Effects of electron beam irradiation on microbial inactivation and quality of kimchi paste during storage

The effects of electron beam irradiation on microbial inactivation and quality of noninoculated and inoculated (Listeria monocytogenes) kimchi pastes were examined. Kimchi paste samples were irradiated at doses of 2, 4, 6, 8 and 10 kGy and stored for 21 days at 4 °C. Irradiation (10 kGy) reduced the populations of total aerobic bacteria, lactic acid bacteria, and yeast and moulds in the samples by 1.72, 2.24 and 0.86 log CFU g^?1, respectively, compared to the control. In particular, coliforms were not detected at 8 and 10 kGy, and the population of L. monocytogenes in inoculated samples was significantly decreased by 2.67 log CFU g^?1. Electron beam irradiation delayed the changes in O₂ and CO₂ concentrations, pH, acidity and reducing sugar content observed in kimchi paste during storage. These results suggest that electron beam irradiation can be used to improve the microbiological safety and shelf life of kimchi paste.     

Immobilization of laccase on carbon nanomaterials

Laccase from Trametes versicolor was readily immobilized on carbon nanomaterials including multiwalled carbon nanotubes (MWNTs), carboxylated multiwalled carbon nanotubes (MWNT-COOHs), and graphene oxides (GOs), by physical adsorption without using coupling agents. The immobilized amount of laccase strongly depends on the pH of the aqueous buffers of the immobilization mixture. As the pH of the aqueous buffer for immobilization increases, the immobilized amount of laccase decreases. The activity of the immobilized laccase on the three carbon nanomaterials exhibits a bell-shaped dependence on the pH of the immobilization solution with maximum activity at pH 6 or 7. When the immobilization solution becomes acidic or basic, the activity of the immobilized laccase declines significantly. The amount and the activity of immobilized laccase were maximum for graphene oxides as substrate material for immobilization.     

Grafting maleic anhydride onto EVA and effect on the physical and rheological properties of PETG/EVA-g-MAH blends

Maleic anhydride (MAH) was grafted onto ethylene vinyl acetate copolymer (EVA) in the internal mixer in the presence of dicumyl peroxide (DCP), and a prepared reactive compatibilizer, MAH-g-EVA, was blended with Poly (ethylene glycol-co-cyclohexane-1,4-dimethanol terephthalate) (PETG). The gel content determination and element analysis (EA) was performed to confirm the grafting reaction. It was found that grafting reaction of MAH on to EVA could compete with crosslinking reaction of DCP during the modification process. In addition, the introduction of small amount of MAH showed a great effect on reducing gel content by decreasing crosslink reaction. As MAH content increased, grafted MAH concentration increased, whereas the grafting yield decreased. It was also confirmed that MAH-g-EVA acted as a reactive compatibilizer in the blend with PETG, and enhanced compatibility by reacting with the hydroxyl end groups, OH, of PETG. Addition of EVA in the blend leads a plastic deformation of PETG, and MAH had a great effect on enhancing interfacial adhesion resulting in significant increasing of % strain; however, improved compatibility could not be changed much in low strain tensile strength and Young's modulus. © 2012 Wiley Periodicals, Inc. J Appl Polym Sci, 2012     

An MCFC operation optimization strategy based on PID auto-tuning control

Molten Carbonate Fuel Cell (MCFC) has been emerging as a promising renewable power system. It is still challenging to operate the MCFC to meet its varying demands because of its nonlinearity and complex dynamics. This paper proposes a novel MCFC operation framework based on PID auto-tuning control. A case study is presented to illustrate the applicability of the strategy with some comments.     

Predicting the Lifespan and Retweet Times of Tweets Based on Multiple Feature Analysis

In social network services, such as Facebook, Google+, Twitter, and certain postings attract more people than others. In this paper, we propose a novel method for predicting the lifespan and retweet times of tweets, the latter being a proxy for measuring the popularity of a tweet. We extract information from retweet graphs, such as posting times; and social, local, and content features, so as to construct prediction knowledge bases. Tweets with a similar topic, retweet pattern, and properties are sequentially extracted from the knowledge base and then used to make a prediction. To evaluate the performance of our model, we collected tweets on Twitter from June 2012 to October 2012. We compared our model with conventional models according to the prediction goal. For the lifespan prediction of a tweet, our model can reduce the time tolerance of a tweet lifespan by about four hours, compared with conventional models. In terms of prediction of the retweet times, our model achieved a significantly outstanding precision of about 50%, which is much higher than two of the conventional models showing a precision of around 30% and 20%, respectively.