Jiangnan University

About: Jiangnan University is a education organization based out in Wuxi, China. It is known for research contribution in the topics: Fermentation & Catalysis. The organization has 27410 authors who have published 29034 publications receiving 450182 citations. The organization is also known as: Jiāngnán Dàxué & Southern Yangtze University.

Present and Future of Surface-Enhanced Raman Scattering

Abstract: The discovery of the enhancement of Raman scattering by molecules adsorbed on nanostructured metal surfaces is a landmark in the history of spectroscopic and analytical techniques. Significant experimental and theoretical effort has been directed toward understanding the surface-enhanced Raman scattering (SERS) effect and demonstrating its potential in various types of ultrasensitive sensing applications in a wide variety of fields. In the 45 years since its discovery, SERS has blossomed into a rich area of research and technology, but additional efforts are still needed before it can be routinely used analytically and in commercial products. In this Review, prominent authors from around the world joined together to summarize the state of the art in understanding and using SERS and to predict what can be expected in the near future in terms of research, applications, and technological development. This Review is dedicated to SERS pioneer and our coauthor, the late Prof. Richard Van Duyne, whom we lost during the preparation of this article.

Particle swarm optimization with particles having quantum behavior

Abstract: In this paper, inspired by the analysis of convergence of PSO, we study the individual particle of a PSO system moving in a quantum multidimensional space and establish a quantum delta potential well model for PSO. After that, a trial method of parameter control and QDPSO is proposed. The experiment result shows much advantage of QDPSO to the traditional PSO.

Parameter-less Auto-weighted multiple graph regularized Nonnegative Matrix Factorization for data representation

Abstract: Recently, multiple graph regularizer based methods have shown promising performances in data representation However, the parameter choice of the regularizer is crucial to the performance of clustering and its optimal value changes for different real datasets To deal with this problem, we propose a novel method called Parameter-less Auto-weighted Multiple Graph regularized Nonnegative Matrix Factorization (PAMGNMF) in this paper PAMGNMF employs the linear combination of multiple simple graphs to approximate the manifold structure of data as previous methods do Moreover, the proposed method can automatically learn an optimal weight for each graph without introducing an additive parameter Therefore, the proposed PAMGNMF method is easily applied to practical problems Extensive experimental results on different real-world datasets have demonstrated that the proposed method achieves better performance than the state-of-the-art approaches

Trends in microwave- related drying of fruits and vegetables

Abstract: Microwave (MW)-related (MW-assisted or MW-enhanced) combination drying is a rapid dehydration technique that can be applied to specific foods, particularly to fruits and vegetables. Increasing concerns over product quality and production costs have motivated the researchers to investigate and the industry to adopt combination drying technologies. The advantages of MW-related combination drying include the following: shorter drying time, improved product quality, and flexibility in producing a wide variety of dried products. But current applications are limited to small categories of fruits and vegetables due to high start-up costs and relatively complicated technology as compared to conventional convection drying. MW-related combination drying takes advantages of conventional drying methods and microwave heating, leading to better processes than MW drying alone. This paper presents a comprehensive review of recent progresses in MW-related combined drying research and recommendations for future research to bridge the gap between laboratory research and industrial applications.

Breast cancer intrinsic subtype classification, clinical use and future trends

Abstract: Breast cancer is composed of multiple subtypes with distinct morphologies and clinical implications. The advent of microarrays has led to a new paradigm in deciphering breast cancer heterogeneity, based on which the intrinsic subtyping system using prognostic multigene classifiers was developed. Subtypes identified using different gene panels, though overlap to a great extent, do not completely converge, and the avail of new information and perspectives has led to the emergence of novel subtypes, which complicate our understanding towards breast tumor heterogeneity. This review explores and summarizes the existing intrinsic subtypes, patient clinical features and management, commercial signature panels, as well as various information used for tumor classification. Two trends are pointed out in the end on breast cancer subtyping, i.e., either diverging to more refined groups or converging to the major subtypes. This review improves our understandings towards breast cancer intrinsic classification, current status on clinical application, and future trends.