A new deep intuitionistic fuzzy time series forecasting method based on long short-term memory

The Journal of Supercomputing - In recent years, deep artificial neural networks can have better forecasting performance than many other artificial neural networks. The long short-term memory...     

Chemical,thermal, and mechanical properties and ultraviolet transmittance of novel nano-hydroxyapatite/polyethylene terephthalate milk bottles

Polyethylene terephthalate (PET)/nano-hydroxyapatite (nHAp) composite granules were obtained using twin-screw extruder. Preforms were prepared by injection molding and then PET/nHAp bottles were produced by blow molding. For PET bottles with nHAp, the migration amounts of carboxylic acid (COOH), acetaldehyde (AA), diethylene glycol (DEG), and isophthalic acid (IPA); glass transition temperature (T_g); melting temperature (T_m); and the maximum crystallization temperature (T_cry) were measured. The load-carrying capacity, burst strength, stress cracking, and regional material distribution tests were carried out on the bottles. X-ray diffraction, scanning electron microscopy, Fourier transform infrared spectroscopy, differential scanning calorimetry, and ultraviolet transmittance analyses were conducted to explain the changes in mechanical, chemical, physical properties, and light transmission of bottles. It was found out that the COOH amount increased and the AA content decreased with increasing nHAp amount. On the other hand, no change was observed in the amounts of DEG and IPA. Although the mechanical properties such as load-carrying capacity and burst strength of the bottles have improved, it has been determined that the standard environmental stress crack resistance test procedure cannot be applied to such a composite. Experimental findings indicate that nHAp disrupts the chemical structure of PET and it isolates harmful chemicals such as AA by forming intermolecular bonds. Moreover, with the addition of up to 0.8% nHAp, PET bottles block the light transmission approximately 80% within 400–700 nm wave length zone. The study demonstrates that the PET/nHAp composite bottles can be used in the food industry, particularly in the packaging of milk and milk products which are vulnerable to light exposure.     

Order-disorder phase transition in NH4AlF4

In this study we calculate the specific heat C_VI for NH₄AlF₄ due to the nearest-neighbor interactions between the NH⁺ ₄ tetrahedra using an Ising model superimposed on an Einstein and/or Debye model. The specific heat C_VI calculated using a power-law formula is in good agreement with the observed C_P for the NH₄AlF₄ system. This is an indication that NH₄AlF₄ undergoes a weak first-order or a nearly second-order phase transition as predicted by our model.     

Signal‐to‐noise ratios for measuring saliency of features extracted by eigenvector methods from ophthalmic arterial Doppler signals

Abstract: Features are used to represent patterns with minimal loss of important information. The feature vector, which is composed of the set of all features used to describe a pattern, is a reduced‐dimensional representation of that pattern. Medical diagnostic accuracies can be improved when the pattern is simplified through representation by important features. By identifying a set of salient features, the noise in a classification model can be reduced, resulting in more accurate classification. In this study, a signal‐to‐noise ratio saliency measure was employed to determine the saliency of input features of recurrent neural networks (RNNs) used in classification of ophthalmic arterial Doppler signals. Eigenvector methods were used to extract features representing the ophthalmic arterial Doppler signals. The RNNs used in the ophthalmic arterial Doppler signal classification were trained for the signal‐to‐noise ratio screening method. The application results of the signal‐to‐noise ratio screening method to the ophthalmic arterial Doppler signals demonstrated that classification accuracies of RNNs with salient input features are higher than those of RNNs with salient and non‐salient input features.     

Feature extraction by autoregressive spectral analysis using maximum likelihood estimation: internal carotid arterial Doppler signals

Abstract: In this study, Doppler signals recorded from the internal carotid artery (ICA) of 97 subjects were processed by personal computer using classical and model-based methods. Fast Fourier transform (classical method) and autoregressive (model-based method) methods were selected for processing the ICA Doppler signals. The parameters in the autoregressive method were found by using maximum likelihood estimation. The Doppler power spectra of the ICA Doppler signals were obtained by using these spectral analysis techniques. The variations in the shape of the Doppler spectra as a function of time were presented in the form of sonograms in order to obtain medical information. These Doppler spectra and sonograms were then used to compare the applied methods in terms of their frequency resolution and the effects in determination of stenosis and occlusion in the ICA. Reliable information on haemodynamic alterations in the ICA can be obtained by evaluation of these sonograms.     

Estimating Construction Material Indices with ARIMA and Optimized NARNETs

Construction Industry operates relying on various key economic indicators. One of these indicators is material prices. On the other hand, cost is a key concern in all operations of the construction industry. In the uncertain conditions, reliable cost forecasts become an important source of information. Material cost is one of the key components of the overall cost of construction. In addition, cost overrun is a common problem in the construction industry, where nine out of ten construction projects face cost overrun. In order to carry out a successful cost management strategy and prevent cost overruns, it is very important to find reliable methods for the estimation of construction material prices. Material prices have a time dependent nature. In order to increase the foreseeability of the costs of construction materials, this study focuses on estimation of construction material indices through time series analysis. Two different types of analysis are implemented for estimation of the future values of construction material indices. The first method implemented was Autoregressive Integrated Moving Average (ARIMA), which is known to be successful in estimation of time series having a linear nature. The second method implemented was Non-Linear Autoregressive Neural Network (NARNET) which is known to be successful in modeling and estimating of series with non-linear components. The results have shown that depending on the nature of the series, both these methods can successfully and accurately estimate the future values of the indices. In addition, we found out that Optimal NARNET architectures which provide better accuracy in estimation of the series can be identified/discovered as result of grid search on NARNET hyperparameters.     

Automatic detection of electroencephalographic changes using adaptive neuro-fuzzy inference system employing Lyapunov exponents

This paper describes the application of adaptive neuro-fuzzy inference system (ANFIS) model for automatic detection of electroencephalographic changes. Decision making was performed in two stages: feature extraction by computation of Lyapunov exponents and classification by the ANFIS trained with the backpropagation gradient descent method in combination with the least squares method. Five types of electroencephalogram (EEG) signals were classified by five ANFIS classifiers. To improve diagnostic accuracy, the sixth ANFIS classifier (combining ANFIS) was trained using the outputs of the five ANFIS classifiers as input data. The proposed ANFIS model combined the neural network adaptive capabilities and the fuzzy logic qualitative approach. Some conclusions concerning the saliency of features on classification of the EEG signals were obtained through analysis of the ANFIS. The performance of the ANFIS model was evaluated in terms of training performance and classification accuracies and the results confirmed that the proposed ANFIS model has potential in classifying the EEG signals.     

Dynamic soil–structure interaction analysis of layered unbounded media via a coupled finite element/boundary element/scaled boundary finite element model

In this paper, a coupled model based on finite element method (FEM), boundary element method (BEM) and scaled boundary FEM (SBFEM) (also referred to as the consistent infinitesimal finite element cell method) for dynamic response of 2D structures resting on layered soil media is presented. The SBFEM proposed by Wolf and Song (Finite‐element Modelling of Unbounded Media. Wiley: England, 1996) and BEM are used for modelling the dynamic response of the unbounded media (far‐field). The standard FEM is used for modelling the finite region (near‐field) and the structure. In SBFEM, which is a semi‐analytical technique, the radiation condition at infinity is satisfied exactly without requiring the fundamental solution. This method, also eliminates the need for the discretization of interfaces between different layers. In both SBFEM and BEM, the spatial dimension is decreased by one. The objective of the development of this coupled model is to combine advantages of above‐mentioned three numerical models to solve various soil–structure interaction (SSI) problems efficiently and effectively. These three methods are coupled (FE–BE–SBFEM) via substructuring method, and a computer programme is developed for the harmonic analyses of SSI systems. The coupled model is established in such a way that, depending upon the problem and far‐field properties, one can choose BEM and/or SBFEM in modelling related far‐field region(s). Thus, BEM and/or SBFEM can be used efficiently in modelling the far‐field. The proposed model is applied to investigate dynamic response of rigid and elastic structures resting on layered soil media. To assess the proposed SSI model, several problems existing in the literature are chosen and analysed. The results of the proposed model agree with the results presented in the literature for the chosen problems. The advantages of the model are demonstrated through these comparisons. Copyright © 2004 John Wiley & Sons, Ltd.     

Fuel bioadditive production by chitosan/sulfosuccinic acid catalytic membrane

Global warming is one of the biggest environmental problems of late. The use of renewable raw material resources as biomass and its conversion into alternative energy resources have attracted considerable attention during the recent years. Ethyl levulinate (EL) is a kind of fuel bioadditive for use in diesel and biodiesel engines, which is produced by the esterification of ethanol and levulinic acid. In the present study, sulfosuccinic acid (SSA) catalysts supported on chitosan catalytic membrane is used for the synthesis of EL. The effects of temperature, catalyst amount, and molar feed ratio were studied.