Mining frequent patterns has been studied popularly in data mining area. However, little work has been done on mining patterns when the database has an influx of fresh data constantly. In these dynamic scenarios, effi...Mining frequent patterns has been studied popularly in data mining area. However, little work has been done on mining patterns when the database has an influx of fresh data constantly. In these dynamic scenarios, efficient maintenance of the discovered patterns is crucial. Most existing methods need to scan the entire database repeatedly, which is an obvious disadvantage. In this paper, an efficient incremental mining algorithm, Incremental-Mining (IM), is proposed for maintenance of the frequent patterns when incremental data come. Based on the frequent pattern tree (FP-tree) structure, IM gives a way to make the most of the things from the previous mining process, and requires scanning the original data once at most. Furthermore, IM can identify directly the differential set of frequent patterns, which may be more informative to users. Moreover, IM can deal with changing thresholds as well as changing data, thus provide a full maintenance scheme. IM has been implemented and the performance study shows it outperforms three other incremental algorithms: FUP, DB-tree and re-running frequent pattern growth (FP-growth). Keywords data mining - association rule mining - frequent pattern mining - incremental mining Supported by the National Basic Research 973 Program of China under Grant No.G1999032705.Xiu-Li Ma received the Ph.D. degree in computer science from Peking University in 2003. She is currently a postdoctoral researcher at National Lab on Machine Perception of Peking University. Her main research interests include data warehousing, data mining, intelligent online analysis, and sensor network.Yun-Hai Tong received the Ph.D. degree in computer software from Peking University in 2002. He is currently an assistant professor at School of Electronics Engineering and Computer Science of Peking University. His research interests include data warehousing, online analysis processing and data mining.Shi-Wei Tang received the B.S. degree in mathematics from Peking University in 1964. Now, he is a professor and Ph.D. su展开更多
With the rapid development and wide use of Global Positioning System in technology tools, such as smart phones and touch pads, many people share their personal experience through their trajectories while visiting plac...With the rapid development and wide use of Global Positioning System in technology tools, such as smart phones and touch pads, many people share their personal experience through their trajectories while visiting places of interest. Therefore, trajectory query processing has emerged in recent years to help users find their best trajectories. However, with the huge amount of trajectory points and text descriptions, such as the activities practiced by users at these points, organizing these data in the index becomes tedious. Therefore, the parallel method becomes indispensable. In this paper, we have investigated the problem of distributed trajectory query processing based on the distance and frequent activities. The query is specified by start and final points in the trajectory, the distance threshold, and a set of frequent activities involved in the point of interest of the trajectory.As a result, the query returns the shortest trajectory including the most frequent activities with high support and high confidence. To simplify the query processing, we have implemented the Distributed Mining Trajectory R-Tree index(DMTR-Tree). For this method, we initially managed the large trajectory dataset in distributed R-Tree indexes.Then, for each index, we applied the frequent itemset Apriori algorithm for each point to select the frequent activity set. For the faster computation of the above algorithms, we utilized the cluster computing framework of Apache Spark with MapReduce as the programing model. The experimental results show that the DMTR-Tree index and the query-processing algorithm are efficient and can achieve the scalability.展开更多
The South China Sea（SCS）,the largest marginal sea of the Northwest Pacific Ocean,is characterized by frequent occurrence of energetic mesoscale eddies.The eddy diameters range from 100 to 300 km.The eddy lifespan va...The South China Sea（SCS）,the largest marginal sea of the Northwest Pacific Ocean,is characterized by frequent occurrence of energetic mesoscale eddies.The eddy diameters range from 100 to 300 km.The eddy lifespan varies from several days to several months with the longest time of seven months（Zheng et al.,2017）.The eddy disturbance reaches down to the ocean bottom layer.展开更多
Shake table testing was performed to investigate the dynamic stability of a mid-dip bedding rock slope under frequent earthquakes. Then, numerical modelling was established to further study the slope dynamic stability...Shake table testing was performed to investigate the dynamic stability of a mid-dip bedding rock slope under frequent earthquakes. Then, numerical modelling was established to further study the slope dynamic stability under purely microseisms and the influence of five factors, including seismic amplitude, slope height, slope angle, strata inclination and strata thickness, were considered. The experimental results show that the natural frequency of the slope decreases and damping ratio increases as the earthquake loading times increase. The dynamic strength reduction method is adopted for the stability evaluation of the bedding rock slope in numerical simulation, and the slope stability decreases with the increase of seismic amplitude, increase of slope height, reduction of strata thickness and increase of slope angle. The failure mode of a mid-dip bedding rock slope in the shaking table test is integral slipping along the bedding surface with dipping tensile cracks at the slope rear edge going through the bedding surfaces. In the numerical simulation, the long-term stability of a mid-dip bedding slope is worst under frequent microseisms and the slope is at risk of integral sliding instability, whereas the slope rock mass is more broken than shown in the shaking table test. The research results are of practical significance to better understand the formation mechanism of reservoir landslides and prevent future landslide disasters.展开更多
Association rules mining is a major data mining field that leads to discovery of associations and correlations among items in today’s big data environment. The conventional association rule mining focuses mainly on p...Association rules mining is a major data mining field that leads to discovery of associations and correlations among items in today’s big data environment. The conventional association rule mining focuses mainly on positive itemsets generated from frequently occurring itemsets (PFIS). However, there has been a significant study focused on infrequent itemsets with utilization of negative association rules to mine interesting frequent itemsets (NFIS) from transactions. In this work, we propose an efficient backward calculating negative frequent itemset algorithm namely EBC-NFIS for computing backward supports that can extract both positive and negative frequent itemsets synchronously from dataset. EBC-NFIS algorithm is based on popular e-NFIS algorithm that computes supports of negative itemsets from the supports of positive itemsets. The proposed algorithm makes use of previously computed supports from memory to minimize the computation time. In addition, association rules, i.e. positive and negative association rules (PNARs) are generated from discovered frequent itemsets using EBC-NFIS algorithm. The efficiency of the proposed algorithm is verified by several experiments and comparing results with e-NFIS algorithm. The experimental results confirm that the proposed algorithm successfully discovers NFIS and PNARs and runs significantly faster than conventional e-NFIS algorithm.展开更多
In recent years, network traffic data have become larger and more complex, leading to higher possibilities of network intrusion. Traditional intrusion detection methods face difficulty in processing high-speed network...In recent years, network traffic data have become larger and more complex, leading to higher possibilities of network intrusion. Traditional intrusion detection methods face difficulty in processing high-speed network data and cannot detect currently unknown attacks. Therefore, this paper proposes a network attack detection method combining a flow calculation and deep learning. The method consists of two parts: a real-time detection algorithm based on flow calculations and frequent patterns and a classification algorithm based on the deep belief network and support vector machine(DBN-SVM). Sliding window(SW) stream data processing enables real-time detection, and the DBN-SVM algorithm can improve classification accuracy. Finally, to verify the proposed method, a system is implemented.Based on the CICIDS2017 open source data set, a series of comparative experiments are conducted. The method's real-time detection efficiency is higher than that of traditional machine learning algorithms. The attack classification accuracy is 0.7 percentage points higher than that of a DBN, which is 2 percentage points higher than that of the integrated algorithm boosting and bagging methods. Hence, it is suitable for the real-time detection of high-speed network intrusions.展开更多
Frequent itemset mining （FIM） is a popular data mining issue adopted in many fields, such as commodity recommendation in the retail industry, log analysis in web searching, and query recommendation （or related sea...Frequent itemset mining （FIM） is a popular data mining issue adopted in many fields, such as commodity recommendation in the retail industry, log analysis in web searching, and query recommendation （or related search）. A large number of FIM algorithms have been proposed to obtain better performance, including parallelized algorithms for processing large data volumes. Besides, incremental FIM algorithms are also proposed to deal with incremental database updates. However, most of these incremental algorithms have low parallelism, causing low efficiency on huge databases. This paper presents two parallel incremental FIM algorithms called IncMiningPFP and IncBuildingPFP, implemented on the MapReduce framework. IncMiningPFP preserves the FP-tree mining results of the original pass, and utilizes them for incremental calculations. In particular, we propose a method to generate a partial FP-tree in the incremental pass, in order to avoid unnecessary mining work. Further, some of the incremental parallel tasks can be omitted when the inserted transactions include fewer items. IncbuildingPFP preserves the CanTrees built in the original pass, and then adds new transactions to them during the incremental passes. Our experimental results show that IncMiningPFP can achieve significant speedup over PFP （Parallel FPGrowth） and a sequential incremental algorithm （CanTree） in most cases of incremental input database, and in other cases IncBuildingPFP can achieve it.展开更多
Ever since the impoundment of Three Gorges Reservoir（TGR）, the seismicity in head region of TGR has increased significantly. Coupled with wide fluctuation of water level each year, it becomes more important to study...Ever since the impoundment of Three Gorges Reservoir（TGR）, the seismicity in head region of TGR has increased significantly. Coupled with wide fluctuation of water level each year, it becomes more important to study the deformation forecasting of landslides beside TGR. As a famous active landslide beside TGR, Huangtupo riverside landslide is selected for a case study. Based on long term water level fluctuation and seismic monitoring, three typical adverse conditions are determined. With the established 3D numerical landslide model, seepage-dynamic coupling calculation is conducted under the seismic intensity of V degree. Results are as follows： 1. the dynamic water pressure formed by water level fluctuation will intensify the deformation of landslide; 2. under seismic load, the dynamic hysteresis is significant in defective geological bodies, such as weak layer and slip zone soil, because of much higher damping ratios, the seismic accelerate would be amplified in these elements; 3. microseisms are not intense enough to cause the landslide instability suddenly, but long term deformation accumulation effect of landslide should be paid more attention; 4. in numerical simulation, the factors of unbalance force and excess pore pressure also can be used in forecasting deformation tendency of landslide.展开更多
OBJECTIVE:To explore the correlation between diagnostic information of tongue and gastroscopy results of patients with chronic gastritis.METHODS:Frequent pattern growth(FP-Growth),SPSS Modeler was used to analyze the ...OBJECTIVE:To explore the correlation between diagnostic information of tongue and gastroscopy results of patients with chronic gastritis.METHODS:Frequent pattern growth(FP-Growth),SPSS Modeler was used to analyze the correlation rules between the image information of tongue parameters and the characteristics of the stomach and duodenum seen under gastroscopy.RESULTS:Ranking in order of confidence:cyanotic tongue,slippery fur,yellow fur and spotted tongue were sequently associated with both gastric antrum mucosal hyperemia or edema and gastric antrum mucosal erythema/macula.L,one value of tongue coating color,which counted among(30,60),tooth-marked tongue and b,one value of tongue coating color,which counted in the range of(5,20)were sequently associated with gastric antrum mucosal erythema/macula.A,one value of tongue body color,which counted in the range of(0,20),was related to both gastric antrum mucosal hyperemia or edema and gastric antrum mucosal erythema/macula.a,one value of tongue coating color,which counted in the range of(15,35),was associated with gastric antrum mucosal erythema/macula.There are a total of 9 strong correlation rules.CONCLUSIONS:Cyanotic tongue,slippery fur,yellow fur,the CIE Lab value of tongue coating,a,the value of tongue body color,spotted tongue,and tooth-marked tongue are all related to the gastric antrum mucosal hyperemia or edema and gastric antrum mucosal erythema/macula.The conditions of gastric mucosa could be predicted by the examination of the above related image information of tongue.展开更多
May marks the beginning of the annual harvest of caterpillar fungus in Bachen County of Nagqu City,Xizang Autonomous Region.As the rainy season sets in,the vast grasslands receive frequent downpours.Regardless of whet...May marks the beginning of the annual harvest of caterpillar fungus in Bachen County of Nagqu City,Xizang Autonomous Region.As the rainy season sets in,the vast grasslands receive frequent downpours.Regardless of whether you are in the downtown area or out on the grasslands,you can frequently witness stunning rainbows.展开更多
目的探讨射频消融(RFCA)治疗频发室性早搏后左室功能和结构的变化。方法收集2006年1月—2011年8月成功行射频消融治疗的频发室性早搏病例59例。比较手术前、后NYHA心功能分级及超声心动图LVEF、LVEDD、LVESD、IVSd、LVPWd等参数的变化...目的探讨射频消融(RFCA)治疗频发室性早搏后左室功能和结构的变化。方法收集2006年1月—2011年8月成功行射频消融治疗的频发室性早搏病例59例。比较手术前、后NYHA心功能分级及超声心动图LVEF、LVEDD、LVESD、IVSd、LVPWd等参数的变化。结果与术前比较,成功消融根治室性早搏后,患者胸闷、心悸症状缓解,NYHA心功能分级明显好转(P<0.05),超声心动图各参数均明显缩小(LVEDD 51.2 mm±4.9 mm vs44.3 mm±4.2 mm,P<0.05,LVESD 32.8 mm±5.1 mm vs 27.2 mm±3.5 mm,P<0.05,IVSd 8.9 mm±0.8 nn vs7.5 mm±0.9 mm,P<0.05,LVPWd 8.8 mm±0.9 mm vs 7.5 mm±0.9 mm,P<0.05);LVEF显著提高(0.64±0.12vs 0.71±0.07,P<0.05)。结论 RFCA是改善频发室性早搏患者症状、心脏结构及功能状况的有效方法。展开更多
DearEditor,Pendulous fibroma,also called acrochordon,is a benign skin neoplasm,originating from the dermis.Usually,small in size,it can arise from any point of the dermis(frequently from the axillary area,inguinal reg...DearEditor,Pendulous fibroma,also called acrochordon,is a benign skin neoplasm,originating from the dermis.Usually,small in size,it can arise from any point of the dermis(frequently from the axillary area,inguinal region,eyelid,or neck),and in some cases,it can be unesthetic.Pendulous fibromas are more frequent in obese subjects,and most of the time,they are sporadic,although sometimes,they can be observed in Birt-Hogg-Dube syndrome.In some rare cases,they can reach considerable dimensions.Occasionally,they are associated with diabetes mellitus,obesity,and intestinal polyps.Often,the pedicle undergoes torsion,causing pain and ischemic necrosis of the fibroma,which requires rapid removal of the tumor.We present a case of giant pendulous fibroma of the scrotum.The patient has given his consent to the publication of his clinical information and photographic images in an anonymous form.展开更多
It is of great significance to improve the efficiency of railway production and operation by realizing the fault knowledge association through the efficient data mining algorithm.However,high utility quantitative freq...It is of great significance to improve the efficiency of railway production and operation by realizing the fault knowledge association through the efficient data mining algorithm.However,high utility quantitative frequent pattern mining algorithms in the field of data mining still suffer from the problems of low time-memory performance and are not easy to scale up.In the context of such needs,we propose a related degree-based frequent pattern mining algorithm,named Related High Utility Quantitative Item set Mining(RHUQI-Miner),to enable the effective mining of railway fault data.The algorithm constructs the item-related degree structure of fault data and gives a pruning optimization strategy to find frequent patterns with higher related degrees,reducing redundancy and invalid frequent patterns.Subsequently,it uses the fixed pattern length strategy to modify the utility information of the item in the mining process so that the algorithm can control the length of the output frequent pattern according to the actual data situation and further improve the performance and practicability of the algorithm.The experimental results on the real fault dataset show that RHUQI-Miner can effectively reduce the time and memory consumption in the mining process,thus providing data support for differentiated and precise maintenance strategies.展开更多
文摘Mining frequent patterns has been studied popularly in data mining area. However, little work has been done on mining patterns when the database has an influx of fresh data constantly. In these dynamic scenarios, efficient maintenance of the discovered patterns is crucial. Most existing methods need to scan the entire database repeatedly, which is an obvious disadvantage. In this paper, an efficient incremental mining algorithm, Incremental-Mining (IM), is proposed for maintenance of the frequent patterns when incremental data come. Based on the frequent pattern tree (FP-tree) structure, IM gives a way to make the most of the things from the previous mining process, and requires scanning the original data once at most. Furthermore, IM can identify directly the differential set of frequent patterns, which may be more informative to users. Moreover, IM can deal with changing thresholds as well as changing data, thus provide a full maintenance scheme. IM has been implemented and the performance study shows it outperforms three other incremental algorithms: FUP, DB-tree and re-running frequent pattern growth (FP-growth). Keywords data mining - association rule mining - frequent pattern mining - incremental mining Supported by the National Basic Research 973 Program of China under Grant No.G1999032705.Xiu-Li Ma received the Ph.D. degree in computer science from Peking University in 2003. She is currently a postdoctoral researcher at National Lab on Machine Perception of Peking University. Her main research interests include data warehousing, data mining, intelligent online analysis, and sensor network.Yun-Hai Tong received the Ph.D. degree in computer software from Peking University in 2002. He is currently an assistant professor at School of Electronics Engineering and Computer Science of Peking University. His research interests include data warehousing, online analysis processing and data mining.Shi-Wei Tang received the B.S. degree in mathematics from Peking University in 1964. Now, he is a professor and Ph.D. su
基金partially supported by the National Natural Science Foundation of China (Nos. U1509216 and 61472099)the National Sci-Tech Support Plan (No. 2015BAH10F01)+1 种基金the Scientific Research Foundation for the Returned Overseas Chinese Scholars of Heilongjiang Provience (No. LC2016026)MOECMicrosoft Key Laboratory of Natural Language Processing and Speech, Harbin Institute of Technology
文摘With the rapid development and wide use of Global Positioning System in technology tools, such as smart phones and touch pads, many people share their personal experience through their trajectories while visiting places of interest. Therefore, trajectory query processing has emerged in recent years to help users find their best trajectories. However, with the huge amount of trajectory points and text descriptions, such as the activities practiced by users at these points, organizing these data in the index becomes tedious. Therefore, the parallel method becomes indispensable. In this paper, we have investigated the problem of distributed trajectory query processing based on the distance and frequent activities. The query is specified by start and final points in the trajectory, the distance threshold, and a set of frequent activities involved in the point of interest of the trajectory.As a result, the query returns the shortest trajectory including the most frequent activities with high support and high confidence. To simplify the query processing, we have implemented the Distributed Mining Trajectory R-Tree index(DMTR-Tree). For this method, we initially managed the large trajectory dataset in distributed R-Tree indexes.Then, for each index, we applied the frequent itemset Apriori algorithm for each point to select the frequent activity set. For the faster computation of the above algorithms, we utilized the cluster computing framework of Apache Spark with MapReduce as the programing model. The experimental results show that the DMTR-Tree index and the query-processing algorithm are efficient and can achieve the scalability.
基金The National Natural Science Foundation of China under contract Nos 41476009 and U1405233the IPOVAR Project under contract Nos GASI-IPOVAI-01-02 and GASI-02-SCS-YGST2-02the Foundation of Guangdong Province for Outstanding Young Teachers in University under contract No.YQ2015088
文摘The South China Sea（SCS）,the largest marginal sea of the Northwest Pacific Ocean,is characterized by frequent occurrence of energetic mesoscale eddies.The eddy diameters range from 100 to 300 km.The eddy lifespan varies from several days to several months with the longest time of seven months（Zheng et al.,2017）.The eddy disturbance reaches down to the ocean bottom layer.
基金National Natural Science Foundation of China under Grant No. 41372356the College Cultivation Project of the National Natural Science Foundation of China under Grant No. 2018PY30+1 种基金the Basic Research and Frontier Exploration Project of Chongqing,China under Grant No. cstc2018jcyj A1597the Graduate Scientific Research and Innovation Foundation of Chongqing,China under Grant No. CYS18026。
文摘Shake table testing was performed to investigate the dynamic stability of a mid-dip bedding rock slope under frequent earthquakes. Then, numerical modelling was established to further study the slope dynamic stability under purely microseisms and the influence of five factors, including seismic amplitude, slope height, slope angle, strata inclination and strata thickness, were considered. The experimental results show that the natural frequency of the slope decreases and damping ratio increases as the earthquake loading times increase. The dynamic strength reduction method is adopted for the stability evaluation of the bedding rock slope in numerical simulation, and the slope stability decreases with the increase of seismic amplitude, increase of slope height, reduction of strata thickness and increase of slope angle. The failure mode of a mid-dip bedding rock slope in the shaking table test is integral slipping along the bedding surface with dipping tensile cracks at the slope rear edge going through the bedding surfaces. In the numerical simulation, the long-term stability of a mid-dip bedding slope is worst under frequent microseisms and the slope is at risk of integral sliding instability, whereas the slope rock mass is more broken than shown in the shaking table test. The research results are of practical significance to better understand the formation mechanism of reservoir landslides and prevent future landslide disasters.
文摘Association rules mining is a major data mining field that leads to discovery of associations and correlations among items in today’s big data environment. The conventional association rule mining focuses mainly on positive itemsets generated from frequently occurring itemsets (PFIS). However, there has been a significant study focused on infrequent itemsets with utilization of negative association rules to mine interesting frequent itemsets (NFIS) from transactions. In this work, we propose an efficient backward calculating negative frequent itemset algorithm namely EBC-NFIS for computing backward supports that can extract both positive and negative frequent itemsets synchronously from dataset. EBC-NFIS algorithm is based on popular e-NFIS algorithm that computes supports of negative itemsets from the supports of positive itemsets. The proposed algorithm makes use of previously computed supports from memory to minimize the computation time. In addition, association rules, i.e. positive and negative association rules (PNARs) are generated from discovered frequent itemsets using EBC-NFIS algorithm. The efficiency of the proposed algorithm is verified by several experiments and comparing results with e-NFIS algorithm. The experimental results confirm that the proposed algorithm successfully discovers NFIS and PNARs and runs significantly faster than conventional e-NFIS algorithm.
基金supported by the National Key Research and Development Program of China(2017YFB1401300,2017YFB1401304)the National Natural Science Foundation of China(61702211,L1724007,61902203)+3 种基金Hubei Provincial Science and Technology Program of China(2017AKA191)the Self-Determined Research Funds of Central China Normal University(CCNU)from the Colleges’Basic Research(CCNU17QD0004,CCNU17GF0002)the Natural Science Foundation of Shandong Province(ZR2017QF015)the Key Research and Development Plan–Major Scientific and Technological Innovation Projects of Shandong Province(2019JZZY020101)。
文摘In recent years, network traffic data have become larger and more complex, leading to higher possibilities of network intrusion. Traditional intrusion detection methods face difficulty in processing high-speed network data and cannot detect currently unknown attacks. Therefore, this paper proposes a network attack detection method combining a flow calculation and deep learning. The method consists of two parts: a real-time detection algorithm based on flow calculations and frequent patterns and a classification algorithm based on the deep belief network and support vector machine(DBN-SVM). Sliding window(SW) stream data processing enables real-time detection, and the DBN-SVM algorithm can improve classification accuracy. Finally, to verify the proposed method, a system is implemented.Based on the CICIDS2017 open source data set, a series of comparative experiments are conducted. The method's real-time detection efficiency is higher than that of traditional machine learning algorithms. The attack classification accuracy is 0.7 percentage points higher than that of a DBN, which is 2 percentage points higher than that of the integrated algorithm boosting and bagging methods. Hence, it is suitable for the real-time detection of high-speed network intrusions.
基金This work was supported by the National High Technology Research and Development 863 Program of China under Grant Nos. 2015AA011505, 2015AA015306, and 2012AA010902, the National Natural Science Foundation of China under Grant Nos. 61202055, 61221062, 61521092, 61303053, 61432016, 61402445, and 61672492, and the National Key Research and Development Program of China under Grant No. 2016YFB1000402.
文摘Frequent itemset mining （FIM） is a popular data mining issue adopted in many fields, such as commodity recommendation in the retail industry, log analysis in web searching, and query recommendation （or related search）. A large number of FIM algorithms have been proposed to obtain better performance, including parallelized algorithms for processing large data volumes. Besides, incremental FIM algorithms are also proposed to deal with incremental database updates. However, most of these incremental algorithms have low parallelism, causing low efficiency on huge databases. This paper presents two parallel incremental FIM algorithms called IncMiningPFP and IncBuildingPFP, implemented on the MapReduce framework. IncMiningPFP preserves the FP-tree mining results of the original pass, and utilizes them for incremental calculations. In particular, we propose a method to generate a partial FP-tree in the incremental pass, in order to avoid unnecessary mining work. Further, some of the incremental parallel tasks can be omitted when the inserted transactions include fewer items. IncbuildingPFP preserves the CanTrees built in the original pass, and then adds new transactions to them during the incremental passes. Our experimental results show that IncMiningPFP can achieve significant speedup over PFP （Parallel FPGrowth） and a sequential incremental algorithm （CanTree） in most cases of incremental input database, and in other cases IncBuildingPFP can achieve it.
基金financially supported by the National Natural Science Foundation of China (Nos. 51409011 and 51309029)the Basic Scientific Research Operating Expenses of Central-Level Public Academies and Institutes (Nos. CKSF2014057/YT and CKSF2015051/YT)
文摘Ever since the impoundment of Three Gorges Reservoir（TGR）, the seismicity in head region of TGR has increased significantly. Coupled with wide fluctuation of water level each year, it becomes more important to study the deformation forecasting of landslides beside TGR. As a famous active landslide beside TGR, Huangtupo riverside landslide is selected for a case study. Based on long term water level fluctuation and seismic monitoring, three typical adverse conditions are determined. With the established 3D numerical landslide model, seepage-dynamic coupling calculation is conducted under the seismic intensity of V degree. Results are as follows： 1. the dynamic water pressure formed by water level fluctuation will intensify the deformation of landslide; 2. under seismic load, the dynamic hysteresis is significant in defective geological bodies, such as weak layer and slip zone soil, because of much higher damping ratios, the seismic accelerate would be amplified in these elements; 3. microseisms are not intense enough to cause the landslide instability suddenly, but long term deformation accumulation effect of landslide should be paid more attention; 4. in numerical simulation, the factors of unbalance force and excess pore pressure also can be used in forecasting deformation tendency of landslide.
基金Key Special Project of the National Key Research and Development Program of Ministry of Science and Technology(No.2017YFB1002300):Topic One:Multimodal Heterogeneous Efficient Acquisition of Traditional Chinese Medicine Big Data and Resource Library Construction(No.2017YFB1002301)and Topic Three:Multi-Scale Cognition Methods and Treatment Analysis Model of Traditional Chinese Medicine Based on Deep Learning(No.2017YFB1002303)from Big Data-Driven Traditional Chinese Medicine Intelligent Auxiliary Diagnostic Service SystemGraduation Design of“Cultivation Program”for Cross-cultivation of High-level Talents in Beijing Colleges and Universities in 2010(Scientific Research):the Research on the Clinical Diagnosis and Prediction System of Gastric Precancerous Lesions Based on Artificial Intelligence+2 种基金National Natural Science Foundation of China(No.30701071)the Sixth Batch of Academic Experience Inheritance of Traditional Chinese Medicine Experts(2017)“3+3”Project of Beijing Traditional Chinese Medicine Inheritance(No.2012-SZ-C-41)。
文摘OBJECTIVE:To explore the correlation between diagnostic information of tongue and gastroscopy results of patients with chronic gastritis.METHODS:Frequent pattern growth(FP-Growth),SPSS Modeler was used to analyze the correlation rules between the image information of tongue parameters and the characteristics of the stomach and duodenum seen under gastroscopy.RESULTS:Ranking in order of confidence:cyanotic tongue,slippery fur,yellow fur and spotted tongue were sequently associated with both gastric antrum mucosal hyperemia or edema and gastric antrum mucosal erythema/macula.L,one value of tongue coating color,which counted among(30,60),tooth-marked tongue and b,one value of tongue coating color,which counted in the range of(5,20)were sequently associated with gastric antrum mucosal erythema/macula.A,one value of tongue body color,which counted in the range of(0,20),was related to both gastric antrum mucosal hyperemia or edema and gastric antrum mucosal erythema/macula.a,one value of tongue coating color,which counted in the range of(15,35),was associated with gastric antrum mucosal erythema/macula.There are a total of 9 strong correlation rules.CONCLUSIONS:Cyanotic tongue,slippery fur,yellow fur,the CIE Lab value of tongue coating,a,the value of tongue body color,spotted tongue,and tooth-marked tongue are all related to the gastric antrum mucosal hyperemia or edema and gastric antrum mucosal erythema/macula.The conditions of gastric mucosa could be predicted by the examination of the above related image information of tongue.
文摘May marks the beginning of the annual harvest of caterpillar fungus in Bachen County of Nagqu City,Xizang Autonomous Region.As the rainy season sets in,the vast grasslands receive frequent downpours.Regardless of whether you are in the downtown area or out on the grasslands,you can frequently witness stunning rainbows.
文摘目的探讨射频消融(RFCA)治疗频发室性早搏后左室功能和结构的变化。方法收集2006年1月—2011年8月成功行射频消融治疗的频发室性早搏病例59例。比较手术前、后NYHA心功能分级及超声心动图LVEF、LVEDD、LVESD、IVSd、LVPWd等参数的变化。结果与术前比较,成功消融根治室性早搏后,患者胸闷、心悸症状缓解,NYHA心功能分级明显好转(P<0.05),超声心动图各参数均明显缩小(LVEDD 51.2 mm±4.9 mm vs44.3 mm±4.2 mm,P<0.05,LVESD 32.8 mm±5.1 mm vs 27.2 mm±3.5 mm,P<0.05,IVSd 8.9 mm±0.8 nn vs7.5 mm±0.9 mm,P<0.05,LVPWd 8.8 mm±0.9 mm vs 7.5 mm±0.9 mm,P<0.05);LVEF显著提高(0.64±0.12vs 0.71±0.07,P<0.05)。结论 RFCA是改善频发室性早搏患者症状、心脏结构及功能状况的有效方法。
文摘DearEditor,Pendulous fibroma,also called acrochordon,is a benign skin neoplasm,originating from the dermis.Usually,small in size,it can arise from any point of the dermis(frequently from the axillary area,inguinal region,eyelid,or neck),and in some cases,it can be unesthetic.Pendulous fibromas are more frequent in obese subjects,and most of the time,they are sporadic,although sometimes,they can be observed in Birt-Hogg-Dube syndrome.In some rare cases,they can reach considerable dimensions.Occasionally,they are associated with diabetes mellitus,obesity,and intestinal polyps.Often,the pedicle undergoes torsion,causing pain and ischemic necrosis of the fibroma,which requires rapid removal of the tumor.We present a case of giant pendulous fibroma of the scrotum.The patient has given his consent to the publication of his clinical information and photographic images in an anonymous form.
基金supported by the Research on Key Technologies and Typical Applications of Big Data in Railway Production and Operation(P2023S006)the Fundamental Research Funds for the Central Universities(2022JBZY023).
文摘It is of great significance to improve the efficiency of railway production and operation by realizing the fault knowledge association through the efficient data mining algorithm.However,high utility quantitative frequent pattern mining algorithms in the field of data mining still suffer from the problems of low time-memory performance and are not easy to scale up.In the context of such needs,we propose a related degree-based frequent pattern mining algorithm,named Related High Utility Quantitative Item set Mining(RHUQI-Miner),to enable the effective mining of railway fault data.The algorithm constructs the item-related degree structure of fault data and gives a pruning optimization strategy to find frequent patterns with higher related degrees,reducing redundancy and invalid frequent patterns.Subsequently,it uses the fixed pattern length strategy to modify the utility information of the item in the mining process so that the algorithm can control the length of the output frequent pattern according to the actual data situation and further improve the performance and practicability of the algorithm.The experimental results on the real fault dataset show that RHUQI-Miner can effectively reduce the time and memory consumption in the mining process,thus providing data support for differentiated and precise maintenance strategies.
