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ERp44 controls the localization and transport of diverse proteins in the early secretory pathway. The mechanisms that allow client recognition and the source of the oxidative power for forming intermolecular disulfides are as yet unknown. Here we present the structure of ERp44 bound to a client, peroxiredoxin 4. Our data reveal that ERp44 binds the oxidized form of peroxiredoxin 4 via thiol-disulfide...
In deregulated power markets, forecasting electricity loads is one of the most essential tasks for system planning, operation and decision making. Based on an integration of two machine learning techniques: a hybrid evolutionary algorithm which combines PSO and Artificial Fish Swarm Algorithm Search approach based on test-sample error estimate criterion (PSO-AFSAS-TEE) and support vector regression...
Electricity demand forecasting is an important index to make power development plan and dispatch the loading of generating units in order to meet system demand. In order to improve the accuracy of the forecasting, we apply the feedforward neural network for electricity demand forecasting. Inspired by the idea of artificial fish swarm algorithm, in this paper we proposed one hybrid evolutionary algorithm...
Nowadays, owing to the accelerated growth in the development of information technology, the aviation industry plays a prominent role in Chinese economy. Therefore, the accurate prediction for the number of international airlines helps in making major decisions in technological reform. In order to analyze the number of international airlines in China efficiently, in this paper, fuzzy adaptive PSO algorithm...
In the last decade, the use of artificial neural networks (ANN) has become widely accepted in medical applications for accuracy for predictive inference, with potential to support and flexible non-linear modelling of large data sets. Feedforward neural network (FNN) is a kind of artificial neural networks, which has a better structure and been widely used. But there are still many drawbacks if we...
Short-term electricity demand forecasting for the next hour to several days out is one of the most important tools by which an electric utility plans and dispatches the loading of generating units in order to meet system demand. But there exists chaos in electricity systems to a great extent. Complicated electricity systems are nonlinear systems and the forecasting is very complex in nature and quite...
Forecasting electricity consumption is an important index for system planning, operation and decision making. In order to improve the accuracy of the forecasting, we apply an integrated architecture to optimize the prediction. Based on an integration of two machine learning techniques: artificial fish swarm algorithm search approach based on test-sample error estimate criterion (AFSAS-TEE) and support...
In recent years, the multilayer feedforward neural network (FNN) has been received considerable attention and have been extensively used in many fields. Levenberg-Marquardt back-propagation (LMBP) algorithm as an FNN training method has some limitations associated with overfitting, local optimum problems and slow convergence rate. In order to overcome the limitations, some people proposed particle...
Time series analysis is an important and complex problem in machine learning. Support vector machine (SVM) has recently emerged as a powerful technique for solving problems in regression, but its performance mainly depends on the parameters selection of it. Parameters selection for SVM is very complex in nature and quite hard to solve by conventional optimization techniques, which constrains its application...
In this paper, a new approach to ARMA model identification using evolutionary particle swarm optimization (PSO) algorithm has been proposed. ARMA is a popular method to analyze stationary univariate time series data. Stationarity checking, model identification, model estimation and model checking are usually four main stages to build an ARMA model and model identification is the most important stage...
Recently some cities' investments on fix assets increase too fast that lead to a property bubble. In order to prevent the overheating of real estate investment, this paper presents a pre-warning system developed to monitor and provide pre-warning to the governmental decision makers in real estate market. In the overall structure plan, the warning classification system is the most important so that...
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