Composition operators on weighted Bergman spaces induced by doubling weights

Given a doubling weightωon the unit disk D,let A_(ω)^(p) be the space of all the holomorphic functions f,where∥f∥A_(ω)^(p):=(∫_(D)|f(z)|_(p)ω(z)dA(z))^(1/p)<∞.We completely characterize the topological connectedness of the set of composition operators on A_(ω)^(p).As an application,we construct an interesting example which reveals that two composition operators on A_(α)^(p) in the same path component may fail to have a compact difference and give a negative answer to the Shapiro-Sundberg question in the(standard)weighted Bergman space.In addition,we completely describe the central compactness of any finite linear combinations of composition operators on A_(ω)^(p) in three terms:a Julia-Carathéodory-type function-theoretic characterization,a power-type characterization,and a Carleson-type measure-theoretic characterization.

Intelligent Voltage Control Method in Active Distribution Networks Based on Averaged Weighted Double Deep Q-network Algorithm

High penetration of distributed renewable energy sources and electric vehicles(EVs)makes future active distribution network(ADN)highly variable.These characteristics put great challenges to traditional voltage control methods.Voltage control based on the deep Q-network(DQN)algorithm offers a potential solution to this problem because it possesses humanlevel control performance.However,the traditional DQN methods may produce overestimation of action reward values,resulting in degradation of obtained solutions.In this paper,an intelligent voltage control method based on averaged weighted double deep Q-network(AWDDQN)algorithm is proposed to overcome the shortcomings of overestimation of action reward values in DQN algorithm and underestimation of action reward values in double deep Q-network(DDQN)algorithm.Using the proposed method,the voltage control objective is incorporated into the designed action reward values and normalized to form a Markov decision process(MDP)model which is solved by the AWDDQN algorithm.The designed AWDDQN-based intelligent voltage control agent is trained offline and used as online intelligent dynamic voltage regulator for the ADN.The proposed voltage control method is validated using the IEEE 33-bus and 123-bus systems containing renewable energy sources and EVs,and compared with the DQN and DDQN algorithms based methods,and traditional mixed-integer nonlinear program based methods.The simulation results show that the proposed method has better convergence and less voltage volatility than the other ones.

Adjusted Log-rank Test with Double Inverse Weighting under Dependent Censoring

It is a common issue to compare treatment-specific survival and the weighted log-rank test is the most popular method for group comparison. However, in observational studies, treatments and censoring times are usually not independent, which invalidates the weighted log-rank tests. In this paper, we propose adjusted weighted log-rank tests in the presence of non-random treatment assignment and dependent censoring. A double-inverse weighted technique is developed to adjust the weighted log-rank tests. Specifically, inverse probabilities of treatment and censoring weighting are involved to balance the baseline treatment assignment and to overcome dependent censoring, respectively. We derive the asymptotic distribution of the proposed adjusted tests under the null hypothesis, and propose a method to obtain the critical values. Simulation studies show that the adjusted log-rank tests have correct sizes whereas the traditional weighted log-rank tests may fail in the presence of non-random treatment assignment and dependent censoring. An application to oropharyngeal carcinoma data from the Radiation Therapy Oncology Group is provided for illustration.

On the Multi-Dimensional Duality Principle of Sawyer Type

A multi-dimensional version of the duality principle of Sawyer type [1] is obtained whenever the corresponding weight satisfies some doubling property.