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.

Systematic review of interventions to improve the provision of information for adults with primary brain tumours and their caregivers

Background: Adults with primary brain tumors and their caregivers have significant information needs. This review assessed the effect of interventions to improve information provision for adult primary brain tumor patients and/or their caregivers. Methods: We included randomized or nonrandomized trials testing educational interventions that had outcomes of information provision, knowledge, understanding, recall, or satisfaction with the intervention, for adults diagnosed with primary brain tumors and/or their family or caregivers. PubMed, MEDLINE, EMBASE and Cochrane Reviews databases were searched for studies published between 1980 and June 2014. Results: Two randomized controlled, one non-randomized controlled, and 10 single group pre-post trials enrolled more than 411 participants. Five group, four practice/process change and four individual interventions assessed satisfaction (12 studies), knowledge (four studies) or information provision (2 studies). Nine studies reported high rates of satisfaction. Three studies showed statistically significant improvements over time in knowledge and two showed greater information was provided to intervention than control group participants, although statistical testing was not performed. Discussion: The trials assessed intermediate outcomes such as satisfaction, and only 4/13 reported on knowledge improvements. Few trials had a randomized controlled design and risk of bias was either evident or could not be assessed in most domains.

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.

Cooperative game play with avatars and agents: Differences in brain activity and the experience of play

The current study sought to identify the impact of whether teammates in a cooperative videogame were controlled by other humans (avatars) or by the game (agents). The impact on player experience was explored through both subjective questionnaire measures and brain wave activity measurement (electroencephalography). Play with human teammates was associated with a greater sense of relatedness, but less competence and flow than play with other computer-controlled teammates. In terms of brain activity, play with human teammates was associated with greater activity in the alpha, theta and beta power bands than play with computer-controlled teammates. Overall, the results suggest that play with human teammates involves greater cognitive activity in terms of 'mentalising' than play with computer-controlled teammates. Additionally, the associations between subjective measures of player experience and brain activity are described. Limitations of the current study are identified and key directions for future research are discussed.

Genetic analysis of structural brain connectivity using DICCCOL models of diffusion MRI in 522 twins

Genetic and environmental factors affect white matter connectivity in the normal brain, and they also influence diseases in which brain connectivity is altered. Little is known about genetic influences on brain connectivity, despite wide variations in the brain's neural pathways. Here we applied the 'DICCCOL' framework to analyze structural connectivity, in 261 twin pairs (522 participants, mean age: 21.8 y ± 2.7SD). We encoded connectivity patterns by projecting the white matter (WM) bundles of all 'DICCCOLs' as a tracemap (TM). Next we fitted an A/C/E structural equation model to estimate additive genetic (A), common environmental (C), and unique environmental/error (E) components of the observed variations in brain connectivity. We found 44 'heritable DICCCOLs' whose connectivity was genetically influenced (α2>1%); half of them showed significant heritability (α2>20%). Our analysis of genetic influences on WM structural connectivity suggests high heritability for some WM projection patterns, yielding new targets for genome-wide association studies.

Elucidating the neuropsychological profile of apathetic syndrome and disinhibition syndrome in a brain-injured population in Oman

The purpose of this study was to compare the neuropsychological performance of two frontal dysexecutive phenotypes - disinhibited&' syndrome (DS) and &'apathetic&' syndrome (AS) following a traumatic brain injury in a non-western population, Oman. Methods: The study compared the performance of DS and AS in neuropsychological measures including those tapping into verbal reasoning ability/working memory/attention planning/goal-directed behavior and affective ranges. Results: The present analysis showed that DS and AS participants did not differ on indices measuring working memory/attention and affective ranges. However, the two cohorts differed significantly in measures of planning/goal-directed behaviour. Conclusion: This study lays the groundwork for further scrutiny in delineating the different characteristics of what has been previously labelled as frontal dysexecutive phenotype. This study indicates that DS and AS are marked with specific neuropsychological deficits.

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.

First-Line afatinib versus chemotherapy in patients with non–small cell lung cancer and common epidermal growth factor receptor gene mutations and brain metastases

Introduction Metastatic spread to the brain is common in patients with non–small cell lung cancer (NSCLC), but these patients are generally excluded from prospective clinical trials. The studies, phase III study of afatinib or cisplatin plus pemetrexed in patients with metastatic lung adenocarcinoma with EGFR mutations (LUX-Lung 3) and a randomized, open-label, phase III study of BIBW 2992 versus chemotherapy as first-line treatment for patients with stage IIIB or IV adenocarcinoma of the lung harbouring an EGFR activating mutation (LUX-Lung 6) investigated first-line afatinib versus platinum-based chemotherapy in epidermal growth factor receptor gene (EGFR) mutation-positive patients with NSCLC and included patients with brain metastases; prespecified subgroup analyses are assessed in this article. Methods For both LUX-Lung 3 and LUX-Lung 6, prespecified subgroup analyses of progression-free survival (PFS), overall survival, and objective response rate were undertaken in patients with asymptomatic brain metastases at baseline (n = 35 and n = 46, respectively). Post hoc analyses of clinical outcomes was undertaken in the combined data set (n = 81). Results In both studies, there was a trend toward improved PFS with afatinib versus chemotherapy in patients with brain metastases (LUX-Lung 3: 11.1 versus 5.4 months, hazard ratio [HR] = 0.54, p = 0.1378; LUX-Lung 6: 8.2 versus 4.7 months, HR = 0.47, p = 0.1060). The magnitude of PFS improvement with afatinib was similar to that observed in patients without brain metastases. In combined analysis, PFS was significantly improved with afatinib versus with chemotherapy in patients with brain metastases (8.2 versus 5.4 months; HR, 0.50; p = 0.0297). Afatinib significantly improved the objective response rate versus chemotherapy in patients with brain metastases. Safety findings were consistent with previous reports. Conclusions These findings lend support to the clinical activity of afatinib in EGFR mutation–positive patients with NSCLC and asymptomatic brain metastases.

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.