Fertility transition in China: causes and trends

"Decentralised policy formation and implementation is one of the salient features of China's family planning programme that is the most important driving force of China's fertility transition. This paper reviews the differentials in local policy and programmes, and examines their effects on patterns of fertility transition across China. Major consequences of the differential fertility transition, such as varied processes of population ageing and labour force supply etc. at provincial level, are then discussed. The author argues that the success of government population policy to a large extent is determined by socio-economic and cultural factors. On the other hand, regional demographic patterns have profound impact on China's regional and rural-urban differences in terms of socio-economic development. Large scale migration is one of such examples." (author's abstract

Leveraging Smartphone Sensor Data for Human Activity Recognition

Using smartphones for human activity recognition (HAR) has a wide range of applications including healthcare, daily fitness recording, and anomalous situations alerting. This study focuses on human activity recognition based on smartphone embedded sensors. The proposed human activity recognition system recognizes activities including walking, running, sitting, going upstairs, and going downstairs. Embedded sensors (a tri-axial accelerometer and a gyroscope sensor) are employed for motion data collection. Both time-domain and frequency-domain features are extracted and analyzed. Our experiment results show that time-domain features are good enough to recognize basic human activities. The system is implemented in an Android smartphone platform. While the focus has been on human activity recognition systems based on a supervised learning approach, an incremental clustering algorithm is investigated. The proposed unsupervised (clustering) activity detection scheme works in an incremental manner, which contains two stages. In the first stage, streamed sensor data will be processed. A single-pass clustering algorithm is used to generate pre-clustered results for the next stage. In the second stage, pre-clustered results will be refined to form the final clusters, which means the clusters are built incrementally by adding one cluster at a time. Experiments on smartphone sensor data of five basic human activities show that the proposed scheme can get comparable results with traditional clustering algorithms but working in a streaming and incremental manner. In order to develop more accurate activity recognition systems independent of smartphone models, effects of sensor differences across various smartphone models are investigated. We present the impairments of different smartphone embedded sensor models on HAR applications. Outlier removal, interpolation, and filtering in pre-processing stage are proposed as mitigating techniques. Based on datasets collected from four distinct smartphones, the proposed mitigating techniques show positive effects on 10-fold cross validation, device-to-device validation, and leave-one-out validation. Improved performance for smartphone based human activity recognition is observed. With the efforts of developing human activity recognition systems based on supervised learning approach, investigating a clustering based incremental activity recognition system with its potential applications, and applying techniques for alleviating sensor difference effects, a robust human activity recognition system can be trained in either supervised or unsupervised way and can be adapted to multiple devices with being less dependent on different sensor specifications

Structure controllability of complex network based on preferential matching

Minimum driver node sets (MDSs) play an important role in studying the structural controllability of complex networks. Recent research has shown that MDSs tend to avoid high-degree nodes. However, this observation is based on the analysis of a small number of MDSs, because enumerating all of the MDSs of a network is a #P problem. Therefore, past research has not been sufficient to arrive at a convincing conclusion. In this paper, first, we propose a preferential matching algorithm to find MDSs that have a specific degree property. Then, we show that the MDSs obtained by preferential matching can be composed of high- and medium-degree nodes. Moreover, the experimental results also show that the average degree of the MDSs of some networks tends to be greater than that of the overall network, even when the MDSs are obtained using previous research method. Further analysis shows that whether the driver nodes tend to be high-degree nodes or not is closely related to the edge direction of the network

Mild Synthesis of Perylene Tetracarboxylic Monoanhydrides with Potential Applications in Organic Optoelectronics

Perylene tetracarboxylic derivatives are considered good n-type semi-conductors. In past decades, there has been extensive study on their synthesis and electronic properties. Because of the high electron affinity, the ability to form π-π stacks, they have been widely utilized in organic photovoltaic solar cells, field-effect transistors and light-emitting diodes. Many of those applications prefer unsymmetrically substituted perylene tetracarboxylic derivatives. Perylene monoanhydrides are the most versatile intermediates for the preparation of unsymmetrically substituted perylene tetracarboxylic derivatives. In this thesis, I focused on introducing new synthesis methods for perylene monoanhydrides with labile functional group and their potential applications in organic optoelectronics. In Chapter 1, it is the general background of perylene tetracarboxylic derivatives including (a) redox properties, optical properties and liquid crystalline phase, (b) synthesis routes, (c) perylene diimides oligomers and their properties. In Chapter 2, perylene monoimide monoanhydride dimer was prepared and applied as an intermediate to synthesize perylene oligomers from monomer to pentamer, breaking the previous record of trimer. And their absorption coefficients and reduction potentials were studied. In Chapter 3, a tandem palladium-catalyzed deallylation/cyclization reaction was designed to occur under a mild condition: weakly acidic (acetic acid) and room temperature, which enables the synthesis of perylene monoanhydrides with many labile R groups for the first time. In Chapter 4, an unsymmetrically substituted perylene monoanhydride diester was prepared through a “one-pot three-step” synthetic strategy and served as the key starting material for a functional group-tolerant, transition metal-free synthesis of perylene monoanhydrides. Perylene monoanhydrides with a wide range of viable functional groups were prepared which could be further converted to other unsymmetrically substituted perylene derivatives. In Chapter 5, by tuning the length of the alkyl swallow tail on perylene tetracarboxylic derivatives, a bundled-stack discotic columnar liquid crystalline phase was observed, which is the first perylene compounds that display discotic columnar liquid crystalline phase within one or two alkyl chains. And highly robust charge transport performance is expected. Novel synthesis methods for preparing perylene tetracarboxylic derivatives are important as providing new perylene-based materials. The characterization is critical as well, which including infrared spectroscopy, ultra-violet, nuclear magnetic resonance, differential scanning calorimetry, gel permeation chromatography and wide-angle X-ray diffraction. In general, the achievements in my research contributed a significant advance in the field of developing perylene tetracarboxylic derivatives as semiconducting materials

Experimental Study on the Influence of “Active Campus” Plan on Physical Fitness and Sports Interest

The exploration of “Activity Campus” action plan for elementary and middle schools in China has been gradually prevailed, but the impact of this plan on students has not been clarified. Based on this, this study is to explores the impact of the Activity Campus action plan on students\u27 physical fitness and sport learning interest through research in “Activity Campus” plan, and provides theoretical support for implementation of the “Activity Campus” plan. 164 fourth-grade students from Shandong Province participate in this experiment, 80 in the experimental group and 84 in the control group. The Primary School Students’ Sports Learning Interest Scale was used to investigate the changes in students\u27 mental health and the Physical Health Test Standard was used to measure students\u27 physical and health levels. The scale and measurement tools have high reliability. At the same time, using independent sample T test analyze the data before and after experiment by spss21.0. For physical fitness level, no significant difference between experimental group and control group in the BMI, 50-meter run, one-minute skipping rope, one-minute sit-ups, and total physical fitness scores before the experiment. After the experiment, the control group had significant differences in vital capacity, 50-meter running, one-minute sit-ups, and total physical fitness for sports learning interest, also no significant difference between experimental group and control group before experiment among the degree of sports participation, positive interest in sports learning, negative interest in sports learning, degree of autonomous learning, and total score in sports learning interest. After experiment, there were significant differences between experimental group and control group in all of above. The research results show that “Activity Campus” plan can effectively improve students\u27 physical fitness and health in terms of speed, strength, endurance, and flexibility, and can effectively improve students\u27 interest in participating in sports and their ability to learn independently. This shows that implementation of the “Activity Campus” action plan in China has certain practical significance

Altering nodes types in controlling complex networks

Controlling a complex network towards a desired state is of great importance in many applications. A network can be controlled by inputting suitable external signals into some selected nodes, which are called driver nodes. Previous works found there exist two control modes in dense networks: distributed and centralized modes. For networks with the distributed mode, most of the nodes can be act as driver nodes; and those with the centralized mode, most of the nodes never be the driver nodes. Here we present an efficient algorithm to change the control type of nodes, from input nodes to redundant nodes, which is done by reversing edges of the network. We conclude four possible cases when reversing an edge and show the control mode can be changed by reversing very few in-edges of driver nodes. We evaluate the performance of our algorithm on both synthetic and real networks. The experimental results show that the control mode of a network can be easily changed by reversing a few elaborately selected edges, and the number of possible driver nodes is dramatically decreased. Our methods provide the ability to design the desired control modes of the network for different control scenarios, which may be used in many application regions