Condensation polymerization of pyrogallol and benzaldehyde

Novolac-type polycondensation of benzaldehyde (B) and pyrogallol (P) has been carried out at 60°C, 75°C and 90°C and at B/P mole ratios of 1.5 and 3.0 using phosphoric acid as catalyst. The reaction follows a 2nd order rate law. By using GC consumption data of benzaldehyde and pyrogallol, kinetic parameters such as the overall rate constants, activation energies (E_a) and logA values are investigated. The activation energies for 1.5 and 3.0 B/P mole ratios are found as 62.3 kJ mol^?1 and 56.4 kJ mol^?1, respectively. The molecular weights of the resins determined by measuring intrinsic viscosities (25°C, THF) are in the range of 0.03 to 0.07 dL g^?1 at various temperatures and B/P mole ratios.     

Assessment of the energy and exergy utilization efficiencies in the Turkish agricultural sector

This study deals with evaluating the energy and exergy utilization efficiencies in the Turkish agricultural sector over a 12‐year period from 1990 to 2001. In the energy and exergy analyses, two main energy sources, namely fuels and electricity, are taken into consideration, while the sectoral energy and exergy efficiencies are compared for this period. These main energy sources include diesel for tractors and other vehicles, and electricity for pumps. Overall energy utilization efficiencies are obtained to vary between 29.1 and 41.1%, while overall exergy utilization efficiencies are found to range from 27.9 to 37.4% in the analysed years, respectively. It may be concluded that the present technique proposed here may be used as a useful tool in analysing and evaluating the energy and exergy utilization efficiencies, identifying energy efficiency and/or energy conservation opportunities and dictating the energy strategies of countries. Copyright © 2005 John Wiley & Sons, Ltd.     

Medical image segmentation with transform and moment based features and incremental supervised neural network

In this study, a novel incremental supervised neural network (ISNN) is proposed for the segmentation of medical images. Performance of the ISNN is investigated for tissue segmentation in medical images obtained from various imaging modalities. Two feature extraction methods based on transform and moments are comparatively investigated to segment the tissues in medical images. Two-dimensional (2D) continuous wavelet transform (CWT) and the moments of the gray-level histogram (MGH) are computed in order to form the feature vectors of ultrasound (US) bladder and phantom images, X-ray computerized tomography (CT) and magnetic resonance (MR) head images. In the 2D-CWT method, feature vectors are formed by the intensity of one pixel of each wavelet-plane of different energy bands. The MGH represents the tissues within the sub-windows by using the spatial variation of image intensities. In this study, the ISNN and Grow and Learn (GAL) network are employed for the segmentation task. It is observed that the ISNN has significantly eliminated the disadvantages of the GAL network in the segmentation of the medical images.     

Aeroelastic tailoring using lamination parameters

The aim of the present work is to passively reduce the induced drag of the rear wing of a Formula One car at high velocity through aeroelastic tailoring. The angle-of-attack of the rear wing is fixed and is determined by the required downforce needed to get around a turn. As a result, at higher velocity, the amount of downforce and related induced drag increases. The maximum speed on a straight part is thus reduced due to the increase in induced drag. A fibre reinforced composite torsion box with extension-shear coupled upper and lower skins is used leading to bending-torsion coupling. Three-dimensional static aeroelastic analysis is performed loosely coupling the Finite Element code Nastran and the Computational Fluid Dynamics panel code VSAERO using ModelCenter. A wing representative of Formula One rear wings is optimised for minimum induced drag using a response surface methodology. Results indicate that a substantial induced drag reduction is achievable while maintaining the desired downforce during low speed turns.     

Vic-dioximes: A new class of sensitive materials for chemical gas sensing

Vic-dioximes, a class of organic chemical compounds, are proposed and characterized for the first time as sensitive materials for volatile organic compound sensing with sorption based chemical gas sensors. Their peculiar sensing properties described in this work originate in the oxime functional group which is a powerful H bond donor interacting strongly but reversibly with H bond acceptors. These specific interactions result in a high preferential enrichment of analyte molecules with H bonding acceptor capabilities in the sensitive material. Accordingly, sensitivity and selectivity for these compounds of vic-dioxime based sensors are high. The advantageous sensing properties are demonstrated in this work with quartz crystal microbalance sensors using 11 selected volatile organic compounds and a set of vic-dioximes varied in their substituents. Vic-dioximes with short alkylthiol substituents were found highly sensitive to such H bond acceptors as organic amines, alcohols, and esters with partition coefficients up to 26,000. At the same time they showed low affinity for aromatic compounds and chlorocarbons. Vic-dioximes are considered powerful sensing materials and interesting for practical use in chemical gas sensor arrays.     

Routing table updating by using intelligent agents

In a network, one of the important problems is making an efficient routing decision. Many studies have been carried out on making a decision and several routing algorithms have been developed. In a network environment, every node has a routing table and these routing tables are used for making routing decisions. Nowadays, intelligent agents are used to make routing decisions. Intelligent agents have been inspired by social insects such as ants. One of the intelligent agent types is self a cloning ant. In this study, a self cloning ant colony approach is used. Self cloning ants are a new synthetic ant type. This ant assesses the situation and multiplies through cloning or destroying itself. It is done by making a routing decision and finding the optimal path. This study explains routing table updating by using the self cloning ant colony approach. In a real net, this approach has been used and routing tables have been created and updated for every node.     

An experimental study for the effect of different clearances on burr, smooth-sheared and blanking force on aluminium sheet metal

In this study, an experimental study was carried out to examine the effect of different clearances on smooth-sheared depth, burr height and blanking force. Aluminium sheet metals with 0.8, 1, and 1.5 mm thicknesses were used in the experiments. These experiments were carried out in circular blanking dies to 6, 8, 10, and 12 mm diameters. Six different clearances (0.009, 0.064, 0.12, 0.175, 0.231 and 0.287 mm) were used for every material and diameter. The results of the experiment show that burr, smooth-sheared and punch force is strongly related to the clearance value. The results are in agreement with the results of previous theoretical and experimental work in the literature.     

On disconjugacy and stability criteria for discrete Hamiltonian systems

By making use of new Lyapunov type inequalities, we establish disconjugacy and stability criteria for discrete Hamiltonian systems. The stability criteria are given when the system is periodic.     

A deep learning approach with Bayesian optimization and ensemble classifiers for detecting denial of service attacks

Detecting malicious behavior is important for preventing security threats in a computer network. Denial of Service (DoS) is among the popular cyber attacks targeted at web sites of high‐profile organizations and can potentially have high economic and time costs. In this paper, several machine learning methods including ensemble models and autoencoder‐based deep learning classifiers are compared and tuned using Bayesian optimization. The autoencoder framework enables to extract new features by mapping the original input to a new space. The methods are trained and tested both for binary and multi‐class classification on Digiturk and Labris datasets, which were introduced recently for detecting various types of DDoS attacks. The best performing methods are found to be ensembles though deep learning classifiers achieved comparable level of accuracy.     

Preparation of dye sensitized titanium oxide nanoparticles for solar cell applications

A new method for synthesis of titanium dioxide (TiO₂)-dye nanoparticles is reported. TiO₂ nanocrystals were obtained at 150 and 200 °C by using chemically bonded TiO₂-sensitizer dye as a precursor. Titanium tetraisopropoxide was first modified with a dye molecule and then precipitated by dropping into acidic water. A strongly colored precipitate was obtained. Hydrothermal growth of a colloidal solution was carried out in a Teflon-lined stainless steel autoclave. Dye sensitized solar cell efficiencies obtained were comparable and fill factor values were close to the analogous cells prepared by the use of conventional TiO₂ paste techniques. This method allows the use of different substrates together with nanocrystalline TiO₂ for many technological applications.