsidiary. The use of artefacts, conventionally, modelled through EA will be considered in these network situations. Two general considerations for EA extensions are explored for the identified requirements at the level of the network: extension of artefacts through the network and alignment of network level artefacts with individual organization artefacts. The list of requirements provides the basis for a constructivist extension of EA in the following ways. Firstly, for descriptive aspects, it offers constructivist insights to guide extensions for particular EA techniques and concepts. Secondly, for prescriptive aspects it defines a set of capability dimensions, which improve the analysis and assessment of organization capabilities for business network situations.

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We report on the comparative study of magnetotransport properties of large-area vertical few-layer graphene networks with different morphologies, measured in a strong (up to 10 T) magnetic field over a wide temperature range. The petal-like and tree-like graphene networks grown by a plasma enhanced CVD process on a thin (500 nm) silicon oxide layer supported by a silicon wafer demonstrate a significant difference in the resistance-magnetic field dependencies at temperatures ranging from 2 to 200 K. This behaviour is explained in terms of the effect of electron scattering at ultra-long reactive edges and ultra-dense boundaries of the graphene nanowalls. Our results pave a way towards three-dimensional vertical graphene-based magnetoelectronic nanodevices with morphology-tuneable anisotropic magnetic properties. © The Royal Society of Chemistry 2013.

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The effect of nitrogen on the growth of vertically oriented graphene nanosheets on catalyst-free silicon and glass substrates in a plasma-assisted process is studied. Different concentrations of nitrogen were found to act as versatile control knobs that could be used to tailor the length, number density and structural properties of the nanosheets. Nanosheets with different structural characteristics exhibit markedly different optical properties. The nanosheet samples were treated with a bovine serum albumin protein solution to investigate the effects of this variation on the optical properties for biosensing through confocal micro-Raman spectroscopy and UV-Vis spectrophotometry. © 2012 Optical Society of America.

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Realizing the promise of molecularly targeted inhibitors for cancer therapy will require a new level of knowledge about how a drug target is wired into the control circuitry of a complex cellular network. Here we review general homeostatic principles of cellular networks that enable the cell to be resilient in the face of molecular perturbations, while at the same time being sensitive to subtle input signals. Insights into such mechanisms may facilitate the development of combination therapies that take advantage of the cellular control circuitry, with the aim of achieving higher efficacy at a lower drug dosage and with a reduced probability of drug-resistance development.

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Tailoring the density of random single-walled carbon nanotube (SWCNT) networks is of paramount importance for various applications, yet it remains a major challenge due to the insufficient catalyst activation in most growth processes. Here we report on a simple and effective method to maximise the number of active catalyst nanoparticles using catalytic chemical vapor deposition (CCVD). By modulating short pulses of acetylene into a methane-based CCVD growth process, the density of SWCNTs is dramatically increased by up to three orders of magnitude without increasing the catalyst density and degrading the nanotube quality. In the framework of a vapor-liquid-solid model, we attribute the enhanced growth to the high dissociation rate of acetylene at high temperatures at the nucleation stage, which can be effective in both supersaturating the larger catalyst nanoparticles and overcoming the nanotube nucleation energy barrier of the smaller catalyst nanoparticles. These results are highly relevant to numerous applications of random SWCNT networks in next-generation energy, sensing and biomedical devices. © 2011 The Royal Society of Chemistry.

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Controlled synthesis of both single-walled carbon nanotube and carbon nanowire networks using the same CVD reactor and Fe/Al2O3 catalyst by slightly altering the hydrogenation and temperature conditions is demonstrated. Structural, bonding and electrical characterization using SEM, TEM, Raman spectroscopy, and temperature-dependent resistivity measurements suggest that the nanotubes are of a high quality and a large fraction (well above the common 33% and possibly up to 75%) of them are metallic. On the other hand, the carbon nanowires are amorphous and semiconducting and feature a controlled sp2/sp3 ratio. The growth mechanism which is based on the catalyst nanoisland analysis by AFM and takes into account the hydrogenation and temperature control effects explains the observed switch-over of the nanostructure growth modes. These results are important to achieve the ultimate control of chirality, structure, and conductivity of one-dimensional all-carbon networks.

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The electronic transport in both intrinsic and acid-treated single-walled carbon nanotube networks containing more than 90% semiconducting nanotubes is investigated using temperature-dependent resistance measurements. The semiconducting behavior observed in the intrinsic network is attributed to the three-dimensional electron hopping mechanism. In contrast, the chemical doping mechanism in the acid-treated network is found to be responsible for the revealed metal-like linear resistivity dependence in a broad temperature range. This effective method to control the electrical conductivity of single-walled carbon nanotube networks is promising for future nanoscale electronics, thermometry, and bolometry. © 2010 American Institute of Physics.

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Using advanced visualization techniques, a comprehensive visualization of all the stages of the self-organized growth of internetworked nanostructures on plasma-exposed surface has been made. Atomistic kinetic Monte Carlo simulation for the initial stage of deposition, with 3-D visualization of the whole system and half-tone visualization of the density field of the adsorbed atoms, makes it possible to implement a multiscale predictive modeling of the development of the nanoscale system.

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The fields of molecular biology and cell biology are being flooded with complex genomic and proteomic datasets of large dimensions. We now recognize that each molecule in the cell and tissue can no longer be viewed as an isolated entity. Instead, each molecule must be considered as one member of an interacting network. Consequently, there is an urgent need for mathematical models to understand the behavior of cell signaling networks in health and in disease.

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Molecular interactions that underlie pathophysiological states are being elucidated using techniques that profile proteomicend points in cellular systems. Within the field of cancer research, protein interaction networks play pivotal roles in the establishment and maintenance of the hallmarks of malignancy, including cell division, invasion, and migration. Multiple complementary tools enable a multifaceted view of how signal protein pathway alterations contribute to pathophysiological states.One pivotal technique is signal pathway profiling of patient tissue specimens. This microanalysis technology provides a proteomic snapshot at one point in time of cells directly procured from the native context of a tumor micro environment. To study the adaptive patterns of signal pathway events over time, before and after experimental therapy, it is necessary to obtain biopsies from patients before, during, and after therapy. A complementary approach is the profiling of cultured cell lines with and without treatment. Cultured cell models provide the opportunity to study short-term signal changes occurring over minutes to hours. Through this type of system, the effects of particular pharmacological agents may be used to test the effects of signal pathway inhibition or activation on multiple endpoints within a pathway. The complexity of the data generated has necessitated the development of mathematical models for optimal interpretation of interrelated signaling pathways. In combination,clinical proteomic biopsy profiling, tissue culture proteomic profiling, and mathematical modeling synergistically enable a deeper understanding of how protein associations lead to disease states and present new insights into the design of therapeutic regimens.

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We propose a reliable and ubiquitous group key distribution scheme that is suitable for ad hoc networks. The scheme has self-initialisation and self-securing features. The former feature allows a cooperation of an arbitrary number of nodes to initialise the system, and it also allows node admission to be performed in a decentralised fashion. The latter feature allows a group member to determine the group key remotely while maintaining the system security. We also consider a decentralised solution of establishing secure point-to-point communication. The solution allows a new node to establish a secure channel with every existing node if it has pre-existing secure channels with a threshold number of the existing nodes.