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Incidence and Survivability of Acute Lymphocytic Leukemia Patients in the United States: Analysis of SEER Data Set from 2000-2019
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作者 Ishan Ghosh Sudipto Mukherjee 《Journal of Cancer Therapy》 2024年第4期141-163,共23页
The main goal of this research is to assess the impact of race, age at diagnosis, sex, and phenotype on the incidence and survivability of acute lymphocytic leukemia (ALL) among patients in the United States. By takin... The main goal of this research is to assess the impact of race, age at diagnosis, sex, and phenotype on the incidence and survivability of acute lymphocytic leukemia (ALL) among patients in the United States. By taking these factors into account, the study aims to explore how existing cancer registry data can aid in the early detection and effective treatment of ALL in patients. Our hypothesis was that statistically significant correlations exist between race, age at which patients were diagnosed, sex, and phenotype of the ALL patients, and their rate of incidence and survivability data were evaluated using SEER*Stat statistical software from National Cancer Institute. Analysis of the incidence data revealed that a higher prevalence of ALL was among the Caucasian population. The majority of ALL cases (59%) occurred in patients aged between 0 to 19 years at the time of diagnosis, and 56% of the affected individuals were male. The B-cell phenotype was predominantly associated with ALL cases (73%). When analyzing survivability data, it was observed that the 5-year survival rates slightly exceeded the 10-year survival rates for the respective demographics. Survivability rates of African Americans patients were the lowest compared to Caucasian, Asian, Pacific Islanders, Alaskan Native, Native Americans and others. Survivability rates progressively decreased for older patients. Moreover, this study investigated the typical treatment methods applied to ALL patients, mainly comprising chemotherapy, with occasional supplementation of radiation therapy as required. The study demonstrated the considerable efficacy of chemotherapy in enhancing patients’ chances of survival, while those who remained untreated faced a less favorable prognosis from the disease. Although a significant amount of data and information exists, this study can help doctors in the future by diagnosing patients with certain characteristics. It will further assist the health care professionals in screening potential patients and early detection of cases. This could also save the lives of elderly patients who have a higher mortality rate from this disease. 展开更多
关键词 Acute Lymphocytic Leukemia SURVIVABILITY INCIDENCE DEMOGRAPHY SEER data Set
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A physics-informed neural network for simulation of finite deformation in hyperelastic-magnetic coupling problems
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作者 WANG Lei LUO Zikun +1 位作者 LU Mengkai TANG Minghai 《Applied Mathematics and Mechanics(English Edition)》 SCIE EI CSCD 2024年第10期1717-1732,共16页
Recently,numerous studies have demonstrated that the physics-informed neural network(PINN)can effectively and accurately resolve hyperelastic finite deformation problems.In this paper,a PINN framework for tackling hyp... Recently,numerous studies have demonstrated that the physics-informed neural network(PINN)can effectively and accurately resolve hyperelastic finite deformation problems.In this paper,a PINN framework for tackling hyperelastic-magnetic coupling problems is proposed.Since the solution space consists of two-phase domains,two separate networks are constructed to independently predict the solution for each phase region.In addition,a conscious point allocation strategy is incorporated to enhance the prediction precision of the PINN in regions characterized by sharp gradients.With the developed framework,the magnetic fields and deformation fields of magnetorheological elastomers(MREs)are solved under the control of hyperelastic-magnetic coupling equations.Illustrative examples are provided and contrasted with the reference results to validate the predictive accuracy of the proposed framework.Moreover,the advantages of the proposed framework in solving hyperelastic-magnetic coupling problems are validated,particularly in handling small data sets,as well as its ability in swiftly and precisely forecasting magnetostrictive motion. 展开更多
关键词 physics-informed neural network(PINN) deep learning hyperelastic-magnetic coupling finite deformation small data set
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Question classification in question answering based on real-world web data sets
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作者 袁晓洁 于士涛 +1 位作者 师建兴 陈秋双 《Journal of Southeast University(English Edition)》 EI CAS 2008年第3期272-275,共4页
To improve question answering (QA) performance based on real-world web data sets,a new set of question classes and a general answer re-ranking model are defined.With pre-defined dictionary and grammatical analysis,t... To improve question answering (QA) performance based on real-world web data sets,a new set of question classes and a general answer re-ranking model are defined.With pre-defined dictionary and grammatical analysis,the question classifier draws both semantic and grammatical information into information retrieval and machine learning methods in the form of various training features,including the question word,the main verb of the question,the dependency structure,the position of the main auxiliary verb,the main noun of the question,the top hypernym of the main noun,etc.Then the QA query results are re-ranked by question class information.Experiments show that the questions in real-world web data sets can be accurately classified by the classifier,and the QA results after re-ranking can be obviously improved.It is proved that with both semantic and grammatical information,applications such as QA, built upon real-world web data sets, can be improved,thus showing better performance. 展开更多
关键词 question classification question answering real-world web data sets question and answer web forums re-ranking model
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Reconstruction of incomplete satellite SST data sets based on EOF method 被引量:2
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作者 DING Youzhuan WEI Zhihui +2 位作者 MAO Zhihua WANG Xiaofei PAN Delu 《Acta Oceanologica Sinica》 SCIE CAS CSCD 2009年第2期36-44,共9页
As for the satellite remote sensing data obtained by the visible and infrared bands myers,on, the clouds coverage in the sky over the ocean often results in missing data of inversion products on a large scale, and thi... As for the satellite remote sensing data obtained by the visible and infrared bands myers,on, the clouds coverage in the sky over the ocean often results in missing data of inversion products on a large scale, and thin clouds difficult to be detected would cause the data of the inversion products to be abnormal. Alvera et a1.(2005) proposed a method for the reconstruction of missing data based on an Empirical Orthogonal Functions (EOF) decomposition, but his method couldn't process these images presenting extreme cloud coverage(more than 95%), and required a long time for recon- struction. Besides, the abnormal data in the images had a great effect on the reconstruction result. Therefore, this paper tries to improve the study result. It has reconstructed missing data sets by twice applying EOF decomposition method. Firstly, the abnormity time has been detected by analyzing the temporal modes of EOF decomposition, and the abnormal data have been eliminated. Secondly, the data sets, excluding the abnormal data, are analyzed by using EOF decomposition, and then the temporal modes undergo a filtering process so as to enhance the ability of reconstruct- ing the images which are of no or just a little data, by using EOF. At last, this method has been applied to a large data set, i.e. 43 Sea Surface Temperature (SST) satellite images of the Changjiang River (Yangtze River) estuary and its adjacent areas, and the total reconstruction root mean square error (RMSE) is 0.82℃. And it has been proved that this improved EOF reconstruction method is robust for reconstructing satellite missing data and unreliable data. 展开更多
关键词 EOF SST Changjiang River estuary Missing data sets
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Influence of image data set noise on classification with a convolutional network 被引量:2
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作者 Wei Tao Shuai Liguo Zhang Yulu 《Journal of Southeast University(English Edition)》 EI CAS 2019年第1期51-56,共6页
To evaluate the influence of data set noise, the network in network(NIN) model is introduced and the negative effects of different types and proportions of noise on deep convolutional models are studied. Different typ... To evaluate the influence of data set noise, the network in network(NIN) model is introduced and the negative effects of different types and proportions of noise on deep convolutional models are studied. Different types and proportions of data noise are added to two reference data sets, Cifar-10 and Cifar-100. Then, this data containing noise is used to train deep convolutional models and classify the validation data set. The experimental results show that the noise in the data set has obvious adverse effects on deep convolutional network classification models. The adverse effects of random noise are small, but the cross-category noise among categories can significantly reduce the recognition ability of the model. Therefore, a solution is proposed to improve the quality of the data sets that are mixed into a single noise category. The model trained with a data set containing noise is used to evaluate the current training data and reclassify the categories of the anomalies to form a new data set. Repeating the above steps can greatly reduce the noise ratio, so the influence of cross-category noise can be effectively avoided. 展开更多
关键词 image recognition data set noise deep convolutional network filtering of cross-category noise
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Clustering method based on data division and partition 被引量:1
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作者 卢志茂 刘晨 +2 位作者 S.Massinanke 张春祥 王蕾 《Journal of Central South University》 SCIE EI CAS 2014年第1期213-222,共10页
Many classical clustering algorithms do good jobs on their prerequisite but do not scale well when being applied to deal with very large data sets(VLDS).In this work,a novel division and partition clustering method(DP... Many classical clustering algorithms do good jobs on their prerequisite but do not scale well when being applied to deal with very large data sets(VLDS).In this work,a novel division and partition clustering method(DP) was proposed to solve the problem.DP cut the source data set into data blocks,and extracted the eigenvector for each data block to form the local feature set.The local feature set was used in the second round of the characteristics polymerization process for the source data to find the global eigenvector.Ultimately according to the global eigenvector,the data set was assigned by criterion of minimum distance.The experimental results show that it is more robust than the conventional clusterings.Characteristics of not sensitive to data dimensions,distribution and number of nature clustering make it have a wide range of applications in clustering VLDS. 展开更多
关键词 CLUSTERING DIVISION PARTITION very large data sets (VLDS)
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An Evaluation of the Reliability of Complex Systems Using Shadowed Sets and Fuzzy Lifetime Data 被引量:3
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作者 Olgierd Hryniewicz 《International Journal of Automation and computing》 EI 2006年第2期145-150,共6页
In this paper, we consider the problem of the evaluation of system reliability using statistical data obtained from reliability tests of its elements, in which the lifetimes of elements are described using an exponent... In this paper, we consider the problem of the evaluation of system reliability using statistical data obtained from reliability tests of its elements, in which the lifetimes of elements are described using an exponential distribution. We assume that this lifetime data may be reported imprecisely and that this lack of precision may be described using fuzzy sets. As the direct application of the fuzzy sets methodology leads in this case to very complicated and time consuming calculations, we propose simple approximations of fuzzy numbers using shadowed sets introduced by Pedrycz (1998). The proposed methodology may be simply extended to the case of general lifetime probability distributions. 展开更多
关键词 Estimation of reliability fuzzy reliability data shadowed sets.
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Oil-gas reservoir in the Mesozoic strata in the Chaoshan depression,northern South China Sea:a new insight from long off set seismic data 被引量:1
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作者 Tao XING Guangjian ZHONG +2 位作者 Wenhuan ZHAN Zhongquan ZHAO Xi CHEN 《Journal of Oceanology and Limnology》 SCIE CAS CSCD 2022年第4期1377-1387,共11页
The Chaoshan depression,a Mesozoic basin in the Dongsha sea area,northern South China Sea,is characterized by well-preserved Mesozoic strata,being good conditions for oil-gas preservation,promising good prospects for ... The Chaoshan depression,a Mesozoic basin in the Dongsha sea area,northern South China Sea,is characterized by well-preserved Mesozoic strata,being good conditions for oil-gas preservation,promising good prospects for oil-gas exploration.However,breakthrough in oil-gas exploration in the Mesozoic strata has not been achieved due to less seismic surveys.New long-off set seismic data were processed that acquired with dense grid with single source and single cable.In addition,the data were processed with 3D imaging method and fi ner processing was performed to highlight the target strata.Combining the new imaging result and other geological information,we conducted integrated interpretation and proposed an exploratory well A-1-1 for potential hydrocarbon.The result provides a reliable basis for achieving breakthroughs in oil and gas exploration in the Mesozoic strata in the northern South China Sea. 展开更多
关键词 Chaoshan depression Mesozoic strata oil and gas exploration long off set seismic data integrated interpretation exploratory well
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THRFuzzy:Tangential holoentropy-enabled rough fuzzy classifier to classification of evolving data streams 被引量:1
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作者 Jagannath E.Nalavade T.Senthil Murugan 《Journal of Central South University》 SCIE EI CAS CSCD 2017年第8期1789-1800,共12页
The rapid developments in the fields of telecommunication, sensor data, financial applications, analyzing of data streams, and so on, increase the rate of data arrival, among which the data mining technique is conside... The rapid developments in the fields of telecommunication, sensor data, financial applications, analyzing of data streams, and so on, increase the rate of data arrival, among which the data mining technique is considered a vital process. The data analysis process consists of different tasks, among which the data stream classification approaches face more challenges than the other commonly used techniques. Even though the classification is a continuous process, it requires a design that can adapt the classification model so as to adjust the concept change or the boundary change between the classes. Hence, we design a novel fuzzy classifier known as THRFuzzy to classify new incoming data streams. Rough set theory along with tangential holoentropy function helps in the designing the dynamic classification model. The classification approach uses kernel fuzzy c-means(FCM) clustering for the generation of the rules and tangential holoentropy function to update the membership function. The performance of the proposed THRFuzzy method is verified using three datasets, namely skin segmentation, localization, and breast cancer datasets, and the evaluated metrics, accuracy and time, comparing its performance with HRFuzzy and adaptive k-NN classifiers. The experimental results conclude that THRFuzzy classifier shows better classification results providing a maximum accuracy consuming a minimal time than the existing classifiers. 展开更多
关键词 data stream classification fuzzy rough set tangential holoentropy concept change
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A Middleware for Polyglot Persistence and Data Portability of Big Data PaaS Cloud Applications
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作者 Kiranbir Kaur Sandeep Sharma Karanjeet Singh Kahlon 《Computers, Materials & Continua》 SCIE EI 2020年第11期1625-1647,共23页
Vendor lock-in can occur at any layer of the cloud stack-Infrastructure,Platform,and Software-as-a-service.This paper covers the vendor lock-in issue at Platform as a Service(PaaS)level where applications can be creat... Vendor lock-in can occur at any layer of the cloud stack-Infrastructure,Platform,and Software-as-a-service.This paper covers the vendor lock-in issue at Platform as a Service(PaaS)level where applications can be created,deployed,and managed without worrying about the underlying infrastructure.These applications and their persisted data on one PaaS provider are not easy to port to another provider.To overcome this issue,we propose a middleware to abstract and make the database services as cloud-agnostic.The middleware supports several SQL and NoSQL data stores that can be hosted and ported among disparate PaaS providers.It facilitates the developers with data portability and data migration among relational and NoSQL-based cloud databases.NoSQL databases are fundamental to endure Big Data applications as they support the handling of an enormous volume of highly variable data while assuring fault tolerance,availability,and scalability.The implementation of the middleware depicts that using it alleviates the efforts of rewriting the application code while changing the backend database system.A working protocol of a migration tool has been developed using this middleware to facilitate the migration of the database(move existing data from a database on one cloud to a new database even on a different cloud).Although the middleware adds some overhead compared to the native code for the cloud services being used,the experimental evaluation on Twitter(a Big Data application)data set,proves this overhead is negligible. 展开更多
关键词 Cloud computing platform as a service MIDDLEWARE polyglot persistence SQL NOSQL data migration tool Twitter data set
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Robustness Evaluation of Remote-Sensing Image Feature Detectors with TH Priori-Information Data Set
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作者 Yiping Duan Xiaoming Tao +1 位作者 Xijia Liu Ning Ge 《China Communications》 SCIE CSCD 2020年第10期218-228,共11页
In this paper,we build a remote-sensing satellite imagery priori-information data set,and propose an approach to evaluate the robustness of remote-sensing image feature detectors.The building TH Priori-Information(TPI... In this paper,we build a remote-sensing satellite imagery priori-information data set,and propose an approach to evaluate the robustness of remote-sensing image feature detectors.The building TH Priori-Information(TPI)data set with 2297 remote sensing images serves as a standardized high-resolution data set for studies related to remote-sensing image features.The TPI contains 1)raw and calibrated remote-sensing images with high spatial and temporal resolutions(up to 2 m and 7 days,respectively),and 2)a built-in 3-D target area model that supports view position,view angle,lighting,shadowing,and other transformations.Based on TPI,we further present a quantized approach,including the feature recurrence rate,the feature match score,and the weighted feature robustness score,to evaluate the robustness of remote-sensing image feature detectors.The quantized approach gives general and objective assessments of the robustness of feature detectors under complex remote-sensing circumstances.Three remote-sensing image feature detectors,including scale-invariant feature transform(SIFT),speeded up robust features(SURF),and priori information based robust features(PIRF),are evaluated using the proposed approach on the TPI data set.Experimental results show that the robustness of PIRF outperforms others by over 6.2%. 展开更多
关键词 REMOTE-SENSING TH data set image feature robustness evaluation
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Evolution algorithm for water storage forecasting response to climate change with little data sets:the Wolonghu Wetland,China
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作者 尼庆伟 叶人珍 +1 位作者 杨凤林 雷坤 《Journal of Harbin Institute of Technology(New Series)》 EI CAS 2011年第2期127-133,共7页
An attempt of applying a novel genetic programming(GP) technique,a new member of evolution algorithms,has been made to predict the water storage of Wolonghu wetland response to the climate change in northeastern part ... An attempt of applying a novel genetic programming(GP) technique,a new member of evolution algorithms,has been made to predict the water storage of Wolonghu wetland response to the climate change in northeastern part of China with little data set.Fourteen years(1993-2006) of annual water storage and climatic data set of the wetland were taken for model training and testing.The results of simulations and predictions illustrated a good fit between calculated water storage and observed values(MAPE=9.47,r=0.99).By comparison,a multilayer perceptron(MLP)(a popular artificial neural network model) method and a grey model(GM) with the same data set were applied for performances estimation.It was found that GP technique had better performances than the other two methods both in the simulation step and predicting phase and the results were analyzed and discussed.The case study confirmed that GP method is a promising way for wetland managers to make a quick estimation of fluctuations of water storage in some wetlands under condition of little data set. 展开更多
关键词 water storage little data set evolution algorism Wolonghu wetland
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A dataset of scientific literature on floods,1990-2017
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作者 Zhang Hongyue Li Guoqing +2 位作者 Huang Mingrui Qing Xiuling Zhang Huarong 《中国科学数据(中英文网络版)》 CSCD 2018年第3期76-85,共10页
With an increasing number of scientific achievements published,it is particularly important to conduct literature-based knowledge discovery and data mining.Flood,as one of the most destructive natural disasters,has be... With an increasing number of scientific achievements published,it is particularly important to conduct literature-based knowledge discovery and data mining.Flood,as one of the most destructive natural disasters,has been the subject of numerous scientific publications.On January 1,2018,we conducted literature data collection and processing on flood research and categorized the retrieved paper records into Whole SCI Dataset(WS)and High-Citation SCI Dataset(HCS).These data sets can serve as basic data for bibliometric analysis to identify the status of global flood research during 1990-2017.Our study shows that while the Chinese Academy of Sciences was the most productive institution during this period,the United States was the most productive country.Besides,our keyword analysis reveals the potential popular issues and future trends of flood research. 展开更多
关键词 literature data sets FLOOD WS HCS
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Hybrid Warehouse Model and Solutions for Climate Data Analysis
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作者 Hasan Hashim 《Journal of Computer and Communications》 2020年第10期75-98,共24页
Recently, due to the rapid growth increment of data sensors, a massive volume of data is generated from different sources. The way of administering such data in a sense storing, managing, analyzing, and extracting ins... Recently, due to the rapid growth increment of data sensors, a massive volume of data is generated from different sources. The way of administering such data in a sense storing, managing, analyzing, and extracting insightful information from the massive volume of data is a challenging task. Big data analytics is becoming a vital research area in domains such as climate data analysis which demands fast access to data. Nowadays, an open-source platform namely MapReduce which is a distributed computing framework is widely used in many domains of big data analysis. In our work, we have developed a conceptual framework of data modeling essentially useful for the implementation of a hybrid data warehouse model to store the features of National Climatic Data Center (NCDC) climate data. The hybrid data warehouse model for climate big data enables for the identification of weather patterns that would be applicable in agricultural and other similar climate change-related studies that will play a major role in recommending actions to be taken by domain experts and make contingency plans over extreme cases of weather variability. 展开更多
关键词 data Warehouse HADOOP NCDC data Set WEATHER
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ERA5再分析10m风速数据在“两洋一海”的适用性分析 被引量:5
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作者 陈君芝 施晓晖 温敏 《气象》 CSCD 北大核心 2023年第1期39-51,共13页
西太平洋-南海-东印度洋(以下简称“两洋一海”)地区对我国的天气气候、国家安全和社会经济有重要影响,但由于资料条件的限制,现有的海上高风速事件研究主要集中于近海,导致对“两洋一海”地区远洋高风速事件的时空分布、变化特征及其... 西太平洋-南海-东印度洋(以下简称“两洋一海”)地区对我国的天气气候、国家安全和社会经济有重要影响,但由于资料条件的限制,现有的海上高风速事件研究主要集中于近海,导致对“两洋一海”地区远洋高风速事件的时空分布、变化特征及其机理仍然不够了解,急需利用新的高分辨率资料进行深入的研究。目前欧州中期天气预报中心第五代全球大气再分析资料(ERA5)再分析近地面10 m风速数据与现场观测风速的比较研究还相对较少,因此本文将“两洋一海”地区的国际海洋大气综合数据集(ICOADS)锚定浮标观测资料与ERA5进行了对比分析。结果表明:ERA5再分析10 m风速数据能够较好地表现出海面风场的分布特点和变化特征。ERA5再分析资料具有较高的时空分辨率、较长的时间序列以及完整的数据记录,将其用于海上高风速事件的气候分析是可行的,且具有一定的优势。需要注意的是,ERA5再分析风速总体上存在低估实测风速的系统偏差,尤其是实测风速较大时,ERA5偏离于实测风速的现象更为明显。 展开更多
关键词 ERA5 ICOADS(International Comprehensive Ocean-Atmosphere data Set) 近地面10m风速 两洋一海 高风速事件
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Cross-Validation Convolution Neural Network-Based Algorithm for Automated Detection of Diabetic Retinopathy
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作者 S.Sudha A.Srinivasan T.Gayathri Devi 《Computer Systems Science & Engineering》 SCIE EI 2023年第5期1985-2000,共16页
The substantial vision loss due to Diabetic Retinopathy(DR)mainly damages the blood vessels of the retina.These feature changes in the blood vessels fail to exist any manifestation in the eye at its initial stage,if t... The substantial vision loss due to Diabetic Retinopathy(DR)mainly damages the blood vessels of the retina.These feature changes in the blood vessels fail to exist any manifestation in the eye at its initial stage,if this problem doesn’t exhibit initially,that leads to permanent blindness.So,this type of disorder can be only screened and identified through the processing of fundus images.The different stages in DR are Micro aneurysms(Ma),Hemorrhages(HE),and Exudates,and the stages in lesion show the chance of DR.For the advancement of early detection of DR in the eye we have developed the CNN-based identification approach on the fundus blood lesion image.The CNN-based automated detection of DR proposes the novel Graph cutter-built background and foreground superpixel segmentation technique and the foremost classification of fundus images feature was done through hybrid classifiers as K-Nearest Neighbor(KNN)classifier,Support Vector Machine(SVM)classifier,and Cascaded Rotation Forest(CRF)classifier.Over this classifier,the feature cross-validation made the classification more accurate and the comparison is made with the previous works of parameters such as specificity,sensitivity,and accuracy shows that the hybrid classifier attains excellent performance and achieves an overall accuracy of 98%.Among these Cascaded Rotation Forest(CRF)classifier has more accuracy than others. 展开更多
关键词 CNN networking SEGMENTATION hybrid classifier data set CROSSVALIDATION fundus image
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Application of Self-Organizing Feature Map Neural Network Based on K-means Clustering in Network Intrusion Detection 被引量:5
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作者 Ling Tan Chong Li +1 位作者 Jingming Xia Jun Cao 《Computers, Materials & Continua》 SCIE EI 2019年第7期275-288,共14页
Due to the widespread use of the Internet,customer information is vulnerable to computer systems attack,which brings urgent need for the intrusion detection technology.Recently,network intrusion detection has been one... Due to the widespread use of the Internet,customer information is vulnerable to computer systems attack,which brings urgent need for the intrusion detection technology.Recently,network intrusion detection has been one of the most important technologies in network security detection.The accuracy of network intrusion detection has reached higher accuracy so far.However,these methods have very low efficiency in network intrusion detection,even the most popular SOM neural network method.In this paper,an efficient and fast network intrusion detection method was proposed.Firstly,the fundamental of the two different methods are introduced respectively.Then,the selforganizing feature map neural network based on K-means clustering(KSOM)algorithms was presented to improve the efficiency of network intrusion detection.Finally,the NSLKDD is used as network intrusion data set to demonstrate that the KSOM method can significantly reduce the number of clustering iteration than SOM method without substantially affecting the clustering results and the accuracy is much higher than Kmeans method.The Experimental results show that our method can relatively improve the accuracy of network intrusion and significantly reduce the number of clustering iteration. 展开更多
关键词 K-means clustering self-organizing feature map neural network network security intrusion detection NSL-KDD data set
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Dynamic change and diagnosis of physical, chemical and biological properties in bauxite residue disposal areas 被引量:4
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作者 GUO Ying ZHU Feng +2 位作者 WU Chuan TIAN Tao XUE Sheng-guo 《Journal of Central South University》 SCIE EI CAS CSCD 2019年第2期410-421,共12页
Vegetation encroachment occurred in bauxite residue disposal area(BRDA)following natural weathering processes,whilst the typical indicators of soil formation are still uncertain.Residue samples were collected from the... Vegetation encroachment occurred in bauxite residue disposal area(BRDA)following natural weathering processes,whilst the typical indicators of soil formation are still uncertain.Residue samples were collected from the BRDA in Central China,and related physical,chemical and biological indicators of bauxite residue with different storage years were determined.The indicators of soil formation in bauxite residue were selected using principal component analysis,factor analysis,and comprehensive evaluation to establish soil quality diagnostic index model on disposal areas.Following natural weathering processes,the texture of bauxite residue changed from silty loam to sandy loam.The pH and EC decreased,whilst porosity,nutrient element content and microbial biomass increased.The identified minimum data set(MDS)included available phosphorus(AP),moisture content(MC),C/N,sand content,total nitrogen(TN),microbial biomass carbon(MBC),and pH.The soil quality index of bauxite residue increased,and the relative soil quality index decreased from 1.89 to 0.15,which indicated that natural weathering had a significant effect on improveing the quality of bauxite residue and forming a new soil-like matrix.The diagnostic model of bauxite residue was established to provide data support for the regeneration on disposal area. 展开更多
关键词 bauxite residue disposal area soil properties minimum data set diagnostic indices natural weathering soil formation in bauxite residue
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Preliminary results of the High Energetic Particle Package on-board the China Seismo-Electromagnetic Satellite 被引量:5
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作者 Wei Chu JianPing Huang +5 位作者 XuHui Shen Ping Wang XinQiao Li ZhengHua An YanBing Xu XiaoHua Liang 《Earth and Planetary Physics》 2018年第6期489-498,共10页
The high energetic particle package(HEPP) on-board the China Seismo-Electromagnetic Satellite(CSES) was launched on February 2, 2018. This package includes three independent detectors: HEPP-H, HEPP-L, and HEPP-X. HEPP... The high energetic particle package(HEPP) on-board the China Seismo-Electromagnetic Satellite(CSES) was launched on February 2, 2018. This package includes three independent detectors: HEPP-H, HEPP-L, and HEPP-X. HEPP-H and HEPP-L can detect energetic electrons from 100 keV to approximately 50 MeV and protons from 2 MeV to approximately 200 MeV. HEPP-X can measure solar X-rays in the energy range from 1 keV to approximately 20 keV. The objective of the HEPP payload was to provide a survey of energetic particles with high energy, pitch angle, and time resolutions in order to gain new insight into the space radiation environments of the near-Earth system. Particularly, the HEPP can provide new measurements of the magnetic storm related precipitation of electrons in the slot region, and the dynamics of radiation belts. In this paper, the HEPP scientific data sets are described and initial results are provided.The scientific data can show variations in the flux of energetic particles during magnetic storms. 展开更多
关键词 CSES energetic particles HEPP data sets data quality preliminary results
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Application of Lyapunov exponent algorithm in balise signal chaotic oscillator detection 被引量:1
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作者 ZHANG Hongyan WANG Ruifeng 《Journal of Measurement Science and Instrumentation》 CAS CSCD 2021年第3期281-286,共6页
To improve the detection accuracy of the balise uplink signal transmitted in a strong noise environment,we use chaotic oscillator to detect the balise uplink signal based on the characteristics of the chaotic system t... To improve the detection accuracy of the balise uplink signal transmitted in a strong noise environment,we use chaotic oscillator to detect the balise uplink signal based on the characteristics of the chaotic system that is sensitive to initial conditions and immune to noise.Combining with the principle of Duffing oscillator system used in weak signal detection and uplink signal feature,the methods and steps of using Duffing oscillator to detect the balise signal are presented.Furthermore,the Lyapunov exponent algorithm is used to calculate the critical threshold of the Duffing oscillator detection system.Thus,the output states of the system can be quantitatively judged to achieve demodulation of the balise signal.The simulation results show that the chaotic oscillator detection method for balise signal based on Lyapunov exponent algorithm not only improves the accuracy and efficiency of threshold setting,but also ensures the reliability of balise signal detection. 展开更多
关键词 Duffing oscillator Lyapunov exponent Jacobian algorithm small data sets balise uplink signal
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