To achieve control objectives for extremely largescale complex networks using standard methods is essentially intractable. In this work, a theory of the approximate control of complex network systems is proposed and developed by the use of graphon theory and the theory of infinite dimensional systems. First, the graphon dynamical system models are formulated in an appropriate infinite dimensional space in order to represent arbitrary-size networks of linear dynamical systems, and to define the convergence of sequences of network systems with limits in the space. The exact controllability and the approximate controllability of graphon dynamical systems are then investigated. Second, the minimum energy state-to-state control problem and the linear quadratic regulator problem for systems on complex networks are considered. The control problem for the graphon limit systems is solved in each case and an approximation is defined which yields control laws for the original control problems. Furthermore, the convergence properties of the approximation schemes are established. A systematic control design methodology is developed within this framework. Finally, numerical examples of networks with randomly sampled weightings are presented to illustrate the effectiveness of the graphon control methodology.

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Following the dramatic socioeconomic transition since the 1980s in China, some people became unemployed and experienced a significant drop in income. The aim of this study was to assess the effects of low income on health-related quality of life (HRQOL) among the population in northeast China. A total of 5100 individuals in northeast China were randomly sampled and investigated using the 36-item Short-Form Health Survey (SF-36) from November 2005 to October 2006. According to the monthly per capita income level, the population was divided into different groups for analysis. Multiple linear regressions showed that low income, older age, disease, and unemployment were the important factors that could lead to worse HRQOL. Covariance analysis showed that there were significant differences in HRQOL scores among the subgroups of the low-income population. When the income level increased, HRQOL scores improved. This study could provide valuable information for planning integrated economic and public health policies to improve the health of people living in poverty.

Glioblastoma (GBM) is the most common malignant primary brain tumor with a poor prognosis. The current standard treatment regimen represented by temozolomide/radiotherapy has an average survival time of 14.6 months, while the 5-year survival rate is still less than 5%. New therapeutics are still highly needed to improve the therapeutic outcome of GBM treatment. The blood-brain barrier (BBB) is the main barrier that prevents therapeutic drugs from reaching the brain. Nanotechnologies that enable drug delivery across the BBB hold great promise for the treatment of GBM. This review summarizes various drug delivery systems used to treat glioma and focuses on their approaches for overcoming the BBB to enhance the accumulation of small molecules, protein and gene drugs, etc. in the brain.

The calibration of the performance of an SFM (scanning force microscope) cantilever has gained more and more interest in the past years, particularly due to increasing applications of SFMs for the determination of the mechanical properties of materials, such as biological structures and organic molecules. In this paper, a MEMS-based nano-force actuator with a force resolution up to nN (10 −9 N) is presented to quantitatively determine the stiffness of an SFM cantilever. The principle, structure design and realization of the nano-force actuator are detailed. Preliminary experiments demonstrate that the long-term self-calibration stability of the actuator is better than 3.7 × 10 −3 N m −1 (1σ ) over 1 h. With careful calibration of the stiffness of the actuator, the MEMS actuator has the capability to determine the stiffness of various types of cantilevers (from 100 N m −1 down to 0.1 N m −1 ) with high accuracy. In addition, thanks to the large displacement and force range (up to 8 μm and 1 mN, respectively) of the actuator, the calibration procedure with our MEMS nano-force actuator features simple and active operation, and therefore applicability for different types of quantitative SFMs.