Effect of CR and FT-paraffin versus SBS modification in terms of conventional and rheological properties

In this study, the rheological and conventional properties of binders prepared with crumb rubber (CR) and FT-paraffin were compared to styrene-butadiene-styrene (SBS)-modified binders’ properties. The objective of this study was to determine which modified binders in different combinations of the CR and the FT-paraffin exhibited better performance than the SBS-modified binders, in terms of conventional and rheological properties. Results showed that the additives exhibited different performances for different types of tests. Many of the combinations including the common usage of the CR and the FT-paraffin performed better at high temperature than the SBS modification. Overall, the test results show that the binders prepared by 6% CR with 3 and 4% FT-paraffin and those prepared with 8% CR with 3% FT-paraffin were better binders than the 4% SBS-modified binder, in terms of low and high temperature properties.     

Stability under vacuum of silicon trap detectors and their use as transfer instruments in cryogenic radiometry

The stability of the responsivity of trap detectors under vacuum has been studied by means of a special chamber designed for the test of photodetectors at low pressure. The first experiments at a wavelength of 647 nm show that the responsivity variations are smaller than the uncertainties of the measurements, approximately 3 parts in 10(5), when the detector operates successively in air, under vacuum, and then again in air. Calculations based on experiments with single windowless photodiodes indicate that the change in trap responsivity that is due to vacuum effects should be smaller than 1 part in 10(5), at least in the visible part of the wavelength range. This stability makes trap detectors suitable for cryogenic radiometry when one uses transfer detectors under vacuum.     

Derivation of empirical equations for neutronic performance in a thorium fusion breeder with various coolants using regression analysis

In this paper, regression analyses (RA) are presented for the neutronic calculation of ThO₂ mixed ²⁴⁴CmO₂ fuel with different neutronic parameters for various coolants, natural lithium, Li₂₀Sn₈₀ and Flinabe, respectively. The tritium breeding ratio (TBR), energy multiplication factor (M), total fission rate (Σ_f) and ²³²Th(n, γ) reaction is computed by XSDRNPM. In addition, this numerical results are estimated by RA depends on neutronic parameters and the empirical equations for neutronic performance are acquired. The results obtained by using XSDRNPM and the results of the RA, obtained empirical equations, are compared. The empirical equations indicate that RA can successfully be used for the prediction of the neutronic performance parameters in the hybrid reactor with a high degree of accuracy. In addition, correlation matrix is calculated to determined statistical relationships between variables TBR, M, Σ_f, and ²³²Th(n, γ).     

An assessment of total RMR classification system using unified simulation model based on artificial neural networks

Engineering design has great importance in the cost and safety of engineering structures. Rock mass rating (RMR) system has become a reliable and widespread pre-design system for its ease of use and variety in engineering applications such as tunnels, foundations, and slopes. In RMR system, six parameters are employed in classifying a rock mass: uniaxial compressive strength of intact rock material (UCS), rock quality designation (RQD), spacing of discontinuities (SD), condition of discontinuities (CD), condition of groundwater (CG), and orientation of discontinuities (OD). The ratings of the first three parameters UCS, RQD, and SD are determined via graphic readings where the last three parameters CD, CG, and OD are estimated by the tables that are composed of interval valued linguistic expressions. Because of these linguistic expresions, the estimated rating values of the last three become fuzzy especially when the related conditions are close to border of any two classes. In such cases, these fuzzy situations could lead up incorrect rock class estimations. In this study, an empirical database based on the linguistic expressions for CD, CG, and OD is developed for training Artificial Neural Network (ANN) classifiers. The results obtained from graphical readings and ANN classifiers are unified in a simulation model (USM). The data obtained from five different tunnels, which were excavated for derivation purpose, are used to evaluate classification results of conventional method and proposed model. Finally, it is noted that more accurate and realistic ratings are reached by means of proposed model.     

Variational sensitivity analysis and design optimization

Variational method (VM) is employed to derive the co-state equations, boundary (transversality) conditions, and functional sensitivity derivatives. The converged solutions of the state equations together with the steady state solution of the co-state equations are integrated along the domain boundary to uniquely determine the functional sensitivity derivatives with respect to the design function. The application of the variational method to aerodynamic shape optimization problems is demonstrated on internal flow problems at supersonic Mach number range of 1.5. Optimization results for flows with and without shock phenomena are presented. The study shows that while maintaining the accuracy of aerodynamical objective function and constraint within the reasonable range for engineering prediction purposes, variational method provides a substantial gain in computational efficiency, i.e., computer time and memory, when compared with the finite difference computations.     

Pitch angle control in wind turbines above the rated wind speed by multi-layer perceptron and radial basis function neural networks

In wind energy conversion systems, one of the operational problems is the changeability and discontinuity of wind. In most cases, wind speed can fluctuate rapidly. Hence, quality of produced energy becomes an important problem in wind energy conversion plants. Several control techniques have been applied to improve the quality of power generated from wind turbines. Pitch control is the most efficient and popular power control method, especially for variable-speed wind turbines. It is a useful method for power regulation above the rated wind speed. This paper proposes an artificial neural network-based pitch angle controller for wind turbines. In the simulations, a variable-speed wind turbine is modeled, and its operation is observed by using two types of artificial neural network controllers. These are multi-layer perceptrons with back propagation learning algorithm and radial basis function network. It is shown that the power output was successfully regulated during high wind speed, and as a result overloading or outage of the wind turbine was prevented.     

Patterns: from system design to software testing

Design patterns are used extensively in the design of software systems. Patterns codify effective solutions for recurring design problems and allow software engineers to reuse these solutions, tailoring them appropriately to their particular applications, rather than reinventing them from scratch. In this paper, we consider the following question: How can system designers and implementers test whether their systems, as implemented, are faithful to the requirements of the patterns used in their design? A key consideration underlying our work is that the testing approach should enable us, in testing whether a particular pattern P has been correctly implemented in different systems designed using P, to reuse the common parts of this effort rather than having to do it from scratch for each system. Thus in the approach we present, corresponding to each pattern P, there is a set of pattern test case templates (PTCTs). A PTCT codifies a reusable test case structure designed to identify defects associated with applications of P in all systems designed using P. Next we present a process using which, given a system designed using P, the system tester can generate a test suite from the PTCTs for P that can be used to test the particular system for bugs in the implementation of P in that system. This allows the tester to tailor the PTCTs for P to the needs of the particular system by specifying a set of specialization rules that are designed to reflect the scenarios in which the defects codified in this set of PTCTs are likely to manifest themselves in the particular system. We illustrate the approach using the Observer pattern.     

Contribution of laser microdissection-based technology to proteomic analysis in hepatocellular carcinoma developing on cirrhosis

Hepatocellular carcinoma (HCC) is a major cause of cancer worldwide. Proteomic studies provide opportunities to uncover targets for the diagnosis and treatment of this disease. However, in HCC developing in a setting of cirrhosis, the detection of proteome alterations may be hampered by the increased cellular heterogeneity of tissue when analysing global liver homogenates. The aim of this study was to evaluate whether the identification of proteome alterations in these HCC cases was improved when the differential protein profile between tumour and non-tumour areas of liver was determined using hepatocytes isolated by laser microdissection (LM). Differential profiles established with LM-hepatocytes and liver section homogenates using 2-DE and MS exhibited noticeable differences: 30% of the protein spots with deregulated expression in tumorous LM-samples did not display any modification in homogenates; conversely 15% of proteins altered in tumorous homogenates were not impaired in LM-hepatocytes. These alterations resulted from the presence in cirrhotic liver of fibrotic stroma which displayed a protein pattern different from that determined in LM-hepatocytes. In conclusion, our data demonstrate the interest of LM in distinguishing between fibrotic and hepatocyte proteome alterations and thus the benefit of LM to proteome studies of HCC developing in a context of cirrhosis.     

Neural computing models for prediction of permeability coefficient of coarse-grained soils

Correlations are very significant from the earliest days; in some cases, it is essential as it is difficult to measure the amount directly, and in other cases it is desirable to ascertain the results with other tests through correlations. Soft computing techniques are now being used as alternate statistical tool, and new techniques such as artificial neural networks, fuzzy inference systems, genetic algorithms, and their hybrids were employed for developing the predictive models to estimate the needed parameters, in the recent years. Determination of permeability coefficient (k) of soils is very important for the definition of hydraulic conductivity and is difficult, expensive, time-consuming, and involves destructive tests. In this paper, use of some soft computing techniques such as ANNs (MLP, RBF, etc.) and ANFIS (adaptive neuro-fuzzy inference system) for prediction of permeability of coarse-grained soils was described and compared. As a result of this paper, it was obtained that the all constructed soft computing models exhibited high performance for predicting k. In order to predict the permeability coefficient, ANN models having three inputs, one output were applied successfully and exhibited reliable predictions. However, all four different algorithms of ANN have almost the same prediction capability, and accuracy of MLP was relatively higher than RBF models. The ANFIS model for prediction of permeability coefficient revealed the most reliable prediction when compared with the ANN models, and the use of soft computing techniques will provide new approaches and methodologies in prediction of some parameters in soil mechanics.     

A combination of Genetic Algorithm,Particle Swarm Optimization and Neural Network for palmprint recognition

In this study, a new approach to the palmprint recognition phase is presented. 2D Gabor filters are used for feature extraction of palmprints. After Gabor filtering, standard deviations are computed in order to generate the palmprint feature vector. Genetic Algorithm-based feature selection is used to select the best feature subset from the palmprint feature set. An Artificial Neural Network (ANN) based on hybrid algorithm combining Particle Swarm Optimization (PSO) algorithm with back-propagation algorithms has been applied to the selected feature vectors for recognition of the persons. Network architecture and connection weights of ANN are evolved by a PSO method, and then, the appropriate network architecture and connection weights are fed into ANN. Recognition rate equal to 96% is obtained by using conjugate gradient descent algorithm.