The use of natural (Pinus pinaster) resin in the production of printing ink and the printability effect

Alkyd resins are generally used in the production of printing inks. All industries look for alternative raw materials in the production of ink with the growing inclination toward using natural products. Resins forming the vehicle of the ink to be obtained from natural resources will provide benefits for the environment, nature, and living creatures. The aim of the study was to promote the use of natural resin in the ink system. Natural Pinus pinaster resin was added into vegetable and mineral oil-based solvents in pure form with alkyd resin in different amounts and ink varnishes of different combinations were prepared. Then, printing inks were produced from these varnishes in pure and hybrid form. Following the assessment of the rheological properties of the inks prepared, printing tests were conducted to assess the printing quality parameters. Ideal mixing ratios of the natural resins in the ink were determined for printability. The environmental importance and advantages of the use of natural resins were discussed. Recommendations were given in line with the results to encourage widespread use of natural resins in near future.     

Cellular neural network with trapezoidal activation function

This paper presents a cellular neural network (CNN) scheme employing a new non‐linear activation function, called trapezoidal activation function (TAF). The new CNN structure can classify linearly non‐separable data points and realize Boolean operations (including eXclusive OR) by using only a single‐layer CNN. In order to simplify the stability analysis, a feedback matrix W is defined as a function of the feedback template A and 2D equations are converted to 1D equations. The stability conditions of CNN with TAF are investigated and a sufficient condition for the existence of a unique equilibrium and global asymptotic stability is derived. By processing several examples of synthetic images, the analytically derived stability condition is also confirmed. Copyright © 2005 John Wiley & Sons, Ltd.     

A new deep intuitionistic fuzzy time series forecasting method based on long short-term memory

The Journal of Supercomputing - In recent years, deep artificial neural networks can have better forecasting performance than many other artificial neural networks. The long short-term memory...     

Maximization of total surface area of Pt-SnO<Subscript>2</Subscript>/Al<Subscript>2</Subscript>O<Subscript>3</Subscript> catalyst by the Taguchi method

The maximization of the total surface area of Pt-SnO₂/Al₂O₃ catalyst was studied by using the Taguchi method of experimental design. The catalysts were prepared by sol-gel method. The effects of HNO₃, H₂O and aluminum nitrate concentrations and the stirring rate on the total surface area were studied at three levels of each. L₉ orthogonal array leading nine experiments was used in the experimental design. The parameter levels that give maximum total surface area were determined and experimentally verified. In the range of conditions studied it was found that, medium levels of HNO₃ and H₂O concentration and lower levels of aluminum nitrate concentration and stirring rate maximize the total surface area.     

Improved phase performance for rotman lens

Rotman lenses are used to obtain multiple beams from a single array. Although the beams produced by the feed antennas at focal points have no path length errors, the beams produced by feed antennas at off focal points may have large path length errors. These path length errors cause deterioration in the multiple beams. In this article, two novel methods are introduced to obtain feed curves which reduce the path length errors of off focal feed points significantly, compared with the commonly used circular and elliptical feed curves. The first method obtains feed curve points based on having zero path length error at three chosen points of the radiating array for each beam direction. The second method uses the particle swarm optimization method for obtaining optimum feed points for each beam direction. The results show that there is a very significant drop in the level of the maximum path length errors (in the order of about 1:4). © 2012 Wiley Periodicals, Inc. Int J RF and Microwave CAE 23: 634–638, 2013.     

Deep Opacity Maps

We present a new method for rapidly computing shadows from semi‐transparent objects like hair. Our deep opacity maps method extends the concept of opacity shadow maps by using a depth map to obtain a per pixel distribution of opacity layers. This approach eliminates the layering artifacts of opacity shadow maps and requires far fewer layers to achieve high quality shadow computation. Furthermore, it is faster than the density clustering technique, and produces less noise with comparable shadow quality. We provide qualitative comparisons to these previous methods and give performance results. Our algorithm is easy to implement, faster, and more memory efficient, enabling us to generate high quality hair shadows in real‐time using graphics hardware on a standard PC.     

A multiperiod set covering location model for dynamic redeployment of ambulances

Emergency medical service (EMS) providers continually seek ways to improve system performance particularly the response time to incidents. The demand for ambulances fluctuate throughout the week, depending on the day of week, and even the time of day, therefore EMS operators can improve system performance by dynamic relocation/redeployment of ambulances in response to fluctuating demand patters. The objective of the model is to determine the minimum number of ambulances and their locations for each time cluster in which significant changes in demand pattern occur while meeting coverage requirement with a predetermined reliability. The model is further enhanced by calculating ambulance specific busy probabilities and validated by a comprehensive simulation model. Computational results on experimental data sets and data from an EMS agency are provided.     

Using fuzzy inference system for architectural space analysis

Though architectural space is the main source and the only indispensable component of any architectural construction, in many cases its boundaries are uncertain, leading intuitive spatial design. Creating a mathematical model of architectural space with concrete results will offer many possibilities for design process in analysing spatial organization, independently from in architect's experience and intuitions. This paper presents a fuzzy inference system based spatial analysis model for spatial analysis for architectural design which brings many advantages to design process. The aim of this article is to investigate the potential of a fuzzy system with a Mamdami inference engine, considering different numbers of membership functions. Two venues have been selected and the fuzzy inference system based spatial analysis model is applied. For better judgement, outcomes of the model have been compared to depthmap analysis model. The results of the model indicate that fuzzy inference system based spatial analysis model performs very well, even with the limited and imprecise data. Such prototype can evolve into a tool for identifying spatial formations for improvements during the architectural design process.