The spark of life:The role of electric fields in regulating cell behaviour using the eye as a model system

Endogenous electric fields (EF) have long been known to influence cell behaviour during development, neural cell tropism, wound healing and cell behaviour generally. The effect is based on short circuiting of electrical potential differences across cell and tissue boundaries generated by ionic segregation. Recent in vitro and in vivo studies have shown that EF regulate not only cell movement but orientation of cells during mitosis, an effect which may underlie shaping of tissues and organs. The molecular basis of this effect is founded on receptor-mediated cell signalling events and alterations in cytoskeletal function as revealed in studies of gene deficient cells. Remarkably, not all cells respond directionally to EF in the same way and this has consequences, for instance, for lens development and vascular remodelling. The physical basis of EF effect may be related to changes induced in 'bound water' at the cell surface, whose organisation in association with trans-membrane proteins (e.g. receptors) is disrupted when EF are generated. Copyright © 2007 S. Karger AG.

Day-Ahead Optimal Charging/Discharging Scheduling for Electric Vehicles in Micro-Grids

A micro-grid is an autonomous system which can be operated and connected to an external system or isolated with the help of energy storage systems (ESSs). While the daily output of distributed generators (DGs) strongly depends on the temporal distribution of natural resources such as wind and solar, unregulated electric vehicle (EV) charging demand will deteriorate the imbalance between the daily load and generation curves. In this paper, a statistical model is presented to describe daily EV charging/discharging behaviour. An optimisation problem is proposed to obtain economic operation for the micro-grid based on this model. In day-ahead scheduling, with estimated information of power generation and load demand, optimal charging/discharging of EVs during 24 hours is obtained. A series of numerical optimization solutions in different scenarios is achieved by serial quadratic programming. The results show that optimal charging/discharging of EVs, a daily load curve can better track the generation curve and the network loss and required ESS capacity are both decreased. The paper also demonstrates cost benefits for EVs and operators.

On the scaling behavior of electric conductivity in [C(4)mim][NTf2]

In this work we examine, for the first time, the molar conductivity behavior of the deeply supercooled room temperature ionic liquid [C₄mim][NTf₂] in the temperature, pressure and volume thermodynamic space in terms of density scaling (TV^γ)⁻¹ combined with the equation of state (EOS). The exponent γ_σ determined from the Avramov model analysis is compared with the coefficient obtained from the viscosity studies carried out at moderate temperatures. Therefore, the experimental results presented herein provide the answer to the long-standing question regarding the validity of thermodynamic scaling of ionic liquids over a wide temperature range, i.e. from the normal liquid state to the glass transition point. Finally, we investigate the relationship between the dynamic and thermodynamic properties of [C₄mim][NTf₂] represented by scaling exponent γ and Grüneisen constant γ_G, respectively.

A well-to-wheel analysis of electric vehicles in the all-island Single Electricity Market

The European Union has set a target for 10% renewable energy in transport by 2020 to be met using biofuels and electric vehicles. In the case of biofuels, the biofuel must achieve greenhouse gas savings of 35% relative to the fossil fuel replaced. For biofuels, greenhouse gas savings can be calculated using life cycle analysis or the European Union default values. In contrast, all electricity used in transport is considered to be the same, regardless of the source or the type of electric vehicle. However, the choice of the electric vehicle and electricity source will have a major impact on the greenhouse gas saving. In this paper the initial findings of a well-to-wheel analysis of electric vehicle deployment in Northern Ireland are presented. The key finding indicates that electric vehicles require least amount of energy per mile on a well-to-wheel basis, consume the fewest resources, even accommodating inefficient fuel production, in comparison to standard internal combustion engine and hybrid vehicles.

Investigation into the potential of using micro gas turbines in plug-in hybrid electric vehicles

The transport sector is considered to be one of the most dependent sectors on fossil fuels. Meeting ecological, social and economic demands throughout the sector has got increasingly important in recent times. A passenger vehicle with a more environmentally friendly propulsion system is the hybrid electric vehicle. Combining an internal combustion engine and an electric motor offers the potential to reduce carbon dioxide emissions. The overall objective of this research is to provide an appraisal of the use of a micro gas turbine as the range extender in a plug-in hybrid electric vehicle. In this application, the gas turbine can always operate at its most efficient operating point as its only requirement is to recharge the battery. For this reason, it is highly suitable for this purpose. Gas turbines offer many benefits over traditional internal combustion engines which are traditionally used in this application. They offer a high power-to-weight ratio, multi-fuel capability and relatively low emission levels due to continuous combustion.

A new self-learning TLBO algorithm for RBF neural modelling of batteries in electric vehicles

One of the main purposes of building a battery model is for monitoring and control during battery charging/discharging as well as for estimating key factors of batteries such as the state of charge for electric vehicles. However, the model based on the electrochemical reactions within the batteries is highly complex and difficult to compute using conventional approaches. Radial basis function (RBF) neural networks have been widely used to model complex systems for estimation and control purpose, while the optimization of both the linear and non-linear parameters in the RBF model remains a key issue. A recently proposed meta-heuristic algorithm named Teaching-Learning-Based Optimization (TLBO) is free of presetting algorithm parameters and performs well in non-linear optimization. In this paper, a novel self-learning TLBO based RBF model is proposed for modelling electric vehicle batteries using RBF neural networks. The modelling approach has been applied to two battery testing data sets and compared with some other RBF based battery models, the training and validation results confirm the efficacy of the proposed method.

Optimal Scheduling Methods to Integrate Plug-In Electric Vehicles with the Power System: A Review

The introduction of the Tesla in 2008 has demonstrated to the public of the potential of electric vehicles in terms of reducing fuel consumption and green-house gas from the transport sector. It has brought electric vehicles back into the spotlight worldwide at a moment when fossil fuel prices were reaching unexpected high due to increased demand and strong economic growth. The energy storage capabilities from of fleets of electric vehicles as well as the potentially random discharging and charging offers challenges to the grid in terms of operation and control. Optimal scheduling strategies are key to integrating large numbers of electric vehicles and the smart grid. In this paper, state-of-the-art optimization methods are reviewed on scheduling strategies for the grid integration with electric vehicles. The paper starts with a concise introduction to analytical charging strategies, followed by a review of a number of classical numerical optimization methods, including linear programming, non-linear programming, dynamic programming as well as some other means such as queuing theory. Meta-heuristic techniques are then discussed to deal with the complex, high-dimensional and multi-objective scheduling problem associated with stochastic charging and discharging of electric vehicles. Finally, future research directions are suggested.

Synchronous Islanding Control in the Presence of Hydro-electric Generation

This paper introduces a novel load sharing algorithm to enable island synchronization. The system model used for development is based on an actual system for which historical measurement and fault data is available and is used to refine and test the algorithms performance and validity. The electrical system modelled is selected due to its high-level of hydroelectric generation and its history of islanding events. The process of developing the load sharing algorithm includes a number of steps. Firstly, the development of a simulation model to represent the case study accurately - this is validated by way of matching system behavior based on data from historical island events. Next, a generic island simulation is used to develop the load sharing algorithm. The algorithm is then tested against the validated simulation model representing the case study area selected. Finally, a laboratory setup is described which is used as validation method for the novel load sharing algorithm.

The mouse and human IGSF6 (DORA) genes map to the inflammatory bowel disease 1 locus and are embedded in an intron of a gene of unknown function.

We have previously characterized IGSF6 (DORA), a novel member of the immunoglobulin superfamily (IGSF) from human and rat expressed in dendritic and myeloid cells. Using a probe from the open reading frame of the rat cDNA, we isolated a cosmid which contains the entire mouse gene. By comparative analysis and reverse transcriptase polymerase chain reaction, we defined the intron/exon structure and the mRNA of the mouse gene and, with respect to human BAC clones, the human gene. The genes span 10 kb (mouse) and 12 kb (human), with six exons arranged in a manner similar to other members of the IGSF. All intron/exon boundaries follow the GT-AG rule. Expression of the mouse Igsf6 gene is restricted to cells of the immune system, particularly macrophages. Northern blot revealed a single mRNA of 2.5 kb, in contrast to the human gene which is expressed as two mRNAs of 1 and 2.5 kb. The human and mouse genes were localized to a locus associated with inflammatory bowel disease. Analysis of the flanking regions of the Igsf6 gene revealed the presence of an unrelated gene, transcribed from the opposite strand of the DNA and oriented such that the Igsf6 gene is encoded entirely within an intron. An identical organization is seen in human. This gene of unknown function is transcribed and processed, contains homologues in Caenorhabditis elegans and prokaryotes, and is expressed in most organs in the mouse.

Proton Probing Measurement of Electric and Magnetic Fields Generated by ns and ps Laser-Matter Interactions

The use of laser-accelerated protons as a particle probe for the detection of electric fields in plasmas has led in recent years to a wealth of novel information regarding the ultrafast plasma dynamics following high intensity laser-matter interactions. The high spatial quality and short duration of these beams have been essential to this purpose. We will discuss some of the most recent results obtained with this diagnostic at the Rutherford Appleton Laboratory (UK) and at LULI - Ecole Polytechnique (France), also applied to conditions of interest to conventional Inertial Confinement Fusion. In particular, the technique has been used to measure electric fields responsible for proton acceleration from solid targets irradiated with ps pulses, magnetic fields formed by ns pulse irradiation of solid targets, and electric fields associated with the ponderomotive channelling of ps laser pulses in under-dense plasmas.