Professional development and social networks in digital content industry micro businesses

This study explores the professional development strategies of digital content professionals in Australian micro businesses. This thesis presents the argument that as these professionals are working in cutting edge creative fields where digital technology drives ongoing change, formal education experiences may be less important than for other professionals, and that specific types of online and face-to-face socially mediated informal learning strategies may be critical to currency. This thesis documents the findings of a broad survey of industry professionals' learning needs and development strategies, in conjunction with rich data from in-depth interviews and social network analyses.

Artificial neural networks to predict activity type and energy expenditure in youth

Previous studies have demonstrated that pattern recognition approaches to accelerometer data reduction are feasible and moderately accurate in classifying activity type in children. Whether pattern recognition techniques can be used to provide valid estimates of physical activity (PA) energy expenditure in youth remains unexplored in the research literature. Purpose: The objective of this study is to develop and test artificial neural networks (ANNs) to predict PA type and energy expenditure (PAEE) from processed accelerometer data collected in children and adolescents. Methods: One hundred participants between the ages of 5 and 15 yr completed 12 activity trials that were categorized into five PA types: sedentary, walking, running, light-intensity household activities or games, and moderate-to-vigorous intensity games or sports. During each trial, participants wore an ActiGraph GTIM on the right hip, and (V) Over dotO(2) was measured using the Oxycon Mobile (Viasys Healthcare, Yorba Linda, CA) portable metabolic system. ANNs to predict PA type and PAEE (METs) were developed using the following features: 10th, 25th, 50th, 75th, and 90th percentiles and the lag one autocorrelation. To determine the highest time resolution achievable, we extracted features from 10-, 15-, 20-, 30-, and 60-s windows. Accuracy was assessed by calculating the percentage of windows correctly classified and root mean square en-or (RMSE). Results: As window size increased from 10 to 60 s, accuracy for the PA-type ANN increased from 81.3% to 88.4%. RMSE for the MET prediction ANN decreased from 1.1 METs to 0.9 METs. At any given window size, RMSE values for the MET prediction ANN were 30-40% lower than the conventional regression-based approaches. Conclusions: ANNs can be used to predict both PA type and PAEE in children and adolescents using count data from a single waist mounted accelerometer.

Invisible connections : investigating older people's emotions and social relations around objects

The advent of the Internet of Things creates an interest in how people might interrelate through and with networks of internet enabled objects. With an emphasis on fostering social connection and physical activity among older people, this preliminary study investigated objects that people over the age of 65 years viewed as significant to them. We conducted contextual interviews in people's homes about their significant objects in order to understand the role of the objects in their lives, the extent to which they fostered emotional and social connections and physical activity, and how they might be augmented through internet connection. Discussion of significant objects generated considerable emotion in the participants. We identified objects of comfort and routine, objects that exhibited status, those that fostered independence and connection, and those that symbolized relationships with loved ones. These findings lead us to consider implications for the design of interconnected objects.

Mechanics of microfilaments networks : a cross-scales study

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.

The role and future challenges of wireless communication networks for cooperative autonomous city vehicles

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.

Nowhere to hide: informal disease surveillance networks tracing state behaviour

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.

Effects of wireless sensor network uncertainties on output-only modal analysis employing merged data of multiple tests

The use of Wireless Sensor Networks (WSNs) for vibration-based Structural Health Monitoring (SHM) has become a promising approach due to many advantages such as low cost, fast and flexible deployment. However, inherent technical issues such as data asynchronicity and data loss have prevented these distinct systems from being extensively used. Recently, several SHM-oriented WSNs have been proposed and believed to be able to overcome a large number of technical uncertainties. Nevertheless, there is limited research verifying the applicability of those WSNs with respect to demanding SHM applications like modal analysis and damage identification. Based on a brief review, this paper first reveals that Data Synchronization Error (DSE) is the most inherent factor amongst uncertainties of SHM-oriented WSNs. Effects of this factor are then investigated on outcomes and performance of the most robust Output-only Modal Analysis (OMA) techniques when merging data from multiple sensor setups. The two OMA families selected for this investigation are Frequency Domain Decomposition (FDD) and data-driven Stochastic Subspace Identification (SSI-data) due to the fact that they both have been widely applied in the past decade. Accelerations collected by a wired sensory system on a large-scale laboratory bridge model are initially used as benchmark data after being added with a certain level of noise to account for the higher presence of this factor in SHM-oriented WSNs. From this source, a large number of simulations have been made to generate multiple DSE-corrupted datasets to facilitate statistical analyses. The results of this study show the robustness of FDD and the precautions needed for SSI-data family when dealing with DSE at a relaxed level. Finally, the combination of preferred OMA techniques and the use of the channel projection for the time-domain OMA technique to cope with DSE are recommended.

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.