Characteristics of Bangladeshi corporate networks

Over the past 40 years, Bangladesh has been undergoing economic reforms and institutional transitions to a market economy. An important product of this strategic transition has been the emergence of interlocking directorates, where a director sits on multiple directorate boards of corporations. Given this background, this thesis seeks to examine the attributes of the corporate network of interlocking directorships in Bangladesh. To date, the study of interlocking directorates has concentrated on Western economies. This study provides the results from a systematic exploration of the corporate network of Bangladesh based on data drawn from 100 largest listed companies in 2010.

Diffusion tensor of water in partially aligned fibre networks

Monte Carlo simulations were used to investigate the relationship between the morphological characteristics and the diffusion tensor (DT) of partially aligned networks of cylindrical fibres. The orientation distributions of the fibres in each network were approximately uniform within a cone of a given semi-angle (θ0). This semi-angle was used to control the degree of alignment of the fibres. The networks studied ranged from perfectly aligned (θ0 = 0) to completely disordered (θ0 = 90°). Our results are qualitatively consistent with previous numerical models in the overall behaviour of the DT. However, we report a non-linear relationship between the fractional anisotropy (FA) of the DT and collagen volume fraction, which is different to the findings from previous work. We discuss our results in the context of diffusion tensor imaging of articular cartilage. We also demonstrate how appropriate diffusion models have the potential to enable quantitative interpretation of the experimentally measured diffusion-tensor FA in terms of collagen fibre alignment distributions.

Neurons Activated During Fear Memory Consolidation and Reconsolidation are Mapped to a Common and New Topography in the Lateral Amygdala

A key question in neuroscience is how memory is selectively allocated to neural networks in the brain. This question remains a significant research challenge, in both rodent models and humans alike, because of the inherent difficulty in tracking and deciphering large, highly dimensional neuronal ensembles that support memory (i.e., the engram). In a previous study we showed that consolidation of a new fear memory is allocated to a common topography of amygdala neurons. When a consolidated memory is retrieved, it may enter a labile state, requiring reconsolidation for it to persist. What is not known is whether the original spatial allocation of a consolidated memory changes during reconsolidation. Knowledge about the spatial allocation of a memory, during consolidation and reconsolidation, provides fundamental insight into its core physical structure (i.e., the engram). Using design-based stereology, we operationally define reconsolidation by showing a nearly identical quantity of neurons in the dorsolateral amygdala (LAd) that expressed a plasticity-related protein, phosphorylated mitogen-activated protein kinase, following both memory acquisition and retrieval. Next, we confirm that Pavlovian fear conditioning recruits a stable, topographically organized population of activated neurons in the LAd. When the stored fear memory was briefly reactivated in the presence of the relevant conditioned stimulus, a similar topography of activated neurons was uncovered. In addition, we found evidence for activated neurons allocated to new regions of the LAd. These findings provide the first insight into the spatial allocation of a fear engram in the LAd, during its consolidation and reconsolidation phase.

Honeycomb nanogold networks with highly active sites

The formation of macroporous honeycomb gold using an electrochemically generated hydrogen bubble template is described. The synthesis procedure induces the formation of highly active surfaces with enhanced electrocatalytic and surface enhanced Raman scattering properties.

The effect of varied test instructions on neuropsychological performance following mild traumatic brain injury : an investigation of ‘diagnosis threat’

Diagnosis threat is a psychosocial factor that has been proposed to contribute to poor outcomes following mild traumatic brain injury (mTBI). This threat is thought to impair the cognitive test performance of individuals with mTBI because of negative injury stereotypes. University students (N= 45, 62.2% female) with a history of mTBI were randomly allocated to a diagnosis threat (DT, n=15), reduced threat (DT-reduced, n=15) or neutral (n=15) group. The reduced threat condition invoked a positive stereotype (i.e., that people with mTBI can perform well on cognitive tests). All participants were given neutral instructions before they completed baseline tests of: a) objective cognitive function across a number of domains; b) psychological symptoms; and, c) PCS symptoms, including self-reported cognitive and emotional difficulties. Participants then received either neutral, DT or DT-reduced instructions, before repeating the tests. Results were analyzed using separate mixed model ANOVAs; one for each dependent measure. The only significant result was for the 2 X 3 ANOVA on an objective test of attention/working memory, Digit Span, p<.05, such that the DT-reduced group performed better than the other groups, which were not different from each other. Although not consistent with predictions or earlier DT studies, the absence of group differences on most tests fits with several recent DT findings. The results of this study suggest that it is timely to reconsider the role of DT as a unique contributor to poor mTBI outcome.

From science to management : using Bayesian networks to learn about Lyngbya

Toxic blooms of Lyngbya majuscula occur in coastal areas worldwide and have major ecological, health and economic consequences. The exact causes and combinations of factors which lead to these blooms are not clearly understood. Lyngbya experts and stakeholders are a particularly diverse group, including ecologists, scientists, state and local government representatives, community organisations, catchment industry groups and local fishermen. An integrated Bayesian Network approach was developed to better understand and model this complex environmental problem, identify knowledge gaps, prioritise future research and evaluate management options.

Tangled Webs : Using Bayesian Networks in the Fight Against Infection

Bayesian networks (BNs) provide a statistical modelling framework which is ideally suited for modelling the many factors and components of complex problems such as healthcare-acquired infections. The methicillin-resistant Staphylococcus aureus (MRSA) organism is particularly troublesome since it is resistant to standard treatments for Staph infections. Overcrowding and understa�ng are believed to increase infection transmission rates and also to inhibit the effectiveness of disease control measures. Clearly the mechanisms behind MRSA transmission and containment are very complicated and control strategies may only be e�ective when used in combination. BNs are growing in popularity in general and in medical sciences in particular. A recent Current Content search of the number of published BN journal articles showed a fi�ve fold increase in general and a six fold increase in medical and veterinary science from 2000 to 2009. This chapter introduces the reader to Bayesian network (BN) modelling and an iterative modelling approach to build and test the BN created to investigate the possible role of high bed occupancy on transmission of MRSA while simultaneously taking into account other risk factors.

A Bayesian network approach to modelling temporal behaviour of Lyngbya majuscula bloom initiation

Lyngbya majuscula is a cyanobacterium (blue-green algae) occurring naturally in tropical and subtropical coastal areas worldwide. Deception Bay, in Northern Moreton Bay, Queensland, has a history of Lyngbya blooms, and forms a case study for this investigation. The South East Queensland (SEQ) Healthy Waterways Partnership, collaboration between government, industry, research and the community, was formed to address issues affecting the health of the river catchments and waterways of South East Queensland. The Partnership coordinated the Lyngbya Research and Management Program (2005-2007) which culminated in a Coastal Algal Blooms (CAB) Action Plan for harmful and nuisance algal blooms, such as Lyngbya majuscula. This first phase of the project was predominantly of a scientific nature and also facilitated the collection of additional data to better understand Lyngbya blooms. The second phase of this project, SEQ Healthy Waterways Strategy 2007-2012, is now underway to implement the CAB Action Plan and as such is more management focussed. As part of the first phase of the project, a Science model for the initiation of a Lyngbya bloom was built using Bayesian Networks (BN). The structure of the Science Bayesian Network was built by the Lyngbya Science Working Group (LSWG) which was drawn from diverse disciplines. The BN was then quantified with annual data and expert knowledge. Scenario testing confirmed the expected temporal nature of bloom initiation and it was recommended that the next version of the BN be extended to take this into account. Elicitation for this BN thus occurred at three levels: design, quantification and verification. The first level involved construction of the conceptual model itself, definition of the nodes within the model and identification of sources of information to quantify the nodes. The second level included elicitation of expert opinion and representation of this information in a form suitable for inclusion in the BN. The third and final level concerned the specification of scenarios used to verify the model. The second phase of the project provides the opportunity to update the network with the newly collected detailed data obtained during the previous phase of the project. Specifically the temporal nature of Lyngbya blooms is of interest. Management efforts need to be directed to the most vulnerable periods to bloom initiation in the Bay. To model the temporal aspects of Lyngbya we are using Object Oriented Bayesian networks (OOBN) to create ‘time slices’ for each of the periods of interest during the summer. OOBNs provide a framework to simplify knowledge representation and facilitate reuse of nodes and network fragments. An OOBN is more hierarchical than a traditional BN with any sub-network able to contain other sub-networks. Connectivity between OOBNs is an important feature and allows information flow between the time slices. This study demonstrates more sophisticated use of expert information within Bayesian networks, which combine expert knowledge with data (categorized using expert-defined thresholds) within an expert-defined model structure. Based on the results from the verification process the experts are able to target areas requiring greater precision and those exhibiting temporal behaviour. The time slices incorporate the data for that time period for each of the temporal nodes (instead of using the annual data from the previous static Science BN) and include lag effects to allow the effect from one time slice to flow to the next time slice. We demonstrate a concurrent steady increase in the probability of initiation of a Lyngbya bloom and conclude that the inclusion of temporal aspects in the BN model is consistent with the perceptions of Lyngbya behaviour held by the stakeholders. This extended model provides a more accurate representation of the increased risk of algal blooms in the summer months and show that the opinions elicited to inform a static BN can be readily extended to a dynamic OOBN, providing more comprehensive information for decision makers.

Enterprise social networks : a business model perspective

Enterprise Social Networks continue to be adopted by organisations looking to increase collaboration between employees, customers and industry partners. Offering a varied range of features and functionality, this technology can be distinguished by the underlying business models that providers of this software deploy. This study identifies and describes the different business models through an analysis of leading Enterprise Social Networks: Yammer, Chatter, SharePoint, Connections, Jive, Facebook and Twitter. A key contribution of this research is the identification of consumer and corporate models as extreme approaches. These findings align well with research on the adoption of Enterprise Social Networks that has discussed bottom-up and top-down approaches. Of specific interest are hybrid models that wrap a corporate model within a consumer model and may, therefore, provide synergies on both models. From a broader perspective, this can be seen as the merging of the corporate and consumer markets for IT products and services.

Effect of dangling chains on the structure and physical properties of a tightly crosslinked poly(ethylene glycol) network

Major imperfections in crosslinked polymers include loose or dangling chain ends that lower the crosslink d., thereby reducing elastic recovery and increasing the solvent swelling. These imperfections are hard to detect, quantify and control when the network is initiated by free radical reactions. As an alternative approach, the sol-​gel synthesis of a model poly(ethylene glycol) (PEG-​2000) network is described using controlled amts. of bis- and mono-​triethoxy silyl Pr urethane PEG precursors to give silsesquioxane (SSQ, R-​SiO1.5) structures as crosslink junctions with a controlled no. of dangling chains. The effect of the no. of dangling chains on the structure and connectivity of the dried SSQ networks has been detd. by step-​crystn. differential scanning calorimetry. The role that micelle formation plays in controlling the sol-​gel PEG network connectivity has been studied by dynamic light scattering of the bis- and mono-​triethoxy silyl precursors and the networks have been characterized by 29Si solid state NMR, sol fraction and swelling measurements. These show that the dangling chains will increase the mesh size and water uptake. Compared to other end-​linked PEG hydrogels, the SSQ-​crosslinked networks show a low sol fraction and high connectivity, which reduces solvent swelling, degree of crystallinity and the crystal transition temp. The increased degree of freedom in segment movement on the addn. of dangling chains in the SSQ-​crosslinked network facilitates the packing process in crystn. of the dry network and, in the hydrogel, helps to accommodate more water mols. before reaching equil.

Multiscale investigation of microfilament networks disruption in living cells

Actin is the most abundantly distributed protein in living cells which plays critical roles in the cell interior force generation and transmission. The fracture mechanism of microfilament networks, whose principle component is actin, would provide insights which can contribute to the understandings of self-protective characters of cytoskeleton. In this study, molecular simulations are conducted to investigate the molecular mechanisms of disruption of microfilament networks from the viewpoint of biophysics. By employing a coarse-grained (CG) model of actin filament networks, we focused on the ultimate strength and crack growth mode of microfilament networks that have dependency on the crack length. It can be found that, the fracture mechanism of microfilament network has dependency on the structural properties of microfilament networks. The structure flaws marginally change the strength of microfilament networks which would explain the self-protective characters of cytoskeleton.

Networked masculinities and social networking sites : a call for the analysis of men and contemporary digital media

It is of course recognised that technology can be gendered and implicated in gender relations. However, it continues to be the case that men’s experiences with technology are underexplored and the situation is even more problematic where digital media is concerned. Over the past 30 years we have witnessed a dramatic rise in the pervasiveness of digital media across many parts of the world and as associated with wide ranging aspects of our lives. This rise has been fuelled over the last decade by the emergence of Web 2.0 and particularly Social Networking Sites (SNS). Given this context, I believe it is necessary for us to undertake more work to understand men’s engagements with digital media, the implications this might have for masculinities and the analysis of gender relations more generally. To begin to unpack this area, I engage theorizations of the properties of digital media networks and integrate this with the masculinity studies field. Using this framework, I suggest we need to consider the rise in what I call networked masculinities – those masculinities (co)produced and reproduced with digitally networked publics. Through this analysis I discuss themes related to digital mediators, relationships, play and leisure, work and commerce, and ethics. I conclude that as masculinities can be, and are being, complicated and given agency by advancing notions and practices of connectivity, mobility, classification and convergence, those engaged with masculinity studies and digital media have much to contribute.

Brain-inspired sensor fusion for navigating robots

Current state of the art robot mapping and navigation systems produce impressive performance under a narrow range of robot platform, sensor and environmental conditions, in contrast to animals such as rats that produce “good enough” maps that enable them to function under an incredible range of situations. In this paper we present a rat-inspired featureless sensor-fusion system that assesses the usefulness of multiple sensor modalities based on their utility and coherence for place recognition, without knowledge as to the type of sensor. We demonstrate the system on a Pioneer robot in indoor and outdoor environments with abrupt lighting changes. Through dynamic weighting of the sensors, the system is able to perform correct place recognition and mapping where the static sensor weighting approach fails.

Predicting voltage unbalance impacts of plug-in electric vehicles penetration in residential low-voltage distribution networks

Plug-in electric vehicles will soon be connected to residential distribution networks in high quantities and will add to already overburdened residential feeders. However, as battery technology improves, plug-in electric vehicles will also be able to support networks as small distributed generation units by transferring the energy stored in their battery into the grid. Even though the increase in the plug-in electric vehicle connection is gradual, their connection points and charging/discharging levels are random. Therefore, such single-phase bidirectional power flows can have an adverse effect on the voltage unbalance of a three-phase distribution network. In this article, a voltage unbalance sensitivity analysis based on charging/discharging levels and the connection point of plug-in electric vehicles in a residential low-voltage distribution network is presented. Due to the many uncertainties in plug-in electric vehicle ratings and connection points and the network load, a Monte Carlo-based stochastic analysis is developed to predict voltage unbalance in the network in the presence of plug-in electric vehicles. A failure index is introduced to demonstrate the probability of non-standard voltage unbalance in the network due to plug-in electric vehicles.