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.

Formation of carbon nanoscrolls from graphene nanoribbons : a molecular dynamics study

Carbon nanoscrolls (CNSs) are one of the carbon-based nanomaterials similar to carbon nanotubes (CNTs) but are not widely studied in spite of their great potential applications. Their practical applications are hindered by the challenging fabrication of the CNSs. A physical approach has been proposed recently to fabricate the CNS by rolling up a monolayer graphene nanoribbon (GNR) around a CNT driven by the interaction energy between them. In this study, we perform extensive molecular dynamics (MD) simulations to investigate the various factors that impact the formation of the CNS from GNR. Our simulation results show that the formation of the CNS is sensitive to the length of the CNT and temperature. When the GNR is functionalized with hydrogen, the formation of the CNS is determined by the density and distribution of the hydrogen atoms. Graphyne, the allotrope of graphene, is inferior to graphene in the formation of the CNS due to the weaker bonds and the associated smaller atom density. The mechanism behind the rolling of GNR into CNS lies in the balance between the GNR–CNT van der Waals (vdW) interactions and the strain energy of GNR. The present work reveals new important insights and provides useful guidelines for the fabrication of the CNS.

Mathematical modelling of intramembranous bone formation during fracture healing

During fracture healing, many complex and cryptic interactions occur between cells and bio-chemical molecules to bring about repair of damaged bone. In this thesis two mathematical models were developed, concerning the cellular differentiation of osteoblasts (bone forming cells) and the mineralisation of new bone tissue, allowing new insights into these processes. These models were mathematically analysed and simulated numerically, yielding results consistent with experimental data and highlighting the underlying pattern formation structure in these aspects of fracture healing.

γ-radiation-induced γH2AX formation occurs preferentially in actively transcribing euchromatic loci

The central dogma in radiation biology is that nuclear DNA is the critical target with respect to radiosensitivity. In accordance with the theoretical expectations, and in the absence of a conclusive model, the general consensus in the field has been to view chromatin as a homogeneous template for DNA damage and repair. This paradigm has been called into question by recent findings indicating a disparity in γ-irradiation-induced γH2AX foci formation in euchromatin and heterochromatin. Here, we have extended those studies and provide evidence that γH2AX foci form preferentially in actively transcribing euchromatin following γ-irradiation.

Emotional experiences of preservice science teachers in online learning : the formation, disruption and maintenance of social bonds

The enactment of learning to become a science teacher in online mode is an emotionally charged experience. We attend to the formation, maintenance and disruption of social bonds experienced by online preservice science teachers as they shared their emotional online learning experiences through blogs, or e-motion diaries, in reaction to videos of face-to-face lessons. A multi-theoretic framework drawing on microsociological perspectives of emotion informed our hermeneutic interpretations of students’ first-person accounts reported through an e-motion diary. These accounts were analyzed through our own database of emotion labels constructed from the synthesis of existing literature on emotion across a range of fields of inquiry. Preservice science teachers felt included in the face-to-face group as they watched videos of classroom transactions. The strength of these feelings of social solidarity were dependent on the quality of the video recording. E-motion diaries provided a resource for interactions focused on shared emotional experiences leading to formation of social bonds and the alleviation of feelings of fear, trepidation and anxiety about becoming science teachers. We offer implications to inform practitioners who wish to improve feelings of inclusion amongst their online learners in science education.

Identifying influential users of micro-blogging services : a dynamic action-based network approach

In this paper, we present a dynamic model to identify influential users of micro-blogging services. Micro-blogging services, such as Twitter, allow their users (twitterers) to publish tweets and choose to follow other users to receive tweets. Previous work on user influence on Twitter, concerns more on following link structure and the contents user published, seldom emphasizes the importance of interactions among users. We argue that, by emphasizing on user actions in micro-blogging platform, user influence could be measured more accurately. Since micro-blogging is a powerful social media and communication platform, identifying influential users according to user interactions has more practical meanings, e.g., advertisers may concern how many actions – buying, in this scenario – the influential users could initiate rather than how many advertisements they spread. By introducing the idea of PageRank algorithm, innovatively, we propose our model using action-based network which could capture the ability of influential users when they interacting with micro-blogging platform. Taking the evolving prosperity of micro-blogging into consideration, we extend our actionbaseduser influence model into a dynamic one, which could distinguish influential users in different time periods. Simulation results demonstrate that our models could support and give reasonable explanations for the scenarios that we considered.

Diffusion-driven formation of MoS2 nanotube bundles containing MoS2 nanopods

MoS2 nanotube bundles along with embedded nested fullerenes were formed in a gas phase reaction of molybdenum carbonyl and H2S gas with the assistance of I2. The amorphous Mo-S-I intermediates obtained through quenching a modified MOCVD reaction in a large temperature gradient were annealed at elevated temperature in an inert atmosphere. Under the influence of the iodine the amorphous precursor formed a surface film with an enhanced mobility of the molybdenum and sulfur components. Point defects within the MoS2 layers combined with the enhanced surface diffusion lead to a scrolling of the inherently instable MoS2 lamellae.

Correlation based method for phase identification in a three phase LV distribution network

Low voltage distribution networks feature a high degree of load unbalance and the addition of rooftop photovoltaic is driving further unbalances in the network. Single phase consumers are distributed across the phases but even if the consumer distribution was well balanced when the network was constructed changes will occur over time. Distribution transformer losses are increased by unbalanced loadings. The estimation of transformer losses is a necessary part of the routine upgrading and replacement of transformers and the identification of the phase connections of households allows a precise estimation of the phase loadings and total transformer loss. This paper presents a new technique and preliminary test results for a method of automatically identifying the phase of each customer by correlating voltage information from the utility's transformer system with voltage information from customer smart meters. The techniques are novel as they are purely based upon a time series of electrical voltage measurements taken at the household and at the distribution transformer. Experimental results using a combination of electrical power and current of the real smart meter datasets demonstrate the performance of our techniques.

Consumer phase identification in a three phase unbalanced LV distribution network

A new technique is presented for automatically identifying the phase connection of domestic customers. Voltage information from a reference three phase house is correlated with voltage information from other customer electricity meters on the same network to determine the highest probability phase connection. The techniques are purely based upon a time series of electrical voltage measurements taken by the household smart meters and no additional equipment is required. The method is demonstrated using real smart meter datasets to correctly identify the phase connections of 75 consumers on a low voltage distribution feeder.

Convolutional Neural Network-based place recognition

Recently Convolutional Neural Networks (CNNs) have been shown to achieve state-of-the-art performance on various classification tasks. In this paper, we present for the first time a place recognition technique based on CNN models, by combining the powerful features learnt by CNNs with a spatial and sequential filter. Applying the system to a 70 km benchmark place recognition dataset we achieve a 75% increase in recall at 100% precision, significantly outperforming all previous state of the art techniques. We also conduct a comprehensive performance comparison of the utility of features from all 21 layers for place recognition, both for the benchmark dataset and for a second dataset with more significant viewpoint changes.

A hybrid quality function deployment and cybernetic analytic network process model for project manager selection

Identifying appropriate decision criteria and making optimal decisions in a structured way is a complex process. This paper presents an approach for doing this in the form of a hybrid Quality Function Deployment (QFD) and Cybernetic Analytic Network Process (CANP) model for project manager selection. This involves the use of QFD to translate the owner's project management expectations into selection criteria and the CANP to weight the expectations and selection criteria. The supermatrix approach then prioritises the candidates with respect to the overall decision-making goal. A case study is used to demonstrate the use of the model in selecting a renovation project manager. This involves the development of 18 selection criteria in response to the owner's three main expectations of time, cost and quality.

Towards a quantitative theory of epidermal calcium profile formation in unwounded skin

We propose and mathematically examine a theory of calcium profile formation in unwounded mammalian epidermis based on: changes in keratinocyte proliferation, fluid and calcium exchange with the extracellular fluid during these cells' passage through the epidermal sublayers, and the barrier functions of both the stratum corneum and tight junctions localised in the stratum granulosum. Using this theory, we develop a mathematical model that predicts epidermal sublayer transit times, partitioning of the epidermal calcium gradient between intracellular and extracellular domains, and the permeability of the tight junction barrier to calcium ions. Comparison of our model's predictions of epidermal transit times with experimental data indicates that keratinocytes lose at least 87% of their volume during their disintegration to become corneocytes. Intracellular calcium is suggested as the main contributor to the epidermal calcium gradient, with its distribution actively regulated by a phenotypic switch in calcium exchange between keratinocytes and extracellular fluid present at the boundary between the stratum spinosum and the stratum granulosum. Formation of the extracellular calcium distribution, which rises in concentration through the stratum granulosum towards the skin surface, is attributed to a tight junction barrier in this sublayer possessing permeability to calcium ions that is less than 15 nm/s in human epidermis and less than 37 nm/s in murine epidermis. Future experimental work may refine the presented theory and reduce the mathematical uncertainty present in the model predictions.

Facebook for adults ? A longitudinal study of MBA student’s use of an enterprise social network for collaborative learning

The rapid pace of social media means that our understanding of the way in which it facilitates the learning process continues to lag. The findings of a longitudinal study of an executive MBA cohort over the period of eight months in their use of the social media application is presented. Over time the ownership and use of the Yammer site shifted to become student driven and facilitated. The motivations behind the site’s use, perceived advantages and disadvantages and changes in usage patterns are documented. The case provides a useful insight into the way in which students used this technology to facilitate their learning goals and how patterns of behaviour changed in response to the changing needs of the cohort.

A Hybrid Queue-based Bayesian Network framework for passenger facilitation modelling

This paper presents a novel framework for the modelling of passenger facilitation in a complex environment. The research is motivated by the challenges in the airport complex system, where there are multiple stakeholders, differing operational objectives and complex interactions and interdependencies between different parts of the airport system. Traditional methods for airport terminal modelling do not explicitly address the need for understanding causal relationships in a dynamic environment. Additionally, existing Bayesian Network (BN) models, which provide a means for capturing causal relationships, only present a static snapshot of a system. A method to integrate a BN complex systems model with stochastic queuing theory is developed based on the properties of the Poisson and exponential distributions. The resultant Hybrid Queue-based Bayesian Network (HQBN) framework enables the simulation of arbitrary factors, their relationships, and their effects on passenger flow and vice versa. A case study implementation of the framework is demonstrated on the inbound passenger facilitation process at Brisbane International Airport. The predicted outputs of the model, in terms of cumulative passenger flow at intermediary and end points in the inbound process, are found to have an R2 goodness of fit of 0.9994 and 0.9982 respectively over a 10 h test period. The utility of the framework is demonstrated on a number of usage scenarios including causal analysis and ‘what-if’ analysis. This framework provides the ability to analyse and simulate a dynamic complex system, and can be applied to other socio-technical systems such as hospitals.

A supervisory load-leveling approach to improve the voltage profile in distribution network

This paper suggests a supervisory control for storage units to provide load leveling in distribution networks. This approach coordinates storage units to charge during high generation and discharge during peak load times, while utilized to improve the network voltage profile indirectly. The aim of this control strategy is to establish power sharing on a pro rata basis for storage units. As a case study, a practical distribution network with 30 buses is simulated and the results are provided.