Construct design for efficient, effective and high-throughput gene silencing in plants

Post-transcriptional silencing of plant genes using anti-sense or co-suppression constructs usually results in only a modest proportion of silenced individuals. Recent work has demonstrated the potential for constructs encoding self-complementary 'hairpin' RNA (hpRNA) to efficiently silence genes. In this study we examine design rules for efficient gene silencing, in terms of both the proportion of independent transgenic plants showing silencing, and the degree of silencing. Using hpRNA constructs containing sense/anti-sense arms ranging from 98 to 853 nt gave efficient silencing in a wide range of plant species, and inclusion of an intron in these constructs had a consistently enhancing effect. Intron-containing constructs (ihpRNA) generally gave 90-100% of independent transgenic plants showing silencing. The degree of silencing with these constructs was much greater than that obtained using either co-suppression or anti-sense constructs. We have made a generic vector, pHANNIBAL, that allows a simple, single PCR product from a gene of interest to be easily converted into a highly effective ihpRNA silencing construct. We have also created a high-throughput vector, pHELLSGATE, that should facilitate the cloning of gene libraries or large numbers of defined genes, such as those in EST collections, using an in vitro recombinase system. This system may facilitate the large-scale determination and discovery of plant gene functions in the same way as RNAi is being used to examine gene function in Caenorhabditis elegans.

The operating schedule for battery energy storage companies in electricity market

This paper presents a series of operating schedules for Battery Energy Storage Companies (BESC) to provide peak shaving and spinning reserve services in the electricity markets under increasing wind penetration. As individual market participants, BESC can bid in ancillary services markets in an Independent System Operator (ISO) and contribute towards frequency and voltage support in the grid. Recent development in batteries technologies and availability of the day-ahead spot market prices would make BESC economically feasible. Profit maximization of BESC is achieved by determining the optimum capacity of Energy Storage Systems (ESS) required for meeting spinning reserve requirements as well as peak shaving. Historic spot market prices and frequency deviations from Australia Energy Market Operator (AEMO) are used for numerical simulations and the economic benefits of BESC is considered reflecting various aspects in Australia’s National Electricity Markets (NEM).

Climate change and IMO technical and operational measures for reduction of emissions of greenhouse gas from ships

According to a study conducted by the International Maritime organisation (IMO) shipping sector is responsible for 3.3% of the global Greenhouse Gas (GHG) emissions. The 1997 Kyoto Protocol calls upon states to pursue limitation or reduction of emissions of GHG from marine bunker fuels working through the IMO. In 2011, 14 years after the adoption of the Kyoto Protocol, the Marine Environment Protection Committee (MEPC) of the IMO has adopted mandatory energy efficiency measures for international shipping which can be treated as the first ever mandatory global GHG reduction instrument for an international industry. The MEPC approved an amendment of Annex VI of the 1973 International Convention for the Prevention of Pollution from Ships (MARPOL 73/78) to introduce a mandatory Energy Efficiency Design Index (EEDI) for new ships and the Ship Energy Efficiency Management Plan (SEEMP) for all ships. Considering the growth projections of human population and world trade the technical and operational measures may not be able to reduce the amount of GHG emissions from international shipping in a satisfactory level. Therefore, the IMO is considering to introduce market-based mechanisms that may serve two purposes including providing a fiscal incentive for the maritime industry to invest in more energy efficient manner and off-setting of growing ship emissions. Some leading developing countries already voiced their serious reservations on the newly adopted IMO regulations stating that by imposing the same obligation on all countries, irrespective of their economic status, this amendment has rejected the Principle of Common but Differentiated Responsibility (the CBDR Principle), which has always been the cornerstone of international climate change law discourses. They also claimed that negotiation for a market based mechanism should not be continued without a clear commitment from the developed counters for promotion of technical co-operation and transfer of technology relating to the improvement of energy efficiency of ships. Against this backdrop, this article explores the challenges for the developing counters in the implementation of already adopted technical and operational measures.

A novel energy efficient controllable stiffness joint

Achieving energy efficient legged locomotion is an important goal for the future of robot mobility. This paper presents a novel joint for legged locomotion that is energy efficient for two reasons. The first reason is the configuration of the elastic elements and actuator which we show analytically has lower energy losses than the typical arrangement. The second is that the joint stiffness, and hence stance duration, is controllable without requiring any energy from the actuator. Further, the joint stiffness can be changed significantly during the flight phase, from zero to highly rigid. Results obtained from a prototype hopper, demonstrate that the joint allows continuous and peak hopping via torque control. The results also demonstrate that the hopping frequency can be varied between 2.2Hz and 4.6Hz with associated stance duration of between 0.35 and 0.15 seconds.

Testing second order cyclostationarity in the squared envelope spectrum of non-white vibration signals

Cyclostationary models for the diagnostic signals measured on faulty rotating machineries have proved to be successful in many laboratory tests and industrial applications. The squared envelope spectrum has been pointed out as the most efficient indicator for the assessment of second order cyclostationary symptoms of damages, which are typical, for instance, of rolling element bearing faults. In an attempt to foster the spread of rotating machinery diagnostics, the current trend in the field is to reach higher levels of automation of the condition monitoring systems. For this purpose, statistical tests for the presence of cyclostationarity have been proposed during the last years. The statistical thresholds proposed in the past for the identification of cyclostationary components have been obtained under the hypothesis of having a white noise signal when the component is healthy. This need, coupled with the non-white nature of the real signals implies the necessity of pre-whitening or filtering the signal in optimal narrow-bands, increasing the complexity of the algorithm and the risk of losing diagnostic information or introducing biases on the result. In this paper, the authors introduce an original analytical derivation of the statistical tests for cyclostationarity in the squared envelope spectrum, dropping the hypothesis of white noise from the beginning. The effect of first order and second order cyclostationary components on the distribution of the squared envelope spectrum will be quantified and the effectiveness of the newly proposed threshold verified, providing a sound theoretical basis and a practical starting point for efficient automated diagnostics of machine components such as rolling element bearings. The analytical results will be verified by means of numerical simulations and by using experimental vibration data of rolling element bearings.

A recommendation approach dealing with multiple market segments

A new community and communication type of social networks - online dating - are gaining momentum. With many people joining in the dating network, users become overwhelmed by choices for an ideal partner. A solution to this problem is providing users with partners recommendation based on their interests and activities. Traditional recommendation methods ignore the users’ needs and provide recommendations equally to all users. In this paper, we propose a recommendation approach that employs different recommendation strategies to different groups of members. A segmentation method using the Gaussian Mixture Model (GMM) is proposed to customize users’ needs. Then a targeted recommendation strategy is applied to each identified segment. Empirical results show that the proposed approach outperforms several existing recommendation methods.

Efficient top-k retrieval with signatures

This paper describes a new method of indexing and searching large binary signature collections to efficiently find similar signatures, addressing the scalability problem in signature search. Signatures offer efficient computation with acceptable measure of similarity in numerous applications. However, performing a complete search with a given search argument (a signature) requires a Hamming distance calculation against every signature in the collection. This quickly becomes excessive when dealing with large collections, presenting issues of scalability that limit their applicability. Our method efficiently finds similar signatures in very large collections, trading memory use and precision for greatly improved search speed. Experimental results demonstrate that our approach is capable of finding a set of nearest signatures to a given search argument with a high degree of speed and fidelity.

An efficient proactive route maintenance process for reliable data transmissions in sensor networks

For industrial wireless sensor networks, maintaining the routing path for a high packet delivery ratio is one of the key objectives in network operations. It is important to both provide the high data delivery rate at the sink node and guarantee a timely delivery of the data packet at the sink node. Most proactive routing protocols for sensor networks are based on simple periodic updates to distribute the routing information. A faulty link causes packet loss and retransmission at the source until periodic route update packets are issued and the link has been identified as broken. We propose a new proactive route maintenance process where periodic update is backed-up with a secondary layer of local updates repeating with shorter periods for timely discovery of broken links. Proposed route maintenance scheme improves reliability of the network by decreasing the packet loss due to delayed identification of broken links. We show by simulation that proposed mechanism behaves better than the existing popular routing protocols (AODV, AOMDV and DSDV) in terms of end-to-end delay, routing overhead, packet reception ratio.

Future-proofing the pharmacy profession in a hypercompetitive market

This paper highlights the hypercompetitive nature of the current pharmacy landscape in Australia and to suggest either a superior level of differentiation strategy or a focused differentiation strategy targeting a niche market as two viable, alternative business models to cost leadership for small, independent community pharmacies. A description of the Australian health care system is provided as well as background information on the current community pharmacy environment in Australia. The authors propose a differentiation or focused differentiation strategy based on cognitive professional services (CPS) which must be executed well and of a superior quality to competitors' services. Market research to determine the services valued by target customers and that they are willing to pay for is vital. To achieve the superior level of quality that will engender high patient satisfaction levels and loyalty, pharmacy owners and managers need to develop, maintain and clearly communicate service quality specifications to the staff delivering these services. Otherwise, there will be a proliferation of pharmacies offering the same professional services with no evident service differential. However, to sustain competitive advantage over the long-term, these smaller, independent community pharmacies will need to exploit a broad core competency base in order to be able to continuously introduce new sources of competitive advantage. With the right expertise, the authors argue that smaller, independent community pharmacies can successfully deliver CPS and sustain profitability in a hypercompetitive market.

A hybrid genetic algorithm for the energy-efficient virtual machine placement problem in data centers

Server consolidation using virtualization technology has become an important technology to improve the energy efficiency of data centers. Virtual machine placement is the key in the server consolidation technology. In the past few years, many approaches to the virtual machine placement have been proposed. However, existing virtual machine placement approaches consider the energy consumption by physical machines only, but do not consider the energy consumption in communication network, in a data center. However, the energy consumption in the communication network in a data center is not trivial, and therefore should be considered in the virtual machine placement. In our preliminary research, we have proposed a genetic algorithm for a new virtual machine placement problem that considers the energy consumption in both physical machines and the communication network in a data center. Aiming at improving the performance and efficiency of the genetic algorithm, this paper presents a hybrid genetic algorithm for the energy-efficient virtual machine placement problem. Experimental results show that the hybrid genetic algorithm significantly outperforms the original genetic algorithm, and that the hybrid genetic algorithm is scalable.

OSCE online : making assessment of clinical competencies transparent, accountable & authentic

Creating an authentic assessment which at once assesses competencies, scene management, communication and overall patient care is challenging in the competitive tertiary education market. Increasing student numbers and the cost of evaluating scenario based competencies serve to ensure the need for consistent objectivity and need for timely feedback to students on their performance. Objective structured clinical examination (OSCE) is currently the most flexible approach to competency based formative and summative assessment and widely used within paramedic degree programs. Students are understandably compelled to perform well and can be frustrated by not receiving timely and appropriate feedback. Increasingly a number of products aimed at providing a more efficient and paperless approach have begun to enter the market. These products, it is suggested are aimed at medicine programs and not at allied health professions and limited to one operating system and therefore ignore issues surrounding equity and accessibility. OSCE Online aims to address this gap in the market and is tailored to these disciplines. The application will provide a service that can be both tailored and standardised from a pre-written bank, depending upon requirement to fit around the needs of clinical competency assessment. Delivering authentic assessments to address student milestones in their training to become paramedics is the cornerstone of OSCE Online. By not being restricted to a specific device it will address issues of functionality, adaptability, accessibility, authenticity and importantly: transparency and accountability by producing contemporaneous data allowing issues to be easily identified and rectified.

Exploring the international market selection of small to medium sized enterprises

Research into the international market selection (IMS) of small to medium sized enterprises (SMEs) commonly identifies psychic distance and networks as being the most important determinants of a firm’s IMS. Whether regional factors, such as bilateral and multilateral regional integration, are important as determinants of IMS is not well understood. This paper utilises a multiple case study method through in-depth interviews to investigate, in the context of the current business environment, how important regionalisation, psychic distance and networks are as determinants of IMS among SMEs in the food and beverage industries within Australia and Malaysia. The study found regional considerations to be important to the IMS of Malaysian but not Australian firms, while psychic distance was considered an important determinant on IMS by only half of the sampled firms. The role of networks, however, was considered the most important determinant of IMS among all the sampled firms.

Defining expected cost for the air conditioning to avoid a price spike of electricity market under DSR model

This work presents a demand side response model (DSR) which assists small electricity consumers, through an aggregator, exposed to the market price to proactively mitigate price and peak impact on the electrical system. The proposed model allows consumers to manage air-conditioning when as a function of possible price spikes. The main contribution of this research is to demonstrate how consumers can minimise the total expected cost by optimising air-conditioning to account for occurrences of a price spike in the electricity market. This model investigates how pre-cooling method can be used to minimise energy costs when there is a substantial risk of an electricity price spike. The model was tested with Queensland electricity market data from the Australian Energy Market Operator and Brisbane temperature data from the Bureau of Statistics during hot days on weekdays in the period 2011 to 2012.

An efficient hybrid evolutionary optimization algorithm based on PSO and SA for clustering

The K-means algorithm is one of the most popular techniques in clustering. Nevertheless, the performance of the K-means algorithm depends highly on initial cluster centers and converges to local minima. This paper proposes a hybrid evolutionary programming based clustering algorithm, called PSO-SA, by combining particle swarm optimization (PSO) and simulated annealing (SA). The basic idea is to search around the global solution by SA and to increase the information exchange among particles using a mutation operator to escape local optima. Three datasets, Iris, Wisconsin Breast Cancer, and Ripley’s Glass, have been considered to show the effectiveness of the proposed clustering algorithm in providing optimal clusters. The simulation results show that the PSO-SA clustering algorithm not only has a better response but also converges more quickly than the K-means, PSO, and SA algorithms.

An efficient hybrid evolutionary optimization algorithm for daily volt/var control at distribution system including DGs

This paper presents a new hybrid evolutionary algorithm based on Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) for daily Volt/Var control in distribution system including Distributed Generators (DGs). Due to the small X/R ratio and radial configuration of distribution systems, DGs have much impact on this problem. Since DGs are independent power producers or private ownership, a price based methodology is proposed as a proper signal to encourage owners of DGs in active power generation. Generally, the daily Volt/Var control is a nonlinear optimization problem. Therefore, an efficient hybrid evolutionary method based on Particle Swarm Optimization and Ant Colony Optimization (ACO), called HPSO, is proposed to determine the active power values of DGs, reactive power values of capacitors and tap positions of transformers for the next day. The feasibility of the proposed algorithm is demonstrated and compared with methods based on the original PSO, ACO and GA algorithms on IEEE 34-bus distribution feeder.