468 research outputs found
sj-docx-3-cll-10.1177_09636897231177356 – Supplemental material for Dexamethasone Induces Senescence-Associated Changes in Trabecular Meshwork Cells by Increasing ROS Levels Via the TGFβ/Smad3-NOX4 Axis
No full textSupplemental material, sj-docx-3-cll-10.1177_09636897231177356 for Dexamethasone Induces Senescence-Associated Changes in Trabecular Meshwork Cells by Increasing ROS Levels Via the TGFβ/Smad3-NOX4 Axis by Haijun Li, Jing Ren, Huiling Cui, Di Wang, Rumeng Zhao, Xiaohui Liu, Shuai Tian, Jing Wang, Jingyi Zhang, Peng Li, Rick F. Thorne and Shichao Duan in Cell Transplantation</p
sj-docx-2-cll-10.1177_09636897231177356 – Supplemental material for Dexamethasone Induces Senescence-Associated Changes in Trabecular Meshwork Cells by Increasing ROS Levels Via the TGFβ/Smad3-NOX4 Axis
No full textSupplemental material, sj-docx-2-cll-10.1177_09636897231177356 for Dexamethasone Induces Senescence-Associated Changes in Trabecular Meshwork Cells by Increasing ROS Levels Via the TGFβ/Smad3-NOX4 Axis by Haijun Li, Jing Ren, Huiling Cui, Di Wang, Rumeng Zhao, Xiaohui Liu, Shuai Tian, Jing Wang, Jingyi Zhang, Peng Li, Rick F. Thorne and Shichao Duan in Cell Transplantation</p
sj-docx-1-cll-10.1177_09636897231177356 – Supplemental material for Dexamethasone Induces Senescence-Associated Changes in Trabecular Meshwork Cells by Increasing ROS Levels Via the TGFβ/Smad3-NOX4 Axis
No full textSupplemental material, sj-docx-1-cll-10.1177_09636897231177356 for Dexamethasone Induces Senescence-Associated Changes in Trabecular Meshwork Cells by Increasing ROS Levels Via the TGFβ/Smad3-NOX4 Axis by Haijun Li, Jing Ren, Huiling Cui, Di Wang, Rumeng Zhao, Xiaohui Liu, Shuai Tian, Jing Wang, Jingyi Zhang, Peng Li, Rick F. Thorne and Shichao Duan in Cell Transplantation</p
UAV aided network association in space-air-ground communication networks
No full textUnmanned aerial vehicles (UAVs) cooperating with satellites and base stations (BSs) constitute a space-air-ground three-tier heterogeneous network, which is beneficial in terms of both providing the seamless coverage as well as of improving the capacity for the users. However, cross-tier interference may be inevitable among these tightly embraced heterogeneous networks. In our paper, we propose a two-stage joint hovering altitude and power control solution for the resource allocation problem. Furthermore, Lagrange dual decomposition and concave-convex procedure (CCP) method are used to solve this problem. Finally, simulation results show the effectiveness of our proposed two-stage joint optimization algorithm in terms of UAV network's total throughput.</p
Holocene peat Hg anomalies
No full textThis project examines the major explosive volcanic eruptions recorded in peat. Hg data from three peat profiles, the Hongyuan and Dangxiong peat from the Tibetan Plateau and the Pinet peat record from France, were synthesized here to support our conclusions. All the Hg data and the MATLAB script are prepared for the manuscript entitled "Tibetan peat saw Holocene global major explosive volcanic eruptions".Corresponding author: Haijun PENG, [email protected].</p
Machine learning paradigms for next-generation wireless networks
No full textNext-generation wireless networks are expected to support extremely high data rates and radically new applications, which require a new wireless radio technology paradigm. The challenge is that of assisting the radio in intelligent adaptive learning and decision making, so that the diverse requirements of next-generation wireless networks can be satisfied. Machine learning is one of the most promising artificial intelligence tools, conceived to support smart radio terminals. Future smart 5G mobile terminals are expected to autonomously access the most meritorious spectral bands with the aid of sophisticated spectral efficiency learning and inference, in order to control the transmission power, while relying on energy efficiency learning/inference and simultaneously adjusting the transmission protocols with the aid of quality of service learning/inference. Hence we briefly review the rudimentary concepts of machine learning and propose their employment in the compelling applications of 5G networks, including cognitive radios, massive MIMOs, femto/small cells, heterogeneous networks, smart grid, energy harvesting, device-todevice communications, and so on. Our goal is to assist the readers in refining the motivation, problem formulation, and methodology of powerful machine learning algorithms in the context of future networks in order to tap into hitherto unexplored applications and services
Information-sharing outage-probability analysis of vehicular networks
No full textIn vehicular networks, information dissemination/sharing among vehicles is of salient importance. Although diverse mechanisms have been proposed in the existing literature, the related information credibility issues have not been investigated. Against this background, in this paper, we propose a credible information-sharing mechanism capable of ensuring that the vehicles do share genuine road traffic information (RTI). We commence with the outage-probability analysis of information sharing in vehicular networks under both a general scenario and a specific highway scenario. Closed-form expressions are derived for both scenarios, given the specific channel settings. Based on the outage-probability expressions, we formulate the utility of RTI sharing and design an algorithm for promoting the sharing of genuine RTI. To verify our theoretical analysis and the proposed mechanism, we invoke a real-world dataset containing the locations of Beijing taxis to conduct our simulations. Explicitly, our simulation results show that the spatial distribution of the vehicles obeys a Poisson point process (PPP), and our proposed credible RTI sharing mechanism is capable of ensuring that all vehicles indeed do share genuine RTI with each other
Thirty years of machine learning: the road to Pareto-optimal wireless networks
No full textFuture wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of thecomplex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (Het-Nets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios offuture wireless networks
sj-docx-1-cll-10.1177_09636897231162526 – Supplemental material for Elevated Angiotensin-II Levels Contribute to the Pathogenesis of Open-Angle Glaucoma Via Inducing the Expression of Fibrosis-Related Genes in Trabecular Meshwork Cells Through a ROS/NOX4/SMAD3 Axis
No full textSupplemental material, sj-docx-1-cll-10.1177_09636897231162526 for Elevated Angiotensin-II Levels Contribute to the Pathogenesis of Open-Angle Glaucoma Via Inducing the Expression of Fibrosis-Related Genes in Trabecular Meshwork Cells Through a ROS/NOX4/SMAD3 Axis by Haijun Li, Huiling Cui, Jing Ren, Di Wang, Rumeng Zhao, Shichao Zhu, Siqing Liu, Xiaohui Liu, Shuai Tian, Yuanyuan Zhang, Panpan Zhao, Peng Li, Rick F. Thorne and Shichao Duan in Cell Transplantation</p
The research and realization of multi-platform real-time message-oriented middleware in large-scale air traffic control system
No full text- …
