Continuous-wave mud telemetry digital communication system design and the simulation test

AbstractThis paper researched on the continuous wave mud telemetry MWD system based on the frequency modulation (FM) transmission mode. The digital communication system based on the continuous wave mud telemetry was designed. The system architecture design includes the ground signal transceiver devices, the bottom signal transceiver devices, as well as the third part of data transmission channel. In the initial stage of the system design, the wind tunnel simulation tests could be employed. The structure of the wind tunnel test model was designed according to the similarity principle, and a series of wind tunnel simulation tests were carried out for data transmission. Test results showed that the continuous wave mud telemetry MWD system based on the FM transmission mode could achieve higher data transfer rate, improve job reliability, and enhance the adaptability to the environment

Empirical analysis of the ship-transport network of China

Structural properties of the ship-transport network of China (STNC) are studied in the light of recent investigations of complex networks. STNC is composed of a set of routes and ports located along the sea or river. Network properties including the degree distribution, degree correlations, clustering, shortest path length, centrality and betweenness are studied in different definition of network topology. It is found that geographical constraint plays an important role in the network topology of STNC. We also study the traffic flow of STNC based on the weighted network representation, and demonstrate the weight distribution can be described by power law or exponential function depending on the assumed definition of network topology. Other features related to STNC are also investigated.Comment: 20 pages, 7 figures, 1 tabl

Numerical Analysis of the Liquid-Gas-Solid Three Phase Flow Inside AWJ Nozzle

The multiphase flows inside the two abrasive waterjet (AWJ) nozzles with different abrasive inlet tube angles are simulated using the standard k-ε turbulence model based on the Euler-Lagrangian approach. The volume of fluid (VOF) method is employed to simulate the water-air multiphase flows. And, the abrasive particles are treated as dilute dispersed phase and tracked with the discrete particle method (DPM). The results indicate that the abrasive inlet tube angle has little impact on the water-phase flows. Further analysis shows that a larger abrasive inlet tube angle can enhance the particle accelerations. The particle number independence analysis is conducted, and the results indicate that sufficient particles should be tracked in order to obtain statistically representative results. The effects of particle initial velocities, particle shape factors, and the restitution coefficients on the predicted particle movements are further analyzed for the two nozzles with abrasive inlet tube angles of 45° and 60°. The results reveal that at the current velocity range, the particle initial velocities have little impact on the predicted particle velocities. However, both the shape factors and the restitution coefficients play an important role on the calculated particle velocities. The results provide a deeper understanding of particle acceleration performance inside the AWJ nozzles with different abrasive inlet tube angles

Growing small-world networks based on a modified BA model

We propose a simple growing model for the evolution of small-world networks. It is introduced as a modified BA model in which all the edges connected to the new nodes are made locally to the creator and its nearest neighbors. It is found that this model can produce small-world networks with power-law degree distributions. Properties of our model, including the degree distribution, clustering, and the average path length are compared with that of the BA model. Since most real networks are both scale-free and small-world networks, our model may provide a satisfactory description for empirical characteristics of real networks.Comment: 4 pages, 4 figure

Willingness to Pay for Social Health Insurance among Informal Sector Workers in Wuhan, China: a Contingent Valuation Study

Background: Most of the about 140 million informal sector workers in urban China do not have health insurance. A 1998 central government policy leaves it to the discretion of municipal governments to offer informal sector workers in cities voluntary participation in a social health insurance for formal sector workers, the so-called 'basic health insurance' (BHI). Methods: We used the contingent valuation method to assess the maximum willingness to pay (WTP) for BHI among informal sector workers, including unregistered rural-to-urban migrants, in Wuhan City, China. We selected respondents in a two-stage self-weighted cluster sampling scheme. Results: On average, informal sector workers were willing to pay substantial amounts for BHI (30 Renminbi (RMB), 95% confidence interval (CI) 27-33) as well as substantial proportions of their incomes (4.6%, 95% CI 4.1-5.1%). Average WTP increased significantly when any one of the copayments of the BHI was removed in the valuation: to 51 RMB (95% CI 46-56) without reimbursement ceiling; to 43 RMB (95% CI 37-49) without deductible; and to 47 RMB (95% CI 40-54) without coinsurance. WTP was higher than estimates of the cost of BHI based on past health expenditure or on premium contributions of formal sector workers. Predicted coverage with BHI declined steeply with the premium contribution at low contribution levels. When we applied equity weighting in the aggregation of individual WTP values in order to adjust for inequity in the distribution of income, mean WTP for BHI increased with inequality aversion over a plausible range of the aversion parameter. Holding other factors constant in multiple regression analysis, for a 1% increase in income WTP for BHI with different copayments increased by 0.434-0.499% (all p < 0.0001), and for a 1% increase in past health care expenditure WTP increased by 0.076-0.148% (all p < 0.0004). Being male, a migrant, or without permanent employment significantly decreased WTP for BHI. Education was not a significant determinant of WTP for BHI. Conclusion: Our results suggest that Chinese municipal governments should allow informal sector workers to participate in the BHI. From a normative perspective, BHI for informal sector workers is likely to increase social welfare because average WTP for BHI is significantly higher than estimates of the average cost of BHI. We further find that informal sector workers do not value the BHI as a mechanism to recover the relatively frequent but small financial losses associated with common illnesses, but because it protects against the rare but large financial losses associated with catastrophic care. From a behavioural perspective, our results predict that at a price equal to the average premium contribution of formal sector workers 35% of informal sector workers will enrol in the BHI. Subsidies and changes in insurance attributes (e.g. including catastrophic care and portability) should be effective in increasing BHI coverage. In addition, coverage should expand with rising incomes among informal sector workers in China. Finally, adverse selection will be unlikely to be a large problem, if the BHI is offered to informal sector workers

Symplectic Structure-Aware Hamiltonian (Graph) Embeddings

In traditional Graph Neural Networks (GNNs), the assumption of a fixed embedding manifold often limits their adaptability to diverse graph geometries. Recently, Hamiltonian system-inspired GNNs are proposed to address the dynamic nature of such embeddings by incorporating physical laws into node feature updates. In this work, we present SAH-GNN, a novel approach that generalizes Hamiltonian dynamics for more flexible node feature updates. Unlike existing Hamiltonian-inspired GNNs, SAH-GNN employs Riemannian optimization on the symplectic Stiefel manifold to adaptively learn the underlying symplectic structure during training, circumventing the limitations of existing Hamiltonian GNNs that rely on a pre-defined form of standard symplectic structure. This innovation allows SAH-GNN to automatically adapt to various graph datasets without extensive hyperparameter tuning. Moreover, it conserves energy during training such that the implicit Hamiltonian system is physically meaningful. To this end, we empirically validate SAH-GNN's superior performance and adaptability in node classification tasks across multiple types of graph datasets.Comment: 5 pages main content with 3 pages appendi