A Novel Approach for Bolted T-Stub Connections

This paper reports an investigation into new connection types and their behaviors determined using full-scale experiments. T-shaped connections were created using the IPE standard profile. The aim of this study was to analyze the influence of T connections based on the IPE standard profile, height of beam to height of T-stub joint (H) of T-stub joints, and lengths (X) of T-stub joints on the behavior of steel connections, in order to provide the necessary data for improving Eurocode 3 and enable efficient use of residue IPE standard profiles and back to the consumption cycle. While the moment resistance values increased with an increase in H from H_min to H_max in model groups with X of 126 mm, and the energy dissipation increased with an increase in H from H_min to H_max and also with an increase in the lengths (X) of T-stub joints from 54 to 126 mm.     

An optimum feature extraction method for texture classification

Texture can be defined as a local statistical pattern of texture primitives in observer’s domain of interest. Texture classification aims to assign texture labels to unknown textures, according to training samples and classification rules. In this paper a novel method, which is an intelligent system for texture classification is introduced. It used a combination of genetic algorithm, discrete wavelet transform and neural network for optimum feature extraction from texture images. An algorithm called the intelligent system, which processes the pattern recognition approximation, is developed. We tested the proposed method with several texture images. The overall success rate is about 95%.     

Modeling and simulation of intracellular dynamics: choosing an appropriate framework

Systems biology is a reemerging paradigm which, among other things, focuses on mathematical modeling and simulation of biochemical reaction networks in intracellular processes. For most simulation tools and publications, they are usually characterized by either preferring stochastic simulation or rate equation models. The use of stochastic simulation is occasionally accompanied with arguments against rate equations. Motivated by these arguments, we discuss in this paper the relationship between these two forms of representation. Toward this end, we provide a novel compact derivation for the stochastic rate constant that forms the basis of the popular Gillespie algorithm. Comparing the mathematical basis of the two popular conceptual frameworks of generalized mass action models and the chemical master equation, we argue that some of the arguments that have been put forward are ignoring subtle differences and similarities that are important for answering the question in which conceptual framework one should investigate intracellular dynamics.     

An Effective Model for Indirect Trust Computation in Pervasive Computing Environment

The performance of indirect trust computation models (based on recommendations) can be easily compromised due to the subjective and social-based prejudice of the provided recommendations. Eradicating the influence of such recommendation remains an important and challenging issue in indirect trust computation models. An effective model for indirect trust computation is proposed which is capable of identifying dishonest recommendations. Dishonest recommendations are identified by using deviation based detecting technique. The concept of measuring the credibility of recommendation (rather than credibility of recommender) using fuzzy inference engine is also proposed to determine the influence of each honest recommendation. The proposed model has been compared with other existing evolutionary recommendation models in this field, and it is shown that the model is more accurate in measuring the trustworthiness of unknown entity.     

Modeling a ground-coupled heat pump system by a support vector machine

This paper reports on a modeling study of ground coupled heat pump (GCHP) system performance (COP) by using a support vector machine (SVM) method. A GCHP system is a multi-variable system that is hard to model by conventional methods. As regards the SVM, it has a superior capability for generalization, and this capability is independent of the dimensionality of the input data. In this study, a SVM based method was intended to adopt GCHP system for efficient modeling. The Lin-kernel SVM method was quite efficient in modeling purposes and did not require a pre-knowledge about the system. The performance of the proposed methodology was evaluated by using several statistical validation parameters. It is found that the root-mean squared (RMS) value is 0.002722, the coefficient of multiple determinations (R²) value is 0.999999, coefficient of variation (cov) value is 0.077295, and mean error function (MEF) value is 0.507437 for the proposed Lin-kernel SVM method. The optimum parameters of the SVM method were determined by using a greedy search algorithm. This search algorithm was effective for obtaining the optimum parameters.The simulation results show that the SVM is a good method for prediction of the COP of the GCHP system. The computation of SVM model is faster compared with other machine learning techniques (artificial neural networks (ANN) and adaptive neuro-fuzzy inference system (ANFIS)); because there are fewer free parameters and only support vectors (only a fraction of all data) are used in the generalization process.     

Hydroformylation of 1-octene using rhodium–phosphite catalyst in a thermomorphic solvent system

The use of a liquid–liquid biphasic thermomorphic or temperature-dependent multicomponent solvent (TMS) system, in which the catalyst accumulates in one of the liquid phases and the product goes preferably to the other liquid phase, can be an enabling strategy of commercial hydroformylation processes with high selectivity, efficiency and ease of product separation and catalyst recovery. This paper describes the synthesis of n-nonanal, a commercially important fine chemical, by the hydroformylation reaction of 1-octene using a homogeneous catalyst consisting of HRh(PPh₃)₃(CO) and P(OPh)₃ in a TMS-system consisting of propylene carbonate (PC), dodecane and 1,4-dioxane. At a reaction temperature of 363 K, syngas pressure of 1.5 MPa and 0.68 mM concentration of the catalyst, HRh(CO)(PPh₃)₃, the conversion of 1-octene and the yield of total aldehyde were 97% and 95%, respectively. With a reaction time of 2 h and a selectivity of 89.3%, this catalytic system can be considered as highly reactive and selective compared to conventional ones. The resulting total turnover number was 600, while the turnover frequency was 400 h^?1. The effects of increasing the concentration of 1-octene, catalyst loading, partial pressure of CO and H₂ and temperature on the rate of reaction have been studied at 353, 363 and 373 K. The rate was found to be first order with respect to concentrations of the catalyst and 1-octene, and the partial pressure of H₂. The dependence of the reaction rate on the partial pressure of CO showed typical substrate inhibited kinetics. The kinetic behavior differs significantly from the kinetics of conventional systems employing HRh(CO)(PPh₃)₃ in organic solvents. Most notable are the lack of olefin inhibition and the absence of a critical catalyst concentration. A mechanistic rate equation has been proposed and the kinetic parameters evaluated with an average error of 5.5%. The activation energy was found to be 69.8 kJ/mol.     

Recent Applications of Polymer Blends in Gas Separation Membranes

Polymeric membranes are extensively used for gas separations but their performance is limited by the upper bound trade‐off discovered by Robeson in 1991. Among the attractive modifications available to increase the performance of polymeric membranes, polymer blending is a unique technique because it offers a time‐ and cost‐effective method of tuning the properties of membranes. A variety of polymer blends has been explored in recent years. The application of polymer blends in gas separation membranes is described by critically analyzing the performance of polymer blend membranes. Polymer blend membranes of different polymer pairs are reviewed and evaluated in terms of phase behavior, permeability, and selectivity.