Propylene Epoxidation: High-Throughput Screening of Supported Metal Catalysts Combinatorially Prepared by Rapid Sol–Gel Method

The gas phase oxidation of propylene using molecular oxygen was studied on a variety of supported metal catalysts. The most promising PO activity was obtained for Cu supported on high surface area SiO₂ and the multimetallic systems exhibit synergistic effects that increased the desired PO yield by several folds for Ag promoted with Cu on SiO₂ after screening a large number of catalysts by a high throughput testing technique.     

Donor-acceptor-donor type conjugated polymers for electrochromic applications: benzimidazole as the acceptor unit

Donor-acceptor-donor (DAD) type benzimidazole (BIm) and 3,4 ethylenedioxythiophene (EDOT) bearing monomers were synthesized and electrochemically polymerized. Pendant group at 2-C position of the imidazole ring was functionalized with phenyl (P1), EDOT (P2) and ferrocene (P3) in order to observe substituent effect on electrochemical and electrochromic properties of corresponding polymers. Spectroelectrochemical results showed that different pendant groups resulted in polymers with slightly different optical band gaps (1.75, 1.69 and 1.77 eV respectively) and different number of achievable colored states. Optoelectronic performance and comparison of results with other well known π-accepting benzazole bearing DAD type polymers were reported in detail.     

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.     

Hydrogen production by aqueous phase catalytic reforming of glycerine

Hydrogen is believed to be the one of the main energy carriers in the near future. In this research glycerine, which is produced in large quantities as a by-product of biodiesel process, was converted to hydrogen aiming to contribute to clean energy initiative. Conversion of glycerol to hydrogen was achieved via aqueous-phase reforming (APR) with Pt/Al₂O₃ catalyst. The experiments were carried out in an autoclave reactor and a continuous fixed-bed reactor. The effects of reaction temperature (160-280 °C), feed flow rate (0.05-0.5 mL/dak) and feed concentration (5-85 wt-% glycerine) on product distribution were investigated. Optimum temperature for hydrogen production with APR was determined as 230 °C. Maximum gas production rate was found at the feed flow rates around 0.1 mL/min. It was also found that hydrogen concentration in the gas product increased with decreasing glycerol concentration in the feed.     

An expert system for detection of electrocardiographic changes in patients with partial epilepsy using wavelet-based neural networks

Abstract: In this study a wavelet‐based neural network model, employing the multilayer perceptron, is presented for the detection of electrocardiographic changes in patients with partial epilepsy. Decision making is performed in two stages: feature extraction using the wavelet transform, and multilayer perceptron neural networks (MLPNNs) trained with the backpropagation, delta‐bar‐delta, extended delta‐bar‐delta and quick propagation algorithms as classifiers. The classification results, the values of statistical parameters and performance evaluation parameters of the MLPNNs trained with different algorithms are compared. Two types of electrocardiogram beats (normal and partial epilepsy) obtained from the MIT‐BIH database were classified with accuracy varying from 90.00% to 97.50% by the MLPNNs trained with different algorithms.     

Comparison of different classification algorithms in clinical decision-making

Abstract: This paper gives an integrated view of implementing automated diagnostic systems for clinical decision-making. Because of the importance of making the right decision, better classification procedures are necessary for clinical decisions. The major objective of the paper is to be a guide for readers who want to develop an automated decision support system for clinical practice. The purpose was to determine an optimum classification scheme with high diagnostic accuracy for this problem. Several different classification algorithms were tested and benchmarked for their performance. The performance of the classification algorithms is illustrated on two data sets: the Pima Indians diabetes and the Wisconsin breast cancer. The present research demonstrates that the support vector machines achieved diagnostic accuracies which were higher than those of other automated diagnostic systems.     

Investigating Neutronic Parameters of a Thorium Fusion Breeder with Recurrent Neural Networks

Artificial neural networks (ANNs) have recently been introduced to the nuclear engineering applications as a fast and flexible vehicle to modeling, simulation and optimization. In this paper, a new approach based on recurrent neural networks (RNNs) was presented for the neutronic parameters of a thorium fusion breeder. The results of the RNNs implemented for the tritium breeding ratio computation, energy multiplication factor and net ²³³U production in a thorium fusion breeder and the results available in the literature obtained by using Scale 4.3 were compared. The drawn conclusions confirmed that the proposed RNNs could provide an accurate computation of the tritium breeding ratio computation, the energy multiplication factor and the net ²³³U production of the thorium fusion breeder.     

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.     

Gold and Cobalt Oxide Nanoparticles Modified Poly-Propylene Poly-Ethylene Glycol Membranes in Poly (ε-Caprolactone) Conduits Enhance Nerve Regeneration in the Sciatic Nerve of Healthy Rats

Reconstruction of nerve defects is a clinical challenge. Autologous nerve grafts as the gold standard treatment may result in an incomplete restoration of extremity function. Biosynthetic nerve conduits are studied widely, but still have limitations. Here, we reconstructed a 10 mm sciatic nerve defect in healthy rats and analyzed nerve regeneration in poly (ε-caprolactone) (PCL) conduits longitudinally divided by gold (Au) and gold-cobalt oxide (AuCoO) nanoparticles embedded in poly-propylene poly-ethylene glycol (PPEG) membranes (AuPPEG or AuCoOPPEG) and compared it with unmodified PPEG-membrane and hollow PCL conduits. After 21 days, we detected significantly better axonal outgrowth, together with higher numbers of activated Schwann cells (ATF3-labelled) and higher HSP27 expression, in reconstructed sciatic nerve and in corresponding dorsal root ganglia (DRG) in the AuPPEG and AuCoOPPEG groups; whereas the number of apoptotic Schwann cells (cleaved caspase 3-labelled) was significantly lower. Furthermore, numbers of activated and apoptotic Schwann cells in the regenerative matrix correlated with axonal outgrowth, whereas HSP27 expression in the regenerative matrix and in DRGs did not show any correlation with axonal outgrowth. We conclude that gold and cobalt-oxide nanoparticle modified membranes in conduits improve axonal outgrowth and increase the regenerative performance of conduits after nerve reconstruction.     

Prediction of the colorimetric parameters and mass loss of heat‐treated bamboo: Comparison of multiple linear regression and artificial neural network method

In this study, the colorimetric parameters (L*, a*, b*) and mass loss of heat‐treated bamboo were investigated, and the obtained results were modeled by using two methods: multiple linear regression (MLR) and artificial neural network (ANN). First, bamboo samples were exposed to heat treatment at different temperatures (110°C, 140°C, 170°C, and 200°C) and durations (15, 30, 45, 60, 75, 90, and 115 minutes) in a laboratory oven. Then, the colorimetric parameters (L*, a*, b*) and mass loss of each sample were measured after each period of heat treatment. All data were modeled by using two methods separately for each parameter and the performances of these proposed methods were compared. It was found that color change and mass loss increased with increasing temperature and duration of heat treatment. Mean absolute percentage error (MAPE) values of all obtained MLR ranged from 0.64% to 10.63%, while the all MAPE values of ANN were found to be lower than 1.5%. Based on these results, it can be said that MLR and ANN could be used to evaluate the changes on the selected properties of heat‐treated bamboo samples. On the other hand, it should be emphasized that the ANN gave more accurate results than the MLR method because of its learning capability.