Tongji University

About: Tongji University is a education organization based out in Shanghai, China. It is known for research contribution in the topics: Population & Adsorption. The organization has 76116 authors who have published 81176 publications receiving 1248911 citations. The organization is also known as: Tongji & Tóngjì Dàxué.

Electrode Materials of Sodium-Ion Batteries toward Practical Application

Abstract: Advances in developing affordable batteries are vital for integrating renewable and environmentally friendly energy sources into the power grid. Benefiting from the abundance of sodium resources, sodium-ion batteries (SIBs) have attracted great attention as one of the most promising energy storage and conversion devices for grid-scale energy storage systems. From this perspective, we present a succinct and critical survey of the emerging electrode materials, such as layered transition-metal oxides, polyanionic compounds, Prussian blue analogue cathode materials, and hard carbon anode materials, that have potential value for large-scale applications.

Programmed cell death and its role in inflammation

Abstract: Cell death plays an important role in the regulation of inflammation and may be the result of inflammation. The maintenance of tissue homeostasis necessitates both the recognition and removal of invading microbial pathogens as well as the clearance of dying cells. In the past few decades, emerging knowledge on cell death and inflammation has enriched our molecular understanding of the signaling pathways that mediate various programs of cell death and multiple types of inflammatory responses. This review provides an overview of the major types of cell death related to inflammation. Modification of cell death pathways is likely to be a logical therapeutic target for inflammatory diseases.

Predicting Quality of Service for Selection by Neighborhood-Based Collaborative Filtering

Abstract: Quality-of-service-based (QoS) service selection is an important issue of service-oriented computing. A common premise of previous research is that the QoS values of services to target users are supposed to be all known. However, many of QoS values are unknown in reality. This paper presents a neighborhood-based collaborative filtering approach to predict such unknown values for QoS-based selection. Compared with existing methods, the proposed method has three new features: 1) the adjusted-cosine-based similarity calculation to remove the impact of different QoS scale; 2) a data smoothing process to improve prediction accuracy; and 3) a similarity fusion approach to handle the data sparsity problem. In addition, a two-phase neighbor selection strategy is proposed to improve its scalability. An extensive performance study based on a public data set demonstrates its effectiveness.