Beihang University

About: Beihang University is a education organization based out in Beijing, China. It is known for research contribution in the topics: Computer science & Control theory. The organization has 67002 authors who have published 73507 publications receiving 975691 citations. The organization is also known as: Beijing University of Aeronautics and Astronautics.

Seeking new, highly effective thermoelectrics

Abstract: Operating across a wide temperature range is a priority for thermoelectric materials Thermoelectric technology can directly and reversibly convert heat to electrical energy. Although thermoelectric energy conversion will never be as efficient as a steam engine (1), improving thermoelectric performance can potentially make a technology commercially competitive. Thermoelectric conversion efficiency is estimated by the so-called dimensionless figure of merit, ZT = S2σT/κ, where S, σ, T, and κ denote the Seebeck coefficient, electrical conductivity, working temperature, and thermal conductivity, respectfully . These parameters are strongly coupled, and improving the final ZT is challenging as a result. Strategies for boosting thermoelectric performance include nanostructuring, band engineering, nanomagnetic compositing, high-throughput screening, and others (2). Many of these strategies create a high ZT in a narrow range of temperatures, limiting the overall energy conversion. Finding materials with wider operating temperature ranges may require rethinking development strategies.

A Review on Applications of Computational Methods in Drug Screening and Design

Abstract: Drug development is one of the most significant processes in the pharmaceutical industry. Various computational methods have dramatically reduced the time and cost of drug discovery. In this review, we firstly discussed roles of multiscale biomolecular simulations in identifying drug binding sites on the target macromolecule and elucidating drug action mechanisms. Then, virtual screening methods (e.g., molecular docking, pharmacophore modeling, and QSAR) as well as structure- and ligand-based classical/de novo drug design were introduced and discussed. Last, we explored the development of machine learning methods and their applications in aforementioned computational methods to speed up the drug discovery process. Also, several application examples of combining various methods was discussed. A combination of different methods to jointly solve the tough problem at different scales and dimensions will be an inevitable trend in drug screening and design.

RegionKNN: A Scalable Hybrid Collaborative Filtering Algorithm for Personalized Web Service Recommendation

Abstract: Several approaches to web service selection and recommendation via collaborative filtering have been studied, but seldom have these studies considered the difference between web service recommendation and product recommendation used in e-commerce sites. In this paper, we present RegionKNN, a novel hybrid collaborative filtering algorithm that is designed for large scale web service recommendation. Different from other approaches, this method employs the characteristics of QoS by building an efficient region model. Based on this model, web service recommendations will be generated quickly by using modified memory-based collaborative filtering algorithm. Experimental results demonstrate that apart from being highly scalable, RegionKNN provides considerable improvement on the recommendation accuracy by comparing with other well-known collaborative filtering algorithms.