Impacts of varnishes and impregnation chemicals on combustion properties of oak (Quercus petreae lipsky)

This study was performed to determine the effects of varnishing after impregnation with boron compounds on combustion properties of oak. For this aim, the test samples prepared from oak (Quercus petreae Liebl.) wood were impregnated according to ASTM D 1413‐99 with boric acid (Ba) and borax (Bx) by vacuum technique. After impregnation, surfaces were coated by cellulosic (Cv), synthetic (Sn), polyurethane (Pu), waterborne (Wb), acrylic (Ac), and acid hardening (Ah) varnishes in accordance to ASTM D 3023. Combustion properties of samples after varnishing process were determined, according to ASTM E 160‐50. According to material and process types, combustion temperature was the highest in Bx and Cv, the lowest in Ba and Ac. An important difference was not observed between without flame source combustion (WFSC) and flame source combustion (FSC). According to combustion type, impregnation material and varnish type, combustion temperature was the highest value in WFSC + Bx + acid hardening varnish combination and the lowest in WFSC + Ba + acrylic varnish combination. As a result, the tested varnishes showed an increasing impact but boron compounds showed a decreasing impact on combustion properties of oak. In consequence, for usage areas having a high risk of fire, impregnation of wood material with boron compounds before varnishing will decrease combustion temperature and provide security. © 2007 Wiley Periodicals, Inc. J Appl Polym Sci 2008     

Comparison between some empirical equations of state for polymers

Some commonly used empirical equations of state for polymers are considered: the Spencer-Gilmore equation with two and three adjustable parameters, the Whitaker-Griskey equation, and the Rehage-Breuer equation. Also, a new equation is proposed: the Inverse Volume equation. These equations are evaluated with regard to fitting experimental P-V-T data and agreement with experimental data on isothermal compressibility and thermal expansion coefficient. The adjustable parameters for each equation are determined with the help of Rosenbrock's optimum-seeking technique. Analysis of the residuals on specific volume for a variety of materials suggests that the Spencer-Gilmore equation with three adjustable parameters, the Rehage-Breuer and the Inverse Volume equations yield the smallest and most random residuals and thus the least systematic error. The same three equations mentioned above yield results in good agreement with experimental isothermal compressibility data. However, among all the equations considered in this study, the Inverse Volume equation yields the best agreement with experimental thermal expansion coefficient data. Furthermore, it is the only equation to correctly predict the rise in thermal expansion coefficient with increasing temperature.     

Fuzzy functions based ARX model and new fuzzy basis function models for nonlinear system identification

In this study, auto regressive with exogenous input (ARX) modeling is improved with fuzzy functions concept (FF-ARX). Fuzzy function with least squares estimation (FF-LSE) method has been recently developed and widely used with a small improvement with respect to least squares estimation method (LSE). FF-LSE is structured with only inputs and their membership values. This proposed model aims to increase the capability of the FF-LSE by widening the regression matrix with lagged input–output values. In addition, by using same idea, we proposed also two new fuzzy basis function models. In the first, basis of the fuzzy system and lagged input–output values are structured together in the regression matrix and named as “L-FBF”. Secondly, instead of using basis function, the membership values of the lagged input–output values are used in the regression matrix by using Gaussian membership functions, called “M-FBF”. Therefore, the power of the fuzzy basis function is also enhanced. For the corresponding models, antecedent part parameters for the input vectors are determined with fuzzy c-means (FCM) clustering algorithm. The consequent parameters of the all models are estimated with the LSE. The proposed models are utilized and compared for the identification of nonlinear benchmark problems.     

Fabrication of ultraviolet photoconductive sensor using a novel aluminium-doped zinc oxide nanorod-nanoflake network thin film prepared via ultrasonic-assisted sol-gel and immersion methods

Unique and novel thin films with aluminium (Al)-doped zinc oxide (ZnO) nanostructures consisting of nanorod-nanoflake networks were prepared for metal-semiconductor-metal (MSM)-type ultraviolet (UV) photoconductive sensor applications. These nanostructures were grown on a glass substrate coated with a seed layer using a combination of ultrasonic-assisted sol-gel and immersion methods. The synthesised ZnO nanorods had diameters varying from 10 to 40 nm. Very thin nanoflake structures were grown vertically and horizontally on top of the nanorod array. The thin film had good ZnO crystallinity with a root mean square roughness of approximately 13.59 nm. The photocurrent properties for the Al-doped ZnO nanorod-nanoflake thin films were more than 1.5 times greater than those of the seed layer when the sensor was illuminated with 365 nm UV light at a density of 5 mA/cm². The responsivity of the device was found to be dependent on the bias voltage. We found that similar photocurrent curves were produced over eight cycles, which indicated that the UV sensing capability of the fabricated sensor was highly reproducible. Our results provide a new approach for utilising the novel structure of Al-doped ZnO thin films with a nanorod-nanoflake network for UV sensor applications. To the best of our knowledge, UV photoconductive sensors using Al-doped ZnO thin films with a nanorod-nanoflake network have not yet been reported.     

Characterising and exploiting workloads of highly interactive video-on-demand

This paper presents a detailed characterisation of user behaviour for a series of interactive video experiments over a 12 month period, in which we served popular sporting and musical content. In addition to generic VCR-like features, our custom-built video-on-demand application provides advanced interactivity features such as bookmarking. The dramatic impact of such functionality on how users consume content is studied and analysed. We discuss in detail how this user behaviour can be exploited by content distributors to improve user experience. Specifically, we study how simple dynamic bookmark placement and interactivity-aware content pre-fetching and replication can reduce the impact of highly interactive media on CDN performance.     

Optimum loading of machines in a flexible manufacturing system using a mixed-integer linear mathematical programming model and genetic algorithm

Machine loading problem in a flexible manufacturing system (FMS) encompasses various types of flexibility aspects pertaining to part selection and operation assignments. The evolution of flexible manufacturing systems offers great potential for increasing flexibility by ensuring both cost-effectiveness and customized manufacturing at the same time. This paper proposes a linear mathematical programming model with both continuous and zero-one variables for job selection and operation allocation problems in an FMS to maximize profitability and utilization of system. The proposed model assigns operations to different machines considering capacity of machines, batch-sizes, processing time of operations, machine costs, tool requirements, and capacity of tool magazine. A genetic algorithm (GA) is then proposed to solve the formulated problem. Performance of the proposed GA is evaluated based on some benchmark problems adopted from the literature. A statistical test is conducted which implies that the proposed algorithm is robust in finding near-optimal solutions. Comparison of the results with those published in the literature indicates supremacy of the solutions obtained by the proposed algorithm for attempted model.     

Attribute weighted Naive Bayes classifier using a local optimization

The Naive Bayes classifier is a popular classification technique for data mining and machine learning. It has been shown to be very effective on a variety of data classification problems. However, the strong assumption that all attributes are conditionally independent given the class is often violated in real-world applications. Numerous methods have been proposed in order to improve the performance of the Naive Bayes classifier by alleviating the attribute independence assumption. However, violation of the independence assumption can increase the expected error. Another alternative is assigning the weights for attributes. In this paper, we propose a novel attribute weighted Naive Bayes classifier by considering weights to the conditional probabilities. An objective function is modeled and taken into account, which is based on the structure of the Naive Bayes classifier and the attribute weights. The optimal weights are determined by a local optimization method using the quasisecant method. In the proposed approach, the Naive Bayes classifier is taken as a starting point. We report the results of numerical experiments on several real-world data sets in binary classification, which show the efficiency of the proposed method.     

On the capacity of the state‐dependent interference relay channel

This paper considers the state‐dependent interference relay channel (SIRC) in which one of the two users may operate as a secondary user and the relay has a noncausal access to the signals from both users. For discrete memoryless SIRC, we first establish the achievable rate region by carefully merging Han‐Kobayashi rate splitting encoding technique, superposition encoding, and Gelfand‐Pinsker encoding technique. Then, based on the achievable rate region that we derive, the capacity of the SIRC is established in many different scenarios including (a) the weak interference regime, (b) the strong interference regime, and (c) the very strong interference regime. This means that our capacity results contain all available known results in the literature. Next, the achievable rate region and the associated capacity results are also evaluated in the case of additive Gaussian noise. Additionally, many numerical examples are investigated to show the value of our theoretical derivations.