Yan Hu

Dr. Yan Hu


Research Focus & Metrics

Academic Career Length

15Years (2012-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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5 August 2016 in SpringerPlus
Verified
Journal Article
Open access
PDF file
YHYan Hu
Yan Hu
JZJun Zhang
Jun Zhang
XBXiaomei Bai
Xiaomei Bai
SYShuo Yu
Shuo Yu
ZYZhuo Yang
Zhuo Yang
With the support of cloud computing techniques, social coding platforms have changed the style of software development. Github is now the most popular social coding platform and project hosting service. Software developers of various levels keep entering Github, and use Github to save their public and private software projects. The large amounts of software developers and software repositories on Github are posing new challenges to the world of software engineering. This paper tries to tackle one of the important problems: analyzing the importance and influence of Github repositories. We proposed a HITS based influence analysis on graphs that represent the star relationship between Github users and repositories. A weighted version of HITS is applied to the overall star graph, and generates a different set of top influential repositories other than the results from standard version of HITS algorithm. We also conduct the influential analysis on per-month star graph, and study the monthly influence ranking of top repositories.

27 January 2016 in Cluster Computing
Verified
Journal Article
Closed access
PDF file
YHYan Hu
Yan Hu
JYJun Yan
Jun Yan
KCKim-Kwang Raymond Choo
Kim-Kwang Raymond Choo

31 October 2012 in International Journal of Advancements in Computing Technology
Verified
Journal Article
Closed access
Jifeng XuanJifeng Xuan
Jifeng Xuan
YHYan Hu
Yan Hu
HJHe Jiang
He Jiang

17 July 2026 in Pharmaceutics
Verified
Journal Article
Open access
PDF file
XXXiaotian Xu
Xiaotian Xu
YHYan Hu
Yan Hu
HLHaifang Li
Haifang Li
YWYu Wang
Yu Wang
JSJiayi Sun
Jiayi Sun
Kai-Rong QinKai-Rong Qin
Kai-Rong Qin
Vascular stents are crucial devices in the treatment of cardiovascular diseases, and their structural design and function critically affect therapeutic efficacy and patient prognosis. Conventional stents can effectively restore vascular patency by providing mechanical support to blood vessels. However, they still face significant challenges including restenosis, thrombosis, and limited adaptability to complex patient-specific lesion characteristics. To address these limitations, drug delivery offers an important strategy to modulate the pathological microenvironment, enhance long-term vascular healing, and reduce systemic side effects. Meanwhile, advances in computational simulations have provided powerful tools for optimizing stent design through structural mechanics, hemodynamics, and drug release modeling. Computational approaches enable the rational design of stent architectures with improved mechanical stability, vascular compatibility, and therapeutic regulation. Consequently, the development of vascular stents is evolving toward the synergistic integration of drug delivery, structural optimization, and intelligent design. This review summarizes the latest advances in functional vascular stents, clinical applications and computational design. This work aims to provide valuable insights for the engineering of efficient, precise, and intelligent vascular stents for cardiovascular therapies.

6 November 2012
Verified
Book Chapter
Closed access
PDF file
YHYan Hu
Yan Hu
HJHe Jiang
He Jiang

16 April 2017 in arXiv
Verified
Preprint
Open access
PDF file
HJHe Jiang
He Jiang
JZJingyuan Zhang
Jingyuan Zhang
JXJifeng Xuan
Jifeng Xuan
ZRZhilei Ren
Zhilei Ren
YHYan Hu
Yan Hu
In this paper, we propose a Hybrid Ant Colony Optimization algorithm (HACO) for Next Release Problem (NRP). NRP, a NP-hard problem in requirement engineering, is to balance customer requests, resource constraints, and requirement dependencies by requirement selection. Inspired by the successes of Ant Colony Optimization algorithms (ACO) for solving NP-hard problems, we design our HACO to approximately solve NRP. Similar to traditional ACO algorithms, multiple artificial ants are employed to construct new solutions. During the solution construction phase, both pheromone trails and neighborhood information will be taken to determine the choices of every ant. In addition, a local search (first found hill climbing) is incorporated into HACO to improve the solution quality. Extensively wide experiments on typical NRP test instances show that HACO outperforms the existing algorithms (GRASP and simulated annealing) in terms of both solution uality and running time.