Nitrogen management in reservoir catchments through constructed wetland systems.

In this study, nitrogen removal was investigated in pilot-scale subsurface flow (SSF) and in free water surface flow (FWS) constructed wetlands installed in the campus of TUBITAK-Marmara Research Center, Gebze, near Istanbul, Turkey. The main purposes of this study are to apply constructed wetlands for the protection of water reservoirs and to reuse wastewater. Experiments were carried out at continuous flow reactors. The effects of the type of plants on the removal were investigated by using emergent (Canna, Cyperus, Typhia spp., Phragmites spp., Juncus, Poaceae, Paspalum and Iris.), submerged (Elodea, Egeria) and floating (Pistia, Salvina and Lemna) marsh plants at different conditions. During the study period HLRs were 30, 50, 70, 80 and 120 L m(2)d(-1) respectively. The average annual NH4-N, NO(3)-N, organic N and TN treatment efficiencies in SSF and FWS wetlands are 81% and 68%, 37% and 49%, 75% and 68%, 47% and 53%, respectively. Nitrification, denitrification and ammonification rate constant (k20) values in SSF and FNS systems have been found as 0.898 d(-1) and 0.541 d(-1), 0.488 d(-1) and 0.502 d(-1), 0.986 d(-1) and 0.908 respectively. Two types of the models (first-order plug flow and multiple regression) were tried to estimate the system performances.     

Enhancing electrochromic properties of polypyrrole by silsesquioxane nanocages

A new monomer, octa(thiophenephenyl)silsesquioxane (OThiophenePS) was synthesized via click chemistry. The chemical structure of OThiophenePS was characterized by nuclear magnetic resonance (¹H NMR) and Fourier transform infrared (FT-IR) spectroscopies. Electrochemical polymerization of pyrrole with OThiophenePS was performed resulting in polypyrrole-attached, polyhedral oligomeric silsesquioxane (OPS–PPy). The spectroelectrochemical studies show that the electrochromic properties of (OPS–PPy) are superior to those of polypyrrole (PPy). This great improvement can be attributed to the more accessible doping sites and the facile ion movement during the redox switching brought by the loose packing of the PPy chains.     

Robust control of variable speed autonomous underwater vehicle

Set point tracking control of autonomous underwater vehicle (AUV) via robust model predictive control (RMPC) is considered. Input-constrained RMPC with integral action, which has been developed in our previous work, is used to control the AUV in this study. In order to derive a RMPC control rule, non-linear dynamics of AUV with six degree of freedom is linearized at certain operating points. So, horizontal and vertical plane dynamics of system are represented by linear models which have polytopic uncertainties. Since the derived control rule will be used in real time, the computation time should be reduced. To overcome this computational time problem and get rid of trial–error step of Algorithm 1, a new algorithm is proposed here. The simulations are carried out using the control rule based on this algorithm and these results are presented.     

Preserving spatial information and overcoming variations in appearance for face recognition

Local feature-based approaches mainly aim to achieve robustness to variations in facial images by assuming that only some parts of the facial images may be affected. However, such approaches may lose spatial information. In this study, a compromise feature extraction scheme is studied which extracts local features while preserving spatial information. The proposed scheme exploits an ensemble of classifiers where each member is constructed using randomly selected design parameters including the size, number and location of sub-images for local feature extraction. Experiments conducted on FERET and ORL databases have shown that proposed scheme surpasses the local feature-based reference systems which focus on either local information or preserving spatial information.     

Affiliated marketing

In information-intensive environments some firms may be sending their customers to other firms’ or affiliates’ websites in order to generate additional sales for their affiliates. Although this may provide a choice for the customers, from a firm’s point of view such strategies have the potential to cannibalize own sales. Hence, when customers purchase from other firms’ websites, the firm may loose its own customers. This study analyzes the optimality of such strategies using an analytical framework. The findings show that a firm may increase its prices and profit when its own customers purchase from other firm websites. An analysis of customer surplus and total welfare show that such strategies may have adverse effects. The results show that customer surplus and total welfare may decrease as a result of affiliations.     

Soft groups and normalistic soft groups

Soft set theory, proposed by Molodtsov, has been regarded as an effective mathematical tool to deal with uncertainties. In this paper, first we correct some of the problematic cases in a previous paper by Akta? and Çag˜man [H. Akta?, N. Çag˜man, Soft sets and soft groups, Inf. Sci. 177 (2007) 2726-2735]. Moreover, we introduce the concepts of normalistic soft group and normalistic soft group homomorphism, study their several related properties, and investigate some structures that are preserved under normalistic soft group homomorphisms.     

An expert system for automated recognition of patients with obstructive sleep apnea using electrocardiogram recordings

Obstructive sleep apnea (OSA) is a highly prevalent sleep disorder. The traditional diagnosis methods of the disorder are cumbersome and expensive. The ability to automatically identify OSA from electrocardiogram (ECG) recordings is important for clinical diagnosis and treatment. In this study, we proposed an expert system based on discrete wavelet transform (DWT), fast-Fourier transform (FFT) and least squares support vector machine (LS-SVM) for the automatic recognition of patients with OSA from nocturnal ECG recordings. Thirty ECG recordings collected from normal subjects and subjects with sleep apnea, each of approximately 8 h in duration, were used throughout the study. The proposed OSA recognition system comprises three stages. In the first stage, an algorithm based on DWT was used to analyze ECG recordings for the detection of heart rate variability (HRV) and ECG-derived respiration (EDR) changes. In the second stage, an FFT based power spectral density (PSD) method was used for feature extraction from HRV and EDR changes. Then, a hill-climbing feature selection algorithm was used to identify the best features that improve classification performance. In the third stage, the obtained features were used as input patterns of the LS-SVM classifier. Using the cross-validation method, the accuracy of the developed system was found to be 100% for using a subset of selected combination of HRV and EDR features. The results confirmed that the proposed expert system has potential for recognition of patients with suspected OSA by using ECG recordings.     

Ternary encoding based feature extraction for binary text classification

A novel framework for termset based feature extraction is proposed for binary text classification. The proposed approach is based on the encoding of the terms within a termset. The ternary codes ‘+1’ and ‘?1’ are used to represent the class that the term supports, whereas ‘0’ denotes no support to any of the classes. Four different encoding schemes are proposed where the term weights and the term occurrence probabilities in the positive and negative documents are used to define the ternary code of a given term. The ternary patterns are utilized to define novel features by splitting them into positive and negative codes where each code is treated as a different feature extractor. Use of the derived features individually and together with bag of words representation are both investigated. The histograms of the resultant features are also employed to study the improvements that can be achieved using a small number of additional features to augment bag of words representation. Experiments conducted on four benchmark datasets with different characteristics have shown that the proposed feature extraction framework provides significant improvements compared to the bag of words representation.     

Conducting carbon/polymer composites as a catalyst support for proton exchange membrane fuel cells

Carbon/poly(3,4‐ethylene dioxythiophene) (C/PEDOT) composites are synthesized by in situ chemical oxidative polymerization of EDOT monomer on carbon black in order to decrease carbon corrosion that occurred in carbon‐supported catalysts used in proton exchange membrane fuel cell. The effects of different dopants including polystyrene sulfonic acid, p‐toluenesulfonic acid and camphorsulfonic acid with the addition of ethylene glycol or dimethyl sulfoxide on the properties of the composites are investigated. The synthesized composites are characterized by X‐ray diffraction, Fourier transform infrared spectroscopy, thermogravimetric analysis, surface area analysis and scanning electron microscope. Electrical conductivity is determined by using the four‐point probe technique. Electrochemical oxidation characteristics of the synthesized C/PEDOT composites are investigated by cyclic voltammetry by applying 1.2 V for 24 h. The composite prepared at 25 °C with p‐toluenesulfonic acid and ethylene glycol shows the best carbon corrosion resistance. Platinum‐supported catalyst by using this composite was prepared using microwave irradiation technique, and it was seen that the prepared catalyst did not significantly lose its hydrogen oxidation and oxygen reduction reaction activities after electrochemical oxidation. Copyright © 2013 John Wiley & Sons, Ltd.