204 research outputs found
Blockchain Network Analysis: A Comparative Study of Decentralized Banks
Decentralized finance (DeFi) is known for its unique mechanism design, which
applies smart contracts to facilitate peer-to-peer transactions. The
decentralized bank is a typical DeFi application. Ideally, a decentralized bank
should be decentralized in the transaction. However, many recent studies have
found that decentralized banks have not achieved a significant degree of
decentralization. This research conducts a comparative study among mainstream
decentralized banks. We apply core-periphery network features analysis using
the transaction data from four decentralized banks, Liquity, Aave, MakerDao,
and Compound. We extract six features and compare the banks' levels of
decentralization cross-sectionally. According to the analysis results, we find
that: 1) MakerDao and Compound are more decentralized in the transactions than
Aave and Liquity. 2) Although decentralized banking transactions are supposed
to be decentralized, the data show that four banks have primary external
transaction core addresses such as Huobi, Coinbase, Binance, etc. We also
discuss four design features that might affect network decentralization. Our
research contributes to the literature at the interface of decentralized
finance, financial technology (Fintech), and social network analysis and
inspires future protocol designs to live up to the promise of decentralized
finance for a truly peer-to-peer transaction network
PeP: a Point enhanced Painting method for unified point cloud tasks
Point encoder is of vital importance for point cloud recognition. As the very
beginning step of whole model pipeline, adding features from diverse sources
and providing stronger feature encoding mechanism would provide better input
for downstream modules. In our work, we proposed a novel PeP module to tackle
above issue. PeP contains two main parts, a refined point painting method and a
LM-based point encoder. Experiments results on the nuScenes and KITTI datasets
validate the superior performance of our PeP. The advantages leads to strong
performance on both semantic segmentation and object detection, in both lidar
and multi-modal settings. Notably, our PeP module is model agnostic and
plug-and-play. Our code will be publicly available soon
Optimization of extraction of polyphenols from Sorghum Moench using response surface methodology, and determination of their antioxidant activities
Purpose: To employ response surface methodology (RSM) hinged on a central composite design (CCD) for the optimization of the extraction of polyphenols from Sorghum moench (Sorghum M).Methods: The combined influence of independent variables were assessed with RSM. Total phenolic content (TPC) determination was carried out using Folin-Ciocalteu method. Derivative compounds of phenolic acid were assayed using high performance liquid (HPLC). Antioxidant potential was determined through 1,1-diphenyl-2- picrylhydrazyl (DPPH) radical scavenging test.Results: The optimized extraction conditions were: 60.37 % ethanol, temperature of 59.07 oC and 2.97 h of extraction duration, which resulted in the extraction of maximum amount of TPC, i.e., 313 mg GAE/100g dry weight. The interactions between temperature and ethanol concentration, and between extraction time and ethanol concentration had significant effects of TPC (p < 0.05). Under these conditions, there was a consistency between the projected and actual experimental levels of polyphenols. A positive correlation was found between TPC and DPPH radical scavenging activity (r=0.67, p <0.05). Furthermore, ferulic acid correlated positively with p-coumaric acid (r = 0.54, p <0.01).Conclusion: These results underscore the usefulness of conditions for extraction in accuratequantification of antioxidants and phenolic compounds from Sorghum M, for possible application in large scale commercial extraction.Keywords: Response surface methodology, Sorghum moench, Polyphenols, Antioxidant
A moving least square immersed boundary method for SPH with thin-walled structures
This paper presents a novel method for smoothed particle hydrodynamics (SPH)
with thin-walled structures. Inspired by the direct forcing immersed boundary
method, this method employs a moving least square method to guarantee the
smoothness of velocity near the structure surface. It simplifies thin-walled
structure simulations by eliminating the need for multiple layers of boundary
particles, and improves computational accuracy and stability in
three-dimensional scenarios. Supportive three-dimensional numerical results are
provided, including the impulsively started plate and the flow past a cylinder.
Results of the impulsively started test demonstrate that the proposed method
obtains smooth velocity and pressure in the, as well as a good match to the
references results of the vortex wake development. In addition, results of the
flow past cylinder test show that the proposed method avoids mutual
interference on both side of the boundary, remains stable for three-dimensional
simulations while accurately calculating the forces acting on structure.Comment: 15 pages,11 figure
A CMAC-Based Systematic Design Approach of an Adaptive Embedded Control Force Loading System
In this chapter, an adaptive embedded control system is developed to measure yield strength of the material plate with an applied load. A systematic approach is proposed to handle special requirements of embedded control systems which are different from computer-based control systems as there are much less computational power and hardware resources available. Efficient control algorithm has to be designed to remove CPU burden so that the microcontroller has enough power available. A three-step approach is proposed to address the embedded control issue: Firstly, the mathematical description of the whole system is studied using both theoretical and experimental methods. A mathematical model is derived from the physical models of each component used, and an experiment is retrieved by employing Levy’s method and least square estimation to identify specific parameters of the system model. Secondly, an adaptive feedforward plus feedback controller is designed and simulated as a preparation for the embedded system implementation. The Cerebellar Model Articulation Controller (CMAC) is chosen as the feedforward part, and a PD controller is used as the feedback part to train the CMAC. Finally, the proposed algorithm is applied to the embedded system, and experiments are conducted to verify both the identified model and designed controller
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