Efficient group communication for large-scale parallel clustering

Global communication requirements and load imbalance of some parallel data mining algorithms are the major obstacles to exploit the computational power of large-scale systems. This work investigates how non-uniform data distributions can be exploited to remove the global communication requirement and to reduce the communication cost in iterative parallel data mining algorithms. In particular, the analysis focuses on one of the most influential and popular data mining methods, the k-means algorithm for cluster analysis. The straightforward parallel formulation of the k-means algorithm requires a global reduction operation at each iteration step, which hinders its scalability. This work studies a different parallel formulation of the algorithm where the requirement of global communication can be relaxed while still providing the exact solution of the centralised k-means algorithm. The proposed approach exploits a non-uniform data distribution which can be either found in real world distributed applications or can be induced by means of multi-dimensional binary search trees. The approach can also be extended to accommodate an approximation error which allows a further reduction of the communication costs.

Transmission of wireless power by magnetic resonance

The work involves investigation of a type of wireless power system wherein its analysis will yield the construction of a prototype modeled as a singular technological artifact. It is through exploration of the artifact that forms the intellectual basis for not only its prototypical forms, but suggestive of variant forms not yet discovered. Through the process it is greatly clarified the role of the artifact, its most suitable application given the constraints on the delivery problem, and optimization strategies to improve it. In order to improve maturity and contribute to a body of knowledge, this document proposes research utilizing mid-field region, efficient inductive-transfer for the purposes of removing wired connections and electrical contacts. While the description seems enough to state the purpose of this work, it does not convey the compromises of having to redraw the lines of demarcation between near and far-field in the traditional method of broadcasting. Two striking scenarios are addressed in this thesis: Firstly, the mathematical explanation of wireless power is due to J.C. Maxwell's original equations, secondly, the behavior of wireless power in the circuit is due to Joseph Larmor's fundamental works on the dynamics of the field concept. A model of propagation will be presented which matches observations in experiments. A modified model of the dipole will be presented to address the phenomena observed in the theory and experiments. Two distinct sets of experiments will test the concept of single and two coupled-modes. In a more esoteric context of the zero and first-order magnetic field, the suggestion of a third coupled-mode is presented. Through the remaking of wireless power in this context, it is the intention of the author to show the reader that those things lost to history, bound to a path of complete obscurity, are once again innovative and useful ideas.

Efficient wireless power delivery for biomedical implants

Power delivery for biomedical implants is a major consideration in their design for both measurement and stimulation. When performed by a wireless technique, transmission efficiency is critically important not only because of the costs associated with any losses but also because of the nature of those losses, for example, excessive heat can be uncomfortable for the individual involved. In this study, a method and means of wireless power transmission suitable for biomedical implants are both discussed and experimentally evaluated. The procedure initiated is comparable in size and simplicity to those methods already employed; however, some of Tesla’s fundamental ideas have been incorporated in order to obtain a significant improvement in efficiency. This study contains a theoretical basis for the approach taken; however, the emphasis here is on practical experimental analysis.

Azide monoliths as convenient flow reactors for efficient Curtius rearrangement reactions

The preparation and use of an azide-containing monolithic reactor is described for use in a flow chemistry device and in particular for conducting Curtius rearrangement reactions via acid chloride inputs.

Energy-efficient hybrid system for Wireless Body Area Network Applications

Wireless Body Area Networks (WBANs) consist of a number of miniaturized wearable or implanted sensor nodes that are employed to monitor vital parameters of a patient over long duration of time. These sensors capture physiological data and wirelessly transfer the collected data to a local base station in order to be further processed. Almost all of these body sensors are expected to have low data-rate and to run on a battery. Since recharging or replacing the battery is not a simple task specifically in the case of implanted devices such as pacemakers, extending the lifetime of sensor nodes in WBANs is one of the greatest challenges. To achieve this goal, WBAN systems employ low-power communication transceivers and low duty cycle Medium Access Control (MAC) protocols. Although, currently used MAC protocols are able to reduce the energy consumption of devices for transmission and reception, yet they are still unable to offer an ultimate energy self-sustaining solution for low-power MAC protocols. This paper proposes to utilize energy harvesting technologies in low-power MAC protocols. This novel approach can further reduce energy consumption of devices in WBAN systems.

The ebbing hegemon? An evolutionary perspective on the emergence of holistic governance and the efficient role of Institutional Investors in Environmental, Social and Governance issues (ESG)

This paper seeks to chronicle the roots of corporate governance form its narrow shareholder perspective to the current bourgeoning stakeholder approach while giving cognizance to institutional investors and their effective role in ESG in light of the King Report III of South Africa. It is aimed at a critical review of the extant literature from the shareholder Cadbury epoch to the present day King Report novelty. We aim to: (i) offer an analytical state of corporate governance in the Anglo-Saxon world, Middle East and North Africa (MENA), Far East Asia and Africa; and (ii) illuminate the lead role the king Report of South Africa is playing as the bellwether of the stakeholder approach to corporate governance as well as guiding the role of institutional investors in ESG.

Attribution of extreme weather events in Africa: A preliminary exploration of the science and policy implications

At the most recent session of the Conference of the Parties (COP19) in Warsaw (November 2013) the Warsaw international mechanism for loss and damage associated with climate change impacts was established under the United Nations Framework Convention on Climate Change (UNFCCC). The mechanism aims at promoting the implementation of approaches to address loss and damage associated with the adverse effects of climate change. Specifically, it aims to enhance understanding of risk management approaches to address loss and damage. Understanding risks associated with impacts due to highly predictable (slow onset) events like sea-level rise is relatively straightforward whereas assessing the effects of climate change on extreme weather events and their impacts is much more difficult. However, extreme weather events are a significant cause of loss of life and livelihoods, particularly in vulnerable countries and communities in Africa. The emerging science of probabilistic event attribution is relevant as it provides scientific evidence on the contribution of anthropogenic climate change to changes in risk of extreme events. It thus provides the opportunity to explore scientifically-backed assessments of the human influence on such events. However, different ways of framing attribution questions can lead to very different assessments of change in risk. Here we explain the methods of, and implications of different approaches to attributing extreme weather events with a focus on Africa. Crucially, it demonstrates that defining the most appropriate attribution question to ask is not a science decision but needs to be made in dialogue with those stakeholders who will use the answers.

Computationally efficient rule-based classification for continuous streaming data

Advances in hardware and software technologies allow to capture streaming data. The area of Data Stream Mining (DSM) is concerned with the analysis of these vast amounts of data as it is generated in real-time. Data stream classification is one of the most important DSM techniques allowing to classify previously unseen data instances. Different to traditional classifiers for static data, data stream classifiers need to adapt to concept changes (concept drift) in the stream in real-time in order to reflect the most recent concept in the data as accurately as possible. A recent addition to the data stream classifier toolbox is eRules which induces and updates a set of expressive rules that can easily be interpreted by humans. However, like most rule-based data stream classifiers, eRules exhibits a poor computational performance when confronted with continuous attributes. In this work, we propose an approach to deal with continuous data effectively and accurately in rule-based classifiers by using the Gaussian distribution as heuristic for building rule terms on continuous attributes. We show on the example of eRules that incorporating our method for continuous attributes indeed speeds up the real-time rule induction process while maintaining a similar level of accuracy compared with the original eRules classifier. We termed this new version of eRules with our approach G-eRules.

Towards a parallel computationally efficient approach to scaling up data stream classification

Advances in hardware technologies allow to capture and process data in real-time and the resulting high throughput data streams require novel data mining approaches. The research area of Data Stream Mining (DSM) is developing data mining algorithms that allow us to analyse these continuous streams of data in real-time. The creation and real-time adaption of classification models from data streams is one of the most challenging DSM tasks. Current classifiers for streaming data address this problem by using incremental learning algorithms. However, even so these algorithms are fast, they are challenged by high velocity data streams, where data instances are incoming at a fast rate. This is problematic if the applications desire that there is no or only a very little delay between changes in the patterns of the stream and absorption of these patterns by the classifier. Problems of scalability to Big Data of traditional data mining algorithms for static (non streaming) datasets have been addressed through the development of parallel classifiers. However, there is very little work on the parallelisation of data stream classification techniques. In this paper we investigate K-Nearest Neighbours (KNN) as the basis for a real-time adaptive and parallel methodology for scalable data stream classification tasks.

[M(CO)4(2,2′-bipyridine)] (M = Cr, Mo, W) as efficient catalysts for electrochemical reduction of CO2 to CO at a gold electrode

Group 6 complexes of the type [M(CO)4(bpy)] (M=Cr, Mo, W) are capable of behaving as electrochemical catalysts for the reduction of CO2 at potentials less negative than those for the reduction of the radical anions [M(CO)4(bpy)].−. Cyclic voltammetric, chronoamperometric and UV/Vis/IR spectro-electrochemical data reveal that five-coordinate [M(CO)3(bpy)]2− are the active catalysts. The catalytic conversion is significantly more efficient in N-methyl-2-pyrrolidone (NMP) compared to tetrahydrofuran, which may reflect easier CO dissociation from 1e−-reduced [M(CO)4(bpy)].− in the former solvent, followed by second electron transfer. The catalytic cycle may also involve [M(CO)4(H-bpy)]− formed by protonation of [M(CO)3(bpy)]2−, especially in NMP. The strongly enhanced catalysis using an Au working electrode is remarkable, suggesting that surface interactions may play an important role, too.

Highly efficient neural differentiation of human somatic stem cells, isolated by minimally invasive periodontal surgery

Neural stem cells (NSCs) are potential sources for cell therapy of neurodegenerative diseases and for drug screening. Despite their potential benefits, ethical and practical considerations limit the application of NSCs derived from human embryonic stem cells (ES) or adult brain tissue. Thus, alternative sources are required to satisfy the criteria of ready accessibility, rapid expansion in chemically defined media and reliable induction to a neuronal fate. We isolated somatic stem cells from the human periodontium that were collected during minimally invasive periodontal access flap surgery as part of guided tissue regeneration therapy. These cells could be propagated as neurospheres in serum-free medium, which underscores their cranial neural crest cell origin. Culture in the presence of epidermal growth factor (EGF) and fibroblast growth factor-2 (FGF-2) under serum-free conditions resulted in large numbers of nestin-positive/Sox-2-positive NSCs. These periodontium-derived (pd) NSCs are highly proliferative and migrate in response to chemokines that have been described as inducing NSC migration. We used immunocytochemical techniques and RT-PCR analysis to assess neural differentiation after treatment of the expanded cells with a novel induction medium. Adherence to substrate, growth factor deprivation, and retinoic acid treatment led to the acquisition of neuronal morphology and stable expression of markers of neuronal differentiation by more than 90% of the cells. Thus, our novel method might provide nearly limitless numbers of neuronal precursors from a readily accessible autologous adult human source, which could be used as a platform for further experimental studies and has potential therapeutic implications.

Efficient animal-serum free 3D cultivation method for adult human neural crest-derived stem cell therapeutics

Due to their broad differentiation potential and their persistence into adulthood, human neural crest-derived stem cells (NCSCs) harbour great potential for autologous cellular therapies, which include the treatment of neurodegenerative diseases and replacement of complex tissues containing various cell types, as in the case of musculoskeletal injuries. The use of serum-free approaches often results in insufficient proliferation of stem cells and foetal calf serum implicates the use of xenogenic medium components. Thus, there is much need for alternative cultivation strategies. In this study we describe for the first time a novel, human blood plasma based semi-solid medium for cultivation of human NCSCs. We cultivated human neural crest-derived inferior turbinate stem cells (ITSCs) within a blood plasma matrix, where they revealed higher proliferation rates compared to a standard serum-free approach. Three-dimensionality of the matrix was investigated using helium ion microscopy. ITSCs grew within the matrix as revealed by laser scanning microscopy. Genetic stability and maintenance of stemness characteristics were assured in 3D cultivated ITSCs, as demonstrated by unchanged expression profile and the capability for self-renewal. ITSCs pre-cultivated in the 3D matrix differentiated efficiently into ectodermal and mesodermal cell types, particularly including osteogenic cell types. Furthermore, ITSCs cultivated as described here could be easily infected with lentiviruses directly in substrate for potential tracing or gene therapeutic approaches. Taken together, the use of human blood plasma as an additive for a completely defined medium points towards a personalisable and autologous cultivation of human neural crest-derived stem cells under clinical grade conditions.

The BioDICE Taverna plugin for clustering and visualization of biological data: a workflow for molecular compounds exploration

Background: In many experimental pipelines, clustering of multidimensional biological datasets is used to detect hidden structures in unlabelled input data. Taverna is a popular workflow management system that is used to design and execute scientific workflows and aid in silico experimentation. The availability of fast unsupervised methods for clustering and visualization in the Taverna platform is important to support a data-driven scientific discovery in complex and explorative bioinformatics applications. Results: This work presents a Taverna plugin, the Biological Data Interactive Clustering Explorer (BioDICE), that performs clustering of high-dimensional biological data and provides a nonlinear, topology preserving projection for the visualization of the input data and their similarities. The core algorithm in the BioDICE plugin is Fast Learning Self Organizing Map (FLSOM), which is an improved variant of the Self Organizing Map (SOM) algorithm. The plugin generates an interactive 2D map that allows the visual exploration of multidimensional data and the identification of groups of similar objects. The effectiveness of the plugin is demonstrated on a case study related to chemical compounds. Conclusions: The number and variety of available tools and its extensibility have made Taverna a popular choice for the development of scientific data workflows. This work presents a novel plugin, BioDICE, which adds a data-driven knowledge discovery component to Taverna. BioDICE provides an effective and powerful clustering tool, which can be adopted for the explorative analysis of biological datasets.

Exploration of the neural correlates of cerebral palsy for sensorimotor BCI control

Cerebral palsy (CP) includes a broad range of disorders, which can result in impairment of posture and movement control. Brain-computer interfaces (BCIs) have been proposed as assistive devices for individuals with CP. Better understanding of the neural processing underlying motor control in affected individuals could lead to more targeted BCI rehabilitation and treatment options. We have explored well-known neural correlates of movement, including event-related desynchronization (ERD), phase synchrony, and a recently-introduced measure of phase dynamics, in participants with CP and healthy control participants. Although present, significantly less ERD and phase locking were found in the group with CP. Additionally, inter-group differences in phase dynamics were also significant. Taken together these findings suggest that users with CP exhibit lower levels of motor cortex activation during motor imagery, as reflected in lower levels of ongoing mu suppression and less functional connectivity. These differences indicate that development of BCIs for individuals with CP may pose additional challenges beyond those faced in providing BCIs to healthy individuals.