SoK: Blockchain Decentralization

Blockchain empowers a decentralized economy by enabling distributed trust in a peer-to-peer network. However, surprisingly, a widely accepted definition or measurement of decentralization is still lacking. We explore a systematization of knowledge (SoK) on blockchain decentralization by comprehensively analyzing existing studies in various aspects. First, we establish a taxonomy for analyzing blockchain decentralization in the five facets of consensus, network, governance, wealth, and transaction. We find a lack of research on the transaction aspects that closely characterize user behavior. Second, we apply Shannon entropy in information theory to propose a decentralization index for blockchain transactions. We show that our index intuitively measures levels of decentralization in peer-to-peer transactions by simulating blockchain token transfers. Third, we apply our index to empirically analyze the dynamics of DeFi token transfers by three methods of description, prediction, and causal inference. In the descriptive analysis, we observe that levels of decentralization converge inter-temporally, regardless of the initial levels. A comparative study across DeFi applications shows that exchange and lending are more decentralized than payment and derivatives across DeFi applications. Second, in the predictive analysis, we also discover that a greater return of Ether, the native coin of the Ethereum blockchain, predicts a greater transaction decentralization in stablecoin that include Ether as collateral. Third, in an event study of causal inference, we find the change of Ethereum Transaction Fee Mechanism to EIP-1559 significantly changes the decentralization level of DeFi transactions. Finally, we identify future research directions

Tribological performances of fabric self-lubricating liner with different weft densities under severe working conditions

Several woven fabric self-lubricating liners with weft densities of 200-450 root/10cm in a spacing of 50 root/10cm have been prepared to investigate the tribological performances of the liner under severe working conditions, such as low velocity and heavy load (110, 179 and 248 MPa) and high velocity and light load (9, 18 and 27 m/min) by utilizing the self-lubricating liner performance assessment tester, and MMU-5G friction and wear tester respectively. The worn surface is characterized using confocal laser scanning microscopy. The tribological results show that the fabric self-lubricating liners with different weft densities share almost the same tribological property variation tendency. Fabric tightness affects the wear rate and the stability of wear resistance of liners under severe working conditions. The overall level of friction coefficient and the wear rate of liners with different weft densities are influenced by the cold flow degree of the polymer. In addition, proper weft density improves the tribological properties of liner and a preferred weft density for the liner under severe working conditions is found to be 300-350 root/10cm

Adsorption and desorption characteristics of coal seam gas under infrared radiation

Infrared radiation technology can enhance rock permeability and promote methane desorption in coalbed methane thermal recovery. In this study, an experimental system with infrared radiation is developed to investigate the adsorption/desorption behavior of coal under different water contents. The results demonstrate that higher power levels of infrared radiation lead to decreased adsorption capacity and increased desorption capacity in coal. Specifically, employing 50 W infrared radiation results in a 30.9% increase in desorption capacity. Higher moisture content intensifies the desorption hysteresis effect, while this adverse impact can be mitigated by infrared radiation with greater power levels, exhibiting a stronger ability to reduce desorption-induced hysteresis. Additionally, a critical pressure for infrared radiation is established. Before and after this pressure, the influence of infrared radiation power on pressure sensitivity differs significantly. Finally, an improved Langmuir adsorption model considering infrared radiation power and moisture content is proposed and validated using experimental data. Our research expands the application of infrared radiation technology for enhanced coalbed methane recovery during actual mining operations.Document Type: Original articleCited as: Tu, Y., Zhang, Y., Dong, Y., Ma, Y. Adsorption and desorption characteristics of coal seam gas under infrared radiation. Capillarity, 2023, 8(3): 53-64. https://doi.org/10.46690/capi.2023.09.0

Performance Analysis and Enhancement of Deep Convolutional Neural Network - Application to Gearbox Condition Monitoring

Convolutional neural network has been widely investigated for machinery condition monitoring, but its performance is highly affected by the learning of input signal representation and model structure. To address these issues, this paper presents a comprehensive deep convolutional neural network (DCNN) based condition monitoring framework to improve model performance. First, various signal representation techniques are investigated for better feature learning of the DCNN model by transforming the time series signal into different domains, such as the frequency domain, the time–frequency domain, and the reconstructed phase space. Next, the DCNN model is customized by taking into account the dimension of model, the depth of layers, and the convolutional kernel functions. The model parameters are then optimized by a mini-batch stochastic gradient descendent algorithm. Experimental studies on a gearbox test rig are utilized to evaluate the effectiveness of presented DCNN models, and the results show that the one-dimensional DCNN model with a frequency domain input outperforms the others in terms of fault classification accuracy and computational efficiency. Finally, the guidelines for choosing appropriate signal representation techniques and DCNN model structures are comprehensively discussed for machinery condition monitoring

Increased transgene expression mediated by recombinant adeno-associated virus in human neuroglia cells under microgravity conditions

The space environment has the special characteristics of radiation, noise particularity and weightlessness, all of which have adverse effects on astronauts’ muscles, bones, neurons and immune system. Some reports have shown that chemotherapy and radiotherapy can increase the activity of the recombinant adeno-associated virus (AAV) which is widely used in gene therapy. In this paper, recombinant AAV2 (rAAV2) was first packaged with the enhanced green fluorescence protein (eGFP) gene and used to infect neuroglia cells including the U87 and U251 cell lines, under microgravity conditions; it was then detected by fluorescence microscopy and flow cytometry. The results show that microgravity affects the adhesion ability of cells, promotes transgene expression induced by rAAV2 and causes changes of viral infection receptors at different time points. These findings broaden the current understanding of the microgravity effects on rAAV, with significant implications in gene therapy and the mechanisms of increased virus pathogenicity under space microgravity.

Effect of doubly fed induction generatortidal current turbines on stability of a distribution grid under unbalanced voltage conditions

This paper analyses the effects of doubly fed induction generator (DFIG) tidal current turbines on a distribution grid under unbalanced voltage conditions of the grid. A dynamic model of an electrical power system under the unbalanced network is described in the paper, aiming to compare the system performance when connected with and without DFIG at the same location in a distribution grid. Extensive simulations of investigating the effect of DFIG tidal current turbine on stability of the distribution grid are performed, taking into account factors such as the power rating, the connection distance of the turbine and the grid voltage dip. The dynamic responses of the distribution system are examined, especially its ability to ride through fault events under unbalanced grid voltage conditions. The research has shown that DFIG tidal current turbines can provide a good damping performance and that modern DFIG tidal current power plants, equipped with power electronics and low-voltage ride-through capability, can stay connected to weak electrical grids even under the unbalanced voltage conditions, whilst not reducing system stability