Search

This paper presents a decomposition analysis of energy-related CO2 emission in China for the period 1991–2006 divided into three equal time intervals. The complete decomposition method developed by Sun is used to analyze the nature of the four factors: CO2 intensity, energy intensity, structural changes and economic activity. The results show that economic activity has the largest positive effect in CO2 emission changes in all the major economic sectors and China has achieved a considerable decrease in CO2 emission mainly due to the improved energy intensity. However, the impact of CO2 intensity and structural changes is relatively small. Structural changes only exhibit positive effect to the CO2 mitigation in agricultural sector, and CO2 intensity also contributes to the decrease of CO2 emission in transportation sector. Moreover, a formula about CO2 mitigation is presented in this paper, which shows that China has made a significant contribution to reducing global CO2 emission. [Copyright &y& Elsevier]

An outbreak of Ulva prolifera poses a massive threat to coastal ecology in the Southern Yellow Sea, China (SYS). It is a necessity to extract its area and monitor its development accurately. At present, Ulva prolifera monitoring by remote sensing imagery is mostly based on a fixed threshold or artificial visual interpretation for threshold selection, which has large errors. In this paper, an adaptive threshold model based on Google Earth Engine (GEE) is proposed and applied to extract U. prolifera in the SYS. The model first applies the Floating Algae Index (FAI) or Normalized Difference Vegetation Index (NDVI) algorithm on the preprocessed remote sensing images and then uses the Canny Edge Filter and Otsu threshold segmentation algorithm to extract the threshold automatically. The model is applied to Landsat8/OLI and Sentinel-2/MSI images, and the confusion matrix and cross-sensor comparison are used to evaluate the accuracy and applicability of the model. The verification results show that the model extraction of U. prolifera based on the FAI algorithm has higher accuracy (R2 = 0.99, RMSE = 5.64) and better robustness. However, when the average cloud cover is more than 70% in the image (based on the statistical results of multi-year cloud cover information), the model based on the NDVI algorithm has better applicability and can extract the algae distributed at the edge of the cloud. When the model uses the FAI algorithm, it is named FAI-COM (model based on FAI, the Canny Edge Filter, and Otsu thresholding). And when the model uses the NDVI algorithm, it is named NDVI-COM (model based on NDVI, the Canny Edge Filter, and Otsu thresholding). Therefore, the final extraction results are generated by supplementing NDVI-COM results on the basis of FAI-COM extraction results in this paper. The F1-score of U. prolifera extracted results is above 0.85. The spatiotemporal distribution of U. prolifera in the South Yellow Sea from 2016 to 2020 is obtained through the model calculation. Overall, the coverage area of U. prolifera shows a decreasing trend over the five years. It is found that the delay in recovery time of Porphyra yezoensis culture facilities in the Northern Jiangsu Shoal and the manual salvage and cleaning-up of U. prolifera in May are among the reasons for the smaller interannual scale of algae in 2017 and 2018. [ABSTRACT FROM AUTHOR]

In order to extract quantitative water-leaving information from the Thematic Mapper (TM) image accurately in inland waters, atmospheric correction is a necessary step. Based on former researchers' results, the paper presents two atmospheric correction algorithms based on meteorological data (MD) and on Moderate Resolution Imaging Spectroradiometer (MODIS) Vicarious Calibration (MVC) for TM image in inland waters according to the theory of radiative transfer. Studying Taihu lake, China, in this paper we derived water remote sensing reflectance from a TM image of 26 July 2004 by these two atmospheric correction algorithms and we compare the results with that of dark object subtraction (DOS) and 6S code. The results show that the effect of atmospheric correction based on meteorological data and MODIS Vicarious Calibration is much better than that of DOS and 6S code. Although the MD is more accurate, MVC may be an ideal choice for TM images in inland water because TERRA MODIS images can be acquired easily than collecting meteorological data at the time of satellites passing over. [ABSTRACT FROM AUTHOR]

Building a distributed agricultural information system (DAIS) is an important task to implement agriculture informatisation. Due to the limitations of network flux and the characters of spatial data, access to huge agricultural geospatial datasets often suffers from slow or unreliable network connections. This limits the development and application of WebGIS in agriculture. To improve the response speed for access to the agricultural geospatial data, this paper introduces a solution for vector data simplification based on the improved Douglas-Peucker algorithm and a binary line generalisation (BLG) tree. Accordingly, this paper discusses the techniques for progressive transmission of vector data, i.e., the architecture of WebGIS, data organising and coding on the server-side. The experimental results show that the method proposed in this paper can not only effectively reduce system response time, but also preserve the topology relation between spatial data, which sequentially improves the performance and quality of DAIS. [ABSTRACT FROM AUTHOR]

This paper proposes an elastic demand network equilibrium model for networks with transit and walking modes. In Hong Kong, the multi-mode transit system services over 90% of the total journeys and the demand on it is continuously increasing. Transit and walking modes are related to each other as transit passengers have to walk to and from transit stops. In this paper, the multi-mode elastic-demand network equilibrium problem is formulated as a variational inequality problem where the combined mode and route choices are modeled in a hierarchical logit structures and the total travel demand for each origin-destination pair is explicitly given by an elastic demand function. In addition, the capacity constraint for transit vehicles and the effects of bi-directional flows on walkways are considered in the proposed model. All these congestion effects are taken into account for modeling the travel choices. A solution algorithm is developed to solve the multi-mode elastic-demand network equilibrium model. It is based on a Block Gauss-Seidel decomposition approach coupled with the method of successive averages. A numerical example is used to illustrate the application of the proposed model and solution algorithm. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, a two-dimensional numerical calculation algorithm for the water quality modeling in the Hengyang City section of the Xiangjiang River is researched considering the effect of the Dayuandu navigational key project. The research river is winding and has two branches resulted from an isle. The numerical calculation algorithm for the water quality modeling is set up on the basis of applying topographic map of the river course and the finite element method. The calculation result for the water quality modeling includes the concentration fields for various pollutants. The numerical calculation algorithm for the water quality modeling set up in this paper can he applied to shallow river with similar topographically complicated river course. Key words: two-dimensional river; water quality; numerical calculation algorithm. [ABSTRACT FROM AUTHOR]

Under the reliability consideration of the multi-energy system, more and more regional networks are in meshed topology. This paper presents a novel method for the regional meshed power–gas system based on the reformulated and tightened cone relaxations. The proposed model defines two energy flows, which are relaxed as convex constraints in the optimization. In the power network, considering the phase angle of each node in meshed networks, a series of conic relaxation-based constraints are employed to represent the alternating current power flow. In the gas network, the conic relaxations are imposed with convex hulls and cones to the gas flow, taking the transmission directions and line-pack constraints into account. To enhance the quality of the relaxation, we propose an approach to tighten the feasible region through two steps. The first step is to tighten the lower and upper bounds of the variables through an optimality-based linear program. The second step is to solve the relaxed tightened optimization with new bounds through a mixed-integer second-order cone program. The advantage and applicability of the proposed approach are tested over a real case study, which shows the improvement of solution quality and optimization speed. • A novel tightened conic-relaxed optimization model for the meshed power–gas system is proposed. • Two convexified energy flows are defined in the model, in which physical characteristics and operation states are considered. • A tightened approach for relaxed optimization is proposed, which enhances the solution quality efficiently. • A two-step optimization-based algorithm is designed to solve the proposed model. • Simulation results based on a real case in China indicate the superiority of the proposed approach. [ABSTRACT FROM AUTHOR]

Waste sorting is an imperative and significant issue in China, of which sorted-waste collection and transportation are indispensable parts. Despite its vital yet practical significance, few studies research mathematical models or algorithms of waste collection and transportation from the perspective of waste sorting. To address this issue, we extend a novel transportation model for the waste management system, namely, capacitated location routing problem with queuing time (CLRPQT) and design a cross-entropy and simulated-annealing based hyper-heuristic algorithm (CE-SAHH) for it. The main idea of this paper is three-fold: (1) As a particular property of this problem, source nodes cannot but need to be served by more than one vehicle that causes queuing time between a heterogeneous fleet of vehicles, which is novel in terms of the proposed model; (2) For the methodological contribution, a character encoding scheme, new decoding procedure, and local search strategy are designed embedded in the proposed method; (3) An integration of simulated annealing strategy and the cross-entropy-based hyper-heuristic algorithm is developed to overcome the combinatorial optimization problem with a more complex solution of this study. Finally, the results and analysis of three numeric experiments on benchmark datasets, new instances of CLRPQT, and simulation data in Shanghai, China, verify the effectiveness and universality of the proposed model and method. • A novel model with interactional routes for sorted-waste transportation is proposed. • A cross-entropy-based hyper-heuristic algorithm is proposed and performs well. • The novel model and method based on sorted waste enrich the waste management system. • Real-life data is used to verify the method and some managerial insights are drawn. [ABSTRACT FROM AUTHOR]

Estimating gross primary production (GPP) and net primary production (NPP) are significant important in studying carbon cycles. Using models driven by multi-source and multi-scale data is a promising approach to estimate GPP and NPP at regional and global scales. With a focus on data that are openly accessible, this paper presents a GPP and NPP model driven by remotely sensed data and meteorological data with spatial resolutions varying from 30 m to 0.25 degree and temporal resolutions ranging from 3 hours to 1 month, by integrating remote sensing techniques and eco-physiological process theories. Our model is also designed as part of the Multi-source data Synergized Quantitative (MuSyQ) Remote Sensing Production System. In the presented MuSyQ-NPP algorithm, daily GPP for a 10-day period was calculated as a product of incident photosynthetically active radiation (PAR) and its fraction absorbed by vegetation (FPAR) using a light use efficiency (LUE) model. The autotrophic respiration (Ra) was determined using eco-physiological process theories and the daily NPP was obtained as the balance between GPP and Ra. To test its feasibility at regional scales, our model was performed in an arid and semi-arid region of Heihe River Basin, China to generate daily GPP and NPP during the growing season of 2012. The results indicated that both GPP and NPP exhibit clear spatial and temporal patterns in their distribution over Heihe River Basin during the growing season due to the temperature, water and solar influx conditions. After validated against ground-based measurements, MODIS GPP product (MOD17A2H) and results reported in recent literature, we found the MuSyQ-NPP algorithm could yield an RMSE of 2.973 gC m-2 d-1 and an R of 0.842 when compared with ground-based GPP while an RMSE of 8.010 gC m-2 d-1 and an R of 0.682 can be achieved for MODIS GPP, the estimated NPP values were also well within the range of previous literature, which proved the reliability of our modelling results. This research suggested that the utilization of multi-source data with various scales would help to the establishment of an appropriate model for calculating GPP and NPP at regional scales with relatively high spatial and temporal resolution. [ABSTRACT FROM AUTHOR]

This paper is concerned with the scheduling of Electrical Multiple Units (EMUs) under the condition of their utilization on one sector or within several interacting sectors. Based on the introduction of the train connection graph which describes the possible connection relationship between trains, the integer programming model of EMU circulation planning is constructed. In order to analyzing the resolution of the model, a heuristic which shares the characteristics with the existing methods is introduced first. This method consists of two stages: one is a greedy strategy to construct a feasible circulation plan fragment, and another is to apply a stochastic disturbance to it to generate a whole feasible solution or get a new feasible solution. Then, an exact branch and bound method which is based on graph designing is proposed. Due to the complexity, the lower bound is computed through a polynomial approximation algorithm which is a modification from the one solving the degree constraint minimum 1-tree problem. Then, a branching strategy is designed to cope with the maintenance constraints. Finally, we report extensive computational results on a railway corridor in which the sectors possess the basic feature of railway networks. [ABSTRACT FROM AUTHOR]

Microblogging is increasingly becoming one of the most popular online social media for people to express ideas and emotions. The amount of socially generated content from this medium is enormous. Text mining techniques have been intensively applied to discover the hidden knowledge and emotions from this huge dataset. In this paper, we propose a modified version of hidden Markov model (HMM) classifier, called self-adaptive HMM, whose parameters are optimized by Particle Swarm Optimization algorithms. Since manually labeling large-scale dataset is difficult, we also employ the entropy to decide whether a new unlabeled tweet shall be contained in the training dataset after being assigned an emotion using our HMM-based approach. In the experiment, we collected about 200,000 Chinese tweets from Sina Weibo. The results show that the F-score of our approach gets 76% on happiness and fear and 65% on anger, surprise, and sadness. In addition, the self-adaptive HMM classifier outperforms Naive Bayes and Support Vector Machine on recognition of happiness, anger, and sadness. [ABSTRACT FROM AUTHOR]

For the search engine, error-input query is a common phenomenon. This paper uses web log as the training set for the query error checking. Through the n-gram language model that is trained by web log, the queries are analyzed and checked. Some features including query words and their number are introduced into the model. At the same time data smoothing algorithm is used to solve data sparseness problem. It will improve the overall accuracy of the n-gram model. The experimental results show that it is effective. [ABSTRACT FROM AUTHOR]

According to the features of Chinese personal name, we present an approach for Chinese personal name recognition based on conditional random fields (CRF) and knowledge base in this paper. The method builds multiple features of CRF model by adopting Chinese character as processing unit, selects useful features based on selection algorithm of knowledge base and incremental feature template, and finally implements the automatic recognition of Chinese personal name from Chinese document. The experimental results on open real corpus demonstrated the effectiveness of our method and obtained high accuracy rate and high recall rate of recognition. [ABSTRACT FROM AUTHOR]

Natural gas, an efficient, eco-friendly and clean green energy, has become one of the important energy structures of various countries in the world, accurately predicting the production of natural gas can help the national energy agency solve "gas shortage" problem. To accurately predict natural gas production in China, this paper establishes an optimized grey system model with weighted fractional accumulation generation operation (abbreviated as WFNGBM(1,1,N)). The proposed model has all the advantages of the GMP(1,1,N) model, NGBM(1,1) model and weighted fractional accumulation generation operation, which makes it have excellent prediction performance. Moreover, five outstanding intelligent optimization algorithms (whale optimization algorithm, marine predators algorithm, grasshopper optimization algorithm, equilibrium optimization algorithm and arithmetic optimization algorithm) are used to solve the hyperparameters of the WFNGBM(1,1,N) model. It is found that the WFNGBM(1,1,N) model has the characteristics of convertibility and small sample modeling, which indicates that it is a small sample prediction model with strong compatibility. After confirming the feasibility of the proposed model compared with its competing models by using natural gas production in Germany, Italy and Canada as examples, the proposed model is used to study China's natural gas production. The results show that this model is very suitable for predicting and analyzing natural gas production in China. Based on this, the WFNGBM(1,1,N) model is used to estimate China's natural gas production in the next three years, and some reasonable suggestions are given according to the development trend of natural gas production. • The concept of weighted fractional accumulation generation operation is given. • An optimized nonlinear grey Bernoulli prediction model is established. • Five algorithms are used to solve the parameters of the proposed model. • The proposed model is used to study natural gas production in China. [ABSTRACT FROM AUTHOR]

In brain MR images, the noise and low-contrast significantly deteriorate the segmentation results. In this paper, we propose an automatic unsupervised segmentation method integrating dual-tree complex wavelet transform (DT-CWT) with K-mean algorithm for brain MR image. Firstly, a multi-dimensional feature vector is constructed based on the intensity, the low-frequency subband of DT-CWT and spatial position information. Then, a spatial constrained K-mean algorithm is presented as the segmentation system. The proposed method is validated by extensive experiments using both simulated and real T1-weighted MR images, and compared with the state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we develop a novel feature selection algorithm based on the genetic algorithm (GA) using a specifically devised trace-based separability criterion. According to the scores of class separability and variable separability, this criterion measures the significance of feature subset, independent of any specific classification. In addition, a mutual information matrix between variables is used as features for classification, and no prior knowledge about the cardinality of feature subset is required. Experiments are performed by using a standard lung cancer dataset. The obtained solutions are verified with three different classifiers, including the support vector machine (SVM), the back-propagation neural network (BPNN), and the K-nearest neighbor (KNN), and compared with those obtained by the whole feature set, the F-score and the correlation-based feature selection methods. The comparison results show that the proposed intelligent system has a good diagnosis performance and can be used as a promising tool for lung cancer diagnosis. [ABSTRACT FROM AUTHOR]

• The modified CES function takes into account the stages of technological progress. • The improved PSO algorithm has fast convergence speed and high accuracy. • The method of calculating the contribution rate is scientific and reliable. • China's economic growth factor contribution rate is given. In the analysis of the economic growth factors, people often use the CES (constant elasticity of substitution) production function model to calculate the contribution rate of influencing factors to economic growth. However, the traditional CES production function model does not consider the period characteristic of technological progress on economic growth, so this paper puts forward a modified CES production function model. On the parameter estimation of the model, this paper presents an improved PSO (particle swarm optimization) algorithm with high precision and fast convergence. Using the modified CES production function model, this paper provides a scientific method to calculate the contribution rates of all factors influencing the economic growth. Finally, this paper performs the empirical analysis of the contribution rates of all factors influencing China's economic growth. [ABSTRACT FROM AUTHOR]

Displaying 3D geological models and performing corresponding analyses in web browsers are trends in "smart city" development, but these tasks are often limited by the web browser and network transmission; therefore, large quantities of data cannot be quickly displayed or analysed, thereby constraining 3D geological models implemented in web browsers. The data redundancy and computational complexity of 3D geological models must be urgently optimized. Because of the data structure, it is difficult to further optimize the data redundancy and computational complexity of the current commonly used triangular prism (TP) model. If the model is sufficiently large, it can be difficult to display and analyse information in a web browser. Therefore, a new data structure and algorithm are necessary to optimize the data and number of calculations in 3D geological models. Thus, this paper uses a two-dimensional projection to simplify the data and proposes a new data structure for a 3D geological model based on a vertically projected triangulated network (VPTN) to describe the TP model. Based on the vertical sides of a TP, the horizontal coordinates and topology of the 3D model can be recorded by a 2D vertically projected triangulated network. According to the new data structure, this paper proposes a corresponding cutting algorithm that takes advantage of a VPTN to increase the computational speed. The VPTN model and TP model of a city in eastern China are built. The comparison of the modelling and cutting results of the two models is carried out, and the results show that the VPTN model can completely record the spatial coordinate information and topological information of the 3D geological model, reduce the memory space occupied by the model and decrease the number of calculations. The research results meet the needs of rapidly displaying 3D geological models and performing related analyses with a large data volume in web browsers. Moreover, these findings provide a reference for further optimization of 3D geological models. • The data structure of VPTN model can record the 3D model by a 2D network. • The data structure of VPTN model can decrease the data volume of model. • The cutting algorithm can reduce the calculations in the cutting process. [ABSTRACT FROM AUTHOR]

Cascade hydropower stations have good regulation and storage capacity and they can be used as a regulatory and compensatory "medium" to compensate for the instability of wind–photovoltaic power generation. This paper presents a short-term multi-objective coordinated dispatching model based on wind–photovoltaic–hydro heterogeneous energy hybrid power generation. The multi-objectives of the model included accepting new energy for power generation to the greatest possible extent, tracing the load curve of the grid with minimum differences, and minimizing the discarded water volume of the cascade reservoirs. To reduce the complexity of the problem, the model was decomposed into two-stage optimization model and solved by using the firefly algorithm. This model was applied to a national-level wind–photovoltaic–hydro complementary power generation base in China. The research results verified the validity of the model and showed that it was feasible to compensate for the wind–photovoltaic power output fluctuation by the cascade hydropower stations and to supply power to the grid by bundling these three power sources. During this process, the cascade hydropower stations certainly made some sacrifices and increased the amount of discarded water. The new operational strategy proposed in this paper can promote the low-carbon power dispatching and optimization of the energy structure. • A framework for a heterogeneous energy complementary power system was proposed. • A new grid–source coordinated dispatching model was developed. • A two-stage decoupled optimization model was built for a multi-objective solution. • The developed framework and the method were assessed in Yalong River in China. [ABSTRACT FROM AUTHOR]

Abstract: In order to simulate the wheel wear behaviour of freight cars, the rail vehicle's multi-body dynamical model and wheel wear simulation programme were combined as a wheel wear simulation tool in this paper. Multi-body dynamical models of China's heavy haul freight cars such as the C80, C80H, C70 and C70H, were built in SIMPACK software and the track system was built based on the China's Ring-line. For the wheel/rail rolling contact, the semi-Hertzian method and FASTSIM algorithm were applied to solve the normal and tangential problem respectively. The shapes of worn wheel profiles in the simulation agree well with the field measurements, but wear rates from the simulation are larger than those found in field measurements. This discrepancy is contributed to two factors. The first factor is the wheel material. The CL60 wheel steel used in China's freight car, is harder than the BS11 wheel steel used in Zobory's wheel/rail wear experiments. The second factor is the impact of a material's elastic shear deformation on the slip velocity in contact patch. This impact will increase the wear rate and was considered in the present wear simulation. [Copyright &y& Elsevier]

Food crop is a kind of special commodities. Logistics distribution process from field to table and from acquisition of raw material to final customers all have special requirements. Due to various reasons, development of food crop logistics distribution in China are still in a relatively backward level, which lead to great loss in logistics distribution process and serious food crop safety incidents. At this stage, food crop logistics distribution problems not only show on storage technology but also show more on management of whole process of food crop logistics distribution. Therefore, launch of food crop logistics distribution research and exploration of new approach for solving food crop logistics distribution fundamentally is of great practical significance. And research result can make scientific contribution for enriching theory system of food crop logistics distribution. Applying theory and method of management of food crops, logistics distribution management and system theory, this paper establishes distribution costs model by literature analysis and site survey adopting basic mode of data algorithm. It is beneficial to redefine food crop logistics distribution system and clearly analyze the elements, structure, function and characteristics of food crop logistics distribution system. [ABSTRACT FROM AUTHOR]

As to the applied range and optimization of grey forecast model, according to the principle that grey GM(1,1) model's parameters are grey and adjustable, the connotation model and parameter packet formulas of GM(1,1, β) were put forward in this paper, and some properties of the model and model's optimization algorithm was analyzed. The results show that the objective valuing region of parameter a of GM(1,1, β) model is (-∞+ ∞), and the objective valuing region of parameter a of GM(1,1) model is (- 2,+2), the objective valuing region of parameter a is determined by the background value coefficient, and it has nothing with raw data sequence; GM(1,1, β) grey differential equation completely suits for the homogeneous exponential function series. Finally, the example of simulating and forecasting the per capita income of urban dwellers in China shows that the grey differential equation GM(1,1, β) is valid. [ABSTRACT FROM AUTHOR]

Dynamic optimization is likely to play an important role in the reliability, quality, and efficiency of future power system, specifically for microgrid with renewable generation. This paper presents an improved hybrid optimization algorithm, called information exchange particle swarm optimization (IEPSO), to deal with the problem of dynamic optimal control in a microgrid with multiple distributed generations (DGs), such as wind power, small hydro, and energy storage. Based on the actual application of DG in Southwest China, we propose an optimization model for grid-connected microgrid that introduces a contribution rate of energy storage into the model and incorporates practical engineering constraints. The performance of different optimization control strategies for active and reactive power is investigated, and the results indicate that the IEPSO algorithm is efficient for dynamic optimization, and the dynamic contribution rate control strategy for energy storage is more effective than static strategy. [ABSTRACT FROM AUTHOR]

Variation of streamflow and sediment load in the Yellow River basin has received considerable attention due to its drastic reduction during the past several decades. This paper presents a detailed investigation on the changes of streamflow and sediment load from 1952 to 2011 using monthly observations at four gauging stations along the Yellow River. The results show significant decreasing trends for both streamflow and sediment load at all four gauging stations over the past 60 years. The wavelet transform demonstrated discontinuous periodicities from 1969 to 1973 and after 1986 due to the construction of large reservoirs and implementation of numerous soil and water conservations practices. The sediment rating curves with the power-law function was applied to investigate the relationship between discharge and sediment load. The results indicate distinct variations of the relationship between streamflow and sediment and implied significant hydro-morphological changes within different periods. The reducing sediment supply from the source region and the increased erosive power of the river are detected at Lanzhou station, while the decrease of the transport capacity at Toudaoguai is caused by severe siltation. Significant changes in the relationship between streamflow and sediment load are found at Huayuankou and Gaocun stations, which are largely induced by evident sediment income and trapping effects of large reservoirs. It is estimated that numerous reservoirs have strongly altered the regime and magnitude of streamflow and trapped large amount of sediment, leading to severe siltation and evident reduction of their total volumes. A decrease in precipitation, incoming water from the upper reaches, soil and water conservation measures as well as water consumption contribute most to the significant reduction of streamflow. The decrease of sediment load mainly resulted from various soil and water conservation measures and trapping in reservoirs from 1986 to 2011. [ABSTRACT FROM AUTHOR]

Stock index forecasting is an important tool for both the investors and the government organizations. However, due to the inherent large volatility, high noise, and nonlinearity of the stock index, stock index forecasting has been a challenging task for a long time. This paper aims to develop a novel hybrid stock index forecasting model named BSO-GNN based on the brain storm optimization (BSO) approach and the grey neural network (GNN) model by taking full advantage of the grey model in dealing with data with small samples and the neural network in handling nonlinear fitting problems. Moreover, the new developed BSOGNN, which initializes the parameters in grey neural network with the BSO algorithm, has great capability in overcoming the deficiencies of the traditional GNN model with randomly initialized parameters through solving the local optimum and low forecasting accuracy problems. The performance of the proposed BSO-GNN model is evaluated under the normalization and nonnormalization preprocessing situations. Experimental results from the Shanghai Stock Exchange (SSE) Composite Index, the Shenzhen Composite Index, and the HuShen 300 Index opening price forecasting show that the proposed BSO-GNN model is effective and robust in the stock index forecasting and superior to the individual GNN model. [ABSTRACT FROM AUTHOR]

Information fusion is a challenging task to extract the required information for high-level applications from various homogeneous or heterogeneous sensors. This paper summarizes some improvements in recent five years in the presented structure of information fusion, including estimation fusion, image fusion and others. Some technologies of information fusion and their variants are discussed. Although the state-of-the-art of the algorithms for information fusion have been proposed, there still remains some fundamental challenges with regard to exploiting the emerging multi-sensors' characteristics and their special structures. Finally, some potential prospects of estimation fusion and image fusion are discussed. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, a hybrid forecasting approach, which combines the Ensemble Empirical Mode Decomposition (EEMD) and the Support Vector Machine (SVM), is proposed to improve the quality of wind speed forecasting. The essence of the methodology incorporates three phases. First, the original data of wind speed are decomposed into a number of independent Intrinsic Mode Functions (IMFs) and one residual series by EEMD using the principle of decomposition. In order to forecast these IMFs, excepting the highest frequency acquired by EEMD, the respective estimates are yielded using the SVM algorithm. Finally, these respective estimates are combined into the final wind speed forecasts using the principle of ensemble. The proposed hybrid method is examined by forecasting the mean monthly wind speed of three wind farms located in northwest China. The obtained results confirm an observable improvement for the forecasting validity of the proposed hybrid approach. This tool shows great promise for the forecasting of intricate time series which are intrinsically highly volatile and irregular. [Copyright &y& Elsevier]

In view of the situations that the locations of high-speed rail terminals in some cities are far from the city centers, this paper put forward an approach to design feeder bus lines considering two aspects of demands. One is recent demand for transporting high-speed rail passengers between city centers and rail terminals; the other is long-term demand for directly connecting the bus system in city centers and the bus system in the emerging city unit around rail terminals. The optimization model for selecting the bus lines was built based on the two aspects of demands and then the related solution ant-colony algorithm was designed. The case of designing the feeder bus lines for anting rail terminals on Shanghai-Nanjing intercity railway was used to illustrate the model and the algorithm. The index showed the optimized feeder bus lines have the characters of quick and reliable service quality and good potential for the transition to express lines in the future. [ABSTRACT FROM AUTHOR]

Since the late 1970s, special education in the People's Republic of China has experienced significant reform and fast development. However, education for children with severe developmental disabilities, especially autism spectrum disorders (ASDs), is still the greatest challenge in the field. This paper aims to give readers an overview of what is happening to children with ASDs in China. We first address the issue of prevalence of ASDs, and then offer an introduction to the diagnostic process. After that, a review of disability-related legislation is provided, followed by a description of current treatment options and available educational services. Finally we introduce all extent service providers and their roles. [ABSTRACT FROM AUTHOR]

Historically, change detection reviews have examined and categorized algorithms based on their techniques for the remote sensing community. Here, we synthesize urban land change algorithms by the types of information they provide to a diverse and growing set of user communities. Two goals of the paper are first to synthesize past and current change detection studies to examine urban land change to help users of remote sensing algorithms understand and navigate the vast variety of available methods, and second to identify gaps in knowledge for the urban remote sensing community. We analyzed 644 peer-reviewed research papers published in English-language journals and conducted a systematic review of urban land change algorithms. All papers included in our study focus on urban land change ; studies that concentrate on single-date urban classification or mapping for one point in time without an explicit urban land change component are not included in this analysis. The review showed five key results and knowledge gaps. First, most urban change detection algorithms are being developed and applied for only a few regions, with 75% of studies focused on high or upper-middle-income countries and the majority of these on China or the United States. This suggests a major gap in geographic coverage as well as the need for more studies on cities in low and lower-middle-income countries. Second, the results show that 41% of the algorithms have been developed or applied for cities of over 5 million inhabitants. This focus on large cities is problematic given that only 11% of the world's urban population lives in cities with populations greater than 5 million and that most future urban growth will occur in small- and medium-sized towns and cities with populations of fewer than 1 million people. Third, our analysis shows that 62% of the studies use three or fewer time points to measure urban land change with an average study length of 17 years. Since rapidly growing urban areas are highly dynamic, this suggests that existing algorithms using only a few time points are likely missing urban transitions that can only be captured with high temporal frequency analysis. Fourth, we find that urban expansion is the most commonly monitored type of urban land change. Comparatively fewer studies characterized intra-urban change or three-dimensional structural change. Fifth and finally, the results show that an overwhelming majority—87%—of all studies identify only one urban class, highlighting a need for more studies that distinguish intra-urban variation and differentiate multiple urban classes. Our analysis shows that it is very difficult—nearly impossible—to compare across algorithms. Thus, for users of urban land change information, it is difficult to navigate the literature and know which algorithms are most appropriate for a particular use. Taken together, this points to the need for improved reproducibility, replicability, and comparability of studies in order to help harmonize urban land change information across regions and countries. This is especially important given the growing user communities of urban land change products and analysis, especially from policy and practice. • We conduct a systematic review of the urban land change algorithm literature. • Most studies monitor urban change at low temporal frequencies. • The majority of urban land change is characterized using only one urban class. • Over 75% of studies focus on high or upper-middle level income countries. • Key knowledge gaps include in geographic coverage, city size, and land transitions. [ABSTRACT FROM AUTHOR]

Aims: This study aimed to evaluate the health‐related quality of life of nursing workers and influential factors. Background: Nursing workers are under great occupational stress. However, few population‐level studies of nursing workers' health conditions have been reported from China. Design This was a cross‐sectional study conducted in 12 medical institutions in east China between January and March 2015. Methods: Social‐demographic information, nursing workers' work‐related information and health‐related quality of life using the Chinese (mainland) version of the Short Form 36 health survey were collected by a self‐administrated questionnaire. Results: Nursing workers reported significantly lower scores of the vitality, mental health dimensions, and mental component summary of the Short Form 36 survey compared with general population reference values. They scored significantly higher for five other dimensions and comparably for the physical role dimension. Pre‐job training, families' positive attitudes to their job, and good relationships with clients were positively related with nursing workers' mental well‐being. Conclusion: To promote nursing workers' mental health, compulsory work‐related training, voluntary psychological counselling, and targeted legislation are needed. SUMMARY STATEMENT: What is already known about this topic? Shortages of professional medical staff in medical institution at all levels will not be quickly met, and nursing workers are thought to be an important supplement in China.Population‐level study on nursing workers' point of view of their work and health condition has been little reported in China. What this paper adds? Nursing workers were mentally vulnerable regarding their vitality and mental health scores.Job training, families' positive attitudes to their job, and good relationships with clients were all protective factors for nursing workers' mental well‐being. The implications of this paper: Compulsory work‐related training for nursing workers provided by intermediary companies can help promote nursing workers' well‐being.Voluntary psychological counselling provided by hospitals can also be helpful to build a supportive work environment for nursing workers.Governments need to regulate this market and protect nursing workers' rights and benefits by legislation. [ABSTRACT FROM AUTHOR]

The objective and accurate prediction of energy consumption can supply an important reference and advance indicator for government to implement economic policies and energy development strategy. On account of the complexity and uncertainty of the energy system, this paper establishes a novel flexible grey multivariable model by introducing a power exponential term, a linear correct term and a random disturbance term. The novel model has the advantages in capturing the dynamic characteristics of the energy system, also it can be compatible with eight existing grey models when some parameters are assigned certain values. Additionally, to further promote the prediction performance of the novel model, the grey wolf optimizer is employed to determine the power indexes of the model. To demonstrate its performance, the proposed model is utilized to predict the energy consumption of three major provinces in China, and the fitting and prediction results of the novel model are compared with those provided by diversified competing models. The results illustrated that the novel model is superior to other competing models, offering more accurate and better performance. Finally, based on the results, several proposals for energy development are put forward for decision-makers. • The flexible multivariable grey model can adapt to more energy system structures. • The novel model can be flexibly compatible with the eight existing grey models. • A specific solution algorithm for the novel model's power indexes is discussed. • The novel model can be easily used to energy system with higher prediction accuracy. [ABSTRACT FROM AUTHOR]

• A linear components extraction method is proposed to identify the daily fluctuating patterns of the solar power series. • A scenarios generation algorithm is proposed to provide forecasting linear trend data. • Temporal distributed deep learning models are developed based on the detected daily fluctuating patterns of the PV power generation. • The proposed algorithm can provide accurate day-ahead solar power forecasts for distributed PV farms without local weather information. In recent years, the integration of distributed photovoltaic has witnessed explosive growth because of its great economic and environmental benefits. However, the intermittence and randomicity of PV power generation are challenging the stable and economic operation of active distribution system, especially when the local historical and forecasting meteorological data are not available. Thus, this paper presents a novel Gated Recurrent Unit based hybrid model for forecasting distributed PV power generations. The proposed method uses locally historical PV power generation data to forecast one-day-ahead solar power time series with five-minute intervals. First, a linear components extraction method is proposed to identify the daily fluctuating patterns of the solar power series. Then, temporal distributed Gated Recurrent Unit models are developed based on the detected daily fluctuating patterns of the PV power generation. A scenarios generation algorithm is proposed to provide forecasting linear trend data for each Gated Recurrent Unit model. Finally, the daily linear series scenarios, the linear and nonlinear parts of the historical data are fed into the Gated Recurrent Unit to forecast the PV generation. Data collected from an actual distributed PV farm in southeastern China are utilized to validate the effectiveness of the proposed model. The results show that the proposed model can provide a nominalized root mean square error of 6.83% and a nominalized mean absolute error of 4.12%. Compared to a single Gated Recurrent Unit forecasting model, the developed model has improved the forecasting accuracy by 62.7%. [ABSTRACT FROM AUTHOR]

• A satellite image-based photovoltaic power forecasting model is proposed. • A nonlinear cloud movement forecasting method based on deep learning is proposed. • An algorithm for intra-hour cloud condition estimation is established. • An algorithm for recognizing the cloud region that blocks sunlight is designed. The rapid variation of clouds is the main factor that causes the fluctuation of photovoltaic power. 1 1 This work was supported by the National Key Research and Development Program of China under Grant 2019YFE0118400. The satellite images contain plenty of information about clouds, applicable for photovoltaic power forecast. However, in practice, two main factors obstruct the application of the satellite images: 1) the relatively low update frequency of the satellite images mismatches the photovoltaic power forecasting frequency, and 2) the cloud region that blocks the sunlight changes significantly with time. In this paper, a novel satellite image-based approach for photovoltaic power forecast is proposed to overcome these obstacles and achieve accurate forecasting results. Firstly, concerning the hourly updated satellite images, a nonlinear cloud movement forecasting model, considering the thickness and shape changes of the cloud, is presented to forecast the hourly variation of the images. Secondly, an active cloud region selection rule is derived based on the changing solar position to dynamically select the cloud region that blocks the concerned photovoltaic power station in a satellite image. Thirdly, a sequential cloud region selection algorithm is provided to estimate the intra-hour variation of the cloud to match the photovoltaic power forecasting frequency. Finally, the photovoltaic power is predicted using the XGBoost algorithm concerning the effects of the cloud and other influencing factors. Testing results show that the proposed method can achieve more accurate photovoltaic power forecasts using the low update frequency satellite images. Meanwhile, the superior performance compared with other benchmarks also verifies the effectiveness of considering cloud information obtained by the proposed method for photovoltaic power forecast. [ABSTRACT FROM AUTHOR]

[Display omitted] • GA-PLS, UVE-PLS, VCPA-PLS and CARS-PLS were comparatively investigated for CP detection. • CARS-PLS model achieved optimum results, the RPD was 4.87. • A SERS-based method for CP was developed with a LOD of 0.01 μg/mL. • The LODs found were lower than the People's Republic of China set the standard of CP in pollution-free rice. Due to the continuous development and progress of society and more and more attention to the quality and safety of food, rapid testing of pesticides in food is of great significance. In this paper, surface-enhanced Raman spectroscopy (SERS) and chemometric algorithms were employed collectively to quantify chlorpyrifos (CP) residues in rice samples. The SERS spectra from different concentrations (0.01–1000 μg/mL) of CP were collected using AgNPs-deposited-ZnO nanoflower (NFs)-like nanoparticles (Ag@ZnO NFs) SERS sensor. Four quantitative chemometric models for CP were comparatively studied, and the competitive adaptive reweighted sampling-partial least squares model achieved the best prediction and practical applicability for predicting CP levels with a limit of detection of 0.01 µg/mL. The results of the student's t -test showed no significant difference between this method and high-performance liquid chromatography (HPLC), and good relative standard deviation (RSD) indicated that this method could be used for the detection of CP in rice. [ABSTRACT FROM AUTHOR]

In this paper two different geophysical electro-magnetic techniques, long-offset time domain EM and the magnetotelluric method, are jointly inverted using a variation of Bayesian inversion called Hybrid Monte-Carlo. Long-offset time domain EM is a method that records Earth response to a controlled source square wave signal, while the magnetotelluric method records Earth response to naturally occurring electro-magnetic signals. We obtain better understanding of subsurface resistivity structures because the information they provide is complementary and joint inversion reduces the set of permissible inversion models. In most situations where long-offset time domain EM is recorded magnetotelluric data is also collected, since receiver instrumentation is shared. Hybrid Monte Carlo uses the gradient of the forward model to enable larger jumps through the solution space than are permissible using the standard Metropolis-Hastings algorithm, avoiding the pitfall of random walk behavior and giving faster convergence. The method is successfully applied to a survey area near the edge of Wuhan China. We obtain inversion results in a highly urbanized area, where the magnetotelluric method is typically compromised by background electro-magnetic noise. • HMC reduces random walks and gives faster convergence. • The joint-inversion of MT and LOTEM combines complimentary EM responses. • Problems with difficult combinations of conductors and resistor would benefit. • This is the first joint inversion of EM data using HMC. [ABSTRACT FROM AUTHOR]

The reservoir rocks in the volcanic strata of the Jilin oil field are characterized by great complexity and diversity in composition and structure of lithology. To enhance the rate of lithology identification in subsurface is very laborious. However, lithology identification is often ignored in quantitative studies, though it is the basis for reservoir characterization. In this paper, an ensemble learning algorithm named gradient boosting decision tree (GBDT) was used to establish the classification model for the volcanic lithology identification of the Lower Cretaceous Yingcheng Formation in the Songliao Basin, NE China. At the same time, support vector machine (SVM), logistic regression (LR) and decision tree (DT) classification models were also adopted in contrast with the classification accuracy of GBDT model. Subsequently, the optimal key parameters for each model were determined by employing validation curves and GridSearchCv. These results indicate that the GBDT model is superior to the single classifier and can accurately distinguish the lithologic interface of breccia tuff and rhyolite. Moreover, it also has better recognition ability for thin layer. It was concluded that the ensemble learning algorithm GBDT has significantly enhanced the accuracy of lithology identification and can be used as a lithologic identification technology. • The GBDT model is applied well in volcanic lithology identification. • Parameter optimization is conducted to optimize the classifier. • The GBDT model is superior to the single classifier. • The GBDT model and has better recognition ability for thin layer. [ABSTRACT FROM AUTHOR]

• A short-term reservoir optimal operation considering water propagation was proposed in this paper. • NPOA was applied to the short-term cascade reservoirs optimal operation. • The rationality and feasibility of the model and algorithm were verified. Difficulty remains to represent water propagation impact to construct water balance constraints for the Short-Term Cascade Reservoirs Optimal Operation (SCROO), due to the complex water propagation mechanism existing between cascade reservoirs. The imprecise representation of water propagation impact hinders the implementation of hydropower generation schemes and leads to operational risks. Thus, a modified SCROO model considering water propagation impact was proposed in this study to improve the accuracy of cascade operation. Specifically, water propagation is expressed using the Muskingum method in the modified SCROO model, and characterized by two variables, including propagation time related to the magnitude of the upstream reservoir's outflow and flow attenuation related to the propagation time, which were identified using the successive approximation method under water balance constraints within the modified SCROO model. Subsequently, this research applied the Nested Progressive Optimality Algorithm (NPOA) in the model calculation innovatively, in which the stage benefit function and nest computation could be improved. The modified model and algorithm developed herein were applied and validated on cascade reservoirs along the Yalung River in China by comparing it with traditional methods and algorithms. The results show that: 1) Compared to traditional methods, considering water propagation within water balance constraints as a dynamic parameter can calculate the inflow of downstream reservoir more accurately and mitigate risks in the actual operation of cascade reservoirs. Besides, the reservoir inflow calculated by traditional methods will lead to different magnitudes of benefit loss; and 2) the NPOA can significantly reduce dimensionality problems caused by the water propagation impact and greatly improve the actual hydropower generation than that of a traditional optimal algorithm, Multi-Stage Dynamic Programming (MSDP). Compared to the MSDP, the NOPA is more applicable in a cascade system with a larger number of reservoirs. The modified model and algorithm can significantly improve the effectiveness of cascade reservoir optimal operation schemes and provide more information for decision-makers. [ABSTRACT FROM AUTHOR]

Currently, remote sensing technologies were widely employed in the dynamic monitoring of the land. This paper presented an algorithm named fuzzy nonlinear proximal support vector machine (FNPSVM) by basing on ETM+ remote sensing image. This algorithm is applied to extract various types of lands of the city Da’an in northern China. Two multi-category strategies, namely “one-against-one” and “one-against-rest” for this algorithm were described in detail and then compared. A fuzzy membership function was presented to reduce the effects of noises or outliers on the data samples. The approaches of feature extraction, feature selection, and several key parameter settings were also given. Numerous experiments were carried out to evaluate its performances including various accuracies (overall accuracies and kappa coefficient), stability, training speed, and classification speed. The FNPSVM classifier was compared to the other three classifiers including the maximum likelihood classifier (MLC), back propagation neural network (BPN), and the proximal support vector machine (PSVM) under different training conditions. The impacts of the selection of training samples, testing samples and features on the four classifiers were also evaluated in these experiments. [ABSTRACT FROM AUTHOR]

Unlike the other fuzzy neural networks, in this paper the fuzzy system and the neural network are separated, and they are corresponding to structure identification and parameter identification. The fuzzy model is generated automatically by on-line clustering method and fuzzy support vector machines. This fuzzy model is not updated, but its modeling error is compensated by a neural network. The neural network acts as the parameter identification model. The benefits of this new method are the structure is simple, and the physical meanings of each part of the model are clear. For the neural compensator, a new stable and fast training algorithm is proposed. Finally, this modeling method is successfully applied to model a magnetic tube recovery ratio in a metal company in China. [ABSTRACT FROM AUTHOR]

A feature-weighted Support Vector Machine regression algorithm is introduced in this paper. We note that the classical SVM is based on the assumption that all the features of the sample points supply the same contribution to the target output value. However, this assumption is not always true in real problems. In the proposed new algorithm, we give different weight values to different features of the samples in order to improve the performance of SVM. In our algorithm, firstly, a measure named grey correlation degree is applied to evaluate the correlation between each feature and the target problem, and then the values of the grey correlation degree are used as weight values assigned to the features. The proposed method is tested on sample stock data sets selected from China Shenzhen A-share market. The result shows that the new version of SVM can improve the accuracy of the prediction. [ABSTRACT FROM AUTHOR]

In this paper, we apply finite-mixture-model-based clustering algorithms to cluster post-landfall tracks of tropical cyclones (TCs) making landfall over China. Because existing studies find that landfall surfaces or elevations affect post-landfall TC movements, we also take account of elevations in addition to time orders in this model. Our study reveals three clusters, with cluster 1 making landfall in Hainan province and moving across the western coast of Guangdong province. Most of the TC tracks in cluster 1 move over the ocean and make secondary landfalls over Yunnan province of China and Vietnam. Cluster 1 finally dissipates inland and moves westward as a result of the westward-shift subtropical high, westward steering flow, easterly vertical wind shear and strong mountainous blocking. Cluster 2 makes landfall over Guangdong and Fujian provinces. TCs in cluster 2 subsequently move inland and disappear due largely to westward-shift subtropical high, easterly steering flow, easterly vertical wind shear and relatively strong mountainous blocking. Cluster 3 makes landfall along the Fujian and Zhejiang coast and sustains a long period of time, recurving mostly to the mid-latitude region owing to the surrounding eastward-shift subtropical high, westerly vertical wind shear, weak mountainous blocking and westerly steering flow. Because cluster 2 is significantly associated with La Niña events, TCs more likely make landfall over southeastern China coast and move westward or northwestward without recurving. Cluster 3 sustains a longer time than clusters 1 and 2 in spite of its weak horizontal and vertical water vapor supply. TCs in cluster 3 interact actively with westerlies during the post-landfall period. However, we cannot observe any analogous interactions with the mid-latitude westerlies in clusters 1 and 2. TCs of clusters 1 and 2 are influenced by summer monsoon flows. Moreover, summer monsoon exerts a greater influence on cluster 1 than cluster 2. The composite 200 hPa divergence of cluster 3 is stronger than that of clusters 1 and 2. This explains to some degree why cluster 3 sustains longer than clusters 1 and 2 after making landfall. [ABSTRACT FROM AUTHOR]

Abstract: Addressing the transmission and storage problems of aurora images that Arctic Yellow River Station of China faces, the present paper proposes a two-stage lossless compression algorithm based on weighted motion compensation and context-based modeling. As no specialized compression algorithms for aurora images are available, proposing a new algorithm that offers a satisfactory lossless compression performance for aurora images is necessary. The proposed algorithm utilizes the weighted motion compensation to obtain the motion vector based on the unusual movement characteristics of aurora images. Subsequently, the context-based model is combined with the motion vector. Experimental results indicated that the proposed algorithm outperformed the state-of-the-art lossless compression methods. [Copyright &y& Elsevier]

The accuracy of annual electric load forecasting plays an important role in the economic and social benefits of electric power systems. The least squares support vector machine (LSSVM) has been proven to offer strong potential in forecasting issues, particularly by employing an appropriate meta-heuristic algorithm to determine the values of its two parameters. However, these meta-heuristic algorithms have the drawbacks of being hard to understand and reaching the global optimal solution slowly. As a novel meta-heuristic and evolutionary algorithm, the fruit fly optimization algorithm (FOA) has the advantages of being easy to understand and fast convergence to the global optimal solution. Therefore, to improve the forecasting performance, this paper proposes a LSSVM-based annual electric load forecasting model that uses FOA to automatically determine the appropriate values of the two parameters for the LSSVM model. By taking the annual electricity consumption of China as an instance, the computational result shows that the LSSVM combined with FOA (LSSVM-FOA) outperforms other alternative methods, namely single LSSVM, LSSVM combined with coupled simulated annealing algorithm (LSSVM-CSA), generalized regression neural network (GRNN) and regression model. [ABSTRACT FROM AUTHOR]

Abstract: To deal with water resources crisis caused by climate change and intensified human activities, the Hetao Irrigation District of Inner Mongolia in North China has been experiencing rehabilitation for water-saving since 1999. This has significantly changed its regional hydrological cycle and thus water use patterns over different land use types. In this paper, Surface Energy Balance Algorithm for Land (SEBAL) model fed with MODIS data was applied in Hetao area from 2000 to 2010 to examine the spatial and temporal patterns of evapotranspiration (ET). The SEBAL estimated ET agreed well with that from other methods in the study area. The results indicate that inter-annual variability in ET over agricultural land, water body, woodland and irrigated grassland are primarily explained by the variation of reference ET. So it is with the seasonal variability in agricultural land ET on monthly basis. However, the inter-annual variations of ET over sandy land, Gobi desert and mountain areas are mainly controlled by precipitation. Over the study period, a reduction in river water diversion for irrigation has not reduced the agricultural land ET, indicating no significant impact on agricultural production. But ET over the non-irrigated grassland tends to decrease, which was likely caused by declining groundwater table in recent years. [Copyright &y& Elsevier]

Abstract: In this paper, firstly a modified particle swarm optimization algorithm (MPSO) is developed, in which the mean value of past optimal positions for each particle and the mutation operation are considered for avoiding premature. In the optimization test, MPSO performs better than particle swarm optimization algorithm (PSO). Then MPSO is applied to solve four portfolio optimization models with the real data from the Hong Kong Stock Market, and optimal values are obtained when the number of swarm , respectively. Finally, actual return rates of these models are calculated in numerical experiments, and it is illustrated from these graphs of actual return rates that when considering higher return, Cai’s model performs better in short-term investment. [Copyright &y& Elsevier]

Abstract: Remote diagnose of fish diseases for farmers is unrealized in China, but use of mobile phones and remote analysis based on image processing can be feasible due to the widespread use of mobile phones with camera features in rural areas. This paper presents a novel method of classifying species of fish based on color and texture features and using a multi-class support vector machine (MSVM). Fish images were acquired and sent by smartphone, and the method utilized was comprised of the following stages. Color and texture subimages of fish skin were obtained from original images. Color features, statistical texture features and wavelet-based texture features of the color and texture subimages were extracted, and six groups of feature vectors were composed. LIBSVM software was tested using leave-one-out cross validation to find the best group for classification in feature selection procedure. Two multi-class support vector machines based on a one-against-one algorithm were constructed for classification. The feature selection results showed that the Bior4.4 wavelet filter in HSV color space achieved greater accuracy than the other feature groups. The classification results indicate that only the DAGMSVM meets the requirement of time efficiency for the system. The results of this study suggest that the best classification model for fish species recognition is composed of a wavelet domain feature extractor with Bior4.4 wavelet filter in HSV color space and a one-against-one algorithm based DAGMSVM classifier. [Copyright &y& Elsevier]

Based on the origin-destination-based Frank-Wolfe algorithm proposed by Anthony Chen (2002) which means the shortest-path of all OD pairs with the same origin was searched in the algorithm at one time and the link flow was updated by using one-OD-at-a-time flow update strategy, the initialization, linear search and shortest path search of ODBFW algorithm was improved in this paper, and the improved ODBFW algorithm was proposed. The improved ODBFW algorithm was used for Sioux Falls network which was common used in international similar researches and the road network of Zhucheng City, Shandong Province, China to compare the feasibility, efficiency and stability of the standard FW algorithm, ODBFW algorithm and improved ODBFW algorithm. The results indicated that the improved ODBFW algorithm has more efficiency and stability than standard FW algorithm. The time used for the improved ODBFW algorithm to reach convergence is nearly 15% less than the time used for the original algorithm in small road network, and is nearly 5% less than that in large road network. [ABSTRACT FROM AUTHOR]

Abstract: Case-based reasoning (CBR) method has been widely used to study environmental and spatial problems since the 1990s. Spatial relations among geographic cases and between case and environment (hereinafter to be referred as spatial relations) were not well considered in most of the previous studies. However, these relations are extremely important in geographic problems solving as spatially closer geographic phenomena are more likely to be similar than those are disperse in space. This paper presents a generic application paradigm based on CBR to solve geographic problems. To better consider the spatial relations, a new component of “Geographic Environment” was added into the standard CBR case representation model. A rough set-based algorithm was used to prune essential spatial relations, which were then used to extract key decision rules and retrieve similar past cases for the new problem. Standard CBR directly accepts the solution that was derived from the past similar cases to solve the new problem. In this study, however, solution was not accepted unless it also satisfied the key decision rules. An illustrating example was used to demonstrate how the general framework and algorithm could be applied to solve geographic problems. The algorithm was then evaluated by examining two datasets, the 2003 land use in the Pearl River Estuary and land use change from 1995 to 2000 in Zhuhai city of China. These two datasets were also examined by the standard CBR and Bayesian Networks (BNs) methods. Comparison of the stratified 10-fold cross-validation results indicated that the algorithm proposed in this study yielded statistically significant higher overall validation accuracy than the standard CBR and BNs methods. [Copyright &y& Elsevier]

In this paper,we propose a class of general partially linear varying-coefficient transformation models for ranking data. In the models, the functional coefficients are viewed as nuisance parameters and approximated by B-spline smoothing approximation technique. The B-spline coefficients and regression parameters are estimated by rank-based maximum marginal likelihood method. The three-stage Monte Carlo Markov Chain stochastic approximation algorithm based on ranking data is used to compute estimates and the corresponding variances for all the B-spline coefficients and regression parameters. Through three simulation studies and a Hong Kong horse racing data application, the proposed procedure is illustrated to be accurate, stable and practical. [ABSTRACT FROM PUBLISHER]