Applying Enterprise Architecture to Business Networks

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.

Large networks of vertical multi-layer graphenes with morphology-tunable magnetoresistance

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.

Deterministic control of structural and optical properties of plasma-grown vertical graphene nanosheet networks via nitrogen gas variation

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.

Proteins, drug targets and the mechanisms they control : the simple truth about complex networks

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.

3-Orders-of-magnitude density control of single-walled carbon nanotube networks by maximizing catalyst activation and dosing carbon supply

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.

Controlled synthesis of a large fraction of metallic single-walled carbon nanotube and semiconducting carbon nanowire networks

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.

Controlled electronic transport in single-walled carbon nanotube networks : selecting electron hopping and chemical doping mechanisms

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.

Simulation and visualization of self-assembled nanodevice networks synthesized via plasma–surface interaction

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.

A control theoretic paradigm for cell signaling networks : a simple complexity for a sensitive robustness

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.

Brain decoding based on functional magnetic resonance imaging using machine learning : a comparative study

Brain decoding of functional Magnetic Resonance Imaging data is a pattern analysis task that links brain activity patterns to the experimental conditions. Classifiers predict the neural states from the spatial and temporal pattern of brain activity extracted from multiple voxels in the functional images in a certain period of time. The prediction results offer insight into the nature of neural representations and cognitive mechanisms and the classification accuracy determines our confidence in understanding the relationship between brain activity and stimuli. In this paper, we compared the efficacy of three machine learning algorithms: neural network, support vector machines, and conditional random field to decode the visual stimuli or neural cognitive states from functional Magnetic Resonance data. Leave-one-out cross validation was performed to quantify the generalization accuracy of each algorithm on unseen data. The results indicated support vector machine and conditional random field have comparable performance and the potential of the latter is worthy of further investigation.

Dopamine transporter genotype predicts behavioural and neural measures of response inhibition

The ability to inhibit unwanted actions is a heritable executive function that may confer risk to disorders such as attention deficit hyperactivity disorder (ADHD). Converging evidence from pharmacology and cognitive neuroscience suggests that response inhibition is instantiated within frontostriatal circuits of the brain with patterns of activity that are modulated by the catecholamines dopamine and noradrenaline. A total of 405 healthy adult participants performed the stop-signal task, a paradigmatic measure of response inhibition that yields an index of the latency of inhibition, termed the stop-signal reaction time (SSRT). Using this phenotype, we tested for genetic association, performing high-density single-nucleotide polymorphism mapping across the full range of autosomal catecholamine genes. Fifty participants also underwent functional magnetic resonance imaging to establish the impact of associated alleles on brain and behaviour. Allelic variation in polymorphisms of the dopamine transporter gene (SLC6A3: rs37020; rs460000) predicted individual differences in SSRT, after corrections for multiple comparisons. Furthermore, activity in frontal regions (anterior frontal, superior frontal and superior medial gyri) and caudate varied additively with the T-allele of rs37020. The influence of genetic variation in SLC6A3 on the development of frontostriatal inhibition networks may represent a key risk mechanism for disorders of behavioural inhibition.

Modeling of protein signaling networks in clinical proteomics

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.

Securing multicast groups in ad hoc networks

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.

Security and Privacy in Vehicular Ad-Hoc Networks : Survey and the Road Ahead

Vehicular Ad-hoc Networks (VANETs) can make roads safer, cleaner, and smarter. It can offer a wide range of services, which can be safety and non-safety related. Many safety-related VANETs applications are real-time and mission critical, which would require strict guarantee of security and reliability. Even non-safety related multimedia applications, which will play an important role in the future, will require security support. Lack of such security and privacy in VANETs is one of the key hindrances to the wide spread implementations of it. An insecure and unreliable VANET can be more dangerous than the system without VANET support. So it is essential to make sure that “life-critical safety” information is secure enough to rely on. Securing the VANETs along with appropriate protection of the privacy drivers or vehicle owners is a very challenging task. In this work we summarize the attacks, corresponding security requirements and challenges in VANETs. We also present the most popular generic security policies which are based on prevention as well detection methods. Many VANETs applications require system-wide security support rather than individual layer from the VANETs’ protocol stack. In this work we will review the existing works in the perspective of holistic approach of security. Finally, we will provide some possible future directions to achieve system-wide security as well as privacy-friendly security in VANETs.

Detection of sinkhole attack in wireless sensor networks

Generally wireless sensor networks rely of many-to-one communication approach for data gathering. This approach is extremely susceptible to sinkhole attack, where an intruder attracts surrounding nodes with unfaithful routing information, and subsequently presents selective forwarding or change the data that carry through it. A sinkhole attack causes an important threat to sensor networks and it should be considered that the sensor nodes are mostly spread out in open areas and of weak computation and battery power. In order to detect the intruder in a sinkhole attack this paper suggests an algorithm which firstly finds a group of suspected nodes by analyzing the consistency of data. Then, the intruder is recognized efficiently in the group by checking the network flow information. The proposed algorithm's performance has been evaluated by using numerical analysis and simulations. Therefore, accuracy and efficiency of algorithm would be verified.