Nanyang Technological University

About: Nanyang Technological University is a education organization based out in Singapore, Singapore. It is known for research contribution in the topics: Computer science & Catalysis. The organization has 48003 authors who have published 112815 publications receiving 3294199 citations. The organization is also known as: NTU & Universiti Teknologi Nanyang.

Further Improvement of Free-Weighting Matrices Technique for Systems With Time-Varying Delay

Abstract: A novel method is proposed in this note for stability analysis of systems with a time-varying delay. Appropriate Lyapunov functional and augmented Lyapunov functional are introduced to establish some improved delay-dependent stability criteria. Less conservative results are obtained by considering the additional useful terms (which are ignored in previous methods) when estimating the upper bound of the derivative of Lyapunov functionals and introducing the new free-weighting matrices. The resulting criteria are extended to the stability analysis for uncertain systems with time-varying structured uncertainties and polytopic-type uncertainties. Numerical examples are given to demonstrate the effectiveness and the benefits of the proposed method

Should we stay or should we go: mechanisms and ecological consequences for biofilm dispersal.

Abstract: In most environments, bacteria reside primarily in biofilms, which are social consortia of cells that are embedded in an extracellular matrix and undergo developmental programmes resulting in a predictable biofilm 'life cycle'. Recent research on many different bacterial species has now shown that the final stage in this life cycle includes the production and release of differentiated dispersal cells. The formation of these cells and their eventual dispersal is initiated through diverse and remarkably sophisticated mechanisms, suggesting that there are strong evolutionary pressures for dispersal from an otherwise largely sessile biofilm. The evolutionary aspect of biofilm dispersal is now being explored through the integration of molecular microbiology with eukaryotic ecological and evolutionary theory, which provides a broad conceptual framework for the diversity of specific mechanisms underlying biofilm dispersal. Here, we review recent progress in this emerging field and suggest that the merging of detailed molecular mechanisms with ecological theory will significantly advance our understanding of biofilm biology and ecology.

The effects of entrepreneurial personality, background and network activities on venture growth*

Abstract: This study investigates the effects of entrepreneurial personality traits, background and networking activities on venture growth among 168 Chinese entrepreneurs in small and medium sized businesses in Singapore. Personality traits include need for achievement, internal locus of control, self-reliance and extroversion; background comprises education and experience; networking activities consist of size and frequency of communication networks. A structural equation modelling technique – partial least squares (PLS) – is used to estimate a path model with latent variables. The results indicate that experience, networking activities, and number of partners as well as internal locus of control and need for achievement all have positive impact on venture growth. Two other personality traits, self-reliance and extroversion have negative impact on number of partners and positive impact on networking activities, respectively. The impact of education on venture growth, however, is moderated by firm size, positive for larger firms and negative for smaller firms. Our findings indicate that among all the factors that we have considered, an entrepreneur’s industrial and managerial experience is the dominating factor affecting venture growth.

Time-aware point-of-interest recommendation

Abstract: The availability of user check-in data in large volume from the rapid growing location based social networks (LBSNs) enables many important location-aware services to users. Point-of-interest (POI) recommendation is one of such services, which is to recommend places where users have not visited before. Several techniques have been recently proposed for the recommendation service. However, no existing work has considered the temporal information for POI recommendations in LBSNs. We believe that time plays an important role in POI recommendations because most users tend to visit different places at different time in a day, \eg visiting a restaurant at noon and visiting a bar at night. In this paper, we define a new problem, namely, the time-aware POI recommendation, to recommend POIs for a given user at a specified time in a day. To solve the problem, we develop a collaborative recommendation model that is able to incorporate temporal information. Moreover, based on the observation that users tend to visit nearby POIs, we further enhance the recommendation model by considering geographical information. Our experimental results on two real-world datasets show that the proposed approach outperforms the state-of-the-art POI recommendation methods substantially.