Zobrazeno 1 - 10
of 98
pro vyhledávání: '"ZONGHAO LI"'
Publikováno v:
Orthopaedic Surgery, Vol 16, Iss 6, Pp 1487-1492 (2024)
The accurate fenestration, screw implantation and assisting stabilizing‐plate placement in surgery of benign tumors in the proximal femur needs be defined easily. The aim of this study was to investigate the value of 3D printed multifunctional guid
Externí odkaz:
https://doaj.org/article/f28f72e81ef84e988e87de1b867d0d75
Publikováno v:
Orthopaedic Surgery, Vol 16, Iss 5, Pp 1246-1251 (2024)
Objective Percutaneous CT‐guided radiofrequency ablation (CT‐RFA) is a widely accepted procedure for treatment of osteoid osteomas. However, the application of CT‐RFA was restricted as a result of some drawbacks, such as radiation exposure, and
Externí odkaz:
https://doaj.org/article/b61eaa890b0b45a28a4b5699f45cddb7
Autor:
Zonghao Li, Hongyan Hu, Jing Zhou, Quan Wang, Lei Zhang, Xuanyi Shen, Chengguo Mei, Zhengyuan He, Yehua Jiang
Publikováno v:
Journal of Materials Research and Technology, Vol 29, Iss , Pp 3011-3023 (2024)
The aim of this study was to address insufficient load-bearing capacity, and lack of antibacterial properties of porous Ti6Al4V (TC4) alloy, porous TC4-5Cu alloy with designed honeycomb structure were prepared by additive manufacturing technology. Th
Externí odkaz:
https://doaj.org/article/6529adfe77564dfebdc33e31589d8039
Autor:
Zonghao Li, Anthony Chan Carusone
Publikováno v:
IEEE Access, Vol 12, Pp 150032-150045 (2024)
This paper presents a fully open-sourced AMS integrated circuit optimization framework based on reinforcement learning (RL). Specifically, given a certain circuit topology and target specifications, this framework optimizes the circuit in both schema
Externí odkaz:
https://doaj.org/article/9dd4790acacb4f449b20f852e1205f2a
Publikováno v:
Biosensors, Vol 14, Iss 8, p 381 (2024)
Exercise-induced muscle injury is one of the most common types of sports injuries. Skeletal muscle troponin I (skTnI) serves as an ideal biomarker in assessing such injuries, facilitating timely detection and evaluation. In this study, we develop a f
Externí odkaz:
https://doaj.org/article/d1c5931ade404316a2d1b8da53db2569
Autor:
Ning Ran, Wenxiang Li, Renjie Zhang, Caorui Lin, Jianping Zhang, Zhijian Wei, Zonghao Li, Zhongze Yuan, Min Wang, Baoyou Fan, Wenyuan Shen, Xueying Li, Hengxing Zhou, Xue Yao, Xiaohong Kong, Shiqing Feng
Publikováno v:
Bioactive Materials, Vol 25, Iss , Pp 766-782 (2023)
Spinal cord injury (SCI) causes motor, sensory and automatic impairment due to rarely axon regeneration. Developing effective treatment for SCI in the clinic is extremely challenging because of the restrictive axonal regenerative ability and disconne
Externí odkaz:
https://doaj.org/article/57c6cf815e774e76a778eae999127c20
Publikováno v:
Shiyou shiyan dizhi, Vol 45, Iss 2, Pp 338-346 (2023)
As the largest hydrocarbon-rich sag in the Junggar Basin, the limited research on geochemical characteristics, sedimentary environment and bio-precursors of source rocks in the Fukang Sag has seriously restricted the understanding of the poor proven
Externí odkaz:
https://doaj.org/article/303949683a2c4b4e8509c5d43aecff55
Publikováno v:
IEEE Access, Vol 11, Pp 115998-116010 (2023)
Traffic sign recognition and detection is a key technology in automatic vehicle driving and driver assistance systems. However, existing traffic sign recognition algorithms suffer from problems such as large model size, complex computation, high comp
Externí odkaz:
https://doaj.org/article/240f5c763cb9418f82f40d2c4aba660a
Publikováno v:
Frontiers in Physics, Vol 11 (2023)
By the continuous development of aerospace, petroleum exploration, and other industrial fields, the fiber-optic acoustic sensor (FOAS) with high reliability is a desideration sensor, which can be used for noise monitoring in the extremely harsh envir
Externí odkaz:
https://doaj.org/article/a1d5787795da425bbd0cabbcc03a3433
MSCPDPLab: A MATLAB toolbox for transfer learning based multi-source cross-project defect prediction
Publikováno v:
SoftwareX, Vol 21, Iss , Pp 101286- (2023)
Software defect prediction (SDP) plays an important role in allocating testing resources and improving testing efficiency. Multi-source cross-project defect prediction (MSCPDP) based on transfer learning refers to transferring defect knowledge from m
Externí odkaz:
https://doaj.org/article/03b263170b104d2094e171925962853b