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Fractional-order derivative is attracting more and more attention of researchers in image processing because of its better property in restoring more texture than the total variation. To improve the performance of fractional-order variation model in image restoration, a truncated fractional-order variation model was proposed in Chan and Liang [Truncated fractional-order variation model for image restoration, J. Oper. Res. Soc. China]. In this paper, we propose a dual approach to solve this truncated fractional-order variation model on noise removal. The proposed algorithm is based on the dual approach proposed by Chambolle [An algorithm for total variation minimisation and applications, J. Math Imaging Vis. 20 (2004), pp. 89–97]. Conversely, the Chambolle's dual approach can be treated as a special case of the proposed algorithm with fractional order α = 1. The work of this paper modifies the result in Zhang et al. [Adaptive fractional-order multi-scale method for image denoising, J. Math. Imaging Vis. 43(1) (2012), pp. 39–49. Springer Netherlands 0924–9907, Computer Science, pp. 1–11, 2011], where the convergence is not analysed. Based on the truncation, the convergence of the proposed dual method can be analysed and the convergence criteria can be provided. In addition, the accuracy of the reconstruction is improved after the truncation is taken. [ABSTRACT FROM AUTHOR]

The multi-objective optimisation of high-speed rail profile with small radius curve is studied in the paper. A multi-objective mathematical model for rail profile optimisation of high-speed railway is established. The CN60 rail profile is parameterised into a series of generalised functions of design variables. In order to guarantee the smoothness of the rail profile and meet the maximum grinding depth of rail in China, the constraints are employed. The wheel-rail vertical clearance and equivalent conicity of wheelset are taken as objective functions, and a rail wear prediction programme is compiled. Contact line method is employed to complete the detection algorithm of wheel-rail contact points. Finally, NSGA-II Algorithm is adopted to solve the Pareto-optimal front of the optimisation model. A set of solutions are retrieved from the Pareto optimal front solution as the optimised profile. The optimised rail profile and the original rail profile are matched with the LMA wheel profile (a certain worn type of wheel profiles for EMU in China) respectively. It is testified that the rail profile could effectively reduce the rail wear and improve curving performance. The new method proposed in this paper can provide some reference for the optimisation design of high-speed rail profile with small radius curve. [ABSTRACT FROM AUTHOR]

Visually inspecting integrated circuit (IC) wire bonding defects is important to ensuring the product quality after the packaging process. The availability of IC X-ray images offers an unprecedented opportunity of studying the image-based automatic IC wire inspection. In this paper, a data-driven method consists of data pre-processing, feature engineering, and classification is developed to address such problem. The data pre-processing is composed of a chip identification algorithm for locating and separating IC chip image patches from the raw images as well as a wire segmentation algorithm for obtaining the wire region. Next, geometric features extracted from the segmented wires are fed into classification models for identifying defects. Five data mining methods are utilised to develop classification models. The vision detection system (VDS) and convolutional neural networks (CNN) are considered as benchmarks. In computational studies, the effectiveness of the developed method is validated by using X-ray images collected from a semiconductor back-end factory in Mainland China. A comparative analysis is conducted to determine the most suitable classifier for the developed method in the chip classification and the SVM model is finally selected. Advantages of the developed method are verified by benchmarking against the VDS and CNN. [ABSTRACT FROM AUTHOR]

Concentration of power in terms of user traffic and copyright content is most evident in content platforms in China. Such concentration has generated an unexpected impact on the way we understand and appreciate creativity, on copyright enforcement and determination of liability on content platforms, and on the regulation of the cultural market by the government. Specifically, the concentration of power in content platforms has not only curbed direct online piracies to a large extent but has also accelerated the fragmentation of copyright enforcement and spawned the need for algorithmic recommendation and filtering systems, which in turn has reinforced the cultural censorship system of China. This paper argues that the employment of algorithms by platforms must be treated with prudence: the algorithmic decision-making systems employed by platforms must be transparent as much as possible, and remedies must be provided for concerned users. The algorithms employed by content platforms must be adjusted to reflect not just the interests of the platforms but also the public interest in accessing and delivering information and local policy considerations. This paper suggests that our regulatory framework should reflect the algorithmic turn of content platforms in its legal and non-legal instruments and alleviate their negative impact on society. [ABSTRACT FROM AUTHOR]

We propose a mathematical model to solve the railway transit network design problem, considering the total time spent in the travel of all the passengers on the network as the optimization goal. The number of passengers to be transported, train accommodating capacity, station capacity, railway section capacity, and available train number is taken as the constraints in the model. Then, we design a heuristic algorithm - fuzzy plant growth simulation algorithm to solve the model. A computing case, based on the data of the railway network of part of East China is presented to prove the efficiency and effectiveness of the proposed model and algorithm. The approach presented in this paper can help to design the service plan in the view of the railway network for the railway operators. [ABSTRACT FROM AUTHOR]

Leaf area index (LAI), officially listed as one of essential climate variables, quantifies the structure and amount of vegetation and characterizes the interaction between vegetation and climate. The advanced MEdium Resolution Spectral Imager (MERSI-II) onboard FengYun-3D (FY-3D) can provide twice-daily global observations of earth at a spatial resolution of 250 m. Therefore, it has great potential for promoting the improvement of global LAI products and boosting the development of earth system modelling. However, the existing methods are mostly sensor-specific, they can not be directly applied to MERSI-II data for LAI generation. In this paper, we proposed a sensor-adaptive approach for LAI estimation based on MERSI-II observations. This method is composed of an LAI retrieval look-up table based on radiative transfer theory, which was calibrated by a global optimizing algorithm to adapt MERSI-II characteristics, and a backup algorithm training neural networks with MERSI-II Normalized Difference Vegetation Index (NDVI) and Moderate Resolution Imaging Spectroradiometer (MODIS) LAI. We evaluated the MERSI-II LAI retrievals by intercomparison to MODIS products over mainland China and direct validation using ground-based upscaled LAI reference maps. The assessments demonstrate that (1) the derived MERSI-II LAI products agree well with the MODIS benchmarks in both spatial and temporal respects; (2) compared to the MODIS LAI, the MERSI-II LAI retrievals with less gaps show great potential in improving spatiotemporal continuity; (3) validation versus in-situ measurements reveals acceptable accuracy of the MERSI-II LAI products with a R2 of 0.85 and RMSE of 0.82; (4) the proposed parametric optimization strategy could successfully transplant MODIS LAI algorithm to MERSI-II data. [ABSTRACT FROM AUTHOR]

This paper proposes the nonnegative adaptive elastic-net for simultaneous nonnegative estimation and variable selection in sparse high-dimensional linear regression models. By inheriting the good features of adaptive elastic-net, the nonnegative adaptive elastic-net enjoys the oracle property even in high-dimensional settings where the dimension of covariates can be much larger than the sample size. Through the simulation, we show that the newly proposed method deals with the collinearity problem better than alternative procedures in the literature. To make the proposed method practically feasible, we extend the multiplicative updates algorithm for implementation. Finally, we illustrate the favorable finite-sample performance of the proposed method through tracking the CSI 300 index, an important stock market index in China. [ABSTRACT FROM AUTHOR]

In the algorithmic era, both users and the platform battle for visibility. Chinese fans are savvy users who explore the hidden algorithms behind platform functions. With the collectively developed algorithmic imaginary, digital fandom communities negotiate with the platform over algorithms to optimize the visibility of celebrities they endorse. Drawing from participatory observation and semi-structured interviews in Chinese online fandoms of an idol group, INTO1, we detailed how fans as digital users collectively explore, interpret, and creatively utilize algorithms to increase their idol's visibility. We conclude that visibility, as a representation of algorithm power, is co-defined through the constant push-and-pull between digital users and the platform. This paper contributes to both algorithm and fandom studies by describing large-scale non-professional users' daily construction of the algorithmic imaginary in the unique context of Chinese fandom and beyond. It also discusses broader civic implications of fans' algorithmic practices to wider digital users in China. [ABSTRACT FROM AUTHOR]

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The vulnerable children refer to the special group of children with deviation in the process of children's psychological development and personality formation due to growth dilemmas. This may incur a series of serious social and family problems. The vulnerable children mainly cover the children suffering from children's psychological problems, such as childhood autism, autism, social anxiety and hypersensitivity, fear, depression, and PTSD arising from other factors. At present, the research results at home and abroad mainly focus on the psychological dynamic correlation investigation and solution discussion of a certain kind of difficult factor in the children's psychological development based on statistical data by the experimental methods, such as scale and model, and there is a blind spot in the humanistic orientation theory construction of psychological treatment for vulnerable children, causing the social reflection on children's psychological predicament from the humanistic perspective cannot be performed in related researches and going against searching for universal and integral theoretical paradigm for solving related problems. Sophisticated technologies for the observations have emerged increasingly for enabling the psychological features of vulnerable children through developmental cognitive neuroscience experiments. This paper introduces humanistic art therapy theory, focuses on the construction of a theoretical paradigm, and verifies its effectiveness based on the experimental results on the psychological development of vulnerable children, with an efficient performance. This study mainly refers to children with difficulty in social inclusion and psychological development. The results showed that two kinds of art therapy can obviously improve the psychological disorders of vulnerable children. The goal was to enhance self-cognition, strengthen emotional interaction, and implement positive motivation. [ABSTRACT FROM AUTHOR]

This paper investigates China's macroeconomic fluctuations by using the agent-based New Keynesian (ABNK) model. The model features bounded rationality, heterogeneous expectations, and adaptive learning. The model is estimated by the Bayesian method combined with the differential evolution algorithm and is analyzed using the impulse response, forecast performance, and variance decomposition approach. The estimation results show that in the real economy, in addition to rational agents, there are also boundedly rational agents with adaptive learning expectations. The impulse responses of macroeconomic variables to shocks are more sensitive and last longer than those from the dynamic stochastic general equilibrium (DSGE) model. The ABNK model exhibits better out-of-sample forecast performance than both the vector auto-regression (VAR) and DSGE models. [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]

The paper considers the design of a multimodal and multilayer inbound logistics network for iron ore to be delivered from suppliers to steel plants. To make cost-efficient logistics and transportation planning decisions, steel companies should account for uncertainty in the demand for iron ore and the economies of scale during transshipment process in the port, which are two critical elements affecting decisions. To this end, we propose a two-stage nonlinear stochastic programming model with the first stage determining the choice of the port to perform transshipment operations and the needed capacity, and the second stage selecting transportation modes after demand uncertainty has been realized. To solve this problem, we first reformulate and linearize the model based on a quantity discount policy applied to the transshipment ports, and then, we develop a scenario-based decomposition algorithm. We conduct a case study based on the data of a steel company in China to illustrate the applicability of our proposed model. Moreover, we perform numerical experiments to demonstrate the effectiveness of our algorithm. [ABSTRACT FROM AUTHOR]

Ride comfort indices are used to evaluate the vibration of rail vehicles and to measure the discomfort of passengers. Among the various ride comfort indices, the Sperling index is widely used in China and other countries. However, there are several different methods for determining the Sperling index from dynamic simulations and performance measurements of rail vehicles, and the results obtained by different algorithms are inconsistent. It is therefore difficult to make an accurate evaluation with the different calculation results. In this paper, a comparison is made between algorithms based on time domain and frequency domain analysis and using the second and third powers of the acceleration. The different algorithms are then summarised into a unified equation, and the consistency of the results is analysed by this equation. It is found that only the r.m.s-based algorithm in the time or frequency domain is stable when analysis is carried out over different sample times. The time-frequency consistency of the r.m.s-based algorithms is verified by Parseval's theorem and using calculation results from time domain simulations. [ABSTRACT FROM AUTHOR]

The surface displacement by seasonally freezing bulge and thawing subsidence are the main hazards for engineering construction in permafrost regions, especially for the Qinghai-Tibet railway. One of the main problems is how to monitor the frozen ground's displacement in the process of construction and protection of the Qinghai-Tibetan railway. The technology of Permanent Scatterers (PS) has been successfully used to detect the long-term subsidence at urban areas. For detecting the subsidence of the frozen earth on Qinghai-Tibet Plateau, this paper extended the capability of the technology of PS to investigate deformation phenomena in vegetated area. The paper analyses an interferometric phase model, and presents improved PS algorithms for separating different components in interferometric phase. The proposed technique is implemented using ENVISAT ASAR images to detect the deformation over permafrost region of Qinghai-Tibet Plateau. The results are in concordance with results provided by a traditional ground levelling, which encourages future development using the Permanent Scatterers method to analyse deformation of the frozen earth on Qinghai-Tibet Plateau. [ABSTRACT FROM AUTHOR]

The post-disaster need assessment for the affected area is of significance for the effective distribution of relief supplies. This paper innovatively concentrates on the integrated routing optimization problem for the rapid need assessment stage and the detailed need assessment stage. A bi-objective model with consideration of the effects of site familiarity and route familiarity is proposed. The proposed nonlinear model is equivalently transformed into a mixed-integer linear programming. A tabu search algorithm is designed to solve the proposed model, and its solving accuracy is validated by comparing with the solution of CPLEX based on a small-scale problem. To demonstrate the efficiency of the proposed model and algorithm for large-scale problems, the case of Ya'an earthquake in China is investigated. The experimental results reveal that comparing to the separate routing optimization and the sequential routing optimization, the integrated routing optimization can achieve a better coordination between the decisions of two stages. [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]

The mismatching patient flow distribution in the health care system in urban China is a great social issue that attracts lots of public attention. In this research, we propose a simulation-based optimisation method using the multi-fidelity optimisation with ordinal transformation (OT) and optimal sampling (OS) () algorithm to evaluate the patient flow distribution, so as to continuously improve the hierarchical health care service system. The low-fidelity model applying the queueing network theory is constructed for the OT part of the, followed by a high-fidelity but time-consuming discrete event simulation model for the OS part. An empirical study on the background of the hierarchical health care delivery system in China is presented, where the proposedmethod is implemented to optimise the system profit by guiding the patient flow distribution. A comparison with other widely used simulation optimisation methods sustains the efficacy of thewith the evidence that acquiring effective information from the low-fidelity model indeed retrenches the computing budget used to explore the feasible domain. [ABSTRACT FROM PUBLISHER]

Both the lack of the downward regulation capacity and the transmission capacity limit have led to considerable wind power curtailments in China. The battery energy storage system (BESS) provides a new solution to reduce the wind power curtailments due to its relatively high energy density and flexible installed location. In this paper, a new bi-level programming model is proposed to optimize the size and location of BESS for reducing the wind curtailments. The revenues of BESS, when it is located either at the wind farm or the load center, are considered comprehensively. Moreover, a numerical optimization algorithm is developed to solve the proposed model. Simulation results demonstrate the validity of the proposed model and developed algorithm. They also reveal that the BESS located at the load center offers more net profit compared to the one located at the wind farm. Subsequently, sensitivity analysis is performed to assess the effect of key parameters on the optimal values of BESS. [ABSTRACT FROM AUTHOR]

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]

Most multiple-flow-direction algorithms (MFDs) use a flow-partition coefficient (exponent) to determine the fractions draining to all downslope neighbours. The commonly used MFD often employs a fixed exponent over an entire watershed. The fixed coefficient strategy cannot effectively model the impact of local terrain conditions on the dispersion of local flow. This paper addresses this problem based on the idea that dispersion of local flow varies over space due to the spatial variation of local terrain conditions. Thus, the flow-partition exponent of an MFD should also vary over space. We present an adaptive approach for determining the flow-partition exponent based on local topographic attribute which controls local flow partitioning. In our approach, the influence of local terrain on flow partition is modelled by a flow-partition function which is based on local maximum downslope gradient (we refer to this approach as MFD based on maximum downslope gradient, MFD-md for short). With this new approach, a steep terrain which induces a convergent flow condition can be modelled using a large value for the flow-partition exponent. Similarly, a gentle terrain can be modelled using a small value for the flow-partition exponent. MFD-md is quantitatively evaluated using four types of mathematical surfaces and their theoretical 'true' value of Specific Catchment Area (SCA). The Root Mean Square Error (RMSE) shows that the error of SCA computed by MFD-md is lower than that of SCA computed by the widely used SFD and MFD algorithms. Application of the new approach using a real DEM of a watershed in Northeast China shows that the flow accumulation computed by MFD-md is better adapted to terrain conditions based on visual judgement. [ABSTRACT FROM AUTHOR]

This paper presents a synthesis technique of the dual offset shaped reflector antenna using Differential Evolution with Global and Local neighborhoods (DEGL) algorithm. The main reflector and subreflector are parameterized by Zernike polynomial expansion. With these expansion coefficients, the antenna is optimized to achieve a contoured beam covering the China territory in Ku-band. The performance of the antenna is calculated by Physical Optics (PO) and Physical Theory of Diffraction (PTD) techniques. The calculated results are validated by the commercial software GRASP9.3.01. [ABSTRACT FROM AUTHOR]

In this paper we present an improved watershed segmentation algorithm for settlement mapping from medium resolution satellite data over plain areas in China. The algorithm can increase the computational efficiency of the fastest reported watershed segmentation algorithm by 30-40%. We apply this method to a selected study area in southern Hebei Province, China. We acquired a Landsat Enhanced Thematic Mapper Plus (ETM + ) image over this area in May 2000, two Landsat Thematic Mapper (TM) images in August 2004 and April 2005, and two Beijing-1 satellite images in May 2006 and May 2007. The three types of images have three similar spectral bands (green, red and near-infrared) with similar spatial resolution (30-32 m). Only the red and near-infrared bands were used in image segmentation for settlement area extraction. The extracted settlement results are compared with manual interpretation results by two people. We assumed the human interpretation results are of higher accuracy than the segmentation results. Our results indicated that our settlement area extraction method is effective. With high quality images, the overall accuracies are nearly 94%, the kappa coefficient can be greater than 0.85. [ABSTRACT FROM AUTHOR]

A remote sensing approach is proposed in this paper for monitoring instantaneous underwater light climate in Dahuchi Lake, China. Water constituents that are optically significant to the lake water can be retrieved through this method based on an artificial neural network (ANN) algorithm with a satisfactory accuracy. In this study, the ANN was trained by spectral simulations which were generated by a water colour simulation model (WASI). An underwater light attenuation model has been built by fitting the field measurements and applied to calculate photosynthetically available radiation (PAR) at the bottom of the lake from the inputs of hyperspectral reflectance above the water surface and the water depth. Results show that this method can predict the concentration of suspended particle matter (SPM) with a high accuracy and explain more than half of the underwater PAR variation with a standard deviation of 94.8 μmol s-1 m-2. In order to test the portability of the algorithm, it has also been extended to three bands of Landsat TM image. Compared with hyperspectral inputs, ANN with broadband inputs has lower but still acceptable accuracy on SPM retrieval. The advantage of this method lies in its time-efficient and cost-efficient monitoring of underwater light climate, i.e. the simulations generated by a semi-physical model can be used for ANN training, instead of using a large number of field measurements. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive knowledge-based similarity classifier that uses remote sensing images and other auxiliary data to map spatial heterogeneity, for stratifying sample units distributed at the geographical landscape in order to improve the precision of the estimate of interest. Our method emphasises the decrease of bias so as to produce the high-quality stratifying frame. For this purpose, the method takes some necessary measures such as use of auxiliary variables including spectral bands, physical and socioeconomic data to help cluster analysis, correlation analysis between auxiliary variables and the goal variable to remove irrelevant data and consideration of spatial correlation in cluster analysis through the density-based unsupervised learning etc. Furthermore, considering the time-consuming characteristic of clustering huge spatiotemporal datasets, the method uses non-parametric supervised learning to induce rules for clustered classes. The rules could be efficiently used to group pixels into different classes of similarity. Then in the method, the pixel-level similarity image was vectorised into polygons with different group labels, thus producing the vector map of geospatial heterogeneity as an easy-to-use stratification frame. Last, to have an accurate estimation of the goal variable, our method re-divided sample units while the units covered by different strata and considered the effect of the sample size in the estimation algorithm. In the survey case of the cultivated land area, the proposed method achieves higher accuracy and a better coefficient of relative efficiency (RE) of stratification with its estimate closer to the observed value in comparison with other stratification strategies, e.g., k-means, SOM and those similar to eco-regions. Our method has potential practical merits as a good stratification strategy can increase the precision and considerably save the cost of sampling for many large regions, such as those in China, to be surveyed. [ABSTRACT FROM AUTHOR]

The DInSAR technique with a multibaseline is becoming popular nowadays to investigate slow urban deformation. In this paper, we focus on deformation retrieval in large areas, including urban and suburban areas. Based on the multibaseline DInSAR algorithm proposed by Mora, three extensions are derived. First, least-squares adjustment and error-controlling methods are used to obtain stable deformation velocity and height error estimations. The least-squares QR factorizaiton algorithm is emphasized to solve large, linear, and sparse functions. Second, a new complex network is presented to limit noise effects on the Delaunay triangular network. Third, by combining complex and Delaunay networks, large-area deformation is investigated, from centre urban areas to suburban areas. The enhanced algorithm is performed to investigate the subsidence of Cangzhou, Hebei province (northern China) during 1993-1997 by using 9 ERS SLC data. The experimental results show serious subsidence in the region and are validated by levelling data and groundwater wells data. Compared with levelling data, the estimation errors of linear deformation velocity in urban areas are in the range of (-2, 2) mm year-1, and in suburban areas, the errors are in the range of (-26, 15) mm year-1, which is sufficiently feasible to determine the status of subsidence relative to the maximum deformation velocity of about -100 mm year-1. The subsidence centres in urban areas are consistent with the spatial distribution of groundwater wells, which provides evidence that groundwater overexploitation is the main cause of subsidence in Cangzhou. The closure of wells will be a good way to control subsidence in the future. [ABSTRACT FROM AUTHOR]

Word retrieval deficits are common concerns for persons with aphasia (PWA). Treatment approaches can be divided into two processes, namely phonological and semantic, based on models suggesting that word activation consists of activating the semantic representation and phonological form of the target word. The role of syntax, however, has received less attention in models of retrieval and in the treatment of word retrieval deficits. This study investigated whether a fixed noun phrase can support word finding in a PWA. Chinese is a classifier language where a classifier is obligatory in the quantitative noun phrase. The language uses two types of prenominal classifiers: sortal (serves to count individual nouns) and mensural (serves to count the units of nouns) classifiers. A Cantonese Chinese speaking PWA (LCH) with marked word retrieval deficits participated in this 15 bi-weekly, 90-minute treatment sessions, which focused on use of classifier cues to aid naming on confrontation naming. Twelve classifiers (6 sortal and 6 mensural) were selected to train target nouns. Target nouns were selected to pair with selected classifiers. Responses to cues were collected during each treatment session, documenting changes over time. Leveraging the relationship between target nouns and obligatory prenominal classifiers resulted in the participant's improved picture naming for trained nouns as well as generalization to select untrained nouns. We report significant differences in the efficacy of sortal versus mensural classifiers to serve as primes for word retrieval. Sortal classifiers were significantly more effective than mensural classifiers, for both cueing trained and untrained target nouns. For this speaker of a classifier language, use of classifiers served as viable cues for word retrieval and that sortal classifiers were more effective than mensural classifiers. Though word order does not distinguish between these two types of classifiers, their relative priming efficiency can be observed at the deep structure level. Findings suggest that a morphosyntactic approach to address word retrieval in a Chinese speaker may be beneficial but that great care must be taken in the creation of treatment materials. These factors must be further explored with additional participants and with a wider range of classifiers. [ABSTRACT FROM AUTHOR]

Monitoring the risk of intussusception after the introduction of rotavirus vaccines is recommended by the World Health Organization (WHO). Although the validity of intussusception monitoring using electronic health records (EHRs) has been confirmed previously, no similar studies have been conducted in China. We aimed to verify the diagnosis and determine an algorithm with the best performance for identification of intussusception using Chinese EHR databases. Using the Regional Health Information Platform in Ningbo, patients aged 0–72 months from 2015 to 2021 with any related visits for intussusception were included. The algorithms were based on diagnostic codes or keywords in different clinical scenarios, and their performance was evaluated with positive predictive value (PPV) and sensitivity in line with the Brighton guidelines. Brighton level 1 intussusception was confirmed in 2958 patients with 3246 episodes. Fine-tuned algorithms combining the appearance of the relevant ICD-10 codes or the Chinese keyword 'Chang Tao' in any diagnostic reports with the results of enema treatments or related surgeries showed the highest sensitivity, while the highest PPV was obtained by further criteria based on typical radiographic appearances. Intussusception could be identified and validated internally using EHRs in Ningbo. [ABSTRACT FROM AUTHOR]

Understanding urban mobility patterns is constrained by our limited capabilities to extract and visualize spatio-temporal regularities from large amounts of mobility data. Moving flocks, defined as groups of people traveling along over a pre-defined time duration, can reveal collective moving patterns at aggregated spatio-temporal scales, thereby facilitating the discovery of urban mobility structure and travel demand patterns. In this study, we extend classical trajectory-oriented flock mining algorithms to discover moving flocks of transit passengers, accounting for the constraints of multi-modal transit networks. We develop a map-centered visual analytics approach by integrating the flock mining algorithm with interactive visualization designs of discovered flocks. Novel interactive visualizations are designed and implemented to support the exploration and analyses of discovered moving flocks at different spatial and temporal scales. The visual analytics approach is evaluated using a real-world smart card dataset collected in Shenzhen City, China, validating its applicability in capturing and mapping dynamic mobility patterns over a large metropolitan area. [ABSTRACT FROM AUTHOR]

To improve the performance of enterprise network information security, we proposed a behaviour analytics model that established a unique behaviour pattern for each user and identifies untrusted interactive behaviour. First, a series of behaviour characteristics was constructed by observing user behaviours. These characteristics were then used by a big data analysis method called hidden Markov model to model the behaviour of trusted users. Next, a forward algorithm calculated the probability of observation sequences from users with the same and different positions. Finally, untrusted interactive behaviours were identified by comparing the observation sequence probability sets of trusted and untrusted users. The proposed method was applied to the Enterprise Resource Planning system used by a publishing house to identify the credibility of its user behaviour. The highest false positive rates obtained were 0.74% and 5.26% for users in different positions and the same position, respectively. These results verify that the model is effective in identifying untrusted interactive behaviours. [ABSTRACT FROM AUTHOR]

Rice is the most important food crop in China, which is of great significance to the country's food security and regional stability. Therefore, it is very important to accurately monitor the paddy rice planting area and its changes. In this study, an adaptive-stacking method based on Google Earth Engine (GEE) was proposed to identify paddy rice using multi-temporal Sentinel-1 and Sentinel-2 remote sensing data. The classification accuracy and Kappa coefficient are 88.53% and 0.85, respectively. Moreover, the user accuracy and producer accuracy of paddy rice reached 86.58% and 86.92%, respectively. The results demonstrate that the superiority of the strategy that integrates multi-temporal optical and SAR data, and the potential of using adaptive-Stacking algorithm based on the GEE for paddy classification. [ABSTRACT FROM AUTHOR]

Factor strategies have gained growing popularity in industry with the fast development of machine learning. Usually, multi-factors are fed to an algorithm for some cross-sectional return predictions, which are then further used to construct a long-short portfolio. Instead of predicting the value of the stock return, emerging studies predict a ranked stock list using the mature learn-to-rank technology. In this study, we propose a new listwise learn-to-rank loss function which aims to emphasize both the top and the bottom of a rank list. Our loss function, motivated by the long-short strategy, is endogenously shift-invariant and can be viewed as a direct generalization of ListMLE. Under different transformation functions, our loss can lead to consistency with binary classification loss or permutation level 0-1 loss. A probabilistic explanation for our model is also given as a generalized Plackett-Luce model. Based on a dataset of 68 factors in the China A-share market from 2006 to 2019, our empirical study has demonstrated the strength of our method which achieves an out-of-sample annual return of 38% with Sharpe ratio 2. [ABSTRACT FROM AUTHOR]

The increasing and widespread usage of social media enables the investigation of public preference using the web as a device. Public sentiment as expressed in 44,319 massive open online course (MOOCs) related microblogs from January to December 2017 was examined on Sina Weibo (the Chinese equivalent of Twitter) to obtain broad insight into how MOOCs are viewed by the public in the Chinese educational landscape. Despite the unstable upward trend of public interest in MOOCs over the past 12 months, the public opinion on MOOCs was largely positive. Content and sentiment analyses were conducted to facilitate a better understanding of what is communicated on social media. A general model of public opinions of MOOCs in China has been developed based on the findings. Individuals were classified into a threefold typology based on the sources and purposes of how this recent form of distance education was perceived. Based on the seven themes, the public views towards MOOCs were differentiated among 'promoters', 'commenters' and 'experiencers'.Implications of the findings were also discussed. [ABSTRACT FROM AUTHOR]

We aimed to predict the mortality of patients with craniotomy in ICU by using predictive models to extract the high-risk factors leading to the death of patients from a retrospective a study. Five machine-learning (ML) algorithms were applied for training on mortality predictive models with the data from a surgical intensive care unit (ICU) database of the Fujian Provincial Hospital in China. The accuracy, precision, recall, f1 score and the area under the receiver operator characteristic curve (AUC) were used to evaluate the performance of different models, and the calibration of the model was evaluated by brier score. We demonstrated that eXtreme Gradient Boosting (XGBoost) was more suitable for the task, demonstrating a AUC of 0.84. We analyzed the feature importance with the Local Interpretable Model-agnostic Explanations (LIME) analysis and further identified the high-risk factors of mortality in ICU through this study. This study established the mortality predictive model of patients who had undergone craniotomy in ICU. Identification of the factors that had great influence on mortality has the potential to provide auxiliary decision support for clinical medical staff on their practices. [ABSTRACT FROM AUTHOR]

Combining accounting-type firm data and transactions-type customs data has become increasingly important for research in international and industrial economics. The statistical authorities in several countries such as the United States or France provide such linked data without details on sources, and researchers have to assume that the matching is correct and the firm identifiers are unique and flawless in the source data. For some other countries such as Switzerland or China, firm and customs data contain information which permits such linking ex post using string matching based on firm names and their meta-information like addresses. Due to spelling and typos, such matching is prone to some errors. Obtaining the largest-possible number of high-quality matches helps avoid potential biases while keeping crucial details. We report on a new algorithm which improves considerably the hitherto available linking efforts of the National Bureau of Statistics firm-level and the Customs trade data for China. [ABSTRACT FROM AUTHOR]

To cope with the nonlinear and nonstationarity challenges faced by conventional runoff forecasting models and improve daily runoff prediction accuracy, a hybrid model-based "feature decomposition-component prediction-result reconstruction" named VMD-LSTM-PSO was proposed. First, variational mode decomposition (VMD) was used to decompose the original daily runoff series into a discrete number of intrinsic mode functions (IMFs) to produce clearer signals. Then, for each IMF, a long short-term memory (LSTM) network was applied to establish the prediction model, and a particle swarm optimization (PSO) algorithm was utilised to optimise the number of hidden layer nodes and the learning rate of the LSTM. The model was applied in three hydrologic stations in the main stream of the Yellow River of China and compared with the complementary ensemble empirical mode decomposition coupled long short-term memory and particle swarm optimization (CEEMD-LSTM-PSO), wavelet transform coupled long short-term memory and particle swarm optimization (WT-LSTM-PSO) and LSTM-PSO models. Based on its high predictive accuracy and stability, the novel model promises to be a preferred data-driven tool for hydrological forecasting in practice. [ABSTRACT FROM AUTHOR]

The purpose of this study is to provide a new pricing strategy for the container terminal handling charge (THC) of terminals. A dynamic pricing model is established by using the Back Propagation Neural Network (BPNN) algorithm and the Time-Driven Activity-Based Costing method. This pricing strategy can dynamically amend the price of the container THC based on handling demand, recent charge standards per container, and handling time for a particular customer. To some extent, this dynamic pricing strategy can provide a valuable reference for terminals in pricing decisions. This case study implemented the dynamic pricing model at the Shanghai ShengDong International Container Terminal, one of the largest container terminals in China. The results show that this pricing model dynamically adjusts the container THC depending on customers' handling conditions. Besides, this dynamic pricing model is more precise than the traditional contractual pricing used at the terminal. Compared with the BPNN algorithm, the optimized BPNN algorithm has faster convergence speed and less learning error. Moreover, this pricing method is universally applicable to the container THC pricing problem of most terminals. [ABSTRACT FROM AUTHOR]

The constrained serial dictatorship mechanism is a popular method used in school admissions. However, due to the number of combinations possible, simulating the behaviour of students in their selection of schools and computing the best strategy for them to select a limited number of schools is currently a highly complex and time-consuming endeavour. The purpose of this study is to propose a recursive algorithm to significantly reduce this computational time and to provide an empirical application by employing this algorithm. To do this, I studied how the behaviours and utilities of students will change if provided with more choices for college admissions in Guangxi in China. The results indicate that neither the behaviours nor the utilities change significantly with the provision of more choices, which implies that increasing the available choices of schools for students in Guangxi would be a waste of resources. [ABSTRACT FROM AUTHOR]

Low consumer-grade cameras attached to small unmanned aerial vehicles (UAV) can easily acquire high spatial resolution images, leading to convenient forest monitoring at small-scales for forest managers. However, most studies were carried out in the low canopy density and flat ground plantations to detect individual trees. We selected overlapping canopy plantation in mountainous area in the eastern of China and acquired high spatial resolution UAV RGB images to detect individual trees. A total of 402 reference trees were located in three rectangle plots (900 m2). To enhance the confidence of the tested individual tree detection method, clear-cutting and Real-Time Kinematic (RTK) were used to obtain the truth values in the plots. A novel method for semi-automatic individual tree detection was proposed based on a local-maximum algorithm and UAV-derived DSM data (LAD) in this study. The detection accuracy of LAD was compared with commonly used methods based on UAV-derived orthophoto images, local-maximum algorithm (LAO), object-oriented feature segmentation (OFS), multiscale segmentation technique (MST) and manual visual interpretation (MVI). The overall accuracy (OA (%) decreased in the order of LAD (84.5%) > MST (69.1%) > OFS (65.1%) > MVI (64.1%) > LAO (59.1%). LAD had only 15.5%s omission errors (OM (%), which was less than half of the other four methods in comparison. It was noteworthy that MVI had 35.9% OM %, which revealed that MVI should be used carefully as the truth value. LAD showed similar repeated detection error (RP (%) and completely wrong detection (CW (%), while the other four methods had obviously higher CW % than the RP %. From our results, it can be concluded that the proposed LAD method may help improving the accuracy of individual tree detection to an acceptable accuracy (>80%) in dense mountain forests, and has practical advantages in future research direction to assess tree attributes from UAV RGB image. [ABSTRACT FROM AUTHOR]

Accurate streamflow prediction information is of great importance for water resource planning and management. The goal of this research is to develop a hybrid model for forecasting short-term runoff time series, where the variational mode decomposition (VMD) is first used to decompose the original nonlinear natural streamflow into numerous subcomponents with different frequencies and resolutions. Second, the extreme learning machine (ELM) is used to excavate the complicated input–output relationship hidden in each subcomponent, and the emerging sine cosine algorithm (SCA) is used to determine the suitable network parameter for each ELM model. Finally, the forecasting results of all the modelled subcomponents are summarized to form the forecasting result for original streamflow. Based on several statistical evaluation measures, the feasibility of the hybrid method is investigated in runoff forecasting for Danjiangkou Reservoir in China. The results indicate that the hybrid method can produce superior forecasting results compared to several control methods, providing accurate streamflow prediction information for operators. [ABSTRACT FROM AUTHOR]

The Bayesian Maximum Entropy (BME) algorithm that can model larger-scale spatial heterogeneity and integrate multiple types of data is a better spatial estimation algorithm in avoiding the circular problem and improving the estimation accuracy of parameters than original empirical geostatistic and spatial statistic methods. To obtain higher-resolution and higher-accuracy soil moisture (SM) data, this study used the BME algorithm to integrate the multiple environmental factor variables related to SM as auxiliary data (11 data sets in total) such as the Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST), and Albedo products with 1 kilometre (km) grid resolution from the Moderate-resolution Imaging Spectroradiometer (MODIS), the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM) satellite data, and the Slope, Aspect, Plan curvature, Profile curvature, Surface roughness, Wetness index, Relief amplitude products generated from ASTER GDEM data. First, the FengYun 3-B satellite (FY3-B) SM product with 25 km grid resolution was downscaled to 1 km grid resolution using the visible, infrared (IR), and microwave fusion method. The downscaled FY3-B SM data and auxiliary data were used to generate the weighted probability soft data (SD) under four cases, case 1: 500 sample points of SD, case 2: 450 sample points of SD, case 3: 400 sample points of SD, and case 4: 350 sample points of SD. Particularly, we used two methods, the multivariable correlation analysis method and principal component analysis (PCA) method to get the weight values of environmental factor variables, i.e. NDVI, LST, Albedo, the Digital Elevation Model (DEM), Slope, Aspect, Plan curvature, Profile curvature, Surface roughness, Wetness index, and Relief amplitude with SM. Then, the in-situ SM measurements as hard data (HD) were used to calibrate the weighted probability SD in the procedure of BME SM estimation. The SM under four cases were estimated from the weighted probability SD and HD using BME algorithm. Finally, comparisons of the estimated SM using BME algorithm with in-situ SM measurements at maize study area were carried out in this study. Our results indicated that the accuracy of the estimated SM using BME algorithm, the root-mean-square error (RMSE) = 0.049 cm3 cm−3, the correlation coefficient (r) = 0.639, the unbiased RMSE (RMSEu) = 0.047 cm3 cm−3, and Bias = 0.002 cm3 cm−3, under case 1 based on PCA method was obviously better than the downscaled FY3-B SM (RMSE = 0.079 cm3 cm−3, r = 0.096, RMSEu = 0.069 cm3 cm−3, and Bias = 0.039 cm3 cm−3) that was generated by the visible, IR, and microwave fusion method. We concluded that integrating auxiliary data into SM estimation using BME algorithm could further improve the downscaled FY3-B SM that was generated by visible, IR, and microwave fusion method. [ABSTRACT FROM AUTHOR]

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This article develops the critical concept of digital utility through studying the case of DiDi Chuxing and the platformization of transport services in urban China. By examining DiDi's business model, its datafication strategies, its relations with the Chinese government, and its labor management systems, the article demonstrates how the platformization of transport is emblematic of a private company becoming a digital utility provider. With technological imagination and practical inconsistency, this process remediates service delivery while reworking infrastructures and redefining the access to public and private services. We argue that platform companies are able to become digital utility suppliers because of their capacity to straddle the public and the private sectors, their aspiration to become "ecosystem builders," and their heavy reliance on the constant intensive labor of users, particularly drivers, to produce data. However, these factors also make instability a definitive feature of digital utility companies in their present condition. Morphing into the terrain of utilities is a common undertaking by DiDi and similar platform companies. To problematize the logics of digital utility, especially its labor-intensive datafication processes and its complex relations with regulators, provides a conceptual anchor for further debates on the infrastructuralization of platforms and the platformization of society. [ABSTRACT FROM AUTHOR]

This study examines the use of "algorithms in everyday labor" to explore the labor conditions of three Chinese food delivery platforms: Baidu Deliveries, Eleme, and Meituan. In particular, it examines how delivery workers make sense of these algorithms through the parameters of temporality, affect, and gamification. The study also demonstrates that in working for food delivery platforms, couriers are not simply passive entities that are subjected to a digital "panopticon." Instead, they create their own "organic algorithms" to manage and, in some cases, even subvert the system. The results of the approach used in this study demonstrate that digital labor has become both more accessible and more precarious in contemporary China. Based on these results, the notion of "algorithmic making and remaking" is suggested as a topic in future research on technology and digital labor. [ABSTRACT FROM AUTHOR]

The central aim of this experiment was to compare acoustic parameters, formant frequencies and vowel space area (VSA), in adolescents with hearing-impaired (HI) and their normal-hearing (NH) peers; for kinematic parameters, the movements of vocal organs, especially the lips, jaw and tongue, during vowel production were analysed. The participants were 12 adolescents with different degrees of hearing impairment. The control group consisted of 12 age-matched NH adolescents. All participants were native Chinese speakers who were asked to produce the Mandarin vowels /a/, /i/ and /u/, with subsequent acoustic and kinematic analysis. There was significant difference between the two groups. Additionally, the HI group produced more exaggerated mouth and less tongue movements in all vowels, compared to their NH peers. Results were discussed regarding possible relationship between acoustic data, articulatory movements and degree of hearing loss to provide an integrative assessment of acoustic and kinematic characteristics of individuals with hearing loss. [ABSTRACT FROM AUTHOR]

Objectives: Because of limited spectral resolution, Mandarin-speaking cochlear implant (CI) users have difficulty perceiving fundamental frequency (F0) cues that are important to lexical tone recognition. To improve Mandarin tone recognition in CI users, we implemented and evaluated a novel real-time algorithm (C-tone) to enhance the amplitude contour, which is strongly correlated with the F0 contour.Methods: The C-tone algorithm was implemented in clinical processors and evaluated in eight users of the Nurotron NSP-60 CI system. Subjects were given 2 weeks of experience with C-tone. Recognition of Chinese tones, monosyllables, and disyllables in quiet was measured with and without the C-tone algorithm. Subjective quality ratings were also obtained for C-tone.Results: After 2 weeks of experience with C-tone, there were small but significant improvements in recognition of lexical tones, monosyllables, and disyllables (P < 0.05 in all cases). Among lexical tones, the largest improvements were observed for Tone 3 (falling-rising) and the smallest for Tone 4 (falling). Improvements with C-tone were greater for disyllables than for monosyllables. Subjective quality ratings showed no strong preference for or against C-tone, except for perception of own voice, where C-tone was preferred.Discussion: The real-time C-tone algorithm provided small but significant improvements for speech performance in quiet with no change in sound quality. Pre-processing algorithms to reduce noise and better real-time F0 extraction would improve the benefits of C-tone in complex listening environments.Conclusions: Chinese CI users' speech recognition in quiet can be significantly improved by modifying the amplitude contour to better resemble the F0 contour. [ABSTRACT FROM AUTHOR]

Artificial neural networks are sophisticated modelling and prediction tools capable of extracting complex, non-linear relationships between predictor (input) and predicted (output) variables. This study explores this capacity by modelling non-linearities in the hardiness-modulated burnout process with a neural network. Specifically, two multi-layer feed-forward artificial neural networks are concatenated in an attempt to model the composite non-linear burnout process. Sensitivity analysis, a Monte Carlo–based global simulation technique, is then utilised to examine the first-order effects of the predictor variables on the burnout sub-dimensions and consequences. Results show that (1) this concatenated artificial neural network approach is feasible to model the burnout process, (2) sensitivity analysis is a prolific method to study the relative importance of predictor variables and (3) the relationships among variables involved in the development of burnout and its consequences are to different degrees non-linear. Practitioner Summary: Many relationships among variables (e.g. stressors and strains) are not linear, yet researchers use linear methods such as Pearson correlation or linear regression to analyse these relationships. Artificial neural network analysis is an innovative method to analyse non-linear relationships and in combination with sensitivity analysis superior to linear methods. [ABSTRACT FROM AUTHOR]

A unique commercial operation in China that delivers express parcels within the storage compartments (belly-holds) of highway passenger coaches is introduced. A new delivery mode, called ‘direct + transfer’ delivery, is proposed. On the basis of this mode, a model to optimize the delivery path is constructed by considering the parcel arrival patterns, coach departure times, and belly-hold capacity in the initial origin. The solution algorithm is designed. In a case study, the probability distribution function of the parcel arrival pattern is fitted, and the delivery paths of parcels from a city to other cities in a region in China are calculated and analyzed. [ABSTRACT FROM AUTHOR]

The present study examined genetic variability among Clonorchis sinensis isolates from four different geographical localities (Guangzhou, Nanning, Jiamusi and Daqing) and host species (cats, dogs, human and rabbits) in Mainland China by sequence analyses of two mitochondrial DNA (mtDNA) genes, namely NADH dehydrogenase subunits 2, 5 ( nad2 and nad5) and ribosomal internal transcribed spacer 1 (ITS1). A portion of the ITS1, nad2 (p nad2) and nad5 (p nad5) was amplified by polymerase chain reaction separately from adult C. sinensis individuals and the amplicons were subjected to sequencing from both directions. The length of the sequences of ITS1, p nad2 and p nad5 was 643, 666 and 771 bp, respectively. The intraspecific sequence variations within C. sinensis were 0-1.7% for ITS1, 0-1.4% for p nad2 and 0-0.9% for p nad5. The interspecific sequence variations within other zoonotic trematodes, which were published previously, were 4.5-84.9% for ITS1, 21.9-43.6% for p nad2 and 19.2-48.9% for p nad5. The A+T contents of the sequences were 45.26-45.88% (ITS1), 62.91-63.51% (p nad2) and 58.24-58.63% (p nad5). Phylogenetic analyses using ribosomal and mitochondrial sequence data set, with three different computational algorithms (Bayesian inference, maximum parsimony and maximum likelihood), all revealed distinct groups with high statistical support. These findings demonstrated the existence of low-level intraspecific variations in ribosomal DNA (rDNA) and mtDNA sequences among C. sinensis isolates from four different regions and hosts in China and elucidated that mtDNA sequences and rDNA sequences provided reliable genetic markers for phylogenetic studies of zoonotic trematodes. [ABSTRACT FROM AUTHOR]