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This paper presents a novel and robust two-stage pursuit strategy for the incomplete-information impulsive space pursuit-evasion missions considering the J2 perturbation. The strategy firstly models the impulsive pursuit-evasion game problem into a far-distance rendezvous stage and a close-distance game stage according to the perception range of the evader. For the far-distance rendezvous stage, it is transformed into a rendezvous trajectory optimization problem and a new objective function is proposed to obtain the pursuit trajectory with the optimal terminal pursuit capability. For the close-distance game stage, a closed-loop pursuit approach is proposed using one of the reinforcement learning algorithms, i.e., the deep deterministic policy gradient algorithm, to solve and update the pursuit trajectory for the incomplete-information impulsive pursuit-evasion missions. The feasibility of this novel strategy and its robustness to different initial states of the pursuer and evader and to the evasion strategies are demonstrated for the sun-synchronous orbit pursuit-evasion game scenarios. The results of the Monte Carlo tests show that the successful pursuit ratio of the proposed method is over 91% for all the given scenarios.

This paper proposes an economic analysis method for distributed energy storage applications in distribution networks, and constructs a visual simulation platform. Firstly, the influence of photovoltaic / wind power fluctuation on load peak-valley difference in distribution network under different permeability is analyzed, and the technical and economic characteristics of different distributed energy storage systems are compared. Secondly, in order to facilitate the analysis and display the analysis results, a visual simulation platform is constructed based on MATLAB / Simulink software. The simulation results of IEEE 33-Bus System show that the method described in this paper can effectively analyze the economy of load peak-shaving and valley-filling by different energy storage configuration methods, and the visualization platform can clearly and intuitively show the results of the above analysis method.

Accompanied by energy structure transformation and the depletion of fossil fuels, large-scale distributed power sources and electric vehicles are accessed to distribution network that result in the load peak-valley gap increasing. Energy storage system (ESS) possessed the characteristics such as quick response, precise control and energy bidirectional flow. Therefore, the configuration of ESS in grid is a feasible measure to reduce the difference between peak load and valley load. This paper presents a superior control strategy that uses distributed energy storage to smooth load fluctuation of grid. Set the time scale to one day, constraints such as energy storage capacity, power and state of charge are taken into account, and the calculation examples are designed based on the proposed the evaluation index compares the effect of optimized control strategy given in this paper with the current common constant power strategy, which proves the feasibility and effect of the presented method.

The intelligent network distribution is helpful to improve the reliability and operation economy of the power grid by converting most of the manual controlling into the active intervention of distribution automation. It covers the functions such as real-time monitoring, autonomous line fault diagnosis, and load transfer by coordinative controlling the master station and each terminal of the line. In this manuscript, taken the Ruyi New District of Huhhot as an example, the intellectualized controlling for the distribution network of the main station and related terminals has been researched. Based on the power quality requirements of the district, the specific indicators of the improved distribution automation in the main station and each terminal have been determined. The novel technology and application of the intelligent distribution network are prospected. The proposed ideas to intellectualize the distribution network will be beneficial to develop an application in the west district of Inner Mongolia regions.

With the increasingly penetration of renewable energy sources in the active distribution network, the distribution network operator would face greater challenges in in terms of planning and operation. Such being the case, in order to meet the uncertain development of distributed generations and load demands, a bi-level robust planning method of active distribution network with uncertain renewable energy sources is studied in this paper. To start with, the uncertain scenario sets for distributed generations and loads are established. Then, a robust planning model for active distribution network is developed, aiming at minimizing the curtailment cost of wind power, solar power and load. Moreover, the investment level and operation level of the planning model are effectively associated, and the original model is converted into a mixed integer second-order cone programming problem for rapid solving by means of second-order cone relaxation. Finally, the effectiveness of proposed method is verified by the simulation results in the modified IEEE 33-bus system.

Background Although the transient nature of transgene expression using first-generation adenovirus (Ad) vectors is well known, the exact mechanisms responsible for this phenomenon are uncertain. Methods Rats were given intramuscular (i.m.) injections of a first-generation Ad containing the human fibroblast growth factor 4 (hFGF-4) gene driven by the cytomegalovirus (CMV) promoter and enhancer (CMV-PE). The copy number of hFGF-4 mRNA and viral DNA was measured in the same muscles by quantitative RT-PCR and quantitative PCR at times between 1 h and 84 days after virus injection. Quantitative Southern blot analysis for the intact hFGF-4 transcription unit DNA was also performed, and the methylation status of the CMV-PE DNA in the muscle was determined using bisulfite sequencing. Results The copy number of hFGF-4 mRNA peaked at 6 h then decreased 56-fold by 24 h, and a further 240-fold between days 3 and 28. Although the viral DNA copy number also decreased 23-fold between 6 h and 28 days, the ratio of copies of hFGF-4 mRNA per copy of viral DNA decreased 385-fold over this period. Methylation of the CMV-PE DNA in the muscle at both CpG and non-CpG sites was observed 24 h after virus administration and had increased at day 7. Conclusions Decreased transcription associated with extensive methylation of the CMV-PE was the major mechanism responsible for the decrease in transgene mRNA levels. Strategies for preventing transcriptional silencing will be valuable for improving the duration of transgene expression from adenoviral vectors. Copyright © 2004 John Wiley & Sons, Ltd.

Paid search advertising has been a major form of online advertising in recent years. In this form of advertising, an advertiser submits a list of keywords to major search engines. When one of the keywords matches the query keyword that a search engines user submits, the ad of this advertiser will have a chance to be shown on the search result page. If the user is interested and clicks on the ad, the advertiser will be billed of each clickthrough with a predetermined cost-per-click fee by the search engine, regardless whether the user purchases anything after entering the advertiser’s website. The advertiser will try to make a profit by hoping a higher probability that a clickthrough can end with a sale. So in an ad campaign the fundamental question the advertiser wants to ask is: what are the good keywords that can attract more clickthrough traffic form search engines, and more importantly have higher sale conversion rate given these clickthrough traffic? Several marketing literatures have addressed this issue but their keywords selection and evaluation methods need human interactions. Our goal is to try to develop a statistical learning method that can automate the keyword evaluation processes. This paper is our pilot study and we want to know whether such statistical learning method can really the same or even better job than human As a comparison, we compared our result with another study with the same data but using mainly manual evaluation process. The result shows our method has better prediction accuracy.