Non-isospectral extension of the Volterra lattice hierarchy, and Hankel determinants

For the first two equations of the Volterra lattice hierarchy and the first two equations of its non-autonomous (non-isospectral) extension, we present Riccati systems for functions c_j(t), j=0,1,..., such that an expression in terms of Hankel determinants built from them solves these equations on the right half of the lattice. This actually achieves a complete linearization of these equations of the extended Volterra lattice hierarchy.Comment: 31 pages, 3rd version: introduction extended, part of Section 2 moved there, Appendix D added, additional references, to appear in Nonlinearit

Stimulating Ideological Education in Elementary School English Teaching with Interest

The 21st century is an informationalized and diversified society, which makes people realize the importance of English learning. Many countries have taken English teaching as an important part of basic education. Because people attach great importance to English, how to learn English well is particularly important. As a special group, elementary school students have a strong interest in new things, Besides, “interest is the best teacher”, so teachers should seize the characteristics of elementary school students, try every means to improve their interest in English learning, push students to seek knowledge, strengthen ideological education, study problems and get good grades. This paper will make a summary of the existing research from the two aspects of teachers and classrooms and focus on the study of how to improve students’ interest in English learning after class, in order to improve students’ interest in English learning through teachers’ methods and parents’ close cooperation

Learning-Based Resource Allocation in Cloud Data Center Using Advantage Actor-Critic

This is the author accepted manuscript. The final version is available from IEEE via the DOI in this record.Due to the ever-changing system states and various user demands, resource allocation in cloud data center is faced with great challenges in dynamics and complexity. Although there are solutions that focus on this problem, they cannot effectively respond to the dynamic changes of system states and user demands since they depend on the prior knowledge of the system. Therefore, it is still an open challenge to realize automatic and adaptive resource allocation in order to satisfy diverse system requirements in cloud data center. To cope with this challenge, we propose an advantage actor-critic based reinforcement learning (RL) framework for resource allocation in cloud data center. First, the actor parameterizes the policy (allocating resources) and chooses continuous actions (scheduling jobs) based on the scores (evaluating actions) from the critic. Next, the policy is updated by gradient ascent and the variance of policy gradient can be significantly reduced with the advantage function. Simulations using Google cluster-usage traces show the effectiveness of the proposed method in cloud resource allocation. Moreover, the proposed method outperforms classic resource allocation algorithms in terms of job latency and achieves faster convergence speed than the traditional policy gradient method

Choice of sewage sludge thermochemical disposal methods from multi‐ perspective analysis

Thermochemical conversion disposal methods for sewage sludge usually include incineration, gasification and pyrolysis. Incineration technology is relatively mature and the incineration ash can be potentially used for phosphorus (P) recovery. Gasification can be used to recover syngas which is convenient to be used for power & heat generation. While through pyrolysis, syngas of high quality, tar and char can be recovered. To make a proper choice from them, these techniques are compared from perspective of technology maturity, investment, operation cost, environmental impact and acceptability of the public. Technology maturity is evaluated by comparing industrial applications. Investment and operation cost are evaluated based on practical operation experiences; environmental impacts are evaluated based on life cycle assessment; and acceptability of the public is based on a questionnaire survey. Based on a scenario with capacity of 100 t/d in eastern China, investment are comparable for the three technologies within the range of 250,000-400,000 RMB yuan/(ton.d) with gasification close to the higher side; the operation cost varies in the range of 140 - 400 RMB yuan/ton with incineration the highest; pyrolysis corresponds to the lowest environmental impacts and the highest acceptability of the public, however the pyrolysis technology is not fully developed, especially the durable pyrolysis reactor and the application of pyrolysis char