A bifurcated pathway to thiazoles and imidazoles using a modular flow microreactor

A scalable method for the preparation of 4,5-disubstituted thiazoles and imidazoles as distinct regioisomeric products using a modular flow microreactor has been devised. The process makes use of microfluidic reaction chips and packed immobilized-reagent columns to effect bifurcation of the reaction pathway.

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.

Obstacle avoidance routing scheme through optimal sink movement for home monitoring and mobile robotic consumer devices

In recent years, ZigBee has been proven to be an excellent solution to create scalable and flexible home automation networks. In a home automation network, consumer devices typically collect data from a home monitoring environment and then transmit the data to an end user through multi-hop communication without the need for any human intervention. However, due to the presence of typical obstacles in a home environment, error-free reception may not be possible, particularly for power constrained devices. A mobile sink based data transmission scheme can be one solution but obstacles create significant complexities for the sink movement path determination process. Therefore, an obstacle avoidance data routing scheme is of vital importance to the design of an efficient home automation system. This paper presents a mobile sink based obstacle avoidance routing scheme for a home monitoring system. The mobile sink collects data by traversing through the obstacle avoidance path. Through ZigBee based hardware implementation and verification, the proposed scheme successfully transmits data through the obstacle avoidance path to improve network performance in terms of life span, energy consumption and reliability. The application of this work can be applied to a wide range of intelligent pervasive consumer products and services including robotic vacuum cleaners and personal security robots1.

Distributed parallelization of greedy Mobile Network Optimization algorithms

The Mobile Network Optimization (MNO) technologies have advanced at a tremendous pace in recent years. And the Dynamic Network Optimization (DNO) concept emerged years ago, aimed to continuously optimize the network in response to variations in network traffic and conditions. Yet, DNO development is still at its infancy, mainly hindered by a significant bottleneck of the lengthy optimization runtime. This paper identifies parallelism in greedy MNO algorithms and presents an advanced distributed parallel solution. The solution is designed, implemented and applied to real-life projects whose results yield a significant, highly scalable and nearly linear speedup up to 6.9 and 14.5 on distributed 8-core and 16-core systems respectively. Meanwhile, optimization outputs exhibit self-consistency and high precision compared to their sequential counterpart. This is a milestone in realizing the DNO. Further, the techniques may be applied to similar greedy optimization algorithm based applications.

LEGO® bricks as building blocks for centimeter-scale biological environments

LEGO bricks are commercially available interlocking pieces of plastic that are conventionally used as toys. We describe their use to build engineered environments for cm-scale biological systems, in particular plant roots. Specifically, we take advantage of the unique modularity of these building blocks to create inexpensive, transparent, reconfigurable, and highly scalable environments for plant growth in which structural obstacles and chemical gradients can be precisely engineered to mimic soil.

A simple and versatile 2-dimensional platform to study plant germination and growth under controlled humidity

We describe a simple, inexpensive, but remarkably versatile and controlled growth environment for the observation of plant germination and seedling root growth on a flat, horizontal surface over periods of weeks. The setup provides to each plant a controlled humidity (between 56% and 91% RH), and contact with both nutrients and atmosphere. The flat and horizontal geometry of the surface supporting the roots eliminates the gravitropic bias on their development and facilitates the imaging of the entire root system. Experiments can be setup under sterile conditions and then transferred to a non-sterile environment. The system can be assembled in 1-2 minutes, costs approximately 8.78$ per plant, is almost entirely reusable (0.43$ per experiment in disposables), and is easily scalable to a variety of plants. We demonstrate the performance of the system by germinating, growing, and imaging Wheat (Triticum aestivum), Corn (Zea mays), and Wisconsin Fast Plants (Brassica rapa). Germination rates were close to those expected for optimal conditions.

Ball milling as an effective route for the preparation of doped bornite: synthesis, stability and thermoelectric properties

Bornite, Cu5FeS4, is a naturally-occuring mineral with an ultralow thermal conductivity and potential for thermoelectric power generation. We describe here a new, easy and scalable route to synthesise bornite, together with the thermoelectric behaviour of manganese-substituted derivatives, Cu5Fe1-xMnxS4 (0 ≤ x ≤ 0.10). The electrical and thermal transport properties of Cu5Fe1-xMnxS4 (0 ≤ x ≤ 0.10), which are p-type semiconductors, were measured from room temperature to 573 K. The stability of bornite was investigated by thermogravimetric analysis under inert and oxidising atmospheres. Repeated measurements of the electrical transport properties confirm that bornite is stable up to 580 K under an inert atmosphere, while heating to 890 K results in rapid degradation. Ball milling leads to a substantial improvement in the thermoelectric figure of merit of unsusbtituted bornite (ZT = 0.55 at 543 K), when compared to bornite prepared by conventional high-temperature synthesis (ZT < 0.3 at 543 K). Manganese-substituted samples have a ZT comparable to that of unsubstituted bornite.

Fast adaptive real-time classification for data streams with concept drift

An important application of Big Data Analytics is the real-time analysis of streaming data. Streaming data imposes unique challenges to data mining algorithms, such as concept drifts, the need to analyse the data on the fly due to unbounded data streams and scalable algorithms due to potentially high throughput of data. Real-time classification algorithms that are adaptive to concept drifts and fast exist, however, most approaches are not naturally parallel and are thus limited in their scalability. This paper presents work on the Micro-Cluster Nearest Neighbour (MC-NN) classifier. MC-NN is based on an adaptive statistical data summary based on Micro-Clusters. MC-NN is very fast and adaptive to concept drift whilst maintaining the parallel properties of the base KNN classifier. Also MC-NN is competitive compared with existing data stream classifiers in terms of accuracy and speed.

CDASH: a cloud-enabled program for structure solution from powder diffraction data

The simulated annealing approach to crystal structure determination from powder diffraction data, as implemented in the DASH program, is readily amenable to parallelization at the individual run level. Very large scale increases in speed of execution can be achieved by distributing individual DASH runs over a network of computers. The CDASH program delivers this by using scalable on-demand computing clusters built on the Amazon Elastic Compute Cloud service. By way of example, a 360 vCPU cluster returned the crystal structure of racemic ornidazole (Z0 = 3, 30 degrees of freedom) ca 40 times faster than a typical modern quad-core desktop CPU. Whilst used here specifically for DASH, this approach is of general applicability to other packages that are amenable to coarse-grained parallelism strategies.

Distributed access control and privacy for the internet of me

This article presents an experimental scalable message driven IoT and its security architecture based on Decentralized Information Flow Control. The system uses a gateway that exports SoA (REST) interfaces to the internet simplifying external applications whereas uses DIFC and asynchronous messaging within the home environment.

Efficiency issues of evolutionary k-means

One of the top ten most influential data mining algorithms, k-means, is known for being simple and scalable. However, it is sensitive to initialization of prototypes and requires that the number of clusters be specified in advance. This paper shows that evolutionary techniques conceived to guide the application of k-means can be more computationally efficient than systematic (i.e., repetitive) approaches that try to get around the above-mentioned drawbacks by repeatedly running the algorithm from different configurations for the number of clusters and initial positions of prototypes. To do so, a modified version of a (k-means based) fast evolutionary algorithm for clustering is employed. Theoretical complexity analyses for the systematic and evolutionary algorithms under interest are provided. Computational experiments and statistical analyses of the results are presented for artificial and text mining data sets. (C) 2010 Elsevier B.V. All rights reserved.

Directed self-assembly of quantum structures by nanomechanical stamping using probe tips

We demonstrate that nanomechanically stamped substrates can be used as templates to pattern and direct the self-assembly of epitaxial quantum structures such as quantum dots. Diamond probe tips are used to indent or stamp the surface of GaAs( 100) to create nanoscale volumes of dislocation-mediated deformation, which alter the growth surface strain. These strained sites act to bias nucleation, hence allowing for selective growth of InAs quantum dots. Patterns of quantum dots are observed to form above the underlying nanostamped template. The strain state of the patterned structures is characterized by micro-Raman spectroscopy. The potential of using nanoprobe tips as a quantum dot nanofabrication technology are discussed.

Trust-By-Contract: Modelling, Analysing and Predicting Behaviour in Software Architectures

Architecture description languages (ADLs) are used to specify high-level, compositional views of a software application. ADL research focuses on software composed of prefabricated parts, so-called software components. ADLs usually come equipped with rigorous state-transition style semantics, facilitating verification and analysis of specifications. Consequently, ADLs are well suited to configuring distributed and event-based systems. However, additional expressive power is required for the description of enterprise software architectures – in particular, those built upon newer middleware, such as implementations of Java’s EJB specification, or Microsoft’s COM+/.NET. The enterprise requires distributed software solutions that are scalable, business-oriented and mission-critical. We can make progress toward attaining these qualities at various stages of the software development process. In particular, progress at the architectural level can be leveraged through use of an ADL that incorporates trust and dependability analysis. Also, current industry approaches to enterprise development do not address several important architectural design issues. The TrustME ADL is designed to meet these requirements, through combining approaches to software architecture specification with rigorous design-by-contract ideas. In this paper, we focus on several aspects of TrustME that facilitate specification and analysis of middleware-based architectures for trusted enterprise computing systems.