Shanghai Jiao Tong University

About: Shanghai Jiao Tong University is a education organization based out in Shanghai, Shanghai, China. It is known for research contribution in the topics: Population & Cancer. The organization has 157524 authors who have published 184620 publications receiving 3451038 citations. The organization is also known as: Shanghai Communications University & Shanghai Jiaotong University.

Fluorine-Free Synthesis of High-Purity Ti3 C2 Tx (T=OH, O) via Alkali Treatment.

Abstract: MXenes, 2D compounds generated from layered bulk materials, have attracted significant attention in energy-related fields. However, most syntheses involve HF, which is highly corrosive and harmful to lithium-ion battery and supercapacitor performance. Here an alkali-assisted hydrothermal method is used to prepare a MXene Ti3 C2 Tx (T=OH, O). This route is inspired from a Bayer process used in bauxite refining. The process is free of fluorine and yields multilayer Ti3 C2 Tx with ca. 92 wt % in purity (using 27.5 m NaOH, 270 °C). Without the F terminations, the resulting Ti3 C2 Tx film electrode (ca. 52 μm in thickness, ca. 1.63 g cm-3 in density) is 314 F g-1 via gravimetric capacitance at 2 mV s-1 in 1 m H2 SO4 . This surpasses (by ca. 214 %) that of the multilayer Ti3 C2 Tx prepared via HF treatments. This fluorine-free method also provides an alkali-etching strategy for exploring new MXenes for which the interlayer amphoteric/acidic atoms from the pristine MAX phase must be removed.

Design, fabrication and characterization of PCL electrospun scaffolds—a review

Abstract: The expanding interest in electrospinning fibers for bioengineering includes a significant use of polyesters, including poly(3-caprolactone) (PCL). This review summarizes literature on PCL and selected blends, and provides extensive descriptions of the broad range of parameters used in manufacturing such electrospun fibers. Furthermore the chemical, physical and biological approaches for characterizing the electrospun material are described and opinions offered on important information to include in future publications with this electrospun material.

Collaborative filtering and deep learning based recommendation system for cold start items

Abstract: Recommender system is a specific type of intelligent systems, which exploits historical user ratings on items and/or auxiliary information to make recommendations on items to the users. It plays a critical role in a wide range of online shopping, e-commercial services and social networking applications. Collaborative filtering (CF) is the most popular approaches used for recommender systems, but it suffers from complete cold start (CCS) problem where no rating record are available and incomplete cold start (ICS) problem where only a small number of rating records are available for some new items or users in the system. In this paper, we propose two recommendation models to solve the CCS and ICS problems for new items, which are based on a framework of tightly coupled CF approach and deep learning neural network. A specific deep neural network SADE is used to extract the content features of the items. The state of the art CF model, timeSVD++, which models and utilizes temporal dynamics of user preferences and item features, is modified to take the content features into prediction of ratings for cold start items. Extensive experiments on a large Netflix rating dataset of movies are performed, which show that our proposed recommendation models largely outperform the baseline models for rating prediction of cold start items. The two proposed recommendation models are also evaluated and compared on ICS items, and a flexible scheme of model retraining and switching is proposed to deal with the transition of items from cold start to non-cold start status. The experiment results on Netflix movie recommendation show the tight coupling of CF approach and deep learning neural network is feasible and very effective for cold start item recommendation. The design is general and can be applied to many other recommender systems for online shopping and social networking applications. The solution of cold start item problem can largely improve user experience and trust of recommender systems, and effectively promote cold start items.

Complex networks: topology, dynamics and synchronization

Abstract: Dramatic advances in the field of complex networks have been witnessed in the past few years. This paper reviews some important results in this direction of rapidly evolving research, with emphasis...