Impact of mobility constraints on epidemic broadcast mechanisms in delay-tolerant networks

In this paper, we investigate the effect of mobility constraints on epidemic broadcast mechanisms in DTNs (Delay-Tolerant Networks). Major factors affecting epidemic broadcast performances are its forwarding algorithm and node mobility. The impact of forwarding algorithm and node mobility on epidemic broadcast mechanisms has been actively studied in the literature, but those studies generally use unconstrained mobility models. The objective of this paper is therefore to quantitatively investigate the effect of mobility constraints on epidemic broadcast mechanisms. We evaluate the performances of three classes of epidemic broadcast mechanisms - P-BCAST (PUSH-based BroadCast), SA-BCAST (Self-Adaptive BroadCast), and HP-BCAST (History-based P-BCAST) - with a random waypoint mobility model with mobility constraints. Our finding includes that the existence of mobility constraints significantly improves the reach ability and dissemination speed of epidemic broadcast mechanisms while degrading their efficiency.

Polysynaptic potentials within the lateral amygdala networks as indicators of reverberatory activity

Synaptic plasticity in the lateral amygdala (LA) may underlie auditory fear conditioning. Hebb postulated that sustained activity in reverberating cellular ensembles can facilitate temporal coincidence detection. Our anatomical data show that LA neurons have extensive local axon collaterals that are topographically organized and that could provide the anatomical basis for reverberatory activity...

Deriving word association networks from text corpora

This article presents and evaluates a model to automatically derive word association networks from text corpora. Two aspects were evaluated: To what degree can corpus-based word association networks (CANs) approximate human word association networks with respect to (1) their ability to quantitatively predict word associations and (2) their structural network characteristics. Word association networks are the basis of the human mental lexicon. However, extracting such networks from human subjects is laborious, time consuming and thus necessarily limited in relation to the breadth of human vocabulary. Automatic derivation of word associations from text corpora would address these limitations. In both evaluations corpus-based processing provided vector representations for words. These representations were then employed to derive CANs using two measures: (1) the well known cosine metric, which is a symmetric measure, and (2) a new asymmetric measure computed from orthogonal vector projections. For both evaluations, the full set of 4068 free association networks (FANs) from the University of South Florida word association norms were used as baseline human data. Two corpus based models were benchmarked for comparison: a latent topic model and latent semantic analysis (LSA). We observed that CANs constructed using the asymmetric measure were slightly less effective than the topic model in quantitatively predicting free associates, and slightly better than LSA. The structural networks analysis revealed that CANs do approximate the FANs to an encouraging degree.

Service Interface Synthesis in Business Networks

Mismatches between services needing to interoperate have been addressed through the adaptation of structural and behavioural interfaces of services, which in practice incur long lead time through manual, coding effort. We propose a framework, complementary to con- ventional service adaptation, to synthesise service interfaces in the open setting of business networks, allowing consumers to introspect service interfaces and formulate service invocations. The framework also allows evolved service requests, as new features of service capabilities are discov- ered, through interactions with other, similar services. Finally the frame- work fosters reuse of adaptation efforts through normalisation of struc- tural and behavioural interfaces of similar services. This paper provides a first exposition of the service interface synthesis framework, describing patterns containing novel requirements for unilateral service adaptation and detailing the interface synthesis technique. Complex examples of ser- vices drawn from commercial logistic systems are then used to validate the synthesis technique and identify open challenges and future research directions.

Prior and posterior linear pooling for combining expert opinions: Uses and impact on Bayesian Networks

The use of expert knowledge to quantify a Bayesian Network (BN) is necessary when data is not available. This however raises questions regarding how opinions from multiple experts can be used in a BN. Linear pooling is a popular method for combining probability assessments from multiple experts. In particular, Prior Linear Pooling (PrLP), which pools opinions then places them into the BN is a common method. This paper firstly proposes an alternative pooling method, Posterior Linear Pooling (PoLP). This method constructs a BN for each expert, then pools the resulting probabilities at the nodes of interest. Secondly, it investigates the advantages and disadvantages of using these pooling methods to combine the opinions of multiple experts. Finally, the methods are applied to an existing BN, the Wayfinding Bayesian Network Model, to investigate the behaviour of different groups of people and how these different methods may be able to capture such differences. The paper focusses on 6 nodes Human Factors, Environmental Factors, Wayfinding, Communication, Visual Elements of Communication and Navigation Pathway, and three subgroups Gender (female, male),Travel Experience (experienced, inexperienced), and Travel Purpose (business, personal) and finds that different behaviors can indeed be captured by the different methods.

Voltage balance improvement in urban low voltage distribution networks

This project is a step forward in developing effective methods to mitigate voltage unbalance in urban residential networks. The method is proposed to reduce energy losses and improve quality of service in strongly unbalanced low-voltage networks. The method is based on phase swapping as well as optimal placement and sizing of Distribution Static Synchronous Compensator (D-STATCOM) using a Particle Swarm Optimisation method.

Establishing international networks: The internationalisation of Calleija Jewellers and their partnership with the exclusive Aston Martin

Calleija, a small to medium sized (SME) Australian and internationally recognised fine jeweller has secured a significant strategic global partnership with one of the world’s best-known luxury automobile brands, Aston Martin. Forging this international relationship to produce an elegant fine jewellery collection has given rise to a new network between the Australian jewellery industry and the European automobile industry. Calleija’s exclusive association with Aston Martin consolidates a shared passion for the finest quality and craftsmanship which was inspired by Aston Martin’s Supercar, the One-77. This inspiration lead to John Calleija being chosen by Aston Martin to design this latest high-luxury offering in which each design is limited to only 77 pieces utilising 30 unique designs (Calleija, 2012). The story behind Calleija’s internationalisation to the United Kingdom (UK) and their subsequent business-to-business strategic partnership with Aston Martin is no doubt a good sign for the Australian jewellery industry.

The role of innovation intermediaries in collaborative networks of small and medium-sized enterprises

The major challenge of European Union’s agricultural industry is to ensure sustainable supply of quality food that meets the demands of a rapidly growing population, changing dietary patterns, increased competition for land use, and environmental concerns. Investments in research and innovation, which facilitate integration of external knowledge in food chain operations, are crucial to undertaking such challenges. This paper addresses how SMEs successfully innovate within collaborative networks with the assistance of innovation intermediaries. In particular, we explore the roles of innovation intermediaries in knowledge acquisition, knowledge assimilation, knowledge, transformation, and knowledge exploitation in open innovation initiatives from the wine industry through the theoretical lens of absorptive capacity. Based on two case studies from the wine industry, we identified seven key activities performed by innovation intermediaries that complement SMEs’ ability to successfully leverage external sources of knowledge for innovation purposes. These activities are articulation of knowledge needs and innovation capabilities, facilitation of social interactions, establishment of complementary links, implementation of governance structures, conflict management, enhancement of transparency, and mediation of communication. Our in-depth qualitative study of two innovation intermediaries in the wine industry has several important implications that contribute to research and practice.

Development of the 'rich club' in brain connectivity networks from 438 adolescents & adults aged 12 to 30

The 'rich club' coefficient describes a phenomenon where a network's hubs (high-degree nodes) are on average more intensely interconnected than lower-degree nodes. Networks with rich clubs often have an efficient, higher-order organization, but we do not yet know how the rich club emerges in the living brain, or how it changes as our brain networks develop. Here we chart the developmental trajectory of the rich club in anatomical brain networks from 438 subjects aged 12-30. Cortical networks were constructed from 68×68 connectivity matrices of fiber density, using whole-brain tractography in 4-Tesla 105-gradient high angular resolution diffusion images (HARDI). The adult and younger cohorts had rich clubs that included different nodes; the rich club effect intensified with age. Rich-club organization is a sign of a network's efficiency and robustness. These concepts and findings may be advantageous for studying brain maturation and abnormal brain development.

Genetics of path lengths in brain connectivity networks: HARDI-based maps in 457 adults

Brain connectivity analyses are increasingly popular for investigating organization. Many connectivity measures including path lengths are generally defined as the number of nodes traversed to connect a node in a graph to the others. Despite its name, path length is purely topological, and does not take into account the physical length of the connections. The distance of the trajectory may also be highly relevant, but is typically overlooked in connectivity analyses. Here we combined genotyping, anatomical MRI and HARDI to understand how our genes influence the cortical connections, using whole-brain tractography. We defined a new measure, based on Dijkstra's algorithm, to compute path lengths for tracts connecting pairs of cortical regions. We compiled these measures into matrices where elements represent the physical distance traveled along tracts. We then analyzed a large cohort of healthy twins and show that our path length measure is reliable, heritable, and influenced even in young adults by the Alzheimer's risk gene, CLU.

L-dopa modulates functional connectivity in striatal cognitive and motor networks: A double-blind placebo-controlled study

Functional connectivity (FC) analyses of resting-state fMRI data allow for the mapping of large-scale functional networks, and provide a novel means of examining the impact of dopaminergic challenge. Here, using a double-blind, placebo-controlled design, we examined the effect of L-dopa, a dopamine precursor, on striatal resting-state FC in 19 healthy young adults.Weexamined the FC of 6 striatal regions of interest (ROIs) previously shown to elicit networks known to be associated with motivational, cognitive and motor subdivisions of the caudate and putamen (Di Martino et al., 2008). In addition to replicating the previously demonstrated patterns of striatal FC, we observed robust effects of L-dopa. Specifically, L-dopa increased FC in motor pathways connecting the putamen ROIs with the cerebellum and brainstem. Although L-dopa also increased FC between the inferior ventral striatum and ventrolateral prefrontal cortex, it disrupted ventral striatal and dorsal caudate FC with the default mode network. These alterations in FC are consistent with studies that have demonstrated dopaminergic modulation of cognitive and motor striatal networks in healthy participants. Recent studies have demonstrated altered resting state FC in several conditions believed to be characterized by abnormal dopaminergic neurotransmission. Our findings suggest that the application of similar experimental pharmacological manipulations in such populations may further our understanding of the role of dopaminergic neurotransmission in those conditions.

Identifying rate-limiting nodes in large-scale cortical networks for visuospatial processing: An illustration using fMRI

With the advent of functional neuroimaging techniques, in particular functional magnetic resonance imaging (fMRI), we have gained greater insight into the neural correlates of visuospatial function. However, it may not always be easy to identify the cerebral regions most specifically associated with performance on a given task. One approach is to examine the quantitative relationships between regional activation and behavioral performance measures. In the present study, we investigated the functional neuroanatomy of two different visuospatial processing tasks, judgement of line orientation and mental rotation. Twenty-four normal participants were scanned with fMRI using blocked periodic designs for experimental task presentation. Accuracy and reaction time (RT) to each trial of both activation and baseline conditions in each experiment was recorded. Both experiments activated dorsal and ventral visual cortical areas as well as dorsolateral prefrontal cortex. More regionally specific associations with task performance were identified by estimating the association between (sinusoidal) power of functional response and mean RT to the activation condition; a permutation test based on spatial statistics was used for inference. There was significant behavioral-physiological association in right ventral extrastriate cortex for the line orientation task and in bilateral (predominantly right) superior parietal lobule for the mental rotation task. Comparable associations were not found between power of response and RT to the baseline conditions of the tasks. These data suggest that one region in a neurocognitive network may be most strongly associated with behavioral performance and this may be regarded as the computationally least efficient or rate-limiting node of the network.

Multiple stages classification of Alzheimer’s disease based on structural brain networks using generalized low rank approximations (GLRAM)

To classify each stage for a progressing disease such as Alzheimer’s disease is a key issue for the disease prevention and treatment. In this study, we derived structural brain networks from diffusion-weighted MRI using whole-brain tractography since there is growing interest in relating connectivity measures to clinical, cognitive, and genetic data. Relatively little work has usedmachine learning to make inferences about variations in brain networks in the progression of the Alzheimer’s disease. Here we developed a framework to utilize generalized low rank approximations of matrices (GLRAM) and modified linear discrimination analysis for unsupervised feature learning and classification of connectivity matrices. We apply the methods to brain networks derived from DWI scans of 41 people with Alzheimer’s disease, 73 people with EMCI, 38 people with LMCI, 47 elderly healthy controls and 221 young healthy controls. Our results show that this new framework can significantly improve classification accuracy when combining multiple datasets; this suggests the value of using data beyond the classification task at hand to model variations in brain connectivity.

Tuning the resonance properties of 2D carbon nanotube networks towards a mechanical resonator

The capabilities of the mechanical resonator-based nanosensors in detecting ultra-small mass or force shifts have driven a continuing exploration of the palette of nanomaterials for such application purposes. Based on large-scale molecular dynamics simulations, we have assessed the applicability of a new class of carbon nanomaterials for nanoresonator usage, i.e. the single-wall carbon nanotube (SWNT) network. It is found that SWNT networks inherit excellent mechanical properties from the constituent SWNTs, possessing a high natural frequency. However, although a high quality factor is suggested from the simulation results, it is hard to obtain an unambiguous Q-factor due to the existence of vibration modes in addition to the dominant mode. The nonlinearities resulting from these extra vibration modes are found to exist uniformly under various testing conditions including different initial actuations and temperatures. Further testing shows that these modes can be effectively suppressed through the introduction of axial strain, leading to an extremely high quality factor in the order of 109 estimated from the SWNT network with 2% tensile strain. Additional studies indicate that the carbon rings connecting the SWNTs can also be used to alter the vibrational properties of the resulting network. This study suggests that the SWNT network can be a good candidate for applications as nanoresonators.