Effect of laser focus in two-color synthesized waveform on generation of soft x-ray high harmonics

Synthesis of multi-color laser pulses has been developed as a promising way to improve low conversion efficiency of high-order harmonic generation(HHG). Here we systematically study the effect of laser focus in a two-color waveform on generation of macroscopic HHG in soft x-rays. We find that the dependence of HHG yields on laser focus at low or high gas pressure is sensitive to the characteristics of single-atom harmonic response, in which “short”-or “long”-trajectory emissions can be selectively controlled by changing the waveform of two-color synthesized laser pulse. We uncover the phase-matching mechanism of HHG in the gas medium by examining the propagation of the two-color waveform and the evolution of time-frequency emissions of high-harmonic field. We further reveal that the nonlinear effects, such as geometric phase, atomic dispersion, and plasma defocusing, are responsible for modification of two-color waveform upon propagation. This work can be used to find better macroscopic conditions for generating soft x-ray HHG by employing two-color optimized waveforms.

Prediction of maximum upward displacement of shield tunnel linings during construction using particle swarm optimization-random forest algorithm

During construction,the shield linings of tunnels often face the problem of local or overall upward movement after leaving the shield tail in soft soil areas or during some large diameter shield projects.Differential floating will increase the initial stress on the segments and bolts which is harmful to the service performance of the tunnel.In this study we used a random forest(RF)algorithm combined particle swarm optimization(PSO)and 5-fold cross-validation(5-fold CV)to predict the maximum upward displacement of tunnel linings induced by shield tunnel excavation.The mechanism and factors causing upward movement of the tunnel lining are comprehensively summarized.Twelve input variables were selected according to results from analysis of influencing factors.The prediction performance of two models,PSO-RF and RF(default)were compared.The Gini value was obtained to represent the relative importance of the influencing factors to the upward displacement of linings.The PSO-RF model successfully predicted the maximum upward displacement of the tunnel linings with a low error(mean absolute error(MAE)=4.04 mm,root mean square error(RMSE)=5.67 mm)and high correlation(R^(2)=0.915).The thrust and depth of the tunnel were the most important factors in the prediction model influencing the upward displacement of the tunnel linings.

Improved Proximal Policy Optimization Algorithm for Sequential Security-constrained Optimal Power Flow Based on Expert Knowledge and Safety Layer

In recent years,reinforcement learning(RL)has emerged as a solution for model-free dynamic programming problem that cannot be effectively solved by traditional optimization methods.It has gradually been applied in the fields such as economic dispatch of power systems due to its strong selflearning and self-optimizing capabilities.However,existing economic scheduling methods based on RL ignore security risks that the agent may bring during exploration,which poses a risk of issuing instructions that threaten the safe operation of power system.Therefore,we propose an improved proximal policy optimization algorithm for sequential security-constrained optimal power flow(SCOPF)based on expert knowledge and safety layer to determine active power dispatch strategy,voltage optimization scheme of the units,and charging/discharging dispatch of energy storage systems.The expert experience is introduced to improve the ability to enforce constraints such as power balance in training process while guiding agent to effectively improve the utilization rate of renewable energy.Additionally,to avoid line overload,we add a safety layer at the end of the policy network by introducing transmission constraints to avoid dangerous actions and tackle sequential SCOPF problem.Simulation results on an improved IEEE 118-bus system verify the effectiveness of the proposed algorithm.

Dynamic State Estimation for Integrated Electricity-gas Systems Based on Kalman Filter

In recent years, integrated electricity-gas systems(IEGSs) have attracted widespread attention. The unifiedscheduling and control of the IEGS depends on high-precisionoperating data. To this end, it is necessary to establish anappropriate state estimation (SE) model for IEGS to filter theraw measured data. Considering that power systems and naturalgas systems have different time scales and sampling periods, thispaper proposes a dynamic state estimation (DSE) method basedon a Kalman filter that can consider the dynamic characteristicsof natural gas pipelines. First, the standardized state transitionequations for the gas system are developed by applying the finitedifference method to the partial differential equations (PDEs) ofthe gas system;then the DSE model for IEGS is formulatedbased on a Kalman filter;also, the measurements from theelectricity system and the gas system with different samplingperiods are fused to ensure the observability of DSE by using theinterpolation method. The IEEE 39-bus electricity system and the18-nodes Belgium gas system are integrated as the test systems.Simulation results verify the proposed method’s accuracy andcalculation efficiency.

Data-driven Robust State Estimation Through Off-line Learning and On-line Matching

To overcome the shortcomings of model-driven state estimation methods, this paper proposes a data-driven robust state estimation (DDSE) method through off-line learning and on-line matching. At the off-line learning stage, a linear regression equation is presented by clustering historical data from supervisory control and data acquisition (SCADA), which provides a guarantee for solving the over-learning problem of the existing DDSE methods;then a novel robust state estimation method that can be transformed into quadratic programming (QP) models is proposed to obtain the mapping relationship between the measurements and the state variables (MRBMS). The proposed QP models can well solve the problem of collinearity in historical data. Furthermore, the off-line learning stage is greatly accelerated from three aspects including reducing historical categories, constructing tree retrieval structure for known topologies, and using sensitivity analysis when solving QP models. At the on-line matching stage, by quickly matching the current snapshot with the historical ones, the corresponding MRBMS can be obtained, and then the estimation values of the state variables can be obtained. Simulations demonstrate that the proposed DDSE method has obvious advantages in terms of suppressing over-learning problems, dealing with collinearity problems, robustness, and computation efficiency.

Fast Decoupled Multi-energy Flow Calculation for Integrated Energy System

In recent years,as a promising option to improve the overall efficiency of energy utilization and absorptive capacity of renewable energies,the integrated energy system(IES)has raised great interest in academies and industries.Multi-energy flow(MF)calculation,which differs from the traditional power flow calculation,plays a basic role in analyzing IES.MF calculation based on Newton-Raphson method has been proposed in literature,but its calculation efficiency is not high.In this paper,a fast decoupled MF(FDMF)calculation method for IES is proposed.Its main idea is to replace the original Jacobian matrix of MF calculation based on Newton-Raphson method with a diagonal and constant Jacobian matrix by the transformation.The simulations demonstrate that the proposed FDMF method can increase the calculation efficiency by at least 4 times with high calculation accuracy.

Data-driven Power Flow Method Based on Exact Linear Regression Equations

Power flow(PF)is one of the most important calculations in power systems.The widely-used PF methods are the Newton-Raphson PF(NRPF)method and the fast-decoupled PF(FDPF)method.In smart grids,power generations and loads become intermittent and much more uncertain,and the topology also changes more frequently,which may result in significant state shifts and further make NRPF or FDPF difficult to converge.To address this problem,we propose a data-driven PF(DDPF)method based on historical/simulated data that includes an offline learning stage and an online computing stage.In the offline learning stage,a learning model is constructed based on the proposed exact linear regression equations,and then the proposed learning model is solved by the ridge regression(RR)method to suppress the effect of data collinearity.In online computing stage,the nonlinear iterative calculation is not needed.Simulation results demonstrate that the proposed DDPF method has no convergence problem and has much higher calculation efficiency than NRPF or FDPF while ensuring similar calculation accuracy.

A mixed-integer linear programming approach for robust state estimation

In this paper,a mixed integer linear programming(MILP)formulation for robust state estimation(RSE)is proposed.By using the exactly linearized measurement equations instead of the original nonlinear ones,the existingmixed integer nonlinear programming formulation for RSE is converted to a MILP problem.The proposed approach not only guarantees to find the global optimum,but also does not have convergence problems.Simulation results on a rudimentary 3-bus system and several IEEE standard test systems fully illustrate that the proposed methodology is effective with high efficiency.

Expression of human FUS protein in Drosophila leads to progressive neurodegeneration

Mutations in the Fused in sarcoma/Translated in liposarcoma gene(FUS/TLS,FUS)have been identified among patients with amyotrophic lateral sclerosis(ALS).FUS protein aggregation is a major pathological hallmark of FUS proteinopathy,a group of neurodegenerative diseases characterized by FUS-immunoreactive inclusion bodies.We prepared transgenic Drosophila expressing either the wild type(Wt)or ALS-mutant human FUS protein(hFUS)using the UAS-Gal4 system.When expressing Wt,R524S or P525L mutant FUS in photoreceptors,mushroom bodies(MBs)or motor neurons(MNs),transgenic flies show age-dependent progressive neural damages,including axonal loss in MB neurons,morphological changes and functional impairment in MNs.The transgenic flies expressing the hFUS gene recapitulate key features of FUS proteinopathy,representing the first stable animal model for this group of devastating diseases.

Optimal layout model of feeder automation equipment oriented to the type of fault detection and local action

In feeder automation transformation there are difficulties in equipment and location selection.To help with this,an optimal layout model of feeder automation equipment oriented to the type of fault detection and local action is pro-posed.It analyzes the coordination relationship of the three most common types of automation equipment,i.e.,fault indicator,over-current trip switch and non-voltage trip switch in the fault handling process,and the explicit expres-sions of power outage time caused by a fault on different layouts of the above three types of equipment are given.Given constraints of power supply reliability and the goal of minimizing the sum of equipment-related capital invest-ment and power interruption cost,a mixed-integer quadratic programming model for optimal layout is established,in which the functional failure probability of equipment is linearized using the 3δprinciple in statistics.Finally,the basic characteristics of the proposed model are illustrated by different scenarios on the IEEE RBTS-BUS6 system.It can not only take into account fault location and fault isolation to enhance user power consumption perception,but also can guide precise investment to improve the operational quality and efficiency of a power company.

Seismic effects on reinforcement load and lateral deformation of geosynthetic-reinforced soil walls

Current design methods for the internal stability of geosynthetic-reinforced soil(GRS)walls postulate seismic forces as inertial forces,leading to pseudo-static analyses based on active earth pressure theory,which yields unconservative reinforcement loads required for seismic stability.Most seismic analyses are limited to the determination of maximum reinforcement strength.This study aimed to calculate the distribution of the reinforcement load and connection strength required for each layer of the seismic GRS wall.Using the top-down procedure involves all of the possible failure surfaces for the seismic analyses of the GRS wall and then obtains the reinforcement load distribution for the limit state.The distributions are used to determine the required connection strength and to approximately assess the facing lateral deformation.For sufficient pullout resistance to be provided by each reinforcement,the maximum required tensile resistance is identical to the results based on the Mononobe-Okabe method.However,short reinforcement results in greater tensile resistances in the mid and lower layers as evinced by compound failure frequently occurring in GRS walls during an earthquake.Parametric studies involving backfill friction angle,reinforcement length,vertical seismic acceleration,and secondary reinforcement are conducted to investigate seismic impacts on the stability and lateral deformation of GRS walls.