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At present, the development of value chain mainly shows that the added value is positive; otherwise, the industrial value chain will be eliminated. Therefore, the construction and measurement of the industrial value chain height can effectively avoid the collapse of the industrial value chain caused by the low system value of the value chain. In this paper, the mobile virtualization channel allocation algorithm is used to construct the multi-objective industrial value chain height index, and the actual industrial value chain height in China is measured. This paper first describes the channel allocation algorithm of mobile virtualization, extracts the allocation rules of the algorithm, constructs the model of multi-objective industrial value chain, then measures the value chain of eight regions in China combined with the elements of technology, production, and marketing in the height of value chain, and quantitatively compares the results of value chain height of different regions according to the embeddedness and depth. Finally, it puts forward the improvement scheme for the measurement results and the problems faced by the value chain. The research shows that the average value chain height of South China is about 70.86, while the average value chain height of Northwest China is about 46.58, ranging from 40.58 to 60.45. Through quantitative data, the value chain height of South China is generally higher than that of Northwest China by more than 50%, which may be related to the innovation of technology and the imitation form of production. Therefore, this paper proposes to strengthen the technological innovation in the upstream of the value chain, so as to drive the development of the regional value chain. [ABSTRACT FROM AUTHOR]

Global warming has constituted a major global problem. Carbon dioxide emissions from the burning of fossil fuels are the main cause of global warming. Therefore, carbon dioxide emission forecasting has attracted widespread attention. Aiming at the problem of carbon dioxide emissions forecasting, this paper proposes a new hybrid forecasting model of carbon dioxide emissions, which combines the marine predator algorithm (MPA) and multi-kernel support vector regression. For further strengthening the prediction accuracy, a novel variant of MPA is proposed, called EGMPA, which introduces the elite opposition-based learning strategy and the golden sine algorithm into MPA. Algorithm test results show that EGMPA can effectively improve the convergence speed and optimization accuracy. The carbon dioxide emission data of China from 1965 to 2020 are taken as the research objects. Root-mean-square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) are used to evaluate the performance of the proposed model. The proposed multi-kernel support vector regression model is used to forecast China's carbon dioxide emissions during the "14th Five-Year Plan" period. The results show that the proposed model has RMSE of 37.43 Mt, MAE of 30.63 Mt, and MAPE of 0.32%, which significantly improves the prediction accuracy and can accurately and effectively predict China's carbon dioxide emissions. During the "14th Five-Year Plan" period, China's carbon dioxide emissions will continue to show an increasing trend, but the growth rate will slow down significantly. [ABSTRACT FROM AUTHOR]

An effective financial distress prediction model is essential, as financial distress of listed companies will cause big problems for listed company owners, shareholders, banks, and regulators. Due to the special circumstances of China, Chinese listed companies that received the 'special treatment' label are considered to be in financial distress in this paper. We construct a relatively new and complete financial distress data set of Chinese listed companies from 2014 to 2019. Their 14 financial indicators of year t - 2 are used to predict whether they will receive the ST label in year t. When building the prediction model, we first propose GA-MLP algorithm in which the Multi-Layer Perception (MLP) is optimized using Genetic Algorithm (GA) which could automatically determine the parameter values and the number of neurons in each hidden layer at the same time, and then a novel diversity-considered GA-MLP ensemble algorithm (DGAMLPE) is proposed to improve the performance. We objectively test the proposed algorithms on the constructed financial distress data set and five other datasets, and compare them with other 7 algorithms in terms of accuracy, F1-score, and AUC. Experimental results show that DGAMLPE performs very well on the financial distress data set, and it outperform other tested algorithms for the data set tested. [ABSTRACT FROM AUTHOR]

This paper presents a massively parallel approach of the multilevel fast multipole algorithm (PMLFMA) on homegrown many-core SW26010 cluster of China, noted as (SW-PMLFMA), for 3-D electromagnetic scattering problems. In this approach, the multilevel fast multipole algorithm (MLFMA) octree is first partitioned among management processing elements (MPEs) of SW26010 processors following the ternary partitioning scheme using the message passing interface (MPI). Then, the computationally intensive parts of the PMLFMA on each MPI process, matrix filling, aggregation and disaggregation are accelerated by using all the 64 computing processing elements (CPEs) in the same core group of the MPE via the Athread parallel programming model. Different parallelization strategies are designed for many-core accelerators to ensure a high computational throughput. In coincidence with the special characteristic of local Lagrange interpolation, the compressed sparse row (CSR) and the compressed sparse column (CSC) sparse matrix storage format is used for storing interpolation and anterpolation matrices, respectively, together with a specially designed cache mechanism of hybrid dynamic and static buffers using the scratchpad memory (SPM) to improve data access efficiency. Numerical results are included to demonstrate the efficiency and versatility of the proposed method. The proposed parallel scheme is shown to have excellent speedup. [ABSTRACT FROM AUTHOR]

The early stage lung cancer often appears as ground-glass nodules (GGNs). The diagnosis of GGN as preinvasive lesion (PIL) or invasive adenocarcinoma (IA) is very important for further treatment planning. This paper proposes an automatic GGNs' invasiveness classification algorithm for the adenocarcinoma. 1431 clinical cases and a total of 1624 GGNs (3–30 mm) were collected from Shanghai Cancer Center for the study. The data is in high-resolution computed tomography (HRCT) format. Firstly, the automatic GGN detector which is composed by a 3D U-Net and a 3D multi-receptive field (multi-RF) network detects the location of GGNs. Then, a deep 3D convolutional neural network (3D-CNN) called Attention-v1 is used to identify the GGNs' invasiveness. The attention mechanism was introduced to the 3D-CNN. This paper conducted a contract experiment to compare the performance of Attention-v1, ResNet, and random forest algorithm. ResNet is one of the most advanced convolutional neural network structures. The competition performance metrics (CPM) of automatic GGN detector reached 0.896. The accuracy, sensitivity, specificity, and area under curve (AUC) value of Attention-v1 structure are 85.2%, 83.7%, 86.3%, and 92.6%. The algorithm proposed in this paper outperforms ResNet and random forest in sensitivity, accuracy, and AUC value. The deep 3D-CNN's classification result is better than traditional machine learning method. Attention mechanism improves 3D-CNN's performance compared with the residual block. The automatic GGN detector with the addition of Attention-v1 can be used to construct the GGN invasiveness classification algorithm to help the patients and doctors in treatment. [ABSTRACT FROM AUTHOR]

This paper is a continuation of our previous paper [Front. Math. China, 2017, 12(5): 1023–1043] where global algorithms for computing the maximal eigenpair were introduced in a rather general setup. The efficiency of the global algorithms is improved in this paper in terms of a good use of power iteration and two quasi-symmetric techniques. Finally, the new algorithms are applied to Hua's economic optimization model. [ABSTRACT FROM AUTHOR]

In seismic data processing, low-frequency random noise with non-Gaussian and non-stationary characteristics heavily contaminates the reflected signals in Tarim area, which brings great difficulties in interpretation of seismic records in northwest China. To achieve more satisfied resolution, more greater fidelity, together with much higher increased signal-to-noise ratio (SNR), this paper proposes a SNR enhancement method based on the combination of variational mode decomposition (VMD) and Semi-soft Go Decomposition (Semi-Soft GoDec), named VMD-SSGoDec, which can realize the simplification of low-rank extraction in time–frequency representation (TFR) domain. Firstly, each trace of the rough seismic record is decomposed into several modes to reconstruct a component matrix by VMD. Due to the semi-low rank or approximate low-rank character of the desert low-frequency noise component matrix in TFR domain, secondly, we apply the Semi-soft GoDec, a low-rank matrix estimation to extract the low-frequency random noise components from the VMD results obtained in the first step. Repeating the above single-trace procedure to each trace rather than decomposing the entire record but use low-rank estimation once can lead to a more reduced dimension of the component matrix, and thus simplify the low-rank selection in Semi-soft GoDec. Finally, with the extracted random noise results in the second step, we can obtain the denoised record by making a difference with the original input. The proposed algorithm is tested by both synthetic record and field desert seismic data. Experimental results show outstanding advantages in low-frequency noise attenuation comparing with those of f-x deconvolution and SSWT-OptShrink. Both low-frequency random noise and surface waves are almost thoroughly attenuated by the proposed method, while the reflected signals are left nearly intact, revealing a significant enhancement in SNR. [ABSTRACT FROM AUTHOR]

In recent years, the convergence of the Internet of Things (IoT) and machine learning technologies has instigated advancements in IoT, and machine learning has revolutionized SMEs, reshaping their role in regional economic growth. These technologies provide SMEs with innovative tools, fostering connectivity within regional ecosystems. This transformation is crucial for China's economic realignment in its 'new normal' phase, with enterprises driving social and economic progress. This study proposed the XGBoost algorithm and IoT data enhance collaboration among SMEs in regional economies, fostering innovation and driving economic development. The role of IoT and machine learning in promoting sustainable regional economic prosperity. Technological innovation is essential for enterprise survival and growth in a competitive landscape. This paper examines the influence of environmental factors and geography on economic development across China's regions. It focuses on SME coupling and coordination, contributing to regional economic progress through performance evaluation. IoT integration provides SMEs with abundant real-time data, enabling deep insights into customer behavior, supply chains, and production. Simultaneously, the XGBoost algorithm efficiently processes and extracts actionable insights from this data. Data from 11 provinces in the Yangtze River economic belt highlights Jiangsu Province's leadership in regional innovation performance for 2015 and 2020. The empirical results, grounded in datasets amalgamating information from these provinces, attest to the potential of this IoT and XGBoost-driven approach. Boasting an impressive accuracy rate of 91.7%, surpassing alternative machine learning techniques like SVM, KNN, DT, RF, and LR, this research underscores the effectiveness of this integrated strategy in optimizing SME operations. Additionally, it computes the coupling coordination degree, innovation environment ranking, mean value, and numerical changes from 2010 to 2020, with an average innovation coupling coordination degree of 0.1624 across 30 Chinese provinces. [ABSTRACT FROM AUTHOR]

Precise segmentation of the hippocampus is essential for various human brain activity and neurological disorder studies. To overcome the small size of the hippocampus and the low contrast of MR images, a dual multilevel constrained attention GAN for MRI-based hippocampus segmentation is proposed in this paper, which is used to provide a relatively effective balance between suppressing noise interference and enhancing feature learning. First, we design the dual-GAN backbone to effectively compensate for the spatial information damage caused by multiple pooling operations in the feature generation stage. Specifically, dual-GAN performs joint adversarial learning on the multiscale feature maps at the end of the generator, which yields an average Dice coefficient (DSC) gain of 5.95% over the baseline. Next, to suppress MRI high-frequency noise interference, a multilayer information constraint unit is introduced before feature decoding, which improves the sensitivity of the decoder to forecast features by 5.39% and effectively alleviates the network overfitting problem. Then, to refine the boundary segmentation effects, we construct a multiscale feature attention restraint mechanism, which forces the network to concentrate more on effective multiscale details, thus improving the robustness. Furthermore, the dual discriminators D1 and D2 also effectively prevent the negative migration phenomenon. The proposed DMCA-GAN obtained a DSC of 90.53% on the Medical Segmentation Decathlon (MSD) dataset with tenfold cross-validation, which is superior to the backbone by 3.78%. [ABSTRACT FROM AUTHOR]

Removing motion blur has been an important issue in computer vision literature. Motion blur is caused by the relative motion between the camera and the photographed object. However, in recent years, some achievements have been made in the research of image deblurring by using deep learning algorithms. In this paper, an enhanced adversarial network model is proposed. The proposed model can use the weight of feature channel to generate sharp image and eliminate draughtboard artefacts. In addition, the mixed loss function enables the network to output high-quality image. The proposed approach is tested using GOPRO datasets and Lai datasets. In the GOPRO datasets, the peak signal-to-noise ratio of the proposed approach is up to 28.674, and DeblurGAN is 27.454. And the structural similarity measure can be achieved up to 0.969, and DeblurGAN is 0.939. Furthermore, the images were obtained from China's Chang'e 3 Lander to test the new algorithm. Due to the elimination of the chessboard effect, the deblurred image has a better visual appearance. The proposed method achieved higher performance and efficiency in qualitative and quantitative aspects using the benchmark dataset experiments. The results also provided various insights into the design and development of the camera pointing system, which was mounted on the Lander for capturing images of the moon and rover for Chang'e space mission. [ABSTRACT FROM AUTHOR]

Slime mould algorithm (SMA) is extensively used in engineering applications such as the parameter estimation of photovoltaic models, image segmentation, economic emission dispatch, etc. To improve SMA's inherent search stagnation, slow convergence speed and poor transition ability when transferring from exploration to exploitation, especially for optimization problems with high dimensions and many local optimal values, a multi-strategy improved slime mould algorithm (MSMA) is proposed in this paper. The elite chaotic search strategy (ECSS) is adopted to improve the ability of the algorithm to explore near the elite individuals. Moreover, the z parameter of SMA, which originally is a constant, is substituted by a nonlinear convergence factor with chaotic disturbance to enhance the algorithm's transition ability between exploration and exploitation. MSMA is compared with other 9 classical or advanced metaheuristic algorithms by optimizing 12 benchmark functions, and the comparative results show that MSMA has better performance in solving accuracy, convergence speed and robustness. Finally, to verify the effectiveness of MSMA, MSMA and the other 9 algorithms are applied to a real cascade hydropower reservoirs system along Dadu River in China to maximize the annual power generation. The numerical simulation results show that with the proposed MSMA, the average power generation in the wet year, the normal year and the dry year are 1.11%–19.22%, 0.35%–13.21% and 0.84%–12.85% more than with other algorithms respectively, which verifies the superiority and effectiveness of MSMA for cascade reservoirs operation problem. [ABSTRACT FROM AUTHOR]

Loess landslides are one of the geological hazards prevalent in mountainous areas of Loess Plateau, seriously threatening people's lives and property safety. Accurate identification of landslides is a prerequisite for reducing the risk of landslide hazards. Traditional landslide interpretation methods often have the disadvantage of being laborious and difficult to use on a large scale compared with the recently developed deep learning-based landslide detection methods. In this study, we propose an improved deep learning model, landslide detection- you only look once (LD-YOLO), based on the existing you only look once (YOLO) model for the intelligent identification of old and new landslides in loess areas. Specifically, remote sensing images of landslides in Baoji City, Shaanxi Province, China are acquired from the Google Earth Engine platform. The landslide images of Baoji City (excluding Qianyang County) are used to establish a loess landslide dataset for training the model. The landslide data of Qianyang County is used to verify the detection performance of the model. The focal and efficient IoU (Focal-EIoU) loss function and efficient channel attention (ECA) mechanism are incorporated into the 7th version of YOLO (YOLOv7) model to construct the LD-YOLO model, which makes it more suitable for the landslide detection task. The experiments yielded an improved LD-YOLO model with average precision of 92.05%, precision of 92.31%, recall of 90.28%, and F1-score of 91.28% for loess landslide detection. The landslides in Qianyang County were divided into two test sets, new landslides and old landslides, which were used to test the detection performance of LD-YOLO for both types of landslides. The results show that LD-YOLO detects old landslides with a detection precision of 82.75% and a recall of 80%. When detecting new landslides, the detection precision is 94.29% and the recall is 91.67%. It indicates that our proposed LD-YOLO model has strong detection performance for both new and old landslides in loess areas. Through a proposed solution that can realize the accurate detection of landslides in loess areas, this paper provides a valuable reference for the application of deep learning methods in landslide identification. [ABSTRACT FROM AUTHOR]

In recent years, the rapid development of fresh food e-commerce in China has brought about more development opportunities for the cold chain logistics industry but has also presented new challenges. With the development of cloud computing and big data technology, it is increasingly important to study the application of big data and cloud computing technology in cold chain logistics. The purpose of this paper is to study the intelligent algorithm of cold chain logistics distribution optimization based on big data cloud computing analysis. Based on the constituent elements of the cold chain distribution problem and using cloud computing technology to obtain real-time traffic information in the transportation system through a unified access interface, this article analyses the distribution time and cost of refrigerated vehicles, thereby establishing a cold chain distribution vehicle path optimization model. By analysing the parallel programming mode of cloud computing, the parallel design and analysis of a coarse-grained genetic algorithm are used to solve the simulation model of the established optimization model. The experimental results show that the method of optimizing cold chain logistics vehicle routing using cloud computing is effective. When comparing 1, 2, 4, and 8 processors, the execution times are 19.89, 14.52, 8.12, and 6.41, respectively. It can be seen that the more processors there are, the shorter the calculation time. [ABSTRACT FROM AUTHOR]

In order to realize the automatic epileptic seizure detection, feature extraction and classification of electroencephalogram (EEG) signals are performed on the interictal, the pre-ictal, and the ictal status of epilepsy patients. There is no effective strategy for selecting the number of channels and spatial filters in feature extraction of multichannel EEG data. Therefore, this paper combined sparse idea and greedy search algorithm to improve the feature extraction of common space pattern. The feature extraction can effectively overcome the repeating selection problem of feature pattern in the eigenvector space by the traditional method. Then we used the Fisher linear discriminant analysis to realize the classification. The results show that the proposed method can get high classification accuracy using fewer data. For 10 subjects, the averaged accuracy of epilepsy detection is more than 99%. So, the detection of an epileptic seizure based on sparse features using Fisher linear discriminant analysis classifiers is more suitable for a reliable, automatic epileptic seizure detection system to enhance the patient's care and the quality of life. [ABSTRACT FROM AUTHOR]

Logistics information will have a certain impact on the price of agricultural products. Therefore, the price of agricultural products can be analyzed and predicted based on the logistics information. Firstly, based on logistics information, a basic model of agricultural product price is constructed. Then, support vector machine prediction algorithm and ensemble learning prediction method are applied to analyze the price relationship of agricultural products and emotional characteristics are added. Secondly, the price data of all kinds of agricultural products in various provinces of China are collected, and the algorithms constructed in this paper are used to test the price data. The results show that the accuracy of each model is improved after adding emotional feature index, which indicates that emotional features can better supplement the shortcomings of digital features, thereby improving the accuracy of prediction. [ABSTRACT FROM AUTHOR]

With the prosperity of e-commerce, ordering food online has become increasingly prevalent nowadays. Derived from the dispatching problem in Meituan, a real online food delivery (OFD) platform in China, this paper addresses an OFD problem (OFDP). To solve the OFDP efficiently, an effective matching algorithm with adaptive tie-breaking strategy (MAATS) is proposed by collaboratively fusing the optimization methods with machine learning (ML) techniques. First, to efficiently generate a partial solution with a certain quality, a best-matching heuristic is proposed. Second, to break the ties occurring in the best-matching heuristic and obtain a complete solution with high quality, multiple tie-breaking operators are designed. Third, to adapt to different scenarios, the tie-breaking operators are utilized in a dynamic way which is achieved by using ML methods including decision trees and a specially-designed deep neural network. Fourth, problem-specific features are extracted as decision information to assist the ML models to predict the best tie-breaking operator for use in the current scenario. Preliminary offline simulations are carried out on real historical data sets to validate the effectiveness of the proposed algorithm. Moreover, rigorous online A/B tests are conducted to evaluate the performance of MAATS in practical applications. The results of offline and online tests demonstrate both the effectiveness of MAATS to solve the OFDP and the application value to improve customer satisfaction and delivery efficiency on Meituan platform. [ABSTRACT FROM AUTHOR]

Secondary use of medical big data is increasingly popular in healthcare services and clinical research. Understanding the logic behind medical big data demonstrates tendencies in hospital information technology and shows great significance for hospital information systems that are designing and expanding services. Big data has four characteristics - Volume, Variety, Velocity and Value (the 4 Vs) - that make traditional systems incapable of processing these data using standalones. Apache Hadoop MapReduce is a promising software framework for developing applications that process vast amounts of data in parallel with large clusters of commodity hardware in a reliable, fault-tolerant manner. With the Hadoop framework and MapReduce application program interface (API), we can more easily develop our own MapReduce applications to run on a Hadoop framework that can scale up from a single node to thousands of machines. This paper investigates a practical case of a Hadoop-based medical big data processing system. We developed this system to intelligently process medical big data and uncover some features of hospital information system user behaviors. This paper studies user behaviors regarding various data produced by different hospital information systems for daily work. In this paper, we also built a five-node Hadoop cluster to execute distributed MapReduce algorithms. Our distributed algorithms show promise in facilitating efficient data processing with medical big data in healthcare services and clinical research compared with single nodes. Additionally, with medical big data analytics, we can design our hospital information systems to be much more intelligent and easier to use by making personalized recommendations. [ABSTRACT FROM AUTHOR]

The disassembly line is the best way to deal with large-scale waste electrical and electronic equipment. Balancing of disassembly line is a hot and challenging problem in recent years. Given the uncertainty factors including corrosion and deformation of parts and components of waste products, this paper introduces the destructive mode and uncertainty disassembly time into the disassembly line and establishes a multi-objective disassembly line balancing model, considering partial destructive mode and U-shaped layout. The model aims to reduce the number of stations, balance the workload and reduce energy consumption while increasing the disassembly profit. A new multi-objective discrete flower pollination algorithm is proposed to solve the problem. Both task assignment and disassembly modes are considered in the encoding and decoding strategies of the flowers. Combining the discrete characteristics of the problem, the cross-pollination and self-pollination behaviors of the algorithm are redefined. The performance of the proposed algorithm is verified by solving two classical examples and by comparing with seven meta-heuristic algorithms. Then the proposed model and method are applied to a television disassembly line of a disassembly enterprise in China. The disassembly schemes of the proposed algorithm are superior to that of the five classical multi-objective algorithms. The results show that the proposed method can improve the performance of the disassembly line. [ABSTRACT FROM AUTHOR]

In order to prevent water inrush from working face floor, it is an urgent problem to predict destroyed floor depth. In this paper, based on the collected samples of floor failure depth of north China type coal fields, the factors that influence the development of floor failure depth were obtained by principal component analysis(PCA), that is, mine pressure, working face information, tectonic conditions, lithology, and sedimentary conditions of coal seam. Then, the parameters of support vector regression (SVR) were optimized by genetic algorithm (GA), the PCA-GA-SVR prediction model of floor failure depth was established, we put the last 4 samples into the model and verified its excellent generalization ability. Finally, we applied the PCA-GA-SVR model to the floor failure depth prediction of Zhaizhen coal mine, comparing the predicted value of the model with the field measured value using double side seal borehole water injection device, the effectiveness of the model was demonstrated. [ABSTRACT FROM AUTHOR]

With the approaching of the aging of the population in China, the risk of heart disease increases with age. Atrial fibrillation as a common heart disease has seriously affected people's lives and health. A study of atrial fibrillation, dynamic electrocardiogram is usually used to analyze atrial fibrillation. But the accuracy of this analytical method may be artificially disturbed, which causes errors in the process of data analysis. Therefore, the computation analysis is carried by combining the automatic detection algorithm. By using the calculation of computer algorithm, the accuracy of data analysis of dynamic electrocardiogram can be increased. And through the test of automatic detection algorithm, the effectiveness of the algorithm can be found. [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]

Devising a method that can select cases based on the performance levels of trainees and the characteristics of cases is essential for developing a personalized training program in radiology education. In this paper, we propose a novel hybrid prediction algorithm called content-boosted collaborative filtering (CBCF) to predict the difficulty level of each case for each trainee. The CBCF utilizes a content-based filtering (CBF) method to enhance existing trainee-case ratings data and then provides final predictions through a collaborative filtering (CF) algorithm. The CBCF algorithm incorporates the advantages of both CBF and CF, while not inheriting the disadvantages of either. The CBCF method is compared with the pure CBF and pure CF approaches using three datasets. The experimental data are then evaluated in terms of the MAE metric. Our experimental results show that the CBCF outperforms the pure CBF and CF methods by 13.33 and 12.17 %, respectively, in terms of prediction precision. This also suggests that the CBCF can be used in the development of personalized training systems in radiology education. [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]

Objectives: To explore the comparative performance and develop the mapping algorithms between EQ-5D-5L and SF-6Dv2 in China. Methods: Respondents recruited from the Chinese general population completed both EQ-5D-5L and SF-6Dv2 during face-to-face interviews. Ceiling/floor effects were reported. Discriminative validity in self-reported chronic conditions was investigated using the effect sizes (ES). Test–retest reliability was evaluated using intra-class correlation coefficient (ICC) and Bland–Altman plots in a subsample. Correlation and absolute agreements between the two measures were estimated with Spearman's rank correlation coefficient and ICC, respectively. Ordinary least squares (OLS), generalized linear model, Tobit model, and robust MM-estimator were explored to estimate mapping equations between EQ-5D-5L and SF-6Dv2. Results: 3320 respondents (50.3% males; age 18–90 years) were recruited. 51.1% and 12.2% of respondents reported no problems on all EQ-5D-5L and SF-6Dv2 dimensions, respectively. The mean EQ-5D-5L utility was higher than SF-6Dv2 (0.947 vs. 0.827, p < 0.001). Utilities were significantly different across all chronic conditions groups for both measures. The mean absolute difference of utilities between the two tests for EQ-5D-5L was smaller (0.033 vs. 0.043) than SF-6Dv2, with a slightly higher ICC (0.859 vs. 0.827). Fair agreement (ICC = 0.582) was observed in the utilities between the two measures. Mapping algorithms generated by the OLS models performed the best according to the goodness-of-fit indicators. Conclusions: Both measures showed comparable discriminative validity. Systematic differences in utilities were found, and on average, the EQ-5D-5L generates higher values than the SF-6Dv2. Mapping algorithms between the EQ-5D-5L and SF-6Dv2 are reported to enable transformations between these two measures in China. [ABSTRACT FROM AUTHOR]

In this paper, a time-varying AR model is constructed by using the vector-space algorithm of compactly-supported biorthonormal wavelet transform. It is developed for forecasting narrow monetary multipliers in China. [ABSTRACT FROM AUTHOR]

This study utilized the deep belief network (DBN) model to predict permeability, which has been shown to be superior in data fitting compared to traditional machine learning models. However, the DBN model requires optimization of multiple hyper-parameters in modeling, making it challenging to ensure that the model parameters are optimal. To address this issue, the improved quantum-behaved particle swarm optimization (IQPSO) algorithm was introduced to optimize the DBN parameters and propose an IQPSO–DBN model. The research focused on the tight sandstone reservoirs of the Baiyanghe Formation in the geothermal field of Zhangye Basin, Northern China. Two experiments were designed to evaluate the prediction ability of the IQPSO–DBN model, and it was compared with other models such as stepwise iteration, support vector regression, group method of data handling, and extreme gradient boosting methods. The results demonstrated that the IQPSO–DBN model can effectively predict permeability in pure data drive, and it had the lowest prediction error compared to the other models. Therefore, the combination of the DBN model and the IQPSO algorithm can be a powerful tool for permeability prediction, which can have significant implications in geothermal fields and other related fields. [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]

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]

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]

Lung cancer screening using computed tomography (CT) has increased the detection rate of small pulmonary nodules and early-stage lung adenocarcinoma. It would be clinically meaningful to accurate assessment of the nodule histology by CT scans with advanced deep learning algorithms. However, recent studies mainly focus on predicting benign and malignant nodules, lacking of model for the risk stratification of invasive adenocarcinoma. We propose an ensemble multi-view 3D convolutional neural network (EMV-3D-CNN) model to study the risk stratification of lung adenocarcinoma. We include 1075 lung nodules (≤30 mm and ≥4 mm) with preoperative thin-section CT scans and definite pathology confirmed by surgery. Our model achieves a state-of-art performance of 91.3% and 92.9% AUC for diagnosis of benign/malignant and pre-invasive/invasive nodules, respectively. Importantly, our model outperforms senior doctors in risk stratification of invasive adenocarcinoma with 77.6% accuracy [i.e., Grades 1, 2, 3]). It provides detailed predictive histological information for the surgical management of pulmonary nodules. Finally, for user-friendly access, the proposed model is implemented as a web-based system (https://seeyourlung.com.cn). [ABSTRACT FROM AUTHOR]

The assessment of bone age is important for evaluating child development, optimizing the treatment for endocrine diseases, etc. And the well-known Tanner-Whitehouse (TW) clinical method improves the quantitative description of skeletal development based on setting up a series of distinguishable stages for each bone individually. However, the assessment is affected by rater variability, which makes the assessment result not reliable enough in clinical practice. The main goal of this work is to achieve a reliable and accurate skeletal maturity determination by proposing an automated bone age assessment method called PEARLS, which is based on the TW3-RUS system (analysis of the radius, ulna, phalanges, and metacarpal bones). The proposed method comprises the point estimation of anchor (PEA) module for accurately localizing specific bones, the ranking learning (RL) module for producing a continuous stage representation of each bone by encoding the ordinal relationship between stage labels into the learning process, and the scoring (S) module for outputting the bone age directly based on two standard transform curves. The development of each module in PEARLS is based on different datasets. Finally, corresponding results are presented to evaluate the system performance in localizing specific bones, determining the skeletal maturity stage, and assessing the bone age. The mean average precision of point estimation is 86.29%, the average stage determination precision is 97.33% overall bones, and the average bone age assessment accuracy is 96.8% within 1 year for the female and male cohorts. [ABSTRACT FROM AUTHOR]

In this paper, we will present a simple algorithm to estimate the temporal and spatial distribution of dust mass concentration by combining PM10 and conventional meteorological observations. The efficiency of the algorithm has been demonstrated by applying it to analyze the dust source, transport, and dissipation of the dust storm which occurred in the west region of Pa-tan-chi-lin Desert at 0200 BST 27 March, 2004. [ABSTRACT FROM AUTHOR]

Facial image based characterization and analysis of ethnicity, which is an important index of human demography, have become increasingly popular in the research areas of pattern recognition, computer vision, and machine learning. Many applications, such as face recognition and facial expression recognition, are affected by ethnicity information of individuals. In this study, we first create a human face database, which focuses on human ethnicity information and includes individuals from eight ethnic groups in China. This dataset can be used to conduct psychological experiments or evaluate the performance of computational algorithms. To evaluate the usefulness of this created dataset, some critical landmarks of these face images are detected and three types of features are extracted as ethnicity representations. Next, the ethnicity manifolds are learnt to demonstrate the discriminative power of the extracted features. Finally, ethnicity classifications with different popular classifiers are conducted on the constructed database, and the results indicate the effectiveness of the proposed features. [ABSTRACT FROM AUTHOR]

The reverse transcription-quantitative polymerase chain reaction (RT-qPCR) approach has been widely used to detect the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, instead of using it alone, clinicians often prefer to diagnose the coronavirus disease 2019 (COVID-19) by utilizing a combination of clinical signs and symptoms, laboratory test, imaging measurement (e.g., chest computed tomography scan), and multivariable clinical prediction models, including the electronic nose. Here, we report on the development and use of a low cost, noninvasive method to rapidly sniff out COVID-19 based on a portable electronic nose (GeNose C19) integrating an array of metal oxide semiconductor gas sensors, optimized feature extraction, and machine learning models. This approach was evaluated in profiling tests involving a total of 615 breath samples composed of 333 positive and 282 negative samples. The samples were obtained from 43 positive and 40 negative COVID-19 patients, respectively, and confirmed with RT-qPCR at two hospitals located in the Special Region of Yogyakarta, Indonesia. Four different machine learning algorithms (i.e., linear discriminant analysis, support vector machine, stacked multilayer perceptron, and deep neural network) were utilized to identify the top-performing pattern recognition methods and to obtain a high system detection accuracy (88–95%), sensitivity (86–94%), and specificity (88–95%) levels from the testing datasets. Our results suggest that GeNose C19 can be considered a highly potential breathalyzer for fast COVID-19 screening. [ABSTRACT FROM AUTHOR]

The article describes the development of a rule extraction approach to discover the nonlinear relationships between key parameters and variances handling measures of clinical pathway (CP). Topics covered include the combination of hybrid genetic double multi-group cooperative particle swam optimization algorithm (PSO) and discrete PSO algorithm and the use of Wisconsin breast cancer data sets and CP variances data sets of osteosarcoma preoperative chemotherapy in validating the method.

In view of the lack of long-term AOD (Aerosol Optical Depth) data, PSO (Particle Swarm Optimization) algorithm is introduced and joint used with NLSM (the nonlinear least square method) to improve visibility-AOD retrieval method, which is referred to as the PSO-M-Elterman model and significantly increases data available rate by 8% and correlation by about 20% with the true value in the experimental group. The mean absolute error, the proportion of the smaller absolute error and the root mean square error in the PSO-M-Elterman model experimental group are 0.0314 and 91.23%, 0.0509 respectively, which significantly outperforms other groups. The main increase of AOD was found in the eastern region (South China, East China, Central China) and Taklimakan with the trend coefficients of 2.67, 2.46, 2.13, and 1.45 (×10−3 yr−1) in recent 55 years, which may not be interpreted by the influence of relative humidity. Long-term change of AOD in east China is mainly caused by human activity, and the AOD is higher in cities with a larger population and more human activity. The PSO-M-Elterman model can maximize the advantage of visibility sequence length to obtain long-term AOD inversion results. [ABSTRACT FROM AUTHOR]

Landslide hazard assessment is critical for preventing and mitigating landslide disasters. The tuning of hyperparameters is of great importance to achieve better accuracy in a landslide hazard assessment model. In this study, a novel approach is proposed for landslide hazard assessment with support vector machine (SVM) as the primary model and Bayesian optimization (BO) algorithm as the parameter tuning method. This study describes 1711 historical landslide disaster points in Nanping City, and a total of 12 landslide conditioning factors including elevation, slope, aspect, curvature, lithology, soil type, soil erosion, rainfall, river, land use, highway, and railway were selected. The multicollinearity diagnosis was performed on the factors using the Spearman correlation coefficient. For model validation, 1711 landslides and 1711 non-landslides were collected as the dataset and divided into a training dataset (50 %) and a testing dataset (50 %). The performance of the model was evaluated by the confusion matrix and receiver operating characteristic (ROC) curve. The results of the confusion matrix accuracy and the area under the ROC curve showed that the BO-SVM model (89.53 %, 0.97) performed better than the SVM model (84.91 %, 0.93). In addition, the landslide hazard maps generated by the BO-SVM model had better overall results than that by the SVM model. [ABSTRACT FROM AUTHOR]

Highlights: Actual geological data, accurate models and precise samples are critical for ore targeting. The RF-based prediction model is more applicable for mapping mineral prospectivity than other algorithms in this study. The determination of sample set is more important than algorithm if there is not enough field data. Successful delineation of high potential targets for exploration in maturely-explored orefields is still a tough challenge. A reliable prediction model achieved by integration of various ore-related geological factors and exploration information in the 3D space is an effective approach to deal with this challenge. The Anqing orefield has been intensively exploited for decades, and thereby the possible potential left must be at depth. The accumulated abundant data of exploration and research provide us a possibility for carrying out machine learning-based 3D modeling. The 3D block models of the main geological bodies, resistivity and volumetric strain field in this orefield were used as multi-resource geological data to construct prediction models by using weight-of-evidence and machine learning methods. Through performance evaluation and comparison, the following conclusions were obtained: (1) it is more scientific and reasonable to use all the geological prospecting factors concurrently for mineral prospectivity mapping (MPM) rather than use one or a part of them; (2) random forest (RF) algorithm seems capable of MPM because of its high accuracy and reliability in prediction; and (3) rational selection of training and learning samples, especially, those from actual geological objects and exploration engineering, plays a more critical role in MPM than algorithm and methods themselves. Two different RF prediction models were obtained for MPM in the east and the surrounding part of this orefield based on the outcome of geophysical prospecting. The spaces with prediction probabilities higher than 0.508 in the east part and 0.501 in the surroundings take up only 3.71% volume of the whole orefield, but contain 95.92% of the mineralized blocks. The high potential targets are most likely parts of the above spaces with high prediction probabilities that have not been drilled yet up to now. [ABSTRACT FROM AUTHOR]

The PQ index is an integrated grouting process parameter that represents the relationship between grouting pressure and grout flow. By tracking the variation in the PQ index, operators can understand and control the grouting process. Therefore, accurate and reliable PQ index prediction is important for grouting process guidance. However, because the original grouting process parameter time series has chaotic characteristics and noise interference, traditional point prediction suffers from inevitable prediction errors and cannot quantify the uncertainties associated with the prediction. In this study, to cope with these deficiencies, a hybrid interval prediction model for the PQ index based on an improved extreme learning machine with a lower upper bound estimation method is proposed. In this model, the lower upper bound estimation method is used to construct an advanced extreme learning machine with interval prediction ability to perceive the uncertainties of the prediction. The improved grey wolf optimizer algorithm is applied to optimize the parameters of the extreme learning machine. Moreover, to enhance the prediction ability, cooperating with the hybrid data preprocessing method based on a noise reduction and decomposition strategy, a feature selection method is developed to identify the chaotic characteristics of the PQ index time series and determine the optimal input form of the interval prediction model. The proposed model is applied to predict the PQ value in the grouting process at a hydropower project in China. The results reveal that the proposed model can construct high-quality prediction intervals, which is superior to the performance of other benchmark models and has high potential for practical applications in grouting projects. [ABSTRACT FROM AUTHOR]

Rockburst is a kind of complex and catastrophic dynamic geological disaster in the development and utilization of underground space, which seriously threatens the safety of personnel and environment. Due to the suddenness in time and randomness in space, the prediction of rockburst becomes a great challenge. Microseismic monitoring is capable to continuously capture rock microfracture signals in real time, which offers an effective means for rockburst prediction. With the explosive growth of monitoring data, the conventional manual forecasting methods are laborious and time-consuming. Therefore, artificial intelligence was introduced to improve prediction efficiency. A novel tree-based algorithm was proposed. Its basic idea was to automatically recognize precursory microseismic sequences for the real-time prediction of rockburst intensity. The database consisting of 1500 microseismic events was analyzed. To establish precursory microseismic sequences, dimensionality reduction of the database was first implemented by t-SNE algorithm. Then, k-means clustering algorithm was employed for labelling 1500 microseismic events. Before that, canopy algorithm was adopted to determine the number of clusters. Finally, 300 precursory microseismic sequences were formed by the grouping rule. They were further partitioned into two parts through stratified sampling: 70% for training and 30% for validation. The validation results indicated that the precursor tree with pruning achieved a higher prediction accuracy of 98.9% than one without pruning on the validation set. And the increase was separately 12.2%, 9.2% and 28.6% on the whole validation set and each classes (low/moderate rockburst). In comparison with low rockburst, moderate rockburst was minority class. The improved accuracy on moderate rockburst suggested that pruning can enhance the recognition ability of precursor tree for the minority class. Additionally, two extra rockburst cases were collected from a diversion tunnel in northwestern China, which provided a complete workflow about how to apply the built precursor tree model to achieve field rockburst warning in engineering practice. The tree-based algorithm served as a new and promising way for the real-time rockburst prediction, which successfully integrated field microseismic monitoring and artificial intelligence. [ABSTRACT FROM AUTHOR]

Aim: The European Society for Clinical and Economic Aspects of Osteoporosis, Osteoarthritis and Musculoskeletal Diseases (ESCEO) algorithm for the management of knee osteoarthritis (OA) is available worldwide from 2014, but in 2019 an update was published. Based on this algorithm, a Working Group (WG), including ESCEO members and Chinese experts, wished to see how the new ESCEO algorithm was perceived by Chinese experts in knee OA and how it was integrated into their clinical practice. Methods: A WG was held between members of the international ESCEO task force and a group of Chinese experts. Results: Non-pharmacological approach should be combined with pharmacological interventions. In step 1, symptomatic slow-acting drugs for osteoarthritis (SYSADOA) are the most important background drugs. Evidence, supported by high-quality research, is available only for crystalline glucosamine sulfate (pCGS) and chondroitin sulfate. Topical NSAIDs could be used as an additional option. In step 2, oral NSAIDs could be useful, but cardiovascular/renal/gastrointestinal profiles of the patients should be considered. Intra-articular hyaluronic acid and corticosteroids are alternative to oral NSAIDs, but the evidence is still limited. If steps 1 and 2 are not sufficient, weak opioids could be used. Overall, the conclusions of the ESCEO algorithm are accepted in China for products available in this country. The WG suggests the importance of economic studies, specifically made in China. Conclusion: This work provides evidence-based advice to establish a treatment algorithm in knee OA, for practical implementation in clinical practice in China. [ABSTRACT FROM AUTHOR]

Optimization back analysis is the most common approach to displacement back analysis for underground engineering. However, this is a non-convex problem that requires the use of nature-inspired global optimization algorithms. Therefore, the present study will investigate on the suitability of six state-of-the-art nature-inspired algorithms for elastic back analysis and elastic–plastic back analysis. These algorithms include improved genetic algorithm, immunized evolutionary programming, particle swarm optimization, continuous ant colony optimization, artificial bee colony and black hole algorithm. Numerical results indicate that immunized evolutionary programming is overall the best algorithm followed by the black hole algorithm; while, the improved genetic algorithm is the worst optimizer. Meanwhile, using elastic back analysis, the sensitivity analysis of the main input parameters for these nature-inspired optimization algorithms has been conducted. At last, the comparative results have been verified by using in one real underground roadway in Huainan coal mine of China. [ABSTRACT FROM AUTHOR]

Inputs of phosphorus (P) during baseflow period usually come from groundwater, bed sediments, and some other sources. Baseflow P can have critical effects on nutrient enrichment of surface waters in some intensively cultivated agricultural watersheds. This study was conducted to estimate the baseflow dissolved phosphorus (DP) export in a typical rainy agricultural watershed of eastern China using a recursive tracing source algorithm (RTSA) and reveal the rules and trends of baseflow DP loads and concentrations. Results indicated that RTSA provided a satisfactory prediction for baseflow DP load (Nash-Sutcliffe efficiency (NSE) = 0.72, R2 = 0.74). From 2003 to 2012, the annual baseflow DP loads ranged from 0.159 (2004) to 0.771 (2012) kg/ha which contributed about 64.3% of the mean total annual DP export in stream (0.597 kg/ha). The annual flow-weighted DP concentrations in streamflow (0.076–0.125 mg/L) and baseflow (0.076–0.137 mg/L) far exceeded the eutrophication threshold of DP (0.01 mg/L). Significantly increasing trends were obtained in the streamflow and baseflow DP loads and the flow-weighted concentrations (Mann-Kendall test, Zs > 2.56, p < 0.01) because of the changes of hydro-meteorological conditions. This indicates that, in the context of global climate change, baseflow DP export would be one of key issues for nonpoint source pollution control in the intensive agricultural watershed. [ABSTRACT FROM AUTHOR]

In recent years, companies depend on the Internet for posting job advertisements and attracting qualified personnel. However, with the vast number of Internet users and the tremendous amount of information on the Internet, it is difficult to accurately evaluate the credibility of the information that candidates provide on the Internet. Therefore, we propose an approach to assess information credibility in terms of trustworthiness and authority to identify unreliable user profiles in online professional social networks. Our approach calculates the trustworthiness probabilities of user profile information using the Tree Augmented Naïve Bayes (TAN) classifier. It also measures the authority of individual users by applying the PageRank algorithm for analyzing the user interactions in the professional social networks. Finally, a group of LinkedIn users' profiles is selected for conducting experiments to validate the proposed approach. Experiments based on a real-world scenario show that our approach integrating the TAN Bayes and PageRank algorithm outperforms other existing approaches in classification accuracy. In addition, the approach has been applied to another social network, namely, Maimai in China to further demonstrate its usefulness. [ABSTRACT FROM AUTHOR]

High-precision areal rainfall is crucial for hydrometeorological coupled forecasts. The accuracy of quantitative precipitation estimates (QPE) is improved by merging radar-rain gauge data with an integration approach based on a statistical weight matrix in the Yishu River catchment, China. First, a local Z-R relationship (Z = 85R1.82) is reconstructed using a genetic optimization algorithm to minimize the error from different precipitation patterns and climate zones. Next, based on the local Z-R relationship, six methods of merging radar-rain gauge data are respectively adapted to improve the accuracy of QPE, as follows: mean field bias (MFB), Kalman filter (KLM), optimum interpolation (OPT), variation method (VAR), two-step calibration of KLM and OPT (KOP), and two-step calibration of KLM and VAR (KVR). The results indicate that QPE accuracy is clearly improved, and is in good agreement with rain gauge observations, after the six merging methods are applied. Among these methods, KOP performs the best, reducing the mean relative error from 55.2 to 15.1%. An innovative aspect of this work is the inclusion of an integrated ideology based on a statistical weight matrix, which further improves the accuracy of QPE by incorporating the advantages of each estimation mode. The results further show that the accuracy of QPE derived from the integration approach is higher than that obtained by any individual method; QPE values are similar to those obtained the automatic rain gauge network in both the spatial distribution and location of the intense precipitation centers, and better reflects the precipitation status over the ground surface. This approach could serve as a promising conventional method for QPE in the study region. [ABSTRACT FROM AUTHOR]

As has been demonstrated, the long short-term memory (LSTM) algorithm has the special ability to process sequenced data; however, LSTM suffers from high dimensionality, and its structure is too complex, leading to overfitting. In this research, we propose a new method, RFG-LSTM, which uses a rectified forgetting gate (RFG) to restructure the LSTM. The rectified forgetting gate is a function that can limit the boundary of an input sequence, so it can reduce the dimensionality and complexity of a neural network. Through theoretical analysis, we demonstrate that RFG-LSTM is monotonic, just as LSTM is; additionally, the stringency does not change in the new algorithm. Thus, RFG-LSTM also has the ability to process sequenced data. Based on the real trading scenario of China's A stock market, we construct a multi-factor alpha portfolio with RFG-LSTM. The experimental results show that the RFG-LSTM model can objectively learn the characteristics and rules of the A stock market, and this can contribute to a portfolio investment strategy. [ABSTRACT FROM AUTHOR]

A many-core parallel approach of the multilevel fast multipole algorithm (MLFMA) based on the Athread parallel programming model is presented on the homegrown many-core SW26010 CPU of China. In the proposed many-core implementation of MLFMA, the data access efficiency is improved by using data structures based on the structure of array. The adaptive workload distribution strategies are adopted on different MLFMA tree levels to ensure full utilization of computing capability and the scratchpad memory. A double buffering scheme is specially designed to make communication overlapped computation. The resulting Athread-based many-core implementation of the MLFMA is capable of solving real-life problems with over one million unknowns with a remarkable speedup. The capability and efficiency of the proposed method are analyzed through the examples of computing scattering by spheres and a practical aerocraft. Numerical results show that with the proposed parallel scheme, the total speedup ratios from 6.4 to 8.0 can be achieved, compared with the CPU master core. [ABSTRACT FROM AUTHOR]

Identification of geochemical anomalies from geological background is of great significance in the exploration of complex mineralization systems. For a 2D problem, the window-based local singularity mapping has been used widely to identify the distribution patterns of geochemical data. However, the optimal window parameters for calculating the singularity index are hard to determine. Previous studies commonly use the default parameters when applying singularity mapping. In this study, window-based local singularity mapping was performed and improved by comprehensive analysis of multiple parameters to explore geochemical anomalies associated with gold mineralization in the Xishan deposit, North China, with the aim of revealing undiscovered mineralization. By using Au anomalies as an example, the parameters that may influence the result of window-based local singularity processing have been analyzed and discussed to improve the mapping result. The parameters include the average concentration calculation algorithm, the shape of the sliding windows, the window size increment and the number of windows. Success-rate curves and area under the success-rate curve have been used to assess the spatial correlation of the singularity map with the known mineral occurrences. While square sliding window is the most regularly used window shape, circular and elliptical windows can be alternative choices. We found that the directions of major axis of the ellipses parallel or quasi-parallel to geological strike fit the locations of ore deposits better if the mineralization system is controlled by regional faults. After taking the influence of different parameters into account, geochemical anomalies were successfully separated from background and have been enhanced compared to anomalies identified solely from concentration values. Singularity–quantile analysis has been applied to recognize and separate multiple geochemical anomaly populations based on the singularity map in frequency and spatial domain. While the Au concentration map shows quite scattered strong and weak geochemical anomalies, the linear regions of positive singularity resolved by singularity–quantile analysis coincide well with the location of regional faults and alteration zones, which might indicate footprints of ore-forming fluids. Based on the singularity maps of multi-elements (Au, Ag, Cu and Pb), we resolved two prospect areas of mineralization bounded by regional faults, hydrothermally altered rocks and lamprophyres with positive singularity, which warrant further investigation for undiscovered mineralization. [ABSTRACT FROM AUTHOR]

The new 1° × 1° resolution global Full Data Daily Analysis Version 2018 published by the Global Precipitation Climatology Centre (GPCC) of Deutscher Wetterdienst was compared with an analysis of the measurements from the national dataset over the mainland of China with regard to four of the 27 ETCCDI indices (http://etccdi.pacificclimate.org/list%5f27%5findices.shtml) commonly used to determine extreme precipitation (Rx5day, R10mm, CDD and SDII). After extreme value check, integrity check, and homogeneity check, the observations from 2327 surface stations covering the years from 1982 to 2016 fulfilled the criteria for the evaluation. The in situ daily precipitation data were interpolated onto a 1° × 1° grid over the mainland of China by employing Shepard's angular and distance weighting algorithm. The four aforementioned indices were then calculated on the national station–based analysis being referred to as STA. Moreover, the aforementioned gridded GPCC Full Data Daily product was directly utilized to calculate the same indices (FDDA). The China national means of Rx5day, R10mm, CDD and SDII calculated from FDDA and STA had similar variations and trends with high correlation coefficients, and the mean biases between FDDA and STA were 2.5 mm, 1.2 days, 0.0 day and 0.3 mm respectively. The trends of Rx5day, R10mm and SDII are increasing, whereas the trend of CDD is negative. The distributions of the grid mean and the grid trends of indices over China from FDDA and STA show similar patterns too, indicating that the FDDA shows a surprisingly high fidelity in reproducing almost the same patterns in the four ETCCDI indices chosen compared with the STA-based analysis. [ABSTRACT FROM AUTHOR]