Method for Assessing the Motor Coordination of Runners Based on the Analysis of Multichannel EMGs

In this paper, we propose a method to evaluate the motor coordination of runners based on the analysis of amplitude and spatiotemporal dynamics of multichannel electromyography. A new diagnostic index for the coordination of runners was proposed, including the amplitude of electromyography, the spatiotemporal stability coefficient, and the symmetry coefficient of muscle force. The motor coordination of 13 professional runners was studied. Detailed anthropometric information was recorded about the professional runners. It has been found that professional athletes are characterized by the stability of movement repetition (more than 83%) and the high degree of symmetry of muscle efforts of the left and right legs (more than 81%) regardless of the changes in load during running at a speed of 8–12 km/hr. Scientific and technological means can support the scientific training of athletes. The end of the Winter Olympic Games has shown us the powerful power of a series of intelligent scientific equipment, including electro-magnetic gun, in sports training. We also look forward to the continuous innovation of these advanced technologies, which will contribute to the intelligent development of sports scientific research

Research on Frequency Adaptability of Permanent Magnet Synchronous Generator

In this paper, a study on frequency adaptability of permanent magnet synchronous generator (PMSG) is carried out, the influence mechanism of the frequency changes on PMSG is revealed. It is proposed that setting the converter protection setting value and PLL parameters reasonably can ensure that the grid frequency change has little effect on the PMSG. The simulation of frequency adaptability of PMSG is realized on Matlab/Simulink, and the simulation results verify the correctness of the conclusion

Novel molecular hepatocellular carcinoma subtypes and RiskScore utilizing apoptosis-related genes

Abstract Hepatocellular carcinoma (HCC) is the third leading cause of global cancer-related deaths. Despite immunotherapy offering hope for patients with HCC, only some respond to it. However, it remains unclear how to pre-screen eligible patients. Our study aimed to address this issue. In this study, we identified 13 prognostic genes through univariate Cox regression analysis of 87 apoptosis-related genes. Subsequently, these 13 genes were analyzed using ConsensusClusterPlus, and patients were categorized into three molecular types: C1, C2, and C3. A prognostic model and RiskScore were constructed using Lasso regression analysis of 132 significant genes identified between C1 and C3. We utilized quantitative polymerase chain reaction to confirm the model’s transcript level in Huh7 and THLE2 cell lines. Both molecular subtypes and RiskScores effectively predicted patients benefiting from immunotherapy. Cox regression analysis revealed RiskScore as the most significant prognosis factor, suggesting its clinical application potential and providing a foundation for future experimental research

Small-Signal Stability Analysis for Power System Frequency Regulation with Renewable Energy Participation

With the improvement of the permeability of wind and photovoltaic (PV) energy, it has become one of the key problems to maintain the small-signal stability of the power system. Therefore, this paper analyzes the small-signal stability in a power system integrated with wind and solar energy. First, a mathematical model for small-signal stability analysis of power systems including the wind farm and PV station is established. And the characteristic roots of the New England power system integrated with wind energy and PV energy are obtained to study their small-signal stability. In addition, the validity of the theory is verified by the voltage drop of different nodes, which proves that power system integrated with wind-solar renewable energy participating in the frequency regulation can restore the system to the rated frequency in the shortest time and, at the same time, can enhance the robustness of each unit

Research on Frequency Adaptability of Photovoltaic Power Generation

In this paper, photovoltaic power generation(PV) and the asynchronously grid-connected power grid are taken as the research objects, and the frequency adaptability of PV to power grid is studied. The influence mechanism of grid frequency variation on PV is revealed, and it is proposed that the frequency tolerance range of PV is mainly determined by the setting value of inverter protection and PLL parameters. The whole process simulation of wind turbine adaptability under frequency change is realized on Matlab/Simulink, and the simulation results verify the correctness of the conclusion

Biobjective Optimization-Based Frequency Regulation of Power Grids with High-Participated Renewable Energy and Energy Storage Systems

Large-scale renewable energy sources connected to the grid bring new problems and challenges to the automatic generation control (AGC) of the power system. In order to improve the dynamic response performance of AGC, a biobjective of complementary control (BOCC) with high-participation of energy storage resources (ESRs) is established, with the minimization of total power deviation and the minimization of regulation mileage payment. To address this problem, the strength Pareto evolutionary algorithm is employed to quickly acquire a high-quality Pareto front for BOCC. Based on the entropy weight method (EWM), grey target decision-making theory is designed to choose a compromise dispatch scheme that takes both of the operating economy and power quality into account. At last, an extended two-area load frequency control (LFC) model with seven AGC units is taken to verify the effectiveness and the performance of the proposed method

Nonlinear Observer-Based Robust Passive Control of Doubly-Fed Induction Generators for Power System Stability Enhancement via Energy Reshaping

The large-scale penetration of wind power might lead to degradation of the power system stability due to its inherent feature of randomness. Hence, proper control designs which can effectively handle various uncertainties become very crucial. This paper designs a novel robust passive control (RPC) scheme of a doubly-fed induction generator (DFIG) for power system stability enhancement. The combinatorial effect of generator nonlinearities and parameter uncertainties, unmodelled dynamics, wind speed randomness, is aggregated into a perturbation, which is rapidly estimated by a nonlinear extended state observer (ESO) in real-time. Then, the perturbation estimate is fully compensated by a robust passive controller to realize a globally consistent control performance, in which the energy of the closed-loop system is carefully reshaped through output feedback passification, such that a considerable system damping can be injected to improve the transient responses of DFIG in various operation conditions of power systems. Six case studies are carried out while simulation results verify that RPC can rapidly stabilize the disturbed DFIG system much faster with less overshoot, as well as supress power oscillations more effectively compared to that of linear proportional-integral-derivative (PID) control and nonlinear feedback linearization control (FLC)

Fluid shear stress induces cell migration via RhoA-YAP1-autophagy pathway in liver cancer stem cells

Fluid shear stress (FSS) regulates the metastasis of hepatocellular carcinoma (HCC), but the role of the RhoA-YAP1-autophagy pathway in HCC remains unclear. Due to the core role of liver cancer stem cells (LCSCs) in HCC metastasis and recurrence, we explored the RhoA-YAP1-autophagy pathway in LCSCs under FSS. Our results indicate that LCSCs have stronger proliferation and cell spheroidization abilities. FSS (1 dyn/cm2) upregulated the migration of LCSCs and autophagy protein markers, inducing LC3B aggregation and autophagosome formation in LCSCs. Mechanistically, FSS promoted YAP1 dephosphorylation and transport to the nucleus, which is mediated by RhoA, inducing autophagy. Finally, inhibition of autophagy suppressed cell migration in LCSCs under FSS. In conclusion, FSS promoted the migration of LCSCs via the RhoA-YAP1-autophagy pathway

A simple data assimilation method to improve atmospheric dispersion based on Lagrangian puff model

To model the atmospheric dispersion of radionuclides released from nuclear accident is very important for nuclear emergency. But the uncertainty of model parameters, such as source term and meteorological data, may significantly affect the prediction accuracy. Data assimilation (DA) is usually used to improve the model prediction with the measurements. The paper proposed a parameter bias transformation method combined with Lagrangian puff model to perform DA. The method uses the transformation of coordinates to approximate the effect of parameters bias. The uncertainty of four model parameters is considered in the paper: release rate, wind speed, wind direction and plume height. And particle swarm optimization is used for searching the optimal parameters. Twin experiment and Kincaid experiment are used to evaluate the performance of the proposed method. The results show that the proposed method can effectively increase the reliability of model prediction and estimate the parameters. It has the advantage of clear concept and simple calculation. It will be useful for improving the result of atmospheric dispersion model at the early stage of nuclear emergency