Synthesis and characterization of new thermally stable poly(ester‐imide)s derived from 2,2′‐bis(4‐trimellitimidophenoxy)biphenyl and 2,2′‐bis(4‐trimellitimidophenoxy)‐1,1′‐binaphthyl and various aromatic dihydroxy compounds

A series of new alternating aromatic poly(ester‐imide)s were prepared by the polycondensation of the preformed imide ring‐containing diacids, 2,2′‐bis(4‐trimellitimidophenoxy)biphenyl (2a) and 2,2′‐bis(4‐trimellitimidophenoxy)‐1,1′‐binaphthyl (2b) with various aromatic dihydroxy compounds in the presence of pyridine and lithium chloride. A model compound (3) was also prepared by the reaction of 2b with phenol, its synthesis permitting an optimization of polymerization conditions. Poly(ester‐imides) were fully characterized by FTIR, UV‐vis and NMR spectroscopy. Both biphenylene‐ and binaphthylene‐based poly(ester‐imide)s exhibited excellent solubility in common organic solvents such as tetrahydrofuran, m‐cresol, pyridine and dichloromethane. However, binaphthylene‐based poly(ester‐imide)s were more soluble than those of biphenylene‐based polymers in highly polar organic solvents, including N‐methyl‐2‐pyrrolidone, N,N‐dimethylacetamide, N,N‐dimethylformamide and dimethyl sulfoxide. From differential scanning calorimetry thermograms, the polymers showed glass‐transition temperatures between 261 and 315 °C. Thermal behaviour of the polymers obtained was characterized by thermogravimetric analysis, and the 10 % weight loss temperatures of the poly(ester‐imide)s was in the range 449–491 °C in nitrogen. Furthermore, crystallinity of the polymers was estimated by means of wide‐angle X‐ray diffraction. The resultant poly(ester‐imide)s exhibited nearly an amorphous nature, except poly(ester‐imide)s derived from hydroquinone and 4,4′‐dihydroxybiphenyl. In general, polymers containing binaphthyl units showed higher thermal stability but lower crystallinity than polymers containing biphenyl units. Copyright © 2005 Society of Chemical Industry     

Study of the morphology and mechanical properties of polypropylene composites with silica or rice‐husk

The mechanical, morphological behavior and water absorption characteristics of polypropylene (PP) and silica, or PP and rice‐husk, composites have been studied. The silica used in this study as filler was a commercial type produced from soluble glass or rice husks. The compatibilizing effect of PP grafted with monomethyl itaconate (PP‐g‐MMI) and/or with vinyltriethoxysilane (PP‐g‐VTES) as polar monomers on the mechanical properties and water absorption was also investigated. In general, a high loading of the studied fillers in the polymer matrix increases the stiffness and the water absorption capacity. This effect is more noticeable in the tensile modulus of the PP/silica composite with PP‐g‐VTES as compatibilizer. However, the increase of the rice‐husk charge as a natural filler in the PP matrix decreases the stiffness, and in the presence of PP‐g‐MMI as compatibilizer in PP/rice‐husk, the tensile modulus and water absorption of the composite were improved. The better adhesion and phase continuity in the PP/silica and PP/rice‐husk composites with different compatibilizers was confirmed by the morphological study. Copyright © 2004 Society of Chemical Industry     

Reconstruction of reflectance spectra using weighted principal component analysis

The weighted principal component analysis technique is employed for reconstruction of reflectance spectra of surface colors from the related tristimulus values. A dynamic eigenvector subspace based on applying certain weights to reflectance data of Munsell color chips has been formed for each particular sample and the color difference value between the target, and Munsell dataset is chosen as a criterion for determination of weighting factors. Implementation of this method enables one to increase the influence of samples which are closer to target on extracted principal eigenvectors and subsequently diminish the effect of those samples which benefit from higher amount of color difference. The performance of the suggested method is evaluated in spectral reflectance reconstruction of three different collections of colored samples by the use of the first three Munsell bases. The resulting spectra show considerable improvements in terms of root mean square error between the actual and reconstructed reflectance curves as well as CIELAB color difference under illuminant A in comparison to those obtained from the standard PCA method. © 2008 Wiley Periodicals, Inc. Col Res Appl, 33, 360–371, 2008     

Genetic algorithm hybridized with multilayer perceptron to have an economical slope stability design

The present work aimed to evaluate and optimize the design of an artificial neural network (ANN) combined with an optimization algorithm of genetic algorithm (GA) for the calculation of slope stability safety factors (SF) in a pure cohesive slope. To make datasets of training and testing for the developed predictive models, 630 finite element limit equilibrium (FELE) analyses were performed. Similar to many artificial intelligence-based solutions, the database was involved in 189 testing datasets (e.g., 30% of the entire database) and 441 training datasets; for example, a range of 70% of the total database. Moreover, variables of multilayer perceptron (MLP) algorithm (for example, number of nodes in any hidden layer) and the algorithm of GA like population size was optimized by utilizing a series of trial and error process. The parameters in input, which were used in the analysis, consist of slope angle (β), setback distance ratio (b/B), applied stresses on the slope (F_y) and undrained shear strength of the cohesive soil (C_u) where the output was taken SF. The obtained network outputs for both datasets from MLP and GA-MLP models are evaluated according to many statistical indices. A total of 72 MLP trial and error (e.g., parameter study) the optimal architecture of 4 × 8 × 1 were determined for the MLP structure. Both proposed techniques result in a proper performance; however, according to the statistical indices, the GA–MLP model can somewhat accomplish the least mean square error (MSE) when compared to MLP. In an optimized GA–MLP network, coefficient of determination (R²) and root mean square error (RMSE) values of (0.975, and 0.097) and (0.969, and 0.107) were found, respectively, to both of the normalized training and testing datasets.

     

Pd/Cu-Free Cobalt-Catalyzed Suzuki and Heck Using Green Bio-Magnetic Hybrid and DFT-Based Theoretical Study

Catalysis Letters - Several highly efficient and magnetically recyclable cobalt catalytic systems were prepared using magnetic chitosan and some safe and available organic compounds...     

Analytical Modeling of Short-Channel Fully-Depleted Triple Work Function Metal Gate (TWFMG) SOI MESFET

Silicon - In this paper, a new structure: triple work function metal gate SOI MESFET, intended for integration into the deep-submicron CMOS technology, is proposed. The gate of the device consists...     

Segregation Neutralised Steels: Microstructural Banding Elimination from Dual-Phase Steel Through Alloy Design Accounting for Inherent Segregation

Metallurgical and Materials Transactions A - Banding in commercial dual-phase steels, such as banded ferrite and pearlite or ferrite and martensite microstructures, is inherited from segregation...     

Application of neuro-fuzzy techniques in oil pipeline ultrasonic nondestructive testing

This paper presents a novel approach to the problem of nondestructive pipeline testing using ultrasonic imaging. The identification of the flaw type and its dimensions are the most important problems in the pipeline inspection. Unlike typical methods, a decision based neural network is used for the detection of flaws. We train a generalized regression neural network to determine the dimensions of the corrosions and generate the whole image of both the internal and external walls of the oil pipeline. As an improvement to the detection algorithm, we introduce fuzzy decision-based neural network algorithms for the detection and classification of the corrosions. The simulation and experimental systems results show that these new methods outperform the existing methods.     

A talking profile to distinguish identical twins

Identical twins pose a great challenge to face recognition due to high similarities in their appearances. Motivated by the psychological findings that facial motion contains identity signatures and the observation that twins may look alike but behave differently, we develop a talking profile to use the identity signatures in the facial motion to distinguish between identical twins. The talking profile for a subject is defined as a collection of multiple types of usual face motions from the video. Given two talking profiles, we compute the similarities of the same type of face motion in both profiles and then perform the classification based on those similarities. To compute the similarity of each type of face motion, we give higher weights to more abnormal motions which are assumed to carry more identity signature information.