Detailed study on fusion characteristics of rigid poly(vinyl chloride) nanocomposites: The comparison of using multiple regression analysis and artificial neural network

Fusion behavior of poly(vinyl chloride) (PVC) compounds plays an important role in the development of physical properties of processed material. The fusion characteristics in PVC processing are governed by material variables that affect the fusion with some interactions. In this research, the aim was to characterize the effects of formulation ingredients on fusion characteristics of PVC. Four material parameters, including the contents of nanoclay (NC), azodicarbonamide, calcium stearate, and processing aid, are proposed as affecting variables. The fusion time (FT) as well as fusion factor (FF) are considered fusion indicators and are experimentally determined in some different levels of affecting parameters. The multivariable regression analysis (MRA) and the Artificial Neural Network (ANN) modeling are considered as two analytical methods. The regression analysis result for the FT denotes, in part, significant linear and quadratic effects of NC and also its significant interactions with azodicarbonamide and calcium stearate, whereas that of FF indicates only a linear effect of NC. ANN modeling is performed with a three‐layer (input, hidden, and output) neural network. The results of the comparison of the MRA and ANN predictions with experimental values are reported as the correlation coefficient (R²), mean‐square error, and mean absolute percentage error for both FF and FT parameters. The obtained values clearly denote that the ANN results are more precise and especially more general than those of MRA. However, in the case of FT, improvement of the ANN modeling is much greater than that of FF. J. VINYL ADDIT. TECHNOL., 21:147–155, 2015. © 2014 Society of Plastics Engineers     

Is the List of Incomplete Open Cubes Complete?

Variations of Incomplete Open Cubes is the major project by the twentieth-century conceptual artist Sol LeWitt. In this paper we interpret the enumerative component of the project as embeddings of graphs. This formulation permits use of an algorithm to check the completeness of the list of the structures produced by the artist. Our conclusion is that the artist found the correct number of structures (that is, 122), but that his list contains a mistake in the presentation of a pair of incomplete cubes, a discovery that appears not to have been noted before.     

A novel context-aware recommender system based on a deep sequential learning approach (CReS)

Neural Computing and Applications - With an increase in online longitudinal users’ interactions, capturing users’ precise preferences and giving accurate recommendations have become an...     

Site-Specific PEGylation of Therapeutic Proteins

The use of proteins as therapeutics has a long history and is becoming ever more common in modern medicine. While the number of protein-based drugs is growing every year, significant problems still remain with their use. Among these problems are rapid degradation and excretion from patients, thus requiring frequent dosing, which in turn increases the chances for an immunological response as well as increasing the cost of therapy. One of the main strategies to alleviate these problems is to link a polyethylene glycol (PEG) group to the protein of interest. This process, called PEGylation, has grown dramatically in recent years resulting in several approved drugs. Installing a single PEG chain at a defined site in a protein is challenging. Recently, there is has been considerable research into various methods for the site-specific PEGylation of proteins. This review seeks to summarize that work and provide background and context for how site-specific PEGylation is performed. After introducing the topic of site-specific PEGylation, recent developments using chemical methods are described. That is followed by a more extensive discussion of bioorthogonal reactions and enzymatic labeling.