Cellulose NANOFIBER‐polyethylene nanocomposites modified by polyvinyl alcohol

The uniform dispersion of cellulose nanofibers (CNFs) in non‐polar polymer matrices is a primary problem to overcome in creating novel nanocomposites from these materials. The aim of this study was to produce CNF‐polyethylene (PE) nanocomposites by melt compounding followed by injection molding to investigate the possibility of using polyvinyl alcohol (PVA) to improve the dispersion of CNF in the PE matrix. The tensile strength of CNF‐ filled composites was 17.4 MPa with the addition of 5 wt % CNF–PVA, which was 25% higher than the strength of neat PE. The tensile modulus of elasticity increased by 40% with 5% CNF–PVA addition. Flexural properties also significantly increased with increased CNF loading. Shear viscosity increased with increasing CNF content. The elastic moduli of the PE/CNF composites from rheological measurements were greater than those of the neat PE matrix because of the intrinsic rigidity of CNF. Melt creep compliance decreased by about 13% and 45% for the composites with 5 wt % CNF and 10 wt % CNF, respectively. It is expected that the PVA carrier system can contribute to the development of a process methodology to effectively disperse CNFs containing water in a polymer matrix. © 2015 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2016 , 133, 42933.     

Synthesis and Structural Characterization of Novel Nanostructured Aromatic Optically Active Poly(Ester–Amide)s Derived from S-tyrosine Containing Symmetric Diol and Aromatic Diacid Chlorides

In this study, at first N,N′-bis[2-(methyl-3-(4-hydroxyphenyl)propanoate)]terephthaldiamide, as a new chiral monomer based on tyrosine amino acid, was synthesized from the reaction of S-tyrosine methyl ester and terephthaloyl dichloride. Then novel nanostructured aromatic optically active and eco-friendly poly(ester–amide)s based on tyrosine amino acid were synthesized by the solution polycondensation of the new diol and a number of aromatic diacid chlorides. The resulting poly(ester–amide)s exhibited good yields, solubility, inherent viscosities, and thermal stability. All polymers were characterized by Fourier transform infrared, ¹H NMR, elemental analysis, and specific rotation. They were also studied by X-ray diffraction, thermogravimetric analysis, and field emission scanning electron microscopy.     

Cover Image,Volume 67, Issue 2

The cover image, by Tayebe Nazari and Hamid Garmabi, is based on the Research Article The effects of processing parameters on the morphology of PLA/PEG melt electrospun fibers, DOI: 10.1002/pi.5486 .

     

Retraction Note to: ANN model to predict the effects of composition and heat treatment parameters on transformation start temperature of microalloyed steels

Neural Computing and Applications - The Editor-in-Chief has retracted this article because it significantly overlaps with a number of previously published articles.     

ACNN-TL: attention-based convolutional neural network coupling with transfer learning and contextualized word representation for enhancing the performance of sentiment classification

Due to the rapid growth of textual information on the web, analyzing users' opinions about particular products, events or services is now considered a crucial and challenging task that has changed sentiment analysis from an academic endeavor to an essential analytic tool in cognitive science and natural language understanding. Despite the remarkable success of deep learning models for textual sentiment classification, they are still confronted with some limitations. Convolutional neural network is one of the deep learning models that has been excelled at sentiment classification but tends to need a large amount of training data while it considers that all words in a sentence have equal contribution in the polarity of a sentence and its performance is highly dependent on its accompanying hyper-parameters. To overcome these issues, an Attention-Based Convolutional Neural Network with Transfer Learning (ACNN-TL) is proposed in this paper that not only tries to take advantage of both attention mechanism and transfer learning to boost the performance of sentiment classification but also language models, namely Word2Vec and BERT, are used as its the backbone to better express sentence semantics as word vector. We conducted our experiment on widely-studied sentiment classification datasets and according to the empirical results, not only the proposed ACNN-TL achieved comparable or even better classification results but also employing contextual representation and transfer learning yielded remarkable improvement in the classification accuracy.

     

Analytical modeling of tensile strength of functionally graded steels

In the present study, the tensile strength of ferritic and austenitic functionally graded steel produced by electroslag remelting has been modeled by artificial neural networks. Functionally graded steel containing graded layers of ferrite and austenite may be fabricated via diffusion of alloying elements during remelting stage. Vickers microhardness profile of the specimen has been obtained experimentally and modeled with artificial neural networks. To build the model for graded ferritic and austenitic steels, training, testing and validation using respectively 174 and 120 experimental data were conducted. According to the input parameters, in the neural networks model, the Vickers microhardness of each layer was predicted. A good-fit equation that correlates the Vickers microhardness of each layer to its corresponding chemical composition was achieved by the optimized network for both ferritic- and austenitic-graded steels. Afterward, the Vickers microhardness of each layer in functionally graded steels was related to the yield stress of the corresponding layer and by assuming Holloman relation for stress–strain curve of each layer, they were acquired. Finally, by applying the rule of mixtures, tensile strength of functionally graded steels configuration was found through a numerical method. The obtained results from the proposed model are in good agreement with those acquired from the experiments.     

Artificial neural networks for prediction compressive strength of geopolymers with seeded waste ashes

In this paper, we reconsider and analyze our previous paper a novel hash algorithm construction based on chaotic neural network, then present equal-length and unequal-length forgery attacks against its security in detail, and then propose a significantly improved approach by utilizing a method of complicated nonlinear computation to enhance the security of the original hash algorithm. Theoretical analysis and computer simulation indicate that the improved algorithm can completely resist the two kinds of forgery attacks and also shows other better performance than the original one, such as better message and key sensitivity, statistical properties, which can satisfy the performance requirements of a more secure hash function.     

Dual Effect of Low‐Dosage Poly(Ethylene Oxides) on Methane Hydrate Formation

Time‐dependent isochoric formation of methane hydrate was investigated in the presence of low‐dose poly(ethylene oxides) (PEOs). The effect of different molecular weights of PEO on methane hydrate nucleation time and storage capacity was studied and compared. Kinetic measurements revealed a dual effect of PEO, including inhibition and stabilization effects, on methane hydrate formation. The nature and type of the effect arises from the difference in molecular weights and concentration ranges of PEOs. These parameters directly affect the nucleation time and storage capacity of methane hydrate. Generally, in comparison with pure water, PEO improved the storage capacity of methane hydrate. PEO (1000 kD) at a concentration of 0.5 wt % exhibits a significant kinetic inhibitory performance. However, it was an efficient low‐dosage hydrate stabilizer at a concentration of 0.25 wt %, along with producing gas‐rich methane hydrate suitable for gas fuel storage and transportation.