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.     

Performance evaluation of a submerged membrane bioreactor for the treatment of brackish oil and natural gas field produced water

Produced water, which is co-produced during oil and gas manufacturing, represents one of the largest sources of oily wastewaters. Therefore, treatment of this produced water may improve the economic viability and lead to a new source of water for beneficial use. In this study a submerged hollow fiber membrane bioreactor (MBR) has been studied experimentally for the treatment of brackish oil and natural gas field produced water. This type of wastewater is also characterized with relatively moderate to high amount of salt, oil and total petroleum hydrocarbons (TPH). However, the bacteria which are growing in conventional activated sludge and MBR cannot survive at these strict conditions, therefore acclimation of the bacteria is of vital importance. The performance of the biological system, membrane permeability, the rate and extent of TPH biodegradability have been investigated under different sludge age and F/M ratios. The results obtained by gas chromatography analyses showed that the MBR system could be very effective in the removal of TPH from produced water and a significant improvement in the effluent quality was achieved.     

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.     

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.     

Termite Resistance of MDF Panels Treated with Various Boron Compounds

In this study, the effects of various boron compounds on the termite resistance of MDF panels were evaluated. Either borax (BX), boric acid (BA), zinc borate (ZB), or sodium perborate tetrahydrate (SPT) were added to urea-formaldehyde (UF) resin at target contents of 1%, 1.5%, 2% and 2.5% based on dry fiber weight. The panels were then manufactured using 12% urea-formaldehyde resin and 1% NH₄Cl. MDF samples from the panels were tested against the subterranean termites, Coptotermes formosanus Shiraki. Laboratory termite resistance tests showed that all samples containing boron compounds had greater resistance against termite attack compared to untreated MDF samples. At the second and third weeks of exposure, nearly 100% termite mortalities were recorded in all boron compound treated samples. The highest termite mortalities were determined in the samples with either BA or BX. Also, it was found that SPT showed notable performance on the termite mortality. As chemical loadings increased, termite mortalities increased, and at the same time the weight losses of the samples decreased.     

Multi-machine earliness and tardiness scheduling problem: an interconnected neural network approach

This paper addresses the problem of scheduling a set of independent jobs with sequence-dependent setups and distinct due dates on non-uniform multi-machines to minimize the total weighted earliness and tardiness, and explores the use of artificial neural networks as a valid alternative to the traditional scheduling approaches. The objective is to propose a dynamical gradient neural network, which employs a penalty function approach with time varying coefficients for the solution of the problem which is known to be NP-hard. After the appropriate energy function was constructed, the dynamics are defined by steepest gradient descent on the energy function. The proposed neural network system is composed of two maximum neural networks, three piecewise linear and one log-sigmoid network all of which interact with each other. The motivation for using maximum networks is to reduce the network complexity and to obtain a simplified energy function. To overcome the tradeoff problem encountered in using the penalty function approach, a time varying penalty coefficient methodology is proposed to be used during simulation experiments. Simulation results of the proposed approach on a scheduling problem indicate that the proposed coupled network yields an optimal solution which makes it attractive for applications of larger sized problems.     

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.