CFRP strengthened CFST columns under vehicular impact

Concrete filled steel tubular (CFST) columns are increasingly used in bridge piers and high-rise buildings due to their excellent axial load bearing capacity. These columns may experience severe damage or failure due to transverse impact of vehicle collisions. In this study, numerical investigation is carried out to evaluate the effect of carbon fibre reinforced polymer (CFRP) strengthening CFST columns under vehicular impact. The CFRP composites damage mechanisms are simulated to account four different failure criteria. The cohesive elements are introduced as interface element to properly simulate the adhesively bonded regime. Simplified vehicle model is also developed to represent real vehicle behaviour. The FE analysis results show that externally bonded CFRP composites improve the impact resistance capacity compared to bare CFST column.

Comparing China's city transportation and economic networks

The city system has been a prevailing research issue in the fields of urban geography and regional economics. Not only do the relationships between cities in the city system exist in the form of rankings, but also in a more general network form. Previous work has examined the spatial structure of the city system in terms of its separate industrial networks, such as in transportation and economic activity, but little has been done to compare different networks. To rectify this situation, this study analyzes and reveals the spatial structural features of China’s city system by comparing its transportation and economic urban networks, thus providing new avenues for research on China’s city network. The results indicate that these two networks relate with each other by sharing structural equivalence with a basic diamond structure and a layered intercity structure decreasing outwards from the national centers. A decoupling effect also exists between them as the transportation network contributes to a balanced regional development, while the economic network promotes agglomeration economies. The law of economic development and the government both play important roles in the articulation between these two networks, and the gap between them can be shortened by related policy reforms and the improvement of the transportation network.

Evaluating the effects of international exposure from networks ties and mimetic pressure on clustered SMEs in Brazil

Research on the internationalisation of small and medium-sized enterprises (SMEs) has received increasing attention in recent years due to the important role they play in today’s economic environment. Internationalisation prompting, or awareness, is an already recognised phase of the innovation-related stages model (I-model). This phase of awareness is closely related to the international exposure that a firm may experience during the occasion when it realises its competitors are already internationalising. Although the literature has discussed the various forms in which international exposure may happen, there has been limited attention given to the extent of its effect on the internationalisation of clustered SMEs that behave according to the I-Model. This study will assess the applicability of the I-Model in a dynamic, competitive and co-operative setting of an industrial cluster. It also evaluates the impact (if any) of international exposure derived from networks and the mimetic pressure that these firms may experience due to their embeddedness in an industrial cluster. Results from this study will indicate the effectiveness of the improved adapted model that will provide a richer insight for both academic researchers and policy makers.

Knowledge networks and Australian Football League coach development: People of influence

Australian Football League (AFL) generally recognised as the ‘national game’ in Australia has a well established program of coach development. However, research examining AFL coaches’ work and how they learn to perform that work has hitherto not been conducted. The effective preparation of coaches is of prime concern to the AFL and should be informed by an examination of how coaches within the code come to know how to do their coaching work. Therefore, the purpose of this AFL-funded research was to inform coach development programs for current and aspiring AFL coaches.

Using enterprise social networks as innovation platforms

Do enterprise social network platforms in an organization make the company more innovative? In theory, through communication, collaboration, and knowledge exchange, innovation ideas can easily be expressed, shared, and discussed with many partners in the organization. Yet, whether this guarantees innovation success remains to be seen. The authors studied how innovation ideas moved--or not--from an enterprise social network platform to regular innovation processes at a large Australian retailer. They found that the success of innovation ideas depends on how easily understandable the idea is on the platform, how long it has been discussed, and how powerful the social network participants are in the organization. These findings inform management strategies for the governance of enterprise social network use and the organizational innovation process.

Posters versus lurkers. Improving participation in enterprise social networks through management interventions

This thesis investigated the phenomenon of underutilised Enterprise social networks (ESNs). Guided by established theories, we identified key reasons that drive ESN members to either post (i.e., create content) or lurk (i.e., read others' content) and examined the influence of three management interventions - aim to boost participation - on lurkers' and posters' beliefs and participation. We test our model with data collected from 366 members in Google⁺ communities in a large Australian retail organization. We find that posters and lurkers are motivated and hindered by different factors. Moreover, management interventions do not – always – yield the hoped-for results among lurkers.

Predicting the temporal response of seagrass meadows to dredging using Dynamic Bayesian Networks

Predicting temporal responses of ecosystems to disturbances associated with industrial activities is critical for their management and conservation. However, prediction of ecosystem responses is challenging due to the complexity and potential non-linearities stemming from interactions between system components and multiple environmental drivers. Prediction is particularly difficult for marine ecosystems due to their often highly variable and complex natures and large uncertainties surrounding their dynamic responses. Consequently, current management of such systems often rely on expert judgement and/or complex quantitative models that consider only a subset of the relevant ecological processes. Hence there exists an urgent need for the development of whole-of-systems predictive models to support decision and policy makers in managing complex marine systems in the context of industry based disturbances. This paper presents Dynamic Bayesian Networks (DBNs) for predicting the temporal response of a marine ecosystem to anthropogenic disturbances. The DBN provides a visual representation of the problem domain in terms of factors (parts of the ecosystem) and their relationships. These relationships are quantified via Conditional Probability Tables (CPTs), which estimate the variability and uncertainty in the distribution of each factor. The combination of qualitative visual and quantitative elements in a DBN facilitates the integration of a wide array of data, published and expert knowledge and other models. Such multiple sources are often essential as one single source of information is rarely sufficient to cover the diverse range of factors relevant to a management task. Here, a DBN model is developed for tropical, annual Halophila and temperate, persistent Amphibolis seagrass meadows to inform dredging management and help meet environmental guidelines. Specifically, the impacts of capital (e.g. new port development) and maintenance (e.g. maintaining channel depths in established ports) dredging is evaluated with respect to the risk of permanent loss, defined as no recovery within 5 years (Environmental Protection Agency guidelines). The model is developed using expert knowledge, existing literature, statistical models of environmental light, and experimental data. The model is then demonstrated in a case study through the analysis of a variety of dredging, environmental and seagrass ecosystem recovery scenarios. In spatial zones significantly affected by dredging, such as the zone of moderate impact, shoot density has a very high probability of being driven to zero by capital dredging due to the duration of such dredging. Here, fast growing Halophila species can recover, however, the probability of recovery depends on the presence of seed banks. On the other hand, slow growing Amphibolis meadows have a high probability of suffering permanent loss. However, in the maintenance dredging scenario, due to the shorter duration of dredging, Amphibolis is better able to resist the impacts of dredging. For both types of seagrass meadows, the probability of loss was strongly dependent on the biological and ecological status of the meadow, as well as environmental conditions post-dredging. The ability to predict the ecosystem response under cumulative, non-linear interactions across a complex ecosystem highlights the utility of DBNs for decision support and environmental management.

Using Bayesian networks to model surveillance in complex plant and animal health systems

In this chapter we consider biosecurity surveillance as part of a complex system comprising many different biological, environmental and human factors and their interactions. Modelling and analysis of surveillance strategies should take into account these complexities, and also facilitate the use and integration of the many types of different information that can provide insight into the system as a whole. After a brief discussion of a range of options, we focus on Bayesian networks for representing such complex systems. We summarize the features of Bayesian networks and describe these in the context of surveillance.

A flexible tool for integrated planning of active distribution networks

This paper presents a flexible and integrated planning tool for active distribution network to maximise the benefits of having high level s of renewables, customer engagement, and new technology implementations. The tool has two main processing parts: “optimisation” and “forecast”. The “optimization” part is an automated and integrated planning framework to optimize the net present value (NPV) of investment strategy for electric distribution network augmentation over large areas and long planning horizons (e.g. 5 to 20 years) based on a modified particle swarm optimization (MPSO). The “forecast” is a flexible agent-based framework to produce load duration curves (LDCs) of load forecasts for different levels of customer engagement, energy storage controls, and electric vehicles (EVs). In addition, “forecast” connects the existing databases of utility to the proposed tool as well as outputs the load profiles and network plan in Google Earth. This integrated tool enables different divisions within a utility to analyze their programs and options in a single platform using comprehensive information.

Revocation and update of trust in autonomous delay tolerant networks

A Delay Tolerant Network (DTN) is a dynamic, fragmented, and ephemeral network formed by a large number of highly mobile nodes. DTNs are ephemeral networks with highly mobile autonomous nodes. This requires distributed and self-organised approaches to trust management. Revocation and replacement of security credentials under adversarial influence by preserving the trust on the entity is still an open problem. Existing methods are mostly limited to detection and removal of malicious nodes. This paper makes use of the mobility property to provide a distributed, self-organising, and scalable revocation and replacement scheme. The proposed scheme effectively utilises the Leverage of Common Friends (LCF) trust system concepts to revoke compromised security credentials, replace them with new ones, whilst preserving the trust on them. The level of achieved entity confidence is thereby preserved. Security and performance of the proposed scheme is evaluated using an experimental data set in comparison with other schemes based around the LCF concept. Our extensive experimental results show that the proposed scheme distributes replacement credentials up to 35% faster and spreads spoofed credentials of strong collaborating adversaries up to 50% slower without causing any significant increase on the communication and storage overheads, when compared to other LCF based schemes.

Decentralised key management for delay tolerant networks

Public key authentication is the verification of the identity-public key binding, and is foundational to the security of any network. The contribution of this thesis has been to provide public key authentication for a decentralised and resource challenged network such as an autonomous Delay Tolerant Network (DTN). It has resulted in the development and evaluation of a combined co-localisation trust system and key distribution scheme evaluated on a realistic large geographic scale mobility model. The thesis also addresses the problem of unplanned key revocation and replacement without any central authority.

The application of islanded photovoltaic cells and battery storage on the reliability of distribution networks

Renewable energy resources, in particularly PV and battery storage are increasingly becoming part of residential and agriculture premises to manage their electricity consumption. This thesis addresses the tremendous technical, financial and planning challenges for utilities created by these increases, by offering techniques to examine the significance of various renewable resources in electricity network planning. The outcome of this research should assist utilities and customers for adequate planning that can be financially effective.

Privacy architectures in social networks’ state-of-the-art survey

The use of social networking has exploded, with millions of people using various web- and mobile-based services around the world. This increase in social networking use has led to user anxiety related to privacy and the unauthorised exposure of personal information. Large-scale sharing in virtual spaces means that researchers, designers and developers now need to re-consider the issues and challenges of maintaining privacy when using social networking services. This paper provides a comprehensive survey of the current state-of-the-art privacy in social networks for both desktop and mobile uses and devices from various architectural vantage points. The survey will assist researchers and analysts in academia and industry to move towards mitigating many of the privacy issues in social networks.

Understanding data flow and security requirements in wireless Body Area Networks for healthcare

The Body Area Network (BAN) is an emerging technology that focuses on monitoring physiological data in, on and around the human body. BAN technology permits wearable and implanted sensors to collect vital data about the human body and transmit it to other nodes via low-energy communication. In this paper, we investigate interactions in terms of data flows between parties involved in BANs under four different scenarios targeting outdoor and indoor medical environments: hospital, home, emergency and open areas. Based on these scenarios, we identify data flow requirements between BAN elements such as sensors and control units (CUs) and parties involved in BANs such as the patient, doctors, nurses and relatives. Identified requirements are used to generate BAN data flow models. Petri Nets (PNs) are used as the formal modelling language. We check the validity of the models and compare them with the existing related work. Finally, using the models, we identify communication and security requirements based on the most common active and passive attack scenarios.

Social networks and use of social supports of minority elders in East Harlem

Considerable empirical research substantiates the importance of social networks on health and well-being in later life. A study of ethnic minority elders living in two low income public housing buildings in East Harlem was undertaken to gain an understanding of the relationship between their health status and social networks. Findings demonstrate that elders with supportive housing had better psychological outcomes and used significantly more informal supports when in need. However, elders with serious health problems had poorer outcomes regardless of their level of social support. This study highlights the potential of supportive living environments to foster social integration and to optimise formal and informal networks.