Studies on modeling and control of spouted bed reactors—II: Control

Control and operability characteristics of an adiabatic spouted bed reactor with a first order reaction are investigated. The reactor transient response to changing operating conditions is analyzed and the generic control problems are formulated. Reduced-order state space models suitable for process control studies are constructed by utilizing the methods of orthogonal collocation and singular perturbation.Based on modern control and estimation techniques a hierarchy of different control systems is systematically generated. The assessment of alternative d     

Predicting direction of stock price index movement using artificial neural networks and support vector machines: The sample of the Istanbul Stock Exchange

Prediction of stock price index movement is regarded as a challenging task of financial time series prediction. An accurate prediction of stock price movement may yield profits for investors. Due to the complexity of stock market data, development of efficient models for predicting is very difficult. This study attempted to develop two efficient models and compared their performances in predicting the direction of movement in the daily Istanbul Stock Exchange (ISE) National 100 Index. The models are based on two classification techniques, artificial neural networks (ANN) and support vector machines (SVM). Ten technical indicators were selected as inputs of the proposed models. Two comprehensive parameter setting experiments for both models were performed to improve their prediction performances. Experimental results showed that average performance of ANN model (75.74%) was found significantly better than that of SVM model (71.52%).     

Visualization of fibrous and thread-like data

Thread-like structures are becoming more common in modern volumetric data sets as our ability to image vascular and neural tissue at higher resolutions improves. The thread-like structures of neurons and micro-vessels pose a unique problem in visualization since they tend to be densely packed in small volumes of tissue. This makes it difficult for an observer to interpret useful patterns from the data or trace individual fibers. In this paper we describe several methods for dealing with large amounts of thread-like data, such as data sets collected using Knife-Edge Scanning Microscopy (KESM) and Serial Block-Face Scanning Electron Microscopy (SBF-SEM). These methods allow us to collect volumetric data from embedded samples of whole-brain tissue. The neuronal and microvascular data that we acquire consists of thin, branching structures extending over very large regions. Traditional visualization schemes are not sufficient to make sense of the large, dense, complex structures encountered. In this paper, we address three methods to allow a user to explore a fiber network effectively. We describe interactive techniques for rendering large sets of neurons using self-orienting surfaces implemented on the GPU. We also present techniques for rendering fiber networks in a way that provides useful information about flow and orientation. Third, a global illumination framework is used to create high-quality visualizations that emphasize the underlying fiber structure. Implementation details, performance, and advantages and disadvantages of each approach are discussed.     

Adhesive surface interactions of cellulose nanocrystals from different sources

Adhesion plays an important role in the final properties of nanocomposites. This study explored the surface interaction of cellulose nanocrystals (CNCs) and the effect of CNC sources on adhesion between individual CNCs and the Si tip of an AFM cantilever using a force mapping technique called FMap. The adhesion between CNCs and a Si tip from five different sources has been studied: cotton, Whatman filter paper, hemp, softwood chemical kraft pulp, and softwood-dissolving pulp (alistaple). Mica was used as the background substrate to act as an internal standard. This study’s findings suggest that adhesion is not the same for all CNCs. Transmission electron microscopy and atomic force microscopy were used to determine the size and shape of each CNC. The experimental quantitative data showed that adhesion between CNCs and the Si tip has a close correlation with the diameter of the CNCs. X-ray photoelectron spectroscopy confirmed the presence of sulfate groups on the surface of the CNCs and a correlation between adhesion and surface chemistry of the CNCs was observed.