Research on Ultimate Loading Carrying Capacities in Concrete Bridge Deck Slabs with Consideration of Compressive Membrane Action

In the investigation of real loading capacities in concrete bridge deck slabs,the study of this type of structure was carried out with consideration of compressive membrane action.A series of experimental test of steel-concrete bridge structures was developed with the analysis of influences from the varying of structural parameters on loading capacities,including reinforcement percentages,supporting beam sizes and concrete compressive strength.Through the study of the experimental results,it was found that the real structural loading capacities are larger than those predicted by current design methods.Therefore,based on the previous research,a prediction method for loading capacities of concrete bridge deck slabs was established with consideration of CMA,which was built based on the plastic ultimate analysis.In this method,the lateral restraint stiffness subjected by concrete bridge deck slabs was provided.The proposed theoretical model is capable of predicting the loading capacities of this type of structure accurately with comparison of results from several bridge deck experimental tests.

NADPH oxidase-4 mediates protection against chronic load-induced stress in mouse hearts by enhancing angiogenesis

Cardiac failure occurs when the heart fails to adapt to chronic stresses. Reactive oxygen species (ROS)-dependent signaling is implicated in cardiac stress responses but the role of different ROS sources remains unclear. Here, we report that NADPH oxidase-4 (Nox4) facilitates cardiac adaptation to chronic stress. Unlike other Nox proteins, Nox4 activity is regulated mainly by its expression level which increased in cardiomyocytes during stresses such as pressure overload or hypoxia. To investigate the functional role of Nox4 during the cardiac response to stress, we generated mice with a genetic deletion of Nox4 or a cardiomyocyte-targeted overexpression of Nox4. Basal cardiac function was normal in both models but Nox4-null animals developed exaggerated contractile dysfunction, hypertrophy and cardiac dilatation during exposure to chronic overload whereas Nox4-transgenic mice were protected. Investigation of mechanisms underlying this protective effect revealed a significant Nox4-dependent preservation of myocardial capillary density after pressure overload. Nox4 enhanced stress-induced activation of cardiomyocyte Hif1 and the release of VEGF, resulting in an increased paracrine angiogenic activity. These data indicate that cardiomyocyte Nox4 is a novel inducible regulator of myocardial angiogenesis, a key determinant of cardiac adaptation to overload stress. Our results also have wider relevance to the use of non-specific antioxidant approaches in cardiac disease and may provide an explanation for the failure of such strategies in many settings.

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.