Group Cohesion in Experiential Growth Groups: A Multilevel Growth Analysis.

Group cohesion is considered a key group therapeutic factor; however, limited knowledge exists on the session-to-session development of group cohesion in the context of training groups or on the factors that may predict such developments. We utilized multilevel growth modeling to examine the longitudinal changes in 44 counselor trainees' ratings of group cohesion in seven experiential growth groups across four semesters. We found significant between-person variability in the growth trajectory of group cohesion and identified trainee self-disclosure and gender as significant predictors of group cohesion. The results indicated the complexity of within- and between-person changes in group cohesion in training groups and the need for further research to identify potential predictors of these changes. We also discussed ways for group facilitators to attend to self-disclosure as an important marker of group cohesion.

A new characterization methodology for starch gelatinization.

A gelatinization degree control system, with a combination of Artificial Neural Networks (ANNs) and computer vision, was successfully developed. An intelligent measurement framework was purposely designed to achieve a precise investigation on phase transition and morphology change of starch in real time, as well as a process control during gelatinization. Base on a variation of birefringence number, the degree of gelatinization (DG) control system provided a direct and fast methodology without subjective uncertainty in studying starch gelatinization. In the course, the whole system was a cascade structure with the hot-stage temperature chosen as the inner-loop parameter, thus the granule morphology and birefringence at different DG could be easily observed and compared in real time, and the relative transition temperature was simultaneously calculated.

Development and preparation of active starch films carrying tea polyphenol.

Starch films incorporated with tea polyphenol (TP) were developed to produce active food packaging. The effect of the incorporation of TP with different content on the structure, physicochemical properties, antioxidant activity and antimicrobial activity of the starch films was systematically evaluated. Results showed that TP was well dispersed in the starch matrix, which induced a slight influence on the surface and barrier properties of the films. TP addition led to an important improvement in antioxidant capability, as well as inhibition efficiency against the microorganisms of S. aureus and E. coli. However, a decrease in mechanical properties of films was observed. Moreover, a new automatic counting method which combined the computer vision and machine learning algorithm was developed to identify and count the colonies, and the method performed much faster without subjective uncertainty.