Development of a contra-rotating tidal current turbine and analysis of performance

A contra-rotating marine current turbine has a number of attractive features: nearzero reactive torque on the support structure, near-zero swirl in the wake, and high relative inter-rotor rotational speeds. Modified blade element modelling theory has been used to design and predict the characteristics of such a turbine, and a model turbine and test rig have been constructed. Tests in a towing tank demonstrated the feasibility of the concept. Power coefficients were high for such a small model and in excellent agreement with predictions, confirming the accuracy of the computational modelling procedures. Highfrequency blade loading data were obtained in the course of the experiments. These show the anticipated dynamic components for a contra-rotating machine. Flow visualization of the wake verified the lack of swirl behind the turbine. A larger machine is presently under construction for sea trials

Design and testing of a contra-rotating tidal current turbine

A contra-rotating marine current turbine has a number of attractive features: nearzero reactive torque on the support structure, near-zero swirl in the wake, and high relative inter-rotor rotational speeds. Modified blade element modelling theory has been used to design and predict the characteristics of such a turbine, and a model turbine and test rig have been constructed. Tests in a towing tank demonstrated the feasibility of the concept. Power coefficients were high for such a small model and in excellent agreement with predictions, confirming the accuracy of the computational modelling procedures. High-frequency blade loading data were obtained in the course of the experiments. These show the anticipated dynamic components for a contra-rotating machine. Flow visualization of the wake verified the lack of swirl behind the turbine. A larger machine is presently under construction for sea trials

Unexpected evolutionary proximity of eukaryotic and cyanobacterial enzymes responsible for biosynthesis of retinoic acid and its oxidation

Biosynthesis of retinoic acid from retinaldehyde (retinal) is catalysed by an aldehyde dehydrogenase (ALDH) and its oxidation by cytochrome P450 enzymes (CYPs). Herein we show by phylogenetic analysis that the ALDHs and CYPs in the retinoic acid pathway in animals are much closer in evolutionary terms to cyanobacterial orthologs than would be expected from the standard models of evolution

Alcohol consumption in young adults: the role of multisensory imagery.

Accepted 19.11.2013Little is known about the subjective experience of alcohol desire and craving in young people. Descriptions of alcohol urges continue to be extensively used in the everyday lexicon of young, non-dependent drinkers. Elaborated Intrusion (EI) Theory contends that imagery is central to craving and desires, and predicts that alcohol-related imagery will be associated with greater frequency and amount of drinking. This study involved 1,535 age stratified 18- 25 year olds who completed an alcohol–related survey that included the Imagery scale of the Alcohol Craving Experience (ACE) questionnaire. Imagery items predicted 12-16% of the variance in concurrent alcohol consumption. Higher total Imagery subscale scores were linearly associated with greater drinking frequency and lower self-efficacy for moderate drinking. Interference with alcohol imagery may have promise as a preventive or early intervention target in young people

Assessment of motivation to control alcohol use: The motivational thought frequency and state motivation scales for alcohol control

publisher: Elsevier articletitle: Assessment of motivation to control alcohol use: The motivational thought frequency and state motivation scales for alcohol control journaltitle: Addictive Behaviors articlelink: http://dx.doi.org/10.1016/j.addbeh.2016.02.038 content_type: article copyright: © 2016 Elsevier Ltd. All rights reserved

Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks

Multivariate time series forecasting is an important machine learning problem across many domains, including predictions of solar plant energy output, electricity consumption, and traffic jam situation. Temporal data arise in these real-world applications often involves a mixture of long-term and short-term patterns, for which traditional approaches such as Autoregressive models and Gaussian Process may fail. In this paper, we proposed a novel deep learning framework, namely Long- and Short-term Time-series network (LSTNet), to address this open challenge. LSTNet uses the Convolution Neural Network (CNN) and the Recurrent Neural Network (RNN) to extract short-term local dependency patterns among variables and to discover long-term patterns for time series trends. Furthermore, we leverage traditional autoregressive model to tackle the scale insensitive problem of the neural network model. In our evaluation on real-world data with complex mixtures of repetitive patterns, LSTNet achieved significant performance improvements over that of several state-of-the-art baseline methods. All the data and experiment codes are available online.Comment: Accepted by SIGIR 201