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Wind power in China is developing rapidly. However, wind power curtailment has become increasingly severe, reaching 15% in 2015. The root cause of wind power curtailment in China is that its power system has insufficient flexibility. We analyze how to achieve a reasonable allocation of ancillary service costs for wind power plants, using a cooperative game approach. The analysis shows that, while the current allocation approach in the Beijing, Tianjin, and Tangshan power grid is based only on wind feed-in tariffs, an allocation method based on both the feed-in tariff and the capacity credit of wind would be more reasonable.

This paper addresses project portfolio selection with divisibility, where cash flow and opportunity cost are simultaneously considered for the first time. If a project is selected, fixed assets req...

Coordinating the relationship between the national pipeline network company and the provincial natural gas company is the most crucial contradiction in China's natural gas market reform. This paper innovatively incorporates the comprehensive evaluation method, modified spatial gravity model, and social network analysis method to investigate the regional natural gas market to solve this dilemma. The result of the case study is as follows. (1) The development of the natural gas market in China's provinces is uneven, among which resourceful provinces and eastern provinces are relatively strong. (2) The densely connected areas of the natural gas market are mainly concentrated in the Beijing-Tianjin-Hebei region, the Yangtze-River-Delta region, and the Sichuan-Chongqing region. (3) Our work can clarify the node attributes and different efficacy of provinces. The core provinces all belong to the net spillover block, and they are located at key positions and roles in the obtained spatial correlation network. (4) Market participants, commodity diversification and openness are the prominent factors influencing the proposed spatial correlation network. This paper's major achievement is the reasonable division of five regional natural gas markets. The conclusion can help policymakers clarify the overall natural gas market spatial pattern and provinces' market potential and functions.

Ovarian cancer is one of the leading lethal gynecological cancers, causing serious harm to the health of female populations. Growing studies emphasize that lncRNAs serve as significant regulators in the tumorigenesis and evolution of numerous malignancies, including ovarian cancer. Recently, the oncogenic activity of lncRNA ARAP1-AS1 has been justified in a variety of cancers. However, the potential function of ARAP1-AS1 in ovarian cancer development is still unclear. Herein, we firstly revealed the expression profile of ARAP1-AS1 in ovarian cancer. Compared to normal samples and cells, upregulation of ARAP1-AS1 was observed in tissues and cells of ovarian cancer. Therewith, it was disclosed that knockdown of ARAP1-AS1 alleviated the carcinogenicity of ovarian cancer cells. Besides, our findings delineated that ARAP1-AS1 silence inhibited the expression of oncogene PLAGL2. Considering that ARAP1-AS1 was principally expressed in the the cytoplasm of ovarian cancer cells, we speculated that ARAP1-AS1 facilitated ovarian cancer progression via functioning as a ceRNA. Further investigations indicated that ARAP1-AS1 promoted PLAGL2 expression by competitively binding with miR-4735-3p. Of note, ARAP1-AS1 contributed to the malignant phenotypes of ovarian cancer cells through modulation of miR-4735-3p/PLAGL2 axis, revealing ARAP1-AS1 as a promising therapeutic target for ovarian cancer patients.

Objective: Elevated expression of Yes-associated protein (YAP1) involves in the pathogenesis of cervical cancer. Bioinformatics analysis showed a targeting relationship between miR-205 and the 3′-U...

Particle swarm optimization (PSO) is an evolutionary algorithm that is well known for its simplicity and effectiveness. It usually has strong global search capability but has the drawback of being easily trapped by local optima. A scaling mutation strategy and an elitist learning strategy are presented in this paper. Based on these strategies, an improved PSO variant (LSERPSO) is developed through a local search and ring topology strategy. The new scaling mutation strategy involved an exploration and exploitation balance focusing on mutation operation. A collection of elite individuals is maintained such that an array of current particles can learn from them. A ring topology-based neighborhood structure is adopted to maintain the population diversity and to reduce the possibility of particles being trapped in local optima. Finally, a quasi-Newton-based local search is incorporated to enhance the fine-grained capability. The effects of these proposed strategies and their cooperation are verified step by step. The performance of LSERPSO is comprehensively studied using IEEE CEC2015 benchmark functions.

As a significant pollutant emitter, China has used natural gas as a bridge to transition from fossil energy to renewable energy. However, as alternative energy sources for natural gas, oil and coal have hindered natural gas development. This paper proposed a novel method for examining the impact of alternation energy competition on natural gas consumption (NGC) based on wavelet spectrum transform and the Structural Vector autoregression model. Multi-time domain Granger causality test, cross-wavelet transform, and coherent wavelet transform are introduced to analyze the complicated and controversial relationship between oil prices, coal prices, natural gas prices, and NGC. First, the comprehensive transaction price index, namely LNG price, thermal coal price, coking coal price, gasoline price, diesel price, and fuel oil price, were used as the influence variables. Second, the raw data is processed by wavelet decomposition, and non-stationary series is decomposed into several detailed sequences and approximate sequences with different frequencies to extract data features. The results reveal that; (1) the high-scale correlation fluctuation exists between LNG price, thermal coal price, diesel price, coking coal price, and NGC in the different time domain, (2) the analysis results of diesel prices, thermal coal prices, and fuel oil prices indicate that the influences of energy competition on NGC are beyond doubt, (3) in short-term, coal prices have a more significant impact on natural gas prices than oil prices in China (4) the NGC is more susceptible to fluctuations of thermal coal price and diesel price. Overall, our findings provide noteworthy suggestions for the policymakers in making natural gas planning.

Spectral and spatial regularized semi-supervised learning is investigated for classification of hyperspectral images. In spectral regularizer, sum of minimum distance (SMD) is introduced to determine graph adjacency relationships and local manifold learning (LML) is employed for edge weighting. The resulted SMD_LML regularizer is able to constrain the prediction vectors to preserve the local geometry of each neighborhood. In spatial regularizer, spatial neighbors with similar spectra are enforced to have similar predictions. The two regularizers capture different relations of data points and play complementary roles. By combining SMD_LML regularizer and spatial regularizer in graph based semi-supervised learning, the local properties of both spectral neighborhood and spatial neighborhood can be preserved in the prediction domain. Experiments with AVIRIS and ROSIS hyperspectral images demonstrated that SMD can produce more accurate adjacency relations than the other two popular distance measurements. The classification with SMD_LML and spatial regularizer achieved significant improvements than several spectral and spatial based methods.

Battery swapping station (BSS) is a new mode of supplying power for electric vehicle (EV). As the pre-stage of BSS construction, location decision problem plays an important role in its whole life cycle, which directly affects the economic profits. However, most of the existing researches are about its operation and management problem. Therefore, this paper proposes a site selection framework for the BSS based on MCDM (Multi-criteria decision making) method. Firstly, three new criteria are considered and the criteria system is established on the basis of existing research. Then, considering the information inadequacies in the decision-making process, triangular fuzzy numbers are adopted to deal with uncertainties. Afterwards, in view of the strong dependence among criteria, the fuzzy DEMATEL (Decision Making Trail and Evaluation Laboratory) method is applied to determine the weights of criteria. And then, the FOWA (Fuzzy ordered Weighted Averaging) operator is adopted to aggregate the evaluation values on alternatives of experts and the fuzzy MULTIMOORA (Multi-Objective Optimization by Ratio Analysis plus the Full Multiplicative Form) approach is used to rank the alternatives. Finally, a case in Beijing, China with sensitivity analysis and comparison results also illustrate the effectiveness and robustness of the proposed approach. This paper provides a critical tool for investors to select the most appropriate alternative.

The ever-increasing electricity price and energy consumption in high-speed railway industry push railway companies to seek a promising way to realize their sustainable developments. Making full use of the solar resource along with high-speed railways can be a potential solution to cut the electricity bill, bring more profit to railway companies and realize the decarbonization of high-speed railway industry. This paper studies the optimal planning of distributed photovoltaic generation (DPVG) and energy storage system (ESS) for the traction power supply system (TPSS) of high-speed railway. A quantitative method is proposed to study the time and space characteristics of photovoltaic generation and electricity demand of high-speed trains. An integrated cost-benefit analysis framework is developed to evaluate the effect of DPVG and ESS on the economy of TPSS. To derive the optimal planning scheme and energy management strategy of DPVG and ESS, a mathematical programming model with the objective of minimizing the total cost is proposed to seek the most economical solution. A hybrid global optimal solution approach is developed to solve the model. A real-world case of Beijing-Baoding high-speed railway in China is used to illustrate the capability and characteristics of the proposed model. The computational results show that DPVG is able to supply 32 .5 % electricity demand of high-speed trains. The integration of DPVG and ESS can help railway company save 4.2 million CNY each year in Beijing-Baoding high-speed railway. This paper demonstrates the potential and applicability of DPVG and ESS in high-speed railway industry.

Multi-person Visual focus of attention (M-VFOA) and spontaneous smile (SS) recognition are important for persons’ behavior understanding and analysis in class. Recently, promising results have been reported using special hardware in constrained environment. However, M-VFOA and SS remain challenging problems in natural and crowd classroom environment, e.g. various poses, occlusion, expressions, illumination and poor image quality, etc. In this study, a robust and un-invasive M-VFOA and SS recognition system has been developed based on continuous head pose estimation in the natural classroom. A novel cascaded multi-task Hough forest (CM-HF) combined with weighted Hough voting and multi-task learning is proposed for continuous head pose estimation, tip of the nose location and SS recognition, which improves accuracies of recognition and reduces the training time. Then, M-VFOA can be recognized based on estimated head poses, environmental cues and prior states in the natural classroom. Meanwhile, SS is classified using CM-HF with local cascaded mouth-eyes areas normalized by the estimated head poses. The method is rigorously evaluated for continuous head pose estimation, multi-person VFOA recognition, and SS recognition on some public available datasets and real-class video sequences. Experimental results show that our method reduces training time greatly and outperforms the state-of-the-art methods for both performance and robustness with an average accuracy of 83.5% on head pose estimation, 67.8% on M-VFOA recognition and 97.1% on SS recognition in challenging environments.

Web service selection, as an important part of web service composition, has direct influence on the quality of composite service. Many works have been carried out to find the efficient algorithms for quality of service (QoS)-aware service selection problem in recent years. In this paper, a negative selection immune algorithm (NSA) is proposed, and as far as we know, this is the first time that NSA is introduced into web service selection problem. Domain terms and operations of NSA are firstly redefined in this paper aiming at QoS-aware service selection problem. NSA is then constructed to demonstrate how to use negative selection principle to solve this question. Thirdly, an inconsistent analysis between local exploitation and global planning is presented, through which a local alteration of a composite service scheme can transfer to the global exploration correctly. It is a general adjusting method and independent to algorithms. Finally, extensive experimental results illustrate that NSA, especially for NSA with consistency weights adjusting strategy (NSA+), significantly outperforms particle swarm optimization and clonal selection algorithm for QoS-aware service selection problem. The superiority of NSA+ over others is more and more evident with the increase of component tasks and related candidate services.

Fully constrained least squares (FCLS) method has been widely applied in estimating land cover abundances within an image pixel through spectral unmixing. With FCLS, if given a large spectra library, researchers have to examine the appropriateness of all endmember signatures, and may mistakenly choose the ones that do not exist in the pixel. Such erroneously selected endmember signatures may lead to overestimation of the non-existing endmembers. In this article, A priori FCLS unmixing method based on sparse unmixing was been pro posed. This methodology contains three steps, including: 1) estimating the abundance of each mixed pixel using sparse unmixing with all the endmembers, 2) chosing the number, type, and signatures of endmembers in every mixed pixel and 3) estimating land cover abundances by FCLS only with chosen endmember signatures. Experimental results suggest that the developed a priori FCLS method is with apparently better performance when compared to those of traditional FCLS and sparse unmixing models. Especially, the improvements of the a priori FCLS are more significant with a larger spectral library and/or a better signal to noise ratio (SNR).

Between 2006 and 2010 the installed capacity of wind power in China has doubled and by 2010 China's cumulative installed capacity of wind power ranked the first in the world, surpassing the United States. However, the rapid expansion of installed capacity has not been matched by grid connection, and this deficiency has aroused the concern of both policy makers and scholars. Unlike most of the current studies which focus on technical strategies in China's wind power industry, this paper analyzes the problem from a policy perspective. The paper analyzes the four challenges that large scale wind power integration in China faces: the uncoordinated development between wind power capacity and power grids; the lack of suitable technical codes for wind power integration; the unclear nature of the grid companies’ responsibility for grid connection; and the inadequate economic incentives for grid enterprises. To address these problems, the paper recommends that the government: formulates policies to better coordinate the development of wind power and the planning and construction of power grids; establishes grid codes that reflect in particular the requirements to be met by users of power transmission and distribution networks; and integrates administrative intervention and economic incentive policies to stimulate the grid enterprises’ enthusiasm to absorb wind power generation.

It is important to study the tourists' behaviors for protecting and managing the nature heritage resorts. Based on the study of visitors' individual spatial behaviors, the author developed a tourists' spatial behaviors simulator (TSBS), which employs GIS to manage the spatial data, analyze and display the simulation results, Multi Agent System to simulate spatial behaviors of numerous individual visitors. In TSBS, tourist resort is represented by a network. There are 2 kinds of network elements: nodes, and links. Each tourist group is simulated by an agent that stored the attributes of tourist group and can update autonomously in some predetermined rules. The simulation process is controlled by the simulation clock. Agents' speed, location and other d-attributes are updated at every clock step. All these results will be recorded in Agent state table. By analyzing this table, all the tourists' behaviors in scenic area can be visualized and quantified. In this paper, tourists' behaviors in Mount Emei listed in word heritage list are simulated based on TSBS. The results show that TSBS can simulate the tourists' behaviors relative accurately and quickly.