Power dissipation in nanoscale conductors: classical, semi-classical and quantum dynamics

Modelling Joule heating is a difficult problem because of the need to introduce correct correlations between the motions of the ions and the electrons. In this paper we analyse three different models of current induced heating (a purely classical model, a fully quantum model and a hybrid model in which the electrons are treated quantum mechanically and the atoms are treated classically). We find that all three models allow for both heating and cooling processes in the presence of a current, and furthermore the purely classical and purely quantum models show remarkable agreement in the limit of high biases. However, the hybrid model in the Ehrenfest approximation tends to suppress heating. Analysis of the equations of motion reveals that this is a consequence of two things: the electrons are being treated as a continuous fluid and the atoms cannot undergo quantum fluctuations. A means for correcting this is suggested.

Power dissipation in nanoscale conductors

A previous tight-binding model of power dissipation in a nanoscale conductor under an applied bias is extended to take account of the local atomic topology and the local electronic structure. The method is used to calculate the power dissipated at every atom in model nanoconductor geometries: a nanoscale constriction, a one-dimensional atomic chain between two electrodes with a resonant double barrier, and an irregular nanowire with sharp corners. The local power is compared with the local current density and the local density of states. A simple relation is found between the local power and the current density in quasiballistic geometries. A large enhancement in the power at special atoms is found in cases of resonant and anti-resonant transmission. Such systems may be expected to be particularly unstable against current-induced modifications.

A simple model of atomic interactions in noble metals based explicitly on electronic structure

A total energy tight-binding model with a basis of just one s state per atom is introduced. It is argued that this simplest of all tight-binding models provides a surprisingly good description of the structural stability and elastic constants of noble metals. By assuming inverse power scaling laws for the hopping integrals and the repulsive pair potential, it is shown that the density matrix in a perfect primitive crystal is independent of volume, and structural energy differences and equations of state are then derived analytically. The model is most likely to be of use when one wishes to consider explicitly and self-consistently the electronic and atomic structures of a generic metallic system, with the minium of computation expense. The relationship to the free-electron jellium model is described. The applicability of the model to other metals is also considered briefly.

Effects of n-3 long chain polyunsaturated fatty acids on depressed mood: Systematic review of published trials.

Background: Greater dietary intakes of n–3 long-chain polyunsaturated fatty acids (n–3 PUFAs) may be beneficial for depressed mood. Objective: This study aimed to systematically review all published randomized controlled trials investigating the effects of n–3 PUFAs on depressed mood. Design: Eight medical and health databases were searched over all years of records until June 2006 for trials that exposed participants to n–3 PUFAs or fish, measured depressed mood, were conducted on human participants, and included a comparison group. Results: Eighteen randomized controlled trials were identified; 12 were included in a meta-analysis. The pooled standardized difference in mean outcome (fixed-effects model) was 0.13 SDs (95% CI: 0.01, 0.25) in those receiving n–3 PUFAs compared with placebo, with strong evidence of heterogeneity (I2 = 79%, P <0.001). The presence of funnel plot asymmetry suggested that publication bias was the likely source of heterogeneity. Sensitivity analyses that excluded one large trial increased the effect size estimates but did not reduce heterogeneity. Metaregression provided some evidence that the effect was stronger in trials involving populations with major depression—the difference in the effect size estimates was 0.73 (95% CI: 0.05, 1.41; P = 0.04), but there was still considerable heterogeneity when trials that involved populations with major depression were pooled separately (I2 = 72%, P <0.001). Conclusions: Trial evidence that examines the effects of n–3 PUFAs on depressed mood is limited and is difficult to summarize and evaluate because of considerable heterogeneity. The evidence available provides little support for the use of n–3 PUFAs to improve depressed mood. Larger trials with adequate power to detect clinically important benefits are required.

Comparison of LIMPET contra-rotating wells turbine with theoretical and model test predictions

The performance of the contra-rotating Wells turbine installed in the LIMPET wave power station is compared to the predicted performance from theoretical analysis and model tests. A reasonable agreement was found between the predicted and measured turbine damping characteristic, however the turbine efficiency was found to be poorly predicted. It is postulated that this is due to the unsteady nature and mal-distribution of flow through the LIMPET turbine, which were not considered in the predictions. It is suggested that the reduced performance of the contra-rotating Wells turbine makes biplane or monoplane Wells turbines with guide vanes better solutions for OWC's.

Artificial Intelligence Model for Flowable Concrete Mixtures Used in Underwater Construction and Repair

This study explores using artificial neural networks to predict the rheological and mechanical properties of underwater concrete (UWC) mixtures and to evaluate the sensitivity of such properties to variations in mixture ingredients. Artificial neural networks (ANN) mimic the structure and operation of biological neurons and have the unique ability of self-learning, mapping, and functional approximation. Details of the development of the proposed neural network model, its architecture, training, and validation are presented in this study. A database incorporating 175 UWC mixtures from nine different studies was developed to train and test the ANN model. The data are arranged in a patterned format. Each pattern contains an input vector that includes quantity values of the mixture variables influencing the behavior of UWC mixtures (that is, cement, silica fume, fly ash, slag, water, coarse and fine aggregates, and chemical admixtures) and a corresponding output vector that includes the rheological or mechanical property to be modeled. Results show that the ANN model thus developed is not only capable of accurately predicting the slump, slump-flow, washout resistance, and compressive strength of underwater concrete mixtures used in the training process, but it can also effectively predict the aforementioned properties for new mixtures designed within the practical range of the input parameters used in the training process with an absolute error of 4.6, 10.6, 10.6, and 4.4%, respectively.

A 3-D well-differentiated model of pediatric bronchial epithelium demonstrates unstimulated morphological differences between asthmatic and nonasthmatic cells

There is a need for reproducible and effective models of pediatric bronchial epithelium to study disease states such as asthma. We aimed to develop, characterize, and differentiate an effective, an efficient, and a reliable three-dimensional model of pediatric bronchial epithelium to test the hypothesis that children with asthma differ in their epithelial morphologic phenotype when compared with nonasthmatic children. Primary cell cultures from both asthmatic and nonasthmatic children were grown and differentiated at the air-liquid interface for 28 d. Tight junction formation, MUC5AC secretion, IL-8, IL-6, prostaglandin E2 production, and the percentage of goblet and ciliated cells in culture were assessed. Well-differentiated, multilayered, columnar epithelium containing both ciliated and goblet cells from asthmatic and nonasthmatic subjects were generated. All cultures demonstrated tight junction formation at the apical surface and exhibited mucus production and secretion. Asthmatic and nonasthmatic cultures secreted similar quantities of IL-8, IL-6, and prostaglandin E2. Cultures developed from asthmatic children contained considerably more goblet cells and fewer ciliated cells compared with those from nonasthmatic children. A well-differentiated model of pediatric epithelium has been developed that will be useful for more in vivo like study of the mechanisms at play during asthma.

Graphical determination of network limits for wind power integration

A graphical method is presented for determining the capability of individual system nodes to accommodate wind power generation. The method is based upon constructing a capability chart for each node at which a wind farm is to be connected. The capability chart defines the domain of allowable power injections at the candidate node, subject to constraints imposed by voltage limits, voltage stability and equipment capability limits being satisfied. The chart is first derived for a two-bus model, before being extended to a multi-node power system. The graphical method is employed to derive the chart for a two-node system, as well as its application to a multi-node power system, considering the IEEE 30-bus test system as a case study. Although the proposed method is derived with the intention of determining the wind farm capacity to be connected at a specific node, it can be used for the analysis of a PQ bus loading as well as generation.

The power of shell rapping influences rates of eviction in hermit crabs

Hermit crabs fight for ownership of shells, and shell exchange may occur after a period of shell rapping, involving the initiating or attacking crab bringing its shell rapidly and repeatedly into contact with the shell of the noninitiator or defender, in a series of bouts. The temporal pattern of rapping contains information about the motivation and/or relative resource holding potential (RHP) of the initiator and acts as a repeated signal of stamina. Here we investigated the role of the force with which the rapping is performed and how this is related to the temporal pattern of rapping by rubberizing the external surface of shells. Initiators that are prevented from rapping with their usual level of force persist with the activity for longer over the whole encounter but use fewer raps per bout and are less likely to effect an exchange than those supplied with control shells. The fact that the force of rapping affects the likelihood of a crab being victorious suggests that either the force of rapping contains information about motivation or RHP or that force directly affects noninitiators, reducing their ability to maintain an adequate grip on their shells. The data suggest that shell rapping is an agonistic signal rather than one that provides information useful to the noninitiator, as has been suggested by the negotiation model of shell exchange.

Short, medium and long term load forecasting model and virtual load forecaster based on radial basis neural networks

Artificial neural networks (ANNs) can be easily applied to short-term load forecasting (STLF) models for electric power distribution applications. However, they are not typically used in medium and long term load forecasting (MLTLF) electric power models because of the difficulties associated with collecting and processing the necessary data. Virtual instrument (VI) techniques can be applied to electric power load forecasting but this is rarely reported in the literature. In this paper, we investigate the modelling and design of a VI for short, medium and long term load forecasting using ANNs. Three ANN models were built for STLF of electric power. These networks were trained using historical load data and also considering weather data which is known to have a significant affect of the use of electric power (such as wind speed, precipitation, atmospheric pressure, temperature and humidity). In order to do this a V-shape temperature processing model is proposed. With regards MLTLF, a model was developed using radial basis function neural networks (RBFNN). Results indicate that the forecasting model based on the RBFNN has a high accuracy and stability. Finally, a virtual load forecaster which integrates the VI and the RBFNN is presented.