Search

Copyright of China Rural Water & Hydropower is the property of China Rural Water & Hydropower Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Arid Zone Research / Ganhanqu Yanjiu is the property of Arid Zone Research Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Coal Science & Technology (0253-2336) is the property of Coal Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Atomic Energy Science & Technology is the property of Editorial Board of Atomic Energy Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Chinese Journal of High Pressure Physics is the property of Chinese Journal of High Pressure Physics Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Objective To investigate the application value of combining the Demirjian's method with machine learning algorithms for dental age estimation in northern Chinese Han children and adolescents. Methods Oral panoramic images of 10 256 Han individuals aged 5 to 24 years in northern China were collected. The development of eight permanent teeth in the left mandibular was classified into different stages using the Demirjian's method. Various machine learning algorithms, including support vector regression (SVR), gradient boosting regression (GBR), linear regression (LR), random forest regression (RFR), and decision tree regression (DTR) were employed. Age estimation models were constructed based on total, female, and male samples respectively using these algorithms. The fitting performance of different machine learning algorithms in these three groups was evaluated. Results SVR demonstrated superior estimation efficiency among all machine learning models in both total and female samples, while GBR showed the best performance in male samples. The mean absolute error (MAE) of the optimal age estimation model was 1.246 3, 1.281 8 and 1.153 8 years in the total, female and male samples, respectively. The optimal age estimation model exhibited varying levels of accuracy across different age ranges, which provided relatively accurate age estimations in individuals under 18 years old. Conclusion The machine learning model developed in this study exhibits good age estimation efficiency in northern Chinese Han children and adolescents. However, its performance is not ideal when applied to adult population. To improve the accuracy in age estimation, the other variables can be considered. [ABSTRACT FROM AUTHOR]

The evolution of urban innovation space development varies greatly, and it is of great practical significance to pay attention to its causes and the key influencing factors of its evolution. Taking Wuhan main city as an example, the machine learning algorithm, and Random Forest Modeling Algorithm is used to analyze the influencing factors on the evolution of Wuhan's innovation space. The results show that the Random Forest Modeling algorithm fits the dataset well, can effectively capture the complex nonlinear characteristics of the influencing factors and the evolution of innovation space, and reveals that the important factors affecting the evolution of the innovation space are population density, distance from commercial centers, subway station density and road network density. Finally, we make suggestions for the future development of urban innovation space based on this evidence. [ABSTRACT FROM AUTHOR]

Copyright of West China Journal of Stomatology is the property of Sichuan University, West China College of Stomatology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Petroleum Geology & Recovery Efficiency is the property of Petroleum Geology & Recovery Efficiency and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Telecommunication Engineering is the property of Telecommunication Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Coal Geology & Exploration is the property of Xian Research Institute of China Coal Research Institute and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

For the uncertainties of demand, exchange rates and tariffs in export cross-border trade, the machine learning driven supply chain network optimization of export cross-border E-commerce enterprises is studied by utilizing rich-data in E-commerce. A two-stage stochastic programming model including overseas warehouse, border warehouse location and inventory decisions is developed. Based on historical data, the machine learning techniques are adopted to construct stochastic trading scenarios required by the proposed model. Thus, the 'predictive to perspective' study is realized. Moreover, the model is applied to an apparel enterprise who sells its products to Southeast Asian. The sensitivity analysis is performed with the consideration of "Regional Comprehensive Economic Partnership (RCEP)" which comes into operation recently. The results show that the random forest algorithm outperforms other machine learning techniques in our case and the implementation of RCEP can bring the significant decrease of the cross-border logistics costs and the development of overseas warehouses. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Mine Automation is the property of Industry & Mine Automation Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Asian Journals of Ecotoxicology is the property of Gai Kan Bian Wei Hui and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In order to construct a high-precision prediction model of forest surface litter moisture content, this paper takes three typical forests in Harbin City, Heilongjiang Province as the research object, and uses a self-made portable meteorological factor detector for real-time detection. Three representative machine learning methods of RF ( Random Forest ), BP ( Back Propagation ), and LSTM ( Long Short Term Memory ) were used to construct litter moisture content prediction models for the three stands. Results showed that, the prediction accuracy of the prediction model constructed by different forest stands under different machine learning methods was different. The prediction accuracy of the three stands in different machine learning models was LSTM > BP > RF. LSTM had the best effect in the study of surface litter moisture content prediction model. The results of this study indicated that LSTM was outstanding in the prediction of surface litter moisture content, which had important theoretical significance and practical guiding value for improving the accuracy of surface litter moisture content model based on meteorological element method. [ABSTRACT FROM AUTHOR]

Given the problem that the communication distance and quality of the vehicle-mounted ultrashort wave radio are affected by ground attachments and topography in the actual combat environment, a radio wave propagation prediction model of vehicle-mounted ultrashort wave radio is established based on ray tracing and machine learning. The integrated modeling of armored combat vehicle and vehicle antenna is established to obtain the antenna radiation pattern, and combined with electronic images, the radio wave propagation simulation model based on ray tracing technology is established. Based on the machine learning algorithm of the random forest and data results for the simulation model, the radio wave propagation prediction model based on the random forest was established. Compared with traditional classical radio wave propagation models such as the Egli and Okumura-Hata models, the radio wave propagation prediction model based on the random forest has higher accuracy. The root mean square error reaches 2.190 1 dB, and the coefficient of determination reaches 0.960 1. It can accurately predict radio wave propagation in the tactical communication environment. [ABSTRACT FROM AUTHOR]

A digital twin construction method based on the reduced order model library and machine learning was proposed for structures under dynamic loads. Firstly, the high-fidelity finite element models were established according to the possible damage states occurring during the service of the physical structures. Secondly, the Krylov subspace order reduction method was used to reduce the orders of the models and the reduced order models were assembled to a library. Finally, the random forest machine learning algorithm was used to train the model selector, infer the current state of the physical structure through the sensor data from the structure, and then drive the digital twin to evolve with the physical structure. A physical frame structure was designed and manufactured to simulate the damages of different degrees at different points, and verify the proposed digital twin construction method for dynamic structures. [ABSTRACT FROM AUTHOR]

The registration quality assessment is usually given to human experts, which is time-consuming. The commonly used Dice score only focuses on the error at the edge of the tissue, which is difficult to assess the registration result within the tissue. To address these issues, this paper proposed a method to predict registration errors based on machine learning (PREML) in lung CT images.This method firstly constructed three types of features, such as deformation field statistical features, deformation field physiologically realistic features and image similarity features, then expanded the number of features by pooling, and finally used random forest regression to predict non-rigid registration errors. Moreover, it used an adaptive random perturbation to simulate the spatial distribution of lung registration errors to further improve the capability of error characterization of statistical features. The proposed method achieved a mean absolute error of 1.245±2.500 mm from ground truth on lung CT image datasets, outperforming the baseline method. The results show that PREML method has the advantages of high accuracy and robustness, enhancing the safety and effectiveness of registration algorithms in clinical applications. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In order to improve the accuracy of gesture recognition based on surface electromyography (sEMG), an improved deep forest combined hand motion recognition method was proposed. The extreme gradient boosting (XGBoost) tree was introduced into the deep forest model to form the cascade structure of deep forest together with the random forest and the complete random forest. The deep forest model integrates three different tree-based classifiers at each level, a total of four decision forests including a random forest, an extreme random forest and two extreme gradient boosting trees. The classification performance was improved by using the complementarity between different learning algorithms. In order to evaluate the performance of the model, the sEMG signals of 4 healthy subjects were collected for the verification experiment of hand action recognition, and compared with random forest, support vector machine, one-dimensional and two-dimensional convolutional neural networks algorithms. The result shows that the average recognition accuracy of the method for 16 commonly used hand actions is 94.14%, and the classification accuracy of sEMG signals is high. [ABSTRACT FROM AUTHOR]

Copyright of Journal of China University of Petroleum is the property of China University of Petroleum and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Asian Journals of Ecotoxicology is the property of Gai Kan Bian Wei Hui and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Marketization of rural idle homesteads can be promoted for “three rights separation” of rural homesteads. The perceived risk can be one of the most important identification factors in the farmers’ homestead marketization in the reform of the homestead system. In this study, an evaluation system was constructed to identify the risk of homestead marketization from the perspective of farmers’ risk perception, including the risk of the farmers’ equity, cultivated land protection, collective return, social stability, ecological environment, and cultural inheritance risk. Taking Chongqing Dazu, Yongchuan, and other typical homestead system reform pilots as empirical areas, a Variable Fuzzy set was adopted to evaluate the risk level of farmers’ homestead marketization. Subsequently, an efficient machine learning model (called Random Forest, RF) was employed to identify the risk of farmers’ homestead marketization. Finally, risk prevention and control strategies were proposed under different marketization patterns. The results showed that: 1) An inverted “U” shaped fluctuation trend was found in the risk level of farmers’ homestead marketization. Specifically, the risk probability of direct marketization (43.38%) and green indicators marketization (32.14%) were significantly higher than that of marketization of reclamation indicators (13.73%), and marketization of planning adjustment (12.99%). 2) There were some differences in the various risk factors for the four types of marketization patterns. The farmers’ equity risk (46.74%) was higher in the direct marketization, whereas, the cultural inheritance risk (4.76%) was lower. The social stability risk (35.66%) was higher in the marketization of planning adjustment, while the ecological environment risk (5.85%) was lower. The cultivated land protection risk (29.13%) was higher in the marketization of reclamation indicators, while the collective return risk (2.05%) was lower. The farmers’ equity risk (34.24%) was higher in the green indicators marketization, while the ecological environment risk (1.20%) was lower. 3) There were some differences in the risk mechanism. The same risk factor also shared the heterogeneity in the homestead marketization of four types. 4) The differentiated prevention strategies of marketization risks were considered the timing, and the cooperation of various participants, such as farmers, collectives, and governments. The government, the collective, and the farmer were united to focus on the “prevention in advance” in the direct marketization. The collective was supposed to focus on the “prevention in advance” in the marketization of planning adjustment, while the government was on the “prevention afterward”. By contrast, the government was supposed to focus on the “prevention in the event” in the marketization of reclamation indicators, while the collective was on the “prevention afterward”. Only the government was supposed to focus on the “prevention in advance” in the green indicators marketization. In conclusion, the reform of homestead marketization should set the tone of “steady and prudent promotion”. It was different from the complete marketization of state-owned land. But the marketization was limited using “three rights separation”. Meanwhile, future homestead marketization should avoid homogenization patterns. More importantly, it is urgent to establish an income distribution mechanism and risk aversion system, in order to share the benefits and risks by farmers, collectives, and governments. The findings can provide the decisionmaking on rural residential land consolidation. The homestead risk database can be expected to deepen the reform of the homestead system. [ABSTRACT FROM AUTHOR]

Soil organic carbon (SOC) responds significantly to the surface layer, but also shows different responses with depth. Jianghan Plain plays an important role in the agroecosystem of the Yangtze River Economic Belt, but its SOC baseline remains unclear. In this study, 66 profiles were collected from 2009 to 2012 in Jianghan Plain, and SOC content prediction models were constructed based on nine environmental factors as covariates using the random forest method for three depths：0–30, >30–60 cm, and >60–100 cm. We constructed a baseline map of SOC at 30 m resolution and estimated the SOC stock. The results showed that SOC contents decreased with increasing soil depth, and were generally high in the east-central part and low in the west. The model had the highest prediction accuracy (R² =0.45, RMSE= 3.28 g·kg−1 ) for the topsoil (0– 30 cm) . Temperature, clay content, and precipitation were the three most important covariables for the model. The soil organic carbon storage at 1 m depth in Jianghan Plain was 183.75 Tg, and >30–100 cm accounted for approximately 59% of the total storage at 1 m depth. We conclude that it is necessary to focus on deep soils when evaluating the soil carbon pool and sequestration potential. Our new estimate can serve as a base for both horizontal and vertical variation of SOC. [ABSTRACT FROM AUTHOR]

The geographic distribution data of the three precious hardwood broad-leaved tree species, Fraxinus mandshurica, Juglans mandshurica and Phellodendron amurense in the Maoershan area were collected through investigation and consulting data. And the suitability distribution of three precious hardwood broad-leaved tree species in Maoershan area was studied by using GIS technology and random forest model, combined with the topographic and soil data of the target tree species sub-compartment. Results showed that, the accuracy rates of the random forest models in the ten datasets were all above 70%. When Ntree(number of trees grown)= 500, the average accuracy of the random forest model was 75.2%; when Ntree = 1000, the average accuracy of the random forest model was 75.4%, and the AUC of the group datasets were all above 0.7, and the average AUC was 0.746. In the ten datasets, mean decrease accuracy and mean decrease Gini of altitude were the largest, mean decrease accuracy of slope position was the smallest, and mean decrease Gini of landform was the smallest. The target tree species had the highest growth suitability within the altitude range of 200-300 m. As the altitude increased above 300 m, the growth suitability of the target tree species was lower as the altitude increased. When the slope was 0°-10° and 30°-35°, the growth suitability was the highest in the range. As the slope position changed from top to bottom, the growth suitability of the target tree species was getting higher and higher. The growth suitability in the three directions of southwest, south and north was the highest. The growth suitability was highest in plain areas. When A layer (eluvial horizon) thickness was greater than 15 cm, the growth suitability of the target tree species became higher and higher. When AB layer (eluvial horizon and deposition layer) thickness was in the range of 10-25 cm, the growth of the target tree species was the most suitable. The order of affecting environmental factors importance sorting of the suitability distribution of the three precious hardwood broad-leaved tree species in the Maoershan area was altitude, AB layer thickness, slope, slope aspect, A layer thickness, landform and slope position in descending order. They were mainly suitable for distribution in the southern and northwestern areas of Maoer Mountain, with an altitude of 200-300 m, a gentle slope, a soil layer thickness of 15-25 cm, a north-south aspect, a plain landform, and a downhill position. [ABSTRACT FROM AUTHOR]

Copyright of Study on Optical Communications / Guangtongxin Yanjiu is the property of Study on Optical Communications Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Objectives: To address the problem of internal inconsistency of classification targets in existing three dimensional(3D) point cloud data segmentation and classification methods. we propose a high-precision classification method for indoor point cloud jointly optimized by super voxel random forest and long short-term memory (LSTM) neural network. Methods: The method takes into account that the super voxel structure has the characteristics of internal feature consistency, divides the original point cloud into super voxels, and uses super voxels as the basic unit for multivariate feature calculation to build a super voxel random forest classification model for indoor point cloud to achieve coarse classification of point cloud data. On this basis, LSTM is introduced to train and predict the neural network model for the hyper voxel neighborhood connectivity of coarse classification to achieve the optimization of hyper voxel coarse classification results. The validity and accuracy of the proposed classification method are verified based on the open dataset. Results: The results show that the classification accuracy of the proposed classification method can reach 83.2% for 13 types of elements in the open dataset. The training data of the LSTM optimization network proposed in this paper used only the label information of region 1 for model training, while other deep learning frameworks used regions 1-5 for model training, so from the perspective of training data requirements, the point cloud data classification framework proposed in this paper can achieve a relatively better prediction result with a small portion of the training data set. The super voxel-based LSTM optimization method approach has high classification accuracy on objects with obvious set features such as ceiling, floor and wall, however, it is inferior to the deep learning algorithm RandLA-Net in classifying objects with complex structures such as chair, sofa and bookcase. Conclusions: In this paper, we consider the association characteristics between different types of elements embedded in the connection relations among super voxels, and introduce LSTM to train and predict the model for the coarse classification of super voxel neighborhood connection relations to achieve the optimization of coarse classification results of super voxels. The proposed method can achieve better classification accuracy when trained with small samples compared with the classical deep learning framework. [ABSTRACT FROM AUTHOR]

High - resolution meteorological data serve as an important data basis for fine - scale meteorological services. Using the hourly 2 - meter air temperature grid data from January 2020 to March 2021 and the terrain factors such as altitude, longitude, and latitude, this study aimed to enhance the resolution of 2 - meter air temperature grid data with a resolution of 1 km to 100 m through downscaling based on four machine learning methods, namely LightGBM ( LGB), XGBoost (XGB), gradient boosting tree ( GBT), and random forest ( RF) . Then, this study conducted the weighted fusion of downscaling results of different models. Finally, the downscaling results of different models were compared with the bilinear interpolation results, and the results are as follows. The results of each downscaling model were relatively consistent with the observational data. Compared with the bilinear interpolation results, the results of the LGB, XGB, and RF models had similar spatial structures but were more detailed. All downscaling models yielded the same spatio - temporal distribution characteristics of errors. Compared with the bilinear interpolation results, the data of the LGB, XGB, and GBT models showed significantly higher precision, and their root mean square ems ( RMSEs) decreased by 5. 2%, 4. 1%, and 4. 6%, respectively. Meanwhile, the RMSE after weighted fusion decreased by 5. 9%, which was higher than that of any single machine learning model. The downscaling results of the LGB, XGB, and GBT models were improved to a certain degree compared with the bilinear interpolation results under different terrain conditions, especially in high - altitude areas (above 600 m) . The correlation coefficients of results of the LGB, XGB, and BGT models and model based on weighted fusion increased by 0. 45%, 0. 40%, 0. 63%, and 0. 66%, respectively, and their RMSEs decreased by 9.1%, 8.0%, 12. 7%, and 13. 1%, respectively. These results indicate that the downscaling model based on the weighted fusion of different machine learning methods can both improve spatial resolution and maintain data precision and, thus, is suitable for downscaling research on 2 - meter air temperature data in the study area. This study can be used as a reference for developing high - resolution data products. [ABSTRACT FROM AUTHOR]

Copyright of Rolling Stock (1002-7602) is the property of Rolling Stock Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Clean Coal Technology is the property of Clean Coal Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Railway Standard Design is the property of Railway Standard Design Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Railway Science & Engineering is the property of Journal of Railway Science & Engineering Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Fishway is one of the common measures to maintain the continuity of rivers and protect fish resources in the world. Among them, the hydraulic characteristics of the early fishway cannot match the swimming ability and habits of the domestic fish, particularly in the early stage of construction. Fortunately, the internal structure can be optimized to improve the efficiency of fishways operation in recent years. However, the complicated physical experiment depends greatly on seasonal, temperature, light, and external factors. Additionally, the test cost is ever increasing in a large amount of fish and feeding. Therefore, it is a high demand to predict the fish migration routes in the field of fish conservation using mathematical models. Taking Schizothorax prenanti as the research object, this study aims to simulate fish migration in the vertical slot fishway. The original data set was divided using upstream data monitored in the previous test, according to the swimming characteristics of Schizothorax prenanti. Random Forest (RF), XGBoost, K-Nearest Neighbors (KNN), and Gradient Boosting Decision Tree (GBDT) were used to establish the multiple Behavior classification model (BCM), and Swimming speed regression model (SSRM). According to the evaluation indexes of the classification and regression model, the optimal model was selected to establish the mapping relationship between the fish behaviors and hydraulic indexes using the Eulerian-Lagrangian-agent method (ELAM) framework, and then the fish migration model was constructed. The improved model also considered the influence of velocity (magnitude, vector, and velocity gradient) and inertia factors on the movement of fish. Specifically, the perception area of the fish body was represented by a two-dimensional circular plane with the inductive distance as the radius, particularly with the center of the fish body as the origin. Finally, the simulated and the observed track were compared to verify the reliability of the fish migration model. The results showed that the RF model performed the best to predict the swimming behavior classification and swimming speed regression. The classification accuracy of swimming behavior was 0.804, the recall rate was 0.794, and the F1 score was 0.798. The R2 values were all greater than 0.75 for the swimming speed regression using the three data sets. The predicted trajectories of the migration model were better agreed with the actual. Most virtual fish successively passed through the mainstream and the right backflow area, indicating better consistency with the actual fish passing experiment. A comparison was made to obtain the characteristic track of the numerical simulation and the actual fish test. Consequently, the fish migration model can be expected to fully reflect the basic behavior characteristics of the target fish, and then to better predict the migration route of Schizothorax prenanti. The finding can also provide a strong reference to evaluate the fishway flow field for the protection measures in the fish industry. [ABSTRACT FROM AUTHOR]

Copyright of Mining & Metallurgy (10057854) is the property of Beijing Research Institute of Mining & Metallurgy Technology Group and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

BACKGROUND: Radiomics can be used to quantify image heterogeneity. Whether radiomics can be used to screen out features such as fingerprint from MRI images of osteoporotic vertebral bodies to predict the occurrence of new fracture is worth studying. OBJECTIVE: To explore the feasibility of constructing a machine learning prediction model for thoracolumbar refracture after vertebral augmentation through combining MRI radiomics features and clinical information. METHODS: This study retrospectively collected the data of patients who were diagnosed with osteoporotic vertebral compression fracture by MRI and treated with percutaneous vertebral augmentation in Chengdu First People’s Hospital from May 2014 to April 2019. PyRadiomics was used to extract the imaging features of T1 sequences of vertebral MRI at the T11-L2 segments before percutaneous vertebral augmentation. All models were constructed in the training set, and prediction performance evaluation was performed in the validation set. Feature dimension reduction was conducted by applying least absolute shrinkage and selection operator regression. The corresponding refracture prediction models were constructed by multivariate logistic regression, random forest and adaptive lifting algorithm analysis using clinical parameters, selected features or the integrating of both. The diagnostic efficacy of the model was evaluated using the receiver operating characteristic curve. The decision analysis curve was used to compare the clinical value of each model. RESULTS AND CONCLUSION: (1) A total of 336 vertebrae were included in 135 patients, of which 67 vertebrae had refractures. 1 746 features were extracted from each vertebra, and 13 important features were obtained through dimension reduction. (2) Among the three models, area under curve of the combined model in the training set and validation set was significantly higher than that of the clinical model (P < 0.05), and the decision analysis curve also showed that the net benefit of the combined model in predicting thoracolumbar refracture was higher than that of the clinical model in most threshold intervals. (3) The results indicated that it was feasible to construct a refracture prediction model based on MRI T1 sequence imaging and clinical information, which could help to identify the vertebrae with high risk of refracture at early stage. [ABSTRACT FROM AUTHOR]

Broken-winged chicken carcasses can be one of the most common defects in broiler slaughter plants. Manual detection cannot fully meet the large-scale production, due to the high labor intensity with the low efficiency and accuracy. Therefore, it is a high demand to rapidly and accurately detect broken wings on chicken carcasses. This study aims to realize the rapid inspection of broken-winged chicken carcasses in the progress of broiler slaughter, in order to improve the production efficiency for the cost-saving slaughter line. 1053 broiler carcass images were collected from a broiler slaughter line using a computer vision system. Rapid identification was then constructed for the broken wing defects. Specifically, the front view of the chicken carcass was obtained in the machine vision system. The preprocessing was then deployed to obtain the chicken carcass images without the background, including the weighted average (graying), two-dimensional median filtering (denoising), and iterative (threshold segmentation). The code was also written in the MATLAB platform. After that, a total of 11 characteristic values were calculated, covering the exact distance starting from the left and right ends of the chicken carcass image to the centroid and the difference (d1, d2, and dc), the heights of the lowest point in the two wings and their difference (h1, h2, and hc), the areas of the two wings and ratio of them (S1, S2, and Sr), squareness (R), and width-length ratio (rate). As such, the eight principal components were achieved in the principal component analysis after the reduction of several dimensions. Separately, the principal components and characteristic values were imported into the specific model of linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), random forest (RF), support vector machine (SVM), and BP neural network. Among them, the input parameter of the VGG16 model was from the RGB maps of the chicken carcass with the removed background. Finally, a comparison was made for the F1-scores and total accuracy of each model. Thus, the highest recall rate of 91.13% was achieved in the RF model with the characteristic values as the input parameters among the shallow learning models. The second higher recognition accuracy and F1-score were 96.28% and 92.58%, respectively in the SVM model with the characteristic values as input parameters. The highest total accuracy of model recognition was achieved in the quadratic discriminant and SVM models with the characteristic values as the input parameters, both up to a proportion of 91.78%. Moreover, the F-score and total accuracy of the VGG16 model were the highest among the total model combinations, with respective rates of 94.35% and 93.28%, respectively. In terms of the prediction time of models, the shortest prediction time was obtained in the SVM model with the principal components as the input parameters. Specifically, the capacity was found to determine 353 sample images in 0.000 9 s, with an average speed of 3.92×105 images per second. By contrast, the longest prediction time was observed in the VGG16 model, where 24.46 s to determine 253 sample images, with an average speed of 10.34 images per second. In conclusion, the VGG16 model can be expected to serve as the best classification of broken wings in chicken carcasses. [ABSTRACT FROM AUTHOR]

Objectives: To explore the relationship between urban ecological environment and human activities is an important research content in the current urbanization process. With the in-depth development of the era of big data, multi-source data ubiquitous in the Internet has been fully excavated, which has played an important role in promoting the research of urban ecological environment. Methods: Based on multisource data, we propose to construct human activity indicators (residential area walkability index, street vitality index, urban function mixing index) using point of interest(POI), OpenStreetMap (OSM) and residential area data, and urban ecological environment indicator (remote sensing ecological index)using remote sensing images. Combing machine learning models such as polynomial regression(PLR), random forest regression( RFR), extreme gradient boosting regression(XGB) and support vector regression machine(SVR), it is effective to make regression analysis on urban ecological environment and human activity indicators. By comparing the performance of different models in this dataset, the relationship between the urban ecological environment and human activities is revealed.Results:We demonstrate the application of our method using a case study of Nanchang city. The results show that: (1)The three indexes of human activities all present a central high and gradually decrease to the surroundings, while the urban ecological environment indicators show an opposite trend. (2) From the analysis of the performance results of each model on the dataset, XGB has the best regression effect, followed by PLR.(3)There is a strong negative correlation between the urban ecological environment and human activities, and the street vitality index, the urban function mixing index are more relevant to the urban ecological environment, and the walkability index of the residential area is less relevant to the urban ecological environment. (4)In areas where human activities have less impact, the urban ecological environment will be disturbed by other factors, resulting in the low prediction accuracy, while the prediction accuracy in areas with strong human activities is high.Conclusions: Using multisource data and machine learning models can provide an important reference for exploring the relationship between urban ecological environment and human activities. [ABSTRACT FROM AUTHOR]

In order to quickly and accurately predict the slope stability, an intelligent evaluation method of slope stability based on machine learning was proposed. Based on the characteristics of 618 slopes in China, 6 typical slope parameters, including gravity (γ), cohesion (C), internal friction angle (φ), slope angle (β), slope height (H) and pore water pressure (P), were selected to establish the slope stability evaluation dataset. Slope stability prediction models were established using gradient boosting machine (GBM), support vector machine (SVM), artificial neural network (ANN) and random forest (RF) in the machine learning algorithm. The models were trained and learned using the training set. The model parameters were adjusted using the 5-fold cross validation and grid-search. The ability of the model to classify the slope stability was tested through the test set, and the optimal model was determined. The results show that by comparing the area under the receiver operating characteristic curve (AUC) and F1Score of GBM, SVM, ANN and RF algorithms, the AUC value and F1Score of RF algorithm are 0.969 and 0.904, respectively; RF algorithm has the best evaluation index and is more suitable for analyzing slope stability. Based on the different slope stability prediction models established by deleting different characteristic variables of RF algorithm, the characteristic parameters in descending order of sensitivity are γ, β, H, φ, P, C. According to the different sensitivity factors, the slope protection measures are proposed based on the sensitivity of the characteristic parameters. [ABSTRACT FROM AUTHOR]

【Objective】With the development of social economy and the acceleration of urbanization, China is facing the double tests of quantity and quality. In order to better monitor and protect cultivated land, it is very important to identify and extract cultivated land efficiently and accurately. Aiming at the shortcomings of using optical data for cultivated land extraction and single machine remote sensing image processing, the present study introduces radar data combined with optical data for cultivated land extraction based on Google Earth Engine (GEE) platform. 【Method】The sentinel-1 and sentinel-2 images of the study area were obtained by GEE platform for preprocessing, and the VV and VH channel information of sentinel-1 and NDVI, NDWI and NDBI of sentinel-2 were extracted. At the same time, the gray level co-occurrence matrix (GLCM) was used to extract the texture information of the study area, and then the minimum distance.Three machine learning algorithms, support vector machine and random forest were used to identify and extract cultivated land in the study area. Finally, the results of cultivated land extraction were processed by morphological processing of open operation. 【Result】Taking Guangzhou city, Guangdong province as an example, the results showed that compared with optical data only, the introduction of radar data improved the accuracy of cultivated land extraction. The overall accuracy of cultivated land extraction based on minimum distance, support vector machine and random forest algorithm was improved by 0.05, 0.09 and 0.10,respectively. Among them, the random forest algorithm had the best extraction effect, and the overall accuracy reached 0.85. Morphological processing removed the wrong small spots in cultivated land extraction, and further improved the accuracy of cultivated land extraction. The overall accuracy was 0.91 and kappa was 0.83.【Conclusion】Based on GEE platform, the study introduced radar data and morphological processing of cultivated land extraction results, which significantly improved the extraction accuracy and provides an effective means for large-scale cultivated land extraction. [ABSTRACT FROM AUTHOR]

Copyright of CT Theory & Applications is the property of Editorial Department of CT Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Acta Agriculturae Zhejiangensis is the property of Acta Agriculturae Zhejiangensis Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Objectives: Machine learning classifier‑based water body extraction methods for polarimetric synthetic aperture radar (PolSAR) images have high reliability but typically require a great number of training samples. Consequently, it is very difficult and time‑consuming to manually collect enough training samples when extracting water body from multi‑temporal PolSAR images. To this problem, transfer learning is used to reduce the labor cost of querying new samples and improve the timeliness of water body extraction of multi‑temporal PolSAR images. Methods: Firstly, an optimal source domain image from multi‑temporal images is automatically selected according to the distribution difference between images, and the other images are taken as target domain images. Secondly, a group of training samples are queried in the source domain image as source sample set, and the same number of unlabeled samples are randomly sampled from each target domain image as their target domain sample set. And the knowledge of source domain samples is transferred to target domain samples via the transfer learning method. Finally, a random forest classifier‑based water body extraction model is trained using the target domain sample set, and is used for the water body extraction of target domain images. Results: We have conducted experiments using six PolSAR images and two kinds of transfer learning methods, the results show that:(1) The label transfer accuracy and the water body extraction accuracy are positively correlated. (2) Inductive transfer learning methods achieve higher label transfer accuracy and lower standard deviation. (3) A smaller distribution difference between source and target domain images indicate a greater transferability, and thus a better water body extraction accuracy. (4) The water body extraction results of inductive transfer learning methods have a higher rate of missing detection, while the results of transductive transfer learning methods have a higher rate of false detection. Conclusions: In the water body extraction of multi‑temporal PolSAR images, the use of transfer learning methods can significantly reduce the number of manually labeled samples needed to construct high‑performance classifiers, while maintaining the water body extraction accuracy at a high level. It has great application potentiality in the emergency response of flood disaster. [ABSTRACT FROM AUTHOR]

Copyright of Journal of South China Normal University (Natural Science Edition) / Huanan Shifan Daxue Xuebao (Ziran Kexue Ban) is the property of Journal of South China Normal University (Natural Science Edition) Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of South China University of Technology (Natural Science Edition) is the property of South China University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

For the landslide susceptibility prediction (LSP) based on machine learning (ML) models, the reasonable selection of negative samples has an important influence on the LSP performance. Generally, the main selection methods include randomly selecting from the whole study area or from the specific attribute areas such as low slopes. The negative samples selected by the above methods are often inaccurate or biased, resulting in low accuracy and low reliability of LSP. To solve this problem, the coupling model of ML and information value (IV) method was proposed for LSP. Taking Ruijin City as the study area, the attribute values of the environmental factors were transformed into the IV values of the contribution to the landslide to obtain the very low and low susceptibility areas. The negative samples were randomly selected in the above areas for the training and validation of machine learning models. The new coupling models of IV-SVM and IV-RF were constructed for the LSP of Ruijin. Further, IV-SVM and IV-RF models were compared with the single SVM and RF model with negative samples randomly selected from the whole study area, as well as the low-slope SVM and RF model with negative samples randomly selected from specific attribute areas with a slope less than 2°. Finally, Kappa coefficient (KC) and receiver operating characteristic (ROC) curve were used to verify and compare the modeling results. The AUC values of the ROC curve and KC of IV-SVM and IV-RF models were 0.828, 0.920 and 0.876, 0.988, which were higher than those of single SVM, RF model and low-slope SVM, RF model, respectively. Meanwhile, IV-SVM and IV-RF models have a smaller mean value and larger standard deviation of a susceptibility probability distribution. Results showed that: 1) IV-SVM and IV-RF models had the higher LSP accuracies than those of the single SVM, RF model and low-slope SVM, RF model, respectively; 2) RF model had higher LSP accuracy compared to the SVM model; 3) The coupling model such as IV-RF could address the inaccuracy of negative sample sampling existing in the single model and the shortcomings of the low slope model in the selection of slope interval, thus improving the LSP accuracy. In conclusion, this study provided a new idea for the negative sample sampling method for LSP using ML models. [ABSTRACT FROM AUTHOR]

Copyright of Shipin Kexue/ Food Science is the property of Food Science Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In view of the slow detection speed of traditional grain flour species, this study implemented the random forest algorithm-assisted microwave nondestructive testing technology to identify grain flour species efficiently and accurately based on ZYNQ platform. By analyzing the hardware implementation of the random forest model, this paper proposed an improved model parameter structure, which could effectively save the consumption of hardware storage resources. In order to shorten the prediction time of the algorithm and reduce the power consumption of the system, it introduced the early termination recognition mechanism in the hardware implementation to avoid the unnecessary decision tree prediction process on the premise of keeping the accuracy unchanged. In view of the Zedboard development board, it designed a storage scheme for model parameters to make full use of on-board resources and ensure the normal operation of the system. The experimental results show that the implementation time of the scheme with ZYNQ is about 54. 2% shorter than that of the random forest algorithm with traditional CPU, and no loss of recognition accuracy is caused. [ABSTRACT FROM AUTHOR]

Copyright of Modern Food Science & Technology is the property of Editorial Office of Modern Food Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)