Investigation of electrochromic properties of poly(3,5-bis(4-methoxyphenyl)dithieno[3,2-b;2′,3′-d]thiophene)

Electropolymerization of 3,5-bis(4-methoxyphenyl)dithieno[3,2-b;2′,3′-d]thiophene BMPhDTT, having strong electron-donating methoxy groups, was performed, utilizing potentiodynamic method. The homopolymer was characterized by cyclic voltammetry (CV), Fourier transform infrared (FTIR) and UV–vis spectroscopy. Spectroelectrochemical and electrochromic properties of the homopolymer film were investigated and a PBMPhDTT/PEDOT device was constructed to understand its characteristics in detail. It was revealed that the potential range of 0.0–2.0 V is suitable for operating the device between yellow and blue colors. It indicated a good open circuit memory and stability.     

Collaborative and Cognitive Network Platforms: Vision and Research Challenges

In this paper, we present a visionary concept referred to as Collaborative and Cognitive Network Platforms (CCNPs) as a future-proof solution for creating a dependable, self-organizing and self-managing communication substrate for effective ICT solutions to societal problems. CCNP creates a cooperative communication platform to support critical services across a range of business sectors. CCNP is based on the personal network (PN) technology which is an inherently cooperative environment prototyped in the Dutch Freeband PNP2008 and the European Union IST MAGNET projects. In CCNP, the cognitive control plane strives to exploit the resources to better satisfy the requirements of networked applications. CCNP facilitates collaboration inherently. Through cognition in the cognitive control plane, CCNP becomes a self-managed substrate. The self-managed substrate, in this paper, is defined as cognitive and collaborative middleware on which future applications run without user intervention. Endemic sensor networks may be incorporated into the CCNP concept to feed its cognitive control plane. In this paper, we present the CCNP concept and discuss the research challenges related to collaboration and cognition.     

Classification of Spectral Patterns Obtained from Eustachian Tube Sonometry

Spectral patterns of sound transmission through the Eustachian tube (ET) have been obtained in a series of experiments designed to identify ET dysfunction. Previous studies of ET function using sonometry have relied on heuristic and somewhat arbitrary methods in interpreting the data. In this study, an automated classification algorithm was developed to separate these sonograms into three distinct groups. From a total of 150 sample spectra, 75 were used in the formation of learning sets. The remaining spectra were classified into these three groups using standard Bayesian techniques. Both parametric and nonparametric methods were applied in estimating conditional probability density functions. Results of classification are compared with an independent test of ET function. Agreement between our classifier and the results of the independent test was as good as 97.3 percent. The results of this study indicate that an automated classification procedure can effectively distinguish among the three major types of sonograms obtained from ET sonometry.     

Inferring Phylogenetic Trees Using Answer Set Programming

We describe the reconstruction of a phylogeny for a set of taxa, with a character-based cladistics approach, in a declarative knowledge representation formalism, and show how to use computational methods of answer set programming to generate conjectures about the evolution of the given taxa. We have applied this computational method in two domains: historical analysis of languages and historical analysis of parasite-host systems. In particular, using this method, we have computed some plausible phylogenies for Chinese dialects, for Indo-European language groups, and for Alcataenia species. Some of these plausible phylogenies are different from the ones computed by other software. Using this method, we can easily describe domain-specific information (e.g., temporal and geographical constraints), and thus prevent the reconstruction of some phylogenies that are not plausible. This paper is a revised and extended version of [3].     

Forecasting highway casualties under the effect of railway development policy in Turkey using artificial neural networks

This study presents forecast of highway casualties in Turkey using nonlinear multiple regression (NLMR) and artificial neural network (ANN) approaches. Also, the effect of railway development on highway safety using ANN models was evaluated. Two separate NLMR and ANN models for forecasting the number of accidents (A) and injuries (I) were developed using 27 years of historical data (1980–2006). The first 23 years data were used for training, while the remaining data were utilized for testing. The model parameters include gross national product per capita (GNP-C), numbers of vehicles per thousand people (V-TP), and percentage of highways, railways, and airways usages (TSUP-H, TSUP-R, and TSUP-A, respectively). In the ANN models development, the sigmoid and linear activation functions were employed with feed-forward back propagation algorithm. The performances of the developed NLMR and ANN models were evaluated by means of error measurements including mean absolute percentage error (MAPE), mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R ²). ANN models were used for future estimates because NLMR models produced unreasonably decreasing projections. The number of road accidents and as well as injuries was forecasted until 2020 via different possible scenarios based on (1) taking TSUPs at their current trends with no change in the national transport policy at present, and (2) shifting passenger traffic from highway to railway at given percentages but leaving airway traffic with its current trend. The model results indicate that shifting passenger traffic from the highway system to railway system resulted in a significant decrease on highway casualties in Turkey.     

Potential Anomaly Separation and Archeological Site Localization Using Genetically Trained Multi‐level Cellular Neural Networks

In this paper, a supervised algorithm for the evaluation of geophysical sites using a multi‐level cellular neural network (ML‐CNN) is introduced, developed, and applied to real data. ML‐CNN is a stochastic image processing technique based on template optimization using neighborhood relationships of the pixels. The separation/enhancement and border detection performance of the proposed method is evaluated by various interesting real applications. A genetic algorithm is used in the optimization of CNN templates. The first application is concerned with the separation of potential field data of the Dumluca chromite region, which is one of the rich reserves of Turkey; in this context, the classical approach to the gravity anomaly separation method is one of the main problems in geophysics. The other application is the border detection of archeological ruins of the Hittite Empire in Turkey. The Hittite civilization sites located at the Sivas‐Altinyayla region of Turkey are among the most important archeological sites in history, one reason among others being that written documentation was first produced by this civilization.     

The production of β-SiAlON ceramics with low amounts of additive,at low sintering temperature

In the present work nano-sized powder of β-SiAlON was produced using a wet milling process. Different milling times and mediums (methyl ethyl keton, ethanol and toluene as solvents, polyethyleneglicol, oleic acid, sodium tripolyphosphate and polyvinylpyrrolidon as dispersants) were performed for the determination of the most efficient milling system. The powders were produced using a conventional process (the ball to powder ratio was 1:1.5, at 300 rpm, for 1.5 h) having a few hundred nanometer particle size, and these were used as standard powders in this study. The nano-sized β-SiAlON starting powders (<100 nm) were sintered at lower temperatures than that of the conventional powders. The amount of Y₂O₃ in powders (～130 nm), produced by high energy milling process, was fewer than conventional powders (5 wt.%). The results of the powder size, sintering behavior and mechanical properties of this sample were compared to those of the standard powder and its sintered sample. This sample, produced using the nano-powder, was investigated, and densified at 150 °C lower than that of the standard sample. Even though the amount of Y₂O₃ was decreased, the hardness of the samples was better than that of the standard sample.