Processes supported by process-aware information systems are subject to continuous and often subtle changes due to evolving operational,organizational,or regulatory factors.These changes,referred to as incremental con...Processes supported by process-aware information systems are subject to continuous and often subtle changes due to evolving operational,organizational,or regulatory factors.These changes,referred to as incremental concept drift,gradually alter the behavior or structure of processes,making their detection and localization a challenging task.Traditional process mining techniques frequently assume process stationarity and are limited in their ability to detect such drift,particularly from a control-flow perspective.The objective of this research is to develop an interpretable and robust framework capable of detecting and localizing incremental concept drift in event logs,with a specific emphasis on the structural evolution of control-flow semantics in processes.We propose DriftXMiner,a control-flow-aware hybrid framework that combines statistical,machine learning,and process model analysis techniques.The approach comprises three key components:(1)Cumulative Drift Scanner that tracks directional statistical deviations to detect early drift signals;(2)a Temporal Clustering and Drift-Aware Forest Ensemble(DAFE)to capture distributional and classification-level changes in process behavior;and(3)Petri net-based process model reconstruction,which enables the precise localization of structural drift using transition deviation metrics and replay fitness scores.Experimental validation on the BPI Challenge 2017 event log demonstrates that DriftXMiner effectively identifies and localizes gradual and incremental process drift over time.The framework achieves a detection accuracy of 92.5%,a localization precision of 90.3%,and an F1-score of 0.91,outperforming competitive baselines such as CUSUM+Histograms and ADWIN+Alpha Miner.Visual analyses further confirm that identified drift points align with transitions in control-flow models and behavioral cluster structures.DriftXMiner offers a novel and interpretable solution for incremental concept drift detection and localization in dynamic,process-aware systems.By integrating statistical signal accumulation,temporal behavior profiling,and structural process mining,the framework enables finegrained drift explanation and supports adaptive process intelligence in evolving environments.Its modular architecture supports extension to streaming data and real-time monitoring contexts.展开更多
Solid backfilling mining technology, which decreases the height of fissure zone and caving zone, and alleviates the subsidence, is a new technology for farmland conservation. Based on the situation analysis of farmlan...Solid backfilling mining technology, which decreases the height of fissure zone and caving zone, and alleviates the subsidence, is a new technology for farmland conservation. Based on the situation analysis of farmland destruction in mining area, three ways for farmland protection were proposed. In order to improve the feasibility of this technology, the limited filling materials should be used to increase resources recovery ratio, and then the surplus materials could be backfilled into goaf. An index, namely farmland conservation ability, was put forward to optimize the ways for farmland conservation. At last, the Wanbei coal mine was taken as a case for farmland conservation. It was shown that 3240 t dull coal was substituted and 52 hm2 farmland was conserved by solid backfilling mining in this coal mine.展开更多
Since the 1960 s, mining science and technology in China has experienced two technical innovations, i.e.the ‘‘Masonry Beam Theory(MBT)" and ‘‘Transfer Rock Beam Theory(TRBT)". Based on those theories, th...Since the 1960 s, mining science and technology in China has experienced two technical innovations, i.e.the ‘‘Masonry Beam Theory(MBT)" and ‘‘Transfer Rock Beam Theory(TRBT)". Based on those theories, the conventional mining method(being called the 121 mining method) was established, consisting of excavating two tunnels with a pillar left for mining a working panel. However, with increasing mining depth,engineering geological disasters in the underground caverns have been frequently encountered. In addition, the use of the coal-pillar mining results in a large amount of coal resources unexploited. In order to address the problems above, the ‘‘Roof Cut Short-Arm Beam Theory(RCSBT), being called the 110 mining method)" was proposed by He Manchao in 2008. The 110 mining method features the mining of one coal seam panel, excavating necessarily only one roadway tunnel and leaving no pillars. Realization of the 110 mining method includes the following steps:(1) directional pre-splitting roof cutting,(2) supporting the roof by using high Constant Resistance Large Deformation bolt/cable(CRLD), and(3) blocking gangue by hydraulic props. This paper presents an overview of the principles, techniques and application of the 110 mining method. Special emphasis is placed on the numerical simulation of the geostress distribution found in the mining panel using the 110 method compared to that of the 121 method. In addition, the stress distribution on the ‘‘short beam" left by the roof cutting when performing the 110 method was also investigated using both numerical simulation and theoretical formulation.展开更多
Workers exposed to hot and humid conditions suffer from heat stress that affects their concentration and can potentially lead to an increase in workplace accidents. To minimize heat stress, protective equipment may be...Workers exposed to hot and humid conditions suffer from heat stress that affects their concentration and can potentially lead to an increase in workplace accidents. To minimize heat stress, protective equipment may be worn, such as personal cooling garments. This paper presents and discusses the performances, advantages and disadvantages of existing personal cooling garments, namely air-cooled, liquid-cooled, phase change, hybrid, gas expansion and vacuum desiccant cooling garments, and a thermoelectric cooling technology. The main objective is to identify the cooling technique that would be most suitable for deep mining workers. It appears that no cooling technology currently on the market is perfectly compatible with this type of mining environment. However, combining two or more cooling technologies into a single hybrid system could be the solution to an optimized cooling garment for deep mines.展开更多
A novel binary particle swarm optimization for frequent item sets mining from high-dimensional dataset(BPSO-HD) was proposed, where two improvements were joined. Firstly, the dimensionality reduction of initial partic...A novel binary particle swarm optimization for frequent item sets mining from high-dimensional dataset(BPSO-HD) was proposed, where two improvements were joined. Firstly, the dimensionality reduction of initial particles was designed to ensure the reasonable initial fitness, and then, the dynamically dimensionality cutting of dataset was built to decrease the search space. Based on four high-dimensional datasets, BPSO-HD was compared with Apriori to test its reliability, and was compared with the ordinary BPSO and quantum swarm evolutionary(QSE) to prove its advantages. The experiments show that the results given by BPSO-HD is reliable and better than the results generated by BPSO and QSE.展开更多
The continuous destruction and frauds prevailing due to phishing URLs make it an indispensable area for research.Various techniques are adopted in the detection process,including neural networks,machine learning,or hy...The continuous destruction and frauds prevailing due to phishing URLs make it an indispensable area for research.Various techniques are adopted in the detection process,including neural networks,machine learning,or hybrid techniques.A novel detection model is proposed that uses data mining with the Particle Swarm Optimization technique(PSO)to increase and empower the method of detecting phishing URLs.Feature selection based on various techniques to identify the phishing candidates from the URL is conducted.In this approach,the features mined from the URL are extracted using data mining rules.The features are selected on the basis of URL structure.The classification of these features identified by the data mining rules is done using PSO techniques.The selection of features with PSO optimization makes it possible to identify phishing URLs.Using a large number of rule identifiers,the true positive rate for the identification of phishing URLs is maximized in this approach.The experiments show that feature selection using data mining and particle swarm optimization helps tremendously identify the phishing URLs based on the structure of the URL itself.Moreover,it can minimize processing time for identifying the phishing website instead.So,the approach can be beneficial to identify suchURLs over the existing contemporary detecting models proposed before.展开更多
Sentiment analysis or opinion mining(OM)concepts become familiar due to advances in networking technologies and social media.Recently,massive amount of text has been generated over Internet daily which makes the patte...Sentiment analysis or opinion mining(OM)concepts become familiar due to advances in networking technologies and social media.Recently,massive amount of text has been generated over Internet daily which makes the pattern recognition and decision making process difficult.Since OM find useful in business sectors to improve the quality of the product as well as services,machine learning(ML)and deep learning(DL)models can be considered into account.Besides,the hyperparameters involved in the DL models necessitate proper adjustment process to boost the classification process.Therefore,in this paper,a new Artificial Fish Swarm Optimization with Bidirectional Long Short Term Memory(AFSO-BLSTM)model has been developed for OM process.The major intention of the AFSO-BLSTM model is to effectively mine the opinions present in the textual data.In addition,the AFSO-BLSTM model undergoes pre-processing and TF-IFD based feature extraction process.Besides,BLSTM model is employed for the effectual detection and classification of opinions.Finally,the AFSO algorithm is utilized for effective hyperparameter adjustment process of the BLSTM model,shows the novelty of the work.A complete simulation study of the AFSO-BLSTM model is validated using benchmark dataset and the obtained experimental values revealed the high potential of the AFSO-BLSTM model on mining opinions.展开更多
Coal resources are very abundant in Zibo, Shandong Province, China. A lot of mining sewages are discharged during the coal mining. The mining sewage is characterized by high ρ (SO 2- 4 ), hardness and totall...Coal resources are very abundant in Zibo, Shandong Province, China. A lot of mining sewages are discharged during the coal mining. The mining sewage is characterized by high ρ (SO 2- 4 ), hardness and totally dissolved solids (TDS). Mining sewage in the southern Zibo is acidic, where heavy metals and benzene are detected. The Xiaofu River is polluted when mining sewage flows into it, so that the Mengshan Reservoir is polluted by Xiaofu River. The groundwater is polluted in Zichuan by the leaking of the Xiaofu River. The polluted Xiaofu River is thus used to irrigate the farmland in Mashang Mengshui zone. The irrigation water affects the quality of shallow groundwater. The laboratory soil column test shows that SO 2- 4, Cl -, Ca 2+ and Mg 2+ can migrate through vadose soil, especially SO 2- 4 and Cl -.展开更多
File semantic has proven effective in optimizing large scale distributed file system.As a consequence of the elaborate and rich I/O interfaces between upper layer applications and file systems,file system can provide ...File semantic has proven effective in optimizing large scale distributed file system.As a consequence of the elaborate and rich I/O interfaces between upper layer applications and file systems,file system can provide useful and insightful information about semantic.Hence,file semantic mining has become an increasingly important practice in both engineering and research community.Unfortunately,it is a challenge to exploit file semantic knowledge because a variety of factors coulda ffect this information exploration process.Even worse,the challenges are exacerbated due to the intricate interdependency between these factors,and make it difficult to fully exploit the potentially important correlation among various semantic knowledges.This article proposes a file access correlation miming and evaluation reference（FARMER） model,where file is treated as a multivariate vector space,and each item within the vector corresponds a separate factor of the given file.The selection of factor depends on the application,examples of factors are file path,creator and executing program.If one particular factor occurs in both files,its value is non-zero.It is clear that the extent of inter-file relationships can be measured based on the likeness of their factor values in the semantic vectors.Benefit from this model,FARMER represents files as structured vectors of identifiers,and basic vector operations can be leveraged to quantify file correlation between two file vectors.FARMER model leverages linear regression model to estimate the strength of the relationship between file correlation and a set of influencing factors so that the ＂bad knowledge＂ can be filtered out.To demonstrate the ability of new FARMER model,FARMER is incorporated into a real large-scale object-based storage system as a case study to dynamically infer file correlations.In addition FARMER-enabled optimize service for metadata prefetching algorithm and object data layout algorithm is implemented.Experimental results show that is FARMER-enabled prefetching algorithm is shown to reduce the metadata operations latency by approximately 30%-40% when compared to a state-of-the-art metadata prefetching algorithm and a commonly used replacement policy.展开更多
Background:This study gets a classic prescription of Song Dynasty medicine for the treatment of waist and leg pain through analyzing the inheritance of traditional Chinese medicine auxiliary platform.Further,the poten...Background:This study gets a classic prescription of Song Dynasty medicine for the treatment of waist and leg pain through analyzing the inheritance of traditional Chinese medicine auxiliary platform.Further,the potential mechanism of the classic prescription was analyzed based on molecular docking and network pharmacology.Methods:Based on the frequency statistics,association rules and cluster analysis,the core herbal combination and the classic prescription was digged out.Use of network pharmacology methods and molecular docking to explore the pharmacological mechanism of classic prescriptions for treatment of lumbar disc herniation.Then gene ontology biological function annotation and Kyoto Encyclopedia of Genes and Genomes enrichment of pathways were performed.Finally,the compounds of herbs were docked with the important targets of MMP1 and CRP.Results:The high-frequency Chinese medicines for treating waist and leg pain were found and we further unearthed the“Rougui-Fuzi-Niuxi(Cinnamoni cortex-Aconm lateralis radix praeparaia-Achyranthis bidentatae radix”as the core herbal combination,and matched the classic ancient prescription of Chinese medicine Jiawei Shenzhuo decoction(CAPCMJWSZD).The targets of CAPCMJWSZD were mapped to the targets of lumbar disc herniation and 48 potential targets were obtained.The core potential targets were obtained in the protein-protein interaction network,such as CRP,IL2,FOS,MMP1,CASP3.Through the DAVID database,a total of 129 gene ontology function annotation terms(P<0.01)and 91 Kyoto Encyclopedia of Genes and Genomes pathways(P<0.01)were obtained.Molecular docking results showed that quercetin has the lowest binding energy for docking with MMP1and CRP,and these two methods of molecular docking are most likely to occur.Conclusion:The most important bioactive components in CAPCMJWSZD can eliminate inflammation and slow disc degeneration through some potential targets,such as CRP,IL-2,MMP1,and these targets can rich in the following pathways,such as metalloendopeptidase activity,MAP kinase activity,osteoclast differentiation,et al.展开更多
Frequent item sets mining plays an important role in association rules mining. A variety of algorithms for finding frequent item sets in very large transaction databases have been developed. Although many techniques w...Frequent item sets mining plays an important role in association rules mining. A variety of algorithms for finding frequent item sets in very large transaction databases have been developed. Although many techniques were proposed for maintenance of the discovered rules when new transactions are added, little work is done for maintaining the discovered rules when some transactions are deleted from the database. Updates are fundamental aspect of data management. In this paper, a decremental association rules mining algorithm is present for updating the discovered association rules when some transactions are removed from the original data set. Extensive experiments were conducted to evaluate the performance of the proposed algorithm. The results show that the proposed algorithm is efficient and outperforms other well-known algorithms.展开更多
文摘Processes supported by process-aware information systems are subject to continuous and often subtle changes due to evolving operational,organizational,or regulatory factors.These changes,referred to as incremental concept drift,gradually alter the behavior or structure of processes,making their detection and localization a challenging task.Traditional process mining techniques frequently assume process stationarity and are limited in their ability to detect such drift,particularly from a control-flow perspective.The objective of this research is to develop an interpretable and robust framework capable of detecting and localizing incremental concept drift in event logs,with a specific emphasis on the structural evolution of control-flow semantics in processes.We propose DriftXMiner,a control-flow-aware hybrid framework that combines statistical,machine learning,and process model analysis techniques.The approach comprises three key components:(1)Cumulative Drift Scanner that tracks directional statistical deviations to detect early drift signals;(2)a Temporal Clustering and Drift-Aware Forest Ensemble(DAFE)to capture distributional and classification-level changes in process behavior;and(3)Petri net-based process model reconstruction,which enables the precise localization of structural drift using transition deviation metrics and replay fitness scores.Experimental validation on the BPI Challenge 2017 event log demonstrates that DriftXMiner effectively identifies and localizes gradual and incremental process drift over time.The framework achieves a detection accuracy of 92.5%,a localization precision of 90.3%,and an F1-score of 0.91,outperforming competitive baselines such as CUSUM+Histograms and ADWIN+Alpha Miner.Visual analyses further confirm that identified drift points align with transitions in control-flow models and behavioral cluster structures.DriftXMiner offers a novel and interpretable solution for incremental concept drift detection and localization in dynamic,process-aware systems.By integrating statistical signal accumulation,temporal behavior profiling,and structural process mining,the framework enables finegrained drift explanation and supports adaptive process intelligence in evolving environments.Its modular architecture supports extension to streaming data and real-time monitoring contexts.
基金Project(50834004)supported by the National Natural Science Foundation of ChinaProject(LEDM2009B01)supported by Key Laboratory for Land Environment and Disaster Monitoring of SBSM,ChinaProject(SKLGP2010K002)supported by Opening Fund of State Key Laboratory of Geohazard Prevention and Geoenvironment Protection,Chengdu University of Technology,China
文摘Solid backfilling mining technology, which decreases the height of fissure zone and caving zone, and alleviates the subsidence, is a new technology for farmland conservation. Based on the situation analysis of farmland destruction in mining area, three ways for farmland protection were proposed. In order to improve the feasibility of this technology, the limited filling materials should be used to increase resources recovery ratio, and then the surplus materials could be backfilled into goaf. An index, namely farmland conservation ability, was put forward to optimize the ways for farmland conservation. At last, the Wanbei coal mine was taken as a case for farmland conservation. It was shown that 3240 t dull coal was substituted and 52 hm2 farmland was conserved by solid backfilling mining in this coal mine.
文摘Since the 1960 s, mining science and technology in China has experienced two technical innovations, i.e.the ‘‘Masonry Beam Theory(MBT)" and ‘‘Transfer Rock Beam Theory(TRBT)". Based on those theories, the conventional mining method(being called the 121 mining method) was established, consisting of excavating two tunnels with a pillar left for mining a working panel. However, with increasing mining depth,engineering geological disasters in the underground caverns have been frequently encountered. In addition, the use of the coal-pillar mining results in a large amount of coal resources unexploited. In order to address the problems above, the ‘‘Roof Cut Short-Arm Beam Theory(RCSBT), being called the 110 mining method)" was proposed by He Manchao in 2008. The 110 mining method features the mining of one coal seam panel, excavating necessarily only one roadway tunnel and leaving no pillars. Realization of the 110 mining method includes the following steps:(1) directional pre-splitting roof cutting,(2) supporting the roof by using high Constant Resistance Large Deformation bolt/cable(CRLD), and(3) blocking gangue by hydraulic props. This paper presents an overview of the principles, techniques and application of the 110 mining method. Special emphasis is placed on the numerical simulation of the geostress distribution found in the mining panel using the 110 method compared to that of the 121 method. In addition, the stress distribution on the ‘‘short beam" left by the roof cutting when performing the 110 method was also investigated using both numerical simulation and theoretical formulation.
文摘Workers exposed to hot and humid conditions suffer from heat stress that affects their concentration and can potentially lead to an increase in workplace accidents. To minimize heat stress, protective equipment may be worn, such as personal cooling garments. This paper presents and discusses the performances, advantages and disadvantages of existing personal cooling garments, namely air-cooled, liquid-cooled, phase change, hybrid, gas expansion and vacuum desiccant cooling garments, and a thermoelectric cooling technology. The main objective is to identify the cooling technique that would be most suitable for deep mining workers. It appears that no cooling technology currently on the market is perfectly compatible with this type of mining environment. However, combining two or more cooling technologies into a single hybrid system could be the solution to an optimized cooling garment for deep mines.
文摘A novel binary particle swarm optimization for frequent item sets mining from high-dimensional dataset(BPSO-HD) was proposed, where two improvements were joined. Firstly, the dimensionality reduction of initial particles was designed to ensure the reasonable initial fitness, and then, the dynamically dimensionality cutting of dataset was built to decrease the search space. Based on four high-dimensional datasets, BPSO-HD was compared with Apriori to test its reliability, and was compared with the ordinary BPSO and quantum swarm evolutionary(QSE) to prove its advantages. The experiments show that the results given by BPSO-HD is reliable and better than the results generated by BPSO and QSE.
基金The authors would like to thank the Deanship of Scientific Research at Shaqra University for supporting this work.
文摘The continuous destruction and frauds prevailing due to phishing URLs make it an indispensable area for research.Various techniques are adopted in the detection process,including neural networks,machine learning,or hybrid techniques.A novel detection model is proposed that uses data mining with the Particle Swarm Optimization technique(PSO)to increase and empower the method of detecting phishing URLs.Feature selection based on various techniques to identify the phishing candidates from the URL is conducted.In this approach,the features mined from the URL are extracted using data mining rules.The features are selected on the basis of URL structure.The classification of these features identified by the data mining rules is done using PSO techniques.The selection of features with PSO optimization makes it possible to identify phishing URLs.Using a large number of rule identifiers,the true positive rate for the identification of phishing URLs is maximized in this approach.The experiments show that feature selection using data mining and particle swarm optimization helps tremendously identify the phishing URLs based on the structure of the URL itself.Moreover,it can minimize processing time for identifying the phishing website instead.So,the approach can be beneficial to identify suchURLs over the existing contemporary detecting models proposed before.
基金The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work under grant number(RGP 2/142/43).
文摘Sentiment analysis or opinion mining(OM)concepts become familiar due to advances in networking technologies and social media.Recently,massive amount of text has been generated over Internet daily which makes the pattern recognition and decision making process difficult.Since OM find useful in business sectors to improve the quality of the product as well as services,machine learning(ML)and deep learning(DL)models can be considered into account.Besides,the hyperparameters involved in the DL models necessitate proper adjustment process to boost the classification process.Therefore,in this paper,a new Artificial Fish Swarm Optimization with Bidirectional Long Short Term Memory(AFSO-BLSTM)model has been developed for OM process.The major intention of the AFSO-BLSTM model is to effectively mine the opinions present in the textual data.In addition,the AFSO-BLSTM model undergoes pre-processing and TF-IFD based feature extraction process.Besides,BLSTM model is employed for the effectual detection and classification of opinions.Finally,the AFSO algorithm is utilized for effective hyperparameter adjustment process of the BLSTM model,shows the novelty of the work.A complete simulation study of the AFSO-BLSTM model is validated using benchmark dataset and the obtained experimental values revealed the high potential of the AFSO-BLSTM model on mining opinions.
基金The paper is supported by National Natural Science Foundation of China( Nos.4963 2 0 90 and4983 2 0 0 5 )
文摘Coal resources are very abundant in Zibo, Shandong Province, China. A lot of mining sewages are discharged during the coal mining. The mining sewage is characterized by high ρ (SO 2- 4 ), hardness and totally dissolved solids (TDS). Mining sewage in the southern Zibo is acidic, where heavy metals and benzene are detected. The Xiaofu River is polluted when mining sewage flows into it, so that the Mengshan Reservoir is polluted by Xiaofu River. The groundwater is polluted in Zichuan by the leaking of the Xiaofu River. The polluted Xiaofu River is thus used to irrigate the farmland in Mashang Mengshui zone. The irrigation water affects the quality of shallow groundwater. The laboratory soil column test shows that SO 2- 4, Cl -, Ca 2+ and Mg 2+ can migrate through vadose soil, especially SO 2- 4 and Cl -.
基金Project supported by the National Basic Research Program of China (Grant Nos. 2004CB318201,2011CB302300)the US National Science Foundation (Grant No. CCF-0621526)+1 种基金the National Natural Science Foundation of China (Grant No. 60703046)HUST-SRF (Grant No.2007Q021B)
文摘File semantic has proven effective in optimizing large scale distributed file system.As a consequence of the elaborate and rich I/O interfaces between upper layer applications and file systems,file system can provide useful and insightful information about semantic.Hence,file semantic mining has become an increasingly important practice in both engineering and research community.Unfortunately,it is a challenge to exploit file semantic knowledge because a variety of factors coulda ffect this information exploration process.Even worse,the challenges are exacerbated due to the intricate interdependency between these factors,and make it difficult to fully exploit the potentially important correlation among various semantic knowledges.This article proposes a file access correlation miming and evaluation reference（FARMER） model,where file is treated as a multivariate vector space,and each item within the vector corresponds a separate factor of the given file.The selection of factor depends on the application,examples of factors are file path,creator and executing program.If one particular factor occurs in both files,its value is non-zero.It is clear that the extent of inter-file relationships can be measured based on the likeness of their factor values in the semantic vectors.Benefit from this model,FARMER represents files as structured vectors of identifiers,and basic vector operations can be leveraged to quantify file correlation between two file vectors.FARMER model leverages linear regression model to estimate the strength of the relationship between file correlation and a set of influencing factors so that the ＂bad knowledge＂ can be filtered out.To demonstrate the ability of new FARMER model,FARMER is incorporated into a real large-scale object-based storage system as a case study to dynamically infer file correlations.In addition FARMER-enabled optimize service for metadata prefetching algorithm and object data layout algorithm is implemented.Experimental results show that is FARMER-enabled prefetching algorithm is shown to reduce the metadata operations latency by approximately 30%-40% when compared to a state-of-the-art metadata prefetching algorithm and a commonly used replacement policy.
基金The 2020 Scientific Research Project of Hebei Provincial Administration of Traditional Chinese Medicine(NO:2020365)The 2019 Hebei University College Student Innovation Training Project(S201910075030).
文摘Background:This study gets a classic prescription of Song Dynasty medicine for the treatment of waist and leg pain through analyzing the inheritance of traditional Chinese medicine auxiliary platform.Further,the potential mechanism of the classic prescription was analyzed based on molecular docking and network pharmacology.Methods:Based on the frequency statistics,association rules and cluster analysis,the core herbal combination and the classic prescription was digged out.Use of network pharmacology methods and molecular docking to explore the pharmacological mechanism of classic prescriptions for treatment of lumbar disc herniation.Then gene ontology biological function annotation and Kyoto Encyclopedia of Genes and Genomes enrichment of pathways were performed.Finally,the compounds of herbs were docked with the important targets of MMP1 and CRP.Results:The high-frequency Chinese medicines for treating waist and leg pain were found and we further unearthed the“Rougui-Fuzi-Niuxi(Cinnamoni cortex-Aconm lateralis radix praeparaia-Achyranthis bidentatae radix”as the core herbal combination,and matched the classic ancient prescription of Chinese medicine Jiawei Shenzhuo decoction(CAPCMJWSZD).The targets of CAPCMJWSZD were mapped to the targets of lumbar disc herniation and 48 potential targets were obtained.The core potential targets were obtained in the protein-protein interaction network,such as CRP,IL2,FOS,MMP1,CASP3.Through the DAVID database,a total of 129 gene ontology function annotation terms(P<0.01)and 91 Kyoto Encyclopedia of Genes and Genomes pathways(P<0.01)were obtained.Molecular docking results showed that quercetin has the lowest binding energy for docking with MMP1and CRP,and these two methods of molecular docking are most likely to occur.Conclusion:The most important bioactive components in CAPCMJWSZD can eliminate inflammation and slow disc degeneration through some potential targets,such as CRP,IL-2,MMP1,and these targets can rich in the following pathways,such as metalloendopeptidase activity,MAP kinase activity,osteoclast differentiation,et al.
文摘Frequent item sets mining plays an important role in association rules mining. A variety of algorithms for finding frequent item sets in very large transaction databases have been developed. Although many techniques were proposed for maintenance of the discovered rules when new transactions are added, little work is done for maintaining the discovered rules when some transactions are deleted from the database. Updates are fundamental aspect of data management. In this paper, a decremental association rules mining algorithm is present for updating the discovered association rules when some transactions are removed from the original data set. Extensive experiments were conducted to evaluate the performance of the proposed algorithm. The results show that the proposed algorithm is efficient and outperforms other well-known algorithms.
