Extracting trust information from security system of a service

The issue of trust is a research problem in emerging open environments, such as ubiquitous networks. Such environments are highly dynamic and they contain diverse number of services and autonomous entities. Entities in open environments have different security needs from services. Trust computations related to the security systems of services necessitate information that meets needs of each entity. Obtaining such information is a challenging issue for entities. In this paper, we propose a model for extracting trust information from the security system of a service based on the needs of an entity. We formally represent security policies and security systems to extract trust information according to needs of an entity. The formal representation ensures an entity to extract trust information about a security property of a service and trust information about whole security system of the service. The proposed model is applied to Dental Clinic Patient Service as a case study with two scenarios. The scenarios are analyzed experimentally with simulations. The experimental evaluation shows that the proposed model provides trust information related to the security system of a service based on the needs of an entity and it is applicable in emerging open environments.     

Exploring brain connectivity with two-dimensional neural maps

We introduce two-dimensional neural maps for exploring connectivity in the brain. For this, we create standard streamtube models from diffusion-weighted brain imaging data sets along with neural paths hierarchically projected into the plane. These planar neural maps combine desirable properties of low-dimensional representations, such as visual clarity and ease of tract-of-interest selection, with the anatomical familiarity of 3D brain models and planar sectional views. We distribute this type of visualization both in a traditional stand-alone interactive application and as a novel, lightweight web-accessible system. The web interface integrates precomputed neural-path representations into a geographical digital-maps framework with associated labels, metrics, statistics, and linkouts. Anecdotal and quantitative comparisons of the present method with a recently proposed 2D point representation suggest that our representation is more intuitive and easier to use and learn. Similarly, users are faster and more accurate in selecting bundles using the 2D path representation than the 2D point representation. Finally, expert feedback on the web interface suggests that it can be useful for collaboration as well as quick exploration of data.     

A new ECG beat clustering method based on kernelized fuzzy c-means and hybrid ant colony optimization for continuous domains

The kernelized fuzzy c-means algorithm uses kernel methods to improve the clustering performance of the well known fuzzy c-means algorithm by mapping a given dataset into a higher dimensional space non-linearly. Thus, the newly obtained dataset is more likely to be linearly seprable. However, to further improve the clustering performance, an optimization method is required to overcome the drawbacks of the traditional algorithms such as, sensitivity to initialization, trapping into local minima and lack of prior knowledge for optimum paramaters of the kernel functions. In this paper, to overcome these drawbacks, a new clustering method based on kernelized fuzzy c-means algorithm and a recently proposed ant based optimization algorithm, hybrid ant colony optimization for continuous domains, is proposed. The proposed method is applied to a dataset which is obtained from MIT–BIH arrhythmia database. The dataset consists of six types of ECG beats including, Normal Beat (N), Premature Ventricular Contraction (PVC), Fusion of Ventricular and Normal Beat (F), Artrial Premature Beat (A), Right Bundle Branch Block Beat (R) and Fusion of Paced and Normal Beat (f). Four time domain features are extracted for each beat type and training and test sets are formed. After several experiments it is observed that the proposed method outperforms the traditional fuzzy c-means and kernelized fuzzy c-means algorithms.     

An artificial neural network model for the effects of chicken manure on ground water

In the areas where broiler industry is located, poultry manure from chicken farms could be a major source of ground water pollution, and this may have extensive effects particularly when the farms use nearby ground water as their fresh water supply. Therefore the prediction the extent of this pollution, either from rigorous mathematical diffusion modeling or from the perspective of experimental data evaluation bears importance. In this work, we have investigated modeling of the effects of chicken manure on ground water by artificial neural networks. An ANN model was developed to predict the total coliform in the ground water well in poultry farms. The back-propagation algorithm was employed for training and testing the network, and the Levenberg-Marquardt algorithm was utilized for optimization. The MATLAB 7.0 environment with Neural Network Toolbox was used for coding. Given the associated input parameters such as the number of chickens, type of manure pool management and depth of well, the model estimates the possible amount of total coliform in the wells to a satisfactory degree. Therefore it is expected to be of help in future for estimating the ground water pollution resulting from chicken farms.     

Optimum topology design of various geometrically nonlinear latticed domes using improved harmony search method

Domes are elegant and economical structures used in covering large areas. They are built in various forms. According to their form, they are given special names such as lamella, network, and geodesic domes. In this paper, optimum topological design algorithm is presented that determines the optimum number of rings, the optimum height of crown and tubular section designations for the member groups of these domes. The design algorithm developed has a routine that generates the data required for the geometry of these domes automatically. The minimum weight of each dome is taken as the objective function. The design constraints are implemented according to the provision of LRFD-AISC (Load and Resistance Factor Design–American Institute of Steel Constitution). The optimum topological design problem that considers these constraints turns out to be discrete programming problem. Improved harmony search algorithm is suggested to determine its optimum solution. The design algorithm also considers the geometric nonlinearity of these dome structures. Design examples are presented to demonstrate the effectiveness and robustness of the design optimization algorithm developed.     

Methacrylate-functionalized POSS influence on cross-linking and mechanical properties of styrene-butadiene rubber

Iranian Polymer Journal - The addition of methacrylate-functional polyhedral oligomeric silsesquioxane (MA-POSS) nanoparticles to styrene-butadiene rubber (SBR) composites was evaluated in terms of...     

Aromatic products of 340 °C supercritical-toluene extraction of two Turkish lignites: an n.m.r. study

Fractions of Elbistan and Seyitomer (Turkish) lignites, extracted with supercritical toluene at 340 °C and 8 MPa, have been separated by solvent extraction and silica-gel chromatography. Analyses by n.m.r. and i.r. spectroscopies and other methods have been combined in structural-analysis schemes to yield information about the average molecule in aromatic extracts. Carbon aromaticities, f_a, derived from 22.63 MHz ¹H-decoupled pulse Fourier-transform (PFT) ¹³C-n.m.r. are more widely spread for Elbistan (0.34–0.56) than for Seyitomer (0.40–0.43), and are lower than for supercritical-gas (SCG) products from bituminous coals. ¹³C-n.m.r. also reveals the presence of aromatic ether-O in polar fractions. Narrow aromatic signals in 100 MHz ¹H-n.m.r. spectra suggest the presence of single-aromatic-ring average structures. In the hexane-soluble aromatics, 27% (Elbistan) and 29% (Seyitomer) of the available sites are substituted by alkyI groups, some of which are at least eight carbon atoms long; the hexane-soluble polar and asphaltene/asphaltol fractions contain fewer such groups.     

PHOTOCATALYTIC OXIDATION OF ALDEHYDES/PCE USING POROUS ANATASE TITANIA AND VISIBLE-LIGHT-RESPONSIVE BROOKITE TITANIA

We have synthesized an annealed porous aerogel titania (LUAG2), which demonstrates a very high photocatalytic activity for aldehydes and perchloroethylene (PCE) photocatalytic oxidation (PCO) in gas phase under blacklight and fluorescent light irradiation. LUAG2 has a BET surface area of 237 m²/g and a porosity of 0.31 (volume fraction). X-ray diffraction (XRD) analysis shows LUAG2 is nearly pure anatase. It has improved the destruction of PCE and aldehydes as a group by 10-34% with black light compared to Degussa P-25. The optimum water vapor to butyraldehyde molar ratio is around 1/3. LUAG2 also shows better mineralization to CO₂ than Degussa P-25 TiO₂ does. Under irradiation of a 4 W fluorescent lamp LUAG2 gives a consistently higher conversion than that of Degussa P-25. The highly active photocatalyst indicates potential applications in indoor and outdoor environmental pollution control. A visible-light-responsive TiO₂, NTB 200, is also investigated for comparison purposes.     

Grafting of poly(ethylene terephthalate) fibers with methacrylic acid using benzoyl peroxide

The graft copolymerization of methacrylic acid onto poly(ethylene terephthalate) fibers, by the aid of benzoyl peroxide, have been investigated. The graft yield increased up to 85°C, and then decreased with the further increase in temperature. The maximum graft yield was obtained at benzoyl peroxide concentration of 4.0 × 10^?3 mol/L. The increase in the concentration of monomer was found to increase the graft yield. The change in the graft yield was followed by the experiments carried out using different water/solvent mixtures. Also, the change in the properties of polye (thylene terephthalate) fibers grafted with methacrylic acid such as moisture regain, density, and diameter were investigated.     

An investigation of some Schiff bases as corrosion inhibitors for austenitic chromium–nickel steel in H2SO4

The corrosion inhibition of austenitic chromium–nickel steel by two Schiff bases, N-(1-toluidine)salicylaldimine and N-(2-hydroxyphenyl)salicylaldimine, was investigated in sulphuric acid medium. The effect of concentration and temperature on inhibition properties was determined. It was found that when the concentrations of inhibitor were increased the inhibition efficiencies () and surface coverage () increased. Some thermodynamic parameters such as free energy of adsorption, G _ads, and enthalpy, H, were determined for the Schiff bases. Experimental results agree with the Temkin isotherm for N-(1-toluidine)salicylaldimine, but the Langmuir isotherm is more appropriate for N-(2-hydroxyphenyl)salicylaldimine.