Intrinsics and dynamics of fat grafts : an in vitro study

Background Despite a revived interest in fat grafting procedures, clinicians still fail to demonstrate clearly the in vivo behavior of fat grafts as a dynamic tissue substitute. However, the basic principles in cellular biology teach us that cells can survive and develop, provided that a structural matrix exists that directs their behavior. The purpose of this in vitro study was to analyze that behavior of crude fat grafts, cultured on a three-dimensional laminin-rich matrix. Methods Nonprocessed, human fat biopsy specimens (approximately 1 mm) were inoculated on Matrigel-coated wells to which culture medium was added. The control group consisted of fat biopsy specimens embedded in medium alone. The cellular proliferation pattern was followed over 6 weeks. Additional cultures of primary generated cellular spheroids were performed and eventually subjected to adipogenic differentiation media. Results A progressive outgrowth of fibroblast-like cells from the core fat biopsy specimen was observed in both groups. Within the Matrigel group, an interconnecting three-dimensional network of spindle-shaped cells was established. This new cell colony reproduced spheroids that functioned again as solitary sources of cellular proliferation. Addition of differentiation media resulted in lipid droplet deposition in the majority of generated cells, indicating the initial steps of adipogenic differentiation. Conclusions The authors noticed that crude, nonprocessed fat biopsy specimens do have considerable potential for future tissue engineering-based applications, provided that the basic principles of developmental, cellular biology are respected. Spontaneous in vitro expansion of the stromal cells present in fat grafts within autologous and injectable matrices could create "off-the-shelf" therapies for reconstructive procedures.

SWIRLnet : portable anemometer network for wind speed measurements of land-falling tropical cyclones

Wind speed measurement systems are sparse in the tropical regions of Australia. Tropical cyclone wind speeds impacting communities are often ‘guestimated’ from analyzing damaged structures. A re-locatable anemometer system is required to enable measurements of wind speeds. This paper discusses design criteria of the tripods and tie down system, proposed deployment of the anemometers, instrumentation, and data logging. Preliminary assessment of the anemometer response indicates a reliable system for 1 second response, however, it is noted that the Australian building code and wind loading standard uses a moving average time of approximately 0.2 seconds for its wind speed design criteria.

Wind speed measurements of land-falling tropical cyclones using SWIRLnet, a portable anemometer network

Wind speed measurement systems are sparse in the tropical regions of Australia. Given this, tropical cyclone wind speeds impacting communities are seldom measured and often only ‘guestimated’ by analysing the extent of damage to structures. In an attempt to overcome this dearth of data, a re-locatable network of anemometers to be deployed prior to tropical cyclone landfall is currently being developed. This paper discusses design criteria of the network’s tripods and tie down system, proposed deployment of the anemometers, instrumentation and data logging. Preliminary assessment of the anemometer response indicates a reliable system for measuring the spectral component of wind with frequencies of approximately 1 Hz. This system limitation highlights an important difference between the capabilities of modern instrumentation and that of the Dines anemometer (around 0.2 seconds) that was used to develop much of the design criteria within the Australian building code and wind loading standard.

A gray-box neural network-based model identification and fault estimation scheme for nonlinear dynamic systems

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.

Co-movements in commodity prices : a note based on network analysis

This article analyses co-movements in a wide group of commodity prices during the time period 1992–2010. Our methodological approach is based on the correlation matrix and the networks inside. Through this approach we are able to summarize global interaction and interdependence, capturing the existing heterogeneity in the degrees of synchronization between commodity prices. Our results produce two main findings: (a) we do not observe a persistent increase in the degree of co-movement of the commodity prices in our time sample, however from mid-2008 to the end of 2009 co-movements almost doubled when compared with the average correlation; (b) we observe three groups of commodities which have exhibited similar price dynamics (metals, oil and grains, and oilseeds) and which have increased their degree of co-movement during the sampled period.

Social network resilience following organisational change

This paper presents two key findings from a longitudinal study examining the dynamics of social networks during organisational change. One, the degree to which users seek new sources of information while adapting to the change. Two, the degree to which social networks display structural resilience when undergoing significant structural and technological change. Users reported an increase in advice ties post-implementation, however a proportionally higher increase in ties within their work group compared to the wider network was identified. The results also supported the supposition that while IT driven change may initially disrupt social networks some networks possess a high degree of resilience, with key players reasserting their original positions of influence following the initial phase of change related disruption.

A parallel approach to social network generation and agent-based epidemic simulation

Understanding the dynamics of disease spread is essential in contexts such as estimating load on medical services, as well as risk assessment and interven- tion policies against large-scale epidemic outbreaks. However, most of the information is available after the outbreak itself, and preemptive assessment is far from trivial. Here, we report on an agent-based model developed to investigate such epidemic events in a stylised urban environment. For most diseases, infection of a new individual may occur from casual contact in crowds as well as from repeated interactions with social partners such as work colleagues or family members. Our model therefore accounts for these two phenomena. Given the scale of the system, efficient parallel computing is required. In this presentation, we focus on aspects related to paralllelisation for large networks generation and massively multi-agent simulations.

Autonomous helicopter hover using an artificial neural network

Details the developments to date of an unmanned air vehicle (UAV) based on a standard size 60 model helicopter. The design goal is to have the helicopter achieve stable hover with the aid of an INS and stereo vision. The focus of the paper is on the development of an artificial neural network (ANN) that makes use of only the INS data to generate hover commands, which are used to directly manipulate the flight servos. Current results show that networks incorporating some form of recurrency (state history) offer little advantage over those without. At this stage, the ANN has partially maintained periods of hover even with misaligned sensors.

Green Falcon IV: An Unmanned Aerial System and an integrated Wireless Sensor Network for remote sensing tasks

This report summarises the development of an Unmanned Aerial System and an integrated Wireless Sensor Network (WSN), suitable for the real world application in remote sensing tasks. Several aspects are discussed and analysed to provide improvements in flight duration, performance and mobility of the UAV, while ensuring the accuracy and range of data from the wireless sensor system.