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.     

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.     

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.     

A minisum location problem with regional demand considering farthest Euclidean distances

We consider a continuous multi-facility location-allocation problem that aims to minimize the sum of weighted farthest Euclidean distances between (closed convex) polygonal and/or circular demand regions, and facilities they are assigned to. We show that the single facility version of the problem has a straightforward second-order cone programming formulation and can therefore be efficiently solved to optimality. To solve large size instances, we adapt a multi-dimensional direct search descent algorithm to our problem which is not guaranteed to find the optimal solution. In a special case with circular and rectangular demand regions, this algorithm, if converges, finds the optimal solution. We also apply a simple subgradient method to the problem. Furthermore, we review the algorithms proposed for the problem in the literature and compare all these algorithms in terms of both solution quality and time. Finally, we consider the multi-facility version of the problem and model it as a mixed integer second-order cone programming problem. As this formulation is weak, we use the alternate location-allocation heuristic to solve large size instances.     

Chemometric approach to evaluate trace metal concentrations in some spices and herbs

Herbs (mint, thyme and rosemary) and spices (black pepper, chili pepper, cinnamon, cumin, sweet red pepper and turmeric) were analysed using atomic spectrometry and then subjected to chemometric evaluation in an attempt to classify them using their trace metallic analyte concentrations (As, Ba, Ca, Cd, Co, Cr, Cu, Fe, Mg, Mn, Ni, Sr and Zn). Trace metals in acid digests of these materials were determined using both inductively coupled plasma-atomic emission spectrometry and inductively coupled plasma-mass spectrometry. The chemometric techniques of principal component analysis (PCA), linear discriminant analysis (LDA) and cluster analysis (CA) were used for the classification studies. These herbs and spices were classified into five groups by PCA and CA. When the results of these techniques were compared with those from LDA, it was found that all group members determined by PCA and CA are in the predicted group that 100.0% of original grouped cases correctly classified by LDA.     

Resistance variation of conductive ink applied by the screen printing technique on different substrates

This research study focuses on the application of conductive ink by the screen printing technique to evaluate the potential of creating printed electrodes and to investigate the effect of washing upon electrical resistance and flexibility. Two conductive inks were applied by a conventional screen printing method on four different textile substrates, 100% cotton, 50%/50% cotton/polyester, 100% polyester and 100% polyamide. The inks were also applied on a multifibre fabric. Atmospheric plasma treatment was applied to improve the adhesion to the samples, and the resistance values were compared with those of non‐treated samples. The values were measured before and after cleaning and washing tests, which were performed to simulate domestic treatment for garments to predict the behaviour of the inks after normal usage of the fabrics. Comfort properties like stiffness of the fabrics were also evaluated after five and 10 washing cycles. It was observed that PE 825 ink forms a thicker film on the fabric surface, contributing to the loss of flexibility of the textile. However, PE 825 ink also produced the best results in terms of durability and lower values of resistance. Polyamide fabrics lost their conductive property after five washing cycles due to weak bonding between the ink and the fibres, whereas cotton fibres provided the best results.     

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.     

Small-diameter vascular grafts of Bombyx mori silk fibroin prepared by a combination of electrospinning and sponge coating

The present study aimed to produce small-diameter grafts made of a silk fibroin by electrospinning. In order to reinforce the electrospun silk fibroin graft (ES), the graft was coated with a silk sponge (ESSC). Physical properties such as a diameter of the electrospun silk fibers were influenced by a concentration of fibroin solution. Ultimate tensile strength (UTS) of the ESSC graft was improved compared to the ES graft. However, the ESSC graft was less compliant than the ES graft. Importantly, water permeability of the ESSC graft was within the range of which endothelialization was promoted in previous studies.     

Freezing Range,Melt Quality,and Hot Tearing in Al-Si Alloys

Metallurgical and Materials Transactions A - In this study, three different aluminum-silicon alloys (A356, A413, and A380) that have different solidification morphology and solidification ranges...