Probiotical cell fragments (PCFs) are structural components of the probiotic cell lysate(s) and exhibit similar beneficial effects on the host as live probiotic bacteria. With cell fragment technology (CFT™), the structural fragments are isolated and purified from live probiotic cells. While observed to be strain-dependent as in the case of live probiotics, orally administered PCFs demonstrated a broad spectrum of immune modulation functions; anti-allergy; anti-inflammation; anti-bacterial and anti-viral properties; anti-mutagenic; and radioprotective and detoxification abilities in humans and animals. The PCFs mechanisms of action include events of motifs of cell wall peptidoglycans (PGs), DNA motifs, nucleotide containing components, lipoteichoic acids (LTAs), surface layer (S-layer) proteins, and cellular carbohydrates. Different immunological in vivo-in vitro tests have shown that PCFs, essentially, have the ability to stimulate the macrophages, and induce cytokines such as interleukins (ILs), tumor necrosis factors (TNFs), interferons (IFNs), and natural killer (NK) cells. PCFs may be used as ingredients for foods and beverages or as nutritional supplements with long term stability and shelf-life up to 5 years. PCFs may also be used as health restorative ingredients in cosmetic products. The outcome of probiotics CFT™ stands as an advantage to the food and pharmaceutical industries, regarding the formulation of unique products with unadulterated sensory characteristics of origin. Hence, PCFs are being characterized here as "novel nutraceutical ingredients" for health maintenance in both humans and animals.

The paper mainly studies the influences of trust transfer on the establishment of consumers' initial trust. Based on the theory of signal transmission and self-efficiency, the study builds a trust transfer model aiming at the same subject between different environments. The results shows that when the consumers' perceived change of environment is little, prior successful experiences will improve the consumers' perceptions of self-ability, which probably lowers the effect of bank's role on the establishment of initial trust. Therefore, banks should cultivate consumers' perceptions of their relative advantages in the original environment and thus improve the consumers' dependency in the new environment to avoid the loss of consumers and build a long-term relationship.

A causal relationship among key critical success factors of successful entrepreneurs in Thailand is proposed. A new business innovation management model is established using data sampling of 250 successful entrepreneurs from various industry sectors in Thailand. The data were analyzed by performing mean, standard deviation calculations, factor analysis and causal factors affected by LISREL program. Quantitative results showed that the model is consistent with empirical data.

Corporate Social Responsibility (CSR) is becoming a new requirement for a successful business in the 21st century. This trend continues to be strengthened throughout the industry and the construction industry cannot be exceptional. Extensive literature reviews on CSR in the construction industry have been conducted to find out the current status.

Convolutional Neural Networks (CNN) has been a very popular area in large scale data processing and many works have demonstrate that CNN is a very promising tool in many field, e.g., image classification and image retrieval. Theoretically, CNN features can become better and better with the increase of CNN layers. But on the other side more layers can dramatically increase the computational cost on the same condition of other devices. In addition to CNN features, how to dig out the potential information contained in the features is also an important aspect. In this paper, we propose a novel approach utilize deep CNN to extract image features and then introduce a Regularized Locality Preserving Indexing (RLPI) method which can make features more differentiated through learning a new space of the data space. First, we apply deep networks (VGG-net) to extract image features and then introduce Regularized Locality Preserving Indexing (RLPI) method to train a model. Finally, the new feature space can be generated through this model and then can be used to image retrieval.