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     

基于不同爆轰产物的格尼速度估算新方法（英）

A new method for prediction of Gurney velocity of explosives is introduced in which energy output is correlated with the heat of detonation, the number of moles of gaseous products of detonation per gram of explosive and the average molecular weight of gaseous products. It is assumed that the CHNO explosive reacts to form products composed of N2 , CO, H2O, CO2, H2,O2 and C（s） as determined by the oxygen balance of the unreacted compound. Good agreement is obtained between measured and calculated values of Gurney velocity as compared to previous correlations which assumed the reaction products to consist of N2 , H2O, CO2 and either C（s） or O2.     

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.

     

A novel approach to predict shear strength of tilted angle connectors using artificial intelligence techniques

Shear connectors play a prominent role in the design of steel-concrete composite systems. The behavior of shear connectors is generally determined through conducting push-out tests. However, these tests are costly and require plenty of time. As an alternative approach, soft computing (SC) can be used to eliminate the need for conducting push-out tests. This study aims to investigate the application of artificial intelligence (AI) techniques, as sub-branches of SC methods, in the behavior prediction of an innovative type of C-shaped shear connectors, called Tilted Angle Connectors. For this purpose, several push-out tests are conducted on these connectors and the required data for the AI models are collected. Then, an adaptive neuro-fuzzy inference system (ANFIS) is developed to identify the most influencing parameters on the shear strength of the tilted angle connectors. Totally, six different models are created based on the ANFIS results. Finally, AI techniques such as an artificial neural network (ANN), an extreme learning machine (ELM), and another ANFIS are employed to predict the shear strength of the connectors in each of the six models. The results of the paper show that slip is the most influential factor in the shear strength of tilted connectors and after that, the inclination angle is the most effective one. Moreover, it is deducted that considering only four parameters in the predictive models is enough to have a very accurate prediction. It is also demonstrated that ELM needs less time and it can reach slightly better performance indices than those of ANN and ANFIS.

     

Incorporation of a Regularization Term to Control Negative Correlation in Mixture of Experts

Combining accurate neural networks (NN) in the ensemble with negative error correlation greatly improves the generalization ability. Mixture of experts (ME) is a popular combining method which employs special error function for the simultaneous training of NN experts to produce negatively correlated NN experts. Although ME can produce negatively correlated experts, it does not include a control parameter like negative correlation learning (NCL) method to adjust this parameter explicitly. In this study, an approach is proposed to introduce this advantage of NCL into the training algorithm of ME, i.e., mixture of negatively correlated experts (MNCE). In this proposed method, the capability of a control parameter for NCL is incorporated in the error function of ME, which enables its training algorithm to establish better balance in bias-variance-covariance trade-off and thus improves the generalization ability. The proposed hybrid ensemble method, MNCE, is compared with their constituent methods, ME and NCL, in solving several benchmark problems. The experimental results show that our proposed ensemble method significantly improves the performance over the original ensemble methods.     

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...