Search

Objectives: Global navigation satellite system (GNSS) interferometric reflectometry (GNSS-IR) is a new passive remote sensing technique for determining surface environment parameters, which places an important part in the inversion of earth's surface properties, such as soil moisture monitoring, snow parameter retrieval, and vegetation remote sensing, etc. GNSS-IR offers several benefits over the traditional soil moisture inversion approach, including all-weather capability, high temporal precision, and cheap cost. Methods: Considering the fact that the existing soil moisture inversion algorithms only utilize one single feature of GNSS reflected signal and from the perspective of increasing data availability, this paper proposes a GNSS-IR soil moisture inversion approach that integrates multi-type feature data by utilizing phase, amplitude, and frequency extracted GNSS signals reflected by soil. The main work is to effectively filter all available features extracted from the original GNSS signal-to-noise ratio observations. The feasibility and effect of the suggested method are compared and evaluated using three machine learning models, including least squares support vector machine (LSSVM), random forest (RF), and back propagation neural network (BPNN). Results: Comparing the inversion effects of above three models, BPNN model has the best inversion effect, followed by RF model, and LSSVM model is the worst. The results show that the correlation coefficients between the reference value and soil moisture inversed by the multi-feature fusion method LSSVM, RF, and BPNN models are 0.830, 0.953, and 0.980, respectively, and the corresponding root mean square errors are 0.045, 0.035 and 0.032 cm³ /cm³ Conclusions: Compared with the single feature inversion method, both the accuracy and correlation coefficient of soil moisture inversion increase significantly. The results demonstrated that the proposed method has higher inversion accuracy and reliability than the single feature inversion method. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. 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 Forensic Science & Technology is the property of Institute of Forensic Science, Ministry of Public Security 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 Chongqing University of Posts & Telecommunications (Natural Science Edition) is the property of Chongqing University of Posts & Telecommunications 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 Chongqing University of Posts & Telecommunications (Natural Science Edition) is the property of Chongqing University of Posts & Telecommunications 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.)

Artificial intelligence (AI) technologies, including machine learning, deep learning, natural language processing, generative AI, the metaverse and other iterations, are rapidly changing the landscape of education. Related technologies not only enhance the teaching and learning process but also improve the quality and availability of educational content. AI facilitates educational transformation, reshapes teaching models, and helps students achieve their personalized learning needs, thus improving learning outcomes, learning efficiencies, and teaching practices. Despite the many AI application cases in nursing management and clinical practice, the application of AI in nursing education remains in its infancy. Machine learning has been used to predict the academic performance and graduation results of nursing students, thereby facilitating the early identification of additional support needs. Natural language processing technology has been used to develop chatbots and virtual teachers to assist learning, providing personalized learning support to help students overcome learning obstacles. Also, generative AI technologies such as ChatGPT (chat generative pre-trained transformer) have been used to create simulated patient cases and as a tool for grading academic writing automatically. Moreover, the combination of generative AI technology and the metaverse has introduced new possibilities to nursing education, allowing students to learn in a more-immersive virtual environment. Despite the significant benefits brought by AI to nursing education, its implementation and integration still face multiple challenges, including high costs, the need to provide technical training to teachers, and the need to address issues such as academic integrity and data privacy. The authors hope this article will help promote interdisciplinary cooperation between nursing educators and information and communication experts and the development of AI-assisted teaching to open a new chapter in nursing education. [ABSTRACT FROM AUTHOR]

Copyright of Pearl River is the property of Pearl River 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 Practical Medicine / Shiyong Yixue Zazhi is the property of Journal of Practical Medicine 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.)

Osteoporosis (OP) is one of the main problems affecting the health of middle-aged and elderly people, and its most serious complication is osteoporotic fracture (OPF), which also has a high disability and mortality rate. The accurate screening and diagnosis of OP mainly rely on imaging analysis, and the diagnosis result rely on the subjective judgment of doctors. Therefore, the traditional imaging method of OP have certain challenges to the accurate screening, diagnosis and differentiation of OP. The cross-fusion of machine learning (ML) and imaging images effectively improves the diagnostic efficiency of clinicians, while realizing the prediction of unknown diseases (such as OPF). Recently, significant progress has been made in the screening and diagnosis of OP and in the diagnosis and prediction of OPF. This paper will review the recent related studies on ML model-assisted screening and diagnosis of OP based on imaging data, so as to provide new ideas for accurate clinical diagnosis and treatment of OP. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Ground Improvement is the property of Journal of Ground Improvement 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 Shenzhen University Science & Engineering is the property of Editorial Department of Journal of Shenzhen University Science & 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.)

The pupose of this review was to explore the application of machine learning in the realm of flavor molecule research, particularly in the analysis of the flavor of Jasmine tea. The study of flavor molecules is fundamental to understand and optimize the taste and quality of food, especially tea. The introduction of machine learning technology has opened new horizons for the identification and analysis of flavor molecules. This paper firstly outlined the basic concepts and research methods of flavor molecules, and a detailed discussion on the application of machine learning in deciphering the relationships between molecular structures and flavor characteristics, as well as in the analysis, prediction and optimization of the flavor and intelligent processing of Jasmine tea and other applications, put forward the research prospect. Thereby, this review could enhance the quality of Jasmine tea and provide technical support for the further development of the tea industry.

The safety in the coal-producing mines in China is continuously improving, but coal and gas outburst accidents still occur. The prediction of coal and gas outbursts allows the scientific application of outburst prevention measures, which can ensure the safe coal mining to a certain extent. Machine learning is an interdisciplinary field involving probability theory, statistics, and computer science, which can explore the nonlinear relationship between outburst accidents and its associated indicators. The application of machine learning in coal and gas outburst prediction has received relatively widespread attention, and with the rapid progress of artificial intelligence and computer technology, it will play a greater role in the field of outburst prediction. Therefore, this paper provides a comprehensive review of the research on machine learning in coal and gas outburst prediction, analyzes the difficulties in outburst prediction and prospects its development direction. Firstly, the paper provides a brief overview of the research status on the hypothesis, occurrence mechanism, and prediction index selection of coal and gas outbursts. Then, it summarizes the research progress in the field of outburst prediction, including the application of support vector machines, neural networks, extreme learning machines, and ensemble learning algorithms. In addition, it also points out the existing problems in the current research, such as imbalanced samples, missing data indicators, and small sample sizes. Finally, the paper gives an outlook on the developments of coal and gas outburst prediction based on machine learning, including improving algorithm performance, optimizing feature engineering, and increasing sample size. With the continuous improvement of computer performance, more powerful models may be proposed, which can further improve the prediction accuracy of outburst accidents.

Copyright of Pearl River is the property of Pearl River 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 Pearl River is the property of Pearl River 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.)

Introduction: This study used artificial intelligence and strategic management to predict sports industry development in Taiwan. Through public government information, we aimed to determine the dynamics of the sports industry and analyze the current situation of domestic sports industries and sports industry sectors. We also explored national sports policies and development issues to construct a Taiwan Sports Industry Gross Domestic Product Forecasting Model that aligns with the requirements of government agencies and the sports industry. Methods: This study explored time series data for 12 sports industries and 40 sports industry sectors from 2009 to 2018. We used artificial neural networks to predict future gross domestic product (GDP). We also used the moving average method to calculate the trend direction and used strategic management and brand positioning analysis to analyze industry positioning and trend development. Results: Only one sports industry, Sports Venue or Facility Construction, had an optimistic forecast for growth, while the GDP of the remaining 11 sports industries was predicted to decline. The average GDP forecasting accuracy for the sports industry was 94% in 2017 and 97.2% in 2018. Among the 40 sports industry sectors, eight had growth forecasts, while the GDP of the other 32 sectors was predicted to decline. The average GDP forecasting accuracy for sports industry sectors was 99.5% in 2017 and 99.4% in 2018. In the strategic matrix analysis, all 12 sports industries showed a growth trend. All sports industries showed a consistent upward trend in the GDP time series, indicating overall continuous growth in the sports industry. Similarly, among the 40 sports industry sectors, the Star and Cash Cow sectors showed growth trends and trend lines in the strategic positioning matrix and GDP time series. In contrast, the Question Marks and Dogs sectors showed declining trends and trend lines. Conclusion: This study makes six specific recommendations: (1) Seek solutions for the sports industry through prediction and estimation, (2) use strategic management to chart the course for developing the sports industry, (3) explore and propose future trends in the sports industry, (4) explore and propose future trends in the various sports industry sectors, (5) define the scope of the sports industry and address significant data management and its implications, and (6) foster research and development in technology management and its academic applications. These recommendations aim to provide valuable insights and guidance for the advancement and strategic planning of the sports industry, considering future trends and technological advances. [ABSTRACT FROM AUTHOR]

Copyright of Smart Rail Transit is the property of Smart Rail Transit 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.)

The sample balance is crucial for machine learning. The importance of certain classes may be higher than its number on the imbalanced datasets. This paper studies the imbalanced datasets for speech emotion recognition. Firstly, the imbalanced baseline datasets EMODB and IEMOCAP are augmented with different signal-to-noise ratios, and the datasets EMODBM and IEMOCAPM are constructed. Secondly, six techniques namely SMOTE, RandomOverSampler, SMOTEENN, ADASYN, TomekLinks and SMOTETomek are adopted to resample the baseline datasets, and the augmented datasets are constructed to achieve the category balance. Thirdly, 21-dimensional low-level descriptor features are extracted from the baseline datasets and the augmented datasets. Finally, a novel model MA-CapsNet is proposed to validate the effectiveness of the resampling techniques. The results show that all types of emotion samples are basically balanced after resampling, which makes the learning of the model MA-CapsNet fairer. In addition, the model MA-CapsNet has better robustness on the resampling datasets. [ABSTRACT FROM AUTHOR]

Hepatic encephalopathy is a clinical syndrome of central nervous system dysfunction caused by liver insufficiency. It severely affects the quality of life of patients and may lead to death. Accurate prediction of the risk of developing hepatic encephalopathy is crucial for early intervention and treatment. In order to identify the risk of hepatic encephalopathy in patients in advance, many studies have been devoted to efforts to develop tools and methods to identify the risk of hepatic encephalopathy as early as possible, so as to develop preventive and early management strategies. Most conventional hepatic encephalopathy risk prediction models currently assess the probability of a patient developing hepatic encephalopathy by analysing factors such as clinical data and biochemical indicators, however, their accuracy, sensitivity and positive predictive value are not high. The application of artificial intelligence to clinical predictive modelling is a very hot and promising area, which can use large amounts of data and complex algorithms to improve the accuracy and efficiency of diagnosis and prognosis. To date, there have been few studies using AI techniques to predict hepatic encephalopathy. Therefore, this paper reviews the research progress of hepatic encephalopathy risk prediction models, and also discusses the prospect of AI application in hepatic encephalopathy risk prediction models. It also points out the challenges and future research directions of AI in HE risk prediction model research in order to promote the development and clinical application of hepatic encephalopathy risk prediction models. [ABSTRACT FROM AUTHOR]

Exploring the particle size distribution of suspended load and bed material load in river channels helps to analyze the overall sedimentation situation of river sediment and provide feedback to guide the optimization of reservoir operation. In order to systematically grasp the distribution pattern of suspended load and bed material load particle size in the lower Yellow River, this paper collected and organized the water and sediment series data from 6 key sections such as Huayuankou in the lower Yellow River. Recursive feature elimination algorithm was used for variable selection, and a prediction model for sediment particle size in the lower Yellow River was built based on different machine learning algorithms. The results show that the variable selection algorithm can effectively extract the main influencing factors in the model, and when using machine learning models to predict the sediment particle size of different sections, the overall performance is good on the test set. The R² values of suspended load and bed material load particle size prediction under each section optimization model are between 0.64 and 0.89, and 0.37 and 0.72 respectively. At the same time, when using 2020 data to further verify the predictive effect of the model, the predicted and actual values of suspended load and bed material load particle sizes R² are also up to 0.609 7 and 0.445 6 respectively, which indicates that the built model can effectively achieve the prediction of sediment particle sizes in the lower Yellow River channel. [ABSTRACT FROM AUTHOR]

The compressive strength of concrete is a crucial parameter in the design and evaluation of building structures,as it directly impacts the quality and safety of buildings. To solve the current problem that the prediction of concrete compressive strength by the existing machine learning models suffers from issues such as long prediction times and low precision, which cannot meet the real-time and accuracy requirements of the prediction on construction sites, this paper proposes a novel method for predicting concrete compressive strength based on the Tuna Swarm Optimization Algorithm（TSO-ELM）. The proposed method utilizes TSO to optimize the offset value of the connection weight in the initial parameters of the ELM hidden layer, thereby enhancing the prediction accuracy of ELM. In the simulation experiment, the prediction speed of ELM, the optimization ability of TSO, and the generalization of the TSO-ELM model are verified using two sets of concrete data. The results demonstrate that the proposed method can significantly improve prediction speed and accuracy with fewer iterations and better generalization, providing a new approach for the timely prediction of concrete compressive strength in field construction. [ABSTRACT FROM AUTHOR]

With the emergence of various delay-sensitive applications, how to improve the system's delay performance has be come a hot topic in academic. However, it is difficult for existing multi-domain embedding algorithms to meet the delay per- formance requirements of virtual networks. Therefore, to solve this problem, this paper proposed a TDS-VNE algorithm. In the node embedding phase, it defined a propagation delay evaluation function(D). In the link embedding phase, it defined a path delay sensing parameter. Simulation results show that the proposed embedding algorithm reduces the average network propaga tion delay and has good results on the virtual network request acceptance rate and long-term benefit-cost ratio [ABSTRACT FROM AUTHOR]

Copyright of Automotive Digest is the property of Automotive Digest 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.)

The traditional monitoring model for concrete dam displacement often ignores analyses of pertinent information within the residual sequence, leading to suboptimal predictive performance for dam displacement. The purpose of this paper is to improve the accuracy of prediction models. To address this issue, a dual-layer displacement prediction model for concrete dam, which considers signal residual amendment, is proposed. Firstly, influential factors affecting dam displacement are selected based on the statistical model of traditional concrete dam displacement. Secondly, the sparrow search algorithm (SSA) is employed to determine the hyperparameters of extreme learning machines (ELM) in order to establish the single-layer prediction model SSA-ELM, which yields single-layer model displacement prediction values for dams. Next, the Variational Mode Decomposition (VMD) technique along with the Minimum Sample Entropy (SE) and correlation analysis is utilized, the residual sequence is decomposed and reconstructed. Finally, by using the SSA-ELM model to correct the reconstructed sequence and combining the corrected values with the single-layer model displacement prediction values, the dual-layer prediction model SSA-ELM-VMDr+ is constructed, allowing for the final displacement prediction values. The engineering validation of the model demonstrates that the dual-layer prediction model exhibits higher prediction accuracy and stronger generalization ability compared with other models. Moreover, the model effectively extracts valuable information from the residual and overcomes noise interference. This paper provides a new reference for dam safety monitoring, health service diagnosis and management operations. [ABSTRACT FROM AUTHOR]

Copyright of Engineering Mechanics / Gongcheng Lixue is the property of Engineering Mechanics 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 Forensic Science & Technology is the property of Institute of Forensic Science, Ministry of Public Security 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 Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. 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 Cryptologic Research is the property of Editorial Board of Journal of Cryptologic Research 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.)

Copyright of Journal of Shanghai Jiao Tong University (1006-2467) is the property of Journal of Shanghai Jiao Tong University 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 Modern Educational Technology is the property of Editorial Board of Modern Educational Technology, Tsinghua University 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.)

The research on 5G-R (5G-Railway) performance monitoring and troubleshooting technology is an important means to improve the level of 5G-R operation. In view of the difficulties in obtaining various network performance data, the different granularity of railway management and technical personnel's concern about the quality of 5G-R network, the insufficient support of intelligent means, and the lack of system level network performance evaluation system design, it is urgent to research how to make full use of multi-source data to establish an intelligent evaluation system for 5G-R network performance to support the development of intelligent railway. The characteristics and composition of the data sources used for evaluation are deeply analyzed, such as DPI, Uu interface, NMS north-bound interface, as well as drive test. The idea of judging the overall health status of 5G-R based on the theory of deterioration degree is proposed, and the network abnormalities and failures are analyzed by using machine learning technology. On the basis of the above key technology research, a micro-service-based 5G-R operation quality evaluation system architecture is proposed. The key technology research and system architecture design for 5G-R operation quality evaluation in this paper will provide technical support for the R&D of future 5G-R operation and maintenance support systems. [ABSTRACT FROM AUTHOR]

Accurate prediction of ice resistance plays an important role in ensuring the safety of ship sailing in polar navigation in ice areas. In recent years, machine learning has been widely used in the field of ships, among which artificial neural network (ANN) is a common method. The focus of this paper is to design an ANN model for predicting the ice resistance of polar ships. According to the traditional empirical and semi-empirical formula, appropriate input characteristic parameters are selected. The radial basis function (RBF) neural network model is built based on a large number of ship model test data, and the genetic algorithm (GA) is used to optimize the model. The research shows that the radial basis function neural network model optimized by genetic algorithm (RBF-GA) based on seven characteristic parameters input has good generalization effect. Compared with the model test and full-scale test data, the average error is about 8%, which shows that the RBF-GA model has a high accuracy, and can be used as a tool for ice resistance prediction.

With the increasing diversity and complexity of material requirements for underground water conservancy and water pipeline networks, the efficient and convenient design of special materials to meet individual needs through machine learning has become a hot topic of concern. Traditional supervised learning methods are all based on a large dataset to train models, but obtaining large datasets for special materials required in deeply buried underground water pipeline networks and high-end military equipment, such as rare and high-entropy alloys, etc. requires extremely high cost and a long period. To solve this problem, we propose a small sample expansion model-RX-SMOGN, using XGBoost and RFECV algorithms for feature screening. We enrich the dataset with the SMOGN algorithm. In this paper, the phase structure of high-entropy alloys is used as the research object, and traditional machine learning models are trained to predict them to verify the effectiveness of the RX-SMOGN model. From the results of 5-fold cross-verification and 4 evaluation indicators, it can be seen that the RX-SMOGN model fully improves the performance of the machine learning model, provides a more convenient method for alloy material design, and fully improves the efficiency of alloy material design. [ABSTRACT FROM AUTHOR]

As the most dynamic component of terrestrial water cycling, soil moisture plays a pivotal role in the formation, transformation, and consumption of surface water. Simultaneously, it serves as a crucial parameter influencing hydrological processes, vegetation status, and climatic conditions. Therefore, highprecision, high spatiotemporal resolution soil moisture data holds significant importance across various fields, including agriculture, forestry, and meteorology. Presently, traditional methods such as the timeconsuming and labor-intensive drying-weighing technique are inadequate for large-scale monitoring demands, highlighting the advantages of remote sensing methods. However, optical sensors are susceptible to cloud cover and vegetation obscuration, while microwave remote sensing technology faces challenges in balancing spatial and temporal resolutions. The space-borne global navigation satellite system-reflectometry (GNSS R) technology, characterized by short revisit cycles and pseudo-random sampling, presents a new opportunity for soil moisture retrieval. This paper begins by introducing the fundamental theory of soil moisture retrieval using GNSS-R and analyzes error sources during the retrieval process. This includes the scattering and attenuation effects of surface factors such as vegetation and surface roughness on reflected signals, as well as the impact of water bodies on reflectivity. Existing error correction methods and their limitations are summarized, and potential avenues for improvement are discussed. Subsequently, the research progress in recent years regarding space-borne GNSS-R soil moisture retrieval is analyzed from three perspectives: (1) The development status of space-borne GNSS-R constellation. (2)An overview of algorithms for space-borne GNSS-R soil moisture retrieval, including empirical models, semi-empirical models, and machine learning methods. (3) Advancements and directions in integrating GNSS-R with other data for soil moisture retrieval. In conclusion, based on the scattering mechanism of reflected signals, the selection of auxiliary data, and the utilization of incident angle information, this paper discusses the technical challenges faced in current soil moisture retrieval using space-borne GNSS-R technology and provides insights into future research directions. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. 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 Data Acquisition & Processing / Shu Ju Cai Ji Yu Chu Li is the property of Editorial Department of Journal of Nanjing University of Aeronautics & Astronautics 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 Data Acquisition & Processing / Shu Ju Cai Ji Yu Chu Li is the property of Editorial Department of Journal of Nanjing University of Aeronautics & Astronautics 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.)

Copyright of Natural Gas Industry is the property of Natural Gas Industry Journal Agency 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 Transactions of China Pulp & Paper is the property of China Pulp & Paper Magazines Publisher 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.)

ObjectiveThis paper focuses on using reinforcement learning-based automatic generation technology to generate loading and unloading schemes for the liquid cargo tanks of oil tankers. MethodsUsing the cargo capacity of an actual operating oil tanker as the input and the loading rates of the cargo tank and ballast water tank as the targets, an intelligent agent and environment are built based on Unity ML-Agents. The agent is trained using the PyTorch framework, and a reward function calculation method that comprehensively considers the loading time and changes in the trim amplitude is proposed. Finally, example analysis is carried out to validate the feasibility of the proposed method. ResultsThe results show that the trained agent can learn effective strategies for achieving the autonomous generation of liquid cargo tank loading schemes. ConclusionsThis study proves that it is reasonable and feasible to apply reinforcement learning to solve the problem of the autonomous generation of liquid cargo tank loading schemes under multi-objective conditions.

Wound age estimation is the core content in the practice of forensic medicine. Accurate estimation of wound age is a scientific question that needs to be urgently solved by forensic scientists at home and abroad. Metabolomics techniques can effectively detect endogenous metabolites produced by internal or external stimulating factors and describe the dynamic changes of metabolites in vivo . It has the advantages of strong operability, high detection efficiency and accurate quantitative results. Machine learning algorithm has special advantages in processing high-dimensional data sets, which can effectively mine biological information and truly reflect the physiological, disease or injury state of the body. It is a new technical means for efficiently processing high-throughput big data. This paper reviews the status and advantages of metabolomic techniques combined with machine learning algorithm in the research of wound age estimation, and provides new ideas for this research.

As a result of the development of industrial intelligence, intelligent control systems have been equipped in industrial production systems, and one of the important requirements is intelligent anomaly detection. However, achieving intelligent anomaly detection is often difficult due to the streaming data format of dynamic runtime parameters and the coupling of high-dimensional data, which make it challenging to perform reliable and efficient anomaly detection. To this end, this paper proposes an anomaly detection method based on joint distribution. Firstly, the dynamic operating data was sampled from both real-time detection and overall detection perspectives. Then, the joint distribution was modeled using empirical Copula. Finally, the anomaly score was determined based on the model. The dataset of the drainage system in a hydropower station on Dadu River was used to investigate the effectiveness of the proposed method, and the results show that this method is more efficient than traditional anomaly detection methods and improves area under curve(AUC) value and average precision. Meanwhile, the interpretability of this method also provides a reliable basis for subsequent troubleshooting and maintenance of the drainage system. [ABSTRACT FROM AUTHOR]

Local SGD is used in distributed machine learning to alleviate communication bottleneck. However, it increases the time gap between heterogeneous cluster workers in local computation with the characteristics of local multi-round iteration, which bring about large synchronization wait time and the problem of stale parameters. Regarding the above problems, based on the Local SGD algorithm, this paper proposed a dynamic partial synchronization communication method（LPSP）. This method makes full use of the advantage of Local SGD with two-layer decision. After each worker finishes each epoch of local gradient calculation, it determines its communication possibility based on local training situation. And parameter server divides the synchronization worker sets to minimize the synchronization wait time. Based on above strategies, the communication overhead of Local SGD method is reduced and the negative influence of straggler is effectively controlled. Experiments show that LPSP can achieve up to 0.75 to 1.26 times acceleration without loss of training accuracy, and up to 5.14% accuracy improvement, which can effectively speed up the convergence of model training. [ABSTRACT FROM AUTHOR]

Copyright of Automobile Technology is the property of Automobile Technology 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.)

The North Mountain landslide in Lijie Town has been in a long-term creeping deformation state and has experienced multiple landslide and debris flow disasters. Monitoring the surface deformation of landslide to grasp the surface deformation pattern of disaster body is a reliable basis for realizing early warning prediction of geological disaster. In this paper, a machine learning model is introduced to predict the relevant data, and a long and short-term memory network is used to predict the landslide deformation by monitoring the displacement data of North Mountain in Lijie, and the prediction results are compared with the actual data and analyzed. In this paper, root mean square error, mean absolute error, coefficient of determination and explainable variance are used to evaluate the prediction results, among which the coefficient of determination and explainable variance reach 0.99. It shows that the long short-term memory network used in this paper achieves good prediction performance in the prediction of landslide deformation in the North Mountain of Lijie. [ABSTRACT FROM AUTHOR]

Copyright of Computer Engineering & Science / Jisuanji Gongcheng yu Kexue is the property of Computer Engineering & Science 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 the last couple of years, there has been an increasing need for Human-Computer Interaction (HCI) systems that do not require touching the devices to control them, such as ATMs, self service kiosks in airports, terminals in public offices, among others. The use of hand gestures offers a natural alternative to achieve control without touching the devices. This paper presents a solution that allows the recognition of hand gestures by analyzing three-dimensional landmarks using deep learning. These landmarks are extracted by using a model created with machine learning techniques from a single standard RGB camera in order to define the skeleton of the hand with 21 landmarks distributed as follows: one on the wrist and four on each finger. This study proposes a deep neural network that was trained with 9 gestures receiving as input the 21 points of the hand. One of the main contributions, that considerably improves the performance, is a first layer of normalization and transformation of the landmarks. In our experimental analysis, we reach an accuracy of 99.87% recognizing of 9 hand gestures. [ABSTRACT FROM AUTHOR]

Interatomic potential is a key component of large-scale atomic simulation of materials. For scientific problems in complex environments such as high temperature，high pressure and irradiation，the interactions between atoms are often very complex. The empirical force field only considers two-body，three-body or four-body interactions between atoms. The physical assumption is simple，and it is often difficult to accurately describe the potential energy surface of complex environment. Machine learning force fields can obtain potential energy surfaces that are more accurate than empirical force fields. In this paper，a machine learning force field based on physical model is proposed for BaZrO3，the most common perovskite system，to describe the static properties，phase stability and mechanical properties of BaZrO3. The density functional theory database is used to train the machine learning force field based on physical model，and the static properties，phase stability and mechanical properties are calculated. For the static properties，the elastic constants C11，C12 and C44 were computed using both a pure machine-learning force field and a machine-learning force field based on a physical model，and the simulation results were much better than the empirical force field when compared to the DFT with errors of 0.34%，8.75% and 10.71% for the former，and 0.34%，2.5% and 7.14% for the latter. As for the phase stability，it is found that the machine learning force field based on physical model inherits the advantage of the empirical force field in maintaining the phase stability，which is better than the pure machine learning force field. For mechanical properties，Young's modulus of four different crystal directions of BaZrO3 are calculated. It was found that the errors between the calculated and experimental values for the machine learning force field and the machine learning force field based on the physical model were 9.22% and 1.6%，which were much lower than the results of the empirical force field. It can be seen that integrating physical models into the development of machine learning force field is an important way to improve the accuracy of atomic simulations.