| 1. |
- Wang, Longwei, et al.
(författare)
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A Molybdenum Disulfide Nanozyme with Charge-Enhanced Activity for Ultrasound-Mediated Cascade-Catalytic Tumor Ferroptosis
- 2023
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Ingår i: Angewandte Chemie - International Edition. - : Wiley. - 1433-7851 .- 1521-3773. ; 62:11
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Tidskriftsartikel (refereegranskat)abstract
- The deficient catalytic activity of nanozymes and insufficient endogenous H2O2 in the tumor microenvironment (TME) are major obstacles for nanozyme-mediated catalytic tumor therapy. Since electron transfer is the basic essence of catalysis-mediated redox reactions, we explored the contributing factors of enzymatic activity based on positive and negative charges, which are experimentally and theoretically demonstrated to enhance the peroxidase (POD)-like activity of a MoS2 nanozyme. Hence, an acidic tumor microenvironment-responsive and ultrasound-mediated cascade nanocatalyst (BTO/MoS2@CA) is presented that is made from few-layer MoS2 nanosheets grown on the surface of piezoelectric tetragonal barium titanate (T-BTO) and modified with pH-responsive cinnamaldehyde (CA). The integration of pH-responsive CA-mediated H2O2 self-supply, ultrasound-mediated charge-enhanced enzymatic activity, and glutathione (GSH) depletion enables out-of-balance redox homeostasis, leading to effective tumor ferroptosis with minimal side effects.
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| 2. |
- Zhang, Zhuo, et al.
(författare)
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Demonstration of three‐dimensional indoor visible light positioning with multiple photodiodes and reinforcement learning
- 2020
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Ingår i: Sensors. - : MDPI AG. - 1424-8220. ; 20:22, s. 1-14
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Tidskriftsartikel (refereegranskat)abstract
- To provide high‐quality location‐based services in the era of the Internet of Things, visible light positioning (VLP) is considered a promising technology for indoor positioning. In this paper, we study a multi‐photodiodes (multi‐PDs) three‐dimensional (3D) indoor VLP system enhanced by reinforcement learning (RL), which can realize accurate positioning in the 3D space without any off-line training. The basic 3D positioning model is introduced, where without height information of the receiver, the initial height value is first estimated by exploring its relationship with the received signal strength (RSS), and then, the coordinates of the other two dimensions (i.e., X and Y in the horizontal plane) are calculated via trilateration based on the RSS. Two different RL processes, namely RL1 and RL2, are devised to form two methods that further improve horizontal and vertical positioning accuracy, respectively. A combination of RL1 and RL2 as the third proposed method enhances the overall 3D positioning accuracy. The positioning performance of the four presented 3D positioning methods, including the basic model without RL (i.e., Benchmark) and three RL based methods that run on top of the basic model, is evaluated experimentally. Experimental results verify that obviously higher 3D positioning accuracy is achieved by implementing any proposed RL based methods compared with the benchmark. The best performance is obtained when using the third RL based method that runs RL2 and RL1 sequentially. For the testbed that emulates a typical office environment with a height difference between the receiver and the transmitter ranging from 140 cm to 200 cm, an average 3D positioning error of 2.6 cm is reached by the best RL method, demonstrating at least 20% improvement compared to the basic model without performing RL.
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| 3. |
- Zhang, Zhuo, et al.
(författare)
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Iterative point-wise reinforcement learning for highly accurate indoor visible light positioning
- 2019
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Ingår i: Optics Express. - : OPTICAL SOC AMER. - 1094-4087 .- 1094-4087. ; 27:16, s. 22161-22172
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Tidskriftsartikel (refereegranskat)abstract
- Iterative point-wise reinforcement learning (IPWRL) is proposed for highly accurate indoor visible light positioning (VLP). By properly updating the height information in an iterative fashion, the IPWRL not only effectively mitigates the impact of non-deterministic noise but also exhibits excellent tolerance to deterministic errors caused by the inaccurate a priori height information. The principle of the IPWRL is explained, and the performance of the IPWRL is experimentally evaluated in a received signal strength (RSS) based VLP system and compared with other positioning algorithms, including the conventional RSS algorithm, the k-nearest neighbors (KNN) algorithm and the PWRL algorithm where iterations exclude. Unlike the supervised machine learning method, e.g., the KNN, whose performance is highly dependent on the training process, the proposed IPWRL does not require training and demonstrates robust positioning performance for the entire tested area. Experimental results also show that when a large height information mismatch occurs, the IPWRL is able to first correct the height information and then offers robust positioning results with a rather low positioning error, while the positioning errors caused by the other algorithms are significantly higher.
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