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In the China VI regulations for light-duty vehicles, the (RDE) test is introduced as a supplementary test procedure. In the actual test process, the RDE results are more significantly influenced by driving behavior and vehicle type. To reduce the test cost, the NOx and PN prediction models are established based on the GA-BP method. The results showed that the coefficients of determination of the GA-BP model for NOx and PN predictions are all greater than 0.9 and are linearly highly correlated at the instantaneous emission level. At the overall emission level, the overall error of the GA-BP model is less than 7% for NOx prediction and less than 6% for PN prediction. The model has high accuracy for both instantaneous and overall emissions of light-duty vehicles. This provides practical engineering value for guiding the RDE test. [ABSTRACT FROM AUTHOR]

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

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

The quality of talent has increased across all fields due to the constant growth of different industries and the growing job saturation. Real-time job information on recruitment platforms can, therefore, accurately reflect the demand for talent from businesses, serving as a basis for the creation of training policies in schools. In international competition, the development of talents, especially top-level talents, will become more and more crucial. Growing in importance is China's economy and social development. The evaluation of higher vocational and technical talents, however, should also be assessed from a variety of angles, given the diversification of talent training objectives and teaching methods, as well as the expansion of teaching functions. An emerging machine learning technology called deep learning (DL) has been developed to bring machine learning closer to the goals of artificial intelligence. This essay offers a thorough evaluation of the depth of deep learning as it relates to the development of innovative talent in schools. The entire school must be strengthened. It is demonstrated that the average execution time is slashed by 0.0024 s, and the learning sample size error of the DL model is reduced by 0.05276 when compared to the Apriori method. As a result, implementing and researching the DL model can significantly improve both the overall teaching quality of schools and their capacity for innovation. [ABSTRACT FROM AUTHOR]

The prevalence of low clouds significantly affects flight safety in Southwest China. However, relevant cloud parameters, especially low cloud-base height (LCBH), lack accurate forecasts. Based on the hourly atmospheric vertical profiles of ERA5 from 2008 to 2019, we developed a new algorithm for estimating LCBH by combining relative humidity (RH) threshold methods with convective condensation level (CCL) (RHs-CCL). To evaluate the performance of RHs-CCL, we use it to estimate the hourly LCBH of airports in Southwest China and compare the results with those based on the ground-based observations and the ERA5 CBH data. Using the observations as a ground truth, we compare the RHs-CCL algorithm with several existing algorithms with the following findings: 1) The correlation coefficient between RHs-CCL and observations reaches 0.5 on average, and the error of RHs-CCL is smaller than those of existing algorithms, with the minimum mean absolute error and root-mean-square error at the four airports studies being able to reach 243 and 321 m. 2) The bias score of RHs-CCL is 0.97 on average, and low clouds classification utilizing RHs-CCL attains the highest accuracy, up to 86%. 3) The errors of ERA5 CBH are the largest when compared with the others. 4) By implementing convective cloud occurrence condition and CCL, RHs-CCL has better applicability in regions of enhanced convective activity. These results suggest the potential of RHs-CCL as an algorithm moving forward for improvement of the LCBH estimates based upon high-resolution reanalysis products and for better predictions of the LCBH utilizing outputs from numerical weather prediction models. Significance Statement: The new algorithm developed in this study can accurately estimate low cloud-base heights from vertical profiles of atmospheric variables. It provides us a much more computationally efficient approach for predicting low cloud-base height relative to running cloud models, which is critical for weather forecasting at locations lacking computational resources and/or cloud modeling capability. In areas such as Southwest China, low clouds are very common, and they pose major threats to aviation safety. The new algorithm has been successfully integrated into the daily operation at Guiyang Airport in Southwest China and demonstrated excellent skills in estimating cloud-base heights. The implementation of the algorithm in aviation forecasting over a broader region is on the horizon. [ABSTRACT FROM AUTHOR]

Social participation comprises comprehensive interactions between individual health and relevant environmental characteristics, which play an increasingly important role in promoting older adults' health and quality of life. Although many studies have examined impacts of various factors on the social participation of older adults, some internal mechanisms between health characteristics and environmental characteristics have not been revealed. This study aimed to examine the moderating effects of health characteristics on the relationships among psychological resources, social network support, community support and social participation of Chinese older adults. Based on some key health indicators, this study used the two‐step cluster algorithm to divide the participants aged over 60 years from the Chinese Longitudinal Aging Social Survey in 2016 (N = 8669) into low (47.2%), moderate (33.8%) and high (18%) health risk groups. A multiple‐group structural equation model was then built to examine the effects of psychological resources, social network support and community support on social participation across the three groups. The results suggested that with the increase in health risks, the contributions of psychological resources and community support to social participation decreased from 28.6% to 23.7% and from 61.5% to 50.7%, respectively, while that of social network support increased from 9.9% to 25.6%. These variations suggest that individual health has a significant moderating effect on the psychological and social compensating mechanisms of the social participation among Chinese older adults. [ABSTRACT FROM AUTHOR]

Background: Antidepressant (AD) algorithm is an important tool to support treatment decision-making and improve management of major depressive disorder (MDD). However, little is known about its concordance with realworld practice. This study aimed to assess the concordance between the longitudinal treatment patterns and AD algorithm recommended by a clinical practice guideline in China. Methods: Data were obtained from the electronic medical records of Shanghai Mental Health Center (SMHC), one of the largest mental health institutions in China. We examined the concordance between clinical practice and the Canadian Network for Mood and Anxiety Treatments (CANMAT) algorithm among a cohort composed of 19,955 MDD patients. The longitudinal characteristics of treatment regimen and duration were described to identify the specific inconsistencies. Demographics and health utilizations of the algorithm-concordant and -discordant subgroups with optimized treatment were measured separately. Results: The overall proportion of algorithm-concordant treatment significantly increased from 84.45% to 86.03% during the year of 2015-2017. Among the patients who received recommended first-line drugs with subsequent optimized treatment (n = 2977), the concordance proportion was 27.24%. Mirtazapine and trazodone were the most used drugs for adjunctive strategy. Inadequate or extended duration before optimized treatment are common inconsistency. The median length of follow-up for algorithm-concordant (n = 811) and algorithm-discordant patients (n = 2166) were 153 days (Q1-Q3 = 79-328) and 368 days (Q1-Q3 = 181-577) respectively, and the average number of clinical visits per person-year was 13.07 and 13.08 respectively. Conclusion: Gap existed between clinical practice and AD algorithm. Improved access to evidence-based treatment is required, especially for optimized strategies during outpatient follow-up. [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]

In this study, a deep learning model is proposed to predict groundwater levels. The model is able to accurately complete the prediction task even when the data utilized are insufficient. The hybrid model that we have developed, CNN-LSTM-ML, uses a combined network structure of convolutional neural networks (CNN) and long short-term memory (LSTM) network to extract the time dependence of groundwater level on meteorological factors, and uses a meta-learning algorithm framework to ensure the network's performance under sample conditions. The study predicts groundwater levels from 66 observation wells in the middle and lower reaches of the Heihe River in arid regions and compares them with other data-driven models. Experiments show that the CNN-LSTM-ML model outperforms other models in terms of prediction accuracy in both the short term (1 month) and long term (12 months). Under the condition that the training data are reduced by 50%, the MAE of the proposed model is 33.6% lower than that of LSTM. The results of ablation experiments show that CNN-LSTM-ML is 26.5% better than the RMSE of the original CNN-LSTM structure. The model provides an effective method for groundwater level prediction and contributes to the sustainable management of water resources in arid regions. [ABSTRACT FROM AUTHOR]

White matter hyperintensities (WMH) are imaging manifestations frequently observed in various neurological disorders, yet the clinical application of WMH quantification is limited. In this study, we designed a series of dedicated WMH labeling protocols and proposed a convolutional neural network named 2D VB-Net for the segmentation of WMH and other coexisting intracranial lesions based on a large dataset of 1,045 subjects across various demographics and multiple scanners using 2D thick-slice protocols that are more commonly applied in clinical practice. Using our labeling pipeline, the Dice consistency of the WMH regions manually depicted by two observers was 0.878, which formed a solid basis for the development and evaluation of the automatic segmentation system. The proposed algorithm outperformed other state-ofthe-art methods (uResNet, 3D V-Net and Visual Geometry Group network) in the segmentation of WMH and other coexisting intracranial lesions and was well validated on datasets with thick-slice magnetic resonance (MR) images and the 2017 medical image computing and computer assisted intervention WMH Segmentation Challenge dataset (with thin-slice MR images), all showing excellent effectiveness. Furthermore, our method can subclassify WMH to display the WMH distributions and is very lightweight. Additionally, in terms of correlation to visual rating scores, our algorithm showed excellent consistency with the manual delineations and was overall better than those from other competing methods. In conclusion, we developed an automatic WMH quantification framework for multiple application scenarios, exhibiting a promising future in clinical practice. [ABSTRACT FROM AUTHOR]

The purpose is to reduce students' psychological pressure and improve their quality of study and life. Here, 400 language-impaired students in the public elective psychology course at Northwestern University and the undergraduate psychology class at Xi'an Foreign Studies University in the 2018-2019 academic year are randomly selected as the research objects for this music psychology experiment. The students were divided into different experimental groups using the Questionnaire Survey (QS) method to analyze the students' psychological reactions to Baroque, classical, and romantic music styles. Then, it further discusses the students' emotional response and audiovisual synaesthesia, as well as their recognition and choice of music style. The results show that there are significant differences in the same emotional response intensity of the subjects to different styles of music creation. The music expression is consistent with the actual feelings of the subjects. The tonality and color density of audiovisual synaesthesia vary with the style of music creation. Different music creation styles generate different associations in students' minds, thus showing different psychological reactions. The QS results indicate that soft and soothing music can relieve students' learning pressure most, while music with a strong sense of rhythm and vitality has no significant effect. Therefore, different music creation styles affect students' learning pressure differently. This work discusses the influence of different music creation styles on the mental health of contemporary college students and provides a reference for music therapy to relieve students' learning pressure in the future. [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]

As states increasingly use algorithms to improve the legibility of society, particularly during the COVID-19 pandemic, it is common for concerns about the expanding power of the algorithm or the state to be raised in a deterministic manner. However, how are the algorithms for states' legibility projects enacted, contested, and reconfigured? Drawing on interviews and media data, this study fills this gap by examining Health Code (jiankangma), the Chinese contact tracing and risk assessment algorithmic system that serves as the COVID-19 health passport. I first explore the intensive and invisible work and infrastructures that enact and stabilize Health Code's sociotechnical assemblage. I then show how this assemblage is frequently challenged and destabilized by errors, breakdowns, and exclusions. Facing unintended engagements from heterogeneous social actors, local interests, and power hierarchies, Health Code reassembles into multiple and contradictory assemblages at different periods and social localities. Finally, I examine how people game and bypass the algorithm's surveillance with their agencies. Recognizing this messiness and heterogeneity contributes to a more nuanced and realistic understanding of states' use of algorithms, including the risks. Doing so also urges us to rethink the politics of citizenship and inequality in the digital age beyond inclusion. [ABSTRACT FROM AUTHOR]

As one of the largest Internet of Things systems in the world, China's expressway electronic toll collection (ETC) generates nearly one billion pieces of transaction data every day, recording the traffic trajectories of almost all vehicles on the expressway, which has great potential application value. However, there are inevitable missed transactions and false transactions in the expressway ETC system, which leads to certain false and missing rates in ETC data. In this work, a dynamic search step SegrDTW algorithm based on an improved DTW algorithm is proposed according to the characteristics of expressway ETC data with origin–destination (OD) data constraints and coupling between the gantry path and the vehicle trajectory. Through constructing the spatial window of segment retrieval, the spatial complexity of the DTW algorithm is effectively reduced, and the efficiency of the abnormal ETC data detection is greatly improved. In real traffic data experiments, the SegrDTW algorithm only needs 3.36 s to measure the abnormal events of a single set of OD path data for 10 days. Compared with the mainstream algorithms, the SegrDTW performs best. Therefore, the proposal provides a feasible method for the abnormal event detection of expressway ETC data in a province and even the whole country. [ABSTRACT FROM AUTHOR]

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

With the development of science and technology and the progress of social civilization, people have entered the era of big data. People's personal information, privacy data, financial status, and financial information have become more and more transparent. Personal privacy has also changed from traditional physical privacy to portrait privacy and further changed to digital privacy based on artificial intelligence, big data, algorithms, and other technologies. At this stage, a new type of privacy problem has been formed, that is, deep learning privacy. In order to better protect the right to privacy, we should effectively block the protection dilemma of privacy and improve the construction of algorithms in the era of artificial intelligence, strengthen our own awareness of prevention, understand the current situation of infringing and protecting the right to privacy, fully protect the right to privacy, and promote the legal construction of the right to privacy in the era of artificial intelligence. However, China's traditional privacy protection system cannot fully adapt to this change, and this highlights the lack of rules and hidden dangers of efficiency of privacy protection under the influence of penetrating monitoring, mandatory sharing, structural discrimination, and interactive assimilation of artificial intelligence. Therefore, based on the infringement mechanism of artificial intelligence on personal privacy, we should build a monitoring and identification function filing system, establish a segmentation and selective agreement rule system, build a comparative antidiscrimination rule system, innovate the "cloud blocking" rule system to protect our privacy, and comprehensively address the major challenges posed by new technologies such as artificial intelligence to China's privacy protection system. [ABSTRACT FROM AUTHOR]

The accurate prediction of surface settlement caused by large-diameter shield tunneling is crucial for the safety of the tunnel environment. However, due to the complexity and uncertainty of the rock-machine interaction and groundwater variation, it is difficult to predict the settlement by developing traditional theoretical methods. Recently, a big number of data obtained from the Chunfeng shield tunnel in China provides the possibility to predict the settlement using machine-learning methods. In this study, the equipment parameters, the geological parameters, and the monitored settlements are used to establish the models. Three machine-learning algorithms (i.e., long-short-term memory (LSTM), random forest (RF), and gated recurrent unit (GRU)) are used to predict the surface settlement. Three indicators, mean absolute error (MAE), accuracy (ACC), and coefficient of determination ( R 2 ), are selected to evaluate the prediction performance. Results demonstrated that the filtering and selection of model parameters is vitally important to the accuracy of model prediction. Among the three machine-learning algorithms, the LSTM algorithm gives the best accuracy in predicting the maximum surface settlement and can effectively predict the settlement development in different strata. [ABSTRACT FROM AUTHOR]

The global economic trends and the winds of technological change have elevated the status of integration between industry and education for innovation and entrepreneurship. Technologies and the Internet have significantly changed all aspects of our lives. College education has improved as a result of the enormous benefits of the Internet and big data. Reforms and development have still a lot of application space in the training and practice of educational technology talents of a college education. To improve the performance evaluation capabilities of educational technology experts in Chinese universities through education and "Internet +" has become a challenge that has to be addressed. This article is under such a formal background to have a detailed study about the current college education in China. In this study, an analysis is presented for the performance evaluation of the education technology talent training in modern universities based on big data technology. A fuzzy evaluation algorithm based on composite elements is proposed to evaluate the multielement and multilevel college education system. Experimental results illustrate the feasibility and effectiveness of the proposed algorithm. The proposed method has the potential to solve the complicated performance evaluation problem in colleges. [ABSTRACT FROM AUTHOR]

The disposal of recyclable waste in China primarily relies on manual sorting, which is inefficient, suffers from harsh working environment and high cost. In this study, an intelligent recyclable waste sorting system is proposed, which employs RGB image as visual information input. Then the class and coordinates of the moving waste on the conveyor belt are obtained through object detection algorithm. Finally, the recyclable waste is picked up by the robotic arm and delivered to different bins. As recyclable waste has various appearance, it collects 36 572 frames of waste pictures and establishes a recyclable waste dataset for model training. This is to improve the generalization ability of the object detection model. Based on YOLOv4, an attention-based detection algorithm, Attn-YOLOv4 is proposed, and the mAP of Attn-YOLOv4 algorithm is 0.16% higher than that of the original YOLOv4. Besides, a multithreaded object tracking algorithm is proposed, which achieves an accuracy of 0.945 within a tolerance of 20 mm. Furthermore, a post-processing module acquires the orientations of the waste, which is further converted to world coordinates and orientations for the robotic arm. In the end, the feasibility and practical effect of the intelligent recyclable waste sorting system are verified and evaluated on a continuous recycling production line. [ABSTRACT FROM AUTHOR]

Objective. This study aims to evaluate the clinical effects of Ling Gui Zhu Gan formula (LGZG), a famous TCM formula, for the management of serum lipids and obesity and preliminarily elucidates the bioactive components and the potential mechanism. Methods. Cluster analysis was adopted to investigate the TCM herbs and their frequency of occurrence for treating hyperlipidemia and obesity in an academic experience database of Chinese famous TCM doctors (http://www.gjmlzy.com:83). Then, relevant randomized controlled trials (RCTs) about LGZG supplementation in improving lipid levels and obesity were retrieved and analyzed. Lastly, the integration of network pharmacology, as well as greedy algorithms, which are theoretically well founded for the set cover in computer science, was exploited to identify the bioactive components of LGZG and to reveal potential mechanisms for attenuation or reversal of hyperlipidemia and obesity. Results. Based on the cluster analysis of 104 cases in TCM academic experience database, four TCM herbs in LGZG showed high-use frequency for treating hyperlipidemia and obesity. Meta-analysis on 19 randomized controlled trials (RCTs) with 1716 participants indicated that LGZG supplementation significantly decreased the serum levels of total triglycerides, total cholesterol, low-density lipoprotein cholesterol, BMI, and body weight and increased high-density lipoprotein cholesterol, compared with clinical control groups. No serious adverse effect was detected in all studies. Twenty-one bioactive components of LGZG, mainly flavonoids (i.e., naringenin, kaempferol, and kumatakenin), saponins (i.e., hederagenin), and fatty acids (i.e., eicosenoic acid), had the potential benefits possibly by regulating multiple targets such as PTPN1, CYP19A1, and ESR2, as well as a few complex pathways including the TNF signaling pathway, PPAR signaling pathway, arachidonic acid metabolism, fat digestion, and absorption. Conclusion. The present study has proved the clinical value of LGZG as a complementary treatment for attenuation or reversal of hyperlipidemia and obesity. More high-quality clinical and experimental studies in the future are demanded to verify its effects and the precise mechanism of action. [ABSTRACT FROM AUTHOR]

Background. Chronic sinusitis (CRS) was a chronic inflammation that originated in the nasal mucosa and affected the health of most people around the world. Chronic rhinosinusitis with nasal polyps (CRSwNP) was one kind of chronic sinusitis. Emerging research had suggested that long noncoding RNAs (lncRNAs) played vital parts in inflammatories and inflammation development. Methods. We acquired GEO data to analyze the differential expression between the miRNA, immune genes, TF, and lncRNA data in CRSWNP and the corresponding control tissues. Bioinformatic analysis by coexpression of endogenous RNA network and competitive way enrichment, analysis, and forecasting functions of these noncoding RNA. The different pathway expressions in CRSwNP patients were confirmed using GSVA to analyze the differentially expressed immune genes and TF data sets in CRSwNP patients. The differential immune gene and transcription factor data set in CRSwNP perform functional notes and protein-protein interaction (PPI) network structure. We predicted the potential genes and RNAs related to CRSWNP by constructing a ceRNA network. In addition, we also used 19 hub immune genes to predict the potential drugs of CRSWNP. lncRNA biomarkers in CRSwNP were identified by lncRNAs LASSO regression. The CIBERSORT algorithm was used to contrast the divergence in immune infiltrations between CRSwNP and usual inferior turbinate organizations in 22 immunocyte subgroups. Results. We identified a total of 48 miRNAs, 304 lncRNAs, 92 TFs, and 525 immune genes as CRSwNP-specific RNAs. GO and KEGG pathways both analyzed differentially expressed immune genes and transcription factor data sets. We predicted the potential genes GNG7, TUSC8, LINC01198, and has-miR-6776-5p by constructing ceRNA and PPI networks. At the same time, we found that the above genes were involved in two important pathways: chemokine signal path and PI3K/AKT signal path. In addition, we predicted 5 small molecule drugs to treat CRSwNP by analyzing 19 central immune genes, namely, danazol, ikarugamycin, semustine, cefamandole, and molindone. Finally, we identified 5 biomarkers in CRSwNP, namely, LINC01198, LINC01094, LINC01798, LINC01829, and LINC01320. Conclusions. We had identified CRSwNP-related miRNAs, lncRNAs, TFs, and immune genes, which may be making use of latent therapeutic target for CRSwNP. At the same time, we identified 5 lncRNA biomarkers in CRSwNP. The results of this study showed that LINC01198 promoted the progression of CRSwNPs through spongy miR-6776-5p. Our studies provide a new way for further analyses of the pathogenesis of CRSwNP. [ABSTRACT FROM AUTHOR]

Electricity theft behaviors have caused great harm to the economic benefits of power companies and the secure operation of power systems, thus electricity theft detection is paid much attention in the actual power supply management. In this work, a two-step electricity theft detection strategy is proposed to identify electricity theft users and predict potentially stolen electricity (PSE) for maximizing economic return. In the first step, a neural network model called convolutional autoencoder (CAE) is proposed for electricity theft identification, and the convolutional layer is adopted in CAE to extract and identify the abnormalities of electricity theft users against the uniformity and periodicity of normal power consumption features. In the second step, the PSE of each identified electricity theft user is predicted by the improved regression algorithm named Tr-XGBoost, which combines the extreme gradient boosting (XGBoost) algorithm and transfer adaptive boosting (TrAdaBoost) training strategy. The propsoed Tr-XGBoost could learn the relationship between the extracted electricity features and the PSE of each electricity theft user, and then the predicted PSE can be used to determine the list of electricity theft users to be inspected for maximizing economic return. Case studies on both the IEEE 33-bus test system and a low-voltage distribution system of a province in China show that the proposed two-step electricity theft detection strategy can improve the accuracy of electricity theft identification, and obtain a larger economic return because of a more accurate result of PSE prediction than other state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

In recent years, the China's rapid economic development and living standards of people have been improved, and the usage of clothing, food, housing, and transportation has increased. However, due to the prevalence of sports injuries and their high frequency of incidence, sports injury incidents do occur from time to time. Sports injury will not only affect the normal training of athletes and sports enthusiasts but also adversely affect their health. An estimation model of sports injury is designed based on an improved recursive neural network algorithm to reduce the incidence of sports injury in the process of physical exercise so that athletes and sports enthusiasts can reduce sports injury, which is conducive to physical health and sports health. [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]

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]

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

This article has first presented an overview and a brief design to the system, and then analyzed the system design goal and design principle. The role of the resource representation model in the Internet of Things is more important than the significance of the database schema for the web information system. In the current "vertical" application model, the device resources of the perception layer and the application entities of the business layer are in a tightly coupled relationship. In the process of system operation, it changes flexibly as the situation changes. This article will combine the characteristics of resources in the Internet of Things, from the perspectives of resources and services, and refer to the basic structure of the traditional Internet resource reference model OWL-S to appropriately expand and tailor it, and describe the relationship between Internet of Things device resources and application services. Therefore, the resource representation model oriented to the Internet of Things service in this article is proposed and simulated and verified. To a certain extent, this model can provide support for the unified description of IoT resources and services, so as to better adapt to the development needs of diversified IoT services. Based on the application and service in the Internet of Things environment restricted by time and space correlation and limited resources, the article discusses and proposes a multiterminal selection algorithm for Internet of Things business, and converts it into a mathematical combination optimization problem. Based on the ant colony optimization algorithm, a solution is given, which can realize the optimization of equipment combination scheme under resource-constrained conditions to maximize the total benefit value of the service, and can effectively improve the utilization rate of terminal resources. In the research on the analysis of the development strategy of China's industrial design industry in the era of intelligent interconnection, the analysis of the development strategy of the industrial design industry in the era of intelligent interconnection through analysis tools of industrial strategy reveals the new trend of industrial design in the era of intelligent interconnection. The analysis and formulation of industrial strategy are complicated, and it is a systematic project involving all aspects. The rational use of corresponding strategic analysis tools can further enhance the scientificity and rationality of industrial design strategy. [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]

Automatic identification system (AIS)-based ship trajectory data are important for analysing maritime activities. As the data accumulate over time, trajectory compression is needed to alleviate the pressure of data storage, migration and usage. The grating algorithm, as a vector data compression algorithm with high compression performance and low computation complexity, has been considered as a very promising approach for ship trajectory compression. This algorithm needs the threshold to be set for each trajectory which limits the applicability over a large number of different trajectories. To solve this problem, a dynamic adaptive threshold grating compression algorithm is developed. In this algorithm, the threshold for each trajectory is dynamically generated using an effective approaching strategy. The developed algorithm is tested with a complex trajectory dataset from the Qiongzhou Strait, China. In comparison with the traditional grating method, our algorithm has improved advantages in the ease of use, the applicability to different trajectories and compression performance, all of which can better support relevant applications, such as ship trajectory data storage and rapid cartographic display. [ABSTRACT FROM AUTHOR]

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

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

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

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

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

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

• The newly developed two-phase model class selection algorithm for void detection is firstly proposed. • The time-domain Bayesian model updating is successfully conducted for the CA void detection of the ballastless slab track by determining the most probable values (MPVs) of mortar stiffness scaling factors. • Not only the void location but also the severity of CA mortar are detected for ballastless slab track. This paper reports the feasibility study on the use of measured vibration of a railway slab track system in detecting void on the cement-emulsified asphalt (CA) mortar layer utilizing the Bayesian approach. By following the specification of the China Railway Track System (CRTS)-I ballastless slab track structure, two scaled models (with and without void in the CA mortar layer) were built and tested in the laboratory to demonstrate and verify the proposed CA void detection methodology. A three-dimensional finite element model was built using ABAQUS to calculate the time-domain data of the ballastless track system for Bayesian model class selection and model updating. A new two-phase model class selection algorithm was developed for identifying the CA void region. The proposed methodology identified the distribution of CA mortar stiffness using impact hammer test data from the scaled CRTS-I ballastless slab panel under laboratory conditions. The model updating results are consistent with the simulated CA mortar stiffness distribution. In addition, the posterior uncertainties of the identified CA mortar stiffness under different sensor configurations were quantitatively investigated. The results from the experimental case studies show that the proposed Bayesian methodology is feasible to detect CA void even with only a single accelerometer with the associated posterior uncertainties being kept at an acceptable level. [ABSTRACT FROM AUTHOR]

The coarseModerate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD) product (spatial resolution: 3 km) retrieved by the dark-target algorithm always generates the missing values when being adopted to estimate the ground-level PM2.5 concentrations. In this study, we developed a two-stage random forest using MODIS 3-km AOD to obtain the PM2.5 concentrations with full coverage in a contiguous coastal developed region, i.e., Yangtze River delta--Fujian--Pearl River delta (YRD--FJ--PRD) region of China. A first-stage random forest--integrated six meteorological fields was employed to predict the missing values of AOD product, and the combined AOD (i.e., random forest--derived AOD and MODIS 3-km AOD) incorporated with other ancillary variables were developed for predicting PM2.5 concentrations within a second-stage random forest model. The results showed that the first-stage random forest could explain 94% of the AOD variability over YRD--FJ--PRD region, and we achieved a site-based cross validation (CV) R² of 0.87 and a time-based CV R² of 0.85. The full-coverage PM2.5 concentrations illustrated a spatial pattern with annual-mean PM2.5 of 46, 40, and 35 µg m-3 in YRD, PRD, and FJ, respectively, sharing the same trend with previous studies. Our results indicated that the proposed two-stage random forest model could be effectively used for PM2.5 estimation in different areas. [ABSTRACT FROM AUTHOR]

Because of the relatively low accuracy of the current asphalt pavement performance prediction, a new pavement performance prediction model was established based on the particle swarm optimization (PSO) algorithm and support vector machine regression (SVR) algorithm. First, the SVR algorithm was introduced into the model to deal with the nonlinear regression. Then the PSO algorithm was applied to improve the searching efficiency and parameter continuity of the SVR algorithm. The pavement inspection data of an expressway in eastern China from 2006 to 2015 were used to verify the results, proving the feasibility of the PSO-SVR prediction model. The research results show that the model using particle swarm optimization has a fast convergence speed, and the optimized support vector machine has better rutting prediction performance and perfect generalization, and the prediction accuracy and reliability are higher than those of unoptimized support vector machine model. [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]

Over the last two decades, the identification of ancient artifacts has been regarded as one of the most challenging tasks for archaeologists. Chinese people consider these artifacts as symbols of their cultural heritage. The development of technology has helped in the identification of ancient artifacts to a greater extent. The study preferred machine-learning algorithms to identify the ancient artifacts found throughout China. The major cities of China were selected for the study and classified the cities based on different features like temple, modern city, harbour, battle, and South China. The study used a decision tree algorithm for recognition and gradient boosting for perception aspects. According to the findings of the study, the algorithms produced 98% accuracy and prediction in detecting ancient artifacts in China. The proposed models provide a good indicator for detecting archaeological site locations. [ABSTRACT FROM AUTHOR]

Deriving large-scale and high-quality precipitation products from satellite remote-sensing spectral data is always challenging in quantitative precipitation estimation (QPE), and limited studies have been conducted even using China's latest Fengyun-4A (FY-4A) geostationary satellite. Taking three rainstorm events over South China as examples, a machine-learning-based regression model was established using the random forest (RF) method to derive QPE from FY-4A observations, in conjunction with cloud parameters and physical quantities. The cross-validation results indicate that both daytime (DQPE) and nighttime (NQPE) RF algorithms performed well in estimating QPE, with the bias score, correlation coefficient and root-mean-square error of DQPE (NQPE) of 2.17 (2.42), 0.79 (0.83) and 1.77 mmh-1 (2.31 mmh-1), respectively. Overall, the algorithm has a high accuracy in estimating precipitation under the heavy-rain level or below. Nevertheless, the positive bias still implies an overestimation of precipitation by the QPE algorithm, in addition to certain misjudgements from non-precipitation pixels to precipitation events. Also, the QPE algorithm tends to underestimate the precipitation at the rainstorm or even above levels. Compared to single-sensor algorithms, the developed QPE algorithm can better capture the spatial distribution of land-surface precipitation, especially the centre of strong precipitation. Marginal difference between the data accuracy over sites in urban and rural areas indicate that the model performs well over space and has no evident dependence on landscape. In general, our proposed FY-4A QPE algorithm has advantages for quantitative estimation of summer precipitation over East Asia. [ABSTRACT FROM AUTHOR]

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]

Background: Chronic obstructive pulmonary disease (COPD) is a chronic respiratory disease that seriously threatens people's health, with high morbidity and mortality worldwide. At present, the clinical diagnosis methods of COPD are time-consuming, invasive, and radioactive. Therefore, it is urgent to develop a non-invasive and rapid COPD severity diagnosis technique suitable for daily screening in clinical practice.Results: This study established an effective model for the preliminary diagnosis of COPD severity using lung sounds with few channels. Firstly, the time-frequency-energy features of 12 channels lung sounds were extracted by Hilbert-Huang transform. And then, channels and features were screened by the reliefF algorithm. Finally, the feature sets were input into a support vector machine to diagnose COPD severity, and the performance with Bayes, decision tree, and deep belief network was compared. Experimental results show that high classification performance using only 4-channel lung sounds of L1, L2, L3, and L4 channels can be achieved by the proposed model. The accuracy, sensitivity, and specificity of mild COPD and moderate + severe COPD were 89.13%, 87.72%, and 91.01%, respectively. The classification performance rates of moderate COPD and severe COPD were 94.26%, 97.32%, and 89.93% for accuracy, sensitivity, and specificity, respectively.Conclusion: This model provides a standardized evaluation with high classification performance rates, which can assist doctors to complete the preliminary diagnosis of COPD severity immediately, and has important clinical significance. [ABSTRACT FROM AUTHOR]

The spatial decomposition of demographic data at a fine resolution is a classic and crucial problem in the field of geographical information science. The main objective of this study was to compare twelve well-known machine learning regression algorithms for the spatial decomposition of demographic data with multisource geospatial data. Grid search and cross-validation methods were used to ensure that the optimal model parameters were obtained. The results showed that all the global regression algorithms used in the study exhibited acceptable results, besides the ordinary least squares (OLS) algorithm. In addition, the regularization method and the subsetting method were both useful for alleviating overfitting in the OLS model, and the former was better than the latter. The more competitive performance of the nonlinear regression algorithms than the linear regression algorithms implies that the relationship between population density and influence factors is likely to be non-linear. Among the global regression algorithms used in the study, the best results were achieved by the k-nearest neighbors (KNN) regression algorithm. In addition, it was found that multi-sources geospatial data can improve the accuracy of spatial decomposition results significantly, and thus the proposed method in our study can be applied to the study of spatial decomposition in other areas. [ABSTRACT FROM AUTHOR]

Nowadays, domestic motor manufacturers often use high-speed circuit and magnetic circuit parameters in engine models. In addition, the calculation accuracy is low and cannot fully meet the requirements of a modern engine design. The field-circuit combination algorithm is based on electric field analysis, which avoids many rough assumptions and empirical formulas; its calculations are more accurate and reliable than traditional algorithms. It is used to analyze and calculate the steady-state operating conditions and characteristics of motors, which can effectively improve the design level of China's boss synchronous motors. This article mainly introduces software algorithms and auxiliary FDTD methods. In this article, we use the field-circuit coupling algorithm to develop an electromagnetic calculation software package for synchronous motors and establish a mathematical model for the potential field-circuit coupling algorithm. We use the cassette electromagnetic calculation program to solve the model, evaluate the synchronous motor, and use historical data to modify the model and improve the accuracy of the development-state evaluation of the electromagnetic calculation software package for the synchronous motor based on the motor electromagnetic circuit coupling algorithm. The experimental results of this article show that the field circuit combination algorithm increases the development of the electromagnetic calculation software package for synchronous motors by 55% and reduces the false alarm rate and false alarm rate. Finally, by comparing the phases of traditional algorithms, we analyzed the excitation circuit combination algorithm to calculate the parameters and performance of the synchronous motor. [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]

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]

• A new ET model (SW_OPT) integrates the SW and the Ts-VI model using PSO. • E, T and ET from SW_OPT were validated in a typical irrigated area of China. • SW_OPT calculates ET and its components with high accuracy. • SW_OPT is significantly more accurate compared to SW_Ori. • SW_OPT has low sensitivity to most of input and empirical parameters. Daily actual evapotranspiration (ET) and its components – soil evaporation (E) and vegetation transpiration (T) – play a key role in water resource management of irrigated areas. Nevertheless, due to large uncertainties in the parameterization of the resistances in irrigated areas, traditional ET models do not always provide accurate ET estimates, especially for its components. This uncertainty is mainly due to the difficulty of determining the empirical parameters accurately of soil and canopy resistances which is debated for a long time and the error in input variables. This paper proposed an optimized Shuttleworth-Wallace model (SW) using the particle swarm optimization (PSO) algorithm to integrate the original SW with the surface temperature-vegetation index (Ts-VI) triangle models (named Shuttleworth & Wallace_Temperature Vegetation Index model, SW_TVI).The performance of the SW_TVI model was significantly improved by optimizing the soil and canopy resistances in the original SW model using discontinuous regional ET estimates from the Ts-VI model under clear-sky conditions. Compared with the original SW model, the root mean squared error (RMSE) and mean absolute deviation (MAD) of the SW_TVI model were reduced by more than 30%, against in situ measurements in the Heihe River Basin. The Bias of the T/ET ratio was also significantly reduced from -22.3% for the original SW model to -5.5% for the SW_TVI model. The annual contributions of E and T to ET were about 20% and 80%, respectively, and had a strong seasonal variation in the typical irrigated area of China. In summary, the SW_TVI model shows three outstanding advantages: (1) it estimates daily continuous E, T, and total ET with high accuracy; (2) it is very robust and insensitive or slightly sensitive to most input variables and empirical parameters; (3) it is independent from ground data. This new SW_TVI model will benefit water resources management in irrigated areas, particularly in arid and semi-arid regions. [ABSTRACT FROM AUTHOR]